Intelligent techniques for recommender systems

This thesis focuses on the data sparsity issue and the temporal dynamic issue in the context of collaborative filtering, and addresses them with imputation techniques, low-rank subspace techniques and optimizations techniques from the machine learning perspective. A comprehensive survey on the development of collaborative filtering techniques is also included

Pedagogical framework for environmental science

Pedagogical framework for environmental scienc

Monetising user generated content using data mining techniques

Social media systems such as YouTube are gaining phenomenal popularity. As they face increasing pres-sure and dificulties monetising the large amount of user-generated content, there are intense interests in technologies capable of delivering revenue to the own-ers. In this paper, we propose to use data min-ing techniques to help companies increase their rev-enue stream. Our approach differs principally in the underlying monetisation model and hence, the algo-rithms and data utilised. Our new model assumes both consumer and commercial content being entirely user-generated. We first present an algorithm to demonstrate one of possible monetisation technique that could be used in social media systems such as YouTube. A large volume of real-data harvested from YouTube will also be discussed and made available for the community to potentially kick start research in this direction. © 2009, Australian Computer Society, Inc

Google hacking defence based on honey pages

 Many web servers contain some dangerous pages (we name them eigenpages) that can indicate their vulnerabilities. Therefore, some worms such as Santy locate their targets by searching for these eigenpages in search engines with well-crafted queries. In this paper, we focus on the modeling and containment of these special worms targeting web applications. We propose a containment system based on honey pots. We make search engines randomly insert a few honey pages that will induce visitors to the pre-established honey pots among the search results for the arriving queries. And then infectious can be detected and reported to the search engines when their malicious scans hit the honey pots. We find that the Santy worm can be well stopped by inserting no more than two honey pages in every one hundred search results. We also solve the challenging issue to dynamically generate matching honey pages for those dynamically arriving queries. Finally, a prototype is implemented to prove the technical feasibility of this system. © 2013 by CESER Publications

Root Cause Analysis of Traffic Anomalies Using Uneven Diffusion Model

Detection and analysis of traffic anomalies are important for the development of intelligent transportation systems. In particular, the root causes of traffic anomalies in road networks as well as their propagation and influence to the surrounding areas are highly meaningful. The root cause analysis of traffic anomalies aims to identify those road segments, where the traffic anomalies are detected by the traffic statuses significantly deviating from the usual condition and are originated due to incidents occurring in those roads such as traffic accidents or social events. The existing methods for traffic anomaly root cause analysis detect all traffic anomalies first and then apply, implicitly or explicitly, specified causal propagation rules to infer the root cause. However, these methods require reliable detection techniques to accurately identify all traffic anomalies and extensive domain knowledge of city traffic to specify plausible causal propagation rules in road networks. In contrast, this paper proposes an innovative and integrated root cause analysis method. The proposed method is featured by 1) defining a visible outlier index as the probabilistic indicator of traffic anomalies/disturbances and 2) automatically learning spatiotemporal causal relationship from historical data to build an uneven diffusion model for root cause analysis. The accuracy and effectiveness of the proposed method have been demonstrated by experiments conducted on a trajectory dataset with 2.5 billion location records of 27 266 taxies in Shenzhen city

RIMS: a real-time and intelligent monitoring system for live-broadcasting platforms

Personal live shows on Internet streaming platforms currently are blooming as one of the most popular applications on mobile phones and especially attracting millions of young generation users. The content supervision on live streaming platforms, in which there are thousands or hundreds of show rooms for performing and chatting synchronously, is a major concern with the development of this new service. Traditional image captures and real-time content analysis experience huge difficulties such as processing delay, data overwhelming, and matching overhead. In this paper, we propose a comprehensive method to monitor real-time live stream and to identify illegal or unchartered live misbehaviors intelligently based on various proposed aspects instead of image analysis only. The proposed system called RIMS makes use of several novel indicators on show room status rather than analyzing images solely to support real-time requirements. Three detecting techniques are adopted: self-adaptive threshold-based abnormal traffic detection, sensitive Danmaku comment perception, and frame difference analysis. RIMS can detect dramatically increasing of user number in a show room, filter sensitive words in Danmaku, and capture segmentation of video scenes by frame difference analysis. We deploy our system to monitor a typical live- broadcasting platform called panda.tv, and overall accuracy of detection via three indicators reaches 90.1%. The application of RIMS can change current supervison methods on live platforms that they totally rely on real-time manual review or after the event check. The key techniques in RIMS can also be widely employed in many other mobile applications in edge computing such as video surveillance in Internet of Things and mobile short video sharing

Time cumulative complexity modeling and analysis for space-based networks

In this paper, the notion of the cumulative time varying graph (C-TVG) is proposed to model the high dynamics and relationships between ordered static graph sequences for space-based information networks (SBINs). In order to improve the performance of management and control of the SBIN, the complexity and social properties of the SBIN's high dynamic topology during a period of time is investigated based on the proposed C-TVG. Moreover, a cumulative topology generation algorithm is designed to establish the topology evolution of the SBIN, which supports the C-TVG based complexity analysis and reduces network congestions and collisions resulting from traditional link establishment mechanisms between satellites. Simulations test the social properties of the SBIN cumulative topology generated through the proposed C-TVG algorithm. Results indicate that through the C-TVG based analysis, more complexity properties of the SBIN can be revealed than the topology analysis without time cumulation. In addition, the application of attack on the SBIN is simulated, and results indicate the validity and effectiveness of the proposed C-TVG and C-TVG based complexity analysis for the SBIN

Clustering Interval-valued Data Using an Overlapped Interval Divergence

As a common problem in data clustering applications, how to identify a suitable proximity measure between data instances is still an open problem. Especially when interval-valued data is becoming more and more popular, it is expected to have a suitable distance for intervals. Existing distance measures only consider the lower and upper bounds of intervals, but overlook the overlapped area between intervals. In this paper, we introduce a novel proximity measure for intervals, called Overlapped Interval Divergence (OLID), which extends the existing distances by considering the relationship between intervals and their overlapped "area". Furthermore, the proposed OLID measure is also incorporated into di®erent adaptive clustering frameworks. The experiment results show that the proposed OLID is more suitable for interval data than the Hausdor® distance and the cityblock distance. © 2009, Australian Computer Society, Inc

AppTCP: The design and evaluation of application-based TCP for e-VLBI in fast long distance networks

Electric Very Long Baseline Interferometry (e-VLBI) is a typical astronomical interferometry used in radio astronomy. It allows observations of an object that are made simultaneously by many radio telescopes to be combined, emulating a telescope with the size equal to the maximum separation between the telescopes. The main requirements of transporting e-VLBI data are the high and constant transmission rate. However, the traditional TCP and its variants cannot meet these requirements. In an effort to solve the problem of transporting e-VLBI data in fast long distance networks, we propose an application-based TCP (AppTCP) congestion control algorithm, using Closed-Loop Control theory to keep the stable and constant transmission rate. AppTCP can swiftly reach the required transmission rate by increasing the congestion control window, and keep the transmission rate and allows the other TCP flows to share the remaining bandwidth. We further conduct extensive experiments in both fast long distance network test-bed and actual national networks (i.e., from Beijing to Shanghai in China) and international networks (i.e., from Hongkong in China to Chicago in USA) to evaluate and verify the performance and effectiveness of AppTCP. The results show that the AppTCP can effectively utilize the link capacity and maintain the constant rate during the data transmission, and its performance significantly outperforms that of the existing TCP variants

A new method for preparing alumina nanofibers by electrospinning technology

A new method for synthesizing alumina (Al2O3) nanofibers through the electrospinning method was reported. The spinning solutions of anhydrous aluminium chloride/polyvinylpyrrolidone (AlCl3/PVP), which were prepared by the sol-gel process of the mixture of AlCl3,PVP, ethanol and redistilled water, were electrospun to form AlCl3/PVP organic-inorganic hybrid fibers. Alumina nanofibers with average diameters of 100—800 nm were obtained by calcinations of the as-prepared fibers. The fibers were characterized by SEM, TG-DTA, FTIR, XPS and XRD. The results showed that with the increase of the concentration of spinning solution, the diameter of fibers also increased, and that the diameter of fibers decreased with the increase of the applied voltage and calcination temperature. The uncrystalline Al2O3,γ-Al2O3and α-Al2O3were obtained after calcinations of about 5 h at 450, 900 and 1100°C, respectively. © 2011, SAGE Publications. All rights reserved