Computer Sciences Commons^™

Increasing The Robustness Of Machine Learning By Adversarial Attacks, Gourab Mukhopadhyay

Theses and Dissertations

By perturbation or physical attacks any machine can be fooled into predicting something else other than the intended output. There are training data based on which the model is trained to predict unknown things. The objective was to create noises and shades of different levels on the images and do experiments for measuring accuracy and making the model classify the traffic signs. When it comes to adding shades to the pictures, pixels were modified for three different layers of the pictures. The experiment also shows that with the shadows getting deeper, the accuracies drop significantly. Here, some changes in pixels …

Smart Airports: Artificial Intelligence–Enabled Internet Of Things Networks Using Blockchain Technology, Edwin Ongola

Journal of Aviation Technology and Engineering

This article provides a perspective on how an internet of heterogeneous self-service airport terminal systems can be used for data collection, which is stored on a private or consortium blockchain depending on the ownership or operations of an airport or both. Such a setup would help to increase efficiency, reduce costs, and improve traveler experience at airport terminals. Moreover, it would allow airports to gather data directly from passengers as opposed to waiting to receive the same data from airlines. Subsequently, this data, now on a blockchain system, becomes a data source for other applications such as machine learning. In …

Memetic Memory As Vital Conduits Of Troublemakers In Digital Culture, Alexander O. Smith, Jordan Loewen-Colón

School of Information Studies - Post-doc and Student Scholarship

Recent fears of data capitalism and colonialism often argue using implicit assumptions about cybernetic technology’s ability to automate data about culture. As such, the level of data granularity made possible by cybernetic engineering can be used to dominate society and culture. Here we unpack these implicit assumptions about the datafication of culture through memes, which both act as cultural data and cultural memory. Using Alexander Galloway’s critical method of protocological analysis and descriptions of media tactics, we respond to fears of cybernetic domination. Protocols – the source by which cybernetic technologies enable automated datafication – enables us to respond to …

Vysion Software, Isaias Hernandez-Dominguez Jr, Chander Luderman Miller

2024 Symposium

Vision loss presents significant challenges in daily life. Existing solutions for blind and visually impaired individuals are often limited in functionality, expensive, or complex to use. Vysion Software addresses this gap by developing a user-friendly, all-in-one AI companion app that provides features including text summarization, real-time audio descriptions, and AI-enhanced navigation. This project details the development plan, initial functionalities, and future vision for Vysion Software.

Sequential Decision Learning For Social Good And Fairness, Dexun Li

Dissertations and Theses Collection (Open Access)

Sequential decision learning is one of the key research areas in artificial intelligence. Typically, a sequence of events is observed through a transformation that introduces uncertainty into the observations and based on these observations, the recognition process produces a hypothesis of the underlying events. This learning process is characterized by maximizing the sum of the reward signals. However, many real-life problems are inherently constrained by limited resources. Besides, when the learning algorithms are used to inform decisions involving human beings (e.g., Security and justice, health intervention, etc), they may inherit the potential, pre-existing bias in the dataset and exhibit similar …

Research On The Solutions Generation And Evaluation In Scenario-Based Intelligence Service Based On Multi-Source Data Aggregation, Yuefen Wang, Xiaoyi Dong, Jin He

Journal of Scientific Information Research

[Purpose/significance]Faced with the challenges of big data and artificial intelligence technology, knowledge services are undergoing profound changes, starting from the task scenarios of user needs, this paper explores the scenario-based intelligence service process that supports and matches different industries and their business scenarios. [Method/process]This paper takes the scenario-based intelligence service R-S model as the core, builds the scenario-based intelligence service solutions generation and evaluation process framework based on multi-source data aggregation, briefly describes the main contents and operation of multi-source data aggregation, and takes an organization's "Russia-Ukraine conflict" equipment information quick perception intelligence service as an example, discusses in detail …

A Deep Learning Method To Predict Bacterial Adp-Ribosyltransferase Toxins, Dandan Zheng, Siyu Zhou, Lihong Chen, Guansong Pang, Jian Yang

Research Collection School Of Computing and Information Systems

Motivation: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members. Results: We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity …

Generalization Analysis Of Deep Nonlinear Matrix Completion, Antoine Ledent, Rodrigo Alves

Research Collection School Of Computing and Information Systems

We provide generalization bounds for matrix completion with Schatten $p$ quasi-norm constraints, which is equivalent to deep matrix factorization with Frobenius constraints. In the uniform sampling regime, the sample complexity scales like $\widetilde{O}\left( rn\right)$ where $n$ is the size of the matrix and $r$ is a constraint of the same order as the ground truth rank in the isotropic case. In the distribution-free setting, the bounds scale as $\widetilde{O}\left(r^{1-\frac{p}{2}}n^{1+\frac{p}{2}}\right)$, which reduces to the familiar $\sqrt{r}n^{\frac{3}{2}}$ for $p=1$. Furthermore, we provide an analogue of the weighted trace norm for this setting which brings the sample complexity down to $\widetilde{O}(nr)$ in all …

A Bottom-Up Multi-Disciplinary Approach For Sustainability Education: Un-Sdg 13.3, Benjamin Gan, Thomas Menkhoff, Eng Lieh Ouh, Kevin Cheong

Research Collection School Of Computing and Information Systems

Teaching both information systems and business undergraduates to break the current inertia in sustainability action requires innovative teaching & learning approaches as well as inter-disciplinary knowledge inputs. This study presents a bottom-up T&L approach delivered by a group of educators from different disciplines aimed at addressing UN-SDG Goal 13 ‘Climate Action’ with a novel approach. Integrating a problem-centric community project assignment into existing courses, our students worked on different disciplinary elements such as persuasive technologies and awareness campaigns to help to address local sustainability initiatives by community partners. We collected data to measure how students’ motivation, engagement, teamwork, and community …

Decentralized Consensus And Governance For Collaborative Intelligence, Huiwen Liu

Dissertations and Theses Collection (Open Access)

The data economy today is becoming increasingly collaborative in nature. Take business intelligence, for example. To unleash the full potential of big data, it is essential to integrate multi-source data depicting entities from a multi-faceted and multi-modal perspective, which, not surprisingly, is not achievable by any company alone. In collaborative intelligence, there are two core issues, namely "trust" and "incentive". The core mechanisms to solve these two problems are consensus and tokenization separately.

To solve the trust problem more effectively, we propose a systematic consensus evaluation framework to investigate whether existing consensus algorithms can do so. After a lot of …

Towards Human-Centered Proactive Conversational Agents, Yang Deng, Lizi Liao, Zhonghua Zheng, Grace Hui Yang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recent research on proactive conversational agents (PCAs) mainly focuses on improving the system's capabilities in anticipating and planning action sequences to accomplish tasks and achieve goals before users articulate their requests. This perspectives paper highlights the importance of moving towards building human-centered PCAs that emphasize human needs and expectations, and that considers ethical and social implications of these agents, rather than solely focusing on technological capabilities. The distinction between a proactive and a reactive system lies in the proactive system's initiative-taking nature. Without thoughtful design, proactive systems risk being perceived as intrusive by human users. We address the issue by …

Exploring The Market Impact Of Web3 Identity Imitation In Ethereum Name Service, Ping Fan Ke, Yi Meng Lau

Research Collection School Of Computing and Information Systems

Digital identities are paramount in today’s digital landscape. However, in the Web3 ecosystem, the absence of a central governing body leaves digital identities, such as domain names, vulnerable to cybersquatting and identity imitation. This study examines the market impact of identity imitation in the Web3 ecosystem. By scrutinizing trading activities within Web3 domain names from Ethereum Name Service (ENS) and its imitator, "Ether Name Service," we found that the presence of a newly imitating domain name increases the subsequent resale value of the authentic domain name. Additionally, we find a positive correlation between the resale value of the imitating domain …

Towards Automated Slide Augmentation To Discover Credible And Relevant Links, Dilan Dinushka Senarath Arachchige, Christopher M. Poskitt, Kwan Chin (Xu Guangjin) Koh, Heng Ngee Mok, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning from concise educational materials, such as lecture notes and presentation slides, often prompts students to seek additional resources. Newcomers to a subject may struggle to find the best keywords or lack confidence in the credibility of the supplementary materials they discover. To address these problems, we introduce Slide++, an automated tool that identifies keywords from lecture slides, and uses them to search for relevant links, videos, and Q&As. This interactive website integrates the original slides with recommended resources, and further allows instructors to 'pin' the most important ones. To evaluate the effectiveness of the tool, we trialled the system …

Diffusion Models For Generative Outfit Recommendation, Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma, Jizhi Zhang, Xiangnan He

Research Collection School Of Computing and Information Systems

Outfit Recommendation (OR) in the fashion domain has evolved through two stages: Pre-defined Outfit Recommendation and Personalized Outfit Composition. However, both stages are constrained by existing fashion products, limiting their effectiveness in addressing users' diverse fashion needs. Recently, the advent of AI-generated content provides the opportunity for OR to transcend these limitations, showcasing the potential for personalized outfit generation and recommendation.To this end, we introduce a novel task called Generative Outfit Recommendation (GOR), aiming to generate a set of fashion images and compose them into a visually compatible outfit tailored to specific users. The key objectives of GOR lie in …

Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang

Research Collection School Of Computing and Information Systems

In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …

Broadening The View: Demonstration-Augmented Prompt Learning For Conversational Recommendation, Quang Huy Dao, Yang Deng, Dung D. Le, Lizi Liao

Research Collection School Of Computing and Information Systems

Conversational Recommender Systems (CRSs) leverage natural language dialogues to provide tailored recommendations. Traditional methods in this field primarily focus on extracting user preferences from isolated dialogues. It often yields responses with a limited perspective, confined to the scope of individual conversations. Recognizing the potential in collective dialogue examples, our research proposes an expanded approach for CRS models, utilizing selective analogues from dialogue histories and responses to enrich both generation and recommendation processes. This introduces significant research challenges, including: (1) How to secure high-quality collections of recommendation dialogue exemplars? (2) How to effectively leverage these exemplars to enhance CRS models?To tackle …

Large Language Model Powered Agents For Information Retrieval, An Zhang, Yang Deng, Yankai Lin, Xu Chen, Ji-Rong Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The vital goal of information retrieval today extends beyond merely connecting users with relevant information they search for. It also aims to enrich the diversity, personalization, and interactivity of that connection, ensuring the information retrieval process is as seamless, beneficial, and supportive as possible in the global digital era. Current information retrieval systems often encounter challenges like a constrained understanding of queries, static and inflexible responses, limited personalization, and restricted interactivity. With the advent of large language models (LLMs), there's a transformative paradigm shift as we integrate LLM-powered agents into these systems. These agents bring forth crucial human capabilities like …

Comparative Analysis Of Hate Speech Detection: Traditional Vs. Deep Learning Approaches, Haibo Pen, Nicole Anne Huiying Teo, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with …

Performance Analysis Of Llama 2 Among Other Llms, Donghao Huang, Zhenda Hu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Llama 2, an open-source large language model developed by Meta, offers a versatile and high-performance solution for natural language processing, boasting a broad scale, competitive dialogue capabilities, and open accessibility for research and development, thus driving innovation in AI applications. Despite these advancements, there remains a limited understanding of the underlying principles and performance of Llama 2 compared with other LLMs. To address this gap, this paper presents a comprehensive evaluation of Llama 2, focusing on its application in in-context learning — an AI design pattern that harnesses pre-trained LLMs for processing confidential and sensitive data. Through a rigorous comparative …

Jigsaw: Edge-Based Streaming Perception Over Spatially Overlapped Multi-Camera Deployments, Ila Gokarn, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

We present JIGSAW, a novel system that performs edge-based streaming perception over multiple video streams, while additionally factoring in the redundancy offered by the spatial overlap often exhibited in urban, multi-camera deployments. To assure high streaming throughput, JIGSAW extracts and spatially multiplexes multiple regions-of-interest from different camera frames into a smaller canvas frame. Moreover, to ensure that perception stays abreast of evolving object kinematics, JIGSAW includes a utility-based weighted scheduler to preferentially prioritize and even skip object-specific tiles extracted from an incoming stream of camera frames. Using the CityflowV2 traffic surveillance dataset, we show that JIGSAW can simultaneously process 25 …

Fedstem-Adl: A Federated Spatial-Temporal Episodic Memory Model For Adl Prediction, Doudou Wu, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Learning of Activities of Daily Living (ADLs) provides insights into an individual’s habits, lifestyle, and well-being. However, it is crucial to address data privacy concerns in practical situations when learning the ADL routines of individuals. In this paper, we introduce FedSTEM-ADL, a federated spatial-temporal episodic memory model to address this privacy issue. FedSTEM-ADL utilizes a federation of Spatial-Temporal Episodic Memory for ADLs (STEM-ADL) for federated learning, wherein multiple local STEM-ADL models from individual users are combined into a global model while preserving the privacy of the original data. Specifically, each local model is designed to learn the spatio-temporal ADL routines …

Is There A Space In Landslide Susceptibility Modelling: A Case Study Of Valtellina Valley, Northern Italy, Min Naing Khant, Mei Yi Victoria Grace Ann, Tin Seong Kam

Research Collection School Of Computing and Information Systems

Landslides pose significant and ever-threatening risks to human life and infrastructure worldwide. Landslide susceptibility modelling is an emerging field of research seeking to determine contributing factors of these events. Yet, previous studies rarely explored the spatial variation of different landslide factors. Hence, this study aims to demonstrate the potential contribution of spatial nonstationarity in landslide susceptibility modelling using Global Logistic Regression (GLR) and Geographically Weighted Logistic Regression (GWLR). The second objective of this study is to demonstrate the important role of data preparation, data sampling, variable sensing, and variable selections in landslide susceptibility modelling. Using Valtellina Valley in Northern Italy …

The Information Content Of Financial Statement Fraud Risk: An Ensemble Learning Approach, Wei Duan, Nan Hu, Fujing Xue

Research Collection School Of Computing and Information Systems

This study aims to assess the financial statement fraud risk ex ante and empirically explore its information content to help improve decision-making and daily operations. We propose an ex-ante fraud risk index by adopting an ensemble learning approach and a theoretically grounded framework. Our ensemble learning model systematically examines the fraud process and deals effectively with the unique challenges in the financial fraud setting, which yields superior prediction performance. More importantly, we empirically examine the information content of our estimated ex-ante fraud risk from the perspective of operational efficiency. Our empirical results find that the estimated ex-ante fraud risk is …

An Exploratory Study Of Conventional Machine Learning And Large Language Models For Sentiment Analysis, Cui Zou, Jingyuan Cai, Langtao Chen, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Sentiment analysis is the use of natural language processing to identify affective states and determine people’s opinions in various analytical applications such as customer reviews and social media analyses. Large language models (LLMs) such as GPT-4o demonstrate impressive performance in text generation tasks. Despite numerous studies in the extant literature, few have compared the performance of conventional machine learning models with LLMs for sentiment analysis. This study aims to fill this gap by conducting an evaluation of these models using a balanced dataset of 2,000 IMDb movie reviews. Our study shows that GPT-4o achieves the highest performance, while GPT-3.5 and …

A Computational Aesthetic Design Science Study On Online Video Based On Triple-Dimensional Multimodal Analysis, Zhangguang Kang, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

Computational video aesthetic prediction refers to using models that automatically evaluate the features of videos to produce their aesthetic scores. Current video aesthetic prediction models are designed based on bimodal frameworks. To address their limitations, we developed the Triple-Dimensional Multimodal Temporal Video Aesthetic neural network (TMTVA-net) model. The Long Short-Term Memory (LSTM) forms the conceptual foundation for the design framework. In the multimodal transformer layer, we employed two distinct transformers: the multimodal transformer and the feature transformer, enabling the acquisition of modality-specific patterns and representational features uniquely adapted to each modality. The fusion layer has also been redesigned to compute …

Understanding And Fighting Scams: Media, Language, Appeals And Effects, Shuhua Zhou, Xiao Fan Liu, Fiona Fui-Hoon Nah, S. Harrison, X. Zhang, S. Zhen, D. Yeung, J. Hsiao, R. Lc, A. Chan, X. Wang, C. Jiang, F. Lin, J. Li, A. Wong, L. Chan, B. George, P. Li

Research Collection School Of Computing and Information Systems

Scams are fraudulent activities aiming to deceive individuals into relinquishing money, property, or rights, and they have proliferated in the context of widespread misinformation and disinformation. In this paper, we propose strategies and a research plan to address key questions about the exploitation of new communication technologies by scammers, the prevalence and nature of different scam types, and the language characteristics and appeals used in scamming content. We aim to develop a comprehensive taxonomy of scams and identify factors that contribute to their persuasiveness. Additionally, we propose the use of advanced technologies, including artificial intelligence, physiological measures, and brain mapping, …

Optimizing Cybersecurity Operations Using Data-Driven Intelligence, Jalal Ghadermazi

USF Tampa Graduate Theses and Dissertations

Cybersecurity operations centers (CSOCs) play a crucial role in safeguarding organizations from cyber threats. CSOC operations are divided into two main areas: Intrusion detection systems (IDS) and security response team (SRT) operations. Machine learning (ML) and deep learning (DL) advancements have significantly improved IDSs. IDS can be either flow-based, suitable for offline analysis, or packet-based, which analyze traffic in real-time. However, packet-based IDS often treat packets independently, ignoring the sequential nature of network communication. Additionally, recent ML/DL approaches also struggle with capturing global and structural information and novel attack detection due to their reliance on labeled data. The SRT within …

The Institutional Challenges Of A Quantified Self Study: An Attempt To Ascertain How Data Collected From A Mobile Device Can Be An Indicator Of Personal Mental Health Over Time, Julian Lazaras

University Honors Theses

The adoption of an application of new technology always comes with a bias, this is never more true for the case of human behavioral analytics within higher education. While movements such as the quantified self movement make strides to reinterpret the realm of data analytics, psychology, and computer science, there are inevitably limitations to the adoption and application of such approaches within the standard realm of research. Herein is presented a case where an effort to evaluate the prospect of use of mobile phone data as secondary indicators of personal mental health through the lens of data analysis was put …

The Efficacy Of Using Machine Learning Techniques For Identifying And Classifying “Fake News”, Muhammad Islam

Dissertations, Theses, and Capstone Projects

In today's digital world, detecting fake news has emerged as a critical challenge, one that has significant effects on democracy and public discourse at large both regionally and globally. This research studies how diversity of news sources in training datasets affects how well machine learning models can classify fake vs true news. I used the Linear Support Vector Classification (LinearSVC) to create and compare two classification models: one was trained on a dataset that only had real news from a singular source, Reuters (Dataset 1), and the other was trained on a dataset that contained real news from Reuters, The …

Anomaly Heterogeneity Learning For Open-Set Supervised Anomaly Detection, Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang

Research Collection School Of Computing and Information Systems

Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies, current OSAD methods can often largely reduce false positive errors. However, these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution, rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to …