【今日新增】SCI期刊专刊截稿信息9条

全文截稿: 2017-06-01

影响因子: 0.8

期刊难度: ★★★

CCF分类: C类

网址: http://www.springer.com/journal/10619/about

Distributed transaction processing is an old and deeply rooted field in the intersection of databases and distributed systems. Recent advances in cloud computing systems have reinvigorated the field. Distributed transactions are being reconsidered for an entirely changed landscape.

This special journal issue focuses on conceptual, systems, and algorithmic aspects of large-scale, Massively Parallel Processing and/or geo-distributed transaction processing. Since a large body of work already addresses several facets of this problem, we are seeking contributions that push the start-of-the-art towards novel transaction processing paradigms, as well as those that provide a deep understanding and assessment of the status quo and real-world experiences. The main goal of the special issue is to provide a sanpshot of the state of the art and a useful point of reference for research in the area of distributed transaction processing.

This special issue seeks articles describing significant research contributions in the domain of distributed OLTP. Areas of interest include, but not limited to, the following:
- Concurrency control
- Relaxing transaction consistency semantics
- Cloud and/or on-premises geo-distributed OLTP
- OLTP over geo-replicated stores
- OLTP over graphs
- Partitioning techniques
- Exploitation of modern hardware to support transaction processing
- Real-world transaction processing applications
- New SQL systems

全文截稿: 2017-06-30

影响因子: 1.446

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/computer-networks

Software-defined Networking (SDN) and Network Functions Virtualization (NFV) are envisioned to massively change network management by enabling a more flexible management of complex networks. While the aim of SDN is to split the control and data plane and to introduce open interfaces between these layers, NFV abstracts network functions from dedicated hardware to virtual machines running on commodity hardware. Consequently, applying SDN/NFV is claimed to have a high business advantage in terms of cost savings and additional revenue sources for network operators, new opportunities for solution providers, and opening new business models.

However, major performance challenges arise when realizing SDN/NFV given the overheads imposed by software and virtualization stacks. At the same time, e.g., the outsourcing of network control or the relocation of network functions to cloud services create new challenges on data privacy and network security.

This special issue of the Computer Networks Journal solicits original, high-quality papers that present, analyze and discuss solutions to improve the security and privacy in SDN/NFV, mechanisms to achieve high packet processing performances in virtualized environments, as well as performance benchmarking aspects and standards. Related topics, such as new security mechanisms enabled by SDN/NFV (e.g. mitigation of DDoS attacks), validation, verification and certification of network functions, design of energy efficient NFV networks, new algorithms controlling the function placement, as well as new services offerings enabled by SDN/NFV (e.g. to improve the end-user experience), techno-economic aspects (e.g. new pricing and business models), and others are also within the scope of the special issue.

全文截稿: 2017-06-30

影响因子: 1.446

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/computer-networks

Cloud-assisted Cyber-Physical Systems (CPS) feature a tight coupling between embedded computing devices and their physical environment. CPSes can be viewed as the bridge between physical components/processes and the cyber space. Specifically, the notion of CPSes is to use computing (e.g. sensing, analyzing and predicting), communication (e.g. interaction, intervene and interface management), and controlling (e.g. inter-operation, evolving and evidence-based certification) to make intelligent and autonomous systems. Recent years have seen a dramatic rise in the development of CPSes services, including ubiquitous health care, smart electricity grid, and smart buildings. However, the fast-growing data volume is hard to process. The present CPSes cannot support ultra-fast computing, and thus it cannot provide real-time and reliable services to meet the requirements, which are essential for mission-critical systems. Fortunately, cloud infrastructures and platforms can provide flexible and on-demand processing power and high-capacity storage for data streams, as well as provisioning of a variety of services using telecommunication and networking technologies. Thus, the large-scale nature of CPSes can be effectively and efficiently supported and assisted by cloud systems, which is referred to as cloud-assisted CPSes (Cloud-CPS).

The coupling of cloud systems and CPSes, though advantageous, is subject to new forms of risks that have not been considered adequately in the traditional computing domain. CPSes often collect sensitive and private information about the physical environment. A loss of security for a CPS can therefore have significant negative impact including loss of privacy, potential physical harm, discrimination, and abuse. Though numerous security primitives have been developed in the cyber domain to address the very same problems, their applicability to the Cloud-CPSes domain is still questionable due to the reason that they are usually complex to implement and oblivious to cyber-physical interactions.

The goal of this special issue is to unveil and address the security and privacy aspects associated to the Cloud-CPeSs.

Suggested topics include, but are not limited to the following.
- Secure data sharing in Cloud-CPSes
- Big data security and privacy in Cloud-CPSes
- Secure computation in Cloud-CPSes
- Location privacy in Cloud-CPSes
- Lightweight block ciphers for low-resource devices in Cloud-CPSes
- Searchable encryption in Cloud-CPSes
- End to end secure communications in Cloud-CPSes
- Access control in Cloud-CPSes
- Key management in Cloud-CPSes

全文截稿: 2017-08-01

影响因子: 1.64

期刊难度: ★★★★

CCF分类: B类

网址: http://www.journals.elsevier.com/computers-and-security

Our increasingly interconnected society relies on the security of the ICT infrastructures underpinning public transportations, financial services, smart factories and cities, etc. Moreover, these ICT infrastructures are a rich source of data (e.g., data collected from our personal and daily activities, such as trip information and medical records), which can be subject to criminal exploitation and abuse. Therefore, it is unsurprising that security and privacy protection remain ongoing research topics.

Another key societal challenges is climate change, and increasingly it is recognized that when we design ICT infrastructures, including security and privacy protection solutions, we should ensure their impact on the environment. In other words, we need to design green solutions for security and privacy (e.g., efficiency or reduced energy usage during data exchange and processing), including for lightweight devices and deployment (e.g., Internet of Things, Wireless Sensor Networks and Mobile Computing).

The aim of this special issue is to solicit contributions from both academia and industry describing novel lightweight or green sustainable security and privacy solutions, as well as concrete use cases, which can be deployed in our ICT infrastructures underpinning the various critical infrastructure sectors.

Topics of interest include, but are not limited to:
- Methods and models for sustainable security solutions
- Optimization techniques for sustainable security solutions
- Cloud automation techniques for sustainable security solutions
- Efficient cloud brokering services
- Cloud pricing models for sustainable security solutions
- Security driven cloud configuration management
- Efficient computing, communication and security architectures
- Energy-aware secure mobile solutions
- Energy-aware Internet of Things solutions
- Energy-efficient management and control of ICT resources  
- Energy efficiency in data centers  
- Energy efficiency and quality of service
- Biometric-based sustainable tools
- Energy consumption measurements, models, and monitoring tools
- Security and Privacy for resource-constrained devices
- Algorithms for reduced power, energy and heat
- Lightweight cryptography algorithms and protocols

全文截稿: 2017-08-15

影响因子: 1.115

期刊难度: ★★★

CCF分类: 无

网址: https://www.computer.org/computer-magazine/

Organized increasingly around computers and the information economy, our modern lives require very little physical activity, and, for nourishment, we have embraced the convenience and thriftiness associated with fast, processed foods. As a result, people living in developed nations face serious health challenges related to sedentary lifestyles and diseases arising from nutritional lapses. It is therefore essential that we find new ways to improve health so that we can live longer and age well. Whereas research has traditionally focused on biomedical science and advances in clinical practices, recent efforts have made significant strides in finding novel ways to promote behavioral changes to improve health outcomes. In fact, avoiding unhealthy habits is currently a major epidemiological priority. To achieve this goal, innovative approaches that automatically and autonomously identify troublesome behaviors are essential in leading to lasting, beneficial behavioral changes.

With an increasing number of smart, ubiquitous sensing systems developed and deployed across the globe, we are primed to help coach people to have healthier behaviors. The increasing accountability associated with app- and device-based behavior tracking not only provides timely and personalized information and support, but also gives us an incentive to set goals and do more.

The guest editors of this special issue aim to bring together the latest advances, experiences, findings, and developments related to smart sensing, modeling, and understanding of human behavior and how these can be useful in service of automated or smart coaching in support of adopting healthier habits and improving health.

We invite novel, innovative, and exciting contributions relating to sensor systems used for behavior monitoring and coaching, the ethical and societal aspects of such technologies, and how best to model behavior to improve the effectiveness of e-coaching strategies. Additional topics for this special issue could also include:
- wearable, mobile, and ubiquitous health sensing systems;
- participative, crowd, and collective sensing;
- collective e-coaching;
- context-awareness and semantic modeling;
- physical and virtual coaching systems;
- e-coaching system design;
- technology-enabled behavioral change;
- persuasive tactics and associated ethics their use;
- e-coaching applications to address specific domains such as weight control, sleep deprivation, or exercise promotion; and
- managing clinical trials and evaluation frameworks.

全文截稿: 2017-09-01

影响因子: 1.361

期刊难度: ★★★

CCF分类: 无

网址: www.computer.org/cise

Much attention is given to computational science advances coming from renowned universities and government-funded projects in western countries like the United States. But many advances in HPC, visualization, and scientific computing algorithms are also coming from developing countries.

This special issue of Computing in Science & Engineering examines the state of computational science in developing countries. Areas of interest include, but are not limited to:
- education
- computing infrastructure
- network infrastructure
- application development

全文截稿: 2017-10-01

影响因子: 2.857

期刊难度: ★★★

CCF分类: 无

网址: http://www.journals.elsevier.com/applied-soft-computing/

Prognostic health management (PHM), studying machine failure detection and management of its life-cycle, is a research area of growing interest because of the economic cost associated with undiagnosed machine failure. A complex manufacturing plant usually consists of a number of massive inter-related components. A failure of a particular component frequently imposes a complete shutdown of the plant process meaning a complete stop of the production cycle. An equipment failure imposes millions of dollars in costs for repair materials, labour and interruption of production cycles, since components are manufactured half way across the world and there may only be few places where they are manufactured. Aging of machinery and its components makes machinery vulnerable to failures. This problem cannot be completely addressed by regular maintenance, carried out at pre-scheduled time periods and requires "maintenance on-demand", during the specific time period, when the machine is likely to fail. The optimization of machinery service and the minimization of life-cycle costs demand advanced soft computing approaches to predict when a machine will no longer be able to perform with satisfactory functionality as well as to monitor a machine condition while running the process without interruption and to alert operators when a fault comes into picture. Prognostics or prediction of the remaining useful life (RUL) plays a crucial role in PHM to provide accurate decision support for maintenance on-demand. While fault detection has been well researched, the prognostics of the likely occurrence of a fault before it occurs has recently started to be a major focus of investigation. Note that accurate prediction of a machinery's RUL leads to flexibility of maintenance on demand such as advanced scheduling of maintenance activities, proactive allocation of replacement parts and enhanced fleet deployment decisions based on the estimated progression of component life consumption. The prediction of RUL aims to make use of the monitoring information of in-service machinery and its past operation profile in order for RUL to be identified before a failure occurs. Nonetheless, development of a reliable predictive methodology to feed accurate information of lifetime of machinery or to monitor tool condition in real-time remains a very complex issue to be dealt with. This special issue aims to bring together research works of soft computing including but not limited to metaheuristic, fuzzy system, neural system, hybrid and probabilistic systems with application to the PHM. Special attention will be paid toward algorithmic development of advanced soft computing to address advanced issues of PHM in various application domains.


The main topics of this special session include, but are not limited to, the following:

[Basic Methodologies]
- Advanced soft computing for fault detection and diagnosis
- Advanced soft computing for tool condition monitoring
- Advanced soft computing for estimation of tool's remaining useful life

[Advanced Concepts]
- Appropriate handling of data uncertainty in various forms in PHM
- Data stream analytics for PHM
- Big data analytics for PHM
- Techniques to address drifts and shifts for PHM
- On-line dynamic dimension reduction for PHM
- Feature selection and extraction techniques for PHM
- Sample selection and active learning for PHM
- Reliability in model predictions and parameters for PHM
- Domain adaptation, importance weighting and sampling for PHM
- Parameter-low and -insensitive learning methods for PHM
- On-line complexity reduction to emphasize transparent, more compact models for PHM
- Unsupervised approach for PHM
- Anomaly detection for PHM
- Outlier detection for PHM
- Noise Cancellation for PHM

[Applications]
- Complex manufacturing process
- Data stream modelling and identification (supervised and unsupervised)
- Online fault detection and decision support systems
- Online media stream classification
- Predictive maintenance and prognostics
- Fault isolation
- Process control and condition monitoring
- Modelling in high throughput production systems
- Adaptive chemometric models in dynamic chemical processes
- High-speed machining process
- Robotics, Intelligent Transport and Advanced Manufacturing
- Optimization of complex manufacturing systems
- Feedback control systems
- Intelligent Control Systems

全文截稿: 2017-11-01

影响因子: 1.115

期刊难度: ★★★

CCF分类: 无

网址: https://www.computer.org/computer-magazine/

Twenty-eight years ago, Tim Berners-Lee defined the technical foundations for the World Wide Web. Since then, its adoption has driven some of the most significant societal changes in history, thanks to both exponential growth and related technological reverberations. The Web's influence has transformed business models and the future of work, empowered our ability to realize human rights and mobilize grassroots social movements, and reinvented our goals around human health and the pursuit of happiness. The Web's infrastructure has also created a paradigm shift in how we learn, communicate, and seek entertainment.

Of course, with every technological advancement, there is a cost. For one, the digital divide-that is, the quality of life disparity between those who have and do not have access to the Web-puts added pressure on the poorest and most vulnerable societies around the globe. Another challenge we face due to the success of the Web is how best to balance data ownership requirements and protection of an individual's privacy. The dangers of failing to meet this particular challenge could result in a crisis of trust stemming from potentially overwhelming surveillance or an inability to prevent physical or virtual crimes on a massive scale. Ultimately, the Web's future success or failure will rely on whether it abides by principles of fairness, inclusivity, and open governance.

In 2006, Web Science was created as the field to explore all areas of the Web from a socio-technical perspective, including mathematical properties, engineering principles, and social impact [T. Berners-Lee et al., Science, 2006, vol. 313, no. 5788, pp. 769-771]. Eleven years on, we are still learning.

The guest editors of this special issue seek articles that help establish the current state of the art and science in Web Science as well as highlight major challenges and barriers to the Web's future development.

Articles could address topics such as
- architecture and philosophy of the Web;
- social machines: collective intelligence, collaborative/peer production, and the future of work;
- Web-enabled social movements;
- Web economics, social entrepreneurship, and innovation;
- socio-technical analysis of the emergence and impacts of online social and information networks;
- governance, democracy, access, and intellectual property;
- personal data and privacy;
- security and trust;
- the digital divide: global access and development;
- knowledge, education, and scholarship on and through the Web ;
- Web Science and the Internet of Things; and
- future Internet.

全文截稿: 2017-11-01

影响因子: 1.361

期刊难度: ★★★

CCF分类: 无

网址: www.computer.org/cise

Computation is widely accepted today as a third "pillar" of science alongside theory and experimentation. Nearly every science and engineering field relies on computational modeling and data analysis for both research and education. Even large-scale computing, or supercomputing, is becoming highly accessible, thanks largely to advances in low-cost cluster computing. We are truly living in an era of supercomputing democratization.

Therefore, it is no surprise that the last decade has yielded several major scientific discoveries that supercomputing enabled. Just last year, a major discovery in gravitational physics - the first-ever direct detection of gravitational waves (which Einstein theorized a century ago) - was possible partly due to supercomputer simulations of black hole binary systems. Another recent discovery involved the detailed simulation of a full-length p53 protein and is likely to boost anti-cancer drug discovery.

The goal of this special issue is to showcase recent scientific and engineering advances that supercomputing enabled, as well as to explore supercomputing's potential for enabling further discovery. We invite the scientific computing community to submit original papers on this topic. Any area of science or engineering is welcome. We are particularly interested in advances that have had a great impact on a given field.

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