计算机类 | SCI期刊专刊截稿信息5条

计算机综合与前沿

Medical Image Analysis

Special Issue on Medical Imaging with Deep Learning

全文截稿: 2018-08-01

影响因子: 4.188

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：人工智能 - 2区

  • 小类 : 计算机：跨学科应用 - 2区

  • 小类 : 工程：生物医学 - 2区

  • 小类 : 核医学 - 2区

网址: http://www.journals.elsevier.com/medical-image-analysis

This Special Issue will select the best papers presented at the conference International Conference on Medical Imaging with Deep Learning, held in Amsterdam, 4-6th July 2018. Please submit your paper following the instructions on https://midl.amsterdam/

Authors will be invited after acceptance of their paper for the conference or after their presentation at the conference. They will be requested to extend their paper to a full paper in Medical Image Analysis.

We invite submissions on all topics related to medical imaging using deep learning. Topics discussed will include, but are not limited to:

- Semantic segmentation of medical images

- (Multi-modal) image registration with deep learning

- Interventional image analysis with deep learning (e.g. endoscopic imaging, image-guided surgery)

- Computer-aided detection and diagnosis

- Image synthesis and reconstruction

- Transfer learning for medical imaging

- Multi-dimensional algorithms (3D, 4D and beyond)

- Learning with noisy labels

- Learning with sparse data/labels

- Unsupervised deep learning

- Uncertainty estimation

- Integration of imaging and clinical data

- Data augmentation for medical images

- End-to-end learning for prognosis and treatment selection

- Thorough evaluation studies demonstrating application of deep learning algorithms

软件工程

IEEE Transactions on Services Computing

Special Issue on Holistic Technologies for Managing Internet of Things Services

全文截稿: 2018-10-15

影响因子: 3.52

CCF分类: B类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：信息系统 - 2区

  • 小类 : 计算机：软件工程 - 1区

网址: http://www.computer.org/web/tsc

详细信息请点击链接 ： https://www.computer.orghttps://www.computer.org/cms/Computer.org/transactions/cfps/cfp_tscsi_htmits.pdf

图形学与多媒体

Signal Processing: Image Communication

Special Issue: “Advanced Deep Learning for Image Super-Resolution”

全文截稿: 2018-10-31

影响因子: 2.244

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 工程：电子与电气 - 3区

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

The aim of image super-resolution (SR) is to recover low-resolution (LR) input image or video to a visually desirable high-resolution (HR) one. HR images have more pixel densities and excellent details than LR images.Imaging techniques have been rapidly developed in the last decades, and the resolution has reached a new level. Image SR has a significant impact on many applications, such as remote sensing, video surveillance, medical image and face recognition. SR has attracted huge interest and presently is one of the hot research topics in image processing and computer vision.

Previously the image SR methods were simple and fast. Image restoration is the process of taking a corrupted image and estimating the original image, which is known to be an ill-posed inverse problem. For the past couple of years, researchers attracted to learning-based image SR. They applied machine learning and deep learning techniques to reconstruct the LR image. Recently, deep neural networks have shown their superior performance in SR. Though, there are many deep learning and image SR problems remain intact, e.g. different types of corruption, new applications, new architectures, large-scale images, depth images.

Hence, the scope of this special issue is to provide a forum for researchers to focus on deep learning for image SR. To do this, we invite papers (including a survey paper) in modeling, algorithm, system, and application of deep learning-based SR and to establish the latest efforts of relevant researchers.

The list of possible topics includes, but not limited to:

- New deep learning models for SR

- Deep learning for SR for special types of images

- Deep learning for SR with different or unknown types of corruption

- Deep learning for depth image SR

- Deep learning for SR in remote sensing, video surveillance, face recognition and medical imaging

- Deep learning with the traditional SR approaches

- New objective functions of deep learning for SR

- Survey /Review of deep learning for SR

- Deep Hybrid learning models and depth image SR

计算机体系结构,并行与分布式计算

Journal of Parallel and Distributed Computing

Special Issue on Theories, Algorithms and Modeling for Privacy Computing

全文截稿: 2019-01-30

影响因子: 1.93

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/

With more data including personal information being hosted online such as cloud, privacy leakage is becoming one of serious concerns in online community. In practice, different temporal, spatial or application cases often demand different privacy protection solutions. Most of traditional approaches are case by case or based on a specific application circumstance. Therefore, we need to develop a systematic and quantized privacy characterization towards systematic computing model describing the relationships between protection level, profit and loss as well as the complexity of integrated privacy protection models Privacy computing is emerging as a paradigm to systematically scope privacy protection and related techniques. This special issue focuses on fundamental challenges of privacy computing – theories, algorithms and modelling while applications and general principles are not in the focus. Papers on theoretical foundations and algorithms with strong analytical contributions are encouraged for submission.

Topics of interests include, but are not limited to:

- Parallel, Distributed and Scalable modelling for privacy protection

- Theories and foundations for privacy computing

- Languages and compilers for privacy computing

- Privacy computing model and modelling

- Privacy quantification, formal description

- Privacy operation and modelling

- Data utility and privacy loss theories

- Context adaptive cryptology for privacy protection

- Data analytics oriented privacy models and reasoning

- Private information exchange and sharing models

- Privacy inference and reasoning

计算机科学与技术

Electronic Commerce Research and Applications

Technology Advances and Marketing Futures

全文截稿: 2019-08-31

影响因子: 1.954

CCF分类: 无

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：跨学科应用 - 3区

网址: https://www.journals.elsevier.com/electronic-commerce-research-and-applications

Purpose. The role of technologies within the Macromarketing field has been overlooked yet their impacts have been profound on society and marketing. Technologies have disrupted market structures in both what and how value is created and delivered to a range of stakeholders such as firms, customers and others. In recent years, technological advances include search tools, social media, content marketing, big data, cryptocurrencies, self-monitoring and the quantified-self, in-home and in-car voice-activated assistants, and the Internet of Things (IoT). Increasingly, these have AI-based (artificial intelligence) algorithms and smart-device interfaces that have influenced major shifts in how markets operate and consumers experience traditional and emerging products and services.

Examples include sensor-based technologies that automate supply chains in firms and service systems. Automata, including robots and AI devices, provide novel services and engagement platforms, such as policing, health, and customer service desk information. Today, robots are being considered as caregivers, providing cognitive and affective support that encompasses teaching, learning and emotional agency for consumers. We also see a rise in automated social-presence actors: technology infusions for service contexts that deliver consistent consumer experiences.

Relevance. What makes these technology applications pertinent to marketers? It is not only the human-like ways in which devices process data, but how their outputs are viewed by users as demonstrating emotion, empathy and human-level understanding, evoking feelings of attachment. Drawing on robotics and AI, Marketing researchers are predicting rapid convergence in the next 10 to 30 years of AI-based and intelligent augmentation (IA) systems in support of people. Robots will evolve from programmed tools to semi-autonomous and autonomous entities, and extend their anthropomorphic projection to become legal non-persons, displaying a person-like consciousness that raises important questions about the nature of human relationships.

Technology researchers predict that humanoid robots and cyborgs will become the dominant form of service provider in the future. Preliminary research suggests, however, there is consumer fear of such hybridity. Some researchers have reported that consumers believe they may lose their humanness as they augment themselves with new cyborg-driven capabilities. Today, increased computer processing capacities support new industrial applications of technologies to replace the human workforce in an increasingly diverse range of contexts. As such, there is a need to understand the breadth of issues that will impact stakeholders for marketing-related activities. To what extent will the technologies emancipate customers and transform markets for the benefit of the stakeholders in the process?

Conference. This VSI is related to the 2018 Macromarketing Conference, to be held in Leipzig, Germany on July 10-13. The related URL for the Conference’s Call for Papers is available at: http://society.macromarketing.org/announcement/conference/society/Macromarketing-Conference-2018-CfP/, and the related track is named the same as this CFP.

Topics. In this VSI, we call for papers that address any aspect of the roles of emergent technologies and their application in disrupting and transforming markets. Topics may be conceptual or issues-based, applied or practice-based, on:

- Market structures and roles of emergent technologies in their development;

- Technology-led market adaptations and their influence on customers and firms;

- Decision-support systems and algorithmic design for markets and marketing structures;

- Interface design (device-led or ubiquitous) and their influence on human behavior;

- Big data and open data initiatives and the roles of facilitating structures; legislation, market forces, etc.;

- Impacts of supply chain technologies, IoT, cryptocurrencies, machine learning, etc.;

- Diffusion and effects of AI and IA-based service actors;

- Ethical considerations related to emerging technologies for marketing and service design;

- Case studies of specific technologies, such as quantified-self, autonomous vehicles, drones, and so on.

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