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In this paper, a multimodal function optimization problem consisting of multiple maximums and multiple minimums is solved using an improved particle swarm optimization (PSO) algorithm. In the proposed scheme, the original population needs to be randomly divided into two main groups in the first stage. One group is to tackle the maximum optimization of the multimodal function and the other then focuses on the function minimum optimization. In the second stage, each group is split up into several subgroups in order to seek for function optimums simultaneously. There is no relation among subgroups and each subgroup can individually seek for one of function optimums. To achieve that, it is necessary to enroll the best particle information of each subgroup. It means that the proposed structure contains a number of best particles, not a single global best particle. The third stage is to modify the velocity updating formula of the algorithm where the global best particle is simply replaced by the best particle of each subgroup. Under the proposed scheme, multiple maxima and minima of the multimodal function can probably be solved separately and synchronously. Finally, many different kinds of multimodal function problems are illustrated to certify the applicability of the presented method, including one maximum and one minimum, two maximums and two minimums, multiple maximums and multiple minimums, and a complex engineering optimization problem with inequality conditions. [ABSTRACT FROM AUTHOR]

Pythagorean fuzzy sets (PFSs) as a new generalization of fuzzy sets (FSs) can handle uncertain information more flexibly in the process of decision making. In our real life, we also may encounter a hesitant fuzzy environment. In view of the effective tool of hesitant fuzzy sets (HFSs) for expressing the hesitant situation, we introduce HFSs into PFSs and extend the existing research work of PFSs. Concretely speaking, this paper considers that the membership degree and the non-membership degree of PFSs are expressed as hesitant fuzzy elements. First, we propose a new concept of hesitant Pythagorean fuzzy sets (HPFSs) by combining PFSs with HFSs. It provides a new semantic interpretation for our evaluation. Meanwhile, the properties and the operators of HPFSs are studied in detail. For the sake of application, we focus on investigating the normalization method and the distance measures of HPFSs in advance. Then, we explore the application of HPFSs to multi-criteria decision making (MCDM) by employing the technique for order preference by similarity to ideal solution (TOPSIS) method. A new extension of TOPSIS method is further designed in the context of MCDM with HPFSs. Finally, an example of the energy project selection is presented to elaborate on the performance of our approach. [ABSTRACT FROM AUTHOR]

Support Vector Data Description (SVDD) is a support vector based learning algorithm for anomaly detection. In this method, the target is to form a boundary around the normalcy data by building a hyper-sphere. To gain noticeable accuracy, a control parameter is used to regulate the hyper-sphere volume. The value of this parameter depends on the data characteristics. Thus, there is no proper way to estimate it. On the other hand, the number of free parameters increases in the more improved versions of SVDD. In this paper, an evolutionary algorithm, Chaotic Bat Algorithm, is used with the aim of designing effective description of data. The proposed method, weighted SVDD based on Chaotic Bat Algorithm (WSVDD-CBA) is constructed based on a new weight and ergodicity of chaotic functions and automatic switching between global and local searches of Bat Algorithm (BA). To evaluate this method several experiments have been conducted based on 10-fold cross-validation over some data sets from UCI repository. Experimental results show the superiority of the proposed algorithm to state-of-the-art methods in the terms of classification accuracy, precision and recall rate measures. [ABSTRACT FROM AUTHOR]

In this paper, a modified epsilon-deviation degree method of ranking fuzzy numbers is proposed. The epsilon-deviation degree method and other ranking methods are available in the literature and applied in the field of decision-making. Despite of the merits, some limitations and shortcomings are observed in these methods. Namely, (1) these methods cannot distinguish fuzzy numbers sharing the same support and different cores, (2) these methods cannot distinguish crisp-valued fuzzy numbers with different heights, (3) these methods also cannot make a preference between a crisp-valued fuzzy number and an arbitrary fuzzy number, (4) if the expectation values of the centroid points are the same for the fuzzy numbers to be compared, then these methods give an incorrect ranking, (5) if fuzzy numbers depict compensation of areas, then these methods fail to give a proper ranking, and (6) further inconsistency in ranking the fuzzy numbers and their images is also observed. Hence, a modified epsilon-deviation degree method is developed, based on the concept of the ill-defined magnitude ‘value’ and the angle of the fuzzy set. The proposed method bears all the properties of epsilon-deviation degree method and overcome all the limitations and shortcomings of this method and other existing methods. Various sets of fuzzy numbers are considered for comparative study between the existing ranking methods and the proposed method for validation. Further, the proposed method seems to outperform in all situations. Risk analysis problem under uncertain environment are often studied under fuzzy domain. Hence, a study is done by applying the proposed method to risk analysis in poultry farming. [ABSTRACT FROM AUTHOR]

This paper proposes an approach for modeling employee turnover in a call center using the versatility of supervised self-organizing maps. Two main distinct problems exist for the modeling employee turnover: first, to predict the employee turnover at a given point in the sales agent's trial period, and second to analyze the turnover behavior under different performance scenarios by using psychometric information about the sales agents. Identifying subjects susceptible to not performing well early on, or identifying personality traits in an individual that does not fit with the work style is essential to the call center industry, particularly when this industry suffers from high employee turnover rates. Self-organizing maps can model non-linear relations between different attributes and ultimately find conditions between an individual's performance and personality attributes that make him more predisposed to not remain long in an organization. Unlike other models that only consider performance attributes, this work successfully uses psychometric information that describes a sales agent's personality, which enables a better performance in predicting turnover and analyzing potential personality profiles that can identify agents with better prospects of a successful career in a call center. The application of our model is illustrated and real data are analyzed from an outbound call center. [ABSTRACT FROM AUTHOR]

Segmentation of Magnetic Resonance Imaging (MRI) brain image data has a significant impact on the computer guided medical image diagnosis and analysis. However, due to limitation of image acquisition devices and other related factors, MRI images are severely affected by the noise and inhomogeneity artefacts which lead to blurry edges in the intersection of the intra-organ soft tissue regions, making the segmentation process more difficult and challenging. This paper presents a novel two-stage fuzzy multi-objective framework (2sFMoF) for segmenting 3D MRI brain image data. In the first stage, a 3D spatial fuzzy c-means (3DSpFCM) algorithm is introduced by incorporating the 3D spatial neighbourhood information of the volume data to define a new local membership function along with the global membership function for each voxel. In particular, the membership functions actually define the underlying relationship between the voxels of a close cubic neighbourhood and image data in 3D image space. The cluster prototypes thus obtained are fed into a 3D modified fuzzy c-means (3DMFCM) algorithm, which further incorporates local voxel information to generate the final prototypes. The proposed framework addresses the shortcomings of the traditional FCM algorithm, which is highly sensitive to noise and may stuck into a local minima. The method is validated on a synthetic image volume and several simulated and in-vivo 3D MRI brain image volumes and found to be effective even in noisy data. The empirical results show the supremacy of the proposed method over the other FCM based algorithms and other related methods devised in the recent past. [ABSTRACT FROM AUTHOR]

Krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been successfully used to solve numerous complex optimization problems. This paper proposed a novel hybrid of KH algorithm with harmony search (HS) algorithm, namely, H-KHA, to improve the global (diversification) search ability. The enhancement includes adding global search operator (improvise a new solution) of the HS algorithm to the KH algorithm for improving the exploration search ability by a new probability factor, namely, Distance factor, thereby moving krill individuals toward the best global solution. The effectiveness of the proposed H-KHA is tested on seven standard datasets from the UCI Machine Learning Repository that are commonly used in the domain of data clustering, also six common text datasets that are used in the domain of text document clustering. The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) enhanced the results in terms of accurate clusters and high convergence rate. Mostly, the performance of H-KHA is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new two-tier decision making framework with linguistic preferences for scientific decision making. The major reason for adopting linguistic preference is to ease the process of rating of alternatives by allowing decision makers (DMs) to strongly emphasize their opinion on each alternative. In the first tier, aggregation is done using a newly proposed operator called linguistic based aggregation (LBA), which aggregates linguistic terms directly without making any conversion. The main motivation for this proposal is driven by the previous studies on aggregation theory which reveals that conversion leads to loss of information and formation of virtual sets which are no longer sensible and rational for decision making process. Secondly, in the next tier, a new ranking method called IFSP (intuitionistic fuzzy set based PROMETHEE) is proposed which is an extension to PROMETHEE (preference ranking organization method for enrichment evaluation) under intuitionistic fuzzy set (IFS) context. Unlike previous ranking methods, this ranking method follows a new formulation by considering personal choice of the DMs over each alternative. The main motivation for such formulation is derived from the notion of not just obtaining a suitable alternative but also coherently satisfying the DMs’ viewpoint during decision process. Finally, the practicality of the framework is tested by using supplier selection (SS) problem for an automobile factory. The strength and weakness of the proposed LBA-IFSP framework are verified by comparing with other methods under the realm of theoretical and numerical analysis. The results from the analysis infer that proposed LBA-IFSP framework is rationally coherent to DMs’ viewpoint, moderately consistent with other methods and highly stable and robust against rank reversal issue. [ABSTRACT FROM AUTHOR]

Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate , takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature. [ABSTRACT FROM AUTHOR]

In this paper, we present two approaches for non-domination level update problem. The first one is a space efficient non-domination level update (SENLU) approach. The second one is a binary search tree based efficient non-domination level update (BST-ENLU) approach which uses the basic property of binary search tree. Although the space complexity of BST-ENLU approach is higher than SENLU approach in case of insertion, but in terms of number of dominance comparisons, BST-ENLU approach can outperform SENLU approach. Thus, these two approaches are complementary to each other. The comparative results show that in case where all the solutions are in different fronts, the maximum number of dominance comparisons using BST-ENLU approach is very less than ENLU approach. A tree based approach is introduced to identify the correct position of the solution to be deleted efficiently. Also a theoretical upper bound to the maximum number of dominance comparisons is obtained for both the proposed approaches in case of both insertion and deletion operations. [ABSTRACT FROM AUTHOR]

Objective video quality assessment (VQA) plays an important role in controlling video quality. Most of the existing VQA methods measure motion-related temporal distortion based on optical-flow methods, which are not consistently reliable in modeling general visual dynamics. This paper presents a full-reference temporal distortion measure based on spacetime texture , a uniform and distributive descriptor of a broad set of spacetime structures. Our method measures the distortion of spacetime texture in video with a motion-tuning strategy, which effectively captures temporal distortion along the motion trajectories. Then it estimates self-information based visual saliency for spatial pooling by reusing the motion descriptors. We evaluated our method on two public VQA databases with a wide variety of distortion types, in which the videos were viewed on a large screen or mobile devices. The results show that our method correlates highly with the subjective quality and has high computational efficiency. [ABSTRACT FROM AUTHOR]

Let P be an orthogonal polygon with n vertices. A sliding camera travels back and forth along an orthogonal line segment s ⊆ P corresponding to its trajectory . The camera sees a point p ∈ P if there is a point q ∈ s such that p q ‾ is a line segment normal to s that is completely contained in P . In the Minimum-Cardinality Sliding Cameras (MCSC) problem , the objective is to find a set S of sliding cameras of minimum cardinality to guard P (i.e., every point in P can be seen by some sliding camera in S ), while in the Minimum-Length Sliding Cameras (MLSC) problem the goal is to find such a set S so as to minimize the total length of trajectories along which the cameras in S travel. In this paper, we answer questions posed by Katz and Morgenstern (2011) by presenting the following results: (i) the MLSC problem is polynomially tractable even for orthogonal polygons with holes, (ii) the MCSC problem is NP -complete when P is allowed to have holes, and (iii) an O ( n 3 log ⁡ n ) -time 2-approximation algorithm for the MCSC problem on [NE]-star-shaped orthogonal polygons with n vertices (similarly, [NW]-, [SE]-, or [SW]-star-shaped orthogonal polygons). [ABSTRACT FROM AUTHOR]

The seven papers included in this special issue represent a selection of extended contributions presented at the 15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020, held in Burgos, Spain, September 2020, and organized by University of Burgos and BISITE and GICAP Research Groups. SOCO 2020 international conference represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines which investigate, simulate, and analyse very complex issues and phenomena. This special issue is aimed at practitioners, researchers and postgraduate students who are engaged in developing and applying advanced intelligent systems principles to solving real-world problems on the mentioned fields. [ABSTRACT FROM AUTHOR]

Deterministic finite automata are widely used in control systems: from abstract protocols such as TCP to mechanical devices such as elevators or traffic lights. Some of these systems are quite complex and can be defined only in terms of formal language theory. In this paper, we propose new approach for synthesizing finite automata from a dictionary of some language that uses neural networks. The results show that the proposed approach works correctly and quickly for automata with up to six states and four characters in the alphabet. For larger automata, the neural network suffers from the vanishing gradient problem, which is a big topic for further research. [ABSTRACT FROM AUTHOR]

Zero-shot learning (ZSL) extends the conventional image classification technique to a more challenging situation where the testing image categories are not seen in the training samples. Most studies on ZSL utilize side information such as attributes or word vectors to bridge the relations between the seen classes and the unseen classes. However, existing approaches on ZSL typically exploit a shared space for each type of side information independently, which cannot make full use of the complementary knowledge of different types of side information. To this end, this paper presents an MBFA-ZSL approach to embed different types of side information as well as the visual feature into one shared space. Specifically, we first develop an algorithm named Multi-Battery Factor Analysis (MBFA) to build a unified semantic space, and then employ multiple types of side information in it to achieve the ZSL. The close-form solution makes MBFA-ZSL simple to implement and efficient to run on large datasets. Extensive experiments on the popular AwA, CUB, and SUN datasets show its superiority over the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

We propose in this paper the Security Policy Language (SePL), which is a formal language for capturing and integrating distributed security policies. The syntax of SePL includes several operators for the integration of policies and it is endowed with a denotational semantics that is a generic semantics, i.e., which is independent of any evaluation environment. We prove the completeness of SePL with respect to set theory. Furthermore, we provide a formalization of a large subset of the eXtensible Access Control Markup Language (XACML), which is the well-known standard informal specification language of Web security policies. We also provide a semantics for XACML policy combining algorithms. [ABSTRACT FROM AUTHOR]

Domain experts typically have detailed knowledge of the concepts that are used in their domain; however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance. [ABSTRACT FROM AUTHOR]

Empirical evidence suggests the effectiveness of robotics as a learning complementary tool in tertiary education. In this context, some experiences benefited from the link between educational practice and theory. However, a comprehensive survey on initiatives that explores this link in universities and colleges is missing. This work systematically reviews quantitatively assessed robots applications, grounded in learning theories, in tertiary institutions. By applying a protocol review in different bibliographic databases, 15 papers were selected for synthesis. As a result, experiences developing non-robotic concepts and skills in universities and colleges were found. In most of the cases, Computer Science and Engineering undergraduate courses were involved. In addition, empirical results reported by the selected publications suggest that some literature proposals can be useful in practice. Based on the panorama obtained, this work also points out future directions for practitioners and researchers in education. [ABSTRACT FROM AUTHOR]

In this paper we present the Large Crater Clustering (LCC) tool set, an ArcGIS plugin that supports the quantitative approximation of a primary impact location from user-identified locations of possible secondary impact craters or the long-axes of clustered secondary craters. The identification of primary impact craters directly supports planetary geologic mapping and topical science studies where the chronostratigraphic age of some geologic units may be known, but more distant features have questionable geologic ages. Previous works (e.g., McEwen et al., 2005; Dundas and McEwen, 2007) have shown that the source of secondary impact craters can be estimated from secondary impact craters. This work adapts those methods into a statistically robust tool set. We describe the four individual tools within the LCC tool set to support: (1) processing individually digitized point observations (craters), (2) estimating the directional distribution of a clustered set of craters, back projecting the potential flight paths (crater clusters or linearly approximated catenae or lineaments), (3) intersecting projected paths, and (4) intersecting back-projected trajectories to approximate the local of potential source primary craters. We present two case studies using secondary impact features mapped in two regions of Mars. We demonstrate that the tool is able to quantitatively identify primary impacts and supports the improved qualitative interpretation of potential secondary crater flight trajectories. [ABSTRACT FROM AUTHOR]

Many pervasive computing applications depend upon maps for support of location based services. Individuals can determine their location outdoors on these maps via GPS. Indoor pervasive applications may also need to know the layout of buildings, however indoor maps are less prevalent. This paper presents iFrame, a dynamic approach that leverages existing mobile sensing capabilities for constructing indoor floor plans. We explore how iFrame users may collaborate and contribute to constructing 2-dimensional indoor maps by merely carrying smartphones. A deployment study shows iFrame is an unattended approach that provides a skeleton map of a real building effectively and automatically. [ABSTRACT FROM AUTHOR]

The active disturbance rejection control (ADRC), first proposed by Jingqing Han in the 1980s is an unconventional design strategy. It has been acknowledged to be an effective control strategy in the absence of proper models and in the presence of model uncertainty. Its power was originally demonstrated by numerical simulations, and later by many engineering practices. For the theoretical problems, namely, the convergence of the tracking differentiator which extracts the derivative of reference signal; the extended state observer used to estimate not only the state but also the “total disturbance”, by the output; and the extended state observer based feedback, progresses have also been made in the last few years from nonlinear lumped parameter systems to distributed parameter systems. The aim of this paper is to review the origin, idea and development of this new control technology from a theoretical perspective. Emphasis will be focused on output feedback stabilization for uncertain systems described by partial differential equations. [ABSTRACT FROM AUTHOR]

The purpose of this research is to know exactly what a ‘Smart City’ and its aspects are. Furthermore, to ensure this ‘Smart City’ aspects and their problems can be implemented in a game, so the game will be able to teach people about missed problem solving solutions. This research paper will discuss about the influence gained from playing a game upon city management skills, and careful planning on what must be done first, in order to maintain or increase the well-being of the citizen and the city welfare. By implementing real life applicable condition in the game which will be played by a person through a sandbox simulation style of gameplay, the player will be able to learn and experience certain types of city problems and the solutions to mitigate those problems without actually making it occur in a real city with real casualties, so it will be a safe vessel to gain new knowledge or to rediscover old effective methods, regarding city management, through a game. The results will portray, how can ‘Smart City’ aspects be implemented into a game, and how a city management simulation game can influence other people’ actions on their city management knowledge. [ABSTRACT FROM AUTHOR]

In this paper, the problem of H ∞ filtering for descriptor systems with strict linear matrix inequalities (LMIs) is investigated. The necessary and sufficient conditions for the solvability and the expression of the solution are obtained for both continuous-time and discrete-time descriptor systems. The proposed continuous-time filters extend the results presented by Xu and Lam (2006) by removing semi definite conditions and introducing new formulation for the filter parameters which does not require determination of intermediate variables. For discrete-time descriptors, differing from existing methods that provide sufficient conditions only, two necessary and sufficient conditions are introduced. New proposed design methods propose less conservative results comparing with existing works. Two numerical examples are presented to demonstrate the applicability of the proposed methods. [ABSTRACT FROM AUTHOR]

We offer an interdisciplinary study of computer science and social science, analyzing behavior surrounding three types of online events: political events, social events, and non-public events. Based on the intrinsic characteristics of the three event types, this paper creates an effective method to predict such events. We continuously followed and recorded data every 10 min for 10 months from September 14, 2012 to July 11, 2013, and collected over 14 million “hot” posts from Sina Weibo, the largest microblogging provider in China. After removing spammers and noises, we developed a database of 4180 hot online events and 7,761,395 threads. We found that people’s online behavior regarding event types varies in terms of follow-up statistics and the predictability of events. The Chinese are, typically, quite concerned with social affairs that relate most closely to their personal interests and preferences. People tend to cluster around political events more often than social events and non-public events. This is demonstrated by an algorithm embedded with a clustering growth pattern of events, which predicts the popularity of online political events above others. The statistical findings are justified by Habermas’ public sphere theory and the theory of vertical/horizontal collectivism/individualism. This research provides an interesting piece of computational social science work to assist in the analysis of incentives concerning China’s collective events. [ABSTRACT FROM AUTHOR]

Systems based on wireless sensor have been presented as an alternative to location and navigation for visually impaired in environments where GPS is not effective. However, dealing with the information obtained from these sensors may present some challenges. The difficulties described by several authors are related mainly to the oscillation of the signals, which are susceptible to numerous interfering agents such as the scene itself, the presence of people, etc. It is necessary to increase the accuracy of the location in all the techniques observed. Therefore, this paper proposes to apply data fusion of wi-fi sensors with inertial sensors to allow visually impaired to location and navigation in indoor places using aspects of healthcare like sensor based systems embedded in wearable devices. The methodology adopted was associate redundant fusion and complementary fusion to construct a database obtained from several readings of the wi-fi sensors and an inertial sensor to construct a reference position to compare the data of the test readings to this reference and indicate the reliability in an indoor location. The results show that RSSI with SNR and magnetometer with gyroscope provide high error rates with raw and isolated data, but when fused, they show a more efficient location, decreasing the error rates observed in isolated mode. The error margins between the real position and the position indicated by the system were 0,386 m, 0,336 m and 0,148 m to RSSI with SNR, inertial and fusion application, respectively. [ABSTRACT FROM AUTHOR]

Activity recognition has been a hot topic for decades, from the scientific research to the development of off-the-shelf commercial products. Since people perform the activities differently, to avoid overfitting, building a general model with activity data of various users is required before the deployment for personal use. However, annotating a large amount of activity data is expensive and time-consuming. In this paper, we build a general model for activity recognition with a limited amount of labelled data. We combine Latent Dirichlet Allocation (LDA) and AdaBoost to jointly train a general activity model with partially labelled data. After that, when AdaBoost is used for online prediction, we combine it with graphical models (such as HMM and CRF) to exploit the temporal information in human activities to smooth out the accidental misclassifications. Experiments with publicly available datasets show that we are able to obtain the accuracy of more than 90% with 1% labelled data. [ABSTRACT FROM AUTHOR]

Many real-world networks have a topological structure characterized by cohesive groups of vertices. To perform the task of identifying such subsets of vertices, community detection in networks has aroused the interest of researchers and practitioners alike. In spite of the existence of various efficient community detection algorithms in the literature, most of them uses global information about the network, not applicable to distributed networks. This paper proposes a genetic-based algorithm to detect communities in directed networks based on local information to generate the offspring. The major difference between the proposed strategy and those found in the literature is the way of exploiting target regions of interest in the solution space. This step is directly influenced by the crossover operator that depends largely on the individual representation. In the introduced strategy, GA-LP, the individual is locally stored in the vertices as labels, what brings more flexibility in the system to be adapted to address applications that involve, for example, dynamic networks. In computational experiments, the proposed strategy showed an outstanding performance, being fast, achieving the best results on average in the networks tested. [ABSTRACT FROM AUTHOR]

The n -dimensional torus T ( k 1 , k 2 , … , k n ) (including the k -ary n -cube Q n k ) is one of the most popular interconnection networks. A paired k -disjoint path cover (paired k -DPC for short) of a graph is a set of k disjoint paths joining k distinct source-sink pairs that cover all vertices of the graph. In this paper, we consider the paired 2-DPC problem of n -dimensional torus. Assuming k i ≥ 3 for i = 1 , 2 , … , n , with at most one k i being even, then T ( k 1 , k 2 , … , k n ) with at most 2 n − 3 faulty edges always has a paired 2-DPC. And the upper bound 2 n − 3 of edge faults tolerated is optimal. The result is a supplement of the results of Chen [3] and [4] . [ABSTRACT FROM AUTHOR]

This paper presents a teaching methodology mixing elements from the domains of music and informatics as a key enabling to expose primary school pupils to basic aspects of computational thinking. This methodology is organized in two phases exploiting LEGO ® bricks respectively as a physical tool and as a metaphor in order to let participants discover a simple notation encoding several basic concepts of the classical musical notation. The related activities, grounded on active learning theory, challenge groups of students to solve musical encoding problems of increasing difficulty. [ABSTRACT FROM AUTHOR]

Haptic technologies allow for adding a new “touching” modality into virtual scenes. 3D reconstruction of a real life scene results, however, often in millions of polygons which cannot be simultaneously visualized and haptically rendered. In this paper, we propose a way of haptic interaction with real life scenes where multiple original images of the real scenes are augmented with reconstructed polygon meshes. We present our solution to the problems of haptic model alignment with the images and interactive haptic rendering of large polygon meshes with reconstruction artifacts. In particular, the presented collision detection algorithm is not restricted by any hypothesis and robust enough to support smooth interaction with millions of polygons. The feasibility and usability of the proposed solution is evaluated in a user study. [ABSTRACT FROM AUTHOR]

In recent decade, a wave of apprehension swept across the Pearl of Oriental with regards to the disappearance of high value tangible and intangible heritages due to urban renewal and redevelopment. The flourish of computer science and engineering, however, offers a rosy spectacle for heritage preservation. Virtual heritage is no longer an idea in cloud-cuckoo-land. In this research paper, we propose the ground breaking five dimensional GIS virtual heritage. Whilst traditional two dimensional GIS stores the information of X and Y axis, the recent three dimensional GIS includes the information of building height and other information is useful to urban planners when they make decision in change in use of land. The fourth dimension includes time with collective memories of Hong Kongers. The invaluable intangible heritage can be kept by record of oral history. Changes in ethnic minorities, different walks of lives provide dazzling and interesting angles of the modern city. We also include the concept of the fifth dimension of automated virtual and augmented heritage with drone and robots’ help in taking 360 degree virtual reality videos and photos. Whilst strand of literatures on VR, AR, 3D GIS and memories scatter in different areas of research, combination of all these technologies is the first of its kind. Research on automated VR and AR is scarce or non-exist. It is expected that the 5D GIS virtual heritage offers a new angle to historian, economists, sociologists, urban planners. [ABSTRACT FROM AUTHOR]

Face verification in the wild, remains a challenging problem. This paper makes two contributions: first, for improving face recognition in the wild, at least in terms of pose variations, we propose a method for aligning faces by employing single-3D face model as reference produced by FaceGen Modeller. Second, we develop a novel face descriptor based on Gabor Filters. The proposed descriptor relies on combination of Gabor magnitude and Gabor phase informations into an unified framework, which is capable to overcome standard representations in the most popular benchmark “Labeled Faces in the Wild” (LFW). This compact descriptor has a better recognition performance, reaches an accuracy of 97.29% on the LFW dataset. [ABSTRACT FROM AUTHOR]

Services on pervasive computing are delegated or offloaded to cloud computing platforms, because pervasive computing devices have only limited resources. However, access control models in pervasive computing environments and cloud computing platforms are different, where the former tends to be context-dependent and the latter to be subject-based, e.g., role-based access control (RBAC). To connect such different models, we propose a framework for managing context-aware services executed at pervasive computing and cloud computing. The framework is constructed as a world model for specifying contextual information in the real world. This paper describes the design and prototype implementation of the proposed model. [ABSTRACT FROM AUTHOR]

Security threats are becoming very sophisticated and pervasive everywhere. Phishing threats in particular has a changeable nature and short life cycle that complicates the detection process. In this paper, we introduce a Multi-Agent System (MAS) as an adaptive intelligent technique that acts on top of distributed Case-Based Reasoning (CBR) Phishing Detection Systems (CBR-PDSs) as a Phishing Detection System Architecture (PDSA) that runs on large scale globally to constitute a robust worldwide Phishing Threat Intelligence (PTI) environment. The global collaborations of PTI introduces a proactive phishing detection technique, quarantines phishing threats via global threats sharing, and minimizes users’ susceptibilities to hard-to-detect spear or advanced phishing attacks. Also, combining two intelligent systems in a unified interactive architecture facilitates the prediction process, increases the accuracy rate, easily tackles the dynamic and changeable behaviors of advanced phishing threats, and minimizes the false negative rate as well. The proposed architecture illustrates the consolidated interaction between intelligent agents and distributed CBR-PDSs in a PTI framework. [ABSTRACT FROM AUTHOR]

The increasing volume of emails has led to the emergence of problems caused by unsolicited email, commonly referred to as Spam. One of the most commonly presentation used in Spam Filter is the BoW (Bag-of-words). However, this approach has a number of weaknesses, mainly the fact that the word order is lost; hence different emails can have the same representation since the same words are used, and it ignores the relationship between words, which can lead to poor performance. This paper proposes a new Spam filter based on PV-DM (Paragraph Vector-Distributed Memory) in order to overcome the limitations of the BoW representation. [ABSTRACT FROM AUTHOR]

We present in this paper different architectures of IDPS. We will also discuss measures that define the effectiveness of IPS and finally the very recent work of standardization and homogenization of IPS. The purpose of this work is the design and the realization of an IDPS (intrusion detection and prevention system) inspired from natural immune systems. The study of biological systems to get inspired from them for the resolution of computer science problems is an axis of the artificial intelligence field which gave rise to robust and effective methods (ants colonies, genetic algorithms, neuron networks…) by their natural function, the immune system's the interest of researchers in the intrusion detection field, taking into account the similarities of NIS (Natural Immune System) and IDPS objectives. Within the framework of this work, we conceived an IDPS inspired from natural immune system and implemented by using a directed approach. A platform was developed and tests were carried out in order to assess our system performances. [ABSTRACT FROM AUTHOR]

In this paper a type of elliptic interface problem with jump coefficients is studied by being reformulated to a mixture of elliptic/mixed elliptic interface problem, which is analogous to the steady state fluid-structure interaction problem to some extent. A mixture of standard- and mixed finite element method is developed for the presented elliptic interface problem, and its well-posedness and convergence theories are analyzed and validated by numerical experiments. [ABSTRACT FROM AUTHOR]

In the field of 3D head modeling and animation, feature points are often needed to mark important regions of the face and can be used to animate or deform the input model. However, an automatic detection of features remains a challenging task. This paper presents a novel approach to feature detection based on curvature and its derived descriptors, such as shape index, curvedness and Willmore energy. Four important feature regions are detected using the proposed approach - eyes, nose, mouth, ears. For each region, feature points are detected. Results show that the feature points are detected with sufficient accuracy for further use. [ABSTRACT FROM AUTHOR]

In 1974 Lawson and Hanson produced a seminal active set strategy to solve least-squares problems with non-negativity constraints that remains popular today. In this paper we present TNT-NN, a new active set method for solving non-negative least squares (NNLS) problems. TNT-NN uses a different strategy not only for the construction of the active set but also for the solution of the unconstrained least squares sub-problem. This results in dramatically improved performance over traditional active set NNLS solvers, including the Lawson and Hanson NNLS algorithm and the Fast NNLS (FNNLS) algorithm, allowing for computational investigations of new types of scientific and engineering problems. For the small systems tested (5000 × 5000 or smaller), it is shown that TNT-NN is up to 95 × faster than FNNLS. Recent studies in rock magnetism have revealed a need for fast NNLS algorithms to address large problems (on the order of 10 5 × 10 5 or larger). We apply the TNT-NN algorithm to a representative rock magnetism inversion problem where it is 60× faster than FNNLS. We also show that TNT-NN is capable of solving large (45000 × 45000) problems more than 150 × faster than FNNLS. These large test problems were previously considered to be unsolvable, due to the excessive execution time required by traditional methods. [ABSTRACT FROM AUTHOR]

The problem of user privacy enforcement in online social networks (OSN) cannot be ignored and, in recent years, Facebook and other providers have improved considerably their privacy protection tools. However, in OSN’s the most powerful data protection “weapons” are the users themselves. The behavior of an individual acting in an OSN highly depends on her level of privacy attitude: an aware user tends not to share her private information, or the private information of her friends, while an unaware user could not recognize some information as private, and could share it without care to her contacts. In this paper, we experimentally study the role of the attitude on privacy of an individual and her friends on information propagation in social networks. We model information diffusion by means of an extension of the Susceptible-Infectious-Recovered (SIR) epidemic model that takes into account the privacy attitude of users. We employ this diffusion model in stochastic simulations on a synthetic social network, designed for miming the characteristics of the Facebook social graph. [ABSTRACT FROM AUTHOR]

In the last decades, one issue that has received a lot of attention in classification problems is how to obtain better classifications. This problem becomes even more complicated when the number of classes is high. In this multiclass scenario, it is assumed that the class labels are independent of each other, and thus, most techniques and methods proposed to improve the performance of the classifiers rely on it. An alternative way to address the multiclass problem is to hierarchically distribute the classes in a collection of multiclass subproblems by reducing the number of classes involved in each local subproblem. In this paper, we propose a new method for inducing a class hierarchy from the confusion matrix of a multiclass classifier. Then, we use the class hierarchy to learn a tree-like hierarchy of classifiers for solving the original multiclass problem in a similar way as the top-down hierarchical classification approach does for working with hierarchical domains. We experimentally evaluate the proposal on a collection of multiclass datasets showing that, in general, the generated hierarchies not only outperforms the original (flat) classification but also hierarchical approaches based on other alternative ways of constructing the class hierarchy. [ABSTRACT FROM AUTHOR]

Complex networks are commonly used to model urban street networks, which allows aiding the analysis of criminal activities in cities. Despite several works focusing on such application, there is a lack of a clear methodology focused in the analysis of crime behavior. In this sense, we propose a methodology for employing complex networks in the analysis of criminality spread within criminal areas of a city. Here, we evaluate synthetic cases of crime propagation concerning real criminal data from the North American city of San Francisco — CA. Our results confirm the effectiveness of our methodology in analyzing the crime behavior by means of criminality spread. Hence, this paper renders further development and planning on public safety in cities. [ABSTRACT FROM AUTHOR]

This year’s ICCS conference theme promotes the use of computational science as a means to foster multidisciplinarity and synergies with other fields. Our thesis is that this trend towards multidisciplinarity should be accompanied by the use of best practices issued from the software engineering community in order to avoid obtaining overly complex and tangled code, difficult to validate, to maintain and to port. In this paper we argue for the need of applying separation of concerns principles when the development involves scientists from various application fields. We overview several strategies that may be used to achieve this separation, focusing mainly on two approaches drawn from our previous experiences with multidisciplinary projects, addressing two distinct patterns of multi-domain interaction that may occur in scientific software development. [ABSTRACT FROM AUTHOR]

In this paper, we study the global stability and attractivity of the endemic equilibrium for a network-based SIS epidemic model with nonmonotone incidence rate. The model was introduced in Li (2015). We prove that the endemic equilibrium is globally asymptotically stable if α (a parameter of this model) is sufficiently large, and is globally attractive if the transmission rate λ satisfies λ λ c ∈ ( 1 , 2 ] , where λ c is the epidemic threshold. Some numerical experiments are also presented to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new fuzzy time series (FTS) forecasting method based on optimal partitions of intervals in the universe of discourse and optimal weighting vectors of two-factors second-order fuzzy-trend logical relationship groups (TSFTLRGs). The proposed method uses particle swarm optimization (PSO) techniques to obtain the optimal partitions of intervals and the optimal weighting vectors simultaneously. The proposed FTS forecasting method outperforms the existing methods for forecasting the TAIEX and the NTD/USD exchange rates in terms of forecasting accuracy rates. It provides us with a useful way to deal with forecasting problems to get higher forecasting accuracy rates. [ABSTRACT FROM AUTHOR]

SACM (Structured Assurance Case Metamodel) is a standard for assurance case specification and exchange. It consists of an argumentation metamodel and an evidence metamodel for justifying that a system satisfies certain requirements. For assurance of safety-critical systems, SACM can be used to manage safety evidence and to specify safety cases. The standard is a promising initiative towards harmonizing and improving system assurance practices, but its suitability for safety evidence management needs to be further studied. To this end, this paper studies how SACM 1.1 supports this activity according to requirements from industry and from prior work. We have analysed the notion of evidence in SACM, its evidence lifecycle, the classes and associations of the evidence metamodel, and the link of this metamodel with the argumentation one. As a result, we have identified several improvement opportunities and extension possibilities in SACM. The notions of evidence and evidence assertion should be clarified, the overlaps between metamodel elements should be reduced, and a wider support to the lifecycle of the artefacts used as safety evidence could be provided. Addressing these aspects will allow SACM to better fit safety evidence management needs and practices, especially beyond the scope of a safety case. The results and the conclusions drawn are especially valuable for practitioners interested in SACM adoption and vendors interested in developing tool support for SACM-based safety evidence management. [ABSTRACT FROM AUTHOR]

There is an increase use of security driven approaches to support software development activities, such as requirements, design and implementation. The objective of this paper is to identify the existing software security approaches used in the software development lifecycle (SDLC). In order to meet our goal, we conducted a systematic mapping study to identify the primary studies on the use of software security techniques in SDLC. In total, we selected and categorized 118 primary studies. After analyzing the selected studies, we identified 52 security approaches and we categorized them in to five main categories, namely, ‘secure requirements modeling’, ‘vulnerability identification, adaption and mitigation’, ‘software security focused process’, ‘extended UML-based secure modeling profiles’, ‘non UML-based secure modeling notations’. The results show that the most frequently used approaches are static analysis and dynamic analysis that provide security checks in the coding phase. In addition, our results show that many studies in this review considered security checks around the coding stage of software development. This work will assist software development organizations in better understanding the existing software security approaches used in the software development lifecycle. It can also provide researchers with a firm basis on which to develop new software security approaches. [ABSTRACT FROM AUTHOR]

Introduced in the mid-1970s as an intermediate step in proving a long-standing conjecture on arithmetic progressions, Szemerédi’s regularity lemma has emerged over time as a fundamental tool in different branches of graph theory, combinatorics and theoretical computer science. Roughly, it states that every graph can be approximated by the union of a small number of random-like bipartite graphs called regular pairs. In other words, the result provides us a way to obtain a good description of a large graph using a small amount of data, and can be regarded as a manifestation of the all-pervading dichotomy between structure and randomness. In this paper we will provide an overview of the regularity lemma and its algorithmic aspects, and will discuss its relevance in the context of pattern recognition research. [ABSTRACT FROM AUTHOR]

Over the past decade, independent evaluations have become commonplace in many areas of experimental computer science, including face and gesture recognition. A key attribute of many successful independent evaluations is a curated data set. Desired aspects associated with these data sets include appropriateness to the experimental design, a corpus size large enough to allow statistically rigorous characterization of results, and the availability of comprehensive metadata that allow inferences to be made on various data set attributes. In this paper, we review a ten-year biometric sampling effort that enabled the creation of several key biometrics challenge problems. We summarize the design and execution of data collections, identify key challenges, and convey some lessons learned. [ABSTRACT FROM AUTHOR]