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Articles 781 - 810 of 2149
Full-Text Articles in Software Engineering
Crossasr: Efficient Differential Testing Of Automatic Speech Recognition Via Text-To-Speech, Muhammad Hilmi Asyrofi, Thung Ferdian, David Lo, Lingxiao Jiang
Crossasr: Efficient Differential Testing Of Automatic Speech Recognition Via Text-To-Speech, Muhammad Hilmi Asyrofi, Thung Ferdian, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Automatic speech recognition (ASR) systems are ubiquitous parts of modern life. It can be found in our smartphones, desktops, and smart home systems. To ensure its correctness in recognizing speeches, ASR needs to be tested. Testing ASR requires test cases in the form of audio files and their transcribed texts. Building these test cases manually, however, is tedious and time-consuming.To deal with the aforementioned challenge, in this work, we propose CrossASR, an approach that capitalizes the existing Text-To-Speech (TTS) systems to automatically generate test cases for ASR systems. CrossASR is a differential testing solution that compares outputs of multiple ASR …
Message From The General Co-Chairs And The Program Co-Chairs, Christoph Treude, Hongyu Zhang, Kelly Blincoe, Zhenchang Xing
Message From The General Co-Chairs And The Program Co-Chairs, Christoph Treude, Hongyu Zhang, Kelly Blincoe, Zhenchang Xing
Research Collection School Of Computing and Information Systems
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Deepsonar: Towards Effective And Robust Detection Of Ai-Synthesized Fake Voices, Run Wang, Felix Juefei-Xu, Yihao Huang, Qing Guo, Xiaofei Xie, Lei Ma, Yang Liu
Deepsonar: Towards Effective And Robust Detection Of Ai-Synthesized Fake Voices, Run Wang, Felix Juefei-Xu, Yihao Huang, Qing Guo, Xiaofei Xie, Lei Ma, Yang Liu
Research Collection School Of Computing and Information Systems
With the recent advances in voice synthesis, AI-synthesized fake voices are indistinguishable to human ears and widely are applied to produce realistic and natural DeepFakes, exhibiting real threats to our society. However, effective and robust detectors for synthesized fake voices are still in their infancy and are not ready to fully tackle this emerging threat. In this paper, we devise a novel approach, named DeepSonar, based on monitoring neuron behaviors of speaker recognition (SR) system, i.e., a deep neural network (DNN), to discern AI-synthesized fake voices. Layer-wise neuron behaviors provide an important insight to meticulously catch the differences among inputs, …
Regression Testing Of Massively Multiplayer Online Role-Playing Games, Yuechen Wu, Yingfeng Chen, Xiaofei Xie, Bing Yu, Changjie Fan, Lei Ma
Regression Testing Of Massively Multiplayer Online Role-Playing Games, Yuechen Wu, Yingfeng Chen, Xiaofei Xie, Bing Yu, Changjie Fan, Lei Ma
Research Collection School Of Computing and Information Systems
Regression testing aims to check the functionality consistency during software evolution. Although general regression testing has been extensively studied, regression testing in the context of video games, especially Massively Multiplayer Online Role-Playing Games (MMORPGs), is largely untouched so far. One big challenge is that game testing requires a certain level of intelligence in generating suitable action sequences among the huge search space, to accomplish complex tasks in the MMORPG. Existing game testing mainly relies on either the manual playing or manual scripting, which are labor-intensive and time-consuming. Even worse, it is often unable to satisfy the frequent industrial game evolution. …
Towards Systematically Deriving Defence Mechanisms From Functional Requirements Of Cyber-Physical Systems, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt
Towards Systematically Deriving Defence Mechanisms From Functional Requirements Of Cyber-Physical Systems, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated the development of different attack detection mechanisms, such as those that monitor for violations of invariants, i.e. properties that always hold in normal operation. Given the complexity of CPSs, several existing approaches focus on deriving invariants automatically from data logs, but these can miss possible system behaviours if they are not represented in that data. Furthermore, resolving any design flaws identified in this process is costly, as the CPS is already built. In this position paper, we propose a systematic method for deriving invariants before a CPS is …
Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan
Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan
Research Collection School Of Computing and Information Systems
This Innovative Practice Full Paper discusses our course design on software architecture to meet the learning expectations of two groups of software engineers. Software engineers with working experiences frequently find themselves the need to upskill in their lifelong learning journey. Their learning expectations are shaped not just by their need to know but also other learning characteristics such as their working experiences. In many cases, we design courses based on the required learning outcomes and assessment criteria. In this paper, we wish to find out whether our course design on software architecture has met the learning expectations of our students …
Sentiment Analysis For Software Engineering: How Far Can Pre-Trained Transformer Models Go?, Ting Zhang, Bowen Xu, Thung Ferdian, Stefanus Agus Haryono, David Lo, Lingxiao Jiang
Sentiment Analysis For Software Engineering: How Far Can Pre-Trained Transformer Models Go?, Ting Zhang, Bowen Xu, Thung Ferdian, Stefanus Agus Haryono, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Extensive research has been conducted on sentiment analysis for software engineering (SA4SE). Researchers have invested much effort in developing customized tools (e.g., SentiStrength-SE, SentiCR) to classify the sentiment polarity for Software Engineering (SE) specific contents (e.g., discussions in Stack Overflow and code review comments). Even so, there is still much room for improvement. Recently, pre-trained Transformer-based models (e.g., BERT, XLNet) have brought considerable breakthroughs in the field of natural language processing (NLP). In this work, we conducted a systematic evaluation of five existing SA4SE tools and variants of four state-of-the-art pre-trained Transformer-based models on six SE datasets. Our work is …
Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu
Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu
Research Collection School Of Computing and Information Systems
Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online …
What Is The Vocabulary Of Flaky Tests?, Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo D'Amorim, Christoph Treude, Antonia Bertolino
What Is The Vocabulary Of Flaky Tests?, Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo D'Amorim, Christoph Treude, Antonia Bertolino
Research Collection School Of Computing and Information Systems
Flaky tests are tests whose outcomes are non-deterministic. Despite the recent research activity on this topic, no effort has been made on understanding the vocabulary of flaky tests. This work proposes to automatically classify tests as flaky or not based on their vocabulary. Static classification of flaky tests is important, for example, to detect the introduction of flaky tests and to search for flaky tests after they are introduced in regression test suites. We evaluated performance of various machine learning algorithms to solve this problem. We constructed a data set of flaky and non-flaky tests by running every test case, …
The Impact Of Dynamics Of Collaborative Software Engineering On Introverts: A Study Protocol, Ingrid Nunes, Christoph Treude, Fabio Calefato
The Impact Of Dynamics Of Collaborative Software Engineering On Introverts: A Study Protocol, Ingrid Nunes, Christoph Treude, Fabio Calefato
Research Collection School Of Computing and Information Systems
Background: Collaboration among software engineers through face-to-face discussions in teams has been promoted since the adoption of agile methods. However, these discussions might demote the contribution of software engineers who are introverts, possibly leading to sub-optimal solutions and creating work environments that benefit extroverts. Objective: We aim to evaluate whether providing software engineers with time to work individually and reason about a collective problem is a setting that makes introverts more comfortable to interact and contribute more, ultimately leading to better solutions. Method: We plan to conduct a between-subjects study, with teams in a control group that design a software …
Evaluating Methods For Students To Identify And Clarify Doubts In Computing Design Courses, Eng Lieh Ouh, Benjamin Gan
Evaluating Methods For Students To Identify And Clarify Doubts In Computing Design Courses, Eng Lieh Ouh, Benjamin Gan
Research Collection School Of Computing and Information Systems
This full paper evaluates the effectiveness of doubts identification and clarification methods applied in undergraduate computing design courses. Many undergraduate courses in computing require students to understand abstract design concepts. Exposed to the design concepts for the first time, students need to be able to identify and clarify their doubts about the abstract concepts in order to make the right design decisions. In this study, we seek to evaluate the effectiveness of six methods that help students to identify and clarify their doubts. These methods vary in their timing (immediate or delayed), communication style (online or face-to-face) and participation style …
Generating Question Titles For Stack Overflow From Mined Code Snippets, Zhipeng Gao, Xin Xia, John Grundy, David Lo, Yuan-Fang Li
Generating Question Titles For Stack Overflow From Mined Code Snippets, Zhipeng Gao, Xin Xia, John Grundy, David Lo, Yuan-Fang Li
Research Collection School Of Computing and Information Systems
Stack Overflow has been heavily used by software developers as a popular way to seek programming-related information from peers via the internet. The Stack Overflow community recommends users to provide the related code snippet when they are creating a question to help others better understand it and offer their help. Previous studies have shown that a significant number of these questions are of low-quality and not attractive to other potential experts in Stack Overflow. These poorly asked questions are less likely to receive useful answers and hinder the overall knowledge generation and sharing process. Considering one of the reasons for …
Fakepolisher: Making Deepfakes More Detection-Evasive By Shallow Reconstruction, Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, Geguang Pu
Fakepolisher: Making Deepfakes More Detection-Evasive By Shallow Reconstruction, Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, Geguang Pu
Research Collection School Of Computing and Information Systems
At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image. Such artifact patterns can be easily exploited (by recent methods) for difference detection of real and GAN-synthesized images. However, the existing detection methods put much emphasis on the artifact patterns, which can become futile if such artifact patterns were reduced.Towards reducing the artifacts in the synthesized images, in this paper, we devise a simple yet powerful approach termed FakePolisher that performs shallow reconstruction of fake images through a learned linear dictionary, intending to effectively and …
Deeprhythm: Exposing Deepfakes With Attentional Visual Heartbeat Rhythms, Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, Jianjun Zhao
Deeprhythm: Exposing Deepfakes With Attentional Visual Heartbeat Rhythms, Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors. Motivated by the fact that remote visual photoplethysmography (PPG) is made possible by monitoring the minuscule periodic changes of skin color due to blood pumping through the face, we conjecture that normal heartbeat rhythms found in the real face videos will be disrupted or even entirely broken in a DeepFake video, making it a potentially powerful indicator for DeepFake detection. In this work, we propose DeepRhythm, a DeepFake …
Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu
Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu
Research Collection School Of Computing and Information Systems
Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems suffer from unprecedented security concerns. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack works at the semantic level that perturbs pixels spatially in a coherent manner. To tackle the black-box attack problem, we devise a simple yet effective …
Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn
Experimental Comparison Of Features And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Wei Minn
Research Collection School Of Computing and Information Systems
Android platform has dominated the smart phone market for years now and, consequently, gained a lot of attention from attackers. Malicious apps (malware) pose a serious threat to the security and privacy of Android smart phone users. Available approaches to detect mobile malware based on machine learning rely on features extracted with static analysis or dynamic analysis techniques. Dif- ferent types of machine learning classi ers (such as support vector machine and random forest) deep learning classi ers (based on deep neural networks) are then trained on extracted features, to produce models that can be used to detect mobile malware. …
Marble: Model-Based Robustness Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Yi Li, Xiaofei Xie, Lei Ma, Yang Liu, Jianjun Zhao
Marble: Model-Based Robustness Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Yi Li, Xiaofei Xie, Lei Ma, Yang Liu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
State-of-the-art deep learning (DL) systems are vulnerable to adversarial examples, which hinders their potential adoption in safetyand security-critical scenarios. While some recent progress has been made in analyzing the robustness of feed-forward neural networks, the robustness analysis for stateful DL systems, such as recurrent neural networks (RNNs), still remains largely uncharted. In this paper, we propose Marble, a model-based approach for quantitative robustness analysis of real-world RNN-based DL systems. Marble builds a probabilistic model to compactly characterize the robustness of RNNs through abstraction. Furthermore, we propose an iterative refinement algorithm to derive a precise abstraction, which enables accurate quantification of …
How (Not) To Find Bugs: The Interplay Between Merge Conflicts, Co-Changes, And Bugs, Luis Amaral, Marcos C. Oliveira, Welder Luz, José Fortes, Rodrigo Bonifacio, Daniel Alencar, Eduardo Monteiro, Gustavo Pinto, David Lo
How (Not) To Find Bugs: The Interplay Between Merge Conflicts, Co-Changes, And Bugs, Luis Amaral, Marcos C. Oliveira, Welder Luz, José Fortes, Rodrigo Bonifacio, Daniel Alencar, Eduardo Monteiro, Gustavo Pinto, David Lo
Research Collection School Of Computing and Information Systems
Context: In a seminal work, Ball et al. [1] investigate if the information available in version control systems could be used to predict defect density, arguing that practitioners and researchers could better understand errors "if [our] version control system could talk". In the meanwhile, several research works have reported that conflict merge resolution is a time consuming and error-prone task, while other contributions diverge about the correlation between co-change dependencies and defect density. Problem: The correlation between conflicting merge scenarios and bugs has not been addressed before, whilst the correlation between co-change dependencies and bug density has been only investigated …
Cats Are Not Fish: Deep Learning Testing Calls For Out-Of-Distribution Awareness, David Berend, Xiaofei Xie, Lei Ma, Lingjun Zhou, Yang Liu, Chi Xu, Jianjun Zhao
Cats Are Not Fish: Deep Learning Testing Calls For Out-Of-Distribution Awareness, David Berend, Xiaofei Xie, Lei Ma, Lingjun Zhou, Yang Liu, Chi Xu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
As Deep Learning (DL) is continuously adopted in many industrial applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. According to the fundamental assumption of deep learning, the DL software does not provide statistical guarantee and has limited capability in handling data that falls outside of its learned distribution, i.e., out-of-distribution (OOD) data. Although recent progress has been made in designing novel testing techniques for DL software, which can detect thousands of …
Smart Contract Repair, Xiao Liang Yu, Omar Al-Bataineh, David Lo, Abhik Roychoudhury
Smart Contract Repair, Xiao Liang Yu, Omar Al-Bataineh, David Lo, Abhik Roychoudhury
Research Collection School Of Computing and Information Systems
Smart contracts are automated or self-enforcing contracts that can be used to exchange assets without having to place trust in third parties. Many commercial transactions use smart contracts due to their potential benefits in terms of secure peer-to-peer transactions independent of external parties. Experience shows that many commonly used smart contracts are vulnerable to serious malicious attacks, which may enable attackers to steal valuable assets of involving parties. There is, therefore, a need to apply analysis and automated repair techniques to detect and repair bugs in smart contracts before being deployed. In this work, we present the first general-purpose automated …
Dct: An Scalable Multi-Objective Module Clustering Tool, Ana Paula M. Tarchetti, Luis Henrique Vieira Amaral, Marcos C. Oliveira, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Dct: An Scalable Multi-Objective Module Clustering Tool, Ana Paula M. Tarchetti, Luis Henrique Vieira Amaral, Marcos C. Oliveira, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Research Collection School Of Computing and Information Systems
Maintaining complex software systems is a timeconsuming and challenging task. Practitioners must have a general understanding of the system’s decomposition and how the system’s developers have implemented the software features (probably cutting across different modules). Re-engineering practices are imperative to tackle these challenges. Previous research has shown the benefits of using software module clustering (SMC) to aid developers during re-engineering tasks (e.g., revealing the architecture of the systems, identifying how the concerns are spread among the modules of the systems, recommending refactorings, and so on). Nonetheless, although the literature on software module clustering has substantially evolved in the last 20 …
Accelerating All-Sat Computation With Short Blocking Clauses, Yueling Zhang, Geguang Pu, Jun Sun
Accelerating All-Sat Computation With Short Blocking Clauses, Yueling Zhang, Geguang Pu, Jun Sun
Research Collection School Of Computing and Information Systems
The All-SAT (All-SATisfiable) problem focuses on finding all satisfiable assignments of a given propositional formula, whose applications include model checking, automata construction, and logic minimization. A typical ALL-SAT solver is normally based on iteratively computing satisfiable assignments of the given formula. In this work, we introduce BASOLVER, a backbone-based All-SAT solver for propositional formulas. Compared to the existing approaches, BASOLVER generates shorter blocking clauses by removing backbone variables from the partial assignments and the blocking clauses. We compare BASOLVER with 4 existing ALL-SAT solvers, namely MBLOCKING, BC, BDD, and NBC. Experimental results indicate that although finding all the backbone variables …
Towards Generating Thread-Safe Classes Automatically, Haichi Wang, Zan Wang, Jun Sun, Shuang Lin, Ayesha Sadiq, Yuan Fang Li
Towards Generating Thread-Safe Classes Automatically, Haichi Wang, Zan Wang, Jun Sun, Shuang Lin, Ayesha Sadiq, Yuan Fang Li
Research Collection School Of Computing and Information Systems
The existing concurrency model for Java (or C) requires programmers to design and implement thread-safe classes by explicitly acquiring locks and releasing locks. Such a model is error-prone and is the reason for many concurrency bugs. While there are alternative models like transactional memory, manually writing locks remains prevalent in practice. In this work, we propose AutoLock, which aims to solve the problem by fully automatically generating thread-safe classes. Given a class which is assumed to be correct with sequential clients, AutoLock automatically generates a thread-safe class which is linearizable, and does it in a way without requiring a specification …
Towards Interpreting Recurrent Neural Networks Through Probabilistic Abstraction, Guoliang Dong, Jingyi Wang, Jun Sun, Yang Zhang, Xinyu Wang, Ting Dai, Jin Song Dong, Xingen Wang
Towards Interpreting Recurrent Neural Networks Through Probabilistic Abstraction, Guoliang Dong, Jingyi Wang, Jun Sun, Yang Zhang, Xinyu Wang, Ting Dai, Jin Song Dong, Xingen Wang
Research Collection School Of Computing and Information Systems
Neural networks are becoming a popular tool for solving many realworld problems such as object recognition and machine translation, thanks to its exceptional performance as an end-to-end solution. However, neural networks are complex black-box models, which hinders humans from interpreting and consequently trusting them in making critical decisions. Towards interpreting neural networks, several approaches have been proposed to extract simple deterministic models from neural networks. The results are not encouraging (e.g., low accuracy and limited scalability), fundamentally due to the limited expressiveness of such simple models.In this work, we propose an approach to extract probabilistic automata for interpreting an important …
Human-Like Summaries From Heterogeneous And Time-Windowed Software Development Artefacts, Mahfouth Alghamdi, Christoph Treude, Markus Wagner
Human-Like Summaries From Heterogeneous And Time-Windowed Software Development Artefacts, Mahfouth Alghamdi, Christoph Treude, Markus Wagner
Research Collection School Of Computing and Information Systems
Automatic text summarisation has drawn considerable interest in the area of software engineering. It is challenging to summarise the activities related to a software project, (1) because of the volume and heterogeneity of involved software artefacts, and (2) because it is unclear what information a developer seeks in such a multi-document summary. We present the first framework for summarising multi-document software artefacts containing heterogeneous data within a given time frame. To produce human-like summaries, we employ a range of iterative heuristics to minimise the cosine-similarity between texts and high-dimensional feature vectors. A first study shows that users find the automatically …
The Impact Of Automated Feature Selection Techniques On The Interpretation Of Defect Models, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
The Impact Of Automated Feature Selection Techniques On The Interpretation Of Defect Models, Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude
Research Collection School Of Computing and Information Systems
The interpretation of defect models heavily relies on software metrics that are used to construct them. Prior work often uses feature selection techniques to remove metrics that are correlated and irrelevant in order to improve model performance. Yet, conclusions that are derived from defect models may be inconsistent if the selected metrics are inconsistent and correlated. In this paper, we systematically investigate 12 automated feature selection techniques with respect to the consistency, correlation, performance, computational cost, and the impact on the interpretation dimensions. Through an empirical investigation of 14 publicly-available defect datasets, we find that (1) 94–100% of the selected …
Wait For It: Identifying 'On-Hold' Self-Admitted Technical Debt, Rungroj Maipradit, Christoph Treude, Hideaki Hata, Kenichi Matsumoto
Wait For It: Identifying 'On-Hold' Self-Admitted Technical Debt, Rungroj Maipradit, Christoph Treude, Hideaki Hata, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
Self-admitted technical debt refers to situations where a software developer knows that their current implementation is not optimal and indicates this using a source code comment. In this work, we hypothesize that it is possible to develop automated techniques to understand a subset of these comments in more detail, and to propose tool support that can help developers manage self-admitted technical debt more effectively. Based on a qualitative study of 333 comments indicating self-admitted technical debt, we first identify one particular class of debt amenable to automated management: on-hold self-admitted technical debt (on-hold SATD), i.e., debt which contains a condition …
Urban Scale Trade Area Characterization For Commercial Districts With Cellular Footprints, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Zheng Yang
Urban Scale Trade Area Characterization For Commercial Districts With Cellular Footprints, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Zheng Yang
Research Collection School Of Computing and Information Systems
Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data …
Persona Perception Scale: Development And Exploratory Validation Of An Instrument For Evaluating Individuals' Perceptions Of Personas, Joni Salminen, Joao M. Santos, Haewoon Kwak, Jisun An, Soon-Gyo Jung
Persona Perception Scale: Development And Exploratory Validation Of An Instrument For Evaluating Individuals' Perceptions Of Personas, Joni Salminen, Joao M. Santos, Haewoon Kwak, Jisun An, Soon-Gyo Jung
Research Collection School Of Computing and Information Systems
Although used in many domains, the evaluation of personas is difficult due to the lack of validated measurement instruments. To tackle this challenge, we propose the Persona Perception Scale (PPS), a survey instrument for evaluating how individuals perceive personas. We develop the scale by reviewing relevant literature from social psychology, persona studies, and Human-Computer Interaction to find relevant constructs and items for measuring persona perceptions. Following initial pilot testing, we conduct an exploratory validation of the scale with 412 respondents and find that the constructs and items of the scale perform satisfactorily for deployment. The research has implications for both …
Detectif: Unified Detection And Correction Of Iot Faults In Smart Homes, Madhumita Maliick, Archan Misra, Niloy Ganguly, Youngki Lee
Detectif: Unified Detection And Correction Of Iot Faults In Smart Homes, Madhumita Maliick, Archan Misra, Niloy Ganguly, Youngki Lee
Research Collection School Of Computing and Information Systems
This paper tackles the problem of detecting a comprehensive set of sensor faults that can occur in IoT-instrumented smart homes customized to infer Activities of Daily Living (ADL) from the activation of sensor sets. Specifically, sensors can suffer faults that (a) span durations that vary between several seconds to hours, (b) can result in both missing or false-alarm sensor-events. Previous fault detection approaches are geared primarily to identify missing faults (absence of sensor readings) of a permanent (very long-lived) nature, or sporadic false-alarm events. We propose DetectIF, a fault-detection framework that detects faults of varying time duration, and identifies both …