Digital Commons Network^™

An Idealist’S Approach For Smart Contract Correctness, Duy Tai Nguyen, Hong Long Pham, Jun Sun, Quang Loc Le

Research Collection School Of Computing and Information Systems

In this work, we experiment an idealistic approach for smart contract correctness verification and enforcement, based on the assumption that developers are either desired or required to provide a correctness specification due to the importance of smart contracts and the fact that they are immutable after deployment. We design a static verification system with a specification language which supports fully compositional verification (with the help of function specifications, contract invariants, loop invariants and call invariants). Our approach has been implemented in a tool named iContract which automatically proves the correctness of a smart contract statically or checks the unverified part …

Followupqg: Towards Information-Seeking Follow-Up Question Generation, Yan Meng, Liangming Pan, Yixin Cao, Min-Yen Kan

Research Collection School Of Computing and Information Systems

Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of realworld information-seeking follow-up question generation (FQG), which aims to generate follow-up questions seeking a more in-depth understanding of an initial question and answer. We construct FOLLOWUPQG, a dataset1 of over 3K real-world (initial question, answer, follow-up question) tuples collected from a Reddit forum providing layman-friendly explanations for open-ended questions. In contrast to existing datasets, questions in FOLLOWUPQG use more diverse pragmatic strategies to seek information, and they also show higher-order cognitive skills (such as applying and relating). We evaluate current question …

Opportunities For Spatial Database Research In The Context Of Preference Queries, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

This is the outline of the keynote speech at LocalRec@ACM SIGSPATIAL 2023. The main objective of the talk is to point out opportunities for spatial database researchers in the area of preference-based querying. We will commence with an overview of the standard queries for multi-objective decision making, and demonstrate their direct connection to recommendations and to market analysis. In this context, there is a number of specific decision criteria, and user preferences are represented as vectors with as many dimensions. We will demonstrate how and why this type of preferences are natural to actual applications and practical for the support …

Towards Llm-Based Fact Verification On News Claims With A Hierarchical Step-By-Step Prompting Method, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

While large pre-trained language models (LLMs) have shown their impressive capabilities in various NLP tasks, they are still underexplored in the misinformation domain. In this paper, we examine LLMs with in-context learning (ICL) for news claim verification, and find that only with 4-shot demonstration examples, the performance of several prompting methods can be comparable with previous supervised models. To further boost performance, we introduce a Hierarchical Step-by-Step (HiSS) prompting method which directs LLMs to separate a claim into several subclaims and then verify each of them via multiple questionsanswering steps progressively. Experiment results on two public misinformation datasets show that …

Krover: A Symbolic Execution Engine For Dynamic Kernel Analysis, Pansilu Madhura Bhashana Pitigalaarachchi Pitigala Arachchillage, Xuhua Ding, Haiqing Qiu, Haoxin Tu, Jiaqi Hong, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

We present KRover, a novel kernel symbolic execution engine catered for dynamic kernel analysis such as vulnerability analysis and exploit generation. Different from existing symbolic execution engines, KRover operates directly upon a live kernel thread's virtual memory and weaves symbolic execution into the target's native executions. KRover is compact as it neither lifts the target binary to an intermediary representation nor uses QEMU or dynamic binary translation. Benchmarked against S2E, our performance experiments show that KRover is up to 50 times faster but with one tenth to one quarter of S2E memory cost. As shown in our four case studies, …

Faire: Repairing Fairness Of Neural Networks Via Neuron Condition Synthesis, Tianlin Li, Xiaofei Xie, Jian Wang, Qing Guo, Aishan Liu, Lei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have achieved tremendous success in many applications, while it has been demonstrated that DNNs can exhibit some undesirable behaviors on concerns such as robustness, privacy, and other trustworthiness issues. Among them, fairness (i.e., non-discrimination) is one important property, especially when they are applied to some sensitive applications (e.g., finance and employment). However, DNNs easily learn spurious correlations between protected attributes (e.g., age, gender, race) and the classification task and develop discriminatory behaviors if the training data is imbalanced. Such discriminatory decisions in sensitive applications would introduce severe social impacts. To expose potential discrimination problems in DNNs …

Laf: Labeling-Free Model Selection For Automated Deep Neural Network Reusing, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

pplying deep learning (DL) to science is a new trend in recent years, which leads DL engineering to become an important problem. Although training data preparation, model architecture design, and model training are the normal processes to build DL models, all of them are complex and costly. Therefore, reusing the open-sourced pre-trained model is a practical way to bypass this hurdle for developers. Given a specific task, developers can collect massive pre-trained deep neural networks from public sources for reusing. However, testing the performance (e.g., accuracy and robustness) of multiple deep neural networks (DNNs) and recommending which model should be …

Pro-Cap: Leveraging A Frozen Vision-Language Model For Hateful Meme Detection, Rui Cao, Ming Shan Hee, Adriel Kuek, Wen Haw Chong, Roy Ka-Wei Lee, Jing Jiang

Research Collection School Of Computing and Information Systems

Hateful meme detection is a challenging multimodal task that requires comprehension of both vision and language, as well as cross-modal interactions. Recent studies have tried to fine-tune pre-trained vision-language models (PVLMs) for this task. However, with increasing model sizes, it becomes important to leverage powerful PVLMs more efficiently, rather than simply fine-tuning them. Recently, researchers have attempted to convert meme images into textual captions and prompt language models for predictions. This approach has shown good performance but suffers from non-informative image captions. Considering the two factors mentioned above, we propose a probing-based captioning approach to leverage PVLMs in a zero-shot …

Matk: The Meme Analytical Tool Kit, Ming Shan Hee, Aditi Kumaresan, Nguyen Khoi Hoang, Nirmalendu Prakash, Rui Cao, Roy Ka-Wei Lee

Research Collection School Of Computing and Information Systems

The rise of social media platforms has brought about a new digital culture called memes. Memes, which combine visuals and text, can strongly influence public opinions on social and cultural issues. As a result, people have become interested in categorizing memes, leading to the development of various datasets and multimodal models that show promising results in this field. However, there is currently a lack of a single library that allows for the reproduction, evaluation, and comparison of these models using fair benchmarks and settings. To fill this gap, we introduce the Meme Analytical Tool Kit (MATK), an open-source toolkit specifically …

Revisiting Disentanglement And Fusion On Modality And Context In Conversational Multimodal Emotion Recognition, Bobo Li, Hao Fei, Lizi Liao, Yu Zhao, Chong Teng, Tat-Seng Chua, Donghong Ji, Fei Li

Research Collection School Of Computing and Information Systems

It has been a hot research topic to enable machines to understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received consistent attention in recent years, where a diverse range of methods has been proposed for securing better task performance. Most existing works treat MM-ERC as a standard multimodal classification problem and perform multimodal feature disentanglement and fusion for maximizing feature utility. Yet after revisiting the characteristic of MM-ERC, we argue that both the feature multimodality and conversational contextualization should be properly modeled simultaneously during the feature disentanglement …

Partial Annotation-Based Video Moment Retrieval Via Iterative Learning, Wei Ji, Renjie Liang, Lizi Liao, Hao Fei, Fuli Feng

Research Collection School Of Computing and Information Systems

Given a descriptive language query, Video Moment Retrieval (VMR) aims to seek the corresponding semantic-consistent moment clip in the video, which is represented as a pair of the start and end timestamps. Although current methods have achieved satisfying performance, training these models heavily relies on the fully-annotated VMR datasets. Nonetheless, precise video temporal annotations are extremely labor-intensive and ambiguous due to the diverse preferences of different annotators.Although there are several works trying to explore weakly supervised VMR tasks with scattered annotated frames as labels, there is still much room to improve in terms of accuracy. Therefore, we design a new …

Heterogeneous Graph Neural Network With Multi-View Representation Learning, Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu

Research Collection School Of Computing and Information Systems

In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local structures of HGs. On the one hand, most of the existing methods either inadequately model the local structure under specific semantics, or neglect the heterogeneity when aggregating information from the local structure. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain …

Ppdf: A Privacy-Preserving Cloud-Based Data Distribution System With Filtering, Yudi Zhang, Willy Susilo, Fuchun Guo, Guomin Yang

Research Collection School Of Computing and Information Systems

Cloud computing has emerged as a popular choice for distributing data among both individuals and companies. Ciphertext-policy attribute-based encryption (CP-ABE) has been extensively used to provide data security and enable fine-grained access control. With this encryption technique, only users whose attributes satisfy the access policy can access the plaintext. In order to mitigate the computational overhead on users, particularly on lightweight devices, partial decryption has been introduced, where the cloud assists in performing the decryption computations without revealing sensitive information. However, in this process, the cloud obtains the user's attributes, thus infringing on the user's privacy. To address this issue, …

Spatial Data Management For Green Mobility, Christophe Claramunt, Christine Bassem, Demetrios Zeinalipour-Yazti, Baihua Zheng, Goce Trajcevski, Kristian Torp

Research Collection School Of Computing and Information Systems

While many countries are developing appropriate actions towards a greener future and moving towards adopting sustainable mobility activities, the real-time management and planning of innovative transportation facilities and services in urban environments still require the development of advanced mobile data management infrastructures. Novel green mobility solutions, such as electric, hybrid, solar and hydrogen vehicles, as well as public and gig-based transportation resources are very likely to reduce the carbon footprint. However, their successful implementation still needs efficient spatio-temporal data management resources and applications to provide a clear picture and demonstrate their effectiveness. This paper discusses the major data management challenges, …

Github Actions: The Impact On The Pull Request Process, Mairieli Wessel, Joseph Vargovich, Marco Gerosa, Christoph Treude

Research Collection School Of Computing and Information Systems

Software projects frequently use automation tools to perform repetitive activities in the distributed software development process. Recently, GitHub introduced GitHub Actions, a feature providing automated workflows for software projects. Understanding and anticipating the effects of adopting such technology is important for planning and management. Our research investigates how projects use GitHub Actions, what the developers discuss about them, and how project activity indicators change after their adoption. Our results indicate that 1,489 out of 5,000 most popular repositories (almost 30% of our sample) adopt GitHub Actions and that developers frequently ask for help implementing them. Our findings also suggest that …

Metaformer Baselines For Vision, Weihao Yu, Chenyang Si, Pan Zhou, Mi Luo, Yichen Zhou, Jiashi Feng, Shuicheng Yan, Xinchao Wang

Research Collection School Of Computing and Information Systems

Abstract—MetaFormer, the abstracted architecture of Transformer, has been found to play a significant role in achieving competitive performance. In this paper, we further explore the capacity of MetaFormer, again, by migrating our focus away from the token mixer design: we introduce several baseline models under MetaFormer using the most basic or common mixers, and demonstrate their gratifying performance. We summarize our observations as follows: (1) MetaFormer ensures solid lower bound of performance. By merely adopting identity mapping as the token mixer, the MetaFormer model, termed IdentityFormer, achieves >80% accuracy on ImageNet-1K. (2) MetaFormer works well with arbitrary token mixers. When …

A Survey On Aspect-Based Sentiment Analysis: Tasks, Methods, And Challenges, Wenxuan Zhang, Xin Li, Yang Deng, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

As an important fine-grained sentiment analysis problem, aspect-based sentiment analysis (ABSA), aiming to analyze and understand people's opinions at the aspect level, has been attracting considerable interest in the last decade. To handle ABSA in different scenarios, various tasks are introduced for analyzing different sentiment elements and their relations, including the aspect term, aspect category, opinion term, and sentiment polarity. Unlike early ABSA works focusing on a single sentiment element, many compound ABSA tasks involving multiple elements have been studied in recent years for capturing more complete aspect-level sentiment information. However, a systematic review of various ABSA tasks and their …

Leveraging Long Short-Term User Preference In Conversational Recommendation Via Multi-Agent Reinforcement Learning, Yang Deng, Yaliang Li, Bolin Ding, Wai Lam

Research Collection School Of Computing and Information Systems

Conversational recommender systems (CRS) endow traditional recommender systems with the capability of dynamically obtaining users’ short-term preferences for items and attributes through interactive dialogues. There are three core challenges for CRS, including the intelligent decisions for what attributes to ask, which items to recommend, and when to askor recommend, at each conversation turn. Previous methods mainly leverage reinforcement learning (RL) to learn conversational recommendation policies for solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components. These approaches restrict the scalability and generality of CRS and fall short of preserving a stable training …

Rethinking Conversational Agents In The Era Of Large Language Models: Proactivity, Non-Collaborativity, And Beyond, Yang Deng, Wenqiang Lei, Minlie Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems are designed to offer human users social support or functional services through natural language interactions. Typical conversation researches mainly focus on the response-ability of the system, such as dialogue context understanding and response generation. In the era of large language models (LLMs), LLM-augmented conversational systems showcase exceptional capabilities of responding to user queries for different language tasks. However, as LLMs are trained to follow users' instructions, LLM-augmented conversational systems typically overlook the design of an essential property in intelligent conversations, i.e., goal awareness. In this tutorial, we will introduce the recent advances on the design of agent's awareness …

Multimodal Fashion Knowledge Extraction As Captioning, Yifei Yuan, Wenxuan Zhang, Yang Deng, Wai Lam

Research Collection School Of Computing and Information Systems

Social media plays a significant role in boosting the fashion industry, where a massive amount of fashion-related posts are generated every day. In order to obtain the rich fashion information from the posts, we study the task of social media fashion knowledge extraction. Fashion knowledge, which typically consists of the occasion, person attributes, and fashion item information, can be effectively represented as a set of tuples. Most previous studies on fashion knowledge extraction are based on the fashion product images without considering the rich text information in social media posts. Existing work on fashion knowledge extraction in social media is …

Exploration Of Motion Capture System To Investigate Human Shoulder Kinematics, Ola Alsaadi

Electronic Theses and Dissertations

The glenohumeral joint (GH) is commonly conceptualized as a ball-and-socket joint [1], and its center of rotation (COR) is presumed to coincide with the geometric center of the medial-superior region of the humeral head [2]. Recent research has endorsed improvements in COR estimation through invasive and noninvasive techniques, including cadaver studies, stereophotogrammetry, and motion capture (MOCAP) systems. Despite increased interest in wearable technology within human movement analysis, the problem of COR estimation employing MOCAP systems and its validation against bi-planar fluoroscopy remains relatively unexplored.

This study employed a marker-based MOCAP system to compare the accuracy, error, and precision of three …

Assessment Of Self-Efficacy And Practice Change Following Pressure Injury Education In A Long-Term Care Facility, Charity L. Booker

Electronic Theses and Dissertations

The purpose of this scholarly project was to evaluate if pressure injury education improved nursing knowledge and self-efficacy in the prevention and management of pressure injuries with the intent to improve practice. A literature review was conducted to evaluate educational interventions for adult learners and nursing roles in the care process. Existing studies indicated that patient outcomes are improved when nurses have adequate knowledge in pressure injury prevention. This quality improvement project was implemented in a long-term care facility with 28 nursing staff. The Agency for Healthcare Research and Quality pressure injury best practices presentation was the basis for the …

Legal Protection Of Property Rights: A Dynamic Evolution Model, Fali Huang

Research Collection School Of Economics

This paper presents an analysis of a dynamic evolution model of institutions, focusing on the factors affecting the security of property rights such as coercive capacity, political power, legal quality, and private protection. These factors are endogenously determined in equilibrium, allowing legal quality to serve as a summary indicator of property security. The primary finding of the study indicates that the legal protection of property rights tends to increase over time, driven by a decline in the economy's vulnerability to expropriation, particularly due to the growing importance of commercial and industrial activities relative to agriculture. Furthermore, the coexistence of the …

Going Beyond The Call Of Duty Under Conditions Of Economic Threat: Integrating Life History And Temporal Dilemma Perspectives, Nina Sirola

Research Collection Lee Kong Chian School Of Business

Under conditions of economic threat, such as during economic downturns, organizations can benefit from employees’ willingness to go beyond the call of duty and engage in organization-directed citizenship behavior (OCBO). Yet, such behavior is discretionary and competes for time with employees’ other interests and priorities. I integrate life history theory with the temporal dilemma perspective on organizational citizenship behavior to propose that childhood environments sensitize individuals to prioritize different goals in response to economic threat later in life. Consistent with strategies for responding to threat that are functional in their childhood environments, employees from wealthier backgrounds respond to economic threat …

Why Your Organisation Needs A Language Management Strategy, John Fiset, Devasheesh P. Bhave

Research Collection Lee Kong Chian School Of Business

This article discusses the challenges and benefits of managing language dynamics at work, where both standard and hybrid forms of language coexist. It reviews the literature on language-related misunderstanding and its effects on employee outcomes through three pathways: relational, emotional, and informational. It also suggests some interventions to enhance communication effectiveness and foster an inclusive culture in linguistically diverse workplaces.

Data And Model Centric Approaches For Card Fraud Detection, Nahla Barakat, Sara Farag

Artificial Intelligence

Card fraud has become a widespread issue as technology advances and credit cards become more popular. Over the last decade, criminals have found diverse ways to steal card information or create duplicates without the cardholder's knowledge by deploying adaptive approaches against the system. Therefore, detecting suspicious behaviour of a card is critical for preventing fraudulent transactions. However, detecting fraud behaviour is challenging task, given that the transactions’ data are imbalanced, and the adaptive fraudsters' approaches against the system. In this paper, a combination of data and model centric approaches are proposed for card fraud detection. Starting by data analysis and …

Adaptive Learning And Virtual Reality Based Conceptual Framework For Dental Training, Nahla Barakat, Abeer Hamdy, Meriam Sherif

Artificial Intelligence

Recently, there has been increasing interest in utilizing virtual reality (VR) technology in dental education and student training. VR based training can provide students with sufficient training and hands-on experience before entering the clinical setting, which in turn helps avoid risks ranging from small errors to severe consequences. However, previous research in this area is limited and none of the previous work proposed a complete, scalable framework for This paper proposes an improved virtual environment for dental training, through an adaptive learning approach, integrated with the VR environment, which is to the best of our knowledge the first framework proposed …

Quantitative Gait And Balance Outcomes For Ataxia Trials: Consensus Recommendations By The Ataxia Global Initiative Working Group On Digital-Motor Biomarkers, Winfried Ilg, Sarah Milne, Tanja Schmitz-Hübsch, Lisa Alcock, Lukas Beichert, Enrico Bertini, Helen Dawes, Christopher M. Gomez, James Mcnames, Multiple Additional Authors

Electrical and Computer Engineering Faculty Publications and Presentations

With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials.

This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics …

Editorial: Solutions To Water Crises (Related To Actual Interventions), Jenia Mukherjee, Saket Pande, Melissa Haeffner, Sara Marks, Pieter Van Oel, Matthew Sanderson, Adriana Allen

Environmental Science and Management Faculty Publications and Presentations

Water science has become “pluralistic” (Evers et al., 2017) to collectively (yet differently) understand complex water systems with promising combinations of compatible and complementary disciplines. The contemporary context of water science discusses the more severe water-society challenges of the Anthropocene. Yet, the conversation is not definitive; indeed, there are unending debates between quantitative and qualitative research approaches including methodological choices and accuracies along questions of scales, themes, and politics of funding. Transdisciplinary applications and cross-sectoral engagements offer solution-oriented water just trajectories – scientists, practitioners, and user groups designing and deploying “solutions” related to actual interventions in addressing water …

Epigenetic Regulation During Cancer Transitions Across 11 Tumour Types, Nadezhda V Terekhanova, Alla Karpova, Wen-Wei Liang, Siqi Chen, Yize Li, Austin N Southard-Smith, Michael D Iglesia, Michael C Wendl, Reyka G Jayasinghe, Jingxian Liu, Yizhe Song, Song Cao, Andrew Houston, Xiuting Liu, Matthew A Wyczalkowski, Rita Jui-Hsien Lu, Wagma Caravan, Andrew Shinkle, Nataly Naser Al Deen, John M Herndon, Jacqueline Mudd, Kazuhito Sato, Omar M Ibrahim, Chia-Kuei Mo, Sara E Chasnoff, Jason M Held, Russell Pachynski, Julie K Schwarz, William E Gillanders, Albert H Kim, Ravi Vij, John F Dipersio, Sidharth V Puram, Milan G Chheda, Katherine C Fuh, David G Denardo, Ryan C Fields, Feng Chen, Li Ding, Et Al.

2020-Current year OA Pubs

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis