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论文润色 | 2019/07/01 11:35:47  | 241 次浏览

 计算机网络 Computer Communications Special Issue on Software-defined disaster area UAV communication networks for extreme surveillance 全文截稿: 2019-11-30 影响因子: 2.613 CCF分类: C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   • 小类 : 工程:电子与电气 - 3区   • 小类 : 电信学 - 3区 网址: , especially natural disasters, agriculture, water, forest, military, buildings, health monitoring, disaster relief & emergency management, area and industrial surveillance have already been studied from the emerging technologies perspective and most of these surveillance applications have attracted much research attention. Emerging technologies is increasingly becoming the most important and valuable source for insights and information in extreme events. It covers from everyone's experiences to everything happening in the world. There will be lots of emerging technologies in extreme events surveillance video, disaster images, social media, voice and video, to name a few, only if their volumes grow to the extent that the traditional processing and analysis systems may not handle effectively Unmanned Aerial Vehicles (UAVs) empower people to reach endangered areas under emergency situations. By collaborating with each other, multiple UAVs forming a UAV network (UAVNet) could work together to perform specific tasks in a more efficient and intelligent way than having a single UAV. UAV Nets pose special characteristics of high dynamics, unstable aerial wireless links, and UAV collision probabilities. UAV networks may vary from slow dynamic to dynamic; have intermittent links and fluid topology. While it is believed that ad hoc mesh network would be most suitable for UAV networks yet the architecture of multi-UAV networks has been an understudied area. Software Defined Networking (SDN) could facilitate flexible deployment and management of new services and help reduce cost, increase security and availability in networks. Routing demands of UAV networks go beyond the needs of MANETS and VANETS. To address these challenges, we propose a special issue aims to gather latest research and development achievements in the field of software-defined disaster area UAV communication networks for extreme surveillance. Original papers that address the most current issues and challenges are solicited. Topics of interest include, but are not limited to: • Software defined radio test beds and experiments for extreme surveillance • Disaster resilient location detection protocols for extreme events. • Multi-hop and relay based communications for extreme surveillance • Location detection technologies and protocols for extreme surveillance applications • Software defined networks for architecture for under water search and surveillance applications • Software defined networks for efficient Internet of multimedia things smart surveillance applications • Software defined networking for traffic engineering measurement and management • Software defined networking for cybersecurity applications • Software defined extreme scale networks for bigdata applications • Software defined networks with QoS for cloud applications • Interaction, access, visualization of intelligent tools for extreme events • Intelligent -oriented middleware for extreme-critical applications • Quality of service(QoS) and priority aware models for extreme surveillance applications

计算机科学与技术 Digital Investigation Call for Papers: Digital Transformations in Forensic Science 全文截稿: 2019-11-30 影响因子: 1.771 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:跨学科应用 - 4区 网址: , particularly digital forensics, is increasing in criminal, civil, and regulatory matters, including security matters such as terrorist attacks and cyber incidents. This trend is driving decentralization and commoditization of forensic capabilities, placing powerful tools in the hands of non-specialists to deal with immediate problems in isolation, without the support of Forensic Science infrastructures. This separation from Forensic Science principles and practices may lead to errors and omissions, especially in digital forensics, which endangers individual liberties and diminishes trust in Forensic Science as a whole. This separation also diminishes the opportunities for Forensic Science to gain a holistic insight into its fundamental objects of study, thus potentially missing crucial information and jeopardizing its future health/growth. Forensic Science cannot reverse the decentralization movement but can seize the opportunity to connect with disparate deployments of forensic capabilities, receiving data and delivering knowledge. Combining the data being gathered in multitude of different environments generates greater understanding of forensic findings and their usefulness, as well as a more comprehensive understanding of crime, malfeasance, and security-relevant problems. Altering Forensic Science from a diffusion of different diSCIplines into a unified amalgamation of knowledge serving the common purposes of abating crime, strengthening security, and reinforcing the criminal justice system requires a digital transformation. This special issue invites submissions related to digital transformations in Forensic Science, including the following topics of specific interest: + Pertinent opinion pieces, commentary articles and case reports + Legal perspectives on maintaining continuity of evidence and repeatability of forensic findings when forensic capabilities are used in the field by non-SCIentists (e.g., risk of evidence not being preserved for independent examination). + Legal and ethical perspectives on relying on links established using artificial intelligence or machine learning + Solutions and strategies for managing opportunities and challenges of decentralization of forensic capabilities + Rationales and approaches for fusion of physical and/or digital forensic results + Interdependence and independence of Forensic Science and investigative operations + Interrelations between Forensic Science and forensic intelligence + Big data analysis applied to Forensic Science, combining physical and/or digital forensic results + Artificial intelligence / Machine learning applied to Forensic Science + Social media as part of traditional investigations + Quality assurance when forensic capabilities are used in decentralized contexts + Challenges and opportunities associated with biometrics in Forensic Science and digital forensics + Centralised research, development and administration of advanced forensic capabilities, made available throughout a decentralized forensic ecosystem + Strategies for handling the rapid rate of advancement in Forensic Science knowledge and technological developments. + Harmonisation of data formats and exchange of forensic information between tools, organizations and countries + Knowledge management, training and education + Explicability and evaluation of forensic results and automated processing

计算机网络 Computer Communications Special Issue on Advanced Computing and Communication Technologies for Internet of Drones 全文截稿: 2019-12-01 影响因子: 2.613 CCF分类: C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   • 小类 : 工程:电子与电气 - 3区   • 小类 : 电信学 - 3区 网址: (UAVs) which are commonly known as drones can be potential enablers for providing different type of solutions in a futuristic smart city. Initially, drones were restricted to sophistic areas like military operations due to their cost and limited technological advancements. But, nowadays, the advent of more affordable technologies are enabling drones in finding their way to our day to day usage application areas like logistics, remote monitoring, cinematography, agricultural monitoring, search and rescue, and 3D-mapping. The development on Internet of Drones (IoD) is enabling the design of new architectures, standards and technologies to make the drone operations as autonomous as possible. A few of the thrust areas of IoD research are computing, communication, security and privacy, energy and sustainability. The limited computing resources available with the drones so as to keep it lightweight must be utilized effectively in terms of processing, computing, sensing and analyzing. For this reason, in order to enable long operating durations during the flight, efficient mechanisms must be designed to optimize the computing processes and needs of the drones. The limited battery capacity hinders the progression of drones by limiting the flight time. However, the energy-efficient computing procedures and mechanisms can reduce the tendency of energy dissipation thereby elongating the flight times during hover. Moreover, the latest innovations in terms of energy harvesting, wireless energy transfer and solar plates has opened new doors for opportunistic energy replacement during the hover time of the drones. The deployment of drones in sensitive and consumer related applications make it necessary to design sustainable and eco friendly solutions which could utilize the limited computing facilities of drones to provide enhanced quality of services to the users. Although the distributed nature of IoD architecture provides various advantages in terms of scalability and autonomy, but it bring forward various mobility and controlling issues creating the need of special attention to the communication platforms also. The conventional network systems may not be very efficient in dealing with the dynamic requirements of IoD. Latest network paradigms such as software defined networking and network function virtualization provides new potentials of technological advancements in IoD communication architecture. Moreover, the next generation technologies such as-5G and NOMA also have shown a promising trends in the design of advanced communication solutions for IoD. As the drones are generally deployed in tough environments and terrains, therefore it is quite essential to provide a strong network and communication backbone. Otherwise, it may lead to adverse effects on the designed solutions or applications dependent on the drones. Security and privacy are yet another areas of concern for drones which needs focus of the research community as an attack on the IoD can be fatal and may lead to loses of lives and assets. There may be several solutions related to security and privacy but the applicability of these solutions with IoD perspective is questionable. Therefore, the applicability of those solutions in IoD needs to be analyzed keeping in view the nature of applications, limited computing and communication capabilities and the energy and sustainability constraints of drones. Moreover, the deployment of drones in consumer applications dealing with product delivery and logistics make it a compulsion to preserve the identity of users and the sensitive information associated with them. Blockchain can be a viable solution for preserving the data integrity and securing the entire transactional process involved. But, the limited computing capabilities require lightweight solutions which right now are not feasible in the available blockchain variants. Moreover, new cryptographic primitives need to be designed to protect and preserve the IoD from adversaries and attackers. Keeping in view of the need of technological advancements in different enabling domains related to IoD, this special issue provides a platform to the research, academia and industrial technocrats to present their ideas and solutions from various perspectives related to drones. Thus, for this special issue , the deeply investigated works describing both theoretical and practical evaluations related to the design, analysis and implementation of secure and sustainable computing and communication technologies for IoD are invited. Some of the desired topics include, but are not limited to the following: Cloud and big data technologies for drone networks. Edge as a service for drones in smart communities. Implementation and deployment of real-time test beds for 5G-drone setups. Data collection, analytics, processing and delivery using drones in smart city. Distributed caching and security protocols for inter-drone communications. Content dissemination and named data networks for smart communities. Advanced caching and content delivery technologies for IoD Security, integrity and privacy solutions for IoD Energy-aware and sustainable solutions for Inter-drone communications. Energy-efficient computing, processing and analysis in IoD ecosystem. Energy harvesting and wireless energy transfer for drones. Trust management and collaborative solutions for IoD. Lightweight blockchain solutions for IoD applications. Smart contracts and consortium Blockchains for drone enabled consumer applications. Software defined networks and network service chaining for IoD. Artificial Intelligence in IoD operations and autonomy. Artificial intelligence in traffic management in IoD. Machine Learning and deep learning algorithms for IoD. Mobility management and channel modeling in IoD. IoT architectures and protocols of drone communication.

数据库管理与信息检索 Information Processing & Management Special Issue on New Techniques in Media Quality Modeling 全文截稿: 2019-12-01 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: , social media platforms, and cloud storage, the tremendous amount of image, video, and textual signals are cheaply available. As a standard tool to analyze these data, quality model has been pervasively used in domains like intelligent systems and 3D rendering. In the past decades, many shallow quality models have been released and commercialized. Despite their success, conventional quality models might be deficiently effective to handle the massive-scale data nowadays. Potential challenges include (not limited to): First, owing to the significant progress in deep feature engineering, deep quality models have been proposed and satisfactory performance was received. But deep model is conducted in a black-box manner, how to make it interpretable or transparent to quality modeling, and encoding human subjective wills and perception are still unsolved. Second, compared to the fully-annotated signals when modeling small-scale data, it is infeasible to label large-scale image/video at pixel-level due to the unaffordable human resources. In practice, only image/video-level labels or partial labels are available. Even worse, sometimes these weak labels are contaminated. Therefore, how to design a noiserobust weakly-supervised learning algorithm to exploring pixel-level quality-related elements is a tough problem. Third, conventional quality models typically leverage local/global features to evaluate each image/video, where human visual perception cannot be encoded explicitly. Apparently, human visual perception plays a significant role in quality modeling. In the literature, it is difficult to mimic human visual perception, i.e., predicting human gaze behavior and subsequently modeling the visual signal cognition in human brain. In this special issue, we will focus on the recent progress in image/video/text quality modeling and analytics. We aim to explore interpretable, noise-tolerant, and perceptionaware deep models to enhance quality models. Submissions related to new image/video/text benchmarks for testing the performance of quality models are also welcome. The primary objective for this special issue is to foster focused attention on the latest research progress in this cutting-edge area. We intend to attract researchers and practitioners from both industry and academia. Topics of interest include (but are not limited to): o New deep architectures for image/video quality evaluation; o Deep algorithms for enhancing the shallow-feature-based intelligent systems; o Quality-driven image/video processing techniques; o New Quality models in management applications; o Semantic models for deep image/video quality prediction; o New management tools based on deep quality models; o New machine learning algorithms for deep media quality modeling; o Visual quality prediction for photo and video management systems; o Leveraging human interactions to improve deep quality models; o Perception-aware quality models for Internet-scale media retrieval; o Novel deep quality features and their applications in pattern recognition. o Deep models trained using small samples for quality understanding; o Novel photo or video retargeting/cropping/re-composition using deep features; o New datasets, benchmarks, and validation of deep quality models; o Subjective methodologies to estimate the quality in real-world systems; o Novel visualization technologies for deep quality features;

计算机体系结构,并行与分布式计算 Future Generation Computer Systems Special Issue on Advanced Techniques and Emerging Trends in Smart Cyber-Physical Systems 全文截稿: 2019-12-15 影响因子: 4.639 CCF分类: C类 中科院JCR分区:   • 大类 : 工程技术 - 2区   • 小类 : 计算机:理论方式 - 2区 网址: , the new industrial revolution is a revolution of cyber-physical systems, of which the Internet of Things forms a key foundation that has a great impact on the way people live and the way businesses are organized. Cyber-physical systems are often considered feedback systems that integrate computation, networking, and physical processes. The CPSs control the physical processes by utilizing the artificial intelligence to acquire the deep knowledge of the monitored environment. Hence the CPSs are designed to be intelligent to provide highly accurate decisions and appropriate actions promptly. The rapidly growing interconnections between the virtual and physical worlds and the development of new intelligent techniques have created new opportunities for the research for next-generation CPS, that is smart cyber-physical systems (SCPS). The 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, can efficiently manage real-world processes and offers a broad range of novel applications and services. 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. As CPSs hold strong interactions between the cyber and physical components, it plays a significant role in the development of next-generation efficient-smart systems in various real-time applications. This special issue is intended to report high-quality research on recent advances towards 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. This special Issue will report the recent increasing interests in the design and development of intelligent techniques for various applications of Smart Cyber-Physical Systems. Moreover, the authors are expected to investigate state-of-art research issues, architectures, applications and achievements in the field of Cyber-Physical Systems. Unpublished innovative papers which are not currently under review to another journal or conference are solicited in the following relevant areas. Topics of interest include, but are not limited to: Data management and knowledge representation for Cyber-Physical Systems Algorithms, models, and designs for social Cyber-Physical Systems Machine learning for high-performance computing with Cyber-Physical Systems User activities recognition for Cyber-Physical Systems Cryptographic protocols and algorithms for Smart CPS Cryptographic engineering for CPS or IoT devices Data integrity, authentication, and access control for Smart CPS Security in smart grids/smart homes/smart cities/smart transportation Security threat detection theories and technologies Cloud based secure Smart CPS Privacy issues in smart grids/smart homes/smart cities/smart transportations Human Computer Interaction in SCPS Analysis of Network Dynamics in Cyberspace Knowledge Modeling and Management in Cyber-Social Networks Cyber-Social Data Processing and Intelligence Mining Cyber-Physical Hybrid System Design Trust and Reputation in Social Cyberspace Cyber-Enabled Learning Analytics Cyber-Physical Healthcare Services Cyber-Empowered Sentiment Analysis and Mental Computing

计算机综合与前沿 Journal of Biomedical Informatics Call for Papers: Towards Preventive Health Care through Digital Technologies 全文截稿: 2020-02-15 影响因子: 2.882 CCF分类: C类 中科院JCR分区:   • 大类 : 医学 - 3区   • 小类 : 计算机:跨学科应用 - 3区   • 小类 : 医学:信息 - 3区 网址: , even seeking their eventual eradication or, at least, minimizing their severity and progression (impact on patients) [1,2]. Thus, digital approaches to preventive health care do not necessarily focus solely on the prevention of disease occurrence. Every stage of a disease may be tackled along a spectrum, from primordial prevention (i.e., educate people to practice preventive behaviors and habits to avoid diseases or injuries before they start) to tertiary prevention aimed at rehabilitation following significant illness (i.e., reduce impairment by rehabilitation and through re-education; or limiting severity of disability through non-intrusive continuous monitoring and assistive technologies for pervasive health care) [3]. There are intermediate stages along the spectrum of digital preventive health care, such as primary prevention (i.e., detect symptoms before the onset of an illness or injury, known also as disease pre-pathogenesis); or secondary prevention [4] (i.e., increase patient survival by halting or mitigating disease progression, preventing more severe problems and complications through adaptive treatments). Current technological advances have made significant improvements to deal with each of these prevention stages [5,6]. The landscape for digital prevention has evolved in recent years to include the concepts of big data, cloud and fog capabilities for predictive analytics [7] and the use of data gathered through remote monitoring [8] (e.g., mHealth, teleHealth), and real-time patient status follow-up). The main aim of digital preventive medicine is "to work closely with individual patients", monitoring their particular health conditions [9]. This requires implementing proactive health-focused tests for each of the prevention levels [10], with special emphasis on primordial and primary prevention stages, i.e., the ones where diseases or injuries have not yet started, even though some symptomatology might already be manifested. These tests should be remote and non-intrusive for people living independently or in communities [11] and, as far as possible, continuous or in real-time. Vital signs can be monitored, as well as derived markers for specific dimensions, such as functional, cognitive [12], behavioral [13], nutritional [14], and social factors that can be used in diagnosis [15] (e.g., gait analysis, wandering patterns in home environments, daily energy expenditure, changes in social behaviors, sleep patterns, daily intake, or unintentional weight loss). Data acquisition typically must rely on wearable sensors, mobile devices, and mHealth apps, but additional inputs can be derived from static embedded sensors arranged in different environments and from knowledge provided by specialists and caregivers. All these heterogeneous data must be preprocessed locally, partially locally (fog), or entirely remotely (cloud) before applying different inference strategies. The latter will determine whether collected data and a patient's baseline profile match corresponding patterns found in large datasets stored in the cloud. Predictive analytic techniques may be used in each of the prevention levels [16]. The resultant information may then be uploaded to the patient's history stored in an electronic health record which may, in turn, be used in other large scale analyses. In this JBI special issue, we solicit contributions presenting novel methods that focus on acquiring, preprocessing, uploading to the cloud, mining, categorizing, summarizing, integrating and analyzing large datasets of heterogeneous information for any level of preventive health care. Furthermore, novel contributions for the acquisition of vital signs and derived health markers through non-intrusive techniques, such as embodied sensing, environmental sensing, or mHealth solutions, are welcome. Figure 1 illustrates the main aspects of the preventive healthcare ecosystem proposed for inclusion in this special issue. The suggested topics listed below can be discussed in terms of concepts, the state of the art, and standards, but all papers should emphasize the novel methods (and motivating applications) that constitute the paper's contribution to the SCIence of informatics. Primordial digital prevention: New technologies and strategies for preventive healthcare promotion and patient education. Effects of technology on health promotion and patient education. Limitations and drawbacks of technology use in preventive healthcare. The harnessing of social media in improvement of healthcare knowledge. Assessing the impact of digital campaigns on preventive healthcare. Real-time health advice and coaching systems. Gamification for preventive healthcare promotion and patient education. Electronic health/patient record innovations. Health data interoperability (standards, security, privacy policies). Primary digital prevention (detecting symptoms before the onset of the disease): Long-term & remote monitoring for diagnosis. Patient similarity in prediction models based on health data. Gamification for primary prevention. Early detection and prevention of diseases through predictive analytics. Real-time healthcare monitoring for early diagnosis. Environmental healthcare systems (IoT for preventive healthcare). Body area sensor networks and mHealth applications for primary prevention. Fog and cloud computing-based infrastructures for primary prevention.


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