ALBERT

Beschreibung: Living in the “era of social networking”, we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this “socially-generated knowledge” (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely location-based information included within geo-tagged photos that we collect from Flickr, along with plain simple textual information from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into “tile”-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results.

Beschreibung: In order to alleviate the pressure of information overload and enhance consumer satisfaction, personalization recommendation has become increasingly popular in recent years. As a result, various approaches for recommendation have been proposed in the past few years. However, traditional recommendation methods are still troubled with typical issues such as cold start, sparsity, and low accuracy. To address these problems, this paper proposed an improved recommendation method based on trust relationships in social networks to improve the performance of recommendations. In particular, we define trust relationship afresh and consider several representative factors in the formalization of trust relationships. To verify the proposed approach comprehensively, this paper conducted experiments in three ways. The experimental results show that our proposed approach leads to a substantial increase in prediction accuracy and is very helpful in dealing with cold start and sparsity.

Beschreibung: Future Internet, Vol. 9, Pages 51: Local Path Planning of Driverless Car Navigation Based on Jump Point Search Method Under Urban Environment Future Internet doi: 10.3390/fi9030051 Authors: Kaijun Zhou Lingli Yu Ziwei Long Siyao Mo The Jump Point Search (JPS) algorithm is adopted for local path planning of the driverless car under urban environment, and it is a fast search method applied in path planning. Firstly, a vector Geographic Information System (GIS) map, including Global Positioning System (GPS) position, direction, and lane information, is built for global path planning. Secondly, the GIS map database is utilized in global path planning for the driverless car. Then, the JPS algorithm is adopted to avoid the front obstacle, and to find an optimal local path for the driverless car in the urban environment. Finally, 125 different simulation experiments in the urban environment demonstrate that JPS can search out the optimal and safety path successfully, and meanwhile, it has a lower time complexity compared with the Vector Field Histogram (VFH), the Rapidly Exploring Random Tree (RRT), A*, and the Probabilistic Roadmaps (PRM) algorithms. Furthermore, JPS is validated usefully in the structured urban environment.

Beschreibung: Future Internet, Vol. 9, Pages 49: Future Intelligent Systems and Networks Future Internet doi: 10.3390/fi9030049 Authors: Carmen de-Pablos-Heredero n/a

Beschreibung: Future Internet, Vol. 9, Pages 50: ARAAC: A Rational Allocation Approach in Cloud Data Center Networks Future Internet doi: 10.3390/fi9030050 Authors: Ahmad Quttoum Ayoub Alsarhan Abidalrahman Moh’d The expansion of telecommunication technologies touches almost all aspects life that we are living nowadays. Indeed, such technologies have emerged as a fourth essential utility alongside the traditional utilities of electricity, water, and gas. In this context, Cloud Data Center Networks (cloud-DCNs) have been proposed as a promising way to cope with such a high-tech era and with any expected trends in future computing networks. Resources of cloud-DCNs are leased to the interested users in the form of services, such services come in different models that vary between software, platform, and infrastructure. The leasing process of any service model starts with the users (i.e., service tenants). A tenant asks for the service resources, and the cloud-provider allocates the resources with a charge that follows a predefined cost policy. Cloud resources are limited, and those cloud providers have profit objectives to be satisfied. Thus, to comply with the aforementioned promise, the limited resources need to be carefully allocated. Existing allocation proposals in the literature dealt with this problem in varying ways. However, none proposes a win-win allocation model that satisfies both the providers and tenants. This work proposes A Rational Allocation Approach in Cloud Data Center Networks (ARAAC) that efficiently allocates the available cloud resources, in a way that allows for a win-win environment to satisfy both parties: the providers and tenants. To do so, ARAAC deploys the Second Best-Price (SBP) mechanism along with a behavioral-based reputation model. The reputation is built according to the tenants’ utilization history throughout their previous service allocations. The reputation records along with the adoption of the SBP mechanism allows for a locally free-equilibrium approach that allocates the available cloud-DCN resources in an efficient and fair manner. In ARAAC, through an auction scenario, tenants with positive reputation records are awarded by having the required resources allocated at prices that are lower than what they have offered. Compared to other benchmark models, simulation results show that ARAAC can efficiently adapt the behavior of those rational service-tenants to provide for better use of the cloud resources, with an increase in the providers’ profits.

Beschreibung: Future Internet, Vol. 9, Pages 52: Design for Children’s Playful Learning with Robots Future Internet doi: 10.3390/fi9030052 Authors: Maria Lupetti Yuan Yao Haipeng Mi Claudio Germak This article presents an investigation of the implications of designing for children’s playful learning with robots. This study was carried out by adopting a Research through Design approach that resulted in the development of a novel low-anthropomorphic robot called Shybo. The article reports the main phases of the project: the preliminary and exploratory research that was carried out to define a list of design requirements; the design of the robot and its supplementary materials for carrying out playful learning experiences; and the evaluation of the project that involved both parents and children. The robot, in fact, was finally tested as part of a two-hour experience that engaged children in activities related to the associations between sounds and colours. The article presents and discusses the results of this evaluation to point out positive aspects of the experience, emerging issues and hints for future works. These are documented to share lessons learned that might be supportive of the general development of children’s playful learning and cognitive experiences with robots.

Beschreibung: Future Internet, Vol. 9, Pages 47: Cost-Aware IoT Extension of DISSECT-CF Future Internet doi: 10.3390/fi9030047 Authors: Andras Markus Attila Kertesz Gabor Kecskemeti In the age of the Internet of Things (IoT), more and more sensors, actuators and smart devices get connected to the network. Application providers often combine this connectivity with novel scenarios involving cloud computing. Before implementing changes in these large-scale systems, an in-depth analysis is often required to identify governance models, bottleneck situations, costs and unexpected behaviours. Distributed systems simulators help in such analysis, but they are often problematic to apply in this newly emerging domain. For example, most simulators are either too detailed (e.g., need extensive knowledge on networking), or not extensible enough to support the new scenarios. To overcome these issues, we discuss our IoT cost analysis oriented extension of DIScrete event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF). Thus, we present an in-depth analysis of IoT and cloud related pricing models of the most widely used commercial providers. Then, we show how the fundamental properties (e.g., data production frequency) of IoT entities could be linked to the identified pricing models. To allow the adoption of unforeseen scenarios and pricing schemes, we present a declarative modelling language to describe these links. Finally, we validate our extensions by analysing the effects of various identified pricing models through five scenarios coming from the field of weather forecasting.

Beschreibung: Future Internet, Vol. 9, Pages 48: Flow Shop Scheduling Problem and Solution in Cooperative Robotics—Case-Study: One Cobot in Cooperation with One Worker Future Internet doi: 10.3390/fi9030048 Authors: Ahmed Sadik Bodo Urban This research combines between two different manufacturing concepts. On the one hand, flow shop scheduling is a well-known problem in production systems. The problem appears when a group of jobs shares the same processing sequence on two or more machines sequentially. Flow shop scheduling tries to find the appropriate solution to optimize the sequence order of this group of jobs over the existing machines. The goal of flow shop scheduling is to obtain the continuity of the flow of the jobs over the machines. This can be obtained by minimizing the delays between two consequent jobs, therefore the overall makespan can be minimized. On the other hand, collaborative robotics is a relatively recent approach in production where a collaborative robot (cobot) is capable of a close proximity cooperation with the human worker to increase the manufacturing agility and flexibility. The simplest case-study of a collaborative workcell is one cobot in cooperation with one worker. This collaborative workcell can be seen as a special case of the shop flow scheduling problem, where the required time from the worker to perform a specific job is unknown and variable. Therefore, during this research, we implement an intelligent control solution which can optimize the flow shop scheduling problem over the previously mentioned case-study.

Beschreibung: Future Internet, Vol. 9, Pages 54: Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks Future Internet doi: 10.3390/fi9040054 Authors: Nelofar Aslam Kewen Xia Muhammad Haider Muhammad Hadi Wireless sensor networks (WSNs), built from many battery-operated sensor nodes are distributed in the environment for monitoring and data acquisition. Subsequent to the deployment of sensor nodes, the most challenging and daunting task is to enhance the energy resources for the lifetime performance of the entire WSN. In this study, we have attempted an approach based on the shortest path algorithm and grid clustering to save and renew power in a way that minimizes energy consumption and prolongs the overall network lifetime of WSNs. Initially, a wireless portable charging device (WPCD) is assumed which periodically travels on our proposed routing path among the nodes of the WSN to decrease their charge cycle time and recharge them with the help of wireless power transfer (WPT). Further, a scheduling scheme is proposed which creates clusters of WSNs. These clusters elect a cluster head among them based on the residual energy, buffer size, and distance of the head from each node of the cluster. The cluster head performs all data routing duties for all its member nodes to conserve the energy supposed to be consumed by member nodes. Furthermore, we compare our technique with the available literature by simulation, and the results showed a significant increase in the vacation time of the nodes of WSNs.

Beschreibung: Future Internet, Vol. 9, Pages 53: Towards a New Digital Era: Observing Local E-Government Services Adoption in a Chinese Municipality Future Internet doi: 10.3390/fi9030053 Authors: Yao Yang As electronic government services (e-government) evolve, traditional applications such as online portals are encountering challenges in a new digital era in which people thirst for more convenient, diverse channels of communication with their government. Despite the efforts devoted to the promotion of Social Networking Service (SNS) use in e-government for the past few years, empirical studies regarding this ongoing trend are still insufficient. In the local administration of China, a special collaboration between local governments and private Internet enterprises is happening: government services are imported into commercial SNS platforms known as WeChat and Weibo. This research conducts a case study of a Chinese municipality premised upon survey data (N = 605) collected in Chongqing. It examines the determinants of e-services adoption in four major local applications: two traditional government-led services “Government website portal” and “Government official app” (traditional government-led services), and two built-in government services on social networking applications “Weibo Government official account” “WeChat Public Services”. The paper aims to provide a closer understanding of current trends in local government e-services, or in this case, an innovative trial of Chinese e-government practices.

Beschreibung: Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures from a small set of linear measurements. Pervious works mainly focus on the analysis of the existence and uniqueness. In this paper, we address its stability. We prove that the solution to the related convex programming of CPCP gives an estimate that is stable to small entry-wise noise. We also provide numerical simulation results to support our result. Numerical results show that the solution to the related convex program is stable to small entry-wise noise under board condition.

Beschreibung: Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.

Beschreibung: In this paper, to overcome the innate drawbacks of some old methods, we present a new quintic spline method for integro interpolation. The method is free of any exact end conditions, and it can reconstruct a function and its first order to fifth order derivatives with high accuracy by only using the given integral values of the original function. The approximation properties of the obtained integro quintic spline are well studied and examined. The theoretical analysis and the numerical tests show that the new method is very effective for integro interpolation.

Beschreibung: Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark’s Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.

Beschreibung: Sentiment analysis has played a primary role in text classification. It is an undoubted fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous expectations and constantly grows within seconds. It is therefore quite complex to cope with the vast amount of textual data particularly if we also take the incremental production speed into account. Social media, e-commerce, news articles, comments and opinions are broadcasted on a daily basis. A rational solution, in order to handle the abundance of data, would be to build automated information processing systems, for analyzing and extracting meaningful patterns from text. The present paper focuses on sentiment analysis applied in Greek texts. Thus far, there is no wide availability of natural language processing tools for Modern Greek. Hence, a thorough analysis of Greek, from the lexical to the syntactical level, is difficult to perform. This paper attempts a different approach, based on the proven capabilities of gradient boosting, a well-known technique for dealing with high-dimensional data. The main rationale is that since English has dominated the area of preprocessing tools and there are also quite reliable translation services, we could exploit them to transform Greek tokens into English, thus assuring the precision of the translation, since the translation of large texts is not always reliable and meaningful. The new feature set of English tokens is augmented with the original set of Greek, consequently producing a high dimensional dataset that poses certain difficulties for any traditional classifier. Accordingly, we apply gradient boosting machines, an ensemble algorithm that can learn with different loss functions providing the ability to work efficiently with high dimensional data. Moreover, for the task at hand, we deal with a class imbalance issues since the distribution of sentiments in real-world applications often displays issues of inequality. For example, in political forums or electronic discussions about immigration or religion, negative comments overwhelm the positive ones. The class imbalance problem was confronted using a hybrid technique that performs a variation of under-sampling the majority class and over-sampling the minority class, respectively. Experimental results, considering different settings, such as translation of tokens against translation of sentences, consideration of limited Greek text preprocessing and omission of the translation phase, demonstrated that the proposed gradient boosting framework can effectively cope with both high-dimensional and imbalanced datasets and performs significantly better than a plethora of traditional machine learning classification approaches in terms of precision and recall measures.

Beschreibung: With the rapid development of Internet, the traditional computing environment is making a big migration to the cloud-computing environment. However, cloud computing introduces a set of new security problems. Aiming at the virtual machine (VM) escape attack, we study the traditional attack model and attack scenarios in the cloud-computing environment. In addition, we propose an access control model that can prevent virtual machine escape (PVME) by adapting the BLP (Bell-La Padula) model (an access control model developed by D. Bell and J. LaPadula). Finally, the PVME model has been implemented on full virtualization architecture. The experimental results show that the PVME module can effectively prevent virtual machine escape while only incurring 4% to 8% time overhead.

Beschreibung: In this paper, an energy efficient power allocation scheme is proposed for a distributed massive multiple-input multiple-output (MIMO) system with a circular antenna array. Single-antenna users simultaneously transmit signal to the base station (BS) with a large number of distributed antennas. The tight approximation of the energy efficiency (EE) is derived in closed form expressions. Through jointly optimizing the power allocation and the antenna number of BS, an NP-hard problem is formulated to maximize the EE. The equal power allocation is proved to be optimal given the total transmit power and the number of antennas. Finally, the optimal antenna number is determined by one dimension search. It is noteworthy that the NP-hard problem is solved by one dimension search. Simulation results validate the accuracy and the low-complexity of our proposed scheme.

Beschreibung: Analyzing people’s opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users’ real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users’ real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users’ online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users’ real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users’ real mood states and could be applied in further psychological measurements and predictions.

Beschreibung: Wireless sensor networks (WSNs) have been extensively applied in many domains such as smart homes and Internet of Things (IoT). As part of WSNs’ communication protocols, back-off mechanisms play an essential role in the deployment of wireless channels for network nodes and have been at the core of ensuring effective communication. The performance of many back-off algorithms is excellent in light or middle load networks. However, it degrades dramatically in heavy load networks. In this paper, we propose an adaptive contention window medium access control (MAC) protocol to improve the throughput performance under heavy load. By using the number of historical collisions as the parameter in the back-off mechanism to reflect the channel status, the size of the contention window is adjusted automatically, and the throughput of network is then improved. Simulation results show that our optimized MAC protocol has higher throughput and energy efficiency.

Beschreibung: We propose a novel fast iterative thresholding algorithm for image compressive sampling (CS) recovery using three existing denoisers—i.e., TV (total variation), wavelet, and BM3D (block-matching and 3D filtering) denoisers. Through the use of the recently introduced plug-and-play prior approach, we turn these denoisers into CS solvers. Thus, our method can jointly utilize the global and nonlocal sparsity of images. The former is captured by TV and wavelet denoisers for maintaining the entire consistency; while the latter is characterized by the BM3D denoiser to preserve details by exploiting image self-similarity. This composite constraint problem is then solved with the fast composite splitting technique. Experimental results show that our algorithm outperforms several excellent CS techniques.

Beschreibung: This paper looks at the challenges and opportunities of implementing blockchain technology across banking, providing food for thought about the potentialities of this disruptive technology. The blockchain technology can optimize the global financial infrastructure, achieving sustainable development, using more efficient systems than at present. In fact, many banks are currently focusing on blockchain technology to promote economic growth and accelerate the development of green technologies. In order to understand the potential of blockchain technology to support the financial system, we studied the actual performance of the Bitcoin system, also highlighting its major limitations, such as the significant energy consumption due to the high computing power required, and the high cost of hardware. We estimated the electrical power and the hash rate of the Bitcoin network, over time, and, in order to evaluate the efficiency of the Bitcoin system in its actual operation, we defined three quantities: “economic efficiency”, “operational efficiency”, and “efficient service”. The obtained results show that by overcoming the disadvantages of the Bitcoin system, and therefore of blockchain technology, we could be able to handle financial processes in a more efficient way than under the current system.

Beschreibung: Future fifth-generation (5G) cellular systems are set to give a strong boost to the large-scale deployment of Internet of things (IoT). In the view of a future converged 5G-IoT infrastructure, cellular IoT solutions such as narrowband IoT (NB-IoT) and device-to-device (D2D) communications are key technologies for supporting IoT scenarios and applications. However, some open issues still need careful investigation. An example is the risk of threats to privacy and security when IoT mobile services rely on D2D communications. To guarantee efficient and secure connections to IoT services involving exchange of sensitive data, reputation-based mechanisms to identify and avoid malicious devices are fast gaining ground. In order to tackle the presence of malicious nodes in the network, this paper introduces reliability and reputation notions to model the level of trust among devices engaged in an opportunistic hop-by-hop D2D-based content uploading scheme. To this end, social awareness of devices is considered as a means to enhance the identification of trustworthy nodes. A performance evaluation study shows that the negative effects due to malicious nodes can be drastically reduced by adopting the proposed solution. The performance metrics that proved to benefit from the proposed solution are data loss, energy consumption, and content uploading time.

Beschreibung: The impact of the world-wide ageing population has commenced with respect to society in developed countries. Several researchers focused on exploring new methods to improve the quality of life of elderly individuals by allowing them to remain independent and healthy to the maximum possible extent. For example, new walking aids are designed to allow elderly individuals to remain mobile in a safe manner because the importance of walking is well-known. The aim of the present study involves designing a humanoid robot guide as a walking trainer for elderly individuals. It is hypothesized that the same service robot provides an assistive and social contribution with respect to interaction between elderly users by motivating them to walk more and simultaneously provides assistance, such as physical assistance and gait monitoring, while walking. This study includes a detailed statement of the research problem as well as a literature review of existing studies related to walking companion robots. A user-centred design approach is adopted to report the results of the current first feasibility study by using a commercially available humanoid robot known as Pepper developed by Softbank-Aldebaran. A quantitative questionnaire was used to investigate all elements that assess intrinsic motivation in users while performing a given activity. Conversely, basic gait data were acquired through a video analysis to test the capability of the robot to modify the gait of human users. The results in terms of the feedback received from elderly subjects and the literature review improve the design of the walking trainer for elderly individuals.

Beschreibung: This paper presents a DCT-based (DCT: discrete cosine transform) listless set partitioning in hierarchical trees (SPIHT) digital watermarking technique that is robust against several common attacks such as cropping, filtering, sharpening, noise, inversion, contrast manipulation, and compression. The proposed technique is made further robust by the incorporation of the Chinese remainder theorem (CRT) encryption technique. Our scheme is compared with the recently proposed CRT-based DCT technique, CRT-based spatial domain watermarking, and DCT-based inter block correlation techniques. Extensive simulation experiments show better robustness in common image manipulations and, at the same time, the proposed technique successfully makes the watermark perceptually invisible. A better Tamper Assessment Function (TAF) value of 2–15% and a better Normalized Correlation (NC) is achieved compared to some of the above techniques. In particular, the proposed technique shows better robustness on compression attacks at moderate to higher compression ratios. It is possible to maintain the imperceptibility and low TAF for various values by doubling the capacity of the watermark.

Beschreibung: Self-Organization is a spontaneous trend which exists in nature among different organisms. Self-organization refers to the process where some form of an overall order arises in a group due to the local interaction among the members of this group. In manufacturing, a similar definition of a Reconfigurable Manufacturing System (RMS) can be found. RMS is a system where the production components and functions can be modified, rearranged and/or interchanged in a timely and cost-effective manner to quickly respond to the production requirements. The definition of the RMS concept implies that the self-organization is an important key factor to fulfil that concept. A case study where a cooperation among a variable number of Industrial Robots (IRs) and workers is studied to show the importance of the research problem. The goal of the paper is to offer a suitable generic control and interaction architecture solution model, which obtains the self-organization from the RMS point of view. Ultimately, applying the proposed solution concept to the case study.

Beschreibung: Future Internet, Vol. 9, Pages 36: An Extended Framework for Recovering From Trust Breakdowns in Online Community Settings Future Internet doi: 10.3390/fi9030036 Authors: Ruchdee Binmad Mingchu Li Zhen Wang Nakema Deonauth Chettupally Carie The violation of trust as a result of interactions that do not proceed as expected gives rise to the question as to whether broken trust can possibly be recovered. Clearly, trust recovery is more complex than trust initialization and maintenance. Trust recovery requires a more complex mechanism to explore different factors that cause the decline of trust and identify the affected individuals of trust violation both directly and indirectly. In this study, an extended framework for recovering trust is presented. Aside from evaluating whether there is potential for recovery based on the outcome of a forgiveness mechanism after a trust violation, encouraging cooperation between interacting parties after a trust violation through incentive mechanisms is also important. Furthermore, a number of experiments are conducted to validate the applicability of the framework and the findings show that the e-marketplace incorporating our proposed framework results in improved efficiency of trading, especially in long-term interactions.

Beschreibung: Future Internet, Vol. 9, Pages 38: Interference-Aware Opportunistic Dynamic Energy Saving Mechanism for Wi-Fi Enabled IoTs Future Internet doi: 10.3390/fi9030038 Authors: Il-Gu Lee The wireless local area network (WLAN) is one of the most popular wireless technologies offering connectivity today, and one of the candidates for the internet of things (IoTs). However, WLAN’s inefficiency in terms of complexity and relatively large power consumption compared with other wireless standards has been reported as a major barrier for IoTs applications. This paper proposes an interference-aware opportunistic dynamic energy saving mechanism to improve energy efficiency for Wi-Fi enabled IoTs. The proposed scheme optimizes operating clock frequencies adaptively for signal processing when the mobile station transmits packets in partial sub-channels. Evaluation results demonstrate that the proposed scheme improves energy efficiency by approximately 34%.

Beschreibung: Future Internet, Vol. 9, Pages 39: Azure-Based Smart Monitoring System for Anemia-Like Pallor Future Internet doi: 10.3390/fi9030039 Authors: Sohini Roychowdhury Paul Hage Joseph Vazquez Increasing costs of diagnostic healthcare have necessitated the development of hardware independent non-invasive Point of Care (POC) systems. Although anemia prevalence rates in global populations vary between 10% and 60% in various demographic groups, smart monitoring systems have not yet been developed for screening and tracking anemia-like pallor. In this work, we present two cloud platform-hosted POC applications that are directed towards smart monitoring of anemia-like pallor through eye and tongue pallor site images. The applications consist of a front-end graphical user interface (GUI) module and two different back-end image processing and machine learning modules. Both applications are hosted on a browser accessible tomcat server ported to an Azure Virtual Machine (VM). We observe that the first application spatially segments regions of interest from pallor site images with higher pallor classification accuracy and relatively longer processing times when compared to the lesser accurate yet faster second application. Also, both applications achieve 65%–98% accuracy in separating normal images from images with pallor or abnormalities. The optimized front-end module is significantly light-weight with a run-through time ratio of 10−5 with respect to the back-end modules. Thus, the proposed applications are portable and hardware independent, allowing for their use in pallor monitoring and screening tasks.

Beschreibung: Future Internet, Vol. 9, Pages 40: Visual Interface Evaluation for Wearables Datasets: Predicting the Subjective Augmented Vision Image QoE and QoS Future Internet doi: 10.3390/fi9030040 Authors: Brian Bauman Patrick Seeling As Augmented Reality (AR) applications become commonplace, the determination of a device operator’s subjective Quality of Experience (QoE) in addition to objective Quality of Service (QoS) metrics gains importance. Human subject experimentation is common for QoE relationship determinations due to the subjective nature of the QoE. In AR scenarios, the overlay of displayed content with the real world adds to the complexity. We employ Electroencephalography (EEG) measurements as the solution to the inherent subjectivity and situationality of AR content display overlaid with the real world. Specifically, we evaluate prediction performance for traditional image display (AR) and spherical/immersive image display (SAR) for the QoE and underlying QoS levels. Our approach utilizing a four-position EEG wearable achieves high levels of accuracy. Our detailed evaluation of the available data indicates that less sensors would perform almost as well and could be integrated into future wearable devices. Additionally, we make our Visual Interface Evaluation for Wearables (VIEW) datasets from human subject experimentation publicly available and describe their utilization.

Beschreibung: Future Internet, Vol. 9, Pages 41: Study on a Quality Evaluation Method for College English Classroom Teaching Future Internet doi: 10.3390/fi9030041 Authors: Mao-hua Sun Yuan-gang Li Bing He A quality evaluation method is an important means and the main basis on which to evaluate the college English classroom teaching quality of teachers. To overcome the one-sided subjectivity and resulting imprecision of the traditional classroom teaching quality evaluation method, a scientific and reasonable quality evaluation index system for college English classroom teaching is constructed. The fuzzy comprehensive evaluation method and the analytic hierarchy process method are combined to propose an improved multi-level fuzzy comprehensive evaluation model for obtaining a new college English classroom teaching quality evaluation method. In the proposed method, according to the fuzzy characteristics of a college English classroom teaching quality evaluation, the fuzzy comprehensive evaluation method is used to transform the qualitative evaluation indexes into limited quantitative evaluation indexes, then a judgment matrix is constructed to determine the weights among different levels by using the analytic hierarchy process method. Additionally, the college English classroom teaching quality is evaluated in detail. Finally, an actual case of college English classroom teaching is used to verify the effectiveness of the college English classroom teaching quality evaluation method. The results show that the proposed college English classroom teaching method can overcome the subjectivity and randomness shortcomings of the traditional classroom teaching quality evaluation methods, and improve the reliability, accuracy, and objectivity of fuzzy comprehensive evaluation. It is an effective method to evaluate college English classroom teaching quality.

Beschreibung: Future Internet, Vol. 9, Pages 42: A Multimodal Perception Framework for Users Emotional State Assessment in Social Robotics Future Internet doi: 10.3390/fi9030042 Authors: Lorenzo Cominelli Nicola Carbonaro Daniele Mazzei Roberto Garofalo Alessandro Tognetti Danilo De Rossi In this work, we present an unobtrusive and non-invasive perception framework based on the synergy between two main acquisition systems: the Touch-Me Pad, consisting of two electronic patches for physiological signal extraction and processing; and the Scene Analyzer, a visual-auditory perception system specifically designed for the detection of social and emotional cues. It will be explained how the information extracted by this specific kind of framework is particularly suitable for social robotics applications and how the system has been conceived in order to be used in human-robot interaction scenarios.

Beschreibung: Future Internet, Vol. 9, Pages 43: A Survey of Denial-of-Service and Distributed Denial of Service Attacks and Defenses in Cloud Computing Future Internet doi: 10.3390/fi9030043 Authors: Adrien Bonguet Martine Bellaiche Cloud Computing is a computing model that allows ubiquitous, convenient and on-demand access to a shared pool of highly configurable resources (e.g., networks, servers, storage, applications and services). Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are serious threats to the Cloud services’ availability due to numerous new vulnerabilities introduced by the nature of the Cloud, such as multi-tenancy and resource sharing. In this paper, new types of DoS and DDoS attacks in Cloud Computing are explored, especially the XML-DoS and HTTP-DoS attacks, and some possible detection and mitigation techniques are examined. This survey also provides an overview of the existing defense solutions and investigates the experiments and metrics that are usually designed and used to evaluate their performance, which is helpful for the future research in the domain.

Beschreibung: Future Internet, Vol. 9, Pages 44: Multicell Interference Management in Device to Device Underlay Cellular Networks Future Internet doi: 10.3390/fi9030044 Authors: Georgios Katsinis Eirini Tsiropoulou Symeon Papavassiliou In this paper, the problem of interference mitigation in a multicell Device to Device (D2D) underlay cellular network is addressed. In this type of network architectures, cellular users and D2D users share common Resource Blocks (RBs). Though such paradigms allow potential increase in the number of supported users, the latter comes at the cost of interference increase that in turn calls for the design of efficient interference mitigation methodologies. To treat this problem efficiently, we propose a two step approach, where the first step concerns the efficient RB allocation to the users and the second one the transmission power allocation. Specifically, the RB allocation problem is formulated as a bilateral symmetric interaction game. This assures the existence of a Nash Equilibrium (NE) point of the game, while a distributed algorithm, which converges to it, is devised. The power allocation problem is formulated as a linear programming problem per RB, and the equivalency between this problem and the total power minimization problem is shown. Finally, the operational effectiveness of the proposed approach is evaluated via numerical simulations, while its superiority against state of the art approaches existing in the recent literature is shown in terms of increased number of supported users, interference reduction and power minimization.

Beschreibung: Future Internet, Vol. 9, Pages 45: A Robust Image Watermarking Scheme Based on SVD in the Spatial Domain Future Internet doi: 10.3390/fi9030045 Authors: Heng Zhang Chengyou Wang Xiao Zhou With the development of image processing technology, the copyright protection of digital images has become an urgent problem to be solved. As an effective method, the robust digital watermarking technique emerges at a historic moment. Currently, most robust watermarking schemes are performed in the transform domains, such as the discrete cosine transform (DCT) and singular value decomposition (SVD). Compared with spatial domain watermarking schemes, these methods have achieved good performance, such as better robustness and higher security. However, the computational complexity increases with the use of forward and reverse transforms. In this paper, we analyze the SVD-based watermarking scheme and its impact on the spatial domain. Based on this analysis and the mathematical characteristics of SVD, we present a robust image watermarking scheme where a binary watermark is embedded into the largest singular value of each image block in the spatial domain. Several experiments are conducted to verify the performance of the proposed watermarking scheme. The experimental results show that compared with the existing SVD domain watermarking schemes, our proposed method has maintained good robustness against various attacks. Moreover, it avoids the false positive problem existing in traditional SVD-based watermarking schemes and has lower computational complexity.

Beschreibung: Future Internet, Vol. 9, Pages 46: Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs Future Internet doi: 10.3390/fi9030046 Authors: Il-Gu Lee The Internet of Things (IoTs) will change the requirements for wireless connectivity significantly, mainly with regard to service coverage, data rate, and energy efficiency. Therefore, to improve robustness and reliability, WiFi-enabled IoT devices have been developed to use narrowband communication. However, narrowband transmission in WiFi such as IEEE 802.11ah causes relatively higher frequency error due to the reduced subcarrier space, which is larger than legacy wireless local area networks (WLANs) in 2.4/5 GHz frequencies. In a direct conversion receiver, this error degrades the signal quality due to the presence of direct current (DC) offset cancellation circuits. In this paper, a digital carrier frequency offset (CFO) predistortion scheme is proposed for a reliable communication link in dense networks. Evaluation results demonstrate that the proposed scheme can improve received signal quality in terms of packet error rate and error vector magnitude.

Beschreibung: Future Internet, Vol. 9, Pages 55: Botnet Detection Technology Based on DNS Future Internet doi: 10.3390/fi9040055 Authors: Xingguo Li Junfeng Wang Xiaosong Zhang With the help of botnets, intruders can implement a remote control on infected machines and perform various malicious actions. Domain Name System (DNS) is very famous for botnets to locate command and control (C and C) servers, which enormously strengthens a botnet’s survivability to evade detection. This paper focuses on evasion and detection techniques of DNS-based botnets and gives a review of this field for a general summary of all these contributions. Some important topics, including technological background, evasion and detection, and alleviation of botnets, are discussed. We also point out the future research direction of detecting and mitigating DNS-based botnets. To the best of our knowledge, this topic gives a specialized and systematic study of the DNS-based botnet evading and detecting techniques in a new era and is useful for researchers in related fields.

Beschreibung: Future Internet, Vol. 9, Pages 57: TSKT-ORAM: A Two-Server k-ary Tree Oblivious RAM without Homomorphic Encryption Future Internet doi: 10.3390/fi9040057 Authors: Jinsheng Zhang Qiumao Ma Wensheng Zhang Daji Qiao This paper proposes TSKT-oblivious RAM (ORAM), an efficient multi-server ORAM construction, to protect a client’s access pattern to outsourced data. TSKT-ORAM organizes each of the server storages as a k-ary tree and adopts XOR-based private information retrieval (PIR) and a novel delayed eviction technique to optimize both the data query and data eviction process. TSKT-ORAM is proven to protect the data access pattern privacy with a failure probability of 2 - 80 when system parameter k ≥ 128 . Meanwhile, given a constant-size local storage, when N (i.e., the total number of outsourced data blocks) ranges from 2 16 – 2 34 , the communication cost of TSKT-ORAM is only 22–46 data blocks. Asymptotic analysis and practical comparisons are conducted to show that TSKT-ORAM incurs lower communication cost, storage cost and access delay in practical scenarios than the compared state-of-the-art ORAM schemes.

Beschreibung: Future Internet, Vol. 9, Pages 58: Access Control with Delegated Authorization Policy Evaluation for Data-Driven Microservice Workflows Future Internet doi: 10.3390/fi9040058 Authors: Davy Preuveneers Wouter Joosen Microservices offer a compelling competitive advantage for building data flow systems as a choreography of self-contained data endpoints that each implement a specific data processing functionality. Such a ‘single responsibility principle’ design makes them well suited for constructing scalable and flexible data integration and real-time data flow applications. In this paper, we investigate microservice based data processing workflows from a security point of view, i.e., (1) how to constrain data processing workflows with respect to dynamic authorization policies granting or denying access to certain microservice results depending on the flow of the data; (2) how to let multiple microservices contribute to a collective data-driven authorization decision and (3) how to put adequate measures in place such that the data within each individual microservice is protected against illegitimate access from unauthorized users or other microservices. Due to this multifold objective, enforcing access control on the data endpoints to prevent information leakage or preserve one’s privacy becomes far more challenging, as authorization policies can have dependencies and decision outcomes cross-cutting data in multiple microservices. To address this challenge, we present and evaluate a workflow-oriented authorization framework that enforces authorization policies in a decentralized manner and where the delegated policy evaluation leverages feature toggles that are managed at runtime by software circuit breakers to secure the distributed data processing workflows. The benefit of our solution is that, on the one hand, authorization policies restrict access to the data endpoints of the microservices, and on the other hand, microservices can safely rely on other data endpoints to collectively evaluate cross-cutting access control decisions without having to rely on a shared storage backend holding all the necessary information for the policy evaluation.

Beschreibung: Future Internet, Vol. 9, Pages 59: Extensions and Enhancements to “the Secure Remote Update Protocol” Future Internet doi: 10.3390/fi9040059 Authors: Andrew Poulter Steven Johnson Simon Cox This paper builds on previous work introducing the Secure Remote Update Protocol (SRUP), a secure communications protocol for Command and Control applications in the Internet of Things, built on top of MQTT. This paper builds on the original protocol and introduces a number of additional message types: adding additional capabilities to the protocol. We also discuss the difficulty of proving that a physical device has an identity corresponding to a logical device on the network and propose a mechanism to overcome this within the protocol.

Beschreibung: Future Internet, Vol. 9, Pages 56: Review on Semi-Fragile Watermarking Algorithms for Content Authentication of Digital Images Future Internet doi: 10.3390/fi9040056 Authors: Xiaoyan Yu Chengyou Wang Xiao Zhou With the popularity of network and the continuous development of multimedia technology, saving of network bandwidth and copyright protection of multimedia content have gradually attracted people’s attention. The fragile watermark for integrity authentication of image data and protection of copyright has become a hotspot. In the storage and transmission process, image data must be compressed to save network bandwidth. As a result, semi-fragile watermarking techniques, which can be used to distinguish common image processing operations from malicious tampering, are emerging. In this paper, semi-fragile watermarking algorithms for image authentication are surveyed. The basic principles and characteristics about semi-fragile watermarking algorithms are introduced, and several kinds of attack behaviors are also included. Aiming at several typical image-authentication algorithms, advantages and disadvantages are analyzed, and evaluation indexes of various algorithms are compared. Finally, we analyze the key points and difficulties in the study on semi-fragile watermarking algorithms, and the direction about future development is prospected.

Beschreibung: Future Internet, Vol. 9, Pages 60: A Design Space for Virtuality-Introduced Internet of Things Future Internet doi: 10.3390/fi9040060 Authors: Kota Gushima Tatsuo Nakajima Augmented reality (AR) and virtual reality (VR) technologies have been dramatically expanded in recent years. In the near future, we expect that diverse digital services that employ Internet of Things (IoT) technologies enhanced with AR and VR will become more popular. Advanced information technologies will enable the physical world to be fused with the virtual world. These digital services will be advanced via virtuality, which means that things that do not physically exist make people believe in their existence. We propose a design space for digital services that are enhanced via virtuality based on insights extracted from three case studies that we have developed and from discussions in focus groups that analyze how existing commercial IoT products proposed in a commercial crowdfunding platform, Kickstarter, could be enhanced through virtuality. The derived design space offers three dimensions to design a digital service to fuse IoT technologies with virtuality: (1) Taxonomy of IoT; (2) Visualizing Level, and (3) Virtuality Level. The design space will help IoT-based digital service designers to develop advanced future IoT products that incorporate virtuality.

Beschreibung: Future Internet, Vol. 9, Pages 61: An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework Future Internet doi: 10.3390/fi9040061 Authors: Guanqiu Qi Jinchuan Wang Qiong Zhang Fancheng Zeng Zhiqin Zhu Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

Beschreibung: Future Internet, Vol. 9, Pages 63: Collaborative Web Service Discovery and Recommendation Based on Social Link Future Internet doi: 10.3390/fi9040063 Authors: Lijun Duan Hao Tian With the increasing application of web services in varying fields, the demand of effective Web service discovery approaches is becoming unprecedentedly strong. To improve the performance of service discovery, this paper proposes a collaborative Web service discovery and recommendation mechanism based on social link by extracting the latent relationships behind users and services. The presented approach can generate a set of candidate services through a complementary manner, in which service discovery and service recommendation could collaborate according to the formalized social link. The experimental results reveal that the proposed mechanism can effectively improve the efficiency and precision of Web service discovery.

Beschreibung: Future Internet, Vol. 9, Pages 62: A Practical Resource Management Scheme for Cellular Underlaid D2D Networks Future Internet doi: 10.3390/fi9040062 Authors: Tae-Won Ban In this paper, we investigate a resource management scheme for cellular underlaid device-to-device (D2D) communications, which are an integral part of mobile caching networks. D2D communications are allowed to share radio resources with cellular communications as long as the generating interference of D2D communications satisfies an interference constraint to secure cellular communications. Contrary to most of the other studies, we propose a distributed resource management scheme for cellular underlaid D2D communications focusing on a practical feasibility. In the proposed scheme, the feedback of channel information is not required because all D2D transmitters use a fixed transmit power and every D2D transmitter determines when to transmit data on its own without centralized control. We analyze the average sum-rates to evaluate the proposed scheme and compare them with optimal values, which can be achieved when a central controller has the perfect entire channel information and the full control of all D2D communications. Our numerical results show that the average sum-rates of the proposed scheme approach the optimal values in low or high signal-to-noise power ratio (SNR) regions. In particular, the proposed scheme achieves almost optimal average sum-rates in the entire SNR values in practical environments.

Beschreibung: Future Internet, Vol. 9, Pages 64: IAACaaS: IoT Application-Scoped Access Control as a Service Future Internet doi: 10.3390/fi9040064 Authors: Álvaro Alonso Federico Fernández Lourdes Marco Joaquín Salvachúa access control is a key element when guaranteeing the security of online services. However, devices that make the Internet of Things have some special requirements that foster new approaches to access control mechanisms. Their low computing capabilities impose limitations that make traditional paradigms not directly applicable to sensors and actuators. In this paper, we propose a dynamic, scalable, IoT-ready model that is based on the OAuth 2.0 protocol and that allows the complete delegation of authorization, so that an as a service access control mechanism is provided. Multiple tenants are also supported by means of application-scoped authorization policies, whose roles and permissions are fine-grained enough to provide the desired flexibility of configuration. Besides, OAuth 2.0 ensures interoperability with the rest of the Internet, yet preserving the computing constraints of IoT devices, because its tokens provide all the necessary information to perform authorization. The proposed model has been fully implemented in an open-source solution and also deeply validated in the scope of FIWARE, a European project with thousands of users, the goal of which is to provide a framework for developing smart applications and services for the future Internet. We provide the details of the deployed infrastructure and offer the analysis of a sample smart city setup that takes advantage of the model. We conclude that the proposed solution enables a new access control as a service paradigm that satisfies the special requirements of IoT devices in terms of performance, scalability and interoperability.

Beschreibung: Future Internet, Vol. 9, Pages 83: Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN Future Internet doi: 10.3390/fi9040083 Authors: Haipeng Li Hidenori Nakazato Syed Ahmed Since the content delivery unit over Information-Centric Networking (ICN) has shifted from files to the segments of a file named chunks, solely either file-level or chunk-level request probability is insufficient for ICN cache management. In this paper, a Request Expectation Index (RXI) based cache replacement algorithm for streaming content delivery is proposed. In this algorithm, RXI is introduced to serve as a fine-grained and unified estimation criteria of possible future request probability for cached chunks. RXI is customized for streaming content delivery by adopting both file-level and chunk-level request probability and considering the dynamically varied request status at each route as well. Compared to prior work, the proposed algorithm evicts the chunk with the minimum expectation of future request to maintain a high cache utilization. Additionally, simulation results demonstrate that the RXI-based algorithm can remarkably enhance the streaming content delivery performance and can be deployed in complex network scenarios. The proposed results validate that, by taking fine-grained request probability and request status into consideration, the customized in-network caching algorithm can improve the ICN streaming content delivery performance by high cache utilization, fast content delivery, and lower network traffic.

Beschreibung: Future Internet, Vol. 9, Pages 90: An Ontology-Based Approach to Enable Knowledge Representation and Reasoning in Worker–Cobot Agile Manufacturing Future Internet doi: 10.3390/fi9040090 Authors: Ahmed R. Sadik Bodo Urban There is no doubt that the rapid development in robotics technology has dramatically changed the interaction model between the Industrial Robot (IR) and the worker. As the current robotic technology has afforded very reliable means to guarantee the physical safety of the worker during a close proximity interaction with the IR. Therefore, new forms of cooperation between the robot and the worker can now be achieved. Collaborative/Cooperative robotics is the new branch of industrial robotics which empowers the idea of cooperative manufacturing. Cooperative manufacturing significantly depends on the existence of a collaborative/cooperative robot (cobot). A cobot is usually a Light-Weight Robot (LWR) which is capable of operating safely with the human co-worker in a shared work environment. This is in contrast with the conventional IR which can only operate in isolation from the worker workspace, due to the fact that the conventional IR can manipulate very heavy objects, which makes it so dangerous to operate in direct contact with the worker. There is a slight difference between the definition of collaboration and cooperation in robotics. In cooperative robotics, both the worker and the robot are performing tasks over the same product in the same shared workspace but not simultaneously. Collaborative robotics has a similar definition, except that the worker and the robot are performing a simultaneous task. Gathering the worker and the cobot in the same manufacturing workcell can provide an easy and cheap method to flexibly customize the production. Moreover, to adapt with the production demands in the real time of production, without the need to stop or to modify the production operations. There are many challenges and problems that can be addressed in the cooperative manufacturing field. However, one of the most important challenges in this field is the representation of the cooperative manufacturing environment and components. Thus, in order to accomplish the cooperative manufacturing concept, a proper approach is required to describe the shared environment between the worker and the cobot. The cooperative manufacturing shared environment includes the cobot, the co-worker, and other production components such as the product itself. Furthermore, the whole cooperative manufacturing system components need to communicate and share their knowledge, to reason and process the shared information, which eventually gives the control solution the capability of obtaining collective manufacturing decisions. Putting into consideration that the control solution should also provide a natural language which is human readable and in the same time can be understood by the machine (i.e., the cobot). Accordingly, a distributed control solution which combines an ontology-based Multi-Agent System (MAS) and a Business Rule Management System (BRMS) is proposed, in order to solve the mentioned challenges in the cooperative manufacturing, which are: manufacturing knowledge representation, sharing, and reasoning.

Beschreibung: Future Internet, Vol. 9, Pages 89: A Fast and Reliable Broadcast Service for LTE-Advanced Exploiting Multihop Device-to-Device Transmissions Future Internet doi: 10.3390/fi9040089 Authors: Giovanni Nardini Giovanni Stea Antonio Virdis Several applications, from the Internet of Things for smart cities to those for vehicular networks, need fast and reliable proximity-based broadcast communications, i.e., the ability to reach all peers in a geographical neighborhood around the originator of a message, as well as ubiquitous connectivity. In this paper, we point out the inherent limitations of the LTE (Long-Term Evolution) cellular network, which make it difficult, if possible at all, to engineer such a service using traditional infrastructure-based communications. We argue, instead, that network-controlled device-to-device (D2D) communications, relayed in a multihop fashion, can efficiently support this service. To substantiate the above claim, we design a proximity-based broadcast service which exploits multihop D2D. We discuss the relevant issues both at the UE (User Equipment), which has to run applications, and within the network (i.e., at the eNodeBs), where suitable resource allocation schemes have to be enforced. We evaluate the performance of a multihop D2D broadcasting using system-level simulations, and demonstrate that it is fast, reliable and economical from a resource consumption standpoint.

Beschreibung: Algorithms, Vol. 10, Pages 114: Variable Selection in Time Series Forecasting Using Random Forests Algorithms doi: 10.3390/a10040114 Authors: Hristos Tyralis Georgia Papacharalampous Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

Beschreibung: Algorithms, Vol. 10, Pages 115: A Multi-Threading Algorithm to Detect and Remove Cycles in Vertex- and Arc-Weighted Digraph Algorithms doi: 10.3390/a10040115 Authors: Huanqing Cui Jian Niu Chuanai Zhou Minglei Shu A graph is a very important structure to describe many applications in the real world. In many applications, such as dependency graphs and debt graphs, it is an important problem to find and remove cycles to make these graphs be cycle-free. The common algorithm often leads to an out-of-memory exception in commodity personal computer, and it cannot leverage the advantage of multicore computers. This paper introduces a new problem, cycle detection and removal with vertex priority. It proposes a multithreading iterative algorithm to solve this problem for large-scale graphs on personal computers. The algorithm includes three main steps: simplification to decrease the scale of graph, calculation of strongly connected components, and cycle detection and removal according to a pre-defined priority in parallel. This algorithm avoids the out-of-memory exception by simplification and iteration, and it leverages the advantage of multicore computers by multithreading parallelism. Five different versions of the proposed algorithm are compared by experiments, and the results show that the parallel iterative algorithm outperforms the others, and simplification can effectively improve the algorithm's performance.

Beschreibung: Algorithms, Vol. 10, Pages 113: Scale Reduction Techniques for Computing Maximum Induced Bicliques Algorithms doi: 10.3390/a10040113 Authors: Shahram Shahinpour Shirin Shirvani Zeynep Ertem Sergiy Butenko Given a simple, undirected graph G, a biclique is a subset of vertices inducing a complete bipartite subgraph in G. In this paper, we consider two associated optimization problems, the maximum biclique problem, which asks for a biclique of the maximum cardinality in the graph, and the maximum edge biclique problem, aiming to find a biclique with the maximum number of edges in the graph. These NP-hard problems find applications in biclustering-type tasks arising in complex network analysis. Real-life instances of these problems often involve massive, but sparse networks. We develop exact approaches for detecting optimal bicliques in large-scale graphs that combine effective scale reduction techniques with integer programming methodology. Results of computational experiments with numerous real-life network instances demonstrate the performance of the proposed approach.

Beschreibung: Algorithms, Vol. 10, Pages 110: DDC Control Techniques for Three-Phase BLDC Motor Position Control Algorithms doi: 10.3390/a10040110 Authors: Rana Masood Dao Wang Zain Ali Babar Khan In this article, a novel hybrid control scheme is proposed for controlling the position of a three-phase brushless direct current (BLDC) motor. The hybrid controller consists of discrete time sliding mode control (SMC) with model free adaptive control (MFAC) to make a new data-driven control (DDC) strategy that is able to reduce the simulation time and complexity of a nonlinear system. The proposed hybrid algorithm is also suitable for controlling the speed variations of a BLDC motor, and is also applicable for the real time simulation of platforms such as a gimbal platform. The DDC method does not require any system model because it depends on data collected by the system about its Inputs/Outputs (IOS). However, the model-based control (MBC) method is difficult to apply from a practical point of view and is time-consuming because we need to linearize the system model. The above proposed method is verified by multiple simulations using MATLAB Simulink. It shows that the proposed controller has better performance, more precise tracking, and greater robustness compared with the classical proportional integral derivative (PID) controller, MFAC, and model free learning adaptive control (MFLAC).

Beschreibung: Algorithms, Vol. 10, Pages 117: Fabric Weave Pattern and Yarn Color Recognition and Classification Using a Deep ELM Network Algorithms doi: 10.3390/a10040117 Authors: Babar Khan Zhijie Wang Fang Han Ather Iqbal Rana Masood Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.

Beschreibung: Algorithms, Vol. 10, Pages 104: Contract-Based Incentive Mechanism for Mobile Crowdsourcing Networks Algorithms doi: 10.3390/a10030104 Authors: Nan Zhao Menglin Fan Chao Tian Pengfei Fan Mobile crowdsourcing networks (MCNs) are a promising method of data collecting and processing by leveraging the mobile devices’ sensing and computing capabilities. However, because of the selfish characteristics of the service provider (SP) and mobile users (MUs), crowdsourcing participants only aim to maximize their own benefits. This paper investigates the incentive mechanism between the above two parties to create mutual benefits. By modeling MCNs as a labor market, a contract-based crowdsourcing model with moral hazard is proposed under the asymmetric information scenario. In order to incentivize the potential MUs to participate in crowdsourcing tasks, the optimization problem is formulated to maximize the SP’s utility by jointly examining the crowdsourcing participants’ risk preferences. The impact of crowdsourcing participants’ attitudes of risks on the incentive mechanism has been studied analytically and experimentally. Numerical simulation results demonstrate the effectiveness of the proposed contract design scheme for the crowdsourcing incentive.

Beschreibung: Algorithms, Vol. 10, Pages 99: Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems Algorithms doi: 10.3390/a10030099 Authors: Mauricio Sanchez Juan Castro Violeta Ocegueda-Miramontes Leticia Cervantes This work is focused on creating fuzzy granular classification models based on general type-2 fuzzy logic systems when consequents are represented by interval type-2 TSK linear functions. Due to the complexity of general type-2 TSK fuzzy logic systems, a hybrid learning approach is proposed, where the principle of justifiable granularity is heuristically used to define an amount of uncertainty in the system, which in turn is used to define the parameters in the interval type-2 TSK linear functions via a dual LSE algorithm. Multiple classification benchmark datasets were tested in order to assess the quality of the formed granular models; its performance is also compared against other common classification algorithms. Shown results conclude that classification performance in general is better than results obtained by other techniques, and in general, all achieved results, when averaged, have a better performance rate than compared techniques, demonstrating the stability of the proposed hybrid learning technique.

Beschreibung: Algorithms, Vol. 10, Pages 98: Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow Algorithms doi: 10.3390/a10030098 Authors: Fei Chen Xiaohong Bi Ruimin Lyu Zhongwei Hua Yuan Liu Xiaoting Zhang Over the past decades, vehicular ad hoc networks (VANETs) have been a core networking technology to provide drivers and passengers with safety and convenience. As a new emerging technology, the vehicular cloud computing (VCC) can provide cloud services for various data-intensive applications in VANETs, such as multimedia streaming. However, the vehicle mobility and intermittent connectivity present challenges to the large-scale data dissemination with underlying computing and networking architecture. In this paper, we will explore the service scheduling of virtual RSUs for diverse request demands in the dynamic traffic flow in vehicular cloud environment. Specifically, we formulate the RSU allocation problem as maximum service capacity with multiple-source and multiple-destination, and propose a bidirectional RSU allocation strategy. In addition, we formulate the content replication in distributed RSUs as the minimum replication set coverage problem in a two-layer mapping model, and analyze the solutions in different scenarios. Numerical results further prove the superiority of our proposed solution, as well as the scalability to various traffic condition variations.

Beschreibung: Algorithms, Vol. 10, Pages 96: Double-Threshold Cooperative Spectrum Sensing Algorithm Based on Sevcik Fractal Dimension Algorithms doi: 10.3390/a10030096 Authors: Xueying Diao Qianhui Dong Zijian Yang Yibing Li Spectrum sensing is of great importance in the cognitive radio (CR) networks. Compared with individual spectrum sensing, cooperative spectrum sensing (CSS) has been shown to greatly improve the accuracy of the detection. However, the existing CSS algorithms are sensitive to noise uncertainty and are inaccurate in low signal-to-noise ratio (SNR) detection. To address this, we propose a double-threshold CSS algorithm based on Sevcik fractal dimension (SFD) in this paper. The main idea of the presented scheme is to sense the presence of primary users in the local spectrum sensing by analyzing different characteristics of the SFD between signals and noise. Considering the stochastic fluctuation characteristic of the noise SFD in a certain range, we adopt the double-threshold method in the multi-cognitive user CSS so as to improve the detection accuracy, where thresholds are set according to the maximum and minimum values of the noise SFD. After obtaining the detection results, the cognitive user sends local detection results to the fusion center for reliability fusion. Simulation results demonstrate that the proposed method is insensitive to noise uncertainty. Simulations also show that the algorithm presented in this paper can achieve high detection performance at the low SNR region.

Beschreibung: Algorithms, Vol. 10, Pages 90: Local Community Detection Based on Small Cliques Algorithms doi: 10.3390/a10030090 Authors: Michael Hamann Eike Röhrs Dorothea Wagner Community detection aims to find dense subgraphs in a network. We consider the problem of finding a community locally around a seed node both in unweighted and weighted networks. This is a faster alternative to algorithms that detect communities that cover the whole network when actually only a single community is required. Further, many overlapping community detection algorithms use local community detection algorithms as basic building block. We provide a broad comparison of different existing strategies of expanding a seed node greedily into a community. For this, we conduct an extensive experimental evaluation both on synthetic benchmark graphs as well as real world networks. We show that results both on synthetic as well as real-world networks can be significantly improved by starting from the largest clique in the neighborhood of the seed node. Further, our experiments indicate that algorithms using scores based on triangles outperform other algorithms in most cases. We provide theoretical descriptions as well as open source implementations of all algorithms used.

Beschreibung: Algorithms, Vol. 10, Pages 95: A Parallel Two-Stage Iteration Method for Solving Continuous Sylvester Equations Algorithms doi: 10.3390/a10030095 Authors: Manyu Xiao Quanyi Lv Zhuo Xing Yingchun Zhang In this paper we propose a parallel two-stage iteration algorithm for solving large-scale continuous Sylvester equations. By splitting the coefficient matrices, the original linear system is transformed into a symmetric linear system which is then solved by using the SYMMLQ algorithm. In order to improve the relative parallel efficiency, an adjusting strategy is explored during the iteration calculation of the SYMMLQ algorithm to decrease the degree of the reduce-operator from two to one communications at each step. Moreover, the convergence of the iteration scheme is discussed, and finally numerical results are reported showing that the proposed method is an efficient and robust algorithm for this class of continuous Sylvester equations on a parallel machine.

Beschreibung: Algorithms, Vol. 10, Pages 87: Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection Algorithms doi: 10.3390/a10030087 Authors: Mozhdeh Shahbazi Gunho Sohn Jérôme Théau In this paper, a robust technique based on a genetic algorithm is proposed for estimating two-view epipolar-geometry of uncalibrated perspective stereo images from putative correspondences containing a high percentage of outliers. The advantages of this technique are three-fold: (i) replacing random search with evolutionary search applying new strategies of encoding and guided sampling; (ii) robust and fast estimation of the epipolar geometry via detecting a more-than-enough set of inliers without making any assumptions about the probability distribution of the residuals; (iii) determining the inlier-outlier threshold based on the uncertainty of the estimated model. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC (random sample consensus), MSAC, MLESAC, Cov-RANSAC, LO-RANSAC, StaRSAC, Multi-GS RANSAC and least median of squares (LMedS). Experimental results showed that the proposed approach performed better than other methods regarding the accuracy of inlier detection and epipolar-geometry estimation, as well as the computational efficiency for datasets majorly contaminated by outliers and noise.

Beschreibung: Algorithms, Vol. 10, Pages 89: On the Existence of Solutions of Nonlinear Fredholm Integral Equations from Kantorovich’s Technique Algorithms doi: 10.3390/a10030089 Authors: José Ezquerro Miguel Hernández-Verón The well-known Kantorovich technique based on majorizing sequences is used to analyse the convergence of Newton’s method when it is used to solve nonlinear Fredholm integral equations. In addition, we obtain information about the domains of existence and uniqueness of a solution for these equations. Finally, we illustrate the above with two particular Fredholm integral equations.

Beschreibung: Algorithms, Vol. 10, Pages 84: Auxiliary Model Based Multi-Innovation Stochastic Gradient Identification Algorithm for Periodically Non-Uniformly Sampled-Data Hammerstein Systems Algorithms doi: 10.3390/a10030084 Authors: Li Xie Huizhong Yang Due to the lack of powerful model description methods, the identification of Hammerstein systems based on the non-uniform input-output dataset remains a challenging problem. This paper introduces a time-varying backward shift operator to describe periodically non-uniformly sampled-data Hammerstein systems, which can simplify the structure of the lifted models using the traditional lifting technique. Furthermore, an auxiliary model-based multi-innovation stochastic gradient algorithm is presented to estimate the parameters involved in the linear and nonlinear blocks. The simulation results confirm that the proposed algorithm is effective and can achieve a high estimation performance.

Beschreibung: Algorithms, Vol. 10, Pages 94: NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method Algorithms doi: 10.3390/a10030094 Authors: Peng Sun Zhiming Yang Yang Yu Junbao Li Xiyuan Peng As technology scales, negative bias temperature instability (NBTI) becomes one of the primary failure mechanisms for Very Large Scale Integration (VLSI) circuits. Meanwhile, the leakage power increases dramatically as the supply/threshold voltage continues to scale down. These two issues pose severe reliability problems for complementary metal oxide semiconductor (CMOS) devices. Because both the NBTI and leakage are dependent on the input vector of the circuit, we present an input vector control (IVC) method based on a linear programming algorithm, which can co-optimize circuit aging and power dissipation simultaneously. In addition, our proposed IVC method is combined with the supply voltage assignment technique to further reduce delay degradation and leakage power. Experimental results on various circuits show the effectiveness of the proposed combination method.

Beschreibung: Algorithms, Vol. 10, Pages 91: Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets Algorithms doi: 10.3390/a10030091 Authors: Chengyuan Chen Qiang Shen In support of reasoning with sparse rule bases, fuzzy rule interpolation (FRI) offers a helpful inference mechanism for deriving an approximate conclusion when a given observation has no overlap with any rule in the existing rule base. One of the recent and popular FRI approaches is the scale and move transformation-based rule interpolation, known as T-FRI in the literature. It supports both interpolation and extrapolation with multiple multi-antecedent rules. However, the difficult problem of defining the precise-valued membership functions required in the representation of fuzzy rules, or of the observations, restricts its applications. Fortunately, this problem can be alleviated through the use of type-2 fuzzy sets, owing to the fact that the membership functions of such fuzzy sets are themselves fuzzy, providing a more flexible means of modelling. This paper therefore, extends the existing T-FRI approach using interval type-2 fuzzy sets, which covers the original T-FRI as its specific instance. The effectiveness of this extension is demonstrated by experimental investigations and, also, by a practical application in comparison to the state-of-the-art alternative approach developed using rough-fuzzy sets.

Beschreibung: Algorithms, Vol. 10, Pages 86: An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems Algorithms doi: 10.3390/a10030086 Authors: Wei Zheng Yanyan Tan Xiaonan Fang Shengtao Li MOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well. However, there is a class of multi-objective problems, called many-objective optimization problems, but the original MOEA/D cannot solve them well. In this paper, an improved MOEA/D with optimal differential evolution (oDE) schemes is proposed, called MOEA/D-oDE, aiming to solve many-objective optimization problems. Compared with MOEA/D, MOEA/D-oDE has two distinguishing points. On the one hand, MOEA/D-oDE adopts a newly-introduced decomposition approach to decompose the many-objective optimization problems, which combines the advantages of the weighted sum approach and the Tchebycheff approach. On the other hand, a kind of combination mechanism for DE operators is designed for finding the best child solution so as to do the a posteriori computing. In our experimental study, six continuous test instances with 4–6 objectives comparing NSGA-II (nondominated sorting genetic algorithm II) and MOEA/D as accompanying experiments are applied. Additionally, the final results indicate that MOEA/D-oDE outperforms NSGA-II and MOEA/D in almost all cases, particularly in those problems that have complicated Pareto shapes and higher dimensional objectives, where its advantages are more obvious.

Beschreibung: Algorithms, Vol. 10, Pages 83: Fuzzy Fireworks Algorithm Based on a Sparks Dispersion Measure Algorithms doi: 10.3390/a10030083 Authors: Juan Barraza Patricia Melin Fevrier Valdez Claudia Gonzalez The main goal of this paper is to improve the performance of the Fireworks Algorithm (FWA). To improve the performance of the FWA we propose three modifications: the first modification is to change the stopping criteria, this is to say, previously, the number of function evaluations was utilized as a stopping criteria, and we decided to change this to specify a particular number of iterations; the second and third modifications consist on introducing a dispersion metric (dispersion percent), and both modifications were made with the goal of achieving dynamic adaptation of the two parameters in the algorithm. The parameters that were controlled are the explosion amplitude and the number of sparks, and it is worth mentioning that the control of these parameters is based on a fuzzy logic approach. To measure the impact of these modifications, we perform experiments with 14 benchmark functions and a comparative study shows the advantage of the proposed approach. We decided to call the proposed algorithms Iterative Fireworks Algorithm (IFWA) and two variants of the Dispersion Percent Iterative Fuzzy Fireworks Algorithm (DPIFWA-I and DPIFWA-II, respectively).

Beschreibung: Algorithms, Vol. 10, Pages 80: A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes Algorithms doi: 10.3390/a10030080 Authors: Yasser El Khamlichi Abderrahim Tahiri Anouar Abtoy Inmaculada Medina-Bulo Francisco Palomo-Lozano Wireless sensor network (WSN) applications are rapidly growing and are widely used in various disciplines. Deployment is one of the key issues to be solved in WSNs, since the sensor nodes’ positioning affects highly the system performance. An optimal WSN deployment should maximize the collection of the desired interest phenomena, guarantee the required coverage and connectivity, extend the network lifetime, and minimize the network cost in terms of energy consumption. Most of the research effort in this area aims to solve the deployment issue, without minimizing the network cost by reducing unnecessary working nodes in the network. In this paper, we propose a deployment approach based on the gradient method and the Simulated Annealing algorithm to solve the sensor deployment problem with the minimum number of sensor nodes. The proposed algorithm is able to heuristically optimize the number of sensors and their positions in order to achieve the desired application requirements.

Beschreibung: Algorithms, Vol. 10, Pages 85: A New Meta-Heuristics of Optimization with Dynamic Adaptation of Parameters Using Type-2 Fuzzy Logic for Trajectory Control of a Mobile Robot Algorithms doi: 10.3390/a10030085 Authors: Camilo Caraveo Fevrier Valdez Oscar Castillo Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.

Beschreibung: Algorithms, Vol. 10, Pages 69: Cross-Language Plagiarism Detection System Using Latent Semantic Analysis and Learning Vector Quantization Algorithms doi: 10.3390/a10020069 Authors: Anak Agung Putri Ratna Prima Dewi Purnamasari Boma Anantasatya Adhi F. Astha Ekadiyanto Muhammad Salman Mardiyah Mardiyah Darien Jonathan Winata Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction.

Beschreibung: Algorithms, Vol. 10, Pages 59: Contradiction Detection with Contradiction-Specific Word Embedding Algorithms doi: 10.3390/a10020059 Authors: Luyang Li Bing Qin Ting Liu Contradiction detection is a task to recognize contradiction relations between a pair of sentences. Despite the effectiveness of traditional context-based word embedding learning algorithms in many natural language processing tasks, such algorithms are not powerful enough for contradiction detection. Contrasting words such as “overfull” and “empty” are mostly mapped into close vectors in such embedding space. To solve this problem, we develop a tailored neural network to learn contradiction-specific word embedding (CWE). The method can separate antonyms in the opposite ends of a spectrum. CWE is learned from a training corpus which is automatically generated from the paraphrase database, and is naturally applied as features to carry out contradiction detection in SemEval 2014 benchmark dataset. Experimental results show that CWE outperforms traditional context-based word embedding in contradiction detection. The proposed model for contradiction detection performs comparably with the top-performing system in accuracy of three-category classification and enhances the accuracy from 75.97% to 82.08% in the contradiction category.

Beschreibung: Algorithms, Vol. 10, Pages 62: Influence Factors Analysis on the Modal Characteristics of Irregularly-Shaped Bridges Based on a Free-Interface Mode Synthesis Algorithm Algorithms doi: 10.3390/a10020062 Authors: Hanbing Liu Mengsu Zhang Xianqiang Wang Shuai Tian Yubo Jiao In order to relieve traffic congestion, irregularly-shaped bridges have been widely used in urban overpasses. However, the analysis on modal characteristics of irregularly-shaped bridges is not exhaustive, and the effect of design parameters on modal characteristics will be deeply investigated in future studies. In this paper, a novel strategy based on a free-interface mode synthesis algorithm is proposed to evaluate the parameters’ effect on the modal characteristics of irregularly-shaped bridges. First, a complicated, irregularly-shaped bridge is divided into several substructures based on its properties. Then, the modal characteristics of the overall structure can be obtained, only by a few low-order modal parameters of each substructure, using a free-interface mode synthesis method. A numerical model of a typical irregularly-shaped bridge is employed to verify the effectiveness of the proposed strategy. Simulation results reveal that the free-interface mode synthesis method possesses favorable calculation accuracy for analyzing the modal characteristics of irregularly-shaped bridges. The effect of design parameters such as ramp curve radius, diaphragm beam stiffness, cross-section feature, and bearing conditions on the modal characteristics of an irregularly-shaped bridge is evaluated in detail. Analysis results can provide references for further research into and the design of irregularly-shaped bridges.

Beschreibung: Algorithms, Vol. 10, Pages 54: Extending the Applicability of the MMN-HSS Method for Solving Systems of Nonlinear Equations under Generalized Conditions Algorithms doi: 10.3390/a10020054 Authors: Ioannis Argyros Janak Sharma Deepak Kumar We present the semilocal convergence of a multi-step modified Newton-Hermitian and Skew-Hermitian Splitting method (MMN-HSS method) to approximate a solution of a nonlinear equation. Earlier studies show convergence under only Lipschitz conditions limiting the applicability of this method. The convergence in this study is shown under generalized Lipschitz-type conditions and restricted convergence domains. Hence, the applicability of the method is extended. Moreover, numerical examples are also provided to show that our results can be applied to solve equations in cases where earlier study cannot be applied. Furthermore, in the cases where both old and new results are applicable, the latter provides a larger domain of convergence and tighter error bounds on the distances involved.

Beschreibung: Algorithms, Vol. 10, Pages 49: Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network Algorithms doi: 10.3390/a10020049 Authors: Khaled Bouzenad Messaoud Ramdani Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.

Beschreibung: Algorithms, Vol. 10, Pages 53: Application of Gradient Descent Continuous Actor-Critic Algorithm for Bilateral Spot Electricity Market Modeling Considering Renewable Power Penetration Algorithms doi: 10.3390/a10020053 Authors: Huiru Zhao Yuwei Wang Mingrui Zhao Chuyu Sun Qingkun Tan The bilateral spot electricity market is very complicated because all generation units and demands must strategically bid in this market. Considering renewable resource penetration, the high variability and the non-dispatchable nature of these intermittent resources make it more difficult to model and simulate the dynamic bidding process and the equilibrium in the bilateral spot electricity market, which makes developing fast and reliable market modeling approaches a matter of urgency nowadays. In this paper, a Gradient Descent Continuous Actor-Critic algorithm is proposed for hour-ahead bilateral electricity market modeling in the presence of renewable resources because this algorithm can solve electricity market modeling problems with continuous state and action spaces without causing the “curse of dimensionality” and has low time complexity. In our simulation, the proposed approach is implemented on an IEEE 30-bus test system. The adequate performance of our proposed approach—such as reaching Nash Equilibrium results after enough iterations of training are tested and verified, and some conclusions about the relationship between increasing the renewable power output and participants’ bidding strategy, locational marginal prices, and social welfare—is also evaluated. Moreover, the comparison of our proposed approach with the fuzzy Q-learning-based electricity market approach implemented in this paper confirms the superiority of our proposed approach in terms of participants’ profits, social welfare, average locational marginal prices, etc.

Beschreibung: Algorithms, Vol. 10, Pages 50: Hierarchical Parallel Evaluation of a Hamming Code Algorithms doi: 10.3390/a10020050 Authors: Shmuel Klein Dana Shapira The Hamming code is a well-known error correction code and can correct a single error in an input vector of size n bits by adding logn parity checks. A new parallel implementation of the code is presented, using a hierarchical structure of n processors in logn layers. All the processors perform similar simple tasks, and need only a few bytes of internal memory.

Beschreibung: Algorithms, Vol. 10, Pages 51: Adaptive Vector Quantization for Lossy Compression of Image Sequences Algorithms doi: 10.3390/a10020051 Authors: Raffaele Pizzolante Bruno Carpentieri Sergio De Agostino In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm.

Beschreibung: Algorithms, Vol. 10, Pages 45: An Efficient Sixth-Order Newton-Type Method for Solving Nonlinear Systems Algorithms doi: 10.3390/a10020045 Authors: Xiaofeng Wang Yang Li In this paper, we present a new sixth-order iterative method for solving nonlinear systems and prove a local convergence result. The new method requires solving five linear systems per iteration. An important feature of the new method is that the LU (lower upper, also called LU factorization) decomposition of the Jacobian matrix is computed only once in each iteration. The computational efficiency index of the new method is compared to that of some known methods. Numerical results are given to show that the convergence behavior of the new method is similar to the existing methods. The new method can be applied to small- and medium-sized nonlinear systems.

Beschreibung: Algorithms, Vol. 10, Pages 39: From Intrusion Detection to an Intrusion Response System: Fundamentals, Requirements, and Future Directions Algorithms doi: 10.3390/a10020039 Authors: Shahid Anwar Jasni Mohamad Zain Mohamad Fadli Zolkipli Zakira Inayat Suleman Khan Bokolo Anthony Victor Chang In the past few decades, the rise in attacks on communication devices in networks has resulted in a reduction of network functionality, throughput, and performance. To detect and mitigate these network attacks, researchers, academicians, and practitioners developed Intrusion Detection Systems (IDSs) with automatic response systems. The response system is considered an important component of IDS, since without a timely response IDSs may not function properly in countering various attacks, especially on a real-time basis. To respond appropriately, IDSs should select the optimal response option according to the type of network attack. This research study provides a complete survey of IDSs and Intrusion Response Systems (IRSs) on the basis of our in-depth understanding of the response option for different types of network attacks. Knowledge of the path from IDS to IRS can assist network administrators and network staffs in understanding how to tackle different attacks with state-of-the-art technologies.

Beschreibung: Algorithms, Vol. 10, Pages 55: Erratum: Ahmad, F., et al. A Preconditioned Iterative Method for Solving Systems of Nonlinear Equations Having Unknown Multiplicity. Algorithms 2017, 10, 17 Algorithms doi: 10.3390/a10020055 Authors: Fayyaz Ahmad Toseef Bhutta Umar Shoaib Malik Ullah Ali Alshomrani Shamshad Ahmad Shahid Ahmad n/a

Beschreibung: Algorithms, Vol. 10, Pages 57: A Prediction of Precipitation Data Based on Support Vector Machine and Particle Swarm Optimization (PSO-SVM) Algorithms Algorithms doi: 10.3390/a10020057 Authors: Jinglin Du Yayun Liu Yanan Yu Weilan Yan Precipitation is a very important topic in weather forecasts. Weather forecasts, especially precipitation prediction, poses complex tasks because they depend on various parameters to predict the dependent variables like temperature, humidity, wind speed and direction, which are changing from time to time and weather calculation varies with the geographical location along with its atmospheric variables. To improve the prediction accuracy of precipitation, this context proposes a prediction model for rainfall forecast based on Support Vector Machine with Particle Swarm Optimization (PSO-SVM) to replace the linear threshold used in traditional precipitation. Parameter selection has a critical impact on the predictive accuracy of SVM, and PSO is proposed to find the optimal parameters for SVM. The PSO-SVM algorithm was used for the training of a model by using the historical data for precipitation prediction, which can be useful information and used by people of all walks of life in making wise and intelligent decisions. The simulations demonstrate that prediction models indicate that the performance of the proposed algorithm has much better accuracy than the direct prediction model based on a set of experimental data if other things are equal. On the other hand, simulation results demonstrate the effectiveness and advantages of the SVM-PSO model used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables.

Beschreibung: Algorithms, Vol. 10, Pages 42: RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation Algorithms doi: 10.3390/a10020042 Authors: Ziyan Chen Yu Huang Yuexian Liang Yang Wang Xingyu Fu Kun Fu Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word representations; unsupervised methods for many natural language processing (NLP) tasks have been widely researched. In this paper; we focus on a new finding of unsupervised relation extraction; which is called distributional relation representation. Without requiring the pre-defined types; distributional relation representation aims to automatically learn entity vectors and further estimate semantic similarity between these entities. We choose global vectors (GloVe) as our original model to train entity vectors because of its excellent balance between local context and global statistics in the whole corpus. In order to train model more efficiently; we improve the traditional GloVe model by using cosine similarity between entity vectors to approximate the entity occurrences instead of dot product. Because cosine similarity can convert vector to unit vector; it is intuitively more reasonable and more easily converge to a local optimum. We call the improved model RGloVe. Experimental results on a massive corpus of Sina News show that our proposed model outperforms the traditional global vectors. Finally; a graph database of Neo4j is introduced to store these relationships between named entities. The most competitive advantage of Neo4j is that it provides a highly accessible way to query the direct and indirect relationships between entities.

Beschreibung: Algorithms, Vol. 10, Pages 48: Adaptive Mutation Dynamic Search Fireworks Algorithm Algorithms doi: 10.3390/a10020048 Authors: Xi-Guang Li Shou-Fei Han Liang Zhao Chang-Qing Gong Xiao-Jing Liu The Dynamic Search Fireworks Algorithm (dynFWA) is an effective algorithm for solving optimization problems. However, dynFWA easily falls into local optimal solutions prematurely and it also has a slow convergence rate. In order to improve these problems, an adaptive mutation dynamic search fireworks algorithm (AMdynFWA) is introduced in this paper. The proposed algorithm applies the Gaussian mutation or the Levy mutation for the core firework (CF) with mutation probability. Our simulation compares the proposed algorithm with the FWA-Based algorithms and other swarm intelligence algorithms. The results show that the proposed algorithm achieves better overall performance on the standard test functions.

Beschreibung: Future Internet, Vol. 9, Pages 85: Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network Future Internet doi: 10.3390/fi9040085 Authors: Zhen Hong Zehua Wang Wei Cai Victor Leung Device-to-device (D2D) communication is becoming an increasingly important technology in future networks with the climbing demand for local services. For instance, resource sharing in the D2D network features ubiquitous availability, flexibility, low latency and low cost. However, these features also bring along challenges when building a satisfactory resource sharing system in the D2D network. Specifically, user mobility is one of the top concerns for designing a cooperative D2D computational resource sharing system since mutual communication may not be stably available due to user mobility. A previous endeavour has demonstrated and proven how connectivity can be incorporated into cooperative task scheduling among users in the D2D network to effectively lower average task execution time. There are doubts about whether this type of task scheduling scheme, though effective, presents fairness among users. In other words, it can be unfair for users who contribute many computational resources while receiving little when in need. In this paper, we propose a novel blockchain-based credit system that can be incorporated into the connectivity-aware task scheduling scheme to enforce fairness among users in the D2D network. Users’ computational task cooperation will be recorded on the public blockchain ledger in the system as transactions, and each user’s credit balance can be easily accessible from the ledger. A supernode at the base station is responsible for scheduling cooperative computational tasks based on user mobility and user credit balance. We investigated the performance of the credit system, and simulation results showed that with a minor sacrifice of average task execution time, the level of fairness can obtain a major enhancement.

Beschreibung: Future Internet, Vol. 9, Pages 88: Behavioural Verification: Preventing Report Fraud in Decentralized Advert Distribution Systems Future Internet doi: 10.3390/fi9040088 Authors: Stylianos Mamais George Theodorakopoulos Service commissions, which are claimed by Ad-Networks and Publishers, are susceptible to forgery as non-human operators are able to artificially create fictitious traffic on digital platforms for the purpose of committing financial fraud. This places a significant strain on Advertisers who have no effective means of differentiating fabricated Ad-Reports from those which correspond to real consumer activity. To address this problem, we contribute an advert reporting system which utilizes opportunistic networking and a blockchain-inspired construction in order to identify authentic Ad-Reports by determining whether they were composed by honest or dishonest users. What constitutes a user’s honesty for our system is the manner in which they access adverts on their mobile device. Dishonest users submit multiple reports over a short period of time while honest users behave as consumers who view adverts at a balanced pace while engaging in typical social activities such as purchasing goods online, moving through space and interacting with other users. We argue that it is hard for dishonest users to fake honest behaviour and we exploit the behavioural patterns of users in order to classify Ad-Reports as real or fabricated. By determining the honesty of the user who submitted a particular report, our system offers a more secure reward-claiming model which protects against fraud while still preserving the user’s anonymity.

Beschreibung: Future Internet, Vol. 9, Pages 91: High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network Future Internet doi: 10.3390/fi9040091 Authors: Zhufang Kuang Gongqiang Li Junshan Tan Zhigang Chen The link failure due to the secondary users exiting the licensed channels when primary users reoccupy the licensed channels is very important in cognitive wireless mesh networks (CWMNs). A multipath routing and spectrum allocation algorithm based on channel interference and reusability with Quality of Service (QoS) constraints in CWMNs (MRIR) was proposed. Maximizing the throughput and the acceptance ratio of the wireless service is the objective of the MRIR. First, a primary path of resource conservation with QoS constraints was constructed, then, a resource conservation backup path based on channel interference and reusability with QoS constraints was constructed. The MRIR algorithm contains the primary path routing and spectrum allocation algorithm, and the backup path routing and spectrum allocation algorithm. The simulation results showed that the MRIR algorithm could achieve the expected goals and could achieve a higher throughput and acceptance ratio.

Beschreibung: Future Internet, Vol. 9, Pages 92: Social-Aware Relay Selection for Cooperative Multicast Device-to-Device Communications Future Internet doi: 10.3390/fi9040092 Authors: Francesco Chiti Romano Fantacci Laura Pierucci The increasing use of social networks such as Facebook, Twitter, and Instagram to share photos, video streaming, and music among friends has generated a huge increase in the amount of data traffic over wireless networks. This social behavior has triggered new communication paradigms such as device-to-device (D2D) and relaying communication schemes, which are both considered as strong drivers for the next fifth-generation (5G) cellular systems. Recently, the social-aware layer and its relationship to and influence on the physical communications layer have gained great attention as emerging focus points. We focus here on the case of relaying communications to pursue the multicast data dissemination to a group of users forming a social community through a relay node, according to the extension of the D2D mode to the case of device-to-many devices. Moreover, in our case, the source selects the device to act as the relay among different users of the multicast group by taking into account both the propagation link conditions and the relay social-trust level with the constraint of minimizing the end-to-end content delivery delay. An optimization procedure is also proposed in order to achieve the best performance. Finally, numerical results are provided to highlight the advantages of considering the impact of social level on the end-to-end delivery delay in the integrated social–physical network in comparison with the classical relay-assisted multicast communications for which the relay social-trust level is not considered.

Beschreibung: Algorithms, Vol. 10, Pages 81: Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus Algorithms doi: 10.3390/a10030081 Authors: Xiaocong Wei Hongfei Lin Yuhai Yu Liang Yang The literature [-5]contains several reports evaluating the abilities of deep neural networks in text transfer learning. To our knowledge, however, there have been few efforts to fully realize the potential of deep neural networks in cross-domain product review sentiment classification. In this paper, we propose a two-layer convolutional neural network (CNN) for cross-domain product review sentiment classification (LM-CNN-LB). Transfer learning research into product review sentiment classification based on deep neural networks has been limited by the lack of a large-scale corpus; we sought to remedy this problem using a large-scale auxiliary cross-domain dataset collected from Amazon product reviews. Our proposed framework exhibits the dramatic transferability of deep neural networks for cross-domain product review sentiment classification and achieves state-of-the-art performance. The framework also outperforms complex engineered features used with a non-deep neural network method. The experiments demonstrate that introducing large-scale data from similar domains is an effective way to resolve the lack of training data. The LM-CNN-LB trained on the multi-source related domain dataset outperformed the one trained on a single similar domain.

Beschreibung: Algorithms, Vol. 10, Pages 78: An Efficient Algorithm for the Separable Nonlinear Least Squares Problem Algorithms doi: 10.3390/a10030078 Authors: Yunqiu Shen Tjalling Ypma The nonlinear least squares problem m i n y , z ∥ A ( y ) z + b ( y ) ∥ , where A ( y ) is a full-rank ( N + ℓ ) × N matrix, y ∈ R n , z ∈ R N and b ( y ) ∈ R N + ℓ with ℓ ≥ n , can be solved by first solving a reduced problem m i n y ∥ f ( y ) ∥ to find the optimal value y * of y, and then solving the resulting linear least squares problem m i n z ∥ A ( y * ) z + b ( y * ) ∥ to find the optimal value z * of z. We have previously justified the use of the reduced function f ( y ) = C T ( y ) b ( y ) , where C ( y ) is a matrix whose columns form an orthonormal basis for the nullspace of A T ( y ) , and presented a quadratically convergent Gauss–Newton type method for solving m i n y ∥ C T ( y ) b ( y ) ∥ based on the use of QR factorization. In this note, we show how LU factorization can replace the QR factorization in those computations, halving the associated computational cost while also providing opportunities to exploit sparsity and thus further enhance computational efficiency.

Beschreibung: Algorithms, Vol. 10, Pages 93: Post-Processing Partitions to Identify Domains of Modularity Optimization Algorithms doi: 10.3390/a10030093 Authors: William Weir Scott Emmons Ryan Gibson Dane Taylor Peter Mucha We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition—i.e., the parameter-space domain where it has the largest modularity relative to the input set—discarding partitions with empty domains to obtain the subset of partitions that are “admissible” candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.

Beschreibung: Algorithms, Vol. 10, Pages 77: New Methodology to Approximate Type-Reduction Based on a Continuous Root-Finding Karnik Mendel Algorithm Algorithms doi: 10.3390/a10030077 Authors: Emanuel Ontiveros-Robles Patricia Melin Oscar Castillo Interval Type-2 fuzzy systems allow the possibility of considering uncertainty in models based on fuzzy systems, and enable an increase of robustness in solutions to applications, but also increase the complexity of the fuzzy system design. Several attempts have been previously proposed to reduce the computational cost of the type-reduction stage, as this process requires a lot of computing time because it is basically a numerical approximation based on sampling, and the computational cost is proportional to the number of samples, but also the error is inversely proportional to the number of samples. Several works have focused on reducing the computational cost of type-reduction by developing strategies to reduce the number of operations. The first type-reduction method was proposed by Karnik and Mendel (KM), and then was followed by its enhanced version called EKM. Then continuous versions were called CKM and CEKM, and there were variations of this and also other types of variations that eliminate the type-reduction process reducing the computational cost to a Type-1 defuzzification, such as the Nie-Tan versions and similar enhancements. In this work we analyzed and proposed a variant of CEKM by viewing this process as solving a root-finding problem, in this way taking advantage of existing numerical methods to solve the type-reduction problem, the main objective being eliminating the type-reduction process and also providing a continuous solution of the defuzzification.

Beschreibung: Algorithms, Vol. 10, Pages 74: The Isomorphic Version of Brualdi’s and Sanderson’s Nestedness Algorithms doi: 10.3390/a10030074 Authors: Annabell Berger Berit Schreck The discrepancy BR for an m × n 0, 1-matrix from Brualdi and Sanderson in 1998 is defined as the minimum number of 1 s that need to be shifted in each row to the left to achieve its Ferrers matrix, i.e., each row consists of consecutive 1 s followed by consecutive 0 s. For ecological bipartite networks, BR describes a nested set of relationships. Since two different labelled networks can be isomorphic, but possess different discrepancies due to different adjacency matrices, we define a metric determining the minimum discrepancy in an isomorphic class. We give a reduction to k ≤ n minimum weighted perfect matching problems. We show on 289 ecological matrices (given as a benchmark by Atmar and Patterson in 1995) that classical discrepancy can underestimate the nestedness by up to 30%.

Beschreibung: Algorithms, Vol. 10, Pages 71: Bayesian and Classical Estimation of Stress-Strength Reliability for Inverse Weibull Lifetime Models Algorithms doi: 10.3390/a10020071 Authors: Qixuan Bi Wenhao Gui In this paper, we consider the problem of estimating stress-strength reliability for inverse Weibull lifetime models having the same shape parameters but different scale parameters. We obtain the maximum likelihood estimator and its asymptotic distribution. Since the classical estimator doesn’t hold explicit forms, we propose an approximate maximum likelihood estimator. The asymptotic confidence interval and two bootstrap intervals are obtained. Using the Gibbs sampling technique, Bayesian estimator and the corresponding credible interval are obtained. The Metropolis-Hastings algorithm is used to generate random variates. Monte Carlo simulations are conducted to compare the proposed methods. Analysis of a real dataset is performed.

Beschreibung: Algorithms, Vol. 10, Pages 72: Hierarchical Gradient Similarity Based Video Quality Assessment Metric Algorithms doi: 10.3390/a10030072 Authors: Jie Yang Jian Xiong Guan Gui Rongfang Song Wang Luo Xianzhong Long Video quality assessment (VQA) plays an important role in video applications for quality evaluation and resource allocation. It aims to evaluate video quality in a way that is consistent with human perception. In this letter, a hierarchical gradient similarity based VQA metric is proposed inspired by the structure of the primate visual cortex, in which visual information is processed through sequential visual areas. These areas are modeled with the corresponding measures to evaluate the overall perceptual quality. Experimental results on the LIVE database show that the proposed VQA metric significantly outperforms most of the state-of-the-art VQA metrics.

Beschreibung: Algorithms, Vol. 10, Pages 66: A New Approach to Image-Based Estimation of Food Volume Algorithms doi: 10.3390/a10020066 Authors: Hamid Hassannejad Guido Matrella Paolo Ciampolini Ilaria Munari Monica Mordonini Stefano Cagnoni A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people’s health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user’s smartphone. From such a video, six frames are selected based on the pictures’ viewpoints as determined by the smartphone’s orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92 % on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s.

Beschreibung: Algorithms, Vol. 10, Pages 63: Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks Algorithms doi: 10.3390/a10020063 Authors: Prima Purnamasari Anak Ratna Benyamin Kusumoputro The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.

Beschreibung: Algorithms, Vol. 10, Pages 60: Correction: A No Reference Image Quality Assessment Metric Based on Visual Perception. Algorithms 2016, 9, 87 Algorithms doi: 10.3390/a10020060 Authors: Yan Fu Shengchun Wang We would like to make the following change to our article [1]. [...]

Beschreibung: Algorithms, Vol. 10, Pages 76: A Genetic Algorithm Using Triplet Nucleotide Encoding and DNA Reproduction Operations for Unconstrained Optimization Problems Algorithms doi: 10.3390/a10030076 Authors: Wenke Zang Weining Zhang Wenqian Zhang Xiyu Liu As one of the evolutionary heuristics methods, genetic algorithms (GAs) have shown a promising ability to solve complex optimization problems. However, existing GAs still have difficulties in finding the global optimum and avoiding premature convergence. To further improve the search efficiency and convergence rate of evolution algorithms, inspired by the mechanism of biological DNA genetic information and evolution, we present a new genetic algorithm, called GA-TNE+DRO, which uses a novel triplet nucleotide coding scheme to encode potential solutions and a set of new genetic operators to search for globally optimal solutions. The coding scheme represents potential solutions as a sequence of triplet nucleotides and the DNA reproduction operations mimic the DNA reproduction process more vividly than existing DNA-GAs. We compared our algorithm with several existing GA and DNA-based GA algorithms using a benchmark of eight unconstrained optimization functions. Our experimental results show that the proposed algorithm can converge to solutions much closer to the global optimal solutions in a much lower number of iterations than the existing algorithms. A complexity analysis also shows that our algorithm is computationally more efficient than the existing algorithms.

Beschreibung: Algorithms, Vol. 10, Pages 70: An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification Algorithms doi: 10.3390/a10020070 Authors: Ying Mei Guanzheng Tan Zhentao Liu Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

Beschreibung: Algorithms, Vol. 10, Pages 82: Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm Algorithms doi: 10.3390/a10030082 Authors: Cinthia Peraza Fevrier Valdez Patricia Melin This article focuses on the dynamic parameter adaptation in the harmony search algorithm using Type-1 and interval Type-2 fuzzy logic. In particular, this work focuses on the adaptation of the parameters of the original harmony search algorithm. At present there are several types of algorithms that can solve complex real-world problems with uncertainty management. In this case the proposed method is in charge of optimizing the membership functions of three benchmark control problems (water tank, shower, and mobile robot). The main goal is to find the best parameters for the membership functions in the controller to follow a desired trajectory. Noise experiments are performed to test the efficacy of the method.

Beschreibung: Algorithms, Vol. 10, Pages 73: Variable Selection Using Adaptive Band Clustering and Physarum Network Algorithms doi: 10.3390/a10030073 Authors: Huanyu Chen Tong Chen Zhihao Zhang Guangyuan Liu Variable selection is a key step for eliminating redundant information in spectroscopy. Among various variable selection methods, the physarum network (PN) is a newly-introduced and efficient one. However, the whole spectrum has to be equally divided into sub-spectral bands in PN. These division criteria limit the selecting ability and prediction performance. In this paper, we transform the spectrum division problem into a clustering problem and solve the problem by using an affinity propagation (AP) algorithm, an adaptive clustering method, to find the optimized number of sub-spectral bands and the number of wavelengths in each sub-spectral band. Experimental results show that combining AP and PN together can achieve similar prediction accuracy with much less wavelength than what PN alone can achieve.