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计算机类SCI期刊专刊信息6条,期刊,SCI,EditSprings,艾德思

网络 | 2018/12/06 09:54:57  | 471 次浏览



数据库管理与信息检索 Information Fusion Call for papers for special issue of Information Fusion 全文截稿: 2019-03-15 影响因子: 6.639 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 1区   • 小类 : 计算机:人工智能 - 2区   • 小类 : 计算机:理论方式 - 1区 网址: : Algorithms, Architectures, and Applications.  Multi-sensor data fusion embraces methodologies, algorithms and technologies for combining information from multiple sources into a unified picture of the observed phenomenon. Specifically in the context of Body Sensor Networks (BSNs), the general objective of sensor fusion is the integration of information from multiple, heterogeneous, noise- and error-affected sensor data source to draw a more consistent and accurate picture of a subject's physiological, health, emotional, and/or activity status.  About a decade ago wireless sensor network (WSN) technologies and applications led to the introduction of BSNs: a particular type of WSN applied to human health. Since their inception, BSNs promised disruptive changes in several aspects of our daily life. At technological level, a BSN comprises wireless wearable physiological sensors applied to the human body (by means of skin electrodes, elastic straps, or even using smart fabrics) to enable, at low cost, continuous and real-time non-invasive monitoring. Very diversified BSN applications were proposed during the years, including prevention, early detection, and monitoring of cardiovascular, neuro-degenerative and other chronic diseases, elderly assistance at home (fall detection, pills reminder), fitness and wellness, motor rehabilitation assistance, physical activity and gestures detection, emotion recognition, and so on. On of the main key benefits of this technology is the possibility to continuously monitor vital and physiological signs without obstructing user/patient comfort in performing his/her daily activities. Indeed, in the last few years, its diffusion increased enormously with the introduction at mass industrial level of smart wearable devices (particularly smart watches and bracelets) that are able to capture several parameters such as body accelerations, electrocardiogram (ECG), pulse rate, and bio-impedance.  However, since many BSN applications require sophisticated signal processing techniques and algorithms, their design and implementation remain a challenging task still today. Sensed data streams are collected, processed, and transmitted remotely by means of wearable devices with limited resources in terms of energy availability, computational power, and storage capacity. In addition, BSN systems are often characterized by error-prone sensor data that significantly affect signal processing, pattern recognition, and machine learning performances. In this challenging scenario, the use of redundant or complementary data coupled with multi-sensor sensor data fusion methods represents an effective solution to infer high quality information from heavily corrupted or noisy signals, random and systematic error-affected sensor samples, data loss or inconsistency, and so on. Most commercially available networked wearables assume that a single device monitors a plethora of user information. In reality, BSN technology is transitioning to multi-device synchronous measurement environments. With the wearable network becoming more complex, fusion of the data from multiple, potentially heterogeneous, sensor sources become non-trivial tasks that directly impact performance of the activity monitoring application. In particular, we note that the complex processing chain used in BSN designs introduces various levels of data fusion with different levels of complexity and effectiveness. Only in recent years researchers have started developing technical solutions for effective fusion of BSN data.  This special issue aims to provide a forum for academic and industrial communities to report recent theoretical and application results related toAdvances in Multi-Sensor Fusion for Body Sensor Networksfrom the perspectives of algorithms, architectures, and applications.  Manuscripts (which should be original and not previously published either in full or in part or presented even in a more or less similar form at any other forum) covering unpublished researchthat clearly delineate the role of information fusionin the context of body sensor networks are invited.  The manuscript will be judged solely on the basis of new contributions excluding the contributions made in earlier publications. Contributions should be described in sufficient detail to be reproducible on the basis of the material presented in the paper and the references cited therein.  Topics appropriate for this special issue include (but are not necessarily limited to):  Data-level algorithms for multi-sensor fusion in BSNs (e.g. Digital Signal Processing, Coordinate Transforms, Kalman Filtering, Independent Component Analysis)  Feature-level algorithms for multi-sensor fusion in BSNs (e.g. Decision Trees, k-Nearest Neighbor, Naive-Bayes networks, Support Vector Machines)  Decision-level algorithms for multi-sensor fusion in BSNs (e.g. Dempster-Shafer theory, Boosting, bagging, plurality and reputation-based voting, stacking, multi-sensor ensemble)  Deep learning algorithms for better understanding of BSN-collected physiological signals for medical monitoring  Multi-level algorithms for multi-sensor fusion in BSNs  Hardware/software architectures (autonomic, agent-oriented, etc) for collaborative multi-sensor fusion in BSNs  Multi-sensor fusion applications in BSNs for human activity recognition  Multi-sensor fusion applications in BSNs for sport monitoring  Multi-sensor fusion applications in BSNs for emotion recognition  Multi-sensor fusion applications in BSNs for health care monitoring

计算机科学与技术 Swarm and Evolutionary Computation Special Issue on Evolutionary Data Mining for Big Data 全文截稿: 2019-04-30 影响因子: 3.818 CCF分类: 无 中科院JCR分区:   • 大类 : 工程技术 - 2区   • 小类 : 计算机:人工智能 - 2区   • 小类 : 计算机:理论方式 - 2区 网址:   Today, big data has become capital in both academia and industry fields, which is changing our world and the way we live at an unprecedented rate. Recent advances in computing technology allow us to gather and store large amounts of information from various fields, such as Internet, sensor monitoring systems, social networks, mobile communication systems, and transportation systems. Since big data contains greater variety arriving in increasing volumes and with ever-higher velocity, it is essential to develop new data mining and knowledge discovery techniques, and especially using evolutionary computation techniques help in the information retrieval process in a better way compared to traditional retrieval techniques.  Considering big data mining technologies, it still meet some serious problems requiring to be tackled, such as classification, clustering, regression, associate rules mining, and frequent pattern mining. The ability of evolutionary algorithms includes managing a set of solutions, attending multiple objectives, as well as their ability to optimize various values, which allows them to fit very well some parts of the big data mining problems. The global search performed by evolutionary methods frequently provides a valuable complement to the local search of non-evolutionary methods, and combinations of data mining and evolutionary methods often show particular promise in practice. Evolutionary data mining emphasizes the utility of different evolutionary algorithms to various facets of data mining from databases, ranging from theoretical analysis to real-life applications. The primary motivation of applying evolutionary data mining procedures is to automatically and effectively extract relevant information from a voluminous pool of data, which can conduct global search in the solution space (for example, rules, or another form of knowledge representation). It has become a hot trend to address the classical and new-emerging data mining problems in big data with different evolutionary algorithms. The benefits of investigating the combination of data mining and evolutionary computation in the big data scenario have potential to apply in multiple research diSCIplines and industries, including transportation, communications, social networks, medical health, and so on.  II. Themes  The goal of this Special Issue aims to provide an unique opportunity to present the work on state-of-the-art of evolutionary data mining algorithms in the area of big data processing. This will provide a snapshot of the latest advances in the contribution of evolutionary frameworks in big data mining applications to solve optimization problems and optimize criteria. The selected papers will be beneficial to both academia and industry, for delivering the significant research outcomes and inspiring new real-world applications.  The topics of interest include, but are not limited to:  Evolutionary multi-modal optimizations for data mining in big data  Dimensionality reductions using evolutionary computation  Large-scale neuro-evolutionary algorithms for data mining in big data  Evolutionary methods for training deep networks with application to data mining tasks  Data-driven large scale optimizations for data mining in big data  Feature selection and extraction methodologies to attribute reductions in high-dimensional and large-scale data  Evolutionary multi/many-objective optimization for data mining in big data  Evolutionary constrained optimization for data mining  Associate rule mining using evolutionary computation  Convergence analysis of evolutionary algorithms for data mining  Parallelized and distributed realizations of evolutionary algorithms  Adaptive knowledge discoveries using advanced metaheuristic and evolutionary algorithms  Semi-supervised learning and transfer learning with evolutionary optimization  Evolutionary fuzzy systems for big data  Ensemble learning with evolutionary optimization  Quantum-inspired evolutionary algorithm for data mining  Co-evolutionary algorithm for data mining in big data  Multi-label, multi-instance and multi-view problems using the evolutionary algorithm  Low quality and/or noisy big data mining problems using the evolutionary algorithm  Evolutionary optimizations with the dynamic parameter adaptation based on fuzzy systems  Recommender system, graph mining, data stream mining and time series analysis with evolutionary algorithms  Real-world big data applications using evolutionary data mining approaches

数据库管理与信息检索 Information Processing & Management Call for Papers: Knowledge and Language Processing (KLP) 全文截稿: 2019-05-30 影响因子: 3.444 CCF分类: B类 中科院JCR分区:   • 大类 : 工程技术 - 3区   • 小类 : 计算机:信息系统 - 3区 网址: (KLP) is to investigate techniques and application of knowledge engineering and natural language processing, focusing in particular on approaches combining them. This is an extremely interdiSCIplinary emerging research area, at the core of Artificial Intelligence, combining and complementing the SCIentific results from Natural Language Processing and Knowledge Representation and Reasoning.  Topics of interest include, but are not limited to:  NLP for Knowledge:  - NLP tasks for Knowledge Extraction  - NLP for Implicit Knowledge  - NLP for Ontology Population and Learning  - Sentiment Analysis and Opinion Mining for Knowledge Applications  - Interplay between Language Resources and Ontologies  - NLP for Explainable Knowledge  - NL Generation from Structured Information and Linked Data  - Machine Translation techniques for Multi-lingual Knowledge  - NLP for the Web  Knowledge for NLP:  - Knowledge to improve NLP tasks  - Knowledge for Information Retrieval  - Knowledge-based Sentiment Analysis and Opinion Mining  - Ontologies for Language  - Combining Knowledge and Deep Learning for NLP  - Knowledge for Text Summarization and Generation  - Knowledge for Persuasion  - Knowledge-based Machine Translation  - Knowledge for the Web  - Linked Data for NLP  Knowledge and NLP Applications:  - Real-world applications that exploit Knowledge and NLP  - Knowledge and NLP Systems for Big Data scenarios  - Deployment of Knowledge and NLP Systems in specific domains. For example:  - Digital Humanities and Social Sciences  - eGovernment and public administration  - Life SCIences, health and medicine  - News and Data Streams

计算机科学与技术 Physical Communication Special Issue on Mission Critical Communications and Networking for Disaster Management (Submission due: June 1, 2019) 全文截稿: 2019-06-01 影响因子: 1.522 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: , since 3rdgeneration partnership project (3GPP) is giving high priority to the mission critical communications networks and services to provide communication services in disaster areas. Because, the disaster management organizations such as fire brigades, rescue teams, and emergency medical service providers have high priority and broadband demand to exchange information among team and with the victims by using mission-critical voice and data communication. To fulfill these demands, a standalone architecture is desired that can enable communication during the disaster situations. Moreover, broadband in public safety gives law enforcement the advantage to access information, stream video, and collaborate in real time. Law enforcement and border control remains the largest application of public safety LTE. To develop the standalone architecture and technologies, currently many organizations are developing LTE-based disaster support architecture because of its capability to provide the bandwidth efficient solutions.  Technologies used in existing narrowband critical communications networks such as Terrestrial Trunked Radio (TETRA) and Project25 (P25) have been in use for about 20 years now. These networks are well developed and effective in supporting only voice applications but are not suitable for future higher bandwidth applications with the requirements of high data/video transmissions. Thus, the MC services are expected to evolve into the future by taking inputs from critical communications industries regarding architecture, technologies, and service requirements. Since, in disaster situation there is a possibility of complete architectural disaster, so to enable voice/data communication in this situation, the unmanned air vehicle (UAV)/robots can be used to provide the communication setup by enabling the path towards the core network. Moreover, Device-to-Device (D2D), Vehicle-to-Vehicle (V2V), and Machine-to-Machine (M2M) communications are the key drivers in the emergency situation for exchanging information from the MC site in the absence of network infrastructure to provide MC voice and data services. To provide these services 3GPP included the Mission-critical push-to-talk (MCPTT) functionality into LTE standard which is the first major step in a series of MC Services and functionalities demanded by the market. Recently, 3GPP Release 14 added various enhancements to its repertoire related to MCPTT, MC Data, and MC Video. Hence, the Release 14 MC services not only required new protocols and security enhancements but also enhancements in existing MCPTT services to enable reuse of common functionalities. Similarly, the Release 15, that will be the first phase of 5G, is expecting more enhancements in MC services.  The main objective of this special section is to bring most recent advances in physical layer of mission critical communication technologies. Moreover, its goal is to address the challenges related to mission critical services. Authors are required to submit previously unpublished papers in this special issue. Topics of interest include, but not limited to the following:  - Physical layer issues for mission critical communications and network  - Radio resource management schemes for mission critical communications and network  - Spectrum sharing and future spectrum requirements for mission critical communications and network  - UAV/robots detection and localizations protocols for mission critical communications and network  - Location-based services: indoor positioning, navigation and mapping for mission critical communications and network  - Stochastic geometry models for mission critical communications and network  - Disaster resilient 3D location detection protocols suitable for mission critical communications and network  - Channel measurements and modeling for mission critical communications and network  - Mission critical communications and network optimization by targeting low latency applications  - Opportunistic offloading schemes for mission critical communications and network  - Device-to-device (D2D) discovery and communications for mission critical communications and network  - Full duplex communications for mission critical communications and network  - Deep learning algorithms for mission critical communications and network  - Real-time video analytics for situation-aware mission critical communications and network  - Information-driven video communication for mission critical communications and network  - Heterogeneous fog communications and computing for mission critical communications and network  - Connected vehicle and computing platform for mission critical communications and network  - Wireless power transfer protocols for mission critical communications and network  - Software defined radio (SDR) based prototype development for mission critical communications and network

计算机科学与技术 Physical Communication Special Issue on Energy-Aware Future Networked Smart Cities II (submission due: August 1, 2019) 全文截稿: 2019-08-01 影响因子: 1.522 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: , global temperatures will have risen by 2º C from pre-industrial levels. Therefore, there is an urgent need to plan the cities of the future for sustainability. As a major agent for promoting a quality of life compatible with a resource efficient economy, the smart city phenomenon has recently captured the imagination of the academia and the industry alike. 5G will provide expressively faster data speeds, much higher data capacity, better coverage, and lower latency. This amalgamation of elements will go beyond improving our own smartphone usage. It will enable the smart city. Since the Internet of things (IoT) is expected to be a primary driving force for future cities, advanced communication techniques will play a pivotal role in facilitating real-time data acquisition and utilization from distributed sensors. However, future cities will also have to operate within the constraints of the national economy and available power resources. Consequently, the challenges in the realization of smart cities are many and varied, such as low energy consumption requirement, constrained bandwidth and budgetary limitations. In order to overcome these hurdles, it is essential that new concepts and theories for optimizing the network in energy and spectral terms are presented to achieve a robust energy efficient environment monitoring and sustainable transportation network, among other provisions. This special issue is aimed at furthering this effort by forging collaborations through the presentation of state-of-the-art research in physical layer endeavours by scholars from across the globe. The topics include, but are not limited to:  Energy efficient PHY architectures for IoT and autonomous systems  Wireless Energy harvesting technologies: Novel PHY architecture design  Experimental network measurements and characterization for energy aware smart cities  Energy-efficiency and spectral-efficiency for smart cities  PHY design for self-powered or low powered machine-to-machine communications  Energy effective approaches for Cognitive Radio-inspired solution for smart cities  RF Energy harvesting for future smart cities  Resource-efficient cross-layer optimization  Interference management in smart networks  Energy-efficiency and spectral-efficiency  Energy responsive Cooperative communications for smart cities  PHY design for self-powered or low powered machine-to-machine communications  Self-organized networks for energy aware smart cities  Disaster recovery and emergency services for energy constrained smart cities  Smart transportation systems and infrastructure  Applications, deployments, test-beds and experimental experience for communications in energy cognizant smart cities  Self-powered secure IOT applications in PHY point of view  5G wireless communications for Smart Cities  Artificial intelligent inspired techniques for smart cities  Unmanned Ariel Vehicle (UAV) communications for smart city applications

计算机科学与技术 Physical Communication Special Issue on Ultra-Reliable, Low-Latency and Low-Power Transmissions in the Era of Internet-of-Things (submission due: November 1, 2019) 全文截稿: 2019-11-01 影响因子: 1.522 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: , not only for enhancing the data rate of current 4G, but also for the goal of achieving ubiquitous connections for anyone and anything despite of time and location. This goal embraces all emerging applications, such as unmanned or remotely controlled robots/vehicles/offices/factories, augmented/virtual reality, intelligent transportation systems, smart grid/building/city, immersive sensory experience, and the Internet of Things (IoT). Therefore, in order to provide heterogeneous services to massive devices, connections and applications in the 5G networks, advanced transmission technologies with different features and requirements are desired. The massive transmissions in IoT should be able to provide connectivity for primarily low-rate and low-power connectivity for enormous amounts of simple sensor/actuator type of devices, and enable real-time control and automation of dynamic processes in various fields, such as industrial process automation and manufacturing, energy distribution or traffic management and safety. Therefore, apart from the data rate improvement, an efficient and effective IoT system should be the one with ultra-low latency, as well as ultra-high reliability and availability. Moreover, as the devices are commonly powered by the batteries which are developed in a relatively low speed, low-power transmission methods are also desired. Nevertheless, the current research advances usually focus on the throughput improvement for the traditional cellular transmissions, while low power, low latency and high reliability schemes call for attention. Some fundamental problems are still open and require immediate studies, such as: How to provide insights to the fundamental tradeoff between ultra-reliable, low-latency and low-power consumption? How to derive an accurate and appropriate model for the above tradeoff? How can we make a smart decision addressing this tradeoff?  Are there any new applications that can utilize novel ultra-reliable, low-latency and low-power transmissions in the era of IoT?  The goal of this Special Issue is to bring together leading researchers and developers from both industry and academia to discuss and present their views on all the aspects of design of ultra-reliable, low-latency and low-power transmissions to embrace the IoT era. Topics of this special issue include but not limited to:  - Energy efficiency issues in D2D/M2M  - Ultra-reliable and low latency communications (URLLC) in IoT networks  - Finite blocklength performance analysis and optimal design for IoT networks  - IoT networks with edge computing nodes under energy/delay constraints.  - Multiple devices/sensors cooperative communication in IoT networks under energy/delay constraints  - IoT networks with energy harvesting nodes and simultaneous wireless information and power transfer (SWIPT)  - NOMA applications in IoT networks under energy/delay constraints.  - Low-power transmission design for IoT  - Novel transmissions in different IoT applications, such as body centric network, industrial internet, etc.

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