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Articles 3841 - 3870 of 8481
Full-Text Articles in Computer Sciences
Efficient Algorithms For Solving Aggregate Keyword Routing Problems, Qize Jiang, Weiwei Sun, Baihua Zheng, Kunjie Chen
Efficient Algorithms For Solving Aggregate Keyword Routing Problems, Qize Jiang, Weiwei Sun, Baihua Zheng, Kunjie Chen
Research Collection School Of Computing and Information Systems
With the emergence of smart phones and the popularity of GPS, the number of point of interest (POIs) is growing rapidly and spatial keyword search based on POIs has attracted significant attention. In this paper, we study a more sophistic type of spatial keyword searches that considers multiple query points and multiple query keywords, namely Aggregate Keyword Routing (AKR). AKR looks for an aggregate point m together with routes from each query point to m. The aggregate point has to satisfy the aggregate keywords, the routes from query points to the aggregate point have to pass POIs in order to …
Online Collaborative Filtering With Implicit Feedback, Jianwen Yin, Chenghao Liu, Jundong Li, Bing Tian Dai, Yun-Chen Chen, Min Wu, Jianling Sun
Online Collaborative Filtering With Implicit Feedback, Jianwen Yin, Chenghao Liu, Jundong Li, Bing Tian Dai, Yun-Chen Chen, Min Wu, Jianling Sun
Research Collection School Of Computing and Information Systems
Studying recommender systems with implicit feedback has become increasingly important. However, most existing works are designed in an offline setting while online recommendation is quite challenging due to the one-class nature of implicit feedback. In this paper, we propose an online collaborative filtering method for implicit feedback. We highlight three critical issues of existing works. First, when positive feedback arrives sequentially, if we treat all the other missing items for this given user as the negative samples, the mis-classified items will incur a large deviation since some items might appear as the positive feedback in the subsequent rounds. Second, the …
Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras
Maximizing Multifaceted Network Influence, Yuchen Li, Ju Fan, George V. Ovchinnikov, Panagiotis Karras
Research Collection School Of Computing and Information Systems
An information dissemination campaign is often multifaceted, involving several facets or pieces of information disseminating from different sources. The question then arises, how should we assign such pieces to eligible sources so as to achieve the best viral dissemination results? Past research has studied the problem of Influence Maximization (IM), which is to select a set of k promoters that maximizes the expected reach of a message over a network. However, in this classical IM problem, each promoter spreads out the same unitary piece of information. In this paper, we propose the Optimal Influential Pieces Assignment (OIPA) problem, which is …
Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Jiashi Feng
Faster First-Order Methods For Stochastic Non-Convex Optimization On Riemannian Manifolds, Pan Zhou, Xiao-Tong Yuan, Jiashi Feng
Research Collection School Of Computing and Information Systems
SPIDER (Stochastic Path Integrated Differential EstimatoR) is an efficient gradient estimation technique developed for non-convex stochastic optimization. Although having been shown to attain nearly optimal computational complexity bounds, the SPIDERtype methods are limited to linear metric spaces. In this paper, we introduce the Riemannian SPIDER (R-SPIDER) method as a novel nonlinear-metric extension of SPIDER for efficient non-convex optimization on Riemannian manifolds. We prove that for finitesum problems with n components, R-SPIDER converges to an -accuracy stationary point within O min n + √ n 2 , 1 3 stochastic gradient evaluations, which is sharper in magnitude than the prior Riemannian …
Revocable Attribute-Based Encryption With Decryption Key Exposure Resistance And Ciphertext Delegation, Shengmin Xu, Guomin Yang, Yi Mu
Revocable Attribute-Based Encryption With Decryption Key Exposure Resistance And Ciphertext Delegation, Shengmin Xu, Guomin Yang, Yi Mu
Research Collection School Of Computing and Information Systems
Attribute-based encryption (ABE) enables fine-grained access control over encrypted data. A practical and popular approach for handing revocation in ABE is to use the indirect revocation mechanism, in which a key generation centre (KGC) periodically broadcasts key update information for all data users over a public channel. Unfortunately, existing RABE schemes are vulnerable to decryption key exposure attack which has been well studied in the identity-based setting. In this paper, we introduce a new notion for RABE called re-randomizable piecewise key generation by allowing a data user to re-randmomize the combined secret key and the key update to obtain the …
The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau
The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper focuses on a recent variant of the Orienteering Problem (OP), namely the Capacitated Team OP (CTOP) which arises in the logistics industry. In this problem, each node is associated with a demand that needs to be satisfied and a score that need to be collected. Given a set of homogeneous fleet of vehicles, the objective is to find a path for each vehicle in order to maximize the total collected score, without violating the capacity and time budget. We propose an Iterated Local Search (ILS) algorithm for solving the CTOP. Two strategies, either accepting a new solution as …
Perception Coordination Network: A Neuro Framework For Multimodal Concept Acquisition And Binding, You-Lu Xing, Xiao-Feng Shi, Fu-Rao Shen, Jin-Xi Zhao, Jing-Xin Pan, Ah-Hwee Tan
Perception Coordination Network: A Neuro Framework For Multimodal Concept Acquisition And Binding, You-Lu Xing, Xiao-Feng Shi, Fu-Rao Shen, Jin-Xi Zhao, Jing-Xin Pan, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
To simulate the concept acquisition and binding of different senses in the brain, a biologically inspired neural network model named perception coordination network (PCN) is proposed. It is a hierarchical structure, which is functionally divided into the primary sensory area (PSA), the primary sensory association area (SAA), and the higher order association area (HAA). The PSA contains feature neurons which respond to many elementary features, e.g., colors, shapes, syllables, and basic flavors. The SAA contains primary concept neurons which combine the elementary features in the PSA to represent unimodal concept of objects, e.g., the image of an apple, the Chinese …
Wiwear: Wearable Sensing Via Directional Wifi Energy Harvesting, Huy Vu Tran, Archan Misra, Jie Xiong, Rajesh Krishna Balan
Wiwear: Wearable Sensing Via Directional Wifi Energy Harvesting, Huy Vu Tran, Archan Misra, Jie Xiong, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Energy harvesting, from a diverse set of modes such as light or motion, has been viewed as the key to developing batteryless sensing devices. In this paper, we develop the nascent idea of harvesting RF energy from WiFi transmissions, applying it to power a prototype wearable device that captures and transmits accelerometer sensor data. Our solution, WiWear, has two key innovations: 1) beamforming WiFi transmissions to significantly boost the energy that a receiver can harvest ~23 meters away, and 2) smart zero-energy, triggering of inertial sensing, that allows intelligent duty-cycled operation of devices whose transient power consumption far exceeds what …
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Research Collection School Of Computing and Information Systems
The rise of Initial Coin Offerings (ICO) in recent times and their potential for investment opportunities have investors spending a lot of time researching ICOs or having to follow the crowd. This paper aims to explore four broad factors of ICOs: identity, credibility, investor sentiment, and price movement to develop a framework that is useful in determining ICO quality. Research is shown using data sources including public forums, chat groups, web sites, white papers as well as smart contract details. Finally, a system, based on the framework, is proposed that can be used to detect and regulate ICO activities and …
Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr
Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr
Research Collection School Of Computing and Information Systems
Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed …
Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr
Bing: Binarized Normed Gradients For Objectness Estimation At 300fps, Ming-Ming Cheng, Yun Liu, Wen-Yan Lin, Ziming Zhang, Paul L. Rosin, Philip H. S. Torr
Research Collection School Of Computing and Information Systems
Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed …
An Artificial Bee Colony-Based Hybrid Approach For Waste Collection Problem With Midway Disposal Pattern, Qu Wei, Zhaoxia Guo, Hoong Chuin Lau, Zhenggang He
An Artificial Bee Colony-Based Hybrid Approach For Waste Collection Problem With Midway Disposal Pattern, Qu Wei, Zhaoxia Guo, Hoong Chuin Lau, Zhenggang He
Research Collection School Of Computing and Information Systems
This paper investigates a waste collection problem with the consideration of midway disposal pattern. An artificial bee colony (ABC)-based hybrid approach is developed to handle this problem, in which the hybrid ABC algorithm is proposed to generate the better optimum-seeking performance while a heuristic procedure is proposed to select the disposal trip dynamically and calculate the carbon emissions in waste collection process. The effectiveness of the proposed approach is validated by numerical experiments. Experimental results show that the proposed hybrid approach can solve the investigated problem effectively. The proposed hybrid ABC algorithm exhibits a better optimum-seeking performance than four popular …
Api Recommendation For Event-Driven Android Application Development, Weizhao Yuan, Huu Hoang Nguyen, Lingxiao Jiang, Yuting Chen, Jianjun Zhao, Haibo Yu
Api Recommendation For Event-Driven Android Application Development, Weizhao Yuan, Huu Hoang Nguyen, Lingxiao Jiang, Yuting Chen, Jianjun Zhao, Haibo Yu
Research Collection School Of Computing and Information Systems
Context: Software development is increasingly dependent on existing libraries. Developers need help to find suitable library APIs. Although many studies have been proposed to recommend relevant functional APIs that can be invoked for implementing a functionality, few studies have paid attention to an orthogonal need associated with event-driven programming frameworks, such as the Android framework. In addition to invoking functional APIs, Android developers need to know where to place functional code according to various events that may be triggered within the framework.Objective: This paper aims to develop an API recommendation engine for Android application development that can recommend both (1) …
Fine-Grained Geolocation Of Tweets In Temporal Proximity, Wen Haw Chong, Ee Peng Lim
Fine-Grained Geolocation Of Tweets In Temporal Proximity, Wen Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g., restaurants, shops) fromwhich they were posted. This explicitly recovers the venue context that is essential for applications such aslocation-based advertising or user profiling. For this geolocation task, we focus on geolocating tweets that arecontained in tweet sequences. In a tweet sequence, tweets are posted from some latent venue(s) by the sameuser and within a short time interval. This scenario arises from two observations: (1) It is quite common thatusers post multiple tweets in a short time and (2) most tweets are not geocoded. To more accurately geolocatea tweet, …
Distributed Gibbs: A Linear-Space Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Roie Zivan
Distributed Gibbs: A Linear-Space Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Roie Zivan
Research Collection School Of Computing and Information Systems
Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this article, we introduce two new sampling-based DCOP algorithms called Sequential Distributed Gibbs (SD-Gibbs) and Parallel Distributed Gibbs (PD-Gibbs). Both algorithms have memory requirements per agent that is linear in …
Automatic, Highly Accurate App Permission Recommendation, Zhongxin Liu, Xin Xia, David Lo, John Grundy
Automatic, Highly Accurate App Permission Recommendation, Zhongxin Liu, Xin Xia, David Lo, John Grundy
Research Collection School Of Computing and Information Systems
To ensure security and privacy, Android employs a permission mechanism which requires developers to explicitly declare the permissions needed by their applications (apps). Users must grant those permissions before they install apps or during runtime. This mechanism protects users’ private data, but also imposes additional requirements on developers. For permission declaration, developers need knowledge about what permissions are necessary to implement various features of their apps, which is difficult to acquire due to the incompleteness of Android documentation. To address this problem, we present a novel permission recommendation system named PerRec for Android apps. PerRec leverages mining-based techniques and data …
Semantic And Influence Aware K-Representative Queries Over Social Streams, Yanhao Wang, Yuchen Li, Kianlee Tan
Semantic And Influence Aware K-Representative Queries Over Social Streams, Yanhao Wang, Yuchen Li, Kianlee Tan
Research Collection School Of Computing and Information Systems
Massive volumes of data continuously generated on social platforms have become an important information source for users. A primary method to obtain fresh and valuable information from social streams is social search. Although there have been extensive studies on social search, existing methods only focus on the relevance of query results but ignore the representativeness. In this paper, we propose a novel Semantic and Influence aware k-Representative (k-SIR) query for social streams based on topic modeling. Specifically, we consider that both user queries and elements are represented as vectors in the topic space. A k-SIR query retrieves a set of …
See No Evil, Hear No Evil? Dissecting The Impact Of Online Hacker Forums, Wei T. Yue, Qiu-Hong Wang, Kai‐Lung Hui
See No Evil, Hear No Evil? Dissecting The Impact Of Online Hacker Forums, Wei T. Yue, Qiu-Hong Wang, Kai‐Lung Hui
Research Collection School Of Computing and Information Systems
Online hacker forums offer a prominent avenue for sharing hacking knowledge. Using a field dataset culled from multiple sources, we find that online discussion of distributed denial of service (DDOS) attacks in hackforums.net decreases the number of DDOS-attack victims. A 1% increase in discussion decreases DDOS attacks by 0.032% to 0.122%. This means that two DDOS-attack posts per day could reduce the number of victims by 700 to 2,600 per day. We find that discussion topics with similar keywords can variously increase or decrease DDOS attacks, meaning we cannot ascertain the impact of the discussion just by the post nature. …
Microbtc: Efficient, Flexible And Fair Micropayment For Bitcoin Using Hash Chains, Zhiguo Wan, Robert H. Deng, David Kuo Chuen Lee, Ying Li
Microbtc: Efficient, Flexible And Fair Micropayment For Bitcoin Using Hash Chains, Zhiguo Wan, Robert H. Deng, David Kuo Chuen Lee, Ying Li
Research Collection School Of Computing and Information Systems
While Bitcoin gains increasing popularity in different payment scenarios, the transaction fees make it difficult to be applied to micropayment. Given the wide applicability of micropayment, it is crucial for all cryptocurrencies including Bitcoin to provide effective support therein. In light of this, a number of low-cost micropayment schemes for Bitcoin have been proposed recently to reduce micropayment costs. Existing schemes, however, suffer from drawbacks such as high computation cost, inflexible payment value, and possibly unfair exchanges. The paper proposes two new micropayment schemes, namely the basic MicroBTC and the advanced MicroBTC, for Bitcoin by integrating the hash chain technique …
Neural Network Based Detection Of Self-Admitted Technical Debt: From Performance To Explainability, Xiaoxue Ren, Zhenchang Xing, Xin Xia, David Lo, Xinyu Wang, John Grundy
Neural Network Based Detection Of Self-Admitted Technical Debt: From Performance To Explainability, Xiaoxue Ren, Zhenchang Xing, Xin Xia, David Lo, Xinyu Wang, John Grundy
Research Collection School Of Computing and Information Systems
Technical debt is a metaphor to reflect the tradeoff software engineers make between short term benefitsand long term stability. Self-admitted technical debt (SATD), a variant of technical debt, has been proposed to identify debt that is intentionally introduced during software development, e.g., temporary fixes and workarounds. Previous studies have leveraged human-summarized patterns (which represent n-gram phrases that can be used to identify SATD) or text mining techniques to detect SATD in source code comments. However, several characteristics of SATD features in code comments, such as vocabulary diversity, project uniqueness, length and semantic variations, pose a big challenge to the accuracy …
Making Wearable Sensing Less Obtrusive, Huy Vu Tran, Archan Misra
Making Wearable Sensing Less Obtrusive, Huy Vu Tran, Archan Misra
Research Collection School Of Computing and Information Systems
Sensing is a crucial part of any cyber-physical system. Wearable device has its huge potential for sensing applications because it is worn on the user body. However, wearable sensing can cause obtrusiveness to the user. Obtrusiveness can be seen as a perception of a lack of usefulness [1] such as a lag in user interaction channel. In addition, being worn by a user, it is not connected to a power supply, and thus needs to be removed to be charged regularly. This can cause a nuisance to elderly or disabled people. However, there are also opportunities for wearable devices to …
Ict: In-Field Calibration Transfer For Air Quality Sensor Deployments, Yun Cheng, Xiaoxi He, Zimu Zhou, Lothar Thiele
Ict: In-Field Calibration Transfer For Air Quality Sensor Deployments, Yun Cheng, Xiaoxi He, Zimu Zhou, Lothar Thiele
Research Collection School Of Computing and Information Systems
Recent years have witnessed a growing interest in urban air pollution monitoring, where hundreds of low-cost air quality sensors are deployed city-wide. To guarantee data accuracy and consistency, these sensors need periodic calibration after deployment. Since access to ground truth references is often limited in large-scale deployments, it is difficult to conduct city-wide post-deployment sensor calibration. In this work we propose In-field Calibration Transfer (ICT), a calibration scheme that transfers the calibration parameters of source sensors (with access to references) to target sensors (without access to references). On observing that (i) the distributions of ground truth in both source and …
Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen
Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority …
Characterizing And Identifying Reverted Commits, Meng Yan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li
Characterizing And Identifying Reverted Commits, Meng Yan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
In practice, a popular and coarse-grained approach for recovering from a problematic commit is to revert it (i.e., undoing the change). However, reverted commits could induce some issues for software development, such as impeding the development progress and increasing the difficulty for maintenance. In order to mitigate these issues, we set out to explore the following central question: can we characterize and identify which commits will be reverted? In this paper, we characterize commits using 27 commit features and build an identification model to identify commits that will be reverted. We first identify reverted commits by analyzing commit messages and …
Welcome Message From The General Chairs, Nabil I. Alshurafa, Archan Misra, Abhishek Mukherji
Welcome Message From The General Chairs, Nabil I. Alshurafa, Archan Misra, Abhishek Mukherji
Research Collection School Of Computing and Information Systems
No abstract provided.
Careful-Packing: A Practical And Scalable Anti-Tampering Software Protection Enforced By Trusted Computing, Flavio Toffalini, Martín Ochoa, Jun Sun, Jianying Zhou
Careful-Packing: A Practical And Scalable Anti-Tampering Software Protection Enforced By Trusted Computing, Flavio Toffalini, Martín Ochoa, Jun Sun, Jianying Zhou
Research Collection School Of Computing and Information Systems
Ensuring the correct behaviour of an application is a critical security issue. One of the most popular ways to modify the intended behaviour of a program is to tamper its binary. Several solutions have been proposed to solve this problem, including trusted computing and anti-tampering techniques. Both can substantially increase security, and yet both have limitations. In this work, we propose an approach which combines trusted computing technologies and anti-tampering techniques, and that synergistically overcomes some of their inherent limitations. In our approach critical software regions are protected by leveraging on trusted computing technologies and cryptographic packing, without introducing additional …
Suaa: A Secure User Authentication Scheme With Anonymity For The Single & Multi-Server Environments, Nassoro M. R. Lwamo, Liehuang Zhu, Chang Xu, Kashif Sharif, Ximeng Liu, Chuan Zhang
Suaa: A Secure User Authentication Scheme With Anonymity For The Single & Multi-Server Environments, Nassoro M. R. Lwamo, Liehuang Zhu, Chang Xu, Kashif Sharif, Ximeng Liu, Chuan Zhang
Research Collection School Of Computing and Information Systems
The rapid increase in user base and technological penetration has enabled the use of a wide range of devices and applications. The services are rendered to these devices from single-server or highly distributed server environments, irrespective of their location. As the information exchanged between servers and clients is private, numerous forms of attacks can be launched to compromise it. To ensure the security, privacy, and availability of the services, different authentication schemes have been proposed for both single-server and multi-server environments. The primary performance objective of such schemes is to prevent most (if not all) attacks, with minimal computational costs …
Design And Assessment Of Myoelectric Games For Prosthesis Training Of Upper Limb Amputees, Meeralakshmi Radhakrishnan, Asim Smailagic, Brian French, Daniel P. Siewiorek, Rajesh Krishna Balan
Design And Assessment Of Myoelectric Games For Prosthesis Training Of Upper Limb Amputees, Meeralakshmi Radhakrishnan, Asim Smailagic, Brian French, Daniel P. Siewiorek, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
In this paper, we present the design and evaluation of our system, which provides an engaging game-based pre-prosthesis training environment for upper limb transradial amputees. We believe that patients who train using such a training tool will demonstrate significantly higher improvement in functional performance tests using a myoelectric prosthesis than when conventional pre-prosthesis training protocols are used. We re-designed two simple games to be playable using three muscle contractions which are appropriate to pre-prosthesis exercises and are detected by an EMG-based arm sleeve. Through user studies conducted with 16 non-amputee subjects, we show that the proposed games are enjoyable, fun …
Fc2: Cloud-Based Cluster Provisioning For Distributed Machine Learning, Nguyen Binh Duong Ta
Fc2: Cloud-Based Cluster Provisioning For Distributed Machine Learning, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
Training large, complex machine learning models such as deep neural networks with big data requires powerful computing clusters, which are costly to acquire, use and maintain. As a result, many machine learning researchers turn to cloud computing services for on-demand and elastic resource provisioning capabilities. Two issues have arisen from this trend: (1) if not configured properly, training models on cloud-based clusters could incur significant cost and time, and (2) many researchers in machine learning tend to focus more on model and algorithm development, so they may not have the time or skills to deal with system setup, resource selection …
Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar
Evolutionary Trends In The Collaborative Review Process Of A Large Software System, Subhajit Datta, Poulami Sarkar
Research Collection School Of Computing and Information Systems
In this paper, we study the evolutionary trends in the collaborative review process of a large open source software system. As expected, the number of reviews, the number of reviews commented on, as well as the number of reviewers, and the interactions between them show increasing trends over time. But unexpectedly, levels of clustering between developers in their interaction networks show a decreasing trend, even as connections between them increase. In the context of our study, clustering is an indicator of developer collaboration, whereas connection points to how intensely developers work together. Thus the trends we observe can inform how …