Open Access. Powered by Scholars. Published by Universities.®

Numerical Analysis and Scientific Computing Commons™

Open Access. Powered by Scholars. Published by Universities.®

Singapore Management University

Discipline
Keyword
Publication Year
Publication
Publication Type

Articles 361 - 390 of 1060

Full-Text Articles in Numerical Analysis and Scientific Computing

Automatic Short Answer Grading Using Siamese Bidirectional Lstm Based Regression, Arya Prabhudesai, Nguyen Binh Duong Ta Apr 2019

Automatic Short Answer Grading Using Siamese Bidirectional Lstm Based Regression, Arya Prabhudesai, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Automatic student assessment plays an important role in education - it provides instant feedback to learners, and at the same time reduces tedious grading workload for instructors. In this paper, we investigate new machine learning techniques for automatic short answer grading (ASAG). The ASAG problem mainly involves assessing short, natural language responses to given questions automatically. While current research in the field has focused either on feature engineering or deep learning, we propose a new approach which combines the advantages of both. More specifically, we propose a Siamese Bidirectional LSTM Neural Network based Regressor in conjunction with handcrafted features for …

Go to article

Exploring And Visualizing Household Electricity Consumption Patterns In Singapore: A Geospatial Analytics Approach, Yong Ying Tan, Tin Seong Kam Apr 2019

Exploring And Visualizing Household Electricity Consumption Patterns In Singapore: A Geospatial Analytics Approach, Yong Ying Tan, Tin Seong Kam

Research Collection School Of Computing and Information Systems

Despite being a small country-state, electricity consumption in Singa-pore is said to be non-homogeneous, as exploratory data analysis showed that the distributions of electricity consumption differ across and within administrative boundaries and dwelling types. Local indicators of spatial association (LISA) were calculated for public housing postal codes using June 2016 data to discover local clusters of households based on electricity consumption patterns. A detailed walkthrough of the analytical process is outlined to describe the R packages and framework used in the R environment. The LISA results are visualized on three levels: country level, regional level and planning subzone level. At …

Go to article

Discursive Power In Contemporary Media Systems: A Comparative Framework, Andreas Jungherr, Oliver Posegga, Jisun An Apr 2019

Discursive Power In Contemporary Media Systems: A Comparative Framework, Andreas Jungherr, Oliver Posegga, Jisun An

Research Collection School Of Computing and Information Systems

Contemporary media systems are in transition. The constellation of organizations, groups, and individuals contributing information to national and international news flows has changed as a result of the digital transformation. The 'hybrid media system' has proven to be one of the most instructive concepts addressing this change. Its focus on the mutually dependent interconnections between various types of media organizations, actors, and publics has inspired prolific research. Yet the concept can tempt researchers to sidestep systematic analyses of information flows and actors’ differing degrees of influence by treating media systems as a black box. To enable large-scale, empirical comparative studies …

Go to article

Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh Mar 2019

Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh

Research Collection School Of Computing and Information Systems

The rise of Initial Coin Offerings (ICO) in recent times and their potential for investment opportunities have investors spending a lot of time researching ICOs or having to follow the crowd. This paper aims to explore four broad factors of ICOs: identity, credibility, investor sentiment, and price movement to develop a framework that is useful in determining ICO quality. Research is shown using data sources including public forums, chat groups, web sites, white papers as well as smart contract details. Finally, a system, based on the framework, is proposed that can be used to detect and regulate ICO activities and …

Go to article

Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen Mar 2019

Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority …

Go to article

Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin Feb 2019

Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin

Research Collection School Of Computing and Information Systems

The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced …

Go to article

Vistanet: Visual Aspect Attention Network For Multimodal Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw Feb 2019

Vistanet: Visual Aspect Attention Network For Multimodal Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Detecting the sentiment expressed by a document is a key task for many applications, e.g., modeling user preferences, monitoring consumer behaviors, assessing product quality. Traditionally, the sentiment analysis task primarily relies on textual content. Fueled by the rise of mobile phones that are often the only cameras on hand, documents on the Web (e.g., reviews, blog posts, tweets) are increasingly multimodal in nature, with photos in addition to textual content. A question arises whether the visual component could be useful for sentiment analysis as well. In this work, we propose Visual Aspect Attention Network or VistaNet, leveraging both textual and …

Go to article

Adaptive Cost-Sensitive Online Classification, Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang Feb 2019

Adaptive Cost-Sensitive Online Classification, Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang

Research Collection School Of Computing and Information Systems

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, …

Go to article

Cryptocurrency Mining On Mobile As An Alternative Monetization Approach, Nguyen Phan Sinh Huynh, Kenny Choo, Rajesh Krishna Balan, Youngki Lee Feb 2019

Cryptocurrency Mining On Mobile As An Alternative Monetization Approach, Nguyen Phan Sinh Huynh, Kenny Choo, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Can cryptocurrency mining (crypto-mining) be a practical ad-free monetization approach for mobile app developers? We conducted a lab experiment and a user study with 228 real Android users to investigate different aspects of mobile crypto-mining. In particular, we show that mobile devices have computational resources to spare and that these can be utilized for crypto-mining with minimal impact on the mobile user experience. We also examined the profitability of mobile crypto-mining and its stability as compared to mobile advertising. In many cases, the profit of mining can exceed mobile advertising's. Most importantly, our study shows that the majority (72%) of …

Go to article

Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach, Chen Zhang, Steven C. H. Hoi Feb 2019

Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach, Chen Zhang, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

This paper explores machine learning to address a problem of Partially Observable Multi-sensor Sequential Change Detection (POMSCD), where only a subset of sensors can be observed to monitor a target system for change-point detection at each online learning round. In contrast to traditional Multisensor Sequential Change Detection tasks where all the sensors are observable, POMSCD is much more challenging because the learner not only needs to detect on-the-fly whether a change occurs based on partially observed multi-sensor data streams, but also needs to cleverly choose a subset of informative sensors to be observed in the next learning round, in order …

Go to article

Discrete Social Recommendation, Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi Feb 2019

Discrete Social Recommendation, Chenghao Liu, Xin Wang, Tao Lu, Wenwu Zhu, Jianling Sun, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, …

Go to article

Robust Estimation Of Similarity Transformation For Visual Object Tracking, Yang Li, Jianke Zhu, Steven C. H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu Feb 2019

Robust Estimation Of Similarity Transformation For Visual Object Tracking, Yang Li, Jianke Zhu, Steven C. H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu

Research Collection School Of Computing and Information Systems

Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale …

Go to article

Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar Feb 2019

Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar

Research Collection School Of Computing and Information Systems

In this paper, we study the evolutionary trends in the collaborative review process of a large open source software system. As expected, the number of reviews, the number of reviews commented on, as well as the number of reviewers, and the interactions between them show increasing trends over time. But unexpectedly, levels of clustering between developers in their interaction networks show a decreasing trend, even as connections between them increase. In the context of our study, clustering is an indicator of developer collaboration, whereas connection points to how intensely developers work together. Thus the trends we observe can inform how …

Go to article

Driving And Effective Data-Ready Culture: How Companies Can Take On A Datadriven Approach To 11 Business, Johnson Poh Jan 2019

Driving And Effective Data-Ready Culture: How Companies Can Take On A Datadriven Approach To 11 Business, Johnson Poh

MITB Thought Leadership Series

TECHNOLOGY has turned the tables in favour of consumers, enabling them to find goods and services faster and access more choices. Companies now compete more intensely to capture consumers’ mindshare and scour for ways to keep their products relevant. But every coin has two sides. While technology has empowered consumers with choice, it has also offered companies a plethora of data to understand consumers better. This puts the odds in favour of companies that can leverage on data to gain consumer insights and meet their business objectives.

Go to article

Mpa-Ibm Project Safer: Sense-Making Analytics For Maritime Event Recognition, Gavin Yeo, Shiau Hong Lim, Laura Wynter, Hifaz Hassan Jan 2019

Mpa-Ibm Project Safer: Sense-Making Analytics For Maritime Event Recognition, Gavin Yeo, Shiau Hong Lim, Laura Wynter, Hifaz Hassan

Research Collection School Of Computing and Information Systems

Project SAFER, a collaboration between the Singapore Maritime and PortAuthority and the IBM Research Singapore Laboratory, was established to conceptualize,develop, and test new analytics-based technologies to enhance port operations and cater tothe increasing growth in vessel traffic in Singapore. The SAFER system addresses areas inmaritime management that have historically required significant human effort. Through acommon set of machine learning–based models, the SAFER system is able to forecast vesselarrival timings and potential traffic hot spots within port waters as well as to detectunusual behavior of vessels, from illegal bunkering (i.e., transfer of marine fuel) to shipsflouting Singapore regulations. The SAFER project …

Go to article

Analysis Of Bus Ride Comfort Using Smartphone Sensor Data, Hoong-Chor Chin, Xingting Pang, Zhaoxia Wang Jan 2019

Analysis Of Bus Ride Comfort Using Smartphone Sensor Data, Hoong-Chor Chin, Xingting Pang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Passenger comfort is an important indicator that is often used to measure the quality of public transport services. It may also be a crucial factor in the passenger’s choice of transport mode. The typical method of assessing passenger comfort is through a passenger interview survey which can be tedious. This study aims to investigate the relationship between bus ride comfort based on ride smoothness and the vehicle’s motion detected by the smartphone sensors. An experiment was carried out on a bus fixed route within the University campus where comfort levels were rated on a 3-point scale and recorded at 5-second …

Go to article

Modeling Location-Based Social Network Data With Area Attraction And Neighborhood Competition, Thanh Nam Doan, Ee-Peng Lim Jan 2019

Modeling Location-Based Social Network Data With Area Attraction And Neighborhood Competition, Thanh Nam Doan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Modeling user check-in behavior helps us gain useful insights about venues as well as the users visiting them. These insights are important in urban planning and recommender system applications. Since check-in behavior is the result of multiple factors, this paper focuses on studying two venue related factors, namely, area attraction and neighborhood competition. The former refers to the ability of a spatial area covering multiple venues to collectively attract check-ins from users, while the latter represents the extent to which a venue can compete with other venues in the same area for check-ins. We first embark on empirical studies to …

Go to article

Online Burst Events Detection Oriented Real-Time Microblog Message Stream, Guozhong Dong, Jun Gao, Liang Huang, Chunlei Shi Jan 2019

Online Burst Events Detection Oriented Real-Time Microblog Message Stream, Guozhong Dong, Jun Gao, Liang Huang, Chunlei Shi

Research Collection School Of Computing and Information Systems

The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become an important research problem in the field of microblog public opinion. Because of the large amount of real-time microblog message stream and irregular language of microblog message, it is important to process real-time microblog message stream and detect burst events accurately. In this paper, an online burst events detection framework is proposed. In this framework, abnormal messages are detected based on sliding time window and two-level hash table. …

Go to article

Semi-Supervised Deep Embedded Clustering, Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu Jan 2019

Semi-Supervised Deep Embedded Clustering, Yazhou Ren, Kangrong Hu, Xinyi Dai, Lili Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Clustering is an important topic in machine learning and data mining. Recently, deep clustering, which learns feature representations for clustering tasks using deep neural networks, has attracted increasing attention for various clustering applications. Deep embedded clustering (DEC) is one of the state-of-theart deep clustering methods. However, DEC does not make use of prior knowledge to guide the learning process. In this paper, we propose a new scheme of semi-supervised deep embedded clustering (SDEC) to overcome this limitation. Concretely, SDEC learns feature representations that favor the clustering tasks and performs clustering assignments simultaneously. In contrast to DEC, SDEC incorporates pairwise constraints …

Go to article

Template-Based Math Word Problem Solvers With Recursive Neural Networks, Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bing Tian Dai, Heng Tao Shen Jan 2019

Template-Based Math Word Problem Solvers With Recursive Neural Networks, Lei Wang, Dongxiang Zhang, Jipeng Zhang, Xing Xu, Lianli Gao, Bing Tian Dai, Heng Tao Shen

Research Collection School Of Computing and Information Systems

The design of automatic solvers to arithmetic math word problems has attracted considerable attention in recent years and a large number of datasets and methods have been published. Among them, Math23K is the largest data corpus that is very helpful to evaluate the generality and robustness of a proposed solution. The best performer in Math23K is a seq2seq model based on LSTM to generate the math expression. However, the model suffers from performance degradation in large space of target expressions. In this paper, we propose a template-based solution based on recursive neural network for math expression construction. More specifically, we …

Go to article

Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia Jan 2019

Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia

Research Collection School Of Computing and Information Systems

Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …

Go to article

Empathetic Computing For Inclusive Application Design, Kenny Choo Tsu Wei Dec 2018

Empathetic Computing For Inclusive Application Design, Kenny Choo Tsu Wei

Dissertations and Theses Collection (Open Access)

The explosive growth of the ecosystem of personal and ambient computing de- vices coupled with the proliferation of high-speed connectivity has enabled ex- tremely powerful and varied mobile computing applications that are used every- where. While such applications have tremendous potential to improve the lives of impaired users, most mobile applications have impoverished designs to be inclusive– lacking support for users with specific disabilities. Mobile app designers today haveinadequate support to design existing classes of apps to support users with specific disabilities, and more so, lack the support to design apps that specifically target these users. One way to resolve …

Go to article

Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang Dec 2018

Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang

Research Collection School Of Computing and Information Systems

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep …

Go to article

An Essential Applied Statistical Analysis Course Using Rstudio With Project-Based Learning For Data Science, Aldy Gunawan, Michelle L. F. Cheong, Johnson Poh Dec 2018

An Essential Applied Statistical Analysis Course Using Rstudio With Project-Based Learning For Data Science, Aldy Gunawan, Michelle L. F. Cheong, Johnson Poh

Research Collection School Of Computing and Information Systems

This paper presents a newpostgraduate level course, named Applied Statistical Analysis with R. Wepresent the course structure, teaching methodology including the assessmentframework and student feedback. The course covers the basic concepts ofstatistics, the knowledge of applying statistical theory in analyzing real dataand the skill of developing statistical applications with R programminglanguage. The first half of each lesson is dedicated to teaching students thestatistical concepts while the second half focuses on the practical aspects ofimplementing the concepts within the RStudio console. The Project-BasedLearning (PBL) approach is adopted to encourage students to apply the knowledgegained to solve real world problems, answer complex …

Go to article

Data Mining Approach To The Identification Of At-Risk Students, Li Chin Ho, Kyong Jin Shim Dec 2018

Data Mining Approach To The Identification Of At-Risk Students, Li Chin Ho, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educators can benefit from the early detection of at-risk students by understanding factors which may lead to failure or drop-out. Further, educators can devise appropriate intervention measures before the students drop out of the course. Our system was built using SAS ® Enterprise Miner (EM) and SAS ® JMP Pro.

Go to article

Data Mining Approach To The Detection Of Suicide In Social Media: A Case Study Of Singapore, Jane H. K. Seah, Kyong Jin Shim Dec 2018

Data Mining Approach To The Detection Of Suicide In Social Media: A Case Study Of Singapore, Jane H. K. Seah, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In this research, we focus on the social phenomenon of suicide. Specifically, we perform social sensing on digital traces obtained from Reddit. We analyze the posts and comments in that are related to depression and suicide. We perform natural language processing to better understand different aspects of human life that relate to suicide.

Go to article

Using Smart Card Data To Model Commuters’ Responses Upon Unexpected Train Delays, Xiancai Tian, Baihua Zheng Dec 2018

Using Smart Card Data To Model Commuters’ Responses Upon Unexpected Train Delays, Xiancai Tian, Baihua Zheng

Research Collection School Of Computing and Information Systems

The mass rapid transit (MRT) network is playing an increasingly important role in Singapore's transit network, thanks to its advantages of higher capacity and faster speed. Unfortunately, due to aging infrastructure, increasing demand, and other reasons like adverse weather condition, commuters in Singapore recently have been facing increasing unexpected train delays (UTDs), which has become a source of frustration for both commuters and operators. Most, if not all, existing works on delay management do not consider commuters' behavior. We dedicate this paper to the study of commuters' behavior during UTDs. We adopt a data-driven approach to analyzing the six-month' real …

Go to article

Fogfly: A Traffic Light Optimization Solution Based On Fog Computing, Quang Tran Minh, Chanh Minh Tran, Tuan An Le, Binh Thai Nguyen, Triet Minh Tran, Rajesh Krishna Balan Dec 2018

Fogfly: A Traffic Light Optimization Solution Based On Fog Computing, Quang Tran Minh, Chanh Minh Tran, Tuan An Le, Binh Thai Nguyen, Triet Minh Tran, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

This paper provides a fog-based approach to solving the traffic light optimization problem which utilizes the Adaptive Traffic Signal Control (ATSC) model. ATSC systems demand the ability to strictly reflect real-time traffic state. The proposed fog computing framework, namely FogFly, aligns with this requirement by its natures in location-awareness, low latency and affordability to the changes in traffic conditions. As traffic data is updated timely and processed at fog nodes deployed close to data sources (i.e., vehicles at intersections) traffic light cycles can be optimized efficiently while virtualized resources available at network edges are efficiently utilized. Evaluation results show that …

Go to article

Mobility-Driven Ble Transmit-Power Adaptation For Participatory Data Muling, Chung-Kyun Han, Archan Misra, Shih-Fen Cheng Dec 2018

Mobility-Driven Ble Transmit-Power Adaptation For Participatory Data Muling, Chung-Kyun Han, Archan Misra, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

This paper analyzes a human-centric framework, called SmartABLE, for easy retrieval of the sensor values from pervasively deployed smart objects in a campus-like environment. In this framework, smartphones carried by campus occupants act as data mules, opportunistically retrieving data from nearby BLE (Bluetooth Low Energy) equipped smart object sensors and relaying them to a backend repository. We focus specifically on dynamically varying the transmission power of the deployed BLE beacons, so as to extend their operational lifetime without sacrificing the frequency of sensor data retrieval. We propose a memetic algorithm-based power adaptation strategy that can handle deployments of thousands of …

Go to article

Towards Mining Comprehensive Android Sandboxes, Tien-Duy B. Le, Lingfeng Bao, David Lo, Debin Gao, Li Li Dec 2018

Towards Mining Comprehensive Android Sandboxes, Tien-Duy B. Le, Lingfeng Bao, David Lo, Debin Gao, Li Li

Research Collection School Of Computing and Information Systems

Android is the most widely used mobile operating system with billions of users and devices. The popularity of Android apps have enticed malware writers to target them. Recently, Jamrozik et al. proposed an approach, named Boxmate, to mine sandboxes to protect Android users from malicious behaviors. In a nutshell, Boxmate analyzes the execution of an app, and collects a list of sensitive APIs that are invoked by that app in a monitoring phase. Then, it constructs a sandbox that can restrict accesses to sensitive APIs not called by the app. In such a way, malicious behaviors that are not observed …

Go to article

First Prev Next Last