Journal Information
Information Sciences
http://www.journals.elsevier.com/information-sciences/
Impact Factor:
4.832
Publisher:
ELSEVIER
ISSN:
0020-0255
Viewed:
7945
Tracked:
19

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Call For Papers
Information Sciences will publish original, innovative and creative research results. A smaller number of timely tutorial and surveying contributions will be published from time to time.

The journal is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in information, knowledge engineering and intelligent systems. Readers are assumed to have a common interest in information science, but with diverse backgrounds in fields such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioural sciences and biochemistry.

The journal publishes high-quality, refereed articles. It emphasizes a balanced coverage of both theory and practice. It fully acknowledges and vividly promotes a breadth of the discipline of Informations Sciences.

Topics include:

Foundations of Information Science:
Information Theory, Mathematical Linguistics, Automata Theory, Cognitive Science, Theories of Qualitative Behaviour, Artificial Intelligence, Computational Intelligence, Soft Computing, Semiotics, Computational Biology and Bio-informatics.

Implementations and Information Technology:
Intelligent Systems, Genetic Algorithms and Modelling, Fuzzy Logic and Approximate Reasoning, Artificial Neural Networks, Expert and Decision Support Systems, Learning and Evolutionary Computing, Expert and Decision Support Systems, Learning and Evolutionary Computing, Biometrics, Moleculoid Nanocomputing, Self-adaptation and Self-organisational Systems, Data Engineering, Data Fusion, Information and Knowledge, Adaptive ad Supervisory Control, Discrete Event Systems, Symbolic / Numeric and Statistical Techniques, Perceptions and Pattern Recognition, Design of Algorithms, Software Design, Computer Systems and Architecture Evaluations and Tools, Human-Computer Interface, Computer Communication Networks and Modelling and Computing with Words

Applications:
Manufacturing, Automation and Mobile Robots, Virtual Reality, Image Processing and Computer Vision Systems, Photonics Networks, Genomics and Bioinformatics, Brain Mapping, Language and Search Engine Design, User-friendly Man Machine Interface, Data Compression and Text Abstraction and Summarization, Virtual Reality, Finance and Economics Modelling and Optimisation
Last updated by Dou Sun in 2017-08-05
Special Issues
Special Issue on Innovative Smart Methods for Security: Emerging Trends and Research Challenges
Submission Date: 2017-08-17

We are witnessing the advent of novel ICT technologies and solutions such as Smart Cities, Internet of Things, Edge Computing, Fog Computing, Social Computing and Big Data Analytics. They are generating a growing interest from both academic community and industrial practitioners. Due to their applications to critical domains (such as homeland security, disaster management, energy generation and distribution, financial services, and heath care), they are paving the way for new security and privacy challenges that cannot be easily or effectively solved using existing solutions. Such challenges provide opportunities of innovation from two aspects: First, the new technologies and applications are bringing new requirements for security and privacy. For example, within the context of Internet of Things (IoT), the resource constraints environment imposes further limitations on the use of traditional approaches used to protect the system from possible attacks and threats. There is a need of energy efficient solutions. Furthermore, such limitations also provide a breeding ground for new types of attacks; for instance, an adversary can conduct the energy depletion attacks to lower the availability of some nodes in IoT. Similarly, the edge computing brings a new challenge to data security as the data is analyzed at the edges that are outside the traditional defense perimeter. Moreover, traditional security and privacy solutions are not practically implementable in new ICT environments. Hence, there is a need of novel solutions to overcome these limitations. To overcome these limitations, researchers have now started looking at the new breed of security and privacy solutions using machine learning, game theory and optimization strategies. Second, the abundant of data on system behavior is now being captured and processed. These large data set is considered a valuable resource to further improve and strengthen the provided security and privacy. However, the management and analysis of such large data sets for security and privacy demands new techniques. For example, novel techniques for detection of abnormal patterns or situations that may trigger automated recovery actions. Similarly, if the large data sets are not properly managed, they themselves become the target of potential attacks. Therefore, this special issue solicits contributions of novel means to provide security and privacy in the upcoming challenging in emerging ICT environments by using machine learning, game theory, optimization or semantic solutions. It also welcomes submissions that presents practical applications of such solutions in the above-mentioned environments. Authors are invited to submit original papers that describe the latest results and advances in novel theories and their new application within the mentioned novel contexts. Specifically, submitted articles MUST NOT substantially duplicate work that any of the authors have published elsewhere or have submitted in parallel to any other conferences that have proceedings or journals. The requirement is to limit an overlap between a conference paper and journal paper to 30% (max). The papers will be peer reviewed and selected on the basis of their quality and relevance to the topic of this special issue.
Last updated by Dou Sun in 2017-01-14
Special Issue on New energy-optimization challenges in the next generation Internet ecosystem
Submission Date: 2017-09-01

In an increasingly connected world, the emerging ubiquitous, pervasive and distributed computing and communication architectures, may be considered first-class energy consumers and potential actors in steering a more careful usage of energy resources. Indeed, with the ever-increasing demand for bandwidth, connection quality and end-to-end interactivity, computer networks, processing systems and terminal devices are requiring more and more sophisticated and power-hungry technologies. Furthermore, with the diffusion of new paradigms such as the Internet of Things (IoT), bringing with them new kinds of fixed and mobile devices with their own specific features and hardware limitations, the need for controlling and containing energy consumption has caught the interest of both the industrial and academic communities. The communication networks and the distributed infrastructures (e.g. clouds and edge systems), providing runtime and storage services to the IoT devices and network-centric applications, may be considered extremely critical assets from both the energy management and sustainability perspectives. Analogously, ubiquitous end-side devices, often characterized by an extremely hardware and dimension-constrained architecture, become the targets for more energy-efficient communication techniques and protocols and require the use of new lightweight cryptographic techniques for providing privacy, integrity and secure endpoint identification in their transactions. These new challenges require innovative and effective optimization solutions for minimizing power consumption in the next generation telecommunication, computing and processing systems, such as energy-efficient devices and transmission techniques, resource scheduling algorithms and control plane protocols. On the other hand, energy awareness may also introduce new dimensions in security menaces. Indeed, energy-efficient technologies may provide attackers with new opportunities for exploiting specific power-related vulnerabilities. This introduces new energy-based denial of service attacks based on raising the target facility power consumption for both end-side devices and large computing firms or communication networks. This special issue aims at providing an opportunity for researchers and practitioners to contribute with original research and review articles that present the state-of-the-art research outcomes, practical results, latest findings, and future evolutions of energy-aware architectures, energy-efficient communication protocols and resource management algorithms as well as energy optimization techniques in the mobile and ubiquitous Internet of things ecosystem. The objective is to cover several developing topics as well as provide new approaches, models and protection schemes for targeting energy consumption-related security menaces, and to enforce information security and attack resiliency in energy-constrained devices. The topics of interest for this special issue include: - Energy-efficient architectures and protocols for next generation distributed computing and communication infrastructures - Modeling and forecasting energy demand in distributed computing systems, fixed and mobile communication infrastructures, and next generation mobile devices. - Optimization methods and heuristics techniques for containing energy consumption in distributed computing systems, fixed and mobile communication infrastructures, and next generation mobile devices. - Energy-aware routing algorithms, runtime systems, middleware and application design - Energy efficiency considerations in cognitive and software defined networks - Energy consumption measurements, and monitoring tools - Security Mechanisms to protect energy-control facilities for green computers and networks - Security weaknesses introduced by Green-enabled devices - Energy targeting security menaces and Energy Consumption Attacks - Advanced cryptographic techniques and protocols for security enforcement in hardware and power-constrained devices. - Formal security methods and information-theoretic security applications and frameworks for hardware and power-constrained devices.
Last updated by Dou Sun in 2017-08-05
Special Issue on Digital Manifolds in Computer Modeling
Submission Date: 2017-10-15

Digital manifolds are widely used in computer modeling, e.g. for object visualization and simulation purposes. Intuitively reasonable and mathematically rigorous definitions of digital manifolds lead to a better understanding of the topologies of digitized 3D sets and assure the correctness of key topological properties of synthetic surfaces. In particular, sound theoretical foundations of digital manifolds are important for applications in 3D medical imaging (e.g., analysis and simulation of organs), bioinformatics (e.g., protein binding simulations), robotics (e.g., motion planning), security (e.g., biometrics), or engineering (e.g., finite elements stress simulations). For example, in surface reconstruction, one should be able to faithfully model the geometry of the original 3D set: a small hole in a heart surface created by imperfections of the synthetic representation renders the synthetic surface useless for blood flow simulation. Various attempts to establish notions and results, analogous to those known for continuous sets, have often faced difficulties due to ambiguities or “paradoxes” that do not exist in the continuous Euclidean space. Since the late 1960s, difficulties in establishing a sound analogy between continuous and discrete spaces have caused a large number of diverse formal definitions of digital curves or digital surfaces, none of them being fully satisfactory. Moreover, no fully adequate theory exists to model digitizations of digital manifolds in more general settings, such as ones with different genus, or surfaces with more complex geometry, e.g., ones featuring certain singularities. The problem of handling “very large” digital manifolds (in the spirit of the big data issue) is not seriously considered yet either. The main objective of this special issue is to stimulate experts in the field to advance the theory of digital manifolds and their implementation in developing machine intelligence. Authors are expected to propose original ideas, techniques and algorithms, leading to better solutions to open problems, and to formulate critical issues and challenges to researchers in the area. While any work on digital manifolds which presents results of exceptional quality would be relevant to the special issue, expected focus is seen in the study of topological and geometric properties of digital manifolds, and related algorithms and applications. In this regard, specific topics of interest include (but are not limited to): - Topology of digital manifolds; graph representations, skeletons and thinning algorithms - Boundary tracking of digital solids; geometric characteristics of object boundaries; multigrid convergence analysis of metric-based descriptors - Digital manifolds and shape representation, recognition, and analysis Of special interest is the analysis and processing of digital manifolds that feature complex topology and considerable size. This includes: - Processing “very large” digital manifolds. Compressed representations and parallel processing - Generalizations of digital manifolds (pinched digital surfaces, digital manifolds with singularities)
Last updated by Dou Sun in 2017-01-14
Special Issue on Parallel and Distributed Data Mining
Submission Date: 2017-12-01

The sheer volume of new data, which is being generated at an increasingly fast pace, has already produced an anticipated data deluge that is difficult to challenge. We are in the presence of an overwhelming vast quantity of data, owing to how easy is to produce or derive digital data. Even the storage of this massive amount of data is becoming a highly demanding task, outpacing the current development of hardware and software infrastructure. Nonetheless, this effort must be undertaken now for the preservation, organization and long-term maintenance of these precious data. However, the collected data is useless without our ability fully understand and make use of it. Therefore, we need new algorithms to address this challenge. Data mining techniques and algorithms to process huge amount of data in order to extract useful and interesting information have become popular in many different contexts. Algorithms are required to make sense of data automatically and in efficient ways. Nonetheless, even though sequential computer systems performance is improving, they are not suitable to keep up with the increase in the demand for data mining applications and the data size. Moreover, the main memory of sequential systems may not be enough to hold all the data related to current applications. This Special Issue takes into account the increasing interest in the design and implementation of parallel and distributed data mining algorithms. Parallel algorithms can easily address both the running time and memory requirement issues, by exploiting the vast aggregate main memory and processing power of processors and accelerators available on parallel computers. Anyway, parallelizing existing algorithms in order to achieve good performance and scalability with regard to massive datasets is not trivial. Indeed, it is of paramount importance a good data organization and decomposition strategy in order to balance the workload while minimizing data dependences. Another concern is related to minimizing synchronization and communication overhead. Finally, I/O costs should be minimized as well. Creating breakthrough parallel algorithms for high-performance data mining applications requires addressing several key computing problems which may lead to novel solutions and new insights in interdisciplinary applications. Moreover, increasingly the data is spread among different geographically distributed sites. Centralized processing of this data is very inefficient and expensive. In some cases, it may even be impractical and subject to security risks. Therefore, processing the data minimizing the amount of data being exchanged whilst guaranteeing at the same time correctness and efficiency is an extremely important challenge. Distributed data mining performs data analysis and mining in a fundamentally distributed manner paying careful attention to resource constraints, in particular bandwidth limitation, privacy concerns and computing power. The focus of this Special Issue is on all forms of advances in high-performance and distributed data mining algorithms and applications. The topics relevant to the Special Issue include (but are not limited to) the following. - Scalable parallel data mining algorithms using message-passing, shared-memory or hybrid programming paradigms - Exploiting modern parallel architectures including FPGA, GPU and many-core accelerators for parallel data mining applications - Middleware for high-performance data mining on grid and cloud environments - Benchmarking and performance studies of high-performance data mining applications - Novel programming paradigms to support high-performance computing for data mining - Performance models for high-performance data mining applications and middleware - Programming models, tools, and environments for high-performance computing in data mining - Map-reduce based parallel data mining algorithms - Caching, streaming, pipelining, and other optimization techniques for data management in high-performance computing for data mining - Novel distributed data mining algorithms
Last updated by Dou Sun in 2017-08-05
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