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Articles 4471 - 4500 of 8495
Full-Text Articles in Computer Sciences
File-Level Defect Prediction: Unsupervised Vs. Supervised Models, Meng Yan, Yicheng Fang, David Lo, Xin Xia, Xiaohong Zhang
File-Level Defect Prediction: Unsupervised Vs. Supervised Models, Meng Yan, Yicheng Fang, David Lo, Xin Xia, Xiaohong Zhang
Research Collection School Of Computing and Information Systems
Background: Software defect models can help software quality assurance teams to allocate testing or code review resources. A variety of techniques have been used to build defect prediction models, including supervised and unsupervised methods. Recently, Yang et al. [1] surprisingly find that unsupervised models can perform statistically significantly better than supervised models in effort-aware change-level defect prediction. However, little is known about relative performance of unsupervised and supervised models for effort-aware file-level defect prediction. Goal: Inspired by their work, we aim to investigate whether a similar finding holds in effort-aware file-level defect prediction. Method: We replicate Yang et al.'s study …
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu
Research Collection School Of Computing and Information Systems
Data fusion is a fundamental research problem of identifying true values of data items of interest from conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, existing approaches generally assume every data item has exactly one true value, which fails to reflect the real world where data items with multiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items. SourceVote models the endorsement relations among sources by quantifying their two-sided inter-source agreements. In particular, two graphs are constructed to model inter-source relations. Then two aspects …
Tweet Geolocation: Leveraging Location, User And Peer Signals, Wen-Haw Chong, Ee Peng Lim
Tweet Geolocation: Leveraging Location, User And Peer Signals, Wen-Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Which venue is a tweet posted from? We referred this as fine-grained geolocation. To solve this problem effectively, we develop novel techniques to exploit each posting user's content history. This is motivated by our finding that most users do not share their visitation history, but have ample content history from tweet posts. We formulate fine-grained geolocation as a ranking problem whereby given a test tweet, we rank candidate venues. We propose several models that leverage on three types of signals from locations, users and peers. Firstly, the location signals are words that are indicative of venues. We propose a location-indicative …
Interactive Social Recommendation, Xin Wang, Steven C. H. Hoi, Chenghao Liu, Martin Ester
Interactive Social Recommendation, Xin Wang, Steven C. H. Hoi, Chenghao Liu, Martin Ester
Research Collection School Of Computing and Information Systems
Social recommendation has been an active research topic over the last decade, based on the assumption that social information from friendship networks is beneficial for improving recommendation accuracy, especially when dealing with cold-start users who lack sufficient past behavior information for accurate recommendation. However, it is nontrivial to use such information, since some of a person's friends may share similar preferences in certain aspects, but others may be totally irrelevant for recommendations. Thus one challenge is to explore and exploit the extend to which a user trusts his/her friends when utilizing social information to improve recommendations. On the other hand, …
On Analyzing Job Hop Behavior And Talent Flow Networks, Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
On Analyzing Job Hop Behavior And Talent Flow Networks, Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo
Research Collection School Of Computing and Information Systems
Analyzing job hopping behavior is important for theunderstanding of job preference and career progression of working individuals.When analyzed at the workforce population level, job hop analysis helps to gaininsights of talent flow and organization competition. Traditionally, surveysare conducted on job seekers and employers to study job behavior. While surveysare good at getting direct user input to specially designed questions, they areoften not scalable and timely enough to cope with fast-changing job landscape.In this paper, we present a data science approach to analyze job hops performedby about 490,000 working professionals located in a city using their publiclyshared profiles. We develop several …
Introducing People With Asd To Crowd Work, Kotaro Hara, Jeffrey P. Bigham
Introducing People With Asd To Crowd Work, Kotaro Hara, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
Adults with Autism Spectrum Disorders (ASD) are unemployed at a high rate, in part because the constraints and expectations of traditional employment can be difficult for them. In this paper, we report on our work in introducing people with ASD to remote work on a crowdsourcing platform and a prototype tool we developed by working with participants. We conducted a six-week long user-centered design study with three participants with ASD. The early stage of the study focused on assessing the abilities of our participants to search and work on micro-tasks available on the crowdsourcing market. Based on our preliminary findings, …
Temporal Understanding Of Human Mobility: A Multi-Time Scale Analysis, Tongtong Liu, Zheng Yang, Yi Zhao, Chenshu Wu, Zimu Zhou, Yunhao Liu
Temporal Understanding Of Human Mobility: A Multi-Time Scale Analysis, Tongtong Liu, Zheng Yang, Yi Zhao, Chenshu Wu, Zimu Zhou, Yunhao Liu
Research Collection School Of Computing and Information Systems
The recent availability of digital traces generated by cellphone calls has significantly increased the scientific understanding of human mobility. Until now, however, based on low time resolution measurements, previous works have ignored to study human mobility under various time scales due to sparse and irregular calls, particularly in the era of mobile Internet. In this paper, we introduced Mobile Flow Records, flow-level data access records of online activity of smartphone users, to explore human mobility. Mobile Flow Records collect high-resolution information of large populations. By exploiting this kind of data, we show the models and statistics of human mobility at …
Language Inclusion Checking Of Timed Automata With Non-Zenoness, Xinyu Wang, Jun Sun, Ting Wang, Shengchao Qin
Language Inclusion Checking Of Timed Automata With Non-Zenoness, Xinyu Wang, Jun Sun, Ting Wang, Shengchao Qin
Research Collection School Of Computing and Information Systems
Given a timed automaton P modeling an implementation and a timed automaton S as a specification, the problem of language inclusion checking is to decide whether the language of P is a subset of that of S. It is known to be undecidable. The problem gets more complicated if non-Zenoness is taken into consideration. A run is Zeno if it permits infinitely many actions within finite time. Otherwise it is non-Zeno. Zeno runs might present in both P and S. It is necessary to check whether a run is Zeno or not so as to avoid presenting Zeno runs as …
Anomaly Detection For A Water Treatment System Using Unsupervised Machine Learning, Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Anomaly Detection For A Water Treatment System Using Unsupervised Machine Learning, Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Research Collection School Of Computing and Information Systems
In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 …
A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao Wu, Weiwei Sun, Baihua Zheng
A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao Wu, Weiwei Sun, Baihua Zheng
Research Collection School Of Computing and Information Systems
Trajectory outlier detection is a fundamental building block for many location-based service (LBS) applications, with a large application base. We dedicate this paper on detecting the outliers from vehicle trajectories efficiently and effectively. In addition, we want our solution to be able to issue an alarm early when an outlier trajectory is only partially observed (i.e., the trajectory has not yet reached the destination). Most existing works study the problem on general Euclidean trajectories and require accesses to the historical trajectory database or computations on the distance metric that are very expensive. Furthermore, few of existing works consider some specific …
Presence Attestation: The Missing Link In Dynamic Trust Bootstrapping, Zhangkai Zhang, Xuhua Ding, Gene Tsudik, Jinhua Cui, Zhoujun Li
Presence Attestation: The Missing Link In Dynamic Trust Bootstrapping, Zhangkai Zhang, Xuhua Ding, Gene Tsudik, Jinhua Cui, Zhoujun Li
Research Collection School Of Computing and Information Systems
Many popular modern processors include an important hardware security feature in the form of a DRTM (Dynamic Root of Trust for Measurement) that helps bootstrap trust and resists software attacks. However, despite substantial body of prior research on trust establishment, security of DRTM was treated without involvement of the human user, who represents a vital missing link. The basic challenge is: how can a human user determine whether an expected DRTM is currently active on her device? In this paper, we define the notion of “presence attestation”, which is based on mandatory, though minimal, user participation. We present three concrete …
Selective Value Coupling Learning For Detecting Outliers In High-Dimensional Categorical Data, Guansong Pang, Hongzuo Xu, Cao Longbing, Wentao Zhao
Selective Value Coupling Learning For Detecting Outliers In High-Dimensional Categorical Data, Guansong Pang, Hongzuo Xu, Cao Longbing, Wentao Zhao
Research Collection School Of Computing and Information Systems
This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data. Existing outlier detection methods work on a full data space or feature subspaces that are identified independently from subsequent outlier scoring. As a result, they are significantly challenged by overwhelming irrelevant features in high-dimensional data due to the noise brought by the irrelevant features and its huge search space. In contrast, SelectVC works on a clean and condensed data space spanned by selective …
Classification-Based Parameter Synthesis For Parametric Timed Automata, Jiaying Li, Jun Sun, Bo Gao, Étienne Andre
Classification-Based Parameter Synthesis For Parametric Timed Automata, Jiaying Li, Jun Sun, Bo Gao, Étienne Andre
Research Collection School Of Computing and Information Systems
Parametric timed automata are designed to model timed systems with unknown parameters, often representing design uncertainties of external environments. In order to design a robust system, it is crucial to synthesize constraints on the parameters, which guarantee the system behaves according to certain properties. Existing approaches suffer from scalability issues. In this work, we propose to enhance existing approaches through classification-based learning. We sample multiple concrete values for parameters and model check the corresponding non-parametric models. Based on the checking results, we form conjectures on the constraint through classification techniques, which can be subsequently confirmed by existing model checkers for …
Design And Implementation Of A Csi-Based Ubiquitous Smoking Detection System, Xiaolong Zheng, Jilian Wang, Longfei Shangguan, Zimu Zhou, Yunhao Liu
Design And Implementation Of A Csi-Based Ubiquitous Smoking Detection System, Xiaolong Zheng, Jilian Wang, Longfei Shangguan, Zimu Zhou, Yunhao Liu
Research Collection School Of Computing and Information Systems
Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous detection service. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, Smokey, which leverages the patterns smoking leaves on WiFi signal to identify the smoking activity even in the non-line-of-sight and throughwall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detectionbased motion acquisition method …
Efficient And Robust Emergence Of Norms Through Heuristic Collective Learning, Jianye Hao, Jun Sun, Guangyong Chen, Zan Wang, Chao Yu, Zhong Ming
Efficient And Robust Emergence Of Norms Through Heuristic Collective Learning, Jianye Hao, Jun Sun, Guangyong Chen, Zan Wang, Chao Yu, Zhong Ming
Research Collection School Of Computing and Information Systems
In multiagent systems, social norms serves as an important technique in regulating agents’ behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques. In this article, we propose two novel learning strategies under the collective learning framework, collective learning EV-l and collective learning EV-g, to efficiently facilitate the emergence of social norms. Extensive simulations results show that both learning strategies can support the emergence of desirable …
On Negative Results When Using Sentiment Analysis Tools For Software Engineering Research, Robbert Jongeling, Proshanta Sarkar, Subhajit Datta, Alexander Serebrenik
On Negative Results When Using Sentiment Analysis Tools For Software Engineering Research, Robbert Jongeling, Proshanta Sarkar, Subhajit Datta, Alexander Serebrenik
Research Collection School Of Computing and Information Systems
Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact …
Benchmarking Single-Image Reflection Removal Algorithms, Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Benchmarking Single-Image Reflection Removal Algorithms, Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Research Collection School Of Computing and Information Systems
Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems. Various approaches have been proposed and shown to be visually plausible on small datasets collected by their authors. A quantitative comparison of existing approaches using the same dataset has never been conducted due to the lack of suitable benchmark data with ground truth. This paper presents the first captured Single-image Reflection Removal dataset `SIR 2 ' with 40 controlled and 100 wild scenes, ground truth of background and reflection. For each controlled scene, we further provide …
Spatiotemporal Identification Of Anomalies In A Wildlife Preserve, Bharadwaj Kishan, Jason Guan Jie Ong, Yanrong Zhang, Tin Seong Kam
Spatiotemporal Identification Of Anomalies In A Wildlife Preserve, Bharadwaj Kishan, Jason Guan Jie Ong, Yanrong Zhang, Tin Seong Kam
Research Collection School Of Computing and Information Systems
The datasets released for the VAST Challenge 2017 comprise vehicle movement data captured with RFID sensors, chemical emission data from factories captured by gas sensors, and image attributes of the wildlife plant health obtained from satellites, all pertaining to a fictional wildlife preserve. Using visual analytics, a compelling hypothesis is established to link the spatiotemporal datasets to the phenomenon, where the count of a bird specimen is found to decline over a given year. Anomalies in vehicle traffic patterns are linked to proximal factory emissions, and further associated with satellite imagery that show proof of degradation in plant quality in …
Fastshrinkage: Perceptually-Aware Retargeting Toward Mobile Platforms, Zhenguang Liu, Zepeng Wang, Luming Zhang, Rajiv Ratn Shah, Yingjie Xia, Yi Yang, Wei Liu
Fastshrinkage: Perceptually-Aware Retargeting Toward Mobile Platforms, Zhenguang Liu, Zepeng Wang, Luming Zhang, Rajiv Ratn Shah, Yingjie Xia, Yi Yang, Wei Liu
Research Collection School Of Computing and Information Systems
Retargeting aims at adapting an original high-resolution photo/video to a low-resolution screen with an arbitrary aspect ratio. Conventional approaches are generally based on desktop PCs, since the computation might be intolerable for mobile platforms (especially when retargeting videos). Besides, only low-level visual features are exploited typically, whereas human visual perception is not well encoded. In this paper, we propose a novel retargeting framework which fast shrinks photo/video by leveraging human gaze behavior. Specifically, we first derive a geometry-preserved graph ranking algorithm, which efficiently selects a few salient object patches to mimic human gaze shifting path (GSP) when viewing each scenery. …
Delving Into Salient Object Subitizing And Detection, Shengfeng He, Jianbo Jiao, Xiaodan Zhang, Guoqiang Han, Rynson W.H Lau
Delving Into Salient Object Subitizing And Detection, Shengfeng He, Jianbo Jiao, Xiaodan Zhang, Guoqiang Han, Rynson W.H Lau
Research Collection School Of Computing and Information Systems
Subitizing (i.e., instant judgement on the number) and detection of salient objects are human inborn abilities. These two tasks influence each other in the human visual system. In this paper, we delve into the complementarity of these two tasks. We propose a multi-task deep neural network with weight prediction for salient object detection, where the parameters of an adaptive weight layer are dynamically determined by an auxiliary subitizing network. The numerical representation of salient objects is therefore embedded into the spatial representation. The proposed joint network can be trained end-to-end using backpropagation. Experiments show the proposed multi-task network outperforms existing …
Pic2dish: A Customized Cooking Assistant System, Yongsheng An, Yu Cao, Jingjing Chen, Chong-Wah Ngo, Jia Jia, Huanbo Luan, Tat-Seng Chua
Pic2dish: A Customized Cooking Assistant System, Yongsheng An, Yu Cao, Jingjing Chen, Chong-Wah Ngo, Jia Jia, Huanbo Luan, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The art of cooking is always fascinating. Nevertheless, reproducing a delicious dish that one has never encountered before is not easy. Even if the name of dish is known and the corresponding recipe could be retrieved, the right ingredients for cooking the dish may not be available due to factors such as geography region or season. Furthermore, knowing how to cut, cook and control timing may be challenging for one whose has no cooking experience. In this paper, an all-around cooking assistant mobile app, named Pic2Dish, is developed to help users who would like to cook a dish but neither …
Cross-Modal Recipe Retrieval With Rich Food Attributes, Jingjing Chen, Chong-Wah Ngo, Tat-Seng Chua
Cross-Modal Recipe Retrieval With Rich Food Attributes, Jingjing Chen, Chong-Wah Ngo, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Food is rich of visible (e.g., colour, shape) and procedural (e.g., cutting, cooking) attributes. Proper leveraging of these attributes, particularly the interplay among ingredients, cutting and cooking methods, for health-related applications has not been previously explored. This paper investigates cross-modal retrieval of recipes, specifically to retrieve a text-based recipe given a food picture as query. As similar ingredient composition can end up with wildly different dishes depending on the cooking and cutting procedures, the difficulty of retrieval originates from fine-grained recognition of rich attributes from pictures. With a multi-task deep learning model, this paper provides insights on the feasibility of …
Strong Authenticated Key Exchange With Auxiliary Inputs, Rongmao Chen, Yi Mu, Guomin Yang, Willy Susilo, Fuchun Guo
Strong Authenticated Key Exchange With Auxiliary Inputs, Rongmao Chen, Yi Mu, Guomin Yang, Willy Susilo, Fuchun Guo
Research Collection School Of Computing and Information Systems
Leakage attacks, including various kinds of side-channel attacks, allow an attacker to learn partial information about the internal secrets such as the secret key and the randomness of a cryptographic system. Designing a strong, meaningful, yet achievable security notion to capture practical leakage attacks is one of the primary goals of leakage-resilient cryptography. In this work, we revisit the modelling and design of authenticated key exchange (AKE) protocols with leakage resilience. We show that the prior works on this topic are inadequate in capturing realistic leakage attacks. To close this research gap, we propose a new security notion named leakage-resilient …
Combinatorial Auction For Transportation Matching Service: Formulation And Adaptive Large Neighborhood Search Heuristic, Baoxiang Li, Hoong Chuin Lau
Combinatorial Auction For Transportation Matching Service: Formulation And Adaptive Large Neighborhood Search Heuristic, Baoxiang Li, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper considers the problem of matching multiple shippers and multi-transporters for pickups and drop-offs, where the goal is to select a subset of group jobs (shipper bids) that maximizes profit. This is the underlying winner determination problem in an online auction-based vehicle sharing platform that matches transportation demand and supply, particularly in a B2B last-mile setting. Each shipper bid contains multiple jobs, and each job has a weight, volume, pickup location, delivery location and time window. On the other hand, each transporter bid specifies the vehicle capacity, available time periods, and a cost structure. This double-sided auction will be …
Tagscan: Simultaneous Target Imaging And Material Identification With Commodity Rfid Devices, Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi Fang
Tagscan: Simultaneous Target Imaging And Material Identification With Commodity Rfid Devices, Ju Wang, Jie Xiong, Xiaojiang Chen, Hongbo Jiang, Rajesh Krishna Balan, Dingyi Fang
Research Collection School Of Computing and Information Systems
Target imaging and material identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commercial of-the-shelf (COTS) RFID devices. The key intuition is that different materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between …
Visual Sentiment Analysis For Review Images With Item-Oriented And User-Oriented Cnn, Quoc Tuan Truong, Hady W. Lauw
Visual Sentiment Analysis For Review Images With Item-Oriented And User-Oriented Cnn, Quoc Tuan Truong, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Online reviews are prevalent. When recounting their experience with a product, service, or venue, in addition to textual narration, a reviewer frequently includes images as photographic record. While textual sentiment analysis has been widely studied, in this paper we are interested in visual sentiment analysis to infer whether a given image included as part of a review expresses the overall positive or negative sentiment of that review. Visual sentiment analysis can be formulated as image classification using deep learning methods such as Convolutional Neural Networks or CNN. However, we observe that the sentiment captured within an image may be affected …
Every Step You Take, I’Ll Be Watching You: Practical Stepauth-Entication Of Rfid Paths, Kai Bu, Yingjiu Li
Every Step You Take, I’Ll Be Watching You: Practical Stepauth-Entication Of Rfid Paths, Kai Bu, Yingjiu Li
Research Collection School Of Computing and Information Systems
Path authentication thwarts counterfeits in RFID-based supply chains. Its motivation is that tagged products taking invalid paths are likely faked and injected by adversaries at certain supply chain partners/steps. Existing solutions are path-grained in that they simply regard a product as genuine if it takes any valid path. Furthermore, they enforce distributed authentication by offloading the sets of valid paths to some or all steps from a centralized issuer. This not only imposes network and storage overhead but also leaks transaction privacy. We present StepAuth, the first step-grained path authentication protocol that is practically efficient for authenticating products with strict …
Which Packages Would Be Affected By This Bug Report?, Qiao Huang, David Lo, Xin Xia, Qingye Wang, Shanping Li
Which Packages Would Be Affected By This Bug Report?, Qiao Huang, David Lo, Xin Xia, Qingye Wang, Shanping Li
Research Collection School Of Computing and Information Systems
A large project (e.g., Ubuntu) usually contains a large number of software packages. Sometimes the same bug report in such project would affect multiple packages, and developers of different packages need to collaborate with one another to fix the bug. Unfortunately, the total number of packages involved in a project like Ubuntu is relatively large, which makes it time-consuming to manually identify packages that are affected by a bug report. In this paper, we propose an approach named PkgRec that consists of 2 components: a name matching component and an ensemble learning component. In the name matching component, we assign …
Target Material Identification With Commodity Rfid Devices, Xinyi Li, Chao Feng, Nana Ding, Ju Wang, Jie Xiong, Yuhui Ren, Xiaojiang Chen, Dingyi Fang
Target Material Identification With Commodity Rfid Devices, Xinyi Li, Chao Feng, Nana Ding, Ju Wang, Jie Xiong, Yuhui Ren, Xiaojiang Chen, Dingyi Fang
Research Collection School Of Computing and Information Systems
Target material identification plays an important role in many reallife applications. This paper introduces a system that can identify the material type with cheap commercial off-the-shelf (COTS) RFID devices. The key intuition is that different materials cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. However, without knowing either material type, trying to obtain the information is challenging. We propose a method to address this challenge and evaluate the method's performance in real-world environment. The results show that we achieve higher than 94% material identification accuracies for 10 liquids …
Tensor Factorization For Low-Rank Tensor Completion, Pan Zhou, Canyi Lu, Zhouchen Lin, Chao Zhang
Tensor Factorization For Low-Rank Tensor Completion, Pan Zhou, Canyi Lu, Zhouchen Lin, Chao Zhang
Research Collection School Of Computing and Information Systems
Recently, a tensor nuclear norm (TNN) based method [1] was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem. Our method preserves the lowrank structure of a tensor by factorizing it into the product of two tensors of smaller sizes. In the optimization process, our method …