计算机类 | 会议/SCI期刊约稿信息7条

数据库管理与信息检索

KDWEB 2018

International Workshop on Knowledge Discovery on the Web

全文截稿: 2018-04-01

开会时间: 2018-06-05

会议难度: ★★

CCF分类: 无

会议地点: Caceres, Spain

网址：http://www.iascgroup.it/kdweb2018.html

In the current era of digital and social data, the world became more connected, networked, and traceable, with the consequent exponentially growth of data creation, sharing, and storing. In particular, data changed from static, complete, and centralized to dynamic, incomplete, and distributed; furthermore, data rapidly increased its scope and size, with the continuous increase of volumes, varieties, and velocities. All these aspects led to new challenges undertaken by the field of Big Data Analysis. Consequently, there is the need for novel computational techniques and tools able to assist humans in extracting useful information (knowledge) from the huge volumes of data. Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from such data, and is currently widespread in numerous fields, including science, engineering, healthcare, business, and medicine. A major aspect of Knowledge Discovery is to extract valuable knowledge and information from data. Typical tasks are aimed at gathering only relevant information from digital data (e.g., text documents, multimedia files, or webpages), by searching for information within documents and for metadata about documents, as well as searching relational databases and the Web. Recently, the rapid growth of social networks and online services entailed that Knowledge Discovery approaches focused on the World Wide Web (WWW), whose popular use as global information system led to a huge amount of digital data. Typically, a webpage has unstructured or semi-structured textual content, leading to present to users both relevant and irrelevant information. Hence, there is the need of novel techniques and systems able to easily extract information and knowledge from the huge web data.

KDWeb 2018 is focused on the field of Knowledge Discovery from digital data, with particular attention for Data Mining, Machine Learning, and Information Retrieval methods, systems, and applications. KDWeb 2018 is aimed at providing a venue to researchers, scientists, students, and practitioners involved in the fields of Knowledge Discovery on Data Mining, Information Retrieval, and Semantic Web, for presenting and discussing novel and emerging ideas. KDWeb 2018 will contribute to discuss and compare suitable novel solutions based on intelligent techniques and applied in realworld applications. The workshop is hosted by the 18th International Conference on Web Engineering (ICWE 2018).

KDWeb will contribute to propose innovative solutions in the following areas, but not limited to them:  
-Big Data
-Data Mining
-Deep Learning
-Feature Selection and Extraction
-Hierarchical Categorization
-Information Filtering and Retrieval
-Knowledge Discovery in BioInformatics
-Linked Data
-Machine Learning
-Open Data
-Recommender Systems
-Semantic Web
-Semantics and Ontology Engineering
-Social Media
-Social Media Measures
-Text Categorization
-Text Mining
-Web Information Filtering and Retrieval
-Web Mining
-Web of Data
-Web Personalization and Recommendation

信息安全及密码学

AsiaJCIS 2018

Asia Joint Conference on Information Security

全文截稿: 2018-04-20

开会时间: 2018-08-08

会议难度: ★★★

CCF分类: 无

会议地点: Guilin, China

网址：http://gcis.guet.edu.cn/asiajcis2018

Authors are invited to submit original papers. The papers must not substantially duplicate work that any of the authors have published elsewhere or have submitted in parallel to any other conferences that have proceedings. The submission must be anonymous, with no author names, affiliations, acknowledgments, or obvious references. Authors of accepted papers must give oral presentation at the conference. Papers of authors who will not attend the conference will be deleted from the conference proceedings, and will be announced as "no show paper" on the conference site.

Areas of interest include, but not limited to:
-Cryptography
-Network security
-System security
-Application security
-Information security management
-Others

计算机体系结构,并行与分布式计算

Computer

Governments in the Age of Big Data and Smart Cities: Call for Papers

全文截稿: 2018-05-01

影响因子: 1.755

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 3区

  • 小类 : 计算机：软件工程 - 3区

网址: https://www.computer.org/computer-magazine/

Governments at the local and national levels are responsible for passing and enforcing laws the impact the quality of life of the citizens they serve. Elected officials are tasked with legislating all manner of complex issues, with guidance from lobbyists, professional staff, research literature, not to mention polling data. For policy decisions in the age of big data and smart cities, new legislation and day-to-day operations of government agencies could benefit from the vast amounts of data, which would help establish unprecedented precision of information.

In this special issue of Computer, the guest editors seek to cover a range of topics that address challenges of data collection and curation, outcome measurement, complex system modeling, and operational management. More specific topics of interest include but are not limited to the following:

- Smart cities and the collection of city-wide data to guide decision making;
- Enterprise healthcare data and analytics to improve the standard of care and lower the cost of healthcare;
- Modeling and simulation of future trends for government planning;
- Prediction and planning for natural disasters to minimize catastrophic outcomes and accelerate emergency response;
- Disruptive technologies’ impact on small and big businesses, workforce, education, city planning, tax revenue, and long-term infrastructure investment; and
- Expectations of privacy in the context of data-guided decision making by business and government agencies and how this impacts individual businesses and citizens.

软件工程

Information and Software Technology

Special Issue on User Feedback and Software Quality in the Mobile Domain

全文截稿: 2018-06-01

影响因子: 2.694

CCF分类: B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 - 2区

网址: http://www.journals.elsevier.com/information-and-software-technology

The global adoption of mobile devices has increased dramatically over the last few years and mobile devices have become part of our everyday lives. As a consequence, we can observe a continuing growth in the development of millions of software applications that run on mobile devices, commonly referred as "apps" [1]. The high diffusion of mobile apps has encouraged software engineering researchers to support developers overcoming the challenges they are facing in this new competitive development market. Indeed, this huge and important economic market represents a rich opportunity for researchers interested in (i) investigating and understanding the specific issues of mobile app development, and (ii) building novel methods and tools to help developers during the different phases of an app’s lifecycle.

This special issue focuses on research challenges and opportunities related to (i) requirements engineering approaches in the mobile domain (e.g., security and privacy requirements in mobile apps); (ii) development and maintenance strategies to ensure high software quality and overall users satisfaction; (iii) monetization strategies and other success related aspects of mobile app development. A particular, overarching focus is the design and implementation of strategies for user feedback analysis and user involvement (mechanisms for users engagement and fidelization) in the mobile context. In is important to specify that, the main focus of proposed approaches, techniques and empirical studies should have a specific focus on improving software development, thus, have a clear and direct impact on on the efficiency of the overall development process.

Potential topics of interest include (but are not limited to the following):

Requirements in the mobile domain:

- Methodologies and techniques for requirements engineering (elicitation, analysis, prioritization, management, traceability, etc.)

- Stakeholders’ identification, classification and prioritization

- Requirements trade-offs (e.g., performance vs. power consumption)

- Mobile technologies for supporting requirements engineering activities

- Security and privacy requirements in mobile apps

App store analytics and quality of apps for the improvement of software development:

- Apps quality vs. apps success

- Relation(s) between reviews and apps’ characteristics (rating, pricing, etc.)

- Monetization strategies for app developers

- Security and privacy issues

User feedback and user involvement in the mobile context to improve software development:

- Gathering, mining and classification of user feedback from different sources

- Motivational issues of end-users to provide feedback

- User experience and App recommendation

- Crowdsourcing to support requirements engineering activities

- Mechanisms for users engagement and fidelization (e.g., gamification)

数据库管理与信息检索

Information Fusion

Call for papers for a special issue on “Data Fusion in Heterogeneous Networks”

全文截稿: 2018-06-01

影响因子: 5.667

中科院JCR分区:

  • 大类 : 工程技术 - 1区

  • 小类 : 计算机：人工智能 - 2区

  • 小类 : 计算机：理论方法 - 1区

网址: http://www.journals.elsevier.com/information-fusion/

The Information Fusion Journal is planning a special issue on advanced academic and industrial research that explicitly demonstrates the role ofData Fusion in Heterogeneous Networks.

A heterogeneous network is a network composed of multiple types of objects and links based on different networking infrastructures. It involves the Internet, Internet of Things, mobile cellular networks, Mobile Ad Hoc Networks (MANET), Vehicular Networks (VENET), Wireless Sensor Networks (WSN) and others. It also includes their up-layer application networks such as social networks, crowdsourcing, Human-Machine Networks, which are gigantic and interconnected. The heterogeneous network holds such characteristics as networking pervasiveness, structure heterogeneity, data diversification and high complexity. Huge volumes of data are generated in the heterogeneous networks. Similar examples are present everywhere, ranging from social media to scientific, engineering or medical systems, and to online e-commerce systems. The data types can be unstructured text documents, multi-lingual data, networking statistics, and online multi-modal data such as images, text, and audios.

The heterogeneous networks are not only ubiquitous but also form a critical component of modern information infrastructure for knowledge retrieval and knowledge discovery. In such a networking environment, data fusion is definitely indispensable and plays a significance role. It provides a main vehicle to information fusion due to the pervasiveness of networking and various modes of information as well as data transmitted. However, the particular characteristics of heterogeneous networks introduce new challenges on data fusion. Heterogeneous data fusion, incentive for data collection, judgement on data veracity, and data trust management are hard to be overcome by existing solutions in the context of heterogeneous networks. Hence, suitable techniques are badly needed to manage and refine data fusion in the heterogeneous networks.

This special issue aims to bring together researchers and practitioners to discuss various aspects of data fusion in heterogeneous networks, explore key technologies, proposed and investigate technology enablers and innovate new solutions for overcoming major challenges in this research area.

Topics central to this special issue include (but are not necessarily limited to):

- Machine learning, data mining and fusion for heterogeneous networks

- Novel datasets and benchmarks for heterogeneous big data analytics

- Information network learning and generation

- New models of heterogeneous network fusion

- Multimodal data fusion via machine learning methods

- Graph (Network) embedding in heterogeneous networks

- Data fusion in next generation communication networks

- Data fusion trust in large scale heterogeneous networks

- Security related data fusion and mining in heterogeneous networks

计算机科学与技术

Journal of Computational Science

Special Issue on The Development and Application of Advanced Biomedical Imaging

全文截稿: 2018-06-20

影响因子: 1.748

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：跨学科应用 - 4区

  • 小类 : 计算机：理论方法 - 3区

网址: https://www.journals.elsevier.com/journal-of-computational-science

With advancement in biomedical imaging, the amount of data generated by multimodality image techniques (e.g. ranging from Computed Tomography (CT), Magnetic Resonance Imaging (MR), Ultrasound, Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc.) has grown exponentially and the nature of such data is increasingly become more complex. This poses a great challenge on how to develop new advanced imaging methods and computational models for efficient data processing, analysis and modelling in clinical applications and in understanding the underlying biological process.

The purpose of this special issue aims to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of advanced imaging analysis in the multimodel biomedical imaging area. The ultimate goal is to promote research and development of advanced imaging analysis for multimodal biomedical images by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field. To this end, scholars and practitioners from academia and industrial fields are invited to submit high-quality original contributions to this special issue.

Topics of interest include, but are not limited to:

- New algorithms, models and applications of advanced imaging methods

- Multimodal imaging techniques: data acquisition, reconstruction; 2D, 3D, 4D imaging, etc.)

- Translational multimodality imaging and biomedical applications (e.g., detection, diagnostic analysis, quantitative measurements, image guidance of ultrasonography)

- Variational and combinatorial optimisations for biomedical imaging and image analysis

- Advanced Biomedical image analysis ( image processing, Statistical and probabilistic methods for biomedical imaging and image analysis, Machine learning in biomedical imaging and image analysis)

- Visualization

计算机科学与技术

Microprocessors and Microsystems

Special Section on Design for Resilience in Cyber-Physical Systems

全文截稿: 2018-06-20

影响因子: 1.025

CCF分类: C类

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：理论方法 - 4区

  • 小类 : 工程：电子与电气 - 4区

网址: https://www.journals.elsevier.com/microprocessors-and-microsystems

Resilience is fundamentally defined as either “resuming the original shape or position after being bent, compressed, or stretched” or “rising readily again after being depressed”. In a more formal definition, resilience is the persistence of performability when facing changes; a resilient system must survive at some capacity, in order to autonomously recover. On the other hand, Cyber-Physical Systems (CPSs) are composed by integrating and networking physical and computational components which work in dynamic environments experiencing many variabilities, which should be recovered autonomously from possible changes because of almost inaccessibility to repairman. Example applications are smart grids, autonomous driving systems, healthcare systems, robotics systems, and situational awareness real-time systems. Deeply intertwined physical and software components of different spatial and temporal scales in CPSs, exhibiting multiple and distinct behavioral modalities, and interacting with each other in a myriad of ways that change with the context make the analysis and design of such systems more complex. CPSs are also prone to cyber-attacks that can have severe consequences, needing resilience. Thus, the incorporation of resiliency to the design of CPSs is a must to make them dependable. In this special section, we welcome original submissions in all theoretical and application-oriented areas, reporting analysis and design for resilience in CPSs. Topics of interest include (but not limited to):

- Interpretation of resilience in CPSs

- Design techniques to achieve resilience in CPSs

- Secure and Fault-Tolerant CPSs

- Modeling resilience of CPSs

- Analyze and measurement of resiliency of CPSs

- Fault models for CPSs

- The interplay between resilience and other design objectives in CPSs

- Testing and validation techniques for resilient CPSs

- Resilient control in CPSs

- Real-time scheduling techniques for resilient CPSs

- Design tools to develop resilient CPSs

- Case studies

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