Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

Singapore Management University

Discipline
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 3961 - 3990 of 9025

Full-Text Articles in Computer Sciences

End-To-End Deep Reinforcement Learning For Multi-Agent Collaborative Exploration, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan Oct 2019

End-To-End Deep Reinforcement Learning For Multi-Agent Collaborative Exploration, Zichen Chen, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Exploring an unknown environment by multiple autonomous robots is a major challenge in robotics domains. As multiple robots are assigned to explore different locations, they may interfere each other making the overall tasks less efficient. In this paper, we present a new model called CNN-based Multi-agent Proximal Policy Optimization (CMAPPO) to multi-agent exploration wherein the agents learn the effective strategy to allocate and explore the environment using a new deep reinforcement learning architecture. The model combines convolutional neural network to process multi-channel visual inputs, curriculum-based learning, and PPO algorithm for motivation based reinforcement learning. Evaluations show that the proposed method …

Go to article

Multi-Agent Collaborative Exploration Through Graph-Based Deep Reinforcement Learning, Tianze Luo, Budhitama Subagdja, Ah-Hwee Tan, Ah-Hwee Tan Oct 2019

Multi-Agent Collaborative Exploration Through Graph-Based Deep Reinforcement Learning, Tianze Luo, Budhitama Subagdja, Ah-Hwee Tan, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Autonomous exploration by a single or multiple agents in an unknown environment leads to various applications in automation, such as cleaning, search and rescue, etc. Traditional methods normally take frontier locations and segmented regions of the environment into account to efficiently allocate target locations to different agents to visit. They may employ ad hoc solutions to allocate the task to the agents, but the allocation may not be efficient. In the literature, few studies focused on enhancing the traditional methods by applying machine learning models for agent performance improvement. In this paper, we propose a graph-based deep reinforcement learning approach …

Go to article

Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo Sep 2019

Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo

Research Collection School Of Computing and Information Systems

Nowadays, with the rapid growth of open source software (OSS), library reuse becomes more and more popular since a large amount of third- party libraries are available to download and reuse. A deeper understanding on why developers reuse a library (i.e., replacing self-implemented code with an external library) or re-implement a library (i.e., replacing an imported external library with self-implemented code) could help researchers better understand the factors that developers are concerned with when reusing code. This understanding can then be used to improve existing libraries and API recommendation tools for researchers and practitioners by using the developers concerns identified …

Go to article

Preference Learning And Similarity Learning Perspectives On Personalized Recommendation, Duy Dung Le Sep 2019

Preference Learning And Similarity Learning Perspectives On Personalized Recommendation, Duy Dung Le

Dissertations and Theses Collection (Open Access)

Personalized recommendation, whose objective is to generate a limited list of items (e.g., products on Amazon, movies on Netflix, or pins on Pinterest, etc.) for each user, has gained extensive attention from both researchers and practitioners in the last decade. The necessity of personalized recommendation is driven by the explosion of available options online, which makes it difficult, if not downright impossible, for each user to investigate every option. Product and service providers rely on recommendation algorithms to identify manageable number of the most likely or preferred options to be presented to each user. Also, due to the limited screen …

Go to article

Exploiting Approximation, Caching And Specialization To Accelerate Vision Sensing Applications, Nguyen Loc Huynh Sep 2019

Exploiting Approximation, Caching And Specialization To Accelerate Vision Sensing Applications, Nguyen Loc Huynh

Dissertations and Theses Collection (Open Access)

Over the past few years, deep learning has emerged as state-of-the-art solutions for many challenging computer vision tasks such as face recognition, object detection, etc. Despite of its outstanding performance, deep neural networks (DNNs) are computational intensive, which prevent them to be widely adopted on billions of mobile and embedded devices with scarce resources. To address that limitation, we
focus on building systems and optimization algorithms to accelerate those models, making them more computational-efficient.
First, this thesis explores the computational capabilities of different existing processors (or co-processors) on modern mobile devices. It recognizes that by leveraging the mobile Graphics Processing …

Go to article

Detecting Toxicity Triggers In Online Discussions, Hamad Bin Khalifa University, Haewoon Kwak Sep 2019

Detecting Toxicity Triggers In Online Discussions, Hamad Bin Khalifa University, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Despite the considerable interest in the detection of toxic comments, there has been little research investigating the causes -- i.e., triggers -- of toxicity. In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.

Go to article

Automatic Generation Of Non-Intrusive Updates For Third-Party Libraries In Android Applications, Yue Duan, Lian Gao, Jie Hu, Heng Yin Sep 2019

Automatic Generation Of Non-Intrusive Updates For Third-Party Libraries In Android Applications, Yue Duan, Lian Gao, Jie Hu, Heng Yin

Research Collection School Of Computing and Information Systems

Third-Party libraries, which are ubiquitous in Android apps,have exposed great security threats to end users as they rarelyget timely updates from the app developers, leaving manysecurity vulnerabilities unpatched. This issue is due to thefact that manually updating libraries can be technically nontrivialand time-consuming for app developers. In this paper,we propose a technique that performs automatic generationof non-intrusive updates for third-party libraries in Androidapps. Given an Android app with an outdated library and anewer version of the library, we automatically update the oldlibrary in a way that is guaranteed to be fully backward compatibleand imposes zero impact to the library’s interactionswith …

Go to article

Be Sensitive And Collaborative: Analyzing Impact Of Coverage Metrics In Greybox Fuzzing, Jinghan Wang, Yue Duan, Wei Song, Heng Yin, Chengyu Song Sep 2019

Be Sensitive And Collaborative: Analyzing Impact Of Coverage Metrics In Greybox Fuzzing, Jinghan Wang, Yue Duan, Wei Song, Heng Yin, Chengyu Song

Research Collection School Of Computing and Information Systems

Coverage-guided greybox fuzzing has become one of the most common techniques for finding software bugs. Coverage metric, which decides how a fuzzer selects new seeds, is an essential parameter of fuzzing and can significantly affect the results. While there are many existing works on the effectiveness of different coverage metrics on software testing, little is known about how different coverage metrics could actually affect the fuzzing results in practice. More importantly, it is unclear whether there exists one coverage metric that is superior to all the other metrics. In this paper, we report the first systematic study on the impact …

Go to article

Can Earables Support Effective User Engagement During Weight-Based Gym Exercises?, Meeralakshmi Radhakrishnan, Archan Misra Sep 2019

Can Earables Support Effective User Engagement During Weight-Based Gym Exercises?, Meeralakshmi Radhakrishnan, Archan Misra

Research Collection School Of Computing and Information Systems

We explore the use of personal ‘earable’ devices (widely used by gym-goers) in providing personalized, quantified insights and feedback to users performing gym exercises. As in-ear sensing by itself is often too weak to pick up exercise-driven motion dynamics, we propose a novel, low-cost system that can monitor multiple concurrent users by fusing data from (a) wireless earphones, equipped with inertial and physiological sensors and (b) inertial sensors attached to exercise equipment. We share preliminary findings from a small-scale study to demonstrate the promise of this approach, as well as identify open challenges.

Go to article

Puncturable Proxy Re-Encryption Supporting To Group Messaging Service, Tran Viet Xuan Phuong, Willy Susilo, Jongkil Kim, Guomin Yang, Dongxi Liu Sep 2019

Puncturable Proxy Re-Encryption Supporting To Group Messaging Service, Tran Viet Xuan Phuong, Willy Susilo, Jongkil Kim, Guomin Yang, Dongxi Liu

Research Collection School Of Computing and Information Systems

This work envisions a new encryption primitive for many-to-many paradigms such as group messaging systems. Previously, puncturable encryption (PE) was introduced to provide forward security for asynchronous messaging services. However, existing PE schemes were proposed only for one-to-one communication, and causes a significant overhead for a group messaging system. In fact, the group communication over PE can only be achieved by encrypting a message multiple times for each receiver by the sender’s device, which is usually suitable to restricted resources such as mobile phones or sensor devices. Our new suggested scheme enables to re-encrypt ciphertexts of puncturable encryption by a …

Go to article

A Lattice-Based Linkable Ring Signature Supporting Stealth Addresses, Zhen Liu, Khoa Nguyen, Guomin Yang, Huaxiong Wang, Duncan S. Wong Sep 2019

A Lattice-Based Linkable Ring Signature Supporting Stealth Addresses, Zhen Liu, Khoa Nguyen, Guomin Yang, Huaxiong Wang, Duncan S. Wong

Research Collection School Of Computing and Information Systems

First proposed in CryptoNote, a collection of popular privacy-centric cryptocurrencies have employed Linkable Ring Signature and a corresponding Key Derivation Mechanism (KeyDerM) for keeping the payer and payee of a transaction anonymous and unlinkable. The KeyDerM is used for generating a fresh signing key and the corresponding public key, referred to as a stealth address, for the transaction payee. The stealth address will then be used in the linkable ring signature next time when the payee spends the coin. However, in all existing works, including Monero, the privacy model only considers the two cryptographic primitives separately. In addition, to be …

Go to article

Lightweight Fine-Grained Search Over Encrypted Data In Fog Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, Hui Li Sep 2019

Lightweight Fine-Grained Search Over Encrypted Data In Fog Computing, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, Hui Li

Research Collection School Of Computing and Information Systems

Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift …

Go to article

Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, David W. Rosen, Sai-Kit Yeung Sep 2019

Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, David W. Rosen, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotation invariance is often not guaranteed, resulting in networks that generalizes poorly to arbitrary rotations. In this paper, we introduce a novel convolution operator for point clouds that achieves rotation invariance. Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. The well-known point ordering problem is also addressed by a binning approach seamlessly built into the …

Go to article

Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen Sep 2019

Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen

Research Collection School Of Computing and Information Systems

Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper's study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed …

Go to article

Self-Refining Deep Symmetry Enhanced Network For Rain Removal, Hong Liu, Hanrong Ye, Xia Li, Wei Shi, Mengyuan Liu, Qianru Sun Sep 2019

Self-Refining Deep Symmetry Enhanced Network For Rain Removal, Hong Liu, Hanrong Ye, Xia Li, Wei Shi, Mengyuan Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

Rain removal aims to remove the rain streaks on rain images. Traditional methods based on convolutional neural network (CNN) have achieved impressive results. However, these methods are under-performed when dealing with tilted rain streaks, because CNN is not equivariant to object rotations. To tackle this problem, we propose the Deep Symmetry Enhanced Network (DSEN) that explicitly extracts and learns from rotation-equivariant features from rain images. In addition, we design a self-refining strategy to remove rain streaks in a coarse-to-fine manner. The key idea is to reuse DSEN with an information link which passes the gradient flow to the finer stage. …

Go to article

Generating Expensive Relationship Features From Cheap Objects, Xiaogang Wang, Qianru Sun, Tat-Seng Chua, Marcelo Ang Sep 2019

Generating Expensive Relationship Features From Cheap Objects, Xiaogang Wang, Qianru Sun, Tat-Seng Chua, Marcelo Ang

Research Collection School Of Computing and Information Systems

We investigate the problem of object relationship classification of visual scenes. For a relationship object1-predicate-object2 that captures the object interaction, its representation is composed by the combination of object1 and object2 features. As a result, relationship classification models usually bias to the frequent objects, leading to poor generalization to rare or unseen objects. Inspired by the data augmentation methods, we propose a novel Semantic Transform Generative Adversarial Network (ST-GAN) that synthesizes relationship features for rare objects, conditioned on the features from random instances of the objects. Specifically, ST-GAN essentially offers a semantic transform function from cheap object features to expensive …

Go to article

Finding Flaws From Password Authentication Code In Android Apps, Siqi Ma, Elisa Bertino, Surya Nepal, Jianru Li, Ostry Diethelm, Robert H. Deng, Sanjay Jha Sep 2019

Finding Flaws From Password Authentication Code In Android Apps, Siqi Ma, Elisa Bertino, Surya Nepal, Jianru Li, Ostry Diethelm, Robert H. Deng, Sanjay Jha

Research Collection School Of Computing and Information Systems

Password authentication is widely used to validate users’ identities because it is convenient to use, easy for users to remember, and simple to implement. The password authentication protocol transmits passwords in plaintext, which makes the authentication vulnerable to eavesdropping and replay attacks, and several protocols have been proposed to protect against this. However, we find that secure password authentication protocols are often implemented incorrectly in Android applications (apps). To detect the implementation flaws in password authentication code, we propose GLACIATE, a fully automated tool combining machine learning and program analysis. Instead of creating detection templates/rules manually, GLACIATE automatically and accurately …

Go to article

Spatio-Temporal Analysis And Prediction Of Cellular Traffic In Metropolis, Xu Wang, Zimu Zhou, Fu Xiao, Kai Xing, Zheng Yang, Yunhao Liu, Chunyi Peng Sep 2019

Spatio-Temporal Analysis And Prediction Of Cellular Traffic In Metropolis, Xu Wang, Zimu Zhou, Fu Xiao, Kai Xing, Zheng Yang, Yunhao Liu, Chunyi Peng

Research Collection School Of Computing and Information Systems

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatiotemporal dependency even among distant cell towers, which is largely overlooked in …

Go to article

Directional Co-Clustering, Aghiles Salah, Mohamed Nadif Sep 2019

Directional Co-Clustering, Aghiles Salah, Mohamed Nadif

Research Collection School Of Computing and Information Systems

Co-clustering addresses the problem of simultaneous clustering of both dimensions of a data matrix. When dealing with high dimensional sparse data, co-clustering turns out to be more beneficial than one-sided clustering even if one is interested in clustering along one dimension only. Aside from being high dimensional and sparse, some datasets, such as document-term matrices, exhibit directional characteristics, and the L2 normalization of such data, so that it lies on the surface of a unit hypersphere, is useful. Popular co-clustering assumptions such as Gaussian or Multinomial are inadequate for this type of data. In this paper, we extend the scope …

Go to article

A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray Sep 2019

A Case Study On Automated Fuzz Target Generation For Large Codebases, Matthew Kelly, Christoph Treude, Alex Murray

Research Collection School Of Computing and Information Systems

Fuzz Testing is a largely automated testing technique that provides random and unexpected input to a program in attempt to trigger failure conditions. Much of the research conducted thus far into Fuzz Testing has focused on developing improvements to available Fuzz Testing tools and frameworks in order to improve efficiency. In this paper however, we instead look at a way in which we can reduce the amount of developer time required to integrate Fuzz Testing to help maintain an existing codebase. We accomplish this with a new technique for automatically generating Fuzz Targets, the modified versions of programs on which …

Go to article

Enhancing Python Compiler Error Messages Via Stack Overflow, Emillie Thiselton, Christoph Treude Sep 2019

Enhancing Python Compiler Error Messages Via Stack Overflow, Emillie Thiselton, Christoph Treude

Research Collection School Of Computing and Information Systems

Background: Compilers tend to produce cryptic and uninformative error messages, leaving programmers confused and requiring them to spend precious time to resolve the underlying error. To find help, programmers often take to online question-and-answer forums such as Stack Overflow to start discussion threads about the errors they encountered.Aims: We conjecture that information from Stack Overflow threads which discuss compiler errors can be automatically collected and repackaged to provide programmers with enhanced compiler error messages, thus saving programmers' time and energy.Method: We present Pycee, a plugin integrated with the popular Sublime Text IDE to provide enhanced compiler error messages for the …

Go to article

A Common Approach For Consumer And Provider Fairness In Recommendations, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitrios Kleftogiannis Sep 2019

A Common Approach For Consumer And Provider Fairness In Recommendations, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitrios Kleftogiannis

Research Collection School Of Computing and Information Systems

We present a common approach for handling consumer and provider fairness in recommendations. Our solution requires defining two key components, a classification of items and a target distribution, which together define the case of perfect fairness. This formulation allows distinct fairness concepts to be specified in a common framework. We further propose a novel reranking algorithm that optimizes for a desired trade-off between utility and fairness of a recommendation list.

Go to article

Efficient Oblivious Transfer With Membership Verification, Weiwei Liu, Dazhi Sun, Yangguang Tian Sep 2019

Efficient Oblivious Transfer With Membership Verification, Weiwei Liu, Dazhi Sun, Yangguang Tian

Research Collection School Of Computing and Information Systems

In this article, we introduce a new concept of oblivious transfer with membership verification that allows any legitimate group users to obtain services from a service provider in an oblivious manner. We present two oblivious transfer with membership verification schemes, differing in design. In the first scheme, a trusted group manager issues credentials for a pre-determined group of users so that the group of users with a valid group credential can obtain services from the service provider, while the choices made by group users remain oblivious to the service provider. The second scheme avoids the trusted group manager, which allows …

Go to article

Confusion And Information Triggered By Photos In Persona Profiles, Joni Salminen, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Lene Nielsen, Bernard J. Jansen Sep 2019

Confusion And Information Triggered By Photos In Persona Profiles, Joni Salminen, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Lene Nielsen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We investigate whether additional photos beyond a single headshot makes a persona profile more informative without confusing the end user. We conduct an eye-tracking experiment and qualitative interviews with digital content creators after varying the persona in photos via a single headshot, a headshot and photo of the persona in different contexts, and a headshot with photos of different people with key persona attributes the gender and age. Findings show that contextual photos provide significantly more persona information to end users; however, showing photos of multiple people engenders confusion and lowers informativeness. Also, as anticipated, viewing additional photos requires more …

Go to article

Foodai: Food Image Recognition Via Deep Learning For Smart Food Logging, Doyen Sahoo, Hao Wang, Ke Shu, Xiongwei Wu, Hung Le, Palakorn Achananuparp, Ee-Peng Lim, Hoi, Steven C. H. Aug 2019

Foodai: Food Image Recognition Via Deep Learning For Smart Food Logging, Doyen Sahoo, Hao Wang, Ke Shu, Xiongwei Wu, Hung Le, Palakorn Achananuparp, Ee-Peng Lim, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones …

Go to article

Sentence-Level Evidence Embedding For Claim Verification With Hierarchical Attention Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong Aug 2019

Sentence-Level Evidence Embedding For Claim Verification With Hierarchical Attention Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications. It is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of three main components: 1) A coherence-based attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention layer …

Go to article

Improving Urban Crowd Flow Prediction On Flexible Region Partition, Xu Wang, Zimu Zhou, Yi Zhao, Xinglin Zhang, Kai Xing, Fu Xiao, Zheng Yang, Yunhao Liu Aug 2019

Improving Urban Crowd Flow Prediction On Flexible Region Partition, Xu Wang, Zimu Zhou, Yi Zhao, Xinglin Zhang, Kai Xing, Fu Xiao, Zheng Yang, Yunhao Liu

Research Collection School Of Computing and Information Systems

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, …

Go to article

Simulated Annealing For The Multi-Vehicle Cyclic Inventory Routing Problem, Aldy Gunawan, Vincent F. Yu, Audrey Tedja Widjaja, Pieter Vansteenwegen Aug 2019

Simulated Annealing For The Multi-Vehicle Cyclic Inventory Routing Problem, Aldy Gunawan, Vincent F. Yu, Audrey Tedja Widjaja, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

This paper studies the Multi-Vehicle Cyclic Inventory Routing Problem (MV-CIRP) as the extension of the Single-Vehicle CIRP (SV-CIRP). The objective is to minimize both distribution and inventory costs at the customers and to maximize the collected rewards simultaneously. The problem is treated as a single objective optimization problem. A subset of customers is selected for each vehicle including the quantity to be delivered to each customer. For each vehicle, a cyclic distribution plan is developed. We construct a mathematical programming model and propose a simulated annealing (SA) metaheuristic for solving both SV-CIRP and MV-CIRP. For SV-CIRP, experimental results on benchmark …

Go to article

Latent Error Prediction And Fault Localization For Microservice Applications By Learning From System Trace Logs, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Dewei Liu, Qilin Xiang, Chuan He Aug 2019

Latent Error Prediction And Fault Localization For Microservice Applications By Learning From System Trace Logs, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Dewei Liu, Qilin Xiang, Chuan He

Research Collection School Of Computing and Information Systems

In the production environment, a large part of microservice failures are related to the complex and dynamic interactions and runtime environments, such as those related to multiple instances, environmental configurations, and asynchronous interactions of microservices. Due to the complexity and dynamism of these failures, it is often hard to reproduce and diagnose them in testing environments. It is desirable yet still challenging that these failures can be detected and the faults can be located at runtime of the production environment to allow developers to resolve them efficiently. To address this challenge, in this paper, we propose MEPFL, an approach of …

Go to article

Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization Under Uncertainty, Jonathan David Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau Aug 2019

Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization Under Uncertainty, Jonathan David Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. To this end, we …

Go to article

First Prev Next Last