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Research Collection School Of Information Systems

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Full-Text Articles in Physical Sciences and Mathematics

When Human Cognitive Modeling Meets Pins: User-Independent Inter-Keystroke Timing Attacks, Ximing Liu, Yingjiu Li, Robert H. Deng, Bing Chang, Shujun Li Jan 2019

When Human Cognitive Modeling Meets Pins: User-Independent Inter-Keystroke Timing Attacks, Ximing Liu, Yingjiu Li, Robert H. Deng, Bing Chang, Shujun Li

Research Collection School Of Information Systems

This paper proposes the first user-independent inter-keystroke timing attacks on PINs. Our attack method is based on an inter-keystroke timing dictionary built from a human cognitive model whose parameters can be determined by a small amount of training data on any users (not necessarily the target victims). Our attacks can thus be potentially launched on a large scale in real-world settings. We investigate inter-keystroke timing attacks in different online attack settings and evaluate their performance on PINs at different strength levels. Our experimental results show that the proposed attack performs significantly better than random guessing attacks. We further demonstrate that ...


A Tribute To Robert U. Ayres For A Lifetime Of Work In Technological Forecasting And Related Areas, Steven Mark Miller Nov 2018

A Tribute To Robert U. Ayres For A Lifetime Of Work In Technological Forecasting And Related Areas, Steven Mark Miller

Research Collection School Of Information Systems

Bob Ayres was born in the UnitedStates in 1932. For his university studies at the bachelors, masters and PhDlevels, he concentrated in physics and mathematics. When we think of Bob today,we think of his pioneering work across the areas of technological forecasting,industrial metabolism and industrial ecology, and the role of energy andthermodynamics in economic growth. How did a person with a strong fundamentaleducation as a physicist end up as a pioneering thinker and thought leader atthe intersection of energy, environment and economics?


Teaching Adult Learners On Software Architecture Design Skills, Eng Lieh Ouh, Yunghans Irawan Oct 2018

Teaching Adult Learners On Software Architecture Design Skills, Eng Lieh Ouh, Yunghans Irawan

Research Collection School Of Information Systems

Software architectures present high-level views ofsystems, enabling developers to abstract away the unnecessarydetails and focus on the overall big picture. Designing a softwarearchitecture is an essential skill in software engineering and adultlearners are seeking this skill to further progress in their career.With the technology revolution and advancements in this rapidlychanging world, the proportion of adult learners attendingcourses for continuing education are increasing. Their learningobjectives are no longer to obtain good grades but the practicalskills to enable them to perform better in their work and advancein their career. Teaching software architecture to upskill theseadult learners requires contending with the problem ...


Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon Oct 2018

Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon

Research Collection School Of Information Systems

Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code ...


Unearthing The X-Streams:Visualizing Water Contamination, Akangsha Bandalkul, Angad Srivastava, Kishan Bharadwaj Shridhar, Jason Guan Jie Ong, Yanrong Zhang Oct 2018

Unearthing The X-Streams:Visualizing Water Contamination, Akangsha Bandalkul, Angad Srivastava, Kishan Bharadwaj Shridhar, Jason Guan Jie Ong, Yanrong Zhang

Research Collection School Of Information Systems

The datasets released for VAST 2018 Mini Challenge 2 pertain to sensor readings capturing chemical concentrations and physical properties from water bodies in the Boonsong Lekagul wildlife preserve. This challenge is in continuation to the VAST 2017 Challenge, where the company Kasios was identified as the culprit in dumping the chemical - Methylosmoline. In the absence of actual chemical measurements in the soil, challenge participants need to visualize chemical contamination based on the proximal water bodies to identify trends of interest. A horizon plot developed helps to narrow down the complete list of 106 chemicals provided to only 7, from where ...


Simknn: A Scalable Method For In-Memory Knn Search Over Moving Objects In Road Networks, Bin Cao, Chenyu Hou, Suifei Li, Jing Fan, Jianwei Yin, Baihua Zheng, Jie Bao Oct 2018

Simknn: A Scalable Method For In-Memory Knn Search Over Moving Objects In Road Networks, Bin Cao, Chenyu Hou, Suifei Li, Jing Fan, Jianwei Yin, Baihua Zheng, Jie Bao

Research Collection School Of Information Systems

Nowadays, many location-based applications require the ability of querying k-nearest neighbors over a very large scale of5 moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt6 to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split7 into more smaller cells which introduces the side-effect of higher memory cost, i.e., maintaining such a large volume of cells requires a8 much larger memory space at the server side. In this paper, we present SIMkNN, a scalable ...


Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan Oct 2018

Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan

Research Collection School Of Information Systems

This paper shares our insights on exploring theexperiential learning model and risk management process todesign an undergraduate software architecture course. The keychallenge for undergraduate students to appreciate softwarearchitecture design is usually their limited experience in thesoftware industry. In software architecture, the high-level designprinciples are heuristics lacking the absoluteness of firstprinciples which for inexperienced undergraduate students, thisis a frustrating divergence from what they used to value. From aneducator's perspective, teaching software architecture requirescontending with the problem of how to express this level ofabstraction practically and also make the learning realistic. Inthis paper, we propose a model adapting the concepts ofexperiential ...


Efficient Traceable Oblivious Transfer And Its Applications, Weiwei Liu, Yinghui Zhang, Yi Mu, Guomin Yang, Yangguang Tian Sep 2018

Efficient Traceable Oblivious Transfer And Its Applications, Weiwei Liu, Yinghui Zhang, Yi Mu, Guomin Yang, Yangguang Tian

Research Collection School Of Information Systems

Oblivious transfer (OT) has been applied widely in privacy-sensitive systems such as on-line transactions and electronic commerce to protect users’ private information. Traceability is an interesting feature of such systems that the privacy of the dishonest users could be traced by the service provider or a trusted third party (TTP). However, previous research on OT mainly focused on designing protocols with unconditional receiver’s privacy. Thus, traditional OT schemes cannot fulfill the traceability requirements in the aforementioned applications. In this paper, we address this problem by presenting a novel traceable oblivious transfer (TOT) without involvement of any TTP. In the ...


Resonance Attacks On Load Frequency Control Of Smart Grids, Yongdong Wu, Zhuo Wei, Jian Weng, Xin Li, Robert H. Deng Sep 2018

Resonance Attacks On Load Frequency Control Of Smart Grids, Yongdong Wu, Zhuo Wei, Jian Weng, Xin Li, Robert H. Deng

Research Collection School Of Information Systems

Load frequency control (LFC) is widely employed to regulate power plants in modern power generation systems of smart grids. This paper presents a simple and yet powerful type of attacks, referred to as resonance attacks, on LFC power generation systems. Specifically, in a resonance attack, an adversary craftily modifies the input of a power plant according to a resonance source (e.g., rate of change of frequency) to produce a feedback on LFC power generation system, such that the state of the power plant quickly becomes instable. Extensive computer simulations on popular LFC power generation system models which consist of ...


Dsh: Deniable Secret Handshake Framework, Yangguang Tian, Yingjiu Li, Yinghui Zhang, Nan Li, Guomin Yang, Yong Yu Sep 2018

Dsh: Deniable Secret Handshake Framework, Yangguang Tian, Yingjiu Li, Yinghui Zhang, Nan Li, Guomin Yang, Yong Yu

Research Collection School Of Information Systems

Secret handshake is a useful primitive that allows a group of authorized users to establish a shared secret key and authenticate each other anonymously. It naturally provides a certain degree of user privacy and deniability which are also desirable for some private conversations that require secure key establishment. The inherent user privacy enables a private conversation between authorized users without revealing their real identities. While deniability allows authorized users to later deny their participating in conversations. However, deniability of secret handshakes lacks a comprehensive treatment in the literature. In this paper, we investigate the deniability of existing secret handshakes. We ...


Question-Guided Hybrid Convolution For Visual Question Answering, Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven C. H. Hoi, Xiaogang Wang Sep 2018

Question-Guided Hybrid Convolution For Visual Question Answering, Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven C. H. Hoi, Xiaogang Wang

Research Collection School Of Information Systems

In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC)network for Visual Question Answering (VQA). Most state-of-the-art VQA methodsfuse the high-level textual and visual features from the neural network andabandon the visual spatial information when learning multi-modal features.Toaddress these problems, question-guided kernels generated from the inputquestion are designed to convolute with visual features for capturing thetextual and visual relationship in the early stage. The question-guidedconvolution can tightly couple the textual and visual information but alsointroduce more parameters when learning kernels. We apply the groupconvolution, which consists of question-independent kernels andquestion-dependent kernels, to reduce the parameter size and ...


Lightweight Break-Glass Access Control System For Healthcare Internet-Of-Things, Yang Yang, Ximeng Liu, Robert H. Deng Aug 2018

Lightweight Break-Glass Access Control System For Healthcare Internet-Of-Things, Yang Yang, Ximeng Liu, Robert H. Deng

Research Collection School Of Information Systems

Healthcare Internet-of-things (IoT) has been proposed as a promising means to greatly improve the efficiency and quality of patient care. Medical devices in healthcare IoT measure patients' vital signs and aggregate these data into medical files which are uploaded to the cloud for storage and accessed by healthcare workers. To protect patients' privacy, encryption is normally used to enforce access control of medical files by authorized parties while preventing unauthorized access. In healthcare, it is crucial to enable timely access of patient files in emergency situations. In this paper, we propose a lightweight break-glass access control (LiBAC) system that supports ...


Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi Aug 2018

Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi

Research Collection School Of Information Systems

To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias ...


Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang Aug 2018

Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang

Research Collection School Of Information Systems

Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently uncertain due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we introduce the uncertain top-k query (UTK). Given uncertain preferences ...


Security Analysis Of A Large-Scale Concurrent Data Anonymous Batch Verification Scheme For Mobile Healthcare Crowd Sensing, Yinghui Zhang, Jiangang Shu, Ximeng Liu, Jin Li, Dong Zheng Aug 2018

Security Analysis Of A Large-Scale Concurrent Data Anonymous Batch Verification Scheme For Mobile Healthcare Crowd Sensing, Yinghui Zhang, Jiangang Shu, Ximeng Liu, Jin Li, Dong Zheng

Research Collection School Of Information Systems

As an important application of the Internet of Things (IoT) technologies, mobile healthcare crowd sensing (MHCS) still has challenging issues, such as privacy protection and efficiency. Quite recently in IEEE Internet of Things Journal (DOI: 10.1109/JIOT.2018.2828463), Liu et al. proposed a large-scale concurrent data anonymous batch verification scheme for mobile healthcare crowd sensing, claiming to provide batch authentication, non-repudiation, and anonymity. However, after a close look at the scheme, we point out that the scheme suffers two types of signature forgery attacks and hence fails to achieve the claimed security properties. In addition, a reasonable and ...


A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun Aug 2018

A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun

Research Collection School Of Information Systems

Attribute based encryption is suitable for data protection in data outsourcing systems such as cloud computing. However, the leveraging of encryption technique may retrain some routine operations over the encrypted data, particularly in the field of data retrieval. This paper presents an attribute based date retrieval with proxy re-encryption (ABDR-PRE) to provide both fine-grained access control and retrieval over the ciphertexts. The proposed scheme achieves fine-grained data access management by adopting KP-ABE mechanism, a delegator can generate the re-encryption key and search indexes for the ciphertexts to be shared over the target delegatee’s attributes. Throughout the process of data ...


Customer Level Predictive Modeling For Accounts Receivable To Reduce Intervention Actions, Michelle L. F. Cheong, Wen Shi Aug 2018

Customer Level Predictive Modeling For Accounts Receivable To Reduce Intervention Actions, Michelle L. F. Cheong, Wen Shi

Research Collection School Of Information Systems

One of the main costs associated with Accounts receivable (AR) collection is related to the intervention actions taken to remind customers to pay their outstanding invoices. Apart from the cost, intervention actions may lead to poor customer satisfaction, which is undesirable in a competitive industry. In this paper, we studied the payment behavior of invoices for customers of a logistics company, and used predictive modeling to predict if a customer will pay the outstanding invoices with high probability, in an attempt to reduce intervention actions taken, thus reducing cost and improving customer relationship. We defined a pureness measure to classify ...


Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng Aug 2018

Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng

Research Collection School Of Information Systems

In this paper we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T and a budget L, find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1−1/e) approximation ratio. However, the enumeration ...


Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan Aug 2018

Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Information Systems

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports.


Deep Learning For Practical Image Recognition: Case Study On Kaggle Competitions, Xulei Yang, Zeng Zeng, Sin G. Teo, Li Wang, Vijay Chandrasekar, Steven C. H. Hoi Aug 2018

Deep Learning For Practical Image Recognition: Case Study On Kaggle Competitions, Xulei Yang, Zeng Zeng, Sin G. Teo, Li Wang, Vijay Chandrasekar, Steven C. H. Hoi

Research Collection School Of Information Systems

In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions, limited labeled data samples, etc. In this work, we are making efforts to deal with these challenges through a computational framework by incorporating latest developments in deep learning. In terms of two-stage detection scheme, pseudo labeling, data augmentation, cross-validation and ensemble learning, the proposed framework aims to achieve better performances for practical ...


Fusing Multi-Abstraction Vector Space Models For Concern Localization, Yun Zhang, David Lo, Xin Xia, Giuseppe Scanniello, Tien-Duy B. Le, Jianling Sun Aug 2018

Fusing Multi-Abstraction Vector Space Models For Concern Localization, Yun Zhang, David Lo, Xin Xia, Giuseppe Scanniello, Tien-Duy B. Le, Jianling Sun

Research Collection School Of Information Systems

Concern localization refers to the process of locating code units that match a particular textual description. It takes as input textual documents such as bug reports and feature requests and outputs a list of candidate code units that are relevant to the bug reports or feature requests. Many information retrieval (IR) based concern localization techniques have been proposed in the literature. These techniques typically represent code units and textual descriptions as a bag of tokens at one level of abstraction, e.g., each token is a word, or each token is a topic. In this work, we propose a multi-abstraction ...


Server-Aided Attribute-Based Signature With Revocation For Resource-Constrained Industrial-Internet-Of-Things Devices, Hui Cui, Robert H. Deng, Joseph K. Liu, Xun Yi, Yingjiu Li Aug 2018

Server-Aided Attribute-Based Signature With Revocation For Resource-Constrained Industrial-Internet-Of-Things Devices, Hui Cui, Robert H. Deng, Joseph K. Liu, Xun Yi, Yingjiu Li

Research Collection School Of Information Systems

The industrial Internet-of-things (IIoT) can be seen as the usage of Internet-of-things technologies in industries, which provides a way to improve the operational efficiency. An attribute-based signature (ABS) has been a very useful technique for services requiring anonymous authentication in practice, where a signer can sign a message over a set of attributes without disclosing any information about his/her identity, and a signature only attests to the fact that it is created by a signer with several attributes satisfying some claim predicate. However, an ABS scheme requires exponentiation and/or pairing operations in the signature generation and verification algorithms ...


Anonymous Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Xiaohua Jia, Kan Yang, Robert H. Deng Aug 2018

Anonymous Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Xiaohua Jia, Kan Yang, Robert H. Deng

Research Collection School Of Information Systems

With the development of sharing economy, crowdsourcing as a distributed computing paradigm has become increasingly pervasive. As one of indispensable services for most crowdsourcing applications, task matching has also been extensively explored. However, privacy issues are usually ignored during the task matching and few existing privacy-preserving crowdsourcing mechanisms can simultaneously protect both task privacy and worker privacy. This paper systematically analyzes the privacy leaks and potential threats in the task matching and proposes a single-keyword task matching scheme for the multirequester/multiworker crowdsourcing with efficient worker revocation. The proposed scheme not only protects data confidentiality and identity anonymity against the ...


Unobtrusive Detection Of Frailty In Older Adults, Lee Buay Tan, W K P Neranjana Nadee Rodrigo Goonawardene, Hwee-Pink Tan Jul 2018

Unobtrusive Detection Of Frailty In Older Adults, Lee Buay Tan, W K P Neranjana Nadee Rodrigo Goonawardene, Hwee-Pink Tan

Research Collection School Of Information Systems

Sensor technologies have gained attention as an effective means to monitor physical and mental wellbeing of elderly. In this study, we examined the possibility of using passive in-home sensors to detect frailty in older adults based on their day-to-day in-home living pattern. The sensor-based elderly monitoring system consists of PIR motion sensors and a door contact sensor attached to the main door. A set of pre-defined features associated with elderly’s day-to-day living patterns were derived based on sensor data of 46 elderly gathered over two different time periods. A series of feature vectors depicting different behavioral aspects were derived ...


Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang Jul 2018

Modeling Contemporaneous Basket Sequences With Twin Networks For Next-Item Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang

Research Collection School Of Information Systems

Our interactions with an application frequently leave a heterogeneous and contemporaneous trail of actions and adoptions (e.g., clicks, bookmarks, purchases). Given a sequence of a particular type (e.g., purchases)-- referred to as the target sequence, we seek to predict the next item expected to appear beyond this sequence. This task is known as next-item recommendation. We hypothesize two means for improvement. First, within each time step, a user may interact with multiple items (a basket), with potential latent associations among them. Second, predicting the next item in the target sequence may be helped by also learning from another ...


Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh-Nam Doan, Ee-Peng Lim Jul 2018

Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh-Nam Doan, Ee-Peng Lim

Research Collection School Of Information Systems

Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as ...


Online Deep Learning: Learning Deep Neural Networks On The Fly, Doyen Sahoo, Hong Quang Pham, Jing Lu, Steven C. H. Hoi Jul 2018

Online Deep Learning: Learning Deep Neural Networks On The Fly, Doyen Sahoo, Hong Quang Pham, Jing Lu, Steven C. H. Hoi

Research Collection School Of Information Systems

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch setting, requiring the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream. We aim to address an open challenge of ``Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is more challenging as the optimization objective is non-convex, and regular ...


Identifying Elderlies At Risk Of Becoming More Depressed With Internet-Of-Things, Jiajue Ou, Huiguang Liang, Hwee Xian Tan Jul 2018

Identifying Elderlies At Risk Of Becoming More Depressed With Internet-Of-Things, Jiajue Ou, Huiguang Liang, Hwee Xian Tan

Research Collection School Of Information Systems

Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity patterns, extracted from wireless sensor data. To do so, we build predictive models to learn the relationship between depression level changes and behaviors using historical data. We also deploy the system for a group of elderly, in their homes, and run the experiments for more than one year. Our experimental study gives encouraging results, suggesting that our ...


Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang Jul 2018

Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang

Research Collection School Of Information Systems

The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional ...


Situation-Aware Authenticated Video Broadcasting Over Train-Trackside Wifi Networks, Yongdong Wu, Dengpan Ye, Zhuo Wei, Qian Wang, William Tan, Robert H. Deng Jul 2018

Situation-Aware Authenticated Video Broadcasting Over Train-Trackside Wifi Networks, Yongdong Wu, Dengpan Ye, Zhuo Wei, Qian Wang, William Tan, Robert H. Deng

Research Collection School Of Information Systems

Live video programmes can bring in better travel experience for subway passengers and earn abundant advertisement revenue for subway operators. However, because the train-trackside channels for video dissemination are easily accessible to anyone, the video traffic are vulnerable to attacks which may cause deadly tragedies. This paper presents a situation-aware authenticated video broadcasting scheme in the railway network which consists of train, on-board sensor, trackside GSM-R (Global System for Mobile Communications-Railway) device, WiFi AP (Access Point), and train control center. Specifically, the scheme has four modules: (1) a train uses its on-board sensors to obtain its speed, location, and RSSI ...