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Articles 1051 - 1080 of 1897

Full-Text Articles in Artificial Intelligence and Robotics

Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar Aug 2022

Trajectory Optimization For Safe Navigation In Maritime Traffic Using Historical Data, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar, T. K. Satish Kumar

Research Collection School Of Computing and Information Systems

Increasing maritime trade often results in congestion in busy ports, thereby necessitating planning methods to avoid close quarter risky situations among vessels. Rapid digitization and automation of port operations and vessel navigation provide unique opportunities for significantly improving navigation safety. Our key contributions are as follows. First, given a set of future candidate trajectories for vessels in a traffic hotspot zone, we develop a multiagent trajectory optimization method to choose trajectories that result in the best overall close quarter risk reduction. Our novel MILP-based optimization method is more than an order-of-magnitude faster than a standard MILP for this problem, and …

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Crosscbr: Cross‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua Aug 2022

Crosscbr: Cross‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively. However, they either use a unified view without differentiation or loosely combine the predictions of two separate views, while the crucial cooperative association between the two views' representations is overlooked.In this work, we propose to model the cooperative association between the two different views through cross-view contrastive learning. By encouraging …

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Artificial Intelligence In Financial Technology, Keng Siau, Fiona Fui-Hoon Nah, Brenda L. Eschenbrenner, Langtao Chen Aug 2022

Artificial Intelligence In Financial Technology, Keng Siau, Fiona Fui-Hoon Nah, Brenda L. Eschenbrenner, Langtao Chen

Research Collection School Of Computing and Information Systems

AI applications in health care, communications, and arts have brought about rapid and dramatic advances in these fields. Nevertheless, the rapidly expanding potential of AI in the economy and society has raised a set of challenging issues. The fields of AI and financial technology are not spared. How can artificial intelligence (AI) be utilized in financial technology (fintech)? What will be the impact? What actionable objectives are needed to realize value from AI? This research uses a systematic qualitative research methodology, Value-Focused Thinking, to identify the actionable objectives for deriving value from AI in the fintech industry. The results of …

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Joint Chance-Constrained Staffing Optimization In Multi-Skill Call Centers, Tien Thanh Dam, Thuy Anh Ta, Tien Mai Aug 2022

Joint Chance-Constrained Staffing Optimization In Multi-Skill Call Centers, Tien Thanh Dam, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

This paper concerns the staffing optimization problem in multi-skill call centers. The objective is to find a minimal cost staffing solution while meeting a target level for the quality of service (QoS) to customers. We consider a staffing problem in which joint chance constraints are imposed on the QoS of the day. Our joint chance-constrained formulation is more rational capturing the correlation between different call types, as compared to separate chance-constrained versions considered in previous studies. We show that, in general, the probability functions in the joint-chance constraints display S-shaped curves, and the optimal solutions should belong to the concave …

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Towards Improving System Performance In Large Scale Multi-Agent Systems With Selfish Agents, Rajiv Ranjan Kumar Jul 2022

Towards Improving System Performance In Large Scale Multi-Agent Systems With Selfish Agents, Rajiv Ranjan Kumar

Dissertations and Theses Collection (Open Access)

Intelligent agents are becoming increasingly prevalent in a wide variety of domains including but not limited to transportation, safety and security. To better utilize the intelligence, there has been increasing focus on frameworks and methods for coordinating these intelligent agents. This thesis is specifically targeted at providing solution approaches for improving large scale multi-agent systems with selfish intelligent agents. In such systems, the performance of an agent depends on not just his/her own efforts, but also on other agent’s decisions. The complexity of interactions among multiple agents, coupled with the large scale nature of the problem domains and the uncertainties …

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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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A Mean-Field Markov Decision Process Model For Spatial Temporal Subsidies In Ride-Sourcing Markets, Zheng Zhu, Jintao Ke, Hai Wang Jul 2022

A Mean-Field Markov Decision Process Model For Spatial Temporal Subsidies In Ride-Sourcing Markets, Zheng Zhu, Jintao Ke, Hai Wang

Research Collection School Of Computing and Information Systems

Ride-sourcing services are increasingly popular because of their ability to accommodate on-demand travel needs. A critical issue faced by ride-sourcing platforms is the supply-demand imbalance, as a result of which drivers may spend substantial time on idle cruising and picking up remote passengers. Some platforms attempt to mitigate the imbalance by providing relocation guidance for idle drivers who may have their own self-relocation strategies and decline to follow the suggestions. Platforms then seek to induce drivers to system-desirable locations by offering them subsidies. This paper proposes a mean-field Markov decision process (MF-MDP) model to depict the dynamics in ride-sourcing markets …

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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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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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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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Towards Aligning Slides And Video Snippets: Mitigating Sequence And Content Mismatches, Ziyuan Liu, Hady W. Lauw Jul 2022

Towards Aligning Slides And Video Snippets: Mitigating Sequence And Content Mismatches, Ziyuan Liu, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Slides are important form of teaching materials used in various courses at academic institutions. Due to their compactness, slides on their own may not stand as complete reference materials. To aid students’ understanding, it would be useful to supplement slides with other materials such as online videos. Given a deck of slides and a related video, we seek to align each slide in the deck to a relevant video snippet, if any. While this problem could be formulated as aligning two time series (each involving a sequence of text contents), we anticipate challenges in generating matches arising from differences in …

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Dynamic Topic Models For Temporal Document Networks, Ce Zhang, Hady Wirawan Lauw Jul 2022

Dynamic Topic Models For Temporal Document Networks, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Dynamic topic models explore the time evolution of topics in temporally accumulative corpora. While existing topic models focus on the dynamics of individual documents, we propose two neural topic models aimed at learning unified topic distributions that incorporate both document dynamics and network structure. For the first model, by adding a time dimension, we propose Time-Aware Optimal Transport, which measures the probability of a link between two differently timestamped documents using their semantic distance. Since the gradually evolving topological structure of network may also influence the establishment of a new link, for the second model, we further design a Temporal …

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Cosm2ic: Optimizing Real-Time Multi-Modal Instruction Comprehension, Weerakoon Mudiyanselage Dulanga Kaveesha Weerakoon, Vigneshwaran Subbaraju, Minh Anh Tuan Tran, Archan Misra Jul 2022

Cosm2ic: Optimizing Real-Time Multi-Modal Instruction Comprehension, Weerakoon Mudiyanselage Dulanga Kaveesha Weerakoon, Vigneshwaran Subbaraju, Minh Anh Tuan Tran, Archan Misra

Research Collection School Of Computing and Information Systems

Supporting real-time, on-device execution of multi-modal referring instruction comprehension models is an important challenge to be tackled in embodied Human-Robot Interaction. However, state-of-the-art deep learning models are resource-intensive and unsuitable for real-time execution on embedded devices. While model compression can achieve a reduction in computational resources up to a certain point, further optimizations result in a severe drop in accuracy. To minimize this loss in accuracy, we propose the COSM2IC framework, with a lightweight Task Complexity Predictor, that uses multiple sensor inputs to assess the instructional complexity and thereby dynamically switch between a set of models of varying computational intensity …

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Test Mimicry To Assess The Exploitability Of Library Vulnerabilities, Hong Jin Kang, Truong Giang Nguyen, Bach Le, Corina S. Pasareanu, David Lo Jul 2022

Test Mimicry To Assess The Exploitability Of Library Vulnerabilities, Hong Jin Kang, Truong Giang Nguyen, Bach Le, Corina S. Pasareanu, David Lo

Research Collection School Of Computing and Information Systems

Modern software engineering projects often depend on open-source software libraries, rendering them vulnerable to potential security issues in these libraries. Developers of client projects have to stay alert of security threats in the software dependencies. While there are existing tools that allow developers to assess if a library vulnerability is reachable from a project, they face limitations. Call graphonly approaches may produce false alarms as the client project may not use the vulnerable code in a way that triggers the vulnerability, while test generation-based approaches faces difficulties in overcoming the intrinsic complexity of exploiting a vulnerability, where extensive domain knowledge …

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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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A Recommendation System Approach To Tune A Qubo Solver, Siong Thye Goh, Jianyuan Bo, Matthieu Parizy, Hoong Chuin Lau Jul 2022

A Recommendation System Approach To Tune A Qubo Solver, Siong Thye Goh, Jianyuan Bo, Matthieu Parizy, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

There are two major challenges to solving constrained optimization problems using a QuadraticUnconstrained Binary Optimization or QUBO solver (QS). First, we need to tune both the underlyingproblem parameters and the algorithm parameters. Second, the solution returned from a QSmight not be feasible. While it is common to use automated tuners such as SMAC and Hyperopt totune the algorithm parameters, the initial search ranges input for the auto tuner affect the performanceof the QS. In this paper, we propose a framework that resembles the Algorithm Selection(AS) framework to tune algorithm parameters for an annealing-based QS. To cope with constraints,we focus on …

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Legal And Regulatory Issues On Artificial Intelligence, Machine Learning, Data Science, And Big Data, Wai Yee Wan, Michael Tsimplis, Keng Siau, Wei T. Yue, Fiona Fui-Hoon Nah, Gabriel M. Yu Jul 2022

Legal And Regulatory Issues On Artificial Intelligence, Machine Learning, Data Science, And Big Data, Wai Yee Wan, Michael Tsimplis, Keng Siau, Wei T. Yue, Fiona Fui-Hoon Nah, Gabriel M. Yu

Research Collection School Of Computing and Information Systems

Technological innovation creates numerous opportunities for businesses, organizations, and societies. Artificial intelligence, machine learning, data science, and big data provide opportunities for developing self-controlling systems emulating human intelligence. In some instances, these systems surpass the performance of humans. The relationship of innovative technology with the law is an important underpinning factor that is often overlooked. Law may encourage innovation but may also inhibit its development and application by adopting stringent regulatory provisions and liability regimes. This article examines the legal and regulatory issues related to new technologies such as artificial intelligence, machine learning, data science, and big data.

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Ai In Accounting: A Value-Focused Thinking Study, Keng Siau, Fiona Fui-Hoon Nah, B. Eschenbrenner, L. Chen, Y. Qian Jul 2022

Ai In Accounting: A Value-Focused Thinking Study, Keng Siau, Fiona Fui-Hoon Nah, B. Eschenbrenner, L. Chen, Y. Qian

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) has been widely utilized in the accounting industry and profoundly shaped how accounting is practiced nowadays. AI is poised to make significant contributions to various accounting functions such as auditing and fraud detection by providing greater insights to improve the efficiency and effectiveness of decision-making processes. To realize the full potential of AI in accounting, a strategic approach to its implementation is necessary. In this research, we propose to utilize a qualitative study that adopts the Value-Focused Thinking approach to identify the objective necessary to achieve the full value of AI in the accounting domain. Findings from …

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Identifying Legal And Ethical Values In Ai, Keng Siau, Fiona Nah, Chunyan Ding, Fiona Fui-Hoon Nah Jul 2022

Identifying Legal And Ethical Values In Ai, Keng Siau, Fiona Nah, Chunyan Ding, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) applications in health care, education, finance, mining, communications, and arts have brought about rapid and dramatic advances in these fields (Hyder et al., 2019). The rapidly expanding potential of AI in the economy and society has raised a set of legal and ethical issues (Wang and Siau, 2019; Siau and Wang, 2020). In-depth research into the ethical and legal aspects of AI to enable policymakers to introduce effective legislation and regulate AI development and applications is needed. This research uses a systematic qualitative research methodology, Value-Focused Thinking (VFT) (Keeney, 1996; Sheng et al., 2007), to identify the …

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Traffic Behavior Recognition From Traffic Videos Under Occlusion Condition: A Kalman Filter Approach, Junfeng Jiao, Huihai Wang Jul 2022

Traffic Behavior Recognition From Traffic Videos Under Occlusion Condition: A Kalman Filter Approach, Junfeng Jiao, Huihai Wang

Research Collection College of Integrative Studies

Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of …

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Special Section: Reevaluating Markets For Information, Robert John Kauffman, Thomas A. Weber Jul 2022

Special Section: Reevaluating Markets For Information, Robert John Kauffman, Thomas A. Weber

Research Collection School Of Computing and Information Systems

As the use of information as a productive and tradeable asset becomes more pervasive, its—often unintended—side-effects start showing. This prompts us to systematically recognize these effects and, once identified, to think about how to use them to our advantage or else mitigate them as much as may be economically and/or socially desirable. In this Special Section, we have assembled three research papers, which take a look at different interesting instances of this question, namely in the context of regulating peer-to-peer sharing markets, the substitution of knowledge workers (or their skills) by artificial intelligence (AI), and the difficulties in appropriating rents …

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Multimodal Zero-Shot Hateful Meme Detection, Jiawen Zhu, Roy Ka-Wei Lee, Wen Haw Chong Jun 2022

Multimodal Zero-Shot Hateful Meme Detection, Jiawen Zhu, Roy Ka-Wei Lee, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Facebook has recently launched the hateful meme detection challenge, which garnered much attention in academic and industry research communities. Researchers have proposed multimodal deep learning classification methods to perform hateful meme detection. While the proposed methods have yielded promising results, these classification methods are mostly supervised and heavily rely on labeled data that are not always available in the real-world setting. Therefore, this paper explores and aims to perform hateful meme detection in a zero-shot setting. Working towards this goal, we propose Target-Aware Multimodal Enhancement (TAME), which is a novel deep generative framework that can improve existing hateful meme classification …

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High-Resolution Face Swapping Via Latent Semantics Disentanglement, Yangyang Xu, Bailin Deng, Junle Wang, Yanqing Jing, Jia Pan, Shengfeng He Jun 2022

High-Resolution Face Swapping Via Latent Semantics Disentanglement, Yangyang Xu, Bailin Deng, Junle Wang, Yanqing Jing, Jia Pan, Shengfeng He

Research Collection School Of Computing and Information Systems

We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator, deriving structure at-tributes from the shallow layers and appearance attributes from the deeper ones. Identity and pose information within the structure attributes are further separated by introducing a landmark-driven structure transfer latent direction. The disentangled latent code produces rich generative features that incorporate feature blending …

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Deep Learning For Anomaly Detection, Guansong Pang, Charu Aggarwal, Chunhua Shen, Nicu Sebe Jun 2022

Deep Learning For Anomaly Detection, Guansong Pang, Charu Aggarwal, Chunhua Shen, Nicu Sebe

Research Collection School Of Computing and Information Systems

A nomaly detection aims at identifying data points which are rare or significantly different from the majority of data points. Many techniques are explored to build highly efficient and effective anomaly detection systems, but they are confronted with many difficulties when dealing with complex data, such as failing to capture intricate feature interactions or extract good feature representations. Deep-learning techniques have shown very promising performance in tackling different types of complex data in a broad range of tasks/problems, including anomaly detection. To address this new trend, we organized this Special Issue on Deep Learning for Anomaly Detection to cover the …

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Mems Ultrasonic Transducers For Safe, Low-Power And Portable Eye-Blinking Monitoring, Sheng Sun, Jianyuan Wang, Menglun Zhang, Yuan Ning, Dong Ma, Yi Yuan, Pengfei Niu, Zhicong Rong, Zhuochen Wang, Wei Pang Jun 2022

Mems Ultrasonic Transducers For Safe, Low-Power And Portable Eye-Blinking Monitoring, Sheng Sun, Jianyuan Wang, Menglun Zhang, Yuan Ning, Dong Ma, Yi Yuan, Pengfei Niu, Zhicong Rong, Zhuochen Wang, Wei Pang

Research Collection School Of Computing and Information Systems

Eye blinking is closely related to human physiology and psychology. It is an effective method of communication among people and can be used in human–machine interactions. Existing blink monitoring methods include video-oculography, electro-oculograms and infrared oculography. However, these methods suffer from uncomfortable use, safety risks, limited reliability in strong light or dark environments, and infringed informational security. In this paper, we propose an ultrasound-based portable approach for eye-blinking activity monitoring. Low-power pulse-echo ultrasound featuring biosafety is transmitted and received by microelectromechanical system (MEMS) ultrasonic transducers seamlessly integrated on glasses. The size, weight and power consumption of the transducers are 2.5 …

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Imagining New Futures Beyond Predictive Systems In Child Welfare: A Qualitative Study With Impacted Stakeholders, Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, Haiyi Zhu Jun 2022

Imagining New Futures Beyond Predictive Systems In Child Welfare: A Qualitative Study With Impacted Stakeholders, Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, Haiyi Zhu

Research Collection School Of Computing and Information Systems

Child welfare agencies across the United States are turning to datadriven predictive technologies (commonly called predictive analytics) which use government administrative data to assist workers’ decision-making. While some prior work has explored impacted stakeholders’ concerns with current uses of data-driven predictive risk models (PRMs), less work has asked stakeholders whether such tools ought to be used in the first place. In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them …

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