计算机类 | CCF推荐期刊约稿信息7条

软件工程

Journal of Systems and Software

Special Issue on Software Product Line Engineering

全文截稿: 2018-01-15

影响因子: 2.444

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：软件工程 - 2区

  • 小类 : 计算机：理论方法 - 3区

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

We invite papers on innovative techniques in all areas related to software product line (SPL) engineering and variability management. We particularly welcome, but the call is not limited to, papers accepted at the 21st International Systems and Software Product Line Conference (SPLC 2017). http://tinyurl.com/JSS-SPLC17

Topics of interest include, but are not limited to:

- Requirements engineering and domain analysis for SPLs

- Business process management, economics and organizational issues of SPL engineering

- Architecture, design, and implementation of SPLs

- Variability management and modeling

- Advances in testing of configurable systems and SPLs

- Analysis techniques such as model-checking, formal methods of SPLs

- Non-functional properties and performance for SPL engineering

- Multi-product lines, ecosystems product lines of product lines, systems of systems

- Mining and reverse engineering of variability

- Dynamic, adaptive, and reconfigurable systems

- End-user concerns and usability of SPLs

- Open source SPLs, software ecosystems, and supply chains

- Domain-specific (modeling) languages and SPLs

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Innovating the Network for Data Intensive Science - INDIS 2017

全文截稿: 2018-01-31

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/

Many fields of science have been experiencing and continue to experience a large influx of data. Managing, transporting, and architecting systems, as well as building tools to deal with the delivery of these data has become increasingly important. Additionally, the ecosystem of information and communications systems is becoming more complex.

Wide area networks are now an integral and essential part of this data-driven supercomputing ecosystem connecting information sources, data stores, processing, simulation, visualization and user communities together. Furthermore, networks are required to connect research instruments such as photon sources, and large visualization displays.

Networks for data-intensive science have more extreme requirements than general-purpose networks. These requirements not only closely impact the design of processor interconnects in supercomputers and cluster computers, but they also impact campus networks, regional networks and national backbone networks.

The developments in network technologies are tremendous. Speeds of many hundreds of Gigabits and deep programmability of network infrastructure are now common. This enables a fundamentally different approach of integrating networks in supercomputing applications.

The INDIS workshop (https://scinet.supercomputing.org/workshop/) held in conjunction with the SuperComputing conference in Denver in November 2017 provided a venue for the exchange of ideas on the above topics.

For this special issue related to the workshop we encourage research papers that address one or more of these networking needs; and developments that are essential in the information systems infrastructure for the scientific discovery process. Participants to the workshop are invited to submit extended version of their work. Other submissions are also welcome.

Topics of interest include but are not limited to:

- High-speed network protocols

- Network architectures

- Securing high-speed networks

- High performance data transfer applications and techniques

- Science DMZs and other campus network constructs

- Software-defined networking , OpenFlow and NFV

- Optical networking

- Network monitoring and traffic analytics

- Requirements and issues for network quality of service (QoS)

- Network management: diagnostics, troubleshooting, fault management, performance monitoring, configuration management

- Multi-domain networking

计算机科学与技术

Microprocessors and Microsystems

Special Issue on Reconfigurable Communication-centric Systems-on-Chip

全文截稿: 2018-01-31

影响因子: 1.025

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 4区

  • 小类 : 计算机：理论方法 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: https://www.journals.elsevier.com/microprocessors-and-microsystems

Systems-on-Chip (S0Cs) are becoming the dominant devices in the post-PC era. SoCs offer small form-factor, reduced cost and power, and their underlying IP-based design methodology allows for an easy and standardized integration of cores on a single die. These characteristics make SoCs ideal candidates for usage in mobile devices such as smartphones and tablets. Yet, SoC design also exhibits some significant challenges. As the number of cores increases and composition of cores becomes more and more heterogeneous, system design and especially the on-chip communication requirements become more complex. This is especially demanding for SoC designs comprising runtime reconfigurable components. Their runtime flexibility impede a comprehensive requirements analysis at design time and, thus, adaptivity mechanisms have to be incorporated in the overall system design.

This special issue is devoted to extended journal versions of selected papers from the 12th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC’17) held in Madrid, Spain, during July 12 to 14, 2017.

ReCoSoC has established itself as a reference for researchers in the areas of reconfigurable and communication-centric systems-on-chip. This special issue will also include other papers that address the same topics as the ReCoSoC conference.

Topics Include:

- New paradigms for reconfigurable and comm. centric computing

- Self-aware, reconfigurable and adaptive embedded SoCs

- Communication-centric design techniques at various levels

- On-chip communication architectures

- Fault tolerance techniques for SoCs

- Low power design of reconfigurable and multiprocessor SoCs

- Communication-aware multiprocessor embedded systems

- OS and middleware for reconfigurable and multicore SoCs

- Specification languages and system design methodologies

- Verification and evaluation techniques

- Industrial case studies

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Smart Cyber-Physical Systems: towards Pervasive Intelligence systems

全文截稿: 2018-03-15

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/

Cyber Physical Systems (CPS) refer to the seamless integration of computation with physical processes, possibly with humans in the loop. In these systems, embedded computers and networks monitor (through sensors) and control (through actuators) the physical processes, usually with feedback loops where physical processes and computations affect each other.

A key point in these systems is the control of physical processes from the monitoring of variables and the use of computational intelligence to obtain a deep knowledge of the monitored environment, thus providing timely and more accurate decisions and actions. The growing interconnection of physical and virtual worlds, and the development of increasingly sophisticated intelligence techniques, has opened the door to the next generation of CPS, that is referred to as smart cyber-physical systems (sCPS).

sCPS are large‐scale software intensive and pervasive systems, which by combining various data sources (both from physical objects and virtual components), and applying intelligence techniques, are able to efficiently manage real-world processes and offers broad range of novel applications and services.

By equipping physical objects with interfaces to the virtual world, and incorporating intelligent mechanisms to leverage collaboration between these objects, the boundaries between the physical and virtual worlds become blurred. Interactions occurring in the physical world are capable of changing the processing behavior in the virtual world, in a causal relationship that can be exploited for the constant improvement of processes. Intelligent, self-aware, self-managing and self-configuring pervasive systems can be built to improve quality of process across a variety of application domains, helping to address a number of contemporary social and environmental issues.

Components of a sCPS must have a high degree of autonomy while cooperating with each other in a robust, scalable and decentralized way. However, several challenges need to be overcome in order to realize such a paradigm, which is highly multidisciplinary. These challenges range from the design of intelligent physical infrastructures for sensing and communication, data stream processing, data analytics and machine learning techniques to build the intelligence core of these systems through the development of self-adaptive and context-aware software. Moreover safety, social and behavioral issues also need to be considering, when including human beings as an integral part of these highly complex systems.

This special issue is intended to report high-quality research on recent advances toward the realization of the Smart Cyber-Physical Systems paradigm. We are interested in all aspects pertaining to this multidisciplinary paradigm, in particular, in its application to building Smart and sustainable spaces. Topics of interest include, but are not limited to, the following:

- Deep Learning and Deep Computation for CPS

- Big Data and Smart Data

- Social Intelligence and Agent-based Computing

- Ubiquitous Intelligence and Cyber-Physical Computing

- The Internet of Things

- Embedded Hardware, Software & Systems

- Pervasive Devices, Wearable Computers, RFIDs, Sensor technology

- Pervasive Networks and Communications

- Middleware for CPS

- Pervasive device virtualization (PDV)

- Privacy, Security and Trust in CPS

- Context-Aware Computing for CPS

- Situation-Aware Reasoning and Recognition

- Mobile Data Mining and Ubiquitous Data Mining

- Smart Urban Spaces and Smart Homes

- Intelligent Social Networking

- Pervasive Technologies for Intelligent Transportation Systems

- Ambient Intelligence

- HCI for Pervasive Computing

- Semantic Technologies for CPS

- Mobility and Multimedia Data Traffic Modelling

- Rapid Application Development for CPS

- Programming Abstractions for CPS and Pervasive Systems

- Cyber-Physical Hybrid Intelligence

- Cyber-Social Networks

- Cyber-Sociology, Cyber-Culture, and Cyber-Economy

- Cyber-Social Simulation

- Cyber-Behaviour Analytics

- Cyber-Crowdsourcing

- Cyber-Trust, Cyber-Privacy, Cyber-Rights, Cyber-Crime, Cyber-Law

- Cyber-Telepathy, Anticipatory Computing

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special issue on “Time-critical applications on software defined infrastructure”

全文截稿: 2018-04-10

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/

Time-critical applications are industrial and scientific applications with strict, often real-time performance requirements, typically expressed as constraints on the Quality of Service (QoS) (e.g. response time upon detection of a tsunami event) or Quality of Experience (QoE) (e.g. stable delivery of ultra-high definition video to content distributors) presented to their users. Such applications often involve distributed components between which large volumes of data must reliably be transferred—for example applications which provide early disaster warning often include remotely deployed sensors, while many live event broadcast scenarios require direction of multiple video sources; these components exist on the periphery of a larger system with data storage, processing and access services. The development of such applications is usually difficult and costly, because of the strict requirements imposed on the runtime environment, which often require careful engineering of system components and complex internal validation procedures when integrating those components into a single functioning system.

Software-defined infrastructure (SDI) provides virtualised, elastic and controllable on-demand services for hosting networked distributed applications, opening up new possibilities for the provisioning and optimisation of various industrial and e-science workflows. With the increasing ubiquity of online data sources and Internet-of-Things (IoT) connected devices as well as every increasing network capacity driving a proliferation of potential use-cases, the use of SDI provides an appealing alternative to expensive specialised infrastructure, allowing developers and data scientists to be collectively more ambitious than ever before. The complexities of SDI however make it difficult to determine how best to optimise infrastructure for different kinds of application, especially in light of initiatives to provide such infrastructure to the widest possible community in order to encourage new commercial and scientific collaborations. There are a lack of established software engineering methods and tools that fully account for the programmability and controllability offered by SDI, both of which are needed for the optimal development, deployment and execution of time-critical applications in particular, which require certain quality guarantees from the underlying infrastructure before they can run in any unproven environment. Without in-depth understanding of the full relationship between application and infrastructure throughout the development and execution lifecycle, supporting time-critical applications on SDI with strong performance guarantees will prove difficult if not impossible.

This special issue on “time-critical applications on software defined infrastructure” focuses on practical aspects of the design, development and operation of time-critical applications on software-defined infrastructures, addressing questions regarding how such applications can be customised for Cloud and other environments, as well as what actions are necessary to guarantee the performance of time-critical applications in such environments.

This special issue solicits novel and original manuscripts that demonstrate current research in all aspects of time-critical applications and software-defined infrastructures. We are especially interested in both applied research into specific applications that make use of software defined infrastructure and more scholarly research that directly addresses some of the challenges and requirements of managing time-critical applications on programmable infrastructures in general. Particular topics of interest include but are not limited to:

- Real-time task scheduling in software defined infrastructures

- Real-time data quality control in Cloud

- Time-critical application engineering approaches

- Real-time data analytics in Cloud and Fog computing

- Time-critical IoT or Edge computing

- Real-time data assimilation on virtualised infrastructures

- Novel approaches for time-critical data analytics on virtualised infrastructures

- Models for mapping application QoS/QoE constraints to requirements on software defined infrastructures.

- Performance models for time-critical applications on software defined infrastructure.

- Frameworks for knowledge management or monitoring of applications on virtualised infrastructure

- Real-time adaptation approaches for quality-critical applications on virtualised infrastructure

- Time-critical distributed workflows

- Software-defined networking for time-critical applications

- Programming tools or workbenches for time-critical cloud applications

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Artificial Intelligence for Cloud-based Internet of Things (IoT)

全文截稿: 2018-04-30

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/

The Internet of Things (IoT) is a term that has been introduced in recent years to define objects that are able to connect and transfer data via the Internet. ‘Thing’ refers to a device which is connected to the internet and transfers the device information to other devices. The cloud-based IoT is used to connect a wide range of things such as vehicles, mobile devices, sensors, industrial equipment’s and manufacturing machines to develop a various smart systems it includes smart city and smart home, smart grid, smart industry, smart vehicle, smart health and smart environmental monitoring. In the IoT, cloud computing environment has made the task of handling the large volume of data generated by connecting devices easy and provides the IoT devices with resources on-demand.

An increasing number of physical objects are being connected to the Internet at an unprecedented rate realizing the idea of the Internet of Things (IoT). A recent report states that “IoT smart objects are expected to reach 212 billion entities deployed globally by the end of 2020”. Similarly, while the number of connected devices already exceeds the number of humans on the planet by over 2 times, for most enterprises, simply connecting their systems and devices remains the first priority. A recent report state that, “The overall Internet of Things market is projected to be worth more than one billion U.S. dollars annually from 2017 onwards”. As a result, data production at this stage will be 44 times greater than that in 2009, indicating a rapid increase in the volume, velocity and variety of data.

Hence, IoT based smart systems generate a large volume of data often called big data that cannot be processed by traditional data processing algorithms and applications. Here will therefore, by difficulty in storing, processing and visualizing this huge data generated from IoT based system. However, there is highly useful information and so many potential values hidden in the huge volume of IoT based sensor data. IoT based sensor data has gained much attention from researchers in healthcare, bioinformatics, information sciences, policy and decision makers in governments and enterprises. Nowadays, Artificial intelligence methods play a significant role in various environments including business monitoring, healthcare applications, production development, research and development, share market prediction, business process, industrial applications, social network analysis, weather analysis and environmental monitoring.

The IoT and Artificial Intelligence (AI) will play a vital role in numerous ways in the future. There are multiple forces which are driving the growing need for both technologies and more and more industries, governments, engineers, scientists and technologists have started to implement it in manifold circumstances. The potential opportunities and benefits of both AI and IoT can be practiced when they are combined, both at the devices end as well as at server. For example, AI combined with Machine learning can study from the data to analyze and predict the future actions in advance, such as order replacements in marketing and failure of equipment in an industry just in time. Moreover, AI can be used with machine learning in smart-homes to make a truly grand smart home experience. Similarly, AI methods with IoT can be used to analyze the human behavior via Bluetooth signals, motion sensors, or facial-recognition technology and to make the corresponding changes in lighting and room temperatures. This special issue aims to gather recent research works in emerging artificial intelligence methods for processing and storing the data generated from cloud-based Internet of Things.

The following is a non-exhaustive list of topics considered for this special issue:

- AI for smart data storage in cloud-based Internet of Things

- AI for software defined networking in cloud-based Internet of Things

- Intelligent algorithms for cloud-based Internet of Things

- Automated reasoning and inference for cloud-based Internet of Things

- Case-based reasoning in cloud-based Internet of Things

- Knowledge representation in cloud-based Internet of Things

- Agent based algorithms for cloud-based Internet of Things

- Swarm Intelligence algorithms for cloud-based Internet of Things

- Machine learning for cloud-based Internet of Things

- Multi-agent systems for cloud-based Internet of Things

- Natural language processing for cloud-based Internet of Things

- Cognitive aspects of AI in cloud-based Internet of Things

- Intelligent interfaces for cloud-based Internet of Things

- Fuzzy systems for cloud-based Internet of Things

- Neural networks for cloud-based Internet of Things

- Nature Inspired algorithms for cloud-based Internet of Things

- Artificial intelligence for cloud-based Internet of Things

- Genetic algorithms for cloud-based Internet of Things

- Deep learning for cloud-based Internet of Things

- Heterogeneous memory systems design for AI in cloud-based Internet of Things

计算机科学理论

Theoretical Computer Science

Special Issue on Metrics in Graphs and its Applications

全文截稿: 2018-05-15

影响因子: 0.698

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：理论方法 - 4区

网址: http://www.journals.elsevier.com/theoretical-computer-science/

Graph structures are used to model computer networks. Servers, hosts or hubs in a network represent vertices in a graph and edges represent connections between them. Each vertex in a graph is a possible location for an intruder (fault in a computer network, spoiled device) and, in this sense, a correct surveillance of each vertex of the graph to control such a possible intruder is worthwhile. According to these facts, it is desirable to uniquely recognize each vertex of the graph. In connection with this problem, the notion of metric generators (also called resolving sets or locating sets) were introduced in the 1970's and, due to this, the concept of metric dimension in graphs is nowadays well studied, which is also somehow based on the fact that the number of researchers on the topic have significantly increased in the last two decades.

Some of the most frequent studies on metric dimension (and all its variants) concern its applications to several real problems and/or its computational and combinatorial properties. In this sense, the main goal of this special issue of Theoretical Computer Science is devoted to collect several interesting and groundbreaking papers on metric dimension and its related variants which are dealing with its applications in computer sciences and its computational and combinatorial properties as well.

All interested researchers are invited to contribute to this special issue. The topics should relate to metric dimension and should contain applications to computer science problems, or computational and combinatorial aspects of it.

All articles will be thoroughly refereed according to the standards of Theoretical Computer Science. The full papers must be submitted through the Elsevier Editorial System (https://ees.elsevier.com/tcs/default.asp). When submitting your paper, be sure to specify that the paper is a contribution for the Special Issue: Metrics in Graphs and its Applications, so that your paper will be assigned to the guest editors. Please see the Author Instructions on the site if you have not yet submitted a paper through this web-based system. Be sure to note that your work is intended for the Special Issue and to select the article type SI: MeGA.

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