njing Li

Research Collaboration and ITS Topic Evolution: 10 Years at T-ITS

This paper investigates the collaboration patterns and research topic trends in the publications of the IEEE Transactions on Intelligent Transportation Systems (T-ITS) over the past decade. We find that coauthorship is prevalent and that the coauthorship networks possess the scale-free property on high degree nodes. Collaborations usually occur within the same research institutions and countries. Interorganization/region collaboration structures are usually connected through a few productive/high-impact authors. Typical international collaborations are between the U.S. and other countries such as China, Germany, U.K., and Italy. Active topics studied in IEEE T-ITS publications in the past ten years include traffic management and machine vision, among others. Authors can be partitioned into common interest groups, of which machine vision and automatic vehicle control attract more researchers.

A Bibliographic Analysis of the IEEE Transactions on Intelligent Transportation Systems Literature

This paper presents a bibliographic analysis of the papers published in the IEEE Transactions on Intelligent Transportation Systems (T-ITS). We identify the most productive and high-impact authors, institutions, and countries/regions. We find that research on intelligent transportation systems is dominated by U.S. researchers and institutions and that China and Japan are the second most productive countries. According to this analysis, M. M. Trivedi, N. P. Papanikolopoulos, and P. A. Ioannou are the three most productive and influential authors in the IEEE T-ITS, whereas the Massachusetts Institute of Technology, Cambridge, the University of California, San Diego, and the University of Minnesota, Minneapolis, are three of the most productive and influential institutions in the IEEE T-ITS.

Equilibrium Bidding Strategy for GSP Keyword Auctions

The generalized second-price (GSP) mechanism is the most widely-used auction format in sponsored search markets. However, figuring out how to bid on GSP auctions presents major theoretical and computational challenges due to the complex nature of the auction format and the infinite number of equilibria. Our study characterizes various equilibrium bidding behaviors in GSP auctions. We develop an algorithm to identify all pure-strategy Nash equilibria and discuss their distribution in the pure-strategy space. This equilibrium distribution can help advertisers formulate bidding strategies, and help search engines calculate their expected revenues.

Real-time prediction of meme burst

Predicting meme burst is of great relevance to develop security-related detecting and early warning capabilities. In this paper, we propose a feature-based method for real-time meme burst predictions, namely “Semantic, Network, and Time” (SNAT). By considering the potential characteristics of bursty memes, such as the semantics and spatio-temporal characteristics during their propagation, SNAT is capable of capturing meme burst at the very beginning and in real time. Experimental results prove the effectiveness of SNAT in terms of both fixed-time and real-time meme burst prediction tasks.

Associated Activation-Driven Enrichment: Understanding Implicit Information from a Cognitive Perspective

In this paper, we propose a novel text representation paradigm and a set of follow-up text representation models based on cognitive psychology theories. The intuition of our study is that the knowledge implied in a large collection of documents may improve the understanding of single documents. Based on cognitive psychology theories, we propose a general text enrichment framework, study the key factors to enable activation of implicit information, and develop new text representation methods to enrich text with the implicit information. Our study aims to mimic some aspects of human cognitive procedure in which given stimulant words serve to activate understanding implicit concepts. By incorporating human cognition into text representation, the proposed models advance existing studies by mining implicit information from given text and coordinating with most existing text representation approaches at the same time, which essentially bridges the gap between explicit and implicit information. Experiments on multiple tasks show that the implicit information activated by our proposed models matches human intuition and significantly improves the performance of the text mining tasks as well.

A Probabilistic Mechanism Design for Online Auctions

Recently, there has been a rapid growth of the online auctions in e-commerce platforms, in which small and/or medium-sized enterprises (SMEs) heavily depend on the advertising systems. In this paper, we design flexible mechanisms to reduce the competition of SMEs without affecting competitive large companies in order to maximize the profit of e-commerce platform and to keep the ecosystem healthy. A probabilistic pricing mechanism design approach is investigated for online auctions. Utilizing this approach, we introduce the notation of simple mechanisms as a tool for designing new mechanisms. Based on a simple and a classical, the proposed mechanism probabilistic mechanisms are designed and their properties are analyzed. Furthermore, we devise two mechanism design algorithms for different application scenarios. Experiments are presented to demonstrate the flexibility and the effectiveness of the proposed probabilistic mechanism design approach.

Activating topic models from a cognitive perspective

Topic modeling is a popular text mining technique for extracting latent semantics from text. It can be widely applied in intelligence analyzing, anti-terrorist, and various other security related tasks. Most existing topic models only focus exclusively on the text literally, and disregard rich contextual, cultural, and language background, hindering the understanding and discovering of the key clues implied in the text. Based on cognitive psychology theories, we justify the classical psychological activation theory named Adaptive Control of Thought from the perspective of information theory. Then, we propose a fast and loosely-coupled activation presentation of text for topic models. Our method mimics the aspect of human cognitive procedure when facing the activation of new concepts based on word correlations and word frequencies. Experimental results on multiple tasks show that our activation presentation models can significantly improve the performance of the topic models with linear time consumption.

New words enlightened sentiment analysis in social media

Public sentiment permeated through social media is usually regarded as an important measure for hot event detecting, policy making and so forth, hence many governments and intelligence agencies have been launching various initiatives to facilitate theories, technologies and systems toward monitoring its fluctuation. Recently, massive new words are created and widely spread in social media, and they pose a great influence on sentiment analysis. Facing this situation, most previous work still just add those new words into sentiment lexicon, none of the existed researches focuses on the role and influence of new words in emotional expression. In this paper, we pay more attention to the influence of new words and propose two novel new words based sentiment analysis methods, named NWLb and NWSA, the former only with the help of lexicon and the latter further incorporates machine learning, which utilize the distinctive role of new words to improve the effectiveness of sentiment analysis in social media. Experiments on real social media dataset demonstrate the effectiveness and performance of our methods.

Refinement of symmetrical Nash equilibrium for generalized second-price mechanism in sponsored search advertising

Sponsored search advertising is the most prevailing online advertising instrument, also it is the most important and fastest-growing revenue source for auctioneers. In this paper, we propose a new type of equilibrium refinement concept named “stable Nash equilibrium” for this auction game. We illustrate that the set of all stable Nash equilibria (STNE) of a GSP mechanism keyword auction can be efficiently calculated by a recursive procedure. STNE is either the same as the set of the well-known symmetrical Nash equilibrium or a proper subset of it. These findings free both auctioneers and advertisers from complicated strategic thinking. The revenue of a GSP auction on STNE is at least the same as that of the classical VCG mechanism and can be used as a benchmark for evaluating other mechanisms. At the same time, STNE provides advertisers a simple yet effective and stable strategy.

Behavior enhanced deep bot detection in social media

Social bots are regarded as the most common kind of malwares in social platform. They can produce fake messages, spread rumours, and even manipulate public opinions. Recently, massive social bots are created and widely spread in social platform, they bring negative effects to public and netizen security. Bot detection aims to distinguish bots from human and it catches more and more attentions in recent years. In this paper, we propose a behavior enhanced deep model (BeDM) for bot detection. The proposed model regards user content as temporal text data instead of plain text to extract latent temporal patterns. Moreover, BeDM fuses content information and behavior information using deep learning method. To the best of our knowledge, this is the first trial that applies deep neural network in bot detection. Experiments on real world dataset collected from Twitter also demonstrate the effectiveness of our proposed model.