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Articles 5161 - 5190 of 9025

Full-Text Articles in Computer Sciences

Related-Key Secure Key Encapsulation From Extended Computational Bilinear Diffie–Hellman, Brandon Qin, Shengli Liu, Shifeng Sun, Robert H. Deng, Dawu Gu Apr 2017

Related-Key Secure Key Encapsulation From Extended Computational Bilinear Diffie–Hellman, Brandon Qin, Shengli Liu, Shifeng Sun, Robert H. Deng, Dawu Gu

Research Collection School Of Computing and Information Systems

As a special type of fault injection attacks, Related-Key Attacks (RKAs) allow an adversary to manipulate a cryptographic key and subsequently observe the outcomes of the cryptographic scheme under these modified keys. In the real life, related-key attacks are already practical enough to be implemented on cryptographic devices. To avoid cryptographic devices suffering from related-key attacks, it is necessary to design a cryptographic scheme that resists against such attacks. This paper proposes an efficient RKA-secure Key Encapsulation Mechanism (KEM), in which the adversary can modify the secret key sk to any value f(sk), as long as, f is a polynomial …

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On The Effectiveness Of Virtualization Based Memory Isolation On Multicore Platforms, Siqi Zhao, Xuhua Ding Apr 2017

On The Effectiveness Of Virtualization Based Memory Isolation On Multicore Platforms, Siqi Zhao, Xuhua Ding

Research Collection School Of Computing and Information Systems

Virtualization based memory isolation has beenwidely used as a security primitive in many security systems.This paper firstly provides an in-depth analysis of itseffectiveness in the multicore setting; a first in the literature.Our study reveals that memory isolation by itself is inadequatefor security. Due to the fundamental design choices inhardware, it faces several challenging issues including pagetable maintenance, address mapping validation and threadidentification. As demonstrated by our attacks implementedon XMHF and BitVisor, these issues undermine the security ofmemory isolation. Next, we propose a new isolation approachthat is immune to the aforementioned problems. In our design,the hypervisor constructs a fully isolated micro …

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Collective Entity Linking In Tweets Over Space And Time, Wen Haw Chong, Ee-Peng Lim, William Cohen Apr 2017

Collective Entity Linking In Tweets Over Space And Time, Wen Haw Chong, Ee-Peng Lim, William Cohen

Research Collection School Of Computing and Information Systems

We propose collective entity linking over tweets that are close in space and time. This exploits the fact that events or geographical points of interest often result in related entities being mentioned in spatio-temporal proximity. Our approach directly applies to geocoded tweets. Where geocoded tweets are overly sparse among all tweets, we use a relaxed version of spatial proximity which utilizes both geocoded and non-geocoded tweets linked by common mentions. Entity linking is affected by noisy mentions extracted and incomplete knowledge bases. Moreover, to perform evaluation on the entity linking results, much manual annotation of mentions is often required. To …

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Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim Apr 2017

Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content …

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Comparative Relation Generative Model, Maksim Tkachenko, Hady W. Lauw Apr 2017

Comparative Relation Generative Model, Maksim Tkachenko, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Online reviews are important decision aids to consumers. Other than helping users to evaluate individual products, reviews also support comparison shopping by comparing two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of authors. Therefore, we focus on comparative sentences, whereby two products are compared directly by a review author within a sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is three-fold: …

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Empirical Study Of Usage And Performance Of Java Collections, Diego Costa, Artur Andrzejak, Janos Seboek, David Lo Apr 2017

Empirical Study Of Usage And Performance Of Java Collections, Diego Costa, Artur Andrzejak, Janos Seboek, David Lo

Research Collection School Of Computing and Information Systems

Collection data structures have a major impact on the performance of applications, especially in languages such as Java, C#, or C++. This requires a developer to select an appropriate collection from a large set of possibilities, including different abstractions (e.g. list, map, set, queue), and multiple implementations. In Java, the default implementation of collections is provided by the standard Java Collection Framework (JCF). However, there exist a large variety of less known third-party collection libraries which can provide substantial performance benefits with minimal code changes.

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Understanding The Information-Based Transformation Of Strategy And Society, Eric K. Clemons, Rajiv M. Dewan, Robert J. Kauffman, Thomas A. Weber Apr 2017

Understanding The Information-Based Transformation Of Strategy And Society, Eric K. Clemons, Rajiv M. Dewan, Robert J. Kauffman, Thomas A. Weber

Research Collection School Of Computing and Information Systems

The world economy is undergoing dramatic changes, largely driven by the new availability of fine-grained information. Innovative ways of using data—large and small—have also prompted a rethinking of the boundaries for the combination and use of knowledge. The strategic design of information flows in the economy has the upside of higher economic rents and competitive advantage, as well as the downsides of wealth inequality and abuse of power. This has brought a wide range of regulatory challenges. To understand the nature of these sweeping changes, it is important to examine the new ways information is used, and how information flows …

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Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim Apr 2017

Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study …

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Clustering Classes In Packages For Program Comprehension, Xiaobing Sun, Xiangyue Liu, Bin Li, Bixin Li, David Lo, Lingzhi Liao Apr 2017

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 …

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Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao Apr 2017

Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao

Research Collection School Of Computing and Information Systems

Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 …

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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 Apr 2017

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 …

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Vulnerabilities, Attacks, And Countermeasures In Balise-Based Train Control Systems, Yongdong Wu, Jian Weng, Zhe Tang, Xin Li, Robert H. Deng Apr 2017

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.

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Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide Apr 2017

Discovering Anomalous Events From Urban Informatics Data, Kasthuri Jayarajah, Vigneshwaran Subbaraju, Dulanga Kaveesha Weerakoon Mudiyanselage, Archan Misra, La Thanh Tam, Noel Athaide

Research Collection School Of Computing and Information Systems

Singapore's "smart city" agenda is driving the government to provide public access to a broader variety of urban informatics sources, such as images from traffic cameras and information about buses servicing different bus stops. Such informatics data serves as probes of evolving conditions at different spatiotemporal scales. This paper explores how such multi-modal informatics data can be used to establish the normal operating conditions at different city locations, and then apply appropriate outlier-based analysis techniques to identify anomalous events at these selected locations. We will introduce the overall architecture of sociophysical analytics, where such infrastructural data sources can be combined …

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The Crowd Work Accessibility Problem, Saiganesh Swaminathan, Kotaro Hara, Jeffrey P. Bigham Apr 2017

The Crowd Work Accessibility Problem, Saiganesh Swaminathan, Kotaro Hara, Jeffrey P. Bigham

Research Collection School Of Computing and Information Systems

Crowd work is an increasingly prevalent and important kind of work. Because of its flexible nature, crowd work may offer benefits for people with disabilities. Unfortunately, people with disabilities currently lack access to much of this work because the tasks that are posted are often inaccessible. In this paper, we first characterize the accessibility of the tasks posted to a popular crowd marketplace, Amazon Mechanical Turk, by performing manual and automatic checks on 120 tasks from several common types. We then outline research directions that could have positive impact on this problem. Given ongoing and upcoming changes to the world …

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A Secure And Efficient Id-Based Aggregate Signature Scheme For Wireless Sensor Networks, Limin Shen, Jianfeng Ma, Ximeng Liu, Fushan Wei, Meixia Miao Apr 2017

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 …

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Finding Causality And Responsibility For Probabilistic Reverse Skyline Query Non-Answers [Extended Abstract], Yunjun Gao, Qing Liu, Gang Chen, Linlin Zhou, Baihua Zheng Apr 2017

Finding Causality And Responsibility For Probabilistic Reverse Skyline Query Non-Answers [Extended Abstract], Yunjun Gao, Qing Liu, Gang Chen, Linlin Zhou, Baihua Zheng

Research Collection School Of Computing and Information Systems

This paper explores the causality and responsibility problem (CRP) for the non-answers to probabilistic reverse skyline queries (PRSQ). Towards this, we propose an efficient algorithm called CP to compute the causality and responsibility for the non-answers to PRSQ. CP first finds candidate causes, and then, it performs verification to obtain actual causes with their responsibilities, during which several strategies are used to boost efficiency. Extensive experiments using both real and synthetic data sets demonstrate the effectiveness and efficiency of the presented algorithms.

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Online Growing Neural Gas For Anomaly Detection In Changing Surveillance Scenes, Qianru Sun, Hong Liu, Tatsuya Harada Apr 2017

Online Growing Neural Gas For Anomaly Detection In Changing Surveillance Scenes, Qianru Sun, Hong Liu, Tatsuya Harada

Research Collection School Of Computing and Information Systems

Anomaly detection is still a challenging task for video surveillance due to complex environments and unpredictable human behaviors. Most existing approaches train offline detectors using manually labeled data and predefined parameters, and are hard to model changing scenes. This paper introduces a neural network based model called online Growing Neural Gas (online GNG) to perform an unsupervised learning. Unlike a parameter-fixed GNG, our model updates learning parameters continuously, for which we propose several online neighbor-related strategies. Specific operations, namely neuron insertion, deletion, learning rate adaptation and stopping criteria selection, get upgraded to online modes. In the anomaly detection stage, the …

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Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua Apr 2017

Neural Collaborative Filtering, Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback.Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in …

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A Dynamic Programming Approach For Quickly Estimating Large Network-Based Mev Models, Tien Mai, Emma Frejinger, Mogens Fosgereau, Fabian Bastin Apr 2017

A Dynamic Programming Approach For Quickly Estimating Large Network-Based Mev Models, Tien Mai, Emma Frejinger, Mogens Fosgereau, Fabian Bastin

Research Collection School Of Computing and Information Systems

We propose a way to estimate a family of static Multivariate Extreme Value (MEV) models with large choice sets in short computational time. The resulting model is also straightforward and fast to use for prediction. Following Daly and Bierlaire (2006), the correlation structure is defined by a rooted, directed graph where each node without successor is an alternative. We formulate a family of MEV models as dynamic discrete choice models on graphs of correlation structures and show that the dynamic models are consistent with MEV theory and generalize the network MEV model (Daly and Bierlaire, 2006). Moreover, we show that …

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Dark Hazard: Large-Scale Discovery Of Unknown Hidden Sensitive Operations In Android Apps, Xiaorui Pan, Xueqiang Wang, Yue Duan, Xiaofeng Wang, Heng Yin Mar 2017

Dark Hazard: Large-Scale Discovery Of Unknown Hidden Sensitive Operations In Android Apps, Xiaorui Pan, Xueqiang Wang, Yue Duan, Xiaofeng Wang, Heng Yin

Research Collection School Of Computing and Information Systems

Hidden sensitive operations (HSO) such as stealing privacy user data upon receiving an SMS message are increasingly utilized by mobile malware and other potentially-harmful apps (PHAs) to evade detection. Identification of such behaviors is hard, due to the challenge in triggering them during an app’s runtime. Current static approaches rely on the trigger conditions or hidden behaviors known beforehand and therefore cannot capture previously unknown HSO activities. Also these techniques tend to be computationally intensive and therefore less suitable for analyzing a large number of apps. As a result, our understanding of real-world HSO today is still limited, not to …

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Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen Mar 2017

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 …

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Version-Sensitive Mobile App Recommendation, Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, Tat-Seng Chua Mar 2017

Version-Sensitive Mobile App Recommendation, Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Jialie Shen, Shunxiang Wu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Being part and parcel of the daily life for billions of people all over the globe, the domain of mobile Applications (Apps) is the fastest growing sector of mobile market today. Users, however, are frequently overwhelmed by the vast number of released Apps and frequently updated versions. Towards this end, we propose a novel version-sensitive mobile App recommendation framework. It is able to recommend appropriate Apps to right users by jointly exploring the version progression and dual-heterogeneous data. It is helpful for alleviating the data sparsity problem caused by version division. As a byproduct, it can be utilized to solve …

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Probabilistic Public Key Encryption For Controlled Equijoin In Relational Databases, Yujue Wang, Hwee Hwa Pang Mar 2017

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, …

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Social Tag Relevance Learning Via Ranking-Oriented Neighbor Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian Mar 2017

Social Tag Relevance Learning Via Ranking-Oriented Neighbor Voting, Chaoran Cui, Jialie Shen, Jun Ma, Tao Lian

Research Collection School Of Computing and Information Systems

High quality tags play a critical role in applications involving online multimedia search, such as social image annotation, sharing and browsing. However, user-generated tags in real world are often imprecise and incomplete to describe the image contents, which severely degrades the performance of current search systems. To improve the descriptive powers of social tags, a fundamental issue is tag relevance learning, which concerns how to interpret the relevance of a tag with respect to the contents of an image effectively. In this paper, we investigate the problem from a new perspective of learning to rank, and develop a novel approach …

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Effective K-Vertex Connected Component Detection In Large-Scale Networks, Yuan Li, Yuha Zhao, Guoren Wang, Feida Zhu, Yubao Wu, Shenglei Shi Mar 2017

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 …

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Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau Mar 2017

Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker’s daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning …

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Efficient Motif Discovery In Spatial Trajectories Using Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Kai Wang Mar 2017

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 …

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Whole-System Analysis For Understanding Publicly Accessible Functions In Android, Huu Hoang Nguyen, Lingxiao Jiang, Thanh Tho Quan Mar 2017

Whole-System Analysis For Understanding Publicly Accessible Functions In Android, Huu Hoang Nguyen, Lingxiao Jiang, Thanh Tho Quan

Research Collection School Of Computing and Information Systems

Android has become the most popular mobile operating system. Millions of applications, including many malwares, haven been developed for it. Android itself evolves constantly with changing features and higher complexities. It is challenging for application developers to keep up with the changes and maintain the compatibility of their apps across Android versions. Therefore, there are many challenges for application analysis tools to accurately model and analyze app behaviors across Android versions. Even though the overall system architecture of Android and many APIs are documented, many other APIs and implementation details are not, not to mention potential bugs and vulnerabilities. Techniques …

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Scalable Image Retrieval By Sparse Product Quantization, Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen Mar 2017

Scalable Image Retrieval By Sparse Product Quantization, Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen

Research Collection School Of Computing and Information Systems

Fast approximate nearest neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is product quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low-dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors, and thus, inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called sparse product quantization (SPQ) to encoding …

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Location Matters: Geospatial Policy Analytics Over Time For Household Hazardous Waste Collection In California, Kustini Lim-Wavde, Robert John Kauffman, Tin Seong Kam, Gregory S. Dawson Mar 2017

Location Matters: Geospatial Policy Analytics Over Time For Household Hazardous Waste Collection In California, Kustini Lim-Wavde, Robert John Kauffman, Tin Seong Kam, Gregory S. Dawson

Research Collection School Of Computing and Information Systems

By integrating mapping and geospatial data into a county-level datasetfor exploratory analysis, we will demonstrate how to provide useful insightsfor waste managers and local governments regarding spatial patterns ofhousehold hazardous waste (HHW) collection and how it changes over time. We usemap-based visualization to display patterns of spatial intensity and countylocations for HHW collection in California from 2004 to 2015. We use exploratory spatial data analyticsmethods to characterize the spatial distribution of HHW collected per person.When we considered the spatial relationships, we were able to develop andestimate a geographically-weighted regression to explain how different regionalfactors influence the amount of HHW collected. …

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