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IEEESCI期刊专刊信息8条,期刊,专刊,EditSprings,艾德思

网络 | 2018/11/28 09:35:06  | 89 次浏览

sci期刊

 计算机网络 IEEE Signal Processing Magazine IEEE SPM Special Issue on Computational MRI: Compressed Sensing and Beyond 全文截稿: 2018-12-01 影响因子: 7.451 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 1区   • 小类 : 工程:电子与电气 - 1区 网址: 专刊详情请打开链接 :

计算机体系结构,并行与分布式计算 Computer Call for Papers: Data Science and Machine Learning across the Stack (CFP) 全文截稿: 2019-01-01 影响因子: 1.94 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 4区   • 小类 : 计算机:硬件 - 3区   • 小类 : 计算机:软件工程 - 3区 网址: , we seek articles that identify the promise of new techniques, paying careful attention to the realm of the possible and limitations of these techniques. Articles should explain complex technical issues at a level conducive to Computer's broad readership and how lessons learned from their projects can translate to other domains. The issue aims for a diversity of application areas; example topics include but are not restricted to: - Data SCIence solutions to classical computer SCIence problems. A range of conferences (for example, SysML) have sprung up to reflect the growing interest in the intersection of machine learning and systems research. Prominent research has revisited classical problems using the new techniques of machine learning (for example, "learned index structures'). What is the potential for ML and data SCIence to upend conventional wisdom and where are the areas where we can see significant improvement? - New ML programming models and abstractions. As ML permeates more and more domains, programmers need significantly expressive tools to capture problem needs and specify solution strategies. What are the latest approaches to support the new generation of ML programmers and what software abstractions are available? - Customized hardware solutions to ML problems. GPUs and GPU-based computing usher in a quantum leap in ML capability, and new customized hardware solutions are rapidly being proposed to address the growing demand. What are the latest system architectures to support the next generation of ML applications? - Human-in-the-loop ML and mining: How can we accelerate human-in-the-loop ML and offer entirely new paradigms of HCI? How can techniques like crowdsourcing coexist with mining massive data?

计算机网络 IEEE Wireless Communications Safeguarding 5G-and-Beyond Networks with Physical Layer Security 全文截稿: 2019-01-20 影响因子: 9.202 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 1区   • 小类 : 计算机:硬件 - 1区   • 小类 : 计算机:信息系统 - 1区   • 小类 : 工程:电子与电气 - 1区   • 小类 : 电信学 - 1区 网址: (5G) and beyond wireless networks will lead to diverse but unprecedented communication requirements, e.g., ultra-high data rate, massive connectivity, ultra-reliability, and ultra-low latency. Fortunately, these requirements do not need to be met simultaneously. To make the design of future communication techniques more focused, 3GPP has categorized the central usage scenarios of future wireless networks into three broad groups: Enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable and low-latency communication (URLLC). Various candidate technologies have been developed to support the diverse requirements of these three scenarios, including co-located and distributed massive multiple-input multiple-output (MIMO), the use of millimeter wave (mmWave) and terahertz (THz) frequencies, full duplex (FD), non-orthogonal multiple access (NOMA), machine-to-machine (M2M) communication, mobile edge computing, fog networks, and short-packet communication. In 5G-and-beyond networks, an enormous amount of confidential information will be exchanged via wireless channels, e.g., personal privacy, trade secrets, financial information, and military secrets. Therefore, providing security is one of the top priorities in the network design. Physical layer security, operating essentially independently of the higher layers, is now generally recognized as a promising paradigm for securing wireless communication in 5G-and-beyond networks. With the rapid development of physical layer security and 5G communication techniques, it is high time to give the readers of the IEEE Wireless Communications a first in-depth look at the enabling techniques for physical layer security in 5G-and-beyond networks. Specifically, this special issue solicits high-quality tutorial articles addressing the integration of physical layer security into future communication applications and its benefits for providing ultra-secure eMBB, mMTC, and URLLC. To enable physical layer security in 5G-and-beyond networks, we seek original and high-quality submissions related to the core area of this special issue. Topics of interest include, but are not limited to: Advances in the fundamental principles of physical layer security for 5G-and-beyond networks Physical layer security in co-located and distributed massive MIMO systems Secure transmission using physical layer characteristics at mmWave and THz frequencies Integration of physical layer security into FD systems Secure orthogonal and non-orthogonal connectivity to massive numbers of devices Lightweight, energy-efficient, and low-overhead physical layer secure transmission Other physical layer security techniques for eMBB, mMTC, and URLLC applications Prototype, testbed, and performance evaluation of physical layer security and key generation

计算机网络 IEEE Network Recent Advances in Security and Privacy for Future Intelligent Networks 全文截稿: 2019-03-01 影响因子: 7.197 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 2区   • 小类 : 计算机:硬件 - 1区   • 小类 : 计算机:信息系统 - 1区   • 小类 : 工程:电子与电气 - 2区   • 小类 : 电信学 - 2区 网址: (FINs). This trend takes place under a circumstance in which a great number of devices are connected for specific purposes by a variety of novel techniques, e.g. Internet-of-Things, cloud computing, edge/fog computing, big data, etc. In FINs, we can envision the benefits by integrating intelligence into networks, e.g. providing an efficient way to automatically generate and optimize policies based on service requirements, freeing network operators from taxing management and configuration burdens, and enabling self-learning from real-time network data. Contemporarily, these emerging techniques are still at its exploring stage, leaving many privacy and security challenges unaddressed. Existing researchers have already uncovered a great amount of attacks and threats. The situation will be more complicated when incorporating artificial intelligence (AI) techniques into networking, as the AI techniques are facing unknown or new types of privacy and security threats. On the whole, more studies and investigations on strengthening security and enhancing efficiency performance in FINs are needed. Although there are existing works that address the security and privacy challenges in cyber space, this Special Issue (SI) focuses on recent attempts to design intelligent approaches that tackle security and privacy challenges arising in a variety of emerging networks, or to exploit vulnerability and security enhancement for intelligent components already integrated in networks. The purpose of this SI is to provide the researchers from both academia and industry an excellent venue to discuss technical challenges and disseminate their recent advances related to security and privacy techniques for FINs. Topics of interest include, but are not limited to: Secure architectures for future Internet (e.g., Software-Defined Networks) Security of emerging networks (e.g., Internet of Things) Security and privacy of cloud computing Security and privacy of edge/fog computing Security and privacy of blockchains Security enhancement in network protocols Designs, implementation, and deployments of security policies Privacy and anonymity in communication networks Intrusion/anomaly detection and prevention in networks Security and privacy of network algorithms based on machine learning and AI

计算机网络 IEEE Wireless Communications Future Communication Trends towards Internet of Things Services and Applications 全文截稿: 2019-03-01 影响因子: 9.202 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 1区   • 小类 : 计算机:硬件 - 1区   • 小类 : 计算机:信息系统 - 1区   • 小类 : 工程:电子与电气 - 1区   • 小类 : 电信学 - 1区 网址: , communication technologies have taken a critical number of major turns and twists, with a wide variety of wireless technologies, protocols, services and configurations developed and deployed. There have been murmurs of network functions virtualization and software-defined networking in the past ten years, but these technologies have been caught in the spotlight of attention in recent years. Today, network functions, e.g., firewalls, reside in the cloud to increase network agility and scalability while also enabling effective use of network resources. Similarly, mobility has advanced from an employee former luxury to today's business necessity. Mobile devices and smartphones have not only transformed personal communications but also increased business productivity and revolutionized our societies. Consonantly, the Internet of Things (IoT) has been dominating the technological landscape, from autonomous connected vehicles to wearable devices. Communication is pivotal to IoT. Networking technologies allow IoT devices to communicate with other devices, applications and services. There exists a broad range of connectivity technologies available for system engineers and developers building IoT products and systems. Depending on the application, requirements such as range, data features, power demands, intermittent connectivity, interoperability and security will mandate the choice of one or more technologies. With the significant development of intelligent communication technology, IoT is expected to play a major role in several application domains such as e-health, intelligent transportation systems and smart cities. This feature topic focuses on the key challenges and considerations that are related to communications within IoT, including power usage, intermittent connectivity, interoperability, orchestration and security. In this Feature Topic (FT), researchers from academia and practitioners from industry are invited to submit their innovative research on future communication architectures and protocols to support IoT services and applications. This FT aims at addressing advances in research on future communication and networking technologies, covering topics ranging from enabling technologies to emerging applications and industrial experiences. Potential topics include, but not limited to the following: IoT communication architectures, standards and protocol stacks Innovative middleware, frameworks and services for coordinating devices in IoT networks Communication security and privacy for IoT networks Interoperability and dynamic adaptation of IoT networks Practical and innovative applications of IoT networks, including smart cities, intelligent transportation systems and e-health IoT enabling technologies, including low power and energy harvesting, machine-type communications, radio frequency identification, sensors, wireless sensor networks, real-time systems and embedded software Things-centric,>信息安全及密码学 IEEE Security & Privacy Cybersecurity Policy–Call for Papers 全文截稿: 2019-05-01 影响因子: 1.239 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 4区   • 小类 : 计算机:信息系统 - 4区   • 小类 : 计算机:软件工程 - 4区 网址: , significant new trends will emerge that change the landscape of policymaking as it relates to cybersecurity.  Whatever the driver of these changes—new technologies, threat types, or innovative approaches in government, industry, or society—there is a need to anticipate and prepare for them.  This special issue will focus on the future trends impacting cybersecurity policy, with a focus on elucidating the challenges, opportunities, and inter-dependencies that are just around the corner. Topics include, but are not limited to: - How new technologies will affect cyber policy issues, for example, the impact of quantum computing on the encryption debate. - The case for increased emphasis or attention on under-served areas of cybersecurity policy, for example, data security for human genetic information. - Case studies or proposals for innovative ways to advance public-private partnerships for cybersecurity. - How artificial intelligence-driven cyber tools, both offensive and defensive, will change the landscape for cyber conflict. - The next evolution of digital disinformation threats and how societies can prepare. - Implications of emerging cyber threats to critical infrastructure, such as the risks posed to census databases or space assets. - New approaches to establishing international norms of responsible behavior in cyberspace or reducing the risk of cyber conflict between major nation states. - Proposed parameters or objectives for a "cyber moonshot' – a unifying national or international goal to improve cybersecurity in fundamental ways. - Trends that will aid or exacerbate prospects for managing vulnerabilities in software and hardware, for example,  artificial intelligence for safer coding. - How data sharing will evolve and implications of new laws on sharing, privacy, law enforcement, legislation, commerce, and partnerships.

计算机网络 IEEE Wireless Communications Intelligent Radio: When Artificial Intelligence Meets Radio Network 全文截稿: 2019-05-31 影响因子: 9.202 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 1区   • 小类 : 计算机:硬件 - 1区   • 小类 : 计算机:信息系统 - 1区   • 小类 : 工程:电子与电气 - 1区   • 小类 : 电信学 - 1区 网址: (5G) communication system extends radio services to various vertical industries, which renders more complexity and thus challenges to wireless communications. The recent advances in artificial intelligence (AI), including machine learning, data mining, and big data analytic hold significant promise for addressing many complex problems in wireless networks. By extending the intelligence from spectrum access, to network management and service orchestration, Cognitive Radio is on the way to evolve to Intelligent Radio. AI is defined as any process or device that perceives its environment and take actions that maximize the chances of success for some predefined goal. It is appropriate to apply AI technologies to tackle accurate channel modeling, optimized physical layer design, flexible spectrum access, and complicated network deployment, automation, optimization, and management issues in the wireless domain. Emerging machine learning approaches and big data analytic technologies have also brought us excellent opportunities to further investigate the essential and so far unexplored characteristics of wireless networks, and to help make breakthrough in wireless communications, via novel radio and networking techniques, including new architectures as well as sophisticated algorithm and protocol designs. The emerging Intelligent Radio has drawn particular attention to inter-diSCIplinary approaches from wireless communications and the AI research community. This Feature Topic (FT) will bring together leading researchers and developers from both industry and academia. The purpose of this FT is to provide the academic and industrial communities an excellent venue to present and discuss technical challenges and recent advances related to AI for wireless communications and networking. The topics of interest include, but are not limited to: 1. Wireless system design and optimization o AI for channel measurement, modelling and estimation New collaborative spectrum sensing enhanced by AI AI for wireless transmission technologies, including antenna array, beamforming, code book design and signal processing AI for system design, including multi-connectivity, and multi-hop relay AI/machine learning for resource allocation and medium access control Protocol design and optimization using machine learning 2. Network management AI for mobility management, including user association, handoff strategy, and backhaul technology Energy-efficient resource allocation via AI/machine learning algorithms AI for radio network slicing, and resource allocation for shared/virtualized networks AI for network analytics and diagnosis Machine learning, data mining, statistical modeling and big data analytics for network management Evaluating potential limitations of AI solutions for networking 3. Network applications and services Novel deep-learning and convolutional neural network approaches for wireless system applications and services Network architecture and optimization for AI/machine learning applications Machine learning for media and entertainment in wireless networks AI for network security, reliability, and safety. AI for edge caching and storage AI for convergence of communications and computing AI for localization and positioning 4. Network automation Machine learning in network control & automation Proactive network monitoring architecture Self-learning, self-organizing and predictive maintenance protocols and algorithms Open-source AI algorithms and software for networking

图形学与多媒体 IEEE Computer Graphics and Applications Call for Papers: Special Issue on Art and Cultural Heritage 全文截稿: 2019-10-01 影响因子: 1.64 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 4区   • 小类 : 计算机:软件工程 - 3区 网址: 's precious artifacts for future generations has become a major challenge. One practical way to tackle the challenge is to apply visual computing technologies, including computer graphics, virtual and augmented reality (VR/AR), human-computer interaction (HCI), and visualization to provide more interactive, personalized, and knowledge-based experiences for understanding art and heritage. Experiments into digitally enabled presentations have lead to enriching our experiences of our cultural heritage as well as contemporary culture. "Digital humanities' also provides a new and general paradigm in this rapid developing field. For this special issue, we are soliciting papers that describe the use of cutting edge technologies for the digital conservation, presentation and communication for museums, galleries or art/cultural institutions. We are looking for computer graphics, VR/AR, HCI, visualization contributions related to, but not limited to the following topics within this domain: - Digital modeling, representation and manipulation of artifacts - Large scale digital reconstruction or restoration of artifacts - Virtual and augmented reality technologies for museums or galleries - Immersive art experiences in AR, VR, or surrounding environments - Digital exhibition technologies - Human-Computer interaction for physical or virtual exhibitions - Digital narratives and storytelling to the mass audience - Non-photorealistic rendering - Emerging trends in digital humanities - Artistic, cognitive and perceptional study of creative behavior - Experiential art, driven by digital technologies - Design, reproduction, fabrication of cultural and artistic artifacts - Inventive uses of graphics and visualization within the arts and humanities - Innovative computer graphics and visualization applications for cultural institutions

 

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