计算机类 | 高引SCI期刊专刊信息8条

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

Blockchain Technologies for Industrial Internet of Things

全文截稿: 2018-11-30

影响因子: 1.747

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 4区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

In the Industrial Internet of Things (IIoT) era, we have billions of connected things, ranging from autonomous cars, to domestic robots, to traffic sensors, and so on. There are a number of research challenges associated with IIoT, such as security and privacy. In an IIoT setup, cloud servers may be utilized to store and process data from IIoT devices; thus ensuring the security of such data is crucial. It can, however, be challenging for data owners to ensure a fine-grained control over the access and use of their private, sometimes sensitive, data, particularly in a centralized IIoT service architecture. Blockchain can potentially be used to mitigate existing limitations, for example, in the facilitation of a novel decentralization architecture for IIoT. The use of blockchain in IIoT security and privacy is an emerging area, and one that has great potential.

In this special issue we seek to publish recent research efforts and advances in blockchain-enabled IIoT security and privacy solutions. Authors are invited to submit their original unpublished research manuscripts on the topics such as the following.

Topics:

- Edge/fog/cloud computing for blockchain-enabled IIoT services

- Location-based services for blockchain-enabled IIoT services

- Deep learning for blockchain-enabled IIoT services

- Croudsourcing for blockchain-enabled IIoT services

- Security and privacy solutions for blockchain-enabled IIoT services

- Performance evaluation of blockchain-enabled IIoT services

- Blockchain enabled new models and applications in IIoT (e.g. smart home, smart healthcare, smart city, intelligent transportation systems, and financial systems)

- Blockchain platforms and testbeds in IIoT

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

Special Issue on Advanced Electrical and Communication Technologies

全文截稿: 2018-12-15

影响因子: 1.747

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 4区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

Electrical Technology is instrumental and indispensable to our existence in the current context of technology-driven living pattern of present times. In today’s technology-driven world, electrical and communication engineering is the cornerstone and driver of innovation of the devices we utilize daily to improve our quality of life. Electrical, electronics and communication engineering is driven by growth and new engineering ideas.

This special Issue aims to publish high-quality manuscripts on advances in the state-of-the-art of communication and information technology, Smart systems and Electrical technologies. Both theoretical contributions, including new techniques, concepts, and analyses, and practical contributions, including system experiments and prototypes, and new applications, are solicited. Authors are invited to submit manuscripts that present original and unpublished research in all areas related to Emerging Electrical and Communications technologies.

Topics:

The general scope of the Special Issue includes the following:

- Information and communication technologies

- Microwave and Millimeter wave Engineering,

- Antenna design and RF propagation,

- Biomedical Engineering,

- RFID Technology and Applications,

- Smart city Technologies and Systems,

- Smart Energy Systems,

- Embedded Systems and Applications,

- Automotive and Avionic engineering,

- MEMS and NEMS Related Technology,

- Materials Characterization Techniques,

- Nano and microelectronics,

- Optoelectronics.

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

Special Issue on Cognitive computing and automation for human-centered systems

全文截稿: 2018-12-30

影响因子: 1.747

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 4区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The goal of cognitive computing is to create automated human-centred systems that are capable of solving problems without requiring human assistance.

The advanced popularity and scope of automation is obvious, covering not only large-scale framework, but also medium and small-scale products. Automation envelops basic, tedious undertakings, as well as advanced functionalities. However, even in systems in which automation has replaced functionality previously performed by humans, the human is still a central player. As automation becomes “smarter” and more ubiquitous, it is paramount that the human interacts with the controlled systems in a safe and efficient way, to help prevent problems in human-automation interaction.

Topics:

This special issue covers the following topics:

- Personalized feedback and intervention for human centred smart systems

- Artificial intelligent systems for human centred systems

- Adaptive computer vision for human centred systems

- Intelligent interfaces for artificial systems

- Cognitive computing of rehabilitation robot systems, medical healthcare robot, wearable robot systems for personal cooperative assistance

- Hybrid cognitive centred systems

- Augmented cognitive and mechanical systems for human centred systems

- Cognitive systems which ease the exchange of information between human and autonomous systems

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

New Computing Paradigms of Stream Data Mining and Optimization in Non-Stationary Environments

全文截稿: 2018-12-31

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/

Lately the number of application scenarios where fast data streams are produced with varying characteristics along time is growing at a fast pace over very diverse sectors, particularly in industrial systems (prognosis), health (condition monitoring, anomaly detection), telecommunications (ultra-fast resource allocation, fraud detection) and security (intrusion detection over high-speed communication networks) among many others. In these scenarios, data may come from devices, sensors, web sites, social media feeds, applications, and other data-intensive infrastructures and processes alike, hence they are often noisy, heterogeneous in nature and evolve over time. In this context, real-world applications require to deal with changing environments, e.g., the estimation of the best route for a fleet of transport vehicles may depend on eventual traffic jams, weather broadcast and/or the state of the highway; job shop scheduling could depend on changing requirements in the manufacturing plant; market conditions in financial models are subject to news and media.

Such circumstances pose an urgent need for developing efficient computational models for data mining (clustering, classification/regression) and optimization not only to accommodate the high rates at which data streams are delivered, but also to adapt to changes in the conditions that ultimately impact on the patterns and solutions found by such models. These cases, often referred to as online/stream analytics where data mining and optimization models should operate efficiently on dynamic (close to real-time) environments, unchain complex design challenges in their learning algorithms, as many factors need to be jointly considered such as computational complexity, accuracy/optimality, flexibility of the model to adapt to new data distributions and/or time-varying scenarios, latency requirements, etc.

This research area is a merge of topics of interest to many disparate research communities. The novelty will reside initially in how to bridge the gap between tasks of interest to these different communities, by offering hybrid dynamic approaches that are able to efficiently ingest and analyse streaming data sources produced in nonstationary environments.

This special issue focuses on such computational aspects and solicits articles dealing with online data processing models over streaming data, with an emphasis on descriptive analysis (including clustering), predictive modelling and optimization. Specifically, this special issue invites research papers to share latest research insights and present emerging results on theoretical and practical contributions related (but not limited) to:

- Dynamic optimization over time-evolving problem formulations.

- Multi-objective optimization and decision-making methods for nonstationary setups.

- Early classification over data streams.

- Semi-supervised/weakly-supervised predictive models for data stream mining.

- Unsupervised learning over data streams (e.g. clustering).

- Diversity-sensitive model construction for nonstationary concepts.

- Model adaptation to nonstationary datasets (e.g. concept drift).

- Change detection/classification approaches over evolving data streams.

- Design and validation of distributed online learning models and dynamic optimization solvers.

- Hybrid methods blending together elements from machine learning, heuristics and time series analysis.

- Computational complexity reduction strategies for learning models and optimization methods.

- New incremental models for learning/optimization.

- Model self-tuning approaches over data streams.

- Real-world applications of stream mining models and dynamic optimization solvers.

计算机体系结构,并行与分布式计算

Journal of Parallel and Distributed Computing

Security & Privacy in Social Big Data

全文截稿: 2018-12-31

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/

The rapid development of social networks dramatically changes the way people think, work, and interact. As more and more individual users proactively generate, share, and exchange digital contents through social media, social networks have become a key source of big data. However, with such vast interconnectivity, convergence of relationships, and shared user information comes increased security and privacy concerns in social big data. On one hand, users carelessly posting their personal information on social media which can easily have their privacy breached. On the other hand, malicious attackers may manipulate such information to make a profit.

There are two important security and privacy issues in social networks. The first is how to effectively utilize social data while protecting user privacy. The second is how to guarantee the authenticity of social data for an in-depth data analysis. Traditional security mechanisms and models tailored to small-scale or isomorphic data are inadequate to securing social big data which exhibit enormous volume and diverse formats. Therefore, how to develop scalable cryptographic algorithms/protocols and lightweight data mining/organization/optimization models to solve the security and privacy challenges becomes crucial for the successful application of social big data.

About the Topics of Interest

Any topic related to security and privacy aspects, e.g., access control, authorization, authorization, and anonymization, for big data and social networks, will be considered. All aspects of design, theory and realization are of interest. The scope and interests for the special issue include but are not limited to the following list:

(i) Fundamentals and Technologies in Social Networks and Big Data

- Social network models and platforms

- Social network architectures and data models

- Searching and discovery

- Architectures for big data

- Machine learning and deep learning

- Scalable computing models, theories, and algorithms

- Content analysis and data mining

- Novel and incentive applications of social big data in various fields

- Big data transformation, and presentation

- Big data acquisition, integration, cleaning, and best practices

- Large-scale data collection and filtering problem

- Sparse data modeling, compressing, and sensing

(ii) Security and Privacy in Social Networks

- Accountability and audit in social networks

- Authentication and authorization in cloud services;

- Secure access to social networks;

- Big data privacy model in social networks

- New trust mechanism in social networks

- Privacy and security preserving protocol for social networks

- Applications of cryptography in social networks

- Secure data management in social networks;

- Privacy modeling in social networks

- Privacy-preserving social data publishing

- Private information retrieval in social networks

- Measurement studies of security & privacy issues in social networks

- Combating cyber-crime: anti-phishing, anti-spam, anti-fraud techniques

(iii) Security and Privacy in Big Data

- Access control models and anonymization algorithms in big data

- Cryptography in big data and cloud computing

- Data protection and integrity in big data

- Secure searching in big data

- Secure outsourcing computing in big data

- System designs for secure data storage in big data

- Security model and architecture for big data;

- Software and system security for big data;

- Scalability and auditing for big data;

- Security and privacy in big data sharing and visualization;

- Security and privacy in big data mining and analytics;

- Data-centric security and data classification;

- Privacy in big data applications and services;

- Privacy in big data integration and transformation;

- Privacy in big data storage management;

- Threat detection using big data analytics;

- Big data privacy policies and standards

(iv) System, Information and Network Security

- High performance security systems

- Secure system implementation

- Database and system security

- Secure operating systems

- Cryptographic primitives and security protocols

- Disaster recovery

- Provable security

- Key distribution and management

- Intrusion detection and prevention

- Privacy, anonymity and traceability

- Identity management

- Access controls and security mechanisms

- Web & applications security

- Secure routing and network management

- Security in content delivery networks

- Security in high speed network

- Security in optical systems and networks

- Network monitoring

- Network security policies

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on "Data Exploration in the Web 3.0 Age"

全文截稿: 2019-01-20

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/

Currently emerging Web 3.0 environments have provided a strong potential for the integration of data sources, applications and tools. In such a pervasive and highly dynamic scenario, existing techniques for accessing and managing web content seem to be actually inadequate to satisfy the user needs and more automatic ways of exploring, joining and sharing information are needed to improve the usability of web resources.

This raises several important challenges for future data and web mining methods. Such challenges range from the analysis of poorly structured information, such as annotations and tags, to the provision of intelligent methods that support users in searching and integrating information offered by web resources. The overall goal of these challenges is not limited to enhance information retrieval but also includes exploiting the enriched semantics a dataset acquires when used in conjunction with other sources of information. The synergy of different technologies, including semantic web, natural language search, machine learning, recommendation agents and artificial intelligence, can be especially fruitful in this perspective.

Furthermore, in the era of big data and Internet of things, we are increasingly dealing with a huge amount of information generated by heterogeneous sources. Indeed, almost every individual leaves digital traces when interacting with sensor networks, cloud services and positioning services, through a variety of mobile devices and smart objects. A growing attention is thus devoted to the design of suitable approaches for exploring this kind of data, in order to extract actionable knowledge about people, things, and their interactions.

More generally, the dimensionality and the complexity of gathered data is fast increasing in almost all applications domains, giving rise to the need of innovative data analysis approaches.

The goal of this FGCS special issue is to foster the dissemination of top-notch results in all the areas related to Data Exploration in a very broad sense, including contributions from data mining, query languages, semantic analysis, data visualization, graph databases and other fields related to the analysis and exploitation of data.

Topics of Interest

The topics of the call regard original contributions focusing on challenging aspects of data exploration in modern scenarios, in a broad sense. These may for instance be related to the followings:

- Text and data mining, knowledge discovery

- Faceted search and browsing

- Information retrieval

- Data visualization and ux for web 3.0 data

- Querying interfaces and languages including constrained natural languages

- Entity recognition and merging, type classification, record linkage and property ranking

- Privacy and security issues in data exploration

- Recommendation agents and artificial intelligence technologies

- High-dimensional data analysis

- Machine learning and statistical methods for data analysis and processing

- Natural language processing for data extraction

- Platforms and applications exploring data in all domains including social, web, bioinformatics and finance

- Knowledge graph creation, reasoning, and exploration

- Data streams and the internet of things

- Semantic web and linked data analytics

计算机体系结构,并行与分布式计算

Computers & Electrical Engineering

SPECIAL ISSUE ON UBIQUITOUS ARTIFICIAL INTELLIGENCE AND CAPSULE NETWORKS

全文截稿: 2019-02-10

影响因子: 1.747

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 4区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: http://www.journals.elsevier.com/computers-and-electrical-engineering/

Capsule networks will certainly transform the capabilities and possibilities of machine learning in many areas. They help machines understand images by giving them a new aspect, similar to the three-dimensional perspective that humans have. They require less training data and deliver equivariant mapping, promising for image segmentation and object detection. With the use of dynamic routing and reconstruction regularization, the capsule network model would be both rotation-invariant and spatially-aware, addressing its inherent limitations.

The rise of Artificial Intelligence has paved the way for ubiquitous computing, enabled machines to adapt and truly build intelligence to make smart decisions, amplify human creativity, complete high-precision operations, optimize costs, and much more. Increased computing power and sensor data along with improved AI algorithms are driving the trend towards machine learning. AI is a tool that is becoming so useful and ubiquitous that it will soon become a kind of sixth sense.

This special issue aims to gather the latest research and development achievements in recent trends in ubiquitous artificial intelligence and capsule networks, and to promote their applications in all important emerging research fields.

New research articles are solicited in the following areas:

- Capsule networks in deep learning

- Convolutional Neural networks in perceptron algorithm

- Artificial Intelligence systems and ubiquitous robotics

- Smart robots for multi-task performance

- High dimensional date routing in AI

- AI framework for smart network management

- Capsules in the brain networks

- Capsule implementation in the network

- Feed-forward neural networks

- Deep reinforcement algorithms

- Recurrent neural networks in AI

- Mobility management in recurrent neural networks

- Telecommunication computing in ubiquitous AI

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on New, Modern and Advanced Digital Forensic Techniques

全文截稿: 2019-06-30

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/

A digital forensic operation is a technological inspection, acquisition, and examination of digital media and their contents using forensic equipment and special software tools. The objective is to locate, identify, collect and acquire data which may be relevant to an investigation, and may be used as evidence in administrative, disciplinary and judicial procedures. The stages of the digital forensics process require differing specialist training and knowledge. Nowadays there is not a universally accepted process model for digital forensics.

A Digital Forensic branch such as Multimedia Forensics (MF) which deals with the recovery of information can be directly used to measure the trustworthiness of digital multimedia content is nowadays an important area of study because digital multimedia contents (images, audio, video, ...) now play an important role as evidence in a trial. That is why becomes important to analyze and decide if a multimedia content is original and has no modification on it in order to use as evidence in court. Another factor to take in count around any digital forensic tool is the right way to maintain the chain of custody of any kind of digital evidence on it.

Traditional tools may be used in typical cybercrime investigation, but these will not be sufficient or suitable for use in cases of sophisticated cybercrime.

Based on this motivation, this Special Issue invites researchers in all related fields (including but not limited to digital forensics, cyber security, machine learning, pattern recognition, malware forensics, etc.) to join us in a quest for solutions to solve actual and possible future problems on digital forensics. The potential topics of interest of this Special Issue are listed below. Submissions can contemplate original research, serious dataset collection and benchmarking, or critical surveys.

This special issue is focused on cutting-edge research from both academia and industry, with a particular emphasis on novel techniques. 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. We will recommend submission of multimedia with each paper as it significantly increases the visibility, downloads, and citations of articles.

Potential topics include, but are not limited to:

- Antiforensics and anti-antiforensics approaches

- Automated and intelligent methods for adversary profiling

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

- Behavior of cyber criminals on compromised systems

- Big data and digital forensics

- Data loss prevention after crypto ransomware attack

- Detection of data exfiltration using steganography

- Digital forensic triage

- Digital forensics on mobile devices

- Early detection of ransomware threats

- Forensic analysis of malware

- Human aspects of information security

- Improving security awareness

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

- Intelligent forensics tools

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

- Malware and targeted attacks including analysis and attribution

- Malware honey pots

- Mobile application and mobile cloud application security and digital forensics

- Modern forensic tools

- Modern Multimedia forensic

- New and improved digital forensic techniques

- Novel machine and deep learning forensics approach

- Techniques and procedures for analysis of fileless and modern malware

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