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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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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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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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Data-Driven Retail Decision-Making Using Spatial Partitioning And Delineation Of Communities, Ming Hui Tan, Kar Way Tan Jul 2022

Data-Driven Retail Decision-Making Using Spatial Partitioning And Delineation Of Communities, Ming Hui Tan, Kar Way Tan

Research Collection School Of Computing and Information Systems

Urbanisation is resulting in rapid growth in road networks within cities. The evolution of road networks can be indicative of a city's economic growth and it is a field of research gaining prominence in recent years. This paper proposes a framework for spatial partition of large scale road networks that produces appropriately sized geospatial units in order to identify the type of community they serve. To this end, we have developed a three-stage procedure which first partitions the road network using Louvain method, followed by outlining the boundary of each partition using Uber H3 grids before classifying each partition using …

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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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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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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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Threshold Multi-Keyword Search For Cloud-Based Group Data Sharing, Yinbin Miao, Robert H. Deng, Kim-Kwang Raymond Choo, Ximeng Liu, Hongwei Li Jul 2022

Threshold Multi-Keyword Search For Cloud-Based Group Data Sharing, Yinbin Miao, Robert H. Deng, Kim-Kwang Raymond Choo, Ximeng Liu, Hongwei Li

Research Collection School Of Computing and Information Systems

Searchable Encryption (SE) is a popular cryptographic primitive for building ciphertexts retrieval systems with far-reaching applications. However, existing SE schemes generally do not support threshold access control (i.e., data users must collaboratively issue search and decryption operations over encrypted cloud data) in a group-oriented cloud data sharing setting, which is increasingly receiving much attention in the research community. Thus, in this article, we first propose a Threshold Multi-keyword Search (TMS) scheme for cloud-based group data sharing (referred to as basic TMS scheme) by utilizing Shamir’s secret sharing technique, to achieve threshold multi-keyword search, threshold decryption, and short record ciphertext size. …

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Lead1.0: A Large-Scale Annotated Dataset For Energy Anomaly Detection In Commercial Buildings, Manoj Gulati, Pandarasamy Arjunan Jul 2022

Lead1.0: A Large-Scale Annotated Dataset For Energy Anomaly Detection In Commercial Buildings, Manoj Gulati, Pandarasamy Arjunan

Research Collection School Of Computing and Information Systems

Modern buildings are densely equipped with smart energy meters, which periodically generate a massive amount of time-series data yielding a few million data points every day. This data can be leveraged to discover the underlying load and infer their energy consumption patterns, inter-dependencies on environmental factors, and the building’s operational properties. Furthermore, it allows us to simultaneously identify anomalies present in the electricity consumption profiles, which is a big step towards saving energy and achieving global sustainability. However, to date, the lack of large-scale annotated energy consumption datasets hinders the ongoing research in anomaly detection. We contribute to this effort …

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Q2r: A Query-To-Resolution System For Natural-Language Queries, Shiau Hong Lim, Laura Wynter Jul 2022

Q2r: A Query-To-Resolution System For Natural-Language Queries, Shiau Hong Lim, Laura Wynter

Research Collection School Of Computing and Information Systems

We present a system for document retrieval that combines direct classification with standard content-based retrieval approaches to significantly improve the relevance of the retrieved documents. Our system exploits the availability of an imperfect but sizable amount of labeled data from past queries. For domains such as technical support, the proposed approach enhances the system’s ability to retrieve documents that are otherwise ranked very low based on content alone. The system is easy to implement and can make use of existing text ranking methods, augmenting them through the novel Q2R orchestration framework. Q2R has been extensively tested and is in use …

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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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Neural Network Based Scene Text Recognition [Us Patent Us 2022/0237403 A1], Pan Zhou, Peng Tang, Ran Xu, Steven Chu Hong Hoi Jul 2022

Neural Network Based Scene Text Recognition [Us Patent Us 2022/0237403 A1], Pan Zhou, Peng Tang, Ran Xu, Steven Chu Hong Hoi

Research Collection School Of Computing and Information Systems

A system uses a neural network based model to perform scene text recognition. The system achieves high accuracy of prediction of text from scenes based on a neural network architecture that uses double attention mechanism. The neural network based model includes a convolutional neural network component that outputs a set of visual features and an attention extractor neural network component that determines attention scores based on the visual features. The visual features and the attention scores are combined to generate mixed features that are provided as input to a character recognizer component that determines a second attention score and recognizes …

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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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3pc: Three Point Compressors For Communication-Efficient Distributed Training And A Better Theory For Lazy Aggregation, Peter Richtarik, Igor Sokolov, Ilyas Fatkhullin, Elnur Gasanov, Zhize Li, Eduard Gorbunov Jul 2022

3pc: Three Point Compressors For Communication-Efficient Distributed Training And A Better Theory For Lazy Aggregation, Peter Richtarik, Igor Sokolov, Ilyas Fatkhullin, Elnur Gasanov, Zhize Li, Eduard Gorbunov

Research Collection School Of Computing and Information Systems

We propose and study a new class of gradient communication mechanisms for communication-efficient training -- three point compressors (3PC) -- as well as efficient distributed nonconvex optimization algorithms that can take advantage of them. Unlike most established approaches, which rely on a static compressor choice (e.g., Top-$K$), our class allows the compressors to {\em evolve} throughout the training process, with the aim of improving the theoretical communication complexity and practical efficiency of the underlying methods. We show that our general approach can recover the recently proposed state-of-the-art error feedback mechanism EF21 (Richt\'arik et al., 2021) and its theoretical properties as …

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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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Robustness And Personalization In Federated Learning: A Unified Approach Via Regularization, Achintya Kundu, Pengqian Yu, Laura Wynter, Shiau Hong Lim Jul 2022

Robustness And Personalization In Federated Learning: A Unified Approach Via Regularization, Achintya Kundu, Pengqian Yu, Laura Wynter, Shiau Hong Lim

Research Collection School Of Computing and Information Systems

We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of methods is to better accommodate the real-world characteristics found in federated training, such as the lack of IID data across parties, the need for robustness to outliers or stragglers, and the requirement to perform well on party-specific datasets. We achieve this through a problem formulation that allows the central server to employ robust ways of aggregating the local models while keeping the structure of local computation intact. Without making any statistical assumption on the …

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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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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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Hu-Fu: Efficient And Secure Spatial Queries Over Data Federation, Yongxin Tong, Xuchen Pan, Yuxiang Zeng, Yexuan Shi, Chunbo Xue, Zimu Zhou, Xiaofei Zhang, Lei Chen, Yi Xu, Ke Xu, Weifeng Lv Jun 2022

Hu-Fu: Efficient And Secure Spatial Queries Over Data Federation, Yongxin Tong, Xuchen Pan, Yuxiang Zeng, Yexuan Shi, Chunbo Xue, Zimu Zhou, Xiaofei Zhang, Lei Chen, Yi Xu, Ke Xu, Weifeng Lv

Research Collection School Of Computing and Information Systems

Data isolation has become an obstacle to scale up query processing over big data, since sharing raw data among data owners is often prohibitive due to security concerns. A promising solution is to perform secure queries over a federation of multiple data owners leveraging secure multi-party computation (SMC) techniques, as evidenced by recent federation work over relational data. However, existing solutions are highly inefficient on spatial queries due to excessive secure distance operations for query processing and their usage of general-purpose SMC libraries for secure operation implementation. In this paper, we propose Hu-Fu, the first system for efficient and secure …

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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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Decomposing Generation Networks With Structure Prediction For Recipe Generation, Hao Wang, Guosheng Lin, Steven C. H. Hoi, Chunyan Miao Jun 2022

Decomposing Generation Networks With Structure Prediction For Recipe Generation, Hao Wang, Guosheng Lin, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

Recipe generation from food images and ingredients is a challenging task, which requires the interpretation of the information from another modality. Different from the image captioning task, where the captions usually have one sentence, cooking instructions contain multiple sentences and have obvious structures. To help the model capture the recipe structure and avoid missing some cooking details, we propose a novel framework: Decomposing Generation Networks (DGN) with structure prediction, to get more structured and complete recipe generation outputs. Specifically, we split each cooking instruction into several phases, and assign different sub-generators to each phase. Our approach includes two novel ideas: …

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Multi-View Scheduling Of Onboard Live Video Analytics To Minimize Frame Processing Latency, Shengzhong Liu, Tianshi Wang, Hongpeng Guo, Xinzhe Fu, Philip David, Maggie Wigness, Archan Misra, Tarek Abdelzaher Jun 2022

Multi-View Scheduling Of Onboard Live Video Analytics To Minimize Frame Processing Latency, Shengzhong Liu, Tianshi Wang, Hongpeng Guo, Xinzhe Fu, Philip David, Maggie Wigness, Archan Misra, Tarek Abdelzaher

Research Collection School Of Computing and Information Systems

This paper presents a real-time multi-view scheduling framework for DNN-based live video analytics at the edge to minimize frame processing latency. The work is motivated by applications where a higher frame rate is important, not to miss actions of interest. Examples include defense, border security, and intruder detection applications where sensors (in this paper, cameras) are deployed to monitor key roads, chokepoints, or passageways to identify events of interest (and intervene in real-time). Supporting a higher frame rate entails lowering frame processing latency. We assume that multiple cameras are deployed with partially overlapping views. Each camera has access to limited …

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Challenges For Inclusion In Software Engineering: The Case Of The Emerging Papua New Guinean Society, Raula Kula, Christoph Treude, Hideaki Hata, Sebastian Baltes, Igor Steinmacher, Marco Gerosa, Winifred Kula Amini Jun 2022

Challenges For Inclusion In Software Engineering: The Case Of The Emerging Papua New Guinean Society, Raula Kula, Christoph Treude, Hideaki Hata, Sebastian Baltes, Igor Steinmacher, Marco Gerosa, Winifred Kula Amini

Research Collection School Of Computing and Information Systems

Software plays a central role in modern societies, with its high economic value and potential for advancing societal change. In this paper, we characterise challenges and opportunities for a country progressing towards entering the global software industry, focusing on Papua New Guinea (PNG). By hosting a Software Engineering workshop, we conducted a qualitative study by recording talks (n=3), employing a questionnaire (n=52), and administering an in-depth focus group session with local actors (n=5). Based on a thematic analysis, we identified challenges as barriers and opportunities for the PNG software engineering community. We also discuss the state of practices and how …

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A Practical Comparison Of Quantum And Classical Leaderless Consensus, Paul Robert Griffin, Dimple Mevada Jun 2022

A Practical Comparison Of Quantum And Classical Leaderless Consensus, Paul Robert Griffin, Dimple Mevada

Research Collection School Of Computing and Information Systems

Quantum computing is coming of age and being explored in many business areas for either solving difficult problems or improving business processes. Distributed ledger technology (DLT) is now embedded in many businesses and continues to mature. Consensus, at the heart of DLTs, has practical scaling issues and, as we move into needing bigger datasets, bigger networks and more security, the problem is ever increasing. Consensus agreement is a non-deterministic problem which is a good match to quantum computers due to the probabilistic nature of quantum phenomena. In this paper, we show that quantum nodes entangled in a variety of network …

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A Survey On Visual Analysis Of Massive Open Online Courses (Mooc) Data [大型开放式在线课程(Mooc)数据可视分析综述], Zhiguang Zhou, Saqing Wang, Yuhua Liu, Yong Wang, Yigang Wang Jun 2022

A Survey On Visual Analysis Of Massive Open Online Courses (Mooc) Data [大型开放式在线课程(Mooc)数据可视分析综述], Zhiguang Zhou, Saqing Wang, Yuhua Liu, Yong Wang, Yigang Wang

Research Collection School Of Computing and Information Systems

Massive open online courses(MOOC)are free online courses that provide educational resources to users.In this paper,related works are summarized from the following perspectives:data types of MOOC,MOOC data visualization,and visual analysis applications of MOOC data.First,MOOC data is categorized into four types,including text data,video data,topological data,spatio-temporal data,and their concepts and applications are introduced.Then,the common visualization methods of different data types are discussed.Furthermore,the major applications of visual analytics approaches in MOOC data analysis are summarized,including online course learning,online tests and exams,online forum interaction and so on.Finally,the challenges and future work on visual analytics of MOOC data are discussed and concluded.

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Class Re-Activation Maps For Weakly-Supervised Semantic Segmentation, Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, Qianru Sun Jun 2022

Class Re-Activation Maps For Weakly-Supervised Semantic Segmentation, Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

Extracting class activation maps (CAM) is arguably the most standard step of generating pseudo masks for weakly supervised semantic segmentation (WSSS). Yet, we find that the crux of the unsatisfactory pseudo masks is the binary cross-entropy loss (BCE) widely used in CAM. Specifically, due to the sum-over-class pooling nature of BCE, each pixel in CAM may be responsive to multiple classes co-occurring in the same receptive field. To this end, we introduce an embarrassingly simple yet surprisingly effective method: Reactivating the converged CAM with BCE by using softmax crossentropy loss (SCE), dubbed ReCAM. Given an image, we use CAM to …

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