Physical Sciences and Mathematics Commons^™

Out Of Sight, Out Of Mind? How Vulnerable Dependencies Affect Open-Source Projects, Gede Artha Azriadi Prana, Abhishek Sharma, Lwin Khin Shar, Darius Foo, Andrew E. Santosa, Asankhaya Sharma, David Lo

Research Collection School Of Computing and Information Systems

Context: Software developers often use open-source libraries in their project to improve development speed. However, such libraries may contain security vulnerabilities, and this has resulted in several high-profile incidents in re- cent years. As usage of open-source libraries grows, understanding of these dependency vulnerabilities becomes increasingly important. Objective: In this work, we analyze vulnerabilities in open-source libraries used by 450 software projects written in Java, Python, and Ruby. Our goal is to examine types, distribution, severity, and persistence of the vulnerabili- ties, along with relationships between their prevalence and project as well as commit attributes. Method: Our data is obtained …

Sentiment-Oriented Metric Learning For Text-To-Image Retrieval, Quoc Tuan Truong, Hady W. Lauw

Research Collection School Of Computing and Information Systems

In this era of multimedia Web, text-to-image retrieval is a critical function of search engines and visually-oriented online platforms. Traditionally, the task primarily deals with matching a text query with the most relevant images available in the corpus. To an increasing extent, the Web also features visual expressions of preferences, imbuing images with sentiments that express those preferences. Cases in point include photos in online reviews as well as social media. In this work, we study the effects of sentiment information on text-to-image retrieval. Particularly, we present two approaches for incorporating sentiment orientation into metric learning for cross-modal retrieval. Each …

A Fully Dynamic Algorithm For K-Regret Minimizing Sets, Yanhao Wang, Yuchen Li, Raymond Chi-Wing Wong, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Selecting a small set of representatives from a large database is important in many applications such as multi-criteria decision making, web search, and recommendation. The k-regret minimizing set (k-RMS) problem was recently proposed for representative tuple discovery. Specifically, for a large database P of tuples with multiple numerical attributes, the k-RMS problem returns a size-r subset Q of P such that, for any possible ranking function, the score of the top-ranked tuple in Q is not much worse than the score of the kth-ranked tuple in P. Although the k-RMS problem has been extensively studied in the literature, existing methods …

Learning Network-Based Multi-Modal Mobile User Interface Embeddings, Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Rich multi-modal information - text, code, images, categorical and numerical data - co-exist in the user interface (UI) design of mobile applications. UI designs are composed of UI entities supporting different functions which together enable the application. To support effective search and recommendation applications over mobile UIs, we need to be able to learn UI representations that integrate latent semantics. In this paper, we propose a novel unsupervised model - Multi-modal Attention-based Attributed Network Embedding (MAAN) model. MAAN is designed to capture both multi-modal and structural network information. Based on the encoder-decoder framework, MAAN aims to learn UI representations that …

Iotbox: Sandbox Mining To Prevent Interaction Threats In Iot Systems, Hong Jin Kang, Sheng Qin Sim, David Lo

Research Collection School Of Computing and Information Systems

Internet of Things (IoT) apps provide great convenience but exposes us to new safety threats. Unlike traditional software systems, threats may emerge from the joint behavior of multiple apps. While prior studies use handcrafted safety and security policies to detect these threats, these policies may not anticipate all usages of the devices and apps in a smart home, causing false alarms. In this study, we propose to use the technique of mining sandboxes for securing an IoT environment. After a set of behaviors are analyzed from a bundle of apps and devices, a sandbox is deployed, which enforces that previously …

Weakly Supervised Segmentation Via Instance-Aware Propagation, Huang Xin, Qianshu Zhu, Yongtuo Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Peak Response Map (PRM) highlighting the discriminative regions can be extracted from a pre-trained classification network. We can accurately localize instances of each class with the help of these response maps. However, these maps cannot provide reliable information for segmentation even with off-the-shelf object proposals. This is because neither PRM nor the proposals know which regions can be regarded as a complete instance. In this paper, we tackle this problem by proposing an Instance-aware Cue propagation Network (ICN) with a new proposal-matching strategy. In particular, the ICN aims to filter out background distractions and cover the complete instance, while our …

Privacyprimer: Towards Privacy-Preserving Episodic Memory Support For Older Adults, Thivya Kandappu, Vigneshwaran Subbaraju, Qianli Xu

Research Collection School Of Computing and Information Systems

Built-in pervasive cameras have become an integral part of mobile/wearable devices and enabled a wide range of ubiquitous applications with their ability to be "always-on". In particular, life-logging has been identified as a means to enhance the quality of life of older adults by allowing them to reminisce about their own life experiences. However, the sensitive images captured by the cameras threaten individuals' right to have private social lives and raise concerns about privacy and security in the physical world. This threat gets worse when image recognition technologies can link images to people, scenes, and objects, hence, implicitly and unexpectedly …

Enconter: Entity Constrained Progressive Sequence Generation Via Insertion-Based Transformer, Lee Hsun Hsieh, Yang Yin Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Pretrained using large amount of data, autoregressive language models are able to generate high quality sequences. However, these models do not perform well under hard lexical constraints as they lack fine control of content generation process. Progressive insertion-based transformers can overcome the above limitation and efficiently generate a sequence in parallel given some input tokens as constraint. These transformers however may fail to support hard lexical constraints as their generation process is more likely to terminate prematurely. The paper analyses such early termination problems and proposes the ENtity-CONstrained insertion TransformER (ENCONTER), a new insertion transformer that addresses the above pitfall …

Practical Server-Side Wifi-Based Indoor Localization: Addressing Cardinality & Outlier Challenges For Improved Occupancy Estimation, Anuradha Ravi, Archan Misra

Research Collection School Of Computing and Information Systems

Server-side WiFi-based indoor localization offers a compelling approach for passive occupancy estimation (i.e., without requiring active participation by client devices, such as smartphones carried by visitors), but is known to suffer from median error of 6–8 meters. By analyzing the characteristics of an operationally-deployed, WiFi-based passive indoor location system, based on the classical RADAR algorithm, we identify and tackle 2 practical challenges for accurate individual device localization. The first challenge is the low-cardinality issue, whereby only the associated AP generates sufficiently frequent RSSI reports, causing a client to experience large localization error due to the absence of sufficient measurements from …

Efficient Retrieval Of Matrix Factorization-Based Top-K Recommendations: A Survey Of Recent Approaches, Duy Dung Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Top-k recommendation seeks to deliver a personalized list of k items to each individual user. An established methodology in the literature based on matrix factorization (MF), which usually represents users and items as vectors in low-dimensional space, is an effective approach to recommender systems, thanks to its superior performance in terms of recommendation quality and scalability. A typical matrix factorization recommender system has two main phases: preference elicitation and recommendation retrieval. The former analyzes user-generated data to learn user preferences and item characteristics in the form of latent feature vectors, whereas the latter ranks the candidate items based on the …

Dismastd: An Efficient Distributed Multi-Aspect Streaming Tensor Decomposition, Keyu Yang, Yunjun Gao, Yifeng Shen, Baihua Zheng, Lu Chen

Research Collection School Of Computing and Information Systems

Tensor decomposition is a fundamental multidimensional data analysis tool for many data-driven applications, such as social computing, computer vision, and bioinformatics, to name but a few. However, the rapidly increasing streaming data nowadays introduces new challenges to traditional static tensor decomposition. It requires an efficient distributed dynamic tensor decomposition without re-computing the whole tensor from scratch. In this paper, we propose DisMASTD, an efficient distributed multi-aspect streaming tensor decomposition. First, we prove the optimal tensor partitioning problem is NP-hard. Second, we present two heuristic tensor partitioning approaches to ensure the load balancing. Third, we develop a distributed multi-aspect streaming tensor …

Newslink: Empowering Intuitive News Search With Knowledge Graphs, Yueji Yang, Yuchen Li, Anthony Tung

Research Collection School Of Computing and Information Systems

News search tools help end users to identify relevant news stories. However, existing search approaches often carry out in a "black-box" process. There is little intuition that helps users understand how the results are related to the query. In this paper, we propose a novel news search framework, called NEWSLINK, to empower intuitive news search by using relationship paths discovered from open Knowledge Graphs (KGs). Specifically, NEWSLINK embeds both a query and news documents to subgraphs, called subgraph embeddings, in the KG. Their embeddings' overlap induces relationship paths between the involving entities. Two major advantages are obtained by incorporating subgraph …

Digital Sustainability And Its Implications For Finance And Climate Change, Gerard George, Simon J.D. Schillebeeckx

Research Collection Lee Kong Chian School Of Business

As the pandemic forced the entire world to a virtual standstill, nature revived a little. The US emitted 10.3% less CO2 in 2020 than in 2019 and other regions similarly experienced emission declines. Depending on the source, global carbon emissions were down between 4 and 8% in 2020.2 Consumers globally have expressed more concern about sustainability, an observation confirmed by large survey research by Accenture, Kantar, Boston Consulting Group (BCG), and Ipsos.3 In its latest Emissions Gap Report4 , the UN Environment Programme (UNEP) explicitly connected the pandemic to climate change, nature loss, and pollution. Besides the acceleration of business …

Tackling Regional Climate Change And Food Security Issues: An Introduction, Md. Saidul Islam, Edson Kieu

Research Collection Lee Kong Chian School Of Business

Regional organizations are central to effectively addressing the current and future impacts of climate change and food security issues. Climate change and food security issues can only be dealt with through extensive policies that adopt a multi-sector, multi-stakeholder approach. An examination of three regional organizations (the Association of Southeast Asian Nations, the Pacific Islands Forum, and the South Asian Association for Regional Cooperation) can provide new insights into current climate change mitigation policies that will ensure food security for future generations. This opening chapter thus begins with the premise that regional organizations are optimally positioned to address climate change and …

Efficient Algorithms For Trajectory-Aware Mobile Crowdsourcing, Chung-Kyun Han

Dissertations and Theses Collection (Open Access)

Mobile crowdsourcing, a subclass of crowdsourcing dealing with location-specific tasks, is prevalent in our daily life. From sensing urban environment such as noise, air pollution to package delivery, various location-specific tasks are posted on mobile crowdsourcing platforms to tap on the pool of crowdsourced workers. Many digital platforms compete with each other to expand and retain their pool of crowdsourced workers. Comparing with the traditional workforce, crowdsourced workers do not dedicate their time to do tasks fully and have strong spatiotemporal preferences. The ignorance of crowdsourced workers’ mobility patterns and the lack of personalization would lead to crowdsourced workers’ exodus, …

Deep Learning For Anomaly Detection: Challenges, Methods, And Opportunities, Guansong Pang, Longbing Cao, Charu Aggarwal

Research Collection School Of Computing and Information Systems

In this tutorial we aim to present a comprehensive survey of the advances in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data mining and machine learning tasks, but popular deep learning techniques are inapplicable to anomaly detection due to some unique characteristics of anomalies, e.g., rarity, heterogeneity, boundless nature, and prohibitively high cost of collecting large-scale anomaly data. Through this tutorial, audiences would gain a systematic overview of this area, learn the key intuitions, objective functions, underlying assumptions, advantages and disadvantages of different categories of …

Bilateral Variational Autoencoder For Collaborative Filtering, Quoc Tuan Truong, Aghiles Salah, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representations for both sets of objects, i.e., users and items, to predict unknown pairwise interactions. Motivated by the recent successes of deep latent variable models, we propose Bilateral Variational Autoencoder (BiVAE), which arises from a combination of a generative model of dyadic data with two inference models, user- and item-based, parameterized by neural networks. Interestingly, our model can take the form …

Explainable Recommendation With Comparative Constraints On Product Aspects, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item.

In particular, we propose to anchor reference items on the previously adopted items in a user's history. Not only do we aim at providing comparative …

Enhancing Healthcare Professional And Caregiving Staff Informedness With Data Analytics For Chronic Disease Management, Na Liu, Robert John Kauffman

Research Collection School Of Computing and Information Systems

An important area in healthcare to which data analytics can be applied is chronic disease management. The chronic care model is mostly patient-centric, so patients have been considered as the end users of data analytics. The information needs of healthcare providers have been overlooked. Drawing upon the theory of informedness and the transtheoretical model of health behavior change, we use a multicase study approach to investigate the information needs of different caregiving stakeholders in the spectrum of chronic diseases, and how data analytics can be designed to meet the varying needs of professionals and staff to support their informedness.

How Do Users Answer Matlab Questions On Q&A Sites? A Case Study On Stack Overflow And Mathworks, Mahshid Naghashzadeh, Amir Hagshenas, Ashkan Sami, David Lo

Research Collection School Of Computing and Information Systems

MATLAB is an engineering programming language with various toolboxes that has a dedicated Question and Answer (Q&A) platform on the MathWorks website, which is similar to Stack Overflow (SO). Moreover, some MATLAB users ask their questions on SO. This paper aims to compare these two Q&A platforms to see what kind of questions are asked and how developers answer these questions in each platform. The result of our analysis on 80,382 MATLAB questions on SO and 266,367 questions on MathWorks show that MATLAB questions on topics ranging from the MATLAB software installation to questions related to programming received high votes …

Learning To Assess The Quality Of Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia

Research Collection School Of Computing and Information Systems

Due to the limited number of therapists, task-oriented exercises are often prescribed for post-stroke survivors as in-home rehabilitation. During in-home rehabilitation, a patient may become unmotivated or confused to comply prescriptions without the feedback of a therapist. To address this challenge, this paper proposes an automated method that can achieve not only qualitative, but also quantitative assessment of stroke rehabilitation exercises. Specifically, we explored a threshold model that utilizes the outputs of binary classifiers to quantify the correctness of a movements into a performance score. We collected movements of 11 healthy subjects and 15 post-stroke survivors using a Kinect sensor …

Traceable Monero: Anonymous Cryptocurrency With Enhanced Accountability, Yannan Li, Guomin Yang, Wily Susilo, Yong Yu, Man Ho Au, Dongxi Liu

Research Collection School Of Computing and Information Systems

Monero provides a high level of anonymity for both users and their transactions. However, many criminal activities might be committed with the protection of anonymity in cryptocurrency transactions. Thus, user accountability (or traceability) is also important in Monero transactions, which is unfortunately lacking in the current literature. In this paper, we fill this gap by introducing a new cryptocurrency named Traceable Monero to balance the user anonymity and accountability. Our framework relies on a tracing authority, but is optimistic, in that it is only involved when investigations in certain transactions are required. We formalize the system model and security model …

Waste Collection Routing Problem: A Mini-Review Of Recent Heuristic Approaches And Applications, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The waste collection routing problem (WCRP) can be defined as a problem of designing a route to serve all of the customers (represented as nodes) with the least total traveling time or distance, served by the least number of vehicles under specific constraints, such as vehicle capacity. The relevance of WCRP is rising due to its increased waste generation and all the challenges involved in its efficient disposal. This research provides a mini-review of the latest approaches and its application in the collection and routing of waste. Several metaheuristic algorithms are reviewed, such as ant colony optimization, simulated annealing, genetic …

Improving Neural Network Verification Through Spurious Region Guided Refinement, Pengfei Yang, Renjue Li, Jianlin Li, Cheng Chao Huang, Jingyi Wang, Jun Sun, Bai Xue, Lijun Zhang

Research Collection School Of Computing and Information Systems

We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region in the abstraction may be spurious in the sense that it does not contain any true counterexample. Our goal is to identify such spurious regions and use them to guide the abstraction refinement. The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons. …

Privacy-Preserving Federated Deep Learning With Irregular Users, Guowen Xu, Hongwei Li, Yun Zhang, Shengmin Xu, Jianting Ning, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated deep learning has been widely used in various fields. To protect data privacy, many privacy-preserving approaches have also been designed and implemented in various scenarios. However, existing works rarely consider a fundamental issue that the data shared by certain users (called irregular users) may be of low quality. Obviously, in a federated training process, data shared by many irregular users may impair the training accuracy, or worse, lead to the uselessness of the final model. In this paper, we propose PPFDL, a Privacy-Preserving Federated Deep Learning framework with irregular users. In specific, we design a novel solution to reduce …

Can We Classify Cashless Payment Solution Implementations At The Country Level?, Dennis Ng, Robert J. Kauffman, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

This research commentary proposes a 3-D implementation classification framework to assist service providers and business leaders in understanding the kinds of contexts in which more or less successful cashless payment solutions are observed at point-of-sale (PoS) settings. Three constructs characterize the framework: the digitalization of the local implementation environment; the relative novelty of a given payment technology solution in a country at a specific point in time; and the development status of the country’s national infrastructure. The framework is motivated by a need to support cross-country research in this domain. We analyze eight country mini-cases based on an eight-facet (2 …

Tree Effects On Urban Microclimate: Diurnal, Seasonal, And Climatic Temperature Differences Explained By Separating Radiation, Evapotranspiration, And Roughness Effects, Naika Meili, Gabriele Manoli, Paolo Burlando, Jan Carmeliet, Winston T. L. Chow, Andres M. Coutts, Matthias Roth, Erik Velasco, Enrique R. Vivoni, Simone Fatichi

Research Collection School of Social Sciences

Increasing urban tree cover is an often proposed mitigation strategy against urban heat as trees are expected to cool cities through evapotranspiration and shade provision. However, trees also modify wind flow and urban aerodynamic roughness, which can potentially limit heat dissipation. Existing studies show a varying cooling potential of urban trees in different climates and times of the day. These differences are so far not systematically explained as partitioning the individual tree effects is challenging and impossible through observations alone. Here, we conduct numerical experiments removing and adding radiation, evapotranspiration, and aerodynamic roughness effects caused by urban trees using a …

Improving Multi-Hop Knowledge Base Question Answering By Learning Intermediate Supervision Signals, Gaole He, Yunshi Lan, Jing Jiang, Wayne Xin Zhao, Ji Rong Wen

Research Collection School Of Computing and Information Systems

Multi-hop Knowledge Base Question Answering (KBQA) aims to find the answer entities that are multiple hops away in the Knowledge Base (KB) from the entities in the question. A major challenge is the lack of supervision signals at intermediate steps. Therefore, multi-hop KBQA algorithms can only receive the feedback from the final answer, which makes the learning unstable or ineffective. To address this challenge, we propose a novel teacher-student approach for the multi-hop KBQA task. In our approach, the student network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals …

All The Wiser: Fake News Intervention Using User Reading Preferences, Kuan Chieh Lo, Shih Chieh Dai, Aiping Xiong, Jing Jiang, Lun Wei Ku

Research Collection School Of Computing and Information Systems

To address the increasingly significant issue of fake news, we develop a news reading platform in which we propose an implicit approach to reduce people's belief in fake news. Specifically, we leverage reinforcement learning to learn an intervention module on top of a recommender system (RS) such that the module is activated to replace RS to recommend news toward the verification once users touch the fake news. To examine the effect of the proposed method, we conduct a comprehensive evaluation with 89 human subjects and check the effective rate of change in belief but without their other limitations. Moreover, 84% …

Singapore Airlines: Profit Recovery And Aircraft Allocation Models During The Covid-19 Pandemic, Michelle L. F. Cheong, Ulysses M. Z. Chong, Anne N. T. A. Nguyen, Su Yiin Ang, Gabriella P. Djojosaputro, Gordy Adiprasetyo, Kendra L. B. Gadong

Research Collection School Of Computing and Information Systems

COVID-19 has severely impacted the global aviation industry, causing many airlines to downsize or exit the industry. For airlines which attempt to sustain their operations, they will need to respond to the increase in passenger and cargo demand, as countries recover slowly from the crisis due to the availability of vaccines. We built a series of spreadsheet models to first project the COVID-19 recovery rates by countries from 2021 to 2025, then forecast the passenger and cargo demand, using historical data as base figures. Using the financial and operation data, the revenue, expense, and profit can be projected, then an …