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Articles 5461 - 5490 of 8479
Full-Text Articles in Computer Sciences
Should We Use The Sample? Analyzing Datasets Sampled From Twitter's Stream Api, Yazhe Wang, Jamie Callan, Baihua Zheng
Should We Use The Sample? Analyzing Datasets Sampled From Twitter's Stream Api, Yazhe Wang, Jamie Callan, Baihua Zheng
Research Collection School Of Computing and Information Systems
Researchers have begun studying content obtained from microblogging services such as Twitter to address a variety of technological, social, and commercial research questions. The large number of Twitter users and even larger volume of tweets often make it impractical to collect and maintain a complete record of activity; therefore, most research and some commercial software applications rely on samples, often relatively small samples, of Twitter data. For the most part, sample sizes have been based on availability and practical considerations. Relatively little attention has been paid to how well these samples represent the underlying stream of Twitter data. To fill …
Continuous Non-Malleable Key Derivation And Its Application To Related-Key Security, Baodong Qin, Shenli Liu, Tsz Hon Yuen, Robert H. Deng, Kefei Chen
Continuous Non-Malleable Key Derivation And Its Application To Related-Key Security, Baodong Qin, Shenli Liu, Tsz Hon Yuen, Robert H. Deng, Kefei Chen
Research Collection School Of Computing and Information Systems
Related-Key Attacks (RKAs) allow an adversary to observe the outcomes of a cryptographic primitive under not only its original secret key e.g., s, but also a sequence of modified keys ϕ(s), where ϕ is specified by the adversary from a class Φ of so-called Related-Key Derivation (RKD) functions. This paper extends the notion of non-malleable Key Derivation Functions (nm-KDFs), introduced by Faust et al. (EUROCRYPT’14), to continuous nm-KDFs. Continuous nm-KDFs have the ability to protect against any a-priori unbounded number of RKA queries, instead of just a single time tampering attack as in the definition of …
History-Based Controller Design And Optimization For Partially Observable Mdps, Akshat Kumar, Shlomo Zilberstein
History-Based Controller Design And Optimization For Partially Observable Mdps, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Partially observable MDPs provide an elegant framework forsequential decision making. Finite-state controllers (FSCs) are often used to represent policies for infinite-horizon problems as they offer a compact representation, simple-to-execute plans, and adjustable tradeoff between computational complexityand policy size. We develop novel connections between optimizing FSCs for POMDPs and the dual linear programfor MDPs. Building on that, we present a dual mixed integer linear program (MIP) for optimizing FSCs. To assign well-defined meaning to FSC nodes as well as aid in policy search, we show how to associate history-based features with each FSC node. Using this representation, we address another challenging …
Replica Placement For Availability In The Worst Case, Peng Li, Debin Gao, Mike Reiter
Replica Placement For Availability In The Worst Case, Peng Li, Debin Gao, Mike Reiter
Research Collection School Of Computing and Information Systems
We explore the problem of placing object replicas on nodes in a distributed system to maximize the number of objects that remain available when node failures occur. In our model, failing (the nodes hosting) a given threshold of replicas is sufficient to disable each object, and the adversary selects which nodes to fail to minimize the number of objects that remain available. We specifically explore placement strategies based on combinatorial structures called t-packings; provide a lower bound for the object availability they offer; show that these placements offer availability that is c-competitive with optimal; propose an efficient algorithm for computing …
Reliable Patch Trackers: Robust Visual Tracking By Exploiting Reliable Patches, Yang Li, Jianke Zhu, Steven C. H. Hoi
Reliable Patch Trackers: Robust Visual Tracking By Exploiting Reliable Patches, Yang Li, Jianke Zhu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization. In this paper, we propose a new tracking method, Reliable Patch Trackers (RPT), which attempts to identify and exploit the reliable patches that can be tracked effectively through the whole tracking process. Specifically, we present a tracking reliability metric to measure how reliably a patch can be tracked, where a probability model is proposed to estimate the distribution of reliable patches under a sequential Monte Carlo framework. As the reliable patches distributed over …
The Role Of Intermediary In Sustainable Lending: An Economic Analysis Of Crowdfunding Platform, Ling Ge, Zhiling Guo
The Role Of Intermediary In Sustainable Lending: An Economic Analysis Of Crowdfunding Platform, Ling Ge, Zhiling Guo
Research Collection School Of Computing and Information Systems
Is the interest-free crowdfunding platform a promising alternative to the non-zero interest platform? This study investigates the lenders and borrowers’ incentives and choices between an indirect, non-zero interest rate platform intermediated by a field partner and a direct-lending, interest-free platform. We model the field partner as a profit maximizer that filters qualified borrowers to enable the lenders’ capital to be better utilized on the crowdfunding platform. We show that, under certain conditions, both the borrowers and lenders are better off from the existence of the field partner. The existence of field partner is necessary to effectively segment the market and …
Assessing The Opportunities And Challenges With Big Data In The Mobile Payments Ecosystem, Jun Liu, Robert John Kauffman, Dan Ma
Assessing The Opportunities And Challenges With Big Data In The Mobile Payments Ecosystem, Jun Liu, Robert John Kauffman, Dan Ma
Research Collection School Of Computing and Information Systems
Information and communication technology (ICT) is an important driver of mobile payments in the financial services industry. Mobile payments (m-payments) technologies enable new channels for consumer payments for goods and services purchases, and other forms of economic exchange. The m-payments ecosystem involves multiple distinct stakeholders, and a high level of consumer data-sharing. In this paper, we will assess the current m-payments ecosystem, and discuss the challenges and opportunities with big data captured from mpayments transactions. We will also propose new directions to encourage research that will shed the light on how stakeholders can facilitate the successful adoption and realize the …
Probabilistic Inference Techniques For Scalable Multiagent Decision Making, Akshat Kumar, Shlomo Zilberstein, Marc Toussaint
Probabilistic Inference Techniques For Scalable Multiagent Decision Making, Akshat Kumar, Shlomo Zilberstein, Marc Toussaint
Research Collection School Of Computing and Information Systems
Decentralized POMDPs provide an expressive framework for multiagent sequential decision making. However, the complexity of these models---NEXP-Complete even for two agents---has limited their scalability. We present a promising new class of approximation algorithms by developing novel connections between multiagent planning and machine learning. We show how the multiagent planning problem can be reformulated as inference in a mixture of dynamic Bayesian networks (DBNs). This planning-as-inference approach paves the way for the application of efficient inference techniques in DBNs to multiagent decision making. To further improve scalability, we identify certain conditions that are sufficient to extend the approach to multiagent systems …
Aarpa: Combining Mobile And Power-Line Sensing For Fine-Grained Appliance Usage And Energy Monitoring, Nirmalya Roy, Nilavra Pathak, Archan Misra
Aarpa: Combining Mobile And Power-Line Sensing For Fine-Grained Appliance Usage And Energy Monitoring, Nirmalya Roy, Nilavra Pathak, Archan Misra
Research Collection School Of Computing and Information Systems
To promote energy-efficient operations in residential and office buildings, non-intrusive load monitoring (NILM) techniques have been proposed to infer the fine-grained power consumption and usage patterns of appliances from power-line measurement data. Fine-grained monitoring of everyday appliances (such as toasters and coffee makers) can not only promote energy-efficient building operations, but also provide unique insights into the context and activities of individuals. Current building-level NILM techniques are unable to identify the consumption characteristics of relatively low-load appliances, whereas smart-plug based solutions incur significant deployment and maintenance costs. In this paper, we investigate an intermediate architecture, where smart circuit breakers provide …
Dynamic Redeployment To Counter Congestion Or Starvation In Vehicle Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Dynamic Redeployment To Counter Congestion Or Starvation In Vehicle Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet
Research Collection School Of Computing and Information Systems
Extensive usage of private vehicles has led to increased traffic congestion, carbon emissions, and usage of non-renewable resources. These concerns have led to the wide adoption of vehicle sharing (ex: bike sharing, car sharing) systems in many cities of the world. In vehicle-sharing systems, base stations (ex: docking stations for bikes) are strategically placed throughout a city and each of the base stations contain a pre-determined number of vehicles at the beginning of each day. Due to the stochastic and individualistic movement of customers,there is typically either congestion (more than required)or starvation (fewer than required) of vehicles at certain base …
Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei
Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei
Research Collection School Of Computing and Information Systems
From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example, sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the "sparsity problem," but is still far …
Emif: Towards A Scalable And Effective Indexing Framework For Large Scale Music Retrieval, Jialie Shen, Tao Mei, Dacheng Tao, Xuelong Li, Yong Rui
Emif: Towards A Scalable And Effective Indexing Framework For Large Scale Music Retrieval, Jialie Shen, Tao Mei, Dacheng Tao, Xuelong Li, Yong Rui
Research Collection School Of Computing and Information Systems
This article presents a novel indexing framework called EMIF (Effective Music Indexing Framework) to facilitate scalable and accurate content based music retrieval. EMIF system architecture is designed based on a "classification-and-indexing" principle and consists of two main functionality layers: 1) a novel semantic-sensitive classification to identify input music's category and 2) multiple indexing structures - one local indexing structure corresponds to one semantic category. EMIF's layered architecture not only enables superior search accuracy but also reduces query response time significantly. To evaluate the system, a set of comprehensive experimental studies have been carried out using large test collection and EMIF …
Method For Matching Probabilistic Encrypted Data, Hwee Hwa Pang, Xuhua Ding
Method For Matching Probabilistic Encrypted Data, Hwee Hwa Pang, Xuhua Ding
Research Collection School Of Computing and Information Systems
Determining if a first encrypted data of a first data value is equal to a second encrypted data of a second data value. Comprising: a first cyclic group; a second cyclic group including a first element. Applying an operation to the first cyclic group to map its elements to an element in the second cyclic group. Randomly selecting a second element from the first cyclic group; producing the first encrypted data by mapping the second element and the first data value into one or more elements of the first cyclic group. Randomly selecting a third element from the first cyclic …
Online Multimodal Co-Indexing And Retrieval Of Weakly Labeled Web Image Collections, Lei Meng, Ah-Hwee Tan, Cyril Leung, Liqiang Nie, Tan-Seng Chua, Chunyan Miao
Online Multimodal Co-Indexing And Retrieval Of Weakly Labeled Web Image Collections, Lei Meng, Ah-Hwee Tan, Cyril Leung, Liqiang Nie, Tan-Seng Chua, Chunyan Miao
Research Collection School Of Computing and Information Systems
Weak supervisory information of web images, such as captions, tags, and descriptions, make it possible to better understand images at the semantic level. In this paper, we propose a novel online multimodal co-indexing algorithm based on Adaptive Resonance Theory, named OMC-ART, for the automatic co-indexing and retrieval of images using their multimodal information. Compared with existing studies, OMC-ART has several distinct characteristics. First, OMCART is able to perform online learning of sequential data. Second, OMC-ART builds a two-layer indexing structure, in which the first layer co-indexes the images by the key visual and textual features based on the generalized distributions …
Wifi-Based Indoor Line-Of-Sight Identification, Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, Haibin Cai, Yunhao Liu, Lionel M. Ni
Wifi-Based Indoor Line-Of-Sight Identification, Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, Haibin Cai, Yunhao Liu, Lionel M. Ni
Research Collection School Of Computing and Information Systems
Wireless LANs, particularly WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing these applications is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to identify the existence of the Line-Of-Sight (LOS) path acts as a key enabler for adaptive communication, cognitive radios, and robust localization. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with MAC-layer received signal strength. In this paper, we propose two PHY-layer channel-statistics-based features from both the time and frequency domains. To further …
Wifi-Based Indoor Line-Of-Sight Identification, Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, Haibin Cai, Yunhao Liu, Lionel M. Ni
Wifi-Based Indoor Line-Of-Sight Identification, Zimu Zhou, Zheng Yang, Chenshu Wu, Longfei Shangguan, Haibin Cai, Yunhao Liu, Lionel M. Ni
Research Collection School Of Computing and Information Systems
Wireless LANs, particularly WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing these applications is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to identify the existence of the Line-Of-Sight (LOS) path acts as a key enabler for adaptive communication, cognitive radios, and robust localization. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with MAC-layer received signal strength. In this paper, we propose two PHY-layer channel-statistics-based features from both the time and frequency domains. To further …
Integrated Low-Rank-Based Discriminative Feature Learning For Recognition, Pan Zhou, Zhouchen Lin, Chao Zhang
Integrated Low-Rank-Based Discriminative Feature Learning For Recognition, Pan Zhou, Zhouchen Lin, Chao Zhang
Research Collection School Of Computing and Information Systems
Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition …
Technology Investment Decision-Making Under Uncertainty, Robert J. Kauffman, Jun Liu, Dan Ma
Technology Investment Decision-Making Under Uncertainty, Robert J. Kauffman, Jun Liu, Dan Ma
Research Collection School Of Computing and Information Systems
Innovations involving information technology (IT) provide potentially valuable investment opportunities for industry and government organizations. Significant uncertainties are associated with decision-making for IT investment though, a problem that senior executives have been concerned about for a long time. The uncertainties include consumer, market and regulatory responses, IT-driven changes in operational and transactional performance, technology standards and competition, and future market conditions. All these things have an impact on organizations’ willingness to adopt. As a result, traditional capital budgeting, investment experience, and intuition have not been very effective in IT investment decision-making. We propose a new option-based stochastic valuation modeling approach …
Will High-Frequency Trading Practices Transform The Financial Markets In The Asia Pacific Region?, Robert John Kauffman, Yuzhou Hu, Dan Ma
Will High-Frequency Trading Practices Transform The Financial Markets In The Asia Pacific Region?, Robert John Kauffman, Yuzhou Hu, Dan Ma
Research Collection School Of Computing and Information Systems
High-frequency trading (HFT) practices in the global financial markets involve the use of information and communication technologies (ICT), especially the capabilities of high-speed networks, rapid computation, and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds. HFT practices exist because a variety of new technologies have made them possible, and because financial market infrastructure capabilities have also been changing so rapidly. The U.S. markets, such as the National Association for Securities Dealers Automated Quote (NASDAQ) market and the New York Stock Exchange (NYSE), have maintained relevance and …
Showrooming Vs. Competing: How Does Brand Selection Matter?, Qian Tang, Mei Lin
Showrooming Vs. Competing: How Does Brand Selection Matter?, Qian Tang, Mei Lin
Research Collection School Of Computing and Information Systems
In this study, we empirically examine the effect of local shoe store openings on the sales of a competing, major online shoe retailer. Both showrooming and competing effects can play a role: Under the showrooming effect, the local store opening can lead to more online sales for the online retailer, whereas the competing effect created by the local store opening can substitute away the demand for the online retailer. We examine when one effect dominates the other by classifying local stores into single- and mixed-brand stores. We find that the showrooming effect is dominant for a single-brand store opening, and …
Extracting Development Tasks To Navigate Software Documentation, Christoph Treude, Martin P. Robillard, Barthélémy Dagenais
Extracting Development Tasks To Navigate Software Documentation, Christoph Treude, Martin P. Robillard, Barthélémy Dagenais
Research Collection School Of Computing and Information Systems
Knowledge management plays a central role in many software development organizations. While much of the important technical knowledge can be captured in documentation, there often exists a gap between the information needs of software developers and the documentation structure. To help developers navigate documentation, we developed a technique for automatically extracting tasks from software documentation by conceptualizing tasks as specific programming actions that have been described in the documentation. More than 70 percent of the tasks we extracted from the documentation of two projects were judged meaningful by at least one of two developers. We present TaskNavigator, a user interface …
Keeping Pace With Criminals: Designing Patrol Allocation Against Adaptive Opportunistic Criminals, Chao Zhang, Arunesh Sinha, Milind Tambe
Keeping Pace With Criminals: Designing Patrol Allocation Against Adaptive Opportunistic Criminals, Chao Zhang, Arunesh Sinha, Milind Tambe
Research Collection School Of Computing and Information Systems
Police patrols are used ubiquitously to deter crimes in urban areas. A distinctive feature of urban crimes is that criminals react opportunistically to patrol officers' assignments. Compared to strategic attackers (such as terrorists) with a well-laid out plan, opportunistic criminals are less strategic in planning attacks and more flexible in executing them. In this paper, our goal is to recommend optimal police patrolling strategy against such opportunistic criminals. We first build a game-theoretic model that captures the interaction between officers and opportunistic criminals. However, while different models of adversary behavior have been proposed, their exact form remains uncertain. Rather than …
Heuristic Collective Learning For Efficient And Robust Emergence Of Social Norms, Jianye Hao, Jun Sun, Dongping Huang, Yi Cai, Chao Yu
Heuristic Collective Learning For Efficient And Robust Emergence Of Social Norms, Jianye Hao, Jun Sun, Dongping Huang, Yi Cai, Chao Yu
Research Collection School Of Computing and Information Systems
In multiagent systems, social norms is a useful technique in regulating agents’ behaviors to achieve coordination or cooperation among agents. One important research question is to investigate how a desirable social norm can be evolved in a bottom-up manner through local interactions. In this paper, we propose two novel learning strategies under the collective learning framework: collective learning EV-l and collective learning EV-g, to efficiently facilitate the emergence of social norms. Experimental results show that both learning strategies can support the emergence of desirable social norms more efficiently in a much broader range of multiagent interaction scenarios than previous work, …
Relative Localization Of Rfid Tags Using Spatial-Temporal Phase Profiling, Longfei Shangguan, Zheng Yang, Alex X. Liu, Zimu Zhou, Yunhao Liu
Relative Localization Of Rfid Tags Using Spatial-Temporal Phase Profiling, Longfei Shangguan, Zheng Yang, Alex X. Liu, Zimu Zhou, Yunhao Liu
Research Collection School Of Computing and Information Systems
Many object localization applications need the relative locations of a set of objects as oppose to their absolute locations. Although many schemes for object localization using Radio Frequency Identification (RFID) tags have been proposed, they mostly focus on absolute object localization and are not suitable for relative object localization because of large error margins and the special hardware that they require. In this paper, we propose an approach called Spatial-Temporal Phase Profiling (STPP) to RFID based relative object localization. The basic idea of STPP is that by moving a reader over a set of tags during which the reader continuously …
Non-Invasive Detection Of Moving And Stationary Human With Wifi, Chenshu Wu, Zheng Yang, Zimu Zhou, Xuefeng Liu, Yunhao Liu, Jiannong Cao
Non-Invasive Detection Of Moving And Stationary Human With Wifi, Chenshu Wu, Zheng Yang, Zimu Zhou, Xuefeng Liu, Yunhao Liu, Jiannong Cao
Research Collection School Of Computing and Information Systems
Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of channel profile in human-free environments. Based on in-depth understanding of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive …
Discovering The Rise And Fall Of Software Engineering Ideas From Scholarly Publication Data, Subhajit Datta, Santonu Sarkar, Sajeev A. S. M., Nishant Kumar
Discovering The Rise And Fall Of Software Engineering Ideas From Scholarly Publication Data, Subhajit Datta, Santonu Sarkar, Sajeev A. S. M., Nishant Kumar
Research Collection School Of Computing and Information Systems
For researchers and practitioners of a relatively young discipline like software engineering, an enduring concern is to identify the acorns that will grow into oaks -- ideas remaining most current in the long run. Additionally, it is interesting to know how the ideas have risen in importance, and fallen, perhaps to rise again. We analyzed a corpus of 19,000+ papers written by 21,000+ authors across 16 software engineering publication venues from 1975 to 2010, to empirically determine the half-life of software engineering research topics. We adapted existing measures of half-life as well as defined a specific measure based on publication …
Beyond Traits: Social Context Based Personality Model, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann
Beyond Traits: Social Context Based Personality Model, Jaroslaw Kochanowicz, Ah-Hwee Tan, Daniel Thalmann
Research Collection School Of Computing and Information Systems
The relation between individual’s personality and environmental context is a key issue in psychology, recently also in character simulations. This paper contributes to both domains by proposing a socio-cognitive, contextual personality model - a new voice in a century old problem of personality, but also an approach to simulating groups of more humanlike agents. After analyzing the influence of popularity of ‘trait personality models’ on psychology and computer simulation, we propose Social Context based Personality model - a continuation and specification of the Cognitive-Affective Personality System theory. The discussion, model and implementation are provided, followed by an example application in …
Report On The Fg 2015 Video Person Recognition Evaluation, J.R. Beveridge, H. Zhang, B.A. Draper, P.J. Flynn, Z. Feng, P. Huber, J. Kittler, Zhiwu Huang, Li S., Li Y., M. Kan, R. Wang, S. Shan, X. Chen, Li H., G. Hua, V. Struc, J. Krizaj, C. Ding, D. Tao
Report On The Fg 2015 Video Person Recognition Evaluation, J.R. Beveridge, H. Zhang, B.A. Draper, P.J. Flynn, Z. Feng, P. Huber, J. Kittler, Zhiwu Huang, Li S., Li Y., M. Kan, R. Wang, S. Shan, X. Chen, Li H., G. Hua, V. Struc, J. Krizaj, C. Ding, D. Tao
Research Collection School Of Computing and Information Systems
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. Five groups from around the world …
Ambient Rendezvous: Energy Efficient Neighbor Discovery Via Acoustic Sensing, Keyu Wang, Zheng Yang, Zimu Zhou, Yunhao Liu, Lionel M. Ni
Ambient Rendezvous: Energy Efficient Neighbor Discovery Via Acoustic Sensing, Keyu Wang, Zheng Yang, Zimu Zhou, Yunhao Liu, Lionel M. Ni
Research Collection School Of Computing and Information Systems
The continual proliferation of mobile devices has stimulated the development of opportunistic encounter-based networking and has spurred a myriad of proximity-based mobile applications. A primary cornerstone of such applications is to discover neighboring devices effectively and efficiently. Despite extensive protocol optimization, current neighbor discovery modalities mainly rely on radio interfaces, whose energy and wake up delay required to initiate, configure and operate these protocols hamper practical applicability. Unlike conventional schemes that actively emit radio tones, we exploit ubiquitous audio events to discover neighbors passively. The rationale is that spatially adjacent neighbors tend to share similar ambient acoustic environments. We propose …
Mining Patterns Of Unsatisfiable Constraints To Detect Infeasible Paths, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar
Mining Patterns Of Unsatisfiable Constraints To Detect Infeasible Paths, Sun Ding, Hee Beng Kuan Tan, Lwin Khin Shar
Research Collection School Of Computing and Information Systems
Detection of infeasible paths is required in many areas including test coverage analysis, test case generation, security vulnerability analysis, etc. Existing approaches typically use static analysis coupled with symbolic evaluation, heuristics, or path-pattern analysis. This paper is related to these approaches but with a different objective. It is to analyze code of real systems to build patterns of unsatisfiable constraints in infeasible paths. The resulting patterns can be used to detect infeasible paths without the use of constraint solver and evaluation of function calls involved, thus improving scalability. The patterns can be built gradually. Evaluation of the proposed approach shows …