【今日新增】10条SCI期刊专刊截稿信息

全文截稿: 2017-08-30

影响因子: 1.277

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-web-semantics

There is a vast and rapidly increasing quantity of scientific, corporate, government, and crowd-sourced data published on the emerging Data Web. Open Data are expected to play a catalyst role in the way structured information is exploited on a large scale. This offers a great potential for building innovative products and services that create new value from already collected data. It is expected to foster active citizenship (e.g., around the topics of journalism, greenhouse gas emissions, food supply-chains, smart mobility, etc.) and world-wide research according to the "fourth paradigm of science".

Published datasets are openly available on the Web. A traditional view of digitally preserving them by "pickling them and locking them away" for future use, like groceries, conflicts with their evolution. There are a number of approaches and frameworks, such as the Linked Data Stack, that manage a full life-cycle of the Data Web. More specifically, these techniques are expected to tackle major issues such as the synchronisation problem (how to monitor changes), the curation problem (how to repair data imperfections), the appraisal problem (how to assess the quality of a dataset), the citation problem (how to cite a particular version of a linked dataset), the archiving problem (how to retrieve the most recent or a particular version of a dataset), and the sustainability problem (how to support preservation at scale, ensuring long-term access).

Preserving linked open datasets poses a number of challenges, mainly related to the nature of the Linked Data principles and the RDF data model. Since resources are globally interlinked, effective citation measures are required. Another challenge is to determine the consequences that changes to one LOD dataset may have implications to other datasets linked to it. The distributed, dynamic nature of LOD datasets furthermore introduces additional complexity, since external sources that are being linked to may change or become unavailable. Finally, another challenge is to identify means to afford on-going access to continuously assess the quality of such dynamic datasets.

全文截稿: 2017-09-15

影响因子: 1.32

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/

Model development for the simulation of the evolution of artificial and natural systems is essential for the advancement of Science. Recently, the increasing power of computers has allowed to considerably extend the application of parallel computing methodologies in research and industry, but also to the quantitative study of complex phenomena. This has permitted a broad application of numerical methods for differential equation systems (e.g., FEM, FDM, etc.) on one hand, and the application of alternative computational paradigms, such as Cellular Automata, Genetic Algorithms, Neural networks, Swarm Intelligence, etc., on the other. These latter have demonstrated their effectiveness for modelling purposes when traditional simulation methodologies have proven to be impracticable.

This Special issue aims to provide a platform for a multidisciplinary community composed of scholars, researchers, developers, educators, practitioners and experts from world leading Universities, Institutions, Agencies and Companies in Computational Science, and thus in the Parallel Computing for Modelling and Simulation field. The intent is to offer an opportunity to express and confront views on trends, challenges, and state-of-the art in diverse application fields, such as engineering, physics, chemistry, biology, geology, medicine, ecology, sociology, traffic control, economy, etc.

全文截稿: 2017-09-18

影响因子: 0.683

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-systems-architecture/

Embedded software has become a necessity in almost every aspect of our daily life. The types of embedded software range from self-contained applications to those embedded in various devices and services, such as mobile phones, vital sign sensors, medication dispensers, home appliances, engine ignition systems, etc. Many such systems are mission/life-critical and performance-sensitive.

This special issue invites original and high-quality papers that describe research or technical aspects in the area of real-time and embedded systems. It aims at the evaluating of the maturity and directions of embedded and real-time system and ubiquitous computing technology and the investigation of the advances and trends in the technology of embedded and real-time systems and their emerging applications, including the Internet of Things and Cyber-Physical Systems. The following is a non-exhaustive list of topics considered for this special issue:
- Real-Time Operating Systems and Scheduling
- Timing Analysis
- Design and Analysis Tools
- Real-Time Aspects of Wireless Sensor Networks
- Energy Aware Real-Time Methods
- Embedded System Architectures
- Multi-Core Embedded Systems
- Non-Voltaile Memory and Storage
- Embedded Systems and Design Methods for Cyber-Physical Systems
- Ubiquitous and Distributed Embedded Systems and Networks
- Applications and Case Studies of IoT and CPS

全文截稿: 2017-09-30

影响因子: 1.424

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-systems-and-software/

This special issue of the Journal of Systems and Software targets at high quality original publications that have not been previously published and are also not under consideration for publication elsewhere. We particularly encourage submissions of extended full research papers accepted for the EASE conference (where at least 30% is original research, not previously published elsewhere).

The areas within Empirical Software Engineering that are target of this special issue include, but are not limited to:
- New ideas pertaining to empirical evaluation of software engineering technologies, methods, and tools, e.g., transferring and applying empirical methods from other disciplines to empirical software engineering
- Infrastructures and novel techniques/tools for supporting any phase of empirical studies
- Empirical studies using qualitative, quantitative, and mixed methods
- Cross- and multi-disciplinary methods and studies
- Experiments and quasi-experiments
- Case studies, action-research, and field studies
- Survey research
- Systematic literature reviews and mapping studies
- Meta-analysis, qualitative and quantitative synthesis of studies
- Replication of empirical studies and families of studies
- Empirically-based decision making
- Evaluation and comparison of techniques and models
- Development and evaluation of empirical prediction systems or software estimation models
- Mining software engineering repositories
- Modelling, measuring, and assessing product and/or process quality
- Simulation-based studies in software engineering
- Assessing the benefits / costs associated with using certain development technologies
- Industrial experience, Software project experience, and knowledge management
- Software technology transfer to the industry

全文截稿: 2017-09-30

影响因子: 1.277

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-web-semantics

The Journal of Web Semantics invites submissions to a special issue on Semantic Web research and technologies specifically for the Internet of Things / Web of Things. The goal is to demonstrate how this area can benefit from specific research contributions and advances of the Semantic Web.

The existing global networking infrastructure has facilitated the widespread development of cyber-physical systems, through networks of smart objects, pervasively using the internet for connectivity and communication. These "things" that communicate using Internet protocols and make the results of their computation available in real-time have given rise to rapidly evolving, new paradigms of computing that contribute towards realizing a global, distributed infrastructure with a lot of similarities to the Web. Many areas such as smart cities, smart buildings, social networks, wearables, and large-scale sensor deployments, along with applications in diverse domains such as e-health, agriculture, environmental monitoring and e-commerce already demonstrate significant uptake and impact.

However, the exciting and enhanced capabilities of these networks present several unprecedented and complex challenges that need to be overcome before data, device and service interoperability on IoT/WoT networks can deliver all of their predicted potential. Despite being connected, there are a plethora of isolated islands of heterogeneous networks that require heavy lifting of protocols and data, and reconciliation of semantics before they can truly communicate using Internet standards. Additionally, interconnected networks produce a data deluge to the order and scale of big data which will present scalability problems to the network and data analysis and knowledge extraction and management. Besides the well-known paradigm of the Cloud, new approaches such as (mobile) edge computing and fog computing have been proposed to address these problems. The goal is to not transport all data but the relevant data across the Internet. This requires a fundamental rethinking of current architectural paradigms and a decentralization of analysis and knowledge technologies towards the edge and inside the whole Internet. The end of this process may be the convergence of the so far traditionally separated research areas of information processing and communication into a single architectural paradigm. It is clear that semantic technologies will play a vital and central role in achieving this vision.

The focus of this special issue is to showcase novel and disrupting approaches for the semantic Web to aid in this mission. The ability to analyze, represent and integrate data into higher level artefacts from very large distributed information sources, the description and management of the data and technical infrastructure and the mutual influences and interactions among technical infrastructures, knowledge creation and use and social aspects are central research questions for researchers, organizations, and governments.

This special issue wants to bring together cutting-edge research with particular emphasis on novel and innovative techniques applied to real-world scenarios that showcase the distinguishing benefits through the application of Semantic Web approaches, ontologies, and Linked data principles to the important questions and new challenges raised by IoT/WoT.

全文截稿: 2017-10-01

影响因子: 1.32

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/

High-performance computing (HPC) describes the application of parallelization and distribution algorithms or techniques to connected computing units, to perform more complex tasks in a faster manner than a single unit could do alone. Over the past two decades, the operation speeds of HPC has increased exponentially. Riding on such growth, while HPC continuously advances in its traditional domain of theoretical science and software development, it is increasingly becoming a prevalent solution to a wide range of emerging telecommunication technologies.

Edge Computing Networks and Telecommunication technologies support information transmission over significant distance via distributed and connected communication devices. Rapid innovations in transmission, switching, processing, analyzing, and retrieval of information are vital for the success of a wide range of emerging telecommunication technologies, including connected sensors and IoT devices, smart grid, smart cities, software-defined networks, network function virtualization, data-driven cognitive networking, cyber security, green communications, etc. The class of computational problems that need to be tackled, such as combinatorial optimization, agent-based modelling, massive data analysis, parallel discrete event network simulations, pose new challenges in design or develop the above emerging telecommunication technologies. HPC is essential to address these computational challenges.

Greatly relevant is the contribution that modelling and evaluation techniques (both analytical and simulation based) may bring to the design and the implementation of such complex HW/SW architectures, to dominate various concurring requirements and guide decision processes that lead to an effective result.

The aim of this special issue is to explore how HPC as a research tool enhances emerging telecommunication technologies, and hence present a completing panorama of the state-of-the-art quality research efforts on applying HPC to telecommunications. In addition, the discussion of future and emerging challenges, advances, and applications of HPC and related technologies are all the interests of this special issue. It is hoped that the published research issues or solution guidelines will inspire further research in this very important area of various telecommunication system or algorithm design, as well as provide comprehensive information for researchers, students in ICT, program developers, military and government organizations, and business professionals.

全文截稿: 2017-10-02

影响因子: 1.277

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-web-semantics

Digital humanities is a new and emerging field, which brings together humanities scholars, social scientists and computer and information scientists to work on agendas of both fundamental and applied research. The field combines digital semantic technologies and (big) digital heritage data. Digital humanities research is typically driven by core questions in each of these disciplines: on the one hand semantic technologies are applied in novel ways in addressing research questions of humanities and social sciences; on the other hand these areas stimulate the development of novel methods in computer and information sciences. This special issue is calling for the submission of novel and impactful research results demonstrating the design, development, evaluation and use of research methods and infrastructures based on Semantic Web technologies for cultural heritage data and use cases in digital humanities scholarship.

全文截稿: 2017-10-30

影响因子: 0.546

期刊难度: ★★

CCF分类: 无

网址: http://www.jucs.org/jucs

Look at virtually any industry - automotive, avionics, oil, telecommunications, banking, semiconductors, pharmaceuticals - all these are highly dependent on computing for their basic functioning. Science and Technology demand high-performance hardware and high-quality software for making improvements and breakthroughs.

Like machinery replaced craftsmanship in the industrial revolution, computers and intelligent parts are quickly pushing their mechanical counterparts out of the market. Contemporary examples of highly complex hardware/ software systems can be found in projects undertaken by Defense, Telecommunications and a variety of other industries.

The demand for complex hardware/software systems has increased more rapidly than the ability to design, implement, test and maintain them. When the requirements for and dependencies on computers increase, the possibility of crises from computer failures also increases. Needless to say, the reliability of computer systems has become a major concern for our society. Recent literature is replete with horror stories of projects gone awry, generally as a result of problems traced to software.

It makes us wonder whether software is reliable at all, whether we should use software in safetycritical embedded applications. With processors and software permeating safety critical embedded world, the reliability of software is simply a matter of life and death. Are we embedding potential disasters while we embed software into systems?

摘要截稿: 2017-07-31

全文截稿: 2017-11-15

影响因子: 2.452

期刊难度: ★★★★

CCF分类: B类

网址: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2330-1643

The field of neuroscience has fruitfully contributed to a wide variety of other fields, for example, economics, marketing and information systems. In the last decade, wide adoption and influence of neuro-physiological (NP) research tools also led the creation of several new sub-fields, including neuroeconomics, neuromarketing and NeuroIS. There is now a growing interest in the use of NP methods in human-information interaction (HII) and interactive information retrieval (IIR)research. The latter interest has been motivated, at least partially, by researchers who regularly utilize search logs, direct searcher observation and questionnaires and interviews as data collection methods and are concerned with the limitations of these traditional methods. Experimental data obtained from NP methods is expected to complement the more traditional data sources and, together, contribute to improving and deepening the understanding of HII1. The deeper understanding offers potential for the development of new information search models. The long-term and primary goal is to create robust and predictive models that go beyond behavioral data. A secondary and additional goal is to develop new search models that can account for physiological and neurological responses to information stimuli and the influence of cognitive and affective states on users' information behavior. The NP methods of potential usefulness to HII include, functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), electro-encephalography (EEG), magneto-encephalography (MEG), eye-tracking (esp. pupilometry). Example research questions include, investigating which cognitive functions are engaged in assessing relevance; establishing differences in NP signals collected when users are assessing relevant vis-a-vis not relevant information; establishing differences in brain activity between easy and difficult search tasks; relating individual differences in search task performance to differences in activations of brain regions.

Early applications of NP methods to HII has resulted in two emerging threads of active research:(1) the investigation of inferring relevance assessment and (2) the study of human responses to search tasks. The results from the two research threads have been disseminated through a number of recent publications that appeared at major international conferences (e.g., ACM CHIIR, ACM SIGIR, ECIR) as well as (less frequently) in scholarly journals (e.g., JASIST). Importantly, a few of these early publications have garnered best-paper awards at major conferences and scholarly venues (including JASIST).  

Unfortunately, many IIR researchers and more broadly information science scholars in general are largely unaware of the new NP methods and NP applications. This special issue aims to increase the awareness of NP methods and their applicability and to showcase the state-of-the-art work in this area, as well as to to examine challenges in applying NP methods to HII and IIR research.  

全文截稿: 2018-01-30

影响因子: 1.32

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/

In recent years, Cloud-assisted Internet of Things (Cloud-of-Things or in short CoT) has emerged as a revolutionary paradigm that enables intelligent and self-configuring (smart) IoT devices and sensors to be connected with the cloud through the Internet. This new paradigm enables the resource-constrained IoT devices to get the benefit from Cloud's powerful and scalable high-performance computing and massive storage infrastructure for real-time processing and storing of the IoT data as well as analysis of the processed information under context using inherently complex models. At the same time, cloud can benefit from IoT by allowing its users to build applications that can use and handle these smart IoT objects interconnected and controlled through software services using cloud infrastructure. Thus, the CoT paradigm can stimulate the development of innovative and novel applications to various areas such as smart cities, smart homes, smart grids, smart agriculture, smart transportation, smart healthcare, etc. to improve all aspects of people's life.

However, currently the CoT paradigm is facing increasing difficulty to handle the Big data that IoT generates from these application use cases. As billions of previously unconnected devices are now generating more than two exabytes of data each day, it is challenging to ensure low latency and network bandwidth consumption, optimal utilization of computational recourses, scalability and energy efficiency of IoT devices while moving all data to the cloud. Hence, in recent times, this centralized CoT model is undergoing a paradigm shift towards a decentralized model termed as edge computing, that allows data computing, storage and service supply to be moved from Cloud to the local edge devices such as smartphones, smart gateways or routers and local PCs that can offer computing and storage capabilities on a smaller scale in real-time. Edge computing pushes data storage, computing and controls closer to the data source(s) instead of performing in a centralized local server or devices as in the case of Fog computing; therefore, enables each edge device to play its own role of determining what information should be stored or processed locally and what needs to be sent to the cloud for further use. Thus, edge computing complements CoT paradigm in terms of high scalability, low delay, location awareness, and allowing of using local client computing capabilities in real time.

While researchers and practitioners have been making progress within the area of edge computing, still there exists several issues that need to be addressed for CoT paradigm. Some of these issues are: novel network architecture and middleware platform for edge and CoT paradigm considering emerging technologies such as 5G wireless networks, semantic computing; edge analytics for Big data; novel security and privacy methods; social intelligence into the edge node to host CoT applications; and context-aware service management on the edge with effective quality of service (QoS) support and other issues.

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