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Articles 2461 - 2490 of 4271
Full-Text Articles in Entire DC Network
Clustering Classes In Packages For Program Comprehension, Xiaobing Sun, Xiangyue Liu, Bin Li, Bixin Li, David Lo, Lingzhi Liao
Clustering Classes In Packages For Program Comprehension, Xiaobing Sun, Xiangyue Liu, Bin Li, Bixin Li, David Lo, Lingzhi Liao
Research Collection School Of Computing and Information Systems
During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately. With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity. Given a target system for comprehension, developers may first focus on the package comprehension. The packages in the system are of different sizes. For small-sized packages in the system, developers can easily comprehend them. However, for large-sized packages, they are difficult to understand. In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized …
What You See Is Not What You Get: Leakage-Resilient Password Entry Schemes For Smart Glasses, Yan Li, Yao Cheng, Yingjiu Li, Robert H. Deng
What You See Is Not What You Get: Leakage-Resilient Password Entry Schemes For Smart Glasses, Yan Li, Yao Cheng, Yingjiu Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
Smart glasses are becoming popular for users to access various services such as email. To protect these services, password-based user authentication is widely used. Unfortunately, the password based user authentication has inherent vulnerability against password leakage. Many efforts have been put on designing leakage resilient password entry schemes on PCs and mobile phones with traditional input equipment including keyboards and touch screens. However, such traditional input equipment is not available on smart glasses. Existing password entry on smart glasses relies on additional PCs or mobile devices. Such solutions force users to switch between different systems, which causes interrupted experience and …
Vulnerabilities, Attacks, And Countermeasures In Balise-Based Train Control Systems, Yongdong Wu, Jian Weng, Zhe Tang, Xin Li, Robert H. Deng
Vulnerabilities, Attacks, And Countermeasures In Balise-Based Train Control Systems, Yongdong Wu, Jian Weng, Zhe Tang, Xin Li, Robert H. Deng
Research Collection School Of Computing and Information Systems
In modern rail transport systems, balises are widely used to exchange track-train information via air-gap interface. In this paper, we first present the vulnerabilities on the standard balise air-gap interface, and then conduct vulnerability simulations using the system parameters that were specified in the European Train Control System. The simulation results show that the vulnerabilities can be exploited to launch effective and practical attacks, which could lead to catastrophic consequences, such as train derailment or collision. To mitigate the vulnerabilities and attacks, we propose to implement a challenge-response authentication process in the air-gap interface in the existing transport infrastructure.
A Secure And Efficient Id-Based Aggregate Signature Scheme For Wireless Sensor Networks, Limin Shen, Jianfeng Ma, Ximeng Liu, Fushan Wei, Meixia Miao
A Secure And Efficient Id-Based Aggregate Signature Scheme For Wireless Sensor Networks, Limin Shen, Jianfeng Ma, Ximeng Liu, Fushan Wei, Meixia Miao
Research Collection School Of Computing and Information Systems
Affording secure and efficient big data aggregation methods is very attractive in the field of wireless sensor networks (WSNs) research. In real settings, the WSNs have been broadly applied, such as target tracking and environment remote monitoring. However, data can be easily compromised by a vast of attacks, such as data interception and data tampering, etc. In this paper, we mainly focus on data integrity protection, give an identity-based aggregate signature (IBAS) scheme with a designated verifier for WSNs. According to the advantage of aggregate signatures, our scheme not only can keep data integrity, but also can reduce bandwidth and …
I Would Not Plant Apple Trees If The World Will Be Wiped: Analyzing Hundreds Of Millions Of Behavioral Records Of Players During An Mmorpg Beta Test, Qatar Computing Research Institute, The State University Of New York University At Buffalo, Haewoon Kwak, Korea University
I Would Not Plant Apple Trees If The World Will Be Wiped: Analyzing Hundreds Of Millions Of Behavioral Records Of Players During An Mmorpg Beta Test, Qatar Computing Research Institute, The State University Of New York University At Buffalo, Haewoon Kwak, Korea University
Research Collection School Of Computing and Information Systems
In this work, we use player behavior during the closed beta test of the MMORPG ArcheAge as a proxy for an extreme situation: at the end of the closed beta test, all user data is deleted, and thus, the outcome (or penalty) of players' in-game behaviors in the last few days loses its meaning. We analyzed 270 million records of player behavior in the 4th closed beta test of ArcheAge. Our findings show that there are no apparent pandemic behavior changes, but some outlierswere more likely to exhibit anti-social behavior (e.g., player killing). We also found that contrary to the …
Probabilistic Public Key Encryption For Controlled Equijoin In Relational Databases, Yujue Wang, Hwee Hwa Pang
Probabilistic Public Key Encryption For Controlled Equijoin In Relational Databases, Yujue Wang, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
We present a public key encryption scheme for relational databases (PKDE) that allows the owner to control the execution of cross-relation joins on an outsourced server. The scheme allows anyone to deposit encrypted records in a database on the server. Thereafter, the database owner may authorize the server to join any two relations to identify matching records across them, while preventing self-joins that would reveal information on records that are unmatched in the join. The security of our construction is formally proved in the random oracle model based on the computational bilinear Diffie-Hellman assumption. Specifically, before a relation is joined, …
Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen
Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen
Research Collection School Of Computing and Information Systems
Given two object sets Q and O , a metric similarity join finds similar object pairs according to a certain criterion. This operation has a wide variety of applications in data cleaning, data mining, to name but a few. However, the rapidly growing volume of data nowadays challenges traditional metric similarity join methods, and thus, a distributed method is required. In this paper, we adopt a popular distributed framework, namely, MapReduce, to support scalable metric similarity joins. To ensure the load balancing, we present two sampling based partition methods. One utilizes the pivot and the space-filling curve mappings to cluster …
Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi
Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi
Research Collection School Of Computing and Information Systems
Finding components with high connectivity is an important problem in component detection with a wide range of applications, e.g., social network analysis, web-page research and bioinformatics. In particular, k-edge connected component (k-ECC) has recently been extensively studied to discover disjoint components. Yet many real applications present needs and challenges for overlapping components. In this paper, we propose a k-vertex connected component (k-VCC) model, which is much more cohesive and therefore allows overlapping between components. To find k-VCCs, a top-down framework is first developed to find the exact k-VCCs. To further reduce the high computational cost for input networks of large …
Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang
Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang
Research Collection School Of Computing and Information Systems
The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between discrete trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering the pair of most similar subtrajectories, be them parts of the same or of different input trajectories.The identified pair of subtrajectories is called a motif.The adoption of DFD as the similarity measure in motif discovery,although semantically ideal, is hindered by the high computational complexity of DFD calculation. In this paper, we propose a suite …
Ui X-Ray: Interactive Mobile Ui Testing Based On Computer Vision, Chun-Fu Richard Chen, Marco Pistoia, Conglei Shi, Paolo Girolami, Joseph W. Ligman, Yong Wang
Ui X-Ray: Interactive Mobile Ui Testing Based On Computer Vision, Chun-Fu Richard Chen, Marco Pistoia, Conglei Shi, Paolo Girolami, Joseph W. Ligman, Yong Wang
Research Collection School Of Computing and Information Systems
User Interface/eXperience (UI/UX) significantly affects the lifetime of any software program, particularly mobile apps. A bad UX can undermine the success of a mobile app even if that app enables sophisticated capabilities. A good UX, however, needs to be supported of a highly functional and user friendly UI design. In spite of the importance of building mobile apps based on solid UI designs, UI discrepancies- inconsistencies between UI design and implementation-Are among the most numerous and expensive defects encountered during testing. This paper presents UI X-RAY, an interactive UI testing system that integrates computer-vision methods to facilitate the correction of …
Collective Multiagent Sequential Decision Making Under Uncertainty, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Collective Multiagent Sequential Decision Making Under Uncertainty, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Multiagent sequential decision making has seen rapid progress with formal models such as decentralized MDPs and POMDPs. However, scalability to large multiagent systems and applicability to real world problems remain limited. To address these challenges, we study multiagent planning problems where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our work exploits recent advances in graphical models for modeling and inference with a population of individuals such as collective graphical models and the notion of finite partial exchangeability in lifted inference. We develop a collective decentralized MDP model where policies can be computed based …
Empath-D: Empathetic Design For Accessibility, Kenny Tsu Wei Choo, Rajesh Krishna Balan, Kiat Wee Tan, Archan Misra, Youngki Lee
Empath-D: Empathetic Design For Accessibility, Kenny Tsu Wei Choo, Rajesh Krishna Balan, Kiat Wee Tan, Archan Misra, Youngki Lee
Research Collection School Of Computing and Information Systems
We describe our vision for Empath-D, our system to enable Empathetic User Interface Design. Our key idea is to leverage Virtual and Augmented Reality (VR / AR) displays to provide an Immersive Reality environment, where developers/designers can emulate impaired interactions by elderly or disabled users while testing the usability of their applications. Our early experiences with the Empath-D prototype show that Empath-D can emulate a cataract vision impairment of the elderly and guide designers to create accessible web pages with less mental workload.
Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang
Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for …
Decentralized Planning In Stochastic Environments With Submodular Rewards, Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar
Decentralized Planning In Stochastic Environments With Submodular Rewards, Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar
Research Collection School Of Computing and Information Systems
Decentralized Markov Decision Process (Dec-MDP) providesa rich framework to represent cooperative decentralizedand stochastic planning problems under transition uncertainty.However, solving a Dec-MDP to generate coordinatedyet decentralized policies is NEXP-Hard. Researchershave made significant progress in providing approximate approachesto improve scalability with respect to number ofagents. However, there has been little or no research devotedto finding guarantees on solution quality for approximateapproaches considering multiple (more than 2 agents)agents. We have a similar situation with respect to the competitivedecentralized planning problem and the StochasticGame (SG) model. To address this, we identify models in thecooperative and competitive case that rely on submodular rewards,where we show …
Streaming Classification With Emerging New Class By Class Matrix Sketching, Xin Mu, Feida Zhu, Juan Du, Ee-Peng Lim, Zhi-Hua Zhou
Streaming Classification With Emerging New Class By Class Matrix Sketching, Xin Mu, Feida Zhu, Juan Du, Ee-Peng Lim, Zhi-Hua Zhou
Research Collection School Of Computing and Information Systems
Streaming classification with emerging new class is an important problem of great research challenge and practical value. In many real applications, the task often needs to handle large matrices issues such as textual data in the bag-of-words model and large-scale image analysis. However, the methodologies and approaches adopted by the existing solutions, most of which involve massive distance calculation, have so far fallen short of successfully addressing a real-time requested task. In this paper, the proposed method dynamically maintains two low-dimensional matrix sketches to 1) detect emerging new classes; 2) classify known classes; and 3) update the model in the …
Crowdsensing And Analyzing Micro-Event Tweets For Public Transportation Insights, Thoong Hoang, Pei Hua (Xu Peihua) Cher, Philips Kokoh Prasetyo, Ee-Peng Lim
Crowdsensing And Analyzing Micro-Event Tweets For Public Transportation Insights, Thoong Hoang, Pei Hua (Xu Peihua) Cher, Philips Kokoh Prasetyo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Efficient and commuter friendly public transportation system is a critical part of a thriving and sustainable city. As cities experience fast growing resident population, their public transportation systems will have to cope with more demands for improvements. In this paper, we propose a crowdsensing and analysis framework to gather and analyze realtime commuter feedback from Twitter. We perform a series of text mining tasks identifying those feedback comments capturing bus related micro-events; extracting relevant entities; and, predicting event and sentiment labels. We conduct a series of experiments involving more than 14K labeled tweets. The experiments show that incorporating domain knowledge …
Dynamic Repositioning To Reduce Lost Demand In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Dynamic Repositioning To Reduce Lost Demand In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Research Collection School Of Computing and Information Systems
Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing …
An Efficient Approach To Model-Based Hierarchical Reinforcement Learning, Zhuoru Li, Akshay Narayan, Tze-Yun Leong
An Efficient Approach To Model-Based Hierarchical Reinforcement Learning, Zhuoru Li, Akshay Narayan, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the stateof-the-art algorithms, and scales well in very large problems.
Optimizing Expectation With Guarantees In Pomdps, Krishnendu Chatterjee, Guillermo A. Perez, Jean-François Raskin, Dorde Zikelic
Optimizing Expectation With Guarantees In Pomdps, Krishnendu Chatterjee, Guillermo A. Perez, Jean-François Raskin, Dorde Zikelic
Research Collection School Of Computing and Information Systems
A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has been a surge of interest in POMDPs where the goal is to maximize the probability to ensure that the payoff is at least a given threshold, but these approaches do not consider any optimization beyond satisfying this threshold constraint. In this work we go beyond both the "expectation" and "threshold" approaches and consider a "guaranteed payoff optimization (GPO)" problem …
Toward A Collaborative Ai Framework For Assistive Dementia Care, Tze-Yun Leong
Toward A Collaborative Ai Framework For Assistive Dementia Care, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
We envision an integrated framework for supporting the development and deployment of human-aware, general artificial intelligence (AI) that needs to collaborate in uncertain, changing environments. We examine the technology and system requirements of building assistive care agents for dementia or cognitive impaired patients through the continuum of care. We summarize the new AI capabilities and show examples of how an evolving, adaptive development approach would be able to support the basic functionalities and applications in a sound, practical, and scalable manner. We highlight the challenges and the opportunities involved in realizing the proposed framework, and call for future research and …
Safestack+: Enhanced Dual Stack To Combat Data-Flow Hijacking, Yan Lin, Xiaoxiao Tang, Debin Gao
Safestack+: Enhanced Dual Stack To Combat Data-Flow Hijacking, Yan Lin, Xiaoxiao Tang, Debin Gao
Research Collection School Of Computing and Information Systems
SafeStack, initially proposed as a key component of Code Pointer Integrity (CPI), separates the program stack into two distinct regions to provide a safe region for sensitive code pointers. SafeStack can prevent buffer overflow attacks that overwrite sensitive code pointers, e.g., return addresses, to hijack control flow of the program, and has been incorporated into the Clang project of LLVM as a C-based language front-end. In this paper, we propose and implement SafeStack+, an enhanced dual stack LLVM plug-in that further protects programs from data-flow hijacking. SafeStack+ locates data flow sensitive variables on the unsafe stack that could potentially affect …
Exploring Representativeness And Informativeness For Active Learning, Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Wei Liu, Jialie Shen, Dacheng Tao
Exploring Representativeness And Informativeness For Active Learning, Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Wei Liu, Jialie Shen, Dacheng Tao
Research Collection School Of Computing and Information Systems
How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without …
Universally Composable Rfid Mutual Authentication, Chunhua Su, Bagus Santoso, Yingjiu Li, Robert H. Deng, Xinyi Huang
Universally Composable Rfid Mutual Authentication, Chunhua Su, Bagus Santoso, Yingjiu Li, Robert H. Deng, Xinyi Huang
Research Collection School Of Computing and Information Systems
Universally Composable (UC) framework provides the strongest security notion for designing fully trusted cryptographic protocols, and it is very challenging on applying UC security in the design of RFID mutual authentication protocols. In this paper, we formulate the necessary conditions for achieving UC secure RFID mutual authentication protocols which can be fully trusted in arbitrary environment, and indicate the inadequacy of some existing schemes under the UC framework. We define the ideal functionality for RFID mutual authentication and propose the first UC secure RFID mutual authentication protocol based on public key encryption and certain trusted third parties which can be …
Online Hacker Forum Censorship: Would Banning The Bad Guys Attract Good Guys?, Qiu-Hong Wang, Le-Ting Zhang, Meng-Ke Qiao
Online Hacker Forum Censorship: Would Banning The Bad Guys Attract Good Guys?, Qiu-Hong Wang, Le-Ting Zhang, Meng-Ke Qiao
Research Collection School Of Computing and Information Systems
To tackle the ubiquitous cybersecurity threats, a few countries have enacted legislation to criminalize the production, distribution and possession of computer misuse tools. Consequently, online hacker forums, which enable the provision and dissemination of malicious cyber-attack techniques among potential hackers or technology-savvy users, are subject to censorship. This project examines the mixed impacts of online hacker forum censorship on users’ contribution to protection discussion through a natural experiment with large-scale content analysis. We find that while the enforcement indeed reduced the discussion on malicious cyber-attacks, the discussion on cybersecurity protection could increase or decrease in different scenarios. The rationale is …
Discovering Historic Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis
Discovering Historic Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Historic traffic information is valuable in transportation analysis and planning, e.g., evaluating the reliability of routes for representative source-destination pairs. Also, it can be utilized to provide efficient and effective route-search services. In view of these applications, we propose the k traffic-tolerant paths (TTP) problem on road networks, which takes a source-destination pair and historic traffic information as input, and returns k paths that minimize the aggregate (historic) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to find. First, we propose an exact algorithm with effective pruning …
High Impact Bug Report Identification With Imbalanced Learning Strategies, Xinli Yang, David Lo, Xin Xia, Qiao Huang, Jianling Sun
High Impact Bug Report Identification With Imbalanced Learning Strategies, Xinli Yang, David Lo, Xin Xia, Qiao Huang, Jianling Sun
Research Collection School Of Computing and Information Systems
In practice, some bugs have more impact than others and thus deserve more immediate attention. Due to tight schedule and limited human resources, developers may not have enough time to inspect all bugs. Thus, they often concentrate on bugs that are highly impactful. In the literature, high-impact bugs are used to refer to the bugs which appear at unexpected time or locations and bring more unexpected effects (i.e., surprise bugs), or break pre-existing functionalities and destroy the user experience (i.e., breakage bugs). Unfortunately, identifying high-impact bugs from thousands of bug reports in a bug tracking system is not an easy …
Attribute-Based Storage Supporting Secure Deduplication Of Encrypted Data In Cloud, Hui Cui, Robert H. Deng, Yingjiu Li, Guowei Wu
Attribute-Based Storage Supporting Secure Deduplication Of Encrypted Data In Cloud, Hui Cui, Robert H. Deng, Yingjiu Li, Guowei Wu
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/herencrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However,the standard ABE system does not support secure deduplication, which is crucial for eliminating duplicate copies of identical data inorder to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with securededuplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages thestorage. Compared with the prior data deduplication systems, our …
A Two-Sided Collaborative Transparent Display Supporting Workspace Awareness, Jiannan Li, Saul Greenberg, Ehud Sharlin
A Two-Sided Collaborative Transparent Display Supporting Workspace Awareness, Jiannan Li, Saul Greenberg, Ehud Sharlin
Research Collection School Of Computing and Information Systems
Transparent displays naturally support workspace awareness during face-to-face interactions. Viewers see another person’s actions through the display: gestures, gaze, body movements, and what one is manipulating on the display. Yet we can design even better collaborative transparent displays. First, collaborators on either side should be able to directly interact with workspace objects. Second, and more controversially, both sides should be capable of presenting different content. This affords: reversal of images/text in place (so that people on both sides see objects correctly); personal and private territories aligned atop each other; and GUI objects that provide different visuals for feedthrough vs. feedback. …
Rfid Ownership Transfer With Positive Secrecy Capacity Channels, Jorge Munilla, Mike Burmester, Alberto Peinado, Guomin Yang, Willy Susilo
Rfid Ownership Transfer With Positive Secrecy Capacity Channels, Jorge Munilla, Mike Burmester, Alberto Peinado, Guomin Yang, Willy Susilo
Research Collection School Of Computing and Information Systems
RFID ownership transfer protocols (OTPs) transfer tag ownership rights. Recently, there has been considerable interest in such protocols; however, guaranteeing privacy for symmetric-key settings without trusted third parties (TTPs) is a challenge still unresolved. In this paper, we address this issue and show that it can be solved by using channels with positive secrecy capacity. We implement these channels with noisy tags and provide practical values, thus proving that perfect secrecy is theoretically possible. We then define a communication model that captures spatiotemporal events and describe a first example of symmetric-key based OTP that: (i) is formally secure in the …
Concept-Based Interactive Search System, Yi-Jie Lu, Phuong Anh Nguyen, Hao Zhang, Chong-Wah Ngo
Concept-Based Interactive Search System, Yi-Jie Lu, Phuong Anh Nguyen, Hao Zhang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Our successful multimedia event detection system at TREC-VID 2015 showed its strength on handling complex concepts in a query. The system was based on a large number of pre-trained concept detectors for textual-to-visual relation. In this paper, we enhance the system by enabling human-in-the-loop. In order to facilitate a user to quickly find an information need, we incorporate concept screening, video reranking by highlighted concepts, relevance feedback and color sketch to refine a coarse retrieval result. The aim is to eventually come up with a system suitable for both Ad-hoc Video Search and Known-Item Search. In addition, as the increasing …