【今日新增】春季截稿的期刊专刊信息7条

1. Mobile Networks and Applications

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全文截稿：2017-05-10

期刊：Neurocomputing

专刊：Special Issue on Deep Learning for Heterogeneous Big Data Analytics

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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Living in the era of big data, we have been witnessing the dramatic growth of heterogeneous data, which consists of a complex set of cross-media content, such as text, images, videos, audio, graphics, time series sequences, and so on. Such hybrid data comes from multiple sources and hence embodies different feature spaces. This situation is creating new challenges for the design of effective algorithms and developing generalized frameworks to meet heterogeneous computing requirements. Meanwhile, deep learning is revolutionizing diverse key application areas, such as speech recognition, object detection, image classification, and machine translation, with its data-driven representation learning. Thus, it has become critical to explore advanced deep learning techniques for heterogeneous big data analytics, including data acquisition, feature representation, time series analysis, knowledge understanding, and semantic modeling.

This special issue serves as a forum to bring together active researchers from across the world to share their recent advances in this exciting area. We solicit original contributions in four categories, all of which are expected to have an emphasis on deep learning and machine learning: (1) state-of-the-art theories and novel application scenarios related to cross-media big data analytics; (2) novel time series analysis methods and applications; (3) surveys of recent progress in this area; and (4) the building of benchmark datasets.

The list of possible topics includes, but is not limited to:

1.Deep Learning and Cross-Media Methods for Big Data Representation

- Data-driven feature learning via deep learning methods

- Large-scale multimodal data acquisition

- Time series analysis via deep learning or machine learning methods

- Novel datasets and benchmarks for heterogeneous big data analytics

2.Deep Learning and Machine Learning Methods for Big Data Understanding

- Architectural designs of deep neural networks

- Multimodal information fusion via deep learning or machine learning methods

- Multiscale analysis via deep learning or machine learning methods

- Transfer learning via deep learning or machine learning methods

- Multi-task learning via deep learning or machine learning methods

- Big data organization, retrieval and indexing via deep learning or machine learning methods

3.Deep Learning and Machine Learning Applications

- Deep learning or machine learning methods for applications such as (but not limited to): object detection and identification, natural language processing, (multiple) object tracking, human action recognition, cross-modal and multimodal data analysis, etc.

- Semantic segmentation via deep learning or machine learning methods

- Video event modeling via deep learning or machine learning methods

- Question answering analysis and mining via deep learning or machine learning methods

- Image/video captioning and visual question and answering via deep learning or machine learning methods

- Industrial data analysis and mining applications via deep learning or machine learning methods

- Pattern recognition in engineering and biomedical sciences

- Health, economics and other applications over heterogeneous big data

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全文截稿：2017-05-21

期刊：Mobile Networks and Applications

专刊：Special Issue on Won5G: New waveform, Non-Orthogonal Multiple Access, and Networking for 5G

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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Mobile/wireless communications and networking deeply influence human lives, while rapid growth in wireless networking also dramatically stimulates the mobile demands. Currently, the fifth generation (5G) mobile communication system is being studied and standardized around the worldwide. 5G asks for 1000 times system capacity, 10 times spectral efficiency, higher data rates (the peak data rate of 10 Gbps and the minimum guaranteed user data rate of 100Mbps), very large connections (1 million connections per sq.km), and ultra-low latency (radio latency less than 1ms and E2E latency less than 10ms). Unsurprisingly, there are strong demands on a series of new techniques supporting these challenging expectations.

Recently, some of the most promising approaches for 5G are emerging. Some new types of waveform are proposed to flexibly accommodate diversified services or applications with different requirements. By introducing new resource domains to separate multi-users, non-orthogonal multiple access can serve more users in the same frequency and time resource block, which significantly improves the capacity, connections, and spectral efficiency. Several non-orthogonal multiple access approaches have been proposed, such as NOMA in power, sparse code multiple access (SCMA) in code domain, multi-user shared access (MUSA), pattern division multiple access (PDMA), lattice partition multiple access (LPMA), and interleave division multiple access (IDMA). Moreover, to further address the extremely high capacity requirements, ultra-dense heterogeneous networking is considered as a promising architecture for 5G. Especially, providing high capacity and connections through directional transmission and large bandwidth, millimeter wave (mmWave) communications and networking can have captured increasing attentions. However, there are also a series of intractable technologies need to be addressed.

The special issue focuses on the key technologies of new waveform, non-orthogonal multiple access, and networking for 5G.

Topics of interest include, but are not limited to, the following scope:

- Filtered OFDM (FOFDM) for 5G

- Non-OFDM waveforms for 5G

- Waveforms for ultra-reliable low latency communications

- Low power and energy efficient waveforms

- Full-duplex communications

- Fundamental information-theoretic and performance analysis of non-orthogonal multiple access

- Routing algorithm and protocol for multi-cell mmWave backhaul networking

- MAC layer design for 5G heterogeneous networking

- Mobility and handoff control for heterogeneous networking

- Unlicensed spectrum such as WiFi and LTE-u offloading for 5G

- Resource allocation for non-orthogonal multiple access

- MAC layer design such as protocol and frame structure for non-orthogonal multiple access

- Channel coding and modulation for nonorthogonal multiple access

- Multi-user techniques such as OFDMA and MU-MIMO for non-orthogonal multiple access

- non-orthogonal multiple access for unlicensed spectrum such as LTE-u and WiFi

- Cross-layer design and optimization for nonorthogonal multiple access

- Hardware implementations of non-orthogonal multiple access

- Multiple access and networking for ultra-dense wireless network

- Software-defined and virtualizedenabled 5G heterogeneous networking architecture

- Emerging applications of nonorthogonal multiple access and/or 5G heterogeneous networking

- Big data for 5G heterogeneous networking

- Social-aware 5G heterogeneous networking

- Cloud computing and edge computing for 5G heterogeneous networking

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全文截稿：2017-05-30

期刊：Neurocomputing

专刊：Special Issue on Advances in Graph Algorithm 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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The variety of data in real life exhibits structure or connection property in nature. Typical data includes world-wide-web data, biological data, social network data, image data, and so on. Graph provides a natural way to represent and analyze the structure in these types of data, but the related algorithms usually suffer from a high computational and/or storage complexity, and some of them are even NP-complete problems. Therefore, many graph models and optimization algorithms have been proposed to achieve a better balance between efficacy and efficiency. Some methods in machine learning and related fields can be regarded as special cases or applications of graph models/algorithms, such as (graph) clustering, graph kernel, graph based ranking, graph retrieval, energy minimization in computer vision, to name a few.

In recent years, many new applications and algorithms on graphs are emerging to cater for the needs of processing and understanding big data. The objective of the special issue is thus to provide an opportunity for researchers and engineers from both academia and industry to publish their latest and original results on graph models, algorithms and applications.

Topics of interest include, but are not limited to:

- Graph based models and optimization algorithms

- Graph clustering

- Graph embedding

- Graph transformation

- Graph matching

- Graph kernel

- Graph based machine learning

- Graph based ranking

- Applications of graph methods in pattern analysis, computer vision, Web data mining, bioinformatics, cheminformatics, robotics

- Other application of graph methods

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全文截稿：2017-05-31

期刊：Journal of Network and Computer Applications

专刊：Special Issue on Security in Cloud Computing

领域：计算机网络

难度：★★★

CCF分类：C类

影响因子：2.331

网址：http://www.journals.elsevier.com/journal-of-network-and-computer-applications

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Cloud computing describes highly scalable computing resources provided as an external service via the internet on a pay-as-you-go basis. With the rapid development of cloud computing technologies, an increasing number of individual and organizations choose cloud platforms to store and deal with their data of various kinds. Cloud computing has appealing benefits including relief of the storage and computation burden, fine-grained data sharing, and cost savings in terms of hardware and software, etc. Nevertheless, there are many new security challenges in cloud computing environment, which have never been addressed in the traditional computing and network environments. Security and privacy issues have severely impeded the practical adoption of cloud technologies. To address these critical issues, it is indispensable to propose and design new algorithms and methods for security in cloud computing.

The objective of this special issue is to collect high-quality research contributions to address the security concerns in cloud computing. The main topics of interest include but are not limited to the following:

- Security architectures for cloud computing

- Security and privacy in outsourcing data and computation

- Visualization for security in cloud computing

- Cryptographic technologies for secure cloud storage

- Access control mechanisms on cloud data

- Searchable encryption

- Proof of remote data integrity and possession

- Data forensics in clouds

- Authentication protocols for secure cloud computing

- Privacy and accountability in cloud storage