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Articles 3511 - 3540 of 8479
Full-Text Articles in Computer Sciences
Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Research Collection School Of Computing and Information Systems
A fine-grained co-change dependency arises when two fine-grained source-code entities, e.g., a method,change frequently together. This kind of dependency is relevant when considering remodularization efforts (e.g., to keep methods that change together in the same class). However, existing approaches forrecommending refactorings that change software decomposition (such as a move method) do not explorethe use of fine-grained co-change dependencies. In this paper we present a novel approach for recommending move method and move field refactorings, which removes co-change dependencies and evolutionary smells, a particular type of dependency that arise when fine-grained entities that belong to different classes frequently change together. First …
Selective Discrete Particle Swarm Optimization For The Team Orienteering Problem With Time Windows And Partial Scores, Vincent F. Yu, Perwira A. A. N. Redi, Parida Jewpanya, Aldy Gunawan
Selective Discrete Particle Swarm Optimization For The Team Orienteering Problem With Time Windows And Partial Scores, Vincent F. Yu, Perwira A. A. N. Redi, Parida Jewpanya, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This paper introduces the Team Orienteering Problem with Time Windows and Partial Scores (TOPTW-PS),which is an extension of the Team Orienteering Problem with Time Windows (TOPTW). In the context of theTOPTW-PS, each node is associated with a set of scores with respect to a set of attributes. The objective ofTOPTW-PS is to find a set of routes that maximizes the total score collected from a subset of attributes whenvisiting the nodes subject to the time budget and the time window at each visited node. We develop a mathematical model and propose a discrete version of the Particle Swarm Optimization (PSO), …
Clustering Models For Topic Analysis In Graduate Discussion Forums, Mallika Gokarn Nitin, Swapna Gottipati, Venky Shankararaman
Clustering Models For Topic Analysis In Graduate Discussion Forums, Mallika Gokarn Nitin, Swapna Gottipati, Venky Shankararaman
Research Collection School Of Computing and Information Systems
Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This paper proposes the use of clustering models and interactive visualizations to conduct a qualitative analysis of graduate discussion forums. Our goal is to identify the sub-topics and topic evolutions in the discussion forums by applying text mining techniques. Our approach generates insights into the topic analysis in the forums and discovers the students’ cognitive understanding within …
Efficient Meta Learning Via Minibatch Proximal Update, Pan Zhou, Xiao-Tong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng
Efficient Meta Learning Via Minibatch Proximal Update, Pan Zhou, Xiao-Tong Yuan, Huan Xu, Shuicheng Yan, Jiashi Feng
Research Collection School Of Computing and Information Systems
We address the problem of meta-learning which learns a prior over hypothesis from a sample of meta-training tasks for fast adaptation on meta-testing tasks. A particularly simple yet successful paradigm for this research is model-agnostic meta-learning (MAML). Implementation and analysis of MAML, however, can be tricky; first-order approximation is usually adopted to avoid directly computing Hessian matrix but as a result the convergence and generalization guarantees remain largely mysterious for MAML. To remedy this deficiency, in this paper we propose a minibatch proximal update based meta-learning approach for learning to efficient hypothesis transfer. The principle is to learn a prior …
Strongly Secure Authenticated Key Exchange From Supersingular Isogenies, Xiu Xu, Haiyang Xue, Kunpeng Wang, Ho Man Au, Song Tian
Strongly Secure Authenticated Key Exchange From Supersingular Isogenies, Xiu Xu, Haiyang Xue, Kunpeng Wang, Ho Man Au, Song Tian
Research Collection School Of Computing and Information Systems
This paper aims to address the open problem, namely, to find new techniques to design and prove security of supersingular isogeny-based authenticated key exchange (AKE) protocols against the widest possible adversarial attacks, raised by Galbraith in 2018. Concretely, we present two AKEs based on a double-key PKE in the supersingular isogeny setting secure in the sense of CK+, one of the strongest security models for AKE. Our contributions are summarised as follows. Firstly, we propose a strong OW-CPA secure PKE, 2PKEsidh, based on SI-DDH assumption. By applying modified Fujisaki-Okamoto transformation, we obtain a [OW-CCA, OW-CPA] secure KEM, 2KEMsidh. Secondly, we …
Computing Maximum And Minimum With Privacy Preservation And Flexible Access Control, Wenxiu Ding, Zheng Yan, Xinren Qian, Robert H. Deng
Computing Maximum And Minimum With Privacy Preservation And Flexible Access Control, Wenxiu Ding, Zheng Yan, Xinren Qian, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the fast development of Internet of Things, huge volume of data is being collected from various sensors and devices, aggregated at gateways, and processed in the cloud. Due to privacy concern, data are usually encrypted before being outsourced to the cloud. However, encryption seriously impedes both computation over the data and sharing of the computation results. Computing maximum and minimum among a data set are two of the most basic operations in machine learning and data mining algorithms. In this paper, we study how to compute maximum and minimum over encrypted data and control the access to the computation …
Tracking Political Events In Social Media: A Case Study Of Hong Kong Protests, Haodi Qi, Hanyu Jiang, Wende Bu, Chengzi Zhang, Kyong Jin Shim
Tracking Political Events In Social Media: A Case Study Of Hong Kong Protests, Haodi Qi, Hanyu Jiang, Wende Bu, Chengzi Zhang, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In this study, we analyze social conversations about Hong Kong Protests, a series of events that were widely seen and debated in social media in 2019. Our system collects data from Twitter and Reddit using their APIs. It performs sentiment analysis, and the analysis results show changes in public sentiment around major events. Social network analysis reveals influencers - users that are in the center of social conversations. Our interactive Tableau dashboard allows the user to easily monitor live social conversations about Hong Kong protests.
A Unified Variance-Reduced Accelerated Gradient Method For Convex Optimization, Guanghui Lan, Zhize Li, Yi Zhou
A Unified Variance-Reduced Accelerated Gradient Method For Convex Optimization, Guanghui Lan, Zhize Li, Yi Zhou
Research Collection School Of Computing and Information Systems
We propose a novel randomized incremental gradient algorithm, namely, VAriance-Reduced Accelerated Gradient (Varag), for finite-sum optimization. Equipped with a unified step-size policy that adjusts itself to the value of the conditional number, Varag exhibits the unified optimal rates of convergence for solving smooth convex finite-sum problems directly regardless of their strong convexity. Moreover, Varag is the first accelerated randomized incremental gradient method that benefits from the strong convexity of the data-fidelity term to achieve the optimal linear convergence. It also establishes an optimal linear rate of convergence for solving a wide class of problems only satisfying a certain error bound …
Ssrgd: Simple Stochastic Recursive Gradient Descent For Escaping Saddle Points, Zhize Li
Ssrgd: Simple Stochastic Recursive Gradient Descent For Escaping Saddle Points, Zhize Li
Research Collection School Of Computing and Information Systems
We analyze stochastic gradient algorithms for optimizing nonconvex problems. In particular, our goal is to find local minima (second-order stationary points) instead of just finding first-order stationary points which may be some bad unstable saddle points. We show that a simple perturbed version of stochastic recursive gradient descent algorithm (called SSRGD) can find an $(\epsilon,\delta)$-second-order stationary point with $\widetilde{O}(\sqrt{n}/\epsilon^2 + \sqrt{n}/\delta^4 + n/\delta^3)$ stochastic gradient complexity for nonconvex finite-sum problems. As a by-product, SSRGD finds an $\epsilon$-first-order stationary point with $O(n+\sqrt{n}/\epsilon^2)$ stochastic gradients. These results are almost optimal since Fang et al. [2018] provided a lower bound $\Omega(\sqrt{n}/\epsilon^2)$ for finding …
Twenty Years Of Open Source Software: From Skepticism To Mainstream, Gregorio Robles, Igor Steinmacher, Paul Adams, Christoph Treude
Twenty Years Of Open Source Software: From Skepticism To Mainstream, Gregorio Robles, Igor Steinmacher, Paul Adams, Christoph Treude
Research Collection School Of Computing and Information Systems
Open source software (OSS) has conquered the software world. You can see it nearly everywhere, from Internet infrastructure to mobile phones to the desktop. In addition to that, although many OSS practices were viewed with skepticism 20 years ago, several have become mainstream in software engineering today: from development tools such as Git to practices such as modern code reviews.
Pieces Of Contextual Information Suitable For Predicting Co-Changes? An Empirical Study, Igor Scaliante Wiese, Rodrigo Takashi Kuroda, Igor Steinmacher, Gustavo A. Oliva, Reginaldo Ré, Christoph Treude, Marco Aurélio Gerosa
Pieces Of Contextual Information Suitable For Predicting Co-Changes? An Empirical Study, Igor Scaliante Wiese, Rodrigo Takashi Kuroda, Igor Steinmacher, Gustavo A. Oliva, Reginaldo Ré, Christoph Treude, Marco Aurélio Gerosa
Research Collection School Of Computing and Information Systems
Models that predict software artifact co-changes have been proposed to assist developers in altering a software system and they often rely on coupling. However, developers have not yet widely adopted these approaches, presumably because of the high number of false recommendations. In this work, we conjecture that the contextual information related to software changes, which is collected from issues (e.g., issue type and reporter), developers’ communication (e.g., number of issue comments, issue discussants and words in the discussion), and commit metadata (e.g., number of lines added, removed, and modified), improves the accuracy of co-change prediction. We built customized prediction models …
A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja
A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja
Research Collection School Of Computing and Information Systems
A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRP-REC-PR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal …
Salience-Aware Adaptive Resonance Theory For Large-Scale Sparse Data Clustering, Lei Meng, Ah-Hwee Tan, Chunyan Miao
Salience-Aware Adaptive Resonance Theory For Large-Scale Sparse Data Clustering, Lei Meng, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additional steps on representation learning and/or feature weighting. However, adding these usually introduces new parameters and increases computational cost, thus inevitably lowering the robustness of these algorithms when handling massive ill-represented data. To alleviate these issues, this paper presents a class of self-organizing neural networks, called the salience-aware adaptive resonance theory (SA-ART) model. SA-ART extends Fuzzy ART with measures for cluster-wise salient feature modeling. …
Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell
Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell
Research Collection School Of Computing and Information Systems
Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …
Multi-Hop Knowledge Base Question Answering With An Iterative Sequence Matching Model, Yunshi Lan, Shuohang Wang, Jing Jiang
Multi-Hop Knowledge Base Question Answering With An Iterative Sequence Matching Model, Yunshi Lan, Shuohang Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
Knowledge Base Question Answering (KBQA) has attracted much attention and recently there has been more interest in multi-hop KBQA. In this paper, we propose a novel iterative sequence matching model to address several limitations of previous methods for multi-hop KBQA. Our method iteratively grows the candidate relation paths that may lead to answer entities. The method prunes away less relevant branches and incrementally assigns matching scores to the paths. Empirical results demonstrate that our method can significantly outperform existing methods on three different benchmark datasets.
Scompile: Critical Path Identification And Analysis For Smart Contracts, Jialiang Chang, Bo Gao, Hao Xiao, Jun Sun, Yan Cai, Zijiang Yang
Scompile: Critical Path Identification And Analysis For Smart Contracts, Jialiang Chang, Bo Gao, Hao Xiao, Jun Sun, Yan Cai, Zijiang Yang
Research Collection School Of Computing and Information Systems
Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise …
Recommendation-Based Team Formation For On-Demand Taxi-Calling Platforms, Lingyu Zhang, Tianshu Song, Yongxin Tong, Zimu Zhou, Dan Li, Wei Ai, Lulu Zhang, Guobin Wu, Yan Liu, Jieping Ye
Recommendation-Based Team Formation For On-Demand Taxi-Calling Platforms, Lingyu Zhang, Tianshu Song, Yongxin Tong, Zimu Zhou, Dan Li, Wei Ai, Lulu Zhang, Guobin Wu, Yan Liu, Jieping Ye
Research Collection School Of Computing and Information Systems
On-demand taxi-calling platforms often ignore the social engagement of individual drivers. The lack of social incentives impairs the work enthusiasms of drivers and will affect the quality of service. In this paper, we propose to form teams among drivers to promote participation. A team consists of a leader and multiple members, which acts as the basis for various group-based incentives such as competition. We define the Recommendation-based Team Formation (RTF) problem to form as many teams as possible while accounting for the choices of drivers. The RTF problem is challenging. It needs both accurate recommendation and coordination among recommendations, since …
Using Customer Service Dialogues For Satisfaction Analysis With Context-Assisted Multiple Instance Learning, Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang
Using Customer Service Dialogues For Satisfaction Analysis With Context-Assisted Multiple Instance Learning, Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang
Research Collection School Of Computing and Information Systems
Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classification (e.g., well satisfied, met and unsatisfied) and sentimental utterance identification (e.g., positive, neutral and negative). In this paper, we conduct a pilot study on the task of service satisfaction analysis (SSA) based on multi-turn CS dialogues. We propose an extensible Context-Assisted Multiple Instance Learning (CAMIL) model to predict the sentiments of all the customer utterances and then aggregate those sentiments into service …
Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak
Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Gender and racial diversity in the mediated images from the media shape our perception of different demographic groups. In this work, we investigate gender and racial diversity of 85,957 advertising images shared by the 73 top international brands on Instagram and Facebook. We hope that our analyses give guidelines on how to build a fully automated watchdog for gender and racial diversity in online advertisements.
Revisiting Collaboration Through Mixed Reality: The Evolution Of Groupware, Barrett Ens, Joel Lanir, Anthony Tang, Scott Bateman, Gun Lee, Thammathip Piumsomboon, Mark Billinghurst
Revisiting Collaboration Through Mixed Reality: The Evolution Of Groupware, Barrett Ens, Joel Lanir, Anthony Tang, Scott Bateman, Gun Lee, Thammathip Piumsomboon, Mark Billinghurst
Research Collection School Of Computing and Information Systems
Collaborative Mixed Reality (MR) systems are at a critical point in time as they are soon to become more commonplace. However, MR technology has only recently matured to the point where researchers can focus deeply on the nuances of supporting collaboration, rather than needing to focus on creating the enabling technology. In parallel, but largely independently, the field of Computer Supported Cooperative Work (CSCW) has focused on the fundamental concerns that underlie human communication and collaboration over the past 30-plus years. Since MR research is now on the brink of moving into the real world, we reflect on three decades …
Visualizing The Invisible: Occluded Vehicle Segmentation And Recovery, Xiaosheng Yan, Feigege Wang, Wenxi Liu, Yuanlong Yu, Shengfeng He, Jia Pan
Visualizing The Invisible: Occluded Vehicle Segmentation And Recovery, Xiaosheng Yan, Feigege Wang, Wenxi Liu, Yuanlong Yu, Shengfeng He, Jia Pan
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, firstly, to improve the quality of the segmentation completion, we present two coupled discriminators that introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, Occluded Vehicle …
Vireo-Eurecom @ Trecvid 2019: Ad-Hoc Video Search (Avs), Phuong Anh Nguyen, Jiaxin Wu, Chong-Wah Ngo, Francis Danny, Benoit Huet
Vireo-Eurecom @ Trecvid 2019: Ad-Hoc Video Search (Avs), Phuong Anh Nguyen, Jiaxin Wu, Chong-Wah Ngo, Francis Danny, Benoit Huet
Research Collection School Of Computing and Information Systems
In this paper, we describe the systems developed for Ad-hoc Video Search (AVS) task at TRECVID 2019[1] and the achieved results.
Gender And Racial Diversity In Commercial Brands' Advertising Images On Social Media, Jisun An, Haewoon Kwak
Gender And Racial Diversity In Commercial Brands' Advertising Images On Social Media, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Gender and racial diversity in the mediated images from the media shape our perception of different demographic groups. In this work, we investigate gender and racial diversity of 85,957 advertising images shared by the 73 top international brands on Instagram and Facebook. We hope that our analyses give guidelines on how to build a fully automated watchdog for gender and racial diversity in online advertisements.
Vireojd-Mm @ Trecvid 2019: Activities In Extended Video (Actev), Zhijian Hou, Ying-Wei Pan, Ting Yao, Chong-Wah Ngo
Vireojd-Mm @ Trecvid 2019: Activities In Extended Video (Actev), Zhijian Hou, Ying-Wei Pan, Ting Yao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
In this paper, we describe the system developed for Activities in Extended Video(ActEV) task at TRECVid 2019 [1] and the achieved results. Activities in Extended Video(ActEV): The goal of Activities in Extended Video is to spatially and temporally localize the action instances in a surveillance setting. We have participated in previous ActEV prize challenge. Since the only difference between the two challenges is evaluation metric, we maintain previous pipeline [2] for this challenge. The pipeline has three stages: object detection, tubelet generation and temporal action localization. This time we extend the system for two aspects separately: better object detection and …
Eurecom At Trecvid Avs 2019, Danny Francis, Phuong Anh Nguyen, Benoit Huet, Chong-Wah Ngo
Eurecom At Trecvid Avs 2019, Danny Francis, Phuong Anh Nguyen, Benoit Huet, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This notebook reports the model and results of the EURECOM runs at TRECVID AVS 2019.
Special Issue On Multimedia Recommendation And Multi-Modal Data Analysis, Xiangnan He, Zhenguang Liu, Hanwang Zhang, Chong-Wah Ngo, Svebor Karaman, Yongfeng Zhang
Special Issue On Multimedia Recommendation And Multi-Modal Data Analysis, Xiangnan He, Zhenguang Liu, Hanwang Zhang, Chong-Wah Ngo, Svebor Karaman, Yongfeng Zhang
Research Collection School Of Computing and Information Systems
Rich multimedia contents are dominating the Web. In popular social media platforms such as FaceBook, Twitter, and Instagram, there are over millions of multimedia contents being created by users on a daily basis. In the meantime, multimedia data consist of data in multiple modalities, such as text, images, audio, and so on. Users are heavily overloaded by the massive multi-modal data, and it becomes critical to explore advanced techniques for heterogeneous big data analytics and multimedia recommendation. Traditional multimedia recommendation and data analysis technologies cannot well address the problem of understanding users’ preference in the feature-rich multimedia contents, and have …
Characterizing And Predicting Repeat Food Consumption Behavior For Just-In-Time Interventions, Yue Liu, Helena Huey Chong Lee, Palakorn Achananuparp, Ee-Peng Lim, Tzu-Ling Cheng, Shou-De Lin
Characterizing And Predicting Repeat Food Consumption Behavior For Just-In-Time Interventions, Yue Liu, Helena Huey Chong Lee, Palakorn Achananuparp, Ee-Peng Lim, Tzu-Ling Cheng, Shou-De Lin
Research Collection School Of Computing and Information Systems
Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studied in food recommendation research. More importantly, the ability to predict daily eating habits of individuals is crucial to improve the effectiveness of food recommender systems in facilitating healthy lifestyle change. In this study, we analyze the patterns of repeat food consumptions using large-scale consumption data from a popular online fitness community …
Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Chris Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang
Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Chris Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang
Research Collection School Of Computing and Information Systems
The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their effectiveness is challenging, however, as realistic sets of attacks to test them against are not always available. In this paper, we propose smart fuzzing, an automated, machine learning guided technique for systematically finding 'test suites' of CPS network attacks, without requiring any knowledge of the system's control programs or physical processes. Our approach uses predictive machine learning models and metaheuristic search algorithms to guide the fuzzing of …
Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing, Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee
Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing, Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee
Research Collection School Of Computing and Information Systems
We present SmrtFridge, a consumer-grade smart fridge prototype that demonstrates two key capabilities: (a) identify the individual food items that users place in or remove from a fridge, and (b) estimate the residual quantity of food items inside a refrigerated container (opaque or transparent). Notably, both of these inferences are performed unobtrusively, without requiring any explicit user action or tagging of food objects. To achieve these capabilities, SmrtFridge uses a novel interaction-driven, multi-modal sensing pipeline, where Infrared (IR) and RGB video sensing, triggered whenever a user interacts naturally with the fridge, is used to extract a foreground visual image of …
Data Security Issues In Deep Learning: Attacks, Countermeasures, And Opportunities, Guowen Xu, Hongwei Li, Hao Ren, Kan Yang, Robert H. Deng
Data Security Issues In Deep Learning: Attacks, Countermeasures, And Opportunities, Guowen Xu, Hongwei Li, Hao Ren, Kan Yang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep learning has been explored in a wide variety of fields and produced unparalleled performance results. It plays a vital role in daily applications and is also subtly changing the rules, habits, and behaviors of society. However, inevitably, data-based learning strategies are bound to cause potential security and privacy threats, and arouse public as well as government concerns about its promotion to the real world. In this article, we mainly focus on data security issues in deep learning. We first investigate the potential threats of deep learning …