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Articles 1681 - 1710 of 8567
Full-Text Articles in Entire DC Network
18 Million Links In Commit Messages: Purpose, Evolution, And Decay, Tao Xiao, Sebastian Baltes, Hideaki Hata, Christoph Treude, Raula Kula, Takashi Ishio, Kenichi Matsumoto
18 Million Links In Commit Messages: Purpose, Evolution, And Decay, Tao Xiao, Sebastian Baltes, Hideaki Hata, Christoph Treude, Raula Kula, Takashi Ishio, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
Commit messages contain diverse and valuable types of knowledge in all aspects of software maintenance and evolution. Links are an example of such knowledge. Previous work on “9.6 million links in source code comments” showed that links are prone to decay, become outdated, and lack bidirectional traceability. We conducted a large-scale study of 18,201,165 links from commits in 23,110 GitHub repositories to investigate whether they suffer the same fate. Results show that referencing external resources is prevalent and that the most frequent domains other than github.com are the external domains of Stack Overflow and Google Code. Similarly, links serve as …
Understanding The Role Of External Pull Requests In The Npm Ecosystem, Vittunyuta Maeprasart, Supatsara Wattanakriengkrai, Raula Gaikovina Kula, Christoph Treude, Kenichi Matsumoto
Understanding The Role Of External Pull Requests In The Npm Ecosystem, Vittunyuta Maeprasart, Supatsara Wattanakriengkrai, Raula Gaikovina Kula, Christoph Treude, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
The risk to using third-party libraries in a software application is that much needed maintenance is solely carried out by library maintainers. These libraries may rely on a core team of maintainers (who might be a single maintainer that is unpaid and overworked) to serve a massive client user-base. On the other hand, being open source has the benefit of receiving contributions (in the form of External PRs) to help fix bugs and add new features. In this paper, we investigate the role by which External PRs (contributions from outside the core team of maintainers) contribute to a library. Through …
Recognizing Hand Gestures Using Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa
Recognizing Hand Gestures Using Solar Cells, Dong Ma, Guohao Lan, Mahbub Hassan, Wen Hu, B. Mushfika Upama, Ashraf Uddin, Youseef, Moustafa
Research Collection School Of Computing and Information Systems
We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its discernible signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for …
Nodemedic: End-To-End Analysis Of Node.Js Vulnerabilities With Provenance Graphs, Darion Cassel, Wai Tuck Wong, Limin Jia
Nodemedic: End-To-End Analysis Of Node.Js Vulnerabilities With Provenance Graphs, Darion Cassel, Wai Tuck Wong, Limin Jia
Research Collection School Of Computing and Information Systems
Packages in the Node.js ecosystem often suffer from serious vulnerabilities such as arbitrary command injection and code execution. Existing taint analysis tools fall short in providing an end-to-end infrastructure for automatically detecting and triaging these vulnerabilities.We develop NodeMedic, an end-to-end analysis infrastructure that automates test driver creation, performs precise yet scalable dynamic taint propagation via algorithmically tuned propagation policies, and exposes taint provenance information as a provenance graph. Using provenance graphs we develop two post-detection analyses: automated constraint-based exploit synthesis to confirm vulnerabilities; Attack-defense-tree-based rating of flow exploitability.We demonstrate the effectiveness of NodeMedic through a large-scale evaluation of 10,000 Node.js …
Is Web3 Better Than Web2 For Investors? Evidence From Domain Name Auctions, Ping Fan Ke, Yi Meng Lau, Daniel Varghese Hanley
Is Web3 Better Than Web2 For Investors? Evidence From Domain Name Auctions, Ping Fan Ke, Yi Meng Lau, Daniel Varghese Hanley
Research Collection School Of Computing and Information Systems
Blockchain-based assets are commonly believed to attract new investors. To investigate this claim, we compared investor preferences for Web2 and Web3 domain name auctions by analyzing daily auction patterns in Namecheap and OpenSea. Our results indicate that Web3 platforms may attract extreme investors with low or high values as a niche market. We found a significantly higher number of bids per auction, higher average bid prices, and greater price spreads on OpenSea, but a significantly lower number of unique bidders per auction. Our findings highlight the importance of considering the auction platform's characteristics and asset context when evaluating bid patterns …
Learning To Ask Clarification Questions With Spatial Reasoning, Yang Deng, Shuaiyi Li, Wai Lam
Learning To Ask Clarification Questions With Spatial Reasoning, Yang Deng, Shuaiyi Li, Wai Lam
Research Collection School Of Computing and Information Systems
Asking clarifying questions has become a key element of various conversational systems, allowing for an effective resolution of ambiguity and uncertainty through natural language questions. Despite the extensive applications of spatial information grounded dialogues, it remains an understudied area on learning to ask clarification questions with the capability of spatial reasoning. In this work, we propose a novel method, named SpatialCQ, for this problem. Specifically, we first align the representation space between textual and spatial information by encoding spatial states with textual descriptions. Then a multi-relational graph is constructed to capture the spatial relations and enable spatial reasoning with relational …
Binalign: Alignment Padding Based Compiler Provenance Recovery, Maliha Ismail, Yan Lin, Donggyun Han, Debin Gao
Binalign: Alignment Padding Based Compiler Provenance Recovery, Maliha Ismail, Yan Lin, Donggyun Han, Debin Gao
Research Collection School Of Computing and Information Systems
Compiler provenance is significant in investigating the source-level indicators of binary code, like development-environment, source compiler, and optimization settings. Not only does compiler provenance analysis have important security applications in malware and vulnerability analysis, but it is also very challenging to extract useful artifacts from binary when high-level language constructs are missing. Previous works applied machine-learning techniques to predict the source compiler of binaries. However, most of the work is done on the binaries compiled on Linux operating system. We highlight the importance and need to explore Windows compilers and the complicated binaries compiled on the latest versions of these …
Transfer-Once-For-All: Ai Model Optimization For Edge, Achintya Kundu, Laura Wynter, Rhui Dih Lee, Luis Angel Bathen
Transfer-Once-For-All: Ai Model Optimization For Edge, Achintya Kundu, Laura Wynter, Rhui Dih Lee, Luis Angel Bathen
Research Collection School Of Computing and Information Systems
Weight-sharing neural architecture search aims to optimize a configurable neural network model (supernet) for a variety of deployment scenarios across many devices with different resource constraints. Existing approaches use evolutionary search to extract models of different sizes from a supernet trained on a very large data set, and then fine-tune the extracted models on the typically small, real-world data set of interest. The computational cost of training thus grows linearly with the number of different model deployment scenarios. Hence, we propose Transfer-Once-For-All (TOFA) for supernet-style training on small data sets with constant computational training cost over any number of edge …
Context-Aware Neural Fault Localization, Zhuo Zhang, Xiaoguang Mao, Meng Yan, Xin Xia, David Lo, David Lo
Context-Aware Neural Fault Localization, Zhuo Zhang, Xiaoguang Mao, Meng Yan, Xin Xia, David Lo, David Lo
Research Collection School Of Computing and Information Systems
Numerous fault localization techniques identify suspicious statements potentially responsible for program failures by discovering the statistical correlation between test results (i.e., failing or passing) and the executions of the different statements of a program (i.e., covered or not covered). They rarely incorporate a failure context into their suspiciousness evaluation despite the fact that a failure context showing how a failure is produced is useful for analyzing and locating faults. Since a failure context usually contains the transitive relationships among the statements of causing a failure, its relationship complexity becomes one major obstacle for the context incorporation in suspiciousness evaluation of …
Generative Ai And Chatgpt Impact On Technostress Of Teachers, Xuenan Huo, Keng Siau
Generative Ai And Chatgpt Impact On Technostress Of Teachers, Xuenan Huo, Keng Siau
Research Collection School Of Computing and Information Systems
Generative AI, such as ChatGPT, is a disruptive technology with significant impacts on education. While it has the potential to transform the delivery and accessibility of education, it can also undermine educational effectiveness by facilitating academic honesty and creating technostress for educators. This study aims to (i) evaluate the extent to which Generative AI, such as ChatGPT, brings technostress to teachers and (ii) how Generative AI changes teachers' professional identities and the technostress that results from such changes. We hypothesize that techno-eustress and techno-distress are determined by three types of self-discrepancies concerning professional identity construction: actual-ought, actual-ideal, and ought-ideal discrepancies. …
Chatgpt, Can You Generate Solutions For My Coding Exercises? An Evaluation On Its Effectiveness In An Undergraduate Java Programming Course, Eng Lieh Ouh, Benjamin Gan, Kyong Jin Shim, Swavek Wlodkowski
Chatgpt, Can You Generate Solutions For My Coding Exercises? An Evaluation On Its Effectiveness In An Undergraduate Java Programming Course, Eng Lieh Ouh, Benjamin Gan, Kyong Jin Shim, Swavek Wlodkowski
Research Collection School Of Computing and Information Systems
In this study, we assess the efficacy of employing the ChatGPT language model to generate solutions for coding exercises within an undergraduate Java programming course. ChatGPT, a large-scale, deep learning-driven natural language processing model, is capable of producing programming code based on textual input. Our evaluation involves analyzing ChatGPT-generated solutions for 80 diverse programming exercises and comparing them to the correct solutions. Our findings indicate that ChatGPT accurately generates Java programming solutions, which are characterized by high readability and well-structured organization. Additionally, the model can produce alternative, memory-efficient solutions. However, as a natural language processing model, ChatGPT struggles with coding …
Semantic-Based Neural Network Repair, Richard Schumi, Jun Sun
Semantic-Based Neural Network Repair, Richard Schumi, Jun Sun
Research Collection School Of Computing and Information Systems
Recently, neural networks have spread into numerous fields including many safety-critical systems. Neural networks are built (and trained) by programming in frameworks such as TensorFlow and PyTorch. Developers apply a rich set of pre-defined layers to manually program neural networks or to automatically generate them (e.g., through AutoML). Composing neural networks with different layers is error-prone due to the non-trivial constraints that must be satisfied in order to use those layers. In this work, we propose an approach to automatically repair erroneous neural networks. The challenge is in identifying a minimal modification to the network so that it becomes valid. …
Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan
Seed Selection For Testing Deep Neural Networks, Yuhan Zhi, Xiaofei Xie, Chao Shen, Jun Sun, Xiaoyu Zhang, Xiaohong Guan
Research Collection School Of Computing and Information Systems
Deep learning (DL) has been applied in many applications. Meanwhile, the quality of DL systems is becoming a big concern. To evaluate the quality of DL systems, a number of DL testing techniques have been proposed. To generate test cases, a set of initial seed inputs are required. Existing testing techniques usually construct seed corpus by randomly selecting inputs from training or test dataset. Till now, there is no study on how initial seed inputs affect the performance of DL testing and how to construct an optimal one. To fill this gap, we conduct the first systematic study to evaluate …
Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu
Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu
Research Collection School Of Computing and Information Systems
Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the distribution of test data differs from the distribution of training data. In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. The Do-GOOD benchmark defines the underlying mechanisms that result in different distribution shifts and contains 9 OOD datasets covering 3 VDU related …
Silent Compiler Bug De-Duplication Via Three-Dimensional Analysis, Chen Yang, Junjie Chen, Xingyu Fan, Jiajun Jiang, Jun Sun
Silent Compiler Bug De-Duplication Via Three-Dimensional Analysis, Chen Yang, Junjie Chen, Xingyu Fan, Jiajun Jiang, Jun Sun
Research Collection School Of Computing and Information Systems
Compiler testing is an important task for assuring the quality of compilers, but investigating test failures is very time-consuming. This is because many test failures are caused by the same compiler bug (known as bug duplication problem). In particular, this problem becomes much more challenging on silent compiler bugs (also called wrong code bugs), since these bugs can provide little information (unlike crash bugs that can produce error messages) for bug de-duplication. In this work, we propose a novel technique (called D3) to solve the duplication problem on silent compiler bugs. Its key insight is to characterize the silent bugs …
An Efficient Hybrid Genetic Algorithm For The Quadratic Traveling Salesman Problem, Quang Anh Pham, Hoong Chuin Lau, Minh Hoang Ha, Lam Vu
An Efficient Hybrid Genetic Algorithm For The Quadratic Traveling Salesman Problem, Quang Anh Pham, Hoong Chuin Lau, Minh Hoang Ha, Lam Vu
Research Collection School Of Computing and Information Systems
The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which hasbeen studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named thequadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics.The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leadsto a quadratic objective function that is much harder to solve.To efficiently solve …
Few-Shot Event Detection: An Empirical Study And A Unified View, Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun
Few-Shot Event Detection: An Empirical Study And A Unified View, Yubo Ma, Zehao Wang, Yixin Cao, Aixin Sun
Research Collection School Of Computing and Information Systems
Few-shot event detection (ED) has been widely studied, while this brings noticeable discrepancies, e.g., various motivations, tasks, and experimental settings, that hinder the understanding of models for future progress. This paper presents a thorough empirical study, a unified view of ED models, and a better unified baseline. For fair evaluation, we compare 12 representative methods on three datasets, which are roughly grouped into prompt-based and prototype-based models for detailed analysis. Experiments consistently demonstrate that prompt-based methods, including ChatGPT, still significantly trail prototype-based methods in terms of overall performance. To investigate their superior performance, we break down their design elements along …
Take A Break In The Middle: Investigating Subgoals Towards Hierarhical Script Generation, Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun
Take A Break In The Middle: Investigating Subgoals Towards Hierarhical Script Generation, Xinze Li, Yixin Cao, Muhao Chen, Aixin Sun
Research Collection School Of Computing and Information Systems
Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically — Human often decompose a complex task into subgoals, where each subgoal can be further decomposed into steps. To establish the benchmark, we contribute a new dataset, propose several baseline methods, and set up evaluation metrics. Both automatic and human evaluation verify the high-quality of dataset, as well as the effectiveness of incorporating subgoals …
Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu
Cheer: Centrality-Aware High-Order Event Reasoning Network For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Yan Zhang, Zhiwei Liu
Research Collection School Of Computing and Information Systems
Document-level Event Causality Identification (DECI) aims to recognize causal relations between events within a document. Recent studies focus on building a document-level graph for cross-sentence reasoning, but ignore important causal structures — there are one or two “central” events that prevail throughout the document, with most other events serving as either their cause or consequence. In this paper, we manually annotate central events for a systematical investigation and propose a novel DECI model, CHEER, which performs high-order reasoning while considering event centrality. First, we summarize a general GNN-based DECI model and provide a unified view for better understanding. Second, we …
Balancing Privacy And Flexibility Of Cloud-Based Personal Health Records Sharing System, Yudi Zhang, Fuchun Guo, Willy Susilo, Guomin Yang
Balancing Privacy And Flexibility Of Cloud-Based Personal Health Records Sharing System, Yudi Zhang, Fuchun Guo, Willy Susilo, Guomin Yang
Research Collection School Of Computing and Information Systems
The Internet of Things and cloud services have been widely adopted in many applications, and personal health records (PHR) can provide tailored medical care. The PHR data is usually stored on cloud servers for sharing. Weighted attribute-based encryption (ABE) is a practical and flexible technique to protect PHR data. Under a weighted ABE policy, the data user's attributes will be “scored”, if and only if the score reaches the threshold value, he/she can access the data. However, while this approach offers a flexible access policy, the data owners have difficulty controlling their privacy, especially sharing PHR data in collaborative e-health …
Reducing Spatial Labeling Redundancy For Active Semi-Supervised Crowd Counting, Yongtuo Liu, Sucheng Ren, Liangyu Chai, Hanjie Wu, Dan Xu, Jing Qin, Shengfeng He
Reducing Spatial Labeling Redundancy For Active Semi-Supervised Crowd Counting, Yongtuo Liu, Sucheng Ren, Liangyu Chai, Hanjie Wu, Dan Xu, Jing Qin, Shengfeng He
Research Collection School Of Computing and Information Systems
Labeling is onerous for crowd counting as it should annotate each individual in crowd images. Recently, several methods have been proposed for semi-supervised crowd counting to reduce the labeling efforts. Given a limited labeling budget, they typically select a few crowd images and densely label all individuals in each of them. Despite the promising results, we argue the None-or-All labeling strategy is suboptimal as the densely labeled individuals in each crowd image usually appear similar while the massive unlabeled crowd images may contain entirely diverse individuals. To this end, we propose to break the labeling chain of previous methods and …
Beyond "Protected" And "Private": An Empirical Security Analysis Of Custom Function Modifiers In Smart Contracts, Yuzhou Fang, Daoyuan Wu, Xiao Yi, Shuai Wang, Yufan Chen, Mengjie Chen, Yang Liu, Lingxiao Jiang
Beyond "Protected" And "Private": An Empirical Security Analysis Of Custom Function Modifiers In Smart Contracts, Yuzhou Fang, Daoyuan Wu, Xiao Yi, Shuai Wang, Yufan Chen, Mengjie Chen, Yang Liu, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
A smart contract is a piece of application-layer code running on blockchain ledgers and it provides programmatic logic via transaction-based execution of pre-defined functions. Smart contract functions are by default invokable by any party. To safeguard them, the mainstream smart contract language, i.e., Solidity of the popular Ethereum blockchain, proposed a unique language-level keyword called “modifier,” which allows developers to define custom function access control policies beyond the traditional “protected” and “private” modifiers in classic programming languages.In this paper, we aim to conduct a large-scale security analysis of the modifiers used in real-world Ethereum smart contracts. To achieve this, we …
Synthesizing Speech Test Cases With Text-To-Speech? An Empirical Study On The False Alarms In Automated Speech Recognition Testing, Julia Kaiwen Lau, Kelvin Kai Wen Kong, Julian Hao Yong, Per Hoong Tan, Zhou Yang, Zi Qian Yong, Joshua Chern Wey Low, Chun Yong Chong, Mei Kuan Lim, David Lo
Synthesizing Speech Test Cases With Text-To-Speech? An Empirical Study On The False Alarms In Automated Speech Recognition Testing, Julia Kaiwen Lau, Kelvin Kai Wen Kong, Julian Hao Yong, Per Hoong Tan, Zhou Yang, Zi Qian Yong, Joshua Chern Wey Low, Chun Yong Chong, Mei Kuan Lim, David Lo
Research Collection School Of Computing and Information Systems
Recent studies have proposed the use of Text-To-Speech (TTS) systems to automatically synthesise speech test cases on a scale and uncover a large number of failures in ASR systems. However, the failures uncovered by synthetic test cases may not reflect the actual performance of an ASR system when it transcribes human audio, which we refer to as false alarms. Given a failed test case synthesised from TTS systems, which consists of TTS-generated audio and the corresponding ground truth text, we feed the human audio stating the same text to an ASR system. If human audio can be correctly transcribed, an …
Mitigating Adversarial Attacks On Data-Driven Invariant Checkers For Cyber-Physical Systems, Rajib Ranjan Maiti, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt
Mitigating Adversarial Attacks On Data-Driven Invariant Checkers For Cyber-Physical Systems, Rajib Ranjan Maiti, Cheah Huei Yoong, Venkata Reddy Palleti, Arlindo Silva, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
The use of invariants in developing security mechanisms has become an attractive research area because of their potential to both prevent attacks and detect attacks in Cyber-Physical Systems (CPS). In general, an invariant is a property that is expressed using design parameters along with Boolean operators and which always holds in normal operation of a system, in particular, a CPS. Invariants can be derived by analysing operational data of various design parameters in a running CPS, or by analysing the system's requirements/design documents, with both of the approaches demonstrating significant potential to detect and prevent cyber-attacks on a CPS. While …
A Data-Driven Approach For Scheduling Bus Services Subject To Demand Constraints, Brahmanage Janaka Chathuranga Thilakarathna, Thivya Kandappu, Baihua Zheng
A Data-Driven Approach For Scheduling Bus Services Subject To Demand Constraints, Brahmanage Janaka Chathuranga Thilakarathna, Thivya Kandappu, Baihua Zheng
Research Collection School Of Computing and Information Systems
Passenger satisfaction is extremely important for the success of a public transportation system. Many studies have shown that passenger satisfaction strongly depends on the time they have to wait at the bus stop (waiting time) to get on a bus. To be specific, user satisfaction drops faster as the waiting time increases. Therefore, service providers want to provide a bus to the waiting passengers within a threshold to keep them satisfied. It is a two-pronged problem: (a) to satisfy more passengers the transport planner may increase the frequency of the buses, and (b) in turn, the increased frequency may impact …
Singapore's Hospital To Home Program: Raising Patient Engagement Through Ai, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gian Lee Ang, Andy Wee An Ta, Steven M. Miller
Singapore's Hospital To Home Program: Raising Patient Engagement Through Ai, John Abisheganaden, Kheng Hock Lee, Lian Leng Low, Eugene Shum, Han Leong Goh, Christine Gian Lee Ang, Andy Wee An Ta, Steven M. Miller
Research Collection School Of Computing and Information Systems
Because of their complex care needs, many elderly patients are discharged from hospitals only to be readmitted for multiple stays within the following twelve months. John Abisheganaden and his fellow authors describe Singapore’s Hospital to Home program, a community care initiative fueled by artificial intelligence.
Impact Of Difficult Negatives On Twitter Crisis Detection, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint
Impact Of Difficult Negatives On Twitter Crisis Detection, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint
Research Collection School Of Computing and Information Systems
Twitter has become an alternative information source during a crisis. However, the short, noisy nature of tweets hinders information extraction. While models trained with standard Twitter crisis datasets accomplished decent performance, it remained a challenge to generalize to unseen crisis events. Thus, we proposed adding “difficult” negative examples during training to improve model generalization for Twitter crisis detection. Although adding random noise is a common practice, the impact of difficult negatives, i.e., negative data semantically similar to true examples, was never examined in NLP. Most of existing research focuses on the classification task, without considering the primary information need of …
Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai
Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai
Research Collection School Of Computing and Information Systems
Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …
Socialz: Multi-Feature Social Fuzz Testing, Francisco Zanartu, Christoph Treude, Markus Wagner
Socialz: Multi-Feature Social Fuzz Testing, Francisco Zanartu, Christoph Treude, Markus Wagner
Research Collection School Of Computing and Information Systems
Online social networks have become an integral aspect of our daily lives and play a crucial role in shaping our relationships with others. However, bugs and glitches, even minor ones, can cause anything from frustrating problems to serious data leaks that can have farreaching impacts on millions of users. To mitigate these risks, fuzz testing, a method of testing with randomised inputs, can provide increased confidence in the correct functioning of a social network. However, implementing traditional fuzz testing methods can be prohibitively difficult or impractical for programmers outside of the network’s development team. To tackle this challenge, we present …
Mdps As Distribution Transformers: Affine Invariant Synthesis For Safety Objectives, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic
Mdps As Distribution Transformers: Affine Invariant Synthesis For Safety Objectives, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic
Research Collection School Of Computing and Information Systems
Markov decision processes can be viewed as transformers of probability distributions. While this view is useful from a practical standpoint to reason about trajectories of distributions, basic reachability and safety problems are known to be computationally intractable (i.e., Skolem-hard) to solve in such models. Further, we show that even for simple examples of MDPs, strategies for safety objectives over distributions can require infinite memory and randomization.In light of this, we present a novel overapproximation approach to synthesize strategies in an MDP, such that a safety objective over the distributions is met. More precisely, we develop a new framework for template-based …