EFFICIENT INFORMATION INTEGRATION SYSTEM FOR TEMPORAL AND SPATIAL DATA

In this dissertation, I develop a novel inconsistency detection and data fusion method for data integration systems. Inconsistent data may lead to incorrect query results and induce unexplainable outcomes. I propose an inconsistency detection method to find out which data items (e.g., temporal or spatial report) have the higher potential to cause data conflicts as well as to estimate a reasonable consistent reported value. My approach is based on representing overlapping data reports as a characteristic linear system. The characteristic linear system can be used to estimate consistent reported values within overlapping time and space intervals. I explore applicability of the proposed approach in different domains. In particular, I perform temporal data fusion with time-overlapping reports using a historical database. I also experiment with spatial data fusion involving space-overlapping reports using simulation of sensor data sets of robots performing search and rescue task. Finally, I apply the proposed approach to combine temporal and spatial fusion and demonstrate that such multidimensional fusion improves inconsistency detection and target value estimation

Subject-relevant Document Recommendation: A Reference Topic-Based Approach

Knowledge-intensive workers, such as academic researchers, medical professionals or patent engineers, have a demanding need of searching information relevant to their work. Content-based recommender system (CBRS) makes recommendation by analyzing similarity of textual contents between documents and users’ preferences. Although content-based filtering has been one of the promising approaches to document recommendations, it encounters the over-specialization problem. CBRS tends to recommend documents that are similar to what have been in user’s preference profile. Rationally, citations in an article represent the intellectual/affective balance of the individual interpretation in time and domain understanding. A cited article shall be associated with and may reflect the subject domain of its citing articles. Our study addresses the over-specialization problem to support the information needs of researchers. We propose a Reference Topic-based Document Recommendation (RTDR) technique, which exploits the citation information of a focal user’s preferred documents and thereby recommends documents that are relevant to the subject domain of his or her preference. Our primary evaluation results suggest the outperformance of the proposed RTDR to the benchmarks

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page

Cryptanalysis and Improvement of the Robust User Authentication Scheme for Wireless Sensor Networks

Wireless sensor networks are widely used in industrial process control, human health care, environmental control, vehicular tracking and battlefield surveillance, etc. A wireless sensor network consists of lots of sensor nodes and a gateway node. The sensor node usually communicates with the gateway node and users over an ad hoc wireless network. However, due to the open environments, the wireless sensor networks are vulnerable to variety of security threats. Thus, it is a critical issue to adopt a suitable authentication mechanism for wireless sensor networks to enhance security. In 2009, Vaidya et al. proposed a robust user authentication schemes for wireless sensor networks. In this article, we will show that their scheme is vulnerable to the guessing attack and the impersonation attack. Since it needs a secure channel for communications in password changing phase, their scheme is also inconvenient and expensive for users to update passwords. We also propose an improved scheme to remedy the flaws. The improved scheme withstands the replay attack and off-line guessing attack, and the users can freely update their passwords via public channels

HOTEL RECOMMENDATION SYSTEM BASED ON REVIEW AND CONTEXT INFORMATION: A COLLABORATIVE FILTERING APPRO

In most organizations, knowledge sharing is often lacking when it comes to business systems success. This paper investigates factors affecting business systems success in Saudi organisations. Data were collected from private organisations in Saudi Arabia and Partial Least Square approach has been applied to analyse the data. The results show that organisational culture influence knowledge sharing towards business systems success. In addition, both intrinsic motivation and perceived usefulness has positive influence on business system success. This indicates that business system success is built upon the concept of knowledge sharing and user motivation

Cyberbullying Detection on Social Network Services

Social networks such as Facebook or Twitter promote the communication between people but they also lead to some excessive uses on the Internet such as cyberbullying for malicious users. In addition, the accessibility of the social network also allows cyberbullying to occur at anytime and evoke more harm from other users’ dissemination. This study collects cyberbullying cases in Twitter and attempts to establish an auto-detection model of cyberbullying tweets base on the text, readability, sentiment score, and other user information to predict the tweets with harassment and ridicule cyberbullying tweets. The novelty of this study is using the readability analysis that has not been considered in past studies to reflect the author\u27s education level, age, and social status. Three data mining techniques, k-nearest neighbors, support vector machine, and decision tree are used in this study to detect the cyberbullying tweets and select the best performance model for cyberbullying prediction

Attention Allocation for Human Multi-Robot Control: Cognitive Analysis based on Behavior Data and Hidden States

Human multi-robot interaction exploits both the human operator’s high-level decision-making skills and the robotic agents’ vigorous computing and motion abilities. While controlling multi-robot teams, an operator’s attention must constantly shift between individual robots to maintain sufficient situation awareness. To conserve an operator’s attentional resources, a robot with self reflect capability on its abnormal status can help an operator focus her attention on emergent tasks rather than unneeded routine checks. With the proposing self-reflect aids, the human-robot interaction becomes a queuing framework, where the robots act as the clients to request for interaction and an operator acts as the server to respond these job requests. This paper examined two types of queuing schemes, the self-paced Open-queue identifying all robots’ normal/abnormal conditions, whereas the forced-paced shortest-job-first (SJF) queue showing a single robot’s request at one time by following the SJF approach. As a robot may miscarry its experienced failures in various situations, the effects of imperfect automation were also investigated in this paper. The results suggest that the SJF attentional scheduling approach can provide stable performance in both primary (locate potential targets) and secondary (resolve robots’ failures) tasks, regardless of the system’s reliability levels. However, the conventional results (e.g., number of targets marked) only present little information about users’ underlying cognitive strategies and may fail to reflect the user’s true intent. As understanding users’ intentions is critical to providing appropriate cognitive aids to enhance task performance, a Hidden Markov Model (HMM) is used to examine operators’ underlying cognitive intent and identify the unobservable cognitive states. The HMM results demonstrate fundamental differences among the queuing mechanisms and reliability conditions. The findings suggest that HMM can be helpful in investigating the use of human cognitive resources under multitasking environments