ALBERT

Description: Algorithms, Vol. 11, Pages 112: A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring Algorithms doi: 10.3390/a11080112 Authors: Ruhua Wang Ling Li Jun Li In this paper, damage detection/identification for a seven-storey steel structure is investigated via using the vibration signals and deep learning techniques. Vibration characteristics, such as natural frequencies and mode shapes are captured and utilized as input for a deep learning network while the output vector represents the structural damage associated with locations. The deep auto-encoder with sparsity constraint is used for effective feature extraction for different types of signals and another deep auto-encoder is used to learn the relationship of different signals for final regression. The existing SAF model in a recent research study for the same problem processed all signals in one serial auto-encoder model. That kind of models have the following difficulties: (1) the natural frequencies and mode shapes are in different magnitude scales and it is not logical to normalize them in the same scale in building the models with training samples; (2) some frequencies and mode shapes may not be related to each other and it is not fair to use them for dimension reduction together. To tackle the above-mentioned problems for the multi-scale dataset in SHM, a novel parallel auto-encoder framework (Para-AF) is proposed in this paper. It processes the frequency signals and mode shapes separately for feature selection via dimension reduction and then combine these features together in relationship learning for regression. Furthermore, we introduce sparsity constraint in model reduction stage for performance improvement. Two experiments are conducted on performance evaluation and our results show the significant advantages of the proposed model in comparison with the existing approaches.

Description: Algorithms, Vol. 11, Pages 113: Improved Parameter Identification Method for Envelope Current Signals Based on Windowed Interpolation FFT and DE Algorithm Algorithms doi: 10.3390/a11080113 Authors: Xiangfeng Su Huaiqing Zhang Lin Chen Ling Qin Lili Yu Envelope current signals are increasingly emerging in power systems, and their parameter identification is particularly necessary for accurate measurement of electrical energy. In order to analyze the envelope current signal, the harmonic parameters, as well as the envelope parameters, need to be calculated. The interpolation fast Fourier transform (FFT) is a widely used approach which can estimate the signal frequency with high precision, but it cannot calculate the envelope parameters of the signal. Therefore, this paper proposes an improved method based on windowed interpolation FFT (WIFFT) and differential evolution (DE). The amplitude and phase parameters obtained through WIFFT and the envelope parameters estimated by the envelope analysis are optimized using the DE algorithm, which makes full use of the performance advantage of DE. The simulation results show that the proposed method can improve the accuracy of the harmonic parameters and the envelope parameter significantly. In addition, it has good anti-noise ability and high precision.

Description: Algorithms, Vol. 11, Pages 114: Revisiting Chameleon Sequences in the Protein Data Bank Algorithms doi: 10.3390/a11080114 Authors: Mihaly Mezei The steady growth of the Protein Data Bank (PDB) suggests the periodic repetition of searches for sequences that form different secondary structures in different protein structures; these are called chameleon sequences. This paper presents a fast (nlog(n)) algorithm for such searches and presents the results on all protein structures in the PDB. The longest such sequence found consists of 20 residues.

Description: Algorithms, Vol. 11, Pages 115: Color-Based Image Retrieval Using Proximity Space Theory Algorithms doi: 10.3390/a11080115 Authors: Jing Wang Lidong Wang Xiaodong Liu Yan Ren Ye Yuan The goal of object retrieval is to rank a set of images by their similarity compared with a query image. Nowadays, content-based image retrieval is a hot research topic, and color features play an important role in this procedure. However, it is important to establish a measure of image similarity in advance. The innovation point of this paper lies in the following. Firstly, the idea of the proximity space theory is utilized to retrieve the relevant images between the query image and images of database, and we use the color histogram of an image to obtain the Top-ranked colors, which can be regard as the object set. Secondly, the similarity is calculated based on an improved dominance granule structure similarity method. Thus, we propose a color-based image retrieval method by using proximity space theory. To detect the feasibility of this method, we conducted an experiment on COIL-20 image database and Corel-1000 database. Experimental results demonstrate the effectiveness of the proposed framework and its applications.

Description: Future Internet, Vol. 10, Pages 71: Hybrid Approach with Improved Genetic Algorithm and Simulated Annealing for Thesis Sampling Future Internet doi: 10.3390/fi10080071 Authors: Shardrom Johnson Jinwu Han Yuanchen Liu Li Chen Xinlin Wu Sampling inspection uses the sample characteristics to estimate that of the population, and it is an important method to describe the population, which has the features of low cost, strong applicability and high scientificity. This paper aims at the sampling inspection of the master’s degree thesis to ensure their quality, which is commonly estimated by random sampling. Since there are disadvantages in random sampling, a hybrid algorithm combined with an improved genetic algorithm and a simulated annealing algorithm is proposed in this paper. Furthermore, a novel mutation strategy is introduced according to the specialty of Shanghai’s thesis sampling to improve the efficiency of sampling inspection; the acceleration of convergence of the algorithm can also take advantage of this. The new algorithm features the traditional genetic algorithm, and it can obtain the global optimum in the optimization process and provide the fairest sampling plan under the constraint of multiple sampling indexes. The experimental results on the master’s thesis dataset of Shanghai show that the proposed algorithm well meets the requirements of the sampling inspection in Shanghai with a lower time-complexity.

Description: Future Internet, Vol. 10, Pages 70: Multidiscipline Integrated Platform Based on Probabilistic Analysis for Manufacturing Engineering Processes Future Internet doi: 10.3390/fi10080070 Authors: Lijun Zhang Kai Liu Jian Liu Researchers from different disciplines, such as materials science, computer science, safety science, mechanical engineering and controlling engineering, have aimed to improve the quality of manufacturing engineering processes. Considering the requirements of research and development of advanced materials, reliable manufacturing and collaborative innovation, a multidiscipline integrated platform framework based on probabilistic analysis for manufacturing engineering processes is proposed. The proposed platform consists of three logical layers: The requirement layer, the database layer and the application layer. The platform is intended to be a scalable system to gradually supplement related data, models and approaches. The main key technologies of the platform, encapsulation methods, information fusion approaches and the collaborative mechanism are also discussed. The proposed platform will also be gradually improved in the future. In order to exchange information for manufacturing engineering processes, scientists and engineers of different institutes of materials science and manufacturing engineering should strengthen their cooperation.

Description: Future Internet, Vol. 10, Pages 72: Context Analysis of Cloud Computing Systems Using a Pattern-Based Approach Future Internet doi: 10.3390/fi10080072 Authors: Ludger Goeke Nazila Gol Mohammadi Maritta Heisel Cloud computing services bring new capabilities for hosting and offering complex collaborative business operations. However, these advances might bring undesirable side-effects, e.g., introducing new vulnerabilities and threats caused by collaboration and data exchange over the Internet. Hence, users have become more concerned about security and privacy aspects. For secure provisioning of a cloud computing service, security and privacy issues must be addressed by using a risk assessment method. To perform a risk assessment, it is necessary to obtain all relevant information about the context of the considered cloud computing service. The context analysis of a cloud computing service and its underlying system is a difficult task because of the variety of different types of information that have to be considered. This context information includes (i) legal, regulatory and/or contractual requirements that are relevant for a cloud computing service (indirect stakeholders); (ii) relations to other involved cloud computing services; (iii) high-level cloud system components that support the involved cloud computing services; (iv) data that is processed by the cloud computing services; and (v) stakeholders that interact directly with the cloud computing services and/or the underlying cloud system components. We present a pattern for the contextual analysis of cloud computing services and demonstrate the instantiation of our proposed pattern with real-life application examples. Our pattern contains elements that represent the above-mentioned types of contextual information. The elements of our pattern conform to the General Data Protection Regulation. Besides the context analysis, our pattern supports the identification of high-level assets. Additionally, our proposed pattern supports the documentation of the scope and boundaries of a cloud computing service conforming to the requirements of the ISO 27005 standard (information security risk management). The results of our context analysis contribute to the transparency of the achieved security and privacy level of a cloud computing service. This transparency can increase the trust of users in a cloud computing service. We present results of the RestAssured project related to the context analysis regarding cloud computing services and their underlying cloud computing systems. The context analysis is the prerequisite to threat and control identification that are performed later in the risk management process. The focus of this paper is the use of a pattern at the time of design systematic context analysis and scope definition for risk management methods.

Description: Algorithms, Vol. 11, Pages 111: A Weighted Histogram-Based Tone Mapping Algorithm for CT Images Algorithms doi: 10.3390/a11080111 Authors: David Völgyes Anne Catrine Trægde Martinsen Arne Stray-Pedersen Dag Waaler Marius Pedersen Computed Tomography (CT) images have a high dynamic range, which makes visualization challenging. Histogram equalization methods either use spatially invariant weights or limited kernel size due to the complexity of pairwise contribution calculation. We present a weighted histogram equalization-based tone mapping algorithm which utilizes Fast Fourier Transform for distance-dependent contribution calculation and distance-based weights. The weights follow power-law without distance-based cut-off. The resulting images have good local contrast without noticeable artefacts. The results are compared to eight popular tone mapping operators.

Description: Algorithms, Vol. 11, Pages 119: An Opportunistic Network Routing Algorithm Based on Cosine Similarity of Data Packets between Nodes Algorithms doi: 10.3390/a11080119 Authors: Yucheng Lin Zhigang Chen Jia Wu Leilei Wang The mobility of nodes leads to dynamic changes in topology structure, which makes the traditional routing algorithms of a wireless network difficult to apply to the opportunistic network. In view of the problems existing in the process of information forwarding, this paper proposed a routing algorithm based on the cosine similarity of data packets between nodes (cosSim). The cosine distance, an algorithm for calculating the similarity between text data, is used to calculate the cosine similarity of data packets between nodes. The data packet set of nodes are expressed in the form of vectors, thereby facilitating the calculation of the similarity between the nodes. Through the definition of the upper and lower thresholds, the similarity between the nodes is filtered according to certain rules, and finally obtains a plurality of relatively reliable transmission paths. Simulation experiments show that compared with the traditional opportunistic network routing algorithm, such as the Spray and Wait (S&W) algorithm and Epidemic algorithm, the cosSim algorithm has a better transmission effect, which can not only improve the delivery ratio, but also reduce the network transmission delay and decline the routing overhead.

Description: Algorithms, Vol. 11, Pages 120: Probabilistic Interval-Valued Hesitant Fuzzy Information Aggregation Operators and Their Application to Multi-Attribute Decision Making Algorithms doi: 10.3390/a11080120 Authors: Wenying Wu Ying Li Zhiwei Ni Feifei Jin Xuhui Zhu Based on the probabilistic interval-valued hesitant fuzzy information aggregation operators, this paper investigates a novel multi-attribute group decision making (MAGDM) model to address the serious loss of information in a hesitant fuzzy information environment. Firstly, the definition of probabilistic interval-valued hesitant fuzzy set will be introduced, and then, using Archimedean norm, some new probabilistic interval-valued hesitant fuzzy operations are defined. Secondly, based on these operations, the generalized probabilistic interval-valued hesitant fuzzy ordered weighted averaging (GPIVHFOWA) operator, and the generalized probabilistic interval-valued hesitant fuzzy ordered weighted geometric (GPIVHFOWG) operator are proposed, and their desirable properties are discussed. We further study their common forms and analyze the relationship among these proposed operators. Finally, a new probabilistic interval-valued hesitant fuzzy MAGDM model is constructed, and the feasibility and effectiveness of the proposed model are verified by using an example of supplier selection.

Description: Future Internet, Vol. 10, Pages 46: A Tiered Control Plane Model for Service Function Chaining Isolation Future Internet doi: 10.3390/fi10060046 Authors: Håkon Gunleifsen Vasileios Gkioulos Thomas Kemmerich This article presents an architecture for encryption automation in interconnected Network Function Virtualization (NFV) domains. Current NFV implementations are designed for deployment within trusted domains, where overlay networks with static trusted links are utilized for enabling network security. Nevertheless, within a Service Function Chain (SFC), Virtual Network Function (VNF) flows cannot be isolated and end-to-end encrypted because each VNF requires direct access to the overall SFC data-flow. This restricts both end-users and Service Providers from enabling end-to-end security, and in extended VNF isolation within the SFC data traffic. Encrypting data flows on a per-flow basis results in an extensive amount of secure tunnels, which cannot scale efficiently in manual configurations. Additionally, creating secure data plane tunnels between NFV providers requires secure exchange of key parameters, and the establishment of an east–west control plane protocol. In this article, we present an architecture focusing on these two problems, investigating how overlay networks can be created, isolated, and secured dynamically. Accordingly, we propose an architecture for automated establishment of encrypted tunnels in NFV, which introduces a novel, tiered east–west communication channel between network controllers in a multi-domain environment.

Description: Algorithms, Vol. 11, Pages 80: Scheduling a Single Machine with Primary and Secondary Objectives Algorithms doi: 10.3390/a11060080 Authors: Nodari Vakhania We study a scheduling problem in which jobs with release times and due dates are to be processed on a single machine. With the primary objective to minimize the maximum job lateness, the problem is strongly NP-hard. We describe a general algorithmic scheme to minimize the maximum job lateness, with the secondary objective to minimize the maximum job completion time. The problem of finding the Pareto-optimal set of feasible solutions with these two objective criteria is strongly NP-hard. We give the dominance properties and conditions when the Pareto-optimal set can be formed in polynomial time. These properties, together with our general framework, provide the theoretical background, so that the basic framework can be expanded to (exponential-time) implicit enumeration algorithms and polynomial-time approximation algorithms (generating the Pareto sub-optimal frontier with a fair balance between the two objectives). Some available in the literature experimental results confirm the practical efficiency of the proposed framework.

Description: Algorithms, Vol. 11, Pages 85: ILC with Initial State Learning for Fractional Order Linear Distributed Parameter Systems Algorithms doi: 10.3390/a11060085 Authors: Yong-Hong Lan Zhe-Min Cui This paper presents a second order P-type iterative learning control (ILC) scheme with initial state learning for a class of fractional order linear distributed parameter systems. First, by analyzing the control and learning processes, a discrete system for P-type ILC is established, and the ILC design problem is then converted to a stability problem for such a discrete system. Next, a sufficient condition for the convergence of the control input and the tracking errors is obtained by introducing a new norm and using the generalized Gronwall inequality, which is less conservative than the existing one. Finally, the validity of the proposed method is verified by a numerical example.

Description: Algorithms, Vol. 11, Pages 86: Performance Optimal PI controller Tuning Based on Integrating Plus Time Delay Models Algorithms doi: 10.3390/a11060086 Authors: Christer Dalen David Di Ruscio A method for tuning PI controller parameters, a prescribed maximum time delay error or a relative time delay error is presented. The method is based on integrator plus time delay models. The integral time constant is linear in the relative time delay error, and the proportional constant is seen inversely proportional to the relative time delay error. The keystone in the method is the method product parameter, i.e., the product of the PI controller proportional constant, the integral time constant, and the integrator plus time delay model, velocity gain. The method product parameter is found to be constant for various PI controller tuning methods. Optimal suggestions are given for choosing the method product parameter, i.e., optimal such that the integrated absolute error or, more interestingly, the Pareto performance objective (i.e., integrated absolute error for combined step changes in output and input disturbances) is minimised. Variants of the presented tuning method are demonstrated for tuning PI controllers for motivated (possible) higher order process model examples, i.e., the presented method is combined with the model reduction step (process–reaction curve) in Ziegler–Nichols.

Description: Future Internet, Vol. 10, Pages 56: Big Data Perspective and Challenges in Next Generation Networks Future Internet doi: 10.3390/fi10070056 Authors: Kashif Sultan Hazrat Ali Zhongshan Zhang With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.

Description: Algorithms, Vol. 11, Pages 94: Tensor Completion Based on Triple Tubal Nuclear Norm Algorithms doi: 10.3390/a11070094 Authors: Dongxu Wei Andong Wang Xiaoqin Feng Boyu Wang Bo Wang Many tasks in computer vision suffer from missing values in tensor data, i.e., multi-way data array. The recently proposed tensor tubal nuclear norm (TNN) has shown superiority in imputing missing values in 3D visual data, like color images and videos. However, by interpreting in a circulant way, TNN only exploits tube (often carrying temporal/channel information) redundancy in a circulant way while preserving the row and column (often carrying spatial information) relationship. In this paper, a new tensor norm named the triple tubal nuclear norm (TriTNN) is proposed to simultaneously exploit tube, row and column redundancy in a circulant way by using a weighted sum of three TNNs. Thus, more spatial-temporal information can be mined. Further, a TriTNN-based tensor completion model with an ADMM solver is developed. Experiments on color images, videos and LiDAR datasets show the superiority of the proposed TriTNN against state-of-the-art nuclear norm-based tensor norms.

Description: Algorithms, Vol. 11, Pages 93: Layered Graphs: Applications and Algorithms Algorithms doi: 10.3390/a11070093 Authors: Bhadrachalam Chitturi Srijith Balachander Sandeep Satheesh Krithic Puthiyoppil The computation of distances between strings has applications in molecular biology, music theory and pattern recognition. One such measure, called short reversal distance, has applications in evolutionary distance computation. It has been shown that this problem can be reduced to the computation of a maximum independent set on the corresponding graph that is constructed from the given input strings. The constructed graphs primarily fall into a class that we call layered graphs. In a layered graph, each layer refers to a subgraph containing, at most, some k vertices. The inter-layer edges are restricted to the vertices in adjacent layers. We study the MIS, MVC, MDS, MCV and MCD problems on layered graphs where MIS computes the maximum independent set; MVC computes the minimum vertex cover; MDS computes the minimum dominating set; MCV computes the minimum connected vertex cover; and MCD computes the minimum connected dominating set. The MIS, MVC and MDS are computed in polynomial time if k=Θ(log|V|). MCV and MCD are computed polynomial time if k=O((log|V|)α), where α<1. If k=Θ((log|V|)1+ϵ), for ϵ>0, then MIS, MVC and MDS are computed in quasi-polynomial time. If k=Θ(log|V|), then MCV and MCD are computed in quasi-polynomial time.

Description: Algorithms, Vol. 11, Pages 116: A Robust and Energy-Efficient Weighted Clustering Algorithm on Mobile Ad Hoc Sensor Networks † Algorithms doi: 10.3390/a11080116 Authors: Huamei Qi Fengqi Liu Tailong Xiao Jiang Su In an Ad hoc sensor network, nodes have characteristics of limited battery energy, self-organization and low mobility. Due to the mobility and heterogeneity of the energy consumption in the hierarchical network, the cluster head and topology are changed dynamically. Therefore, topology control and energy consumption are growing to be critical in enhancing the stability and prolonging the lifetime of the network. In order to improve the survivability of Ad hoc network effectively, this paper proposes a new algorithm named the robust, energy-efficient weighted clustering algorithm (RE2WCA). For the homogeneous of the energy consumption; the proposed clustering algorithm takes the residual energy and group mobility into consideration by restricting minimum iteration times. In addition, a distributed fault detection algorithm and cluster head backup mechanism are presented to achieve the periodic and real-time topology maintenance to enhance the robustness of the network. The network is analyzed and the simulations are performed to compare the performance of this new clustering algorithm with the similar algorithms in terms of cluster characteristics, lifetime, throughput and energy consumption of the network. The result shows that the proposed algorithm provides better performance than others.

Description: Algorithms, Vol. 11, Pages 121: Stacked-GRU Based Power System Transient Stability Assessment Method Algorithms doi: 10.3390/a11080121 Authors: Feilai Pan Jun Li Bendong Tan Ciling Zeng Xinfan Jiang Li Liu Jun Yang With the interconnection between large power grids, the issue of security and stability has become increasingly prominent. At present, data-driven power system adaptive transient stability assessment methods have achieved excellent performances by balancing speed and accuracy, but the complicated construction and parameters are difficult to obtain. This paper proposes a stacked-GRU (Gated Recurrent Unit)-based transient stability intelligent assessment method, which builds a stacked-GRU model based on time-dependent parameter sharing and spatial stacking. By using the time series data after power system failure, the offline training is performed to obtain the optimal parameters of stacked-GRU. When the application is online, it is assessed by framework of confidence. Basing on New England power system, the performance of proposed adaptive transient stability assessment method is investigated. Simulation results show that the proposed model realizes reliable and accurate assessment of transient stability and it has the advantages of short assessment time with less complex model structure to leave time for emergency control.

Description: Algorithms, Vol. 11, Pages 122: Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms Algorithms doi: 10.3390/a11080122 Authors: Chi-Yi Tsai Kuang-Jui Hsu Humaira Nisar Three-Dimensional (3D) object pose estimation plays a crucial role in computer vision because it is an essential function in many practical applications. In this paper, we propose a real-time model-based object pose estimation algorithm, which integrates template matching and Perspective-n-Point (PnP) pose estimation methods to deal with this issue efficiently. The proposed method firstly extracts and matches keypoints of the scene image and the object reference image. Based on the matched keypoints, a two-dimensional (2D) planar transformation between the reference image and the detected object can be formulated by a homography matrix, which can initialize a template tracking algorithm efficiently. Based on the template tracking result, the correspondence between image features and control points of the Computer-Aided Design (CAD) model of the object can be determined efficiently, thus leading to a fast 3D pose tracking result. Finally, the 3D pose of the object with respect to the camera is estimated by a PnP solver based on the tracked 2D-3D correspondences, which improves the accuracy of the pose estimation. Experimental results show that the proposed method not only achieves real-time performance in tracking multiple objects, but also provides accurate pose estimation results. These advantages make the proposed method suitable for many practical applications, such as augmented reality.

Description: Future Internet, Vol. 10, Pages 81: Interactive 3D Exploration of RDF Graphs through Semantic Planes Future Internet doi: 10.3390/fi10080081 Authors: Fabio Viola Luca Roffia Francesco Antoniazzi Alfredo D’Elia Cristiano Aguzzi Tullio Salmon Cinotti This article presents Tarsier, a tool for the interactive 3D visualization of RDF graphs. Tarsier is mainly intended to support teachers introducing students to Semantic Web data representation formalisms and developers in the debugging of applications based on Semantic Web knowledge bases. The tool proposes the metaphor of semantic planes as a way to visualize an RDF graph. A semantic plane contains all the RDF terms sharing a common concept; it can be created, and further split into several planes, through a set of UI controls or through SPARQL 1.1 queries, with the full support of OWL and RDFS. Thanks to the 3D visualization, links between semantic planes can be highlighted and the user can navigate within the 3D scene to find the better perspective to analyze data. Data can be gathered from generic SPARQL 1.1 protocol services. We believe that Tarsier will enhance the human friendliness of semantic technologies by: (1) helping newcomers assimilate new data representation formats; and (2) increasing the capabilities of inspection to detect relevant situations even in complex RDF graphs.

Description: Future Internet, Vol. 10, Pages 80: A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection Future Internet doi: 10.3390/fi10080080 Authors: Lei Zhang Xiaoli Zhi Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

Description: Future Internet, Vol. 10, Pages 84: Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion Future Internet doi: 10.3390/fi10090084 Authors: Yanli Li Lala Mei Ran Li Changan Wu Frame repetition (FR) is a common temporal-domain tampering operator, which is often used to increase the frame rate of video sequences. Existing methods detect FR forgery by analyzing residual variation or similarity between video frames; however, these methods are easily interfered with by noise, affecting the stability of detection performance. This paper proposes a noise-level based detection method which detects the varying noise level over time to determine whether the video is forged by FR. Wavelet coefficients are first computed for each video frame, and median absolute deviation (MAD) of wavelet coefficients is used to estimate the standard deviation of Gaussian noise mixed in each video frame. Then, fast Fourier transform (FFT) is used to calculate the amplitude spectrum of the standard deviation curve of the video sequence, and to provide the peak-mean ratio (PMR) of the amplitude spectrum. Finally, according to the PMR obtained, a hard threshold decision is taken to determine whether the standard deviation bears periodicity in the temporal domain, in which way FR forgery can be automatically identified. The experimental results show that the proposed method ensures a large PMR for the forged video, and presents a better detection performance when compared with the existing detection methods.

Description: Future Internet, Vol. 10, Pages 85: Predictive Power Management for Wind Powered Wireless Sensor Node Future Internet doi: 10.3390/fi10090085 Authors: Yin Wu Bowen Li Fuquan Zhang A conventional Wireless Sensor Network (WSN) cannot have an infinite lifetime without a battery recharge or replacement. Energy Harvesting (EH), from environmental energy sources, is a promising technology to provide sustainable powering for a WSN. In this paper, we propose and investigate a novel predictive energy management framework that combines the Maximal Power Transferring Tracking (MPTT) algorithm, a predictive energy allocation strategy, and a high efficiency transmission power control mechanism: First, the MPTT optimal working point guarantees minimum power loss of the EH-WSN system; Then, by exactly predicting the upcoming available energy, the power allocation strategy regulates EH-nodes’ duty cycle accurately to minimize the power failure time; Ultimately, the transmission power control module further improves energy efficiency by dynamically selecting the optimum matching transmission power level with minimum energy consumption. A wind energy powered wireless sensor system has been equipped and tested to validate the effectiveness of the proposed scheme. Results indicate that compared with other predictive energy managers, the proposed mechanism incurs relatively low power failure time while maintaining a high-energy conversion rate.

Description: Future Internet, Vol. 10, Pages 86: Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers Future Internet doi: 10.3390/fi10090086 Authors: Samah Alshathri Bogdan Ghita Nathan Clarke The cloud-computing concept has emerged as a powerful mechanism for data storage by providing a suitable platform for data centers. Recent studies show that the energy consumption of cloud computing systems is a key issue. Therefore, we should reduce the energy consumption to satisfy performance requirements, minimize power consumption, and maximize resource utilization. This paper introduces a novel algorithm that could allocate resources in a cloud-computing environment based on an energy optimization method called Sharing with Live Migration (SLM). In this scheduler, we used the Cloud-Sim toolkit to manage the usage of virtual machines (VMs) based on a novel algorithm that learns and predicts the similarity between the tasks, and then allocates each of them to a suitable VM. On the other hand, SLM satisfies the Quality of Services (QoS) constraints of the hosted applications by adopting a migration process. The experimental results show that the algorithm exhibits better performance, while saving power and minimizing the processing time. Therefore, the SLM algorithm demonstrates improved virtual machine efficiency and resource utilization compared to an adapted state-of-the-art algorithm for a similar problem.

Description: Algorithms, Vol. 11, Pages 139: An Auto-Adjustable Semi-Supervised Self-Training Algorithm Algorithms doi: 10.3390/a11090139 Authors: Ioannis E. Livieris Andreas Kanavos Vassilis Tampakas Panagiotis Pintelas Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models.

Description: Algorithms, Vol. 11, Pages 142: Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method Algorithms doi: 10.3390/a11090142 Authors: Wei Gao Hengyi Lv Qiang Zhang Dunbo Cai The satisfiability modulo theories (SMT) problem is to decide the satisfiability of a logical formula with respect to a given background theory. This work studies the counting version of SMT with respect to linear integer arithmetic (LIA), termed SMT(LIA). Specifically, the purpose of this paper is to count the number of solutions (volume) of a SMT(LIA) formula, which has many important applications and is computationally hard. To solve the counting problem, an approximate method that employs a recent Markov Chain Monte Carlo (MCMC) sampling strategy called “flat histogram” is proposed. Furthermore, two refinement strategies are proposed for the sampling process and result in two algorithms, MCMC-Flat1/2 and MCMC-Flat1/t, respectively. In MCMC-Flat1/t, a pseudo sampling strategy is introduced to evaluate the flatness of histograms. Experimental results show that our MCMC-Flat1/t method can achieve good accuracy on both structured and random instances, and our MCMC-Flat1/2 is scalable for instances of convex bodies with up to 7 variables.

Description: Future Internet, Vol. 10, Pages 93: Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks Future Internet doi: 10.3390/fi10100093 Authors: Anwar Said Syed Waqas Haider Shah Hasan Farooq Adnan Noor Mian Ali Imran Jon Crowcroft Caching close to users in a radio access network (RAN) has been identified as a promising method to reduce a backhaul traffic load and minimize latency in 5G and beyond. In this paper, we investigate a novel community detection inspired by a proactive caching scheme for device-to-device (D2D) enabled networks. The proposed scheme builds on the idea that content generated/accessed by influential users is more probable to become popular and thus can be exploited for pro-caching. We use a Clustering Coefficient based Genetic Algorithm (CC-GA) for community detection to discover a group of cellular users present in close vicinity. We then use an Eigenvector Centrality measure to identify the influential users with respect to the community structure, and the content associated to it is then used for pro-active caching using D2D communications. The numerical results show that, compared to reactive caching, where historically popular content is cached, depending on cache size, load and number of requests, up to 30% more users can be satisfied using a proposed scheme while achieving significant reduction in backhaul traffic load.

Description: Future Internet, Vol. 10, Pages 92: Occlusion-Aware Unsupervised Learning of Monocular Depth, Optical Flow and Camera Pose with Geometric Constraints Future Internet doi: 10.3390/fi10100092 Authors: Qianru Teng Yimin Chen Chen Huang We present an occlusion-aware unsupervised neural network for jointly learning three low-level vision tasks from monocular videos: depth, optical flow, and camera motion. The system consists of three different predicting sub-networks simultaneously coupled by combined loss terms and is capable of computing each task independently on test samples. Geometric constraints extracted from scene geometry which have traditionally been used in bundle adjustment or pose-graph optimization are formed as various self-supervisory signals during our end-to-end learning approach. Different from prior works, our image reconstruction loss also takes account of optical flow. Moreover, we impose novel 3D flow consistency constraints over the predictions of all the three tasks. By explicitly modeling occlusion and taking utilization of both 2D and 3D geometry relationships, abundant geometric constraints are formed over estimated outputs, enabling the system to capture both low-level representations and high-level cues to infer thinner scene structures. Empirical evaluation on the KITTI dataset demonstrates the effectiveness and improvement of our approach: (1) monocular depth estimation outperforms state-of-the-art unsupervised methods and is comparable to stereo supervised ones; (2) optical flow prediction ranks top among prior works and even beats supervised and traditional ones especially in non-occluded regions; (3) pose estimation outperforms established SLAM systems under comparable input settings with a reasonable margin.

Description: Future Internet, Vol. 10, Pages 76: SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach Future Internet doi: 10.3390/fi10080076 Authors: Marcio Andrey Teixeira Tara Salman Maede Zolanvari Raj Jain Nader Meskin Mohammed Samaka This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank’s control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.

Description: Future Internet, Vol. 10, Pages 78: Smart Collection of Real-Time Vehicular Mobility Traces Future Internet doi: 10.3390/fi10080078 Authors: Nisrine Ibadah Khalid Minaoui Mohammed Rziza Mohammed Oumsis César Benavente-Peces Mobility trace techniques makes possible drawing the behaviors of real-life movement which shape wireless networks mobility whereabouts. In our investigation, several trace mobility models have been collected after the devices’ deployment. The main issue of this classical procedure is that it produces uncompleted records due to several unpredictable problems occurring during the deployment phase. In this paper, we propose a new procedure aimed at collecting traces while deployment phase failures are avoided, which improves the reliability of data. The introduced procedure makes possible the complete generation of traces with a minimum amount of damage without the need to recover mobile devices or lose them, as it is the case in previous mobility traces techniques. Based on detecting and correcting all accidental issues in real time, the proposed trace scanning offers a set of relevant information about the vehicle status which was collected during seven months. Furthermore, the proposed procedure could be applied to generate vehicular traces. Likewise, it is suitable to record/generate human and animal traces. The research outcomes demonstrate the effectiveness and robustness of the smart collection algorithm based on the proposed trace mobility model.

Description: Future Internet, Vol. 10, Pages 77: Motives for Instagram Use and Topics of Interest among Young Adults Future Internet doi: 10.3390/fi10080077 Authors: Yi-Ting Huang Sheng-Fang Su Instagram is currently the most popular social media app among young people around the world. More than 70% of people between the ages of 12 and 24 are Instagram users. The research framework of this study was constructed based on smartphone addiction and the uses and gratifications theory. We used 27 question items divided into five factors, namely social interaction, documentation, diversion, self-promotion, and creativity, to investigate the motives for Instagram use and topics of interest among university students in Taiwan. A total of 307 valid questionnaires were obtained. The results revealed that on the whole, the motives for Instagram use were mostly to look at posts, particularly involving social interaction and diversion motives. The level of agreement expressed toward motives for creating posts was lower. Gender, professional training background, and level of addiction to Instagram all exert influence on motives for Instagram use. Over half of the students majoring in design followed artisans and celebrities (including designers), and female students noticed ads on Instagram more than male students did.

Description: Future Internet, Vol. 10, Pages 79: Queue Spillover Management in a Connected Vehicle Environment Future Internet doi: 10.3390/fi10080079 Authors: Chuanxiang Ren Wenbo Zhang Lingqiao Qin Bo Sun To alleviate the queue spillovers at intersections of urban roads during rush hours, a solution to the cross-spill problem based on vehicle networking technologies is proposed. This involves using connected vehicle technology, to realize the interactive information on vehicle and intersection signal control. The maximum control distance between intersections is determined by how vehicles are controlled and would travel in that connected environment. A method of calculating overflow tendency towards intersection queuing is also proposed, based on the maximum phase control distance. By this method, the intersection overflow is identified, and then the signal phases are re-optimized according to the requirements of different phases. Finally, overflow prevention control was also performed in this study. The VISSIM simulation results show that the method can better prevent the overflow of queues at intersections.

Description: Algorithms, Vol. 11, Pages 123: Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans Algorithms doi: 10.3390/a11080123 Authors: Yamur K. Al-Douri Hussan Hamodi Jan Lundberg The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

Description: Algorithms, Vol. 11, Pages 127: Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm Algorithms doi: 10.3390/a11080127 Authors: Mingbin Zeng Xu Yang Mengxing Wang Bangjiang Xu In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

Description: Algorithms, Vol. 11, Pages 129: The Fast Detection and Identification Algorithm of Optical Fiber Intrusion Signals Algorithms doi: 10.3390/a11090129 Authors: Zhiyong Sheng Dandan Qu Yuan Zhang Dan Yang With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the real-time requirements of OFPS, this paper presents a fast algorithm to accelerate the detection and recognition processing of optical fiber intrusion signals. The algorithm is implemented in an embedded system that is composed of a digital signal processor (DSP). The processing flow is divided into two parts. First, the dislocation processing method is adopted for the sum processing of original signals, which effectively improves the real-time performance. The filtered signals are divided into two parts and are parallel processed by two DSP boards to save time. Then, the data is input into the identification module for feature extraction and classification. Experiments show that the algorithm can effectively detect and identify the optical fiber intrusion signals. At the same time, it accelerates the processing speed and meets the real-time requirements of OFPS for detection and identification.

Description: Algorithms, Vol. 11, Pages 130: Numerical Modeling of Wave Disturbances in the Process of Ship Movement Control Algorithms doi: 10.3390/a11090130 Authors: Piotr Borkowski The article presents a numerical model of sea wave generation as an implementation of the stochastic process with a spectrum of wave angular velocity. Based on the wave spectrum, a forming filter is determined, and its input is fed with white noise. The resulting signal added to the angular speed of a ship represents disturbances acting on the ship’s hull as a result of wave impact. The model was used for simulation tests of the influence of disturbances on the course stabilization system of the ship.

Description: Algorithms, Vol. 11, Pages 132: Study of Precipitation Forecast Based on Deep Belief Networks Algorithms doi: 10.3390/a11090132 Authors: Jinglin Du Yayun Liu Zhijun Liu Due to the impact of weather forecasting on global human life, and to better reflect the current trend of weather changes, it is necessary to conduct research about the prediction of precipitation and provide timely and complete precipitation information for climate prediction and early warning decisions to avoid serious meteorological disasters. For the precipitation prediction problem in the era of climate big data, we propose a new method based on deep learning. In this paper, we will apply deep belief networks in weather precipitation forecasting. Deep belief networks transform the feature representation of data in the original space into a new feature space, with semantic features to improve the predictive performance. The experimental results show, compared with other forecasting methods, the feasibility of deep belief networks in the field of weather forecasting.

Description: Algorithms, Vol. 11, Pages 131: An efficient algorithm to determine probabilistic bisimulation Algorithms doi: 10.3390/a11090131 Authors: J.F. Groote H.J. Rivera Verduzco E.P. de Vink We provide an algorithm to efficiently compute bisimulation for probabilistic labeled transition systems, featuring non-deterministic choice as well as discrete probabilistic choice. The algorithm is linear in the number of transitions and logarithmic in the number of states, distinguishing both action states and probabilistic states, and the transitions between them. The algorithm improves upon the proposed complexity bounds of the best algorithm addressing the same purpose so far by Baier, Engelen and Majster-Cederbaum (Journal of Computer and System Sciences 60:187–231, 2000). In addition, experimentally, on various benchmarks, our algorithm performs rather well; even on relatively small transition systems, a performance gain of a factor 10,000 can be achieved.

Description: Future Internet, Vol. 10, Pages 87: Log Likelihood Ratio Based Relay Selection Scheme for Amplify and Forward Relaying with Three State Markov Channel Future Internet doi: 10.3390/fi10090087 Authors: Manish Sahajwani Alok Jain Radheyshyam Gamad This paper presents log likelihood ratio (LLR) based relay selection scheme for a cooperative amplify and forward relaying system. To evaluate the performance of the aforementioned system model, a three state Markov chain based fading environment has been presented to toggle among Rayleigh, Rician, and Nakagami-m fading environment. A simulation is carried out while assuming that there is no possibility of direct transmission from the source and destination terminal. Simulation results on the basis of Bit Error Rate (BER), Instantaneous Channel Capacity, and Outage probability have been presented and compared for different cases. In each case, the best performance of the proposed algorithm is obtained with a Binary Phase Shift Keying (BPSK) modulation scheme.

Description: Algorithms, Vol. 11, Pages 136: Mixed Order Fractional Observers for Minimal Realizations of Linear Time-Invariant Systems Algorithms doi: 10.3390/a11090136 Authors: Manuel A. Duarte-Mermoud Javier A. Gallegos Norelys Aguila-Camacho Rafael Castro-Linares Adaptive and non-adaptive minimal realization (MR) fractional order observers (FOO) for linear time-invariant systems (LTIS) of a possibly different derivation order (mixed order observers, MOO) are studied in this paper. Conditions on the convergence and robustness are provided using a general framework which allows observing systems defined with any type of fractional order derivative (FOD). A qualitative discussion is presented to show that the derivation orders of the observer structure and for the parameter adjustment are relevant degrees of freedom for performance optimization. A control problem is developed to illustrate the application of the proposed observers.

Description: Algorithms, Vol. 11, Pages 138: Are Markets Truly Efficient? Experiments Using Deep Learning Algorithms for Market Movement Prediction Algorithms doi: 10.3390/a11090138 Authors: Sanjiv Das Karthik Mokashi Robbie Culkin We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index, but not strongly enough to reject market efficiency. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P 500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

Description: Algorithms, Vol. 11, Pages 140: Complexity of Hamiltonian Cycle Reconfiguration Algorithms doi: 10.3390/a11090140 Authors: Asahi Takaoka The Hamiltonian cycle reconfiguration problem asks, given two Hamiltonian cycles C 0 and C t of a graph G, whether there is a sequence of Hamiltonian cycles C 0 , C 1 , … , C t such that C i can be obtained from C i − 1 by a switch for each i with 1 ≤ i ≤ t , where a switch is the replacement of a pair of edges u v and w z on a Hamiltonian cycle with the edges u w and v z of G, given that u w and v z did not appear on the cycle. We show that the Hamiltonian cycle reconfiguration problem is PSPACE-complete, settling an open question posed by Ito et al. (2011) and van den Heuvel (2013). More precisely, we show that the Hamiltonian cycle reconfiguration problem is PSPACE-complete for chordal bipartite graphs, strongly chordal split graphs, and bipartite graphs with maximum degree 6. Bipartite permutation graphs form a proper subclass of chordal bipartite graphs, and unit interval graphs form a proper subclass of strongly chordal graphs. On the positive side, we show that, for any two Hamiltonian cycles of a bipartite permutation graph and a unit interval graph, there is a sequence of switches transforming one cycle to the other, and such a sequence can be obtained in linear time.

Description: Algorithms, Vol. 11, Pages 141: Generalized Paxos Made Byzantine (and Less Complex) Algorithms doi: 10.3390/a11090141 Authors: Miguel Pires Srivatsan Ravi Rodrigo Rodrigues One of the most recent members of the Paxos family of protocols is Generalized Paxos. This variant of Paxos has the characteristic that it departs from the original specification of consensus, allowing for a weaker safety condition where different processes can have a different views on a sequence being agreed upon. However, much like the original Paxos counterpart, Generalized Paxos does not have a simple implementation. Furthermore, with the recent practical adoption of Byzantine fault tolerant protocols in the context of blockchain protocols, it is timely and important to understand how Generalized Paxos can be implemented in the Byzantine model. In this paper, we make two main contributions. First, we attempt to provide a simpler description of Generalized Paxos, based on a simpler specification and the pseudocode for a solution that can be readily implemented. Second, we extend the protocol to the Byzantine fault model, and provide the respective correctness proof.

Description: Algorithms, Vol. 11, Pages 51: A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel Algorithms doi: 10.3390/a11040051 Authors: Xinxin Lv Qi Zhu Spectrum sensing is the prerequisite of the realization of cognitive radio. So it is a significant part of cognitive radio. In order to stimulate the SUs to sense the spectrum, we combine the incentive mechanism of crowd-sensing with cooperative spectrum sensing effectively, and put forward a crowd cooperative spectrum sensing algorithm with optimal utility of secondary users (SUs) under non-ideal channel which we define SUs’ utility expectation functions related to rewards, sensing time and transmission power. Then, we construct the optimization problem of maximizing the utilities of SUs by optimizing the sensing time and the transmission power, and prove that this problem is a convex optimization problem. The optimal sensing time and transmission power are obtained by using the Karush-Kuhn-Tucker (KKT) conditions. The numerical simulation results show that the spectrum detection performance of algorithm, which we put forward, is improved.

Description: Algorithms, Vol. 11, Pages 54: Dual Market Facility Network Design under Bounded Rationality Algorithms doi: 10.3390/a11040054 Authors: D. G. Mogale Geet Lahoti Shashi Bhushan Jha Manish Shukla Narasimha Kamath Manoj Kumar Tiwari A number of markets, geographically separated, with different demand characteristics for different products that share a common component, are analyzed. This common component can either be manufactured locally in each of the markets or transported between the markets to fulfill the demand. However, final assemblies are localized to the respective markets. The decision making challenge is whether to manufacture the common component centrally or locally. To formulate the underlying setting, a newsvendor modeling based approach is considered. The developed model is solved using Frank-Wolfe linearization technique along with Benders’ decomposition method. Further, the propensity of decision makers in each market to make suboptimal decisions leading to bounded rationality is considered. The results obtained for both the cases are compared.

Description: Future Internet, Vol. 10, Pages 36: Dynamic Linked Data: A SPARQL Event Processing Architecture Future Internet doi: 10.3390/fi10040036 Authors: Luca Roffia Paolo Azzoni Cristiano Aguzzi Fabio Viola Francesco Antoniazzi Tullio Salmon Cinotti This paper presents a decentralized Web-based architecture designed to support the development of distributed, dynamic, context-aware and interoperable services and applications. The architecture enables the detection and notification of changes over the Web of Data by means of a content-based publish-subscribe mechanism where the W3C SPARQL 1.1 Update and Query languages are fully supported and used respectively by publishers and subscribers. The architecture is built on top of the W3C SPARQL 1.1 Protocol and introduces the SPARQL 1.1 Secure Event protocol and the SPARQL 1.1 Subscribe Language as a means for conveying and expressing subscription requests and notifications. The reference implementation of the architecture offers to developers a design pattern for a modular, scalable and effective application development.

Description: Algorithms, Vol. 11, Pages 52: Introduction to Reconfiguration Algorithms doi: 10.3390/a11040052 Authors: Naomi Nishimura Reconfiguration is concerned with relationships among solutions to a problem instance, where the reconfiguration of one solution to another is a sequence of steps such that each step produces an intermediate feasible solution. The solution space can be represented as a reconfiguration graph, where two vertices representing solutions are adjacent if one can be formed from the other in a single step. Work in the area encompasses both structural questions (Is the reconfiguration graph connected?) and algorithmic ones (How can one find the shortest sequence of steps between two solutions?) This survey discusses techniques, results, and future directions in the area.

Description: Future Internet, Vol. 10, Pages 38: MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers Future Internet doi: 10.3390/fi10050038 Authors: He Wu Zhu Pathan To enhance the efficiency of data searching, most data owners store their data files in different cloud servers in the form of cipher-text. Thus, efficient search using fuzzy keywords becomes a critical issue in such a cloud computing environment. This paper proposes a method that aims at improving the efficiency of cipher-text retrieval and lowering storage overhead for fuzzy keyword search. In contrast to traditional approaches, the proposed method can reduce the complexity of Min-Hash-based fuzzy keyword search by using Min-Hash fingerprints to avoid the need to construct the fuzzy keyword set. The method will utilize Jaccard similarity to rank the results of retrieval, thus reducing the amount of calculation for similarity and saving a lot of time and space overhead. The method will also take consideration of multiple user queries through re-encryption technology and update user permissions dynamically. Security analysis demonstrates that the method can provide better privacy preservation and experimental results show that efficiency of cipher-text using the proposed method can improve the retrieval time and lower storage overhead as well.

Description: Algorithms, Vol. 11, Pages 60: Vessel Traffic Risk Assessment Based on Uncertainty Analysis in the Risk Matrix Algorithms doi: 10.3390/a11050060 Authors: Molin Sun Zhongyi Zheng Uncertainty analysis is considered to be a necessary step in the process of vessel traffic risk assessment. The purpose of this study is to propose the uncertainty analysis algorithm which can be used to investigate the reliability of the risk assessment result. Probability and possibility distributions are used to quantify the two types of uncertainty identified in the risk assessment process. In addition, the algorithm for appropriate time window selection is chosen by considering the uncertainty of vessel traffic accident occurrence and the variation trend of the vessel traffic risk caused by maritime rules becoming operative. Vessel traffic accident data from the United Kingdom’s marine accident investigation branch are used for the case study. Based on a comparison with the common method of estimating the vessel traffic risk and the algorithm for uncertainty quantification without considering the time window selection, the availability of the proposed algorithms is verified, which can provide guidance for vessel traffic risk management.

Description: Algorithms, Vol. 11, Pages 58: Computing Fault-Containment Times of Self-Stabilizing Algorithms Using Lumped Markov Chains Algorithms doi: 10.3390/a11050058 Authors: Volker Turau The analysis of self-stabilizing algorithms is often limited to the worst case stabilization time starting from an arbitrary state, i.e., a state resulting from a sequence of faults. Considering the fact that these algorithms are intended to provide fault tolerance in the long run, this is not the most relevant metric. A common situation is that a running system is an a legitimate state when hit by a single fault. This event has a much higher probability than multiple concurrent faults. Therefore, the worst case time to recover from a single fault is more relevant than the recovery time from a large number of faults. This paper presents techniques to derive upper bounds for the mean time to recover from a single fault for self-stabilizing algorithms based on Markov chains in combination with lumping. To illustrate the applicability of the techniques they are applied to a new self-stabilizing coloring algorithm.

Description: Algorithms, Vol. 11, Pages 59: Decision-Making Approach Based on Neutrosophic Rough Information Algorithms doi: 10.3390/a11050059 Authors: Muhammad Akram Nabeela Ishfaq Sidra Sayed Florentin Smarandache Rough set theory and neutrosophic set theory are mathematical models to deal with incomplete and vague information. These two theories can be combined into a framework for modeling and processing incomplete information in information systems. Thus, the neutrosophic rough set hybrid model gives more precision, flexibility and compatibility to the system as compared to the classic and fuzzy models. In this research study, we develop neutrosophic rough digraphs based on the neutrosophic rough hybrid model. Moreover, we discuss regular neutrosophic rough digraphs, and we solve decision-making problems by using our proposed hybrid model. Finally, we give a comparison analysis of two hybrid models, namely, neutrosophic rough digraphs and rough neutrosophic digraphs.

Description: Algorithms, Vol. 11, Pages 63: The Supplier Selection of the Marine Rescue Equipment Based on the Analytic Hierarchy Process (AHP)-Limited Diversity Factors Method Algorithms doi: 10.3390/a11050063 Authors: Minghui Shao Yan Song Biao Wu Yanjie Chang Supplier selection is an important decision-making link in bidding activity. When overall scores of several suppliers are similar, it is hard to obtain an accurate ranking of these suppliers. Applying the Diversity Factors Method (Diversity Factors Method, DFM) may lead to over correction of weights, which would degrade the capability of indexes to reflect the importance. A Limited Diversity Factors Method (Limited Diversity Factors Method, LDFM) based on entropy is presented in this paper in order to adjust the weights, in order to relieve the over correction in DFM and to improve the capability of identification of indexes in supplier selection. An example of salvage ship bidding demonstrates the advantages of the LDFM, in which the raking of overall scores of suppliers is more accurate.

Description: Algorithms, Vol. 11, Pages 66: Single Machine Scheduling Problem with Interval Processing Times and Total Completion Time Objective Algorithms doi: 10.3390/a11050066 Authors: Yuri N. Sotskov Natalja G. Egorova We consider a single machine scheduling problem with uncertain durations of the given jobs. The objective function is minimizing the sum of the job completion times. We apply the stability approach to the considered uncertain scheduling problem using a relative perimeter of the optimality box as a stability measure of the optimal job permutation. We investigated properties of the optimality box and developed algorithms for constructing job permutations that have the largest relative perimeters of the optimality box. Computational results for constructing such permutations showed that they provided the average error less than 0 . 74 % for the solved uncertain problems.

Description: Algorithms, Vol. 11, Pages 65: Control Strategy of Speed Servo Systems Based on Deep Reinforcement Learning Algorithms doi: 10.3390/a11050065 Authors: Pengzhan Chen Zhiqiang He Chuanxi Chen Jiahong Xu We developed a novel control strategy of speed servo systems based on deep reinforcement learning. The control parameters of speed servo systems are difficult to regulate for practical applications, and problems of moment disturbance and inertia mutation occur during the operation process. A class of reinforcement learning agents for speed servo systems is designed based on the deep deterministic policy gradient algorithm. The agents are trained by a significant number of system data. After learning completion, they can automatically adjust the control parameters of servo systems and compensate for current online. Consequently, a servo system can always maintain good control performance. Numerous experiments are conducted to verify the proposed control strategy. Results show that the proposed method can achieve proportional–integral–derivative automatic tuning and effectively overcome the effects of inertia mutation and torque disturbance.

Description: Future Internet, Vol. 10, Pages 42: Test Bed of Semantic Interaction of Smart Objects in the Web of Things Future Internet doi: 10.3390/fi10050042 Authors: Santiago Guerrero-Narváez Miguel-Ángel Niño-Zambrano Dalila-Jhoana Riobamba-Calvache Gustavo-Adolfo Ramírez-González Semantic interaction in the Internet of Things (IoT) is an important concept within current IoT development, given that smart things require further autonomy with greater processing, storage, and communication capacities. The problem is now becoming one of how to get these things to interact and collaborate with each other; to form intelligent environments amongst themselves and thus generate better services for users. This article explores a solution approach that consists in providing collaborative behavior to smart things, through the incorporation of an ontology and an architecture. It makes possible things that can communicate and collaborate with each other, allowing the generation of new services of interaction according to user needs. For this task, a real test bed of smart things was created, in which the proposed solution was deployed (Smart Room). Finally, it was concluded that the creation of these types of test bed is feasible, taking into account that response times and the information delivered by the different managed processes are acceptable. New challenges were encountered, however, such as problems of critical region in test beds with conflicting services and management of multiple users.

Description: Algorithms, Vol. 11, Pages 71: Improving Monarch Butterfly Optimization Algorithm with Self-Adaptive Population Algorithms doi: 10.3390/a11050071 Authors: Hui Hu Zhaoquan Cai Song Hu Yingxue Cai Jia Chen Sibo Huang Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to the parameter p, which is unchanged during the entire optimization process. In our present work, a self-adaptive strategy is introduced to dynamically adjust the butterflies in land 1 and 2. Accordingly, the population size in subpopulation 1 and 2 are dynamically changed as the algorithm evolves in a linear way. After introducing the concept of a self-adaptive strategy, an improved MBO algorithm, called monarch butterfly optimization with self-adaptive population (SPMBO), is put forward. In SPMBO, only generated individuals who are better than before can be accepted as new individuals for the next generations in the migration operation. Finally, the proposed SPMBO algorithm is benchmarked by thirteen standard test functions with dimensions of 30 and 60. The experimental results indicate that the search ability of the proposed SPMBO approach significantly outperforms the basic MBO algorithm on most test functions. This also implies the self-adaptive strategy is an effective way to improve the performance of the basic MBO algorithm.

Description: Algorithms, Vol. 11, Pages 75: A New Oren–Nayar Shape-from-Shading Approach for 3D Reconstruction Using High-Order Godunov-Based Scheme Algorithms doi: 10.3390/a11050075 Authors: Guohui Wang Yuanbo Chu 3D shape reconstruction from images has been an important topic in the field of robot vision. Shape-From-Shading (SFS) is a classical method for determining the shape of a 3D surface from a one intensity image. The Lambertian reflectance is a fundamental assumption in conventional SFS approaches. Unfortunately, when applied to characterize the reflection attribute of the diffuse reflection, the Lambertian model is tested to be inexact. In this paper, we present a new SFS approach for 3D reconstruction of diffuse surfaces whose reflection attribute is approximated by the Oren–Nayar reflection model. The partial differential Image Irradiance Equation (IIR) is set up with this model under a single distant point light source and an orthographic camera projection whose direction coincides with the light source. Then, the IIR is converted into an eikonal equation by solving a quadratic equation that includes the 3D surface shape. The viscosity solution of the resulting eikonal equation is approximated by using the high-order Godunov-based scheme that is accelerated by means of an alternating sweeping strategy. We conduct the experiments on synthetic and real-world images, and the experimental results illustrate the effectiveness of the presented approach.

Description: Future Internet, Vol. 10, Pages 44: Studying Semi-TCP and Its Application in Marine Internet Future Internet doi: 10.3390/fi10060044 Authors: Liang Zhou Sheng-Ming Jiang Chen-Lin Xiong TCP protocol has good performance on the Internet, but its performance is significantly reduced when it is applied to Marine Internet (MI). How to improve the performance of TCP protocol in Marine Internet has become an important research topic. In this paper, an improved Semi-TCP is adopted for Marine Internet, and the implementation scheme of Semi-TCP congestion control is introduced. The exposed terminal problem and congestion control problem of high load networks are analyzed in detail. By using a timer, the congestion control algorithm is improved. Performance analysis and comparison of TCP-Lite, Semi-TCP-RTS, and improved Semi-TCP (Semi-TCP-RTS-V2) are carried out on Exata simulation platform, and the experimental results show that Semi-TCP-RTS-V2 has better transmission performance in ship ad hoc networks.

Description: Future Internet, Vol. 10, Pages 45: A Multiple Access Protocol Based on Gray Forecast for Satellite Network Future Internet doi: 10.3390/fi10060045 Authors: Chong Han Zilong Li Jian Zhou Lijuan Sun Siyu Chen A satellite network’s multiple access protocol must be well designed to efficiently occupy channel resources and meet various qualities of service requirements. In this paper, a combined free/demand assignment multiple access protocol based on gray forecast is proposed. In terms of access, the adoption of the gray forecast method allows the user to book the number of timeslots to have more than the existing number of burst of packets. In terms of bandwidth allocation, a user priority allocation method is used to prioritize users with high needs and focus on the allocation of satellite resources. Experimental results show that the proposed protocol can effectively improve the channel utilization of the entire system. Thus, this protocol has some advantages over existing protocols in network throughput and end-to-end delay.

Description: Algorithms, Vol. 11, Pages 96: Solving Multi-Document Summarization as an Orienteering Problem Algorithms doi: 10.3390/a11070096 Authors: Asma Al-Saleh Mohamed El Bachir Menai With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information overload. This article proposes a novel extractive graph-based approach to solve the multi-document summarization (MDS) problem. To optimize the coverage of information in the output summary, the problem is formulated as an orienteering problem and heuristically solved by an ant colony system algorithm. The performance of the implemented system (MDS-OP) was evaluated on DUC 2004 (Task 2) and MultiLing 2015 (MMS task) benchmark corpora using several ROUGE metrics, as well as other methods. Its comparison with the performances of 26 systems shows that MDS-OP achieved the best F-measure scores on both tasks in terms of ROUGE-1 and ROUGE-L (DUC 2004), ROUGE-SU4, and three other evaluation methods (MultiLing 2015). Overall, MDS-OP ranked among the best 3 systems.

Description: Algorithms, Vol. 11, Pages 95: Experimental Validation of a Novel Auto-Tuning Method for a Fractional Order PI Controller on an UR10 Robot Algorithms doi: 10.3390/a11070095 Authors: Cristina I. Muresan Cosmin Copot Isabela Birs Robin De Keyser Steve Vanlanduit Clara M. Ionescu Classical fractional order controller tuning techniques usually consider the frequency domain specifications (phase margin, gain crossover frequency, iso-damping) and are based on knowledge of a process model, as well as solving a system of nonlinear equations to determine the controller parameters. In this paper, a novel auto-tuning method is used to tune a fractional order PI controller. The advantages of the proposed auto-tuning method are two-fold: There is no need for a process model, neither to solve the system of nonlinear equations. The tuning is based on defining a forbidden region in the Nyquist plane using the phase margin requirement and determining the parameters of the fractional order controller such that the loop frequency response remains out of the forbidden region. Additionally, the final controller parameters are those that minimize the difference between the slope of the loop frequency response and the slope of the forbidden region border, to ensure the iso-damping property. To validate the proposed method, a case study has been used consisting of a pick and place movement of an UR10 robot. The experimental results, considering two different robot configurations, demonstrate that the designed fractional order PI controller is indeed robust.

Description: Algorithms, Vol. 11, Pages 97: A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining Algorithms doi: 10.3390/a11070097 Authors: Lin Tang Lin Liu Jianhou Gan The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously cluster motion words of video into motion topics, and the locations of motion into motion regions, where each motion topic associates with its region. Meanwhile, a hybrid stochastic variational Gibbs sampling algorithm is developed for inference of our region topic model, which has the ability of inferring in real time with massive video stream dataset. We evaluate our method on simulate and real datasets. The comparison with the Gibbs sampling algorithm shows the superiorities of proposed model and its online inference algorithm in terms of anomaly detection.

Description: Algorithms, Vol. 11, Pages 98: Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming Algorithms doi: 10.3390/a11070098 Authors: Li-Hsuan Chen Felix Reidl Peter Rossmanith Fernando Sánchez Villaamil Treedepth is a well-established width measure which has recently seen a resurgence of interest. Since graphs of bounded treedepth are more restricted than graphs of bounded tree- or pathwidth, we are interested in the algorithmic utility of this additional structure. On the negative side, we show with a novel approach that the space consumption of any (single-pass) dynamic programming algorithm on treedepth decompositions of depth d cannot be bounded by (2−ϵ)d·logO(1)n for Vertex Cover, (3−ϵ)d·logO(1)n for 3-Coloring and (3−ϵ)d·logO(1)n for Dominating Set for any ϵ>0. This formalizes the common intuition that dynamic programming algorithms on graph decompositions necessarily consume a lot of space and complements known results of the time-complexity of problems restricted to low-treewidth classes. We then show that treedepth lends itself to the design of branching algorithms. Specifically, we design two novel algorithms for Dominating Set on graphs of treedepth d: A pure branching algorithm that runs in time dO(d2)·n and uses space O(d3logd+dlogn) and a hybrid of branching and dynamic programming that achieves a running time of O(3dlogd·n) while using O(2ddlogd+dlogn) space.

Description: Future Internet, Vol. 10, Pages 61: Personalised and Coordinated Demand-Responsive Feeder Transit Service Design: A Genetic Algorithms Approach Future Internet doi: 10.3390/fi10070061 Authors: Bo Sun Ming Wei Chunfeng Yang Zhihuo Xu Han Wang The purpose of this work is to create an efficient optimization framework for demand-responsive feeder transit services to assign vehicles to cover all pickup locations to transport passengers to a rail station. The proposed methodology features passengers placing a personalized travel order involving the subway schedule chosen by passengers and windows of service time, etc. Moreover, synchronous transfer between the shuttle and feeder bus is fully accounted for in the problem. A mixed-integer linear programming model is formulated to minimize the total travel time for all passengers, which consists of ride-time for vehicles from the pickup locations to the rail station and wait-time for passengers taking the subway beforehand. Different from conventional methods, the proposed model benefits from using a real distribution of passenger demand aggregated from cellular data and travel time or the distance matrix obtained from an open GIS tool. A distributed genetic algorithm is further designed to obtain meta-optimal solutions in a reasonable amount of time. When applied to design a feeder bus system in Nanjing City, China, case study results reveal that the total travel time of the proposed model was reduced by 2.46% compared to the traditional model. Sensitivity analyses were also further performed to investigate the impact of the number of vehicles on the output. Finally, the difference in results of Cplex, standard GA, and the proposed algorithm were compared to prove the validity of the algorithm.

Description: Future Internet, Vol. 10, Pages 60: A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay Future Internet doi: 10.3390/fi10070060 Authors: Li Quan Zhiliang Wang Fuji Ren Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay.

Description: Future Internet, Vol. 10, Pages 64: Dynamic Traffic Scheduling and Congestion Control across Data Centers Based on SDN Future Internet doi: 10.3390/fi10070064 Authors: Dong Sun Kaixin Zhao Yaming Fang Jie Cui Software-defined Networking (SDN) and Data Center Network (DCN) are receiving considerable attention and eliciting widespread interest from both academia and industry. When the traditionally shortest path routing protocol among multiple data centers is used, congestion will frequently occur in the shortest path link, which may severely reduce the quality of network services due to long delay and low throughput. The flexibility and agility of SDN can effectively ameliorate the aforementioned problem. However, the utilization of link resources across data centers is still insufficient, and has not yet been well addressed. In this paper, we focused on this issue and proposed an intelligent approach of real-time processing and dynamic scheduling that could make full use of the network resources. The traffic among the data centers could be classified into different types, and different strategies were proposed for these types of real-time traffic. Considering the prolonged occupation of the bandwidth by malicious flows, we employed the multilevel feedback queue mechanism and proposed an effective congestion control algorithm. Simulation experiments showed that our scheme exhibited the favorable feasibility and demonstrated a better traffic scheduling effect and great improvement in bandwidth utilization across data centers.

Description: Algorithms, Vol. 11, Pages 101: Robust Fuzzy Adaptive Sliding Mode Stabilization for Fractional-Order Chaos Algorithms doi: 10.3390/a11070101 Authors: Bachir Bourouba Samir Ladaci In this paper, a new adaptive fuzzy sliding mode control (AFSMC) design strategy is proposed for the control of a special class of three-dimensional fractional order chaotic systems with uncertainties and external disturbance. The design methodology is developed in two stages: first, an adaptive sliding mode control law is proposed for the class of fractional order chaotic systems without uncertainties, and then a fuzzy logic system is used to estimate the control compensation effort to be added in the case of uncertainties on the system’s model. Based on the Lyapunov theory, the stability analysis of both control laws is provided with elimination of the chattering action in the control signal. The developed control scheme is simple to implement and the overall control scheme guarantees the global asymptotic stability in the Lyapunov sense if all the involved signals are uniformly bounded. In the present work, simulation studies on fractional-order Chen chaotic systems are carried out to show the efficiency of the proposed fractional adaptive controllers.

Description: Algorithms, Vol. 11, Pages 102: Advanced Artificial Neural Networks Algorithms doi: 10.3390/a11070102 Authors: Tin-Chih Toly Chen Cheng-Li Liu Hong-Dar Lin Artificial neural networks (ANNs) have been extensively applied to a wide range of disciplines, such as system identification and control, decision making, pattern recognition, medical diagnosis, finance, data mining, visualization, and others. With advances in computing and networking technologies, more complicated forms of ANNs are expected to emerge, requiring the design of advanced learning algorithms. This Special Issue is intended to provide technical details of the construction and training of advanced ANNs.

Description: Future Internet, Vol. 10, Pages 65: Performance Analysis of Hybrid Optical–Acoustic AUV Swarms for Marine Monitoring Future Internet doi: 10.3390/fi10070065 Authors: Chiara Lodovisi Pierpaolo Loreti Lorenzo Bracciale Silvello Betti Autonomous Underwater Vehicles (AUVs) are assuming an important role in the monitoring and mapping of marine ecosystems, especially for their ability to explore harsh environments. AUV swarm can collect data operating autonomously for long periods enabling new applications in this field. However, the mission duration is usually limited also by the high power consumption required for acoustic transmissions. A new generation of devices complements the acoustic modem with an optical modem that can provide a communication channel with higher capacity and lower power consumption with respect to the acoustic channel. However, the optical link that uses the visible light is very sensitive to the water turbidity that can strongly limit the link coverage. In this paper, we evaluate the networking performances of the Venus vessel, a real AUV prototype equipped with an acoustical modem and an optical modem. The presented analysis aims to evaluate key system parameters allowing to select the best way to set up network communications according to the surrounding conditions (e.g., quality of water) and to the application requirements. Simulation results account for the case of ports or basins, where the water quality is poor and the use of the optical modem is strongly limited by distance. We evaluate system performance in terms of transmission delay in the network and we also provide a power–capacity trade-off.

Description: Algorithms, Vol. 11, Pages 103: Solutions to the Sub-Optimality and Stability Issues of Recursive Pole and Zero Distribution Algorithms for the Approximation of Fractional Order Models Algorithms doi: 10.3390/a11070103 Authors: Jocelyn Sabatier This paper analyses algorithms currently found in the literature for the approximation of fractional order models and based on recursive pole and zero distributions. The analysis focuses on the sub-optimality of the approximations obtained and stability issues that may appear after approximation depending on the pole location of the initial fractional order model. Solutions are proposed to reduce this sub-optimality and to avoid stability issues.

Description: Future Internet, Vol. 10, Pages 66: Enabling Trustworthy Multicast Wireless Services through D2D Communications in 5G Networks Future Internet doi: 10.3390/fi10070066 Authors: Sara Pizzi Chiara Suraci Leonardo Militano Antonino Orsino Antonella Molinaro Antonio Iera Giuseppe Araniti Device-to-device (D2D) communication is considered as one of the key enabling technologies for fifth-generation (5G) networks as it allows data offloading generated by the huge number of connected devices. In this respect, group-oriented services are among the most interesting usage scenarios. Indeed, D2D can improve the performance of the conventional multicast scheme (CMS) in cellular networks, which is known to suffer from low spectral efficiency. Security is a further key field of investigation for 5G systems, as any threat to privacy and security may lead to both deteriorated user experience and inefficient network resources’ utilization. Security issues are even more in focus for D2D connections between devices that are in mutual proximity. To improve the CMS performance and also sustain security requirements of the 5G network, this work proposes a secure D2D data transmission algorithm. Making use of mechanisms such as encryption and signature, this algorithm aims to protect the exchanged data and the privacy of the devices involved in the communication. A simulation campaign conducted using MATLAB shows the ability of the proposed solution to take advantage of the establishment of secure D2D communications and efficiently utilize network resources.

Description: Algorithms, Vol. 11, Pages 108: Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions Algorithms doi: 10.3390/a11070108 Authors: Natalia Alekseeva Ivan Tanev Katsunori Shimohara The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.

Description: Algorithms, Vol. 11, Pages 83: A Combined Syntactical and Statistical Approach for R Peak Detection in Real-Time Long-Term Heart Rate Variability Analysis Algorithms doi: 10.3390/a11060083 Authors: David Pang Tomohiko Igasaki Long-term heart rate variability (HRV) analysis is useful as a noninvasive technique for autonomic nervous system activity assessment. It provides a method for assessing many physiological and pathological factors that modulate the normal heartbeat. The performance of HRV analysis systems heavily depends on a reliable and accurate detection of the R peak of the QRS complex. Ectopic beats caused by misdetection or arrhythmic events can introduce bias into HRV results, resulting in significant problems in their interpretation. This study presents a novel method for long-term detection of normal R peaks (which represent the normal heartbeat in electrocardiographic signals), intended specifically for HRV analysis. The very low computational complexity of the proposed method, which combines and exploits the advantages of syntactical and statistical approaches, enables real-time applications. The approach was validated using the Massachusetts Institute of Technology–Beth Israel Hospital Normal Sinus Rhythm and the Fantasia database, and has a sensitivity, positive predictivity, detection error rate, and accuracy of 99.998, 99.999, 0.003, and 99.996%, respectively.

Description: Algorithms, Vol. 11, Pages 82: Research on Fault Diagnosis of a Marine Fuel System Based on the SaDE-ELM Algorithm Algorithms doi: 10.3390/a11060082 Authors: Yi Wei Yaokun Yue Since the traditional fault diagnosis method of the marine fuel system has a low accuracy of identification, the algorithm solution can easily fall into local optimum, and they are not fit for the research on the fault diagnosis of a marine fuel system. Hence, a fault diagnosis method for a marine fuel system based on the SaDE-ELM algorithm is proposed. First, the parameters of initializing extreme learning machine are adopted by a differential evolution algorithm. Second, the fault diagnosis of the marine fuel system is realized by the fault diagnosis model corresponding to the state training of marine fuel system. Based on the obtained fault data of a marine fuel system, the proposed method is verified. The experimental results show that this method produces higher recognition accuracy and faster recognition speed that are superior to the traditional BP neural network, SVM support vector machine diagnosis algorithm, and the un-optimized extreme learning machine algorithm. The results have important significance relevant to fault diagnosis for a marine fuel system. The algorithm based on SaDE-ELM is an effective and practical method of fault diagnosis for a marine fuel system.

Description: Future Internet, Vol. 10, Pages 50: Fairness and Trust in Virtual Environments: The Effects of Reputation Future Internet doi: 10.3390/fi10060050 Authors: Mirko Duradoni Mario Paolucci Franco Bagnoli Andrea Guazzini Reputation supports pro-social behaviors in a variety of social settings and across different ages. When re-encounters are possible, developing a positive reputation can be a valuable asset that will result in better outcomes. However, in real life, cooperative acts are ambiguous and happen in noisy environments in which individuals can have multiple goals, visibility is reduced, and reputation systems may differ. This study examined how reputation within a virtual environment affects fairness in material allocations and trust in information exchange, in a three-actors interaction game in which each player had an incentive to deceive the others. We compared the results of two experimental conditions, one in which informers could be evaluated, and one without reputational opportunities. A reputational system appeared to enhance both trust and fairness even within a virtual environment under anonymous condition. We tested adolescents and adults finding that they were consistently more generous when visibility was increased, but they showed significantly different patterns in resources allocation and information exchange. Male and female participants, across ages, showed other interesting differences. These findings suggest that reputational effects increase fairness and trust even in a noisy, ambiguous and uncertain environment, but this effect is modulated by age and gender.

Description: Future Internet, Vol. 10, Pages 51: A Driving Behavior Planning and Trajectory Generation Method for Autonomous Electric Bus Future Internet doi: 10.3390/fi10060051 Authors: Lingli Yu Decheng Kong Xiaoxin Yan A framework of path planning for autonomous electric bus is presented. ArcGIS platform is utilized for map-building and global path planning. Firstly, a high-precision map is built based on GPS in ArcGIS for global planning. Then the global optimal path is obtained by network analysis tool in ArcGIS. To facilitate local planning, WGS-84 coordinates in the map are converted to local coordinates. Secondly, a double-layer finite state machine (FSM) is devised to plan driving behavior under different driving scenarios, such as structured driving, lane changing, turning, and so on. Besides, local optimal trajectory is generated by cubic polynomial, which takes full account of the safety and kinetics of the electric bus. Finally, the simulation results show that the framework is reliable and feasible for driving behavior planning and trajectory generation. Furthermore, its validity is proven with an autonomous bus platform 12 m in length.

Description: Future Internet, Vol. 10, Pages 47: Secure Inter-Frame Space Communications for Wireless LANs Future Internet doi: 10.3390/fi10060047 Authors: Il-Gu Lee The internet of things (IoTs) offers a wide range of consumer benefits, from personal portable devices to internet-connected infrastructure. The wireless local area network (WLAN) is one of the potential candidates for IoTs technology to connect billions of smart devices. Long-range WLAN is widely deployed in dense networks as an alternative to cellular networks or satellite internet access because of its low cost, high performance, and existing ecosystem. However, due to the nature of unregulated communications in industrial, scientific, and medical (ISM) bands, WLANs experience interferences from other radios such as radars and frequency hopping devices. Once interference is detected at a WLAN device, the channel is avoided and other channels become crowded. Thus, it degrades network throughput and channel utilization. In this paper, a secure inter-frame space communication system design is proposed for WLANs to share the ISM bands with other radio systems that generate periodic radio signals. The proposed secure inter-frame communication scheme achieves the goal by designing accurate radar detection and secure inter-frame space communication. The simulation results demonstrate that the proposed scheme significantly increases the receiver sensitivity and user datagram protocol throughput.

Description: Future Internet, Vol. 10, Pages 48: On the Future of Legal Publishing Services in the Semantic Web Future Internet doi: 10.3390/fi10060048 Authors: Enrico Francesconi The development of the Semantic Web represents an essential precondition to the definition of new scenarios for the future Internet. This perspective is of particular interest in the legal information domain for the specialized nature of legal information and the peculiarities of the legal users’ information needs. In this paper, the evolution in recent years of the Semantic Web in the legal domain is reviewed, with particular emphasis to the most recent developments related to Linked Open Data initiative and to the role, in the legal Semantic Web, of the Publications Office of the European Union in its two-fold role of public institution and legal publisher.

Description: Future Internet, Vol. 10, Pages 55: Simulating the Cost of Cooperation: A Recipe for Collaborative Problem-Solving Future Internet doi: 10.3390/fi10060055 Authors: Andrea Guazzini Mirko Duradoni Alessandro Lazzeri Giorgio Gronchi Collective problem-solving and decision-making, along with other forms of collaboration online, are central phenomena within ICT. There had been several attempts to create a system able to go beyond the passive accumulation of data. However, those systems often neglect important variables such as group size, the difficulty of the tasks, the tendency to cooperate, and the presence of selfish individuals (free riders). Given the complex relations among those variables, numerical simulations could be the ideal tool to explore such relationships. We take into account the cost of cooperation in collaborative problem solving by employing several simulated scenarios. The role of two parameters was explored: the capacity, the group’s capability to solve increasingly challenging tasks coupled with the collective knowledge of a group, and the payoff, an individual’s own benefit in terms of new knowledge acquired. The final cooperation rate is only affected by the cost of cooperation in the case of simple tasks and small communities. In contrast, the fitness of the community, the difficulty of the task, and the groups sizes interact in a non-trivial way, hence shedding some light on how to improve crowdsourcing when the cost of cooperation is high.

Description: Algorithms, Vol. 11, Pages 90: A Novel Method for Control Performance Assessment with Fractional Order Signal Processing and Its Application to Semiconductor Manufacturing Algorithms doi: 10.3390/a11070090 Authors: Kai Liu YangQuan Chen Paweł D. Domański Xi Zhang The significant task for control performance assessment (CPA) is to review and evaluate the performance of the control system. The control system in the semiconductor industry exhibits a complex dynamic behavior, which is hard to analyze. This paper investigates the interesting crossover properties of Hurst exponent estimations and proposes a novel method for feature extraction of the nonlinear multi-input multi-output (MIMO) systems. At first, coupled data from real industry are analyzed by multifractal detrended fluctuation analysis (MFDFA) and the resultant multifractal spectrum is obtained. Secondly, the crossover points with spline fit in the scale-law curve are located and then employed to segment the entire scale-law curve into several different scaling regions, in which a single Hurst exponent can be estimated. Thirdly, to further ascertain the origin of the multifractality of control signals, the generalized Hurst exponents of the original series are compared with shuffled data. At last, non-Gaussian statistical properties, multifractal properties and Hurst exponents of the process control variables are derived and compared with different sets of tuning parameters. The results have shown that CPA of the MIMO system can be better employed with the help of fractional order signal processing (FOSP).

Description: Algorithms, Vol. 11, Pages 91: emgr—The Empirical Gramian Framework Algorithms doi: 10.3390/a11070091 Authors: Christian Himpe System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality. These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems. Empirical Gramians are an extension to the system Gramians for parametric and nonlinear systems as well as a data-driven method of computation. The empirical Gramian framework - emgr - implements the empirical Gramians in a uniform and configurable manner, with applications such as Gramian-based (nonlinear) model reduction, decentralized control, sensitivity analysis, parameter identification and combined state and parameter reduction.

Description: Future Internet, Vol. 10, Pages 59: Clock Recovery Challenges in DSP-Based Coherent Single-Mode and Multi-Mode Optical Systems Future Internet doi: 10.3390/fi10070059 Authors: Júlio César Medeiros Diniz Francesco Da Ros Darko Zibar We present an analysis of clock recovery algorithms in both polarization division multiplexing systems and mode division multiplexing systems. The impact of inter-polarization time skew and polarization mode dispersion in single-mode fibers, as well as the combined impact of mode mixing and mode group delay spread in multi-mode fibers under different coupling regimes are investigated. Results show that although the clock tone vanishing has a known solution for single-mode systems, in multi-mode systems even for low group delay spread, strong coupling will cause clock tone extinction, making it harder to implement an effective clock recovery scheme.

Description: Algorithms, Vol. 11, Pages 118: Sliding Suffix Tree Algorithms doi: 10.3390/a11080118 Authors: Andrej Brodnik Matevž Jekovec We consider a sliding window W over a stream of characters from some alphabet of constant size. We want to look up a pattern in the current sliding window content and obtain all positions of the matches. We present an indexed version of the sliding window, based on a suffix tree. The data structure of size Θ(|W|) has optimal time queries Θ(m+occ) and amortized constant time updates, where m is the length of the query string and occ is its number of occurrences.

Description: Future Internet, Vol. 10, Pages 34: Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage Future Internet doi: 10.3390/fi10040034 Authors: Rongheng Li Jian Zhang Wenfeng Shen With the rapid development of video surveillance technology, especially the popularity of cloud-based video surveillance applications, video data begins to grow explosively. However, in the cloud-based video surveillance system, replicas occupy an amount of storage space. Also, the slow response to video playback constrains the performance of the system. In this paper, considering the characteristics of video data comprehensively, we propose a dynamic redundant replicas mechanism based on security levels that can dynamically adjust the number of replicas. Based on the location correlation between cameras, this paper also proposes a data cache strategy to improve the response speed of data reading. Experiments illustrate that: (1) our dynamic redundant replicas mechanism can save storage space while ensuring data security; (2) the cache mechanism can predict the playback behaviors of the users in advance and improve the response speed of data reading according to the location and time correlation of the front-end cameras; and (3) in terms of cloud-based video surveillance, our proposed approaches significantly outperform existing methods.

Description: Algorithms, Vol. 11, Pages 55: A Heuristic Approach to Solving the Train Traffic Re-Scheduling Problem in Real Time Algorithms doi: 10.3390/a11040055 Authors: Omid Gholami Johanna Törnquist Krasemann Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solving the real-time train traffic re-scheduling problem. This problem is here interpreted as a blocking job-shop scheduling problem, and a hybrid of the mixed graph and alternative graph is used for modelling the infrastructure and traffic dynamics on a mesoscopic level. A heuristic algorithm is developed and applied to resolve the conflicts by re-timing, re-ordering, and locally re-routing the trains. A part of the Southern Swedish railway network from Karlskrona centre to Malmö city is considered for an experimental performance assessment of the approach. The network consists of 290 block sections, and for a one-hour time horizon with around 80 active trains, the algorithm generates a solution in less than ten seconds. A benchmark with the corresponding mixed-integer program formulation, solved by commercial state-of-the-art solver Gurobi, is also conducted to assess the optimality of the generated solutions.

Description: Future Internet, Vol. 10, Pages 37: Ex Machina: Analytical platforms, Law and the Challenges of Computational Legal Science Future Internet doi: 10.3390/fi10050037 Authors: Nicola Lettieri Antonio Altamura Rosalba Giugno Alfonso Guarino Delfina Malandrino Alfredo Pulvirenti Francesco Vicidomini Rocco Zaccagnino Over the years, computation has become a fundamental part of the scientific practice in several research fields that goes far beyond the boundaries of natural sciences. Data mining, machine learning, simulations and other computational methods lie today at the hearth of the scientific endeavour in a growing number of social research areas from anthropology to economics. In this scenario, an increasingly important role is played by analytical platforms: integrated environments allowing researchers to experiment cutting-edge data-driven and computation-intensive analyses. The paper discusses the appearance of such tools in the emerging field of computational legal science. After a general introduction to the impact of computational methods on both natural and social sciences, we describe the concept and the features of an analytical platform exploring innovative cross-methodological approaches to the academic and investigative study of crime. Stemming from an ongoing project involving researchers from law, computer science and bioinformatics, the initiative is presented and discussed as an opportunity to raise a debate about the future of legal scholarship and, inside of it, about the challenges of computational legal science.

Description: Algorithms, Vol. 11, Pages 62: A Feature-Weighted SVR Method Based on Kernel Space Feature Algorithms doi: 10.3390/a11050062 Authors: Minghua Xie Decheng Wang Lili Xie Support Vector Regression (SVR), which converts the original low-dimensional problem to a high-dimensional kernel space linear problem by introducing kernel functions, has been successfully applied in system modeling. Regarding the classical SVR algorithm, the value of the features has been taken into account, while its contribution to the model output is omitted. Therefore, the construction of the kernel space may not be reasonable. In the paper, a Feature-Weighted SVR (FW-SVR) is presented. The range of the feature is matched with its contribution by properly assigning the weight of the input features in data pre-processing. FW-SVR optimizes the distribution of the sample points in the kernel space to make the minimizing of the structural risk more reasonable. Four synthetic datasets and seven real datasets are applied. A superior generalization ability is obtained by the proposed method.

Description: Future Internet, Vol. 10, Pages 41: Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network Future Internet doi: 10.3390/fi10050041 Authors: Tamotsu Yashima Kazumasa Takami Using ad hoc communication between mobile terminals, MANETs (mobile ad hoc networks) are independent of any communication infrastructure but their communication quality can degrade because, as terminals move about in the service area, routes are constantly disconnected and then re-established. There has been no proposal for a quality metric that models this unstable state, i.e., route nonuniformity. This paper proposes a new concept of route availability (RA) as a metric of route nonuniformity in a MANET and verifies how effectively it represents the quality of service (QoS) of a network or the quality of experience (QoE) of video streaming. We have built an environment that emulates a MANET capable of video streaming, and developed a method of measuring RA for two representative MANET routing methods: AODV (Ad hoc On-Demand Distance Vector) and OLSR (Optimized Link State Routing). We have examined the relationship between RA and conventional network QoS metrics: packet loss rate and throughput. We have also checked RA using a subjective quality assessment test.

Description: Algorithms, Vol. 11, Pages 68: Hybrid Flow Shop with Unrelated Machines, Setup Time, and Work in Progress Buffers for Bi-Objective Optimization of Tortilla Manufacturing Algorithms doi: 10.3390/a11050068 Authors: Victor Hugo Yaurima-Basaldua Andrei Tchernykh Francisco Villalobos-Rodríguez Ricardo Salomon-Torres We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.

Description: Algorithms, Vol. 11, Pages 72: Gray Wolf Optimization Algorithm for Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization Algorithms doi: 10.3390/a11050072 Authors: Yixuan Ren Tao Ye Mengxing Huang Siling Feng In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf’s social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization.

Description: Future Internet, Vol. 10, Pages 43: Botnet Detection Based On Machine Learning Techniques Using DNS Query Data Future Internet doi: 10.3390/fi10050043 Authors: Xuan Dau Hoang Quynh Chi Nguyen In recent years, botnets have become one of the major threats to information security because they have been constantly evolving in both size and sophistication. A number of botnet detection measures, such as honeynet-based and Intrusion Detection System (IDS)-based, have been proposed. However, IDS-based solutions that use signatures seem to be ineffective because recent botnets are equipped with sophisticated code update and evasion techniques. A number of studies have shown that abnormal botnet detection methods are more effective than signature-based methods because anomaly-based botnet detection methods do not require pre-built botnet signatures and hence they have the capability to detect new or unknown botnets. In this direction, this paper proposes a botnet detection model based on machine learning using Domain Name Service query data and evaluates its effectiveness using popular machine learning techniques. Experimental results show that machine learning algorithms can be used effectively in botnet detection and the random forest algorithm produces the best overall detection accuracy of over 90%.

Description: Algorithms, Vol. 11, Pages 77: A Novel Design of Sparse Prototype Filter for Nearly Perfect Reconstruction Cosine-Modulated Filter Banks Algorithms doi: 10.3390/a11050077 Authors: Wei Xu Yi Li Jinghong Miao Jiaxiang Zhao Xin Gao Cosine-modulated filter banks play a major role in digital signal processing. Sparse FIR filter banks have lower implementation complexity than full filter banks, while keeping a good performance level. This paper presents a fast design paradigm for sparse nearly perfect-reconstruction (NPR) cosine-modulated filter banks. First, an approximation function is introduced to reduce the non-convex quadratically constrained optimization problem to a linearly constrained optimization problem. Then, the desired sparse linear phase FIR prototype filter is derived through the orthogonal matching pursuit (OMP) performed under the weighted l 2 norm. The simulation results demonstrate that the proposed scheme is an effective paradigm to design sparse NPR cosine-modulated filter banks.

Description: Algorithms, Vol. 11, Pages 78: A Modified Artificial Bee Colony Algorithm Based on the Self-Learning Mechanism Algorithms doi: 10.3390/a11060078 Authors: Bao Pang Yong Song Chengjin Zhang Hongling Wang Runtao Yang Artificial bee colony (ABC) algorithm, a novel category of bionic intelligent optimization algorithm, was achieved for solving complex nonlinear optimization problems. Previous studies have shown that ABC algorithm is competitive to other biological-inspired optimization algorithms, but there still exist several insufficiencies due to the inefficient solution search equation (SSE), which does well in exploration but poorly in exploitation. To improve accuracy of the solutions, this paper proposes a modified ABC algorithm based on the self-learning mechanism (SLABC) with five SSEs as the candidate operator pool; among them, one is good at exploration and two of them are good at exploitation; another SSE intends to balance exploration and exploitation; moreover, the last SSE with Lévy flight step-size which can generate smaller step-size with high frequency and bigger step-size occasionally not only can balance exploration and exploitation but also possesses the ability to escape from the local optimum. This paper proposes a simple self-learning mechanism, wherein the SSE is selected according to the previous success ratio in generating promising solutions at each iteration. Experiments on a set of 9 benchmark functions are carried out with the purpose of evaluating the performance of the proposed method. The experimental results illustrated that the SLABC algorithm achieves significant improvement compared with other competitive algorithms.

Description: Algorithms, Vol. 11, Pages 36: A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem Algorithms doi: 10.3390/a11040036 Authors: Yu Feng Jianzhong Zhou Li Mo Chao Wang Zhe Yuan Jiang Wu In this paper, a gradient-based cuckoo search algorithm (GCS) is proposed to solve a reservoir-scheduling problem. The classical cuckoo search (CS) is first improved by a self-adaptive solution-generation technique, together with a differential strategy for Lévy flight. This improved CS is then employed to solve the reservoir-scheduling problem, and a two-way solution-correction strategy is introduced to handle variants’ constraints. Moreover, a gradient-based search strategy is developed to improve the search speed and accuracy. Finally, the proposed GCS is used to obtain optimal schemes for cascade reservoirs in the Jinsha River, China. Results show that the mean and standard deviation of power generation obtained by GCS are much better than other methods. The converging speed of GCS is also faster. In the optimal results, the fluctuation of the water level obtained by GCS is small, indicating the proposed GCS’s effectiveness in dealing with reservoir-scheduling problems.

Description: Algorithms, Vol. 11, Pages 40: Connectivity and Hamiltonicity of Canonical Colouring Graphs of Bipartite and Complete Multipartite Graphs Algorithms doi: 10.3390/a11040040 Authors: Ruth Haas Gary MacGillivray A k-colouring of a graph G with colours 1 , 2 , … , k is canonical with respect to an ordering π = v 1 , v 2 , … , v n of the vertices of G if adjacent vertices are assigned different colours and, for 1 ≤ c ≤ k , whenever colour c is assigned to a vertex v i , each colour less than c has been assigned to a vertex that precedes v i in π . The canonical k-colouring graph of G with respect to π is the graph Can k π ( G ) with vertex set equal to the set of canonical k-colourings of G with respect to π , with two of these being adjacent if and only if they differ in the colour assigned to exactly one vertex. Connectivity and Hamiltonicity of canonical colouring graphs of bipartite and complete multipartite graphs is studied. It is shown that for complete multipartite graphs, and bipartite graphs there exists a vertex ordering π such that Can k π ( G ) is connected for large enough values of k. It is proved that a canonical colouring graph of a complete multipartite graph usually does not have a Hamilton cycle, and that there exists a vertex ordering π such that Can k π ( K m , n ) has a Hamilton path for all k ≥ 3 . The paper concludes with a detailed consideration of Can k π ( K 2 , 2 , … , 2 ) . For each k ≥ χ and all vertex orderings π , it is proved that Can k π ( K 2 , 2 , … , 2 ) is either disconnected or isomorphic to a particular tree.

Description: Algorithms, Vol. 11, Pages 41: A Distributed Indexing Method for Timeline Similarity Query Algorithms doi: 10.3390/a11040041 Authors: Zhenwen He Xiaogang Ma Timelines have been used for centuries and have become more and more widely used with the development of social media in recent years. Every day, various smart phones and other instruments on the internet of things generate massive data related to time. Most of these data can be managed in the way of timelines. However, it is still a challenge to effectively and efficiently store, query, and process big timeline data, especially the instant recommendation based on timeline similarities. Most existing studies have focused on indexing spatial and interval datasets rather than the timeline dataset. In addition, many of them are designed for a centralized system. A timeline index structure adapting to parallel and distributed computation framework is in urgent need. In this research, we have defined the timeline similarity query and developed a novel timeline index in the distributed system, called the Distributed Triangle Increment Tree (DTI-Tree), to support the similarity query. The DTI-Tree consists of one T-Tree and one or more TI-Trees based on a triangle increment partition strategy with the Apache Spark. Furthermore, we have provided an open source timeline benchmark data generator, named TimelineGenerator, to generate various timeline test datasets for different conditions. The experiments for DTI-Tree’s construction, insertion, deletion, and similarity queries have been executed on a cluster with two benchmark datasets that are generated by TimelineGenerator. The experimental results show that the DTI-tree provides an effective and efficient distributed index solution to big timeline data.

Description: Algorithms, Vol. 11, Pages 42: Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method Algorithms doi: 10.3390/a11040042 Authors: Muneki Yasuda The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order) spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE) method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

Description: Future Internet, Vol. 10, Pages 33: An Intelligent Content Prefix Classification Approach for Quality of Service Optimization in Information-Centric Networking Future Internet doi: 10.3390/fi10040033 Authors: Cutifa Safitri Yoshihide Yamada Sabariah Baharun Shidrokh Goudarzi Quang Ngoc Nguyen Keping Yu Takuro Sato This research proposes an intelligent classification framework for quality of service (QoS) performance improvement in information-centric networking (ICN). The proposal works towards keyword classification techniques to obtain the most valuable information via suitable content prefixes in ICN. In this study, we have achieved the intelligent function using Artificial Intelligence (AI) implementation. Particularly, to find the most suitable and promising intelligent approach for maintaining QoS matrices, we have evaluated various AI algorithms, including evolutionary algorithms (EA), swarm intelligence (SI), and machine learning (ML) by using the cost function to assess their classification performances. With the goal of enabling a complete ICN prefix classification solution, we also propose a hybrid implementation to optimize classification performances by integration of relevant AI algorithms. This hybrid mechanism searches for a final minimum structure to prevent the local optima from happening. By simulation, the evaluation results show that the proposal outperforms EA and ML in terms of network resource utilization and response delay for QoS performance optimization.

Description: Future Internet, Vol. 10, Pages 35: Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover Future Internet doi: 10.3390/fi10040035 Authors: Asmae Ait Mansour Nourddine Enneya Mohamed Ouadou In this paper, we address the problem of cell association during a handover performed in a dense heterogeneous network, where the preference of a mobile user’s equipment in terms of uplink traffic is not the same as for the downlink traffic. Therefore, since mobility is an intrinsic element of cellular networks, designing a handover from the perspective of the uplink and downlink is mandatory in the context of 5G cellular networks. Based on this arena, we propose a decoupled uplink-downlink handover scheme while making use of femtocells in order to maximize the overall network entity utilities and avoid overloading macrocells. However, the fact that the handover process is performed in a dense heterogeneous network makes the issue NP-hard. Therefore, taking into account the need for self-organizing solutions, we modeled the handover process as a matching game with externalities. Thus, we will provide an aspect of intelligence for the execution of the handover process to mobile user’s equipment (UE). To make the proposition more efficient, we integrate an assignment step to assist the matching game. Hence, the base stations will be investigated and filtered, keeping only the helpful base stations as the players in terms of the quality of service for the uplink and downlink. The numerical results verify the superiority of the proposed context-aware algorithm over traditional downlink handover and traditional decoupled uplink and downlink handover schemes, by improving the load balancing, increasing rates and reducing delays.