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Articles 151 - 169 of 169
Full-Text Articles in Physical Sciences and Mathematics
Identifying Self-Admitted Technical Debt In Open Source Projects Using Text Mining, Qiao Huang, Emad Shihab, Xin Xia, David Lo, Shanping Li
Identifying Self-Admitted Technical Debt In Open Source Projects Using Text Mining, Qiao Huang, Emad Shihab, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Technical debt is a metaphor to describe the situation in which long-term code quality is traded for short-term goals in software projects. Recently, the concept of self-admitted technical debt (SATD) was proposed, which considers debt that is intentionally introduced, e.g., in the form of quick or temporary fixes. Prior work on SATD has shown that source code comments can be used to successfully detect SATD, however, most current state-of-the-art classification approaches of SATD rely on manual inspection of the source code comments. In this paper, we proposed an automated approach to detect SATD in source code comments using text mining. …
Attribute-Based Cloud Storage With Secure Provenance Over Encrypted Data, Hui Cui, Robert H. Deng, Yingjiu Li
Attribute-Based Cloud Storage With Secure Provenance Over Encrypted Data, Hui Cui, Robert H. Deng, Yingjiu Li
Research Collection School Of Computing and Information Systems
To securely and conveniently enjoy the benefits of cloud storage, it is desirable to design a cloud data storage system which protects data privacy from storage servers through encryption, allows fine-grained access control such that data providers can expressively specify who are eligible to access the encrypted data, enables dynamic user management such that the total number of data users is unbounded and user revocation can be carried out conveniently, supports data provider anonymity and traceability such that a data provider’s identity is not disclosed to data users in normal circumstances but can be traced by a trusted authority if …
Upping The Game Of Taxi Driving In The Age Of Uber, Shashi Shekhar Jha, Shih-Fen Cheng, Meghna Lowalekar, Wai Hin Wong, Rajendram Rishikeshan Rajendram, Trong Khiem Tran, Pradeep Varakantham, Nghia Truong Trong, Firmansyah Abd Rahman
Upping The Game Of Taxi Driving In The Age Of Uber, Shashi Shekhar Jha, Shih-Fen Cheng, Meghna Lowalekar, Wai Hin Wong, Rajendram Rishikeshan Rajendram, Trong Khiem Tran, Pradeep Varakantham, Nghia Truong Trong, Firmansyah Abd Rahman
Research Collection School Of Computing and Information Systems
In most cities, taxis play an important role in providing point-to-point transportation service. If the taxi service is reliable, responsive, and cost-effective, past studies show that taxi-like services can be a viable choice in replacing a significant amount of private cars. However, making taxi services efficient is extremely challenging, mainly due to the fact that taxi drivers are self-interested and they operate with only local information. Although past research has demonstrated how recommendation systems could potentially help taxi drivers in improving their performance, most of these efforts are not feasible in practice. This is mostly due to the lack of …
Modelling Domain Relationships For Transfer Learning On Retrieval-Based Question Answering Systems In E-Commerce, Jianfei Yu, Minghui Qiu, Jing Jiang, Jun Huang, Shuangyong Song, Wei Chu, Haiqing Chen
Modelling Domain Relationships For Transfer Learning On Retrieval-Based Question Answering Systems In E-Commerce, Jianfei Yu, Minghui Qiu, Jing Jiang, Jun Huang, Shuangyong Song, Wei Chu, Haiqing Chen
Research Collection School Of Computing and Information Systems
Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning …
Sequential Recommendation With User Memory Networks, Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, Hongyuan Zha
Sequential Recommendation With User Memory Networks, Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, Hongyuan Zha
Research Collection School Of Computing and Information Systems
User preferences are usually dynamic in real-world recommender systems, and a user»s historical behavior records may not be equally important when predicting his/her future interests. Existing recommendation algorithms -- including both shallow and deep approaches -- usually embed a user»s historical records into a single latent vector/representation, which may have lost the per item- or feature-level correlations between a user»s historical records and future interests. In this paper, we aim to express, store, and manipulate users» historical records in a more explicit, dynamic, and effective manner. To do so, we introduce the memory mechanism to recommender systems. Specifically, we design …
Things You May Not Know About Android (Un)Packers: A Systematic Study Based On Whole-System Emulation, Yue Duan, Mu Zhang, Abhishek Vasist Bhaskar, Heng Yin, Xiaorui Pan, Tongxin Li, Xueqiang Wang, Xiaofeng Wang
Things You May Not Know About Android (Un)Packers: A Systematic Study Based On Whole-System Emulation, Yue Duan, Mu Zhang, Abhishek Vasist Bhaskar, Heng Yin, Xiaorui Pan, Tongxin Li, Xueqiang Wang, Xiaofeng Wang
Research Collection School Of Computing and Information Systems
The prevalent usage of runtime packers has complicated Android malware analysis, as both legitimate and malicious apps are leveraging packing mechanisms to protect themselves against reverse engineer. Although recent efforts have been made to analyze particular packing techniques, little has been done to study the unique characteristics of Android packers. In this paper, we report the first systematic study on mainstream Android packers, in an attempt to understand their security implications. For this purpose, we developed DROIDUNPACK, a whole-system emulation based Android packing analysis framework, which compared with existing tools, relies on intrinsic characteristics of Android runtime (rather than heuristics), …
Compressive Representation For Device-Free Activity Recognition With Passive Rfid Signal Strength, Lina Yao, Quan Z. Sheng, Xue Li, Tao Gu, Mingkui Tan, Xianzhi Wang, Sen Wang, Wenjie Ruan
Compressive Representation For Device-Free Activity Recognition With Passive Rfid Signal Strength, Lina Yao, Quan Z. Sheng, Xue Li, Tao Gu, Mingkui Tan, Xianzhi Wang, Sen Wang, Wenjie Ruan
Research Collection School Of Computing and Information Systems
Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system …
Enhanced Vireo Kis At Vbs 2018, Phuong Anh Nguyen, Yi-Jie Lu, Hao Zhang, Chong-Wah Ngo
Enhanced Vireo Kis At Vbs 2018, Phuong Anh Nguyen, Yi-Jie Lu, Hao Zhang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
The VIREO Known-Item Search (KIS) system has joined the Video Browser Showdown (VBS) [1] evaluation benchmark for the first time in year 2017. With experiences learned, the second version of VIREO KIS is presented in this paper. Considering the color-sketch based retrieval, we propose a simple grid-based approach for color query. This method allows the aggregation of color distributions in video frames into a shot representation, and generates the pre-computed rank list for all available queries which reduces computational resources and favors a recommendation module. With focusing on concept based retrieval, we modify our multimedia event detection system at TRECVID …
R3: Reinforced Ranker-Reader For Open-Domain Question Answering, Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, Jing Jiang
R3: Reinforced Ranker-Reader For Open-Domain Question Answering, Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, Jing Jiang
Research Collection School Of Computing and Information Systems
In recent years researchers have achieved considerable success applyingneural network methods to question answering (QA). These approaches haveachieved state of the art results in simplified closed-domain settings such asthe SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selectedpassage, from which the answer to a given question may be extracted. Morerecently, researchers have begun to tackle open-domain QA, in which the modelis given a question and access to a large corpus (e.g., wikipedia) instead of apre-selected passage (Chen et al., 2017a). This setting is more complex as itrequires large-scale search for relevant passages by an information retrievalcomponent, combined with a …
Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods, Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi
Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods, Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on scalable online kernel learning have attempted to overcome this shortcoming, e.g., by imposing a constant budget on the number of support vectors. Although they attempt to bound the number of support vectors at each online learning iteration, most of them fail to bound the number of support vectors for the final output hypothesis, which is often obtained by averaging the series of hypotheses over all the …
Food Photo Recognition For Dietary Tracking: System And Experiment, Zhao-Yan Ming, Jingjing Chen, Yu Cao, Ciarán Forde, Chong-Wah Ngo, Tat Seng Chua
Food Photo Recognition For Dietary Tracking: System And Experiment, Zhao-Yan Ming, Jingjing Chen, Yu Cao, Ciarán Forde, Chong-Wah Ngo, Tat Seng Chua
Research Collection School Of Computing and Information Systems
Tracking dietary intake is an important task for health management especially for chronic diseases such as obesity, diabetes, and cardiovascular diseases. Given the popularity of personal hand-held devices, mobile applications provide a promising low-cost solution to tackle the key risk factor by diet monitoring. In this work, we propose a photo based dietary tracking system that employs deep-based image recognition algorithms to recognize food and analyze nutrition. The system is beneficial for patients to manage their dietary and nutrition intake, and for the medical institutions to intervene and treat the chronic diseases. To the best of our knowledge, there are …
User-Friendly Deniable Storage For Mobile Devices, Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen-Tao Zhu, Yingjiu Li, Zhan. Wang
User-Friendly Deniable Storage For Mobile Devices, Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen-Tao Zhu, Yingjiu Li, Zhan. Wang
Research Collection School Of Computing and Information Systems
Mobile devices are prevalently used to process sensitive data, but traditional encryption may not work when an adversary is able to coerce the device owners to disclose the encryption keys. Plausibly Deniable Encryption (PDE) is thus designed to protect sensitive data against this powerful adversary. In this paper, we present MobiPluto, a user-friendly PDE scheme for denying the existence of sensitive data stored on mobile devices. A salient difference between MobiPluto and the existing PDE systems is that any block-based file systems can be deployed on top of it. To further improve usability and deniability of MobiPluto, we introduce a …
Exact And Heuristic Approaches For The Multi-Agent Orienteering Problem With Capacity Constraints, Wenjie Wang, Hoong Chuin Lau, Shih-Fen Cheng
Exact And Heuristic Approaches For The Multi-Agent Orienteering Problem With Capacity Constraints, Wenjie Wang, Hoong Chuin Lau, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
This paper introduces and addresses a new multiagent variant of the orienteering problem (OP), namely the multi-agent orienteering problem with capacity constraints (MAOPCC). Different from the existing variants of OP, MAOPCC allows a group of visitors to concurrently visit a node but limits the number of visitors simultaneously being served at each node. In this work, we solve MAOPCC in a centralized manner and optimize the total collected rewards of all agents. A branch and bound algorithm is first proposed to find an optimal MAOPCC solution. Since finding an optimal solution for MAOPCC can become intractable as the number of …
Smart Monitoring Via Participatory Ble Relaying, Meeralakshmi Radhakrishnan, Sougata Sen, Archan Misra, Youngki Lee, Rajesh Krishna Balan
Smart Monitoring Via Participatory Ble Relaying, Meeralakshmi Radhakrishnan, Sougata Sen, Archan Misra, Youngki Lee, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
We espouse the vision of a smart object/campus architecture where sensors attached to smart objects use BLE as communication interface, and where smartphones act as opportunistic relays to transfer the data. We explore the feasibility of the vision with real-world Wi-Fi based location traces from our university campus. Our feasibility studies establish that redundancy exists in user movement within the indoor spaces, and that this redundancy can be exploited for collecting sensor data in an opportunistic, yet fair manner. We develop a couple of alternative heuristics that address the BLE energy asymmetry challenge by intelligently duty-cycling the scanning actions of …
Multi-Target Deep Neural Networks: Theoretical Analysis And Implementation, Zeng Zeng, Nanying Liang, Xulei Yang, Steven C. H. Hoi
Multi-Target Deep Neural Networks: Theoretical Analysis And Implementation, Zeng Zeng, Nanying Liang, Xulei Yang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this work, we propose a novel deep neural network referred to as Multi-Target Deep Neural Network (MT-DNN). We theoretically prove that different stable target models with shared learning paths are stable and can achieve optimal solutions respectively. Based on GoogleNet, we design a single model with three different targets, one for classification, one for regression, and one for masks that is composed of 256 × 256 sub-models. Unlike bounding boxes used in ImageNet, our single model can draw the shapes of target objects, and in the meanwhile, classify the objects and calculate their sizes. We validate our single MT-DNN …
Code: Coherence Based Decision Boundaries For Feature Correspondence, Wen-Yan Lin, Fan Wang, Ming-Ming Cheng, Sai-Kit Yeung, Philip H. S. Torr, Jiangbo Lu
Code: Coherence Based Decision Boundaries For Feature Correspondence, Wen-Yan Lin, Fan Wang, Ming-Ming Cheng, Sai-Kit Yeung, Philip H. S. Torr, Jiangbo Lu
Research Collection School Of Computing and Information Systems
A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model. Once computed, the model can filter the …
Collaboration Patterns In Software Developer Network, Didi Surian, Ee-Peng Lim, David Lo
Collaboration Patterns In Software Developer Network, Didi Surian, Ee-Peng Lim, David Lo
Research Collection School Of Computing and Information Systems
In this entry, we mine collaboration patterns from a large software developer network (Surian et al. 2010). We consider high- and low-level patterns. High-level patterns correspond to various network-level statistics that we observe to hold in this network. Low-level patterns are topological subgraph patterns that are frequently observed among developers collaborating in the network. Mining topological subgraph patterns are difficult as it is an NP-hard problem. To address this issue, we use a combination of frequent subgraph mining and graph matching by leveraging the power law property exhibited by a large collaboration graph. The technique is applicable to any software …
Skylens: Visual Analysis Of Skyline On Multi-Dimensional Data, Xun Zhao, Yanhong Wu, Weiwei Cui, Xinnan Du, Yuan Chen, Yong Wang, Dik Lun Lee, Huamin Qu
Skylens: Visual Analysis Of Skyline On Multi-Dimensional Data, Xun Zhao, Yanhong Wu, Weiwei Cui, Xinnan Du, Yuan Chen, Yong Wang, Dik Lun Lee, Huamin Qu
Research Collection School Of Computing and Information Systems
Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e.. the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points …
Anatomy Of Online Hate: Developing A Taxonomy And Machine Learning Models For Identifying And Classifying Hate In Online News Media, Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-Gyu Jung, Haewoon Kwak, Haewoon Kwak, Bernard J. Jansen
Anatomy Of Online Hate: Developing A Taxonomy And Machine Learning Models For Identifying And Classifying Hate In Online News Media, Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-Gyu Jung, Haewoon Kwak, Haewoon Kwak, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both …