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Implementations that follow the functional programming paradigm are being used in more and more domains. As functional programming paradigm has mathematical reference transparency, refinement to functional programs contributes to improving the reliability of the transformation process and simplifying the refinement steps. However, it is a challenge to generate functional programs from specifications. Most existing transformation methods refine specifications into abstract algorithm-level programs based on loop invariants rather than functional programs. This paper proposes a novel functional program generation method based on the partition-recursion refinement rule. It establishes a novel program refinement framework based on functional theory for the first time. This is the first study to regard the whole program refinement process as a composition of abstract functions. This paper designs a recurrence-based algorithm design language (Radl+) and implements a software prototype to map Radl+ algorithms into executable Haskell programs. To prove the feasibility and efficiency of this method, this paper transforms the polynomial multiplication problem from a specification into an executable Haskell program. This case shows that compared with existing approaches, the proposed method can simplify the transformation steps and reduce the number of lines of generated code from 38 to 10. • Novel refinement framework provides a new approach to generating a functional program. • The composition of abstract functions explains the program refinement process. • Substitution rule and Recursion rule have none of the side effects. • Software prototype transforms the polynomial multiplication problem into Haskell program. [ABSTRACT FROM AUTHOR]

In order to cope with uncertainties in a platoon, this paper proposes a reconfigurable multi-agent architecture to address the platoon safety problem by handling two modes: the normal mode and the degraded mode. At this stage of research, the normal mode is characterized by the interaction between agents over a Vehicle-to-Vehicle (V2V) communication network while the degraded mode simply involves sensors for a local perception. The switching from the normal mode to the degraded one is triggered when the communication quality is considered not fully reliable. A PID (Proportional Integral Derivative) controller is proposed to regulate the inter-vehicle distance and orientation. Two models are proposed in this paper: in the first one, the management operations such as splitting and joining are set up while the second is mainly modeled to assess the implemented controller quality. In this paper, the safety of a platoon is represented by the quality of tracking and the inter-vehicle distance. The mentioned features are assessed for both modes through a formal verification using the Uppaal software. We prove the efficiency of the proposed platoon model for several situations such as merging, following or leaving the platoon by verifying different properties using the model checking. The evaluation of the second model, simulated by the Webots software, proves the impact of the number of vehicles on the platoon performance and the vehicle tracking quality. We conclude that when the platoon reaches a certain number of vehicles, the safety criterion is no more reliable. [ABSTRACT FROM AUTHOR]

Security vulnerabilities are among the major concerns of modern software engineering. Successful results of machine learning techniques in various challenging applications have led to an emerging field of research to investigate the effectiveness of machine learning, and more recently, deep learning techniques, for the problem of software vulnerability analysis and discovery. In this paper, we explore the utilization of Graph Neural Networks as the latest trend and progress in the field of artificial neural networks. To this end, we propose an original neural vulnerability analysis approach, using customized intermediate graph representations of programs to train graph neural network models. Experimental results on a public suite of vulnerable programs show that the proposed approach is effective at the task of software vulnerability analysis. Additional empirical experiments answer complementary research questions about the proposed approach. In particular, we present experimental results for the challenging task of cross-project vulnerability analysis, with interesting insights on the capabilities of our novel approach. Furthermore, a software utility that was developed in the course of this study is also published as a further contribution to the research community. [ABSTRACT FROM AUTHOR]

Aggregation theory is devoted to the fusing of several values into a unique output that summarizes the given information. Typically, the aggregation process is formalized in terms of an increasing mathematical function that maps the input values to the result, fulfilling some boundary conditions. However, this formalization can be too restrictive for some scenarios. In some cases, the inputs can be seen as observations of random variables, the aggregation result being also a random variable. In others, the aggregation process can be identified as a program that performs the aggregation rather than a mathematical function. In this direction, the concepts of aggregation of random variables and computable aggregation have been defined in the literature. This paper is devoted to the definition of computable aggregation of random variables, which are computer programs, not functions, that aggregate random variables, not numbers. Special attention is given to different possible alternatives to modelize random variables and monotonicity. The implementation of some examples is also provided. • Different possible representations of random variables in a computation framework are considered. • The definition of computable aggregations of random variables is stated. • The boundary and monotonicity conditions are studied. • The implementation of some remarkable examples is provided. [ABSTRACT FROM AUTHOR]

Software can be delivered either after it has been entirely developed or it can be delivered in phases. When delivered in phases, software can be enhanced either by upgrading the existing modules in forthcoming phases or by modifying the modules that were developed in previous phases. An incorporated multi-period multiple objective optimization model for the enhancement and establishment of a software system is discussed in this paper. The software system's total cost is minimized, and the fitness evaluation score of the software components (commercial off-the-shelf and in-house) for modules that are not outsourced, along with vendors for modules that are outsourced, is maximized. The fitness evaluation of module alternatives (software components and vendors) is conducted based on many important qualitative attributes using the Technique for Order Preference by Similarity to Ideal Solution. The model is constrained to critical parameters, such as compatibility, delivery time and integration of alternatives, along with software reliability. To exemplify the applicability of the suggested framework for optimization, the development of e-commerce software is used as a case study. [ABSTRACT FROM AUTHOR]

• We introduce a novel way to extract features from source code automatically. To the best of our knowledge, we are the first to apply the autoencoder in fault localization. • We present a fault localization approach, ABFL that combines SBFL techniques with the latent representation of source code to improve the accuracy of fault localization. • We evaluate ABFL on 357 bugs from 5 different software projects in the Defects4J benchmark. ABFL substantially outperforms 14 SBFL techniques, ranking 14%, 26%, and 34% faults at the top 1, 3, and 5 of the ranked list, respectively. Fault localization is essential to software debugging. Despite existing techniques, such as mutation analysis, development history and bug reports, have made great contributions to fault localization, the challenge of infeasibility still exits in practice due to expense of mutation analysis, lacking of development history and bug reports. To improve accuracy and feasibility in fault code locating, in this paper, we propose ABFL, an Autoencoder Based practical approach for Fault Localization. ABFL first introduces an autoencoder to extract 32 features from software static source code. Then it employs Spectrum Based Fault Localization (SBFL) techniques to calculate 14 types of scores, which are taken as another group of features in software running time. Finally, relying on the constructed ranking model, ABFL integrates two groups of features together and precisely locates faulty statements in code. The executed extensive experiments on the Defects4J repository show that our approach is superior to the state-of-the-art SBFL techniques, ranking the faulty statement at the 1st, 3rd, and 5th positions with 49, 94, and 123 faults, respectively. [ABSTRACT FROM AUTHOR]

While working on a software specification, designers usually need to evaluate different architectural alternatives to be sure that quality criteria are met. Even when these quality aspects could be expressed in terms of multiple software metrics, other qualitative factors cannot be numerically measured, but they are extracted from the engineers know-how and prior experiences. In fact, detecting not only strong but also weak points in the different solutions seems to fit better with the way humans make their decisions. Putting the human in the loop brings new challenges to the search-based software engineering field, especially for those human-centered activities within the early analysis phase. This paper explores how the interactive evolutionary computation can serve as a basis for integrating the humans judgment into the search process. An interactive approach is proposed to discover software architectures, in which both quantitative and qualitative criteria are applied to guide a multi-objective evolutionary algorithm. The obtained feedback is incorporated into the fitness function using architectural preferences allowing the algorithm to discern between promising and poor solutions. Experimentation with real users has revealed that the proposed interaction mechanism can effectively guide the search towards those regions of the search space that are of real interest to the expert. [ABSTRACT FROM AUTHOR]

This paper considers the use of interpolative coding for lossless chain code compression. The most popular chain codes are used, including Freeman chain code in eight (F8) and four directions (F4), Vertex Chain Code (VCC), and three-orthogonal chain code (3OT). The whole compression pipeline consists of the Burrows–Wheeler transform, Move-To-Front transform and the interpolative coding, which was improved by FELICS and new Ψ-coding. The approach was compared with the state-of-the-art chain code compression algorithms. For VCC, 3OT and F4, the obtained results are slightly better than the existing approaches. However, an important improvement was achieved with F8 chain code, where the presented approach is considerably better. [ABSTRACT FROM AUTHOR]

Information Sciences is a leading international journal in computer science launched in 1968, so becoming fifty years old in 2018. In order to celebrate its anniversary, this study presents a bibliometric overview of the leading publication and citation trends occurring in the journal. The aim of the work is to identify the most relevant authors, institutions, countries, and analyze their evolution through time. The paper uses the Web of Science Core Collection in order to search for the bibliographic information. Our study also develops a graphical mapping of the bibliometric material by using the visualization of similarities (VOS) viewer. With this software, the work analyzes bibliographic coupling, citation and co-citation analysis, co-authorship, and co-occurrence of keywords. The results underline the significant growth of the journal through time and its international diversity having publications from countries all over the world. [ABSTRACT FROM AUTHOR]

Online social networks and recommender systems are two of the most common internet applications, but due to their different nature, they are seldom considered under a single framework. Nevertheless we often rely on friends for advices before purchasing products or services. In other words, information embedded in the online social networks may be relevant to recommender systems and the combination of the two systems may benefit each other. In this paper, we introduce a simple recommendation algorithm based on a diffusion process which integrates the networks of friends and user-product relations. Our results show that social networks improve the accuracy of recommendation for inactive users, and increase the diversity of the recommended products for active users. In addition, our approach outperforms conventional popularity-based algorithms and provides personalized recommendations in the cold-start period. These results shed light on a new design of recommendation algorithms in integrating social information and recommendations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel message embedding algorithm using the optimal weighted modulus. Our algorithm, referred to as a w eighted m odulus and abbreviated by WM( n, M ), conceals an M -ary secret digit in a group of n pixels. Using an n -tuple optimal weight ensures that our algorithm can produce stego pixels with minimal pixel distortion. We adopt four steps to pragmatically accomplish the goal of message embedding. In the first step, we introduce a check-weight algorithm to systematically generate all of the valid weights. Secondly, we classify valid weights by referring to theoretically minimal distortion to produce an optimal weight. Along with the classification, we construct a pixel alteration table which simplifies the process of the message concealment through convenient vector operations. We take advantage of the homogeneous alteration table that is built in this step to decrease the distortion encountered when resolving the problem of pixel underflow or overflow. Finally, we propose a universal bit conversion scheme which transforms a serial secret bit stream into M -ary digits to minimize the transformation loss, thereby achieving maximal embedding capacity. Our algorithm accurately predicts results prior to real message concealment, effectively conveys maximal secret bits and speedily produces stego images with theoretically minimal distortion. The experimental results demonstrate that our algorithm outperforms 11 current state-of-the-art competitors. It is not detectable by the SPAM and RS steganalytic attacks. [ABSTRACT FROM AUTHOR]

Implementing cloud computing empowers numerous paths for Web-based service offerings to meet diverse needs. However, the data security and privacy has become a critical issue that restricts many cloud applications. One of the major concerns in security and privacy is caused by the fact that cloud operators have chances to reach the sensitive data. This concern dramatically increases users’ anxiety and reduces the adoptability of cloud computing in many fields, such as the financial industry and governmental agencies. This paper focuses on this issue and proposes an intelligent cryptography approach, by which the cloud service operators cannot directly reach partial data. The proposed approach divides the file and separately stores the data in the distributed cloud servers. An alternative approach is designed to determine whether the data packets need a split in order to shorten the operation time. The proposed scheme is entitled Security-Aware Efficient Distributed Storage (SA-EDS) model, which is mainly supported by our proposed algorithms, including Alternative Data Distribution (AD2) Algorithm, Secure Efficient Data Distributions (SED2) Algorithm and Efficient Data Conflation (EDCon) Algorithm . Our experimental evaluations have assessed both security and efficiency performances and the experimental results depict that our approach can effectively defend main threats from clouds and requires with an acceptable computation time. [ABSTRACT FROM AUTHOR]

SimRank is a well-studied similarity measure between two nodes in a network. However, evaluating SimRank of all nodes in a network is not only time-consuming but also not pragmatic, since users are only interested in the most similar pairs in many real-world applications. This paper focuses on top- k similarity join based on SimRank. In this work, we first present an incremental algorithm for computing SimRank. On top of that, we derive an iterative batch pruning framework, which is able to iteratively filter out unpromising nodes and obtain the top- k pairs in a fast mode. Specifically, we define the concept of super node such that for a node in the network, the SimRank with its super node is not less than that with any others. Based on this feature, we propose a tight upper bound for each node that can be easily calculated after each iteration. Experiments on both real-life and synthetic datasets demonstrate that our method achieves better performance and scalability, in comparison with the state-of-the-art solution. [ABSTRACT FROM AUTHOR]

As a promising implementation of software systems, service composition has attracted significant attention and research, which constructs complex and value-added applications by composing existing single services to reduce the deployment time and cost. However, as the services on the Internet and the external environment are frequently changeable, these demand the service composition must be adaptive and dynamic to address these changes automatically. Therefore, this paper describes a multi-agent reinforcement learning model for the dynamic optimization of web service composition. In this model, agent can utilize reinforcement learning algorithms to interact with environment in real time to compute optimal composition strategy dynamically, and multi-agents mechanism can keep higher effectiveness in contrast to single-agent reinforcement learning. We propose a distributed Q-learning algorithm, which decompose the task into many sub-tasks and make every agent focus on own sub-task, to accelerate the convergence rate. In addition, we also introduce experience sharing strategy to improve the efficiency. As a result, these methods allow composite service to dynamically adjust itself to fit a varying environment, where the properties of the component services continue changing. Finally, a series of comparable experiments with traditional Q-learning algorithm demonstrate that our algorithms have certain validity, higher efficiency and obvious advantages. [ABSTRACT FROM AUTHOR]

This paper presents three translations from Petri nets to Modeling, Simulation and Verification Language (MSVL) programs. Each translation is directed by one of the three semantics of Petri nets, namely interleaving, concurrency and max-concurrency. Further, for each translation, an equivalence relation between Petri nets and generated MSVL programs is proved. As a result, the supporting tool MSV for MSVL can be used to verify the properties of Petri nets. Case studies are given to show how to do so with MSV under each semantics. [ABSTRACT FROM AUTHOR]

This paper studies the state feedback based output tracking control of probabilistic Boolean control networks (PBCNs) by using the semi-tensor product of matrices. A series of reachable sets (with probability 1) is defined inductively for PBCNs, and some useful properties are obtained for the reachable sets. Based on the properties of the reachable sets, a necessary and sufficient condition is presented for the state feedback based output tracking control of PBCNs. Meanwhile, a constructive procedure is proposed to design state feedback laws for PBCNs to track a constant reference signal. The study of two illustrative examples shows that the obtained new results are very effective. [ABSTRACT FROM AUTHOR]

3D protein structure similarity searching is one of the important processes performed in structural bioinformatics, since it allows for protein function identification and reconstruction of phylogeny for weakly related organisms. Due to the complexity of 3D protein structures and exponential growth of protein structures in public repositories, like the Protein Data Bank, the process is time-consuming and requires increased computational resources. This causes the necessity to prepare computer systems to be able to deal with such huge volumes of macromolecular data. In this paper, we show how 3D protein structure similarity searching can be performed in parallel by distributing MapReduce jobs on the HDInsight cluster in Microsoft Azure commercial cloud. Our solution combines the use of two important computing paradigms that gain popularity in recent years—Hadoop/MapReduce and Cloud computing. Our experiments performed with the use of the whole repository of protein structures from Protein Data Bank confirm that such a technological fusion is very beneficial and can be successfully applied when performing time-consuming computations over biological data. Moreover, appropriate preparation of data allows to reduce the time needed for computations and significantly accelerates the similarity searching. [ABSTRACT FROM AUTHOR]

In machine learning and computer vision fields, a wide range of applications, such as human age estimation and head pose recognition, are related to ordinal data in which there exists an order relationship. To perform such ordinal estimations in a desired metric space, in this paper we first propose a novel ordinal margin metric learning (ORMML) method by separating the data classes with a sequence of margins, which makes the classes distribute orderly in the learned metric space. Then, to cope with more realistic scenarios where the data are sampled with each class across multiple distributions, we present a cross-distribution variant of ORMML, coined as CD-ORMML, by maximizing the correlation between distributions within each class when conducting metric learning. Finally, extensive experiments on synthetic and publicly available image datasets demonstrate the superiority of the proposed methods in performance to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Many real-world face recognition applications can only provide single sample for each person, while most face recognition approaches require a large set of training samples, which leads to single sample per person (SSPP) problem. In this paper, we propose local structure based multi-phase collaborative representation classification (LS_MPCRC) to solve SSPP problem. By adopting the “divide-conquer-aggregate” strategy, we successfully alleviate the dilemma of high data dimensionality and small samples, where we first divide the face into local blocks, and classify each local block, and then integrate all the classification results by voting. For each local block, we further divide it into overlapped local patches and assume that these patches lie in a linear subspace. This subspace assumption reflects local structure relationship of the overlapped patches and makes CRC robust for SSPP problem. Motivated by the fact that the entropy of the class probability distribution is a measure about classification confidence, we further apply multi-phase technique to reduce entropy, where useless classes are eliminated after each phase classification. This strategy finally produces a sparse class probability distribution with higher classification confidence. Experimental results show that the proposed method generalizes well to SSPP problem and outperforms many state-of-the-art methods. It also shows strong robustness to the large variation of expression, illumination, little poses variation, occlusion and time variation. [ABSTRACT FROM AUTHOR]

Software systems tend to become larger and more complicated as the functional requirements increase gradually. Well-modularized software systems are widely believed to be understood, managed and evolved easily. Software module clustering problems (SMCPs) are significant and challenging problems in software engineering whose primary aim is to obtain perfect structures of software systems according to predefined rules. To solve SMCPs, it is important to design a measure which can appropriately evaluate the quality of the structures obtained. So far, the modularization quality (the basic MQ ) is widely used as the conventional measure. However, this measure does not consider global modules and edge directions between two modules. Therefore, in this paper, we design a new modularization quality measure based on the similarity, labeled as MS , to automatically guide optimization algorithms to find a good partition of software systems which considers both global modules and edge directions. To systematically validate the performance of MS , three optimization algorithms, namely hill-climbing algorithm (HC), genetic algorithm (GA), and multi-agent evolutionary algorithm (MAEA), are adopted to optimize MS on 17 real-world software systems with varying sizes. A thorough comparison shows that MS outperforms the basic MQ in guiding the optimization algorithms to find more meaningful module clusters for software systems. [ABSTRACT FROM AUTHOR]

Network operators employ a variety of security policies for protecting the data and services. However, deploying these policies in traditional network is complicated and security vulnerable due to the distributed network control and lack of standard control protocol. Software-defined network provides an ideal paradigm to address these challenges by separating control plane and data plane, and exploiting the logically centralized control. In this paper, we focus on taking the advantage of software-defined networking for security policies enforcement. We propose a two layer OpenFlow switch topology designed to implement security policies, which considers the limitation of flow table size in a single switch, the complexity of configuring security policies to these switches, and load balance among these switches. Furthermore, we introduce a safe way to update the configuration of these switches one by one for better load balance when traffic distribution changes. Specifically, we model the update process as a path in a graph, in which each node represents a security policy satisfied configuration, and each edge represents a single step of safely update. Based on this model, we design a heuristic algorithm to find an optimal update path in real time. Simulations of the update scheme show that our proposed algorithm is effective and robust under an extensive range of conditions. [ABSTRACT FROM AUTHOR]

Users of web search engines are increasingly worried that their query activities may expose what topics they are interested in, and in turn, compromise their privacy. It would be desirable for a search engine to protect the true query intention for users without compromising the precision-recall performance. In this paper, we propose a client-based approach to address this problem. The basic idea is to issue plausible but innocuous pseudo queries together with a user query, so as to mask the user intention. First, we present a privacy model which formulates plausibility and innocuousness, and then the requirements which should be satisfied to ensure that the user intention is protected against a search engine effectively. Second, based on a semantic reference space derived from Wikipedia, we propose an approach to construct a group of pseudo queries that exhibit similar characteristic distribution as a given user query, but point to irrelevant topics, so as to meet the security requirements defined by the privacy model. Finally, we conduct extensive experimental evaluations to demonstrate the practicality and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Robotic intelligence has recently received significant attention in the research community. Application of such artificial intelligence can be used to perform automatic document retrieval and interpretation by a robot through query. So, it is necessary to extract the key information from the document based on the query to produce the desired feedback. For this purpose, in this paper we propose a system for automatic date field extraction from multi-lingual (English, Devnagari and Bangla scripts) handwritten documents. The date is a key piece of information, which can be used in various robotic applications such as date-wise document indexing/retrieval. In order to design the system, first the script of the document is identified, and based on the identified script, word components of each text line are classified into month and non-month classes using word-level feature extraction and classification. Next, non-month words are segmented into individual components and labelled into one of text, digit, punctuation or contraction categories. Subsequently, the date patterns are searched using the labelled components. Both numeric and semi-numeric regular expressions have been used for date part extraction. Dynamic Time Warping (DTW) and profile feature-based approaches are used for classification of month/non-month words. Other date components such as numerals and punctuation marks are recognised using a gradient-based feature and Support Vector Machine (SVM) classifier. The experiments are performed on English, Devnagari and Bangla document datasets and the encouraging results obtained from the system indicate the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

The most applied class of copulas for fitting purposes is undoubtedly the class of Archimedean copulas due to their representation by means of single functions of one variable, i.e., by means of additive generators or the corresponding pseudo-inverses. In this paper, we characterize aggregation functions preserving additive generators (pseudo-inverses of additive generators) of Archimedean copulas. As a by-product, we obtain an efficient method to construct new additive generators from some given ones. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new graph sampling method for online social networks that achieves the following. First, a sample graph should reflect the ratio between the number of nodes and the number of edges of the original graph. Second, a sample graph should reflect the topology of the original graph. Third, sample graphs should be consistent with each other when they are sampled from the same original graph. The proposed method employs two techniques: hierarchical community extraction and densification power law. The proposed method partitions the original graph into a set of communities to preserve the topology of the original graph. It also uses the densification power law which captures the ratio between the number of nodes and the number of edges in online social networks. In experiments, we use several real-world online social networks, create sample graphs using the existing methods and ours, and analyze the differences between the sample graph by each sampling method and the original graph. [ABSTRACT FROM AUTHOR]

Abstract: Uncertainty in real world decision making problems not always has probabilistic nature, in such cases the use of linguistic information to model and manage such an uncertainty has given good results. The adoption of linguistic information implies the accomplishment of processes of computing with words to solve linguistic decision making problems. In the specialized literature, several computational models can be found to carry out such processes. However, there is a shortage of software tools that develop and implement these computational models. The 2-tuple linguistic model has been widely used to operate with linguistic information in decision problems due to the fact that provides linguistic results that are accurate and easy to understand for human beings. Furthermore, another advantage of the 2-tuple linguistic model is the existence of different extensions to accomplish processes of computing with words in complex decision frameworks. Due to these reasons, in this paper a fuzzy linguistic decision tools enhancement suite so-called Flintstones is proposed to solve linguistic decision making problems based on the 2-tuple linguistic model and its extensions. Additionally, the Flintstones website is also presented, this website has been deployed and includes a repository of case studies and datasets for different linguistic decision making problems. Finally, a case study solved by Flintstones is illustrated in order to show its performance, usefulness and effectiveness. [Copyright &y& Elsevier]

Abstract: It is well known that data structures are essential in obtaining efficient algorithms, having a major importance in the software development process. A proper instantiation of a data structure can greatly impact the performance and the efficiency of the software system. In this paper we are focusing on the problem of customizing data structures instances during the execution of software systems using a supervised learning approach. In order to customize a data structure instance according to the software system’s current execution context, a neural network will be used. We experimentally evaluated our technique on a case study, emphasizing the advantages of the proposed approach. The obtained experimental results highlight the potential of our proposal in using a supervised learning based approach for dynamically configuring data structures instances. [Copyright &y& Elsevier]