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- Cloud security (2)
- Software architecture (2)
- (re)delegation (1)
- Abstraction refinement (1)
- Accessibility (1)
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- Active learning (1)
- Adult learners (1)
- Adversarial networks (1)
- Africa (1)
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- Association rule mining (1)
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- Attribute-based encryption (1)
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- Co-location attacks (1)
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- Coercive attack (1)
- Computer architecture (1)
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- Constant size (1)
- Criteria-based encryption (1)
- Customer segmentation (1)
- Cyber-physical system (1)
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Articles 1 - 30 of 40
Full-Text Articles in Physical Sciences and Mathematics
Deep Unsupervised Pixelization, Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie Tan, Guoqiang Han, Tien-Tsin Wong
Deep Unsupervised Pixelization, Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie Tan, Guoqiang Han, Tien-Tsin Wong
Research Collection School Of Computing and Information Systems
In this paper, we present a novel unsupervised learning method for pixelization. Due to the difficulty in creating pixel art, preparing the paired training data for supervised learning is impractical. Instead, we propose an unsupervised learning framework to circumvent such difficulty. We leverage the dual nature of the pixelization and depixelization, and model these two tasks in the same network in a bi-directional manner with the input itself as training supervision. These two tasks are modeled as a cascaded network which consists of three stages for different purposes. GridNet transfers the input image into multi-scale grid-structured images with different aliasing …
Automatically `Verifying’ Discrete-Time Complex Systems Through Learning, Abstraction And Refinement, Jingyi Wang, Jun Sun, Shengchao Qin, Cyrille Jegourel
Automatically `Verifying’ Discrete-Time Complex Systems Through Learning, Abstraction And Refinement, Jingyi Wang, Jun Sun, Shengchao Qin, Cyrille Jegourel
Research Collection School Of Computing and Information Systems
Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to automatically ‘verify’ such complex systems through a combination of learning, abstraction and refinement from a set of system log traces. We assume that log traces and sampling frequency are adequate to capture ‘enough’ behaviour of the system. Given a safety property and the concrete system log traces as input, LAR automatically learns and refines system models, and produces two kinds of outputs. One is a counterexample with a bounded …
Co-Location Resistant Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta
Co-Location Resistant Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
Due to increasing number of avenues for conducting cross-virtual machine (VM) side-channel attacks, the security of public IaaS cloud data centers is a growing concern. These attacks allow an adversary to steal private information from a target user whose VM instance is co-located with that of the adversary. To reduce the probability of malicious co-location, we propose a novel VM placement algorithm called “Previously Co-Located Users First”. We perform a theoretical and empirical analysis of our proposed algorithm to evaluate its resource efficiency and security. Our results, obtained using real-world cloud traces containing millions of VM requests and thousands of …
Early Prediction Of Merged Code Changes To Prioritize Reviewing Tasks, Yuanrui Fan, Xin Xia, David Lo, Shanping Li
Early Prediction Of Merged Code Changes To Prioritize Reviewing Tasks, Yuanrui Fan, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Modern Code Review (MCR) has been widely used by open source and proprietary software projects. Inspecting code changes consumes reviewers much time and effort since they need to comprehend patches, and many reviewers are often assigned to review many code changes. Note that a code change might be eventually abandoned, which causes waste of time and effort. Thus, a tool that predicts early on whether a code change will be merged can help developers prioritize changes to inspect, accomplish more things given tight schedule, and not waste reviewing effort on low quality changes. In this paper, motivated by the above …
Delta Debugging Microservice Systems, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Wenhai Li, Chao Ji, Dan Ding
Delta Debugging Microservice Systems, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Wenhai Li, Chao Ji, Dan Ding
Research Collection School Of Computing and Information Systems
Debugging microservice systems involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, in this paper, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimize failureinducing deltas of circumstances (e.g., deployment, environmental configurations) for effective debugging. Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas following the idea of delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimize, our approach defines a set of …
Is There Space For Violence?: A Data-Driven Approach To The Exploration Of Spatial-Temporal Dimensions Of Conflict, Tin Seong Kam, Vincent Zhi
Is There Space For Violence?: A Data-Driven Approach To The Exploration Of Spatial-Temporal Dimensions Of Conflict, Tin Seong Kam, Vincent Zhi
Research Collection School Of Computing and Information Systems
With recent increases in incidences of political violence globally, the world has now become more uncertain and less predictable. Of particular concern is the case of violence against civilians, who are often caught in the crossfire between armed state or non-state actors. Classical methods of studying political violence and international relations need to be updated. Adopting the use of data analytic tools and techniques of studying big data would enable academics and policy makers to make sense of a rapidly changing world.
An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings, Di Wang, Xiaolin Qian, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Xiaofeng Zhang, Geok See Ng, You Zhou
An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings, Di Wang, Xiaolin Qian, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Xiaofeng Zhang, Geok See Ng, You Zhou
Research Collection School Of Computing and Information Systems
Due to their aptitude in both accurate data processing and human comprehensible reasoning, neural fuzzy inference systems have been widely adopted in various application domains as decision support systems. Especially in real-world scenarios such as decision making in financial transactions, the human experts may be more interested in knowing the comprehensive reasons of certain advices provided by a decision support system in addition to how confident the system is on such advices. In this paper, we apply an integrated autonomous computational model termed genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS) to predict underpricing in initial public …
Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua
Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …
Efficient Attribute-Based Encryption With Blackbox Traceability, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu
Efficient Attribute-Based Encryption With Blackbox Traceability, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu
Research Collection School Of Computing and Information Systems
Traitor tracing scheme can be used to identify a decryption key is illegally used in public-key encryption. In CCS’13, Liu et al. proposed an attribute-based traitor tracing (ABTT) scheme with blackbox traceability which can trace decryption keys embedded in a decryption blackbox/device rather than tracing a well-formed decryption key. However, the existing ABTT schemes with blackbox traceability are based on composite order group and the size of the decryption key depends on the policies and the number of system users. In this paper, we revisit blackbox ABTT and introduce a new primitive called attribute-based set encryption (ABSE) based on key-policy …
Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon
Augmenting And Structuring User Queries To Support Efficient Free-Form Code Search, Raphael Sirres, Tegawendé F. Bissyande, Dongsun Kim, David Lo, Jacques Klein, Kisub Kim, Yves Le Traon
Research Collection School Of Computing and Information Systems
Source code terms such as method names and variable types are often different from conceptual words mentioned in a search query. This vocabulary mismatch problem can make code search inefficient. In this paper, we present COde voCABUlary (CoCaBu), an approach to resolving the vocabulary mismatch problem when dealing with free-form code search queries. Our approach leverages common developer questions and the associated expert answers to augment user queries with the relevant, but missing, structural code entities in order to improve the performance of matching relevant code examples within large code repositories. To instantiate this approach, we build GitSearch, a code …
Simknn: A Scalable Method For In-Memory Knn Search Over Moving Objects In Road Networks, Bin Cao, Chenyu Hou, Suifei Li, Jing Fan, Jianwei Yin, Baihua Zheng, Jie Bao
Simknn: A Scalable Method For In-Memory Knn Search Over Moving Objects In Road Networks, Bin Cao, Chenyu Hou, Suifei Li, Jing Fan, Jianwei Yin, Baihua Zheng, Jie Bao
Research Collection School Of Computing and Information Systems
Nowadays, many location-based applications require the ability of querying k-nearest neighbors over a very large scale of5 moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt6 to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split7 into more smaller cells which introduces the side-effect of higher memory cost, i.e., maintaining such a large volume of cells requires a8 much larger memory space at the server side. In this paper, we present SIMkNN, a scalable and in-memory …
Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan
Exploring Experiential Learning Model And Risk Management Process For An Undergraduate Software Architecture Course, Eng Lieh Ouh, Yunghans Irawan
Research Collection School Of Computing and Information Systems
This paper shares our insights on exploring theexperiential learning model and risk management process todesign an undergraduate software architecture course. The keychallenge for undergraduate students to appreciate softwarearchitecture design is usually their limited experience in thesoftware industry. In software architecture, the high-level designprinciples are heuristics lacking the absoluteness of firstprinciples which for inexperienced undergraduate students, thisis a frustrating divergence from what they used to value. From aneducator's perspective, teaching software architecture requirescontending with the problem of how to express this level ofabstraction practically and also make the learning realistic. Inthis paper, we propose a model adapting the concepts ofexperiential learning …
Teaching Adult Learners On Software Architecture Design Skills, Eng Lieh Ouh, Yunghans Irawan
Teaching Adult Learners On Software Architecture Design Skills, Eng Lieh Ouh, Yunghans Irawan
Research Collection School Of Computing and Information Systems
Software architectures present high-level views ofsystems, enabling developers to abstract away the unnecessarydetails and focus on the overall big picture. Designing a softwarearchitecture is an essential skill in software engineering and adultlearners are seeking this skill to further progress in their career.With the technology revolution and advancements in this rapidlychanging world, the proportion of adult learners attendingcourses for continuing education are increasing. Their learningobjectives are no longer to obtain good grades but the practicalskills to enable them to perform better in their work and advancein their career. Teaching software architecture to upskill theseadult learners requires contending with the problem of …
Visforum: A Visual Analysis System For Exploring User Groups In Online Forums, Siwei Fu, Yong Wang, Yi Yang, Qingqing Bi, Fangzhou Guo, Huamin Qu
Visforum: A Visual Analysis System For Exploring User Groups In Online Forums, Siwei Fu, Yong Wang, Yi Yang, Qingqing Bi, Fangzhou Guo, Huamin Qu
Research Collection School Of Computing and Information Systems
User grouping in asynchronous online forums is a common phenomenon nowadays. People with similar backgrounds or shared interests like to get together in group discussions. As tens of thousands of archived conversational posts accumulate, challenges emerge for forum administrators and analysts to effectively explore user groups in large-volume threads and gain meaningful insights into the hierarchical discussions. Identifying and comparing groups in discussion threads are nontrivial, since the number of users and posts increases with time and noises may hamper the detection of user groups. Researchers in data mining fields have proposed a large body of algorithms to explore user …
Pfix: Fixing Concurrency Bugs Based On Memory Access Patterns, Huarui Lin, Zan Wang, Shuang Liu, Jun Sun, Dongdi Zhang, Guangning Wei
Pfix: Fixing Concurrency Bugs Based On Memory Access Patterns, Huarui Lin, Zan Wang, Shuang Liu, Jun Sun, Dongdi Zhang, Guangning Wei
Research Collection School Of Computing and Information Systems
Concurrency bugs of a multi-threaded program may only manifest with certain scheduling, i.e., they are heisenbugs which are observed only from time to time if we execute the same program with the same input multiple times. They are notoriously hard to fix. In this work, we propose an approach to automatically fix concurrency bugs. Compared to previous approaches, our key idea is to systematically fix concurrency bugs by inferring locking policies from failure inducing memory-access patterns. That is, we automatically identify memory-access patterns which are correlated with the manifestation of the bug, and then conjecture what is the intended locking …
Blockchain Based Efficient And Robust Fair Payment For Outsourcing Services In Cloud Computing, Yinghui Zhang, Robert H. Deng, Ximeng Liu, Dong Zheng
Blockchain Based Efficient And Robust Fair Payment For Outsourcing Services In Cloud Computing, Yinghui Zhang, Robert H. Deng, Ximeng Liu, Dong Zheng
Research Collection School Of Computing and Information Systems
As an attractive business model of cloud computing, outsourcing services usually involve online payment and security issues. The mutual distrust between users and outsourcing service providers may severely impede the wide adoption of cloud computing. Nevertheless, most existing payment solutions only consider a specific type of outsourcing service and rely on a trusted third-party to realize fairness. In this paper, in order to realize secure and fair payment of outsourcing services in general without relying on any third-party, trusted or not, we introduce BCPay, a blockchain based fair payment framework for outsourcing services in cloud computing. We first present the …
Customer Segmentation Using Online Platforms: Isolating Behavioral And Demographic Segments For Persona Creation Via Aggregated User Data, Jisun An, Haewoon Kwak, Soon‑Gyo Jung, Joni Salminen, Bernard J. Jansen
Customer Segmentation Using Online Platforms: Isolating Behavioral And Demographic Segments For Persona Creation Via Aggregated User Data, Jisun An, Haewoon Kwak, Soon‑Gyo Jung, Joni Salminen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer …
Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang
Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang
Research Collection School Of Computing and Information Systems
Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently uncertain due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we introduce the uncertain top-k query (UTK). Given uncertain preferences, that is, …
Learning Representations Of Ultrahigh-Dimensional Data For Random Distance-Based Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Learning Representations Of Ultrahigh-Dimensional Data For Random Distance-Based Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
Research Collection School Of Computing and Information Systems
Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random …
A Unified Approach To Route Planning For Shared Mobility, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Jieping Ye, Ke Xu
A Unified Approach To Route Planning For Shared Mobility, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Jieping Ye, Ke Xu
Research Collection School Of Computing and Information Systems
There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In this …
Compositional Reasoning For Shared-Variable Concurrent Programs, Fuyuan Zhang, Yongwang Zhao, David Sanan, Yang Liu, Alwen Tiu, Shang-Wei Lin, Jun Sun
Compositional Reasoning For Shared-Variable Concurrent Programs, Fuyuan Zhang, Yongwang Zhao, David Sanan, Yang Liu, Alwen Tiu, Shang-Wei Lin, Jun Sun
Research Collection School Of Computing and Information Systems
Scalable and automatic formal verification for concurrent systems is always demanding. In this paper, we propose a verification framework to support automated compositional reasoning for concurrent programs with shared variables. Our framework models concurrent programs as succinct automata and supports the verification of multiple important properties. Safety verification and simulations of succinct automata are parallel compositional, and safety properties of succinct automata are preserved under refinements. We generate succinct automata from infinite state concurrent programs in an automated manner. Furthermore, we propose the first automated approach to checking rely-guarantee based simulations between infinite state concurrent programs. We have prototyped our …
Privacy-Preserving Mining Of Association Rule On Outsourced Cloud Data From Multiple Parties, Lin Liu, Jinshu Su, Rongmao Chen, Ximeng Liu, Xiaofeng Wang, Shuhui Chen, Ho-Fung Fung Leung
Privacy-Preserving Mining Of Association Rule On Outsourced Cloud Data From Multiple Parties, Lin Liu, Jinshu Su, Rongmao Chen, Ximeng Liu, Xiaofeng Wang, Shuhui Chen, Ho-Fung Fung Leung
Research Collection School Of Computing and Information Systems
It has been widely recognized as a challenge to carry out data analysis and meanwhile preserve its privacy in the cloud. In this work, we mainly focus on a well-known data analysis approach namely association rule mining. We found that the data privacy in this mining approach have not been well considered so far. To address this problem, we propose a scheme for privacy-preserving association rule mining on outsourced cloud data which are uploaded from multiple parties in a twin-cloud architecture. In particular, we mainly consider the scenario where the data owners and miners have different encryption keys that are …
Towards Optimal Concolic Testing, Xinyu Wang, Jun Sun, Zhenbang Chen, Peixin Zhang, Jingyi Wang, Yun Lin
Towards Optimal Concolic Testing, Xinyu Wang, Jun Sun, Zhenbang Chen, Peixin Zhang, Jingyi Wang, Yun Lin
Research Collection School Of Computing and Information Systems
Concolic testing integrates concrete execution (e.g., random testing) and symbolic execution for test case generation. It is shown to be more cost-effective than random testing or symbolic execution sometimes. A concolic testing strategy is a function which decides when to apply random testing or symbolic execution, and if it is the latter case, which program path to symbolically execute. Many heuristics-based strategies have been proposed. It is still an open problem what is the optimal concolic testing strategy. In this work, we make two contributions towards solving this problem. First, we show the optimal strategy can be defined based on …
Empath-D: Vr-Based Empathetic App Design For Accessibility, Wonjung Kim, Kenny Tsu Wei Choo, Youngki Lee, Archan Misra, Rajesh Krishna Balan
Empath-D: Vr-Based Empathetic App Design For Accessibility, Wonjung Kim, Kenny Tsu Wei Choo, Youngki Lee, Archan Misra, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
With app-based interaction increasingly permeating all aspects of daily living, it is essential to ensure that apps are designed to be inclusive and are usable by a wider audience such as the elderly, with various impairments (e.g., visual, audio and motor). We propose Empath-D, a system that fosters empathetic design, by allowing app designers, in-situ, to rapidly evaluate the usability of their apps, from the perspective of impaired users. To provide a truly authentic experience, Empath-D carefully orchestrates the interaction between a smartphone and a VR device, allowing the user to experience simulated impairments in a virtual world while interacting …
Dimensionality's Blessing: Clustering Images By Underlying Distribution, Wen-Yan Lin, Jian-Huang Lai, Siying Liu, Yasuyuki Matsushita
Dimensionality's Blessing: Clustering Images By Underlying Distribution, Wen-Yan Lin, Jian-Huang Lai, Siying Liu, Yasuyuki Matsushita
Research Collection School Of Computing and Information Systems
Many high dimensional vector distances tend to a constant. This is typically considered a negative “contrastloss” phenomenon that hinders clustering and other machine learning techniques. We reinterpret “contrast-loss” as a blessing. Re-deriving “contrast-loss” using the law of large numbers, we show it results in a distribution’s instances concentrating on a thin “hyper-shell”. The hollow center means apparently chaotically overlapping distributions are actually intrinsically separable. We use this to develop distribution-clustering, an elegant algorithm for grouping of data points by their (unknown) underlying distribution. Distribution-clustering, creates notably clean clusters from raw unlabeled data, estimates the number of clusters for itself and …
Breathing-Based Authentication On Resource-Constrained Iot Devices Using Recurrent Neural Networks, Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee
Breathing-Based Authentication On Resource-Constrained Iot Devices Using Recurrent Neural Networks, Jagmohan Chauhan, Suranga Seneviratne, Yining Hu, Archan Misra, Aruna Seneviratne, Youngki Lee
Research Collection School Of Computing and Information Systems
Recurrent neural networks (RNNs) have shown promising resultsin audio and speech-processing applications. The increasingpopularity of Internet of Things (IoT) devices makes a strongcase for implementing RNN-based inferences for applicationssuch as acoustics-based authentication and voice commandsfor smart homes. However, the feasibility and performance ofthese inferences on resource-constrained devices remain largelyunexplored. The authors compare traditional machine-learningmodels with deep-learning RNN models for an end-to-endauthentication system based on breathing acoustics.
Analyzing Requirements And Traceability Information To Improve Bug Localization, Michael Rath, David Lo, Patrick Mader
Analyzing Requirements And Traceability Information To Improve Bug Localization, Michael Rath, David Lo, Patrick Mader
Research Collection School Of Computing and Information Systems
Locating bugs in industry-size software systems is time consuming and challenging. An automated approach for assisting the process of tracing from bug descriptions to relevant source code benefits developers. A large body of previous work aims to address this problem and demonstrates considerable achievements. Most existing approaches focus on the key challenge of improving techniques based on textual similarity to identify relevant files. However, there exists a lexical gap between the natural language used to formulate bug reports and the formal source code and its comments. To bridge this gap, state-of-the-art approaches contain a component for analyzing bug history information …
Findings Of A User Study Of Automatically Generated Personas, Joni Salminen, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen
Findings Of A User Study Of Automatically Generated Personas, Joni Salminen, Haewoon Kwak, Jisun An, Soon-Gyo Jung, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We report findings and implications from a semi-naturalistic user study of a system for Automatic Persona Generation (APG) using large-scale audience data of an organization's social media channels conducted at the workplace of a major international corporation. Thirteen participants from a range of positions within the company engaged with the system in a use case scenario. We employed a variety of data collection methods, including mouse tracking and survey data, analyzing the data with a mixed method approach. Results show that having an interactive system may aid in keeping personas at the forefront while making customer-centric decisions and indicate that …
Criteria-Based Encryption, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo
Criteria-Based Encryption, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo
Research Collection School Of Computing and Information Systems
We present a new type of public-key encryption called Criteria-based Encryption (or , for short). Different from Attribute-based Encryption, in , we consider the access policies as criteria carrying different weights. A user must hold some cases (or answers) satisfying the criteria and have sufficient weights in order to successfully decrypt a message. We then propose two Schemes under different settings: the first scheme requires a user to have at least one case for a criterion specified by the encryptor in the access structure, while the second scheme requires a user to have all the cases for each criterion. We …
Virtualization In Wireless Sensor Networks: Fault Tolerant Embedding For Internet Of Things, Omprakash Kaiwartya, Abdul Hanan Abdullah, Yue Cao, Jaime Lloret, Sushil Kumar, Rajiv Ratn Shah, Mukesh Prasad, Shiv Prakash
Virtualization In Wireless Sensor Networks: Fault Tolerant Embedding For Internet Of Things, Omprakash Kaiwartya, Abdul Hanan Abdullah, Yue Cao, Jaime Lloret, Sushil Kumar, Rajiv Ratn Shah, Mukesh Prasad, Shiv Prakash
Research Collection School Of Computing and Information Systems
Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the growing service domain for IoT. Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of a communication link in WSNs impacts many virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. An optimization problem is formulated considering fault tolerance and communication delay as two conflicting objectives. An adapted non-dominated sorting based genetic algorithm (A-NSGA) is developed to …