High-quality and challenging event stream datasets play an important role in the design of an efficient event-driven mechanism that mimics the brain. Although event cameras can provide high dynamic range and low-energy event stream data, the scale is smaller and more difficult to obtain than traditional frame-based data, which restricts the development of neuromorphic computing. Data augmentation can improve the quantity and quality of the original data by processing more representations from the original data. This paper proposes an efficient data augmentation strategy for event stream data: EventMix. We carefully design the mixing of different event streams by Gaussian Mixture Model (GMM) to generate random 3D masks and achieve arbitrary shape mixing of event streams in the spatio-temporal dimension. By computing the relative distances of event streams, we propose a more reasonable way to assign labels to the mixed samples. The experimental results on multiple neuromorphic datasets have shown that our strategy can improve performance on neuromorphic classification tasks as well as neuromorphic human action recognition tasks both for ANNs and SNNs, and we have achieved state-of-the-art performance on DVS-CIFAR10, N-Caltech101, and DVS-Gesture datasets.

Spiking neural network (SNN) has attracted much attention due to its powerful spatio-temporal information representation ability. Capsule Neural Network (CapsNet) does well in assembling and coupling features of different network layers. Here, we propose Spiking CapsNet by combining spiking neurons and capsule structures. In addition, we propose a more biologically plausible Spike Timing Dependent Plasticity routing mechanism. The coupling ability is further improved by fully considering the spatio-temporal relationship between spiking capsules of the low layer and the high layer. We have verified experiments on the MNIST, FashionMNIST, and CIFAR10 datasets. Our algorithm still shows comparable performance concerning other excellent SNNs with typical structures (convolutional, fully-connected) on these classification tasks. Our Spiking CapsNet combines SNN and CapsNet’s strengths and shows strong robustness to noise and affine transformation. By adding different Salt-Pepper and Gaussian noise to the test dataset, the experimental results demonstrate that our algorithm is more resistant to noise than other approaches. As well, our Spiking CapsNet shows strong generalization to affine transformation on the AffNIST dataset. Our code is available at https://github.com/BrainCog-X/Brain-Cog.

Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. These data can be used to better understand people (e.g., personality traits, perceptions, and preferences) and predict their behavior. As a result, a deeper understanding of users and their behavior can benefit a wide range of intelligent applications, such as advertising, social recommender systems, and personalized knowledge management. These applications will also benefit individual users themselves and optimize their experience across a wide variety of domains, such as retail, healthcare, and education. Since mining and understanding user behavior from social media often requires interdisciplinary effort, including machine learning, text mining, human-computer interaction, and social science, our workshop aims to bring together researchers and practitioners from multiple fields to discuss the creation of deeper models of individual users by mining the content that they publish and the social networking behavior that they exhibit.

We organize and present the 5th version of the International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning (Web-KR 2014) as a continuous effort to discuss and provide possible theories and techniques to deal with the barriers for knowledge processing at Web scale. This workshop was held in conjunction with the 2014 ACM International Conference on Information and Knowledge Management (CIKM 2014) on November 3rd, 2014 in Shanghai, China. Compared to previous workshops under the same title, accepted papers of this workshop covers even wider topics in the field. The contributions focus on semantic knowledge extraction, representation, knowledge clustering, inconsistency checking, entity relatedness and linking, query suggestions, etc. Many new approaches are proposed to investigate these topics in the context of Web-scale resources. This summary introduces the major contributions of accepted papers in the Web-KR 2014 workshop.

As a continuous effort for organizing discussions and providing possible theories and techniques to deal with the barriers for knowledge processing at Web scale, the 2013 International Workshop on Web-scale Knowledge Representation, Retrieval and Reasoning (Web-KR 2013) was held in conjunction with the 2013 ACM International Conference on Information and Knowledge Management (CIKM 2013) on November 1st, 2013 at Burlingame, CA, United States. This is the 4th version of the Web-KR workshop. As in previous workshops under the same title, accepted papers of this workshop cover many important topics in the field. This year, the contributions focus on multi-faceted understanding of Web knowledge sources, Web entity linking, deep Web knowledge acquisition, and Web-scale stream reasoning. Many new approaches are proposed to deal with these problems in the context of large scale Web resources. This summary introduces the major contributions of accepted papers in the Web-KR 2013 workshop.

The rapid and perpetual growth of knowledge on the Web has given rise to many grand challenges (such as scalability, inconsistency, uncertainty, distribution and dynamics) for traditional knowledge processing methods and systems. Knowledge representation, retrieval and reasoning methods need to evolve and adapt to the Web to face these challenges and make this vast, heterogenous knowledge useful and accessible. In this light, the International Workshop on Web-scale Knowledge Representation, Retrieval, and Reasoning (Web-KR) is initiated. This workshop serves as the third one in this workshop series. This summary discusses the scope of Web-KR and introduces the advances in this field through the accepted papers in the Web-KR 2012 workshop, co-located with CIKM 2012.

Web-based collaborative knowledge bases collect human knowledge through the Web. They can be used for answering questions or support different knowledge intensive applications on the Web. From a statistical point of view, they usually reveal some interesting characteristics, which can be acquired through statistical analysis to get deeper understanding of these kinds of knowledge bases. In this paper, we build a semantic knowledge base using the triples extracted from a Chinese wiki Web site called Baidu Baike. We make an investigation on the statistical results on the building process and structural characteristics of this knowledge base. We explain what we have observed and inferred and how the conclusion can help to understand the process of building large scale Web based collaborative knowledge bases and how to make them better.

For most scientists, their research interests are dynamically changing all the time. Through an analysis of research interests, we find that all the changes are with some characteristics. Plus, the research interests in the dynamic changing process are not isolated, instead, they are interconnected as a whole to form a holistic structure. We introduce some measurement parameters to track and detect the evolution process, we analyze the structural and dynamic characteristics of research interests through statistical analysis, and we also investigate on how they affect each other. As a possible application, we use observed characteristics of research interests to refine literature search on the Web, which shows that diverse user needs can be satisfied using various observations from research interests as constraints for vague queries. Such effort may provide some hints and various methods to support personalized search, and can be considered as a step forward user centric knowledge retrieval on the Web.

A Search Support Engine (SSE) is implemented based on the basic principles of Information Retrieval Support Systems (IRSS) and Information Seeking Support Systems (ISSS). An SSE aims at meeting the diversity needs from different users, providing various supporting functionalities, tools, etc. for users to perform various tasks beyond the traditional search and browsing provided by current search engines. As an illustrative example, we developed a DBLP search support engine (DBLP-SSE), and we discuss some concrete supporting functionalities, namely, search refinement support, domain analysis support, etc. Each of the functionality focus on a unique perspective supporting users finding useful information and knowledge from the DBLP dataset. The search support engine can be considered as a step towards Knowledge Retrieval (KR) and Web Intelligence (WI).

With the ever-increasing growth of data and information, finding the right knowledge becomes a real challenge and an urgent task. Traditional data and information retrieval systems that support the current web are no longer adequate for knowledge seeking tasks. Knowledge retrieval systems will be the next generation of retrieval system serving those purposes. Basic issues of knowledge retrieval systems are examined and a conceptual framework of such systems is proposed. Theories and Technologies such as theory of knowledge, machine learning and knowledge discovery, psychology, logic and inference, linguistics, etc. are briefly mentioned for the implementation of knowledge retrieval systems. Two applications of knowledge retrieval in rough sets and biomedical domains are presented.