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Full-Text Articles in Entire DC Network
Law-Aware Autonomous Driving, Yang Sun
Law-Aware Autonomous Driving, Yang Sun
Dissertations and Theses Collection (Open Access)
Autonomous driving systems (ADSs) necessitate comprehensive testing prior to deployment in Autonomous Vehicles (AVs). High-fidelity simulators are crucial for this testing, as they can replicate a wide range of scenarios, including those that are difficult or dangerous to recreate in real-world conditions. While previous approaches have demonstrated that test cases can be generated automatically, they often focus on weak oracles (e.g., reaching the destination without collisions) and fail to assess whether the journey was conducted safely and in compliance with some complex property specifications such as traffic laws. In this dissertation, beyond assessing basic properties like energy consumption and proximity …
An Overview Of Ancillary Services Provided By Vehicle-To-Grid Systems, Fazel Mohammadi, Mahmood Mirhashemi
An Overview Of Ancillary Services Provided By Vehicle-To-Grid Systems, Fazel Mohammadi, Mahmood Mirhashemi
Electrical & Computer Engineering and Computer Science Faculty Publications
Vehicle-to-Grid (V2G) systems are emerging as a pivotal technology in modern power systems, offering a range of ancillary services that enhance the stability and reliability of power systems. This paper provides an overview of the key ancillary services provided by V2G systems, highlighting their role in grid modernization. Technical challenges, economic implications, and policy considerations associated with the deployment of V2G systems are explored to assess their potential impact on advancing a more resilient and sustainable energy infrastructure.
Regulating Adaptive Medical Artificial Intelligence: Can Less Oversight Lead To Greater Compliance?, Jiayi Lai, Liang Xu, Xin Fang, Tinglong Dai
Regulating Adaptive Medical Artificial Intelligence: Can Less Oversight Lead To Greater Compliance?, Jiayi Lai, Liang Xu, Xin Fang, Tinglong Dai
Research Collection Lee Kong Chian School Of Business
As of June 2024, the U.S. Food and Drug Administration (FDA) has approved 950 medical artificial intelligence (AI) devices. The current regulatory framework freezes AI algorithms after approval, requiring new submissions for updates to ensure compliance with Good Machine Learning Practices (GMLP). This approach imposes a significant administrative burden, while hindering the ability of AI algorithms to learn from new data. To address these challenges, the FDA has explored a novel pathway known as Predetermined Change Control Plans (PCCP), allowing developers to outline future changes during initial submissions and exempting approved changes from regulatory review. Yet, the impact of this …
Bridging Human And Machine Intelligence: Reverse-Engineering Radiologist Intentions For Clinical Trust And Adoption, Akash Awasthi, Ngan Le, Zhigang Deng, Rishi Agrawal, Carol C. Hu, Hien Van Nguyen
Bridging Human And Machine Intelligence: Reverse-Engineering Radiologist Intentions For Clinical Trust And Adoption, Akash Awasthi, Ngan Le, Zhigang Deng, Rishi Agrawal, Carol C. Hu, Hien Van Nguyen
Computer Science and Computer Engineering Faculty Publications and Presentations
In the rapidly evolving landscape of medical imaging, the integration of artificial intelligence (AI) with clinical expertise offers unprecedented opportunities to enhance diagnostic precision and accuracy. Yet, the "black box" nature of AI models often limits their integration into clinical practice, where transparency and interpretability are important. This paper presents a novel system leveraging the Large Multimodal Model (LMM) to bridge the gap between AI predictions and the cognitive processes of radiologists. This system consists of two core modules, Temporally Grounded Intention Detection (TGID) and Region Extraction (RE). The TGID module predicts the radiologist's intentions by analyzing eye gaze fixation …
An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme For Internet Of Things, Chong Guo, Bei Gong, Muhammad Waqas, Hisham Alasmary, Shanshan Tu, Sheng Chen
An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme For Internet Of Things, Chong Guo, Bei Gong, Muhammad Waqas, Hisham Alasmary, Shanshan Tu, Sheng Chen
Research outputs 2022 to 2026
The Internet of Things (IoT) is a heterogeneous network composed of numerous dynamically connected devices. While it brings convenience, the IoT also faces serious challenges in data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography method that supports fine-grained access control, offering a solution to the IoT’s security issues. However, existing CP-ABE schemes are inefficient and unsuitable for IoT devices with limited computing resources. To address this problem, this paper proposes an efficient pairing-free CP-ABE scheme for the IoT. The scheme is based on lightweight elliptic curve scalar multiplication and supports multi-authority and verifiable outsourced decryption. The proposed scheme …
Collectively Advancing Deep Learning For Animal Detection In Drone Imagery: Successes, Challenges, And Research Gaps, Daniel Axford, Ferdous Sohel, Mathew A. Vanderklift, Amanda J. Hodgson
Collectively Advancing Deep Learning For Animal Detection In Drone Imagery: Successes, Challenges, And Research Gaps, Daniel Axford, Ferdous Sohel, Mathew A. Vanderklift, Amanda J. Hodgson
Research outputs 2022 to 2026
Drones have emerged as a powerful tool in animal detection, significantly advancing wildlife monitoring, conservation, and management by capturing high-resolution, real-time imagery over areas often inaccessible or challenging for human observers to reach. However, manual analysis of drone imagery for animal detection is labour-intensive and time-consuming. The application of deep learning methods, particularly convolutional neural networks, in automating animal detection from drone imagery has the potential to revolutionise wildlife monitoring, conservation, and management protocols. This review provides a comprehensive overview of the increasing use and prospects of deep learning in animal detection using drone imagery. It explores successful applications of …
Safeguarding User-Centric Privacy In Smart Homes, Keyang Yu, Qi Li, Dong Chen, Liting Hu
Safeguarding User-Centric Privacy In Smart Homes, Keyang Yu, Qi Li, Dong Chen, Liting Hu
Computer Science Faculty Research and Publications
Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new …
Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang
Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang
Research Collection School Of Computing and Information Systems
Adversarial attack is a crucial step when evaluating the reliability and robustness of deep neural networks (DNNs) models. Most existing attack approaches apply an end-to-end gradient update strategy to generate adversarial examples for a classification or regression problem. However, few of them consider the non-differentiable DNN models (e.g., coordinate regression model) that prevent end-to-end backpropagation resulting in the failure of gradient calculation. In this paper, we present a new adversarial example generation approach for both untargeted and targeted attacks on coordinate regression models with non-differentiable operations. The novelty of our approach lies in a k-layer penetrating representation, on which we …
Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong
Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract the internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user …
Unlocking Markets: A Multilingual Benchmark To Cross-Market Question Answering, Yifei Yuan, Yang Deng, Anders Sogaard, Mohammad Alliannejadi
Unlocking Markets: A Multilingual Benchmark To Cross-Market Question Answering, Yifei Yuan, Yang Deng, Anders Sogaard, Mohammad Alliannejadi
Research Collection School Of Computing and Information Systems
Users post numerous product-related questions on e-commerce platforms, affecting their purchase decisions. Product-related question answering (PQA) entails utilizing product-related resources to provide precise responses to users. Wepropose a novel task of Multilingual Crossmarket Product-based Question Answering (MCPQA) and define the task as providing answers to product-related questions in a main marketplace by utilizing information from another resource-rich auxiliary marketplace in a multilingual context. We introduce a largescale dataset comprising over 7 million questions from 17 marketplaces across 11 languages. We then perform automatic translation on the Electronics category of our dataset, naming it as McMarket. We focus on two subtasks: …
Ask-Before-Plan : Proactive Language Agents For Real-World Planning, Xuan Zhang, Yang Deng, Zifeng Ren, See-Kiong Ng, Tat-Seng Chua
Ask-Before-Plan : Proactive Language Agents For Real-World Planning, Xuan Zhang, Yang Deng, Zifeng Ren, See-Kiong Ng, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The evolution of large language models (LLMs) has enhanced the planning capabilities of language agents in diverse real-world scenarios. Despite these advancements, the potential of LLM-powered agents to comprehend ambiguous user instructions for reasoning and decision-making is still under exploration. In this work, we introduce a new task, Proactive Agent Planning, which requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction, invoke external tools to collect valid information, and generate a plan to fulfill the user's demands. To study this practical problem, we establish a new benchmark dataset, Ask-before-Plan. To tackle the deficiency of LLMs …
Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe
Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe
Research Collection School Of Computing and Information Systems
Public health practitioners often have the goal of monitoring patients and maximizing patients’ time spent in “favorable” or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model to solve this problem as they are helpful to allocate limited resources among many agents under resource constraints, where patients behave differently depending on whether they are intervened on or not. However, RMABs assume the reward function is known. This is unrealistic in many public health settings because patients face unique challenges and it is impossible for a human to know who is most deserving …
Multimodal Misinformation Detection By Learning From Synthetic Data With Multimodal Llms, Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang
Multimodal Misinformation Detection By Learning From Synthetic Data With Multimodal Llms, Fengzhu Zeng, Wenqian Li, Wei Gao, Yan Pang
Research Collection School Of Computing and Information Systems
Detecting multimodal misinformation, especially in the form of image-text pairs, is crucial. Obtaining large-scale, high-quality real-world fact-checking datasets for training detectors is costly, leading researchers to use synthetic datasets generated by AI technologies. However, the generalizability of detectors trained on synthetic data to real-world scenarios remains unclear due to the distribution gap. To address this, we propose learning from synthetic data for detecting real-world multimodal misinformation through two model-agnostic data selection methods that match synthetic and real-world data distributions. Experiments show that our method enhances the performance of a small MLLM (13B) on real-world fact-checking datasets, enabling it to even …
Scoping Software Engineering For Ai: The Tse Perspective, Sebastián Uchitel, Marsha Chechik, Massimiliano Di Penta, Bram Adams, Nazareno Aguirre, Gabriele Bavota, Domenico Bianculli, Kelly Blincoe, Ana Cavalcanti, Yvonne Dittrich, Filomena Ferrucci, Rashina Hoda, Liguo Huang, David Lo, Et Al.
Scoping Software Engineering For Ai: The Tse Perspective, Sebastián Uchitel, Marsha Chechik, Massimiliano Di Penta, Bram Adams, Nazareno Aguirre, Gabriele Bavota, Domenico Bianculli, Kelly Blincoe, Ana Cavalcanti, Yvonne Dittrich, Filomena Ferrucci, Rashina Hoda, Liguo Huang, David Lo, Et Al.
Research Collection School Of Computing and Information Systems
In recent years, important advances in Artificial Intelligence (AI), and, in particular, in Machine Learning (ML), including Deep Learning (DL) and Large Language Models (LLMs), have caused a substantial increase of submissions to all Software Engineering (SE) venues (conferences and journals) related to SE with and for AI. They are commonly referred to as AI for SE and SE for AI.
Ai And Data Science For Public Policy, Kenneth Benoit
Ai And Data Science For Public Policy, Kenneth Benoit
Research Collection School of Social Sciences
Artificial intelligence (AI) and data science are reshaping public policy by enabling more data-driven, predictive, and responsive governance, while at the same time producing profound changes in knowledge production and education in the social and policy sciences. These advancements come with ethical and epistemological challenges surrounding issues of bias, transparency, privacy, and accountability. This special issue explores the opportunities and risks of integrating AI into public policy, offering theoretical frameworks and empirical analyses to help policymakers navigate these complexities. The contributions explore how AI can enhance decision-making in areas such as healthcare, justice, and public services, while emphasising the need …
The Epistemic Role Of Ai Decision Support Systems: Neither Superiors, Nor Inferiors, Nor Peers, Rand Hirmiz
The Epistemic Role Of Ai Decision Support Systems: Neither Superiors, Nor Inferiors, Nor Peers, Rand Hirmiz
Research Collection School of Social Sciences
Despite the importance of discussions over the epistemic role that artificially intelligent decision support systems ought to play, there is currently a lack of these discussions in both the AI literature and the epistemology literature. My goal in this paper is to rectify this by proposing an account of the epistemic role of AI decision support systems in medicine and discussing what this epistemic role means with regard to how these systems ought to be utilized. In particular, I argue that AI decision support systems are not epistemic superiors, inferiors, or peers. Instead, I recommend that they be classified in …
A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu
A Comprehensive Survey On Relation Extraction: Recent Advances And New Frontiers, Xiaoyan Zhao, Yang Deng, Min Yang, Lingzhi Wang, Rui Zhang, Hong Cheng, Wai Lam, Ying Shen, Ruifeng Xu
Research Collection School Of Computing and Information Systems
Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as knowledge graph completion and question answering. In recent years, deep neural networks have dominated the field of RE and made noticeable progress. Subsequently, the large pre-trained language models (PLMs) have taken the state-of-the-art RE to a new level. This survey provides a comprehensive review of existing deep learning techniques for RE. First, we introduce RE resources, including datasets and evaluation metrics. Second, we propose a new taxonomy to categorize existing works …
Efficient Multiplicative-To-Additive Function From Joye-Libert Cryptosystem And Its Application To Threshold Ecdsa, Haiyang Xue, Ho Man Au, Mengling Liu, Yin Kwan Chan, Handong Cui, Xiang Xie, Hon Tsz Yuen, Chengru Zhang
Efficient Multiplicative-To-Additive Function From Joye-Libert Cryptosystem And Its Application To Threshold Ecdsa, Haiyang Xue, Ho Man Au, Mengling Liu, Yin Kwan Chan, Handong Cui, Xiang Xie, Hon Tsz Yuen, Chengru Zhang
Research Collection School Of Computing and Information Systems
Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes.In this paper, we design a novel MtA by revisiting the Joye-Libert (JL) cryptosystem. Specifically, we revisit JL encryption and propose a JL-based …
Angels Or Demons: Investigating And Detecting Decentralized Financial Traps On Ethereum Smart Contracts, Jiachi Chen, Jiang Hu, Xin Xia, David Lo, John Grundy, Zhipeng Gao, Ting Chen
Angels Or Demons: Investigating And Detecting Decentralized Financial Traps On Ethereum Smart Contracts, Jiachi Chen, Jiang Hu, Xin Xia, David Lo, John Grundy, Zhipeng Gao, Ting Chen
Research Collection School Of Computing and Information Systems
Decentralized Finance (DeFi) uses blockchain technologies to transform traditional financial activities into decentralized platforms that run without intermediaries and centralized institutions. Smart contracts are programs that run on the blockchain, and by utilizing smart contracts, developers can more easily develop DeFi applications. Some key features of smart contracts—self-executed and immutability—ensure the trustworthiness, transparency and efficiency of DeFi applications and have led to a fast-growing DeFi market. However, misbehaving developers can add traps or backdoor code snippets to a smart contract, which are hard for contract users to discover. We call these code snippets in a DeFi smart contract as “DeFi …
Cas: Fusing Dnn Optimization & Adaptive Sensing For Energy-Efficient Multi-Modal Inference, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra
Cas: Fusing Dnn Optimization & Adaptive Sensing For Energy-Efficient Multi-Modal Inference, Dulanga Weerakoon, Vigneshwaran Subbaraju, Joo Hwee Lim, Archan Misra
Research Collection School Of Computing and Information Systems
Intelligent virtual agents are used to accomplish complex multi-modal tasks such as human instruction comprehension in mixed-reality environments by increasingly adopting richer, energy-intensive sensors and processing pipelines. In such applications, the context for activating sensors and processing blocks required to accomplish a given task instance is usually manifested via multiple sensing modes. Based on this observation, we introduce a novel Commit-and-Switch ( CAS ) paradigm that simultaneously seeks to reduce both sensing and processing energy. In CAS , we first commit to a low-energy computational pipeline with a subset of available sensors. Then, the task context estimated by this pipeline …
Experience As Source For Anticipation And Planning : Experiential Policy Learning For Target-Driven Recommendation Dialogues, Quang Huy Dao, Yang Deng, Khanh-Huyen Bui, Dung D. Le, Lizi Liao
Experience As Source For Anticipation And Planning : Experiential Policy Learning For Target-Driven Recommendation Dialogues, Quang Huy Dao, Yang Deng, Khanh-Huyen Bui, Dung D. Le, Lizi Liao
Research Collection School Of Computing and Information Systems
Target-driven recommendation dialogues present unique challenges in dialogue management due to the necessity of anticipating user interactions for successful conversations. Current methods face significant limitations: (I) inadequate capabilities for conversation anticipation, (II) computational inefficiencies due to costly simulations, and (III) neglect of valuable past dialogue experiences. To address these limitations, we propose a new framework, Experiential Policy Learning (EPL), for enhancing such dialogues. EPL embodies the principle of Learning From Experience, facilitating anticipation with an experiential scoring function that estimates dialogue state potential using similar past interactions stored in long-term memory. To demonstrate its flexibility, we introduce Tree-structured EPL (T-EPL) …
Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series …
From Data To Application: Harnessing Big Spatial Data And Spatially Explicit Machine Learning Model For Landslide Susceptibility Prediction And Mapping, Min Naing Khant, Mei Yi Victoria Grace Ann, Tin Seong Kam
From Data To Application: Harnessing Big Spatial Data And Spatially Explicit Machine Learning Model For Landslide Susceptibility Prediction And Mapping, Min Naing Khant, Mei Yi Victoria Grace Ann, Tin Seong Kam
Research Collection School Of Computing and Information Systems
Recent advancements in information and communication technology have significantly enhanced access to extensive geospatial data, presenting a valuable opportunity to leverage big spatial data for improved modeling and predictive capabilities in natural disaster risk assessment. This paper explores the integration of a comprehensive dataset comprising historical landslide events and various geo-environmental variables within a spatially explicit machine learning framework. The study empirically demonstrates that incorporating big spatial data allows a more nuanced understanding of local variations and spatial dependencies. Ultimately, this empirical assessment produces more accurate landslide risk predictions than traditional baseline models. Using Italy’s expansive Valtellina Valley as a …
Eyetraes : Fine-Grained, Low-Latency Eye Tracking Via Adaptive Event Slicing, Argha Sen, Panahetipola Mudiyanselage Nuwan Bandara, Ila Gokarn, Thivya Kandappu, Archan Misra
Eyetraes : Fine-Grained, Low-Latency Eye Tracking Via Adaptive Event Slicing, Argha Sen, Panahetipola Mudiyanselage Nuwan Bandara, Ila Gokarn, Thivya Kandappu, Archan Misra
Research Collection School Of Computing and Information Systems
Eye-tracking technology has gained significant attention in recent years due to its wide range of applications in humancomputer interaction, virtual and augmented reality, and wearable health. Traditional RGB camera-based eye-tracking systems often struggle with poor temporal resolution and computational constraints, limiting their effectiveness in capturing rapid eye movements. To address these limitations, we propose EyeTrAES, a novel approach using neuromorphic event cameras for high-fidelity tracking of natural pupillary movement that shows significant kinematic variance. One of EyeTrAES’s highlights is the use of a novel adaptive windowing/slicing algorithm that ensures just the right amount of descriptive asynchronous event data accumulation within …
Eyegraph : Modularity-Aware Spatio Temporal Graph Clustering For Continuous Event-Based Eye Tracking, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra, Ila Gokarn, Archan Misra
Eyegraph : Modularity-Aware Spatio Temporal Graph Clustering For Continuous Event-Based Eye Tracking, Panahetipola Mudiyanselage Nuwan Bandara, Thivya Kandappu, Archan Misra, Ila Gokarn, Archan Misra
Research Collection School Of Computing and Information Systems
Continuous tracking of eye movement dynamics plays a significant role in developing a broad spectrum of human-centered applications, such as cognitive skills (visual attention and working memory) modeling, human-machine interaction, biometric user authentication, and foveated rendering. Recently neuromorphic cameras have garnered significant interest in the eye-tracking research community, owing to their sub-microsecond latency in capturing intensity changes resulting from eye movements. Nevertheless, the existing approaches for event-based eye tracking suffer from several limitations: dependence on RGB frames, label sparsity, and training on datasets collected in controlled lab environments that do not adequately reflect real-world scenarios. To address these limitations, in …
Effect Of Virtual Reality Technology On Computer Science/Engineering Based Laboratories Education – A Case Study, Saeed Salem Al Shebli
Effect Of Virtual Reality Technology On Computer Science/Engineering Based Laboratories Education – A Case Study, Saeed Salem Al Shebli
Theses
The rapid growth in mobile applications raises critical concerns about the security of apps and users' privacy, especially in permission control. Mobile apps access sensitive information of users, and the current cybersecurity landscape faces a huge challenge in ensuring the least required permissions are granted. This research focuses on designing an advanced permission recommendation system that couples the strengths of Natural Language Processing (NLP) and Machine Learning (ML) in solving most of the existing gaps in permission management, thus guiding which permissions are mostly needed by Android applications.
The research thus follows a multi-classification approach, integrating state-of-the-art ML techniques with …
A Secure And Effective Framework For Key Concept Mining From Educational Content Using Large Language Models, Ashika Sameem Abdul Rasheed
A Secure And Effective Framework For Key Concept Mining From Educational Content Using Large Language Models, Ashika Sameem Abdul Rasheed
Theses
This thesis examines the use of Large Language Models (LLMs) in education, with a focus on improving performance and implementing strong security measures. The research has two main goals, namely, the development of an effective lecture summarization technique using LLMs and identifying and addressing security vulnerabilities in LLM applications according to OWASP (Open Web Application Security Project) guidelines. For the former goal, we have proposed an effective framework for fine-tuning LLMs using real lecture datasets and compared the performance of different LLMs. For the latter goal, we conducted a thorough review of the application dataflow of the proposed framework and …
Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik
Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik
Neutrosophic Systems with Applications
Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with …
Product Perspective From Fuzzy To Neutrosophic Graph Extension- A Review Of Literature, G. Vetrivel, M. Mullai, G. Rajchakit
Product Perspective From Fuzzy To Neutrosophic Graph Extension- A Review Of Literature, G. Vetrivel, M. Mullai, G. Rajchakit
Neutrosophic Systems with Applications
This review article consolidates and tabulates research on the product operations done in fuzzy, intuitionistic fuzzy, and neutrosophic graphs. This article encompasses the previous product discussions on fuzzy graphs and their extensions. This article aims to list the origin, structural properties, applications, etc. done by the researchers and academicians using the product behavior of two graphs on the fuzzified environment. This review provides a clear understanding of enhancements of product approach on graphs from fuzzy to neutrosophic kind.
A Neutrosophic Model For Measuring Evolution, Involution, And Indeterminacy In Species: Integrating Common And Uncommon Traits In Environmental Adaptation, Antonios Paraskevas, Michael Madas
A Neutrosophic Model For Measuring Evolution, Involution, And Indeterminacy In Species: Integrating Common And Uncommon Traits In Environmental Adaptation, Antonios Paraskevas, Michael Madas
Neutrosophic Systems with Applications
In 2017, Professor F. Smarandache introduced the Neutrosophic Theory of Evolution, Involution, and Indeterminacy (or Neutrality) (NToEIaI). He concluded that every theory of evolution is characterized by a certain degree of truth, indeterminacy, and untruth, as in neutrosophic logic. In this perspective, he raised several open questions on evolution, neutrality, and involution that required further research effort. Very recently, in 2024, Smarandache conducted research, from a soft sciences/philosophical viewpoint, on identifying and studying common parts in uncommon things and uncommon parts in common things emphasizing the complexity and interconnectedness of concepts within the context of neutrosophy. In this article, we …