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Articles 931 - 960 of 1897

Full-Text Articles in Artificial Intelligence and Robotics

Mosaic: Spatially-Multiplexed Edge Ai Optimization Over Multiple Concurrent Video Sensing Streams, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra Jun 2023

Mosaic: Spatially-Multiplexed Edge Ai Optimization Over Multiple Concurrent Video Sensing Streams, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

Sustaining high fidelity and high throughput of perception tasks over vision sensor streams on edge devices remains a formidable challenge, especially given the continuing increase in image sizes (e.g., generated by 4K cameras) and complexity of DNN models. One promising approach involves criticality-aware processing, where the computation is directed selectively to "critical" portions of individual image frames. We introduce MOSAIC, a novel system for such criticality-aware concurrent processing of multiple vision sensing streams that provides a multiplicative increase in the achievable throughput with negligible loss in perception fidelity. MOSAIC determines critical regions from images received from multiple vision …

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Multi-Head Attention Graph Convolutional Network Model: End-To-End Entity And Relation Joint Extraction Based On Multi-Head Attention Graph Convolutional Network, Zhihua Tao, Chunping Ouyang, Yongbin Liu, Tonglee Chung, Yixin Cao Jun 2023

Multi-Head Attention Graph Convolutional Network Model: End-To-End Entity And Relation Joint Extraction Based On Multi-Head Attention Graph Convolutional Network, Zhihua Tao, Chunping Ouyang, Yongbin Liu, Tonglee Chung, Yixin Cao

Research Collection School Of Computing and Information Systems

At present, the entity and relation joint extraction task has attracted more and more scholars' attention in the field of natural language processing (NLP). However, most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure information. The adjacency matrix constructed by the dependency tree can convey syntactic information. Dependency trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic description. At the same time, a large amount of irrelevant information will cause redundancy. This paper presents a novel end-to-end entity and relation joint extraction …

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Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Böther, Daniel Martin Katz Jun 2023

Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Böther, Daniel Martin Katz

Research Collection Yong Pung How School Of Law

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce textbased and graph-based methods to identify instances of law smells, confirming their utility in …

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Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Bother, Daniel Martin Katz Jun 2023

Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Bother, Daniel Martin Katz

Research Collection Yong Pung How School Of Law

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in …

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Automatic Scoring Of Speeded Interpersonal Assessment Center Exercises Via Machine Learning: Initial Psychometric Evidence And Practical Guidelines, Louis Hickman, Christoph N. Herde, Filip Lievens, Louis Tay Jun 2023

Automatic Scoring Of Speeded Interpersonal Assessment Center Exercises Via Machine Learning: Initial Psychometric Evidence And Practical Guidelines, Louis Hickman, Christoph N. Herde, Filip Lievens, Louis Tay

Research Collection Lee Kong Chian School Of Business

Assessment center (AC) exercises such as role-plays have established themselves as valuable approaches for obtaining insights into interpersonal behavior, but they are often considered the “Rolls Royce” of personnel assessment due to their high costs. The observation and rating process comprises a substantial part of these costs. In an exploratory case study, we capitalize on recent advances in natural language processing (NLP) by developing NLP-based machine learning (ML) models to investigate the possibility of automatically scoring AC exercises. First, we compared the convergent-related validity and contamination with word count of ML scores based on models that used different NLP methods …

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Venus: A Geometrical Representation For Quantum State Visualization, Shaolun Ruan, Ribo Yuan, Qiang Guan, Yanna Lin, Ying Mao, Weiwen Jiang, Zhepeng Wang, Wei Xu, Yong Wang Jun 2023

Venus: A Geometrical Representation For Quantum State Visualization, Shaolun Ruan, Ribo Yuan, Qiang Guan, Yanna Lin, Ying Mao, Weiwen Jiang, Zhepeng Wang, Wei Xu, Yong Wang

Research Collection School Of Computing and Information Systems

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum …

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Contrabert: Enhancing Code Pre-Trained Models Via Contrastive Learning, Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, Yang. Liu May 2023

Contrabert: Enhancing Code Pre-Trained Models Via Contrastive Learning, Shangqing Liu, Bozhi Wu, Xiaofei Xie, Guozhu Meng, Yang. Liu

Research Collection School Of Computing and Information Systems

Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code representation, they have been further applied in multiple downstream tasks such as clone detection, code search and code translation. However, it is also observed that these state-of-the-art pre-trained models are susceptible to adversarial attacks. The performance of these pre-trained models drops significantly with simple perturbations such as renaming variable names. This weakness may be inherited by their downstream models and thereby amplified at an unprecedented scale. To this end, we propose an approach namely ContraBERT that aims …

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Neural Episodic Control With State Abstraction, Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao May 2023

Neural Episodic Control With State Abstraction, Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency.Generally, episodic control-based approaches are solutions that leveragehighly-rewarded past experiences to improve sample efficiency of DRL algorithms.However, previous episodic control-based approaches fail to utilize the latentinformation from the historical behaviors (e.g., state transitions, topological similarities,etc.) and lack scalability during DRL training. This work introducesNeural Episodic Control with State Abstraction (NECSA), a simple but effectivestate abstraction-based episodic control containing a more comprehensive episodicmemory, a novel state evaluation, and a multi-step state analysis. We evaluate ourapproach to the MuJoCo and Atari tasks in OpenAI gym domains. The experimentalresults indicate that NECSA achieves higher …

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Wearing Masks Implies Refuting Trump?: Towards Target-Specific User Stance Prediction Across Events In Covid-19 And Us Election 2020, Hong Zhang, Haewoon Kwak, Wei Gao, Jisun An May 2023

Wearing Masks Implies Refuting Trump?: Towards Target-Specific User Stance Prediction Across Events In Covid-19 And Us Election 2020, Hong Zhang, Haewoon Kwak, Wei Gao, Jisun An

Research Collection School Of Computing and Information Systems

People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well. The existence of such connections, which we call connected behavior, gives researchers a unique opportunity to predict how one would behave for a future event given their past behaviors. In this work, we propose a framework to conduct connected behavior analysis. Neural stance detection models are trained on Twitter data collected on three seemingly independent topics, i.e., wearing a mask, racial equality, and Trump, to detect people’s stance, which we consider as their online behavior in …

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Generative Stresnet For Crime Prediction, Ba Phong Tran, Hoong Chuin Lau May 2023

Generative Stresnet For Crime Prediction, Ba Phong Tran, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this work, we combine STResnet (Zhang et al., 2017) with VAE Kingma & Welling (2013) to generate crime distribution. The outputs can be used for downstream tasks such as patrol deployment planning Chase et al. (2021).

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Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo May 2023

Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo

Research Collection School Of Computing and Information Systems

Stack Overflow is a popular platform for developers to seek solutions to programming-related problems. However, prior studies identified that developers may suffer from the redundant, useless, and incomplete information retrieved by the Stack Overflow search engine. To help developers better utilize the Stack Overflow knowledge, researchers proposed tools to summarize answers to a Stack Overflow question. However, existing tools use hand-craft features to assess the usefulness of each answer sentence and fail to remove semantically redundant information in the result. Besides, existing tools only focus on a certain programming language and cannot retrieve up-to-date new posted knowledge from Stack Overflow. …

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Fine-Grained Commit-Level Vulnerability Type Prediction By Cwe Tree Structure, Shengyi Pan, Lingfeng Bao, Xin Xia, David Lo, Shanping Li May 2023

Fine-Grained Commit-Level Vulnerability Type Prediction By Cwe Tree Structure, Shengyi Pan, Lingfeng Bao, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Identifying security patches via code commits to allow early warnings and timely fixes for Open Source Software (OSS) has received increasing attention. However, the existing detection methods can only identify the presence of a patch (i.e., a binary classification) but fail to pinpoint the vulnerability type. In this work, we take the first step to categorize the security patches into fine-grained vulnerability types. Specifically, we use the Common Weakness Enumeration (CWE) as the label and perform fine-grained classification using categories at the third level of the CWE tree. We first formulate the task as a Hierarchical Multi-label Classification (HMC) problem, …

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Chronos: Time-Aware Zero-Shot Identification Of Libraries From Vulnerability Reports, Yunbo Lyu, Thanh Le Cong, Hong Jin Kang, Ratnadira Widyasari, Zhipeng Zhao, Xuan-Bach Dinh Le, Ming Li, David Lo May 2023

Chronos: Time-Aware Zero-Shot Identification Of Libraries From Vulnerability Reports, Yunbo Lyu, Thanh Le Cong, Hong Jin Kang, Ratnadira Widyasari, Zhipeng Zhao, Xuan-Bach Dinh Le, Ming Li, David Lo

Research Collection School Of Computing and Information Systems

Tools that alert developers about library vulnerabilities depend on accurate, up-to-date vulnerability databases which are maintained by security researchers. These databases record the libraries related to each vulnerability. However, the vulnerability reports may not explicitly list every library and human analysis is required to determine all the relevant libraries. Human analysis may be slow and expensive, which motivates the need for automated approaches. Researchers and practitioners have proposed to automatically identify libraries from vulnerability reports using extreme multi-label learning (XML). While state-of-the-art XML techniques showed promising performance, their experimental settings do not practically fit what happens in reality. Previous studies …

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What Do Users Ask In Open-Source Ai Repositories? An Empirical Study Of Github Issues, Zhou Yang, Chenyu Wang, Jieke Shi, Thong Hoang, Pavneet Singh Kochhar, Qinghua Lu, Zhenchang Xing, David Lo May 2023

What Do Users Ask In Open-Source Ai Repositories? An Empirical Study Of Github Issues, Zhou Yang, Chenyu Wang, Jieke Shi, Thong Hoang, Pavneet Singh Kochhar, Qinghua Lu, Zhenchang Xing, David Lo

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech recognition, and image processing. The advancement of these AI systems is inseparable from open-source software (OSS). Specifically, many benchmarks, implementations, and frameworks for constructing AI systems are made open source and accessible to the public, allowing researchers and practitioners to reproduce the reported results and broaden the application of AI systems. The development of AI systems follows a data-driven paradigm and is sensitive to hyperparameter settings and data separation. Developers …

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Msrl-Net: A Multi-Level Semantic Relation-Enhanced Learning Network For Aspect-Based Sentiment Analysis, Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan May 2023

Msrl-Net: A Multi-Level Semantic Relation-Enhanced Learning Network For Aspect-Based Sentiment Analysis, Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) aims to analyze the sentiment polarity of a given text towards several specific aspects. For implementing the ABSA, one way is to convert the original problem into a sentence semantic matching task, using pre-trained language models, such as BERT. However, for such a task, the intra- and inter-semantic relations among input sentence pairs are often not considered. Specifically, the semantic information and guidance of relations revealed in the labels, such as positive, negative and neutral, have not been completely exploited. To address this issue, we introduce a self-supervised sentence pair relation classification task and propose a …

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Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria May 2023

Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …

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Consumer Reaction To The Use Of Artificial Intelligence Chatbot On Distribution Of General Insurance In Singapore, Lai Hing Tan May 2023

Consumer Reaction To The Use Of Artificial Intelligence Chatbot On Distribution Of General Insurance In Singapore, Lai Hing Tan

Dissertations and Theses Collection (Open Access)

As technology rapidly permeates all aspects of our lives, it is not unusual to question and even challenge the rationale on why certain industries are slower to adapt to the new digital age. Insurance is a business that is under scrutiny given its traditional ways of selling and legacy challenges. Why is technology investment in insurance companies lagging others? One emerging technological disruption is artificial intelligence (AI). It is the science of designing and building intelligent systems that can complete tasks traditionally performed by humans. AI is expected to fundamentally transform today’s marketplace, for businesses and consumers alike. However, because …

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Algorithms, Leadership, And Morality: Why A Mere Human Effect Drives The Preference For Human Over Algorithmic Leadership, Jack Mcguire, David De Cremer May 2023

Algorithms, Leadership, And Morality: Why A Mere Human Effect Drives The Preference For Human Over Algorithmic Leadership, Jack Mcguire, David De Cremer

Research Collection Lee Kong Chian School Of Business

Algorithms are increasingly making decisions in organizations that carry moral consequences and such decisions are considered to be ordinarily made by leaders. An important consideration to be made by organizations is therefore whether adopting algorithms in this domain will be accepted by employees and whether this practice will harm their reputation. Considering this emergent phenomenon, we set out to examine employees’ perceptions about (a) algorithmic decision-making systems employed to occupy leadership roles and make moral decisions in organizations, and (b) the reputation of organizations that employ such systems. Furthermore, we examine the extent to which the decision agent needs to …

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The Persuasive Design Of Ai-Synthesized Voices, Hannah H. Chang, Anirban Mukherjee May 2023

The Persuasive Design Of Ai-Synthesized Voices, Hannah H. Chang, Anirban Mukherjee

Research Collection Lee Kong Chian School Of Business

We investigate the impact of AI-based, machine-synthesized narrating voices on consumer cognitions and behavior in media-rich environment. Across four studies (plus pretests), we show that the design of AI voices systematically and predictably affects consumer cognition and behavior. Specifically, the designs of AI voices have differential effects in early versus later stages of consumer purchase journey. In situations where the consumers’ attention is already directed to the message, we find that marcomm with more AI voices generates a smaller proportion of favorable thoughts, which leads to a lower purchase likelihood. These results support our conceptualization that hearing more AI voices …

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Re-Evaluating Natural Intelligence In The Face Of Chatgpt, Elvin T. Lim, Tze K Koh May 2023

Re-Evaluating Natural Intelligence In The Face Of Chatgpt, Elvin T. Lim, Tze K Koh

Research Collection College of Integrative Studies

How will new technologies impact the nature of higher education? Before ChatGPT, the world witnessed major shifts led by innovations in information storage and transmission. Papyrus in ancient Egypt, the Gutenberg press in 15th-century Europe, and the internet in the 20th century were all milestones in the mass dissemination of knowledge.

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Exploring A Gradient-Based Explainable Ai Technique For Time-Series Data: A Case Study Of Assessing Stroke Rehabilitation Exercises, Min Hun Lee, Yi Jing Choy May 2023

Exploring A Gradient-Based Explainable Ai Technique For Time-Series Data: A Case Study Of Assessing Stroke Rehabilitation Exercises, Min Hun Lee, Yi Jing Choy

Research Collection School Of Computing and Information Systems

Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in various applications. However, there has been limited exploration of explainable AI techniques on time-series data, especially in the healthcare context. In this paper, we describe a threshold-based method that utilizes a weakly supervised model and a gradient-based explainable AI technique (i.e. saliency map) and explore its feasibility to identify salient frames of time-series data. Using the dataset from 15 post-stroke survivors performing three upper-limb exercises and labels on whether a compensatory motion is observed or …

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Trustworthy And Synergistic Artificial Intelligence For Software Engineering: Vision And Roadmaps, David Lo May 2023

Trustworthy And Synergistic Artificial Intelligence For Software Engineering: Vision And Roadmaps, David Lo

Research Collection School Of Computing and Information Systems

For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engiueering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to …

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Supporting Novices Author Audio Descriptions Via Automatic Feedback, Rosiana Natalie, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara Apr 2023

Supporting Novices Author Audio Descriptions Via Automatic Feedback, Rosiana Natalie, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions (AD) make videos accessible to those who cannot see them. But many videos lack AD and remain inaccessible as traditional approaches involve expensive professional production. We aim to lower production costs by involving novices in this process. We present an AD authoring system that supports novices to write scene descriptions (SD)—textual descriptions of video scenes—and convert them into AD via text-to-speech. The system combines video scene recognition and natural language processing to review novice-written SD and feeds back what to mention automatically. To assess the effectiveness of this automatic feedback in supporting novices, we recruited 60 participants to …

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Open-Set Domain Adaptation By Deconfounding Domain Gaps, Xin Zhao, Shengsheng Wang, Qianru Sun Apr 2023

Open-Set Domain Adaptation By Deconfounding Domain Gaps, Xin Zhao, Shengsheng Wang, Qianru Sun

Research Collection School Of Computing and Information Systems

Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any distractions caused by open-set classes, i.e., the classes “unknown” to the source model. Compared to standard DA, the key of OSDA lies in the separation between known and unknown classes. Existing OSDA methods often fail the separation because of overlooking the confounders (i.e., the domain gaps), which means their recognition of “unknown classes” is not because of class semantics but domain difference (e.g., styles and contexts). We address this issue by explicitly deconfounding domain gaps …

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Design, Development, And Evaluation Of An Interactive Personalized Social Robot To Monitor And Coach Post-Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermudez I Badia Apr 2023

Design, Development, And Evaluation Of An Interactive Personalized Social Robot To Monitor And Coach Post-Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermudez I Badia

Research Collection School Of Computing and Information Systems

Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However, even if people have various physical conditions, most prior work on social robot exercise coaching systems has utilized generic, predefined feedback. The deployment of these systems still remains a challenge. In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation. Through interviews with therapists, we designed how this system interacts with the user and then developed an interactive …

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Mimusa: Mimicking Human Language Understanding For Fine-Grained Multi-Class Sentiment Analysis, Zhaoxia Wang, Zhenda Hu, Seng-Beng Ho, Erik Cambria, Ah-Hwee Tan Apr 2023

Mimusa: Mimicking Human Language Understanding For Fine-Grained Multi-Class Sentiment Analysis, Zhaoxia Wang, Zhenda Hu, Seng-Beng Ho, Erik Cambria, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Sentiment analysis is an important natural language processing (NLP) task due to a wide range of applications. Most existing sentiment analysis techniques are limited to the analysis carried out at the aggregate level, merely providing negative, neutral and positive sentiments. The latest deep learning-based methods have been leveraged to provide more than three sentiment classes. However, such learning-based methods are still black-box-based methods rather than explainable language processing methods. To address this gap, this paper proposes a new explainable fine-grained multi-class sentiment analysis method, namely MiMuSA, which mimics the human language understanding processes. The proposed method involves a multi-level modular …

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Interconnectedness And Financial Stability In The Era Of Artificial Intelligence, Nydia Remolina Leon Apr 2023

Interconnectedness And Financial Stability In The Era Of Artificial Intelligence, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

This chapter explains AI use cases may amplify some threats to the stability of the financial system. It argues that lack of a consistent approach to AI Governance by financial regulators and limited oversight on some AI models in terms of systemic risks need to be addressed to preserve the stability of the financial system. To illustrate the problem and propose specific policy recommendations, the chapter first explains how the widespread use AI in finance impacts systemic risk in the financial sector. Then, it explains why trustworthiness of AI encompasses not only the typical ethical principles about transparency, explainability, fairness, …

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Regulating Artificial Intelligence In International Investment Law, Mark Mclaughlin Apr 2023

Regulating Artificial Intelligence In International Investment Law, Mark Mclaughlin

Research Collection Yong Pung How School Of Law

The interaction between artificial intelligence (AI) and international investment treaties is an uncharted territory of international law. Concerns over the national security, safety, and privacy implications of AI are spurring regulators into action around the world. States have imposed restrictions on data transfer, utilised automated decision-making, mandated algorithmic transparency, and limited market access. This article explores the interaction between AI regulation and standards of investment protection. It is argued that the current framework provides an unpredictable legal environment in which to adjudicate the contested norms and ethics of AI. Treaties should be recalibrated to reinforce their anti-protectionist origins, embed human-centric …

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Engaging Students Through Conversational Chatbots And Digital Content: A Climate Action Perspective, Thomas Menkhoff, Benjamin Gan Apr 2023

Engaging Students Through Conversational Chatbots And Digital Content: A Climate Action Perspective, Thomas Menkhoff, Benjamin Gan

Research Collection Lee Kong Chian School Of Business

In this case study, we report experiences deploying a conversational chatbot as a pre-class and post-class engagement tool for undergraduate students enrolled in sustainability-related courses aimed at educating them about the severity of climate change and the importance of climate action by offsetting one’s carbon footprint (e.g, by planting trees or mangroves in SEA). The intitiative supports the university’s sustainability efforts in general and our new sustainability major in particular aimed at helping students to achieve sustainability-related learning outcomes with reference to climate change and climate action (SDG 13), one of the 17 Sustainable Development Goals established by the United …

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Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee Apr 2023

Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Object detection, while being an attractive interaction method for Augmented Reality (AR), is fundamentally error-prone due to the probabilistic nature of the underlying AI models, resulting in sub-optimal user experiences. In this paper, we explore the effect of three game design concepts, Ambiguity, Transparency, and Controllability, to provide better gameplay experiences in AR games that use error-prone object detection-based interaction modalities. First, we developed a base AR pet breeding game, called Bubbleu that uses object detection as a key interaction method. We then implemented three different variants, each according to the three concepts, to investigate the impact of each design …

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