计算机网络IEEE Signal Processing MagazineIEEE SPM Special Issue on Advances in Radar Systems for Modern Civilian and Commercial Applications全文截稿: 2018-06-10影响因子: 9.654CCF分类: 无中科院JCR分区:• 大类 : 工程技术 - 1区• 小类 : 工程:电子与电气 - 1区网址:

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计算机网络Journal of Network and Computer ApplicationsA Special Issue of Journal of Network and Computer Applications on “Advances in Security & Privacy of IoT”全文截稿: 2018-06-20影响因子: 3.5CCF分类: C类中科院JCR分区:• 大类 : 工程技术 - 2区• 小类 : 计算机:硬件 - 2区• 小类 : 计算机:跨学科应用 - 2区• 小类 : 计算机:软件工程 - 1区网址:

With the rapid development and applications of Internet-of-Things (IoT), objects in the physical world are connected to allow communications with each other and exchange data. The applications of IoT create smart environments over the huge amount of data collected from IoT devices such as automotive, healthcare, environmental monitoring, and many others. However, with the limit computation and storage ability of the IoT devices, many data processing and computing applications have been moved from local devices to cloud computing or other third computing platforms, which offer numerous benefits from both the technology and functionality perspectives. Traditional security techniques face many challenges in these new computing environments, such as the privacy in collaborative machine learning in cloud computing, edge and fog computing, and applications of emerging techniques in IoT such as blockchain and SGX. Thus, efforts are needed to explore the security and privacy issues of IoT applications.

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

- Security & privacy in the IoT communication

- Privacy techniques in Machine learning of IoT

- Security & privacy in outsourced computation in cloud computing

- Authentication techniques in IoT

- Lightweight cryptographic techniques for IoT security

- Collaborative data mining privacy in IoT

- Secure Routing in IoT

- Security & Privacy in Pervasive and Ubiquitous Computing

- Security & Privacy for IoT applications

计算机体系结构,并行与分布式计算IEEE MicroEmerging Memory Technologies — Call for Papers全文截稿: 2018-06-29影响因子: 1.933CCF分类: 无中科院JCR分区:• 大类 : 工程技术 - 3区• 小类 : 计算机:硬件 - 3区• 小类 : 计算机:软件工程 - 3区网址:

Technology scaling of traditional memory technologies, such as SRAM and DRAM, is increasingly constrained by fundamental technology limits. The recent research progress of various emerging memory technologies, such as 3D integrated memory, phase-change RAM (PCM), spin-transfer-torque magnetoresistive RAM (STT-MRAM), and resistive RAM (ReRAM), have drawn tremendous attentions from both academy and industry. As these emerging memory technologies are maturing, it is important for us to understand their pros and cons for improving the performance, power, reliability, and scalability of future computer systems. As such, these technologies bring many research opportunities and challenges for novel architectures, systems, applications, design tools, compilers, and programming models and languages.

This special issue of IEEE Micro will explore exciting academic and industrial research on all topics relating to emerging memory technologies. Such topics include, but are not limited to:

- Workload characterization and benchmarks for applications that are likely to benefit from emerging memory technologies.

- Architectures and microarchitectures for emerging memory technologies.

- Design tools for emerging memory technologies.

- Operating system support for emerging memory technologies.

- Compiler and programming language design for emerging memory technologies.

- Reliability, resiliency, and scalability.

- Prototype experiences with emerging memory technologies.

人工智能NeurocomputingSpecial Issue on Deep Learning for Intelligent Sensing,Decision-Making and Control全文截稿: 2018-06-30影响因子: 3.317CCF分类: C类中科院JCR分区:• 大类 : 工程技术 - 2区• 小类 : 计算机:人工智能 - 3区网址:

Intelligent sensing, especially together with autonomous decision-making and control recently has gained wide attention, with successful showcases in different areas such as the autonomous flying droids, self-driving cars, and amazon kiva systems. One primary ultimate goal is that via active sensing, the computer/machine can learn through either supervised or unsupervised information to perform different tasks.

This fact renders learning a fundamental component for both sensing and control. Among many learning approaches, deep learning has obtained a series of success across various domains including image, speech, text as well as various user-interaction data. The resulting increased sensing capability opens up new possibility for more intelligent decision-making and control. On the other hand, emerging technology e.g. deep reinforcement learning and big data also spur the research for new control paradigm.

The continuously increasing interest in the intersection between intelligent sensing, big data, and deep learning motivates us to organize this special section to study the learning of feature representations for decision-making and control problems.

This special issue will feature original research papers related to (but limited to) learning theory, feature representation, and end-to-end automatic systems for intelligent sensing and control. The survey/vision/review papers are also welcome. The topics of interest include, but not limited to:

- New deep network structure/learning algorithm for intelligent sensing

- Multi-modal/task learning for decision-making and control

- Reinforcement deep learning

- Adversarial deep learning

- Online learning via deep network

- End-to-end learning system for sensing and control

- Visual simultaneous localization and mapping (VSLAM) by deep learning

- Statistical learning for mining and analysis of big data

- New regression/classification model for expert system

- Autonomous robotics with deep learning

数据库管理与信息检索Information SciencesSpecial Issue on Security and Privacy in Machine Learning全文截稿: 2018-07-15影响因子: 4.832CCF分类: B类中科院JCR分区:• 大类 : 工程技术 - 2区• 小类 : 计算机:信息系统 - 1区网址:

Machine learning has been widely applied in many important fields such as health monitoring, decision making, image processing, and financial predictions etc. To obtain more accurate classifier, sufficient training data from a set of data owners are necessary for appropriate learning algorithms. However, a dataset usually contains sensitive information of data owner in most applications, which creates a certain barrier for sharing the data among data owners for machine learning tasks. Protecting data privacy in machine learning is complex and difficult, since the mechanism should enable to perform learning over the dataset meanwhile preserve data privacy. Moreover, due to computation and storage bottlenecks, data storage and learning computation have to be outsourced to cloud servers rather than executed locally, and the cloud computing also makes the problem of privacy leakage more visible. As a result, there is an increasing demand for the development of new security and privacy approaches to guarantee the security, privacy, and availability of data in machine learning.

This feature topic will benefit the research community towards identifying challenges and disseminating the latest methodologies and solutions to security and privacy issues in machine learning. The ultimate objective is to publish high-quality articles presenting open issues, delivering algorithms, protocols, frameworks, and solutions for machine learning related to security and privacy. All received submissions will be sent out for peer review by at least three experts in the field and evaluated with respect to relevance to the special issue, level of innovation, depth of contributions, and quality of presentation. Case studies, which address state-of-art research and state-of-practice industry experiences, are also welcomed. Guest editors will make an initial determination of the suitability and scope of all submissions. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review and the authors will be promptly notified in such cases. Submitted papers must not be under consideration by any other journal or publication.

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

- Privacy-preserving Learning Algorithm

- Privacy-preserving Classification Algorithm

- Secure Data Management in Machine Learning

- Multi-party Secure Computation Techniques for Machine Learning

- Efficient Outsourced Machine Learning Algorithm

- Privacy-preserving Learning Theory

- Privacy-preserving Deep Learning

- Trusted Mechanism for Machine Learning

- Machine Learning with Differential Privacy

- Adversary Machine Learning

- Privacy Standard in Machine Learning Tasks

- Machine Learning Forensics Techniques

- Security & Privacy for Machine Learning Applications

- Light-weighted Secure Machine Learning Techniques in Smart Devices

- Reliability of Machine Learning

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