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Articles 2731 - 2760 of 9024

Full-Text Articles in Computer Sciences

Github Discussions: An Exploratory Study Of Early Adoption, Hideaki Hata, Nicole Novielli, Sebastian Baltes, Raula Kula, Christoph Treude Jan 2022

Github Discussions: An Exploratory Study Of Early Adoption, Hideaki Hata, Nicole Novielli, Sebastian Baltes, Raula Kula, Christoph Treude

Research Collection School Of Computing and Information Systems

Discussions is a new feature of GitHub for asking questions or discussing topics outside of specific Issues or Pull Requests. Before being available to all projects in December 2020, it had been tested on selected open source software projects. To understand how developers use this novel feature, how they perceive it, and how it impacts the development processes, we conducted a mixed-methods study based on early adopters of GitHub discussions from January until July 2020. We found that: (1) errors, unexpected behavior, and code reviews are prevalent discussion categories; (2) there is a positive relationship between project member involvement and …

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Github Repositories With Links To Academic Papers: Public Access, Traceability, And Evolution, Supatsara Wattanakriengkrai, Bodin Chinthanet, Hideaki Hata, Raula Kula, Christoph Treude, Jin Guo, Kenichi Matsumoto Jan 2022

Github Repositories With Links To Academic Papers: Public Access, Traceability, And Evolution, Supatsara Wattanakriengkrai, Bodin Chinthanet, Hideaki Hata, Raula Kula, Christoph Treude, Jin Guo, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

Traceability between published scientific breakthroughs and their implementation is essential, especially in the case of open-source scientific software which implements bleeding-edge science in its code. However, aligning the link between GitHub repositories and academic papers can prove difficult, and the current practice of establishing and maintaining such links remains unknown. This paper investigates the role of academic paper references contained in these repositories. We conduct a large-scale study of 20 thousand GitHub repositories that make references to academic papers. We use a mixed-methods approach to identify public access, traceability and evolutionary aspects of the links. Although referencing a paper is …

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Novel Secure Outsourcing Of Modular Inversion For Arbitrary And Variable Modulus, Chengliang Tian, Jia Yu, Hanlin Zhang, Haiyang Xue, Cong Wang, Kui Ren Jan 2022

Novel Secure Outsourcing Of Modular Inversion For Arbitrary And Variable Modulus, Chengliang Tian, Jia Yu, Hanlin Zhang, Haiyang Xue, Cong Wang, Kui Ren

Research Collection School Of Computing and Information Systems

In cryptography and algorithmic number theory, modular inversion is viewed as one of the most common and time-consuming operations. It is hard to be directly accomplished on resource-constrained clients (e.g., mobile devices and IC cards) since modular inversion involves a great amount of operations on large numbers in practice. To address the above problem, this paper proposes a novel unimodular matrix transformation technique to realize secure outsourcing of modular inversion. This technique makes our algorithm achieve several amazing properties. First, to the best of our knowledge, it is the first secure outsourcing computation algorithm that supports arbitrary and variable modulus, …

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Kg4vis: A Knowledge Graph-Based Approach For Visualization Recommendation, Haotian Li, Yong Wang, Songheng Zhang, Yangqiu Song, Huamin. Qu Jan 2022

Kg4vis: A Knowledge Graph-Based Approach For Visualization Recommendation, Haotian Li, Yong Wang, Songheng Zhang, Yangqiu Song, Huamin. Qu

Research Collection School Of Computing and Information Systems

Visualization recommendation or automatic visualization generation can significantly lower the barriers for general users to rapidly create effective data visualizations, especially for those users without a background in data visualizations. However, existing rule-based approaches require tedious manual specifications of visualization rules by visualization experts. Other machine learning-based approaches often work like black-box and are difficult to understand why a specific visualization is recommended, limiting the wider adoption of these approaches. This paper fills the gap by presenting KG4Vis, a knowledge graph (KG)-based approach for visualization recommendation. It does not require manual specifications of visualization rules and can also guarantee good …

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M2lens: Visualizing And Explaining Multimodal Models For Sentiment Analysis, Xingbo Wang, Jianben He, Zhihua Jin, Muqiao Yang, Yong Wang, Huamin Qu Jan 2022

M2lens: Visualizing And Explaining Multimodal Models For Sentiment Analysis, Xingbo Wang, Jianben He, Zhihua Jin, Muqiao Yang, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. It has become a vibrant and important research topic in natural language processing. Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels. However, current multimodal models with strong performance are often deep-learning-based techniques and work like black boxes. It is not clear how models utilize multimodal information for sentiment predictions. Despite recent advances in techniques for enhancing the explainability of machine learning models, they often target unimodal scenarios (e.g., images, sentences), and …

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Partnering For Value Perfection And Business Sustainability In The Cloud Services Brokerage Market, Richard Shang, Robert John Kauffman Jan 2022

Partnering For Value Perfection And Business Sustainability In The Cloud Services Brokerage Market, Richard Shang, Robert John Kauffman

Research Collection School Of Computing and Information Systems

The cloud computing and services market has advanced in the past ten years. They now include most IT services from fundamental computing to cutting-edge AI capabilities. With the widespread adoption of cloud services, clients are facing the fact that they are utilizing cloud resources at a sub-optimal level. Cloud services brokers (CSBs) grew from the market to fill the needs for cloud resource management and risk mitigation. Based on analysis of the cloud market and the case of cloud services brokerage and related activities in North America, we offer theoretical analysis for how value creation works, its impacts on the …

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Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew Jan 2022

Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew

Research Collection School Of Computing and Information Systems

Rapid technological advances in recent years drastically transformed our world. Amidst modern technological inventions such as smart phones, smart watches and smart home devices, consumers of electronic digital devices experience greatly improved automation, productivity, and efficiency in conducting routine daily tasks, information searching, shopping as well as finding entertainment. In the last few years, the global smart speaker market has undergone significant growth. As technology continues to advance and smart speakers are equipped with innovative features, the adoption of smart speakers will increase and so will consumer expectations. This research paper presents an aspect-specific sentiment analysis of consumer reviews of …

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Steps Before Syntax: Helping Novice Programmers Solve Problems Using The Pcdit Framework, Oka Kurniawan, Cyrille Jegourel, Norman Tiong Seng Lee, Matthieu De Mari, Christopher M. Poskitt Jan 2022

Steps Before Syntax: Helping Novice Programmers Solve Problems Using The Pcdit Framework, Oka Kurniawan, Cyrille Jegourel, Norman Tiong Seng Lee, Matthieu De Mari, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

Novice programmers often struggle with problem solving due to the high cognitive loads they face. Furthermore, many introductory programming courses do not explicitly teach it, assuming that problem solving skills are acquired along the way. In this paper, we present 'PCDIT', a non-linear problem solving framework that provides scaffolding to guide novice programmers through the process of transforming a problem specification into an implemented and tested solution for an imperative programming language. A key distinction of PCDIT is its focus on developing concrete cases for the problem early without actually writing test code: students are instead encouraged to think about …

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Defining Smart Contract Defects On Ethereum, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen Jan 2022

Defining Smart Contract Defects On Ethereum, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen

Research Collection School Of Computing and Information Systems

Smart contracts are programs running on a blockchain. They are immutable to change, and hence can not be patched for bugs once deployed. Thus it is critical to ensure they are bug-free and well-designed before deployment. A Contract defect is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. The detection of contract defects is a method to avoid potential bugs and improve the design of existing code. Since smart contracts contain numerous distinctive features, such as the gas system. decentralized, it is important …

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Toward Video-Conferencing Tools For Hands-On Activities In Online Teaching, Audrey Labrie, Terrance Mok, Anthony Tang, Michelle Lui, Lora Oehlberg, Lev Poretski Jan 2022

Toward Video-Conferencing Tools For Hands-On Activities In Online Teaching, Audrey Labrie, Terrance Mok, Anthony Tang, Michelle Lui, Lora Oehlberg, Lev Poretski

Research Collection School Of Computing and Information Systems

Many instructors in computing and HCI disciplines use hands-on activities for teaching and training new skills. Beyond simply teaching hands-on skills like sketching and programming, instructors also use these activities so students can acquire tacit skills. Yet, current video-conferencing technologies may not effectively support hands-on activities in online teaching contexts. To develop an understanding of the inadequacies of current video-conferencing technologies for hands-on activities, we conducted 15 interviews with university-level instructors who had quickly pivoted their use of hands-on activities to an online context during the early part of the COVID-19 pandemic. Based on our analysis, we uncovered four pedagogical …

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A Quantum Interpretation Of Separating Conjunction For Local Reasoning Of Quantum Programs Based On Separation Logic, Xuan Bach Le, Shang-Wei Lin, Jun Sun, David Sanan Jan 2022

A Quantum Interpretation Of Separating Conjunction For Local Reasoning Of Quantum Programs Based On Separation Logic, Xuan Bach Le, Shang-Wei Lin, Jun Sun, David Sanan

Research Collection School Of Computing and Information Systems

It is well-known that quantum programs are not only complicated to design but also challenging to verify because the quantum states can have exponential size and require sophisticated mathematics to encode and manipulate. To tackle the state-space explosion problem for quantum reasoning, we propose a Hoare-style inference framework that supports local reasoning for quantum programs. By providing a quantum interpretation of the separating conjunction, we are able to infuse separation logic into our framework and apply local reasoning using a quantum frame rule that is similar to the classical frame rule. For evaluation, we apply our framework to verify various …

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Enjoy Your Observability: An Industrial Survey Of Microservice Tracing And Analysis, Bowen Li, Xin Peng, Qilin Xiang, Hanzhang Wang, Tao Xie, Jun Sun, Xuanzhe Liu Jan 2022

Enjoy Your Observability: An Industrial Survey Of Microservice Tracing And Analysis, Bowen Li, Xin Peng, Qilin Xiang, Hanzhang Wang, Tao Xie, Jun Sun, Xuanzhe Liu

Research Collection School Of Computing and Information Systems

Microservice systems are often deployed in complex cloud-based environments and may involve a large number of service instances being dynamically created and destroyed. It is thus essential to ensure observability to understand these microservice systems’ behaviors and troubleshoot their problems. As an important means to achieve the observability, distributed tracing and analysis is known to be challenging. While many companies have started implementing distributed tracing and analysis for microservice systems, it is not clear whether existing approaches fulfill the required observability. In this article, we present our industrial survey on microservice tracing and analysis through interviewing developers and operation engineers …

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Perceptions And Needs Of Artificial Intelligence In Health Care To Increase Adoption: Scoping Review, Han Shi Jocelyn Chew, Palakorn Achananuparp Jan 2022

Perceptions And Needs Of Artificial Intelligence In Health Care To Increase Adoption: Scoping Review, Han Shi Jocelyn Chew, Palakorn Achananuparp

Research Collection School Of Computing and Information Systems

Background: Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. Objective: This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. Methods: A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, …

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On Discovering Motifs And Frequent Patterns In Spatial Trajectories With Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Jiahao Zhang, Kai Wang Jan 2022

On Discovering Motifs And Frequent Patterns In Spatial Trajectories With Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Jiahao Zhang, Kai Wang

Research Collection School Of Computing and Information Systems

The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between two trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering similar subtrajectories within a single trajectory or across multiple trajectories. In this paper, we adopt DFD as the similarity measure, and study two representative trajectory analysis problems, namely, motif discovery and frequent pattern discovery. Due to the time complexity of DFD, these tasks are computationally challenging. We address that challenge with a suite of …

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Towards An Instant Structure-Property Prediction Quality Control Tool For Additive Manufactured Steel Using A Crystal Plasticity Trained Deep Learning Surrogate, Yuhui Tu, Zhongzhou Liu, Luiz Carneiro, Caitriona M. Ryan, Andrew C. Parnell, Sean B. Leen Jan 2022

Towards An Instant Structure-Property Prediction Quality Control Tool For Additive Manufactured Steel Using A Crystal Plasticity Trained Deep Learning Surrogate, Yuhui Tu, Zhongzhou Liu, Luiz Carneiro, Caitriona M. Ryan, Andrew C. Parnell, Sean B. Leen

Research Collection School Of Computing and Information Systems

The ability to conduct in-situ real-time process-structure-property checks has the potential to overcome process and material uncertainties, which are key obstacles to improved uptake of metal powder bed fusion in industry. Efforts are underway for live process monitoring such as thermal and image-based data gathering for every layer printed. Current crystal plasticity finite element (CPFE) modelling is capable of predicting the associated strength based on a microstructural image and material data but is computationally expensive. This work utilizes a large database of input–output samples from CPFE modelling to develop a trained deep neural network (DNN) model which instantly estimates the …

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Hierarchical Control Of Multi-Agent Reinforcement Learning Team In Real-Time Strategy (Rts) Games, Weigui Jair Zhou, Budhitama Subagdja, Ah-Hwee Tan, Darren Wee Sze Ong Dec 2021

Hierarchical Control Of Multi-Agent Reinforcement Learning Team In Real-Time Strategy (Rts) Games, Weigui Jair Zhou, Budhitama Subagdja, Ah-Hwee Tan, Darren Wee Sze Ong

Research Collection School Of Computing and Information Systems

Coordinated control of multi-agent teams is an important task in many real-time strategy (RTS) games. In most prior work, micromanagement is the commonly used strategy whereby individual agents operate independently and make their own combat decisions. On the other extreme, some employ a macromanagement strategy whereby all agents are controlled by a single decision model. In this paper, we propose a hierarchical command and control architecture, consisting of a single high-level and multiple low-level reinforcement learning agents operating in a dynamic environment. This hierarchical model enables the low-level unit agents to make individual decisions while taking commands from the high-level …

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The Power Of Renegotiation And Monitoring In Software Outsourcing: Substitutes Or Complements?, He Huang, Minhui Hu, Robert J. Kauffman, Hongyan Xu Dec 2021

The Power Of Renegotiation And Monitoring In Software Outsourcing: Substitutes Or Complements?, He Huang, Minhui Hu, Robert J. Kauffman, Hongyan Xu

Research Collection School Of Computing and Information Systems

Monitoring and contract renegotiation are two common solutions for addressing information asymmetry and uncertainty between a client and a vendor of software outsourcing services. Monitoring is mostly applied in time-and-materials contracts, as a basis for inspecting and reimbursing the vendor’s efforts in system development. Renegotiation, by contrast, is deployed in fixed-price and time-and-materials contracts to mitigate the loss of surplus from uncertainty after system development. We investigate the interaction between monitoring and renegotiation and examine the corresponding contract choice problem. We find that the client benefits from renegotiation based on two effects: an uncertainty-resolution effect and a post-development incentive effect, …

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A Theory-Driven Self-Labeling Refinement Method For Contrastive Representation Learning, Pan Zhou, Caiming Xiong, Xiao-Tong Yuan Dec 2021

A Theory-Driven Self-Labeling Refinement Method For Contrastive Representation Learning, Pan Zhou, Caiming Xiong, Xiao-Tong Yuan

Research Collection School Of Computing and Information Systems

For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives. Although intuitive, such a native label assignment strategy cannot reveal the underlying semantic similarity between a query and its positives and negatives, and impairs performance, since some negatives are semantically similar to the query or even share the same semantic class as the query. In this work, we first prove that for contrastive learning, inaccurate label assignment heavily impairs its generalization for semantic instance discrimination, while accurate labels benefit its generalization. Inspired by this theory, we propose a novel …

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Fine-Grained Generalization Analysis Of Inductive Matrix Completion, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft Dec 2021

Fine-Grained Generalization Analysis Of Inductive Matrix Completion, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Marius Kloft

Research Collection School Of Computing and Information Systems

In this paper, we bridge the gap between the state-of-the-art theoretical results for matrix completion with the nuclear norm and their equivalent in \textit{inductive matrix completion}: (1) In the distribution-free setting, we prove bounds improving the previously best scaling of \widetilde{O}(rd2) to \widetilde{O}(d3/2√r), where d is the dimension of the side information and rr is the rank. (2) We introduce the (smoothed) \textit{adjusted trace-norm minimization} strategy, an inductive analogue of the weighted trace norm, for which we show guarantees of the order \widetilde{O}(dr) under arbitrary sampling. In the inductive case, a similar rate was previously achieved only under uniform sampling …

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Strategic Behavior And Market Inefficiency In Blockchain-Based Auctions, Ping Fan Ke, Jianqing Chen, Zhiling Guo Dec 2021

Strategic Behavior And Market Inefficiency In Blockchain-Based Auctions, Ping Fan Ke, Jianqing Chen, Zhiling Guo

Research Collection School Of Computing and Information Systems

Blockchain-based auctions play a key role in decentralized finance, such as liquidation of collaterals in crypto-lending. In this research, we show that a Blockchain-based auction is subject to the threat to availability because of the characteristics of the Blockchain platform, which could lead to auction inefficiency or even market failure. Specifically, an adversary could occupy all of the transaction capacity of an auction by sending transactions with sufficiently high transaction fees, and then win the item in an auction with a nearly zero bid price as there are no competitors available. We discuss how to prevent this kind of strategic …

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Broadcast Authenticated Encryption With Keyword Search, Xueqiao Liu, Kai He, Guomin Yang, Willy Susilo, Joseph Tonien, Qiong Huang Dec 2021

Broadcast Authenticated Encryption With Keyword Search, Xueqiao Liu, Kai He, Guomin Yang, Willy Susilo, Joseph Tonien, Qiong Huang

Research Collection School Of Computing and Information Systems

The emergence of public-key encryption with keyword search (PEKS) has provided an elegant approach to enable keyword search over encrypted content. Due to its high computational complexity proportional to the number of intended receivers, the trivial way of deploying PEKS for data sharing with multiple receivers is impractical, which motivates the development of a new PEKS framework for broadcast mode. However, existing works suffer from either the vulnerability to keyword guessing attacks (KGA) or high computation and communication complexity. In this work, a new primitive for keyword search in broadcast mode, named broadcast authenticated encryption with keyword search (BAEKS), is …

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Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter Dec 2021

Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter

Research Collection School Of Computing and Information Systems

One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples. We show that in certain settings, the available data can be dramatically increased through a form of multi-task learning, by exploiting an invariance property in the tasks. We provide a theoretical performance bound for the gain in sample efficiency under this setting. This motivates a new approach to multi-task learning, which involves the design of an appropriate neural network architecture and a prioritized task-sampling strategy. We demonstrate empirically the effectiveness of the proposed approach on two real-world sequential resource allocation tasks where this …

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Deriving Invariant Checkers For Critical Infrastructure Using Axiomatic Design Principles, Cheah Huei Yoong, Venkata Reddy Palleti, Rajib Ranjan Maiti, Arlindo Silva, Christopher M. Poskitt Dec 2021

Deriving Invariant Checkers For Critical Infrastructure Using Axiomatic Design Principles, Cheah Huei Yoong, Venkata Reddy Palleti, Rajib Ranjan Maiti, Arlindo Silva, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) in critical infrastructure face serious threats of attack, motivating research into a wide variety of defence mechanisms such as those that monitor for violations of invariants, i.e. logical properties over sensor and actuator states that should always be true. Many approaches for identifying invariants attempt to do so automatically, typically using data logs, but these can miss valid system properties if relevant behaviours are not well-represented in the data. Furthermore, as the CPS is already built, resolving any design flaws or weak points identified through this process is costly. In this paper, we propose a systematic …

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Russian Logics And The Culture Of Impossible: Part 1. Recovering Intelligentsia Logics, Ksenia Tatarchenko, Anya Yermakova, Liesbeth De Mol Dec 2021

Russian Logics And The Culture Of Impossible: Part 1. Recovering Intelligentsia Logics, Ksenia Tatarchenko, Anya Yermakova, Liesbeth De Mol

Research Collection College of Integrative Studies

This article reinterprets algorithmic rationality by looking at the interaction between mathematical logic, mechanized reasoning, and, later, computing in the Russian Imperial and Soviet contexts to offer a history of the algorithm as a mathematical object bridging the inner and outer worlds, a humanistic vision that we, following logician Vladimir Uspensky, call the “culture of the impossible.” We unfold the deep roots of this vision as embodied in scientific intelligentsia. In Part I, we examine continuities between the turn-of-the-twentieth-century discussions of poznaniye—an epistemic orientation towards the process of knowledge acquisition—and the postwar rise of the Soviet school of mathematical logic. …

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Rmix: Learning Risk-Sensitive Policies For Cooperative Reinforcement Learning Agents, Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich Dec 2021

Rmix: Learning Risk-Sensitive Policies For Cooperative Reinforcement Learning Agents, Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

Research Collection School Of Computing and Information Systems

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaR …

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Video Snapshot: Single Image Motion Expansion Via Invertible Motion Embedding, Qianshu Zhu, Chu Han, Guoqiang Han, Tien-Tsin Wong, Shengfeng He Dec 2021

Video Snapshot: Single Image Motion Expansion Via Invertible Motion Embedding, Qianshu Zhu, Chu Han, Guoqiang Han, Tien-Tsin Wong, Shengfeng He

Research Collection School Of Computing and Information Systems

Unlike images, finding the desired video content in a large pool of videos is not easy due to the time cost of loading and watching. Most video streaming and sharing services provide the video preview function for a better browsing experience. In this paper, we aim to generate a video preview from a single image. To this end, we propose two cascaded networks, the motion embedding network and the motion expansion network. The motion embedding network aims to embed the spatio-temporal information into an embedded image, called video snapshot. On the other end, the motion expansion network is proposed to …

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Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang Dec 2021

Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to …

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Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang Dec 2021

Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang

Research Collection School Of Computing and Information Systems

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry …

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Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh Dec 2021

Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Do my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to individual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ …

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Data Fusion For Trust Evaluation, Zheng Yan, Qinghua Zheng, Laurence T. Yang, Robert H. Deng Dec 2021

Data Fusion For Trust Evaluation, Zheng Yan, Qinghua Zheng, Laurence T. Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Trust evaluation is a process to quantify trust by analyzing the data related to the factors that affect trust. It has been widely applied in many fields to facilitate decision making, system entity collaboration and security establishment. For example, in social networking, trust evaluation helps users make a social decision, reduce the risk of social interactions, and ensure the quality of a social networking environment. In digital communications, trust evaluation can be applied to detect malicious nodes, filter unwanted traffic and improve communication security. In e-commerce and cloud services, trust evaluation helps users selecting an appropriate product or service from …

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