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Treecaps: Tree-Based Capsule Networks For Source Code Processing, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Treecaps: Tree-Based Capsule Networks For Source Code Processing, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Recently program learning techniques have been proposed to process source code based on syntactical structures (e.g., Abstract Syntax Trees) and/or semantic information (e.g., Dependency Graphs). While graphs may be better at capturing various viewpoints of code semantics than trees, constructing graph inputs from code need static code semantic analysis that may not be accurate and introduces noise during learning. On the other hand, syntax trees are precisely defined according to the language grammar and easier to construct and process than graphs. We propose a new tree-based learning technique, named TreeCaps, by fusing capsule networks with tree-based convolutional neural networks, to …
An Exploratory Study On The Introduction And Removal Of Different Types Of Technical Debt In Deep Learning Frameworks, Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li
An Exploratory Study On The Introduction And Removal Of Different Types Of Technical Debt In Deep Learning Frameworks, Jiakun Liu, Qiao Huang, Xin Xia, Emad Shihab, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. …
Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma
Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma
Research Collection School Of Computing and Information Systems
With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students’ problem-solving processes unfold step by step to infer whether students’ problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from …
Graph-Evolving Meta-Learning For Low-Resource Medical Dialogue Generation, Shuai Lin, Pan Zhou, Xiaodan Liang, Jianheng Tang, Ruihui Zhao, Ziliang Chen, Liang Lin
Graph-Evolving Meta-Learning For Low-Resource Medical Dialogue Generation, Shuai Lin, Pan Zhou, Xiaodan Liang, Jianheng Tang, Ruihui Zhao, Ziliang Chen, Liang Lin
Research Collection School Of Computing and Information Systems
Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded dialogue systems often require a large number of dialogue instances to learn as they fail to capture the correlations between different diseases and neglect the diagnostic experience shared among them. To address this issue, we propose a more natural and practical paradigm, i.e., low-resource medical dialogue generation, which can transfer the diagnostic experience from source diseases to target ones with a handful of data for adaptation. It is capitalized on a commonsense knowledge graph to characterize the …
Performance-Based Iadl Evaluation Of Older Adults With Cognitive Impairment Within A Smart Home: A Feasibility Study, Iris Rawtaer, Khalid Abdul Jabbar, Xiao Liu, Thit Thit Htat Ying, Anh Thuy Giang, Philip Lin Kiat Yap, Rachel Chin Yee Cheong, Hwee-Pink Tan, Pius Lee Wei Qi, Shiou Liang Wee, Tze Pin Ng
Performance-Based Iadl Evaluation Of Older Adults With Cognitive Impairment Within A Smart Home: A Feasibility Study, Iris Rawtaer, Khalid Abdul Jabbar, Xiao Liu, Thit Thit Htat Ying, Anh Thuy Giang, Philip Lin Kiat Yap, Rachel Chin Yee Cheong, Hwee-Pink Tan, Pius Lee Wei Qi, Shiou Liang Wee, Tze Pin Ng
Research Collection School Of Computing and Information Systems
Introduction Mild cognitive impairment (MCI) is characterized by subtle deficits that functional assessment via informant-report measures may not detect. Sensors can potentially detect deficits in everyday functioning in MCI. This study aims to establish feasibility and acceptability of using sensors in a smart home for performance-based assessments of two instrumental activities of daily living (IADLs). Methods Thirty-five older adults (>65 years) performed two IADL tasks in a smart home laboratory equipped with sensors and a web camera. Participants' cognitive states were determined using published criteria including measures of global cognition and comprehensive neuropsychological test batteries. Selected subtasks of the …
Chronic Customers Or Increased Awareness? The Dynamics Of Social Media Customer Service, Shujing Sun, Yang Gao, Huaxia Rui
Chronic Customers Or Increased Awareness? The Dynamics Of Social Media Customer Service, Shujing Sun, Yang Gao, Huaxia Rui
Research Collection School Of Computing and Information Systems
Despite that social media has become a promising alternative to traditional call centers, managers hesitate to fully harness its power because they worry that active service intervention may encourage excessive use of the channel by disgruntled customers. This paper sheds light on such a concern by examining the dynamics between brand-level customer complaints and service interventions on social media. Using details of customer-brand interactions of 40 airlines on Twitter, we find that more service interventions indeed cause more customer complaints, accounting for the online customer population and service quality. However, the increased complaints are primarily driven by the awareness enhancement …
Scalable Online Vetting Of Android Apps For Measuring Declared Sdk Versions And Their Consistency With Api Calls, Daoyuan Wu, Debin Gao, David Lo
Scalable Online Vetting Of Android Apps For Measuring Declared Sdk Versions And Their Consistency With Api Calls, Daoyuan Wu, Debin Gao, David Lo
Research Collection School Of Computing and Information Systems
Android has been the most popular smartphone system with multiple platform versions active in the market. To manage the application’s compatibility with one or more platform versions, Android allows apps to declare the supported platform SDK versions in their manifest files. In this paper, we conduct a systematic study of this modern software mechanism. Our objective is to measure the current practice of declared SDK versions (which we term as DSDK versions afterwards) in real apps, and the (in)consistency between DSDK versions and their host apps’ API calls. To successfully analyze a modern dataset of 22,687 popular apps (with an …
Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng
Proxy-Free Privacy-Preserving Task Matching With Efficient Revocation In Crowdsourcing, Jiangang Shu, Kan Yang, Xiaohua Jia, Ximeng Liu, Cong Wang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including ServerLocal Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the …
Context-Aware Retrieval-Based Deep Commit Message Generation, Haoye Wang, Xin Xia, David Lo, Qiang He, Xinyu Wang, John Grundy
Context-Aware Retrieval-Based Deep Commit Message Generation, Haoye Wang, Xin Xia, David Lo, Qiang He, Xinyu Wang, John Grundy
Research Collection School Of Computing and Information Systems
Commit messages recorded in version control systems contain valuable information for software development, maintenance, and comprehension. Unfortunately, developers often commit code with empty or poor quality commit messages. To address this issue, several studies have proposed approaches to generate commit messages from commit diffs. Recent studies make use of neural machine translation algorithms to try and translate git diffs into commit messages and have achieved some promising results. However, these learning-based methods tend to generate high-frequency words but ignore low-frequency ones. In addition, they suffer from exposure bias issues, which leads to a gap between training phase and testing phase. …
Understanding The Inter-Domain Presence Of Research Topics In The Computing Discipline, Subhajit Datta, Rumana Lakdawala, Santonu Sarkar
Understanding The Inter-Domain Presence Of Research Topics In The Computing Discipline, Subhajit Datta, Rumana Lakdawala, Santonu Sarkar
Research Collection School Of Computing and Information Systems
The very nature of scientific inquiry encourages the flow of ideas across research domains in a discipline. Research topics with higher inter-domain presence tend to attract higher attention at individual and organizational levels. This is more pronounced in a discipline like computing, with its deeply intertwined ideas and strong connections with technology. In this paper, we study corpora of research publications across four domains of the computing discipline – covering more than 150,000 papers, involving more than 200,000 authors over 55 years and 175 publication venues – to examine the influences on inter-domain presence of research topics. We find statistically …
Discovering Hidden Topical Hubs And Authorities Across Multiple Online Social Networks, Ka Wei, Roy Lee, Tuan-Anh Hoang, Ee-Peng Lim
Discovering Hidden Topical Hubs And Authorities Across Multiple Online Social Networks, Ka Wei, Roy Lee, Tuan-Anh Hoang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users’ relationship and textual content. However, many of the previous works are confined to …
Adversarial Specification Mining, Hong Jin Kang, David Lo
Adversarial Specification Mining, Hong Jin Kang, David Lo
Research Collection School Of Computing and Information Systems
There have been numerous studies on mining temporal specifications from execution traces. These approaches learn finite-state automata (FSA) from execution traces when running tests. To learn accurate specifications of a software system, many tests are required. Existing approaches generalize from a limited number of traces or use simple test generation strategies. Unfortunately, these strategies may not exercise uncommon usage patterns of a software system. To address this problem, we propose a new approach, adversarial specification mining, and develop a prototype, DICE (Diversity through Counter-Examples). DICE has two components: DICE-Tester and DICE-Miner. After mining Linear Temporal Logic specifications from an input …
Technical Q8a Site Answer Recommendation Via Question Boosting, Zhipeng Gao, Xin Xia, David Lo, John Grundy
Technical Q8a Site Answer Recommendation Via Question Boosting, Zhipeng Gao, Xin Xia, David Lo, John Grundy
Research Collection School Of Computing and Information Systems
Software developers have heavily used online question and answer platforms to seek help to solve their technical problems. However, a major problem with these technical Q&A sites is "answer hungriness" i.e., a large number of questions remain unanswered or unresolved, and users have to wait for a long time or painstakingly go through the provided answers with various levels of quality. To alleviate this time-consuming problem, we propose a novel DeepAns neural network-based approach to identify the most relevant answer among a set of answer candidates. Our approach follows a three-stage process: question boosting, label establishment, and answer recommendation. Given …
Do Blockchain And Iot Architecture Create Informedness To Support Provenance Tracking In The Product Lifecycle?, Somnath Mazumdar, Thomas Jensen, Raghava Rao Mukkamala, Robert John Kauffman, Jan Damsgaard
Do Blockchain And Iot Architecture Create Informedness To Support Provenance Tracking In The Product Lifecycle?, Somnath Mazumdar, Thomas Jensen, Raghava Rao Mukkamala, Robert John Kauffman, Jan Damsgaard
Research Collection School Of Computing and Information Systems
Consumers often lack information about the origin and provenance of the products they buy. They may ask: Is a food product truly organic? Or, what is the origin of the gemstone in the ring I purchased? They also may have sustainability concerns about the footprint of a product at the end of its life. Producers and sellers, meanwhile, wish to know how longitudinal tracking of the provenance of products and their components can boost their sales prices and after-market value, and re- veal new business opportunities. We focus on how the product lifecycle (PLC) can be leveraged to track information …
Smart Contracts: Will Fintech Be The Catalyst For The Next Global Financial Crisis?, Randall Duran, Paul Griffin
Smart Contracts: Will Fintech Be The Catalyst For The Next Global Financial Crisis?, Randall Duran, Paul Griffin
Research Collection School Of Computing and Information Systems
Purpose: This paper aims to examine the risks associated with smart contracts, a disruptive financial technology (FinTech) innovation, and assesses how in the future they could threaten the integrity of the global financial system. Design/methodology/approach: A qualitative approach is used to identify risk factors related to the use of new financial innovations, by examining how over-the-counter (OTC) derivatives contributed to the Global Financial Crisis (GFC) which occurred during 2007 and 2008. Based on this analysis, the potential for similar concerns with smart contracts are evaluated, drawing on the failure of The DAO on the Ethereum blockchain, which involved the loss …
A Continual Deepfake Detection Benchmark: Dataset, Methods, And Essentials, Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Van Gool Luc
A Continual Deepfake Detection Benchmark: Dataset, Methods, And Essentials, Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Van Gool Luc
Research Collection School Of Computing and Information Systems
There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in the real-world scenarios. To simulate the wild scenes, this paper suggests a continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models. The suggested CDDB designs multiple evaluations on the detection over easy, hard, and long sequence of deepfake tasks, with a set of appropriate measures. In addition, we exploit multiple approaches to adapt multiclass incremental learning methods, commonly used in the continual visual recognition, …
Smart Scribbles For Image Matting, Yang Xin, Yu Qiao, Shaozhe Chen, Shengfeng He, Baocai Yin, Qiang Zhang, Xiaopeng Wei, Rynson W. H. Lau
Smart Scribbles For Image Matting, Yang Xin, Yu Qiao, Shaozhe Chen, Shengfeng He, Baocai Yin, Qiang Zhang, Xiaopeng Wei, Rynson W. H. Lau
Research Collection School Of Computing and Information Systems
Image matting is an ill-posed problem that usually requires additional user input, such as trimaps or scribbles. Drawing a fine trimap requires a large amount of user effort, while using scribbles can hardly obtain satisfactory alpha mattes for non-professional users. Some recent deep learning-based matting networks rely on large-scale composite datasets for training to improve performance, resulting in the occasional appearance of obvious artifacts when processing natural images. In this article, we explore the intrinsic relationship between user input and alpha mattes and strike a balance between user effort and the quality of alpha mattes. In particular, we propose an …
Partial Adversarial Behavior Deception In Security Games, Thanh H. Nguyen, Arunesh Sinha, He He
Partial Adversarial Behavior Deception In Security Games, Thanh H. Nguyen, Arunesh Sinha, He He
Research Collection School Of Computing and Information Systems
Learning attacker behavior is an important research topic in security games as security agencies are often uncertain about attackers’ decision making. Previous work has focused on developing various behavioral models of attackers based on historical attack data. However, a clever attacker can manipulate its attacks to fail such attack-driven learning, leading to ineffective defense strategies. We study attacker behavior deception with three main contributions. First, we propose a new model, named partial behavior deception model, in which there is a deceptive attacker (among multiple attackers) who controls a portion of attacks. Our model captures real-world security scenarios such as wildlife …
Privacy-Preserving Outsourced Clinical Decision Support System In The Cloud, Ximeng Liu, Robert H. Deng, Kim-Kwang Raymond Choo, Yang Yang
Privacy-Preserving Outsourced Clinical Decision Support System In The Cloud, Ximeng Liu, Robert H. Deng, Kim-Kwang Raymond Choo, Yang Yang
Research Collection School Of Computing and Information Systems
In this paper, we propose a privacy-preserving clinical decision support system using Naïve Bayesian (NB) classifier, hereafter referred to as Peneus, designed for the outsourced cloud computing environment. Peneus allows one to use patient health information to train the NB classifier privately, which can then be used to predict a patient's (undiagnosed) disease based on his/her symptoms in a single communication round. Specifically, we design secure Single Instruction Multiple Data (SIMD) integer circuits using the fully homomorphic encryption scheme, which can greatly increase the performance compared with the original secure integer circuit. Then, we present a privacy-preserving historical Personal Health …
Facial Emotion Recognition With Noisy Multi-Task Annotations, S. Zhang, Zhiwu Huang, D.P. Paudel, Gool L. Van
Facial Emotion Recognition With Noisy Multi-Task Annotations, S. Zhang, Zhiwu Huang, D.P. Paudel, Gool L. Van
Research Collection School Of Computing and Information Systems
Human emotions can be inferred from facial expressions. However, the annotations of facial expressions are often highly noisy in common emotion coding models, including categorical and dimensional ones. To reduce human labelling effort on multi-task labels, we introduce a new problem of facial emotion recognition with noisy multitask annotations. For this new problem, we suggest a formulation from the point of joint distribution match view, which aims at learning more reliable correlations among raw facial images and multi-task labels, resulting in the reduction of noise influence. In our formulation, we exploit a new method to enable the emotion prediction and …
A Data-Driven Method For Online Monitoring Tube Wall Thinning Process In Dynamic Noisy Environment, Chen Zhang, Jun Long Lim, Ouyang Liu, Aayush Madan, Yongwei Zhu, Shili Xiang, Kai Wu, Rebecca Yen-Ni Wong, Jiliang Eugene Phua, Karan M. Sabnani, Keng Boon Siah, Wenyu Jiang, Yixin Wang, Emily Jianzhong Hao, Hoi, Steven C. H.
A Data-Driven Method For Online Monitoring Tube Wall Thinning Process In Dynamic Noisy Environment, Chen Zhang, Jun Long Lim, Ouyang Liu, Aayush Madan, Yongwei Zhu, Shili Xiang, Kai Wu, Rebecca Yen-Ni Wong, Jiliang Eugene Phua, Karan M. Sabnani, Keng Boon Siah, Wenyu Jiang, Yixin Wang, Emily Jianzhong Hao, Hoi, Steven C. H.
Research Collection School Of Computing and Information Systems
Tube internal erosion, which corresponds to its wall thinning process, is one of the major safety concerns for tubes. Many sensing technologies have been developed to detect a tube wall thinning process. Among them, fiber Bragg grating (FBG) sensors are the most popular ones due to their precise measurement properties. Most of the current works focus on how to design different types of FBG sensors according to certain physical laws and only test their sensors in controlled laboratory conditions. However, in practice, an industrial system usually suffers from harsh and dynamic environmental conditions, and FBG signals are affected by many …
Can We Trust Your Explanations? Sanity Checks For Interpreters In Android Malware Analysis, Min Fan, Wenying Wei, Xiaofei Xie, Yang Liu, Xiaohong Guan, Ting Liu
Can We Trust Your Explanations? Sanity Checks For Interpreters In Android Malware Analysis, Min Fan, Wenying Wei, Xiaofei Xie, Yang Liu, Xiaohong Guan, Ting Liu
Research Collection School Of Computing and Information Systems
With the rapid growth of Android malware, many machine learning-based malware analysis approaches are proposed to mitigate the severe phenomenon. However, such classifiers are opaque, non-intuitive, and difficult for analysts to understand the inner decision reason. For this reason, a variety of explanation approaches are proposed to interpret predictions by providing important features. Unfortunately, the explanation results obtained in the malware analysis domain cannot achieve a consensus in general, which makes the analysts confused about whether they can trust such results. In this work, we propose principled guidelines to assess the quality of five explanation approaches by designing three critical …
3d Dental Biometrics: Automatic Pose-Invariant Dental Arch Extraction And Matching, Xin Zhong, Zhiyuan Zhang
3d Dental Biometrics: Automatic Pose-Invariant Dental Arch Extraction And Matching, Xin Zhong, Zhiyuan Zhang
Research Collection School Of Computing and Information Systems
A novel automatic pose-invariant dental arch extraction and matching framework is developed for 3D dental identification using laser-scanned dental plasters. In our previous attempt [1-5], 3D point-based algorithms have been developed and they have shown a few advantages over existing 2D dental identifications. This study is a continuous effort in developing arch-based algorithms to extract and match dental arch feature in an automatic and pose-invariant way. As best as we know, this is the first attempt at automatic dental arch extraction and matching for 3D dental identification. A Radial Ray Algorithm (RRA) is proposed by projecting dental arch shape from …
Coherence And Identity Learning For Arbitrary-Length Face Video Generation, Shuquan Ye, Chu Han, Jiaying Lin, Guoqiang Han, Shengfeng He
Coherence And Identity Learning For Arbitrary-Length Face Video Generation, Shuquan Ye, Chu Han, Jiaying Lin, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
Face synthesis is an interesting yet challenging task in computer vision. It is even much harder to generate a portrait video than a single image. In this paper, we propose a novel video generation framework for synthesizing arbitrary-length face videos without any face exemplar or landmark. To overcome the synthesis ambiguity of face video, we propose a divide-and-conquer strategy to separately address the video face synthesis problem from two aspects, face identity synthesis and rearrangement. To this end, we design a cascaded network which contains three components, Identity-aware GAN (IA-GAN), Face Coherence Network, and Interpolation Network. IA-GAN is proposed to …
An Efficient Privacy Preserving Message Authentication Scheme For Internet-Of-Things, Jiannan Wei, Tran Viet Xuan Phuong, Guomin Yang
An Efficient Privacy Preserving Message Authentication Scheme For Internet-Of-Things, Jiannan Wei, Tran Viet Xuan Phuong, Guomin Yang
Research Collection School Of Computing and Information Systems
As an essential element of the next generation Internet, Internet of Things (IoT) has been undergoing an extensive development in recent years. In addition to the enhancement of peoples daily lives, IoT devices also generate/gather a massive amount of data that could be utilized by machine learning and big data analytics for different applications. Due to the machine-to-machine communication nature of IoT, data security and privacy are crucial issues that must be addressed to prevent different cyber attacks (e.g., impersonation and data pollution/poisoning attacks). Nevertheless, due to the constrained computation power and the diversity of IoT devices, it is a …
Deep Unsupervised Anomaly Detection, Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin
Deep Unsupervised Anomaly Detection, Tangqing Li, Zheng Wang, Siying Liu, Wen-Yan Lin
Research Collection School Of Computing and Information Systems
This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing normal candidate subset based on clustering and representation learning. The reconstruction error from the learned autoencoder serves as a scoring function to assess the normality of the data. Experimental results …
Why My Code Summarization Model Does Not Work: Code Comment Improvement With Category Prediction, Qiuyuan Chen, Xin Xia, Han Hu, David Lo, Shanping Li
Why My Code Summarization Model Does Not Work: Code Comment Improvement With Category Prediction, Qiuyuan Chen, Xin Xia, Han Hu, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Code summarization aims at generating a code comment given a block of source code and it is normally performed by training machine learning algorithms on existing code block-comment pairs. Code comments in practice have different intentions. For example, some code comments might explain how the methods work, while others explain why some methods are written. Previous works have shown that a relationship exists between a code block and the category of a comment associated with it. In this article, we aim to investigate to which extent we can exploit this relationship to improve code summarization performance. We first classify comments …
Enabling Collaborative Video Sensing At The Edge Through Convolutional Sharing, Kasthuri Jayarajah, Wanniarachchige Dhanuja Tharith Wanniarachchi, Archan Misra
Enabling Collaborative Video Sensing At The Edge Through Convolutional Sharing, Kasthuri Jayarajah, Wanniarachchige Dhanuja Tharith Wanniarachchi, Archan Misra
Research Collection School Of Computing and Information Systems
While Deep Neural Network (DNN) models have provided remarkable advances in machine vision capabilities, their high computational complexity and model sizes present a formidable roadblock to deployment in AIoT-based sensing applications. In this paper, we propose a novel paradigm by which peer nodes in a network can collaborate to improve their accuracy on person detection, an exemplar machine vision task. The proposed methodology requires no re-training of the DNNs and incurs minimal processing latency as it extracts scene summaries from the collaborators and injects back into DNNs of the reference cameras, on-the-fly. Early results show promise with improvements in recall …
Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo
Blockchain-Based Public Auditing And Secure Deduplication With Fair Arbitration, Haoran Yuan, Xiaofeng Chen, Jianfeng Wang, Jiaming Yuan, Hongyang Yan, Willy Susilo
Research Collection School Of Computing and Information Systems
Data auditing enables data owners to verify the integrity of their sensitive data stored at an untrusted cloud without retrieving them. This feature has been widely adopted by commercial cloud storage. However, the existing approaches still have some drawbacks. On the one hand, the existing schemes have a defect of fair arbitration, i.e., existing auditing schemes lack an effective method to punish the malicious cloud service provider (CSP) and compensate users whose data integrity is destroyed. On the other hand, a CSP may store redundant and repetitive data. These redundant data inevitably increase management overhead and computational cost during the …
A Study Of Multi-Task And Region-Wise Deep Learning For Food Ingredient Recognition, Jingjing Chen, Bin Zhu, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang
A Study Of Multi-Task And Region-Wise Deep Learning For Food Ingredient Recognition, Jingjing Chen, Bin Zhu, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composition. In reality, two dishes with the same name do not necessarily share the exact list of ingredients. Therefore, the dishes under the same food category are not mandatorily equal in nutrition content. Nevertheless, due to limited datasets available with ingredient labels, the problem of ingredient recognition is often overlooked. Furthermore, as the number of ingredients is expected to be much less than the number of food categories, …