Computer Sciences Commons^™

On Privacy Of Encrypted Speech Communications, Ye Zhu, Yuanchao Lu, Anil Vikram

Electrical and Computer Engineering Faculty Publications

Silence suppression, an essential feature of speech communications over the Internet, saves bandwidth by disabling voice packet transmissions when silence is detected. However, silence suppression enables an adversary to recover talk patterns from packet timing. In this paper, we investigate privacy leakage through the silence suppression feature. More specifically, we propose a new class of traffic analysis attacks to encrypted speech communications with the goal of detecting speakers of encrypted speech communications. These attacks are based on packet timing information only and the attacks can detect speakers of speech communications made with different codecs. We evaluate the proposed attacks with …

K-Partite Graph Reinforcement And Its Application In Multimedia Information Retrieval, Yue Gao, Meng Wang, Rongrong Ji, Zheng-Jun Zha, Jialie Shen

Research Collection School Of Computing and Information Systems

In many example-based information retrieval tasks, example query actually contains multiple sub-queries. For example, in 3D object retrieval, the query is an object described by multiple views. In content-based video retrieval, the query is a video clip that contains multiple frames. Without prior knowledge, the most intuitive approach is to treat the sub-queries equally without difference. In this paper, we propose a k-partite graph reinforcement approach to fuse these sub-queries based on the to-be-retrieved database. The approach first collects the top retrieved results. These results are regarded as pseudo-relevant samples and then a k-partite graph reinforcement is performed on these …

Identifying Event-Related Bursts Via Social Media Activities, Xin Zhao, Baihan Shu, Jing Jiang, Yang Song, Hongfei Yan, Xiaoming Li

Research Collection School Of Computing and Information Systems

Activities on social media increase at a dramatic rate. When an external event happens, there is a surge in the degree of activities related to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to identify event-related bursts via social media activities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can capture the state context. The experiments …

Lagrangian Relaxation Techniques For Scalable Spatial Conservation Planning, Akshat Kumar, Xiaojian Wu, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

We address the problem of spatial conservation planning in which the goal is to maximize the expected spread of cascades of an endangered species by strategically purchasing land parcels within a given budget. This problem can be solved by standard integer programming methods using the sample average approximation (SAA) scheme. Our main contribution lies in exploiting the separable structure present in this problem and using Lagrangian relaxation techniques to gain scalability over the flat representation. We also generalize the approach to allow the application of the SAA scheme to a range of stochastic optimization problems. Our iterative approach is highly …

Exact Soft Confidence-Weighted Learning, Jialei Wang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the art algorithms (including AROW, NAROW and NHERD), we found that SCW generally achieves better or at least comparable predictive …

Online Kernel Selection: Algorithms And Evaluations, Tianbao Yang, Mehrdad Mahdavi, Rong Jin, Jinfeng Yi, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational …

A Model Checker For Hierarchical Probabilistic Real-Time Systems, Songzheng Song, Jun Sun, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

Real-life systems are usually hard to control, due to their complicated structures, quantitative time factors and even stochastic behaviors. In this work, we present a model checker to analyze hierarchical probabilistic real-time systems. A modeling language called PRTS is used to specify such systems, and automatic zone-abstraction approach, which is probability preserving, is used to generate finite state MDP. We have implemented PRTS in model checking framework PAT so that friendly user interface can be used to edit, simulate and verify PRTS models. Some experiments are conducted to show our tool’s efficiency.

Parameter Synthesis For Hierarchical Concurrent Real-Time Systems, Étienne André, Yang Liu, Jun Sun, Jin Song Dong

Research Collection School Of Computing and Information Systems

Modeling and verifying complex real-time systems, involving timing delays, are notoriously difficult problems. Checking the correctness of a system for one particular value for each delay does not give any information for other values. It is hence interesting to reason parametrically, by considering that the delays are parameters (unknown constants) and synthesize a constraint guaranteeing a correct behavior. We present here Parametric Stateful Timed CSP, a language capable of specifying hierarchical real-time systems with complex data structures. Although we prove that the synthesis is undecidable in general, we present an algorithm for efficient parameter synthesis that behaves well in practice.

Translating Pddl Into Csp# - The Pat Approach, Yi Li, Jing Sun, Jin Song Dong, Yang Liu, Jun Sun

Research Collection School Of Computing and Information Systems

Model checking provides a way to automatically verify hardware and software systems, whereas the goal of planning is to produce a sequence of actions that leads from the initial state to the desired goal state. Recently research indicates that there is a strong connection between model checking and planning problem solving. In this paper, we investigate the feasibility of using a newly developed model checking framework, Process Analysis Toolkit (PAT), to serve as a planning solution provider for upper layer applications. We first carried out a number of experiments on different planning tools in order to compare their performance and …

Mydeal: The Context-Aware Urban Shopping Assistant, Kartik Muralidharan, Swapna Gottipati, Jing Jiang, Narayan Ramasubbu, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

A common problem in large Urban cities, of the sort seen in Asia, is the huge number of retail options available in the city. In particular, it is not uncommon to find multiple malls, each with hundreds of stores inside, just a short distance from each other in almost every part of these cities. These factors make it incredibly hard for consumers to identify stores of interest to them in any particular mall.In response, a number of shopping assistance applications have been created for mobile phones.However, these applications mostly just allow users to know which stores are where or to …

Finding Bursty Topics From Microblogs, Qiming Diao, Jing Jiang, Feida Zhu, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find topics that have bursty patterns on microblogs, we propose a topic model that simultaneousy captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) …

Decision Support For Agent Populations In Uncertain And Congested Environments, Pradeep Reddy Varakantham, Shih-Fen Cheng, Geoff Gordon, Asrar Ahmed

Research Collection School Of Computing and Information Systems

This research is motivated by large scale problems in urban transportation and labor mobility where there is congestion for resources and uncertainty in movement. In such domains, even though the individual agents do not have an identity of their own and do not explicitly interact with other agents, they effect other agents. While there has been much research in handling such implicit effects, it has primarily assumed deterministic movements of agents. We address the issue of decision support for individual agents that are identical and have involuntary movements in dynamic environments. For instance, in a taxi fleet serving a city, …

Logistics Orchestration Modeling And Evaluation For Humanitarian Relief, Hoong Chuin Lau, Zhengping Li, Xin Du, Heng Jiang, Robert De Souza

Research Collection School Of Computing and Information Systems

This paper proposes an orchestration model for post-disaster response that is aimed at automating the coordination of scarce resources that minimizes the loss of human lives. In our setting, different teams are treated as agents and their activities are "orchestrated" to optimize rescue performance. Results from simulation are analysed to evaluate the performance of the optimization model.

Accelerating Learning With Distance Education And Open Courseware, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

What will happen if academia disrupts traditional teaching methods by providing students with online and free academic materials?

Using Computers To Aid In Successful Trading And Investing, Spencer Davis

All Student Theses and Dissertations

In today’s world of high frequency trading and volatile markets, it is next to impossible for the average investor to make good decisions about where to put their money and for how long. There is a multitude of “hype” from many different directions including, television, magazine ads, internet searches, and emails. These companies usually sell computer programs to take the guess work out of it and do the thinking for you. They show successful results and it seems very promising. Unfortunately, most of these programs tend to come up short on their promises over the long run by having dismal …

Adaptive Cgf For Pilots Training In Air Combat Simulation, Teck-Hou Teng, Ah-Hwee Tan, Wee-Sze Ong, Kien-Lip Lee

Research Collection School Of Computing and Information Systems

Training of combat fighter pilots is often conducted using either human opponents or non-adaptive computer-generated force (CGF) inserted with the doctrine for conducting air combat mission. The novelty and challenges of such non-adaptive doctrine-driven CGF is often lost quickly. Incorporating more complex knowledge manually is known to be tedious and time-consuming. Therefore, a study of using adaptive CGF to learn from the real-time interactions with human pilots to extend the existing doctrine is conducted in this work. The goal of this study is to show how an adaptive CGF can be more effective than a non-adaptive doctrine-driven CGF for simulator-based …

On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied in some real datasets, leading to poor performance when the assumption does not hold. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a …

Fast Bounded Online Gradient Descent Algorithms For Scalable Kernel-Based Online Learning, Peilin Zhao, Jialei Wang, Pengcheng Wu, Rong Jin, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem of bounded kernel-based online learning that aims to constrain the number of support vectors by a predefined budget. Although several algorithms have been proposed in literature, they are neither computationally efficient due to their intensive budget maintenance strategy nor effective due to the use of simple Perceptron algorithm. To overcome these limitations, we propose a …

Serious Game Design Principles: The Impact Of Game Design On Learning Outcomes, Michael W. Martin

Computational Modeling & Simulation Engineering Theses & Dissertations

This dissertation examines the research question "How do video game design principles affect learning outcomes in serious games?" This research first develops a theoretical foundation concerning the meaning of the terms "game" and "serious game". This conceptual clarification is broken down into analytic propositions, which state that games have participants, rules, goals and challenges, and synthetic propositions, which state that the games should be intrinsically compelling, provide meaningful choices, and be self encapsulated. Based on these synthetic propositions, three hypotheses were developed. The hypotheses are that games with an enhanced aesthetic presentation, more meaningful choices, or provide player competition will …

Incorporating Memory And Learning Mechanisms Into Meta-Raps, Arif Arin

Engineering Management & Systems Engineering Theses & Dissertations

Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics.

Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning …

Visualizing Digital Collections At Archive-It, Kalpesh Padia

Computer Science Theses & Dissertations

Archive-It, a subscription service from the Internet Archive, allows users to create,maintain, and view digital collections of web resources. The current interface of Archive-It is largely text-based, supporting drill-down navigation using lists of URIs.While this interface provides good searching capabilities, it is not efficient for browsing. In the absence of keywords, a user has to spend large amount of time trying to locate a web page of interest. In order to provide a better visual experience to the user, we have studied the underlying characteristics of Archive-It collections and implemented six different visualizations (treemap, time cloud, bubble chart, image plot, …

Topic Discovery From Tweet Replies, Bingtian Dai, Ee Peng Lim, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

Twitter is a popular online social information network service which allows people to read and post messages up to 140 characters, known as “tweets”. In this paper, we focus on the tweets between pairs of individuals, i.e., the tweet replies, and propose a generative model to discover topics among groups of twitter users. Our model has then been evaluated with a tweet dataset to show its effectiveness.

Enhancing Access Privacy Of Range Retrievals Over B+Trees, Hwee Hwa Pang, Jilian Zhang, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

Users of databases that are hosted on shared servers cannot take for granted that their queries will not be disclosed to unauthorized parties. Even if the database is encrypted, an adversary who is monitoring the I/O activity on the server may still be able to infer some information about a user query. For the particular case of a B+-tree that has its nodes encrypted, we identify properties that enable the ordering among the leaf nodes to be deduced. These properties allow us to construct adversarial algorithms to recover the B+-tree structure from the I/O traces generated by range queries. Combining …

Joint Learning For Coreference Resolution With Markov Logic, Yang Song, Jing Jiang, Xin Zhao, Sujian Li, Houfeng Wang

Research Collection School Of Computing and Information Systems

Pairwise coreference resolution models must merge pairwise coreference decisions to generate final outputs. Traditional merging methods adopt different strategies such as the best first method and enforcing the transitivity constraint, but most of these methods are used independently of the pairwise learning methods as an isolated inference procedure at the end. We propose a joint learning model which combines pairwise classification and mention clustering with Markov logic. Experimental results show that our joint learning system outperforms independent learning systems. Our system gives a better performance than all the learning-based systems from the CoNLL-2011 shared task on the same dataset. Compared …

A Pollution Attack To Public-Key Watermarking Schemes, Yongdong Wu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Public-key watermarking schemes are required to possess two desirable properties: allowing everyone to determine whether a watermark exists in an image or not and ensuring high detection probability in case of malicious modification. In this paper we propose an attack which pollutes the watermark embedded in an image with an optimal colored noise so as to fool the detector of the underlying public-key watermarking scheme. We further show how to apply the proposed pollution attack to public-key subspace watermarking schemes to generate pirated images of high quality but of low detection probability. Our experiment results demonstrate that the proposed pollution …

An Economic Analysis Of The Online Counterfeit Market And The Impact Of Anti-Counterfeit Technology, Xiong Zhang, Zhiling Guo, Wei Thoo Yue

Research Collection School Of Computing and Information Systems

Counterfeiting causes hundreds of billions dollars of losses around the world every year. Due to the growing prominence of online commerce, the seriousness of the situation could soon become much worse. Hence, reaching a clear understanding of the fundamental economic incentives behind this practice is of vital importance. In this paper, we investigate a problem within which a firm selling a counterfeit product engages in price competition with a firm that sells an authentic product to a population of heterogeneous consumers. An online intermediary acts as the facilitator of both firms’ transactions and may consequently be liable for any counterfeit …

Detecting Anomalous Twitter Users By Extreme Group Behaviors, Hanbo Dai, Ee-Peng Lim, Feida Zhu, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Twitter has enjoyed tremendous popularity in the recent years. To help categorizing and search tweets, Twitter users assign hashtags to their tweets. Given that hashtag assignment is the primary way to semantically categorizing and search tweets, it is highly susceptible to abuse by spammers and other anomalous users [1]. Popular hashtags such as #Obama and #ladygaga could be hijacked by having them added to unrelated tweets with the intent of misleading many other users or promoting specific agenda to the users. The users performing this act are known as the hashtag hijackers. As the hijackers usually abuse common sets of …

Formal Analysis Of Pervasive Computing Systems, Yan Liu, Xian Zhang, Jin Song Dong, Yang Liu, Jun Sun, Jit Biswas, Mounir Mokhtari

Research Collection School Of Computing and Information Systems

Pervasive computing systems are heterogenous and complex as they usually involve human activities, various sensors and actuators as well as middleware for system controlling. Therefore, analyzing such systems is highly nontrivial. In this work, we propose to use formal methods for analyzing pervasive computing systems. Firstly, a formal modeling framework is proposed to cover the main characteristics of pervasive computing systems (e.g., context-awareness, concurrent communications, layered architectures). Secondly, we identify the safety requirements (e.g., free of deadlocks and conflicts etc.) and propose their specifications as safety and liveness properties. Finally, we demonstrate our ideas using a case study of a …

Information-Theoretic Multi-View Domain Adaptation, Pei Yang, Wei Gao, Qi Tan, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

We use multiple views for cross-domain document classification. The main idea is to strengthen the views’ consistency for target data with source training data by identifying the correlations of domain-specific features from different domains. We present an Information-theoretic Multi-view Adaptation Model (IMAM) based on a multi-way clustering scheme, where word and link clusters can draw together seemingly unrelated domain-specific features from both sides and iteratively boost the consistency between document clusterings based on word and link views. Experiments show that IMAM significantly outperforms state-of-the-art baselines.

Identifying And Attributing Similar Traces With Greatest Common Factor Analysis, Fred Cohen

Journal of Digital Forensics, Security and Law

This paper presents an algorithm for comparing large numbers of traces to each other and identifying and presenting groups of traces with similar features. It is applied to forensic analysis in which groups of similar traces are automatically identified and presented so that attribution and other related claims may be asserted, and independently confirmed or refuted. The approach of this paper is to identify an approximate algorithm that will find a large subset of greatest common factor similar groups of arbitrary factors in far less time and space than an exact algorithm using examiner-provided selection criteria for factor definition.