ALBERT

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

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

Description: The recent discovery of boron triangular nanotubes competes with carbon in many respects. The closed form of M‐polynomial of nanotubes produces closed forms of many degree‐based topological indices which are numerical parameters of the structure and, in combination, determine properties of the concerned nanotubes. In this report, we give M‐polynomials of boron triangular nanotubes and recover many important topological degree‐based indices of these nanotubes. We also plot surfaces associated with these nanotubes that show the dependence of each topological index on the parameters of the structure.

Description: Symmetry, Vol. 9, Pages 155: On Brane Solutions with Intersection Rules Related to Lie Algebras Symmetry doi: 10.3390/sym9080155 Authors: Vladimir D. Ivashchuk The review is devoted to exact solutions with hidden symmetries arising in a multidimensional gravitational model containing scalar fields and antisymmetric forms. These solutions are defined on a manifold of the form M = M0 x M1 x . . . x Mn , where all Mi with i >= 1 are fixed Einstein (e.g., Ricci-flat) spaces. We consider a warped product metric on M. Here, M0 is a base manifold, and all scale factors (of the warped product), scalar fields and potentials for monomial forms are functions on M0 . The monomial forms (of the electric or magnetic type) appear in the so-called composite brane ansatz for fields of forms. Under certain restrictions on branes, the sigma-model approach for the solutions to field equations was derived in earlier publications with V.N.Melnikov. The sigma model is defined on the manifold M0 of dimension d0 ≠ 2 . By using the sigma-model approach, several classes of exact solutions, e.g., solutions with harmonic functions, S-brane, black brane and fluxbrane solutions, are obtained. For d0 = 1 , the solutions are governed by moduli functions that obey Toda-like equations. For certain brane intersections related to Lie algebras of finite rank—non-singular Kac–Moody (KM) algebras—the moduli functions are governed by Toda equations corresponding to these algebras. For finite-dimensional semi-simple Lie algebras, the Toda equations are integrable, and for black brane and fluxbrane configurations, they give rise to polynomial moduli functions. Some examples of solutions, e.g., corresponding to finite dimensional semi-simple Lie algebras, hyperbolic KM algebras: H2(q, q) , AE3, HA(1)2, E10 and Lorentzian KM algebra P10 , are presented.

Description: Symmetry, Vol. 9, Pages 157: Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets Symmetry doi: 10.3390/sym9080157 Authors: Lili Guo Shanya Cong Heterogeneous networks (HetNets) are regarded as a promising approach to handle the deluge of mobile data traffic. With the co-channel deployment of small cells, the coverage and capacity of the network will be improved. However, the conventional maximum-received-power (MRP) user association scheme and cross-tier interference issue significantly diminish the performance gain provided by small cells. In this paper, we propose a novel location-aware cross-tier cooperation (LA-CTC) scheme for jointly achieving load balancing and interference mitigation in two-tier HetNets. In detail, we define an inner region for each macro base station (MBS) where the femto base stations (FBSs) will be deactivated, and thereby the users within the inner region will only be served by the MBS. Subsequently, for the users located in the outer region, the proposed scheme only uses coordinated multipoint (CoMP) transmission by two tiers of BSs to eliminate the excessive cross-tier interference suffered by offloaded users, whereas users with good locations are served directly by either a MBS or a FBS. Using tools of stochastic geometry, we derived the analytical expressions for the coverage probability and average rate of a randomly chosen user. Meanwhile, the analytical results were validated through Monte Carlo simulations. The numerical results show that the proposed scheme can improve the performance of networks significantly. Moreover, we compare the performance of the proposed scheme with that of the conventional MRP scheme, the cell range expansion (CRE) scheme and the location-aware cross-tier CoMP transmission (LA-CTCT) scheme in the literature. Numerical comparisons revealed that the proposed LA-CTC scheme outperforms the other three schemes.

Description: Symmetry, Vol. 9, Pages 163: Distributed Newton Methods for Strictly Convex Consensus Optimization Problems in Multi-Agent Networks Symmetry doi: 10.3390/sym9080163 Authors: Dong Wang Hualing Ren Fubo Shao Various distributed optimization methods have been developed for consensus optimization problems in multi-agent networks. Most of these methods only use gradient or subgradient information of the objective functions, which suffer from slow convergence rate. Recently, a distributed Newton method whose appeal stems from the use of second-order information and its fast convergence rate has been devised for the network utility maximization (NUM) problem. This paper contributes to this method by adjusting it to a special kind of consensus optimization problem in two different multi-agent networks. For networks with Hamilton path, the distributed Newton method is modified by exploiting a novel matrix splitting techniques. For general connected multi-agent networks, the algorithm is trimmed by combining the matrix splitting technique and the spanning tree for this consensus optimization problems. The convergence analyses show that both modified distributed Newton methods enable the nodes across the network to achieve a global optimal solution in a distributed manner. Finally, the distributed Newton method is applied to solve a problem which is motivated by the Kuramoto model of coupled nonlinear oscillators and the numerical results illustrate the performance of the proposed algorithm.

Description: Symmetry, Vol. 9, Pages 164: Blockchain Security in Cloud Computing: Use Cases, Challenges, and Solutions Symmetry doi: 10.3390/sym9080164 Authors: Jin Park Jong Park Blockchain has drawn attention as the next-generation financial technology due to its security that suits the informatization era. In particular, it provides security through the authentication of peers that share virtual cash, encryption, and the generation of hash value. According to the global financial industry, the market for security-based blockchain technology is expected to grow to about USD 20 billion by 2020. In addition, blockchain can be applied beyond the Internet of Things (IoT) environment; its applications are expected to expand. Cloud computing has been dramatically adopted in all IT environments for its efficiency and availability. In this paper, we discuss the concept of blockchain technology and its hot research trends. In addition, we will study how to adapt blockchain security to cloud computing and its secure solutions in detail.

Description: Symmetry, Vol. 9, Pages 166: Asymmetries in the Maintenance Performance of Spanish Industries before and after the Recession Symmetry doi: 10.3390/sym9080166 Authors: María Carmen Carnero Abstract: Until the last few decades, maintenance has not been considered of special importance by organisations. Thus, the number of studies that assess maintenance performance in a country is still very small, despite the relevance this area has to the level of national competitiveness. This article describes a multicriteria model integrating the fuzzy analytic hierarchy process (FAHP) with multi-attribute utility theory (MAUT) to assess the maintenance performance of large, medium and small enterprises in Spain, before and after the recession, as well as the asymmetries in the state of maintenance between different activity sectors. The weightings are converted to utility functions which allow the final utility of an alternative to be calculated via a Multi-Attribute Utility Function. From the Spanish maintenance data for different industrial sectors in 2005 and 2010, 2400 discrete probability distributions have been produced. Finally, a Monte Carlo simulation is applied for the estimation of the uncertainty. The results show that the economic crisis experienced by Spain since 2008 has negatively affected the level of maintenance applied, rather than it being considered an area that could deliver cost reductions and improvements in productivity and quality to organisations.

Description: Symmetry, Vol. 9, Pages 172: Endpoint Security in Networks: An OpenMP Approach for Increasing Malware Detection Speed Symmetry doi: 10.3390/sym9090172 Authors: Igor Forain Robson de Oliveira Albuquerque Ana Sandoval Orozco Luis García Villalba Tai-Hoon Kim Increasingly sophisticated antivirus (AV) software and the growing amount and complexity of malware demand more processing power from personal computers, specifically from the central processor unit (CPU). This paper conducted performance tests with Clam AntiVirus (ClamAV) and improved its performance through parallel processing on multiple cores using the Open Multi-Processing (OpenMP) library. All the tests used the same dataset constituted of 1.33 GB of data distributed among 2766 files of different sizes. The new parallel version of ClamAV implemented in our work achieved an execution time around 62% lower than the original software version, reaching a speedup of 2.6 times faster. The main contribution of this work is to propose and implement a new version of the ClamAV antivirus using parallel processing with OpenMP, easily portable to a variety of hardware platforms and operating systems.

Description: Symmetry, Vol. 9, Pages 173: Constructions of Helicoidal Surfaces in Euclidean Space with Density Symmetry doi: 10.3390/sym9090173 Authors: Dae Yoon Dong-Soo Kim Young Kim Jae Lee Our principal goal is to study the prescribed curvature problem in a manifold with density. In particular, we consider the Euclidean 3-space R 3 with a positive density function e ϕ , where ϕ = - x 2 - y 2 , ( x , y , z ) ∈ R 3 and construct all the helicoidal surfaces in the space by solving the second-order non-linear ordinary differential equation with the weighted Gaussian curvature and the mean curvature functions. As a result, we give a classification of weighted minimal helicoidal surfaces as well as examples of helicoidal surfaces with some weighted Gaussian curvature and mean curvature functions in the space.

Description: Symmetry, Vol. 9, Pages 174: Chiral Buckybowl Molecules Symmetry doi: 10.3390/sym9090174 Authors: Kuppusamy Kanagaraj Kangjie Lin Wanhua Wu Guowei Gao Zhihui Zhong Dan Su Cheng Yang Buckybowls are polynuclear aromatic hydrocarbons that have a curved aromatic surface and are considered fragments of buckminsterfullerenes. The curved aromatic surface led to the loss of planar symmetry of the normal aromatic plane and may cause unique inherent chirality, so-called bowl chirality, which it is possible to thermally racemize through a bowl-to-bowl inversion process. In this short review, we summarize the studies concerning the special field of bowl chirality, focusing on recent practical aspects of attaining diastereo/enantioenriched chiral buckybowls through asymmetric synthesis, chiral optical resolution, selective chiral metal complexation, and chiral assembly formation.

Description: Symmetry, Vol. 9, Pages 179: NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier Symmetry doi: 10.3390/sym9090179 Authors: Yaman Akbulut Abdulkadir Sengur Yanhui Guo Florentin Smarandache k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for improving its precision. The neutrosophic set (NS) defines three memberships namely T, I and F. T, I, and F shows the truth membership degree, the false membership degree, and the indeterminacy membership degree, respectively. In this paper, the NS memberships are adopted to improve the classification performance of the k-NN classifier. A new straightforward k-NN approach is proposed based on NS theory. It calculates the NS memberships based on a supervised neutrosophic c-means (NCM) algorithm. A final belonging membership U is calculated from the NS triples as U = T + I − F . A similar final voting scheme as given in fuzzy k-NN is considered for class label determination. Extensive experiments are conducted to evaluate the proposed method’s performance. To this end, several toy and real-world datasets are used. We further compare the proposed method with k-NN, fuzzy k-NN, and two weighted k-NN schemes. The results are encouraging and the improvement is obvious.

Description: Symmetry, Vol. 9, Pages 183: Community Detection Based on Differential Evolution Using Social Spider Optimization Symmetry doi: 10.3390/sym9090183 Authors: You-Hong Li Jian-Qiang Wang Xue-Jun Wang Yue-Long Zhao Xing-Hua Lu Da-Long Liu Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising.

Description: Symmetry, Vol. 9, Pages 184: An Efficient and Energy-Aware Cloud Consolidation Algorithm for Multimedia Big Data Applications Symmetry doi: 10.3390/sym9090184 Authors: JongBeom Lim HeonChang Yu Joon-Min Gil It is well known that cloud computing has many potential advantages over traditional distributed systems. Many enterprises can build their own private cloud with open source infrastructure as a service (IaaS) frameworks. Since enterprise applications and data are migrating to private cloud, the performance of cloud computing environments is of utmost importance for both cloud providers and users. To improve the performance, previous studies on cloud consolidation have been focused on live migration of virtual machines based on resource utilization. However, the approaches are not suitable for multimedia big data applications. In this paper, we reveal the performance bottleneck of multimedia big data applications in cloud computing environments and propose a cloud consolidation algorithm that considers application types. We show that our consolidation algorithm outperforms previous approaches.

Description: Symmetry, Vol. 9, Pages 185: An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut Symmetry doi: 10.3390/sym9090185 Authors: Yanhui Guo Yaman Akbulut Abdulkadir Şengür Rong Xia Florentin Smarandache Segmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using neutrosophic graph cut (NGC). An image is presented in neutrosophic set, and an indeterminacy filter is constructed using the indeterminacy value of the input image, which is defined by combining the spatial information and intensity information. The indeterminacy filter reduces the indeterminacy of the spatial and intensity information. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Numerous experiments have been taken to test its performance, and it is compared with a neutrosophic similarity clustering (NSC) segmentation algorithm and a graph-cut-based algorithm. The results indicate that the proposed NGC approach obtains better performances, both quantitatively and qualitatively.

Description: Symmetry, Vol. 9, Pages 190: Chiral Stationary Phases for Liquid Chromatography Based on Chitin- and Chitosan-Derived Marine Polysaccharides Symmetry doi: 10.3390/sym9090190 Authors: João Ribeiro Maria Tiritan Madalena Pinto Carla Fernandes The development of chiral stationary phases (CSPs) for liquid chromatography (LC) revolutionized the enantioseparation and, nowadays, different types of CSPs are commercially available. Polysaccharide-based CSPs are one of the most versatile and widely used for both analytical and preparative applications and they are able to resolve several classes of racemates. Phenylcarbamates of amylose and cellulose derivatives are the most successful; however, polysaccharide-based CSPs comprising marine-derived polysaccharides are also described revealing high chiral recognition abilities and wider range of mobile phases. A literature survey covering the report on chitin and chitosan based CSPs is presented. The chemical structure of the chiral selectors, their development and applications in chiral LC are emphasized.

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

Description: Symmetry, Vol. 9, Pages 192: A Robust Method for Finding the Automated Best Matched Genes Based on Grouping Similar Fragments of Large-Scale References for Genome Assembly Symmetry doi: 10.3390/sym9090192 Authors: Jaehee Jung Jong Kim Young-Sik Jeong Gangman Yi Big data research on genomic sequence analysis has accelerated considerably with the development of next-generation sequencing. Currently, research on genomic sequencing has been conducted using various methods, ranging from the assembly of reads consisting of fragments to the annotation of genetic information using a database that contains known genome information. According to the development, most tools to analyze the new organelles’ genetic information requires different input formats such as FASTA, GeneBank (GB) and tab separated files. The various data formats should be modified to satisfy the requirements of the gene annotation system after genome assembly. In addition, the currently available tools for the analysis of organelles are usually developed only for specific organisms, thus the need for gene prediction tools, which are useful for any organism, has been increased. The proposed method—termed the genome_search_plotter—is designed for the easy analysis of genome information from the related references without any file format modification. Anyone who is interested in intracellular organelles such as the nucleus, chloroplast, and mitochondria can analyze the genetic information using the assembled contig of an unknown genome and a reference model without any modification of the data from the assembled contig.

Description: Symmetry, Vol. 9, Pages 193: M2M Security Technology of CPS Based on Blockchains Symmetry doi: 10.3390/sym9090193 Authors: Shiyong Yin Jinsong Bao Yiming Zhang Xiaodi Huang As the core of intelligent manufacturing, cyber-physical systems (CPS) have serious security issues, especially for the communication security of their terminal machine-to-machine (M2M) communications. In this paper, blockchain technology is introduced to address such a security problem of communications between different types of machines in the CPS. According to the principles of blockchain technology, we designed a blockchain for secure M2M communications. As a communication system, M2M consists of public network areas, device areas, and private areas, and we designed a sophisticated blockchain structure between the public area and private area. For validating our design, we took cotton spinning production as a case study to demonstrate our solution to M2M communication problems under the CPS framework. We have demonstrated that the blockchain technology can effectively solve the safety of expansion of machines in the production process and the communication data between the machines cannot be tampered with.

Description: Symmetry, Vol. 9, Pages 194: Planning of Knotting Based on Manipulation Skills with Consideration of Robot Mechanism/Motion and Its Realization by a Robot Hand System Symmetry doi: 10.3390/sym9090194 Authors: Yuji Yamakawa Akio Namiki Masatoshi Ishikawa Makoto Shimojo This paper demonstrates the relationship between the production process of a knot and manipulation skills. First, we define the description (rope intersections, grasp type and fixation positions) of a knot. Second, we clarify the characteristics of the manipulation skills from the viewpoint of the knot description. Next, in order to obtain the production process of the knot, we propose an analysis method based on the structure of the knot and the characteristics of the manipulation skills. Using the proposed analysis method, we analyzed eight kinds of knots, formed with a single rope, two ropes or a single rope and an object. Finally, in order to validate the production process obtained by the proposed analysis method, we show experimental results of an overhand knot and a half hitch produced by using a robot hand system.

Description: Symmetry, Vol. 9, Pages 153: Vector Similarity Measures between Refined Simplified Neutrosophic Sets and Their Multiple Attribute Decision-Making Method Symmetry doi: 10.3390/sym9080153 Authors: Jiqian Chen Jun Ye Shigui Du A refined single-valued/interval neutrosophic set is very suitable for the expression and application of decision-making problems with both attributes and sub-attributes since it is described by its refined truth, indeterminacy, and falsity degrees. However, existing refined single-valued/interval neutrosophic similarity measures and their decision-making methods are scarcely studied in existing literature and cannot deal with this decision-making problem with the weights of both attributes and sub-attributes in a refined interval and/or single-valued neutrosophic setting. To solve the issue, this paper firstly introduces a refined simplified neutrosophic set (RSNS), which contains the refined single-valued neutrosophic set (RSVNS) and refined interval neutrosophic set (RINS), and then proposes vector similarity measures of RSNSs based on the Jaccard, Dice, and cosine measures of simplified neutrosophic sets in vector space, and the weighted Jaccard, Dice, and cosine measures of RSNSs by considering weights of both basic elements and sub-elements in RSNS. Further, a decision-making method with the weights of both attributes and sub-attributes is developed based on the weighted Jaccard, Dice, and cosine measures of RSNSs under RSNS (RINS and/or RSVNS) environments. The ranking order of all the alternatives and the best one can be determined by one of weighted vector similarity measures between each alternative and the ideal solution (ideal alternative). Finally, an actual example on the selecting problem of construction projects illustrates the application and effectiveness of the proposed method.

Description: Symmetry, Vol. 9, Pages 151: Selecting Project Delivery Systems Based on Simplified Neutrosophic Linguistic Preference Relations Symmetry doi: 10.3390/sym9080151 Authors: Sui-Zhi Luo Peng-Fei Cheng Jian-Qiang Wang Yuan-Ji Huang Project delivery system selection is an essential part of project management. In the process of choosing appropriate transaction model, many factors should be under consideration, such as the capability and experience of proprietors, project implementation risk, and so on. How to make their comprehensive evaluations and select the optimal delivery system? This paper proposes a decision-making approach based on an extended linguistic preference structure: simplified neutrosophic linguistic preference relations (SNLPRs). The basic elements in SNLPRs are simplified neutrosophic linguistic numbers (SNLNs). First, several distance measures of SNLNs are introduced. A distance-based consistency index is provided to measure the consistency degree of a simplified neutrosophic linguistic preference relation (SNLPR). When the SNLPR is not acceptably consistent, a consistency-improving automatic iterative algorithm may be used. Afterwards, a decision-making method with SNLPRs is developed. The example of its application in project delivery systems’ selection is offered, and a comparison analysis is given in the end as well.

Description: Symmetry, Vol. 9, Pages 170: Enantiomeric Separation of Tramadol and Its Metabolites: Method Validation and Application to Environmental Samples Symmetry doi: 10.3390/sym9090170 Authors: Cátia Silva Cláudia Ribeiro Alexandra Maia Virgínia Gonçalves Maria Tiritan Carlos Afonso The accurate assessment of racemic pharmaceuticals requires enantioselective analytical methods. This study presents the development and validation of an enantioselective liquid chromatography with a fluorescence detection method for the concomitant quantification of the enantiomers of tramadol and their metabolites, N-desmethyltramadol and O-desmethyltramadol, in wastewater samples. Optimized conditions were achieved using a Lux Cellulose-4 column 150 × 4.6 mm, 3 µm isocratic elution, and 0.1% diethylamine in hexane and ethanol (96:4, v/v) at 0.7 mL min−1. The samples were extracted using 150 mg Oasis® mixed-mode cation exchange (MCX) cartridges. The method was validated using a synthetic effluent of a laboratory-scale aerobic granular sludge sequencing batch reactor. The method demonstrated to be selective, accurate, and linear (r2 > 0.99) over the range of 56 ng L−1 to 392 ng L−1. The detection and the quantification limits of each enantiomer were 8 ng L−1 and 28 ng L−1 for tramadol and N-desmethyltramadol, and 20 ng L−1 and 56 ng L−1 for O-desmethyltramadol. The feasibility of the method was demonstrated in a screening study in influent and effluent samples from a wastewater treatment plant. The results demonstrated the occurrence of tramadol enantiomers up to 325.1 ng L−1 and 357.9 ng L−1, in the effluent and influent samples, respectively. Both metabolites were detected in influents and effluents.

Description: Symmetry, Vol. 9, Pages 171: A Security Generic Service Interface of Internet of Things (IoT) Platforms Symmetry doi: 10.3390/sym9090171 Authors: Mi Kim Nam Lee Jin Park Internet of Things (IoT) platforms are the key for the development of scalable IoT applications and services that connect real and virtual worlds between objects, systems, and people. However, as the IoT platform market represents a truly new market segment that was almost non-existent a few years ago, the platforms are complex and changing quickly. These IoT platforms perform simple functions such as providing useful information, and others can provide services through collaborations with IoT devices. This situation needs a generic service interface, and results in a range of IoT architectures through not only the configuration setting of IoT devices and resources but also the varied environments of collaboration of each device. Due to these heterogeneities, it is quite challenging to develop applications working with diverse IoT services, and it is even more difficult to maintain such applications. Therefore, this paper presents a security generic service interface with the effective common characteristics of an IoT platform by defining a set of generic interfaces and adopting well-known design patterns. The generic interface solves the heterogeneity-driven problems and makes it possible to effectively adopt a platform-independent Generic Interface that could be operated in diverse IoT platforms.

Description: Symmetry, Vol. 9, Pages 175: The Orthogonality between Complex Fuzzy Sets and Its Application to Signal Detection Symmetry doi: 10.3390/sym9090175 Authors: Bo Hu Lvqing Bi Songsong Dai A complex fuzzy set is a set whose membership values are vectors in the unit circle in the complex plane. This paper establishes the orthogonality relation of complex fuzzy sets. Two complex fuzzy sets are said to be orthogonal if their membership vectors are perpendicular. We present the basic properties of orthogonality of complex fuzzy sets and various results on orthogonality with respect to complex fuzzy complement, complex fuzzy union, complex fuzzy intersection, and complex fuzzy inference methods. Finally, an example application of signal detection demonstrates the utility of the orthogonality of complex fuzzy sets.

Description: Symmetry, Vol. 9, Pages 177: On Characterizations of Directional Derivatives and Subdifferentials of Fuzzy Functions Symmetry doi: 10.3390/sym9090177 Authors: Wei Zhang Yumei Xing Dong Qiu In this paper, based on a partial order, we study the characterizations of directional derivatives and the subdifferential of fuzzy function. At the same time, we also discuss the relation between the directional derivative and the subdifferential.

Description: Symmetry, Vol. 9, Pages 176: Parallelization of Modified Merge Sort Algorithm Symmetry doi: 10.3390/sym9090176 Authors: Zbigniew Marszałek Modern architectures make possible development in new algorithms for large data sets and distributed computing. The newly proposed versions can benefit both from faster computing on the multi core architectures, and intelligent programming techniques that use efficient procedures available in the latest programming studios. Frequently used algorithms to sort arrays of data in NoSQL databases is merge sort, where as NoSQL we understand any database without typical SQL programming interpreter. The author describes how to use the parallelization of the sorting processes for the modified method of sorting by merging for large data sets. The subject of this research is the claim that the parallelization of the sorting method is faster and beneficial for multi-core systems. Presented results show how the number of processors influences the sorting performance. The results are presented in theoretical assumptions and confirmed in practical benchmark tests. The method is compared to other sorting methods like quick sort, heap sort, and merge sort to show potential efficiency.

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

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

Description: Symmetry, Vol. 9, Pages 189: A Study on Interaction of Gaze Pointer-Based User Interface in Mobile Virtual Reality Environment Symmetry doi: 10.3390/sym9090189 Authors: Mingyu Kim Jiwon Lee Changyu Jeon Jinmo Kim This research proposes a gaze pointer-based user interface to provide user-oriented interaction suitable for the virtual reality environment on mobile platforms. For this purpose, a mobile platform-based three-dimensional interactive content is produced to test whether the proposed gaze pointer-based interface increases user satisfaction through the interactions in a virtual reality environment based on mobile platforms. The gaze pointer-based interface—the most common input method for mobile virtual reality content—is designed by considering four types: the visual field range, the feedback system, multi-dimensional information transfer, and background colors. The performance of the proposed gaze pointer-based interface is analyzed by conducting experiments on whether or not it offers motives for user interest, effects of enhanced immersion, provision of new experience, and convenience in operating content. In addition, it is verified whether any negative psychological factors, such as VR sickness, fatigue, difficulty of control, and discomfort in using contents are caused. Finally, through the survey experiment, this study confirmed that it is possible to design different ideal gaze pointer-based interface in mobile VR environment according to presence and convenience.

Description: Symmetry, Vol. 9, Pages 188: On the Form and Growth of Complex Crystals: The Case of Tsai-Type Clusters Symmetry doi: 10.3390/sym9090188 Authors: Jean Taylor Erin Teich Pablo Damasceno Yoav Kallus Marjorie Senechal Where are the atoms in complex crystals such as quasicrystals or periodic crystals with one hundred or more atoms per unit cell? How did they get there? The first of these questions has been gradually answered for many materials over the quarter-century since quasicrystals were discovered; in this paper we address the second. We briefly review a history of proposed models for describing atomic positions in crystal structures. We then present a revised description and possible growth model for one particular system of alloys, those containing Tsai-type clusters, that includes an important class of quasicrystals.

Description: Symmetry, Vol. 9, Pages 191: Forecasting Model Based on Neutrosophic Logical Relationship and Jaccard Similarity Symmetry doi: 10.3390/sym9090191 Authors: Hongjun Guan Shuang Guan Aiwu Zhao The daily fluctuation trends of a stock market are illustrated by three statuses: up, equal, and down. These can be represented by a neutrosophic set which consists of three functions—truth-membership, indeterminacy-membership, and falsity-membership. In this paper, we propose a novel forecasting model based on neutrosophic set theory and the fuzzy logical relationships between the status of historical and current values. Firstly, the original time series of the stock market is converted to a fluctuation time series by comparing each piece of data with that of the previous day. The fluctuation time series is then fuzzified into a fuzzy-fluctuation time series in terms of the pre-defined up, equal, and down intervals. Next, the fuzzy logical relationships can be expressed by two neutrosophic sets according to the probabilities of different statuses for each current value and a certain range of corresponding histories. Finally, based on the neutrosophic logical relationships and the status of history, a Jaccard similarity measure is employed to find the most proper logical rule to forecast its future. The authentic Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series datasets are used as an example to illustrate the forecasting procedure and performance comparisons. The experimental results show that the proposed method can successfully forecast the stock market and other similar kinds of time series. We also apply the proposed method to forecast the Shanghai Stock Exchange Composite Index (SHSECI) to verify its effectiveness and universality.

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

Description: Symmetry, Vol. 9, Pages 195: Solvable Three-Dimensional Product-Type System of Difference Equations with Multipliers Symmetry doi: 10.3390/sym9090195 Authors: Stevo Stević The solvability of the following three-dimensional product-type system of difference equations x n + 1 = α y n a z n − 1 b , y n + 1 = β z n c x n − 1 d , z n + 1 = γ x n f y n − 1 g , n ∈ N 0 , where a , b , c , d , f , g ∈ Z , α , β , γ ∈ C \ { 0 } and x − i , y − i , z − i ∈ C \ { 0 } , i ∈ { 0 , 1 } , is shown. This is the first three-dimensional system of the type with multipliers for which formulas are presented for their solutions in closed form in all the cases.

Description: Symmetry, Vol. 9, Pages 154: Simplified Neutrosophic Exponential Similarity Measures for the Initial Evaluation/Diagnosis of Benign Prostatic Hyperplasia Symptoms Symmetry doi: 10.3390/sym9080154 Authors: Jing Fu Jun Ye When a physician carries out the clinical survey of a patient with benign prostatic hyperplasia (BPH) symptoms to reach the initial evaluation/diagnosis of BPH, the existing initial evaluation method of BPH based on the international prostate symptom score (I-PSS) usually uses the objective evaluation/diagnosis method with crisp values without considering fuzzy information. However, this common evaluation/diagnosis method may lead to the loss of a great deal of useful incomplete, uncertain, and inconsistent information in the clinical survey and initial evaluation process of the BPH symptoms for a patient, resulting in an unreasonable evaluation and diagnosis distortion of the BPH symptoms. To overcome this drawback, this paper aims to propose new exponential similarity measures (ESMs) between simplified neutrosophic sets (SNSs), including single-valued neutrosophic ESMs and interval neutrosophic ESMs, and their initial evaluation/diagnosis method of the BPH symptoms with simplified neutrosophic information. Finally, two evaluation/diagnosis examples of the BPH symptoms are provided to demonstrate the effectiveness and rationality of the proposed method.

Description: Symmetry, Vol. 9, Pages 152: A Study on Big Data Thinking of the Internet of Things-Based Smart-Connected Car in Conjunction with Controller Area Network Bus and 4G-Long Term Evolution Symmetry doi: 10.3390/sym9080152 Authors: Donghwoon Kwon Suwoo Park Jeong-Tak Ryu A smart connected car in conjunction with the Internet of Things (IoT) is an emerging topic. The fundamental concept of the smart connected car is connectivity, and such connectivity can be provided by three aspects, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X). To meet the aspects of V2V and V2I connectivity, we developed modules in accordance with international standards with respect to On-Board Diagnostics II (OBDII) and 4G Long Term Evolution (4G-LTE) to obtain and transmit vehicle information. We also developed software to visually check information provided by our modules. Information related to a user’s driving, which is transmitted to a cloud-based Distributed File System (DFS), was then analyzed for the purpose of big data analysis to provide information on driving habits to users. Yet, since this work is an ongoing research project, we focus on proposing an idea of system architecture and design in terms of big data analysis. Therefore, our contributions through this work are as follows: (1) Develop modules based on Controller Area Network (CAN) bus, OBDII, and 4G-LTE; (2) Develop software to check vehicle information on a PC; (3) Implement a database related to vehicle diagnostic codes; (4) Propose system architecture and design for big data analysis.

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

Description: Symmetry, Vol. 9, Pages 159: Multi-objective Fuzzy Bi-matrix Game Model: A Multicriteria Non-Linear Programming Approach Symmetry doi: 10.3390/sym9080159 Authors: Wei Zhang Yumei Xing Dong Qiu A multi-objective bi-matrix game model based on fuzzy goals is established in this paper. It is shown that the equilibrium solution of such a game model problem can be translated into the optimal solution of a multi-objective, non-linear programming problem. Finally, the results of this paper are demonstrated through a numerical example.

Description: Symmetry, Vol. 9, Pages 158: Intuitionistic-Fuzzy Goals in Zero-Sum Multi Criteria Matrix Games Symmetry doi: 10.3390/sym9080158 Authors: Zia Bashir Jarosław Wątróbski Tabasam Rashid Wojciech Sałabun Jawad Ali The classical matrix theory is deficient to express the vagueness of the real life. The fuzzy set theory has been successfully applied to bridge this gap. Much work has already been done on a two-person zero sum matrix game with fuzzy goals. In continuation, this paper is dedicated to define and study a multi-criteria two-person zero sum game with intuitionistic fuzzy goals. It is shown that solving such games is equivalent to solving two crisp multi object linear programming problems. Our work generalizes the previous study on a multi-criteria game with fuzzy goals by adopting the approach of linear programming with intuitionistic fuzzy sets. Finally, an illustrative numerical example is provided to elaborate the proposed approach.

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

Description: Symmetry, Vol. 9, Pages 165: Schrödinger Equations with Logarithmic Self-Interactions: From Antilinear PT-Symmetry to the Nonlinear Coupling of Channels Symmetry doi: 10.3390/sym9080165 Authors: Miloslav Znojil František Růžička Konstantin Zloshchastiev Schrödinger equations with non-Hermitian, but PT -symmetric quantum potentials V ( x ) found, recently, a new field of applicability in classical optics. The potential acquired there a new physical role of an “anomalous” refraction index. This turned attention to the nonlinear Schrödinger equations in which the interaction term becomes state-dependent, V ( x ) → W ( ψ ( x ) , x ) . Here, the state-dependence in W ( ψ ( x ) , x ) is assumed logarithmic, and some of the necessary mathematical assumptions, as well as some of the potential phenomenological consequences of this choice are described. Firstly, an elementary single-channel version of the nonlinear logarithmic model is outlined in which the complex self-interaction W ( ψ ( x ) , x ) is regularized via a deformation of the real line of x into a self-consistently constructed complex contour C. The new role played by PT -symmetry is revealed. Secondly, the regularization is sought for a multiplet of equations, coupled via the same nonlinear self-interaction coupling of channels. The resulting mathematical structures are shown to extend the existing range of physics covered by the logarithmic Schrödinger equations.

Description: Symmetry, Vol. 9, Pages 160: On Extended Representable Uninorms and Their Extended Fuzzy Implications (Coimplications) Symmetry doi: 10.3390/sym9080160 Authors: Aifang Xie In this work, by Zadeh’s extension principle, we extend representable uninorms and their fuzzy implications (coimplications) to type-2 fuzzy sets. Emphatically, we investigate in which algebras of fuzzy truth values the extended operations are type-2 uninorms and type-2 fuzzy implications (coimplications), respectively.

Description: Symmetry, Vol. 9, Pages 197: Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks Symmetry doi: 10.3390/sym9090197 Authors: Kamran Siddique Zahid Akhtar Haeng-gon Lee Woongsup Kim Yangwoo Kim Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i) performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii) employs logistic regression and extreme gradient boosting techniques for classification; (iii) introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv) uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

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

Description: Symmetry, Vol. 9, Pages 198: A Generalization of Trapezoidal Fuzzy Numbers Based on Modal Interval Theory Symmetry doi: 10.3390/sym9100198 Authors: Lambert Jorba Romà Adillon We propose a generalization of trapezoidal fuzzy numbers based on modal interval theory, which we name “modal interval trapezoidal fuzzy numbers”. In this generalization, we accept that the alpha cuts associated with a trapezoidal fuzzy number can be modal intervals, also allowing that two interval modalities can be associated with a trapezoidal fuzzy number. In this context, it is difficult to maintain the traditional graphic representation of trapezoidal fuzzy numbers and we must use the interval plane in order to represent our modal interval trapezoidal fuzzy numbers graphically. Using this representation, we can correctly reflect the modality of the alpha cuts. We define some concepts from modal interval analysis and we study some of the related properties and structures, proving, among other things, that the inclusion relation provides a lattice structure on this set. We will also provide a semantic interpretation deduced from the modal interval extensions of real continuous functions and the semantic modal interval theorem. The application of modal intervals in the field of fuzzy numbers also provides a new perspective on and new applications of fuzzy numbers.

Description: This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC) Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.

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

Description: Symmetry, Vol. 9, Pages 156: Cloud Generalized Power Ordered Weighted Average Operator and Its Application to Linguistic Group Decision-Making Symmetry doi: 10.3390/sym9080156 Authors: Jianwei Gao Ru Yi In this paper, we develop a new linguistic aggregation operator based on the cloud model for solving linguistic group decision-making problem. First, an improved generating cloud method is proposed so as to transform linguistic variables into clouds, which modifies the limitation of the classical generating cloud method. We then address some new cloud algorithms, such as cloud possibility degree and cloud support degree which can be respectively used to compare clouds and determine the weights. Combining the cloud support degree with power aggregation operator, we develop a new cloud aggregation operator dubbed the cloud generalized power ordered weighted average (CGPOWA) operator. We study the properties of the CGPOWA operator and investigate its family including a wide range of aggregation operators such as the CGPA operator, CPOWA operator, CPOWGA operator, CPWQA operator, CWAA and CWGA operator. Furthermore, a new approach for linguistic group decision-making is presented on the basis of the improved generating cloud method and CGPOWA operator. Finally, an illustrative example is provided to examine the effectiveness and validity of the proposed approach.

Description: Symmetry, Vol. 9, Pages 162: Risk Evaluation in Failure Mode and Effects Analysis Using Fuzzy Measure and Fuzzy Integral Symmetry doi: 10.3390/sym9080162 Authors: Haibin Liu Xinyang Deng Wen Jiang Failure mode and effects analysis (FMEA) is a popular and useful approach applied to examine potential failures in different products, designs, processes, and services. As a vital index, the risk priority number (RPN) can determine the risk priorities of failure modes by some risk factors such as occurrence (O), severity (S), and detection (D). However, in FMEA, the traditional risk priority number approach has some shortcomings, especially in setting the weight of risk factors. This paper presents an improved risk priority number approach based on a fuzzy measure and fuzzy integral. A fuzzy measure is used to reflect the importance of the individual indicators and the indicator set and a fuzzy integral is a nonlinear function defined on the basis of fuzzy measure. The weights of risk factors given by domain experts are seen as fuzzy densities to generate a λ -fuzzy measure which can reflect the weights’ difference and relevance about risk factors. Then, the Choquet integral is used to fuse every value of risk factors about failure modes so as to obtain the comprehensive evaluation result. The result can reflect the comprehensive risk level, so it has a definite physical significance. Finally, an illustrative example and a comparison with another approach are given to show the effectiveness of the proposed approach in the paper.

Description: Symmetry, Vol. 9, Pages 161: Using Knowledge Transfer and Rough Set to Predict the Severity of Android Test Reports via Text Mining Symmetry doi: 10.3390/sym9080161 Authors: Shikai Guo Rong Chen Hui Li Crowdsourcing is an appealing and economic solution to software application testing because of its ability to reach a large international audience. Meanwhile, crowdsourced testing could have brought a lot of bug reports. Thus, in crowdsourced software testing, the inspection of a large number of test reports is an enormous but essential software maintenance task. Therefore, automatic prediction of the severity of crowdsourced test reports is important because of their high numbers and large proportion of noise. Most existing approaches to this problem utilize supervised machine learning techniques, which often require users to manually label a large number of training data. However, Android test reports are not labeled with their severity level, and manual labeling is time-consuming and labor-intensive. To address the above problems, we propose a Knowledge Transfer Classification (KTC) approach based on text mining and machine learning methods to predict the severity of test reports. Our approach obtains training data from bug repositories and uses knowledge transfer to predict the severity of Android test reports. In addition, our approach uses an Importance Degree Reduction (IDR) strategy based on rough set to extract characteristic keywords to obtain more accurate reduction results. The results of several experiments indicate that our approach is beneficial for predicting the severity of android test reports.

Description: Symmetry, Vol. 9, Pages 167: BATCP: Bandwidth-Aggregation Transmission Control Protocol Symmetry doi: 10.3390/sym9080167 Authors: Ismael Amezcua Valdovinos Jesus Perez Diaz Luis Garcia Villalba Tai-hoon Kim The Transmission Control Protocol (TCP) is the most used transport protocol to exchange reliable data between network devices. A considerable number of extensions have been implemented into TCP to achieve better performance. In this paper, we will present, describe, implement, and analyze a new protocol extension called Bandwidth-Aggregation TCP (BATCP), which enables the concurrent use of network interfaces, to improve network performance on multi-homed nodes. BATCP allows the use of multiple TCP connections to accept multiple IP addresses from a multi-homed node, scheduling segments among them based on a scheduling algorithm. Our results show that BATCP achieves full exploitation of each network interface, achieving up to 100 % network utilization using two ADSL connections in real-world scenarios. MultiPath TCP (MPTCP) is currently being standardized, and achieves up to 96 % of network utilization when in ideal conditions. BATCP and MPTCP are the only protocols tested on real-world scenarios. Related work such as the Proxy Inverse Multiplexer, called PRISM, and bandwidth aggregation with Stream Control Transmission Protocol (SCTP) achieve 80 % utilization or less with network simulators.

Description: Symmetry, Vol. 9, Pages 169: An Extended VIKOR Method Using Intuitionistic Fuzzy Sets and Combination Weights for Supplier Selection Symmetry doi: 10.3390/sym9090169 Authors: Jiansen Zhao Xiao-Yue You Hu-Chen Liu Song-Man Wu Supplier selection is a complex multiple criteria decision-making (MCDM) problem, which considers a number of alternative suppliers as well as conflicting and noncommensurable criteria. Considering the fact that it is difficult to precisely determine criteria weights and the ratings of alternatives on each criterion in real-life situations, the VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) method has been modified for intuitionistic fuzzy data in this study for supplier evaluation and selection. Further, we take into account both subjective and objective weights of criteria in the decision-making process, as most supplier selection approaches consider only subjective criteria weights. Finally, two supplier selection examples are provided to illustrate the proposed intuitionistic fuzzy hybrid VIKOR (IFH-VIKOR) method, and its merits are highlighted by comparing it with other relevant approaches.

Description: Symmetry, Vol. 9, Pages 168: An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization Symmetry doi: 10.3390/sym9090168 Authors: Rafał Dreżewski Krzysztof Doroz Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

Description: Symmetry, Vol. 9, Pages 180: Qinling: A Parametric Model in Speculative Multithreading Symmetry doi: 10.3390/sym9090180 Authors: Yuxiang Li Yinliang Zhao Bin Liu Speculative multithreading (SpMT) is a thread-level automatic parallelization technique that can accelerate sequential programs, especially for irregular applications that are hard to be parallelized by conventional approaches. Thread partition plays a critical role in SpMT. Conventional machine learning-based thread partition approaches applied machine learning to offline guide partition, but could not explicitly explore the law between partition and performance. In this paper, we build a parametric model (Qinling) with a multiple regression method to discover the inherent law between thread partition and performance. The paper firstly extracts unpredictable parameters that determine the performance of thread partition in SpMT; secondly, we build a parametric model Qinling with extracted parameters and speedups, and train Qinling offline, as well as apply it to predict the theoretical speedups of unseen applications. Finally, validation is done. Prophet, which consists of an automatic parallelization compiler and a multi-core simulator, is used to obtain real speedups of the input programs. Olden and SPEC2000 benchmarks are used to train and validate the parametric model. Experiments show that Qinling delivers a good performance to predict speedups of unseen programs, and provides feedback guidance for Prophet to obtain the optimal partition parameters.

Description: Symmetry, Vol. 9, Pages 178: A Sparse Signal Reconstruction Method Based on Improved Double Chains Quantum Genetic Algorithm Symmetry doi: 10.3390/sym9090178 Authors: Qiang Guo Guoqing Ruan Jian Wan This paper proposes a novel method of sparse signal reconstruction, which combines the improved double chains quantum genetic algorithm (DCQGA) and the orthogonal matching pursuit algorithm (OMP). Firstly, aiming at the problems of the slow convergence speed and poor robustness of traditional DCQGA, we propose an improved double chains quantum genetic algorithm (IDCQGA). The main innovations contain three aspects: (1) a high density quantum encoding method is presented to reduce the searching space and increase the searching density of the algorithm; (2) the adaptive step size factor is introduced in the chromosome updating, which changes the step size with the gradient of the objective function at the search points; (3) the quantum π / 6 -gate is proposed in chromosome mutation to overcome the deficiency of the traditional NOT-gate mutation with poor performance to increase the diversity of the population. Secondly, for the problem of the OMP algorithm not being able to reconstruct precisely the effective sparse signal in noisy environments, a fidelity orthogonal matching pursuit (FOMP) algorithm is proposed. Finally, the IDCQGA-based OMP and FOMP algorithms are applied to the sparse signal decomposition, and the simulation results show that the proposed algorithms can improve the convergence speed and reconstruction precision compared with other methods in the experiments.

Description: Symmetry, Vol. 9, Pages 181: Sandor Type Fuzzy Inequality Based on the (s,m)-Convex Function in the Second Sense Symmetry doi: 10.3390/sym9090181 Authors: Haiping Ren Guofu Wang Laijun Luo Integral inequalities play critical roles in measure theory and probability theory. Given recent profound discoveries in the field of fuzzy set theory, fuzzy inequality has become a hot research topic in recent years. For classical Sandor type inequality, this paper intends to extend the Sugeno integral. Based on the (s,m)-convex function in the second sense, a new Sandor type inequality is proposed for the Sugeno integral. Examples are given to verify the conclusion of this paper.

Description: Symmetry, Vol. 9, Pages 182: Effects of Handedness and Viewpoint on the Imitation of Origami-Making Symmetry doi: 10.3390/sym9090182 Authors: Natalie Uomini Rebecca Lawson The evolutionary origins of the human bias for 85% right-handedness are obscure. The Apprenticeship Complexity Theory states that the increasing difficulty of acquiring stone tool-making and other manual skills in the Pleistocene favoured learners whose hand preference matched that of their teachers. Furthermore, learning from a viewing position opposite, rather than beside, the demonstrator might be harder because it requires more mental transformation. We varied handedness and viewpoint in a bimanual learning task. Thirty-two participants reproduced folding asymmetric origami figures as demonstrated by a videotaped teacher in four conditions (left-handed teacher opposite the learner, left-handed beside, right-handed opposite, or right-handed beside). Learning performance was measured by time to complete each figure, number of video pauses and rewinds, and similarity of copies to the target shape. There was no effect of handedness or viewpoint on imitation learning. However, participants preferred to produce figures with the same asymmetry as demonstrated, indicating they imitate the teacher's hand preference. We speculate that learning by imitation involves internalising motor representations and that, to facilitate learning by imitation, many motor actions can be flexibly executed using the demonstrated hand configuration. We conclude that matching hand preferences evolved due to socially learning moderately complex bimanual skills.

Description: Symmetry, Vol. 9, Pages 186: An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships Symmetry doi: 10.3390/sym9090186 Authors: Wei He Zhixiong Li Reza Malekian Xinglong Liu Zhihe Duan Automatic Identification System (AIS), as a major data source of navigational data, is widely used in the application of connected ships for the purpose of implementing maritime situation awareness and evaluating maritime transportation. Efficiently extracting featured data from AIS database is always a challenge and time-consuming work for maritime administrators and researchers. In this paper, a novel approach was proposed to extract massive featured data from the AIS database. An Evidential Reasoning rule based methodology was proposed to simulate the procedure of extracting routes from AIS database artificially. First, the frequency distributions of ship dynamic attributes, such as the mean and variance of Speed over Ground, Course over Ground, are obtained, respectively, according to the verified AIS data samples. Subsequently, the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling. In this case, the attributes were characterized into several pieces of evidence, and the evidence can be combined with the Evidential Reasoning rule. In addition, the weight coefficients were trained in a nonlinear optimization model to extract the AIS data more accurately. A real life case study was conducted at an intersection waterway, Yangtze River, Wuhan, China. The results show that the proposed methodology is able to extract data very precisely.

Description: Symmetry, Vol. 9, Pages 187: Spatio-Temporal Symmetry—Point Groups with Time Translations Symmetry doi: 10.3390/sym9090187 Authors: Haricharan Padmanabhan Maggie Kingsland Jason Munro Daniel Litvin Venkatraman Gopalan Spatial symmetries occur in combination with temporal symmetries in a wide range of physical systems in nature, including time-periodic quantum systems typically described by the Floquet formalism. In this context, groups formed by three-dimensional point group symmetry operations in combination with time translation operations are discussed in this work. The derivation of these ’spatio-temporal’ groups from conventional point groups and their irreducible representations is outlined, followed by a complete listing. The groups are presented in a template similar to space group operations, and are visualized using a modified version of conventional stereographic projections. Simple examples of physical processes that simultaneously exhibit symmetry in space and time are identified and used to illustrate the application of spatio-temporal groups.

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

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

Description: A conservation law theorem stated by N. Ibragimov along with its subsequent extensions are shown to be a special case of a standard formula that uses a pair consisting of a symmetry and an adjoint-symmetry to produce a conservation law through a well-known Fréchet derivative identity. Furthermore, the connection of this formula (and of Ibragimov’s theorem) to the standard action of symmetries on conservation laws is explained, which accounts for a number of major drawbacks that have appeared in recent work using the formula to generate conservation laws. In particular, the formula can generate trivial conservation laws and does not always yield all non-trivial conservation laws unless the symmetry action on the set of these conservation laws is transitive. It is emphasized that all local conservation laws for any given system of differential equations can be found instead by a general method using adjoint-symmetries. This general method is a kind of adjoint version of the standard Lie method to find all local symmetries and is completely algorithmic. The relationship between this method, Noether’s theorem and the symmetry/adjoint-symmetry formula is discussed.

Description: In order to improve system performance such as throughput, heterogeneous network (HetNet) has become an effective solution in Long Term Evolution-Advanced (LET-A). However, co-channel interference leads to degradation of the HetNet throughput, because femtocells are always arranged to share the spectrum with the macro base station. In this paper, in view of the serious cross-layer interference in double layer HetNet, the Stackelberg game model is adopted to analyze the resource allocation methods of the network. Unlike the traditional system models only focusing on macro base station performance improvement, we take into account the overall system performance and build a revenue function with convexity. System utility functions are defined as the average throughput, which does not adopt frequency spectrum trading method, so as to avoid excessive signaling overhead. Due to the value scope of continuous Nash equilibrium of the built game model, the gradient iterative algorithm is introduced to reduce the computational complexity. As for the solution of Nash equilibrium, one kind of gradient iterative algorithm is proposed, which is able to intelligently choose adjustment factors. The Nash equilibrium can be quickly solved; meanwhile, the step of presetting adjustment factors is avoided according to network parameters in traditional linear iterative model. Simulation results show that the proposed algorithm enhances the overall performance of the system.

Description: Inspired by the generalized entropies for graphs, a class of generalized degree-based graph entropies is proposed using the known information-theoretic measures to characterize the structure of complex networks. The new entropies depend on assigning a probability distribution about the degrees to a network. In this paper, some extremal properties of the generalized degree-based graph entropies by using the degree powers are proved. Moreover, the relationships among the entropies are studied. Finally, numerical results are presented to illustrate the features of the new entropies.

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

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

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

Description: Cognitive radio (CR) has become a tempting technology that achieves significant improvement in spectrum utilization. To resolve the hidden terminal problem, collaborative spectrum sensing (CSS), which profits from spatial diversity, has been studied intensively in recent years. As CSS is vulnerable to the attacks launched by malicious secondary users (SUs), certain CSS security schemes based on the Dempster–Shafer theory of evidence have been proposed. Nevertheless, the available works only focus on the real-time difference of SUs, like the difference in similarity degree or SNR, to evaluate the credibility of each SU. Since the real-time difference is unilateral and sometimes inexact, the statistical information comprised in SUs’ historical behaviors should not be ignored. In this paper, we propose a robust CSS method based on evidence theory and credibility calculation. It is executed in four consecutive procedures, which are basic probability assignment (BPA), holistic credibility calculation, option and amelioration of BPA and evidence combination via the Dempster–Shafer rule, respectively. Our scheme evaluates the holistic credibility of SUs from both the real-time difference and statistical sensing behavior of SUs. Moreover, considering that the transmitted data increase with the number of SUs increasing, we introduce the projection approximation approach to adjust the evidence theory to the binary hypothesis test in CSS; on this account, both the data volume to be transmitted and the workload at the data fusion center have been reduced. Malicious SUs can be distinguished from genuine ones based on their historical sensing behaviors, and SUs’ real-time difference can be reserved to acquire a superior current performance. Abounding simulation results have proven that the proposed method outperforms the existing ones under the effect of different attack modes and different numbers of malicious SUs.

Description: Long Term Evolution (LTE) is the prominent technology in Fourth Generation (4G) communication standards, which provides higher throughput and better Quality of Service (QoS) to all users. However, users in the cell-edge area are receiving comparatively low QoS due to the distance from eNodeB (eNB) and bad channel conditions. The Conventional Modified Largest Weighted Delay First (MLWDF) algorithm is unable to resolve this issue, as it does not consider the location of the user. This paper proposes an extended MLWDF (EMLWDF) downlink scheduling algorithm to provide better services to the cell-edge user as well as to the cell-center user. The proposed algorithm divides the eNB cell area into inner and outer regions. It includes the distance of the user from attached eNB, received Signal to Interference plus Noise Ratio (SINR) and error probability into the original algorithm. The simulated results are compared with other well-known algorithms and the comparison shows that the proposed algorithm enhances overall 56.23% of cell-edge user throughput and significantly improves the average user throughput, fairness index, and spectral efficiency.

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

Description: The Dombi operations of T-norm and T-conorm introduced by Dombi can have the advantage of good flexibility with the operational parameter. In existing studies, however, the Dombi operations have so far not yet been used for neutrosophic sets. To propose new aggregation operators for neutrosophic sets by the extension of the Dombi operations, this paper firstly presents the Dombi operations of single-valued neutrosophic numbers (SVNNs) based on the operations of the Dombi T-norm and T-conorm, and then proposes the single-valued neutrosophic Dombi weighted arithmetic average (SVNDWAA) operator and the single-valued neutrosophic Dombi weighted geometric average (SVNDWGA) operator to deal with the aggregation of SVNNs and investigates their properties. Because the SVNDWAA and SVNDWGA operators have the advantage of good flexibility with the operational parameter, we develop a multiple attribute decision-making (MADM) method based on the SVNWAA or SVNWGA operator under a SVNN environment. Finally, an illustrative example about the selection problem of investment alternatives is given to demonstrate the application and feasibility of the developed approach.

Description: Chicks (Gallus gallus) learned to run from a starting box to a target located at the end of a runway. At test, colourful and bright distractors were placed just outside the starting box. Dark incubated chicks (maintained in darkness from fertilization to hatching) stopped significantly more often, assessing more the left-side distractor than chicks hatched after late (for 42 h during the last three days before hatching) or early (for 42 h after fertilization) exposure to light. The results show that early embryonic light stimulation can modulate this particular behavioural lateralization comparably to the late application of it, though via a different route.

Description: This work proposes a new and non-blind steganographic scheme for synthesized pitches. Synthesized music is popularly used to demonstrate early versions of compositions conveniently and at low-cost. They can also be utilized to pass secrets or obtain digital rights. The method consists of two procedures, of which the first is the realistic simulation of synthesized pitches using a computer and the second is the hiding of secrets during the generated simulated pitches. The first part of this paper reviews attempts to discover the fundamental patterns of synthesized pitches and to develop a strategy for generating approximate pitches using a computer. The component frequencies are used to generate a pitch in which to hide secrets. Legal receivers receive the referenced composition and frequencies, enabling them to generate the synthesized pitches according to the main frequencies of the referenced composition. Finally, the generated and received pitches are compared to identify the secret bits. As more frequencies are used to hide secret bits, more secret bits can be embedded in the synthesized pitches. The use of more frequencies makes synthesized pitches more realistic compared to real ones. The performance of the proposed method is also compared with that of competing methods and under common attacks.

Description: To improve the accuracy of indoor pedestrian positioning, an indoor pedestrian positioning system with two-order Bayesian estimation based on Extended Kalman Filter (EKF) and Particle Filter (PF) is proposed in this paper. The presented system combines a foot-mounted inertial sensor, a Wi-Fi propagation model and building structure to make good use of these information resources. There are two updates in this system in order to limit the accumulative errors of inertial sensors. In the first update, the inertial navigation system (INS) is the main system in the calculation of pedestrian positioning, and Zero-velocity update (ZUPT) is introduced as the reference to correct the accumulative errors of INS based on EKF. To further limit the accumulative errors of inertial sensors, the estimated results obtained from the first update, including horizontal position information, are introduced as the observations based on PF in the second update; Pedestrian Dead Reckoning (PDR) is the main system in the calculation of pedestrian positioning, and the weight of particles is determined by the Wi-Fi propagation model, building structure information and output of the first update. The results show that the accuracy of positioning is effectively increased.

Description: Rice leaves display lateral asymmetry around the midrib, and the narrow side exhibits higher leaf area-based nitrogen concentration (Na) and soil plant analysis development (SPAD) values than the wider side. However, the difference in the relationship between the SPAD of each side and Na of the corresponding lateral half, and the optimal position along the leaf blade for SPAD measurements are not known. In this study, the relationship between SPAD and Na of both sides of the top three leaves was determined with 17 rice varieties grown over three growing seasons in two locations. The relationship between SPAD and Na displayed leaf lateral asymmetry, in which the wide side reflected a higher coefficient of determination than the narrow side. The ability to estimate Na of the whole leaf was slightly improved by averaging SPAD values across the leaf sides and measured points for the top two leaves. Apparently, it was more accurate and easier to measure SPAD readings on the wide side than the narrow side of rice leaf blade with respect to estimating plant N status. Due to the relatively poor relationship of the upper leaf, and the structural limit for SPAD measurements of the base, this study suggests that the most suitable and representative position for SPAD meter measurement on the leaf blade of rice is the lower-middle part from the leaf apex on the wide side.

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

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

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

Description: The POT family of proton coupled oligopeptide transporters belong to the Major Facilitator Superfamily of secondary active transporters and are found widely distributed in bacterial, plant, fungal and animal genomes. POT transporters use the inwardly directed proton electrochemical gradient to drive the concentrative uptake of di- and tri-peptides across the cell membrane for metabolic assimilation. Mammalian members of the family, PepT1 and PepT2, are responsible for the uptake and retention of dietary protein in the human body, and due to their promiscuity in ligand recognition, play important roles in the pharmacokinetics of drug transport. Recent crystal structures of bacterial and plant members have revealed the overall architecture for this protein family and provided a framework for understanding proton coupled transport within the POT family. An interesting outcome from these studies has been the discovery of symmetrically equivalent structural and functional sites. This review will highlight both the symmetry and asymmetry in structure and function within the POT family and discuss the implications of these considerations in understanding transport and regulation.

Description: In order to capture an eye image of high quality in a gaze-tracking camera, an auto-focusing mechanism is used, which requires accurate focus assessment. Although there has been previous research on focus assessment in the spatial or wavelet domains, there are few previous studies that combine all of the methods of spatial and wavelet domains. Since all of the previous focus assessments in the spatial or wavelet domain methods have disadvantages, such as being affected by illumination variation, etc., we propose a new focus assessment method by combining the spatial and wavelet domain methods for the gaze-tracking camera. This research is novel in the following three ways, in comparison with the previous methods. First, the proposed focus assessment method combines the advantages of spatial and wavelet domain methods by using ε-support vector regression (SVR) with a symmetrical Gaussian radial basis function (RBF) kernel. In order to prevent the focus score from being affected by a change in image brightness, both linear and nonlinear normalizations are adopted in the focus score calculation. Second, based on the camera optics, we mathematically prove the reason for the increase in the focus score in the case of daytime images or a brighter illuminator compared to nighttime images or a darker illuminator. Third, we propose a new criterion to compare the accuracies of the focus measurement methods. This criterion is based on the ratio of relative overlapping amount (standard deviation of focus score) between two adjacent positions along the Z-axis to the entire range of focus score variety between these two points. Experimental results showed that the proposed method outperforms other methods.

Description: The primary benefit of fuzzy systems theory is to approximate system behavior where analytic functions or numerical relations do not exist. In this paper, heuristic fuzzy rules were used with the intention of improving the performance of optimization models, introducing experiential rules acquired from experts and utilizing recommendations. The aim of this paper was to define soft constraints using an adaptive network-based fuzzy inference system (ANFIS). This newly-developed soft constraint was applied to discrete optimization for obtaining optimal solutions. Even though the computational model is based on advanced computational technologies including fuzzy logic, neural networks and discrete optimization, it can be used to solve real-world problems of great interest for design engineers. The proposed computational model was used to find the minimum weight solutions for simply-supported laterally-restrained beams.

Description: Searchable encryption mechanism and attribute-based encryption (ABE) are two effective tools for providing fine-grained data access control in the cloud. Researchers have also taken their advantages to present searchable encryption schemes based on ABE and have achieved significant results. However, most of the existing key word search schemes based on ABE lack the properties of key exposure protection and highly efficient key updating when key leakage happens. To better tackle these problems, we present a key insulated attribute-based data retrieval scheme with key word search (KI-ABDR-KS) for multi-tenant architecture. In our scheme, a data owner can make a self-centric access policy of the encrypted data. Only when the possessing attributes match with the policy can a receiver generate a valid trapdoor and search the ciphertext. The proposed KI-ABDR-KS also provides full security protection when key exposure happens, which can minimize the damage brought by key exposure. Furthermore, the system public parameters remain unchanged during the process of key updating; this will reduce the considerable overheads brought by parameters synchronization. Finally, our KI-ABDR-KS is proven to be secure under chosen-keyword attack and achieves better efficiency compared to existing works.

Description: In this note, we discuss when the concave integral coincides with the pan- integral with respect to the standard arithmetic operations + and ·. The subadditivity of the underlying monotone measure is one sufficient condition for this equality. We show also another sufficient condition, which, in the case of finite spaces, is necessary, too.

Description: The first organocatalyzed sulfoxidation reaction towards enantioenriched (R)-modafinil (Armodafinil®), a drug against narcolepsy, is reported here. A series of chiral organocatalysts, e.g., different chiral BINOL-phosphates, or a fructose-derived N-substituted oxazolidinone ketone (Shi catalyst) were applied for the sulfoxidation reaction with environmentally friendly H2O2 as a convenient oxygen transferring agent. Furthermore, the potential of a biomimetic catalytic system consisting of FeCl3 and a dipeptide-based chiral ligand was demonstrated, which constitutes the most successful asymmetric non-heme iron-catalyzed synthesis of (R)-modafinil so far.

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

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

Description: The neural processes of bird song and song development have become a model for research relevant to human acquisition of language, but in fact, very few avian species have been tested for lateralization of the way in which their audio-vocal system is engaged in perception, motor output and cognition. Moreover, the models that have been developed have been premised on birds with strong vocal dimorphism, with a tendency to species with complex social and/or monomorphic song systems. The Australian magpie (Gymnorhina tibicen) is an excellent model for the study of communication and vocal plasticity with a sophisticated behavioural repertoire, and some of its expression depends on functional asymmetry. This paper summarizes research on vocal mechanisms and presents field-work results of behavior in the Australian magpie. For the first time, evidence is presented and discussed about lateralized behaviour in one of the foremost songbirds in response to specific and specialized auditory and visual experiences under natural conditions. It presents the first example of auditory lateralization evident in the birds’ natural environment by describing an extractive foraging event that has not been described previously in any avian species. It also discusses the first example of auditory behavioral asymmetry in a songbird tested under natural conditions.

Description: The goal of this paper is to compare and analyze the forecasting performance of two artificial neural network models (i.e., MLP (multi-layer perceptron) and DNN (deep neural network)), and to conduct an experimental investigation by data flow, not economic flow. In this paper, we investigate beyond the scope of simple predictions, and conduct research based on the merits and data of each model, so that we can predict and forecast the most efficient outcomes based on analytical methodology with fewer errors. In particular, we focus on identifying two models of neural networks (NN), a multi-layer perceptron (i.e., MLP) model and an excellent model between the neural network (i.e., DNN) model. At this time, predictability and accuracy were found to be superior in the DNN model, and in the MLP model, it was found to be highly correlated and accessible. The major purpose of this study is to analyze the performance of MLP and DNN through a practical approach based on an artificial neural network stock forecasting method. Although we do not limit S&P (i.e., Standard&Poor’s 500 index) to escape other regional exits in order to see the proper flow of capital, we first measured S&P data for 100 months (i.e., 407 weeks) and found out the following facts: First, the traditional artificial neural network (ANN) model, according to the specificity of each model and depending on the depth of the layer, shows the model of the prediction well and is sensitive to the index data; Second, comparing the two models, the DNN model showed better accuracy in terms of data accessibility and prediction accuracy than MLP, and the error rate was also shown in the weekly and monthly data; Third, the difference in the prediction accuracy of each model is not statistically significant. However, these results are correlated with each other, and are considered robust because there are few error rates, thanks to the accessibility to various other prediction accuracy measurement methodologies.

Description: This paper studies cooperative spectrum sensing based on crowdsourcing in cognitive radio networks. Since intelligent mobile users such as smartphones and tablets can sense the wireless spectrum, channel sensing tasks can be assigned to these mobile users. This is referred to as the crowdsourcing method. However, there may be some malicious mobile users that send false sensing reports deliberately, for their own purposes. False sensing reports will influence decisions about channel state. Therefore, it is necessary to classify mobile users in order to distinguish malicious users. According to the sensing reports, mobile users should not just be divided into two classes (honest and malicious). There are two reasons for this: on the one hand, honest users in different positions may have different sensing outcomes, as shadowing, multi-path fading, and other issues may influence the sensing results; on the other hand, there may be more than one type of malicious users, acting differently in the network. Therefore, it is necessary to classify mobile users into more than two classes. Due to the lack of prior information of the number of user classes, this paper casts the problem of mobile user classification as a dynamic clustering problem that is NP-hard. The paper uses the interdistance-to-intradistance ratio of clusters as the fitness function, and aims to maximize the fitness function. To cast this optimization problem, this paper proposes a distributed algorithm for user classification in order to obtain bounded close-to-optimal solutions, and analyzes the approximation ratio of the proposed algorithm. Simulations show the distributed algorithm achieves higher performance than other algorithms.

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

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

Description: An intelligent tour service system including an augmented reality (AR) tour-sharing Application (APP) and a query-answering server was developed in this study to promote tourist attractions involving local Hakka culture in Thailand. Subsequently, use of this APP to navigate Hakka culture tourist attractions in Thailand was observed. The novel random neural networks (RNNs) were proposed to obtain query-answering services, and the practical experimental results showed that the accuracy of RNNs was 99.51%. This study also integrated the Technology Acceptance Model with Uses and Gratifications Theory to predict the gratification, usage intention, and user attitudes toward marketed attractions of the AR tour-sharing APP. A questionnaire survey was conducted, and 446 valid questionnaires were returned. The following results were obtained: (a) self-presentation and perceived usefulness (PU) directly influenced gratification; (b) perceived entertainment indirectly influenced gratification through perceived ease of use and PU, and information sharing indirectly influenced gratification through PU; and (c) gratification was significantly and positively related to usage intention and attitude toward attractions. Based on these results, suggestions that new technology marketing can be used to promote causes other than Hakka tourist attractions established in Thailand can be contrived. For example, the tour-sharing APP developed in this study could be applied to emphasize the characteristics of Thai Hakka culture; users’ fondness for self-presentation and information sharing can be used for word-of-mouth marketing to attract additional visitors. In addition, this research provides a reference for enterprises and marketers regarding the use of AR tour-sharing APPs to market tourist attractions, and also for future related studies.

Description: Wearable devices are being explored and investigated as a promising computing platform as well as a source of personal big data for the post smartphone era. To deal with a series of rapidly developed wearable prototypes, a well-structured strategy is required to assess the prototypes at various development stages. In this paper, we first design and develop variants of advanced wearable user interface prototypes, including joystick-embedded, potentiometer-embedded, motion-gesture and contactless infrared user interfaces for rapidly assessing hands-on user experience of potential futuristic user interfaces. To achieve this goal systematically, we propose a conceptual test framework and present a case study of using the proposed framework in an iterative cyclic process to prototype, test, analyze, and refine the wearable user interface prototypes. We attempt to improve the usability of the user interface prototypes by integrating initial user feedback into the leading phase of the test framework. In the following phase of the test framework, we track signs of improvements through the overall results of usability assessments, task workload assessments and user experience evaluation of the prototypes. The presented comprehensive and in-depth case study demonstrates that the iterative approach employed by the test framework was effective in assessing and enhancing the prototypes, as well as gaining insights on potential applications and establishing practical guidelines for effective and usable wearable user interface development.

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

Description: In recent years, we have seen a significant number of new technological ideas appearing in literature discussing the future of education. For example, E-learning, cloud computing, social networking, virtual laboratories, virtual realities, virtual worlds, massive open online courses (MOOCs), and bring your own device (BYOD) are all new concepts of immersive and global education that have emerged in educational literature. One of the greatest challenges presented to e-learning solutions is the reproduction of the benefits of an educational institution’s physical laboratory. For a university without a computing lab, to obtain hands-on IT training with software, operating systems, networks, servers, storage, and cloud computing similar to that which could be received on a university campus computing lab, it is necessary to use a combination of technological tools. Such teaching tools must promote the transmission of knowledge, encourage interaction and collaboration, and ensure students obtain valuable hands-on experience. That, in turn, allows the universities to focus more on teaching and research activities than on the implementation and configuration of complex physical systems. In this article, we present a model for implementing ecosystems which allow universities to teach practical Information Technology (IT) skills. The model utilizes what is called a “social cloud”, which utilizes all cloud computing services, such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Additionally, it integrates the cloud learning aspects of a MOOC and several aspects of social networking and support. Social clouds have striking benefits such as centrality, ease of use, scalability, and ubiquity, providing a superior learning environment when compared to that of a simple physical lab. The proposed model allows students to foster all the educational pillars such as learning to know, learning to be, learning to live together, and, primarily, learning to do, through hands-on IT training from a MOOCs. An aspect of the model has been verified experimentally and statistically through a course of computer operating systems.

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

Description: Symmetry, Vol. 9, Pages 119: A Novel Rough Set Model in Generalized Single Valued Neutrosophic Approximation Spaces and Its Application Symmetry doi: 10.3390/sym9070119 Authors: Zhi-Lian Guo Yan-Ling Liu Hai-Long Yang In this paper, we extend the rough set model on two different universes in intuitionistic fuzzy approximation spaces to a single-valued neutrosophic environment. Firstly, based on the ( α , β , γ ) -cut relation R ˜ { ( α , β , γ ) } , we propose a rough set model in generalized single-valued neutrosophic approximation spaces. Then, some properties of the new rough set model are discussed. Furthermore, we obtain two extended models of the new rough set model—the degree rough set model and the variable precision rough set model—and study some of their properties. Finally, we explore an example to illustrate the validity of the new rough set model.

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

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

Description: Symmetry, Vol. 9, Pages 123: Expressions of Rock Joint Roughness Coefficient Using Neutrosophic Interval Statistical Numbers Symmetry doi: 10.3390/sym9070123 Authors: Jiqian Chen Jun Ye Shigui Du Rui Yong In nature, the mechanical properties of geological bodies are very complex, and their various mechanical parameters are vague, incomplete, imprecise, and indeterminate. However, we cannot express them by the crisp values in classical probability and statistics. In geotechnical engineering, we need to try our best to approximate exact values in indeterminate environments because determining the joint roughness coefficient (JRC) effectively is a key parameter in the shear strength between rock joint surfaces. In this original study, we first propose neutrosophic interval probability (NIP) and define the confidence degree based on the cosine measure between NIP and the ideal NIP. Then, we propose a new neutrosophic interval statistical number (NISN) by combining the neutrosophic number with the confidence degree to express indeterminate statistical information. Finally, we apply NISNs to express JRC under indeterminate (imprecise, incomplete, and uncertain, etc.) environments. By an actual case, the results demonstrate that NISNs are suitable and effective for JRC expressions and have the objective advantage.