ALBERT

Beschreibung: Hepatic steatosis is the accumulation of fat in the hepatic cells and the liver. Triglycerides and other kinds of molecules are included in the lipids. When there is some defect in the process, hepatic steatosis arise, during which the free fatty acids are taken by the liver and exuded as lipoproteins. Alcohol is the main cause of steatosis when excessive amounts are consumed for a long period of time. In many cases, steatosis can lead to inflammation that is mentioned as steatohepatitis or non-alcoholic steatohepatitis (NASH), which can later lead to fibrosis and finally cirrhosis. For automated detection and quantification of hepatic steatosis, a novel two-stage methodology is developed in this study. Initially, the image is processed in order to become more suitable for the detection of fat regions and steatosis quantification. In the second stage, initial candidate image regions are detected, and then they are either validated or discarded based on a series of criteria. The methodology is based on liver biopsy image analysis, and has been tested using 40 liver biopsy images obtained from patients who suffer from hepatitis C. The obtained results indicate that the proposed methodology can accurately assess liver steatosis.

Beschreibung: Information, Vol. 8, Pages 98: An Information Theory Calculator for Understanding Information and Library Science Applications Information doi: 10.3390/info8030098 Authors: Robert Losee The study of information as proposed in information theory provides a useful tool for studying many aspects of information systems, libraries, and archives. How does a calculator that computes information-theoretic functions contribute to students learning database ideas such as the relationships between various attributes, or columns in a relational database? Understanding the relationships between variables in a professional discipline can be viewed as the core of the discipline, and these information measures are very important to the study of the organization of information. A web-based information-theoretic calculator is examined that enables students to easily and completely process various information-theoretic calculations that are useful in studying database relationships. Students were questioned after using the calculator on a homework assignment. The students’ evaluations of this form of calculator were then examined, and lastly, recommendations about using information-theoretic calculators are suggested. Recommendations are made about the use of this form of calculator in a range of academic disciplines, from computer science and business, to library and information sciences. These recommendations apply to both the academy and professional practice applications in informational domains.

Beschreibung: Information, Vol. 8, Pages 99: Special Issue on Mobile Systems, Mobile Networks, and Mobile Cloud: Security, Privacy, and Digital Forensics Information doi: 10.3390/info8030099 Authors: Lei Chen Wenjia Li Rami Haddad The use of smartphones and mobile devices has become an indispensable part of everyone’s daily life and work [...]

Beschreibung: Information, Vol. 8, Pages 103: A Novel STDM Watermarking Using Visual Saliency-Based JND Model Information doi: 10.3390/info8030103 Authors: Chunxing Wang Teng Zhang Wenbo Wan Xiaoyue Han Meiling Xu The just noticeable distortion (JND) model plays an important role in measuring the visual visibility for spread transform dither modulation (STDM) watermarking. However, the existing JND model characterizes the suprathreshold distortions with an equal saliency level. Visual saliency (VS) has been widely studied by psychologists and computer scientists during the last decade, where the distortions are more likely to be noticeable to any viewer. With this consideration, we proposed a novel STDM watermarking method for a monochrome image by exploiting a visual saliency-based JND model. In our proposed JND model, a simple VS model is employed as a feature to reflect the importance of a local region and compute the final JND map. Extensive experiments performed on the classic image databases demonstrate that the proposed watermarking scheme works better in terms of the robustness than other related methods.

Beschreibung: Information, Vol. 8, Pages 104: Edge Detection Method Based on General Type-2 Fuzzy Logic Applied to Color Images Information doi: 10.3390/info8030104 Authors: Claudia Gonzalez Patricia Melin Oscar Castillo This paper presents a new general type-2 fuzzy logic method for edge detection applied to color format images. The proposed algorithm combines the methodology based on the image gradients and general type-2 fuzzy logic theory to provide a powerful edge detection method. General type-2 fuzzy inference systems are approximated using the α-planes approach. The edge detection method is tested on a database of color images with the idea of illustrating the advantage of applying the fuzzy edge detection approach on color images against grayscale format images, and also when the images are corrupted by noise. This paper compares the proposed method based on general type-2 fuzzy logic with other edge detection algorithms, such as ones based on type-1 and interval type-2 fuzzy systems. Simulation results show that edge detection based on a general type-2 fuzzy system outperforms the other methods because of its ability to handle the intrinsic uncertainty in this problem.

Beschreibung: Information, Vol. 8, Pages 78: A Generalized Triangular Intuitionistic Fuzzy Geometric Averaging Operator for Decision-Making in Engineering and Management Information doi: 10.3390/info8030078 Authors: Daniel Aikhuele Sarah Odofin Triangular intuitionistic fuzzy number (TIFN) is a more generalized platform for expressing imprecise, incomplete, and inconsistent information when solving multi-criteria decision-making problems, as well as for expressing and reflecting the evaluation information in several dimensions. In this paper, the TIFN has been applied for solving multi-criteria decision-making (MCDM) problems, first, by defining some existing triangular intuitionistic fuzzy geometric aggregation operators, and then developing a new triangular intuitionistic fuzzy geometric aggregation operator, which is the generalized triangular intuitionistic fuzzy ordered weighted geometric averaging (GTIFOWGA) operator. Based on these operators, a new approach for solving multicriteria decision-making problems when the weight information is fixed is proposed. Finally, a numerical example is provided to show the applicability and rationality of the presented method, followed by a comparative analysis using similar existing computational approaches.

Beschreibung: Information, Vol. 8, Pages 106: An Extended VIKOR-Based Approach for Pumped Hydro Energy Storage Plant Site Selection with Heterogeneous Information Information doi: 10.3390/info8030106 Authors: Yunna Wu Lingyun Liu Jianwei Gao Han Chu Chuanbo Xu The selection of a desirable site for constructing a pumped hydro energy storage plant (PHESP) plays a vital important role in the whole life cycle. However, little research has been done on the site selection of PHESP, which affects the rapid development of PHESP. Therefore, this paper aims to select the most ideal PHESP site from numerous candidate alternatives using the multi-criteria decision-making (MCDM) technique. Firstly, a comprehensive evaluation criteria system is established for the first time. Then, considering quantitative and qualitative criteria coexist in this system, multiple types of representations, including crisp numerical values (CNVs), triangular intuitionistic fuzzy numbers (TIFNs), and 2-dimension uncertain linguistic variables (2DULVs), are employed to deal with heterogeneous criteria information. To determine the weight of criteria and fully take the preference of the decision makers (DMs) into account, the analytic hierarchy process (AHP) method is applied for criteria weighting. After that, an extended Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is utilized to provide compromise solutions for the PHESP site considering such heterogeneous information. At last, the proposed model is then applied in a case study of Zhejiang province, China to illustrate its practicality and efficiency. The result shows the Changlongshan should be selected as the optimal PHESP.

Beschreibung: Information, Vol. 8, Pages 105: Arabic Handwritten Alphanumeric Character Recognition Using Very Deep Neural Network Information doi: 10.3390/info8030105 Authors: MohammedAli Mudhsh Rolla Almodfer The traditional algorithms for recognizing handwritten alphanumeric characters are dependent on hand-designed features. In recent days, deep learning techniques have brought about new breakthrough technology for pattern recognition applications, especially for handwritten recognition. However, deeper networks are needed to deliver state-of-the-art results in this area. In this paper, inspired by the success of the very deep state-of-the-art VGGNet, we propose Alphanumeric VGG net for Arabic handwritten alphanumeric character recognition. Alphanumeric VGG net is constructed by thirteen convolutional layers, two max-pooling layers, and three fully-connected layers. The proposed model is fast and reliable, which improves the classification performance. Besides, this model has also reduced the overall complexity of VGGNet. We evaluated our approach on two benchmarking databases. We have achieved very promising results, with a validation accuracy of 99.66% for the ADBase database and 97.32% for the HACDB database.

Beschreibung: Information, Vol. 8, Pages 108: Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead Information doi: 10.3390/info8030108 Authors: Tekenate Amah Maznah Kamat Kamalrulnizam Bakar Syed Rahman Muhammad Mohammed Aliyu Abali Waldir Moreira Antonio Oliveira The rising human population in urban environments drives the mission towards smart cities, which envisions a wide deployment of sensors in order to improve the quality of living. In this regard, opportunistic networks (OppNets) present an economical means of collecting delay tolerant data from sensors to their respective gateways for providing various Smart City services. Due to the distributed nature of the network, encounter-based routing protocols achieve acceptable throughput by requiring nodes to exchange and update contact information on an encounter basis. Unfortunately, sufficient insight into the associated overhead is lacking in the literature. Hence, we contribute by modelling contact information overhead and investigating its impact on OppNet routing, particularly in terms of data exchange success and energy consumption on portable handheld devices. Our findings reveal that the expected contact information overhead in Smart City scenarios significantly reduces data exchange success and increases energy consumption on portable handheld devices, thereby threatening the feasibility of the technology. We address this issue by proposing an algorithm that can be incorporated into encounter-based routing protocols to reduce contact information overhead without compromising throughput. Simulation results show that our proposed algorithm reduces the average contact information overhead, increases throughput and reduces average energy consumption.

Beschreibung: Information, Vol. 8, Pages 97: Review of Recent Type-2 Fuzzy Image Processing Applications Information doi: 10.3390/info8030097 Authors: Oscar Castillo Mauricio Sanchez Claudia Gonzalez Gabriela Martinez This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

Beschreibung: Information, Vol. 8, Pages 100: Physical Layer Security and Optimal Multi-Time-Slot Power Allocation of SWIPT System Powered by Hybrid Energy Information doi: 10.3390/info8030100 Authors: Dandan Guo Baogang Li Wei Zhao In this paper, a new approach is proposed to solve the constrained optimization problem of saving grid energy and increasing safety in a simultaneous wireless information and power transfer (SWIPT) system. The traditional grid energy is combined with the renewable energy to form a hybrid energy, which provides power for the system to achieve green wireless transmission. The transfer process of SWIPT system is divided into multiple time slots. The renewable energy is harvested and stored in battery at each time slot. A multi-time-slot artificial noise-assisted transmission strategy is proposed to reduce the signal to noise ratio (SNR) of eavesdropping link. A power allocation algorithm based on multi-time-slot golden section is given, which performs one-dimensional search on the power ratio of artificial noise to determine the transmit power of source node. And then the allocation algorithm is utilized to dynamically configure the harvested renewable energy for each time slot. When the battery capacity is constant, the maximum renewable energy is being used to reduce the grid power consumption. Finally, the performances of proposed schemes are evaluated by simulations in terms of various tradeoffs.

Beschreibung: Information, Vol. 8, Pages 101: An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review Information doi: 10.3390/info8030101 Authors: Peng Jing Hao Huang Long Chen In the last few years, traffic congestion has become a growing concern due to increasing vehicle ownerships in urban areas. Intersections are one of the major bottlenecks that contribute to urban traffic congestion. Traditional traffic signal control systems cannot adjust the timing pattern depending on road traffic demand. This results in excessive delays for road users. Adaptive traffic signal control in a connected vehicle environment has shown a powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g., delay minimization). Connected vehicle technology, as an emerging technology, is a mobile data platform that enables the real-time data exchange among vehicles and between vehicles and infrastructure. Although several reviews about traffic signal control or connected vehicles have been written, a systemic review of adaptive traffic signal control in a connected vehicle environment has not been made. Twenty-six eligible studies searched from six databases constitute the review. A quality evaluation was established based on previous research instruments and applied to the current review. The purpose of this paper is to critically review the existing methods of adaptive traffic signal control in a connected vehicle environment and to compare the advantages or disadvantages of those methods. Further, a systematic framework on connected vehicle based adaptive traffic signal control is summarized to support the future research. Future research is needed to develop more efficient and generic adaptive traffic signal control methods in a connected vehicle environment.

Beschreibung: Information, Vol. 8, Pages 109: Frequent Releases in Open Source Software: A Systematic Review Information doi: 10.3390/info8030109 Authors: Antonio Cesar Brandão Gomes da Silva Glauco de Figueiredo Carneiro Fernando Brito e Abreu Miguel Pessoa Monteiro Context: The need to accelerate software delivery, supporting faster time-to-market and frequent community developer/user feedback are issues that have led to relevant changes in software development practices. One example is the adoption of Rapid Release (RR) by several Open Source Software projects (OSS). This raises the need to know how these projects deal with software release approaches. Goal: Identify the main characteristics of software release initiatives in OSS projects, the motivations behind their adoption, strategies applied, as well as advantages and difficulties found. Method: We conducted a Systematic Literature Review (SLR) to reach the stated goal. Results: The SLR includes 33 publications from January 2006 to July 2016 and reveals nine advantages that characterize software release approaches in OSS projects; four challenge issues; three possibilities of implementation and two main motivations towards the adoption of RR; and finally four main strategies to implement it. Conclusion: This study provides an up-to-date and structured understanding of the software release approaches in the context of OSS projects based on findings systematically collected from a list of relevant references in the last decade.

Beschreibung: Information, Vol. 8, Pages 110: Linguistic Neutrosophic Cubic Numbers and Their Multiple Attribute Decision-Making Method Information doi: 10.3390/info8030110 Authors: Jun Ye To describe both certain linguistic neutrosophic information and uncertain linguistic neutrosophic information simultaneously in the real world, this paper originally proposes the concept of a linguistic neutrosophic cubic number (LNCN), including an internal LNCN and external LNCN. In LNCN, its uncertain linguistic neutrosophic number consists of the truth, indeterminacy, and falsity uncertain linguistic variables, and its linguistic neutrosophic number consists of the truth, indeterminacy, and falsity linguistic variables to express their hybrid information. Then, we present the operational laws of LNCNs and the score, accuracy, and certain functions of LNCN for comparing/ranking LNCNs. Next, we propose a LNCN weighted arithmetic averaging (LNCNWAA) operator and a LNCN weighted geometric averaging (LNCNWGA) operator to aggregate linguistic neutrosophic cubic information and discuss their properties. Further, a multiple attribute decision-making method based on the LNCNWAA or LNCNWGA operator is developed under a linguistic neutrosophic cubic environment. Finally, an illustrative example is provided to indicate the application of the developed method.

Beschreibung: Information, Vol. 8, Pages 111: Cooperative Non-Orthogonal Multiple Access with Energy Harvesting Information doi: 10.3390/info8030111 Authors: Weidong Guo Yunfeng Wang In this paper, we analyze the outage probability of an amplify-and-forward (AF) cooperative non-orthogonal multiple access (NOMA) model in multi-relay multiuser networks. In contrast to conventional cooperative networks, relays in the considered network have no embedded energy supply; they need to rely on the energy harvested from the signals broadcasted by the source for their cooperative NOMA transmission. Based on this structure, a new relay selection scheme is proposed, considering both channel state information (CSI) and battery status of relays. Assuming each relay has infinite or finite energy storage for accumulating energy, we use the infinite or finite Markov chain to capture the evolution of relay batteries and certain simplified assumptions to reduce computational complexity of the Markov chain analysis. The approximate closed-form expressions for the outage probability of the proposed scheme are derived therefrom. All theoretical results are validated by numerical simulations. The impacts of the system parameters, such as relay number, energy harvesting threshold and battery size, on the performance are extensively investigated.

Beschreibung: Information, Vol. 8, Pages 112: Volume Shocks around Announcements in the Chinese Stock Market: An Ex-Post Earnings-Information-Based Study of Speculative Behavior Information doi: 10.3390/info8030112 Authors: Xiangdong Chen Fei Wang Wei Wang Valerie Hunstock The Second Board Market is typical stock market for high tech companies in China. This paper discusses the relationship between trading volume and price changes in the case of high-tech listed companies in the Chinese Second-Board Stock Market. By using the basic concepts proposed by Kim and Verrecchia, and Kandel and Pearson, and contrasting them with ex-post information from earnings releases, the paper provides findings on the speculative behavior of informed traders with a volume shock premium. The paper suggests that these methods may be further applied to investigating investors’ behavior in speculation, especially for the high-tech-company-based Second-Board Stock Market during announcement periods.

Beschreibung: This research evaluates the impact of In-vehicle Signal Advisory System (ITSAS) on signalized arterial. ITSAS provides individual drivers equipped with a mobile communication device with advisory speed information enabling to minimize the time delay and fuel consumption when crossing intersection. Given the instantaneous vehicular driving information, such as position, speed, and acceleration rate, ITSAS produces advisory speed information by taking into consideration the traffic signal changes at a downstream intersection. The advisory speed information includes not only an optimal speed range updated every 300-ft for individual drivers but also a descriptive message to warn drivers stop to ensure safety at the downstream intersection. Unlike other similar Connected Vehicles applications for intersection management, ITSAS does not require Roadside Equipment (RSE) to disseminate the advisory speed information as it is designed to exploit commercial cellular network service (i.e., 3G and 4G-LTE). Thus, ITSAS can be easily plugged into existing traffic control management system to rapidly conduct its implementation without significant additional cost. This research presents the field evaluations of ITSAS on a signalized corridor in New Jersey, which discovered significant travel time savings for the equipped vehicle.

Beschreibung: Information, Vol. 8, Pages 117: Cosine Measures of Linguistic Neutrosophic Numbers and Their Application in Multiple Attribute Group Decision-Making Information doi: 10.3390/info8040117 Authors: Lilian Shi Jun Ye The linguistic neutrosophic numbers (LNNs) can express the truth, indeterminacy, and falsity degrees independently by three linguistic variables. Hence, they are an effective tool for describing indeterminate linguistic information under linguistic decision-making environments. Similarity measures are usual tools in decision-making problems. However, existing cosine similarity measures have been applied in decision-making problems, but they cannot deal with linguistic information under linguistic decision-making environments. To deal with the issue, we propose two cosine similarity measures based on distance and the included angle cosine of two vectors between LNNs. Then, we establish a multiple attribute group decision-making (MAGDM) method based on the cosine similarity measures under an LNN environment. Finally, a practical example about the decision-making problems of investment alternatives is presented to demonstrate the effective applications of the proposed MAGDM method under an LNN environment.

Beschreibung: Information, Vol. 8, Pages 115: Predicting DNA Motifs by Using Multi-Objective Hybrid Adaptive Biogeography-Based Optimization Information doi: 10.3390/info8040115 Authors: Siling Feng Ziqiang Yang Mengxing Huang The computational discovery of DNA motifs is one of the most important problems in molecular biology and computational biology, and it has not yet been resolved in an efficient manner. With previous research, we have solved the single-objective motif discovery problem (MDP) based on biogeography-based optimization (BBO) and gained excellent results. In this study, we apply multi-objective biogeography-based optimization algorithm to the multi-objective motif discovery problem, which refers to discovery of novel transcription factor binding sites in DNA sequences. For this, we propose an improved multi-objective hybridization of adaptive Biogeography-Based Optimization with differential evolution (DE) approach, namely MHABBO, to predict motifs from DNA sequences. In the MHABBO algorithm, the fitness function based on distribution information among the habitat individuals and the Pareto dominance relation are redefined. Based on the relationship between the cost of fitness function and average cost in each generation, the MHABBO algorithm adaptively changes the migration probability and mutation probability. Additionally, the mutation procedure that combines with the DE algorithm is modified. And the migration operators based on the number of iterations are improved to meet motif discovery requirements. Furthermore, the immigration and emigration rates based on a cosine curve are modified. It can therefore generate promising candidate solutions. Statistical comparisons with DEPT and MOGAMOD approaches on three commonly used datasets are provided, which demonstrate the validity and effectiveness of the MHABBO algorithm. Compared with some typical existing approaches, the MHABBO algorithm performs better in terms of the quality of the final solutions.

Beschreibung: Information, Vol. 8, Pages 102: A Robust Timetabling Model for a Metro Line with Passenger Activity Information Information doi: 10.3390/info8030102 Authors: Lai Wei Zhenzhou Yuan Timetable design is crucial to the reliability of a metro service. In terms of the delays caused by passengers’ boarding and alighting behaviors during rush hours, the planned timetable for a metro line with high-frequency service tends to be difficult to implement. General oversaturation events, rather than accidents or track damage, still have a significant impact on metro systems, so that trains are canceled and delayed. When the activity reality diverges from the real-time or historical information, it is imperative that dispatchers present a good solution during the planning stage in order to minimize the nuisance for passengers and reduce the crowding risk. This paper presents a robust timetabling model (RTM) for a metro line with passenger activity information, which takes into account congestion and buffer time adjustments. The main objective pursued by dispatchers in the model is the enhancement of punctuality while minimizing train delays by adjusting the buffer time. By explicitly taking the passenger activity information into account, a mixed integer nonlinear programming (MINLP) model was developed, and a genetic algorithm (GA) is proposed to solve the model. Finally, numerical experiments based on the Batong line of the Beijing Metro were carried out, the results of which verify the effectiveness and efficiency of our method.

Beschreibung: Information, Vol. 8, Pages 107: Bonferroni Mean Operators of Linguistic Neutrosophic Numbers and Their Multiple Attribute Group Decision-Making Methods Information doi: 10.3390/info8030107 Authors: Changxing Fan Jun Ye Keli Hu En Fan Linguistic neutrosophic numbers (LNN) is presented by Fang and Ye in 2017, which can describe the truth, falsity, and indeterminacy linguistic information independently. In this paper, the LNN and the Bonferroni mean operator are merged together to propose a LNN normalized weighted Bonferroni mean (LNNNWBM) operator and a LNN normalized weighted geometric Bonferroni mean (LNNNWGBM) operator and the properties of these two operators are proved. Further, multi-attribute group decision methods are introduced based on the proposed LNNNWBM and LNNNWGBM operators, and then an example is provided to demonstrate the application and validity of the proposed methods. In addition, in order to consider the effect of the parameters p and q on the decision results, different pairs of parameter values are employed to verify the decision results.

Beschreibung: In 1997, the author concluded that living cells use a molecular language (cellese) that is isomorphic with the human language (humanese) based on his finding that the former shared 10 out of the 13 design features of the latter. In 2012, the author postulated that cellese and humanese derived from a third language called the cosmic language (or cosmese) and that what was common among these three kinds of languages was waves—i.e., sound waves for humanese, concentration waves for cellese, and quantum waves for cosmese. These waves were suggested to be the symmetry principle underlying cosmese, cellese, and humanese. We can recognize at least five varieties of waves—(i) electromagnetic; (ii) mechanical; (iii) chemical concentration; (iv) gravitational; and (v) probability waves, the last being non-material, in contrast to the first four, which are all material. The study of waves is called “cymatics” and the invention of CymaScope by J. S. Reid of the United Kingdom in 2002 is expected to accelerate the study of waves in general. CymaScope has been used to visualize not only human sounds (i.e., humanese) but also sounds made by individual cells (cellese) in conjunction with Atomic Force Microscopy (AFM) (unpublished observations of J. Gimzewski of UCLA and J. Reid). It can be predicted that the gravitational waves recently detected by the Interferometer Gravitational-Wave Observatory (LIGO) will be visualized with CymaScope one day, thereby transforming gravitational waves into CymaGlyphs. Since cellese in part depends on RNA concentration waves (or RNA glyphs) and humanese includes hieroglyphs that were decoded by Champollion in 1822, it seems reasonable to use cymaglyphs, RNA glyphs, and hieroglyphs as symbols of cosmese, cellese, and humanese, respectively, all based on the principle of waves as the medium of communication.

Beschreibung: In order to build an efficient security architecture, previous studies have attempted to understand complex system architectures and message flows to detect various attack packets. However, the existing hardware-based single security architecture cannot efficiently handle a complex system structure. To solve this problem, we propose a software-defined networking (SDN) policy-based scheme for an efficient security architecture. The proposed scheme considers four policy functions: separating, chaining, merging, and reordering. If SDN network functions virtualization (NFV) system managers use these policy functions to deploy a security architecture, they only submit some of the requirement documents to the SDN policy-based architecture. After that, the entire security network can be easily built. This paper presents information about the design of a new policy functions model, and it discusses the performance of this model using theoretical analysis.

Beschreibung: As an extension of an intuitionistic fuzzy set, a single-valued neutrosophic set is described independently by the membership functions of its truth, indeterminacy, and falsity, which is a subclass of a neutrosophic set (NS). However, in existing exponential operations and their aggregation methods for neutrosophic numbers (NNs) (basic elements in NSs), the exponents (weights) are positive real numbers in unit intervals under neutrosophic decision-making environments. As a supplement, this paper defines new exponential operations of single-valued NNs (basic elements in a single-valued NS), where positive real numbers are used as the bases, and single-valued NNs are used as the exponents. Then, we propose a single-valued neutrosophic weighted exponential aggregation (SVNWEA) operator based on the exponential operational laws of single-valued NNs and the SVNWEA operator-based decision-making method. Finally, an illustrative example shows the applicability and rationality of the presented method. A comparison with a traditional method demonstrates that the new decision-making method is more appropriate and effective.

Beschreibung: Text summarization namely, automatically generating a short summary of a given document, is a difficult task in natural language processing. Nowadays, deep learning as a new technique has gradually been deployed for text summarization, but there is still a lack of large-scale high quality datasets for this technique. In this paper, we proposed a novel deep learning method to identify high quality document–summary pairs for building a large-scale pairs dataset. Concretely, a long short-term memory (LSTM)-based model was designed to measure the quality of document–summary pairs. In order to leverage information across all parts of each document, we further proposed an improved LSTM-based model by removing the forget gate in the LSTM unit. Experiments conducted on the training set and the test set built upon Sina Weibo (a Chinese microblog website similar to Twitter) showed that the LSTM-based models significantly outperformed baseline models with regard to the area under receiver operating characteristic curve (AUC) value.

Beschreibung: A turbo coded cooperative orthogonal frequency division multiplexing (OFDM) with multiple-input multiple-output (MIMO) antennas scheme is considered, and its performance over a fast Rayleigh fading channel is evaluated. The turbo coded OFDM incorporates MIMO (2 × 2) Alamouti space-time block code. The interleaver design, and its placement always plays a vital role in the performance of a turbo coded cooperation scheme. Therefore, a code-matched interleaver (CMI) is selected as an optimum choice of interleaver and is placed at the relay node. The performance of the CMI is evaluated in a turbo coded OFDM system over an additive white Gaussian noise (AWGN) channel. Moreover, the performance of the CMI is also evaluated in the turbo coded OFDM system with MIMO antennas over a fast Rayleigh fading channel. The modulation schemes chosen are Binary Phase shift keying (BPSK), Quadrature phase shift keying (QPSK) and 16-Quadrature amplitude modulation (16QAM). Soft-demodulators are employed along with joint iterative soft-input soft-output (SISO) turbo decoder at the destination node. Monte Carlo simulated results reveal that the turbo coded cooperative OFDM system with MIMO antennas scheme incorporates coding gain, diversity gain and cooperation gain successfully over the direct transmission scheme under identical conditions.

Beschreibung: Compressed sensing (CS) has become a powerful tool to process data that is correlated in underwater sensor networks (USNs). Based on CS, certain signals can be recovered from a relatively small number of random linear projections. Since the battery-driven sensor nodes work in adverse environments, energy-efficient routing well-matched with CS is needed to realize data gathering in USNs. In this paper, a clustering, uneven-layered, and multi-hop routing based on CS (CS-CULM) is proposed. The inter-cluster transmission and fusion are fulfilled by an improved LEACH protocol, then the uneven-layered, multi-hop routing is adopted to forward the packets fused to sink node for data reconstruction. Simulation results show that CS-CULM can achieve better performances in energy saving and data reconstruction.

Beschreibung: The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists.

Beschreibung: Adaptive coded and modulation (ACM) is an effective measure to resist rain attenuation in Ka-band satellite communications. The accuracy of the estimator for signal-to-noise ratio (SNR) is one of the main factors that affect ACM performance. This paper establishes the channel model of satellite communication in Ka-band and derives and analyzes data-aided (DA) maximum likelihood (ML) and method-of-moments estimators for SNR of continuous phase modulation (CPM) in Ka-band fading channel. Simulations and analysis indicate that the normalized mean square error (NMSE) of the DA ML estimator is closer to the Cramer-Rao bounds (CRB) at low SNR, but the performance worsens with the SNR increases from medium to high values. The M2M4 estimator performs poorly at low SNR and best at medium SNR. When the SNR is low, the performance of the DA ML estimator is better than the M2M4 estimator; however, the two estimators have similar performance when SNR is high. The performances of the two estimators will become increasingly better the greater the lengths of the observation signals become. However, the influence of the signal length will become increasingly smaller as the SNR becomes larger.

Beschreibung: The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols.

Beschreibung: Smartphones and other mobile devices have proliferated in the past five years. The expectation of mobile device users to always be online has led to Wi-Fi networks being offered by a variety of providers. Using these networks introduces multiple security risks. In this work, we assess to what extent the privacy stance of mobile device users corresponds with their actual behavior by conducting a study with 108 participants. Our methodology consists of monitoring Wi-Fi networks that the participants’ devices connect to and the connections made by apps on these devices, for a period of 30 days. Afterwards, participants are surveyed about their awareness and privacy sensitiveness. We show that while a higher expertise in computer networks corresponds to more awareness about the connections made by apps, neither this expertise nor the actual privacy stance of the participant translates to better security habits. Moreover, participants in general were unaware about a significant part of connections made by apps on their devices, a matter that is worsened by the fact that one third of Wi-Fi networks that participants connect to do not have any security enabled. Based on our results, we provide recommendations to network providers, developers and users on how to improve Wi-Fi security for mobile devices.

Beschreibung: The objective of this study was to develop information mining methodology for drought modeling and predictions using historical records of climate, satellite, environmental, and oceanic data. The classification and regression tree (CART) approach was used for extracting drought episodes at different time-lag prediction intervals. Using the CART approach, a number of successful model trees were constructed, which can easily be interpreted and used by decision makers in their drought management decisions. The regression rules produced by CART were found to have correlation coefficients from 0.71–0.95 in rules-alone modeling. The accuracies of the models were found to be higher in the instance and rules model (0.77–0.96) compared to the rules-alone model. From the experimental analysis, it was concluded that different combinations of the nearest neighbor and committee models significantly increase the performances of CART drought models. For more robust results from the developed methodology, it is recommended that future research focus on selecting relevant attributes for slow-onset drought episode identification and prediction.

Beschreibung: Building-up construction is one of several methods for constructing self-dual codes. Recently, a new building-up construction method has been developed by S. Han, in which the existence of a square matrix U such that U U T = - I is essential. In this paper, we completely solve the existence problem for U over Z p m , where p is an arbitrary prime number.

Beschreibung: Single-valued neutrosophic numbers (SVNNs) can express incomplete, indeterminate, and inconsistent information in the real world. Then, the common weighted aggregation operators of SVNNs may result in unreasonably aggregated results in some situations. Based on the hybrid weighted arithmetic and geometric aggregation and hybrid ordered weighted arithmetic and geometric aggregation ideas, this paper proposes SVNN hybrid weighted arithmetic and geometric aggregation (SVNNHWAGA) and SVNN hybrid ordered weighted arithmetic and geometric aggregation (SVNNHOWAGA) operators and investigates their rationality and effectiveness by numerical examples. Then, we establish a multiple-attribute decision-making method based on the SVNNHWAGA or SVNNHOWAGA operator under a SVNN environment. Finally, the multiple-attribute decision-making problem about the design schemes of punching machine is presented as a case to show the application and rationality of the proposed decision-making method.

Beschreibung: Information, Vol. 8, Pages 87: Efficient Listening and Sleeping Scheduling Mechanism Based on Self-Similarity for Duty Cycle Opportunistic Mobile Networks Information doi: 10.3390/info8030087 Authors: Feng Zeng Yueyue Dou Zhigang Chen Hui Liu In opportunistic mobile networks (OppNets), nodes should be in listening state to discover the neighbors for opportunistic message forwarding. While in OppNets, contacts between nodes are sparse, most of the node’s energy is consumed in idle listening state, which highlights the need for energy saving in contact probing. Duty cycle operation can be applied to address this problem. However, it may cause the degradation of network connectivity when the state of node is turned to be sleeping. In this paper, we propose an adaptive scheduling mechanism based on self-similarity, in which LMMSE predictor is used to predict the future contact information. The state of a node will be set as listening or sleeping adaptively according to the predicted result of future contacts with other nodes. Finally, we validate the effectiveness of the proposed mechanism by conducting a large amount of trace-driven simulations, which show that the proposed mechanism outperforms the random working mechanism and periodical working mechanism in terms of the number of effective contacts, delivery ratio, transmission delay and cost.

Beschreibung: Information, Vol. 8, Pages 89: Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images Information doi: 10.3390/info8030089 Authors: Haidar Almubarak R. Stanley William Stoecker Randy Moss A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy clustering ratio over different regions of the lesion over a data set of 517 dermoscopy images consisting of 175 invasive melanomas and 342 benign lesions. Experimental results show that the fuzzy clustering ratio applied over an eight-connected neighborhood on the outer 25% of the skin lesion with an alpha-cut of 0.08 can recognize 92.6% of melanomas with approximately 13.5% false positive lesions. These results show the critical importance of colors in the lesion periphery. Our fuzzy logic-based description of lesion colors offers relevance to clinical descriptions of malignant melanoma.

Beschreibung: Information, Vol. 8, Pages 90: Stabilization of Discrete-Time Markovian Jump Systems by a Partially Mode-Unmatched Fault-Tolerant Controller Information doi: 10.3390/info8030090 Authors: Mo Liu Guoliang Wang In this paper, a kind of fault-tolerant controller is proposed to study the stabilization problem of discrete-time Markovian jump systems, whose operation modes are not only partially-available but also unmatched. Here, such general properties of controller are modeled to be a controller having polytopic forms and uncertainties simultaneously. Based on the proposed model, concise conditions for the existence of such a controller are proposed with linear matrix inequality (LMI) forms, which are extended to consider observer design problem too. Compared with the traditional methods, not only is the designed controller more general but also the established results are fault free and could be solved directly. Finally, numerical examples are used to demonstrate the effectiveness of the proposed methods.

Beschreibung: Information, Vol. 8, Pages 91: Deep Transfer Learning for Modality Classification of Medical Images Information doi: 10.3390/info8030091 Authors: Yuhai Yu Hongfei Lin Jiana Meng Xiaocong Wei Hai Guo Zhehuan Zhao Medical images are valuable for clinical diagnosis and decision making. Image modality is an important primary step, as it is capable of aiding clinicians to access required medical image in retrieval systems. Traditional methods of modality classification are dependent on the choice of hand-crafted features and demand a clear awareness of prior domain knowledge. The feature learning approach may detect efficiently visual characteristics of different modalities, but it is limited to the number of training datasets. To overcome the absence of labeled data, on the one hand, we take deep convolutional neural networks (VGGNet, ResNet) with different depths pre-trained on ImageNet, fix most of the earlier layers to reserve generic features of natural images, and only train their higher-level portion on ImageCLEF to learn domain-specific features of medical figures. Then, we train from scratch deep CNNs with only six weight layers to capture more domain-specific features. On the other hand, we employ two data augmentation methods to help CNNs to give the full scope to their potential characterizing image modality features. The final prediction is given by our voting system based on the outputs of three CNNs. After evaluating our proposed model on the subfigure classification task in ImageCLEF2015 and ImageCLEF2016, we obtain new, state-of-the-art results—76.87% in ImageCLEF2015 and 87.37% in ImageCLEF2016—which imply that CNNs, based on our proposed transfer learning methods and data augmentation skills, can identify more efficiently modalities of medical images.

Beschreibung: Information, Vol. 8, Pages 93: A Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle Information doi: 10.3390/info8030093 Authors: Rulin Huang Jiajia Chen Jian Liu Lu Liu Biao Yu Yihua Wu Robust and quick road curb detection under various situations is critical in developing intelligent vehicles. However, the road curb detection is easily affected by the obstacles in the road area when Lidar based method is applied. A practical road curb detection method using point cloud from a three-dimensional Lidar for autonomous vehicle is reported in this paper. First, a multi-feature, loose-threshold, varied-scope ground segmentation method is presented to increase the robustness of ground segmentation with which obstacles above the ground can be detected. Second, the road curb is detected by applying the global road trend and an extraction-update mechanism. Experiments show the robustness and efficiency of the road curb detection under various environments. The road curb detection method is 10 times the speed of traditional method and the accuracy is much higher than existing methods.

Beschreibung: Information, Vol. 8, Pages 92: A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification Information doi: 10.3390/info8030092 Authors: Xiaocong Wei Hongfei Lin Liang Yang Yuhai Yu Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.

Beschreibung: Information, Vol. 8, Pages 95: Decentralized State-Observer-Based Traffic Density Estimation of Large-Scale Urban Freeway Network by Dynamic Model Information doi: 10.3390/info8030095 Authors: Yuqi Guo Yangzhou Chen Chiyuan Zhang In order to estimate traffic densities in a large-scale urban freeway network in an accurate and timely fashion when traffic sensors do not cover the freeway network completely and thus only local measurement data can be utilized, this paper proposes a decentralized state observer approach based on a macroscopic traffic flow model. Firstly, by using the well-known cell transmission model (CTM), the urban freeway network is modeled in the way of distributed systems. Secondly, based on the model, a decentralized observer is designed. With the help of the Lyapunov function and S-procedure theory, the observer gains are computed by using linear matrix inequality (LMI) technique. So, the traffic densities of the whole road network can be estimated by the designed observer. Finally, this method is applied to the outer ring of the Beijing’s second ring road and experimental results demonstrate the effectiveness and applicability of the proposed approach.

Beschreibung: Information, Vol. 8, Pages 94: Planning of Vehicle Routing with Backup Provisioning Using Wireless Sensor Technologies Information doi: 10.3390/info8030094 Authors: Noélia Correia Nuno Carvalho Gabriela Schütz Wireless sensor technologies can be used by intelligent transportation systems to provide innovative services that lead to improvements in road safety and congestion, increasing end-user satisfaction. In this article, we address vehicle routing with backup provisioning, where the possibility of reacting to overloading/overcrowding of vehicles at certain stops is considered. This is based on the availability of vehicle load information, which can be captured using wireless sensor technologies. After discussing the infrastructure and monitoring tool, the problem is mathematically formalized, and a heuristic algorithm using local search procedures is proposed. Results show that planning routes with backup provisioning can allow fast response to overcrowding while reducing costs. Therefore, sustainable urban mobility, with efficient use of resources, can be provided while increasing the quality of service perceived by users.

Beschreibung: Information, Vol. 8, Pages 96: A Content-Based Image Retrieval Scheme Using an Encrypted Difference Histogram in Cloud Computing Information doi: 10.3390/info8030096 Authors: Dandan Liu Jian Shen Zhihua Xia Xingming Sun Content-based image retrieval (CBIR) has been widely used in many applications. Large storage and computation overheads have made the outsourcing of CBIR services attractive. However, the privacy issues brought by outsourcing have become a big problem. In this paper, a secure CBIR scheme based on an encrypted difference histogram (EDH-CBIR) is proposed. Firstly, the image owner calculates the order or disorder difference matrices of RGB components and encrypts them by value replacement and position scrambling. The encrypted images are then uploaded to the cloud server who extracts encrypted difference histograms as image feature vectors. To search similar images, the query image is encrypted by the image users as the image owner does, and the query feature vector is extracted by the cloud server. The Euclidean distance between query feature vector and image feature vector is calculated to measure the similarity. The security analysis and experiments demonstrate the usability of the proposed scheme.

Beschreibung: Information, Vol. 8, Pages 123: Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things Information doi: 10.3390/info8040123 Authors: Amany Safia Zaher Aghbari Ibrahim Kamel With the rapid development of Internet of Things (IoT), more and more static and mobile sensors are being deployed for sensing and tracking environmental phenomena, such as fire, oil spills and air pollution. As these sensors are usually battery-powered, energy-efficient algorithms are required to extend the sensors’ lifetime. Moreover, forwarding sensed data towards a static sink causes quick battery depletion of the sinks’ nearby sensors. Therefore, in this paper, we propose a distributed energy-efficient algorithm, called the Hilbert-order Collection Strategy (HCS), which uses a mobile sink (e.g., drone) to collect data from a mobile wireless sensor network (mWSN) and detect environmental phenomena. The mWSN consists of mobile sensors that sense environmental data. These mobile sensors self-organize themselves into groups. The sensors of each group elect a group head (GH), which collects data from the mobile sensors in its group. Periodically, a mobile sink passes by the locations of the GHs (data collection path) to collect their data. The collected data are aggregated to discover a global phenomenon. To shorten the data collection path, which results in reducing the energy cost, the mobile sink establishes the path based on the order of Hilbert values of the GHs’ locations. Furthermore, the paper proposes two optimization techniques for data collection to further reduce the energy cost of mWSN and reduce the data loss.

Beschreibung: Information, Vol. 8, Pages 124: Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay Information doi: 10.3390/info8040124 Authors: Licai Zhu Ruchuan Wang Hao Yang The RPL (Routing Protocol for LLN) protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource, so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfigurations.

Beschreibung: Information, Vol. 8, Pages 127: Car-to-Pedestrian Communication Safety System Based on the Vehicular Ad-Hoc Network Environment: A Systematic Review Information doi: 10.3390/info8040127 Authors: Peng Jing Wei Huang Long Chen With the unparalleled growth of motor vehicles, traffic accident between pedestrians and vehicles is one of the most serious issues in the word-wild. Plenty of injuries and fatalities are caused by the traffic accidents and crashes. The connected vehicular ad hoc network as an emerging approach which has the potential to reduce and even avoid accidents have been focused on by many researchers. A large number of car-to-pedestrian communication safety systems based on the vehicular ad hoc network are researching and developing. However, to our limited knowledge, a systematic review about the car-to-pedestrian communication safety system based on the vehicular ad-hoc network has not be written. The purpose and goal of this review is to systematically evaluate and access the reliability of car-to-pedestrian communication safety system based on the vehicular ad-hoc network environment and provide some recommendations for the future works according to throwing some light on the previous literatures. A quality evaluation was developed through established items and instruments tailored to this review. Future works are needed to focus on developing a valid as well as effective communication safety system based on the vehicular ad hoc network to protect the vulnerable road users.

Beschreibung: Information, Vol. 8, Pages 121: Offset Free Tracking Predictive Control Based on Dynamic PLS Framework Information doi: 10.3390/info8040121 Authors: Jin Xin Wang Yue Luo Lin This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS) framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC) controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

Beschreibung: Information, Vol. 8, Pages 126: A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting Information doi: 10.3390/info8040126 Authors: Yi-Chung Hu Peng Jiang Yu-Jing Chiu Jung-Fa Tsai Grey prediction models for time series have been widely applied to demand forecasting because only limited data are required for them to build a time series model without any statistical assumptions. Previous studies have demonstrated that the combination of grey prediction with neural networks helps grey prediction perform better. Some methods have been presented to improve the prediction accuracy of the popular GM(1,1) model by using the Markov chain to estimate the residual needed to modify a predicted value. Compared to the previous Grey-Markov models, this study contributes to apply the functional-link net to estimate the degree to which a predicted value obtained from the GM(1,1) model can be adjusted. Furthermore, the troublesome number of states and their bounds that are not easily specified in Markov chain have been determined by a genetic algorithm. To verify prediction performance, the proposed grey prediction model was applied to an important grey system problem—foreign tourist forecasting. Experimental results show that the proposed model provides satisfactory results compared to the other Grey-Markov models considered.

Beschreibung: Information, Vol. 8, Pages 125: TODIM Method for Single-Valued Neutrosophic Multiple Attribute Decision Making Information doi: 10.3390/info8040125 Authors: Dong-Sheng Xu Cun Wei Gui-Wu Wei Recently, the TODIM has been used to solve multiple attribute decision making (MADM) problems. The single-valued neutrosophic sets (SVNSs) are useful tools to depict the uncertainty of the MADM. In this paper, we will extend the TODIM method to the MADM with the single-valued neutrosophic numbers (SVNNs). Firstly, the definition, comparison, and distance of SVNNs are briefly presented, and the steps of the classical TODIM method for MADM problems are introduced. Then, the extended classical TODIM method is proposed to deal with MADM problems with the SVNNs, and its significant characteristic is that it can fully consider the decision makers’ bounded rationality which is a real action in decision making. Furthermore, we extend the proposed model to interval neutrosophic sets (INSs). Finally, a numerical example is proposed.

Beschreibung: Information, Vol. 8, Pages 128: Neutrosophic N-Structures Applied to BCK/BCI-Algebras Information doi: 10.3390/info8040128 Authors: Young Jun Florentin Smarandache Hashem Bordbar Neutrosophic N -structures with applications in B C K / B C I -algebras is discussed. The notions of a neutrosophic N -subalgebra and a (closed) neutrosophic N -ideal in a B C K / B C I -algebra are introduced, and several related properties are investigated. Characterizations of a neutrosophic N -subalgebra and a neutrosophic N -ideal are considered, and relations between a neutrosophic N -subalgebra and a neutrosophic N -ideal are stated. Conditions for a neutrosophic N -ideal to be a closed neutrosophic N -ideal are provided.

Beschreibung: Information, Vol. 8, Pages 118: A Survey on Information Diffusion in Online Social Networks: Models and Methods Information doi: 10.3390/info8040118 Authors: Mei Li Xiang Wang Kai Gao Shanshan Zhang By now, personal life has been invaded by online social networks (OSNs) everywhere. They intend to move more and more offline lives to online social networks. Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks. From this diffusion process, lots of latent information can be mined. It can be used for market predicting, rumor controlling, and opinion monitoring among other things. However, the research of these applications depends on the diffusion models and methods. For this reason, we survey various information diffusion models from recent decades. From a research process view, we divide the diffusion models into two categories—explanatory models and predictive models—in which the former includes epidemics and influence models and the latter includes independent cascade, linear threshold, and game theory models. The purpose of this paper is to investigate the research methods and techniques, and compare them according to the above categories. The whole research structure of the information diffusion models based on our view is given. There is a discussion at the end of each section, detailing related models that are mentioned in the literature. We conclude that these two models are not independent, they always complement each other. Finally, the issues of the social networks research are discussed and summarized, and directions for future study are proposed.

Beschreibung: Information, Vol. 8, Pages 120: A Novel Hybrid BND-FOA-LSSVM Model for Electricity Price Forecasting Information doi: 10.3390/info8040120 Authors: Weishang Guo Zhenyu Zhao Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and randomness, which make it quite hard to forecast electricity price accurately. In this paper, a novel hybrid model for electricity price forecasting was proposed combining Beveridge-Nelson decomposition (BND) method, fruit fly optimization algorithm (FOA), and least square support vector machine (LSSVM) model, namely BND-FOA-LSSVM model. Firstly, the original electricity price time series were decomposed into deterministic term, periodic term, and stochastic term by using BND model. Then, these three decomposed terms were forecasted by employing LSSVM model, respectively. Meanwhile, to improve the forecasting performance, a new swarm intelligence optimization algorithm FOA was used to automatically determine the optimal parameters of LSSVM model for deterministic term forecasting, periodic term forecasting, and stochastic term forecasting. Finally, the forecasting result of electricity price can be obtained by multiplying the forecasting values of these three terms. The results show the mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) of the proposed BND-FOA-LSSVM model are respectively 3.48%, 11.18 Yuan/MWh and 9.95 Yuan/MWh, which are much smaller than that of LSSVM, BND-LSSVM, FOA-LSSVM, auto-regressive integrated moving average (ARIMA), and empirical mode decomposition (EMD)-FOA-LSSVM models. The proposed BND-FOA-LSSVM model is effective and practical for electricity price forecasting, which can improve the electricity price forecasting accuracy.

Beschreibung: Information, Vol. 8, Pages 130: Neutrosophic Commutative N -Ideals in BCK-Algebras Information doi: 10.3390/info8040130 Authors: Seok-Zun Song Florentin Smarandache Young Jun The notion of a neutrosophic commutative N -ideal in BCK-algebras is introduced, and several properties are investigated. Relations between a neutrosophic N-ideal and a neutrosophic commutative N-ideal are discussed. Characterizations of a neutrosophic commutative N-ideal are considered.

Beschreibung: Information, Vol. 8, Pages 119: A Dynamic Spectrum Allocation Algorithm for a Maritime Cognitive Radio Communication System Based on a Queuing Model Information doi: 10.3390/info8040119 Authors: Jingbo Zhang Jianyu Yang Yiying Zhang Shufang Zhang With the rapid development of maritime digital communication, the demand for spectrum resources is increasing, and building a maritime cognitive radio communication system is an effective solution. In this paper, the problem of how to effectively allocate the spectrum for secondary users (SUs) with different priorities in a maritime cognitive radio communication system is studied. According to the characteristics of a maritime cognitive radio and existing research about cognitive radio systems, this paper establishes a centralized maritime cognitive radio communication model and creates a simplified queuing model with two queues for the communication model. In the view of the behaviors of SUs and primary users (PUs), we propose a dynamic spectrum allocation (DSA) algorithm based on the system status, and analyze it with a two-dimensional Markov chain. Simulation results show that, when different types of SUs have similar arrival rates, the algorithm can vary the priority factor according to the change of users’ status in the system, so as to adjust the channel allocation, decreasing system congestion. The improvement of the algorithm is about 7–26%, and the specific improvement is negatively correlated with the SU arrival rate.

Beschreibung: Information, Vol. 8, Pages 122: Neutrosophic Similarity Score Based Weighted Histogram for Robust Mean-Shift Tracking Information doi: 10.3390/info8040122 Authors: Keli Hu En Fan Jun Ye Changxing Fan Shigen Shen Yuzhang Gu Visual object tracking is a critical task in computer vision. Challenging things always exist when an object needs to be tracked. For instance, background clutter is one of the most challenging problems. The mean-shift tracker is quite popular because of its efficiency and performance in a range of conditions. However, the challenge of background clutter also disturbs its performance. In this article, we propose a novel weighted histogram based on neutrosophic similarity score to help the mean-shift tracker discriminate the target from the background. Neutrosophic set (NS) is a new branch of philosophy for dealing with incomplete, indeterminate, and inconsistent information. In this paper, we utilize the single valued neutrosophic set (SVNS), which is a subclass of NS to improve the mean-shift tracker. First, two kinds of criteria are considered as the object feature similarity and the background feature similarity, and each bin of the weight histogram is represented in the SVNS domain via three membership functions T(Truth), I(indeterminacy), and F(Falsity). Second, the neutrosophic similarity score function is introduced to fuse those two criteria and to build the final weight histogram. Finally, a novel neutrosophic weighted mean-shift tracker is proposed. The proposed tracker is compared with several mean-shift based trackers on a dataset of 61 public sequences. The results revealed that our method outperforms other trackers, especially when confronting background clutter.

Beschreibung: Information, Vol. 8, Pages 147: Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study Information doi: 10.3390/info8040147 Authors: André Mora Tiago Santos Szymon Łukasik João Silva António Falcão José Fonseca Rita Ribeiro This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.

Beschreibung: Information, Vol. 8, Pages 150: Investigating the Statistical Distribution of Learning Coverage in MOOCs Information doi: 10.3390/info8040150 Authors: Xiu Li Chang Men Zhihui Du Jason Liu Manli Li Xiaolei Zhang Learners participating in Massive Open Online Courses (MOOC) have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually obtain a certificate. We discovered this phenomenon after having examined 92 courses on both xuetangX and edX platforms. More specifically, we found that the learning coverage in many courses—one of the metrics used to estimate the learners’ active engagement with the online courses—observes a Zipf distribution. We apply the maximum likelihood estimation method to fit the Zipf’s law and test our hypothesis using a chi-square test. In the xuetangX dataset, the learning coverage in 53 of 76 courses fits Zipf’s law, but in all of 16 courses on the edX platform, the learning coverage rejects the Zipf’s law. The result from our study is expected to bring insight to the unique learning behavior on MOOC.

Beschreibung: Information, Vol. 8, Pages 152: Ensemble of Filter-Based Rankers to Guide an Epsilon-Greedy Swarm Optimizer for High-Dimensional Feature Subset Selection Information doi: 10.3390/info8040152 Authors: Mohammad Dowlatshahi Vali Derhami Hossein Nezamabadi-pour The main purpose of feature subset selection is to remove irrelevant and redundant features from data, so that learning algorithms can be trained by a subset of relevant features. So far, many algorithms have been developed for the feature subset selection, and most of these algorithms suffer from two major problems in solving high-dimensional datasets: First, some of these algorithms search in a high-dimensional feature space without any domain knowledge about the feature importance. Second, most of these algorithms are originally designed for continuous optimization problems, but feature selection is a binary optimization problem. To overcome the mentioned weaknesses, we propose a novel hybrid filter-wrapper algorithm, called Ensemble of Filter-based Rankers to guide an Epsilon-greedy Swarm Optimizer (EFR-ESO), for solving high-dimensional feature subset selection. The Epsilon-greedy Swarm Optimizer (ESO) is a novel binary swarm intelligence algorithm introduced in this paper as a novel wrapper. In the proposed EFR-ESO, we extract the knowledge about the feature importance by the ensemble of filter-based rankers and then use this knowledge to weight the feature probabilities in the ESO. Experiments on 14 high-dimensional datasets indicate that the proposed algorithm has excellent performance in terms of both the error rate of the classification and minimizing the number of features.

Beschreibung: Information, Vol. 8, Pages 154: Certain Concepts in Intuitionistic Neutrosophic Graph Structures Information doi: 10.3390/info8040154 Authors: Muhammad Akram Muzzamal Sitara A graph structure is a generalization of simple graphs. Graph structures are very useful tools for the study of different domains of computational intelligence and computer science. In this research paper, we introduce certain notions of intuitionistic neutrosophic graph structures. We illustrate these notions by several examples. We investigate some related properties of intuitionistic neutrosophic graph structures. We also present an application of intuitionistic neutrosophic graph structures.

Beschreibung: Information, Vol. 8, Pages 155: Face Classification Using Color Information Information doi: 10.3390/info8040155 Authors: Atul Sajjanhar Ahmed Mohammed Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture analysis to improve classification performance of race and gender. The proposed methods for texture analysis are based on Local Binary Pattern and its derivatives. These texture analysis methods are evaluated for six color models (hue, saturation and intensity value (HSV); L*a*b*; RGB; YCbCr; YIQ; YUV) to investigate the effect of each color model. Further, we configure two combinations of color channels to represent color information suitable for gender and race classification of face images. We perform experiments on publicly available face databases. Experimental results show that the proposed approaches are effective for the classification of gender and race.

Beschreibung: Information, Vol. 8, Pages 156: The Emperor of Strong AI Has No Clothes: Limits to Artificial Intelligence Information doi: 10.3390/info8040156 Authors: Adriana Braga Robert Logan Making use of the techniques of media ecology we argue that the premise of the technological Singularity based on the notion computers will one day be smarter that their human creators is false. We also analyze the comments of other critics of the Singularity, as well supporters of this notion. The notion of intelligence that advocates of the technological singularity promote does not take into account the full dimension of human intelligence. They treat artificial intelligence as a figure without a ground. Human intelligence as we will show is not based solely on logical operations and computation, but also includes a long list of other characteristics that are unique to humans, which is the ground that supporters of the Singularity ignore. The list includes curiosity, imagination, intuition, emotions, passion, desires, pleasure, aesthetics, joy, purpose, objectives, goals, telos, values, morality, experience, wisdom, judgment, and even humor.

Beschreibung: Information, Vol. 8, Pages 157: Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging Information doi: 10.3390/info8040157 Authors: Maihemuti Maimaiti Aishan Wumaier Kahaerjiang Abiderexiti Tuergen Yibulayin Uyghur is an agglutinative and a morphologically rich language; natural language processing tasks in Uyghur can be a challenge. Word morphology is important in Uyghur part-of-speech (POS) tagging. However, POS tagging performance suffers from error propagation of morphological analyzers. To address this problem, we propose a few models for POS tagging: conditional random fields (CRF), long short-term memory (LSTM), bidirectional LSTM networks (BI-LSTM), LSTM networks with a CRF layer, and BI-LSTM networks with a CRF layer. These models do not depend on stemming and word disambiguation for Uyghur and combine hand-crafted features with neural network models. State-of-the-art performance on Uyghur POS tagging is achieved on test data sets using the proposed approach: 98.41% accuracy on 15 labels and 95.74% accuracy on 64 labels, which are 2.71% and 4% improvements, respectively, over the CRF model results. Using engineered features, our model achieves further improvements of 0.2% (15 labels) and 0.48% (64 labels). The results indicate that the proposed method could be an effective approach for POS tagging in other morphologically rich languages.

Beschreibung: Information, Vol. 8, Pages 149: NC-TODIM-Based MAGDM under a Neutrosophic Cubic Set Environment Information doi: 10.3390/info8040149 Authors: Surapati Pramanik Shyamal Dalapati Shariful Alam Tapan Kumar Roy A neutrosophic cubic set is the hybridization of the concept of a neutrosophic set and an interval neutrosophic set. A neutrosophic cubic set has the capacity to express the hybrid information of both the interval neutrosophic set and the single valued neutrosophic set simultaneously. As newly defined, little research on the operations and applications of neutrosophic cubic sets has been reported in the current literature. In the present paper, we propose the score and accuracy functions for neutrosophic cubic sets and prove their basic properties. We also develop a strategy for ranking of neutrosophic cubic numbers based on the score and accuracy functions. We firstly develop a TODIM (Tomada de decisao interativa e multicritévio) in the neutrosophic cubic set (NC) environment, which we call the NC-TODIM. We establish a new NC-TODIM strategy for solving multi attribute group decision making (MAGDM) in neutrosophic cubic set environment. We illustrate the proposed NC-TODIM strategy for solving a multi attribute group decision making problem to show the applicability and effectiveness of the developed strategy. We also conduct sensitivity analysis to show the impact of ranking order of the alternatives for different values of the attenuation factor of losses for multi-attribute group decision making strategies.

Beschreibung: Information, Vol. 8, Pages 159: sCwc/sLcc: Highly Scalable Feature Selection Algorithms Information doi: 10.3390/info8040159 Authors: Kilho Shin Tetsuji Kuboyama Takako Hashimoto Dave Shepard Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain the phenomena that the dataset describes, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied intensively in machine learning research. However, while feature selection algorithms that exhibit excellent accuracy have been developed, they are seldom used for analysis of high-dimensional data because high-dimensional data usually include too many instances and features, which make traditional feature selection algorithms inefficient. To eliminate this limitation, we tried to improve the run-time performance of two of the most accurate feature selection algorithms known in the literature. The result is two accurate and fast algorithms, namely sCWC and sLCC. Multiple experiments with real social media datasets have demonstrated that our algorithms improve the performance of their original algorithms remarkably. For example, we have two datasets, one with 15,568 instances and 15,741 features, and another with 200,569 instances and 99,672 features. sCwc performed feature selection on these datasets in 1.4 seconds and in 405 seconds, respectively. In addition, sLcc has turned out to be as fast as sCwc on average. This is a remarkable improvement because it is estimated that the original algorithms would need several hours to dozens of days to process the same datasets. In addition, we introduce a fast implementation of our algorithms: sCwc does not require any adjusting parameter, while sLcc requires a threshold parameter, which we can use to control the number of features that the algorithm selects.

Beschreibung: Information, Vol. 8, Pages 148: Source Code Documentation Generation Using Program Execution Information doi: 10.3390/info8040148 Authors: Matúš Sulír Jaroslav Porubän Automated source code documentation approaches often describe methods in abstract terms, using the words contained in the static source code or code excerpts from repositories. In this paper, we describe DynamiDoc: a simple automated documentation generator based on dynamic analysis. Our representation-based approach traces the program being executed and records string representations of concrete argument values, a return value and a target object state before and after each method execution. Then, for each method, it generates documentation sentences with examples, such as “When called on [3, 1.2] with element = 3, the object changed to [1.2]”. Advantages and shortcomings of the approach are listed. We also found out that the generated sentences are substantially shorter than the methods they describe. According to our small-scale study, the majority of objects in the generated documentation have their string representations overridden, which further confirms the potential usefulness of our approach. Finally, we propose an alternative, variable-based approach that describes the values of individual member variables, rather than the state of an object as a whole.

Beschreibung: Information, Vol. 8, Pages 151: A New Anomaly Detection System for School Electricity Consumption Data Information doi: 10.3390/info8040151 Authors: Wenqiang Cui Hao Wang Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. We investigated five models within electricity consumption data from different schools to detect anomalies in the data. Furthermore, we proposed a hybrid model that combines polynomial regression and Gaussian distribution, which detects anomalies in the data with 0 false negative and an average precision higher than 91%. Based on the proposed model, we developed a data detection and visualization system for a facilities management company to detect and visualize anomalies in school electricity consumption data. The system is tested and evaluated by facilities managers. According to the evaluation, our system has improved the efficiency of facilities managers to identify anomalies in the data.

Beschreibung: Information, Vol. 8, Pages 153: A Routing Protocol Based on Received Signal Strength for Underwater Wireless Sensor Networks (UWSNs) Information doi: 10.3390/info8040153 Authors: Meiju Li Xiujuan Du Kejun Huang Senlin Hou Xiuxiu Liu Underwater wireless sensor networks (UWSNs) are featured by long propagation delay, limited energy, narrow bandwidth, high BER (Bit Error Rate) and variable topology structure. These features make it very difficult to design a short delay and high energy-efficiency routing protocol for UWSNs. In this paper, a routing protocol independent of location information is proposed based on received signal strength (RSS), which is called RRSS. In RRSS, a sensor node firstly establishes a vector from the node to a sink node; the length of the vector indicates the RSS of the beacon signal (RSSB) from the sink node. A node selects the next-hop along the vector according to RSSB and the RSS of a hello packet (RSSH). The node nearer to the vector has higher priority to be a candidate next-hop. To avoid data packets being delivered to the neighbor nodes in a void area, a void-avoiding algorithm is introduced. In addition, residual energy is considered when selecting the next-hop. Meanwhile, we establish mathematic models to analyze the robustness and energy efficiency of RRSS. Lastly, we conduct extensive simulations, and the simulation results show RRSS can save energy consumption and decrease end-to-end delay.

Beschreibung: Information, Vol. 8, Pages 132: Certain Competition Graphs Based on Intuitionistic Neutrosophic Environment Information doi: 10.3390/info8040132 Authors: Muhammad Akram Maryam Nasir The concept of intuitionistic neutrosophic sets provides an additional possibility to represent imprecise, uncertain, inconsistent and incomplete information, which exists in real situations. This research article first presents the notion of intuitionistic neutrosophic competition graphs. Then, p-competition intuitionistic neutrosophic graphs and m-step intuitionistic neutrosophic competition graphs are discussed. Further, applications of intuitionistic neutrosophic competition graphs in ecosystem and career competition are described.

Beschreibung: Information, Vol. 8, Pages 136: Fuzzy Extractor and Elliptic Curve Based Efficient User Authentication Protocol for Wireless Sensor Networks and Internet of Things Information doi: 10.3390/info8040136 Authors: Anup Maurya Vinjamuri Sastry To improve the quality of service and reduce the possibility of security attacks, a secure and efficient user authentication mechanism is required for Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). Session key establishment between the sensor node and the user is also required for secure communication. In this paper, we perform the security analysis of A.K.Das’s user authentication scheme (given in 2015), Choi et al.’s scheme (given in 2016), and Park et al.’s scheme (given in 2016). The security analysis shows that their schemes are vulnerable to various attacks like user impersonation attack, sensor node impersonation attack and attacks based on legitimate users. Based on the cryptanalysis of these existing protocols, we propose a secure and efficient authenticated session key establishment protocol which ensures various security features and overcomes the drawbacks of existing protocols. The formal and informal security analysis indicates that the proposed protocol withstands the various security vulnerabilities involved in WSNs. The automated validation using AVISPA and Scyther tool ensures the absence of security attacks in our scheme. The logical verification using the Burrows-Abadi-Needham (BAN) logic confirms the correctness of the proposed protocol. Finally, the comparative analysis based on computational overhead and security features of other existing protocol indicate that the proposed user authentication system is secure and efficient. In future, we intend to implement the proposed protocol in real-world applications of WSNs and IoT.

Beschreibung: Information, Vol. 8, Pages 138: MR Brain Image Segmentation: A Framework to Compare Different Clustering Techniques Information doi: 10.3390/info8040138 Authors: Laura Caponetti Giovanna Castellano Vito Corsini In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate on specific regions of the brain in order to analyze them. However, segmentation of brain images is a difficult task due to high similarities and correlations of intensity among different regions of the brain image. Among various methods proposed in the literature, clustering algorithms prove to be successful tools for image segmentation. In this paper, we present a framework for image segmentation that is devoted to support the expert in identifying different brain regions for further analysis. The framework includes different clustering methods to perform segmentation of MR images. Furthermore, it enables easy comparison of different segmentation results by providing a quantitative evaluation using an entropy-based measure as well as other measures commonly used to evaluate segmentation results. To show the potential of the framework, the implemented clustering methods are compared on simulated T1-weighted MR brain images from the Internet Brain Segmentation Repository (IBSR database) provided with ground truth segmentation.

Beschreibung: Information, Vol. 8, Pages 139: Structural and Symbolic Information in the Context of the General Theory of Information Information doi: 10.3390/info8040139 Authors: Mark Burgin Rainer Feistel The general theory of information, which includes syntactic, semantic, pragmatic, and many other special theories of information, provides theoretical and practical tools for discerning a very large diversity of different kinds, types, and classes of information. Some of these kinds, types, and classes are more important and some are less important. Two basic classes are formed by structural and symbolic information. While structural information is intrinsically imbedded in the structure of the corresponding object or domain, symbolic information is represented by symbols, the meaning of which is subject to arbitrary conventions between people. As a result, symbolic information exists only in the context of life, including technical and theoretical constructs created by humans. Structural information is related to any objects, systems, and processes regardless of the existence or presence of life. In this paper, properties of structural and symbolic information are explored in the formal framework of the general theory of information developed by Burgin because this theory offers more powerful instruments for this inquiry. Structural information is further differentiated into inherent, descriptive, and constructive types. Properties of correctness and uniqueness of these types are investigated. In addition, predictive power of symbolic information accumulated in the course of natural evolution is considered. The phenomenon of ritualization is described as a general transition process from structural to symbolic information.

Beschreibung: Information, Vol. 8, Pages 135: Enhancement of Low Contrast Images Based on Effective Space Combined with Pixel Learning Information doi: 10.3390/info8040135 Authors: Gengfei Li Guiju Li Guangliang Han Images captured in bad conditions often suffer from low contrast. In this paper, we proposed a simple, but efficient linear restoration model to enhance the low contrast images. The model’s design is based on the effective space of the 3D surface graph of the image. Effective space is defined as the minimum space containing the 3D surface graph of the image, and the proportion of the pixel value in the effective space is considered to reflect the details of images. The bright channel prior and the dark channel prior are used to estimate the effective space, however, they may cause block artifacts. We designed the pixel learning to solve this problem. Pixel learning takes the input image as the training example and the low frequency component of input as the label to learn (pixel by pixel) based on the look-up table model. The proposed method is very fast and can restore a high-quality image with fine details. The experimental results on a variety of images captured in bad conditions, such as nonuniform light, night, hazy and underwater, demonstrate the effectiveness and efficiency of the proposed method.

Beschreibung: Information, Vol. 8, Pages 137: A Distributed Ledger for Supply Chain Physical Distribution Visibility Information doi: 10.3390/info8040137 Authors: Haoyan Wu Zhijie Li Brian King Zina Ben Miled John Wassick Jeffrey Tazelaar Supply chains (SC) span many geographies, modes and industries and involve several phases where data flows in both directions from suppliers, manufacturers, distributors, retailers, to customers. This data flow is necessary to support critical business decisions that may impact product cost and market share. Current SC information systems are unable to provide validated, pseudo real-time shipment tracking during the distribution phase. This information is available from a single source, often the carrier, and is shared with other stakeholders on an as-needed basis. This paper introduces an independent, crowd-validated, online shipment tracking framework that complements current enterprise-based SC management solutions. The proposed framework consists of a set of private distributed ledgers and a single blockchain public ledger. Each private ledger allows the private sharing of custody events among the trading partners in a given shipment. Privacy is necessary, for example, when trading high-end products or chemical and pharmaceutical products. The second type of ledger is a blockchain public ledger. It consists of the hash code of each private event in addition to monitoring events. The latter provide an independently validated immutable record of the pseudo real-time geolocation status of the shipment from a large number of sources using commuters-sourcing.

Beschreibung: Information, Vol. 8, Pages 158: Uncertain Production Scheduling Based on Fuzzy Theory Considering Utility and Production Rate Information doi: 10.3390/info8040158 Authors: Yue Wang Xin Jin Lei Xie Yanhui Zhang Shan Lu Handling uncertainty in an appropriate manner during the real operation of a cyber-physical system (CPS) is critical. Uncertain production scheduling as a part of CPS uncertainty issues should attract more attention. In this paper, a Mixed Integer Nonlinear Programming (MINLP) uncertain model for batch process is formulated based on a unit-specific event-based continuous-time modeling method. Utility uncertainty and uncertain relationship between production rate and utility supply are described by fuzzy theory. The uncertain scheduling model is converted into deterministic model by mathematical method. Through one numerical example, the accuracy and practicability of the proposed model is proved. Fuzzy scheduling model can supply valuable decision options for enterprise managers to make decision more accurate and practical. The impact and selection of some key parameters of fuzzy scheduling model are elaborated.

Beschreibung: Information, Vol. 9, Pages 3: Special Issue on Fuzzy Logic for Image Processing Information doi: 10.3390/info9010003 Authors: Laura Caponetti Giovanna Castellano The increasing availability of huge image collections in different application fields, such as medical diagnosis, remote sensing, transmission and encoding, machine/robot vision, and video processing, microscopic imaging has pressed the need, in the last few last years, for the development of efficient techniques capable of managing and processing large collection of image data [...]

Beschreibung: Information, Vol. 9, Pages 4: An RTT-Aware Virtual Machine Placement Method Information doi: 10.3390/info9010004 Authors: Li Quan Zhiliang Wang Fuji Ren Virtualization is a key technology for mobile cloud computing (MCC) and the virtual machine (VM) is a core component of virtualization. VM provides a relatively independent running environment for different applications. Therefore, the VM placement problem focuses on how to place VMs on optimal physical machines, which ensures efficient use of resources and the quality of service, etc. Most previous work focuses on energy consumption, network traffic between VMs and so on and rarely consider the delay for end users’ requests. In contrast, the latency between requests and VMs is considered in this paper for the scenario of optimal VM placement in MCC. In order to minimize average RTT for all requests, the round-trip time (RTT) is first used as the metric for the latency of requests. Based on our proposed RTT metric, an RTT-Aware VM placement algorithm is then proposed to minimize the average RTT. Furthermore, the case in which one of the core switches does not work is considered. A VM rescheduling algorithm is proposed to keep the average RTT lower and reduce the fluctuation of the average RTT. Finally, in the simulation study, our algorithm shows its advantage over existing methods, including random placement, the traffic-aware VM placement algorithm and the remaining utilization-aware algorithm.

Beschreibung: Information, Vol. 9, Pages 2: Entity Attribute Value Style Modeling Approach for Archetype Based Data Information doi: 10.3390/info9010002 Authors: Shivani Batra Shelly Sachdeva Subhash Bhalla Entity Attribute Value (EAV) storage model is extensively used to manage healthcare data in existing systems, however it lacks search efficiency. This study examines an entity attribute value style modeling approach for standardized Electronic Health Records (EHRs) database. It sustains qualities of EAV (i.e., handling sparseness and frequent schema evolution) and provides better performance for queries in comparison to EAV. It is termed as the Two Dimensional Entity Attribute Value (2D EAV) model. Support for ad-hoc queries is provided through a user interface for better user-interaction. 2D EAV focuses on how to handle template-centric queries as well as other health query scenarios. 2D EAV is analyzed (in terms of minimum non-null density) to make a judgment about the adoption of 2D EAV over n-ary storage model of RDBMS. The primary aim of current research is to handle sparseness, frequent schema evolution, and efficient query support altogether for standardized EHRs. 2D EAV will benefit data administrators to handle standardized heterogeneous data that demands high search efficiency. It will also benefit both skilled and semi-skilled database users (such as, doctors, nurses, and patients) by providing a global semantic interoperable mechanism of data retrieval.

Beschreibung: Information, Vol. 8, Pages 133: Bi-Objective Economic Dispatch of Micro Energy Internet Incorporating Energy Router Information doi: 10.3390/info8040133 Authors: Tian Li Yongqian Li Anqiang Lv Integration of different energy networks will increase additional flexibility to system operation. The key component in such a coupled infrastructure is the energy router, which plays an important role in energy transition and storage to smoothing the prediction error both in renewables and load. The router has the multi-carrier energy generation capability, and builds physical linkages among the power network, heat network, and other networks in the micro energy internet. The economic dispatch problem of the micro energy internet is formulated as a bi-objective optimization problem. Golden section search method is adopted to locate a compromising solution in the sense of Nash Bargaining. Case studies on a typical test system verify the effectiveness of the proposed bi-objective dispatch model and solution method.

Beschreibung: Information, Vol. 8, Pages 134: Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey Information doi: 10.3390/info8040134 Authors: Changyu Liu Qian Zhang Bin Lu Cong Li Action and event recognition in multimedia collections is relevant to progress in cross-disciplinary research areas including computer vision, computational optimization, statistical learning, and nonlinear dynamics. Over the past two decades, action and event recognition has evolved from earlier intervening strategies under controlled environments to recent automatic solutions under dynamic environments, resulting in an imperative requirement to effectively organize spatiotemporal deep features. Consequently, resorting to feature encodings and poolings for action and event recognition in complex multimedia collections is an inevitable trend. The purpose of this paper is to offer a comprehensive survey on the most popular feature encoding and pooling approaches in action and event recognition in recent years by summarizing systematically both underlying theoretical principles and original experimental conclusions of those approaches based on an approach-based taxonomy, so as to provide impetus for future relevant studies.

Beschreibung: This paper investigates the extent to which a mobile data source can be utilised to generate new information intelligence for decision-making in smart city planning processes. In this regard, the Mobility Explorer framework is introduced and applied to the City of Vienna (Austria) by using anonymised mobile phone data from a mobile phone service provider. This framework identifies five necessary elements that are needed to develop complex planning applications. As part of the investigation and experiments a new dynamic software tool, called Mobility Explorer, has been designed and developed based on the requirements of the planning department of the City of Vienna. As a result, the Mobility Explorer enables city stakeholders to interactively visualise the dynamic diurnal population distribution, mobility patterns and various other complex outputs for planning needs. Based on the experiences during the development phase, this paper discusses mobile data issues, presents the visual interface, performs various user-defined analyses, demonstrates the application’s usefulness and critically reflects on the evaluation results of the citizens’ motion exploration that reveal the great potential of mobile phone data in smart city planning but also depict its limitations. These experiences and lessons learned from the Mobility Explorer application development provide useful insights for other cities and planners who want to make informed decisions using mobile phone data in their city planning processes through dynamic visualisation of Call Data Record (CDR) data.

Beschreibung: Identity-based signcryption is a useful cryptographic primitive that provides both authentication and confidentiality for identity-based crypto systems. It is challenging to build a secure identity-based signcryption scheme that can be proven secure in a standard model. In this paper, we address the issue and propose a novel construction of identity-based signcryption which enjoys IND-CCA security and existential unforgeability without resorting to the random oracle model. Comparisons demonstrate that the new scheme achieves stronger security, better performance efficiency and shorter system parameters.

Beschreibung: In order to detect outliers in temperature time series data for improving data quality and decision-making quality related to design and operation, we proposed an algorithm based on sliding window prediction. Firstly, the time series are segmented based on the sliding window. Then, the prediction model is established based on the history data to predict the future value. If the difference between a predicted value and a measured value is larger than the preset threshold value, the sequence point will be judged to be an outlier and then corrected. In this paper, the sliding window and parameter settings of the algorithm are discussed and the algorithm is verified on actual data. This method does not need to pre classify the abnormal points and perform fast, and can handle large scale data. The experimental results show that the proposed algorithm can not only effectively detect outliers in the time series of meteorological data but also improves the correction efficiency notoriously.

Beschreibung: This paper presents a new approach to solve the multi-criteria group decision making (MCGDM) problem where criteria values take the form of 2-tuple linguistic information. Firstly, a 2-tuple hybrid ordered weighted geometric (THOWG) operator is proposed, which synthetically considers the importance of both individual and the ordered position so as to overcome the defects of existing operators. Secondly, combining the advantages of the cloud model and 2-tuple linguistic variable, a new generating cloud method is proposed to transform 2-tuple linguistic variables into clouds. Thirdly, we further define some new cloud algorithms, such as cloud possibility degree and cloud support degree which can be respectively used to compare clouds and determine the criteria weights. Furthermore, a new approach for 2-tuple linguistic group decision making is presented on the basis of the THOWG operator, the improved generating cloud method as well as the new cloud algorithms. Finally, an example of assessing the social effects of biomass power plants (BPPS) is illustrated to verify the application and feasible of the developed approach, and a comparative analysis is also conducted to validate the effectiveness of the proposed method.

Beschreibung: In this paper, we propose a single image dehazing method aiming at addressing the inherent limitations of the extensively employed dark channel prior (DCP). More concretely, we introduce the Gaussian mixture model (GMM) to segment the input hazy image into scenes based on the haze density feature map. With the segmentation results, combined with the proposed sky region detection method, we can effectively recognize the sky region where the DCP cannot well handle this. On the basis of sky region detection, we then present an improved global atmospheric light estimation method to increase the estimation accuracy of the atmospheric light. Further, we present a multi-scale fusion-based strategy to obtain the transmission map based on DCP, which can significantly reduce the blocking artifacts of the transmission map. To further rectify the error-prone transmission within the sky region, an adaptive sky region transmission correction method is also presented. Finally, due to the segmentation-blindness of GMM, we adopt the guided total variation (GTV) to tackle this problem while eliminating the extensive texture details contained in the transmission map. Experimental results verify the power of our method and show its superiority over several state-of-the-art methods.

Beschreibung: The intensive construction of domain-specific knowledge bases (DSKB) has posed an urgent demand for researches about domain-specific entity detection and linking (DSEDL). Joint models are usually adopted in DSEDL tasks, but data imbalance and high computational complexity exist in these models. Besides, traditional feature representation methods are insufficient for domain-specific tasks, due to problems such as lack of labeled data, link sparseness in DSKBs, and so on. In this paper, a two-stage joint (TSJ) model is proposed to solve the data imbalance problem by discriminatively processing entity mentions with different degrees of ambiguity. In addition, three novel methods are put forward to generate effective features by incorporating an unlabeled corpus. One crucial feature involving entity detection is the mention type, extracted by a long short-term memory (LSTM) model trained on automatically annotated data. The other two types of features mainly involve entity linking, including the inner-document topical coherence, which is measured based on entity co-occurring relationships in the corpus, and the cross-document entity coherence evaluated using similar documents. An overall 74.26% F1 value is obtained on a dataset of real-world movie comments, demonstrating the effectiveness of the proposed approach and indicating its potentiality to be used in real-world domain-specific applications.

Beschreibung: In this report, we studied the sampling synchronization of a discrete signal in the receiver of a communication system and found that the frequency of the received signal usually exhibits some unpredictable deviations. We observed many harmonics caused by the frequency deviations of the discrete received signal. These findings indicate that signal sampling synchronization is an important research technique when using discrete Fourier transforms (DFT) to analyze the harmonics of discrete signals. We investigated the influence of these harmonics on the performance of signal sampling and studied the frequency estimation of the received signal. Based on the frequency estimation of the received signal, the sampling rate of the discrete signal was converted using a modified Farrow filter to achieve sampling synchronization for the received signal. The algorithm discussed here can be applied to sampling synchronization for monitoring and control systems. Finally, simulations and experimental results are presented.

Beschreibung: This research presents and studies an evaluation framework for tourism mobile commerce platforms based on tourists’ experience. Synthesizing from prior literature, relevant theories, and the results of online questionnaires, we select 24 evaluation indices for preliminary evaluation. Using exploratory factor analysis method, we then extract from these indices the following five principal factors: interactive experience, infrastructure experience, personalization experience, product or service quality experience, and product operation experience. We further employ the confirmatory factor analysis to test the construction of the evaluation framework and demonstrate that the evaluation framework is both robust and effective. Finally, based on our proposed evaluation framework, we empirically evaluate the most popular mobile commerce platforms (Ctrip and Qunaer) in China by using fuzzy comprehensive evaluation method.

Beschreibung: Compound figure detection on figures and associated captions is the first step to making medical figures from biomedical literature available for further analysis. The performance of traditional methods is limited to the choice of hand-engineering features and prior domain knowledge. We train multiple convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent unit (GRU) networks on top of pre-trained word vectors to learn textual features from captions and employ deep CNNs to learn visual features from figures. We then identify compound figures by combining textual and visual prediction. Our proposed architecture obtains remarkable performance in three run types—textual, visual and mixed—and achieves better performance in ImageCLEF2015 and ImageCLEF2016.

Beschreibung: Citizen participation for social innovation and co-creating urban regeneration proposals can be greatly facilitated by innovative IT systems. Such systems can use Open Government Data, visualise urban proposals in 3D models and provide automated feedback on the feasibility of the proposals. Using such a system as a communication platform between citizens and city administrations provides an integrated top-down and bottom-up urban planning and decision-making approach to smart cities. However, generating automated feedback on citizens’ proposals requires modelling domain-specific knowledge i.e., vocabulary and rules, which can be applied on spatial and temporal 3D models. This paper presents the European Commission funded H2020 smarticipate project that aims to achieve the above challenge by applying it on three smart cities: Hamburg, Rome and RBKC-London. Whilst the proposed system architecture indicates various innovative features, a proof of concept of the automated feedback feature for the Hamburg use case ‘planting trees’ is demonstrated. Early results and lessons learned show that it is feasible to provide automated feedback on citizen-initiated proposals on specific topics. However, it is not straightforward to generalise this feature to cover more complex concepts and conditions which require specifying comprehensive domain languages, rules and appropriate tools to process them. This paper also highlights the strengths of the smarticipate platform, discusses challenges to realise its different features and suggests potential solutions.

Beschreibung: A simplified neutrosophic set is characterized by a truth-membership function, an indeterminacy-membership function, and a falsity-membership function, which is a subclass of the neutrosophic set and contains the concepts of an interval neutrosophic set and a single valued neutrosophic set. It is a powerful structure in expressing indeterminate and inconsistent information. However, there has only been one paper until now—to the best of my knowledge—on the subtraction and division operators in the basic operational laws of neutrosophic single-valued numbers defined in existing literature. Therefore, this paper proposes subtraction operation and division operation for simplified neutrosophic sets, including single valued neutrosophic sets and interval neutrosophic sets respectively, under some constrained conditions to form the integral theoretical framework of simplified neutrosophic sets. In addition, we give numerical examples to illustrate the defined operations. The subtraction and division operations are very important in many practical applications, such as decision making and image processing.

Beschreibung: Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T2w) MRI anatomical data processing, is proposed. This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study. Volume-based metrics, spatial overlap-based metrics and spatial distance-based metrics were used to quantitatively evaluate the accuracy of the obtained segmentation results with respect to the gold-standard boundaries delineated manually by an expert radiologist. The proposed multispectral segmentation method was compared with the same processing pipeline applied on either T2w or T1w MR images alone. The multispectral approach considerably outperforms the monoparametric ones, achieving an average Dice Similarity Coefficient 90.77 ± 1.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively. Combining T2w and T1w MR image structural information significantly enhances prostate gland segmentation by exploiting the uniform gray appearance of the prostate on T1w MRI.

Beschreibung: Recently, Wireless Body Area Networks (WBANs) have become an emerging technology in healthcare, where patients are equipped withwearable and implantable body sensor nodes to gather sensory information for remote monitoring. The increasing development of coordinator devices on patients enables the internetworking of WBANs in heterogeneous wireless networks to deliver physiological information that is collected at remote terminals in a timely fashion. However, in this type of network, providing a seamless handover with a guaranteed Quality of Service (QoS), especially emergency services, is a challenging task. In this paper, we proposed an effective Multi-Attribute Decision-Making (MADM) handover algorithm that guarantees seamless connectivity. A patient’s mobile devices automatically connect to the best network that fulfills the QoS requirements of different types of applications. Additionally, we integrated a Content-Centric Networking (CCN) processing module into different wireless networks to reduce packet loss, enhance QoS and avoid unnecessary handovers by leveraging in-network caching to achieve efficient content dissemination for ubiquitous healthcare. Simulation results proved that our proposed approach forthe model with CCN outperforms the model without CCN and Received Signal Strength Vertical Handoff (RSS-VHD) in terms of the number of handovers, enhancing QoS, packet loss, and energy efficiency.

Beschreibung: Information and symmetry are essential theoretical concepts that underlie the scientific explanation of a variety of complex systems. In spite of clear-cut developments around both concepts, their intersection is really problematic, either in fields related to mathematics, physics, and chemistry, or even more in those pertaining to biology, neurosciences, and social sciences. The present Special Issue explores recent developments, both theoretical and applied, in most of these disciplines.

Beschreibung: In this paper, we jointly consider the inhomogeneity and spatial dimension in large scale wireless networks. We study the effects of topology on the throughput capacity. This problem is inherently difficult since it is complex to handle the interference caused by simultaneous transmission. To solve this problem, we, according to the inhomogeneity of topology, divide the transmission into intra-cluster transmission and inter-cluster transmission. For the intra-cluster transmission, a spheroidal percolation model is constructed. The spheroidal percolation model guarantees a constant rate when a power control strategy is adopted. We also propose a cube percolation mode for the inter-cluster transmission. Different from the spheroidal percolation model, a constant transmission rate can be achieved without power control. For both transmissions, we propose a routing scheme with five phases. By comparing the achievable rate of each phase, we get the rate bottleneck, which is the throughput capacity of the network.

Beschreibung: Electricity demand forecasting can provide the scientific basis for the country to formulate the power industry development strategy and the power-generating target, which further promotes the sustainable, healthy and rapid development of the national economy. In this paper, a new mathematical hybrid method is proposed to forecast electricity demand. In line with electricity demand feature, the framework of joint-forecasting model is established and divided into two procedures: firstly, the modified GM(1,1) model and the Logistic model are used to make single forecasting. Then, the induced ordered weighted harmonic averaging operator (IOWHA) is applied to combine these two single models and make joint-forecasting. Forecasting results demonstrate that this new hybrid model is superior to both single-forecasting approaches and traditional joint-forecasting methods, thus verifying the high prediction validity and accuracy of mentioned joint-forecasting model. Finally, detailed forecasting-outcomes on electricity demand of China in 2016–2020 are discussed and displayed a slow-growth smoothly over the next five years.

Beschreibung: Multi-label classification is a well-known supervised machine learning setting where each instance is associated with multiple classes. Examples include annotation of images with multiple labels, assigning multiple tags for a web page, etc. Since several labels can be assigned to a single instance, one of the key challenges in this problem is to learn the correlations between the classes. Our first contribution assumes labels from a perfect source. Towards this, we propose a novel topic model (ML-PA-LDA). The distinguishing feature in our model is that classes that are present as well as the classes that are absent generate the latent topics and hence the words. Extensive experimentation on real world datasets reveals the superior performance of the proposed model. A natural source for procuring the training dataset is through mining user-generated content or directly through users in a crowdsourcing platform. In this more practical scenario of crowdsourcing, an additional challenge arises as the labels of the training instances are provided by noisy, heterogeneous crowd-workers with unknown qualities. With this motivation, we further augment our topic model to the scenario where the labels are provided by multiple noisy sources and refer to this model as ML-PA-LDA-MNS. With experiments on simulated noisy annotators, the proposed model learns the qualities of the annotators well, even with minimal training data.

Beschreibung: In this paper, four kinds of block diffraction models were proposed on the basis of the uniform geometrical theory of diffraction, and these models were validated by experiments with 45 GHz millimeter wave in the laboratory. The results are in agreement with the theoretical analysis. Some errors exist in the measurement results because of the unsatisfactory experimental environment. Single conducting cylindrical block measurement error was less than 0.5 dB, and single man block measurement error in the school laboratory was less than 1 dB, while in the factory laboratory environment, the peak to peak error reached 1.6 dB. Human body block attenuation was about 5.9–9.2 dB lower than that of the single conducting cylinder. A human body and a conducting cylinder were used together as a block in model (c) and model (d), but the positions of the cylinder in the two models were different. The measurement results showed that the attenuation of model (d) is about 3 dB higher than that of model (c).

Beschreibung: To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC) inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

Beschreibung: Point-of-interest (POI) recommendation has been well studied in recent years. However, most of the existing methods focus on the recommendation scenarios where users can provide explicit feedback. In most cases, however, the feedback is not explicit, but implicit. For example, we can only get a user’s check-in behaviors from the history of what POIs she/he has visited, but never know how much she/he likes and why she/he does not like them. Recently, some researchers have noticed this problem and began to learn the user preferences from the partial order of POIs. However, these works give equal weight to each POI pair and cannot distinguish the contributions from different POI pairs. Intuitively, for the two POIs in a POI pair, the larger the frequency difference of being visited and the farther the geographical distance between them, the higher the contribution of this POI pair to the ranking function. Based on the above observations, we propose a weighted ranking method for POI recommendation. Specifically, we first introduce a Bayesian personalized ranking criterion designed for implicit feedback to POI recommendation. To fully utilize the partial order of POIs, we then treat the cost function in a weighted way, that is give each POI pair a different weight according to their frequency of being visited and the geographical distance between them. Data analysis and experimental results on two real-world datasets demonstrate the existence of user preference on different POI pairs and the effectiveness of our weighted ranking method.

Beschreibung: In this brief overview paper, we analyse information flow in the brain. Although Shannon’s information concept, in its pure algebraic form, has made a number of valuable contributions to neuroscience, information dynamics within the brain is not fully captured by its classical description. These additional dynamics consist of self-organisation, interplay of stability/instability, timing of sequential processing, coordination of multiple sequential streams, circular causality between bottom-up and top-down operations, and information creation. Importantly, all of these processes are dynamic, hierarchically nested and correspond to continuous brain state change, even if the external environment remains constant. This is where metastable coordination comes into play. In a metastable regime of brain functioning, as a result of the simultaneous co-existence of tendencies for independence and cooperation, information is continuously created, preserved for some time and then dissipated through the formation of dynamical and nested spatio-temporal coalitions among simple neuronal assemblies and larger coupled conglomerates of them—so-called delocalised operational modules.