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Articles 57481 - 57510 of 294456

Full-Text Articles in Physical Sciences and Mathematics

On The Spectral Theory Of Linear Differential-Algebraic Operators With Periodic Coefficients, Bader Alshammari Jul 2022

On The Spectral Theory Of Linear Differential-Algebraic Operators With Periodic Coefficients, Bader Alshammari

Theses and Dissertations

In this thesis, the spectral theory of linear differential algebraic equations (DAEs) is considered in detail and extended to treat the weighted spectral theory which generalizes the classical theory, i.e., we develop the spectral theory for the most general DAEs: J df dt + Hf = λWf, (0.0.1) where J is a constant nonzero skew-Hermitian n×n-matrix, both H and W are dperiodic Hermitian n×n-matrices with Lebesgue measurable functions as entries, and W is positive semidefinite and invertible for a.e. t ∈ R (i.e., Lebesgue almost everywhere). Under weakest hypotheses on H and W currently known, called the local index-1 hypotheses, …

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Automatic 3d Cranial Landmark Positioning Based Onsurface Curvature Feature Using Machine Learning, Putu Hendra Suputra, Anggraini Dwi Sensusiati, Myrtati Dyah Artaria, Gijsbertus Jacob Verkerke, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama Jul 2022

Automatic 3d Cranial Landmark Positioning Based Onsurface Curvature Feature Using Machine Learning, Putu Hendra Suputra, Anggraini Dwi Sensusiati, Myrtati Dyah Artaria, Gijsbertus Jacob Verkerke, Eko Mulyanto Yuniarno, I Ketut Eddy Purnama

Knowledge Engineering and Data Science

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious …

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Human Facial Expressions Identification Using Convolutionalneural Network With Vgg16 Architecture, Luther Alexander Latumakulita, Sandy Laurentius Lumintang, Deiby Tineke Salaki, Steven R. Sentinuwo, Alwin Melkie Sambul, Noorul Islam Jul 2022

Human Facial Expressions Identification Using Convolutionalneural Network With Vgg16 Architecture, Luther Alexander Latumakulita, Sandy Laurentius Lumintang, Deiby Tineke Salaki, Steven R. Sentinuwo, Alwin Melkie Sambul, Noorul Islam

Knowledge Engineering and Data Science

The human facial expression identification system is essential in developing human interaction and technology. The development of Artificial Intelligence for monitoring human emotions can be helpful in the workplace. Commonly, there are six basic human expressions, namely anger, disgust, fear, happiness, sadness, and surprise, that the system can identify. This study aims to create a facial expression identification system based on basic human expressions using the Convolutional Neural Network (CNN) with a 16-layer VGG architecture. Two thousand one hundred thirty-seven facial expression images were selected from the FER2013, JAFFE, and MUG datasets. By implementing image augmentation and setting up the …

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Sentiment Analysis Of Amazon Product Reviews Usingsupervised Machine Learning Techniques, Naveed Sultan Jul 2022

Sentiment Analysis Of Amazon Product Reviews Usingsupervised Machine Learning Techniques, Naveed Sultan

Knowledge Engineering and Data Science

Today, everything is sold online, and many individuals can post reviews about different products to show feedback. Serves as feedback for businesses regarding buyer reviews, performance, product quality, and seller service. The project focuses on buyer opinions based on Mobile Phone reviews. Sentiment analysis is the function of analyzing all these data, obtaining opinions about these products and services that classify them as positive, negative, or neutral. This insight can help companies improve their products and help potential buyers make the right decisions. Once the preprocessing is classified on a trained dataset, these reviews must be preprocessed to remove unwanted …

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Using Constraint Programming And Graph Representation Learning For Generating Interpretable Cloud Security Policies, Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar Jul 2022

Using Constraint Programming And Graph Representation Learning For Generating Interpretable Cloud Security Policies, Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar

Research Collection School Of Computing and Information Systems

Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs signifcant (monetary) loss for a commercial organization. Conceptually, cloud security heavily relies on Identity Access Management (IAM) policies that IT admins need to properly confgure and periodically update. Security negligence and human errors often lead to misconfguring IAM policies which may open a backdoor for attackers. To address these challenges, frst, we develop a novel framework that encodes generating optimal IAM policies using constraint programming (CP). We identify reducing dormant permissions of cloud users as an optimality criterion, which intuitively …

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Techniques To Enhance A Qubo Solver For Permutation-Based Combinatorial Optimization, Siong Thye Goh, Jianyuan Bo, Sabrish Gopalakrishnan, Hoong Chuin Lau Jul 2022

Techniques To Enhance A Qubo Solver For Permutation-Based Combinatorial Optimization, Siong Thye Goh, Jianyuan Bo, Sabrish Gopalakrishnan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Many combinatorial optimization problems can be formulated as a problem to determine the order of sequence or to find a corresponding mapping of the objects. We call such problems permutation-based optimization problems. Many such problems can be formulated as a quadratic unconstrained binary optimization (QUBO) or Ising model by introducing a penalty coefficient to the permutation constraint terms. While classical and quantum annealing approaches have been proposed to solve QUBOs to date, they face issues with optimality and feasibility. Here we treat a given QUBO solver as a black box and propose techniques to enhance its performance. First, to ensure …

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Self-Supervised Video Representation Learning By Uncovering Spatio-Temporal Statistics, Jiangliu Wang, Jianbo Jiao, Linchao Bao, Shengfeng He, Wei Liu, Yun-Hui Liu Jul 2022

Self-Supervised Video Representation Learning By Uncovering Spatio-Temporal Statistics, Jiangliu Wang, Jianbo Jiao, Linchao Bao, Shengfeng He, Wei Liu, Yun-Hui Liu

Research Collection School Of Computing and Information Systems

This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc. Then a neural network is built and trained to yield the statistical summaries given the video frames as inputs. In order to alleviate the learning difficulty, we employ several spatial partitioning patterns to encode rough spatial locations instead of exact spatial Cartesian coordinates. …

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Uncovering Inclusivity Gaps In Design Pedagogy Through The Digital Design Marginalization Framework, Jaisie Sin, Cosmin Munteanu, Michael Nixon, Velian Pandeliev, Garreth W. Tigwell, Kristen Shinohara, Anthony Tang, Steve Szigeti Jul 2022

Uncovering Inclusivity Gaps In Design Pedagogy Through The Digital Design Marginalization Framework, Jaisie Sin, Cosmin Munteanu, Michael Nixon, Velian Pandeliev, Garreth W. Tigwell, Kristen Shinohara, Anthony Tang, Steve Szigeti

Research Collection School Of Computing and Information Systems

Designers play a key role in the design of inclusive and socially conscious interfaces. Thus, it is imperative for designers to be thoughtful of the ethical and social implications of design. However, gaps in the foundational training that designers receive (e.g., as university students) can negatively impact their ability to consider the social implications of their design practice. This can result in consequences such as digital marginalization, which, as defined by the Digital Design Marginalization (DDM) framework, is the “pushing away”, whether intentional or not, of a defined group of users from a digital or online service or system, where …

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Efficient Neural Neighborhood Search For Pickup And Delivery Problems, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, Yuejiao Gong, Meng Chee Chee Jul 2022

Efficient Neural Neighborhood Search For Pickup And Delivery Problems, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, Yuejiao Gong, Meng Chee Chee

Research Collection School Of Computing and Information Systems

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even …

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Multi-Objective Evolutionary Algorithm Based On Rbf Network For Solving The Stochastic Vehicle Routing Problem, Yunyun Niu, Jie Shao, Jianhua Xiao, Wen Song, Zhiguang Cao Jul 2022

Multi-Objective Evolutionary Algorithm Based On Rbf Network For Solving The Stochastic Vehicle Routing Problem, Yunyun Niu, Jie Shao, Jianhua Xiao, Wen Song, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi -objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi -objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function net-work (RBFN) is exploited to learn the potential knowledge of individuals, generate hypoth-esis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into …

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Contours Of Virtual Enfreakment In Fighting Game Characters, Sercan Sengun, Peter Mawhorter, James Bowie-Wilson, Yusef Audeh, Haewoon Kwak, D. Fox Harrell Jul 2022

Contours Of Virtual Enfreakment In Fighting Game Characters, Sercan Sengun, Peter Mawhorter, James Bowie-Wilson, Yusef Audeh, Haewoon Kwak, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Characters in fighting videogames1 such as Street Fighter V and Tekken7 typically reveal a phenomenon that we define as virtual enfreakment: their bodies, costumes, and fighting styles are exaggerated (1) in a manner that emphasizes perceived exoticism and (2) to enable them to be easily visually and conceptually distinguishable from one another. Here, using both quantitative and qualitative methods, including crowd-sourced surveys and analyses of game mechanics, we report on the contours of virtual enfreakment in those games. We specifically examine differences in character design across gender, national-origin, and skin-color lines. Disappointingly but not surprisingly, we find racism and sexism …

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Self-Guided Learning To Denoise For Robust Recommendation, Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng Jul 2022

Self-Guided Learning To Denoise For Robust Recommendation, Yunjun Gao, Yuntao Du, Yujia Hu, Lu Chen, Xinjun Zhu, Ziquan Fang, Baihua Zheng

Research Collection School Of Computing and Information Systems

The ubiquity of implicit feedback makes them the default choice to build modern recommender systems. Generally speaking, observed interactions are considered as positive samples, while unobserved interactions are considered as negative ones. However, implicit feedback is inherently noisy because of the ubiquitous presence of noisy-positive and noisy-negative interactions. Recently, some studies have noticed the importance of denoising implicit feedback for recommendations, and enhanced the robustness of recommendation models to some extent. Nonetheless, they typically fail to (1) capture the hard yet clean interactions for learning comprehensive user preference, and (2) provide a universal denoising solution that can be applied to …

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Proceedings Of The 13th International Workshop On Graph Computation Models (Gcm 2022), Reiko Heckel, Christopher M. Poskitt Jul 2022

Proceedings Of The 13th International Workshop On Graph Computation Models (Gcm 2022), Reiko Heckel, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

This volume contains the proceedings of the Thirteenth International Workshop on Graph Computation Models (GCM 2022) , which was held in Nantes, France on 6th July 2022 as part of the STAF federation of conferences. Graphs are common mathematical structures that are visual and intuitive. They constitute a natural and seamless way for system modelling in science, engineering, and beyond, including computer science, biology, and business process modelling. Graph computation models constitute a class of very high-level models where graphs are first-class citizens. The aim of the International GCM Workshop series is to bring together researchers interested in all aspects …

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Xss For The Masses: Integrating Security In A Web Programming Course Using A Security Scanner, Lwin Khin Shar, Christopher M. Poskitt, Kyong Jin Shim, Li Ying Leonard Wong Jul 2022

Xss For The Masses: Integrating Security In A Web Programming Course Using A Security Scanner, Lwin Khin Shar, Christopher M. Poskitt, Kyong Jin Shim, Li Ying Leonard Wong

Research Collection School Of Computing and Information Systems

Cybersecurity education is considered an important part of undergraduate computing curricula, but many institutions teach it only in dedicated courses or tracks. This optionality risks students graduating with limited exposure to secure coding practices that are expected in industry. An alternative approach is to integrate cybersecurity concepts across non-security courses, so as to expose students to the interplay between security and other sub-areas of computing. In this paper, we report on our experience of applying the security integration approach to an undergraduate web programming course. In particular, we added a practical introduction to secure coding, which highlighted the OWASP Top …

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Npc: Neuron Path Coverage Via Characterizing Decision Logic Of Deep Neural Networks, Xiaofei Xie, Tianlin Li, Jian Wang, Lei Ma, Qing Guo, Felix Juefei-Xu, Yang Liu Jul 2022

Npc: Neuron Path Coverage Via Characterizing Decision Logic Of Deep Neural Networks, Xiaofei Xie, Tianlin Li, Jian Wang, Lei Ma, Qing Guo, Felix Juefei-Xu, Yang Liu

Research Collection School Of Computing and Information Systems

Deep learning has recently been widely applied to many applications across different domains, e.g., image classification and audio recognition. However, the quality of Deep Neural Networks (DNNs) still raises concerns in the practical operational environment, which calls for systematic testing, especially in safety-critical scenarios. Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs. However, due to the blackbox nature of DNN, the existing structural coverage criteria are difficult to interpret, making it hard to understand the underlying principles of these criteria. The relationship between the structural coverage and …

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Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method, Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun Sun, Baihua Zheng Jul 2022

Multi-Agent Reinforcement Learning For Traffic Signal Control Through Universal Communication Method, Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm. Experimental results on real datasets demonstrate that UniComm …

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Cross-Lingual Transfer Learning For Statistical Type Inference, Zhiming Li, Xiaofei Xie, Haoliang Li, Zhengzi Xu, Yi Li, Yang Liu Jul 2022

Cross-Lingual Transfer Learning For Statistical Type Inference, Zhiming Li, Xiaofei Xie, Haoliang Li, Zhengzi Xu, Yi Li, Yang Liu

Research Collection School Of Computing and Information Systems

Hitherto statistical type inference systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label large amounts of data. Most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a cross-lingual transfer learning framework, Plato, for statistical type inference, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others, e.g., Python to JavaScript, Java to JavaScript, etc. Plato is powered by a novel kernelized attention mechanism …

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Gbgallery: A Benchmark And Framework For Game Testing, Zhuo Li, Yuechen Wu, Lei Ma, Xiaofei Xie, Yingfeng Chen, Changjie Fan Jul 2022

Gbgallery: A Benchmark And Framework For Game Testing, Zhuo Li, Yuechen Wu, Lei Ma, Xiaofei Xie, Yingfeng Chen, Changjie Fan

Research Collection School Of Computing and Information Systems

Software bug database and benchmark are the wheels of advancing automated software testing. In practice, real bugs often occur sparsely relative to the amount of software code, the extraction and curation of which are quite labor-intensive but can be essential to facilitate the innovation of testing techniques. Over the past decade, several milestones have been made to construct bug databases, pushing the progress of automated software testing research. However, up to the present, it still lacks a real bug database and benchmark for game software, making current game testing research mostly stagnant. The missing of bug database and framework greatly …

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An Empirical Study On Data Distribution-Aware Test Selection For Deep Learning Enhancement, Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Lei Ma, Mike Papadakis, Yves Le Traon Jul 2022

An Empirical Study On Data Distribution-Aware Test Selection For Deep Learning Enhancement, Qiang Hu, Yuejun Guo, Maxime Cordy, Xiaofei Xie, Lei Ma, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

Similar to traditional software that is constantly under evolution, deep neural networks need to evolve upon the rapid growth of test data for continuous enhancement (e.g., adapting to distribution shift in a new environment for deployment). However, it is labor intensive to manually label all of the collected test data. Test selection solves this problem by strategically choosing a small set to label. Via retraining with the selected set, deep neural networks will achieve competitive accuracy. Unfortunately, existing selection metrics involve three main limitations: (1) using different retraining processes, (2) ignoring data distribution shifts, and (3) being insufficiently evaluated. To …

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Ai-Enabled Adaptive Learning Using Automated Topic Alignment And Doubt Detection, Kar Way Tan, Siaw Ling Lo, Eng Lieh Ouh, Wei Leng Neo Jul 2022

Ai-Enabled Adaptive Learning Using Automated Topic Alignment And Doubt Detection, Kar Way Tan, Siaw Ling Lo, Eng Lieh Ouh, Wei Leng Neo

Research Collection School Of Computing and Information Systems

Implementing adaptive learning is often a challenging task at higher learning institutions where the students come from diverse backgrounds and disciplines. In this work, we collected informal learning journals from learners. Using the journals, we trained two machine learning models, an automated topic alignment and a doubt detection model to identify areas of adjustment required for teaching and students who require additional attention. The models form the baseline for a quiz recommender tool to dynamically generate personalized quizzes for each learner as practices to reinforce learning. Our pilot deployment of our AI-enabled Adaptive Learning System showed that our approach delivers …

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Review Of Some Existing Qml Frameworks And Novel Hybrid Classical-Quantum Neural Networks Realising Binary Classification For The Noisy Datasets, N. Schetakis, D. Aghamalyan, Paul Robert Griffin, M. Boguslavsky Jul 2022

Review Of Some Existing Qml Frameworks And Novel Hybrid Classical-Quantum Neural Networks Realising Binary Classification For The Noisy Datasets, N. Schetakis, D. Aghamalyan, Paul Robert Griffin, M. Boguslavsky

Research Collection School Of Computing and Information Systems

One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve AUC-ROC. By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for …

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Structured And Natural Responses Co-Generation For Conversational Search, Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, Tat-Seng Chua Jul 2022

Structured And Natural Responses Co-Generation For Conversational Search, Chenchen Ye, Lizi Liao, Fuli Feng, Wei Ji, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Generating fluent and informative natural responses while maintaining representative internal states for search optimization is critical for conversational search systems. Existing approaches either 1) predict structured dialog acts first and then generate natural response; or 2) map conversation context to natural responses directly in an end-to-end manner. Both kinds of approaches have shortcomings. The former suffers from error accumulation while the semantic associations between structured acts and natural responses are confined in single direction. The latter emphasizes generating natural responses but fails to predict structured acts. Therefore, we propose a neural co-generation model that generates the two concurrently. The key …

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A Low-Cost Virtual Coach For 2d Video-Based Compensation Assessment Of Upper Extremity Rehabilitation Exercises, Ana Rita Coias, Min Hun Lee, Alexandre Bernardino Jul 2022

A Low-Cost Virtual Coach For 2d Video-Based Compensation Assessment Of Upper Extremity Rehabilitation Exercises, Ana Rita Coias, Min Hun Lee, Alexandre Bernardino

Research Collection School Of Computing and Information Systems

Background: The increasing demands concerning stroke rehabilitation and in-home exercise promotion grew the need for affordable and accessible assistive systems to promote patients' compliance in therapy. These assistive systems require quantitative methods to assess patients' quality of movement and provide feedback on their performance. However, state-of-the-art quantitative assessment approaches require expensive motion-capture devices, which might be a barrier to the development of low-cost systems.Methods: In this work, we develop a low-cost virtual coach (VC) that requires only a laptop with a webcam to monitor three upper extremity rehabilitation exercises and provide real-time visual and audio feedback on compensatory motion patterns …

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Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System, Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao Jul 2022

Multi-Level Cross-View Contrastive Learning For Knowledge-Aware Recommender System, Ding Zou, Wei Wei, Xian-Ling Mao, Ziyang Wang, Minghui Qiu, Feida Zhu, Xin Cao

Research Collection School Of Computing and Information Systems

Knowledge graph (KG) plays an increasingly important role in recommender systems. Recently, graph neural networks (GNNs) based model has gradually become the theme of knowledge-aware recommendation (KGR). However, there is a natural deficiency for GNN-based KGR models, that is, the sparse supervised signal problem, which may make their actual performance drop to some extent. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring the contrastive learning in KG-aware recommendation and propose a novel multi-level cross-view contrastive learning mechanism, named MCCLK. Different from traditional contrastive learning methods which …

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What Makes The Story Forward?: Inferring Commonsense Explanations As Prompts For Future Event Generation, Li Lin, Yixin Cao, Lifu Huang, Shu Ang Li, Xuming Hu, Lijie Wen, Jianmin Wang Jul 2022

What Makes The Story Forward?: Inferring Commonsense Explanations As Prompts For Future Event Generation, Li Lin, Yixin Cao, Lifu Huang, Shu Ang Li, Xuming Hu, Lijie Wen, Jianmin Wang

Research Collection School Of Computing and Information Systems

Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluent text generation but also commonsense reasoning to maintain the logical coherence of the entire event story. In this paper, we propose a novel explainable FEG framework, Coep. It highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward. To …

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On Measuring Network Robustness For Weighted Networks, Jianbing Zheng, Ming Gao, Ee-Peng Lim, David Lo, Cheqing Jin, Aoying Zhou Jul 2022

On Measuring Network Robustness For Weighted Networks, Jianbing Zheng, Ming Gao, Ee-Peng Lim, David Lo, Cheqing Jin, Aoying Zhou

Research Collection School Of Computing and Information Systems

Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely used in many applications, such as information diffusion, disease transmission, and network security. However, existing metrics, including node connectivity, edge connectivity, and graph expansion, can be suboptimal for measuring network robustness since they are inefficient to be computed and cannot directly apply to the weighted networks or disconnected networks. In this paper, we define the RR-energy as a new robustness measurement for weighted networks based on the method of spectral analysis. RR-energy can cope …

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Harnessing Confidence For Report Aggregation In Crowdsourcing Environments, Hadeel Alhosaini, Xianzhi Wang, Lina Yao, Zhong Yang, Farookh Hussain, Ee-Peng Lim Jul 2022

Harnessing Confidence For Report Aggregation In Crowdsourcing Environments, Hadeel Alhosaini, Xianzhi Wang, Lina Yao, Zhong Yang, Farookh Hussain, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result …

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Data Quality Matters: A Case Study On Data Label Correctness For Security Bug Report Prediction, Xiaoxue Wu, Wei Zheng, Xin Xia, David Lo Jul 2022

Data Quality Matters: A Case Study On Data Label Correctness For Security Bug Report Prediction, Xiaoxue Wu, Wei Zheng, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

In the research of mining software repositories, we need to label a large amount of data to construct a predictive model. The correctness of the labels will affect the performance of a model substantially. However, limited studies have been performed to investigate the impact of mislabeled instances on a predictive model. To bridge the gap, in this article, we perform a case study on the security bug report (SBR) prediction. We found five publicly available datasets for SBR prediction contains many mislabeled instances, which lead to the poor performance of SBR prediction models of recent studies (e.g., the work of …

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End-To-End Open-Set Semi-Supervised Node Classification With Out-Of-Distribution Detection, Tiancheng Huang, Donglin Wang, Yuan Fang Jul 2022

End-To-End Open-Set Semi-Supervised Node Classification With Out-Of-Distribution Detection, Tiancheng Huang, Donglin Wang, Yuan Fang

Research Collection School Of Computing and Information Systems

Out-Of-Distribution (OOD) samples are prevalent in real-world applications. The OOD issue becomes even more severe on graph data, as the effect of OOD nodes can be potentially amplified by propagation through the graph topology. Recent works have considered the OOD detection problem, which is critical for reducing the uncertainty in learning and improving the robustness. However, no prior work considers simultaneously OOD detection and node classification on graphs in an end-to-end manner. In this paper, we study a novel problem of end-to-end open-set semisupervised node classification (OSSNC) on graphs, which deals with node classification in the presence of OOD nodes. …

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A3gan: Attribute-Aware Anonymization Networks For Face De-Identification, Liming Zhai, Qing Guo, Xiaofei Xie, Lei Ma, Yi Estelle Wang, Yang Liu Jul 2022

A3gan: Attribute-Aware Anonymization Networks For Face De-Identification, Liming Zhai, Qing Guo, Xiaofei Xie, Lei Ma, Yi Estelle Wang, Yang Liu

Research Collection School Of Computing and Information Systems

Face de-identification (De-ID) removes face identity information in face images to avoid personal privacy leakage. Existing face De-ID breaks the raw identity by cutting out the face regions and recovering the corrupted regions via deep generators, which inevitably affect the generation quality and cannot control generation results according to subsequent intelligent tasks (e.g., facial expression recognition). In this work, for the first attempt, we think the face De-ID from the perspective of attribute editing and propose an attribute-aware anonymization network (A3GAN) by formulating face De-ID as a joint task of semantic suppression and controllable attribute injection. Intuitively, the semantic suppression …

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