计算机类 | CCF推荐期刊约稿信息6条

数据库管理与信息检索

Journal of Web Semantics

Special Issue of the JWS on Open Data

全文截稿: 2018-03-15

影响因子: 1.075

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：人工智能 - 4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-web-semantics

The Journal of Web Semantics seeks submissions for a special issue on Open Data to be edited by Pieter Colpart, Deirdre Lee, Elena Simperl and Jürgen Umbrich. Submissions are due March 15th, 2018.

The Open Definition (http://opendefinition.org) defines “open data” in legal terms: anyone should be able to freely access, use, modify, and share the data for any purpose. Following this definition, the potential of Open Data unfolds when it is widely reused. This is reflected in data policies, which mandate the release of key data assets in the public sector using technologies that encourage reuse.

To achieve this, organisations in the public and private sectors typically have defined an Open Data strategy, established Open Data publication, management and governance processes, and set up Open Data portals that act as central points of access to datasets. Portals enable data to be found more easily - for example, if the consumer is looking for a dataset of a given publisher, their data portal would probably be the prime address to find that data. The portal would typically provide visitors a number of features for data discovery, including a search bar, a dataset catalog, and dataset metadata and descriptions. Some portals host data from multiple organisations. For those who release their data openly, a portal can be useful in many ways - from hosting reliable URLs to version management and metadata management.

Our understanding of how and how widely open data is reused is patchy. However, most publishers and portal owners would agree that levels are much lower than ambitioned and that tracking the impact of their data is extremely difficult. This is also due to several technical challenges, including:

- Data access uses a range of protocols( e.g. bulk downloads, REST API, single or compressed files);

- Datasets are published in different formats and refer to different domain ontologies;

- Metadata often lacks key information, while descriptions are unstructured and of variable quality;

- Data terms of use and license information are not always easy to find;

- The tools people use to search and make sense of open data offer a limited user experience.

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Cyber Threat Intelligence and Analytics

全文截稿: 2018-03-31

影响因子: 3.997

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/

In today’s Internet-connected world where technologies underpin almost every facet of our society, cyber security and forensics specialists are increasingly dealing with wide ranging cyber threats in almost real-time conditions. The capability to detect, analyze and defend against such threats in near real-time conditions is not possible without employment of threat intelligence, big data and machine learning techniques. For example, when a significant amount of data is collected from or generated by different security monitoring solutions, intelligent and next generation big-data analytical techniques are necessary to mine, interpret and extract knowledge of these unstructured/structured (big) data. Thus, this gives rise to cyber threat intelligence and analytics solutions, such as big data, artificial intelligence and machine learning, to perceive, reason, learn and act against cyber adversaries tactics, techniques and procedures.

Cyber threat intelligence and analytics is among one of the fastest growing interdisciplinary fields of research bringing together researchers from different fields such as digital forensics, political and security studies, criminology, cyber security, big data analytics, machine learning, etc. to detect, contain and mitigate advanced persistent threats and fight against malicious cyber activities (e.g. organized cyber crimes and state-sponsored cyber threats).

This special issue is focused on cutting-edge research from both academia and industry, with a particular emphasis on novel techniques, combination of tools and so forth to perceive, reason, learn and act on a wide range of cyber threat data collected from different intrusion attempts, malware campaigns and indications of compromise. Only technical papers describing previously unpublished, original, state-of-the-art research, and not currently under review by a conference or a journal will be considered. Extended work must have a significant number of "new and original" contributions along with more than 60% brand "new" material.

Specifically, this issue welcomes two categories of papers: (1) invited articles from qualified experts; and (2) contributed papers from open call with list of addressed topics. Topics of interest include but not limited to:

- Detection and analysis of advanced threat actors tactics, techniques and procedures

- Analytics techniques for detection and analysis of cyber threats

- Application of machine learning tools and techniques in cyber threat intelligence

- Theories and models for detection and analysis of advanced persistent threats

- Automated and smart tools for collection, preservation and analysis of digital evidences

- Threat intelligence techniques for constructing, detecting, and reacting to advanced intrusion campaigns

- Applying machines learning tools and techniques for malware analysis and fighting against cyber crimes

- Intelligent forensics tools, techniques and procedures for cloud, mobile and data-centre forensics

- Intelligent analysis of different types of data collected from different layers of network security solutions

- Threat intelligence in cyber security domain utilising big data solutions such as Hadoop

- Intelligent methods to manage, share, and receive logs and data relevant to variety of adversary groups

- Interpretation of cyber threat and forensic data utilising intelligent data analysis techniques

- Infer intelligence of existing cyber security data generated by different monitoring and defense solutions

- Automated and intelligent methods for adversary profiling

- Automated integration of analysed data within incident response and cyber forensics capabilities

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Semantic Multimedia Data

全文截稿: 2018-03-31

影响因子: 3.997

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/

Multimedia data can be easily uploaded, communicated and shared in community portals. These later allow users to manually tag, comment and annotate the digital content, but they lack a general support for fine-grained semantic descriptions and look-up, especially when talking about things “inside” multimedia content, such as an object in a video or a person depicted in a still image.

Linking Multimedia Data is an active and inter-disciplinary research field in multimedia. Turning a distributed repository of images, video, text, and other signal-based objects (such as various radar signatures) into an easily browsable information and knowledge would certainly transform the underlying data into a more satisfying representation for humans to intelligently navigate. The power of this approach results from the mixture of different modalities of data. While linked textual data is being studied by the web semantics and database communities, adding non-textual data is much more satisfying for human interaction, but comes with the price of more complexity.

While in hypermedia, one mainly focuses on languages and synchronization made between information parts in both temporal and spatial dimensions, we mainly focus in this special issue on the (semi) automatic and collaborative methods for fostering semantics in multimedia data by enriching information (so to come up with a knowledge), for visualization and exploration, and for missing (meta)data estimation. The added complexity is due to objects having multiple facets, depending on their use and who or what system is doing the labeling.

This research area is a blending of topics of interest to many disparate research communities. The novelty will reside initially in how to formulate and implement the boundaries between tasks of interest to these different communities. As the field matures and the diverse nature of the data is accepted as a given by researchers in this area, research advances of a more integrative nature will be the norm, treading into areas that we can only dream of today.

The relevance and significance of this special issue to the FGCS journal

Semantic Data is a hot research topic in the Web Semantics and Database Community. The linkage to multimedia data is an emerging and very important area which investigates the necessary methods to link multimedia data and its fragments and objects identified in the data to other data, and to publish and consume it on the Linked Open Data Cloud. As such, Semantic Multimedia Data will attract more and more attention from academic and industrial communities.

Since FGCS has been the most influential academic journal to promote the exchange of the latest advances in both semantic and knowledge based systems and Big Data registration, processing and analyses, we believe that it is the best address for our “Semantic Multimedia Data” special issue.

Topics of interest include, but are not limited to:

- Multimedia data linking methods

- Collective knowledge, event and entity detection

- Multimedia measures (similarity, quality, data fusion, etc.)

- Semi-supervised, learning-based multimedia data linking methods

- Multimedia on the web sampling techniques

- Identity representation and semantics

- Online personal information management

- Reasoning on connected multimedia data

- Retrieval and annotation of big multimedia data

- Big multimedia data analytics

- Semantics and metadata in multimedia systems

- Link propagation

- Provenance and trust models on links

- Semantic-based multimedia retrieval and annotation

- Emergent semantics in content-based retrieval systems

- Social media and Crowdsourcing

- Evaluation techniques and tools

- Marketplaces, aggregators and indexes for Semantic Multimedia Data

- Applications

- Internet of things

计算机体系结构,并行与分布式计算

Journal of Parallel and Distributed Computing

Special Issue on “Trends on Heterogeneous and Innovative Hardware and Software Systems”

全文截稿: 2018-04-06

影响因子: 1.93

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/

In the last years, there has been a clear move towards heterogeneous hardware and software systems. In this scenario, the hardware and software designer is faced with the need of innovative techniques to manage efficiently such a complex system. The purpose of this special issue is to collect the main recent trends in heterogeneous and innovative systems, including computer architecture, languages and compilers, algorithms, and applications.

Topics of interests include:

- Innovative architectures, including processor, cache, memory, storage, and interconnect

- Power and energy-efficient systems, including GPUs, FPGAs and other accelerator architectures

- Reconfigurable, resilient and fault-tolerant innovative systems

- Heterogeneous application-specific systems

- Languages, compilers, and tools for heterogeneous parallel and distributed programming, including support for emerging domains (Big Data, Deep Learning)

- Heterogeneous systems and innovative Cloud, Grid, cluster, and peer-to-peer systems

- Benchmarking, performance measurements, and analysis of heterogeneous systems

- Modeling and simulation methodology for heterogeneous systems

- Innovative algorithms, and applications for heterogeneous parallel and distributed systems

- Real-world applications and case studies for heterogeneous systems

计算机网络

Computer Networks

Special Issue on “Application Areas and Fundamental Challenges in Network Functions Virtualization”

全文截稿: 2018-04-15

影响因子: 2.516

CCF分类: B类

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：信息系统 - 3区

  • 小类 : 工程：电子与电气 - 3区

  • 小类 : 电信学 - 3区

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

Network Functions Virtualization (NFV) has drastically changed the way networks are operated. NFV allows for the removal of network functions from dedicated network hardware to make them running in virtualized containers (e.g., virtual machines or Linux containers) of commercial-of-the-shelf servers with advanced hypervisor platforms (e.g., Linux KVM or Docker). Bare-metal accelerators will be provided to leverage the most performance-hungry functions.

In this scenario, network functions can be implemented not only in a more cost-effective, and vendor-independent fashion with a dramatically reduced time-to-market, but network operators will be also enabled to compose them into highly customized services (chains). Such custom services will be the key to support new-generation vertical applications with tight and very heterogeneous operating requirements, i.e., from the tactile Internet to mass-scale Internet of Things (IoT) into upcoming 5G facilities. On one hand, the NFV technology will enable network operators to create virtual “slices” of their network, containing NFV customized services, and offer them to vertical industries “as-a-Service”; on the other hand, the same infrastructure could be exploited for hosting at least part of the same vertical applications from different vertical industry areas (e-Health, Industry 4.0, Energy Smart Grid, Automotive, etc.), through emerging paradigms like Mobile Edge or Fog Computing, whose integration with the NFV architecture is still a fully open issue. Thus, there will be the need for evolving current NFV approaches and paradigms not only to customize (access and core) network operations according to these diverse requirements, but also to flexibly and fully integrate them with distributed and network-aware cloud systems.

The evolution of the NFV technology does represent the cornerstone enabler for a new role of telecommunication operators in the upcoming 5G and IT ecosystems, which may directly impact any third-party sectors. Therefore, the fundamental challenges to be addressed span from the design of new programmable network abstractions (e.g., network slicing) to be exposed to vertical industries acting in various areas, to the evolution of current NFV frameworks to support and integrate heterogeneous applications in a scalable and highly automated fashion. A further key objective will be how to maintain network and infrastructural awareness across the various abstracted virtual layers of the tomorrows’ 5G networks.

This SI aims to attract high-quality submissions highlighting the key role of NFV for future telecommunication infrastructures and services, its relationship with the cloud, mobile-edge, and fog computing, covering a broad range of (vertical) application areas, the underlying fundamental challenges and the intrinsic scalability levels provided by this technological paradigm.

Application Areas

- 5G Infrastructure, paradigms and services including Network Slicing and Cloud Radio Access Network (RAN).

- 5G Vertical Applications in fields like Industry 4.0, Tactile Internet, Augmented Reality, Mass-scale IoT and Automotive.

- Mobile Edge and Fog Computing and their integration within 5G.

- Design and scalability issues in softwarized network infrastructures and platforms including lifecycle management and orchestration of slices, NFV services, virtualized network functions and computing/networking resources.

- Hierarchical control schemes to enable highly dynamic and autonomic operations, such as placement service/VNF chaining, among vertical applications, software network platforms and infrastructures.


Fundamental Challenges

- I/O performance problems when using commercial off-the-shelf (COTS) hardware.

- Network design considerations like function placement and function embedding.

- Virtualization techniques and related information models.

- Data/control plane performance modelling to support autonomic (vertical/horizontal) scaling.

- Virtualized and multi-layered resource management and optimization.

- Performance evaluation and benchmarking of physical and virtual network functions and micro functions.

- Flexibility and interoperability among 5G vertical infrastructure, 5G network service platforms, and infrastructures.

- Energy-efficiency and sustainability in NFV architectures.

计算机综合与前沿

Computers in Human Behavior

Call for paper for Special issue on "Reflection of Teaching and Learning Cognition and Behavior in Smart Learning Environments"

全文截稿: 2018-04-15

影响因子: 3.435

网址: https://www.journals.elsevier.com/computers-in-human-behavior

Learning environments are basic conditions and criteria that learning activities must continuously rely upon. Learning environments generally include both tangible and intangible criteria. Intangible criteria mainly refer to psychological environments generated through mutual interactions between people or between people and the tangible environment. Intangible criteria were therefore often described as the soft or spiritual environment. Tangible criteria, on the other hand, refer to the physical environment which could also be referred to as the hard environment or material environment. The modern era is characterized by rapid advancements in information technology (IT). Digitalization is becoming more ubiquitous and extensive with new IT developments being introduced into the field of education. The physical learning environment of learners is transforming from traditional classrooms to diverse smart learning environment, and this has led to corresponding changes to the learners’ learning objectives from simply knowledge-based learning to skill-based learning. Such transformations have led to changes to the learners’ psychological learning environment as well. This Special Issue hopes to invite fellow researchers studying changes to the students ‘mental learning environments and how such changes influenced their learning performance against the backdrop of smart learning environments. The following are the topics that we will be looking at:

1. Smart learning environments and their psychological impact upon learners during the learning process. New media technologies, including gesture recognition and improved virtual reality (VR) technologies, have continued to make their way into the educational sector and gave rise to experience-based learning and other new learning models that make learning environments smarter and easier to use. Studies were conducted to see how smart learning environments encouraged or transformed the learners’ learning emotions and motivations.

2. Studies related to student learning behaviors and psychology of learning under smart learning environments. The establishment of smart learning environments placed greater demands on learning methods. Knowledge building started focusing upon the objective of teamwork, learner evaluation became more diverse, while teacher-oriented learning activities shifted towards student-focused approaches. In other words, smart learning environments changed the way students learn and created new relationships between student learning behaviors and learning psychology.

3. Psychological investigations of concept building processes in various subjects under the smart learning environment. The establishment of concept model is the most important process in the learning process of science and technology subjects. Traditional learning models have limited effects on correcting previously held concepts. Smart learning environments, however, offer a new paradigm to the concept building process. Evaluation studies between educational psychology and concept building were carried out to review explicit changes to student psychology during the process of building conceptual models in various subjects when learning under the smart learning environment. Outcomes could be employed to further encourage student learning.

4. Investigations on technological dependence and Internet addiction among students in smart learning environments. The Internet is characterized by interactivity, promptness, prevalence, and openness and has virtually infiltrated every aspect of the modern lifestyle, and would exert significant influence upon the students’ lifestyles, learning methods, and psychological behaviors. Despite its benefits, Internet technology and smart learning also come with certain risks. These negative risks include Internet hackers, viruses, user addiction, and potential impact to the students’ cognition, emotion, commitment, personality, and social psychology, and could not to be ignored.

图形学与多媒体

Journal of Visual Communication and Image Representation

Special Issue on Feature representations for medical images and activity understanding

全文截稿: 2018-04-15

影响因子: 2.164

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/

This special issue aims to bring together researchers from different areas related to medical images and activity understanding. The past decade has witnessed the explosion of advances on feature extractions, learning and representation methods that emerged as promising and effective approaches to deal with the tasks of understanding medical images and human activities. We expect original and high-quality articles covering data representations, new benchmarks datasets, feature detection description, semantic extraction for scene understanding, scene context, and medical applications.

We invite authors to contribute with high quality paper that will stimulate the research community on the use computer vision, pattern recognition and image processing for medical images problems and activity understanding. Research papers are solicited in, but not limited to, the following topics:

• Data representations, classification methods based on deep learning techniques;

• Feature detection and description;

• Image applications for domain adaptation;

• Extraction of semantic representations for video data representation, such as scene understanding and scene context;

• Applications such as activity recognition, semantic video summarization, video captioning, and action retrieval;

• Data representation and semantic extraction for first-person camera vision;

• Biological and medical imaging.

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