Computer Sciences Commons^™

Business Practice Of Social Media - Platform And Customer Service Adoption, Shujing Sun, Yang Gao, Huaxia Rui

Research Collection School Of Computing and Information Systems

This paper examines the key drivers in business adoptions of the platform and customer service within the context of social media. We carry out the empirical analyses using the decision trajectories of the international airline industry on Twitter. We find that a firm's decision-making is subject to both peer influence and consumer pressure. Regarding peer influence, we find that the odds of both adoptions increase when the extent of peers' adoption increases. We also identify the distinctive role of consumers. Specifically, before the platform adoption, firms learn about potential consequences from consumer reactions to peers' adoptions. Upon the platform adoption, …

A Study Of Multi-Task And Region-Wise Deep Learning For Food Ingredient Recognition, Jingjing Chen, Bin Zhu, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composition. In reality, two dishes with the same name do not necessarily share the exact list of ingredients. Therefore, the dishes under the same food category are not mandatorily equal in nutrition content. Nevertheless, due to limited datasets available with ingredient labels, the problem of ingredient recognition is often overlooked. Furthermore, as the number of ingredients is expected to be much less than the number of food categories, …

Cross-Thought For Sentence Encoder Pre-Training, Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jingjing Liu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines. Our proposed approach also …

Coupled Hierarchical Transformer For Stance-Aware Rumor Verification In Social Media Conversations, Jianfei Yu, Jing Jiang, Ling Min Serena Khoo, Hai Leong Chieu, Rui Xia

Research Collection School Of Computing and Information Systems

The prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV). A number of previous studies focus on leveraging stance classification to enhance RV with multi-task learning (MTL) methods. However, most of these methods failed to employ pre-trained contextualized embeddings such as BERT, and did not exploit inter-task dependencies by using predicted stance labels to improve the RV task. Therefore, in this paper, to extend BERT to obtain thread representations, we first propose a Hierarchical Transformer1 , which divides each long thread into shorter subthreads, …

Effort-Aware Just-In-Time Defect Identification In Practice: A Case Study At Alibaba, Meng Yan, Xin Xia, Yuanrui Fan, David Lo, Ahmed E. Hassan, Xindong Zhang

Research Collection School Of Computing and Information Systems

Effort-aware Just-in-Time (JIT) defect identification aims at identifying defect-introducing changes just-in-time with limited code inspection effort. Such identification has two benefits compared with traditional module-level defect identification, i.e., identifying defects in a more cost-effective and efficient manner. Recently, researchers have proposed various effort-aware JIT defect identification approaches, including supervised (e.g., CBS+, OneWay) and unsupervised approaches (e.g., LT and Code Churn). The comparison of the effectiveness between such supervised and unsupervised approaches has attracted a large amount of research interest. However, the effectiveness of the recently proposed approaches and the comparison among them have never been investigated in an industrial setting.In …

Bugsinpy: A Database Of Existing Bugs In Python Programs To Enable Controlled Testing And Debugging Studies, Ratnadira Widyasari, Sheng Qin Sim, Camellia Lok, Haodi Qi, Jack Phan, Qijin Tay, Constance Tan, Fiona Wee, Jodie Ethelda Tan, Yuheng Yieh, Brian Goh, Ferdian Thung, Hong Jin Kang, Thong Hoang, David Lo, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

The 2019 edition of Stack Overflow developer survey highlights that, for the first time, Python outperformed Java in terms of popularity. The gap between Python and Java further widened in the 2020 edition of the survey. Unfortunately, despite the rapid increase in Python's popularity, there are not many testing and debugging tools that are designed for Python. This is in stark contrast with the abundance of testing and debugging tools for Java. Thus, there is a need to push research on tools that can help Python developers.One factor that contributed to the rapid growth of Java testing and debugging tools …

Deepcommenter: A Deep Code Comment Generation Tool With Hybrid Lexical And Syntactical Information, Boao Li, Meng Yan, Xin Xia, Xing Hu, Ge Li, David Lo

Research Collection School Of Computing and Information Systems

As the scale of software projects increases, the code comments are more and more important for program comprehension. Unfortunately, many code comments are missing, mismatched or outdated due to tight development schedule or other reasons. Automatic code comment generation is of great help for developers to comprehend source code and reduce their workload. Thus, we propose a code comment generation tool (DeepCommenter) to generate descriptive comments for Java methods. DeepCommenter formulates the comment generation task as a machine translation problem and exploits a deep neural network that combines the lexical and structural information of Java methods. We implement DeepCommenter in …

Enhancing Developer Interactions With Programming Screencasts Through Accurate Code Extraction, Lingfeng Bao, Shengyi Pan, Zhenchang Xing, Xin Xia, David Lo, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Programming screencasts have become a pervasive resource on the Internet, which is favoured by many developers for learning new programming skills. For developers, the source code in screencasts is valuable and important. However, the streaming nature of screencasts limits the choice that they have for interacting with the code. Many studies apply the Optical Character Recognition (OCR) technique to convert screen images into text, which can be easily searched and indexed. However, we observe that the noise in the screen images significantly affects the quality of OCRed code.In this paper, we develop a tool named psc2code, which has two components, …

Leveraging Profanity For Insincere Content Detection: A Neural Network Approach, Swapna Gottipati, Annabel Tan, David Jing Shan Chow, Joel Wee Kiat Lim

Research Collection School Of Computing and Information Systems

Community driven social media sites are rich sources of knowledge and entertainment and at the same vulnerable to the flames or toxic content that can be dangerous to various users of these platforms as well as to the society. Therefore, it is crucial to identify and remove such content to have a better and safe online experience. Manually eliminating flames is tedious and hence many research works focus on machine learning or deep learning models for automated methods. In this paper, we primarily focus on detecting the insincere content using neural network-based learning methods. We also integrated the profanity features …

Semi-Analytical Model For Design And Analysis Of On-Orbit Servicing Architecture, Koki Ho, Hai Wang, Paul A. De Trempe, Tristan Sarton Du Jonchay, Kento Tomita

Research Collection School Of Computing and Information Systems

Robotic on-orbit servicing (OOS) is expected to be a key technology and concept for future sustainable space exploration. This paper develops a novel semi-analytical model for OOS system analysis, responding to the growing needs and ongoing trend of robotic OOS. An OOS infrastructure system is considered whose goal is to provide responsive services to the random failures of a set of customer modular satellites distributed in space (e.g., at the geosynchronous orbit). The considered OOS architecture comprises a servicer that travels and provides module-replacement services to the customer satellites, an on-orbit depot to store the spares, and a series of …

Security Analysis Of Permission Re-Delegation Vulnerabilities In Android Apps, Biniam Fisseha Demissie, Mariano Ceccato, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

The Android platform facilitates reuse of app func- tionalities by allowing an app to request an action from another app through inter-process communication mechanism. This fea- ture is one of the reasons for the popularity of Android, but it also poses security risks to end users because malicious, unprivileged apps could exploit this feature to make privileged apps perform privileged actions on behalf of them.

In our journal paper [4], we investigate the hybrid use of program analysis, genetic algorithm based test generation, natu- ral language processing, machine learning techniques for precise detection of permission re-delegation vulnerabilities in Android apps. …

Erica: Enabling Real-Time Mistake Detection And Corrective Feedback For Free-Weights Exercises, Meeralakshmi Radhakrishnan, Darshana Rathnayake, Koon Han Ong, Inseok Hwang, Archan Misra

Research Collection School Of Computing and Information Systems

We present ERICA, a digital personal trainer for users performing free weights exercises, with two key differentiators: (a) First, unlike prior approaches that either require multiple on-body wearables or specialized infrastructural sensing, ERICA uses a single in-ear "earable" device (piggybacking on a form factor routinely used by millions of gym-goers) and a simple inertial sensor mounted on each weight equipment; (b) Second, unlike prior work that focuses primarily on quantifying a workout, ERICA additionally identifies a variety of fine-grained exercising mistakes and delivers real-time, in-situ corrective instructions. To achieve this, we (a) design a robust approach for user-equipment association that …

Base-Package Recommendation Framework Based On Consumer Behaviours In Iptv Platform, Kuruparan Shanmugalingam, Ruwinda Ranganayanke, Chanka Gunawardhaha, Rajitha Navarathna

Research Collection School Of Computing and Information Systems

Internet Protocol TeleVision (IPTV) provides many services such as live television streaming, time-shifted media, and Video On Demand (VOD). However, many customers do not engage properly with their subscribed packages due to a lack of knowledge and poor guidance. Many customers fail to identify the proper IPTV service package based on their needs and to utilise their current package to the maximum. In this paper, we propose a base-package recommendation model with a novel customer scoring-meter based on customers behaviour. Initially, our paper describes an algorithm to measure customers engagement score, which illustrates a novel approach to track customer engagement …

A Geospatial Analytics Approach To Delineate Trade Areas For Quick Service Restaurants (Qsr) In Singapore, Hui Ting Lim

Research Collection School Of Computing and Information Systems

According to Huff, trade area is defined as “a geographically delineated region containing potential customers for whom there exists a probability greater than zero of their purchasing a given class of products or services offered for sale by a particular firm or by a particular agglomeration of firms”. Several methods to delineate a store trade area have been proposed over the years. For drive-time or travel distance analysis method, the trade area is delineated according to how far or how long the customers are willing to travel to patronise the store. Another commonly used method is the Huff Model which …

Learning Personal Conscientiousness From Footprints In E-Learning Systems, Lo Pang-Yun Ting, Shan Yun Teng, Kun Ta Chuang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Personality inference has received widespread attention for its potential to infer psychological well being, job satisfaction, romantic relationship success, and professional performance. In this research, we focus on Conscientiousness, one of the well studied Big Five personality traits, which determines if a person is self-disciplined, organized, and hard-working. Research has shown that Conscientiousness is related to a person's academic and workplace success. For an expert to evaluate a person's Conscientiousness, long-term observation of the person's behavior at work place or at home is usually required. To reduce this evaluation effort as well as to cope with the increasing trend of …

Reducing Estimation Bias Via Triplet-Average Deep Deterministic Policy Gradient, Dongming Wu, Xingping Dong, Jianbing Shen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

The overestimation caused by function approximation is a well-known property in Q-learning algorithms, especially in single-critic models, which leads to poor performance in practical tasks. However, the opposite property, underestimation, which often occurs in Q-learning methods with double critics, has been largely left untouched. In this article, we investigate the underestimation phenomenon in the recent twin delay deep deterministic actor-critic algorithm and theoretically demonstrate its existence. We also observe that this underestimation bias does indeed hurt performance in various experiments. Considering the opposite properties of single-critic and double-critic methods, we propose a novel triplet-average deep deterministic policy gradient algorithm that …

Boosting Privately: Federated Extreme Gradient Boosting For Mobile Crowdsensing, Yang Liu, Zhuo Ma, Ximeng Liu, Siqi Ma, Surya Nepal, Robert H. Deng, Kui Ren

Research Collection School Of Computing and Information Systems

Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key when applying HE to FL. For the naive HE scheme, the server is set to master the secret key. Such a setting causes a serious problem that if the server does not conduct aggregation before decryption, a chance is left for the server to access the user’s update. Inspired by the two challenges, we propose …

An Empirical Study Of Release Note Production And Usage In Practice, Tingting Bi, Xin Xia, David Lo, John Grundy, Thomas Zimmermann

Research Collection School Of Computing and Information Systems

The release note is one of the most important software artifacts that serves as a bridge for communication among stakeholders. Release notes contain a set of crucial information, such as descriptions of enhancements, improvements, potential issues, development, evolution, testing, and maintenance of projects throughout the whole development lifestyle. A comprehensive understanding of what makes a good release note and how to write one for different stakeholders would be highly beneficial. However, in practice, the release note is often neglected by stakeholders and has not to date been systematically investigated by researchers. In this paper, we conduct a mixed methods study …

Using Data Analytics To Predict Students Score, Nang Laik Ma, Gim Hong Chua

Research Collection School Of Computing and Information Systems

Education is very important to Singapore, and the government has continued to invest heavily in our education system to become one of the world-class systems today. A strong foundation of Science, Technology, Engineering, and Mathematics (STEM) was what underpinned Singapore's development over the past 50 years. PISA is a triennial international survey that evaluates education systems worldwide by testing the skills and knowledge of 15-year-old students who are nearing the end of compulsory education. In this paper, the authors used the PISA data from 2012 and 2015 and developed machine learning techniques to predictive the students' scores and understand the …

How Should We Understand The Digital Economy In Asia? Critical Assessment And Research Agenda, Kai Li, Dan J. Kim, Karl R. Lang, Robert J. Kauffman, Maurizio Naldi

Research Collection School Of Computing and Information Systems

By Asian digital economy, we refer to high-tech developments, business and social transformations, and information-driven changes in the region's growth. We discuss its background and foundations, significance in Asia and contribution to removal of historical barriers in traditional business. We assess how new value chains are transforming country-level involvement in worldwide manufacturing and note "smiling curve theory" predictions about the global value chain in Asia for high-tech firms and their economies. The takeaway is that the digital economy in Asian nations involves revamping business processes through technology innovation, government policies for growth, and digital entrepreneurship. We analyze the "digital economy …

A Secure Flexible And Tampering-Resistant Data Sharing System For Vehicular Social Networks, Jianfei Sun, Hu Xiong, Shufan Zhang, Ximeng Liu, Jiaming Yuan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Vehicular social networks (VSNs) have emerged as the promising paradigm of vehicular networks that can improve traffic safety, relieve traffic congestion and even provide comprehensive social services by sharing vehicular sensory data. To selectively share the sensory data with other vehicles in the vicinity and reduce the local storage burden of vehicles, the vehicular sensory data are usually outsourced to vehicle cloud server for sharing and searching. However, existing data sharing systems for VSNs can neither provide secure selective one-to-many data sharing and verifiable data retrieval over encrypted data nor ensure that the integrity of retrieved data. In this paper, …

Empowering Singapore’S Smes: Fintech P2p Lending — A Lifeline For Smes’ Survival?, Grace Lee, Alan Megargel

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic has sent shock waves throughout the world, pushed countries into lockdown, and wreaked havoc on the world’s people and the global economy. The damage to economies around the world caused by the COVID-19 pandemic has far exceeded that of the global financial crisis. While all businesses suffered hugely, it would be of grave consequence if the small and medium-sized enterprises (SMEs), an important segment of every country’s economy, are unable to withstand the shock wave and sustain themselves beyond this pandemic. The COVID-19 pandemic has highlighted the importance of cash flow or working capital for the viability …

Coinwatch: A Clone-Based Approach For Detecting Vulnerabilities In Cryptocurrencies, Qingze Hum, Wei Jin Tan, Shi Ying Tey, Latasha Lenus, Ivan Homoliak, Yun Lin, Jun Sun

Research Collection School Of Computing and Information Systems

Cryptocurrencies have become very popular in recent years. Thousands of new cryptocurrencies have emerged, proposing new and novel techniques that improve on Bitcoin's core innovation of the blockchain data structure and consensus mechanism. However, cryptocurrencies are a major target for cyber-attacks, as they can be sold on exchanges anonymously and most cryptocurrencies have their codebases publicly available. One particular issue is the prevalence of code clones in cryptocurrencies, which may amplify security threats. If a vulnerability is found in one cryptocurrency, it might be propagated into other cloned cryptocurrencies. In this work, we propose a systematic remedy to this problem, …

Cost-Sensitive Deep Forest For Price Prediction, Chao Ma, Zhenbing Liu, Zhiguang Cao, Wen Song, Jie Zhang, Weiliang Zeng

Research Collection School Of Computing and Information Systems

For many real-world applications, predicting a price range is more practical and desirable than predicting a concrete value. In this case, price prediction can be regarded as a classification problem. Although deep forest is recognized as the best solution to many classification problems, a crucial issue limits its direct application to price prediction, i.e., it treated all the misclassifications equally no matter how far away they are from the real classes, since their impacts on the accuracy are the same. This is unreasonable to price prediction as the misclassification should be as close to the real price range as possible …

Highly Efficient And Scalable Multi-Hop Ride-Sharing, Yixin Xu, Lars Kulik, Renata Borovica‐Gajic, Abdullah Aldwyish, Jianzhong Qi

Research Collection School Of Computing and Information Systems

On-demand ride-sharing services such as Uber and Lyft have gained tremendous popularity over the past decade, largely driven by the omnipresence of mobile devices. Ride-sharing services can provide economic and environmental benefits such as reducing traffic congestion and vehicle emissions. Multi-hop ride-sharing enables passengers to transfer between vehicles within a single trip, which significantly extends the benefits of ride-sharing and provides ride opportunities that are not possible otherwise. Despite its advantages, offering real-time multi-hop ride-sharing services at large scale is a challenging computational task due to the large combination of vehicles and passenger transfer points. To address these challenges, we …

Sfuzz: An Efficient Adaptive Fuzzer For Solidity Smart Contracts, Tai D. Nguyen, Long H. Pham, Jun Sun, Yun Lin, Minh Quang Tran

Research Collection School Of Computing and Information Systems

Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platform. Like traditional programs, smart contracts may contain vulnerabilities. Unlike traditional programs, smart contracts cannot be easily patched once they are deployed. It is thus important that smart contracts are tested thoroughly before deployment. In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz. Compared to existing Solidity fuzzers, sFuzz combines the strategy in the AFL fuzzer and …

Multi-User Verifiable Searchable Symmetric Encryption For Cloud Storage, Xueqiao Liu, Guomin Yang, Guomin Yang

Research Collection School Of Computing and Information Systems

In a cloud data storage system, symmetric key encryption is usually used to encrypt files due to its high efficiency. In order allow the untrusted/semi-trusted cloud storage server to perform searching over encrypted data while maintaining data confidentiality, searchable symmetric encryption (SSE) has been proposed. In a typical SSE scheme, a users stores encrypted files on a cloud storage server and later can retrieve the encrypted files containing specific keywords. The basic security requirement of SSE is that the cloud server learns no information about the files or the keywords during the searching process. Some SSE schemes also offer additional …

Exploring And Evaluating Attributes, Values, And Structures For Entity Alignment, Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performance by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective …

Attribute-Based Keyword Search Over Hierarchical Data In Cloud Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Xinghua Li, Qi Jiang, Junwei Zhang

Research Collection School Of Computing and Information Systems

Searchable encryption (SE) has been a promising technology which allows users to perform search queries over encrypted data. However, the most of existing SE schemes cannot deal with the shared records that have hierarchical structures. In this paper, we devise a basic cryptographic primitive called as attribute-based keyword search over hierarchical data (ABKS-HD) scheme by using the ciphertext-policy attribute-based encryption (CP-ABE) technique, but this basic scheme cannot satisfy all the desirable requirements of cloud systems. The facts that the single keyword search will yield many irrelevant search results and the revoked users can access the unauthorized data with the old …

Perceptions, Expectations, And Challenges In Defect Prediction, Zhiyuan Wan, Xin Xia, Ahmed E. Hassan, David Lo, Jianwei Yin, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Defect prediction has been an active research area for over four decades. Despite numerous studies on defect prediction, the potential value of defect prediction in practice remains unclear. To address this issue, we performed a mixed qualitative and quantitative study to investigate what practitioners think, behave and expect in contrast to research findings when it comes to defect prediction. We collected hypotheses from open-ended interviews and a literature review, followed by a validation survey. We received 395 responses from practitioners. Some of our key findings include: 1) Over 90% of respondents are willing to adopt defect prediction techniques. 2) There …