1. Networks
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全文截稿：2017-06-15
期刊：Networks
专刊：Special issue on Drone Delivery Systems
领域：计算机网络
难度：★★★
CCF分类：C类
影响因子：0.943
网址：http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-0037
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Recent advances in autonomous vehicle technology create new opportunities to increase the efficiency and effectiveness of transportation systems. The use of unmanned drones to support last-mile delivery operations is a topic receiving considerable attention. While there has been much research on the technical aspects of the use of autonomous drones and many millions invested in testing by logistics service providers and start-ups, the concept is only recently receiving attention from the academic community in transportation and logistics planning.
This special issue aims to bring together state-of-the-art research related to the design and operations of drone delivery systems. It specifically focuses on optimization approaches (exact and heuristic) to support decision making in these settings. We anticipate submissions that cover various topics related to transportation, monitoring and surveillance systems with aerial and ground drones, including (dynamic) routing and scheduling, facility location and network design.
2. Journal of Grid computing
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全文截稿：2017-06-30
期刊：Journal of Grid computing
专刊：Special Issue on Storage for the Big Data Era
领域：计算机体系结构/并行与分布计算/存储系统
难度：★★★
CCF分类：C类
影响因子：1.561
网址：http://www.springer.com/journal/10723/about
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One of the main conceptual breakthroughs in computer science, invented in theory by the baroness Ada Lovelace and then implemented in the Charles Babbage's Analytic Machine design and the von Neumann computer architecture has been the ability to use 'memory' to store both data and instructions. Following decades of progress in computer science, we are on the fringe of a new Big Data Era that poses significant requirements for the storage of both data and instructions. Undoubtedly, there exist problems for which even exascale computing approaches will not be enough. Immense storage capacity cannot be used without thoughtful storage designs that take care of many important aspects, such as availability versus cost. Storage can and still be used for both programs, that nowadays come in the form of functional Virtual Machine, container or other disk images and for storing raw data, such as file systems, object stores and other more structured forms of storage for data and metadata.
Nowadays, in distributed computing environments such as cloud federations, the functional and nonfunctional requirements for storage play an increasingly important role. Therefore, new methods, approaches and technologies are necessary to address these requirements. Innovative approaches may include innovative forms of cloud storage federations, distribution and content-delivery networks, provenance tracking and replica management strategies, multi-objective optimization of storage etc.
New and innovative storage paradigms have not been sufficiently addressed in the literature. The objectives of this Special Issue of the Grid Computing Journal are therefore to provide focused dissemination of new approaches, methods, and technologies for storage in the Big Data Era.
Topics of interest may include:
- Hierarchical approaches for storing data for the Internet of Things
- Edge/Fog computing concepts for storage
- Data fusion technologies
- Software defined storage
- Storage-related Quality of Service models
- Service Level Agreements for storage
- Measurement methods for storage properties
- Storage approaches focusing on Virtual Machine, container and other disk images
- Federation forms for storage
- Security, privacy and other non-functional aspects of storage
- Storage for Open Data
3. Journal of Universal Computer Science-----------------------
全文截稿：2017-07-01
期刊：Journal of Universal Computer Science
专刊：Special Issue on Computational Intelligence Technologies Meet Medical Informatics - From Prediction to Prognosis
领域：计算机科学理论
难度：★★
影响因子：0.546
网址：http://www.jucs.org/jucs
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Medical informatics is an interdisciplinary scientific field. It deals with the development and application of advanced methods in order to better understand and to improve health care through diagnosis, treatment, prevention of disease, illness, injury, and other physical and mental impairments in human beings.
This special issue spans theoretical, practical, and technical issues in medical informatics and quality health care. Research on applications of prevention, prediction and prognosis topics are appropriate for this special issue. Practical experiences and experiments in using business intelligence and healthcare information technologies are also welcome. We look forward to contributions from academicians, researchers, and educators worldwide.
The papers for the issue should be suitable for experts in the respective fields, but also accessible to wider academic audiences. In view of the publication venue, all papers should contain some relation to computing issues. Furthermore, it is expected that if relevant, authors cite articles from the Journal of Universal Computer Science in their papers. Contributions should fall within the categories (a) research contributions, or (c) surveys (for further elaboration, see http://www.jucs.org/jucs_info/aims).
Contributions adding to research on medical informatics and computational intelligence technologies are solicited in, but not limited to, the following topics:
- Business Intelligence and Data Warehousing
- Cloud Computing and Big Data
- Health/Medicine informatics education
- Risk Evaluation & Modeling
- Knowledge Abstraction, Classification & Summarization
- Patient Safety & Clinical Outcomes
- Public Health Informatics
- Statistics & Quality of Medical Data
- Survival Analysis & Health Hazard Evaluations
4. Mobile Networks and Applications-----------------------
全文截稿：2017-07-01
期刊：Mobile Networks and Applications
专刊：Special Issue on Mobilware 2017: Mobile Network Software-ization and Mobile Middleware
领域：计算机网络
难度：★★★
CCF分类：C类
影响因子：1.538
网址：http://www.springer.com/journal/11036/about
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The recent advances in wireless communications and the proliferation of powerful mobile devices have enabled smart cyber-physical environments where people and devices can seamlessly interact and where mass-market users are willing to receive/contribute to a wide range of mobile services, everywhere and anytime. A key enabler of these pervasive ubiquitous environments is the advancement of software and middleware technologies in various mobile-related sectors, ranging from effective synergic management of wireless communications to mobility/adaptivity support in operating systems, from horizontal support to crowdsourcing in different application domains to dynamic offloading to cloud resources, only to mention a few.
Topics of interest include, but are not limited to, the following scope:
- IoT and SDN in mobile networks
- Fog Computing in wired/wireless and mobile environments
- Fog/Cloud Computing service continuum
- Containerization support for mobile environments
- Security and privacy of mobile-based monitoring systems
- Big data and cloud computing scalable processing of mobile sensors-generated datastreams
- Applications and testbeds in vehicular networks, home networks, and Industrial IoT
- New middleware concepts for mobile devices
- Mobile middleware enabling machine-tomachine communication
- Mobile crowdsourcing and people-centric collaborative sensing
- Smart space/city middleware and applications
- Middleware for mobile cloud computing (e.g., seamless offloading)
- End-to-end architectures for seamless ubiquitous service provisioning and deployment
- Synergic integration of heterogeneous wired and wireless networks
- Opportunistic, delay-tolerant, and store-carry-forward techniques for mobile and wireless networking
- Energy-efficient applications, services, and middleware
- Modeling, simulation, and performance evaluation of mobile wireless systems and services
- Trustworthiness, security, and privacy of mobile and wireless systems
- Impact of IMS, RCS, RCS-e, EPC, LTE, LTE Direct on the evolution of mobile middleware
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全文截稿：2017-07-30
期刊：Neurocomputing
专刊：Special Issue on Deep Neural Networks for Emerging Multimedia Computing and Applications
领域：人工智能
难度：★★★
CCF分类：C类
影响因子：2.392
网址：http://www.journals.elsevier.com/neurocomputing/
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Deep Neural networks have become a crucial technology in the field of multimedia community. They have been exploited in a series of multimedia tasks, such as multimedia content analysis and understanding, retrieval, compression, and transmission. For example, the neural networks Deep Boltzmann Machine (DBM) and Deep Auto-Encoder (DAE) have been widely used for multimodal learning and cross-modal retrieval. The Convolutional Neural Networks (CNN) and their variants have become the basic tools for building deep representations to perceive and understand multimodal information, such as images and audios. Recurrent Neural Networks (RNN) or Long-Short Term Memory (LSTM) can be used for sequence modeling and prediction for high-level semantic data like natural language. However, most existing methods directly borrow the models from the deep neural networks for multimedia tasks without considering the distinctiveness of multimedia data and multimedia tasks. As a result, these methods hardly fit the requirements of these multimedia tasks. Furthermore, the emerging multimedia computing tasks have gained more and more attention, such as visual grounding, multimedia language description, multimedia language description, multimedia refereeing expression, multimedia knowledge extraction and reasoning. In order to cope with these new multimedia tasks, current deep neural networks, including their architectures, training and inference methods, must be adapted or even re-designed. In addition, the new deep neural models will also be needed to deal with emerging multimedia applications, such as webcast live video analysis and understanding, food&health, multimedia application for robot, which are of practical use in real-world use cases. In summary, a number of fundamental issues will have to be solved for emerging multimedia data, multimedia computing and applications. For example, how to train the deep neural network for large-scale, noisy, sparse and diverse multimodal data; How to design novel deep network architectures for emerging multimedia retrieval and recommendation tasks; How to conduct multimedia caption at multi-granularity from global correspondence to local correspondence; How to enhance the deep neural networks to support simultaneous multimedia knowledge extraction and reasoning, to name a few.
This special issue seeks innovative articles that exploit new solutions and applications for deep neural networks in emerging multimedia computing and applications. The list of possible topics includes, but not limited to:
- Novel deep network architectures for large-scale noisy, sparse and diverse multimodal data
- Deep neural networks for multimedia content analysis and understanding
- Deep neural networks for cross-media analysis, knowledge transfer and information sharing
- Enhanced deep neural networks for multimodal data perception and reasoning
- Deep reinforcement learning methods for multimedia-oriented human-machine interaction
- Multi-granularity deep neural networks for multimedia caption
- Efficient deep neural networks for multimedia compression, encoding/decoding
- Recourse-and energy-efficient deep neural networks for high-quality multimedia transmission and communication
- Efficient training and inference methods for multimedia deep neural networks
- Emerging applications of deep neural networks in multimedia search, retrieval, recommendation and management
- Distributed multimedia computing and new hardware architectures for deep neural networks in the multimedia research
- New theory and models of deep neural networks for multimedia computing
- Novel and incentive applications of deep neural networks in various fields
- Other deep learning topics for multimedia computing and emerging multimedia applications.
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全文截稿：2017-07-31
期刊：Signal Processing
专刊：Special Issue on Data-driven Multimedia Processing and Evaluation
领域：计算机图形学与多媒体
难度：★★★
CCF分类：C类
影响因子：2.063
网址：http://www.journals.elsevier.com/signal-processing/
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With the rapid growth of smart sensors and ubiquitous cameras, more affiliated information to various multimedia applications has been collected and stored into the cheap storage devices. For instance, wearable devices can record the instant physiological feedback of audience when they are watching movies, including heart rate, rhythm of the body and facial expression, just to name a few; video streaming service providers (e.g., Youtube, Netflix) can acquire the users' social relationships from other online social network platforms (e.g., Twitter, Facebook). Using powerful big data platforms and analytics tools, such as cloud computing, Hadoop, Spark, TensorFlow, etc., these multimedia data can be utilized to model system evolution, design novel applications, and optimize system performance, and is spurring on tremendous amounts of research and development of related technologies and applications. However, it introduces many technological challenges, such as how to intelligently analyze, mine and understand the fusion information inside from such multimodal big data, and how to utilize the mined insights to design novel applications and optimize the legacy systems.
This special issue aims at providing a forum to discuss the recent advances on big data driven multimedia system modeling, design, evaluation, and optimization. Topics of interest in this special issue include, but are not limited to:
- Novel theory and models for multimedia big data computing
- Deep learning and cloud computing for multimedia big data
- Novel QoS/QoE model of multimedia data
- Security and privacy in multimedia data
- Data driven multimedia networking and systems
- Novel and incentive multimedia big data applications
- Cross-media data fusion
- Security and privacy in the cloud multimedia big data
- Subjective and objective evaluation methods for multimedia data/systems
- Survey on the recent progress in multimedia big data
- Content-based multimedia data processing
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