【今日新增】高难度期刊专刊截稿信息8条

1. Journal of Parallel and Distributed Computing

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全文截稿：2017-06-01

期刊：Journal of Systems Architecture

专刊：Special Issue on Embedded Artificial Intelligence and Smart Computing (EAI-SC)

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：B类

影响因子：0.683

网址：http://www.journals.elsevier.com/journal-of-systems-architecture/

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With recent breakthroughs at multiple fronts in Machine Learning/Deep Learning, Artificial Intelligence (AI) have become the center of attention, not only in the IT circles but also in mainstream media. AI is traditionally dependent on high computation power provided by server farms either in the cloud or on-premise for large-scale, data-intensive model training and inference. While model training requires big data and cloud computing, it is often possible and desirable to implement model inference and online learning on resource-constrained edge devices, such as smart phones and in-vehicle platforms, to avoid the potential latencies and service disruptions due to cloud access. There has been significant recent research progress in embedded software/hardware techniques for AI, ranging from processing elements such as CPU, GPU, FPGA, ASIC, to subsystems of memory hierarchy, storage, and networking. This special issue focuses on the emerging intersection between AI/ML and embedded systems. 

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全文截稿：2017-06-15

期刊：Theoretical Computer Science

专刊：Special Issue on Quantum Structures in Computer Science: Language, Semantics, Retrieval

领域：计算机科学理论

难度：★★★★

CCF分类：B类

影响因子：0.643

网址：http://www.journals.elsevier.com/theoretical-computer-science/

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Recently, the application of the mathematical formalism of quantum theory has showed significant effectiveness in the modeling and prediction of several phenomena involving complex cognitive processes, such as human probability judgment, knowledge representation, decision-making and perception, where traditional Boolean and Bayesian approaches are problematic. This has had a huge impact on applied disciplines, such as informatics, natural language processing, artificial intelligence, human-machine interaction and machine learning. In this regard, the proponents of the special issue Quantum Structures in Computer Science: Language, Semantics, Retrieval are partners in the Consortium Quantum information Access and Retrieval Theory(QUARTZ), involving seven European Institutions, which has recently been awarded by the European Union for a prestigious Marie Curie European Training Network. With the present special issue, we intend to open the novel and powerful quantum theoretical approach to the community of computer and information scientists, explaining how the quantum mechanical formalism addresses the challenges of the dynamic and multi-modal nature of data and user interaction context, and hence can be successfully applied to natural language, semantics and information access and retrieval and related problems. Through a presentation of the state of the art of the discipline, the contributors will explain why traditional algebraic and logical approaches are not sufficient to address the challenges of a dynamic, adaptive and context-aware user-machine interaction and to make a breakthrough in the overall effectiveness of retrieval systems, thus claiming for a genuine theoretical advance. The proponents and contributors believe that this breakthrough can be provided by quantum theory, which integrates abstract vector spaces, probability spaces and logical operations in a single theoretical framework that extends and generalizes the classical vector, probability and logic spaces utilized in semantic analysis, information access and retrieval and natural language processing. The contributors will also investigate how and why a theoretically and empirically motivated quantum theoretical framework gives up the notions of uni-modal features and classical ranking models disconnected from context. Finally, the contributors will investigate theoretical issues and evaluate methods to manage data collections and meet the user's information needs in a dynamic context.

The special issue will include contributions from top experts in computer science and related fields, who have played a pioneering role in the development of the quantum theoretical research programme and are still providing fundamental contributions to it.

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全文截稿：2017-06-30

期刊：World Wide Web Journal

专刊：Special Issue on Deep Mining Big Social Data

领域：交叉/综合/新兴/数据库/数据挖掘/内容检索

难度：★★★★

CCF分类：B类

影响因子：1.539

网址：http://www.springer.com/journal/11280/about

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The internet revolution has made information acquisition easy and cheap so that it has been producing massive web/social data in our real life. The emergence of big social media has lead researchers to study the possibility of their exploitation in order to identify hidden knowledge. However, a huge number of issues appear in obtained big social data. First, there are incomplete social data due to all kinds of reasons, such as security and private information. Second, the structure of social data is different, including structured data (e.g., social web data), semi-structured data (e.g., XML data) and unstructured data (e.g., social networks). Third, the web data are often high-dimensional. However, current computer techniques can only deal with structured, complete and moderate-sized-dimensional data. Moreover, current computer technologies can only mine the basic structure and are not capable of mining their natural complex structure (or deep structure). Hence, there is a huge gap between existing technologies and the real requirements of actual big social data. In this case, deep mining of big social data (such as data preprocessing, deep pattern discovery, pattern fusion, and outlier/noise detection) stands as an interesting promise to relief such a gap. The World Wide Web journal invites papers for a special issue on "Deep Mining Big Social Data" to attract articles that cover existing approaches to mining big social data.

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全文截稿：2017-07-01

期刊：Neural Networks

专刊：Special Issue on Deep Reinforcement Learning in Neural Networks

领域：人工智能

难度：★★★★

CCF分类：B类

影响因子：3.216

网址：http://www.journals.elsevier.com/neural-networks/

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Deep learning (DL) has become highly popular in recent years, among theoretically minded and application-focused researchers alike. Moreover, the idea of deep learning has been combined with reinforcement learning (RL), leading to deep reinforcement learning, which has achieved notable successes in tackling difficult problems, including the achievement of AlphaGo.

However, there are many open questions and issues that need to be addressed with regard to deep RL.  Open questions with regard to deep RL include:

- How do we extend RL algorithms or systems to make them suitable for deep learning? How do we make RL (typically centered on values of states or state-action pairings) appropriately deep?

- How do we do so without jeopardizing useful characteristics of RL?

- What modification and enhancements to learning algorithms are necessary to accomplish deep RL in an effective and/or efficient manner?

- How can we make knowledge within deep RL systems explicit (generating explicit, symbolic, usable knowledge) and enable metacognitive reflection and regulation to some extent?  

- How can deep learning help facilitate planning or model-based reinforcement learning?

- How can hierarchical or modular approaches be applied to deep RL?

- What theoretical/mathematical properties can be obtained with regard to deep RL (e.g., convergence, stability, robustness, and optimality)?

- How do we apply deep RL in real-world scenarios?

The aim of this special issue is to showcase state-of-the-art work in the field of deep RL, addressing some of the above questions and beyond. Although there have no doubt been advances in addressing these questions, there is clearly room for further development. This special issue will provide a platform for deep learning and reinforcement learning researchers to share their work, for the sake of more rapid advances on a solid footing, fully realizing the potential of infusing reinforcement learning and deep learning. It also intends to showcase more effective applications in a variety of fields (robotics, control engineering, data analysis, and so on).

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全文截稿：2017-07-20

期刊：Journal of The Acoustical Society of America

专刊：Special Issue on Ultrasound in Air

领域：计算机图形学与多媒体

难度：★★★★

CCF分类：B类

影响因子：1.572

网址：http://scitation.aip.org/content/asa/journal/jasa

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For the purpose of this Special Issue, the lower limit of the ultrasonic range will be 17.8 kHz because this is the lower limit of the third octave band centered on 20 kHz. However radiations at lower frequencies may be acceptable if there are compelling reasons such as continuity of audiological or calibration procedures or consideration of a device which emits substantially into both the third octave band centered at 20 kHz and the band below that (centered at 16 kHz). The focus includes (but is not restricted to) topics such as:

1.Use/sources of airborne ultrasound for:

- haptic feedback

- power delivery

- communication

- intrusion detection

- warning/positioning systems

- entertainment

- acoustic spotlights

- acoustic beacons

- deterrent

- weaponry

2.Measurement of airborne ultrasound

- Measurement procedures for airborne ultrasound

- Calibration procedures for airborne ultrasonic devices

- Unintentional emission of airborne ultrasound by devices or procedures

3.Airborne ultrasound in zoology

4.Effects of ultrasound in air on humans

5.High frequency audiology

6.Guidelines/standards pertaining to the deployment, exposure or measurement of ultrasound in air