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Articles 11731 - 11760 of 63035

Full-Text Articles in Computer Sciences

Cellphone-Acoustics Based Suas Detection And Tracking, Ryan D. Clendening Mar 2023

Cellphone-Acoustics Based Suas Detection And Tracking, Ryan D. Clendening

Theses and Dissertations

Small Unmanned Aerial Systems (sUAS) are an easily accessible technology that has become an increasingly large threat to US critical systems. This threatening technology demands using fault-tolerant, low-cost, replaceable, and accurate sensing resources, which counter the ubiquitous nature of sUAS [1]. Therefore, the methods developed in this thesis detect and track sUAS using easily accessible sensing resources, such as cellphones. First, we develop an acoustics sensor network-based sUAS detection methodology. In the latter effort, a deep learning model is trained using the acoustics data from the data collection to predict sUAS range from a cellphone. Combined, these two efforts demonstrate …

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The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold Mar 2023

The Electromagnetic Bayonet: Development Of A Scientific Computing Method For Aperture Antenna Optimization, Michael P. Ingold

Theses and Dissertations

The quiet zone of a radar range is the region over which a transmitted EM field approximates a uniform plane wave to within some finite error tolerance. Any target to be measured must physically fit within this quiet zone to prevent excess measurement error. Compact radar ranges offer significant operational advantages for performing RCS measurements but their quiet zone sizes are constrained by space limitations. In this work, a scientific computing approach is used to investigate whether equivalent-current transmitters can be designed that generate larger quiet zones than a conventional version at short range. A time-domain near-field solver, JefimenkoModels, was …

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Spectral Material Classification Of Orbital Objects - Applying Machine Learning To Visible And Near-Infrared Spectral Scenes, Stephen M. Stumpf Mar 2023

Spectral Material Classification Of Orbital Objects - Applying Machine Learning To Visible And Near-Infrared Spectral Scenes, Stephen M. Stumpf

Theses and Dissertations

MSI and HSI techniques allow users to determine the material composition of an object at range. To avoid labor-intensive manual classification, ML is used to determine the most likely material contained in a given pixel of a target image. Previous work primarily focuses on terrestrial applications; this paper extends these techniques into the low-illumination space situational awareness domain, which is of critical importance to national security. HSI datacubes are preprocessed with RL deconvolution as a means of reducing the effects of the optical PSF; then, statistical ML techniques, including k-NN, LDA, QDA, and SVMs are implemented as means of assigning …

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Strategic Action Execution Through Regret Matching In Press Diplomacy, Leif D. White Mar 2023

Strategic Action Execution Through Regret Matching In Press Diplomacy, Leif D. White

Theses and Dissertations

To take most advantage of collaboration, negotiation is paramount to succeed in press Diplomacy. Humans use this construct to work towards self victory or sometimes towards an alternative strategic objective undefined in the game’s rules. To emulate this behavior, this thesis examines how to use communication to enable the victory or defeat of any other player in the game. This research develops a press Diplomacy agent, Lyre, that can work to attain these specific objectives in Diplomacy through the regret matching algorithm (RM). We also study how Lyre can begin Diplomacy with the goal to win, then shift strategies to …

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Temporal Convolutional Neural Networks For Device Discrimination, Ryan T. Zacher Mar 2023

Temporal Convolutional Neural Networks For Device Discrimination, Ryan T. Zacher

Theses and Dissertations

This research uses TCN modifications to CNN classifiers, specifically dilation, causal padding, and residual blocks, to focus on temporal features and improve existing DNA analysis processes. Dilation significantly improves classification accuracy, even detecting features where no other models were able to. The smallest improvement shown is a 3-6dB reduction in SNR to reach a 90% classification accuracy. The maximum improvement is shown in the data-delivery region of the Cisco dataset, with the dilated model being the only model to exceed 90% classification accuracy. The other TCN modifications are shown to have no beneficial effect on the models.

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Spatio-Temporal Heterogeneity In The International Trade Resilience During Covid-19, Wei Luo, Lingfeng He, Zihui Yang, Shirui Zhang, Yong Wang, Dianbo Liu, Sheng Hu, Li He, Jizhe Xia, Min Chen Mar 2023

Spatio-Temporal Heterogeneity In The International Trade Resilience During Covid-19, Wei Luo, Lingfeng He, Zihui Yang, Shirui Zhang, Yong Wang, Dianbo Liu, Sheng Hu, Li He, Jizhe Xia, Min Chen

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns …

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Specification-Based Autonomous Driving System Testing, Yuan Zhou, Yang Sun, Yun Tang, Yuqi Chen, Jun Sun, Christopher M. Poskitt, Yang Liu, Zijiang Yang Mar 2023

Specification-Based Autonomous Driving System Testing, Yuan Zhou, Yang Sun, Yun Tang, Yuqi Chen, Jun Sun, Christopher M. Poskitt, Yang Liu, Zijiang Yang

Research Collection School Of Computing and Information Systems

Autonomous vehicle (AV) systems must be comprehensively tested and evaluated before they can be deployed. High-fidelity simulators such as CARLA or LGSVL allow this to be done safely in very realistic and highly customizable environments. Existing testing approaches, however, fail to test simulated AVs systematically, as they focus on specific scenarios and oracles (e.g., lane following scenario with the "no collision" requirement) and lack any coverage criteria measures. In this paper, we propose AVUnit, a framework for systematically testing AV systems against customizable correctness specifications. Designed modularly to support different simulators, AVUnit consists of two new languages for specifying dynamic …

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Investment And Risk Management With Online News And Heterogeneous Networks, Meng Kiat Gary Ang, Ee-Peng Lim Mar 2023

Investment And Risk Management With Online News And Heterogeneous Networks, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Stock price movements in financial markets are influenced by large volumes of news from diverse sources on the web, e.g., online news outlets, blogs, social media. Extracting useful information from online news for financial tasks, e.g., forecasting stock returns or risks, is, however, challenging due to the low signal-to-noise ratios of such online information. Assessing the relevance of each news article to the price movements of individual stocks is also difficult, even for human experts. In this article, we propose the Guided Global-Local Attention-based Multimodal Heterogeneous Network (GLAM) model, which comprises novel attention-based mechanisms for multimodal sequential and graph encoding, …

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Investigating Guardian Awareness Techniques To Promote Safety In Virtual Reality, Sixuan Wu, Jiannan Li, Maurício Sousa, Tovi Grossman Mar 2023

Investigating Guardian Awareness Techniques To Promote Safety In Virtual Reality, Sixuan Wu, Jiannan Li, Maurício Sousa, Tovi Grossman

Research Collection School Of Computing and Information Systems

Virtual Reality (VR) can completely immerse users in a virtual world and provide little awareness of bystanders in the surrounding physical environment. Current technologies use predefined guardian area visualizations to set safety boundaries for VR interactions. However, bystanders cannot perceive these boundaries and may collide with VR users if they accidentally enter guardian areas. In this paper, we investigate four awareness techniques on mobile phones and smartwatches to help bystanders avoid invading guardian areas. These techniques include augmented reality boundary overlays and visual, auditory, and haptic alerts indicating bystanders' distance from guardians. Our findings suggest that the proposed techniques effectively …

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Learning And Understanding User Interface Semantics From Heterogeneous Networks With Multimodal And Positional Attributes, Meng Kiat Gary Ang, Ee-Peng Lim Mar 2023

Learning And Understanding User Interface Semantics From Heterogeneous Networks With Multimodal And Positional Attributes, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g., applications, screens, view class, and other types of design objects) with multimodal (e.g., textual and visual) and positional (e.g., spatial location, sequence order, and hierarchy level) attributes. We can therefore represent a set of application UIs as a heterogeneous network with multimodal and positional attributes. Such a network not only represents how users understand the visual layout of UIs but also influences how users would interact with applications through these UIs. To model the UI semantics well for different UI annotation, search, and evaluation tasks, …

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The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang Mar 2023

The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang

Research Collection School Of Computing and Information Systems

This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total …

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Of Inventorship And Patent Ownership: Examining The Intersection Between Artificial Intelligence And Patent Law, Cheng Lim Saw, Zheng Wen Samuel Chan Mar 2023

Of Inventorship And Patent Ownership: Examining The Intersection Between Artificial Intelligence And Patent Law, Cheng Lim Saw, Zheng Wen Samuel Chan

Research Collection Yong Pung How School Of Law

Artificial intelligence (“AI”) has garnered much attention in recent years, with capabilities spanning the operation of self-driving cars to the emulation of the great artistic masters of old. The field has now been ostensibly enlarged in light of the professed abilities of AI machines to autonomously generate patentable inventions. This article examines the present state of AI technology and the suitability of existing patent law frameworks in accommodating it. Looking ahead, the authors also offer two recommendations in a bid to anticipate and resolve the challenges that future developments in AI technology might pose to patent law. In particular, the …

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Multi-Modal Api Recommendation, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo Mar 2023

Multi-Modal Api Recommendation, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo

Research Collection School Of Computing and Information Systems

Too many options can be a problem, which is the case for Application Programming Interfaces (APIs). As there are many such APIs, with many more being introduced periodically, it raises the problem of choosing which API to be recommended. Furthermore, numerous APIs are commonly used together with other complementary third-party APIs. It can be challenging for developers to understand how to use each API and to remember all the complementary APIs for the API they want to use. Therefore, an accurate API recommendation approach can improve developers' efficiency in implementing certain functionality. Several approaches have been developed to automatically recommend …

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Improved Object Re-Identification Via More Efficient Embeddings, Ertugrul Bayraktar Mar 2023

Improved Object Re-Identification Via More Efficient Embeddings, Ertugrul Bayraktar

Turkish Journal of Electrical Engineering and Computer Sciences

Object reidentification (ReID) in cluttered rigid scenes is a challenging problem especially when same-looking objects coexist in the scene. ReID is accepted to be one of the most powerful tools for matching the correct identities to each individual object when issues such as occlusion, missed detections, multiple same-looking objects coexisting in the same scene, and disappearance of objects from the view and/or revisiting the same region arise. We propose a novel framework towards more efficient object ReID, improved object reidentification (IO-ReID), to perform object ReID in challenging scenes with real-time processing in mind. The proposed approach achieves distinctive and efficient …

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3d Point Cloud Classification With Acgan-3d And Vacwgan-Gp, Onur Ergün, Yusuf Sahi̇lli̇oğlu Mar 2023

3d Point Cloud Classification With Acgan-3d And Vacwgan-Gp, Onur Ergün, Yusuf Sahi̇lli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning and deep learning techniques are widely used to make sense of 3D point cloud data which became ubiquitous and important due to the recent advances in 3D scanning technologies and other sensors. In this work, we propose two networks to predict the class of the input 3D point cloud: 3D Auxiliary Classifier Generative Adversarial Network (ACGAN-3D) and Versatile Auxiliary Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (VACWGAN-GP). Unlike other classifiers, we are able to enlarge the limited data set with the data produced by generative models. We consequently aim to increase the success of the model by …

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Dminer: Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu Mar 2023

Dminer: Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: arrangement , which describes the position, size, and layout of each view in the display space; and coordination , which indicates the interaction between pairwise views. We build a new dataset containing 854 …

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Green Data Analytics Of Supercomputing From Massive Sensor Networks: Does Workload Distribution Matter?, Zhiling Guo, Jin Li, Ram Ramesh Mar 2023

Green Data Analytics Of Supercomputing From Massive Sensor Networks: Does Workload Distribution Matter?, Zhiling Guo, Jin Li, Ram Ramesh

Research Collection School Of Computing and Information Systems

Energy costs represent a significant share of the total cost of ownership in high performance computing (HPC) systems. Using a unique data set collected by massive sensor networks in a peta scale national supercomputing center, we first present an explanatory model to identify key factors that affect energy consumption in supercomputing. Our analytic results show that, not only does computing node utilization significantly affect energy consumption, workload distribution among the nodes also has significant effects and could effectively be leveraged to improve energy efficiency. Next, we establish the high model performance using in-sample and out-of-sample analyses. We then develop prescriptive …

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A Modified Space Vector Modulation Based Rotor Flux Oriented Control Of Six-Phase Asymmetrical Induction Motor Drive, Krunal Shah, Rakesh Maurya Mar 2023

A Modified Space Vector Modulation Based Rotor Flux Oriented Control Of Six-Phase Asymmetrical Induction Motor Drive, Krunal Shah, Rakesh Maurya

Turkish Journal of Electrical Engineering and Computer Sciences

In view of the attractive features like improved torque density, reduction torque pulsation, superior fault tolerance, reduced power rating of voltage source converter, and sterling noise characteristics of six-phase asymmetrical induction motor (SPAIM) as compared to its three-phase counterpart, the SPAIM is considered for the study. In this paper, mathematical modelling of SPAIM is carried out in the synchronous reference frame and then indirect rotor field-oriented control (IRFOC) of SPAIM using a modified carrier wave-based space vector modulation (SVM) scheme is developed. A Simulink model of the proposed system configuration is developed and a simulation study is carried out. In …

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Machine Learning Techniques For Stock Price Prediction And Graphic Signal Recognition, Junde Chen, Yuxin Wen, Y. A. Nanehkaran, M. D. Suzauddola, Weirong Chen, Defu Zhang Mar 2023

Machine Learning Techniques For Stock Price Prediction And Graphic Signal Recognition, Junde Chen, Yuxin Wen, Y. A. Nanehkaran, M. D. Suzauddola, Weirong Chen, Defu Zhang

Engineering Faculty Articles and Research

Stock market analysis is extremely important for investors because knowing the future trend and grasping the changing characteristics of stock prices will decrease the risk of investing capital for profit. Thereupon, the prediction of stock prices and identifying the graphic signals of candlestick charts, which are two crucial tasks in stock price analysis, attract much attention from investors owing to the returns and risks that coexist in financial markets. To introduce a reliable approach for addressing these challenges, this paper proposes the modeling strategies based on machine learning (ML) techniques. A vector autoregression (VAR)-based rolling prediction model is proposed for …

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User Acceptance And Adoption Of Smart Homes: A Decade Long Systematic Literature Review, Ibrahim Mashal, Ahmed Shuhaiber, Ayman Wael Al-Khatib Mar 2023

User Acceptance And Adoption Of Smart Homes: A Decade Long Systematic Literature Review, Ibrahim Mashal, Ahmed Shuhaiber, Ayman Wael Al-Khatib

All Works

This survey aims to provide a coherent and bibliometric overview of the theories and constructs employed in smart homes acceptance and adoption literature. To achieve the study aims, we con-ducted a systematic search for every article related to the SH concept, services and applications, user acceptance and adoption, and integrated IoT home appliances and devices, in 10 major library databases, namely, IEEE Digital Library, ACM Digital Library, Association for Information Systems (AIS), Elsevier, Emerald, Taylor and Francis, Wiley InterScience, Springer, Inderscience, and Hindawi. These databases contain literature focusing on smart home adoption using IoT tech-nology. 40 research articles of journal …

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An Exploratory Study On Museum Visitor Ship Trends In Singapore, Aldy Gunawan, Chentao Liu, Heranshan S/O Subramaniam, Melissa Tan, Ranice Tan, Clarence Tay, Tasaporn. Visawameteekul Mar 2023

An Exploratory Study On Museum Visitor Ship Trends In Singapore, Aldy Gunawan, Chentao Liu, Heranshan S/O Subramaniam, Melissa Tan, Ranice Tan, Clarence Tay, Tasaporn. Visawameteekul

Research Collection School Of Computing and Information Systems

The COVID-19 outbreak has unpredictably disrupted the operations of numerous museums. Museum visitor experience has a physical, personal, and social context, which are not achievable during the pandemic. Despite the depreciation during the Circuit Breaker period, the disruption also presents an opportunity for local museums to develop new strategies of audience engagement to accommodate the altered audience behavior. This exploratory study analyses data from six Singapore-based museums to understand the visitorship patterns across different ages and genders. The impact of COVID-19 is also analysed. Using R-studio and relevant packages, we conducted statistical tests such as hypothesis testing, Chi-square testing and …

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Flextype: Flexible Text Input With A Small Set Of Input Gestures, Dylan Gaines, Mackenzie M. Baker, Keith Vertanen Mar 2023

Flextype: Flexible Text Input With A Small Set Of Input Gestures, Dylan Gaines, Mackenzie M. Baker, Keith Vertanen

Michigan Tech Publications, Part 1

In many situations, it may be impractical or impossible to enter text by selecting precise locations on a physical or touchscreen keyboard. We present an ambiguous keyboard with four character groups that has potential applications for eyes-free text entry, as well as text entry using a single switch or a brain-computer interface. We develop a procedure for optimizing these character groupings based on a disambiguation algorithm that leverages a long-span language model. We produce both alphabetically-constrained and unconstrained character groups in an offline optimization experiment and compare them in a longitudinal user study. Our results did not show a significant …

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Understanding Adoption Barriers To Dwell-Free Eye-Typing: Design Implications From A Qualitative Deployment Study And Computational Simulations, Per Ola Kristensson, Morten Mjelde, Keith Vertanen Mar 2023

Understanding Adoption Barriers To Dwell-Free Eye-Typing: Design Implications From A Qualitative Deployment Study And Computational Simulations, Per Ola Kristensson, Morten Mjelde, Keith Vertanen

Michigan Tech Publications, Part 1

Eye-typing is a slow and cumbersome text entry method typically used by individuals with no other practical means of communication. As an alternative, prior HCI research has proposed dwell-free eye-typing as a potential improvement that eliminates time-consuming and distracting dwell-timeouts. However, it is rare that such research ideas are translated into working products. This paper reports on a qualitative deployment study of a product that was developed to allow users access to a dwell-free eye-typing research solution. This allowed us to understand how such a research solution would work in practice, as part of users' current communication solutions in their …

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Challenges, Choice, & Change: Experiences And Reflections From The First Semester Of A Technology And Human Futures Course, Briana Christina Bettin Mar 2023

Challenges, Choice, & Change: Experiences And Reflections From The First Semester Of A Technology And Human Futures Course, Briana Christina Bettin

Michigan Tech Publications, Part 1

Society's rapidly increasing reliance on technology has created urgency for future leaders to understand these technologies' potential impacts. News of technical and algorithmic concerns in society are frequent, highlighting the real impacts to real people. Learners may approach computing in order to feel prepared for careers and life in modern society. These learners benefit from understanding the many ways technology may be part of their future careers, their lives, and the lives of others. Beyond this, many wish to use computing to solve problems - but complex societal problems may feel too overwhelming to solve. Greater understanding of the technologies …

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Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meera Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra Mar 2023

Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meera Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve …

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Investigating Noticeable Hand Redirection In Virtual Reality Using Physiological And Interaction Data, Martin Feick, Kora Regitz, Anthony Tang, Jungbluth Mar 2023

Investigating Noticeable Hand Redirection In Virtual Reality Using Physiological And Interaction Data, Martin Feick, Kora Regitz, Anthony Tang, Jungbluth

Research Collection School Of Computing and Information Systems

Hand redirection is effective so long as the introduced offsets are not noticeably disruptive to users. In this work we investigate the use of physiological and interaction data to detect movement discrepancies between a user's real and virtual hand, pushing towards a novel approach to identify discrepancies which are too large and therefore can be noticed. We ran a study with 22 participants, collecting EEG, ECG, EDA, RSP, and interaction data. Our results suggest that EEG and interaction data can be reliably used to detect visuo-motor discrepancies, whereas ECG and RSP seem to suffer from inconsistencies. Our findings also show …

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A Review On Learning To Solve Combinatorial Optimisation Problems In Manufacturing, Cong Zhang, Yaoxin Wu, Yining Ma, Wen Song, Zhang Le, Zhiguang Cao, Jie Zhang Mar 2023

A Review On Learning To Solve Combinatorial Optimisation Problems In Manufacturing, Cong Zhang, Yaoxin Wu, Yining Ma, Wen Song, Zhang Le, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

An efficient manufacturing system is key to maintaining a healthy economy today. With the rapid development of science and technology and the progress of human society, the modern manufacturing system is becoming increasingly complex, posing new challenges to both academia and industry. Ever since the beginning of industrialisation, leaps in manufacturing technology have always accompanied technological breakthroughs from other fields, for example, mechanics, physics, and computational science. Recently, machine learning (ML) technology, one of the crucial subjects of artificial intelligence, has made remarkable progress in many areas. This study thoroughly reviews how ML, specifically deep (reinforcement) learning, motivates new ideas …

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Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meeralakshmi Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra Mar 2023

Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meeralakshmi Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra

Research Collection School Of Computing and Information Systems

We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve …

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Exploring And Repairing Gender Fairness Violations In Word Embedding-Based Sentiment Analysis Model Through Adversarial Patches, Lin Sze Khoo, Jia Qi Bay, Ming Lee Kimberly Yap, Mei Kuan Lim, Chun Yong Chong, Zhou Yang, David Lo Mar 2023

Exploring And Repairing Gender Fairness Violations In Word Embedding-Based Sentiment Analysis Model Through Adversarial Patches, Lin Sze Khoo, Jia Qi Bay, Ming Lee Kimberly Yap, Mei Kuan Lim, Chun Yong Chong, Zhou Yang, David Lo

Research Collection School Of Computing and Information Systems

With the advancement of sentiment analysis (SA) models and their incorporation into our daily lives, fairness testing on these models is crucial, since unfair decisions can cause discrimination to a large population. Nevertheless, some challenges in fairness testing include the unknown oracle, the difficulty in generating suitable test inputs, and the lack of a reliable way of fixing the issues. To fill in these gaps, BiasRV, a tool based on metamorphic testing (MT), was introduced and succeeded in uncovering fairness issues in a transformer-based model. However, the extent of unfairness in other SA models has not been thoroughly investigated. Our …

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Automatic Identification Of Crash-Inducing Smart Contracts, Chao Ni, Cong Tian, Kaiwen Yang, David Lo, Jiachi Chen, Xiaohu Yang Mar 2023

Automatic Identification Of Crash-Inducing Smart Contracts, Chao Ni, Cong Tian, Kaiwen Yang, David Lo, Jiachi Chen, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Smart contract, a special software code running on and resided in the blockchain, enlarges the general application of blockchain and exchanges assets without dependence of external parties. With blockchain's characteristic of immutability, they cannot be modified once deployed. Thus, the contract and the records are persisted on the blockchain forever, including failed transactions that are caused by runtime errors and result in the waste of computation, storage, and fees. In this paper, we refer to smart contracts which will cause runtime errors as crash-inducing smart contracts. However, automatic identification of crash-inducing smart contracts is limited investigated in the literature. The …

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