Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (3560)
- Software Engineering (2205)
- Artificial Intelligence and Robotics (1897)
- Information Security (1108)
- Numerical Analysis and Scientific Computing (1060)
-
- Graphics and Human Computer Interfaces (947)
- Engineering (884)
- Social and Behavioral Sciences (808)
- Business (748)
- Theory and Algorithms (513)
- Computer Engineering (449)
- Programming Languages and Compilers (413)
- Operations Research, Systems Engineering and Industrial Engineering (407)
- OS and Networks (345)
- Communication (326)
- Social Media (264)
- Public Affairs, Public Policy and Public Administration (230)
- Medicine and Health Sciences (197)
- Education (194)
- Transportation (194)
- Management Information Systems (176)
- Data Storage Systems (167)
- E-Commerce (154)
- International and Area Studies (147)
- Technology and Innovation (146)
- Asian Studies (145)
- Health Information Technology (118)
- Higher Education (105)
- Keyword
-
- Machine learning (145)
- Deep learning (129)
- Artificial intelligence (124)
- Social media (82)
- Singapore (73)
-
- Reinforcement learning (72)
- Data mining (70)
- Privacy (67)
- Security (62)
- Cloud computing (60)
- Deep Learning (58)
- Empirical study (55)
- Software engineering (55)
- Optimization (54)
- Online learning (51)
- Visualization (51)
- Neural networks (50)
- Anomaly detection (49)
- Training (49)
- Twitter (49)
- Task analysis (48)
- Blockchain (47)
- Large Language Models (47)
- Natural language processing (47)
- Collaboration (46)
- Feature extraction (45)
- Algorithms (44)
- Access control (43)
- Machine Learning (43)
- Semantics (43)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (8479)
- Dissertations and Theses Collection (Open Access) (189)
- Research Collection Lee Kong Chian School Of Business (59)
- Research Collection Yong Pung How School Of Law (49)
- Research Collection School of Social Sciences (27)
-
- Asian Management Insights (26)
- Research Collection College of Integrative Studies (23)
- Perspectives@SMU (21)
- Research Collection School Of Accountancy (18)
- Dissertations and Theses Collection (15)
- FORCE 2026 (14)
- SMU Press Releases and News (12)
- MITB Thought Leadership Series (11)
- Research Collection School of Computing and Information Systems (11)
- Research Collection Library (10)
- Research@SMU: Connecting the Dots (10)
- PhD Student’s Publications Collection (8)
- LARC Research Publications (7)
- Research Collection School Of Economics (6)
- CCX Research (4)
- SMU Research Data (4)
- Student Publications (4)
- 2024 AI for Research Week (3)
- SCIS Student Publications (3)
- Centre for AI & Data Governance (2019-2025) (2)
- Research Collection Office of Research (2)
- CASTLe: Collection of Articles on Scholarship for Teaching and Learning (1)
- Centre for Computational Law (2022-2025) (1)
- Library Events (1)
- ROSA Journal Articles and Publications (1)
- Publication Type
- File Type
Articles 5101 - 5130 of 9025
Full-Text Articles in Computer Sciences
Country 2.0: Upgrading Cities With Smart Technologies, Steven M. Miller
Country 2.0: Upgrading Cities With Smart Technologies, Steven M. Miller
Asian Management Insights
Advancements in technology are being used to transform our cities into smart cities, but the process is not without its risks.
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.
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.
Robust Object Tracking Via Locality Sensitive Histograms, Shengfeng He, Rynson W.H Lau, Qingxiong Yang, Jiang Wang, Ming-Hsuan Yang
Robust Object Tracking Via Locality Sensitive Histograms, Shengfeng He, Rynson W.H Lau, Qingxiong Yang, Jiang Wang, Ming-Hsuan Yang
Research Collection School Of Computing and Information Systems
This paper presents a novel locality sensitive histogram (LSH) algorithm for visual tracking. Unlike the conventional image histogram that counts the frequency of occurrence of each intensity value by adding ones to the corresponding bin, an LSH is computed at each pixel location, and a floating-point value is added to the corresponding bin for each occurrence of an intensity value. The floating-point value exponentially reduces with respect to the distance to the pixel location where the histogram is computed. An efficient algorithm is proposed that enables the LSHs to be computed in time linear in the image size and the …
Assertion Generation Through Active Learning, Long H. Pham, Jun Sun, Jun Sun
Assertion Generation Through Active Learning, Long H. Pham, Jun Sun, Jun Sun
Research Collection School Of Computing and Information Systems
Program assertions are useful for many program analysis tasks. They are however often missing in practice. In this work, we develop a novel approach for generating likely assertions automatically based on active learning. Our target is complex Java programs which cannot be symbolically executed (yet). Our key idea is to generate candidate assertions based on test cases and then apply active learning techniques to iteratively improve them. The experiments show that active learning really helps to improve the generated assertions.
Feedback-Based Debugging, Yun Lin, Jun Sun, Yinxing Xue, Yang Liu, Jin Song Dong
Feedback-Based Debugging, Yun Lin, Jun Sun, Yinxing Xue, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Software debugging has long been regarded as a time and effort consuming task. In the process of debugging, developers usually need to manually inspect many program steps to see whether they deviate from their intended behaviors. Given that intended behaviors usually exist nowhere but in human mind, the automation of debugging turns out to be extremely hard, if not impossible. In this work, we propose a feedback-based debugging approach, which (1) builds on light-weight human feedbacks on a buggy program and (2) regards the feedbacks as partial program specification to infer suspicious steps of the buggy execution. Given a buggy …
Parametric Model Checking Timed Automata Under Non-Zenoness Assumption, Étienne Andre, Hoang Gia Nguyen, Laure Petrucci, Jun Sun
Parametric Model Checking Timed Automata Under Non-Zenoness Assumption, Étienne Andre, Hoang Gia Nguyen, Laure Petrucci, Jun Sun
Research Collection School Of Computing and Information Systems
Real-time systems often involve hard timing constraints and concurrency, and are notoriously hard to design or verify. Given a model of a real-time system and a property, parametric model-checking aims at synthesizing timing valuations such that the model satisfies the property. However, the counter-example returned by such a procedure may be Zeno (an infinite number of discrete actions occurring in a finite time), which is unrealistic. We show here that synthesizing parameter valuations such that at least one counterexample run is non-Zeno is undecidable for parametric timed automata (PTAs). Still, we propose a semi-algorithm based on a transformation of PTAs …
Towards Distributed Machine Learning In Shared Clusters: A Dynamically-Partitioned Approach, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Shengen Yan
Towards Distributed Machine Learning In Shared Clusters: A Dynamically-Partitioned Approach, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Shengen Yan
Research Collection School Of Computing and Information Systems
Many cluster management systems (CMSs) have been proposed to share a single cluster with multiple distributed computing systems. However, none of the existing approaches can handle distributed machine learning (ML) workloads given the following criteria: high resource utilization, fair resource allocation and low sharing overhead. To solve this problem, we propose a new CMS named Dorm, incorporating a dynamicallypartitioned cluster management mechanism and an utilizationfairness optimizer. Specifically, Dorm uses the container-based virtualization technique to partition a cluster, runs one application per partition, and can dynamically resize each partition at application runtime for resource efficiency and fairness. Each application directly launches …
Search-Driven String Constraint Solving For Vulnerability Detection, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand
Search-Driven String Constraint Solving For Vulnerability Detection, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand
Research Collection School Of Computing and Information Systems
—Constraint solving is an essential technique for detecting vulnerabilities in programs, since it can reason about input sanitization and validation operations performed on user inputs. However, real-world programs typically contain complex string operations that challenge vulnerability detection. State-ofthe-art string constraint solvers support only a limited set of string operations and fail when they encounter an unsupported one; this leads to limited effectiveness in finding vulnerabilities. In this paper we propose a search-driven constraint solving technique that complements the support for complex string operations provided by any existing string constraint solver. Our technique uses a hybrid constraint solving procedure based on …
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.
Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li
Continuous Top-K Monitoring On Document Streams, Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li
Research Collection School Of Computing and Information Systems
The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top-k results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. …
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 …
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim
Research Collection School Of Computing and Information Systems
This study proposes adata-driven approach for benchmarking energy efficiency of warehouse buildings.Our proposed approach provides an alternative to the limitation of existingbenchmarking approaches where a theoretical energy-efficient warehouse was usedas a reference. Our approach starts by defining the questions needed to capturethe characteristics of warehouses relating to energy consumption. Using an existingdata set of warehouse building containing various attributes, we first cluster theminto groups by their characteristics. The warehouses characteristics derivedfrom the cluster assignments along with their past annual energy consumptionare subsequently used to train a decision tree model. The decision tree providesa classification of what factors contribute to different …
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 …
Android Repository Mining For Detecting Publicly Accessible Functions Missing Permission Checks, Huu Hoang Nguyen, Lingxiao Jiang, Thanh Tho Quan
Android Repository Mining For Detecting Publicly Accessible Functions Missing Permission Checks, 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 malware, haven been developed for it. Even though its overall system architecture and many APIs are documented, many other methods and implementation details are not, not to mention potential bugs and vulnerabilities that may be exploited. Manual documentation may also be easily outdated as Android evolves constantly with changing features and higher complexities. Techniques and tool supports are thus needed to automatically extract information from different versions of Android to facilitate whole-system analysis of undocumented code. This paper presents an approach for alleviating the challenges associated …
Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun
Collaborative Topic Regression For Online Recommender Systems: An Online And Bayesian Approach, Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun
Research Collection School Of Computing and Information Systems
Collaborative Topic Regression (CTR) combines ideas of probabilistic matrix factorization (PMF) and topic modeling (such as LDA) for recommender systems, which has gained increasing success in many applications. Despite enjoying many advantages, the existing Batch Decoupled Inference algorithm for the CTR model has some critical limitations: First of all, it is designed to work in a batch learning manner, making it unsuitable to deal with streaming data or big data in real-world recommender systems. Secondly, in the existing algorithm, the item-specific topic proportions of LDA are fed to the downstream PMF but the rating information is not exploited in discovering …
Who Will Leave The Company?: A Large-Scale Industry Study Of Developer Turnover By Mining Monthly Work Report, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Who Will Leave The Company?: A Large-Scale Industry Study Of Developer Turnover By Mining Monthly Work Report, Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is very important for IT companies to retain talented developers and reduce the loss due to developers' departure. Previous studies mainly perform qualitative observations or simple statistical analysis of developers' activity data to understand developer turnover. In this paper, we investigate whether we can predict the turnover of software developers in non-open source companies by automatically analyzing monthly …
Online/Offline Provable Data Possession, Yujue Wang, Qianhong Wu, Bo Qin, Shaohua Tang, Willy Susilo
Online/Offline Provable Data Possession, Yujue Wang, Qianhong Wu, Bo Qin, Shaohua Tang, Willy Susilo
Research Collection School Of Computing and Information Systems
Provable data possession (PDP) allows a user to outsource data with a guarantee that the integrity can be efficiently verified. Existing publicly verifiable PDP schemes require the user to perform expensive computations, such as modular exponentiations for processing data before outsourcing to the storage server, which is not desirable for weak users with limited computation resources. In this paper, we introduce and formalize an online/offline PDP (OOPDP) model, which divides the data processing procedure into offline and online phases. In OOPDP, most of the expensive computations for processing data are performed in the offline phase, and the online phase requires …
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 …
Determining The Impact Regions Of Competing Options In Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung. Yiu
Determining The Impact Regions Of Competing Options In Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung. Yiu
Research Collection School Of Computing and Information Systems
In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a weight vector. The score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting, the user input is a vector (equivalently, a point) in a d-dimensional preference space, where d is the number of data attributes. In this paper we study the problem of determining in which regions of the preference space the weight vector should …
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.
Cryptography And Data Security In Cloud Computing, Zheng Yan, Robert H. Deng, Vijay Varadharajan
Cryptography And Data Security In Cloud Computing, Zheng Yan, Robert H. Deng, Vijay Varadharajan
Research Collection School Of Computing and Information Systems
Cloud computing offers a new way of services by re-arranging various resources and providing them to users based on their demands. It also plays an important role in the next generation mobile networks and services (5G) and Cyber-Physical and Social Computing (CPSC). Storing data in the cloud greatly reduces storage burden of users and brings them access convenience, thus it has become one of the most important cloud services. However, cloud data security, privacy and trust become a crucial issue that impacts the success of cloud computing and may impede the development of 5G and CPSC. First, storing data at …
Provably Secure Attribute Based Signcryption With Delegated Computation And Efficient Key Updating, Hanshu Hong, Yunhao Xia, Zhixin Sun, Ximeng Liu
Provably Secure Attribute Based Signcryption With Delegated Computation And Efficient Key Updating, Hanshu Hong, Yunhao Xia, Zhixin Sun, Ximeng Liu
Research Collection School Of Computing and Information Systems
Equipped with the advantages of flexible access control and fine-grained authentication, attribute based signcryption is diffusely designed for security preservation in many scenarios. However, realizing efficient key evolution and reducing the calculation costs are two challenges which should be given full consideration in attribute based cryptosystem. In this paper, we present a key-policy attribute based signcryption scheme (KP-ABSC) with delegated computation and efficient key updating. In our scheme, an access structure is embedded into user’s private key, while ciphertexts corresponds a target attribute set. Only the two are matched can a user decrypt and verify the ciphertexts. When the access …
A Multi-Agent System For Coordinating Vessel Traffic, Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar
A Multi-Agent System For Coordinating Vessel Traffic, Teck-Hou Teng, Hoong Chuin Lau, Akshat Kumar
Research Collection School Of Computing and Information Systems
Environmental, regulatory and resource constraints affects the safety and efficiency of vessels navigating in and out of the ports. Movement of vessels under such constraints must be coordinated for improving safety and efficiency. Thus, we frame the vessel coordination problem as a multi-agent path-finding (MAPF) problem. We solve this MAPF problem using a Coordinated Path-Finding (CPF) algorithm. Based on the local search paradigm, the CPF algorithm improves on the aggregated path quality of the vessels iteratively. Outputs of the CPF algorithm are the coordinated trajectories. The Vessel Coordination Module (VCM) described here is the module encapsulating our MAPF-based approach for …
Exploiting Anonymity And Homogeneity In Factored Dec-Mdps Through Pre-Computed Binomial Distributions, Rajiv Ranjan Kumar, Pradeep Varakantham
Exploiting Anonymity And Homogeneity In Factored Dec-Mdps Through Pre-Computed Binomial Distributions, Rajiv Ranjan Kumar, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Recent work in decentralized stochastic planning for cooperative agents has focussed on exploiting omogeneity of agents and anonymity in interactions to solve problems with large numbers of agents. Due to a linear optimization formulation that computes joint policy and an objective that indirectly approximates joint expected reward with reward for expected number of agents in all state, action pairs, these approaches have ensured improved scalability. Such an objective closely approximates joint expected reward when there are many agents, due to law of large numbers. However, the performance deteriorates in problems with fewer agents. In this paper, we improve on the …
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 …
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 …
Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang
Discovering Your Selling Points: Personalized Social Influential Tags Exploration, Yuchen Li, Kian-Lee Tan, Ju Fan, Dongxiang Zhang
Research Collection School Of Computing and Information Systems
Social influence has attracted significant attention owing to the prevalence of social networks (SNs). In this paper, we study a new social influence problem, called personalized social influential tags exploration (PITEX), to help any user in the SN explore how she influences the network. Given a target user, it finds a size-k tag set that maximizes this user’s social influence. We prove the problem is NP-hard to be approximated within any constant ratio. To solve it, we introduce a sampling-based framework, which has an approximation ratio of 1−ǫ 1+ǫ with high probabilistic guarantee. To speedup the computation, we devise more …