Computer Sciences Commons^™

Robot: Robustness-Oriented Testing For Deep Learning Systems, Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng

Research Collection School Of Computing and Information Systems

Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a. bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by …

Characterization And Prediction Of Questions Without Accepted Answers On Stack Overflow, Mohamad Yazdaninia, David Lo, Ashkan Sami

Research Collection School Of Computing and Information Systems

A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is important, so these CQAs have a considerable impact on the software development process. However, if developers do not get their expected answers, the websites will not be useful, and software development time will increase. Stack Overflow is the most popular CQA concerning programming problems. According to its rules, the only sign that shows a question poser has achieved the desired answer is …

Sguard: Towards Fixing Vulnerable Smart Contracts Automatically, Tai D. Nguyen, Long H. Pham, Jun Sun

Research Collection School Of Computing and Information Systems

Smart contracts are distributed, self-enforcing programs executing on top of blockchain networks. They have the potential to revolutionize many industries such as financial institutes and supply chains. However, smart contracts are subject to code-based vulnerabilities, which casts a shadow on its applications. As smart contracts are unpatchable (due to the immutability of blockchain), it is essential that smart contracts are guaranteed to be free of vulnerabilities. Unfortunately, smart contract languages such as Solidity are Turing-complete, which implies that verifying them statically is infeasible. Thus, alternative approaches must be developed to provide the guarantee. In this work, we develop an approach …

Adaptive Operating Hours For Improved Performance Of Taxi Fleets, Rajiv Ranjan Kumar, Pradeep Varakantham, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

Taxi fleets and car aggregation systems are an important component of the urban public transportation system. Taxis and cars in taxi fleets and car aggregation systems (e.g., Uber) are dependent on a large number of self-controlled and profit-driven taxi drivers, which introduces inefficiencies in the system. There are two ways in which taxi fleet performance can be optimized: (i) Operational decision making: improve assignment of taxis/cars to customers, while accounting for future demand; (ii) strategic decision making: optimize operating hours of (taxi and car) drivers. Existing research has primarily focused on the operational decisions in (i) and we focus on …

Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Parachuri, Akshat Kumar

Research Collection School Of Computing and Information Systems

In modular reinforcement learning (MRL), a complex decision making problem is decomposed into multiple simpler subproblems each solved by a separate module. Often, these subproblems have conflicting goals, and incomparable reward scales. A composable decision making architecture requires that even the modules authored separately with possibly misaligned reward scales can be combined coherently. An arbitrator should consider different module’s action preferences to learn effective global action selection. We present a novel framework called GRACIAS that assigns fine-grained importance to the different modules based on their relevance in a given state, and enables composable decision making based on modern deep RL …

Cross-Modal Food Retrieval: Learning A Joint Embedding Of Food Images And Recipes With Semantic Consistency And Attention Mechanism;, Hao Wang, Doyen Sahoo, Chenghao Liu, Ke Shu, Achananuparp Palakorn, Ee Peng Lim, Steven Hoi

Research Collection School Of Computing and Information Systems

Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc. In this paper, we investigate cross-modal retrieval between food images and cooking recipes. The goal is to learn an embedding of images and recipes in a common feature space, such that the corresponding image-recipe embeddings lie close to one another. Two major challenges in addressing this problem are 1) large intra-variance and small inter-variance across cross-modal food data; and 2) difficulties in obtaining discriminative recipe representations. To address these …

Interactive Search Vs. Automatic Search: An Extensive Study On Video Retrieval, Phuong-Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This article conducts user evaluation to study the performance difference between interactive and automatic search. Particularly, the study aims to provide empirical insights of how the performance landscape of video search changes, with tens of thousands of concept detectors freely available to exploit for query formulation. We compare three types of search modes: free-to-play (i.e., search from scratch), non-free-to-play (i.e., search by inspecting results provided by automatic search), and automatic search including concept-free and concept-based retrieval paradigms. The study involves a total of 40 participants; each performs interactive search over 15 queries of various difficulty levels using two search modes …

Automated Query Reformulation For Efficient Search Based On Query Logs From Stack Overflow, Kaibo Cao, Chunyang Chen, Sebastian Baltes, Christoph Treude, Xiang Chen

Research Collection School Of Computing and Information Systems

As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we …

Characterising The Knowledge About Primitive Variables In Java Code Comments, Mahfouth Alghamdi, Shinpei Hayashi, Takashi Kobayashi, Christoph Treude

Research Collection School Of Computing and Information Systems

Primitive types are fundamental components available in any programming language, which serve as the building blocks of data manipulation. Understanding the role of these types in source code is essential to write software. Little work has been conducted on how often these variables are documented in code comments and what types of knowledge the comments provide about variables of primitive types. In this paper, we present an approach for detecting primitive variables and their description in comments using lexical matching and advanced matching. We evaluate our approaches by comparing the lexical and advanced matching performance in terms of recall, precision, …

How Do Software Developers Use Github Actions To Automate Their Workflows?, Timothy Kinsman, Mairieli Wessel, Marco Gerosa, Christoph Treude

Research Collection School Of Computing and Information Systems

Automated tools are frequently used in social coding repositories to perform repetitive activities that are part of the distributed software development process. Recently, GitHub introduced GitHub Actions, a feature providing automated work-flows for repository maintainers. Although several Actions have been built and used by practitioners, relatively little has been done to evaluate them. Understanding and anticipating the effects of adopting such kind of technology is important for planning and management. Our research is the first to investigate how developers use Actions and how several activity indicators change after their adoption. Our results indicate that, although only a small subset of …

Research Artifact: The Potential Of Meta-Maintenance On Github, Hideaki Hata, Raula Kula, Takashi Ishio, Christoph Treude

Research Collection School Of Computing and Information Systems

This is a research artifact for the paper “Same File, Different Changes: The Potential of Meta-Maintenance on GitHub”. This artifact is a data repository including a list of studied 32,007 repositories on GitHub, a list of targeted 401,610,677 files, the results of the qualitative analysis for RQ2, RQ3, and RQ4, the results of the quantitative analysis for RQ5, and survey material for RQ6. The purpose of this artifact is enabling researchers to replicate our mixed-methods results of the paper, and to reuse the results of our exploratory study for further software engineering research. This research artifact is available at https://github.com/NAIST-SE/MetaMaintenancePotential …

Same File, Different Changes: The Potential Of Meta-Maintenance On Github, Hideaki Hata, Raula Kula, Takashi Ishio, Christoph Treude

Research Collection School Of Computing and Information Systems

Online collaboration platforms such as GitHub have provided software developers with the ability to easily reuse and share code between repositories. With clone-and-own and forking becoming prevalent, maintaining these shared files is important, especially for keeping the most up-to-date version of reused code. Different to related work, we propose the concept of meta-maintenance-i.e., tracking how the same files evolve in different repositories with the aim to provide useful maintenance opportunities to those files. We conduct an exploratory study by analyzing repositories from seven different programming languages to explore the potential of meta-maintenance. Our results indicate that a majority of active …

The Shifting Sands Of Motivation: Revisiting What Drives Contributors In Open Source, Marco Gerosa, Igor Wiese, Bianca Trinkenreich, Georg Link, Gregorio Robles, Christoph Treude, Igor Steinmacher, Anita Sarma

Research Collection School Of Computing and Information Systems

Open Source Software (OSS) has changed drastically over the last decade, with OSS projects now producing a large ecosystem of popular products, involving industry participation, and providing professional career opportunities. But our field's understanding of what motivates people to contribute to OSS is still fundamentally grounded in studies from the early 2000s. With the changed landscape of OSS, it is very likely that motivations to join OSS have also evolved. Through a survey of 242 OSS contributors, we investigate shifts in motivation from three perspectives: (1) the impact of the new OSS landscape, (2) the impact of individuals' personal growth …

Business-Driven Technical Debt Prioritization: An Industrial Case Study, Rodrigo Rebouças De Almeida, Rafael Do Nascimento Ribeiro, Christoph Treude, ‪Uirá Kulesza

Research Collection School Of Computing and Information Systems

Incorporating the business perspective into prioritizing technical debt is essential to contribute to decision making in industry. In this paper, we evolve and evaluate a businessdriven approach for technical debt prioritization. The approach was evaluated during a five-months industrial case study with business and technical stakeholders’ active participation. The results show that the approach contributed to aligning business criteria between the business and technical stakeholders. We also observed a downward trend in the amount of technical debt that affects high-value business assets. Moreover, we identified eight business factors that affect the decision making related to the prioritization of technical debt. …

Prototypical Contrastive Learning Of Unsupervised Representations, Junnan Li, Pan Zhou, Caiming Xiong, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that bridges contrastive learning with clustering. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it encodes semantic structures discovered by clustering into the learned embedding space. Specifically, we introduce prototypes as latent variables to help find the maximum-likelihood estimation of the network parameters in an Expectation-Maximization framework. We iteratively perform E-step as finding the distribution of prototypes via clustering and M-step as optimizing the network via contrastive learning. We propose ProtoNCE loss, a generalized version of the InfoNCE loss for contrastive …

Tensor Low-Rank Representation For Data Recovery And Clustering, Pan Zhou, Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Multi-way or tensor data analysis has attracted increasing attention recently, with many important applications in practice. This article develops a tensor low-rank representation (TLRR) method, which is the first approach that can exactly recover the clean data of intrinsic low-rank structure and accurately cluster them as well, with provable performance guarantees. In particular, for tensor data with arbitrary sparse corruptions, TLRR can exactly recover the clean data under mild conditions; meanwhile TLRR can exactly verify their true origin tensor subspaces and hence cluster them accurately. TLRR objective function can be optimized via efficient convex programing with convergence guarantees. Besides, we …

Oidpr: Optimized Insulin Dosage Via Privacy‐Preserving Reinforcement Learning, Zuobin Ying, Yun Zhang, Shuanglong Cao, Shengmin Xu, Maode Ma

Research Collection School Of Computing and Information Systems

The precision of insulin dosage is essential in the process of diabetes treatment. The fact is providing precise dosage is almost impossible for clinicians since blood sugar levels are dynamically affected by many factors. Even though some auxiliary dosing systems have been proposed, the required real‐time physical data about the health situation of diabetics is still hard to synchronize to the end‐devices instantly. The traditional personalized drug delivery frameworks for accurate dosing of insulin always collect and transmit medical data in cleartext, which raises privacy problems. In this article, we propose a framework for an optimized insulin dosage via privacy‐preserving …

Digital Banking Accelerator: A Service-Oriented Architecture Starter Kit For Banks, Alan @ Ali Madjelisi Megargel, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

Digital banking refers to the delivery of interactive financial services through online mechanisms which include web and mobile apps. The main barrier to digital banking for traditional banks, is the presence of legacy core banking systems. Service Oriented Architecture (SOA) is a key enabler to overcome this barrier, and a bank’s level of SOA maturity influences its time-to-market capability of delivering new innovative digital banking solutions. However, most traditional banks struggle with implementing an SOA due to a number of technology and organizational challenges, and the overall steep learning curve. This paper proposes a Digital Banking Accelerator, a “starter kit” …

A Novel Dynamic Analysis Infrastructure To Instrument Untrusted Execution Flow Across User-Kernel Spaces, Jiaqi Hong, Xuhua Ding

Research Collection School Of Computing and Information Systems

Code instrumentation and hardware based event trapping are two primary approaches used in dynamic malware analysis systems. In this paper, we propose a new approach called Execution Flow Instrumentation (EFI) where the analyzer execution flow is interleaved with the target flow in user- and kernel-mode, at junctures flexibly chosen by the analyzer at runtime. We also propose OASIS as the system infrastructure to realize EFI with virtues of the current two approaches, however without their drawbacks. Despite being securely and transparently isolated from the target, the analyzer introspects and controls it in the same native way as instrumentation code. We …

Leveraging Multiple Relations For Fashion Trend Forecasting Based On Social Media, Yujuan Ding, Yunshan Ma, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

—Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real complex fashion trends. Moreover, the mainstream solutions for this task are still statistical-based and solely focus on time-series data modeling, which limit the forecast accuracy. Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends. Specifically, it focused on detailed fashion …

Fine-Grained And Controllably Redactable Blockchain With Harmful Data Forced Removal, Huiying Hou, Shidi Hao, Jiaming Yuan, Shengmin Xu, Yunlei Zhao

Research Collection School Of Computing and Information Systems

Notoriously, immutability is one of the most striking properties of blockchains. As the data contained in blockchains may be compelled to redact for personal and legal reasons, immutability needs to be skillfully broken. In most existing redactable blockchains, fine-grained redaction and effective deletion of harmful data are mutually exclusive. To close the gap, we propose a fine-grained and controllably redactable blockchain with harmful data forced removal. In the scheme, the originator of the transaction has fine-grained control over who can perform the redaction and which portions of the transaction can be redacted. The redaction transaction is performed after collecting enough …

Dialogue State Tracking With Incremental Reasoning, Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-theart methods in terms of joint …

Immigrant Families' Health-Related Information Behavior On Instant Messaging Platforms: Health-Related Information Exchange In Immigrant Family Groups On Instant Messaging Platforms, Lev Poretski, Taamannae Taabassum, Anthony Tang

Research Collection School Of Computing and Information Systems

For immigrant families, instant messaging family groups are a common platform for sharing and discussing health-related information. Immigrants often maintain contact with their family abroad and trust information in shared IM family groups more than the information from local authorities and sources. In this study, we aimed to understand health-related information behaviors of immigrant families in their IM family groups. Based on the interviews with 6 participants from immigrant families to Canada, we found that immigrant families’ discourse on IM platforms is motivated by love and care for other family members. The families used local and international sources of information, …

More Kawaii Than A Real-Person Live Streamer: Understanding How The Otaku Community Engages With And Perceives Virtual Youtubers, Zhicong Lu, Chenxinran Shen, Jiannan Li, Hong Shen, Daniel Wigdor

Research Collection School Of Computing and Information Systems

Live streaming has become increasingly popular, with most streamers presenting their real-life appearance. However, Virtual YouTubers (VTubers), virtual 2D or 3D avatars that are voiced by humans, are emerging as live streamers and attracting a growing viewership in East Asia. Although prior research has found that many viewers seek real-life interpersonal interactions with real-person streamers, it is currently unknown what makes VTuber live streams engaging or how they are perceived differently than real-person streamers. We conducted an interview study to understand how viewers engage with VTubers and perceive the identities of the voice actors behind the avatars (i.e., Nakanohito). The …

Visuo-Haptic Illusions For Linear Translation And Stretching Using Physical Proxies In Virtual Reality, Martin Feick, Niko Kleer, André Zenner, Anthony Tang, Antonio Kruger

Research Collection School Of Computing and Information Systems

Providing haptic feedback when manipulating virtual objects is an essential part of immersive virtual reality experiences; however, it is challenging to replicate all of an object’s properties and characteristics. We propose the use of visuo-haptic illusions alongside physical proxies to enhance the scope of proxy-based interactions with virtual objects. In this work, we focus on two manipulation techniques, linear translation and stretching across different distances, and investigate how much discrepancy between the physical proxy and the virtual object may be introduced without participants noticing. In a study with 24 participants, we found that manipulation technique and travel distance significantly affect …

Solving 3d Bin Packing Problem Via Multimodal Deep Reinforcement Learning, Yuan Jiang, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3D BPP, existing methods are only able to handle up to 50 boxes. In this paper, we propose to alleviate this issue via an end-to-end multimodal DRL agent, which sequentially addresses three sub-tasks of sequence, orientation and position, respectively. The resulting architecture enables the agent to solve large-scale instances of 100 boxes …

On Decentralization Of Bitcoin: An Asset Perspective, Ling Cheng, Feida Zhu, Huiwen Liu, Chunyan Miao

Research Collection School Of Computing and Information Systems

Since its advent in 2009, Bitcoin, a cryptography-enabled peer-to-peer digital payment system, has been gaining increasing attention from both academia and industry. An effort designed to overcome a cluster of bottlenecks inherent in existing centralized financial systems, Bitcoin has always been championed by the crypto community as an example of the spirit of decentralization. While the decentralized nature of Bitcoin's Proof-of-Work consensus algorithm has often been discussed in great detail, no systematic study has so far been conducted to quantitatively measure the degree of decentralization of Bitcoin from an asset perspective -- How decentralized is Bitcoin as a financial asset? …

Taiger Ai: Saas Bundling And Unbundling, Singapore Management University

Perspectives@SMU

Software companies bundle support services with their products as standard practice. Is it possible to be different…and profitable?

Machine Learning Based Approaches Towards Robust Android Malware Detection, Jiayun Xu

Dissertations and Theses Collection (Open Access)

The Android platform is becoming increasingly popular and numerous applications (apps) have been developed by organizations to meet the ever increasing market demand over years. Naturally, security and privacy concerns on Android apps have grabbed considerable attention from both academic and industrial
communities. Many approaches have been proposed to detect Android malware in different ways so far, and most of them produce satisfactory performance under the given Android environment settings and labelled samples. However, existing approaches suffer the following robustness problems:

In many Android malware detection approaches, specific API calls are used to build the feature sets, and their feature …

Determining The Number Of Communities In Degree-Corrected Stochastic Block Models, Shujie Ma, Liangjun Su, Yichong Zhang

Research Collection School Of Economics

We propose to estimate the number of communities in degree-corrected stochastic block models based on a pseudo likelihood ratio. For estimation, we consider a spectral clustering together with binary segmentation method. This approach guarantees an upper bound for the pseudo likelihood ratio statistic when the model is over-fitted. We also derive its limiting distribution when the model is under-fitted. Based on these properties, we establish the consistency of our estimator for the true number of communities. Developing these theoretical properties require a mild condition on the average degree: growing at a rate faster than log(n), where n is the number …