大数据 | CCF- B类 | SCI期刊专刊信息3条

全文截稿: 2021-04-01

影响因子: 4.787

CCF分类: B类

中科院JCR分区:

• 大类 : 计算机科学 - 1区

• 小类 : 计算机：信息系统 - 1区

• 小类 : 图书情报与档案管理 - 1区

网址：
http://www.journals.elsevier.com/information-processing-and-management/

As one of the most exciting trends in computing today, social geo-media holds the great potential to fundamentally change how information is accessed and modeled, how knowledge is harvested, processed and extracted, and how business (e.g., branding and customer relationship management) is conducted. Indeed, over the past few years, it has begun to usher in a new and exciting era of knowledge discovery and information retrieval research. An immense amount of information about users and their social interactions based on specific geo location can be harvested from the Web and explored through advanced data mining/information retrieval techniques. In particular, comprehensive analysis of social geo-media data can be leveraged to develop novel application in human activity domain including political elections, tourism management, smart cities and disease spread modeling.

This special issue is devoted to the publications of high-quality papers on three technical developments and practical applications around social, geo-media analytics and retrieval. It will serve as a forum for recent advances in the fields of social and geo-media content analysis, mining, search, and emerging new applications, such as geo-media systems, context-aware advertising, and personalized socio-mobile experience.

全文截稿: 2021-06-30

影响因子: 4.787

CCF分类: B类

中科院JCR分区:

• 大类 : 计算机科学 - 1区

• 小类 : 计算机：信息系统 - 1区

• 小类 : 图书情报与档案管理 - 1区

网址：
http://www.journals.elsevier.com/information-processing-and-management/

Cognition is emerging as a new and promising methodology with the development of cognitive-inspired computing, cognitive-inspired interaction and systems, which has the potential to enable a large class of applications and has emerged with a great potential to change our life. However, recent advances on artificial intelligence (AI), fog computing, big data, and cognitive computational theory show that multidisciplinary cognitive-inspired computing still struggle with fundamental, long-standing problems, such as computational models and decision-making mechanisms based on the neurobiological processes of the brain, cognitive sciences, and psychology. How to enhance human cognitive performance with machine learning, common sense, natural language processing etc. are worth exploring.

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

The objective of this special issue is to bring together state-of-the-art research contributions that address these key aspects of cognitive-inspired computing and applications. Original papers describing completed and unpublished work not currently under review by any other journal/magazine/conference are solicited. Specific topics include, but not limited to, the following:

Cognitive-inspired computing fundamentals

Cognitive-inspired computing systems

Cognitive-inspired computing with big data

Cognitive-inspired intelligent interaction

AI-assisted cognitive computing approaches

Brain analysis for cognitive-inspired computing

Internet of cognitive Things

Cognitive environment, sensing and data

Cognitive robots and agents

Security issue in cognitive-inspired computing

Test-bed, prototype implementation and applications

全文截稿: 2021-07-01

影响因子: 5.91

CCF分类: B类

中科院JCR分区:

• 大类 : 计算机科学 - 1区

• 小类 : 计算机：信息系统 - 1区

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

Autonomous Machine Learning (AML) refers to a learning system having flexible characteristic to evolve both its network structure and parameters on the fly. It is capable of initiating its learning process from scratch with/without a predefined network structure while its knowledge base is automatically constructed in real-time. AML is built upon two fundamental principles: one-pass learning strategy and self-evolving network structure. The former one reflects a situation where a data point is directly discarded once learned to assure bounded memory and computational burdens while the latter lies in the self-reconfiguration aptitude of AML where its network size can increase or reduce in respect to varying data distributions. AMLs have been proven to be useful in handling real-time data streams where a learning system confronts never-ending information flow which does not follow static or predictable data distributions rather drifting overtime with different types, magnitudes and types. Variants of AMLs are capable of quickly reacting to those drifting distributions regardless of how slow, fast, sudden, gradual, cyclic changing distributions might be while retaining computationally light characteristics. In addition, the AMLs have grown into various application domains not only limited to regression, classification, clustering but also control and reinforcement learning. In a nutshell, it is
enabled by the fact that AMLs aim to balance between stability and plasticity of a learning system.

Recent challenges in machine learning renders innovation of AMLs urgently needed. The advent of deep learning technologies is a concrete example. Existing DNNs mostly rely on a static and offline learning principle limiting its feasibility in the streaming environments. On the other hand, DNNs are well-known for its feature learning power being able to handle unstructured problems with large input dimension and target classes. The network structure of DNNs are difficult to evolve because of the absence of local and spatial contexts. The multi-layer nature of DNNs complicate the self-evolving strategy. Insertion of a new layer definitely leads to the catastrophic forgetting problem. Another research opportunity of AMLs is identified in the context of lifelong/continual learning where the goal is not only to adapt to changing environments but also to actualize a lifelong learning agent with knowledge retention property. That is, a learning agent must not suffer from the catastrophic forgetting problem when adapting to a new context. The fact that AMLs are normally designed in the local learning environment should be useful for this purpose. Only relevant knowledge is stimulated by new tasks while others remain silent. The application of AMLs in the transfer learning domain deserves in-depth study. Unlike traditional AML involving only a single stream, the case of multi-streams remains an open issue. The main goal of this problem is to create a domain-invariant network handling both source stream and target stream equally well. The challenge of this topic is evident in the covariate shift problem between source stream and target stream. As with the single stream case, the concept drift occurs here in each stream in different time periods.

This special issue aims to bring together recent research works of AMLs. Particular interest lies in the integration of AMLs in handling advanced issues of machine learning as abovementioned. We solicit original works that have not been published nor under consideration in other publication venues.

Topic of Interest
The topic of interest includes the following but not limited to


Novel network architecture of AMLs

AMLs to handle unstructured problems such as texts, videos, speech, etc.

AMLs to handle weakly supervised learning problem.

AMLs to handle semi-supervised learning problem.

Active learning for AMLs.

AMLs to handle continual learning problem.

AMLs to handle multi-stream problem.