Physical Sciences and Mathematics Commons^™

Deep Weakly-Supervised Anomaly Detection, Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples only (i.e., seen anomalies), and consequently they fail to generalize to those that are not, i.e., new types/classes of anomaly unseen during training. To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two …

Sparsity Brings Vulnerabilities: Exploring New Metrics In Backdoor Attacks, Jianwen Tian, Kefan Qiu, Debin Gao, Zhi Wang, Xiaohui Kuang, Gang Zhao

Research Collection School Of Computing and Information Systems

Nowadays, using AI-based detectors to keep pace with the fast iterating of malware has attracted a great attention. However, most AI-based malware detectors use features with vast sparse subspaces to characterize applications, which brings significant vulnerabilities to the model. To exploit this sparsityrelated vulnerability, we propose a clean-label backdoor attack consisting of a dissimilarity metric-based candidate selection and a variation ratio-based trigger construction. The proposed backdoor is verified on different datasets, including a Windows PE dataset, an Android dataset with numerical and boolean feature values, and a PDF dataset. The experimental results show that the attack can slash the accuracy …

Single-View View Synthesis With Self-Rectified Pseudo-Stereo, Yang Zhou, Hanjie Wu, Wenxi Liu, Zheng Xiong, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo viewpoint, which serves as an auxiliary input to construct the 3D space. In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction. In order to synthesize a structurally correct and detail-preserved stereo image, we propose a self-rectified stereo synthesis to amend erroneous …

Balancing Utility And Fairness In Submodular Maximization, Yanhao Wang, Yuchen Li, Francesco Bonchi, Ying Wang

Research Collection School Of Computing and Information Systems

Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications – including data summarization, influence maximization, and recommendation. In many of these problems, the goal is to find a solution that maximizes the average utility over all users, for each of whom the utility is defined by a monotone submodular function. However, when the population of users is composed of several demographic groups, another critical problem is whether the utility is fairly distributed across different groups. Although the utility and fairness objectives are both desirable, they might contradict each other, and, to the best of our knowledge, …

Ai For Connectivism Learning: Undergraduate Students’ Experiences Of Chatgpt In Advanced Programming Courses, Swapna Gottipati, Kyong Jin Shim, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Advanced programming skills are required for computing courses on merging topics, and students often struggle to develop these skills to solve complex problems. To address this challenge, faculty members provide additional lectures, practice sessions, and educational technology tools. This paper discusses the challenges faced by computer science students in developing advanced programming skills and explores the use of AI chatbots, specifically ChatGPT, as a support tool for learning. We study the engagement and effectiveness of ChatGPT in helping students learn advanced programming skills using two engagement learning frameworks (CIE and MELT) for evaluation. The study involves designing a computing lab …

Multi-Granularity Detector For Vulnerability Fixes, Truong Giang Nguyen, Cong, Thanh Le, Hong Jin Kang, Ratnadira Widyasari, Chengran Yang, Zhipeng Zhao, Bowen Xu, Jiayuan Zhou, Xin Xia, Ahmed E. Hassan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and …

Lean Manufacturing Approach To Increase Packaging Efficiency, Lina Gozali, Irsandy Kurniawan, Aldo Salim, Iveline Anne Marie, Benny Tjahjono, Yun Chia Liang, Aldy Gunawan, Nnovia Hardjo Sie, Yuliani Suseno

Research Collection School Of Computing and Information Systems

The company upon which this paper is based engages in flexible packaging production, especially pharmaceutical products with guaranteed quality, trusted by consumers. Its production process includes printing, laminating, and assembling processes. Production activities are done manually and automatically using machines, so various types of waste are often found in these processes, making the level of plant efficiency nonoptimal. This study aims to identify wastes occurring in the production process, especially the production of pollycelonium with three colour variants as the highest demand product, by applying lean manufacturing concepts. The Current Value Stream Mapping (CVSM) used to map the production process …

Knowledge Representation For Conceptual, Motivational, And Affective Processes In Natural Language Communication, Seng Beng Ho, Zhaoxia Wang, Boon-Kiat Quek, Erik Cambria

Research Collection School Of Computing and Information Systems

Natural language communication is an intricate and complex process. The speaker usually begins with an intention and motivation of what is to be communicated, and what outcomes are expected from the communication, while taking into consideration the listener’s mental model to concoct an appropriate sentence. Likewise, the listener has to interpret the speaker’s message, and respond accordingly, also with the speaker’s mental model in mind. Doing this successfully entails the appropriate representation of the conceptual, motivational, and affective processes that underlie language generation and understanding. Whereas big-data approaches in language processing (such as chatbots and machine translation) have performed well, …

Mastering Stock Markets With Efficient Mixture Of Diversified Trading Experts, Shuo Sun, Xinrun Wang, Wanqi Xue, Xiaoxuan Lou, Bo An

Research Collection School Of Computing and Information Systems

Quantitative stock investment is a fundamental financial task that highly relies on accurate prediction of market status and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, the performance of existing DL methods is unstable with sensitivity to network initialization and hyperparameter selection. One major limitation of existing works is that investment decisions are made based on one individual neural network predictor with high uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle this limitation, we propose AlphaMix, a novel three-stage …

A Survey On Proactive Dialogue Systems: Problems, Methods, And Prospects, Yang Deng, Wenqiang Lei, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side. It is empowered by advanced techniques to progress to more complicated tasks that require strategical and motivational interactions. In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. Furthermore, we discuss challenges that meet the real-world application needs but require a greater research focus in the future. We hope that …

Corporate Trade War Uncertainty And Patent Bubble, Xu Yang, Nan Hu, Peng Liang

Research Collection School Of Computing and Information Systems

This paper draws upon resource dependence theory and investigates how trade policy uncertainty affects firm strategic innovation management in China. Adopting a machine learning approach called Word2Vec from computational linguistics, we construct and validate a measure of firm-level managers’ perceived trade war uncertainty (TWU). We find that TWU has a positive effect on the number of total patent applications, but this positive effect is totally driven by low-quality patents instead of high-quality patents. Moreover, we document that firms have stronger incentives for such strategic innovation behavior when the underlying firms are more financially constrained, and/or when the management is more …

Generative Artificial Intelligence And Metaverse: Future Of Work, Future Of Society, And Future Of Humanity, Yuxin Liu, Keng Siau

Research Collection School Of Computing and Information Systems

The rapid development of Generative Artificial Intelligence (GenAI) and the emergence of the Metaverse are dynamically reshaping our lives and societies. GenAI can enhance the development of Metaverse and empower the applications in Metaverse. Metaverse is also an excellent environment for GenAI to demonstrate its power and usefulness. This interwoven relationship fuels the potential of integrating GenAI and Metaverse. The paper discusses the integration potential of GenAI and Metaverse from four aspects. We further investigate how GenAI, Metaverse, and the integration of GenAI and Metaverse can reshape our future across the realms of work, society, and humanity. This paper offers …

Using Ai And Chatgpt In Brand Storytelling, Runyu Wang, Keng Siau, Zili Zhang

Research Collection School Of Computing and Information Systems

AI Chatbots, like ChatGPT, have attracted widespread attention for their powerful natural languageprocessing capabilities. Artificial intelligence (AI) chatbots, such as Auto-GPT and ChatGPT, canhelpcompanies create brand stories and communicate with stakeholders to enhance corporate communicationand brand equity. In this extended abstract, we discuss our plan to generate brand messages withvaryinglanguage formality and investigate the effect of informed communicators (i.e., human and AI chatbots) and the language formality of brand stories on individuals' attitudes to the brand and examine the roleof psychological distance. Our findings are expected to contribute to the theoretical fundamentals of usingchatbots like ChatGPT in brand storytelling.

Flood: A Flexible Invariant Learning Framework For Out-Of-Distribution Generalization On Graphs, Yang Liu, Xiang Ao, Fuli Feng, Yunshan Ma, Kuan Li, Tat‑Seng Chua, Qing He

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have achieved remarkable success in various domains but most of them are developed under the in-distribution assumption. Under out-of-distribution (OOD) settings, they suffer from the distribution shift between the training set and the test set and may not generalize well to the test distribution. Several methods have tried the invariance principle to improve the generalization of GNNs in OOD settings. However, in previous solutions, the graph encoder is immutable after the invariant learning and cannot be adapted to the target distribution flexibly. Confronting the distribution shift, a flexible encoder with refinement to the target distribution can …

Diffusion Variational Autoencoder For Tackling Stochasticity In Multi-Step Regression Stock Price Prediction, Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem …

Epbc 2010/5491: Weaber Plain Development Project Compliance Report - 1st May 2022 To 30th April 2023, Department Of Primary Industries And Regional Development, Western Australia

Irrigated Agricultural Development in the North

The Weaber Plain Development Project (Goomig Project) was approved on 13 September 2011. The approval reference is EPBC 2010/5491 (the Approval).

The Goomig Project comprises irrigated farmland, associated infrastructure, and a buffer area on the Weaber Plain located about 30km north-northeast of Kununurra in Western Australia. Figure 1 shows the location and layout of the Goomig Project and interface with other projects including the Knox Creek Plain Irrigation Development (EPBC 2014/7143), Sorby Hills Silver Lead Zinc mining project, and Ord River Irrigation Area (ORIA) Stage 1 'Cave Springs' farms.

The Department of Primary Industries and Regional Development is the Goomig …

Bayesian Optimization With Switching Cost: Regret Analysis And Lookahead Variants, Peng Liu, Haowei Wang, Wei Qiyu

Research Collection Lee Kong Chian School Of Business

Bayesian Optimization (BO) has recently received increasing attention due to its efficiency in optimizing expensive-to-evaluate functions. For some practical problems, it is essential to consider the path-dependent switching cost between consecutive sampling locations given a total traveling budget. For example, when using a drone to locate cracks in a building wall or search for lost survivors in the wild, the search path needs to be efficiently planned given the limited battery power of the drone. Tackling such problems requires a careful cost-benefit analysis of candidate locations and balancing exploration and exploitation. In this work, we formulate such a problem as …

Open Data From The Third Observing Run Of Ligo, Virgo, Kagra, And Geo, R. Abbott, H. Abe, F. Acernese, Teviet Creighton, Mario C. Diaz, Francisco Llamas, Soma Mukherjee, Gaukhar Nurbek, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series …

Causal Inference And Machine Learning Methods In Parkinson's Disease Data Analysis, Albert Pierce

Computational and Data Sciences (PhD) Dissertations

This dissertation documents an investigation into Parkinson’s Disease utilizing machine learning and causal inference methods. I will cover a descriptive analysis of Parkinson’s Disease (PD) in a vast, high-quality database and present costs associated with Parkinson’s Disease medications. I also researched a causal inference method assessing the Carbidopa-Levodopa effect on two-year survival and a causal survival analysis on a one-to-five-year survival comparing no drug use and Carbidopa-Levodopa in Parkinson’s Disease patients.

For my classification with Parkinson’s gait, patients were monitored with a smartphone and an additional 6 Inertial Measurement Unit (IMU) sensors to collect clinical gait measures. I used classical …

Computational Analysis Of Antibody Binding Mechanisms To The Omicron Rbd Of Sars-Cov-2 Spike Protein: Identification Of Epitopes And Hotspots For Developing Effective Therapeutic Strategies, Mohammed Alshahrani

Computational and Data Sciences (PhD) Dissertations

The advent of the Omicron strain of SARS-CoV-2 has elicited apprehension regarding its potential influence on the effectiveness of current vaccines and antibody treatments. The present investigation involved the implementation of mutational scanning analyses to examine the impact of Omicron mutations on the binding affinity of four categories of antibodies that target the Omicron receptor binding domain (RBD) of the Spike protein. The study demonstrates that the Omicron variant harbors 23 unique mutations across the RBD regions I, II, III, and IV. Of these mutations, seven are shared between RBD regions I and II, while three are shared among RBD …

Causal Inference Methods For Estimation Of Survival And General Health Status Measures Of Alzheimer’S Disease Patients, Ehsan Yaghmaei

Computational and Data Sciences (PhD) Dissertations

Identifying optimal treatment options with respect to survival of Alzheimer's disease patients is crucially important and previously uninvestigated research question. Our objective was to estimate the causal effects of the most prevalent classes of Alzheimer’s disease drugs, Donepezil and Memantine, and their combined use on Survival and General Health Status Measures of Alzheimer's disease patients for the first five years after initial diagnosis. We carried out a thorough causal inference study using doubly robust estimators, nonparametric bootstrap confidence intervals, Bonferroni corrections for multiple comparisons and analyzing one of the largest high-quality medical databases containing millions of de-identified electronic health records …

Stochastic Processes And Multi-Resolution Analysis: A Trigonometric Moment Problem Approach And An Analysis Of The Expenditure Trends For Diabetic Patients, Isaac Nwi-Mozu

Computational and Data Sciences (PhD) Dissertations

This dissertation is divided into two distinct parts. The main theme of the first part is to study stochastic processes (and related signal processing questions) using tools in wavelet analysis, functional analysis (we use in particular the trigonometric moment problem), the theory of realization of rational functions, and reproducing kernel Hilbert spaces. A novel form of multiresolution analysis is formulated in the discrete case that is used to study some stochastic processes. Using the trigonometric moment problem, we associate with a vector-valued wide-sense stationary process a multiresolution of a new kind. The notion of realization of rational functions was used …

Application Of Machine Learning Algorithms For Elucidation Of Biological Networks From Time Series Gene Expression Data, Krupa Nagori

Computational and Data Sciences (PhD) Dissertations

This dissertation provides a deep dive into understanding gene expression, interaction, regulation, and the intricate mechanisms behind heliotropism and phototropism. Additionally, the research accentuates the significance of machine learning techniques, specifically for gene regulatory networks (GRNs).

Chapter 1 offers an exhaustive benchmarking of GRN methodologies, furthering our comprehension of machine-learning models relevant to GRNs. The evaluation revealed that GRNTE, SWING, and BiXGBoost emerged as top-performing methods in GRN inference. The suitability of these models varies depending on specific research criteria such as computational needs, dataset dimensions, and performance metric emphasis. An innovation of this chapter was the introduction of Colab …

Portuguese Society Of Pastures And Forages, J. Potes, E. V. Lourenço, T. Carita

IGC Proceedings (1977-2023)

The Portuguese Society of Pastures and Forages (SPPF) was born from a course on pastures and forages held in the National Plant Breeding Station of Elvas (where the headquarters of SPPF are located) in 1979. It was organised by the head of the Pastures and Forages Department, David Gomes Crespo, who was the first and founder member. According to Abreu et al. (1999), the structure established was that of a Scientific Society and was considered by the Government as a Service of Public Interest. It gathered inspiration from the British Grassland Society, the Association Francaise pour la Production Fourragére, …

The Activities And Aspirations Of The Estonian Grassland Society, A. Selge, R. Viiralt, Tiina Köster

IGC Proceedings (1977-2023)

The aim of the Estonian Grassland Society (EGS) is to develop the flow of grassland-based knowledge between different stakeholders and multidisciplinary research and to create international contacts. The EGS is a non-profit organisation and more than 100 interdisciplinary researchers, plant breeders, advisors and farmers are actively involved in the work of the society. The interdisciplinary activities are becoming more and more attractive and important and bring together different target groups. At present seminars, which are focused on the adaptation producers to the EU rules and support system for farmers are attractive. Nowadays the essential goal for the EGS is to …

The Role Of The New Zealand Grassland Association In Technology Interaction And Policy Evolution, M. W. Calder

IGC Proceedings (1977-2023)

The New Zealand Grassland Association (NZGA) aims for a two-way communication between grassland researchers and pastoral farmers on research findings and farming trends and opportunities. Annual conferences, special symposia, print and electronic publications as well as the web site are used to further these aims. Policy evolution is indirect, using the relationship with agribusiness sponsors and members to promote research objectives.

Slovenian Grassland Society: Science, Profession And Practice, Branko Kramberger, Matej Vidrih, T. Vidrih, J. Čop

IGC Proceedings (1977-2023)

The Slovenian Grassland Society (SGS) was established in 1993. It has around 120 members. A half of members are active farmers, around 10% are scientists, the rest are employed in extension services or other agricultural enterprises (seed companies, administration bodies, etc.)

Role Of Hellenic Range And Pasture Society In Technology Interaction And Policy Evolution, T. G. Papachristou, P. D. Platis, V. Papanastasis, A. Ainalis

IGC Proceedings (1977-2023)

The Hellenic Range and Pasture Society (HRPS) was founded in 1992 and is a non-profit scientific association. It is composed of 74 members with a common interest in the study, management, and rational use of rangelands and related ecosystems (e.g. pasturelands). The objectives for which the corporation was established are: a) to develop an understanding of rangeland ecosystems and of the principles applicable to the management of rangeland resources (soil, plants, water, and animals), b) to assist all who work with rangelands and pasturelands to keep abreast of new findings and techniques in the science and art of rangeland and …

Technology Interaction And Policy Evolution; Grassland Society Of Southern Australia Inc, L. L. Bennison

IGC Proceedings (1977-2023)

The Grassland Society of Southern Australia was formed in 1959 to provide a forum for the transfer of information, ideas and experiences in relation to all aspects of grassland establishment, maintenance, utilisation, persistence and research. The membership of over 2,000 is composed primarily of primary producers (80%) with extension and research scientists comprising 10%, agribusiness 8% and academia 2%. The major interests of members are wool, lamb and beef production, dairying and cropping. The society produces bimonthly newsletters, holds an annual conference with attendances between 400 and 600 annually and has a branch structure that hosts local events. The society …

Role Of The Grassland Society Of Bosnia And Herzegovina In Grassland Agriculture, S. Alibegovic-Gbic, M. Bezdrob

IGC Proceedings (1977-2023)

The biggest part of Bosnia and Herzegovina is covered with hilly and mountainous terrain, which, from the agricultural perspective, determines the manner of soil usage. Of all agricultural areas in Bosnia and Herzegovina, 55.49% are covered by grassland with that figure increasing to 79.14% in mountainous regions. Therefore, grassland agriculture and its cousin livestock production are the most significant part of the Bosnian economy. In the current situation natural grasslands are largely neglected and give a low yield of forage of low quality. Many areas are completely abandoned and have been gradually overgrown with weeds and bushes. Yet a special …