计算机 | CCF推荐期刊专刊信息5条

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on “Advances in Self Protecting Systems”

全文截稿: 2019-07-15

影响因子: 4.639

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Effectively protecting computer systems from cyber-attacks is a challenging task due to their large scale and the heterogeneity of the underlying hardware and software components. Furthermore, when trying to defend from an attack, the time factor is critical, and any non-guided human resolution attempt could introduce a significant stress and delay to the execution of the proper response. This situation provides the attackers more time to accomplish their objectives.

Several organizations, including the National Institute of Standards and Technology (NIST), have released guidelines and best practices to manage cyber-security related risk (e.g., the NIST Cybersecurity Framework 1.1). However, despite a growing interest in the area in the last 4-5 years, automation of cyber-security operations is still at its early stages.

Automatically defending a computer system encompasses a large number of activities, that range from data capture, management and analysis, to automated decision making and automated system operations.

In this special issue, we solicit high quality contributions that fit with the overarching idea of creating a fully automated protection system based on the Monitor, Analyze, Plan, Execute (MAPE) loop, control theory, bio-inspired solutions, Self-Regenerative Systems, and the like.

Selected papers of the 1st Int. Workshop on Self-Protecting Systems (SPS-2019) are invited to submit an extended version of their work.

Topics of interest for the special issue are:

Distributed and secure data collection and storage for sensing/monitoring

Automated Feature Selection approaches to reduce data dimensionality on cyber-security relevant data

Techniques for automatic correlation of data streams

Self-Evolving Anomaly-Based and Signature-Based Network/Host Intrusion Detection Systems

Attack and defense modeling for threats detection and risk management

Self-Evolving Model-based and Model-free Intrusion response

Attack and defense modeling for reactive and proactive intrusion response

Foundational results for self-protecting systems: Algorithms, artificial intelligence, biological-inspired techniques, control theory, machine learning, operation research, probability and stochastic processes, queueing theory, rule-based systems, and socially-inspired techniques

Software engineering for self-protecting systems: System architectures, services, components and platforms, Goal specification and policies, modeling of security-level agreements, behavior enforcement, IT governance, and security-driven IT management

Self-organizing and organic computing for self-protecting systems: Self-organization principles and organic computing principles borrowed from systems theory, control theory, game theory, decision theory, social theories, biological theories, etc. ; Self-organization, emergent behavior, decentralized control, individual and social/organizational learning, scalability, robustness, goal- and norm-governed behavior, online self-integration for trustworthy self-organizing and organic systems; Infrastructures and architectures for self-organizing systems and organic computing systems.

Implementation of prototypes that integrate cutting edge technologies, e.g., Software Defined Networks, Cloud/Fog/edge computing, Artificial Intelligence, micro-services

Holistic perspective on self-protecting systems i.e., researches that consider the overall picture and propose novel software architectures, frameworks and technologies to ease the realization of self-protecting systems.

图形学与多媒体

Computer-Aided Design

Special Issue on Process Planning for Additive/Hybrid Manufacturing

全文截稿: 2019-08-01

影响因子: 2.947

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：软件工程 - 2区

网址: http://www.journals.elsevier.com/computer-aided-design

Manufacturing processes have driven the evolution of computational tools to support design, planning, and analysis of functional parts and assemblies. Planning for modern, advanced manufacturing technologies such as additive (AM) and hybrid additive and subtractive manufacturing (HM) with single, graded, or composite materials requires coordinating spatial reasoning, evaluation/simulation of material properties, and physics analysis on representations of manufactured artifacts. Process plans may affect the shape (at multiple length scales), material distribution, and physical behavior of the fabricated design. Conversely, fabrication-aware design must take manufacturing constraints into account. Variation from nominal designs due to manufacturing uncertainty should be characterized and represented to evaluate process plans. These challenging research topics must be addressed to synthesize parts, assemblies, and systems so that design tools can take full advantage of the rapid advancement in emergent manufacturing technologies.

The aim of this special issue is to bring together a research community to discuss these critical computational design issues and push forward new methodologies in manufacturing planning for functional parts and assemblies. These efforts will accelerate the development of computational methods for manufacturing planning in AM and HM that relate to the shape, material distribution/properties, and simulated physical performance of fabricated parts. Specific topics of interest include, but are not limited to the following:

Geometric and physical reasoning for AM/HM process planning

Design for AM/HM with single, multiple, or composite materials

Physics simulation of AM/HM processes

Simulating function and performance for parts fabricated with AM/HM

Representing anisotropy and heterogeneity in manufactured micro and bulk structure

Spatial planning and manufacturability analysis for AM/HM

Modeling uncertainty in AM/HM processes

图形学与多媒体

Journal of Visual Communication and Image Representation

Special Issue: Integrating Vision and Language for Semantic Knowledge Reasoning and Transfer

全文截稿: 2019-08-01

影响因子: 1.836

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：信息系统 - 3区

  • 小类 : 计算机：软件工程 - 3区

网址: http://www.journals.elsevier.com/journal-of-visual-communication-and-image-representation/

Due to the explosive growth of visual and textual data (e.g., images, video, blogs) on the Internet and the urgent requirement of joint understanding the heterogeneous data, integrating vision and language to bridge the semantic gap has attracted a huge amount of interest from the computer vision and natural language processing communities. Great efforts have been made to study the intersection of vision and language, and fantastic applications include (i) generating image descriptions using natural language, (ii) visual question answering, (iii) retrieval of images based on textural queries (and vice versa), (iv) generating images/videos from textual descriptions, (v) language grounding and many other related topics.

Though booming recently, it remains challenging as reasoning of the connections between visual contents and linguistic words are difficult. Reasoning is based on semantic knowledge, i.e. people understanding a linguistic word (for example “swan”) involves reasoning the external knowledge of the word (e.g., what swan look like, the sounds they make, how they behave and what their skin feels like.) Although reasoning ability is always claimed in recent studies, most “reasoning” simply uncovers latent connections between visual elements and textual/semantic facts during the training on manually annotated datasets with a large number of image-text pairs. Furthermore, recent studies are always specific to certain datasets that lack generalization ability, i.e., the semantic knowledge obtained from specific dataset cannot be directly transferred to other datasets, as different benchmark may have different characteristics of its own. One potential solution is leveraging external knowledge resources (e.g., social-media sites, expert systems and Wikipedia) as intermediate bridge for knowledge transfer. However, it is still implicit that how to appropriately incorporate the comprehensive knowledge resources for more effective knowledge-based reasoning and transfer across datasets. Towards a broad perspective of applications, integrating vision and language for knowledge reasoning and transfer has yet been well exploited in existing research.

Topics of Interests:

This special issue targets the researchers and practitioners from both the academia and industry to explore how advanced learning models and systems can be leveraged to address the challenges in semantic knowledge reasoning and transfer for joint understanding vision and language. It provides a forum to publish recent state-of-the-art research findings, methodologies, technologies and services in vision-language technology for practical applications. We invite original and high quality submissions addressing all aspects of this field, which is closely related to multimedia search, multi-modal learning, cross-media analysis, cross-knowledge transfer and so on.

Topics of interest include, but are not limited to:

· Big data storage, indexing, and searching

· Deep learning methods for vision and language

· Transfer learning for vision and language

· Cross-media analysis (retrieval, hashing, transfer, reasoning, etc)

· Multi-modal learning and semantic representation learning

· Learning knowledge graph over multi-modal data

· Generating image/video descriptions using natural language

· Visual question answering/generation

· Retrieval of images based on textural queries (and vice versa)

· Generating images/videos from textual descriptions

· Language grounding

计算机体系结构,并行与分布式计算

Journal of Parallel and Distributed Computing

Distributed Applications and Interoperable Systems: Recent Advances and Future Trends

全文截稿: 2019-09-01

影响因子: 1.815

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：理论方法 - 3区

网址: http://www.journals.elsevier.com/journal-of-parallel-and-distributed-computing/

This special issue addresses all practical and conceptual aspects of distributed applications, including their design, modeling, implementation and operation, the supporting middleware, appropriate software engineering methodologies and tools, as well as experimental studies and applications. In particular, the special issue will address novel and advanced distributed systems, like blockchains, IOT and cyber-physical systems, peer-to-peer systems and the techniques which required for the definition and the development of these systems, for instance novel distributed consensus mechanisms, advanced networking techniques, trust and privacy.

Submissions will be judged on their originality, significance, clarity, relevance, and technical correctness.

The topics of interest to the conference include, but are not limited to:

1. Novel and innovative distributed applications and systems, particularly in the areas of

middleware,

cloud, edge and fog computing,

big data processing,

streaming and complex event processing,

distributed social networking,

IoT and cyber-physical systems,

mobile computing,

advanced networking (SDN/NFV),

micro-sevices and service-oriented computing,

peer-to-peer systems, and

data center and internet-scale systems.

2. Novel architectures and mechanisms, particularly in the areas of

publish/subscribe systems,

epidemic protocols,

language-based approaches,

virtualization and resource allocation,

distributed storage,

trusted execution environments,

blockchains, cryptocurrencies and smart contracts, and

distributed consensus mechanisms.

3. System issues and design goals, including

interoperability and adaptation,

self-* properties (e.g., self-organization, self-management,...),

security and practical applications of cryptography,

trust and privacy,

cooperation incentives and fairness,

fault-tolerance and dependability,

scalability and elasticity, and

tail-performance and energy-efficiency.

软件工程

Journal of Systems and Software

Special Issue on Intelligent Software Engineering

全文截稿: 2019-09-15

影响因子: 2.278

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：软件工程 - 2区

  • 小类 : 计算机：理论方法 - 3区

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

Software systems have been playing important roles in business, scientific research, and our everyday lives. It is critical to improve both software productivity and quality, which are major challenges to software engineering researchers and practitioners. As developers work on a project, they leave behind many digital artifacts. These digital trails can provide insights into how software is developed and provide a rich source of information to help improve development practices. For instance, GitHub hosts more than 57M repositories, and is currently used by more than 20M developers. As another example, Stack Overflow has more than 3.9M registered users, 8.8M questions, and 41M comments. The productivity of software developers and testers can be improved using information from these repositories.

In recent years, intelligent software engineering has emerged as a promising means to address these challenges. In intelligent software engineering, Artificial Intelligence (AI) techniques (e.g., deep learning) have been frequently applied to discover knowledge or build intelligent tools from software artifacts (e.g., specifications, source code, documentations, execution logs, code commits and bug reports) to improve software quality and development process (e.g., to obtain the insights for the causes leading to poor software quality, and to help the managers optimize the resources for better productivity). And these techniques have shown a great success in addressing various software engineering problems (e.g., code generation, code recommendation, and bug fix and repair). Therefore, intelligent software engineering has attracted great attention in both software engineering and AI communities.

We invite the submission of high-quality papers describing original and significant work in all areas of intelligent software engineering including (but not limited to): 1) Methodological and technical foundations of intelligent software engineering, 2) Approaches and techniques for knowledge discovery in various software artefacts, and 3) Applications of AI techniques to facilitate specialized tasks in software engineering. We especially encourage submission of extended papers from the 18th National Software Application Conference (NASAC 2019). Topics of interest include but are not limited to:

A. Intelligent software engineering techniques

A1. AI models and techniques for software engineering

A2. Robust and highly scalable algorithms for mining ultra-large-scale software systems

A3. Explainable and actionable AI models

A4. Visualizing AI models

B. Knowledge discovery in software artefacts

B1. Mining software specifications

B2. Mining source code/code commits

B3. Mining execution traces and logs

B4. Mining bug and crash reports

B5. Mining Q&A and social data

C. Intelligent software engineering in specialized tasks

C1. AI techniques for software development and reuse

C2. AI techniques for software maintenance and evolution

C3. AI techniques for software testing and debugging

C4. AI techniques for open source ecosystem best practices

C5. AI techniques for software defect identification and characterization

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