Databases and Information Systems Commons^™

Bank Error In Whose Favor? A Case Study Of Decentralized Finance Misgovernance, Ping Fan Ke, Ka Chung Boris Ng

Research Collection School Of Computing and Information Systems

Decentralized Finance (DeFi) emerged rapidly in recent years and provided open and transparent financial services to the public. Due to its popularity, it is not uncommon to see cybersecurity incidents in the DeFi landscape, yet the impact of such incidents is under-studied. In this paper, we examine two incidents in DeFi protocol that are mainly caused by misgovernance and mistake in the smart contract. By using the synthetic control method, we found that the incident in Alchemix did not have a significant effect on the total value locked (TVL) in the protocol, whereas the incident in Compound caused a 6.13% …

Mining Competitively-Priced Bundle Configurations, Ezekiel Ong Young, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We examine the bundle configuration problem in the presence of competition. Given a competitor's bundle configuration and pricing, we determine what to bundle together, and at what prices, to maximize the target firm's revenue. We highlight the difficulty in pricing bundles and propose a scalable alternative and an efficient search heuristic to refine the approximate prices. Furthermore, we extend the heuristics proposed by previous work to accommodate the presence of a competitor. We analyze the effectiveness of our proposed models through experimentation on real-life ratings-based preference data.

Singlish Checker: A Tool For Understanding And Analysing An English Creole Language, Lee-Hsun Hsieh, Nam Chew Chua, Agus Trisnajaya Kwee, Pei-Chi Lo, Yang-Yin Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

As English is a widely used language in many countries of different cultures, variants of English also known as English creoles have also been created. Singlish is one such English creole used by people in Singapore. Nevertheless, unlike English, Singlish is not taught in schools nor encouraged to be used in formal communications. Hence, it remains to be a low resource language with a lack of up-to-date Singlish word dictionary and computational tools to analyse the language. In this paper, we therefore propose Singlish Checker, a tool that is able to help detecting Singlish text, Singlish words and phrases. To …

On The Robustness Of Diffusion In A Network Under Node Attacks, Alvis Logins, Yuchen Li, Panagiotis Karras

Research Collection School Of Computing and Information Systems

How can we assess a network's ability to maintain its functionality under attacks Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade or Linear Threshold model, susceptible to attacks by an adversarial attacker who disables nodes. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside …

Using Landsat Satellite Imagery To Estimate Groundcover In The Grainbelt Of Western Australia, Justin Laycock, Nick Middleton, Karen Holmes

Resource management technical reports

Maintaining vegetative groundcover is an important component of sustainable agricultural systems and plays a critical function for soil and land conservation in Western Australia’s (WA) grainbelt (the south-west cropping region). This report describes how satellite imagery can be used to quantitatively and objectively estimate total vegetative groundcover, both in near real time and historically across large areas. We used the Landsat seasonal fractional groundcover products developed by the Joint Remote Sensing Research Program from the extensive archive of Landsat imagery. These products provide an estimate of the percentage of green vegetation, non-green vegetation and bare soil for each 30 m …

Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang

Research Collection School Of Computing and Information Systems

Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pretrained RoBERTa language model for hateful meme classification. …

Curiosity-Driven And Victim-Aware Adversarial Policies, Chen Gong, Zhou Yang, Yunpeng Bai, Jieke Shi, Arunesh Sinha, Bowen Xu, David Lo, Xinwen Hou, Guoliang Fan

Research Collection School Of Computing and Information Systems

Recent years have witnessed great potential in applying Deep Reinforcement Learning (DRL) in various challenging applications, such as autonomous driving, nuclear fusion control, complex game playing, etc. However, recently researchers have revealed that deep reinforcement learning models are vulnerable to adversarial attacks: malicious attackers can train adversarial policies to tamper with the observations of a well-trained victim agent, the latter of which fails dramatically when faced with such an attack. Understanding and improving the adversarial robustness of deep reinforcement learning is of great importance in enhancing the quality and reliability of a wide range of DRL-enabled systems. In this paper, …

A Unified Dialogue User Simulator For Few-Shot Data Augmentation, Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang

Research Collection School Of Computing and Information Systems

Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment largescale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with fewshot data. The experiments on a target dataset across multiple domains show …

Implementation Of Empath X Sla Predictive Tool For A Government Agency, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Service Level Agreement (SLA) plays a significant role in the relationship between citizens and the government. It stipulates the quality levels required for the meaningful interaction between the two parties. Most SLA predictive models consider end-to-end duration and frequency of failed service requests as model inputs with little research on the analysis of textual details of the service request. This is an issue for government bodies as the latter do not just want to meet SLA, but also be proactive by knowing the citizens before assisting them. Inclusion of textual data potentially answer to this requirement of knowing the citizen …

Supply Regulation Under The Exclusion Policy In A Ride-Sourcing Market, Xiaonan Li, Xiangyong Li, Hai Wang, Junxin Shi, Yash P. Aneja

Research Collection School Of Computing and Information Systems

On-demand ride-sourcing platforms have quickly emerged and become ubiquitous in our daily lives. Motivated by the rising public concern about service quality in the ride-sourcing market, this paper aims to examine the impact of exclusion policy that can serve as both quality management and supply regulation strategy. With an exclusion policy, the platform excludes low-quality service providers/drivers from the ride-sourcing market by setting a quality threshold of admission (QTA). We propose a model to describe and analyze the market equilibrium under the exclusion policy and present our analytical and numerical results – some of which are non-intuitive and intriguing. Considering …

Question-Attentive Review-Level Recommendation Explanation, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommendation explanations help to improve their acceptance by end users. The form of explanation of interest here is presenting an existing review of the recommended item. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance of each review to the recommendation objective. Our focus is on improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. …

Assessment Design For Digital Education: An Analytics-Based Authentic Assessment Approach, Lim Ming Soon Tristan, Gottipati Swapna, Michelle L. F. Cheong, Christopher Pang, Jun Wei Ng

Research Collection School Of Computing and Information Systems

This study looks to identify an assessment design construct that overcomes known issues in authentic assessment design practices in digital education. These include lack of "freedom-of-choice", lack of focus on multimodal nature of the digital process, and shortage of effective feedbacks. This study proposes an authentic assessment, that combines gamification (G) with heutagogy (H) and multimodality (M) of assessments, building upon learning analytics (A), known as GHMA. Proposed assessment design is a skills-oriented game-based assessment approach. Learners can determine their own goals and create individualized multimodal artefacts; receive cognitive challenge through cognitively complex tasks structured in gamified non-linear learning paths; …

Learning Generalizable Models For Vehicle Routing Problems Via Knowledge Distillation, Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee

Research Collection School Of Computing and Information Systems

Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribution generalization concerns, we bring the knowledge distillation to this field and propose an Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme for learning more generalizable deep models. Particularly, our AMDKD leverages various knowledge from multiple teachers trained on exemplar distributions to yield a light-weight yet generalist student model. Meanwhile, we equip AMDKD with an adaptive strategy that allows the student to concentrate on difficult distributions, so as to absorb hard-to-master knowledge more effectively. Extensive experimental results …

Learning Dynamic Multimodal Implicit And Explicit Networks For Multiple Financial Tasks, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Many financial f orecasting d eep l earning w orks focus on the single task of predicting stock returns for trading with unimodal numerical inputs. Investment and risk management however involves multiple financial t asks - f orecasts o f expected returns, risks and correlations of multiple stocks in portfolios, as well as important events affecting different stocks - to support decision making. Moreover, stock returns are influenced by large volumes of non-stationary time-series information from a variety of modalities and the propagation of such information across inter-company relationship networks. Such networks could be explicit - observed co-occurrences in online …

R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao

Research Collection School Of Computing and Information Systems

Document-level natural language inference (DocNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents. Current datasets and baselines largely follow sentence-level settings, but fail to address the issues raised by longer documents. In this paper, we establish a general solution, named Retrieval, Reading and Fusion (R2F) framework, and a new setting, by analyzing the main challenges of DocNLI: interpretability, long-range dependency, and cross-sentence inference. The basic idea of the framework is to simplify document-level task into a set of sentence-level tasks, and improve both performance and …

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …

Cold Calls To Enhance Class Participation And Student Engagement, Manoj Thulasidas, Aldy Gunawan

Research Collection School Of Computing and Information Systems

The question whether cold calls increase student engagement in the classroom has not been conclusively answered in the literature. This study describes the automated system to implement unbiased, randomized cold calling by posing a question, allowing all students to think first and then calling on a particular student to respond. Since we already have a measure of the level of student engagement as the self-reported classparticipation entries from the students, its correlation to cold calling is also further studied. The results show that there is a statistically significant increase in the class participation reported, and therefore in student engagement, in …

Gamified Online Industry Learning Platform For Teaching Of Foundational Computing Skills, Yi Meng Lau, Rafael Jose Barros Barrios, Gottipati Swapna, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

Online industry learning platforms are widely used by organizations for employee training and upskilling. Courses or lessons offered by these platforms can be generic or specific to an enterprise application. The increased demand of new hires to learn these platforms or who are already certified in some of these courses has led universities to look at the opportunities for integrating online industry learning platforms into their curricula. Universities hope to use these platforms to aid students in their learning of concepts and theories. At the same time, these platforms can equip students with industryrecognized certifications or digital badges. This paper …

Innovative And Effective Spreadsheet Tool For Learning Sentiment Analysis And Prediction, Michelle L. F. Cheong, Jean Yun-Chen Chen

Research Collection School Of Computing and Information Systems

In this paper, we describe a spreadsheet tool which was developed and implemented to teach sentiment analysis and prediction to university students who are less technically inclined. We adopted the “Use-Modify-Create” cycle to ensure that the students learn the computing concepts and practices in a progressive and active learning manner, and we evaluated the effectiveness of the tool using the Learning Object Evaluation Scale for Students (LOES-S). The evaluation shows that our tool performed well in terms of quality, learn and engagement constructs, and our paired T-test shows strong evidence that, on average, the course module with the tool does …

S-Prompts Learning With Pre-Trained Transformers: An Occam's Razor For Domain Incremental Learning, Yabin Wang, Zhiwu Huang, Xiaopeng. Hong

Research Collection School Of Computing and Information Systems

State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain. The independent prompting across domains only …

Coresets For Vertical Federated Learning: Regularized Linear Regression And K-Means Clustering, Lingxiao Huang, Zhize Li, Jialin Sun, Haoyu Zhao

Research Collection School Of Computing and Information Systems

Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexity for VFL is typically very high. In this paper, we propose a unified framework by constructing coresets in a distributed fashion for communication-efficient VFL. We study two important learning tasks in the VFL setting: regularized linear regression and $k$-means clustering, and apply our coreset framework to both problems. We theoretically show that using coresets can drastically alleviate the communication complexity, while nearly maintain the solution quality. Numerical experiments are conducted to corroborate our theoretical findings.

Beer: Fast O(1/T) Rate For Decentralized Nonconvex Optimization With Communication Compression, Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtarik, Yuejie Chi

Research Collection School Of Computing and Information Systems

Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant efforts, the state-of-the-art algorithm in the nonconvex setting still suffers from a slower rate of convergence $O((G/T)^{2/3})$ compared with their uncompressed counterpart, where $G$ measures the data heterogeneity across different clients, and $T$ is the number …

Soteriafl: A Unified Framework For Private Federated Learning With Communication Compression, Zhize Li, Haoyu Zhao, Boyue Li, Yuejie Chi

Research Collection School Of Computing and Information Systems

To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compression. On the other end, privacy-preserving, especially at the client level, is another important desideratum that has not been addressed simultaneously in the presence of advanced communication compression techniques yet. In this paper, we propose a unified framework that enhances the communication efficiency of private federated learning with communication compression. Exploiting both general compression operators and local differential privacy, we first examine a simple algorithm that applies compression directly to differentially-private …

Conreader: Exploring Implicit Relations In Contracts For Contract Clause Extraction, Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam

Research Collection School Of Computing and Information Systems

We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substantial barrier to understanding contracts of high complexity. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts, namely, 1) Long-range Context Relation that captures the correlations of distant clauses; 2) Term-Definition Relation that captures the relation between important terms with their corresponding definitions; and 3) Similar Clause Relation that captures the similarities between clauses of the same type. Then we propose a …

Pacific: Towards Proactive Conversational Question Answering Over Tabular And Textual Data In Finance, Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA. In addition, we propose a novel method, namely UniPCQA, to adapt a hybrid format of input and output content in PCQA into the Seq2Seq problem, including the reformulation of the numerical reasoning process as code generation. UniPCQA performs multi-task …

On The Merge Of K-Nn Graph, Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

k-nearest neighbor graph is a fundamental data structure in many disciplines such as information retrieval, data-mining, pattern recognition, and machine learning, etc. In the literature, considerable research has been focusing on how to efficiently build an approximate k-nearest neighbor graph (k-NN graph) for a fixed dataset. Unfortunately, a closely related issue of how to merge two existing k-NN graphs has been overlooked. In this paper, we address the issue of k-NN graph merging in two different scenarios. In the first scenario, a symmetric merge algorithm is proposed to combine two approximate k-NN graphs. The algorithm facilitates large-scale processing by the …

Rural America Is Still Technologically Behind: Why It Matters Now More Than Ever, Paul Force-Emery Mackie

Social Work Department Publications

No abstract provided.

Farmer Adoption Of Advanced Technology In Agribusiness, Justin W. Belcher

USF Tampa Graduate Theses and Dissertations

Normally, family-owned farms are slow to adopt advanced technologies though these technologies can provide several benefits to the farm and have the potential to increase farm production volumes to help meet future population growth. The goal of this study was to document the factors that influence the adoption decision of advanced technologies by family-owned farms and what strategies can be used to motivate adoption. Case study research was conducted to gather data in a more structured way from family-owned farms typically excluded from past research for the purpose of comparing similarities across similar and dissimilar farms. For generalizing similarities, a …

Designing A Messaging Strategy To Improve Information Security Policy Compliance, Federico Giovannetti

USF Tampa Graduate Theses and Dissertations

Lack of employee compliance with information security policies is a key factor driving security incidents. Information security practitioners struggle to enforce policy compliance while employees try to curtail safeguards in favor of expediency and other perceived business goals. Several studies have shown individual and organizational factors influencing this type of employee behavior. However, few have recommended management-level interventions that can be used as a solution framework by information security practitioners.

This research utilized the Design Science Research (DSR) methodology to develop a management-level intervention based on a messaging strategy that aims to help information security practitioners improve the information security …

Redefining Research In Nanotechnology Simulations: A New Approach To Data Caching And Analysis, Darin Tsai, Alan Zhang, Aloysius Rebeiro

The Journal of Purdue Undergraduate Research

No abstract provided.