【今日新增】中上难度SCI期刊专刊信息8条

全文截稿: 2017-08-15

影响因子: 1.539

期刊难度: ★★★★

CCF分类: B类

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

Today, large collections of web data are explosively created in different fields and have attracted increasing interest in the research community. Big web data can be seen in the social media where thousands of tweets, millions of Facebook "likes", and billions of check-ins on Foursquare are collected to enrich people's daily life. It can also be seen in the finance and business where large amount of stock exchange, online and onsite transactions data flows are captured for inventory monitoring and customer behavior analysis. Big web data provides unprecedented opportunities to address many challenging research problems. Recent success of deep learning has shown that it outperforms state-of-the-art systems in web search, recommendation systems, text analysis, summarization of web data, etc. Therefore, deep learning has a large potential to improve the intelligence of the WWW and the web service systems by efficiently and effectively utilizing big data on the Web. However, deep learning is not omnipotent. Shallow learning is still dominant in fields such as web data storage, real-time computing and association rule mining. It is critical to utilize both deep and shallow learning models to support web-scale data computing and applications.

On the other hand, the explosion of big data raises more challenges for learning and puts urgent needs for novel applications. Given the high volume, high velocity, and high variety of big web data that require new forms of processing to enable efficient retrieval, insight discovery and process optimization, there are a lot of research challenges. For example, based on these unprecedented large amount of data, what kinds of novel tools and deployment platforms can be developed to facilitate data storage? This motivates us to design parallel or distributed platforms. Moreover, how do the traditional query and indexing algorithms (proven efficient and effective in small-sized data) be scaled up to millions and even billions of items? The researchers in this topic produced big data indexing techniques as well as using cloud computing. Besides, it is also important to mine useful information and design interesting applications to fully explore the big data treasure.  

Topics of interest include, but are not limited to:
- Big data storage, indexing, and searching
- Deep learning for web-scale data analysis
- Topics discovering and monitoring from social websites
- Indexing algorithms for large-scale web data retrieval
- Compression techniques for large-scale multimedia retrieval
- Image annotation and classification with deep learning
- Clustering for large-scale multimedia data
- Knowledge mining from large-scale social media
- Storyline summarization for large scale social media
- Efficient optimization algorithms for large-scale learning
- Algorithms and applications with large-scale social media
- Other applications of large scale multimedia data

全文截稿: 2017-10-02

影响因子: 1.602

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/signal-processing-image-communication/

The pervasiveness of new technologies, such as smartphones, tablets and Internet made digital images and videos the primary source of visual information in nowadays society. However, their reliability as a true representation of reality cannot be taken for granted, due to the affordable powerful graphics editing software that can easily alter the original content without any visual trace of the modification.

Nowadays, machine learning techniques and, in particular, Deep Learning have come to play a vital role to deal with a massive amount of unsupervised data. In recent years, deep neural networks, such as deep belief network, deep autoencoder and convolutional neural network (CNN), have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of computer vision tasks, including image classification, speech recognition and so on.

The extensive use of Deep Learning in many areas has motivated and led the multimedia forensics community to comprehend if such technological solution is able to detect image and video manipulations or to exploit source identification. For example, it has been foreseen the proposal of new convolutional network architecture capable of working on any kind of different image formats and of automatically learning manipulation detection features directly from the training data itself. A general data-driven forensics methodology should be devised to accomplish the forensics tasks, independent from the kind of tampering, from the image format and designed to detect many, if not all, editing operations.

Furthermore, it is interesting to investigate the adversarial actions performed on deep learning techniques, to understand how their analysis can be biased and perturbed by means of the injection of fake data or adversarial examples and how the trustworthiness of the produced knowledge is diminished in relation with the kind and intensity of the performed manipulation (e.g. forged images and videos).

The aim of this special issue is to gather image forensic works specifically oriented to deal with deep learning based approach with applications in passive image forensics.

We solicit high-quality original research papers as well as review papers that mainly address these issues and advance the development in image forensics. Submitted papers should not be previously published or be under consideration for publication elsewhere.

Potential topics include, but are not limited to:
- tampering detection machine learning classification
- deep learning tampering detection
- source identification with deep learning
- relationship between adversarial forensics and deep learning

全文截稿: 2017-10-15

影响因子: 0.789

期刊难度: ★★★

CCF分类: 无

网址: http://www.mitpressjournals.org/loi/pres/

It is forecasted that augmented reality (AR) and virtual reality (VR) automotive applications will increase road safety, bring intuitive activities to driving, and finally enhance driving experience. AR/VR technology may also help on the transition towards automated driving. AR head-up-displays (HUDs) may soon overlay 3D navigation instructions onto road geometry and moving obstacles like vulnerable road users (pedestrians, bikers, wheel-chair users) and other vehicles may be highlighted to calm down the driver-passenger and enhance trust in their vehicle's automated operation as the vehicle proves its awareness of its surroundings. VR windshields may allow for dynamic reconfiguration of multi-lane roads based on demand and will, in the long term, remove road signs, traffic lights, road paintings, etc. from the streets.

However, many technological challenges need to be addressed before AR/VR applications will hit the mainstream market. These include how to capture and interpret road geometry through computing intensive sensor fusion, precise vehicle positioning, compensation for vibrations, delays, and jitter, laser projection, driver monitoring via inward facing cameras and designing sophisticated algorithms to generate precise augmentation content in the viewing field of the driver, etc.

Topics of interest for this special issue include, but are not limited to:
- Responsive, adaptive and evolvable behaviors in immersive virtual environments that deal with driver-vehicle interaction in the interior or vehicle-pedestrian interaction in the exterior.
- Multiuser virtual environments.
- Mixed reality and the experience of real and virtual environments .
- Tools, techniques, frameworks and methodologies.
- Case studies of application of augmented or virtual reality in the automotive domain.
- Education in in the automotive field, e.g., driver training, using AR/VR technology.
- Evaluation and validation methodologies for the impact of AR/VR in driving.
- Applications/solutions that deal with cognitive overload, distraction, inattentional blindness, simulation sickness.
- Studies reporting the benefit of AR/VR technology towards attention (span), etc.
- Risk assessment of the use of AR/VR technology for driving and strategies to reduce its risk.
- Social capabilities of virtual/augmented reality technology in automotive environments.
- In- and inter-vehicle gaming applications.

全文截稿: 2017-10-28

影响因子: 1.539

期刊难度: ★★★★

CCF分类: B类

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

This special issue aims to publish research work that covers the full spectrum of geo-social computing including theoretical, empirical, algorithms, models and design research contributions. The rapid development of Web 2.0, location acquisition and wireless communication technologies has fostered a pro-fusion of geo-social networks, such as location-based social networks (LBSNs) and event-based social networks (EBSNs). LBSNs (e.g., Foursquare, Yelp and Google Place) provide users an online platform to check-in at points of interests (e.g., cinemas, galleries and hotels) and share their life experiences in the physical world via mobile devices. The new dimension of geographical location implies extensive knowledge about an individual's behaviors and interests by bridging the gap between online social networks and the physical world. Moreover, newly emerging EBSNs (e.g., Meetup and Plancast) enable users to check-in and share more specific activities/events held in the physical world, ranging from informal get-togethers (e.g., movie nights and dining out) to formal activities (e.g., culture salons and business meetings).

Despite the explosion of interest in social computing, this is the first time to call for papers on geo-social computing. Compared with traditional social computing that only focuses on the social perspective, Geo-Social Computing introduces a new paradigm combining spatial and social dimension. Geo-social computing is fundamentally about computing methods and techniques to understand, model, and facilitate both the social interactions between people and the physical interactions between people and spatial items (e.g., POIs and events). It will bring many benefits to the improved decision making, accurate mobile targeted advertisement, trip planning, richer collaborations, and enhanced problem solving capabilities through a better understanding of human behavior and social interaction in interpersonal, organizational, and societal settings. We welcome submissions that focus on various computation methods and models to exploit and explore the geo-social data generated by both users and GPS devices.

Potential topics include but are not limited to the following:
- User Profiling
- Location-based recommendation
- POI Recommendation
- Event Recommendation
- Community discovery
- Social Link Prediction/Friend Recommendation
- Information Diffusion in geo-social network
- User Mobility Analysis and Modeling
- User Linkage Across platforms or devices
- Influence Maximization in geo-social networks
- Inferring locations of user homes
- Inferring Locations of user generated contents (e.g., images, videos and posts)
- Collective intelligence
- Sentiment Analysis
- Spatial data analysis and mining
- Team Formation and Collaboration  

全文截稿: 2017-10-30

影响因子: 0.643

期刊难度: ★★★★

CCF分类: B类

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

Manuscripts are solicited for a special issue in the journal "Theoretical Computer Science" (TCS) on the topic "Theory and Applications of Graph Searching". The scope of the issue is inspired by, but not limited to papers presented in the 8th International Workshop on Graph Searching: Theory and Algorithms (GRASTA 2017).

Graph searching involves a team of mobile agents (called searchers or pursuers or cops) that aims at capturing a set of escaping agents (called evaders or fugitives or robbers) that hide in a network which may be modeled by a graph. Many variants of graph searching have been studied in the literature, such as pursuit-evasion games or cops and robbers games. These variants are either application driven, i.e., motivated by settings of practical interest, or are inspired by foundational issues at the intersection of Computer Science, Discrete Mathematics, and Artificial Inteligence. As a result, graph searching has attracted significant interest from different areas of Mathematics, Computer Science and Operations Research.

With this Special Issue, we aim to further foster research in the area of Graph Searching by presenting recent results and directions for future research. The purpose of the Special Issue is to highlight recent research both from the applied and the theoretical point of view. Topics of interest include (but are not limited to) the following:
- Graph Searching and Logic
- Graph Parameters Related to Graph Searching
- Graph Searching and Robotics
- Conquest and Expansion Games
- Search, Patrolling and Surveillance Games
- Graph Searching and Robotics
- Conquest and Expansion Games
- Database Theory and Robber and Marshals Games
- Probabilistic Techniques in Graph Searching
- Monotonicity and Connectivity in Graph Searching
- New Variants and Performance Measures of Graph Searching
- Graph Searching and Distributed Computing
- Graph Searching and Network Security
- Searching on Unbounded Domains

全文截稿: 2017-10-31

影响因子: 0.789

期刊难度: ★★★

CCF分类: 无

网址: http://www.mitpressjournals.org/loi/pres/

In recent years, mobile plaMorms and emerging headworn display hardware have ushered in a new wave of AR excitement and use. To realize AR's full potential, however, a thorough understanding of perceptual and cognitive factors and their role in informing design of effective augmented reality systems is highly needed; both in research and industry communities alike. To date, there is neither an in-depth overview of these factors, nor wellfounded knowledge on most effects as gained through formal validation. In particular, longterm usage effects are inadequately understood. The objective of this Special Issue on Perception and Cognition in Augmented Reality is to showcase current work in this area and to raise awareness of the importance of these issues with respect to user performance, user safety, and system usability.  

Areas of Interest  
- Depth and color perception  
- Visual search / information processing  
- Situational awareness  
- Selective, focused or divided aYention  
- JNDs, signal thresholds, and biases  
- Individual differences in perception & cognition  
- Comparisons between AR and VR perceptual issues  
- Cognitive load, mental workload  
- Multisensory issues (sensation, perception & cognition in non-visual AR)  
- Visualization techniques addressing perceptual or cognitive issues  
- Novel visual display devices that target specific perceptual issues  
- Validation methodologies, benchmarks and measurement methods  
- Techniques for conducting longitudinal studies

全文截稿: 2017-10-31

影响因子: 2.595

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/the-journal-of-strategic-information-systems

The Journal of Strategic Information Systems focuses on the strategic management, business and organizational issues associated with the introduction and utilization of information systems (IS), and considers these issues in a global context. We welcome papers from a wide range of disciplines. The special issue is open to a wide variety of existing research methodologies, but it also encourages innovation in research methodologies exploring digital entrepreneurship and digital innovation. Implications of findings for theory and practice are essential. The journal has previously explored how IT governance plays an important role in the successful introduction and utilization of IS (Joachim, Beimborn and Weitzel, 2013; Hadaya and Cassivi, 2012; Schwarz and Hirscheim, 2003) and now invites papers for a Special Issue on "IT Governance of the Internet of Things."

We are witnessing a very significant event in the history of Information Systems. Billions of electronic nodes ("things") are now linked to the Internet. Only a fraction of these "things" will be conventional computing devices, such as laptops, phones etc. Most nodes will be the result of recent and future innovations in sensor technology. These sensors will shape the environment we live in by analyzing, controlling, monitoring and optimizing our world. A shift that will transform our technical and socio-technical landscape is imminent. This shift will be as significant as the introduction of the PC and will integrate several technologies, including mobile communications, cloud computing and data analytics. We will inhabit a world of universal connectivity in which remote computers will have eyes, ears and even hands in the physical world. Our actions and interactions, at all times and everywhere, will be influenced by the Internet of Things (IoT), and our everyday world will be made more intelligent by the capabilities of computing power distributed and embedded into everyday objects connected by the Internet. Thus, we are witnessing the dawn of an era where we will live with an omnipresent IT accompaniment of our daily life-as an active user, as a passive beneficiary, or as a monitored (and even system-guided) individual. Technical breakthroughs have spawned a plethora of technical research domains and have contributed to the creation of an Internet of Things: from mechatronics to materials science, from telecommunications engineering to computing and AI research. IoT scenarios currently discussed in our research communities range from connected consumer electronics, automotive, health care and utilities to intelligent homes and buildings.

But the Internet of Things also rquires leadership and organizational structures. IT governance ensures delivery of the expected benefits of IT in a controlled manner to enhance the long-term success of the enterprise. Broadbent and Weill (2003) consider that "IT governance is about who is entitled to make major decisions, who has input and who is accountable for implementing those decisions. It is not synonymous with IT management. IT governance is about decision rights, whereas IT management is about making and implementing specific IT decisions." Accordingly, Weill (2004, p. 3) defines IT governance as "specifying the framework for decision rights and accountabilities to encourage desirable behaviour in the use of IT."

We need to learn from our experiences with traditional IT governance challenges in order to tackle the emergent issues associated with the IoT phenomenon. In particular, we need to better understand how an independently managed decentralized multiple-root system can be successfully implemented, and how the establishment of new basic governance principles that include transparency and accountability, legitimacy of institutional bodies and ethics, factor into this new form of IT governance. To realize its potential, the Internet of Things will have to be governed effectively, and the IS research community will need to mobilize and respond to the challenge of developing effective governance theories, frameworks, models and processes. The goal of this special issue to address this research challenge.

There is a broad range of exciting questions being evoked by the Internet of Things. Questions addressed by papers suitable for the special issue could include the following, but are by no means limited to:
- How can our IS theories better account for governance in the IoT era?  
- How can our research methods be developed to enable a better understanding of governance of IoT?
- What are the critical institutional logics shaping the governance of IoT? What are the implications for our theories of organizations?
- What are the new roles and responsibilities of IT organizations in the IoT era?
- What are the data governance challenges that are emerging?
- How is IT strategy impacted by IoT?
- How will IoT impact the performance of the firm?
- What are the implications of IoT for privacy and security at the individual, organizational and societal level?
- What new ethical models are needed with respect to governance of the IoT?
- How will theories of IT adoption and diffusion develop to take account of the IoT?
- What is the role of standards in the integration and inter-operability of innovative IoT technologies?
- Realizing and evaluating the benefits of IT investments has always been a challenge for the IT management community. What new methods and approaches are required for evaluating IS in the context of IoT?
- What forms of IT governance are relevant for the complex stakeholder eco-systems that are characteristic of the IoT business landscape?
- IoT based products and services need to be embedded within larger legacy systems system. How can IT governance models support the system integration challenge?
- What new forms of collaboration, open innovation and co-creation are emerging in the era of IoT?
- Cities are designing, coordinating and leading integrated "smart city projects" based on IoT technology. What are the IT governance challenges with respect to smart city initiatives?
- What new business models are being developed specifically to address the economic opportunities being created by the IoT?
- How will IoT impact individual and group behaviour in the workplace? What are the implications for praxiological theory?
- Are new pedagogical models and curricula needed in universities to effectively teach the governance of IoT to students?
- What role can IoT architectures play in providing standardized schema for IoT and promoting effective IT governance?
- How can enterprise architecture support governance of the IoT at the organizational level?

全文截稿: 2017-11-15

影响因子: 0.787

期刊难度: ★★★

CCF分类: C类

网址: http://www.springer.com/journal/11219/about

Testing is one of the most frequently used techniques in practice to assure the quality and the reliability of software systems. It is used both during the development and the operation of such systems. Last years, new technologies appeared for checking not only functional but also nonfunctional requirements. Application areas include but are not restricted to communicating systems such as protocols, middleware, networks, web services, cloud computing systems, wireless applications, control systems, business information systems, embedded and real-time software, software product lines, etc. Despite the decades of research and practical experience on software testing, the underlying theory, methods and tools, industrial use, and in its systematic combined application with other verification techniques are still very challenging. In order to perform testing and evaluate the quality of testing, a number of techniques and metrics have been proposed. As testing techniques we can mention active and passive testing (monitoring) techniques, and as metrics we can consider fault coverage, complexity, performance, etc. These techniques and metrics allow to perform testing and to estimate the testing quality as well as the quality of different test derivation methods.

Topics of interests include but are not limited to:
- Aspects of testing: test derivation, test selection, test coverage, test implementation and execution, test result analysis, test oracles, test management, monitoring and runtime verification, test frameworks.
- Model-based testing: Formal models and modeling languages such as automata, state machines, process algebra, logics, UML, HOL, Markov-chains, test generation from models, model coverage
- Combination of techniques: Techniques that demonstrate how to combine testing and formal (modelbased) verification and analysis to improve quality and reduce effort
- Quality aspects: Functional, interoperability, performance, conformance, security, reliability, robustness, etc.
- Application areas: Communicating systems such as protocols, middleware, networks, web services, cloud computing systems, wireless applications, control systems, business information systems, embedded and real-time software, software product lines, etc.
- Communicating systems such as protocols, middleware, networks, web services, wireless applications, control systems, business information systems, embedded and real-time software, etc.
- Combinations of different testing techniques: In particular combination of techniques for the automated generation of test data  
- Tools and methods: Automated support of any of the testing activities, rigid testing processes, testdriven development, sound metrics and measurements
- Case studies: Case studies and industrial applications involving qualified empirical evaluations

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