计算机类 | 期刊专刊截稿信息9条

人机交互

Behaviour & Information Technology

Big data research for behavior, integrating services and applications: Implications for innovation, policy making and technology transfer

全文截稿: 2018-03-01

影响因子: 1.388

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：控制论 - 4区

网址: http://www.tandfonline.com/toc/tbit20/current

The Big Data and Data Analytics is a new paradigm for the integration of Internet Technology in the human and machine context. Now we can transform raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship.

The Big Data and Data Analytics research domain is a valuable addition to the traditional computer science and web science domains. In the current effort of analyzing, in depth, the impact of this phenomenon to modern information systems, society and economy there are still some missing pieces. Another challenge is the integration of heterogeneous systems and how it impacts in the analysis of data in multicultural and heterogeneous environments.

The emphasis of this special issue is on the behavioral aspects of big data, exploring the relation of Behavior, Big Data and Social Mining towards the deeper understanding of the evolution of the human collective behavior and wisdom, with a special emphasis on users.

The special issue will focus on the research, services and applications related to big data and its implications for policy development and technology transfer. In addition, the special issue will prefer a focus on relevant topics related to users rather than on artificial intelligence, business, marketing or general discussions of big data and knowledge society.

Original and unpublished high-quality research results are solicited to explore several challenging topics which include, but are not limited to:

What can I contribute
- Behavioral implications of big data
- Big data applications related to collaboration learning and behavior change
- Big data interoperability and integration of heterogeneous data sources
- Big data integration and quality management
- Big data integration and cultural barriers
- Big data research as a way to intergrate cultures
- Big data and semantic interoperability
- Internet of things as it applies to the management of human to world interventions
- Knowledge management methods, practices and applications for big data research
- Big data research and its contribution to massive open online courses ecosystem
- Motivation, engagement and social inclusiveness in big data context
- Personalization with smart data for advanced human computer interaction
- Privacy and threats of big data
- Sentiment analysis over big data applications
- Social mining for behavior understanding
- Studies on applications of research

计算机网络

IEEE Wireless Communications

Security and Privacy in the Wireless Internet of Things: Emerging Trends and Challenges

全文截稿: 2018-03-01

影响因子: 8.972

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: http://www.comsoc.org/wirelessmag/

Internet of Things (IoT) is a paradigm that involves a network of physical objects containing embedded technologies to collect, communicate, sense, and interact with their internal states or the external environment through wireless or wired connections. Most of the IoT devices have wireless capabilities (e.g., wi-fi, Bluetooth). With rapid advancements in IoT technology，the number of IoT devices is expected to surpass 50 billion by 2020, which has drawn the attention of attackers who seek to exploit the merits of this new technology for their own benefits. The exposure of resource-constrained IoT devices to the dangers of the Internet opens the door to a plethora of potential security and privacy risks to the IoT, such as attacks against IoT systems and unauthorized access to private information of end-users. As IoT starts to penetrate to virtually all sectors of the society, such as retail, transportation, healthcare, energy supply, and smart cities, security breaches may be catastrophic to the actual users and the physical world. Such threats may diminish the public confidence towards the adoption of the IoT technology.

Although there are existing works that address the security challenges in network and distributed systems, we need to pay more attention the security and privacy challenges emerging from deploying IoT in practical applications with a special emphasis on the wireless IoT devices, infrastructures, wireless networking, and applications. The purpose of this special issue is to provide the academic and industrial communities an excellent venue to present and discuss technical challenges and recent advances related to security and privacy techniques for wireless IoT. The topics of interest include, but are not limited to:

- Intrusion prevention, detection, and response for wireless IoT networks.

- Implementation, deployment and management of network security policies in the IoT.

- Cross-domain trust management in wireless IoT.

- Integrating security in IoT protocols: routing, naming, network management.

- Security for future wireless IoT architectures and designs.

- Secure crowdsourcing in wireless IoT.

- Secure wireless communication protocols in the IoT.

- Security and privacy of wireless IoT systems based on machine learning.

- Privacy and anonymity technologies for wireless IoT.

- Privacy in wireless IoT-based services and applications.

- Privacy in mobile and wireless communications for IoT.

- Privacy-preserving authentication in wireless IoT.

- Privacy-preserving attack detections in wireless IoT.

- Privacy-preserving data aggregation and analysis in wireless IoT applications.

计算机网络

IEEE Communications Magazine

Communications Education and Training: Entrepreneurship and Industry Experience in Education

全文截稿: 2018-04-01

影响因子: 10.435

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

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/commag/

Engineering curricula are rapidly expanding  beyond traditional training in mathematical and scientific principles in order  to better prepare graduates to contribute to a risk-based and fast-paced  innovation economy. Such training generally emphasizes working with diverse  teams to solve real problems and make effective decisions in a technology  context. Often such training is overseen by a diverse group that may include  faculty from both engineering and business schools and senior engineers and  managers from industry with strong entrepreneurial and management backgrounds.

While great progress has been made  in implementing such training, relatively little of the experience gained and  lessons learned are formally shared with the broader community. This feature topic is  intended to support the integration of entrepreneurship and industry experience  into communications engineering curricula by providing educators, researchers  and standards professionals with an opportunity to share their experience, best  practices and case studies.     

Authors from  industry, government and academia are invited to submit papers for this FT  (Feature Topic) of IEEE Communications Magazine on Entrepreneurship and  Industry Experience in Education. The FT scope includes, but is not limited to,  the following topics of interest:

- Case studies of the incorporation of Entrepreneurship and Industry  Experience principles and concepts into communications engineering curricula.  
- Best practices for incorporating and Entrepreneurship and Industry  Experience principles and concepts into communications engineering curricula.
- Case studies of the incorporation of Entrepreneurship and Industry  Experience principles and concepts into professional training.
- Best practices for incorporating Entrepreneurship and Industry Experience  principles and concepts into professional training.
- Development of tools for use in learning about Entrepreneurship and Industry Experience principles and concepts and their impact on design and development.

计算机网络

IEEE Network

AI for Network Traffic Control

全文截稿: 2018-04-01

影响因子: 7.23

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: http://www.comsoc.org/netmag/

Network traffic control, which has been a crucial component of network systems, is able to significantly increase information delivery efficiency as well as resource utilization through monitoring, inspecting and regulating data flows. However, the proliferation of smart mobile devices in the IoT era and the future ultra-dense radio networks have greatly enlarged network scale while introducing highly dynamic topology. Explosive growing data traffic impose considerable pressure on the management of the Internet. In addition, the advances in cloud services together with edge computing and caching technologies have substantially changed the traffic flow models and the service architecture of the Internet, which further pose new challenges on scalable and adaptive traffic control.

Recently, Artificial Intelligence (AI) has made a significant breakthrough in achieving high efficiency and adaptability in a variety of applications, such as healthcare, automotive industry and financial analysis. Naturally, AI can be a very promising approach to deal with dynamic and large-scale topology such that we can explore AI techniques, e.g., statistical learning, feedforward neural networks, deep recurrent neural networks, for intelligent traffic control. Despite all the possibilities offered by AI for network traffic control, there are a number of new challenges including adaptive scheduling of artificial computing, cooperation of heterogeneous intelligent schemes, and computational complexity. These open questions should be carefully studied before we can explore the full potential of AI-inspired approach for intelligent network traffic control.

This special issue aims at soliciting high quality and unpublished work regarding the latest advances in the area of AI-inspired network traffic control. Topics for the issue include, but are not limited to:

- New architectures and mechanisms for AI-based network traffic control

- Machine learning, deep learning for intelligent network traffic control

- Big data driven approach for intelligent network traffic control

- Intelligent network traffic control in wireless networks, e.g., 5G, wireless mesh networks, mobile social networks, wireless sensor networks, crowdsourcing, and vehicular networks

- Joint scheduling of computing, communication, and caching resources

- Energy-efficient design and deployment for AI-based network traffic control

- Cloud/Edge computing and analytics for AI-based traffic control

人机交互

Behaviour & Information Technology

Contribute to our special issue on The impact of interface design for soliciting user’s feedback

摘要截稿: 2018-01-30

全文截稿: 2018-04-28

影响因子: 1.388

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：控制论 - 4区

网址: http://www.tandfonline.com/toc/tbit20/current

Users’ feedback is becoming more and more important in many different contexts of interaction, such as in recommender systems, social network, e-democracy, quantified-self, affective computing, and also in the IOT world. Most of these systems need user feedback for their proper working (i.e. recommender systems, affective computing based systems, reputation systems), or because is linked to their inner nature (e-democracy and social network systems), or for adapting their behaviour according to a specific user’s behaviour while using an object in the real or virtual world (IOT and user-adaptive systems).Hence stimulatingusers to provide explicit feedback becomes an important challenge, especially as users are reluctant to provide it and using and relying on implicit feedback has its limitations.

The excplit and/or implicit collection of users feedback (opinions, ratings, likes, physiological states, usage of virtual or tangible objects, etc.) is a central feature in the the design of such systems, and their design may have an impact on the way the feedback is collected and interpreted.

The proposed special issue will focus on the impact of users’ feedback and on how feedback is solicited and ways to encourage/convince users to provide it.

What can I contribute?
- Rating scales and their design and evaluation
- Micro actionand their design and evaluation
- Evaluation of the use of tags, like, emoticons, icons
- Gamified approaches for eliciting users feedback
- Feedback elicitation in Quantified-self context
- What impacts user feedback in social network
- What impacts user feedback in IOT, expecially in the term of physical interaction
- What impacts user feedback in recommender systems
- What impacts user feedback in the ubiquitous web
- What impacts user feedback in the augmented reality
- Implict modalities for acquiring user feedback: user observation and monitoring, gaze and gesture detection, emotion detection, phisiological state detection etc
- Influence of interaction modalities (e.g. the rating scales widget, the use of gestures, etc) on the ratings

计算机网络

IEEE Communications Magazine

Green Internet of Things

全文截稿: 2018-05-01

影响因子: 10.435

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

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/commag/

As a potential way to dramatically change how human beings work and live, the Internet of Things (IoT) aims to connect every “thing” (e.g., car, people, network) from the physical world, social world to cyber world and let every “thing” interact with each other (e.g., smart transportation, smart grid, smart manufacturing). Since the appearance of the concept of IoT, it is attracting tremendous attention from the academia, industry and government. Championing the IoT concept, IoT is even considered by enthusiasts as the world's third wave of the information industry evolution after the inventions of computer and Internet. However, the greenness of IoT is crucial for the success of IoT. Specifically, various things (e.g., sensor devices, mobile phone terminals, cloud computing systems) in IoT are consuming a large amount of energy. Moreover, the connection of things (e.g., RFID network, GPS network, 5G network) in IoT is with substantial energy consumption. Finally, the interaction of things (e.g., data sensing, data communications, data computing) in IoT needs a lot of energy. In particular, with the prevalence of mobile devices, electronic devices, cameras, social networks, social media, etc., our world is generating big data and multimedia big data, which further aggregate the energy demand in terms of the data transmission of IoT.

Therefore, the aim of this feature topic (FT) is to solicit original papers with novel contributions on the greenness of IoT, from the perspective of energy efficiency. Contributions by the applications of emerging technologies (e.g., social computing, big data computing, fog computing, edge computing, emotional computing, software defined networks) to address the greenness issue of IoT are also welcome.

Topics of interest include (but are not limited to):

- Energy-efficient sensing for green IoT

- Energy-efficient cloud computing for green IoT

- Energy-efficient RFID for green IoT

- Energy-efficient 5G for green IoT

- Energy-efficient communication for green IoT

- Energy-efficient data center for green IoT

- Energy-efficient cyber-physical systems for green IoT

- Social computing for green IoT

- Big data computing for green IoT

- Fog computing for green IoT

- Edge computing for green IoT

- Emotional computing for green IoT

- Software-defined networks for green IoT

- Middleware for green IoT

- Testbeds for green IoT

- Novel applications for green IoT

人工智能

Journal of Experimental and Theoretical Artificial Intelligence

Deep Learning and Artificial Intelligence

全文截稿: 2018-05-15

影响因子: 1.384

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：人工智能 - 4区

网址: http://www.tandfonline.com/toc/teta20/current

The integration of deep learning and artificial intelligence has become a topic of increasing interest for both researchers and developers from academic fields and industries worldwide. Deep learning and artificial intelligence are currently playing a vital role to deal with a massive amount of unsupervised data. Deep neural networks, such as deep belief network, deep auto-encoder and convolutional neural network have shown to be capable of extracting complex statistical features and efficiently learning their representations.

Deep learning and artificial intelligence are currently applied to wide variety of areas including computer vision, image classification, image forensics, video forensics, information fusion, speech recognition, big data analytics, text recognition, multimedia analytics, machine fault diagnosis, biomedical and health informatics, computer aided diagnosis of diseases etc. Deep learning and artificial intelligence can be treated as the most significant breakthrough over the past decade in the field of machine learning.

What can I contribute?
- Deep Learning for Big Data Analysis and Multimedia Retrieval
- Multimodal Deep Learning in Image and Computer Vision
- Deep Learning for Character and Text Recognition
- Multi-sensor and Feature Fusion using Deep Learning Models
- Deep Learning for Computer-Aided Diagnosis Systems
- Hardware Accelerators for Artificial Intelligence Algorithms
- Artificial Intelligence for Mobile Computing and Security
- Intelligent Vehicular Networking and Robotic Systems
- Heterogeneous Memory Systems Design for Artificial Intelligence
- Artificial Intelligence for Tele-Health and Wireless Sensor Networks

计算机网络

IEEE Journal on Selected Areas in Communications

Ultra-Reliable Low-Latency Communications in Wireless Networks

全文截稿: 2018-06-15

影响因子: 8.085

CCF分类: A类

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

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/jsac

Ultra-high reliability and low latency have not been provided by most wireless networks, due to the focus on human-centric communications, delay-tolerant content and reliability levels in the order of 95-99%. New uses of wireless communication are envisioned in areas such as augmented and virtual reality, industrial control, automated driving or flying, robotics, and tactile Internet. In response, new releases of mobile cellular networks are envisaged to support ultra-reliable low-latency communications (URLLC) scenarios with strict requirements in terms of latency (ranging from one to a few milliseconds end-to-end latency) and reliability (higher than 99.9999%). This will require a departure from throughput-oriented system design.
URLLC introduces new research challenges in air interface design, signal processing, resource allocation, network deploy-ment, control/user plane design, protocol stack design, core network and integration in existing wired infrastructures. URLLC rekindles the interest in the long-standing challenge of completely characterizing the non-asymptotic fundamental tradeoffs between delay, throughput and error probability in wireless networks, including both coding and queuing delays. In addition, the highly variable and delay-sensitive nature of network traffic together with the associated control information (metadata) should be incorporated in the conventional communication theoretic framework. Recent advances in combining queuing theory with communication theory promise significant performance gains in terms of latency, reliability and throughput of wireless networks. It still remains unclear how the application of such theoretical concepts to the design of wireless systems can satisfy and guarantee the stringent URLLC requirements.

This special issue aims to bring together contributions from researchers and practitioners focusing on the above mentioned challenges. These topics, together with fundamental advances in the underlying theory as well as real-world deployments of delay-constrained systems form the core of this special issue. We solicit original research work in various areas of im-portance to URLLC. Topics of interest include, but are not limited to, the following:

- Fundamental limits, performance analysis, network theoretic approaches (e.g. stochastic network calcu-lus, timely throughput)

- New waveforms, coding, MIMO techniques, numer-ology and multiple access schemes

- Interference, radio resource and mobility manage-ment

- Network architecture and protocol design for high re-liability-latency guarantees

- Network infrastructure, backhaul & core network issues

- Integration of URLLC links in wired infrastruc-tures

- Co-design of control information and data

- Mission-critical applications, e.g., smart grid, in-dustry automation and control, robotics, tactile In-ternet, and vehicular communications

- Network slicing and network function virtualiza-tion

- Results from real-world deployments, experi-ments, prototypes, and testbeds

数据库管理与信息检索

International Journal of Geographical Information Science

5th Special Issue on Spatial Ecology

全文截稿: 2018-07-15

影响因子: 2.502

CCF分类: C类

中科院JCR分区:

  • 大类 : 地学 - 3区

  • 小类 : 计算机：信息系统 - 3区

  • 小类 : 自然地理 - 4区

网址: http://www.tandfonline.com/toc/tgis20/current

A 5th special issue on spatial ecology has been approved by the Editors and Publisher of the International Journal of GIS. You are encouraged to submit relevant and high quality manuscripts for this special issue (see details below). This special issue continues the tradition of Spatial Ecology publications in the IJGIS.

Spatial Ecology in its broadest sense is the application of geospatial analyses to applications spanning fields such as ecology, forestry, agriculture, environmental management, geography, and global change. The previous special issues reflect the breadth, but also the depth, of spatial ecology.

The first four special issues on spatial ecology can be accessed from http://www.tandfonline.com/toc/tgis20/25/3, http://www.tandfonline.com/toc/tgis20/26/11, http://www.tandfonline.com/toc/tgis20/28/8, and http://www.tandfonline.com/toc/tgis20/30/1.

For this special issue, we are seeking the submission of papers from ecological and related environmental studies, as well as more technical articles including topics such as spatial data infrastructure relevant to ecological applications. We are especially interested in special and novel ways of addressing spatial ecology questions, managing spatial ecological data, and advancing open science in spatial ecology.

Key words and topics for this special issue include scale, geovisualization, spatial data infrastructure for ecological (biodiversity) data, methods to derive ancillary data required for ecological modeling (climate, terrain, soils etc), animal movement including both spatial and temporal analysis, phenology, global databases for ecological studies (biodiversity, NPP, carbon etc), fragmentation and connectivity, biodiversity hotspots and endemism, physical vegetation structure for biomass assessment, palaeoecology and reconstructing past environments with respect to climate change, innovative methods and models for spatial ecological analysis, and open science and new directions for spatial ecology research. Applications across terrestrial, marine and atmospheric ecology are welcome. Relevant cross-over papers between GIS and remote sensing will also be considered.

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