【今日新增】ACM Trans. 专刊截稿信息3条

全文截稿: 2017-05-15

影响因子: 0.705

期刊难度: ★★★★

CCF分类: B类

网址: http://toit.acm.org

Over the years, Service-Oriented Computing (SOC) has evolved into a methodology for software engineering.  SOC has been successfully developed, deployed and experienced in different areas, such as the Web and Grid systems.

However, SOC, in both its foundations and methodologies, has not kept pace with the challenges posed in Internet of Things (IoT) settings. This special section focuses on service management for the Internet of Things, which is an emerging research area for investigating techniques for establishing and maintaining IoT infrastructures, platforms and applications based on the SOC paradigm. The techniques include those for designing, deploying, configuring, and controlling services-based IoT systems and applications.

Topics of interest include:

1.Design time management of IoT services
- Theoretical foundations, design, and specification
- Architectures, infrastructure, and platforms
- Discovery, customization, and composition
- Analytics and reengineering
- Cognitive services  

2.Runtime management of IoT services
- Provisioning and deployment  
- Monitoring and control  
- Adaptation  
- Nonfunctional properties of IoT services
- Security
- Privacy
- Trust

3.Services-based IoT systems and applications  
- Social media
- Health care delivery
- Smart cities, smart homes, and smart transportation
- Finance
- Embedded and real-time services
- RFID

全文截稿: 2017-06-15

影响因子: 0.982

期刊难度: ★★★★

CCF分类: B类

网址: http://tomm.acm.org/

Modeling, processing, recognizing, searching, and retrieving 3D human data (shapes, gestures, interactions) is a well-established research area in Multimedia. In the last decade, there has been a tremendous increase in opportunities for using 3D human data in medicine, security, and human computer interaction, largely driven by the development of effective devices and algorithms for recovering 3D data (e.g., Microsoft Kinect, Intel RealSense, Google Project Tango, and Apple Prime-Sense). Such rich information opens the way to new modes of experiential computing, interactive environments, as well as new multimedia content. This special issue is of interest to an interdisciplinary target audience as well interdisciplinary teams of contributors spanning: applied math, multimedia experiential computing, computational science and engineering, and application domain experts. Several fundamental research problems within the scope include:
- Representations for 3D static and dynamic human data
- Representations for non-rigid 3D objects (face, body)
- Temporal modeling of 3D face/body sequences
- Machine learning techniques for 3D human representations
- Computationally efficient strategies for resource constrained deployments
- Fusing multiple cues: shape, color, texture, motion etc.

This special issue aims to bring together researchers interested in defining new and innovative solutions that advance the way 3D human data are used in multimedia computing, communications and applications such as human behavior understanding from 3D sensors, animation and entertainment, sports analytics, natural interaction, virtual and augmented reality.

Application areas of interest include, but are not limited to:
- Human behavior understanding from dynamic 3D data
- Gesture and action recognition from dynamic 3D data
- Facial expression recognition from static and dynamic 3D data
- Analysis of human (spontaneous) emotions using 3D facial expressions and body gestures
- Deep learning for 3D representations
- Benchmark datasets for static and dynamic 3D data analysis (face and body)
- Applications in tele-rehabilitation, gaming, augmented reality, retail, biometry and surveillance
- Modeling and animation of 3D humans for model based ultra low bitrate transmission
- Motion capture (MoCap) data acquisition and transmission for 3D humans
- Modeling human interaction (human-human, human-robot, human-virtual agent)

全文截稿: 2017-11-21

影响因子: 0.982

期刊难度: ★★★★

CCF分类: B类

网址: http://tomm.acm.org/

Deep Learning has become a crucial technology in the field of multimedia computing. It offers a powerful instrument to automatically produce highlevel abstractions of complex multimedia data, which can be exploited in a number of applications including object detection and recognition, speech-totext, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of deep learning methodologies within the research community, bringing to the development of a number of new technologies and applications, outperforming in most cases the results achieved by traditional machine learning technologies.

In recent years, the possibility of implementing deep learning technologies on mobile devices has gained significant attention. Smartphones, but more in general any mobile component that holds some sensing and processing capability, may potentially become a smart object able to learn and act, either stand-alone or interconnected with other intelligent objects. In this context, deep learning not only can boost the performance of mobile multimedia applications availably nowadays, but could also pave the way towards more sophisticated uses of mobile devices.

The path towards these exciting future scenarios, however, entangles a number of important research challenges. The fundamental deep learning technologies, including deep neural network architectures, training and inference methods, and so on, are hardly adapted to the requirements of the mobile and wireless multimedia environments. Therefore, new generations of mobile processors and chipsets will be required to support intensive and parallel computation, small footprint learning algorithms have to be developed to fit lower computation and lower power consumption requirements, new models of collaborative and distributed processing will be  
needed to deal with higher complexity tasks, and a number of other fundamental issues will have to be solved to ensure reliable, efficient and real-time deep learning technologies for mobile multimedia computing, communications and applications.

The goal of this special issue is to seek original articles examining the state of the art, open research challenges, new solutions and applications for deep learning in mobile multimedia computing, processing and analytics.

All submissions should contain substantial tutorial contents and be accessible to a general audience of researchers and practitioners. Topics of interest include, but are not limited to:
- Hardware architectures for deep learning in the mobile
- Deep network architectures for mobile environments
- Recourse- and energy-efficient deep learning methods
- Efficient inference methods for mobile multimedia deep networks
- Real-time methods and applications of deep learning for mobile multimedia
- Emerging applications of deep learning in mobile multimedia analysis, search, retrieval and management
- Emerging applications of deep learning in self-driving cars, drones and other robotic platforms
- Deep learning performance analysis in mobile multimedia

下载Call4Papers App，获取更多详细内容！