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Articles 4711 - 4740 of 8495
Full-Text Articles in Computer Sciences
Neural Correlates Of User Experience In Gaming, Y. Tejaswini, F. Nah, Keng Siau, L. Chen
Neural Correlates Of User Experience In Gaming, Y. Tejaswini, F. Nah, Keng Siau, L. Chen
Research Collection School Of Computing and Information Systems
The objective of this research is to understand the neural correlates of user states of experience in human-computer interaction using electroencephalogram (EEG). Such user states include flow, boredom, and anxiety that are experienced when a user interacts with a computer-based system. We propose using a within-subjects experiment to collect EEG data to assess and compare the neural correlates of three main states of user experience (i.e., flow, boredom, and anxiety) as well as compare them with the resting state as a baseline. We expect the findings from this research to contribute to an improved understanding of psychophysiological means of assessing …
Choosing An Nlp Library For Analyzing Software Documentation: A Systematic Literature Review And A Series Of Experiments, Fouad N. A. Al Omran, Christoph Treude
Choosing An Nlp Library For Analyzing Software Documentation: A Systematic Literature Review And A Series Of Experiments, Fouad N. A. Al Omran, Christoph Treude
Research Collection School Of Computing and Information Systems
To uncover interesting and actionable information from natural language documents authored by software developers, many researchers rely on "out-of-the-box" NLP libraries. However, software artifacts written in natural language are different from other textual documents due to the technical language used. In this paper, we first analyze the state of the art through a systematic literature review in which we find that only a small minority of papers justify their choice of an NLP library. We then report on a series of experiments in which we applied four state-of-the-art NLP libraries to publicly available software artifacts from three different sources. Our …
Fusing Social Media And Mobile Analytics For Urban Sense-Making, Archan Misra
Fusing Social Media And Mobile Analytics For Urban Sense-Making, Archan Misra
Research Collection School Of Computing and Information Systems
The project was motivated by the observation that urban environments are increasingly characterized by a variety of non-traditional “sensors”, whose data streams can be harnessed to infer a variety of latent events and urban context. For example, users spontaneously generate huge amounts of content (text, images and video) on social network channels, whereas GPS & other sensors on taxis and buses increasingly provide near-real time traces of their movement throughout the city. Similarly, advances in Wi-Fi based sensing allow us to passively capture the individual and collective movement of visitors across various public spaces, such as college campuses, museums and …
Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen
Persona Generation From Aggregated Social Media Data, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Moeed Ahmad, Lene Nielsen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We develop a methodology for persona generation using real time social media data for the distribution of products via online platforms. From a large social media account containing more than 30 million interactions from users from 181 countries engaging with more than 4,200 digital products produced by a global media corporation, we demonstrate that our methodology can first identify both distinct and impactful user segments and then create persona descriptions by automatically adding pertinent features, such as names, photos, and personal attributes. We validate our approach by implementing the methodology into an actual working system that leverages large scale online …
Household Informedness And Policy Analytics For The Collection And Recycling Of Household Hazardous Waste In California, Kustini Lim-Wavde, Robert J. Kauffman, Gregory S. Dawson
Household Informedness And Policy Analytics For The Collection And Recycling Of Household Hazardous Waste In California, Kustini Lim-Wavde, Robert J. Kauffman, Gregory S. Dawson
Research Collection School Of Computing and Information Systems
Collection and recycling of household hazardous waste (HHW) can vary due to differences in household incomes, demographics, material recyclability, and HHW collection programs. We evaluate the role of household informedness, the degree to which households have the necessary information to make utility-maximizing decisions about the handling of their waste. Household informedness seems to be influenced by HHW public education and environmental quality information. We assess the effects of household informedness on HHW collection and recycling using panel data, community surveys, drinking water compliance reports, and census data in California from 2004 to 2012. The results enable the calculation of …
Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing, Truc Viet Le, Baoyang Song, Laura Wynter
Real-Time Prediction Of Length Of Stay Using Passive Wi-Fi Sensing, Truc Viet Le, Baoyang Song, Laura Wynter
Research Collection School Of Computing and Information Systems
The proliferation of wireless technologies in today's everyday life is one of the key drivers of the Internet of Things (IoT). In addition to being an enabler of connectivity, the vast penetration of wireless devices today gives rise to a secondary functionality as a means of tracking and localization of the devices themselves. Indeed, in order to discover and automatically connect to known Wi-Fi networks, mobile devices have to scan and broadcast the so-called probe requests on all available channels, which can be captured and analyzed in a non-intrusive manner. Thus, one of the key applications of this feature is …
Dynamic Nearest Neighbor Queries In Euclidean Space, Sarana Nutanong, Mohammed Eunus Ali, Egemen Tanin, Kyriakos Mouratidis
Dynamic Nearest Neighbor Queries In Euclidean Space, Sarana Nutanong, Mohammed Eunus Ali, Egemen Tanin, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Given a query point q and a set D of data points, a nearest neighbor (NN) query returns the data point p in D that minimizes the distance DIST(q,p), where the distance function DIST(,) is the L2norm. One important variant of this query type is kNN query, which returns k data points with the minimum distances. When taking the temporal dimension into account, the k NN query result may change over a period of time due to changes in locations of the query point and/or data points.
Machine Learning Approaches To Sentiment Analytics, W. Zhao, Keng Siau
Machine Learning Approaches To Sentiment Analytics, W. Zhao, Keng Siau
Research Collection School Of Computing and Information Systems
One key aspect of sentiment analytics is emotion classification. This research studies the use of machine learning approaches to classify human emotion. Two different machine learning approaches were compared in an experimental study. In one approach, emotions from both genders were used to train the machine. In another approach, genders were separated and two separate machines were used to learn the emotions of the two genders. We also manipulated the training sample sizes and study the effect of training sample sizes on the two machine learning approaches. Our preliminary results show that the approach where the genders were separated produces …
Stop Nuclear Smuggling Through Efficient Container Inspection, Xinrun Wang, Qingyu Guo, Bo An
Stop Nuclear Smuggling Through Efficient Container Inspection, Xinrun Wang, Qingyu Guo, Bo An
Research Collection School Of Computing and Information Systems
Since 2003, the U.S. government has spent $850 million on the Megaport Initiative which aims at stopping the nuclear smuggling in international container shipping through advanced inspection facilities including Non-Intrusive Inspection (NII) and Mobile Radiation Detection and Identification System (MRDIS). Unfortunately, it remains a significant challenge to efficiently inspect more than 11.7 million containers imported to the U.S. due to the limited inspection resources. Moreover, existing work in container inspection neglects the sophisticated behavior of the smuggler who can surveil the inspector’s strategy and decide the optimal (sequential) smuggling plan. This paper is the first to tackle this challenging container …
Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim
Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse for building models. Nonetheless, although only a small fraction of tweets are geocoded, we find that they are posted by a substantial proportion of users. Essentially, such users have location history data. Along with tweet posting time, these serve as additional contextual information for geolocation. In designing our geolocation models, we …
Encrypted Data Processing With Homomorphic Re-Encryption, Wenxiu Ding, Zheng Yan, Robert H. Deng
Encrypted Data Processing With Homomorphic Re-Encryption, Wenxiu Ding, Zheng Yan, Robert H. Deng
Research Collection School Of Computing and Information Systems
Cloud computing offers various services to users by re-arranging storage and computing resources. In order to preserve data privacy, cloud users may choose to upload encrypted data rather than raw data to the cloud. However, processing and analyzing encrypted data are challenging problems, which have received increasing attention in recent years. Homomorphic Encryption (HE) was proposed to support computation on encrypted data and ensure data confidentiality simultaneously. However, a limitation of HE is it is a single user system, which means it only allows the party that owns a homomorphic decryption key to decrypt processed ciphertexts. Original HE cannot support …
A Neural Network Model For Semi-Supervised Review Aspect Identification, Ying Ding, Changlong Yu, Jing Jiang
A Neural Network Model For Semi-Supervised Review Aspect Identification, Ying Ding, Changlong Yu, Jing Jiang
Research Collection School Of Computing and Information Systems
Aspect identification is an important problem in opinion mining. It is usually solved in an unsupervised manner, and topic models have been widely used for the task. In this work, we propose a neural network model to identify aspects from reviews by learning their distributional vectors. A key difference of our neural network model from topic models is that we do not use multinomial word distributions but instead embedding vectors to generate words. Furthermore, to leverage review sentences labeled with aspect words, a sequence labeler based on Recurrent Neural Networks (RNNs) is incorporated into our neural network. The resulting model …
Follow-My-Lead: Intuitive Indoor Path Creation And Navigation Using See-Through Interactive Videos, Quentin Roy, Simon T. Perrault, Shengdong Zhao, Richard Davis, Anuroop Pattena Vaniyar, Velko Vechev, Youngki Lee, Archan Misra
Follow-My-Lead: Intuitive Indoor Path Creation And Navigation Using See-Through Interactive Videos, Quentin Roy, Simon T. Perrault, Shengdong Zhao, Richard Davis, Anuroop Pattena Vaniyar, Velko Vechev, Youngki Lee, Archan Misra
Research Collection School Of Computing and Information Systems
We present Follow-My-Lead, an alternative indoor navigation technique that uses visual information recorded on an actual navigation path as a navigational guide. Its design revealed a trade-off between the fidelity of information provided to users and their effort to acquire it. Our first experiment revealed that scrolling through a continuous image stream of the navigation path is highly informative, but it becomes tedious with constant use. Discrete image checkpoints require less effort, but can be confusing. A balance may be struck by adding fast video transitions between image checkpoints, but precise control is required to handle difficult situations. Authoring still …
Core Determining Class And Inequality Selection, Ye Luo, Hai Wang
Core Determining Class And Inequality Selection, Ye Luo, Hai Wang
Research Collection School Of Computing and Information Systems
The relations between unobserved events and observed outcomes can be characterized by a bipartite graph. We propose an algorithm that explores the structure of the graph to construct the "exact Core Determining Class," i.e., the set of irredudant inequalities. We prove that in general the exact Core Determining Class does not depend on the probability measure of the outcomes but only on the structure of the graph. For more general linear inequalities selection problems, we propose a statistical procedure similar to the Dantzig Selector to select the truly informative constraints. We demonstrate performances of our procedures in Monte-Carlo experiments.
What Gets Media Attention And How Media Attention Evolves Over Time: Large-Scale Empirical Evidence From 196 Countries, Jisun An, Haewoon Kwak
What Gets Media Attention And How Media Attention Evolves Over Time: Large-Scale Empirical Evidence From 196 Countries, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
It is known that news topics, covered more frequently and over longer periods of time, are considered to be important to the public. Hence, what gets media attention and how me- dia attention evolves over time has been studied for decades in communication study. However, previous studies are con- fined to a few countries or a few topics, mainly due to lack of longitudinal global data. In this work, we use a large-scale news data compiled from 196 countries to provide empirical analyses of media attention dynamics.
Data-Driven Approach To Measuring The Level Of Press Freedom Using Media Attention Diversity From Unfiltered News, Jisun An, Haewoon Kwak
Data-Driven Approach To Measuring The Level Of Press Freedom Using Media Attention Diversity From Unfiltered News, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Published by Reporters Without Borders every year, the Press Freedom Index (PFI) reflects the fear and tension in the newsroom pushed by the government and private sectors. While the PFI is invaluable in monitoring media environ- ments worldwide, the current survey-based method has in- herent limitations to updates in terms of cost and time. In this work, we introduce an alternative way to measure the level of press freedom using media attention diversity compiled from Unfiltered News.
Design And Implementation Of An Rfid-Based Customer Shopping Behavior Mining System, Zimu Zhou, Longfei Shangguan, Xiaolong Zheng, Lei Yang, Yunhao Liu
Design And Implementation Of An Rfid-Based Customer Shopping Behavior Mining System, Zimu Zhou, Longfei Shangguan, Xiaolong Zheng, Lei Yang, Yunhao Liu
Research Collection School Of Computing and Information Systems
Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate …
Finding Causality And Responsibility For Probabilistic Reverse Skyline Query Non-Answers [Extended Abstract], Yunjun Gao, Qing Liu, Gang Chen, Linlin Zhou, Baihua Zheng
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.
On The Effectiveness Of Virtualization Based Memory Isolation On Multicore Platforms, Siqi Zhao, Xuhua Ding
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 …
Machine Comprehension Using Match-Lstm And Answer Pointer, Shuohang Wang, Jing Jiang
Machine Comprehension Using Match-Lstm And Answer Pointer, Shuohang Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, in SQuAD the answers do not come from a small set of candidate answers and they have variable lengths. We propose an end-to-end neural architecture for the task. The architecture is based on match-LSTM, a model we proposed previously for textual entailment, and Pointer Net, a sequence-to-sequence …
Predicting The Impact Of Software Engineering Topics: An Empirical Study, Santonu Sarkar, Rumana Lakdawala, Subhajit Datta
Predicting The Impact Of Software Engineering Topics: An Empirical Study, Santonu Sarkar, Rumana Lakdawala, Subhajit Datta
Research Collection School Of Computing and Information Systems
Predicting the future is hard, more so in active research areas. In this paper, we customize an established model for citation prediction of research papers and apply it on research topics. We argue that research topics, rather than individual publications, have wider relevance in the research ecosystem, for individuals as well as organizations. In this study, topics are extracted from a corpus of software engineering publications covering 55,000+ papers written by more than 70,000 authors across 56 publication venues, over a span of 38 years, using natural language processing techniques. We demonstrate how critical aspects of the original paper-based prediction …
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 …
Identity-Based Data Outsourcing With Comprehensive Auditing In Clouds, Yujue Wang, Qianhong Wu, Bo Qin, Wenchang Shi, Robert H. Deng, Jiankun Hu
Identity-Based Data Outsourcing With Comprehensive Auditing In Clouds, Yujue Wang, Qianhong Wu, Bo Qin, Wenchang Shi, Robert H. Deng, Jiankun Hu
Research Collection School Of Computing and Information Systems
Cloud storage system provides facilitative file storage and sharing services for distributed clients. To address integrity, controllable outsourcing, and origin auditing concerns on outsourced files, we propose an identity-based data outsourcing (IBDO) scheme equipped with desirable features advantageous over existing proposals in securing outsourced data. First, our IBDO scheme allows a user to authorize dedicated proxies to upload data to the cloud storage server on her behalf, e.g., a company may authorize some employees to upload files to the company's cloud account in a controlled way. The proxies are identified and authorized with their recognizable identities, which eliminates complicated certificate …
Inferring Smartphone Keypress Via Smartwatch Inertial Sensing, Sougata Sen, Karan Grover, Vigneshwaran Subbaraju, Archan Misra
Inferring Smartphone Keypress Via Smartwatch Inertial Sensing, Sougata Sen, Karan Grover, Vigneshwaran Subbaraju, Archan Misra
Research Collection School Of Computing and Information Systems
Due to numerous benefits, sensor-rich smartwatchesand wrist-worn wearable devices are quickly gaining popularity.The popularity of these devices also raises privacy concerns. Inthis paper we explore one such privacy concern: the possibility ofextracting the location of a user’s touch-event on a smartphone,using the inertial sensor data of a smartwatch worn by the useron the same arm. This is a major concern not only because itmight be possible for an attacker to extract private and sensitiveinformation from the inputs provided but also because the attackmode utilises a device (smartwatch) that is distinct from thedevice being attacked (smartphone). Through a user study wefind …
On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee Peng Lim
On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known as …
A Compare-Aggregate Model For Matching Text Sequences, Shuohang Wang, Jing Jiang
A Compare-Aggregate Model For Matching Text Sequences, Shuohang Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a general "compare-aggregate" framework that performs word-level matching followed by aggregation using Convolutional Neural Networks. We particularly focus on the different comparison functions we can use to match two vectors. We use four different datasets to evaluate the model. We find that some simple comparison functions based on element-wise operations can work better than standard neural network and neural tensor network.
Learning Personalized Preference Of Strong And Weak Ties For Social Recommendation, Xin Wang, Steven C. H. Hoi, Martin Ester, Jiajun Bu, Chun Chen
Learning Personalized Preference Of Strong And Weak Ties For Social Recommendation, Xin Wang, Steven C. H. Hoi, Martin Ester, Jiajun Bu, Chun Chen
Research Collection School Of Computing and Information Systems
Recent years have seen a surge of research on social recommendation techniques for improving recommender systems due to the growing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despite the extensive studies, no existing work has attempted to distinguish and learn the personalized preferences between strong and weak ties, two important terms widely used in social sciences, for each individual in social recommendation. In this paper, we first highlight the importance of different types of ties in …
Characterizing Malicious Android Apps By Mining Topic-Specific Data Flow Signatures, Xinli Yang, David Lo, Li Li, Xin Xia, Tegawendé F. Bissyande, Jacques Klein
Characterizing Malicious Android Apps By Mining Topic-Specific Data Flow Signatures, Xinli Yang, David Lo, Li Li, Xin Xia, Tegawendé F. Bissyande, Jacques Klein
Research Collection School Of Computing and Information Systems
Context: State-of-the-art works on automated detection of Android malware have leveraged app descriptions to spot anomalies w.r.t the functionality implemented, or have used data flow information as a feature to discriminate malicious from benign apps. Although these works have yielded promising performance,we hypothesize that these performances can be improved by a better understanding of malicious behavior. Objective: To characterize malicious apps, we take into account both information on app descriptions,which are indicative of apps’ topics, and information on sensitive data flow, which can be relevant todiscriminate malware from benign apps. Method: In this paper, we propose a topic-specific approach to …
Related-Key Secure Key Encapsulation From Extended Computational Bilinear Diffie–Hellman, Brandon Qin, Shengli Liu, Shifeng Sun, Robert H. Deng, Dawu Gu
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 …
Collective Entity Linking In Tweets Over Space And Time, Wen Haw Chong, Ee-Peng Lim, William Cohen
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 …