计算机类 | SCI期刊专刊截稿信息7条

计算机体系结构,并行与分布式计算
Future Generation Computer Systems
Special Issue on Security and Trust in Cloud Application Life-Cycle Management
全文截稿: 2018-11-01
影响因子: 3.997
CCF分类: C类
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 计算机：理论方法 - 2区
网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Virtualization, scalability and flexibility features have enabled the widespread adoption of the cloud computing paradigm by both enterprises and individual users. However, trust and security concerns, related for instance to the protection of sensitive data stored by cloud infrastructures, or to the reliability of cloud applications and providers, are still posing limitations to the full exploitation of the loud potential.

As a matter of fact, cloud computing is currently influencing many daily activities, and cloud customers are recently asking for trusted’’ cloud services and applications: a customer that trusts’’ a cloud service or provider i) expects a specific behavior from the trustee (such as providing valid information or ensure a certain level of data privacy); ii) believes that the expected behavior occurs, and iii) is willing to take a certain amount of risk for that belief, which is proportional to the level of trustworthiness towards that provider.

In order to meet these requirements, cloud application designers and developers should address the potential trust and security issues that are relevant for the customers during the whole applications’ life-cycle management, and should adapt to the flexibility offered by the cloud paradigm, while also considering the relevant constraints posed by the stakeholders. This need is tackled by security-by-design and trust-aware approaches, which aim to build cloud applications and services whose security and trust aspects are addressed from the very early stages of the design process.

This special issue focuses on novel solutions and techniques for the development of trust-aware and secure cloud-based applications. We are particularly interested in contributions that focus on the security-by-design development paradigm, able to cope with the different phases of an application life-cycle. The special issue will therefore emphasize (but will not be limited to) the presentation of innovative aspects related to the elicitation and representation of requirements for trusted cloud applications and services, the definition and application of novel risk analysis techniques, the development of automated solutions for security enforcement, and the identification and implementation of strategies for the evaluation of all the aspects that may influence the trustworthiness of a cloud application.

Scope of Special Issue

• Security-driven cloud service negotiation and selection.
• Tools and techniques for the automatic enforcement of security in clouds.
• Security-by-design approaches and DevOps tools for the management of a cloud application’s life-cycle.
• Security evaluation and security review in the cloud.
• Security monitoring.
• Security modeling and model-based approaches for the security assessment of cloud applications.
• SLA-based trust management.
• Trust-based service selection in Cloud Federations.
• Policies and mechanisms for trusted cloud applications.
• Tools for modeling and simulation of trust mechanisms in cloud applications.
• Auditing for trust certification in cloud applications.

计算机科学与技术
Physical Communication
Physical layer techniques and channel modeling enabling new transportation systems (Submission Due: Nov 1, 2018)
全文截稿: 2018-11-01
影响因子: 1.583
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 4区
• 小类 : 工程：电子与电气 - 4区
• 小类 : 电信学 - 4区
网址: https://www.journals.elsevier.com/physical-communication

Over the past decade the use of wireless communications in Intelligent Transportation Systems (ITS) has attracted a significant attention both from the research community and from industry. Thanks to advances in sensor technology, miniaturization, and electronics, it has become possible to make vehicular, aircraft, and high-speed railway systems progressively more aware of their environment. ITS extensively rely on this data to increase safety, reduce cost and maximize efficiency of their operation. Wireless communications technologies provide a platform for the exchange of this sensory data and are particularly suited for the needs of future ITS. Yet to design, evaluate and optimize the forthcoming ITS applications relying on wireless technology, it is essential to understand the effects of propagation conditions specific to ITS and develop physical layer techniques correspondingly.

In contrast to classical wireless communication systems with a fixed basis station and mobile terminals, the physical layer techniques for wireless communications in ITS scenarios are more complicated and challenging due to the highly dynamic and non-stationary channels. Therefore, thorough investigations of the channel models and corresponding physical layer techniques in different ITS relevant scenarios are necessary.

To promote communication between researchers of physical layer techniques and wireless propagation in ITS environments, we invite investigators to contribute original research articles as well as review articles that will stimulate the continuing efforts to model the wireless channels and develop corresponding physical layer techniques in ITS over vehicular, aircraft and high-speed railway systems:

• Propagation and wireless channel measurement, simulation, and modeling
• MIMO and massive MIMO for ITS
• Communications for high mobility transportation systems
• Radio technologies applied to public transportation systems.
• Physical layer techniques for public transportation control and signaling
• Physical layer techniques for connected vehicles
• High speed communications technology and services for passengers
• Wireless transmission of critical communications
• Wireless technologies for automated and connected vehicles
• Millimeter wave and THz communications enabling smart rail mobility

计算机体系结构,并行与分布式计算
Future Generation Computer Systems
Special Issue on Cognitive-inspired Computing and Applications
全文截稿: 2018-11-10
影响因子: 3.997
CCF分类: C类
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 计算机：理论方法 - 2区
网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Cognition is emerging as a new and promising methodology with the development of cognitive-inspired computing, cognitive-inspired interaction and systems, which has the potential to enable a large class of applications and has emerged with a great potential to change our life. However, recent advances on artificial intelligence (AI), fog computing, big data, and cognitive computational theory show that multidisciplinary cognitive-inspired computing still struggle with fundamental, long-standing problems, such as computational models and decision-making mechanisms based on the neurobiological processes of the brain, cognitive sciences, and psychology. How to enhance human cognitive performance with machine learning, common sense, natural language processing etc. are worth exploring.

The objective of this special issue is to bring together state-of-the-art research contributions that address these key aspects of cognitive-inspired computing and applications. Original papers describing completed and unpublished work not currently under review by any other journal/magazine/conference are solicited. Specific topics include, but not limited to, the following:

• Cognitive-inspired computing fundamentals
• Cognitive-inspired computing systems
• Cognitive-inspired computing with big data
• Cognitive-inspired intelligent interaction
• AI-assisted cognitive computing approaches
• Brain analysis for cognitive-inspired computing
• Internet of cognitive Things
• Cognitive environment, sensing and data
• Cognitive robots and agents
• Security issue in cognitive-inspired computing
• Test-bed, prototype implementation and applications

计算机体系结构,并行与分布式计算
Future Generation Computer Systems
Special Issue on Trusted Cloud-Edges (CE) Computations
全文截稿: 2018-11-30
影响因子: 3.997
CCF分类: C类
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 计算机：理论方法 - 2区
网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Current and future service-based software needs to remain focused towards the development and deployment of large and complex intelligent and networked information systems, required for internet-based and intranet-based systems in organizations, as well to move to IoT integration and big data analytics. Today, service-based software covers a very wide range of application domains as well as technologies and research issues. This has recently found realization through the integration of cloud computing and IoT, forming a revolutionary paradigm, cloud-assisted Internet of things (CoT), that enables intelligent and self-configuring (smart) IoT devices to be connected with the cloud through the Internet. However, huge volume of data generated from real-world applications leads to the operation difficulty of CoT paradigm. More specifically, while billions of connected devices will generate exabytes of data every day, moving all the data from comparably resource-constrained IoT devices to the cloud becomes a big challenge. Hence, the centralized CoT model is undergoing a paradigm shift towards a decentralized model termed as edge computing, where local and distributed edge devices such as smartphones, smart gateways, and local PC with weaker capability than the centralized cloud can offer cloud-like service for only a limited group of devices. While the centralized cloud is still inevitable for the heavyweight computation needs, in this emerging Cloud-Edges (CE) paradigm, the cloud, together with local edge devices jointly offer services and intelligence. CE paradigm complements traditional CoT paradigm in terms of high scalability, low delay, location awareness, and instant local client computing capabilities. Nonetheless, due to the multiple and even highly distributed roles in CE paradigm, vital elements in such CE paradigm are the notions of trust, security, privacy and risk management among the cloud, edge devices, and end devices.

This special issue solicits submissions from both academia and industry presenting novel research in the context of trusted Cloud-Edges (CE) computations, presenting theoretical and practical approaches to cloud, big data, IoT and edge computing trust, security, privacy and risk management. This special issue will provide a special focus on the intersection between cloud paradigm, big data analytics, IoT integration and edge computing, bringing together experts from the four communities to discuss on the vital issues of trust, security, privacy and risk management in cloud computing, shedding the light on novel issues and requirements in domains of big data, IoT, and edge computing. Potential contributions could cover new approaches, methodologies, protocols, tools, or verification and validation techniques. We also welcome review papers that analyze

critically the current status of trust, security, privacy and risk management in the cloud, big data, IoT, and edge computing. Papers from practitioners who encounter trust, security, privacy, and risk management problems, and seek understanding are finally welcome. Best papers from the 7th International Symposium on Secure Virtual Infrastructures - Cloud and Trusted Computing (http://www.otmconferences.org/index.php/conferences/ctc-2018) will be also invited to submit to the special issue.

The following themes are of particular interest:

• Authentication, auditing and accountability in Cloud-Edges
• Edge computing architectures and solution design patterns
• Edge computing for IoT
• Communication and networking protocols for Cloud-Edges
• Fine-grained access control mechanism in Cloud-Edges
• Privacy-preserving computation in Cloud-Edges
• Trust and reputation issues in Cloud-Edges
• Security architecture for Cloud-Edges
• Key management in Cloud-Edges
• IoT communication in Cloud-Edges
• Data caching for big data in Cloud-Edges
• Big data analytics in Cloud-Edges
• Incentive models or techniques for data processing in Cloud-Edges
• Privacy-Enhancing Cryptographic Techniques in Cloud-Edges
• Secure Data Analysis and Private Learning
• Outsourced or Verifiable Computation in Cloud-Edges
• Secure Software-Defined Networking and Virtualization for in Cloud-Edges
• Security for Crowdsourcing in Cloud-Edges

数据库管理与信息检索
Information Processing & Management
Special Issue on Marginalized Communities
全文截稿: 2018-11-30
影响因子: 2.391
CCF分类: B类
中科院JCR分区:
• 大类 : 工程技术 - 3区
• 小类 : 计算机：信息系统 - 3区
网址: http://www.journals.elsevier.com/information-processing-and-management/

The Special Issue aims to investigate issues in relation to empowering marginalised and vulnerable communities in the digital age and the creative design and use of emerging technologies to promote social innovation. Researchers from the disciplines of library and information sciences, human-computer interaction, and community informatics are encouraged to submit their related works.

The intersection between digital information worlds and vulnerable communities is a critical research area within information sciences and human-computer interaction. There have been concerns about issues regarding accessibility, bias, social exclusion, cyber-racism, cyberbullying, digital divide, misinformation, usability, and other information sharing hazards in the information and technology experiences of vulnerable groups and populations.

According to Aday (1994), to be vulnerable is to be in a position of being hurt, marginalised, or ignored, as well as helped, by others. Vulnerable people typically include women and children, ethnic people of colour, immigrants, LBGTQI populations, the homeless, and the elderly (Flaskerud & Winslow, 1998). But it should be noted that not everyone in a particular category is vulnerable. A simplistic label of vulnerability risks ignoring people’s resilience and capacities (Gatehouse et al., 2018; Vines et al., 2014; Vyas & Dillahunt, 2017).

Much remains unknown about vulnerability in the context of emerging technologies and social innovation. For example, how do we define or conceptualise vulnerability? What are the main digital disadvantages for vulnerable communities? What are the unique needs and information behaviours of these communities? To what extent do technologies empower the vulnerable communities and what are the associated challenges? What applied methodologies should researchers adopt and adapt in order to have an impact in the area of racial and social justice? How should we evaluate the role of emerging technologies such as virtual reality, social robots, artificial intelligence, and big data analytics in promoting social and emotional wellbeing and are their uses culturally appropriate? To name a few.

The Special Issue is intended to present a unique collection of outstanding studies addressing the relationships among marginalised and vulnerable communities, emerging technologies, and social innovation in the digital age. We look for theoretical and methodological advances and contributions to this important area of study.

Topics include but are not limited to:

• Definition and conceptualisation of vulnerability
• Vulnerable and marginalised communities’ experience of information technologies
• Big data and vulnerable and marginalised communities
• Digital libraries and vulnerable and marginalised users
• Everyday life information behaviour and technology use of older adults
• Information experience of migrants and refugees
• Information service model for minorities
• Information practices of indigenous people in the technology-penetrated society
• Homeless population in the digital age
• Technology design and use by people with disabilities
• Approaches or methods to study vulnerable and marginalised groups
• Ethical issues and challenges of studying information and technology use with vulnerable and marginalised groups
• Strategies for good practice in the design and deployment of emerging technologies forvulnerable and marginalised groups
• Evaluations of technologies for the public good
• Accessibility and usability guidelines to support people with disabilities

数据库管理与信息检索
Information Sciences
Special Issue on Recent Advances in Type-2 Fuzzy Decision Making: Theories and Applications
全文截稿: 2018-11-30
影响因子: 4.832
CCF分类: B类
中科院JCR分区:
• 大类 : 工程技术 - 2区
• 小类 : 计算机：信息系统 - 1区
网址: http://www.journals.elsevier.com/information-sciences/

The management of uncertainty within decision-making problems is still a very challenging research issue, despite the different proposals developed across the time. One of the most interesting research topics in recent years is the use of type 2 fuzzy sets in decision making processes. As a generalization of fuzzy sets, type-2 fuzzy sets (T2FS) can address higher type uncertainty present in natural and human systems. In the last decade, we have witnessed a rapidly growth of T2FS in multiple criteria decision-making, group decision making, system optimization and control problems and their applications to various fields. Compared with the developments of other fuzzy decision-making areas, the studies on type-2 fuzzy decision makings are relatively recent, a few in the existing literature and lack of systematic and in-depth research in models and solutions. It seems to be the right time to establish new theories, methodologies and tools of type-2 fuzzy in decision making that may lead to new breakthroughs in this area.

As a fundamental theory in decision making and information fusion for fuzzy decision information, T2FS method is quite important because it exhibits many interesting and open problems. Meanwhile, both interval and general type-2 fuzzy decision-making are worth studying in big data environment. This special issue aims at offering a systematic overview of this research field and providing novel and innovative approaches, models and application systems to effectively support decision making in data-driven and knowledge-oriented environments. In particular, new innovative approaches to general type-2 fuzzy decision-making theory and management applications, or newly decision-making models in big data-driven are especially welcome. We warmly invite scholars from worldwide to submit original and high-quality papers to this special issue. It is anticipated that this special issue will help foster future studies on type-2 fuzzy decision making towards forming their own theories and models.

计算机体系结构,并行与分布式计算
Journal of Parallel and Distributed Computing
Special Issue on Advances on Parallel and High Performance Computing for AI Applications
全文截稿: 2018-11-30
影响因子: 1.93
CCF分类: B类
中科院JCR分区:
• 大类 : 工程技术 - 3区
• 小类 : 计算机：理论方法 - 3区
网址: http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/

Artificial Intelligence (AI) and Machine Learning (ML) have grown substantially in popularity in recent years. Much research has been done in both academia and industry, with applications in many areas. For example, deep learning has achieved superhuman performance in image classification. AI/ML have been used to play games such as Chess, Go, Atari and Jeopardy very successfully. In addition, many companies have being using AI and ML in areas such as health care, natural resource management and advertisement.

Most of the AI/ML technologies and applications require heavy use of high performance computers and accelerators. Consequently, High Performance Computing (HPC) is a key component of these systems. Clusters of computers and accelerators (e.g. GPUs) are routinely used to train and run models, both in research and production. On the other hand, ML and AI have also become a "killer application" for HPC and, consequently, have driven much of research in this area. For example, tailored computer architecture has been devised and new parallel programming frameworks developed to accelerate AI/ML models. The objective of this special issue is to bring together the HPC and AI/ML communities to present their applications and solutions to performance issues, and also to present how AI/ML can be used to solve HPC problems.

This is an open call for contributions. Authors are invited to submit papers to this special issue on themes related to the interplay of HPC and AI/ML. A selection of papers from the High Performance Machine Learning Workshop (HPML2018) will be invited to submit extended versions with at least 40% of new material to be considered for this special issue.

Topics of interest include, but are not limited to:

• Machine learning (including deep learning) models
• Large-scale machine learning applications
• Statistical models
• Large-scale data analytics
• Machine learning applied to HPC
• Accelerated Machine Learning
• HPC applied to Machine Learning
• Benchmarking, performance measurements, and analysis of ML models
• Hardware acceleration for ML and AI
• Parallel ML and AI models
• HPC infrastructure and resource management for ML
• Novel HPC architecture for AI/ML

原文发布时间为：2018-06-29
本文作者： Call4Papers
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