【今日新增】7条中高难度SCI期刊专刊截稿信息

1. Journal of Universal Computer Science

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全文截稿：2017-12-22

期刊：Journal of Systems and Software

专刊：Special Issue on Software Engineering Education and Training

领域：软件工程/系统软件/程序设计语言

难度：★★★★

CCF分类：B类

影响因子：1.424

网址：http://www.journals.elsevier.com/journal-of-systems-and-software/

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The Conference on Software Engineering Education and Training (CSEE&T) is now approaching 30 years of longevity. Software engineering education has changed a great deal in that timeframe. In 1986, software engineering education was largely undertaken by industry, with just a few academic software engineering programs in place. The Master of Software Engineering (MSE) reference curriculum changed the landscape of software engineering, resulting in many MSE programs worldwide, not to mention software engineering tracks within Computer Science (CS) masters' programs. In the years that followed, software engineering education emerged at the undergraduate level, with a documented reference curriculum. Software engineering professionalism initiatives resulted in the certification and licensing of software engineers in a number of countries worldwide. We are seeing increased attention to software engineering specialty areas, and many software engineering degree programs have tracks to support these specialties.

On the industry side we are seeing an upheaval in software engineering as we know it. Software engineering is pervasive. Innovations such as Cloud Computing, autonomous vehicles, drones, bioengineering, and other initiatives have made for a rapidly changing landscape. Topics such as software assurance, safety, and reliability have become increasingly important knowledge areas.

As educators we are challenged to keep up with the emerging trends, to identify suitable software engineering techniques, and to incorporate them into our class offerings. We are in a global economy with a software supply chain that can extend across many countries and regions. Each one has their own regulations and laws about safety, security, and privacy. Practicing software engineers change jobs frequently, so the value of in-house training is not as clear as it once was.

In 2017 the conference will to focus on the changes that are taking place in the field, our response to it, and our vision of how we can move forward to meet the ever-increasing demand for qualified software engineers. Furthermore, the 2017 conference has teamed up with the Journal of Systems and Software (JSS) to devote a special issue of the JSS to the select papers from this year's conference.

Authors interested to contribute to the JSS special issue should consider submitting their preliminary work to the CSEE&T.

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全文截稿：2018-01-31

期刊：Pattern Recognition Letters

专刊：Special Issue on Learning Compact Representations for Scalable Visual Recognition and Retrieval

领域：人工智能

难度：★★★

CCF分类：C类

影响因子：1.586

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

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With the explosive growth of visual data, traditional hand-crafted features or learning-based representations will induce inapplicable computational complexity and large memory costs, due to exhausting computations in large-scale and high-dimensional feature space. Therefore, these conventional methods are lack of scalability for large-scale visual applications, e.g. image/video recognition and retrieval. It is highly desirable to learn much more compact feature representations to reduce computational loads for massive visual data and make big data analysis more feasible. In recent years, compact feature learning algorithms have been intensively exploited and attracted broad interests in visual analysis. For instance, benefiting from the hashing technique, we can obtain compact binary representations, based on which efficient XOR computations in the Hamming space can operate in constant time. The above compact feature learning approaches have been proved to achieve promising performance in various large-scale visual applications, such as content-based image/video retrieval and recognition. In addition, these techniques will be essential for the applications on portable/wearable devices.

The special issue will focus on the most recent progress on compact representation learning for a variety of large-scale visual data analysis, such as content-based image/video retrieval, image/video recognition, annotation and segmentation, object detection and recognition, visual processing and affective computing. This special issue will also target on related feature selection, subspace learning and deep learning techniques, which can well handle large-scale visual tasks. The primary objective of this special issue fosters focused attention on the latest research progress in this interesting area.

The special issue seeks for original contributions of work, which addresses the challenges from the compact representation learning and the related efficient representation learning algorithms for large-scale visual data. 

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全文截稿：2018-03-31

期刊：Pattern Recognition Letters

专刊：Special Issue on Multiple-task Learning for Big Data (ML4BD)

领域：人工智能

难度：★★★

CCF分类：C类

影响因子：1.586

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

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Big Data is a term that describes the large volume of data-both structured and unstructured. With the rapid development of new information technologies such as smart phone, mobile game platforms, smart home devices, smart health devices, and wearable computation devices, the amount of created and stored data on global level is almost inconceivable and it just keeps growing. These data is s- large and complex that traditional data processing applications are incapable of dealing with them. There are many challenges when addressing big data problem, such as data acquisition, data curation, data storage, data search, data transfer and sharing, data visualization, data query and retrieval, information security, and data analysis (e.g., prediction, user behavior analysis). Big data requires novel data processing techniques t- solve some of these challenges jointly, which is related t- Multiple-task Learning methodologies.

Multiple-task Learning is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. This can result in improved learning efficiency and prediction accuracy for each task-specific model, when compared t- training each of the models separately. Multiple-task Learning is an approach of inductive transfer that improves generalization by using domain information contained in the training signals of related tasks as inductive bias. It does this by learning multiple tasks in parallel using a shared representation, based on the assumption that all tasks can help each other in learning. MTL works because regularization induced by requiring an algorithm t- perform well on a related task can be superior t- regularization that prevents overfitting by penalizing all complexity uniformly. MTL may be particularly helpful if all related tasks share significant commonalities and are slightly under sampled. With big structured and unstructured data, different tasks on the same data or related data are suitable for MTL framework. Therefore, Multiple-task Learning for Big Data (MTL4BD) have broad applications in many fields, such as online recommendation system, smart home, smart health care, robotics, medical imaging, multimedia application, computer vision, human computer interaction and language processing, etc. We plan t- receive about 45 paper submissions from around 30 Universities and research institutions and we will accept about 15 papers plus a review paper totally.

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全文截稿：2018-06-30

期刊：Pattern Recognition Letters

专刊：Special Issue on Learning and Recognition for Assistive Computer Vision

领域：人工智能

难度：★★★

CCF分类：C类

影响因子：1.586

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

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Assistive Computer Vision refers to systems that support people with physical and mental problems to better perform daily tasks enhancing their quality of life.  The advances in learning and recognizing patterns are allowing a point of view in the definition and development of more efficient and effective assistive frameworks.

In the light of this, it is important to collect the most recent advancements in learning and recognition algorithms to be exploited in different applications to be employed to assist the modern society.

The aim of the special issue is to gather papers in which machine learning and pattern recognition are the key core in the design of advanced assistive computer vision systems to help human in tasks such as:

- Rehabilitation

- Training

- Mobility

- Assessment and diagnosis of physical and cognitive diseases

- Improving quality of Life

- Remote Healthcare

- Safe and security

- Remote Surgery

- Ambient Assisted Living

- Augmented Perception, Attention and Memory

We will invite authors to contribute with high quality paper that will stimulate the research community on building theory and applications of machine learning and pattern recognition to be used in real-life environments for assistive computer vision technologies.