Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (8549)
- Computer Sciences (8532)
- Databases and Information Systems (3451)
- Software Engineering (2164)
- Artificial Intelligence and Robotics (1697)
-
- Information Security (1067)
- Numerical Analysis and Scientific Computing (1024)
- Engineering (956)
- Graphics and Human Computer Interfaces (933)
- Social and Behavioral Sciences (734)
- Business (686)
- Computer Engineering (512)
- Theory and Algorithms (493)
- Operations Research, Systems Engineering and Industrial Engineering (417)
- Programming Languages and Compilers (379)
- OS and Networks (323)
- Communication (310)
- Social Media (248)
- Public Affairs, Public Policy and Public Administration (227)
- Transportation (204)
- Medicine and Health Sciences (186)
- Education (180)
- Management Information Systems (178)
- Data Storage Systems (176)
- E-Commerce (151)
- International and Area Studies (125)
- Asian Studies (124)
- Digital Communications and Networking (123)
- Technology and Innovation (114)
- Health Information Technology (113)
- Keyword
-
- Machine learning (125)
- Deep learning (124)
- Social media (80)
- Artificial intelligence (72)
- Reinforcement learning (69)
-
- Data mining (65)
- Privacy (65)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (55)
- Optimization (55)
- Security (54)
- Singapore (52)
- Visualization (52)
- Task analysis (50)
- Twitter (50)
- Neural networks (49)
- Online learning (49)
- Software engineering (49)
- Training (49)
- Anomaly detection (47)
- Feature extraction (46)
- Collaboration (45)
- Large Language Models (45)
- Blockchain (44)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Natural language processing (39)
- Publication Year
- File Type
Articles 3361 - 3390 of 8722
Full-Text Articles in Entire DC Network
Sentiment Analysis Over Collaborative Relationships In Open Source Software Projects, Lingjia Li, Jian Cao, David Lo
Sentiment Analysis Over Collaborative Relationships In Open Source Software Projects, Lingjia Li, Jian Cao, David Lo
Research Collection School Of Computing and Information Systems
Sentiments and collaboration efficiency are key factors in the success of the open source software (OSS) development process. However, in the software engineering domain, no studies have been conducted to analyze the effect between collaborators' sentiments, and the role of sentiment in collaborative relationships during the development process. In this study, we apply sentiment analysis and statistical analysis on collaboration artifacts over five projects on GitHub. We use sentiment consistency to quantify the relation between sentiments in collaborative relationships. It is found that sentiment consistency is positively correlated with the closeness of collaborative relationships and collaborators' overall sentiment states. We …
Recovering Fitness Gradients For Interprocedural Boolean Flags In Search-Based Testing, Yun Lin, Jun Sun, Gordon Fraser, Ziheng Xiu, Ting Liu, Jin Song Dong
Recovering Fitness Gradients For Interprocedural Boolean Flags In Search-Based Testing, Yun Lin, Jun Sun, Gordon Fraser, Ziheng Xiu, Ting Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
In Search-based Software Testing (SBST), test generation is guided by fitness functions that estimate how close a test case is to reach an uncovered test goal (e.g., branch). A popular fitness function estimates how close conditional statements are to evaluating to true or false, i.e., the branch distance. However, when conditions read Boolean variables (e.g., if(x && y)), the branch distance provides no gradient for the search, since a Boolean can either be true or false. This flag problem can be addressed by transforming individual procedures such that Boolean flags are replaced with numeric comparisons that provide better guidance for …
Probabilistic Value Selection For Space Efficient Model, Gunarto Sindoro Njoo, Baihua Zheng, Kuo-Wei Hsu, Wen-Chih Peng
Probabilistic Value Selection For Space Efficient Model, Gunarto Sindoro Njoo, Baihua Zheng, Kuo-Wei Hsu, Wen-Chih Peng
Research Collection School Of Computing and Information Systems
An alternative to current mainstream preprocessing methods is proposed: Value Selection (VS). Unlike the existing methods such as feature selection that removes features and instance selection that eliminates instances, value selection eliminates the values (with respect to each feature) in the dataset with two purposes: reducing the model size and preserving its accuracy. Two probabilistic methods based on information theory's metric are proposed: PVS and P + VS. Extensive experiments on the benchmark datasets with various sizes are elaborated. Those results are compared with the existing preprocessing methods such as feature selection, feature transformation, and instance selection methods. Experiment results …
Next-Term Grade Prediction: A Machine Learning Approach, Audrey Tedja Widjaja, Lei Wang, Nghia Truong Trong, Aldy Gunawan, Ee-Peng Lim
Next-Term Grade Prediction: A Machine Learning Approach, Audrey Tedja Widjaja, Lei Wang, Nghia Truong Trong, Aldy Gunawan, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
As students progress in their university programs, they have to face many course choices. It is important for them to receive guidance based on not only their interest, but also the "predicted" course performance so as to improve learning experience and optimise academic performance. In this paper, we propose the next-term grade prediction task as a useful course selection guidance. We propose a machine learning framework to predict course grades in a specific program term using the historical student-course data. In this framework, we develop the prediction model using Factorization Machine (FM) and Long Short Term Memory combined with FM …
Graph-To-Tree Learning For Solving Math Word Problems, Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, Ee-Peng Lim
Graph-To-Tree Learning For Solving Math Word Problems, Jipeng Zhang, Lei Wang, Roy Ka-Wei Lee, Yi Bin, Yan Wang, Jie Shao, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution expressions. In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by …
Lightweight And Privacy-Aware Fine-Grained Access Control For Iot-Oriented Smart Health, Jianfei Sun, Hu Xiong, Ximeng Liu, Yinghui Zhang, Xuyun Nie, Robert H. Deng
Lightweight And Privacy-Aware Fine-Grained Access Control For Iot-Oriented Smart Health, Jianfei Sun, Hu Xiong, Ximeng Liu, Yinghui Zhang, Xuyun Nie, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the booming of Internet of Things (IoT), smart health (s-health) is becoming an emerging and attractive paradigm. It can provide an accurate prediction of various diseases and improve the quality of healthcare. Nevertheless, data security and user privacy concerns still remain issues to be addressed. As a high potential and prospective solution to secure IoT-oriented s-health applications, ciphertext policy attribute-based encryption (CP-ABE) schemes raise challenges, such as heavy overhead and attribute privacy of the end users. To resolve these drawbacks, an optimized vector transformation approach is first proposed to efficiently transform the access policy and user attribute set into …
Big Data, Spatial Optimization, And Planning, Kai Cao, Wenwen Li, Richard Church
Big Data, Spatial Optimization, And Planning, Kai Cao, Wenwen Li, Richard Church
Research Collection School Of Computing and Information Systems
Spatial optimization represents a set of powerful spatial analysis techniques that can be used to identify optimal solution(s) and even generate a large number of competitive alternatives. The formulation of such problems involves maximizing or minimizing one or more objectives while satisfying a number of constraints. Solution techniques range from exact models solved with such approaches as linear programming and integer programming, or heuristic algorithms, i.e. Tabu Search, Simulated Annealing, and Genetic Algorithms. Spatial optimization techniques have been utilized in numerous planning applications, such as location-allocation modeling/site selection, land use planning, school districting, regionalization, routing, and urban design. These methods …
Spinfer: Inferring Semantic Patches For The Linux Kernel, Lucas Serrano, Van-Anh Nguyen, Ferdian Thung, Lingxiao Jiang, David Lo, Julia Lawall, Gilles Muller
Spinfer: Inferring Semantic Patches For The Linux Kernel, Lucas Serrano, Van-Anh Nguyen, Ferdian Thung, Lingxiao Jiang, David Lo, Julia Lawall, Gilles Muller
Research Collection School Of Computing and Information Systems
In a large software system such as the Linux kernel, there is a continual need for large-scale changes across many source files, triggered by new needs or refined design decisions. In this paper, we propose to ease such changes by suggesting transformation rules to developers, inferred automatically from a collection of examples. Our approach can help automate large-scale changes as well as help understand existing large-scale changes, by highlighting the various cases that the developer who performed the changes has taken into account. We have implemented our approach as a tool, Spinfer. We evaluate Spinfer on a range of challenging …
Mining And Predicting Micro-Process Patterns Of Issue Resolution For Open Source Software Projects, Yiran Wang, Jian Cao, David Lo
Mining And Predicting Micro-Process Patterns Of Issue Resolution For Open Source Software Projects, Yiran Wang, Jian Cao, David Lo
Research Collection School Of Computing and Information Systems
Addressing issue reports is an integral part of open source software (OSS) projects. Although several studies have attempted to discover the factors that affect issue resolution, few pay attention to the underlying micro-process patterns of resolution processes. Discovering these micro-patterns will help us understand the dynamics of issue resolution processes so that we can manage and improve them in better ways. Of the various types of issues, those relating to corrective maintenance account for nearly half hence resolving these issues efficiently is critical for the success of OSS projects. Therefore, we apply process mining techniques to discover the micro-patterns of …
Expertise Style Transfer: A New Task Towards Better Communication Between Experts And Laymen, Yixin Cao, Ruihao Shui, Liangming Pan, Min-Yen Kan, Zhiyuan Lu, Tat-Seng Chua
Expertise Style Transfer: A New Task Towards Better Communication Between Experts And Laymen, Yixin Cao, Ruihao Shui, Liangming Pan, Min-Yen Kan, Zhiyuan Lu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style …
Tree-Augmented Cross-Modal Encoding For Complex-Query Video Retrieval, Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat-Seng Chua
Tree-Augmented Cross-Modal Encoding For Complex-Query Video Retrieval, Xun Yang, Jianfeng Dong, Yixin Cao, Xun Wang, Meng Wang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
The rapid growth of user-generated videos on the Internet has intensified the need for text-based video retrieval systems. Traditional methods mainly favor the concept-based paradigm on retrieval with simple queries, which are usually ineffective for complex queries that carry far more complex semantics. Recently, embedding-based paradigm has emerged as a popular approach. It aims to map the queries and videos into a shared embedding space where semantically-similar texts and videos are much closer to each other. Despite its simplicity, it forgoes the exploitation of the syntactic structure of text queries, making it suboptimal to model the complex queries. To facilitate …
Deep Learning Of Facial Embeddings And Facial Landmark Points For The Detection Of Academic Emotions, Hua Leong Fwa
Deep Learning Of Facial Embeddings And Facial Landmark Points For The Detection Of Academic Emotions, Hua Leong Fwa
Research Collection School Of Computing and Information Systems
Automatic emotion recognition is an actively researched area as emotion plays a pivotal role in effective human communications. Equipping a computer to understand and respond to human emotions has potential applications in many fields including education, medicine, transport and hospitality. In a classroom or online learning context, the basic emotions do not occur frequently and do not influence the learning process itself. The academic emotions such as engagement, frustration, confusion and boredom are the ones which are pivotal to sustaining the motivation of learners. In this study, we evaluated the use of deep learning on FaceNet embeddings and facial landmark …
Covid-19 Calls For Remote Reskilling And Retraining, C. Zou, W. Zhao, Keng Siau
Covid-19 Calls For Remote Reskilling And Retraining, C. Zou, W. Zhao, Keng Siau
Research Collection School Of Computing and Information Systems
Cui Zou, Wangchuchu Zhao, and Keng Siau respond directly to COVID-19 by framing the skills and training necessary to survive crises. The authors focus on the importance of helping organizations prepare beyond the current pandemic by teaching everyone how to use the technology tools -- and exploit the processes -- around remote working.
Covid-19 Pandemic: Role Of Technology In Transforming Business To The New Normal, Fiona Fui-Hoon Nah, Keng Siau
Covid-19 Pandemic: Role Of Technology In Transforming Business To The New Normal, Fiona Fui-Hoon Nah, Keng Siau
Research Collection School Of Computing and Information Systems
COVID-19 has disrupted our lives and the economy. In this paper, we outline approaches in which information technology can be used to implement business strategies to enhance resilience by coping with, adapting to, and recovering from adversity resulting from the COVID-19 pandemic. We discuss how information technology such as digital supply chain, data analytics, artificial intelligence, machine learning, robotics, digital commerce, and Internet of Things can be used to enhance resilience and continuity of business.
Keen2act: Activity Recommendation In Online Social Collaborative Platforms, Roy Ka-Wei Lee, Thong Hoang, Richard J. Oentaryo, David Lo
Keen2act: Activity Recommendation In Online Social Collaborative Platforms, Roy Ka-Wei Lee, Thong Hoang, Richard J. Oentaryo, David Lo
Research Collection School Of Computing and Information Systems
Social collaborative platforms such as GitHub and Stack Overflow have been increasingly used to improve work productivity via collaborative efforts. To improve user experiences in these platforms, it is desirable to have a recommender system that can suggest not only items (e.g., a GitHub repository) to a user, but also activities to be performed on the suggested items (e.g., forking a repository). To this end, we propose a new approach dubbed Keen2Act, which decomposes the recommendation problem into two stages: the Keen and Act steps. The Keen step identifies, for a given user, a (sub)set of items in which he/she …
What Was Written Vs. Who Read It: News Media Profiling Using Text Analysis And Social Media Context, Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, Preslav. Nakov
What Was Written Vs. Who Read It: News Media Profiling Using Text Analysis And Social Media Context, Ramy Baly, Georgi Karadzhov, Jisun An, Haewoon Kwak, Yoan Dinkov, Ahmed Ali, James Glass, Preslav. Nakov
Research Collection School Of Computing and Information Systems
Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim, either manually or automatically. Thus, it has been proposed to profile entire news outlets and to look for those that are likely to publish fake or biased content. This makes it possible to detect likely “fake news” the moment they are published, by simply checking the reliability of their …
Video-Grounded Dialogues With Pretrained Generation Language Models, Hung Le, Steven C. H. Hoi
Video-Grounded Dialogues With Pretrained Generation Language Models, Hung Le, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Pre-trained language models have shown remarkable success in improving various downstream NLP tasks due to their ability to capture dependencies in textual data and generate natural responses. In this paper, we leverage the power of pre-trained language models for improving video-grounded dialogue, which is very challenging and involves complex features of different dynamics: (1) Video features which can extend across both spatial and temporal dimensions; and (2) Dialogue features which involve semantic dependencies over multiple dialogue turns. We propose a framework by extending GPT-2 models to tackle these challenges by formulating video-grounded dialogue tasks as a sequence-to-sequence task, combining both …
A Review On Eye-Tracking Metrics For Sleepiness, Debasis Roy, Fiona Fui-Hoon Nah
A Review On Eye-Tracking Metrics For Sleepiness, Debasis Roy, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Sleepiness that can arise from sleep deprivation can increase human errors in task performance and create workplace hazards and accidents. Hence, it is critical to detect sleepiness to minimize hazards and human errors. This paper provides a review of the literature on eye tracking metrics that can be used to detect sleepiness. These metrics include blink duration, blink frequency, saccade latency, saccade peak velocity, saccade accuracy, smooth pursuit velocity gain, fixation rate, pupil size, and latency to pupil constriction.
The Prediction Of Delay Time At Intersection And Route Planning For Autonomous Vehicles, Genwang Gou, Yongxin Zhao, Jiawei Liang, Ling Shi
The Prediction Of Delay Time At Intersection And Route Planning For Autonomous Vehicles, Genwang Gou, Yongxin Zhao, Jiawei Liang, Ling Shi
Research Collection School Of Computing and Information Systems
Intelligent Intersections (roundabout and crossroads) management is considered as one of the challenges to significantly improve urban traffic efficiency. Recent researches in artificial intelligence suggest that autonomous vehicles have the possibility of forming intelligent intersection management, and likely to occupy the leading role in future urban traffic. If route planning method can be used for route decision of autonomous vehicle, the urban traffic efficiency can be further improved. In this paper, we propose an Intelligent Intersection Control Protocol (IICP) for controlling autonomous vehicles cross intersection, and recommend route for autonomous vehicles to reduce travel time and improve urban traffic efficiency. …
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization With Nearly Optimal Generalization, Pan Zhou, Xiaotong Yuan
Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization With Nearly Optimal Generalization, Pan Zhou, Xiaotong Yuan
Research Collection School Of Computing and Information Systems
Stochastic variance-reduced gradient (SVRG) algorithms have been shown to work favorably in solving large-scale learning problems. Despite the remarkable success, the stochastic gradient complexity of SVRG-type algorithms usually scales linearly with data size and thus could still be expensive for huge data. To address this deficiency, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly-convex problems that enjoys provably improved data-size-independent complexity guarantees.
Answer Ranking For Product-Related Questions Via Multiple Semantic Relations Modeling, Wenxuan Zhang, Yang Deng, Wai Lam
Answer Ranking For Product-Related Questions Via Multiple Semantic Relations Modeling, Wenxuan Zhang, Yang Deng, Wai Lam
Research Collection School Of Computing and Information Systems
Many E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary diversely in their qualities and thus need to be appropriately ranked for each question to improve user satisfaction. It can be observed that product reviews usually provide useful information for a given question, and thus can assist the ranking process. In this paper, we investigate the answer ranking problem for product-related questions, with the relevant reviews treated as auxiliary information that can be exploited for facilitating …
Bridging Hierarchical And Sequential Context Modeling For Question-Driven Extractive Answer Summarization, Yang Deng, Wenxuan Zhang, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Bridging Hierarchical And Sequential Context Modeling For Question-Driven Extractive Answer Summarization, Yang Deng, Wenxuan Zhang, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Research Collection School Of Computing and Information Systems
Non-factoid question answering (QA) is one of the most extensive yet challenging application and research areas of retrieval-based question answering. In particular, answers to non-factoid questions can often be too lengthy and redundant to comprehend, which leads to the great demand on answer sumamrization in non-factoid QA. However, the multi-level interactions between QA pairs and the interrelation among different answer sentences are usually modeled separately on current answer summarization studies. In this paper, we propose a unified model to bridge hierarchical and sequential context modeling for question-driven extractive answer summarization. Specifically, we design a hierarchical compare-aggregate method to integrate the …
Towards An Optimal Outdoor Advertising Placement: When A Budget Constraint Meets Moving Trajectories, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Towards An Optimal Outdoor Advertising Placement: When A Budget Constraint Meets Moving Trajectories, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Research Collection School Of Computing and Information Systems
In this article, we propose and study the problem of trajectory-driven influential billboard placement: given a set of billboards U (each with a location and a cost), a database of trajectories T, and a budget L, we find a set of billboards within the budget to influence the largest number of trajectories. One core challenge is to identify and reduce the overlap of the influence from different billboards to the same trajectories, while keeping the budget constraint into consideration. We show that this problem is NP-hard and present an enumeration based algorithm with (1-1/e) approximation ratio. However, the enumeration would …
Improving Event Detection Via Open-Domain Event Trigger Knowledge, Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie
Improving Event Detection Via Open-Domain Event Trigger Knowledge, Meihan Tong, Bin Xu, Shuai Wang, Yixin Cao, Lei Hou, Juanzi Li, Jun Xie
Research Collection School Of Computing and Information Systems
Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the small scale of training data, previous methods perform poorly on unseen/sparsely labeled trigger words and are prone to overfitting densely labeled trigger words. To address the issue, we propose a novel Enrichment Knowledge Distillation (EKD) model to leverage external open-domain trigger knowledge to reduce the in-built biases to frequent trigger words in annotations. Experiments on benchmark ACE2005 show that our model outperforms nine strong baselines, is especially effective for unseen/sparsely labeled trigger words. The source code is released on https://github.com/shuaiwa16/ekd.git.
A Systematic Media Frame Analysis Of 1.5 Million New York Times Articles From 2000 To 2017, Haewoon Kwak, Jisun An
A Systematic Media Frame Analysis Of 1.5 Million New York Times Articles From 2000 To 2017, Haewoon Kwak, Jisun An
Research Collection School Of Computing and Information Systems
Framing is an indispensable narrative device for news media because even the same facts may lead to conflicting understandings if deliberate framing is employed. Therefore, identifying media framing is a crucial step to understanding how news media influence the public. Framing is, however, difficult to operationalize and detect, and thus traditional media framing studies had to rely on manual annotation, which is challenging to scale up to massive news datasets. Here, by developing a media frame classifier that achieves state-of-the-art performance, we systematically analyze the media frames of 1.5 million New York Times articles published from 2000 to 2017. By …
Automated Synthesis Of Local Time Requirement For Service Composition, Étienne André, Tian Huat Tan, Manman Chen, Shuang Liu, Jun Sun, Yang Liu, Jin Song Dong
Automated Synthesis Of Local Time Requirement For Service Composition, Étienne André, Tian Huat Tan, Manman Chen, Shuang Liu, Jun Sun, Yang Liu, Jin Song Dong
Research Collection School Of Computing and Information Systems
Service composition aims at achieving a business goal by composing existing service-based applications or components. The response time of a service is crucial, especially in time-critical business environments, which is often stated as a clause in service-level agreements between service providers and service users. To meet the guaranteed response time requirement of a composite service, it is important to select a feasible set of component services such that their response time will collectively satisfy the response time requirement of the composite service. In this work, we use the BPEL modeling language that aims at specifying Web services. We extend it …
Search Me In The Dark: Privacy-Preserving Boolean Range Query Over Encrypted Spatial Data, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Robert H. Deng, Yinbin Miao, Dan Zhu, Zhuoran Ma
Search Me In The Dark: Privacy-Preserving Boolean Range Query Over Encrypted Spatial Data, Xiangyu Wang, Jianfeng Ma, Ximeng Liu, Robert H. Deng, Yinbin Miao, Dan Zhu, Zhuoran Ma
Research Collection School Of Computing and Information Systems
With the increasing popularity of geo-positioning technologies and mobile Internet, spatial keyword data services have attracted growing interest from both the industrial and academic communities in recent years. Meanwhile, a massive amount of data is increasingly being outsourced to cloud in the encrypted form for enjoying the advantages of cloud computing while without compromising data privacy. Most existing works primarily focus on the privacy-preserving schemes for either spatial or keyword queries, and they cannot be directly applied to solve the spatial keyword query problem over encrypted data. In this paper, we study the challenging problem of Privacy-preserving Boolean Range Query …
Recent Advances In Deep Learning For Object Detection, Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi
Recent Advances In Deep Learning For Object Detection, Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, …
Privacy-Enhanced Remote Data Integrity Checking With Updatable Timestamp, Tong Wu, Guomin Yang, Yi Mu, Rongmao Chen, Shengmin Xu
Privacy-Enhanced Remote Data Integrity Checking With Updatable Timestamp, Tong Wu, Guomin Yang, Yi Mu, Rongmao Chen, Shengmin Xu
Research Collection School Of Computing and Information Systems
Remote data integrity checking (RDIC) enables clients to verify whether the outsourced data is intact without keeping a copy locally or downloading it. Nevertheless, the existing RDIC schemes do not support the pay-as-you-go (PAYG) payment model, where the payment is decided by the volume and duration of the outsourced data. Specifically, none of the existing works have considered the client’s control over changes in storage duration. In this paper, we propose an RDIC scheme to simultaneously check the data content and storage duration represented by an updatable timestamp via the third-party auditor (TPA). Also, our proposed scheme achieves indistinguishable privacy …
Interactive Entity Linking Using Entity-Word Representations, Pei Chi Lo, Ee-Peng Lim
Interactive Entity Linking Using Entity-Word Representations, Pei Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. In this paper, we leverage on human intelligence for embedding-based interactive entity linking. We adopt an active learning approach to select mentions for human …