【计算机类】期刊专刊/国际会议截稿信息6条

数据库管理与信息检索

Journal of Global Information Technology Management

Information Technology for Social Innovation: Global IT for social inclusion and transformation

全文截稿: 2018-03-15

影响因子: 0.0

CCF分类: C类

网址: http://www.tandfonline.com/toc/ugit20/current

Social and environmental problems have been increasing rapidly worldwide. Climate change, epidemic diseases, aging societies, unhealthy lifestyles, increasing inequality within and between societies, global energy wars, large scale immigration and racial or gender-based discrimination are examples from the ever-growing list of problems of the modern age. Conventional innovations are unable to solve such fundamental societal challenges, as costs and benefits of innovation are mostly distributed unequally amongst different parts of a society (Guth, 2005). Social Innovation (SI) has been offered as an alternative for attempting the most pressing issues of contemporary societies (Howaldt & Swarz, 2010; Hochgerner, 2012). Moreover, well-designed Socially Influencing Systems (SIS) can empower communities to achieve their wellbeing goals by permanently transforming their lifestyles (Stibe & Larson, 2016).

There are many competing definitions of SI. For example, Bouchard (1999) have defined SI as “any new approach, practice, intervention, or product elaborated to improve a social situation or solve a social problem”. Similarly, (Pol and Ville, 2009, p.881) defines SI as an innovation that has a potential to improve “either the quality or the quantity of life”.  Whereas, Moulaert et al. (2005) suggested SI as “changes in social relations, especially with regards to governance, that enable the above satisfaction, but also increase the level of participation of all but especially deprived groups in society”. Similarly, Harrison & Vezina (2006) emphasized “the coordination of relationships among social actors in the solving of socio-economic problems, with the goal of improving the performance and the well-being of communities” in their definition of SI. Goldenberg (2004) defines SI as “finding concrete ways to deal with social and economic problems so as to make a real difference in the lives of real people”, underlining the concrete nature of SI in designing real solutions. Despite variety of definitions and approaches, further scrutiny is needed on how returns on innovation processes are distributed and how technology-based innovations may serve to marginalize parts of a society, potentially impacting the social and economic structure.

Interactions with and influences of information technology solutions that address environmental, economic, social or ethical problems, can translate to betterment of the artificial, social or natural environment, resulting in better quality of life and work, social inclusion, non-discrimination, equal opportunity for participation. For example, IT-based innovations may decrease carbon footprint, create new possibilities for access, emphasize solidarity, enable resistance and foster equality enabling a more inclusive society, instead of serving for social control, public dominance and political hegemony.

The purpose of this special issue of JGITM is to advance our understanding of social innovations and adoption of information technology against critical social problems across the world by attracting high quality manuscripts in this area. It would provide a platform for academics, policy makers and practitioners to identify and explore the issues, opportunities, and solutions that promote social innovations and find new societal, as well as business value of information and communication technologies. It is also expected to serve as the spring-board for gathering and disseminating experiences gained in implementing IT for social inclusion and transformation to reflect the most pressing problems facing humanity in a variety of social and organizational settings in different countries.

Papers of all theoretical and methodological approaches are welcome. Submissions that cross multiple disciplines including information systems, engineering, management, operations management, applied computer science, social influence, behavioral science, psychology, sociology, etc. are encouraged. Consistent with the focus of JGITM, all submitted papers must address global/international issues associated with social innovation, social inclusion and transformation.

Possible contributions may include but are not limited to, the following topics:

- Adoption of IT for social inclusion and transformation.
- Bottom-of the-pyramid issues relating to IS
- Creating, enabling and negotiating social innovations with IT
- Cryptocurrencies, the block-chain, and the new economic/social mechanisms
- Digital labor markets and social inclusion/exclusion with IT
- Global trends and issues in IT-based social innovation
- Socially engineered societal transformations
- IT implementation challenges in social innovations
- Peer-to-peer online marketplaces and platforms
- Policies and IT solutions for social innovation
- IT solutions for sharing economy and social innovation
- IT-based social innovation for solidarity, equality and resistance
- Socially influencing systems
- Sociotech design for smarter communities
- Social movements and IS
- Societal consequences of emerging IT
- Computer-supported influence for social change
- Avoiding backfires in socially transformative systems
- Supporting and empowering marginalized groups in society with IT
- Sustainable and unsustainable IS/IT
- Sustainable business practices and processes with IT
- Persuasive cities for sustainable wellbeing

计算机综合与前沿

World Wide Web Journal

Big Data for Effective Disaster Management

全文截稿: 2018-04-01

影响因子: 1.405

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.springer.com/journal/11280/about

It is well known that hurricanes, earthquakes, and other natural disasters cause immense physical destruction, loss of life and property around the world. Unfortunately, the frequency and intensity of natural disasters has increased significantly in recent decades, and this trend is expected to continue. Facing these possible and unexpected disasters, disaster management has become a big problem for governments across the world. Recently, however, people’s mobile phone data, GPS trajectories data, location‐based online social networking data, surveillance video data, satellite imagery and IC card data have become readily available and this information has increased explosively. The explosion of this sensing data has become “Big Data”, and offers a new way to circumvent the methodological problems of earlier research for more effective disaster management. As such, big data for more effective disaster management is spurring on tremendous amounts of research and development of related technologies and applications.   

The goal of this special issue is to provide a premier forum for researchers working on big data for disaster management to present their recent research results. It also provides an important opportunity for multidisciplinary studies connecting data mining and big data analytics to disaster management. Topics of interest include, but are not limited to   
- New theory and models for big data computing
- Content analysis and mining for disaster management
- Integration of dedicated sensor and multi‐purpose sensor big data for disaster management
- Integration of heterogeneous extreme events data sources
- Disaster information systems
- Disaster prevention, mitigation, preparedness, response, and recovery using big data
- Big data in international cooperation/responses for disaster management
- Risk reduction using big data
- Big data in interagency network for disaster management
- Pre‐incident training and testing using big data for disaster management
- Use of social media in emergencies
- Filtering of noise and misinformation in open source extreme events data
- Security and privacy in multimedia big data for disaster management
- Interaction, access, visualization of big data for disaster management
- Big data in disaster tourism
- Big data in extreme events management during multi‐hazards

人工智能

NLPCS 2018

International Workshop on Natural Language Processing and Cognitive Science

全文截稿: 2018-04-15

开会时间: 2018-09-11

会议难度: ★

CCF分类: 无

会议地点: Krakow, Poland

网址：http://nlpcs2018.epi.uj.edu.pl

The workshop will provide a forum for researchers and practitioners from the fields of natural language processing, computational linguistics and cognitive science to their ideas and investigations. Given the breadth of the topic, we welcome papers from many perspectives, including but not limited to computational linguistics, psycholinguistics, cognitive psychology, language learning, artificial intelligence, and in particular cognitive neuroscience.

The aim of this workshop is to foster interactions among researchers and practitioners in Natural Language Processing (NLP) by taking a Cognitive Science perspective and learning from recent advances in Cognitive Neuroscience.

计算机综合与前沿

IEEE Transactions on Sustainable Computing

Special Issue on Sustainable Information Security and Forensic Computing

全文截稿: 2018-05-01

影响因子: 0.0

网址: https://www.computer.org/web/tsusc

Modern societies are becoming increasingly reliance on inter-connected digital systems, where commercial activities and government services are delivered. Despite the benefits, it is impossible to overstate the importance of information security and forensics in a highly inter-connected system. To address security threats to network infrastructure devices and sensitive data, many different solutions capable of providing a suitable degree of security and forensic capability have been proposed. However, such solutions have not been properly designed to address important aspects such as computational costs, scalability, energy efficiency and resource usage. This special issue thus focuses on practical aspects of information security and forensics in sustainable computing. We solicit original contributions on novel threats, defences and security, information, tools, and digital forensics applications in sustainable computing. We also seek contributions motivated by taking real-world security and forensic problems and theoretical works that have clear intention for practical applications.

计算机科学与技术

Sustainable Computing

Call for Papers for Special Issue on Internet of Things for Efficient Energy Management of Computing Resources

全文截稿: 2018-05-30

影响因子: 1.8

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：信息系统 - 4区

网址: www.journals.elsevier.com/sustainable-computing

Internet of Things (IoT) vision is to create a hyper-connected global ecosystem in which embedded systems communicate with other whenever needed to deliver highly diversified services to users. IoT enables humans to access, control and manage the operations of the objects working under different information systems. Currently IoT systems are applied in the areas like smart cities, health systems, smart homes, etc. Due to the large number of heterogeneous elements interacting and working under IoT systems, there is an enormous need of resource management for the smooth running of IoT operations. Optimizing the computing resources needed by IoT systems needs to be performed at device layer, networking layer, middleware layer, application service layer and data semantics layer. Managing of computing resources can also be performed by implementing protocols, machine learning, deep learning and artificial intelligence techniques to enhance the scalability, reliability and stability in the operations.

The motivation of this special issue is to solicit the efforts and ongoing research work in the domain of computation resource management using machine learning, deep learning and artificial intelligence in IoT systems. This issue will be elaborating the key aspects of computation resource management, communication protocols and future applications of IoT systems which include IoT-Clouds, IoT based industrial and home environments and integration of IoT concept in upcoming technologies using machine learning, deep learning and artificial intelligence. This will provide the opportunity for research community across the globe to share their ideas on these newly emerging fields of IoT.

Specific topics include, but are not limited to, the following:

- Efficient Energy Management of Computing Resources for
 * IoT Architectures (Things-Centric, Data-Centric and Service-Centric Architectures)
 * Mobile and Cloud-Based IoT Designs
 * IoT based Embedded and Information Systems.
 * IoT Enabling Technologies (Sensor Networks and Machine-Type Communication)
 * IoT Services (Open Service Platform and Semantic Service Management)

- Hybrid Energy Efficient Intelligent Models using Artificial Intelligence in IoT for Industry

- Energy Efficient Meta-Heuristic Algorithms for IoT and Wearable Computing

- Hybrid Energy Optimization Methods and Emerging Real World Applications of IoT in Industry

- Innovative Deep Learning Architectures for Optimizing Time Series Computational Data in IoT

- Energy Efficient Machine Learning and Data Analytics Techniques in IoT for Industry

- Computational Resource Management using Neural Network Modelling for IoT Systems.

- Nature-Inspired Smart Efficient Energy Hybrid Systems for IoT Context-Aware Systems

- Energy Efficient Hybrid IoT Systems for Ambient Living

人工智能

Pattern Recognition Letters

Special issue on “DLVA: Advances in Deep Learning and Visual Analytics for Intelligent Surveillance Systems”

全文截稿: 2018-09-30

影响因子: 1.995

CCF分类: C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

网址: http://www.journals.elsevier.com/pattern-recognition-letters/

The increasing sophistication and diversity of threats to public security have been calling critical demand of developing and deploying reliable, secure, and timely efficient visual intelligent surveillance systems in smart cities. For example, visual surveillance for indoor environments, like metro stations, plays an important role both in the assurance of safety conditions for the public and in the management of the transport network. When designing the next generation security solutions, it is crucial to combine sensing, computing, understanding, communication and prediction in such networked-camera systems. Examples include automated video surveillance platforms and smart camera networked systems that are monitoring the behavior, activities, or other changing information for the purpose of influencing, managing, directing, or protecting people. They exhibit a high-level of awareness beyond primitive actions, in support of persistent and long-term autonomy. However, some core problems such as object identification and tracking, and behavior analysis in intelligent surveillance are still affected by a number of practical problems. They typically involve a variety of representation, reasoning and efficiency mechanisms in the context of an extended distance and period of time and low resolution/frame rate in poor quality capturing conditions. Recent progress in computer vision techniques and related visual analytics offers new prospects for an intelligent surveillance system. A major recent development is the massive success resulting from using the deep learning techniques to enable the significant boosting of visual analysis performance and initiate new research directions to understand visual content. For example, convolutional neural networks have demonstrated superiority on modeling high-level visual concepts, while recurrent neural networks have shown promise in modeling temporal dynamics in videos. It has been and will be seen as resolution to change the whole visual recognition systems. It is expected that the development of deep learning and its related visual analytic methodologies would further influence the field of intelligent surveillance systems.

This special issue will serve a platform to publish state-of-the-art advancements in this domain of research and seeks for original contributions of work, which addresses the challenges from using deep learning and related techniques to understand and promote the ubiquitous intelligent surveillance systems. Original papers to survey the recent progress in this exciting area and highlight potential solutions to common challenging problems are also welcome. The list of possible topics includes, but not limited to:

- Emotion/Gait/Activity/Gesture recognition and prediction

- Large-scale video indexing

- Pedestrian detection in the wild

- Scene understanding and human behavior analysis

- Person re-identification and biometric recognition

- Summarization of long surveillance videos

- Visual analytics for forensics and security applications

- Pedestrian and vehicle navigation tracking

- Face recognition and verification

- Event (abnormal) detection and recognition

- Cloud and distributed for visual surveillance

- Object tracking and segmentation

- Human computer/robot interactions

- Data collections, benchmarking and performance evaluations

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