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Articles 17131 - 17160 of 63011

Full-Text Articles in Computer Sciences

Smart Contract Development: Challenges And Opportunities, Weiqin Zou, David Lo, Pavneet Singh Kochhar, Xuan-Bach D. Le, Xin Xia, Yang Feng, Zhenyu Chen, Baowen Xu Oct 2021

Smart Contract Development: Challenges And Opportunities, Weiqin Zou, David Lo, Pavneet Singh Kochhar, Xuan-Bach D. Le, Xin Xia, Yang Feng, Zhenyu Chen, Baowen Xu

Research Collection School Of Computing and Information Systems

Smart contract, a term which was originally coined to refer to the automation of legal contracts in general, has recently seen much interest due to the advent of blockchain technology. Recently, the term is popularly used to refer to low-level code scripts running on a blockchain platform. Our study focuses exclusively on this subset of smart contracts. Such smart contracts have increasingly been gaining ground, finding numerous important applications (e.g., crowdfunding) in the real world. Despite the increasing popularity, smart contract development still remains somewhat a mystery to many developers largely due to its special design and applications. Are there …

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Interactive Probing Of Multivariate Time Series Prediction Models: A Case Of Freight Rate Analysis, Haonan Xu, Haotian Li, Yong Wang Oct 2021

Interactive Probing Of Multivariate Time Series Prediction Models: A Case Of Freight Rate Analysis, Haonan Xu, Haotian Li, Yong Wang

Research Collection School Of Computing and Information Systems

We present an interactive probing tool to create, modify and analyze what-if scenarios for multivariate time series models. The solution is applied to freight trading, where analysts can carry out sensitivity analysis on freight rates by changing demand and supply-related econometric variables and observing their resultant effects on freight indexes. We utilize various visualization techniques to enable intuitive scenario creation, alteration, and comprehension of time series inputs and model predictions. Our tool proved to be useful to the industry practitioners, demonstrated by a case study where freight traders are given hypothetical market scenarios and successfully generated quantitative freight index projection …

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Visilence: An Interactive Visualization Tool For Error Resilience Analysis, Shaolun Ruan, Yong Wang, Qiang Guan Oct 2021

Visilence: An Interactive Visualization Tool For Error Resilience Analysis, Shaolun Ruan, Yong Wang, Qiang Guan

Research Collection School Of Computing and Information Systems

Soft errors have become one of the major concerns for HPC applications, as those errors can result in seriously corrupted outcomes, such as silent data corruptions (SDCs). Prior studies on error resilience have studied the robustness of HPC applications. However, it is still difficult for program developers to identify potential vulnerability to soft errors. In this paper, we present Visilence, a novel visualization tool to visually analyze error vulnerability based on the control-flow graph generated from HPC applications. Visilence efficiently visualizes the affected program states under injected errors and presents the visual analysis of the most vulnerable parts of an …

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A First Look At Accessibility Issues In Popular Github Projects, Tingting Bi, Xin Xia, David Lo, Aldeida Aleti Oct 2021

A First Look At Accessibility Issues In Popular Github Projects, Tingting Bi, Xin Xia, David Lo, Aldeida Aleti

Research Collection School Of Computing and Information Systems

Accessibility design elements allow people to access software products and services independent of their different abilities. However, accessibility is challenging to handle and whether accessibility is widely considered in software projects is unclear. In this work, we aim to understand if accessibility is a prevalent consideration in practice, what accessibility issues are discussed in GitHub projects, what potential reasons cause accessibility issues, and what solutions (e.g., tools and standards) are applied for addressing accessibility issues. In this work, we collect 11,820 accessibility issues and their threads discussed by developers in popular GitHub projects. We manually analyzed and grouped the collected …

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Solarslam: Battery-Free Loop Closure For Indoor Localisation, Bo Wei, Weitao Xu, Chengwen Luo, Guillaume Zoppi, Dong Ma, Sen Wang Oct 2021

Solarslam: Battery-Free Loop Closure For Indoor Localisation, Bo Wei, Weitao Xu, Chengwen Luo, Guillaume Zoppi, Dong Ma, Sen Wang

Research Collection School Of Computing and Information Systems

In this paper, we propose SolarSLAM, a batteryfree loop closure method for indoor localisation. Inertial Measurement Unit (IMU) based indoor localisation method has been widely used due to its ubiquity in mobile devices, such as mobile phones, smartwatches and wearable bands. However, it suffers from the unavoidable long term drift. To mitigate the localisation error, many loop closure solutions have been proposed using sophisticated sensors, such as cameras, laser, etc. Despite achieving high-precision localisation performance, these sensors consume a huge amount of energy. Different from those solutions, the proposed SolarSLAM takes advantage of an energy harvesting solar cell as a …

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Constrained Contrastive Distribution Learning For Unsupervised Anomaly Detection And Localisation In Medical Images, Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh Oct 2021

Constrained Contrastive Distribution Learning For Unsupervised Anomaly Detection And Localisation In Medical Images, Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual labelling of abnormal samples and the subsequent issues involved in training with extremely class-imbalanced data. Further, UAD approaches can potentially detect and localise any type of lesions that deviate from the normal patterns. One significant challenge faced …

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Impact Of Digital Nudging On Information Security Behavior: An Experimental Study On Framing And Priming In Cybersecurity, Kavya Sharma, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Maggie X. Cheng Oct 2021

Impact Of Digital Nudging On Information Security Behavior: An Experimental Study On Framing And Priming In Cybersecurity, Kavya Sharma, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Maggie X. Cheng

Research Collection School Of Computing and Information Systems

Purpose: Phishing attacks are the most common cyber threats targeted at users. Digital nudging in the form of framing and priming may reduce user susceptibility to phishing. This research focuses on two types of digital nudging, framing and priming, and examines the impact of framing and priming on users' behavior (i.e. action) in a cybersecurity setting. It draws on prospect theory, instance-based learning theory and dual-process theory to generate the research hypotheses. Design/methodology/approach: A 3 × 2 experimental study was carried out to test the hypotheses. The experiment consisted of three levels for framing (i.e. no framing, negative framing and …

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From Continuity To Editability: Inverting Gans With Consecutive Images, Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, Shengfeng He Oct 2021

From Continuity To Editability: Inverting Gans With Consecutive Images, Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of them clearly cannot realize real image editing. In this paper, we resolve this paradox by introducing consecutive images (e.g., video frames or the same person with different poses) into the inversion process. The rationale behind our solution is that the continuity of consecutive images leads to inherent editable directions. This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted codes is semantically …

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Differentiated Learning For Multi-Modal Domain Adaptation, Jianming Lv, Kaijie Liu, Shengfeng He Oct 2021

Differentiated Learning For Multi-Modal Domain Adaptation, Jianming Lv, Kaijie Liu, Shengfeng He

Research Collection School Of Computing and Information Systems

Directly deploying a trained multi-modal classifier to a new environment usually leads to poor performance due to the well-known domain shift problem. Existing multi-modal domain adaptation methods treated each modality equally and optimize the sub-models of different modalities synchronously. However, as observed in this paper, the degrees of domain shift in different modalities are usually diverse. We propose a novel Differentiated Learning framework to make use of the diversity between multiple modalities for more effective domain adaptation. Specifically, we model the classifiers of different modalities as a group of teacher/student sub-models, and a novel Prototype based Reliability Measurement is presented …

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From Contexts To Locality: Ultra-High Resolution Image Segmentation Via Locality-Aware Contextual Correlation, Qi Li, Weixiang Yang, Wenxi Liu, Yuanlong Yu, Shengfeng He Oct 2021

From Contexts To Locality: Ultra-High Resolution Image Segmentation Via Locality-Aware Contextual Correlation, Qi Li, Weixiang Yang, Wenxi Liu, Yuanlong Yu, Shengfeng He

Research Collection School Of Computing and Information Systems

Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultrahigh resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware contextual correlation based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present a contextual semantics refinement …

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Learning To Adversarially Blur Visual Object Tracking, Qing Guo, Ziyi Cheng, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yang Liu, Jianjun Zhao Oct 2021

Learning To Adversarially Blur Visual Object Tracking, Qing Guo, Ziyi Cheng, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Motion blur caused by the moving of the object or camera during the exposure can be a key challenge for visual object tracking, affecting tracking accuracy significantly. In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i.e., adversarial blur attack (ABA). Our main objective is to online transfer input frames to their natural motion-blurred counterparts while misleading the state-of-the-art trackers during the tracking process. To this end, we first design the motion blur synthesizing method for visual tracking based on the generation principle of motion blur, considering the motion information and …

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Using Role Play To Develop An Empathetic Mindset In Executive Education, Siu Loon Hoe, Tamsin Greulich-Smith Oct 2021

Using Role Play To Develop An Empathetic Mindset In Executive Education, Siu Loon Hoe, Tamsin Greulich-Smith

Research Collection School Of Computing and Information Systems

The purpose of this article is to discuss the importance of a role play activity as part of an experiential instructional strategy to develop an empathetic mindset among professionals, managers, and executives (PMEs) attending an executive education program in change management. This article provides an approach and process for management educators and facilitators of executive education programs to introduce and teach role play for the busy executives to learn about empathy. Role play is a useful teaching method that helps adult learners understand the importance of seeing things from another person’s point of view especially within a short period of …

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Direct Differentiable Augmentation Search, Aoming Liu, Zehao Huang, Zhiwu Huang, Huang, Naiyan Wang Oct 2021

Direct Differentiable Augmentation Search, Aoming Liu, Zehao Huang, Zhiwu Huang, Huang, Naiyan Wang

Research Collection School Of Computing and Information Systems

Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets. Consequently, a recent trend is to adopt AutoML technique to learn proper augmentation policy without extensive hand-crafted tuning. In this paper, we propose an efficient differentiable search algorithm called Direct Differentiable Augmentation Search (DDAS). It exploits meta-learning with one-step gradient update and continuous relaxation to the expected training loss for efficient search. Our DDAS can achieve efficient augmentation search without relying on approximations such as Gumbel-Softmax or second order gradient approximation. To further reduce …

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Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong Oct 2021

Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multimodal memes to improve the hateful content classification and explainability. We conduct extensive experiments on two publicly available …

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Aessa Young Professionals Forum Webinar “Technologies And Skills That Will Gearup The Aerospace Industry Post Pandemic” - A Global Perspective With An Emphasis On South Africa October 2021, Linda Vee Weiland Oct 2021

Aessa Young Professionals Forum Webinar “Technologies And Skills That Will Gearup The Aerospace Industry Post Pandemic” - A Global Perspective With An Emphasis On South Africa October 2021, Linda Vee Weiland

Publications

A webinar presentation for AeSSA Young Professionals.

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(2021 Revision) Chapter 2: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana Oct 2021

(2021 Revision) Chapter 2: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

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(2021 Revision) Chapter 1: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana Oct 2021

(2021 Revision) Chapter 1: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

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(2021 Revision) Chapter 3: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana Oct 2021

(2021 Revision) Chapter 3: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

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(2021 Revision) Chapter 5: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana Oct 2021

(2021 Revision) Chapter 5: Essential Aspects Of Physical Design And Implementation Of Relational Databases, Tatiana Malyuta, Ashwin Satyanarayana

Open Educational Resources

No abstract provided.

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Internet Of Things Software And Hardware Architectures And Their Impacts On Forensic Investigations: Current Approaches And Challenges, Abel Alex Boozer, Arun John, Tathagata Mukherjee Sep 2021

Internet Of Things Software And Hardware Architectures And Their Impacts On Forensic Investigations: Current Approaches And Challenges, Abel Alex Boozer, Arun John, Tathagata Mukherjee

Journal of Digital Forensics, Security and Law

The never-before-seen proliferation of interconnected low-power computing devices, patently dubbed the Internet of Things (IoT), is revolutionizing how people, organizations, and malicious actors interact with one another and the Internet. Many of these devices collect data in different forms, be it audio, location data, or user commands. In civil or criminal nature investigations, the data collected can act as evidence for the prosecution or the defense. This data can also be used as a component of cybersecurity efforts. When data is extracted from these devices, investigators are expected to do so using proven methods. Still, unfortunately, given the heterogeneity in …

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The Survey On Cross-Border Collection Of Digital Evidence By Representatives From Polish Prosecutors’ Offices And Judicial Authorities, Paweł Olber Dr Sep 2021

The Survey On Cross-Border Collection Of Digital Evidence By Representatives From Polish Prosecutors’ Offices And Judicial Authorities, Paweł Olber Dr

Journal of Digital Forensics, Security and Law

Dynamic development of IT technology poses new challenges related to the cross-border collection of electronic evidence from the cloud. Many times investigators need to secure data stored on foreign servers directly and then look for solutions on how to turn the data into a legitimate source of evidence. To study the situation and propose solutions, I conducted a survey among Polish representatives of public prosecutors' offices and courts. This paper presents information from digital evidence collection practices across multiple jurisdictions. I stated that representatives from the prosecution and the judiciary in Poland are aware of the issues associated with cross-border …

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Intelligent Optimization Algorithm-Based Path Planning For A Mobile Robot, Qisong Song, Shaobo Li, Jing Yang, Qiang Bai, Jianjun Hu, Xingxing Zhang, Ansi Zhang Sep 2021

Intelligent Optimization Algorithm-Based Path Planning For A Mobile Robot, Qisong Song, Shaobo Li, Jing Yang, Qiang Bai, Jianjun Hu, Xingxing Zhang, Ansi Zhang

Faculty Publications

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). …

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(Linked) Data Quality Assessment: An Ontological Approach, Aparna Nayak, Bojan Bozic, Luca Longo Sep 2021

(Linked) Data Quality Assessment: An Ontological Approach, Aparna Nayak, Bojan Bozic, Luca Longo

Conference papers

The effective functioning of data-intensive applications usually requires that the dataset should be of high quality. The quality depends on the task they will be used for. However, it is possible to identify task-independent data quality dimensions which are solely related to data themselves and can be extracted with the help of rule mining/pattern mining. In order to assess and improve data quality, we propose an ontological approach to report data quality violated triples. Our goal is to provide data stakeholders with a set of methods and techniques to guide them in assessing and improving data quality

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Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard Sep 2021

Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard

James Madison Undergraduate Research Journal (JMURJ)

The dissemination of deep fakes for nefarious purposes poses significant national security risks to the United States, requiring an urgent development of technologies to detect their use and strategies to mitigate their effects. Deep fakes are images and videos created by or with the assistance of AI algorithms in which a person’s likeness, actions, or words have been replaced by someone else’s to deceive an audience. Often created with the help of generative adversarial networks, deep fakes can be used to blackmail, harass, exploit, and intimidate individuals and businesses; in large-scale disinformation campaigns, they can incite political tensions around the …

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Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp Sep 2021

Computer-Aided Diagnosis Of Low Grade Endometrial Stromal Sarcoma (Lgess), Xinxin Yang, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an …

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Users’ Sentiment Analysis Toward National Digital Library Of India: A Quantitative Approach For Understanding User Perception, Ritu Sharma, Sarita Gulati, Amanpreet Kaur, Rupak Chakravarty Sep 2021

Users’ Sentiment Analysis Toward National Digital Library Of India: A Quantitative Approach For Understanding User Perception, Ritu Sharma, Sarita Gulati, Amanpreet Kaur, Rupak Chakravarty

Library Philosophy and Practice (e-journal)

Sentiment analysis is also known as opinion mining. Sentiment analysis is contextual mining of text which identifies and extracts subjective information in textual data. It is extremely used by business, educational organizations, and social media monitoring to gain the general outlook of the wide public regarding their product and policy. The current study looks for gaining insights into user reviews on the National Digital Library of India (NDLI) mobile app (android and iOS). For this purpose, sentiment analysis will be used. It yields an average of 3.64/5 ratings based on 11,861 reviews. The dataset includes a total of 4560 user …

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Tensor Pooling-Driven Instance Segmentation Framework For Baggage Threat Recognition, Taimur Hassan, Samet Akçay, Mohammed Bennamoun, Salman Khan, Naoufel Werghi Sep 2021

Tensor Pooling-Driven Instance Segmentation Framework For Baggage Threat Recognition, Taimur Hassan, Samet Akçay, Mohammed Bennamoun, Salman Khan, Naoufel Werghi

Computer Vision Faculty Publications

Automated systems designed for screening contraband items from the X-ray imagery are still facing difficulties with high clutter, concealment, and extreme occlusion. In this paper, we addressed this challenge using a novel multi-scale contour instance segmentation framework that effectively identifies the cluttered contraband data within the baggage X-ray scans. Unlike standard models that employ region-based or keypoint-based techniques to generate multiple boxes around objects, we propose to derive proposals according to the hierarchy of the regions defined by the contours. The proposed framework is rigorously validated on three public datasets, dubbed GDXray, SIXray, and OPIXray, where it outperforms the state-of-the-art …

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Building Substrate For National Strategy Of New Infrastructure Construction—Practice And Thought Of Information Superbahn Testbed, Xiaohong Wang, Sa Wang, Hongwei Tang, Xiaohui Peng Sep 2021

Building Substrate For National Strategy Of New Infrastructure Construction—Practice And Thought Of Information Superbahn Testbed, Xiaohong Wang, Sa Wang, Hongwei Tang, Xiaohui Peng

Bulletin of Chinese Academy of Sciences (Chinese Version)

This study first introduces the background and status quo of the national strategy of New Infrastructure Construction. Next, the study systematically reviews the development and impact of information infrastructure testbed in USA, as well as the new challenge for information infrastructure in the intelligent era of the ternary integration of man-machine-things. Then, we propose the Information Superbahn technology innovation program and the OneComputer project, which is a large-scale testbed for the next generation information infrastructure. Furthermore, we present the latest progress and some thoughts about methodology of this large project. Finally, this study looks into the project's future development under …

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An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm For First-Order And Zeroth-Order Optimization, Xiyuan Wei, Bin Gu, Heng Huang Sep 2021

An Accelerated Variance-Reduced Conditional Gradient Sliding Algorithm For First-Order And Zeroth-Order Optimization, Xiyuan Wei, Bin Gu, Heng Huang

Machine Learning Faculty Publications

The conditional gradient algorithm (also known as the Frank-Wolfe algorithm) has recently regained popularity in the machine learning community due to its projection-free property to solve constrained problems. Although many variants of the conditional gradient algorithm have been proposed to improve performance, they depend on first-order information (gradient) to optimize. Naturally, these algorithms are unable to function properly in the field of increasingly popular zeroth-order optimization, where only zeroth-order information (function value) is available. To fill in this gap, we propose a novel Accelerated variance-Reduced Conditional gradient Sliding (ARCS) algorithm for finite-sum problems, which can use either first-order or zeroth-order …

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Ensemble Learning Using Error Correcting Output Codes: New Classification Error Bounds, Hieu Nguyen, Mohammed Sarosh Khan, Nicholas Kaegi, Shen-Shyang Ho, Jonathan Moore, Logan Borys, Lucas Lavalva Sep 2021

Ensemble Learning Using Error Correcting Output Codes: New Classification Error Bounds, Hieu Nguyen, Mohammed Sarosh Khan, Nicholas Kaegi, Shen-Shyang Ho, Jonathan Moore, Logan Borys, Lucas Lavalva

College of Science & Mathematics Departmental Research

New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectiveness of the ECOC approach. Bounds are derived for two different models: the first under the assumption that all base classifiers are independent and the second under the assumption that all base classifiers are mutually correlated up to first-order. Moreover, we perform ECOC classification on six datasets and compare their error rates with our bounds to experimentally validate our work and show the effect of correlation on …

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