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Articles 2161 - 2190 of 4271
Full-Text Articles in Entire DC Network
Use Of Artificial Intelligence, Machine Learning, And Autonomous Technologies In The Mining Industry, Z. Hyder, Keng Siau, Fiona Fui-Hoon Nah
Use Of Artificial Intelligence, Machine Learning, And Autonomous Technologies In The Mining Industry, Z. Hyder, Keng Siau, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Mining is an important industrial and economic sector that plays a major role in the economic development of a country and provides many employment opportunities. Implementation of Artificial Intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with the first application to autonomous trucks. The autonomous technologies provide many economic benefits to the mining industry through cost reduction, productivity improvement, reduction in exposure of workers to hazardous conditions, continuous production, and improved safety. However, implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This paper discusses the current status …
Esg And Corporate Financial Performance: Empirical Evidence From China's Listed Power Generation Companies, Changhong Zhao, Yu Guo, Jiahai Yuan, Mengya Wu, Daiyu Li, Yiou Zhou, Jiangang Kang
Esg And Corporate Financial Performance: Empirical Evidence From China's Listed Power Generation Companies, Changhong Zhao, Yu Guo, Jiahai Yuan, Mengya Wu, Daiyu Li, Yiou Zhou, Jiangang Kang
Research Collection School Of Computing and Information Systems
Nowadays, listed companies around the world are shifting from short-term goals of maximizing profits to long-term sustainable environmental, social, and governance (ESG) goals. People have come to realize that ESG has become an important source of the corporate risk and may affect the company's financial performance and profitability. Recent research shows that good ESG performance could improve the financial performance in some countries. Yet, the question of how does ESG affect financial performance has not been thoroughly discussed and studied in China. In this article, we study China's listed power generation groups to explore the relationship between ESG performance and …
Secure And Efficient Outsourcing Of Large-Scale Overdetermined Systems Of Linear Equations, Shiran Pan, Wen-Tao Zhu, Qiongxiao Wang, Bing Chang
Secure And Efficient Outsourcing Of Large-Scale Overdetermined Systems Of Linear Equations, Shiran Pan, Wen-Tao Zhu, Qiongxiao Wang, Bing Chang
Research Collection School Of Computing and Information Systems
We address overdetermined systems of linear equations, where the number of unknowns is smaller than the number of equations so that only approximate solutions exist instead of exact solutions. Such systems are prevalent in many areas of science and engineering, and finding the optimal solutions is mathematically known as the linear least squares (LLS) problem. Real-world overdetermined systems are often large-scale and computationally expensive to solve. Consequently, we are interested in connecting the LLS problem with cloud computing, where a resource-constrained client outsources the problem to a powerful but untrusted cloud. Among several security considerations is that the input of …
Server-Aided Attribute-Based Signature With Revocation For Resource-Constrained Industrial-Internet-Of-Things Devices, Hui Cui, Robert H. Deng, Joseph K. Liu, Xun Yi, Yingjiu Li
Server-Aided Attribute-Based Signature With Revocation For Resource-Constrained Industrial-Internet-Of-Things Devices, Hui Cui, Robert H. Deng, Joseph K. Liu, Xun Yi, Yingjiu Li
Research Collection School Of Computing and Information Systems
The industrial Internet-of-things (IIoT) can be seen as the usage of Internet-of-things technologies in industries, which provides a way to improve the operational efficiency. An attribute-based signature (ABS) has been a very useful technique for services requiring anonymous authentication in practice, where a signer can sign a message over a set of attributes without disclosing any information about his/her identity, and a signature only attests to the fact that it is created by a signer with several attributes satisfying some claim predicate. However, an ABS scheme requires exponentiation and/or pairing operations in the signature generation and verification algorithms, and hence, …
Anonymous Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Xiaohua Jia, Kan Yang, Robert H. Deng
Anonymous Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Xiaohua Jia, Kan Yang, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the development of sharing economy, crowdsourcing as a distributed computing paradigm has become increasingly pervasive. As one of indispensable services for most crowdsourcing applications, task matching has also been extensively explored. However, privacy issues are usually ignored during the task matching and few existing privacy-preserving crowdsourcing mechanisms can simultaneously protect both task privacy and worker privacy. This paper systematically analyzes the privacy leaks and potential threats in the task matching and proposes a single-keyword task matching scheme for the multirequester/multiworker crowdsourcing with efficient worker revocation. The proposed scheme not only protects data confidentiality and identity anonymity against the crowd-server, …
Lightweight Break-Glass Access Control System For Healthcare Internet-Of-Things, Yang Yang, Ximeng Liu, Robert H. Deng
Lightweight Break-Glass Access Control System For Healthcare Internet-Of-Things, Yang Yang, Ximeng Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
Healthcare Internet-of-things (IoT) has been proposed as a promising means to greatly improve the efficiency and quality of patient care. Medical devices in healthcare IoT measure patients' vital signs and aggregate these data into medical files which are uploaded to the cloud for storage and accessed by healthcare workers. To protect patients' privacy, encryption is normally used to enforce access control of medical files by authorized parties while preventing unauthorized access. In healthcare, it is crucial to enable timely access of patient files in emergency situations. In this paper, we propose a lightweight break-glass access control (LiBAC) system that supports …
Pusc: Privacy-Preserving User-Centric Skyline Computation Over Multiple Encrypted Domains, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Yang Yang
Pusc: Privacy-Preserving User-Centric Skyline Computation Over Multiple Encrypted Domains, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng, Yang Yang
Research Collection School Of Computing and Information Systems
In this paper, we present a new privacy-preserving user-centric skyline computation framework over different encrypted domains, which we referred to as PUSC. With PUSC, a user can flexibly obtain the skyline set from different service providers without disclosing user preferences to third parties in the system. Specifically, we introduce a secure user-defined vector dominance protocol to compare the vector dominance relationship between two encrypted vectors, according to user's preference. This serves as the core protocol in PUSC. Detailed security analysis shows that the proposed PUSC achieves the goal of selecting skyline set according to authorized users' preferences without leaking their …
Embedding Wordnet Knowledge For Textual Entailment, Yunshi Lan, Jing Jiang
Embedding Wordnet Knowledge For Textual Entailment, Yunshi Lan, Jing Jiang
Research Collection School Of Computing and Information Systems
In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call “entailment vectors.” We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that …
Knowledge-Aware Attentive Neural Network For Ranking Question Answer Pairs, Ying Shen, Yang Deng, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei
Knowledge-Aware Attentive Neural Network For Ranking Question Answer Pairs, Ying Shen, Yang Deng, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei
Research Collection School Of Computing and Information Systems
Ranking question answer pairs has attracted increasing attention recently due to its broad applications such as information retrieval and question answering (QA). Significant progresses have been made by deep neural networks. However, background information and hidden relations beyond the context, which play crucial roles in human text comprehension, have received little attention in recent deep neural networks that achieve the state of the art in ranking QA pairs. In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences. Specifically, we develop …
Rumor Detection On Twitter With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Kam-Fai Wong
Rumor Detection On Twitter With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel …
Towards 'Verifying' A Water Treatment System, Jingyi Wang, Jun Sun, Yifan Jia, Shengchao Qin, Zhiwu Xu
Towards 'Verifying' A Water Treatment System, Jingyi Wang, Jun Sun, Yifan Jia, Shengchao Qin, Zhiwu Xu
Research Collection School Of Computing and Information Systems
Modeling and verifying real-world cyber-physical systems is challenging, which is especially so for complex systems where manually modeling is infeasible. In this work, we report our experience on combining model learning and abstraction refinement to analyze a challenging system, i.e., a real-world Secure Water Treatment system (SWaT). Given a set of safety requirements, the objective is to either show that the system is safe with a high probability (so that a system shutdown is rarely triggered due to safety violation) or not. As the system is too complicated to be manually modeled, we apply latest automatic model learning techniques to …
Compositional Reasoning For Shared-Variable Concurrent Programs, Fuyuan Zhang, Yongwang Zhao, David Sanan, Yang Liu, Alwen Tiu, Shang-Wei Lin, Jun Sun
Compositional Reasoning For Shared-Variable Concurrent Programs, Fuyuan Zhang, Yongwang Zhao, David Sanan, Yang Liu, Alwen Tiu, Shang-Wei Lin, Jun Sun
Research Collection School Of Computing and Information Systems
Scalable and automatic formal verification for concurrent systems is always demanding. In this paper, we propose a verification framework to support automated compositional reasoning for concurrent programs with shared variables. Our framework models concurrent programs as succinct automata and supports the verification of multiple important properties. Safety verification and simulations of succinct automata are parallel compositional, and safety properties of succinct automata are preserved under refinements. We generate succinct automata from infinite state concurrent programs in an automated manner. Furthermore, we propose the first automated approach to checking rely-guarantee based simulations between infinite state concurrent programs. We have prototyped our …
Face Detection Using Deep Learning: An Improved Faster Rcnn Approach, Xudong Sun, Pengcheng Wu, Steven C. H. Hoi
Face Detection Using Deep Learning: An Improved Faster Rcnn Approach, Xudong Sun, Pengcheng Wu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this paper, we present a new face detection scheme using deep learning and achieve the state-of-the-art detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark
Identifying Elderlies At Risk Of Becoming More Depressed With Internet-Of-Things, Jiajue Ou, Huiguang Liang, Hwee Xian Tan
Identifying Elderlies At Risk Of Becoming More Depressed With Internet-Of-Things, Jiajue Ou, Huiguang Liang, Hwee Xian Tan
Research Collection School Of Computing and Information Systems
Depression in the elderly is common and dangerous. Current methods to monitor elderly depression, however, are costly, time-consuming and inefficient. In this paper, we present a novel depression-monitoring system that infers an elderly’s changes in depression level based on his/her activity patterns, extracted from wireless sensor data. To do so, we build predictive models to learn the relationship between depression level changes and behaviors using historical data. We also deploy the system for a group of elderly, in their homes, and run the experiments for more than one year. Our experimental study gives encouraging results, suggesting that our IoT system …
Situation-Aware Authenticated Video Broadcasting Over Train-Trackside Wifi Networks, Yongdong Wu, Dengpan Ye, Zhuo Wei, Qian Wang, William Tan, Robert H. Deng
Situation-Aware Authenticated Video Broadcasting Over Train-Trackside Wifi Networks, Yongdong Wu, Dengpan Ye, Zhuo Wei, Qian Wang, William Tan, Robert H. Deng
Research Collection School Of Computing and Information Systems
Live video programmes can bring in better travel experience for subway passengers and earn abundant advertisement revenue for subway operators. However, because the train-trackside channels for video dissemination are easily accessible to anyone, the video traffic are vulnerable to attacks which may cause deadly tragedies. This paper presents a situation-aware authenticated video broadcasting scheme in the railway network which consists of train, on-board sensor, trackside GSM-R (Global System for Mobile Communications-Railway) device, WiFi AP (Access Point), and train control center. Specifically, the scheme has four modules: (1) a train uses its on-board sensors to obtain its speed, location, and RSSI …
Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw
Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw
Research Collection School Of Computing and Information Systems
We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.
Experiences & Challenges With Server-Side Wifi Indoor Localization Using Existing Infrastructure, Dheryta Jaisinghani, Rajesh Krishna Balan, Vinayak Naik, Archan Misra, Youngki Lee
Experiences & Challenges With Server-Side Wifi Indoor Localization Using Existing Infrastructure, Dheryta Jaisinghani, Rajesh Krishna Balan, Vinayak Naik, Archan Misra, Youngki Lee
Research Collection School Of Computing and Information Systems
Real-world deployments of WiFi-based indoor localization in large public venues are few and far between as most state-of-the-art solutions require either client or infrastructure-side changes. Hence, even though high location accuracy is possible with these solutions, they are not practical due to cost and/or client adoption reasons. Majority of the public venues use commercial controller-managed WLAN solutions, that neither allow client changes nor infrastructure changes. In fact, for such venues we have observed highly heterogeneous devices with very low adoption rates for client-side apps. In this paper, we present our experiences in deploying a scalable location system for such venues. …
A Survey On Sensor Calibration In Air Pollution Monitoring Deployments, Balz Maah, Zimu Zhou, Lothar Thiele
A Survey On Sensor Calibration In Air Pollution Monitoring Deployments, Balz Maah, Zimu Zhou, Lothar Thiele
Research Collection School Of Computing and Information Systems
Air pollution is a major concern for public health and urban environments. Conventional air pollution monitoring systems install a few highly accurate, expensive stations at representative locations. Their sparse coverage and low spatial resolution are insufficient to quantify urban air pollution and its impacts on human health and environment. Advances in lowcost portable air pollution sensors have enabled air pollution monitoring deployments at scale to measure air pollution at high spatiotemporal resolution. However, it is challenging to ensure the accuracy of these low-cost sensor deployments because the sensors are more error-prone than high-end sensing infrastructures and they are often deployed …
Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh Nam Doan, Ee-Peng Lim
Pacela: A Neural Framework For User Visitation In Location-Based Social Networks, Thanh Nam Doan, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Check-in prediction using location-based social network data is an important research problem for both academia and industry since an accurate check-in predictive model is useful to many applications, e.g. urban planning, venue recommendation, route suggestion, and context-aware advertising. Intuitively, when considering venues to visit, users may rely on their past observed visit histories as well as some latent attributes associated with the venues. In this paper, we therefore propose a check-in prediction model based on a neural framework called Preference and Context Embeddings with Latent Attributes (PACELA). PACELA learns the embeddings space for the user and venue data as well …
Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram
Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram
Research Collection School Of Computing and Information Systems
Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab (specific to the Southeast Asia region). Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multi-agent optimization technology could potentially help taxi drivers compete against more technologically advanced service …
Deep Specification Mining, Tien-Duy B. Le, David Lo
Deep Specification Mining, Tien-Duy B. Le, David Lo
Research Collection School Of Computing and Information Systems
Formal specifications are essential but usually unavailable in software systems. Furthermore, writing these specifications is costly and requires skills from developers. Recently, many automated techniques have been proposed to mine specifications in various formats including finite-state automaton (FSA). However, more works in specification mining are needed to further improve the accuracy of the inferred specifications. In this work, we propose Deep Specification Miner (DSM), a new approach that performs deep learning for mining FSA-based specifications. Our proposed approach uses test case generation to generate a richer set of execution traces for training a Recurrent Neural Network Based Language Model (RNNLM). …
Striving To Earn More: A Survey Of Work Strategies And Tool Use Among Crowd Workers, Toni Kaplan, Susumu Saito, Kotaro Hara, Jeffrey P. Bigham
Striving To Earn More: A Survey Of Work Strategies And Tool Use Among Crowd Workers, Toni Kaplan, Susumu Saito, Kotaro Hara, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
Earning money is a primary motivation for workers on Amazon Mechanical Turk, but earning a good wage is difficult because work that pays well is not easily identified and can be time-consuming to find. We explored the strategies that both low- and high-earning workers use to find and complete tasks via a survey of 360 workers. Nearly all workers surveyed had earning money as their primary goal, and workers used many of the same tools (browser extensions and scripts) and strategies in an attempt to earn more money, regardless of earning level. However, high-earning workers used more tools, were more …
Autonomous Agents In Snake Game Via Deep Reinforcement Learning, Zhepei Wei, Di Wang, Ming Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou
Autonomous Agents In Snake Game Via Deep Reinforcement Learning, Zhepei Wei, Di Wang, Ming Zhang, Ah-Hwee Tan, Chunyan Miao, You Zhou
Research Collection School Of Computing and Information Systems
Since DeepMind pioneered a deep reinforcement learning (DRL) model to play the Atari games, DRL has become a commonly adopted method to enable the agents to learn complex control policies in various video games. However, similar approaches may still need to be improved when applied to more challenging scenarios, where reward signals are sparse and delayed. In this paper, we develop a refined DRL model to enable our autonomous agent to play the classical Snake Game, whose constraint gets stricter as the game progresses. Specifically, we employ a convolutional neural network (CNN) trained with a variant of Q-learning. Moreover, we …
Lattice-Based Dual Receiver Encryption And More, Daode Zhang, Kai Zhang, Bao Li, Xianhui Lu, Haiyang Xue, Jie Li
Lattice-Based Dual Receiver Encryption And More, Daode Zhang, Kai Zhang, Bao Li, Xianhui Lu, Haiyang Xue, Jie Li
Research Collection School Of Computing and Information Systems
Dual receiver encryption (DRE), proposed by Diament et al. at ACM CCS 2004, is a special extension notion of public-key encryption, which enables two independent receivers to decrypt a ciphertext into a same plaintext. This primitive is quite useful in designing combined public key cryptosystems and denial of service attack-resilient protocols. Up till now, a series of DRE schemes are constructed with bilinear pairing groups. In this work, we introduce the first construction of lattice-based DRE. Our scheme is secure against chosen-ciphertext attacks from the standard Learning with Errors (LWE) assumption with a public key of bit-size about 2nmlogq, where …
Online Active Learning With Expert Advice, Shuji Hao, Peiying Hu, Peilin Zhao, Steven C. H. Hoi, Chunyan Miao
Online Active Learning With Expert Advice, Shuji Hao, Peiying Hu, Peilin Zhao, Steven C. H. Hoi, Chunyan Miao
Research Collection School Of Computing and Information Systems
In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it is nearly impossible and highly costly to label all of the instances. In this article, we address this problem with active learning with expert advice, where the ground truth of an …
A Co-Matching Model For Multi-Choice Reading Comprehension, Shuohang Wang, Mo Yu, Shiyu Chang, Jing Jiang
A Co-Matching Model For Multi-Choice Reading Comprehension, Shuohang Wang, Mo Yu, Shiyu Chang, Jing Jiang
Research Collection School Of Computing and Information Systems
Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer. Experimental results on the RACE dataset demonstrate that our approach achieves state-of-the-art performance.
An Integrated Approach For Effective Injection Vulnerability Analysis Of Web Applications Through Security Slicing And Hybrid Constraint Solving, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand
An Integrated Approach For Effective Injection Vulnerability Analysis Of Web Applications Through Security Slicing And Hybrid Constraint Solving, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand
Research Collection School Of Computing and Information Systems
Malicious users can attack Web applications by exploiting injection vulnerabilities in the source code. This work addresses the challenge of detecting injection vulnerabilities in the server-side code of Java Web applications in a scalable and effective way. We propose an integrated approach that seamlessly combines security slicing with hybrid constraint solving; the latter orchestrates automata-based solving with meta-heuristic search. We use static analysis to extract minimal program slices relevant to security from Web programs and to generate attack conditions. We then apply hybrid constraint solving to determine the satisfiability of attack conditions and thus detect vulnerabilities. The experimental results, using …
Tkse: Trustworthy Keyword Search Over Encrypted Data With Two-Side Verifiability Via Blockchain, Yinghui Zhang, Robert H. Deng, Jiangang Shu, Kan Yang, Dong Zheng
Tkse: Trustworthy Keyword Search Over Encrypted Data With Two-Side Verifiability Via Blockchain, Yinghui Zhang, Robert H. Deng, Jiangang Shu, Kan Yang, Dong Zheng
Research Collection School Of Computing and Information Systems
As a very attractive computing paradigm, cloud computing makes it possible for resource-constrained users to enjoy cost-effective and flexible resources of diversity. Considering the untrustworthiness of cloud servers and the data privacy of users, it is necessary to encrypt the data before outsourcing it to the cloud. However, the form of encrypted storage also poses a series of problems, such as: How can users search over the outsourced data? How to realize user-side verifiability of search results to resist malicious cloud servers? How to enable server-side verifiability of outsourced data to check malicious data owners? How to achieve payment fairness …
Towards Optimal Concolic Testing, Xinyu Wang, Jun Sun, Zhenbang Chen, Peixin Zhang, Jingyi Wang, Yun Lin
Towards Optimal Concolic Testing, Xinyu Wang, Jun Sun, Zhenbang Chen, Peixin Zhang, Jingyi Wang, Yun Lin
Research Collection School Of Computing and Information Systems
Concolic testing integrates concrete execution (e.g., random testing) and symbolic execution for test case generation. It is shown to be more cost-effective than random testing or symbolic execution sometimes. A concolic testing strategy is a function which decides when to apply random testing or symbolic execution, and if it is the latter case, which program path to symbolically execute. Many heuristics-based strategies have been proposed. It is still an open problem what is the optimal concolic testing strategy. In this work, we make two contributions towards solving this problem. First, we show the optimal strategy can be defined based on …
Static Analysis Of Context Leaks In Android Applications, Flavio Toffalini, Jun Sun, Martín Cohoa
Static Analysis Of Context Leaks In Android Applications, Flavio Toffalini, Jun Sun, Martín Cohoa
Research Collection School Of Computing and Information Systems
Android native applications, written in Java and distributed in APK format, are widely used in mobile devices. Their specific pattern of use lets the operating system control the creation and destruction of key resources, such as activities and services (contexts). Programmers are not supposed to interfere with such lifecycle events. Otherwise contexts might be leaked, i.e. they will never be deallocated from memory, or be deallocated too late, leading to memory exhaustion and frozen applications. In practice, it is easy to write incorrect code, which hinders garbage collection of contexts and subsequently leads to context leakage.In this work, we present …