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Full-Text Articles in Entire DC Network
Dynamic Student Classification On Memory Networks For Knowledge Tracing, Sein Minn, Michel C. Desmarais, Feida Zhu, Jing Xiao, Jianzong Wang
Dynamic Student Classification On Memory Networks For Knowledge Tracing, Sein Minn, Michel C. Desmarais, Feida Zhu, Jing Xiao, Jianzong Wang
Research Collection School Of Computing and Information Systems
Knowledge Tracing (KT) is the assessment of student’s knowledge state and predicting whether that student may or may not answer the next problem correctly based on a number of previous practices and outcomes in their learning process. KT leverages machine learning and data mining techniques to provide better assessment, supportive learning feedback and adaptive instructions. In this paper, we propose a novel model called Dynamic Student Classification on Memory Networks (DSCMN) for knowledge tracing that enhances existing KT approaches by capturing temporal learning ability at each time interval in student’s long-term learning process. Experimental results confirm that the proposed model …
Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen
Confusion Prediction From Eye-Tracking Data: Experiments With Machine Learning, Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority …
Careful-Packing: A Practical And Scalable Anti-Tampering Software Protection Enforced By Trusted Computing, Flavio Toffalini, Martín Ochoa, Jun Sun, Jianying Zhou
Careful-Packing: A Practical And Scalable Anti-Tampering Software Protection Enforced By Trusted Computing, Flavio Toffalini, Martín Ochoa, Jun Sun, Jianying Zhou
Research Collection School Of Computing and Information Systems
Ensuring the correct behaviour of an application is a critical security issue. One of the most popular ways to modify the intended behaviour of a program is to tamper its binary. Several solutions have been proposed to solve this problem, including trusted computing and anti-tampering techniques. Both can substantially increase security, and yet both have limitations. In this work, we propose an approach which combines trusted computing technologies and anti-tampering techniques, and that synergistically overcomes some of their inherent limitations. In our approach critical software regions are protected by leveraging on trusted computing technologies and cryptographic packing, without introducing additional …
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Picking Flowers In An Ico Garden, Fam Guo Teng, Paul R. Griffin, Andrew Koh
Research Collection School Of Computing and Information Systems
The rise of Initial Coin Offerings (ICO) in recent times and their potential for investment opportunities have investors spending a lot of time researching ICOs or having to follow the crowd. This paper aims to explore four broad factors of ICOs: identity, credibility, investor sentiment, and price movement to develop a framework that is useful in determining ICO quality. Research is shown using data sources including public forums, chat groups, web sites, white papers as well as smart contract details. Finally, a system, based on the framework, is proposed that can be used to detect and regulate ICO activities and …
Api Recommendation For Event-Driven Android Application Development, Weizhao Yuan, Huu Hoang Nguyen, Lingxiao Jiang, Yuting Chen, Jianjun Zhao, Haibo Yu
Api Recommendation For Event-Driven Android Application Development, Weizhao Yuan, Huu Hoang Nguyen, Lingxiao Jiang, Yuting Chen, Jianjun Zhao, Haibo Yu
Research Collection School Of Computing and Information Systems
Context: Software development is increasingly dependent on existing libraries. Developers need help to find suitable library APIs. Although many studies have been proposed to recommend relevant functional APIs that can be invoked for implementing a functionality, few studies have paid attention to an orthogonal need associated with event-driven programming frameworks, such as the Android framework. In addition to invoking functional APIs, Android developers need to know where to place functional code according to various events that may be triggered within the framework.Objective: This paper aims to develop an API recommendation engine for Android application development that can recommend both (1) …
Fine-Grained Geolocation Of Tweets In Temporal Proximity, Wen Haw Chong, Ee Peng Lim
Fine-Grained Geolocation Of Tweets In Temporal Proximity, Wen Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In fine-grained tweet geolocation, tweets are linked to the specific venues (e.g., restaurants, shops) fromwhich they were posted. This explicitly recovers the venue context that is essential for applications such aslocation-based advertising or user profiling. For this geolocation task, we focus on geolocating tweets that arecontained in tweet sequences. In a tweet sequence, tweets are posted from some latent venue(s) by the sameuser and within a short time interval. This scenario arises from two observations: (1) It is quite common thatusers post multiple tweets in a short time and (2) most tweets are not geocoded. To more accurately geolocatea tweet, …
See No Evil, Hear No Evil? Dissecting The Impact Of Online Hacker Forums, Wei T. Yue, Qiu-Hong Wang, Kai‐Lung Hui
See No Evil, Hear No Evil? Dissecting The Impact Of Online Hacker Forums, Wei T. Yue, Qiu-Hong Wang, Kai‐Lung Hui
Research Collection School Of Computing and Information Systems
Online hacker forums offer a prominent avenue for sharing hacking knowledge. Using a field dataset culled from multiple sources, we find that online discussion of distributed denial of service (DDOS) attacks in hackforums.net decreases the number of DDOS-attack victims. A 1% increase in discussion decreases DDOS attacks by 0.032% to 0.122%. This means that two DDOS-attack posts per day could reduce the number of victims by 700 to 2,600 per day. We find that discussion topics with similar keywords can variously increase or decrease DDOS attacks, meaning we cannot ascertain the impact of the discussion just by the post nature. …
Microbtc: Efficient, Flexible And Fair Micropayment For Bitcoin Using Hash Chains, Zhiguo Wan, Robert H. Deng, David Kuo Chuen Lee, Ying Li
Microbtc: Efficient, Flexible And Fair Micropayment For Bitcoin Using Hash Chains, Zhiguo Wan, Robert H. Deng, David Kuo Chuen Lee, Ying Li
Research Collection School Of Computing and Information Systems
While Bitcoin gains increasing popularity in different payment scenarios, the transaction fees make it difficult to be applied to micropayment. Given the wide applicability of micropayment, it is crucial for all cryptocurrencies including Bitcoin to provide effective support therein. In light of this, a number of low-cost micropayment schemes for Bitcoin have been proposed recently to reduce micropayment costs. Existing schemes, however, suffer from drawbacks such as high computation cost, inflexible payment value, and possibly unfair exchanges. The paper proposes two new micropayment schemes, namely the basic MicroBTC and the advanced MicroBTC, for Bitcoin by integrating the hash chain technique …
Neural Network Based Detection Of Self-Admitted Technical Debt: From Performance To Explainability, Xiaoxue Ren, Zhenchang Xing, Xin Xia, David Lo, Xinyu Wang, John Grundy
Neural Network Based Detection Of Self-Admitted Technical Debt: From Performance To Explainability, Xiaoxue Ren, Zhenchang Xing, Xin Xia, David Lo, Xinyu Wang, John Grundy
Research Collection School Of Computing and Information Systems
Technical debt is a metaphor to reflect the tradeoff software engineers make between short term benefitsand long term stability. Self-admitted technical debt (SATD), a variant of technical debt, has been proposed to identify debt that is intentionally introduced during software development, e.g., temporary fixes and workarounds. Previous studies have leveraged human-summarized patterns (which represent n-gram phrases that can be used to identify SATD) or text mining techniques to detect SATD in source code comments. However, several characteristics of SATD features in code comments, such as vocabulary diversity, project uniqueness, length and semantic variations, pose a big challenge to the accuracy …
Characterizing And Identifying Reverted Commits, Meng Yan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li
Characterizing And Identifying Reverted Commits, Meng Yan, Xin Xia, David Lo, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
In practice, a popular and coarse-grained approach for recovering from a problematic commit is to revert it (i.e., undoing the change). However, reverted commits could induce some issues for software development, such as impeding the development progress and increasing the difficulty for maintenance. In order to mitigate these issues, we set out to explore the following central question: can we characterize and identify which commits will be reverted? In this paper, we characterize commits using 27 commit features and build an identification model to identify commits that will be reverted. We first identify reverted commits by analyzing commit messages and …
Distributed Gibbs: A Linear-Space Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Roie Zivan
Distributed Gibbs: A Linear-Space Sampling-Based Dcop Algorithm, Duc Thien Nguyen, William Yeoh, Hoong Chuin Lau, Roie Zivan
Research Collection School Of Computing and Information Systems
Researchers have used distributed constraint optimization problems (DCOPs) to model various multi-agent coordination and resource allocation problems. Very recently, Ottens et al. proposed a promising new approach to solve DCOPs that is based on confidence bounds via their Distributed UCT (DUCT) sampling-based algorithm. Unfortunately, its memory requirement per agent is exponential in the number of agents in the problem, which prohibits it from scaling up to large problems. Thus, in this article, we introduce two new sampling-based DCOP algorithms called Sequential Distributed Gibbs (SD-Gibbs) and Parallel Distributed Gibbs (PD-Gibbs). Both algorithms have memory requirements per agent that is linear in …
Ict: In-Field Calibration Transfer For Air Quality Sensor Deployments, Yun Cheng, Xiaoxi He, Zimu Zhou, Lothar Thiele
Ict: In-Field Calibration Transfer For Air Quality Sensor Deployments, Yun Cheng, Xiaoxi He, Zimu Zhou, Lothar Thiele
Research Collection School Of Computing and Information Systems
Recent years have witnessed a growing interest in urban air pollution monitoring, where hundreds of low-cost air quality sensors are deployed city-wide. To guarantee data accuracy and consistency, these sensors need periodic calibration after deployment. Since access to ground truth references is often limited in large-scale deployments, it is difficult to conduct city-wide post-deployment sensor calibration. In this work we propose In-field Calibration Transfer (ICT), a calibration scheme that transfers the calibration parameters of source sensors (with access to references) to target sensors (without access to references). On observing that (i) the distributions of ground truth in both source and …
Making Wearable Sensing Less Obtrusive, Huy Vu Tran, Archan Misra
Making Wearable Sensing Less Obtrusive, Huy Vu Tran, Archan Misra
Research Collection School Of Computing and Information Systems
Sensing is a crucial part of any cyber-physical system. Wearable device has its huge potential for sensing applications because it is worn on the user body. However, wearable sensing can cause obtrusiveness to the user. Obtrusiveness can be seen as a perception of a lack of usefulness [1] such as a lag in user interaction channel. In addition, being worn by a user, it is not connected to a power supply, and thus needs to be removed to be charged regularly. This can cause a nuisance to elderly or disabled people. However, there are also opportunities for wearable devices to …
Design And Assessment Of Myoelectric Games For Prosthesis Training Of Upper Limb Amputees, Meeralakshmi Radhakrishnan, Asim Smailagic, Brian French, Daniel P. Siewiorek, Rajesh Krishna Balan
Design And Assessment Of Myoelectric Games For Prosthesis Training Of Upper Limb Amputees, Meeralakshmi Radhakrishnan, Asim Smailagic, Brian French, Daniel P. Siewiorek, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
In this paper, we present the design and evaluation of our system, which provides an engaging game-based pre-prosthesis training environment for upper limb transradial amputees. We believe that patients who train using such a training tool will demonstrate significantly higher improvement in functional performance tests using a myoelectric prosthesis than when conventional pre-prosthesis training protocols are used. We re-designed two simple games to be playable using three muscle contractions which are appropriate to pre-prosthesis exercises and are detected by an EMG-based arm sleeve. Through user studies conducted with 16 non-amputee subjects, we show that the proposed games are enjoyable, fun …
A Sampling Approach For Proactive Project Scheduling Under Generalized Time-Dependent Workability Uncertainty, Wen Song, Donghun Kang, Jie Zhang, Zhiguang Cao, Hui Xi
A Sampling Approach For Proactive Project Scheduling Under Generalized Time-Dependent Workability Uncertainty, Wen Song, Donghun Kang, Jie Zhang, Zhiguang Cao, Hui Xi
Research Collection School Of Computing and Information Systems
In real-world project scheduling applications, activity durations are often uncertain. Proactive scheduling can effectively cope with the duration uncertainties, by generating robust baseline solutions according to a priori stochastic knowledge. However, most of the existing proactive approaches assume that the duration uncertainty of an activity is not related to its scheduled start time, which may not hold in many real-world scenarios. In this paper, we relax this assumption by allowing the duration uncertainty to be time-dependent, which is caused by the uncertainty of whether the activity can be executed on each time slot. We propose a stochastic optimization model to …
Multi-Task Learning With Multi-View Attention For Answer Selection And Knowledge Base Question Answering, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, Ying Shen
Multi-Task Learning With Multi-View Attention For Answer Selection And Knowledge Base Question Answering, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Nan Du, Wei Fan, Kai Lei, Ying Shen
Research Collection School Of Computing and Information Systems
Answer selection and knowledge base question answering (KBQA) are two important tasks of question answering (QA) systems. Existing methods solve these two tasks separately, which requires large number of repetitive work and neglects the rich correlation information between tasks. In this paper, we tackle answer selection and KBQA tasks simultaneously via multi-task learning (MTL), motivated by the following motivations. First, both answer selection and KBQA can be regarded as a ranking problem, with one at text-level while the other at knowledge-level. Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), …
An Attribute-Based Framework For Secure Communications In Vehicular Ad Hoc Networks, Hui Cui, Robert H. Deng, Guilin Wang
An Attribute-Based Framework For Secure Communications In Vehicular Ad Hoc Networks, Hui Cui, Robert H. Deng, Guilin Wang
Research Collection School Of Computing and Information Systems
In this paper, we introduce an attribute-based framework to achieve secure communications in vehicular ad hoc networks (VANETs), which enjoys several advantageous features. The proposed framework employs attribute-based signature (ABS) to achieve message authentication and integrity and protect vehicle privacy, which greatly mitigates the overhead caused by pseudonym/private key change or update in the existing solutions for VANETs based on symmetric key, asymmetric key, and identity-based cryptography and group signature. In addition, we extend a standard ABS scheme with traceability and revocation mechanisms and seamlessly integrate them into the proposed framework to support vehicle traceability and revocation by a trusted …
Robust Estimation Of Similarity Transformation For Visual Object Tracking, Yang Li, Jianke Zhu, Steven C. H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu
Robust Estimation Of Similarity Transformation For Visual Object Tracking, Yang Li, Jianke Zhu, Steven C. H. Hoi, Wenjie Song, Zhefeng Wang, Hantang Liu
Research Collection School Of Computing and Information Systems
Most of existing correlation filter-based tracking approaches only estimate simple axis-aligned bounding boxes, and very few of them is capable of recovering the underlying similarity transformation. To tackle this challenging problem, in this paper, we propose a new correlation filter-based tracker with a novel robust estimation of similarity transformation on the large displacements. In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result. Specifically, we employ an efficient phase correlation scheme to deal with both scale …
Security Analysis Of A Large-Scale Concurrent Data Anonymous Batch Verification Scheme For Mobile Healthcare Crowd Sensing, Yinghui Zhang, Jiangang Shu, Ximeng Liu, Jin Li, Dong Zheng
Security Analysis Of A Large-Scale Concurrent Data Anonymous Batch Verification Scheme For Mobile Healthcare Crowd Sensing, Yinghui Zhang, Jiangang Shu, Ximeng Liu, Jin Li, Dong Zheng
Research Collection School Of Computing and Information Systems
As an important application of the Internet of Things (IoT) technologies, mobile healthcare crowd sensing (MHCS) still has challenging issues, such as privacy protection and efficiency. Quite recently in IEEE Internet of Things Journal (DOI: 10.1109/JIOT.2018.2828463), Liu et al. proposed a large-scale concurrent data anonymous batch verification scheme for mobile healthcare crowd sensing, claiming to provide batch authentication, non-repudiation, and anonymity. However, after a close look at the scheme, we point out that the scheme suffers two types of signature forgery attacks and hence fails to achieve the claimed security properties. In addition, a reasonable and rigorous probability analysis indicates …
Understanding Open Ports In Android Applications: Discovery, Diagnosis, And Security Assessment, Daoyuan Wu, Debin Gao, Rocky K. C. Chang, En He, Eric K. T. Cheng, Robert H. Deng
Understanding Open Ports In Android Applications: Discovery, Diagnosis, And Security Assessment, Daoyuan Wu, Debin Gao, Rocky K. C. Chang, En He, Eric K. T. Cheng, Robert H. Deng
Research Collection School Of Computing and Information Systems
Open TCP/UDP ports are traditionally used by servers to provide application services, but they are also found in many Android apps. In this paper, we present the first open-port analysis pipeline, covering the discovery, diagnosis, and security assessment, to systematically understand open ports in Android apps and their threats. We design and deploy a novel on-device crowdsourcing app and its server-side analytic engine to continuously monitor open ports in the wild. Over a period of ten months, we have collected over 40 million port monitoring records from 3,293 users in 136 countries worldwide, which allow us to observe the actual …
Bilateral Dependency Neural Networks For Cross-Language Algorithm Classification, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Bilateral Dependency Neural Networks For Cross-Language Algorithm Classification, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Algorithm classification is to automatically identify the classes of a program based on the algorithm(s) and/or data structure(s) implemented in the program. It can be useful for various tasks, such as code reuse, code theft detection, and malware detection. Code similarity metrics, on the basis of features extracted from syntax and semantics, have been used to classify programs. Such features, however, often need manual selection effort and are specific to individual programming languages, limiting the classifiers to programs in the same language.To recognise the similarities and differences among algorithms implemented in different languages, this paper describes a framework of Bilateral …
Gamification Of Enterprise Systems, Fiona Fui-Hoon Nah, B. Eschenbrenner, C. Claybaugh, P. Koob
Gamification Of Enterprise Systems, Fiona Fui-Hoon Nah, B. Eschenbrenner, C. Claybaugh, P. Koob
Research Collection School Of Computing and Information Systems
Enterprise systems have become an integral part of an organization’s operations. However, they also pose many challenges to organizations from the perspective of implementation, user training, as well as use and acceptance. Without effective usage, enterprise systems may not be able to provide the strategic or competitive advantages that organizations desire. Therefore, organizations mayconsider gamification to enhance training, acceptance, and usage. We discuss the various ways in whichenterprise system challenges can be addressed through the lens of gamification and present a frameworkforgamificationofenterprisesystems. Theframeworkiscomprisedofbasicprinciplesand key design elements of gamification, as well as their application to enterprise systems. The specific principles of …
Unveiling Exception Handling Guidelines Adopted By Java Developers, Hugo Melo, Roberta Coelho, Christoph Treude
Unveiling Exception Handling Guidelines Adopted By Java Developers, Hugo Melo, Roberta Coelho, Christoph Treude
Research Collection School Of Computing and Information Systems
Despite being an old language feature, Java exception handling code is one of the least understood parts of many systems. Several studies have analyzed the characteristics of exception handling code, trying to identify common practices or even link such practices to software bugs. Few works, however, have investigated exception handling issues from the point of view of developers. None of the works have focused on discovering exception handling guidelines adopted by current systems - which are likely to be a driver of common practices. In this work, we conducted a qualitative study based on semi-structured interviews and a survey whose …
Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin
Stock Market Prediction Analysis By Incorporating Social And News Opinion And Sentiment, Zhaoxia Wang, Seng-Beng Ho, Zhiping Lin
Research Collection School Of Computing and Information Systems
The price of the stocks is an important indicator for a company and many factors can affect their values. Different events may affect public sentiments and emotions differently, which may have an effect on the trend of stock market prices. Because of dependency on various factors, the stock prices are not static, but are instead dynamic, highly noisy and nonlinear time series data. Due to its great learning capability for solving the nonlinear time series prediction problems, machine learning has been applied to this research area. Learning-based methods for stock price prediction are very popular and a lot of enhanced …
Adaptive Cost-Sensitive Online Classification, Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang
Adaptive Cost-Sensitive Online Classification, Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang
Research Collection School Of Computing and Information Systems
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered first-order information of data stream. It is insufficient in practice, since many recent studies have proved that incorporating second-order information enhances the prediction performance of classification models. Thus, we propose a family of cost-sensitive online classification algorithms with adaptive regularization in this paper. We theoretically analyze the proposed algorithms and empirically validate their effectiveness and properties in extensive experiments. Then, …
Automatic Code Review By Learning The Revision Of Source Code, Shu-Ting Shi, Ming Li, David Lo, Ferdian Thung, Xuan Huo
Automatic Code Review By Learning The Revision Of Source Code, Shu-Ting Shi, Ming Li, David Lo, Ferdian Thung, Xuan Huo
Research Collection School Of Computing and Information Systems
Code review is the process of manual inspection on the revision of the source code in order to find out whether the revised source code eventually meets the revision requirements. However, manual code review is time-consuming, and automating such the code review process will alleviate the burden of code reviewers and speed up the software maintenance process. To construct the model for automatic code review, the characteristics of the revisions of source code (i.e., the difference between the two pieces of source code) should be properly captured and modeled. Unfortunately, most of the existing techniques can easily model the overall …
Transnfcm: Translation-Based Neural Fashion Compatibility Modeling, Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, Tat-Seng Chua
Transnfcm: Translation-Based Neural Fashion Compatibility Modeling, Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of fashion items, this task is still underexplored. Inspired by the recent advances in multi-relational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner. TransNFCM places items …
Multi-Authority Attribute-Based Keyword Search Over Encrypted Cloud Data, Yibin Miao, Robert H. Deng, Ximeng Liu, Kim-Kwang Raymond. Choo, Hongjun Wu, Hongwei Li
Multi-Authority Attribute-Based Keyword Search Over Encrypted Cloud Data, Yibin Miao, Robert H. Deng, Ximeng Liu, Kim-Kwang Raymond. Choo, Hongjun Wu, Hongwei Li
Research Collection School Of Computing and Information Systems
Searchable Encryption (SE) is an important technique to guarantee data security and usability in the cloud at the same time. Leveraging Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) scheme can achieve keyword-based retrieval and fine-grained access control simultaneously. However, the single attribute authority in existing CP-ABKS schemes is tasked with costly user certificate verification and secret key distribution. In addition, this results in a single-point performance bottleneck in distributed cloud systems. Thus, in this paper, we present a secure Multi-authority CP-ABKS (MABKS) system to address such limitations and minimize the computation and storage burden on resource-limited devices …
Task Relation Networks, Jianshu Li, Pan Zhou, Yunpeng Chen, Jian Zhao, Sujoy Roy, Shuicheng Yan, Jiashi Feng, Terence Sim
Task Relation Networks, Jianshu Li, Pan Zhou, Yunpeng Chen, Jian Zhao, Sujoy Roy, Shuicheng Yan, Jiashi Feng, Terence Sim
Research Collection School Of Computing and Information Systems
Multi-task learning is popular in machine learning and computer vision. In multitask learning, properly modeling task relations is important for boosting the performance of jointly learned tasks. Task covariance modeling has been successfully used to model the relations of tasks but is limited to homogeneous multi-task learning. In this paper, we propose a feature based task relation modeling approach, suitable for both homogeneous and heterogeneous multi-task learning. First, we propose a new metric to quantify the relations between tasks. Based on the quantitative metric, we then develop the task relation layer, which can be combined with any deep learning architecture …
Who Should Be Invited To My Party: A Size-Constrained K-Core Problem In Social Networks, Yu-Liang Ma, Ye Yuan, Feida Zhu, Guo-Ren Wang, Jing Xiao, Jian-Zong Wang
Who Should Be Invited To My Party: A Size-Constrained K-Core Problem In Social Networks, Yu-Liang Ma, Ye Yuan, Feida Zhu, Guo-Ren Wang, Jing Xiao, Jian-Zong Wang
Research Collection School Of Computing and Information Systems
In this paper, we investigate the problem of a size-constrained k-core group query (SCCGQ) in social networks, taking both user closeness and network topology into consideration. More specifically, SCCGQ intends to find a group of h users that has the highest social closeness while being a k-core. SCCGQ can be widely applied to event planning, task assignment, social analysis, and many other fields. In contrast to existing work on the k-core detection problem, which aims to find a k-core in a social network, SCCGQ not only focuses on k-core detection but also takes size constraints into consideration. Although the conventional …