Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (8556)
- Computer Sciences (8539)
- Databases and Information Systems (3453)
- Software Engineering (2164)
- Artificial Intelligence and Robotics (1697)
-
- Information Security (1071)
- Numerical Analysis and Scientific Computing (1025)
- Graphics and Human Computer Interfaces (933)
- Engineering (888)
- Social and Behavioral Sciences (681)
- Business (642)
- Theory and Algorithms (493)
- Computer Engineering (451)
- Operations Research, Systems Engineering and Industrial Engineering (410)
- Programming Languages and Compilers (379)
- OS and Networks (324)
- Communication (297)
- Social Media (240)
- Public Affairs, Public Policy and Public Administration (217)
- Transportation (195)
- Medicine and Health Sciences (181)
- Education (170)
- Management Information Systems (167)
- Data Storage Systems (162)
- E-Commerce (148)
- International and Area Studies (111)
- Asian Studies (110)
- Health Information Technology (110)
- Digital Communications and Networking (104)
- Technology and Innovation (104)
- Keyword
-
- Deep learning (123)
- Machine learning (122)
- Social media (74)
- Artificial intelligence (72)
- Reinforcement learning (69)
-
- Privacy (65)
- Data mining (64)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (54)
- Optimization (54)
- Security (54)
- Visualization (51)
- Task analysis (50)
- Online learning (49)
- Software engineering (49)
- Training (49)
- Neural networks (48)
- Singapore (48)
- Anomaly detection (47)
- Feature extraction (46)
- Twitter (46)
- Large Language Models (45)
- Blockchain (44)
- Collaboration (44)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Natural language processing (39)
- Publication Year
- File Type
Articles 3451 - 3480 of 8597
Full-Text Articles in Entire DC Network
Maptransfer: Urban Air Quality Map Generation For Downscaled Sensor Deployments, Yun Cheng, Xiaoxi He, Zimu Zhou, Lothar Thiele
Maptransfer: Urban Air Quality Map Generation For Downscaled Sensor Deployments, Yun Cheng, Xiaoxi He, Zimu Zhou, Lothar Thiele
Research Collection School Of Computing and Information Systems
Dense deployments of commodity air quality sensors have proven effective to provide spatially-resolved information on urban air pollution in real-time. However, long-term operation of a dense sensor deployment incurs enormous maintenance expenses and efforts. A cost-effective alternative is to first collect measurements with an initial dense deployment and then rely on a small subset of sensors for air quality map generation. To avoid dramatic accuracy degradation in air quality maps generated using the downscaled sparse deployment, we design MapTransfer, an air quality map generation scheme which augments the current sensor measurements from the downscaled sparse deployment with appropriate historical data …
Predictive Task Assignment In Spatial Crowdsourcing: A Data-Driven Approach, Yan Zhao, Kai Zheng, Yue Cui, Han Su, Feida Zhu, Xiaofang Zhou
Predictive Task Assignment In Spatial Crowdsourcing: A Data-Driven Approach, Yan Zhao, Kai Zheng, Yue Cui, Han Su, Feida Zhu, Xiaofang Zhou
Research Collection School Of Computing and Information Systems
With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive …
Are These Comments Triggering? Predicting Triggers Of Toxicity In Online Discussions, Hind Almerekhi, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Are These Comments Triggering? Predicting Triggers Of Toxicity In Online Discussions, Hind Almerekhi, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
Understanding the causes or triggers of toxicity adds a new dimension to the prevention of toxic behavior in online discussions. In this research, we define toxicity triggers in online discussions as a non-toxic comment that lead to toxic replies. Then, we build a neural network-based prediction model for toxicity trigger. The prediction model incorporates text-based features and derived features from previous studies that pertain to shifts in sentiment, topic flow, and discussion context. Our findings show that triggers of toxicity contain identifiable features and that incorporating shift features with the discussion context can be detected with a ROC-AUC score of …
Statistical Modeling Of Harassment Against Reddit Moderators, Hamad Bin Khalifa University, Haewoon Kwak
Statistical Modeling Of Harassment Against Reddit Moderators, Hamad Bin Khalifa University, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Despite the dedication that some volunteer moderators of online communities display when performing their moderation duties, they become targets of hate and harassment by other users. To understand what causes the change in moderator role from heroes to victims, we analyze the responses of 1,818 moderators on Reddit to an online survey about moderation practices and harassment. We built a statistical model and found 6 significant independent variables that affect harassment on moderators, such as the knowledge of community norms, which increases harassment on moderators the most. Our findings imply that vulnerable moderators in toxic communities need countermeasures against harassment.
Attribute-Based Cloud Data Integrity Auditing For Secure Outsourced Storage, Yong Yu, Yannan Li, Bo Yang, Willy Susilo, Guomin Yang, Jian Bai
Attribute-Based Cloud Data Integrity Auditing For Secure Outsourced Storage, Yong Yu, Yannan Li, Bo Yang, Willy Susilo, Guomin Yang, Jian Bai
Research Collection School Of Computing and Information Systems
Outsourced storage such as cloud storage can significantly reduce the burden of data management of data owners. Despite of a long list of merits of cloud storage, it triggers many security risks at the same time. Data integrity, one of the most burning challenges in secure cloud storage, is a fundamental and pivotal element in outsourcing services. Outsourced data auditing protocols enable a verifier to efficiently check the integrity of the outsourced files without downloading the entire file from the cloud, which can dramatically reduce the communication overhead between the cloud server and the verifier. Existing protocols are mostly based …
Two Can Play That Game: An Adversarial Evaluation Of A Cyber-Alert Inspection System, Ankit Shah, Arunesh Sinha, Rajesh Ganesan, Sushil Jajodia, Hasan Cam
Two Can Play That Game: An Adversarial Evaluation Of A Cyber-Alert Inspection System, Ankit Shah, Arunesh Sinha, Rajesh Ganesan, Sushil Jajodia, Hasan Cam
Research Collection School Of Computing and Information Systems
Cyber-security is an important societal concern. Cyber-attacks have increased in numbers as well as in the extent of damage caused in every attack. Large organizations operate a Cyber Security Operation Center (CSOC), which forms the first line of cyber-defense. The inspection of cyber-alerts is a critical part of CSOC operations (defender or blue team). Recent work proposed a reinforcement learning (RL) based approach for the defender’s decision-making to prevent the cyber-alert queue length from growing large and overwhelming the defender. In this article, we perform a red team (adversarial) evaluation of this approach. With the recent attacks on learning-based decision-making …
Understanding The Relation Between Repeat Developer Interactions And Bug Resolution Times In Large Open Source Ecosystems: A Multisystem Study, Subhajit Datta, Reshma Roychoudhuri, Subhashis Majumder
Understanding The Relation Between Repeat Developer Interactions And Bug Resolution Times In Large Open Source Ecosystems: A Multisystem Study, Subhajit Datta, Reshma Roychoudhuri, Subhashis Majumder
Research Collection School Of Computing and Information Systems
Large‐scale software systems are being increasingly built by distributed teams of developers who interact across geographies and time zones. Ensuring smooth knowledge transfer and the percolation of skills within and across such teams remain key challenges for organizations. Towards addressing this challenge, organizations often grapple with questions around whether and how repeat collaborations between members of a team relate to outcomes of important activities. In the context of this paper, the word ‘repeat interaction’ does not imply a greater number of interactions; it refers to repeat interaction between a pair of developers who have collaborated before. In this paper, we …
Recipegpt: Generative Pre-Training Based Cooking Recipe Generation And Evaluation System, Helena Huey Chong Lee, Ke Shu, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, Lav R. Varshney
Recipegpt: Generative Pre-Training Based Cooking Recipe Generation And Evaluation System, Helena Huey Chong Lee, Ke Shu, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, Lav R. Varshney
Research Collection School Of Computing and Information Systems
Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes. We present RecipeGPT, a novel online recipe generation and evaluation system. The system provides two modes of text generations: (1) instruction generation from given recipe title and ingredients; and (2) ingredient generation from recipe title and cooking instructions. Its back-end text generation module comprises a generative pre-trained language model GPT-2 fine-tuned on a large cooking recipe dataset. Moreover, the recipe evaluation module allows the users to conveniently inspect the quality of the generated recipe …
Leakage-Resilient Biometric-Based Remote User Authentication With Fuzzy Extractors, Yangguang Tian, Yingjiu Li, Binanda Sengupta, Nan Li, Chunhua Su
Leakage-Resilient Biometric-Based Remote User Authentication With Fuzzy Extractors, Yangguang Tian, Yingjiu Li, Binanda Sengupta, Nan Li, Chunhua Su
Research Collection School Of Computing and Information Systems
Fuzzy extractors convert biometrics and other noisy data into a cryptographic key for security applications such as remote user authentication. Leakage attacks, such as side channel attacks, have been extensively modelled and studied in the literature. However, to the best of our knowledge, leakage attacks to biometric-based remote user authentication with fuzzy extractors have never been studied rigorously. In this paper, we propose a generic framework of leakage-resilient and privacy-preserving biometric-based remote user authentication that allows an authorized user to securely authenticate herself to a remote authentication server using her biometrics. In particular, the authorized user relies only on her …
How To And How Much? Teaching Ethics In An Interaction Design Course, Bimlesh Wadhwa, Eng Lieh Ouh, Benjamin Gan
How To And How Much? Teaching Ethics In An Interaction Design Course, Bimlesh Wadhwa, Eng Lieh Ouh, Benjamin Gan
Research Collection School Of Computing and Information Systems
How much is sufficient and how should one teach ethics in an Interaction Design curriculum in undergraduate computing program has been a point of dilemma for many HCI educators. We conducted a preliminary study using a mixed method to gather perception on ethics in our interaction design courses at two of the leading Singapore Universities. We answer three research questions specific to an undergraduate HCI course: Is there a need for ethics? Is there sufficient ethics coverage? and how to teach ethics? We surveyed 140 students and interviewed six teachers in two Singapore Universities. Our findings suggest that 92% of …
Artificial Intelligence (Ai) Ethics: Ethics Of Ai And Ethical Ai, Keng Siau, Weiyu Wang
Artificial Intelligence (Ai) Ethics: Ethics Of Ai And Ethical Ai, Keng Siau, Weiyu Wang
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI)-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies. As AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. Even though the concept of “machine ethics” was proposed around 2006, AI ethics is still in the infancy stage. AI ethics …
Google Summer Of Code: Student Motivations And Contributions, Jefferson O. Silva, Igor Scaliante Wiese, Daniel M. Germán, Christoph Treude, Marco Aurélio Gerosa, Igor Steinmacher
Google Summer Of Code: Student Motivations And Contributions, Jefferson O. Silva, Igor Scaliante Wiese, Daniel M. Germán, Christoph Treude, Marco Aurélio Gerosa, Igor Steinmacher
Research Collection School Of Computing and Information Systems
Several open source software (OSS) projects participate in engagement programs like Summers of Code expecting to foster newcomers’ onboarding and receive contributions. However, scant empirical evidence identifies why students join such programs. In this paper, we study the well-established Google Summer of Code (GSoC), which is a 3-month OSS engagement program that offers stipends and mentorship to students willing to contribute to OSS projects. We combined a survey (of students and mentors) and interviews (of students) to understand what motivates students to enter GSoC. Our results show that students enter GSoC for an enriching experience, and not necessarily to become …
Identity-Based Encryption Transformation For Flexible Sharing Of Encrypted Data In Public Cloud, Robert H. Deng, Zheng Qin, Qianhong Wu, Zhenyu Guan, Robert H. Deng, Yujue Wang, Yunya Zhou
Identity-Based Encryption Transformation For Flexible Sharing Of Encrypted Data In Public Cloud, Robert H. Deng, Zheng Qin, Qianhong Wu, Zhenyu Guan, Robert H. Deng, Yujue Wang, Yunya Zhou
Research Collection School Of Computing and Information Systems
With the rapid development of cloud computing, an increasing number of individuals and organizations are sharing data in the public cloud. To protect the privacy of data stored in the cloud, a data owner usually encrypts his data in such a way that certain designated data users can decrypt the data. This raises a serious problem when the encrypted data needs to be shared to more people beyond those initially designated by the data owner. To address this problem, we introduce and formalize an identity-based encryption transformation (IBET) model by seamlessly integrating two well-established encryption mechanisms, namely identity-basedencryption (IBE) and …
Energy-Efficient Distance-Bounding With Residual Charge Computation, Yunhui Zhuang, Anjia Yang, Gerhard Hancke, Duncan S. Wong, Guomin Yang
Energy-Efficient Distance-Bounding With Residual Charge Computation, Yunhui Zhuang, Anjia Yang, Gerhard Hancke, Duncan S. Wong, Guomin Yang
Research Collection School Of Computing and Information Systems
Real-time location systems are often required in industrial applications. In addition to securely determining an item's location, these systems also need to accommodate energy-limited tracking tokens. Distance-bounding protocols enable a Verifier to cryptographically determine an upper-bound on the physical distance to a Prover by measuring the round-trip time of specially designed challenge-response messages. This type of protocols serve as countermeasure to three common attacks on location-based systems and have been extensively studied with the goal of achieving optimal security bounds for the respective attacks. In this paper, we propose a new energy-efficient distance-bounding protocol that protects against all three common …
On The Security Of Lwe Cryptosystem Against Subversion Attacks, Zhichao Yang, Rongmao Chen, Chao Li, Longjiang Qu, Guomin Yang
On The Security Of Lwe Cryptosystem Against Subversion Attacks, Zhichao Yang, Rongmao Chen, Chao Li, Longjiang Qu, Guomin Yang
Research Collection School Of Computing and Information Systems
Subversion of cryptography has received wide attentions especially after the Snowden Revelations in 2013. Most of the currently proposed subversion attacks essentially rely on the freedom of randomness choosing in the cryptographic protocol to hide backdoors embedded in the cryptosystems. Despite the fact that significant progresses in this line of research have been made, most of them mainly considered the classical setting, while the research gap regarding subversion attacks against post-quantum cryptography remains tremendous. Inspired by this observation, we investigate a subversion attack against existing protocol that is proved post-quantum secure. Particularly, we show an efficient way to undetectably subvert …
Reinforced Negative Sampling Over Knowledge Graph For Recommendation, Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua
Reinforced Negative Sampling Over Knowledge Graph For Recommendation, Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples — both informative to model training and reflective of user real needs. In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. Towards this end, we develop a new negative sampling model, Knowledge Graph Policy …
Improving Neural Relation Extraction With Implicit Mutual Relations, Jun Kuang, Yixin Cao, Jianbing Zheng, Xiangnan He, Ming Gao, Aoying Zhou
Improving Neural Relation Extraction With Implicit Mutual Relations, Jun Kuang, Yixin Cao, Jianbing Zheng, Xiangnan He, Ming Gao, Aoying Zhou
Research Collection School Of Computing and Information Systems
Relation extraction (RE) aims at extracting the relation between two entities from the text corpora. It is a crucial task for Knowledge Graph (KG) construction. Most existing methods predict the relation between an entity pair by learning the relation from the training sentences, which contain the targeted entity pair. In contrast to existing distant supervision approaches that suffer from insufficient training corpora to extract relations, our proposal of mining implicit mutual relation from the massive unlabeled corpora transfers the semantic information of entity pairs into the RE model, which is more expressive and semantically plausible. After constructing an entity proximity …
Techniques To Visualize Occluded Graph Elements For 2.5d Map Editing, Kazuyuki Fujita, Daigo Hayashi, Kotaro Hara, Kazuki Takashima, Yoshifumi Kitamura
Techniques To Visualize Occluded Graph Elements For 2.5d Map Editing, Kazuyuki Fujita, Daigo Hayashi, Kotaro Hara, Kazuki Takashima, Yoshifumi Kitamura
Research Collection School Of Computing and Information Systems
We propose an interface with two novel techniques to visualize occluded graph nodes and edges that help the user edit map data with a 2.5D geographical structure (e.g., multi-floor indoor maps). We first design a visualization technique —Repel Signification— that employs micro-animation to signify the graph elements that are overlapping with each other (and potentially erroneous). We also design a technique that enables the user to edit the occluded components with Expansion Interaction, which simultaneously visualizes both in-floor and across-floor occluded connections between the map elements. The combination of the two methods would enable the map editors (non-experts) to effectively …
Voicecoach: Interactive Evidence-Based Training For Voice Modulation Skills In Public Speaking, Xingbo Wang, Haipeng Zeng, Yong Wang, Aoyu Wu, Zhida Sun, Xiaojuan Ma, Qu Huamin
Voicecoach: Interactive Evidence-Based Training For Voice Modulation Skills In Public Speaking, Xingbo Wang, Haipeng Zeng, Yong Wang, Aoyu Wu, Zhida Sun, Xiaojuan Ma, Qu Huamin
Research Collection School Of Computing and Information Systems
The modulation of voice properties, such as pitch, volume, and speed, is crucial for delivering a successful public speech. However, it is challenging to master different voice modulation skills. Though many guidelines are available, they are often not practical enough to be applied in different public speaking situations, especially for novice speakers. We present VoiceCoach, an interactive evidence-based approach to facilitate the effective training of voice modulation skills. Specifically, we have analyzed the voice modulation skills from 2623 high-quality speeches (i.e., TED Talks) and use them as the benchmark dataset. Given a voice input, VoiceCoach automatically recommends good voice modulation …
Dfseer: A Visual Analytics Approach To Facilitate Model Selection For Demand Forecasting, Dong Sun, Zezheng Feng, Yuanzhe Chen, Yong Wang, Jia Zeng, Mingxuan Yuan, Ting-Chuen Pong, Huamin Qu
Dfseer: A Visual Analytics Approach To Facilitate Model Selection For Demand Forecasting, Dong Sun, Zezheng Feng, Yuanzhe Chen, Yong Wang, Jia Zeng, Mingxuan Yuan, Ting-Chuen Pong, Huamin Qu
Research Collection School Of Computing and Information Systems
Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users’ demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model …
Understanding Wikipedia As A Resource For Opportunistic Learning Of Computing Concepts, Martin P. Robillard, Christoph Treude
Understanding Wikipedia As A Resource For Opportunistic Learning Of Computing Concepts, Martin P. Robillard, Christoph Treude
Research Collection School Of Computing and Information Systems
Posts on on-line forums where programmers look for information often include links to Wikipedia when it can be assumed the reader will not be familiar with the linked terms. A Wikipedia article will thus often be the first exposure to a new computing concept for a novice programmer. We conducted an exploratory study with 18 novice programmers by asking them to read a Wikipedia article on a common computing concept that was new to them, while using the think-aloud protocol. We performed a qualitative analysis of the session transcripts to better understand the experience of the novice programmer learning a …
Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang
Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang
Research Collection School Of Computing and Information Systems
Reducing traffic delay is of crucial importance for the development of sustainable transportation systems, which is a challenging task in the studies of stochastic shortest path (SSP) problem. Existing methods based on the probability tail model to solve the SSP problem, seek for the path that minimizes the probability of delay occurrence, which is equal to maximizing the probability of reaching the destination before a deadline (i.e., arriving on time). However, they suffer from low accuracy or high computational cost. Therefore, we design a novel and practical Q-learning approach where the converged Q-values have the practical meaning as the actual …
Heartquake: Accurate Low-Cost Non-Invasive Ecg Monitoring Using Bed-Mounted Geophones, Jaeyeon Park, Hyeon Cho, Rajesh Krishna Balan, Jeonggil Ko
Heartquake: Accurate Low-Cost Non-Invasive Ecg Monitoring Using Bed-Mounted Geophones, Jaeyeon Park, Hyeon Cho, Rajesh Krishna Balan, Jeonggil Ko
Research Collection School Of Computing and Information Systems
This work presents HeartQuake, a low cost, accurate, non-intrusive, geophone-based sensing system for extracting accurate electrocardiogram (ECG) patterns using heartbeat vibrations that penetrate through a bed mattress. In HeartQuake, cardiac activity-originated vibration patterns are captured on a geophone and sent to a server, where the data is filtered to remove the sensor's internal noise and passed on to a bidirectional long short term memory (Bi-LSTM) deep learning model for ECG waveform estimation. To the best of our knowledge, this is the first solution that can non-intrusively provide accurate ECG waveform characteristics instead of more basic abstract features such as the …
Securing Bring-Your-Own-Device (Byod) Programming Exams, Oka Kurniawan, Norman Lee, Christopher M. Poskitt
Securing Bring-Your-Own-Device (Byod) Programming Exams, Oka Kurniawan, Norman Lee, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Traditional pen and paper exams are inadequate for modern university programming courses as they are misaligned with pedagogies and learning objectives that target practical coding ability. Unfortunately, many institutions lack the resources or space to be able to run assessments in dedicated computer labs. This has motivated the development of bring-your-own-device (BYOD) exam formats, allowing students to program in a similar environment to how they learnt, but presenting instructors with significant additional challenges in preventing plagiarism and cheating. In this paper, we describe a BYOD exam solution based on lockdown browsers, software which temporarily turns students' laptops into secure workstations …
Feature Agglomeration Networks For Single Stage Face Detection, Jialiang Zhang, Xiongwei Wu, Steven C. H. Hoi, Jianke Zhu
Feature Agglomeration Networks For Single Stage Face Detection, Jialiang Zhang, Xiongwei Wu, Steven C. H. Hoi, Jianke Zhu
Research Collection School Of Computing and Information Systems
Recent years have witnessed promising results of exploring deep convolutional neural network for face detection. Despite making remarkable progress, face detection in the wild remains challenging especially when detecting faces at vastly different scales and characteristics. In this paper, we propose a novel simple yet effective framework of “Feature Agglomeration Networks” (FANet) to build a new single-stage face detector, which not only achieves state-of-the-art performance but also runs efficiently. As inspired by Feature Pyramid Networks (FPN) (Lin et al., 2017), the key idea of our framework is to exploit inherent multi-scale features of a single convolutional neural network by aggregating …
Pokeme: Applying Context-Driven Notifications To Increase Worker Engagement In Mobile Crowd-Sourcing, Thivya Kandappu, Abhinav Mehrotra, Archan Misra, Mirco Musolesi, Shih-Fen Cheng, Lakmal Buddika Meegahapola
Pokeme: Applying Context-Driven Notifications To Increase Worker Engagement In Mobile Crowd-Sourcing, Thivya Kandappu, Abhinav Mehrotra, Archan Misra, Mirco Musolesi, Shih-Fen Cheng, Lakmal Buddika Meegahapola
Research Collection School Of Computing and Information Systems
In mobile crowd-sourcing systems, simply relying on people to opportunistically select and perform tasks typically leads to drawbacks such as low task acceptance/completion rates and undesirable spatial skews. In this paper, we utilize data from "Smart Campus", a campus-based mobile crowd-sourcing platform, to empirically study and discover whether and how various context-aware notification strategies can help overcome such drawbacks. We first study worker interactions, in the absence of any notifications, to discover some spatio-temporal properties of task acceptance and completion. Based on these insights, we then experimentally demonstrate the effectiveness of two novel, non-personal, context-driven notification strategies, comparing the outcomes …
An Empirical Study On Correlation Between Coverage And Robustness For Deep Neural Networks, Yizhen Dong, Peixin Zhang, Jingyi Wang, Shuang Liu, Jun Sun, Jianye Hao, Xinyu Wang, Li Wang, Jinsong Dong, Ting Dai
An Empirical Study On Correlation Between Coverage And Robustness For Deep Neural Networks, Yizhen Dong, Peixin Zhang, Jingyi Wang, Shuang Liu, Jun Sun, Jianye Hao, Xinyu Wang, Li Wang, Jinsong Dong, Ting Dai
Research Collection School Of Computing and Information Systems
Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs are subject to attacks such as adversarial perturbation and thus must be properly tested. Many coverage criteria for DNN since have been proposed, inspired by the success of code coverage criteria for software programs. The expectation is that if a DNN is well tested (and retrained) according to such coverage criteria, it is more likely to be robust. In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and …
Ifix: Fixing Concurrency Bugs While They Are Introduced, Zan Wang, Haichi Wang, Shuang Liu, Jun Sun, Haoyu Wang, Junjie Chen
Ifix: Fixing Concurrency Bugs While They Are Introduced, Zan Wang, Haichi Wang, Shuang Liu, Jun Sun, Haoyu Wang, Junjie Chen
Research Collection School Of Computing and Information Systems
Concurrency bugs are notoriously hard to identify and fix. A systematic way of avoiding concurrency bugs is to design and implement a locking policy that consistently guards all shared variables. Concurrency bugs thus can be viewed as the result of an illy-designed or poorly implemented locking policy. The trouble is that the locking policy is often not documented, which makes debugging concurrency bugs clueless. We argue that it is too late to debug concurrency bugs after programming is done and we instead detect and fix them while they are being implemented. In this work, we propose an approach named IFIX …
Towards K-Vertex Connected Component Discovery From Large Networks, Li Yuan, Guoren Wang, Yuhai Zhao, Feida Zhu
Towards K-Vertex Connected Component Discovery From Large Networks, Li Yuan, Guoren Wang, Yuhai Zhao, Feida Zhu
Research Collection School Of Computing and Information Systems
In many real life network-based applications such as social relation analysis, Web analysis, collaborative network, road network and bioinformatics, the discovery of components with high connectivity is an important problem. In particular, k-edge connected component (k-ECC) has recently been extensively studied to discover disjoint components. Yet many real scenarios present more needs and challenges for overlapping components. In this paper, we propose a k-vertex connected component (k-VCC) model, which is much more cohesive, and thus supports overlapping between components very well. To discover k-VCCs, we propose three frameworks including top-down, bottom-up and hybrid …
Learning Fault Models Of Cyber Physical Systems, Teck Ping Khoo, Jun Sun, Sudipta Chattopadhyay
Learning Fault Models Of Cyber Physical Systems, Teck Ping Khoo, Jun Sun, Sudipta Chattopadhyay
Research Collection School Of Computing and Information Systems
Cyber Physical Systems (CPSs) comprise sensors and actuators which interact with the physical environment over a computer network to achieve some control objective. Bugs in CPSs can have severe consequences as CPSs are increasingly deployed in safety-critical applications. Debugging CPSs is therefore an important real world problem. Traces from a CPS can be lengthy and are usually linked to different parts of the system, making debugging CPSs a complex and time-consuming undertaking. It is challenging to isolate a component without running the whole CPS. In this work, we propose a model-based approach to debugging a CPS. For each CPS property, …