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Articles 4081 - 4110 of 8481
Full-Text Articles in Computer Sciences
A Strategic Value Appropriation Path For Cloud Computing, Abhishek Kathuria, Arti Mann, Jiban Khuntia, Robert J. Kauffman
A Strategic Value Appropriation Path For Cloud Computing, Abhishek Kathuria, Arti Mann, Jiban Khuntia, Robert J. Kauffman
Research Collection School Of Computing and Information Systems
Cloud-based information management is one of the leading competitive differentiation strategies for firms. With the increasing criticality of information management in value creation and process support, establishing an integrated capability with cloud computing is vital for organizational success in the changing landscape of business competition. These issues have received scant attention, however. We draw on the resource-based view, dynamic capability hierarchy concepts, and the perspective of operand and operant resources to suggest a cloud value appropriation model for firms. We argue that, to appropriate business value from cloud computing, the firm needs to effectively deploy cloud computing and leverage cloud …
Implicit Linking Of Food Entities In Social Media, Wen Haw Chong, Ee Peng Lim
Implicit Linking Of Food Entities In Social Media, Wen Haw Chong, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Dining is an important part in people’s lives and this explains why food-related microblogs and reviews are popular in social media. Identifying food entities in food-related posts is important to food lover profiling and food (or restaurant) recommendations. In this work, we conduct Implicit Entity Linking (IEL) to link food-related posts to food entities in a knowledge base. In IEL, we link posts even if they do not contain explicit entity mentions. We first show empirically that food venues are entity-focused and associated with a limited number of food entities each. Hence same-venue posts are likely to share common food …
Talent Flow Analytics In Online Professional Network, Richard J. Oentaryo, Ee-Peng Lim, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo
Talent Flow Analytics In Online Professional Network, Richard J. Oentaryo, Ee-Peng Lim, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo
Research Collection School Of Computing and Information Systems
Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are conducted on job seekers and employers to study job hop behavior. Beyond surveys, job hop behavior can also be studied in a highly scalable and timely manner using a data-driven approach in response to fast-changing job landscape. Fortunately, the advent of online professional networks (OPNs) has made it possible to perform a large-scale analysis of talent flow. In this …
A Hybrid Model For Identity Obfuscation By Face Replacement, Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, Bernt Schiele
A Hybrid Model For Identity Obfuscation By Face Replacement, Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, Bernt Schiele
Research Collection School Of Computing and Information Systems
As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition, becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as …
Secondary Frequency Stochastic Optimal Control In Independent Microgrids With Virtual Synchronous Generator-Controlled Energy Storage Systems, Ting Yang, Yajian Zhang, Zhaoxia Wang, Haibo Pen
Secondary Frequency Stochastic Optimal Control In Independent Microgrids With Virtual Synchronous Generator-Controlled Energy Storage Systems, Ting Yang, Yajian Zhang, Zhaoxia Wang, Haibo Pen
Research Collection School Of Computing and Information Systems
With the increasing proportion of renewable energy in microgrids (MGs), its stochastic fluctuation of output power has posed challenges to system safety and operation, especially frequency stability. Virtual synchronous generator (VSG) technology, as one effectivemethod, was used to smoothen frequency fluctuation and improve the system's dynamic performance,which can simulate the inertia and damping of the traditional synchronous generator. This study outlines the integration of VSG-controlled energy storage systems (ESSs) and traditional synchronous generators so they jointly participate in secondary frequency regulation in an independent MG. Firstly, a new uncertain state-space model for secondary frequency control is established, considering the measurement …
Accurate And Cost-Effective Traffic Information Acquisition Using Adaptive Sampling: Centralized And V2v Schemes, Shiau Hong Lim, Yeow Khiang Chia, Laura Wynter
Accurate And Cost-Effective Traffic Information Acquisition Using Adaptive Sampling: Centralized And V2v Schemes, Shiau Hong Lim, Yeow Khiang Chia, Laura Wynter
Research Collection School Of Computing and Information Systems
The new generation of GPS-based tolling systems allow for a much higher degree of road sensing than has been available up to now. We propose an adaptive sampling scheme to collect accurate real-time traffic information from large-scale implementations of on-board GPS-based devices over a road network. The goal of the system is to minimize the transmission costs over all vehicles while satisfying requirements in the accuracy and timeliness of the traffic information obtained. The system is designed to make use of cellular communication as well as leveraging additional technologies such as roadside units equipped with WiFi and vehicle-to-vehicle (V2V) dedicated …
Challenges In Learning Uml: From The Perspective Of Diagrammatic Representation And Reasoning, Z. Shen, S. Tan, Keng Siau
Challenges In Learning Uml: From The Perspective Of Diagrammatic Representation And Reasoning, Z. Shen, S. Tan, Keng Siau
Research Collection School Of Computing and Information Systems
Unified modeling language (UML) is widely taught in the information systems (IS) curriculum. To understand UML in IS education, this paper reports on an empirical study that taps into students’ learning of UML. The study uses a concept-mapping technique to identify the challenges in learning UML notational elements. It reveals that some technical properties of UML diagrammatic representation, coupled with students’ cognitive attributes, hinder both perceptual and conceptual processes involved in searching, recognizing, and inferring visual information, which creates learning barriers. This paper also discusses how to facilitate perceptual and conceptual processes in instruction to overcome learning challenges. The study …
A Two-Stage Mechanism For Ordinal Peer Assessment, Zhize Li, Le Zhang, Zhixuan Fang, Jian Li
A Two-Stage Mechanism For Ordinal Peer Assessment, Zhize Li, Le Zhang, Zhixuan Fang, Jian Li
Research Collection School Of Computing and Information Systems
Peer assessment is a major method for evaluating the performance of employee, accessing the contributions of individuals within a group, making social decisions and many other scenarios. The idea is to ask the individuals of the same group to assess the performance of the others. Scores or rankings are then determined based on these evaluations. However, peer assessment can be biased and manipulated, especially when there is a conflict of interests. In this paper, we consider the problem of eliciting the underlying ordering (i.e. ground truth) of n strategic agents with respect to their performances, e.g., quality of work, contributions, …
Welcome Message From The Dysdoc3 2018 Chairs, Martin P. Robillard, Andrian Marcus, Christoph Treude, Michele Lanza
Welcome Message From The Dysdoc3 2018 Chairs, Martin P. Robillard, Andrian Marcus, Christoph Treude, Michele Lanza
Research Collection School Of Computing and Information Systems
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
A Vector Field Design Approach To Animated Transitions, Yong Wang, Daniel Archambault, Carlos E. Scheidegger, Huamin Qu
A Vector Field Design Approach To Animated Transitions, Yong Wang, Daniel Archambault, Carlos E. Scheidegger, Huamin Qu
Research Collection School Of Computing and Information Systems
Animated transitions can be effective in explaining and exploring a small number of visualizations where there are drastic changes in the scene over a short interval of time. This is especially true if data elements cannot be visually distinguished by other means. Current research in animated transitions has mainly focused on linear transitions (all elements follow straight line paths) or enhancing coordinated motion through bundling of linear trajectories. In this paper, we introduce animated transition design, a technique to build smooth, non-linear transitions for clustered data with either minimal or no user involvement. The technique is flexible and simple to …
Cognitive Antecedents Of Family Business Bias In Investment Decisions: A Commentary On 'Risky Decisions And The Family Firm Bias: An Experimental Study Based On Prospect Theory, H. Fang, Keng Siau, E. Memili, J. Dou
Cognitive Antecedents Of Family Business Bias In Investment Decisions: A Commentary On 'Risky Decisions And The Family Firm Bias: An Experimental Study Based On Prospect Theory, H. Fang, Keng Siau, E. Memili, J. Dou
Research Collection School Of Computing and Information Systems
Lude and Prügl explored “family business bias,” a cognitive tendency where the family nature of a firm can often reduce investors’ perceived risk in investments. As a result, investors would display lower risk-avoidance in the gain domain and reinforced risk-seeking in the loss domain. We expanded the authors’ work by introducing four cognitive factors (anchoring, representativeness, stereotype heuristic, and information availability) that can explain the underlying mechanisms behind the prevalence of “family business bias” and other cognitive misperceptions surrounding family businesses when it comes to investment decisions.
Transferring Time-Series Discrete Choice To Link-Based Route Choice In Space: Estimating Vehicle Type Preference Using Recursive Logit Model, Fabian Bastin, Yan Liu, Cinzia Cirillo, Tien Mai
Transferring Time-Series Discrete Choice To Link-Based Route Choice In Space: Estimating Vehicle Type Preference Using Recursive Logit Model, Fabian Bastin, Yan Liu, Cinzia Cirillo, Tien Mai
Research Collection School Of Computing and Information Systems
This paper considers a sequential discrete choice problem in a time domain, formulated and solved as a route choice problem in a space domain. Starting from a dynamic specification of time-series discrete choices, we show how it is transferrable to link-based route choices that can be formulated by a finite path choice multinomial logit model. This study establishes that modeling sequential choices over time and in space are equivalent as long as the utility of the choice sequence is additive over the decision steps, the link-specific attributes are deterministic, and the decision process is Markovian. We employ the recursive logit …
Anonymous Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Xiaohua Jia, Kan Yang, Robert H. Deng
Anonymous Privacy-Preserving Task Matching In Crowdsourcing, Jiangang Shu, Ximeng Liu, Xiaohua Jia, Kan Yang, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the development of sharing economy, crowdsourcing as a distributed computing paradigm has become increasingly pervasive. As one of indispensable services for most crowdsourcing applications, task matching has also been extensively explored. However, privacy issues are usually ignored during the task matching and few existing privacy-preserving crowdsourcing mechanisms can simultaneously protect both task privacy and worker privacy. This paper systematically analyzes the privacy leaks and potential threats in the task matching and proposes a single-keyword task matching scheme for the multirequester/multiworker crowdsourcing with efficient worker revocation. The proposed scheme not only protects data confidentiality and identity anonymity against the crowd-server, …
Deep Learning For Practical Image Recognition: Case Study On Kaggle Competitions, Xulei Yang, Zeng Zeng, Sin G. Teo, Li Wang, Vijay Chandrasekar, Steven C. H. Hoi
Deep Learning For Practical Image Recognition: Case Study On Kaggle Competitions, Xulei Yang, Zeng Zeng, Sin G. Teo, Li Wang, Vijay Chandrasekar, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In past years, deep convolutional neural networks (DCNN) have achieved big successes in image classification and object detection, as demonstrated on ImageNet in academic field. However, There are some unique practical challenges remain for real-world image recognition applications, e.g., small size of the objects, imbalanced data distributions, limited labeled data samples, etc. In this work, we are making efforts to deal with these challenges through a computational framework by incorporating latest developments in deep learning. In terms of two-stage detection scheme, pseudo labeling, data augmentation, cross-validation and ensemble learning, the proposed framework aims to achieve better performances for practical image …
Iterated Local Search Algorithm For The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau
Iterated Local Search Algorithm For The Capacitated Team Orienteering Problem, Aldy Gunawan, Kien Ming Ng, Vincent F. Yu, Gordy Adiprasetyo, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
This paper focuses on a recent variant of the Orienteering Problem (OP), namely the Capacitated Team Orienteering Problem (CTOP). In this problem, each node is associated with a demand that needs to be satisfied and a score that need to be collected. Given a set of homogeneous fleet of vehicles, the main objective is to find a path for each available vehicle in order to maximize the total score, without violating the capacity and time budget of each vehicle. We propose an Iterated Local Search algorithm that has been applied in solving various variants of the OP. We propose two …
Organizational Change In The Artificial Intelligence Age – The Case Of Marketing And Sales, Yin Yang, Keng Siau
Organizational Change In The Artificial Intelligence Age – The Case Of Marketing And Sales, Yin Yang, Keng Siau
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI), robotics, machine learning, and automation are revolutionizing the field of marketing and sales. Although the field of sales and marketing has been impacted by advanced technologies continuously, the impact of AI on sales and marketing field is expected to be transformational and revolutionary. The main threats pose by AI are job losses and upheaval of the society as a result (Siau, 2018; Siau and Wang, 2018). A report published in February 2016 by Citibank in partnership with the University of Oxford predicted that 47% of US jobs are at risk of automation. In UK, it is 35%. …
Sentence Compression With Reinforcement Learning, Liangguo Wang, Jing Jiang, Lejian Liao
Sentence Compression With Reinforcement Learning, Liangguo Wang, Jing Jiang, Lejian Liao
Research Collection School Of Computing and Information Systems
Deletion-based sentence compression is frequently formulated as a constrained optimization problem and solved by integer linear programming (ILP). However, ILP methods searching the best compression given the space of all possible compressions would be intractable when dealing with overly long sentences and too many constraints. Moreover, the hard constraints of ILP would restrict the available solutions. This problem could be even more severe considering parsing errors. As an alternative solution, we formulate this task in a reinforcement learning framework, where hard constraints are used as rewards in a soft manner. The experiment results show that our method achieves competitive performance …
Demand-Aware Charger Planning For Electric Vehicle Sharing, Bowen Du, Yongxin Tong, Zimu Zhou, Qian Tao, Wenjun Zhou
Demand-Aware Charger Planning For Electric Vehicle Sharing, Bowen Du, Yongxin Tong, Zimu Zhou, Qian Tao, Wenjun Zhou
Research Collection School Of Computing and Information Systems
Cars of the future have been predicted as shared and electric. There has been a rapid growth in electric vehicle (EV) sharing services worldwide in recent years. For EV-sharing platforms to excel, it is essential for them to offer private charging infrastructure for exclusive use that meets the charging demand of their clients. Particularly, they need to plan not only the places to build charging stations, but also the amounts of chargers per station, to maximally satisfy the requirements on global charging coverage and local charging demand. Existing research efforts are either inapplicable for their different problem formulations or are …
Fusing Multi-Abstraction Vector Space Models For Concern Localization, Yun Zhang, David Lo, Xin Xia, Giuseppe Scanniello, Tien-Duy B. Le, Jianling Sun
Fusing Multi-Abstraction Vector Space Models For Concern Localization, Yun Zhang, David Lo, Xin Xia, Giuseppe Scanniello, Tien-Duy B. Le, Jianling Sun
Research Collection School Of Computing and Information Systems
Concern localization refers to the process of locating code units that match a particular textual description. It takes as input textual documents such as bug reports and feature requests and outputs a list of candidate code units that are relevant to the bug reports or feature requests. Many information retrieval (IR) based concern localization techniques have been proposed in the literature. These techniques typically represent code units and textual descriptions as a bag of tokens at one level of abstraction, e.g., each token is a word, or each token is a topic. In this work, we propose a multi-abstraction concern …
Probabilistic Collaborative Representation Learning For Personalized Item Recommendation, Aghiles Salah, Hady W. Lauw
Probabilistic Collaborative Representation Learning For Personalized Item Recommendation, Aghiles Salah, Hady W. Lauw
Research Collection School Of Computing and Information Systems
We present Probabilistic Collaborative Representation Learning (PCRL), a new generative model of user preferences and item contexts. The latter builds on the assumption that relationships among items within contexts (e.g., browsing session, shopping cart, etc.) may underlie various aspects that guide the choices people make. Intuitively, PCRL seeks representations of items reflecting various regularities between them that might be useful at explaining user preferences. Formally, it relies on Bayesian Poisson Factorization to model user-item interactions, and uses a multilayered latent variable architecture to learn representations of items from their contexts. PCRL seamlessly integrates both tasks within a joint framework. However, …
A Formal Specification And Verification Framework For Timed Security Protocols, Li Li, Jun Sun, Yang Liu, Meng Sun, Jin Song Dong
A Formal Specification And Verification Framework For Timed Security Protocols, Li Li, Jun Sun, Yang Liu, Meng Sun, Jin Song Dong
Research Collection School Of Computing and Information Systems
Nowadays, protocols often use time to provide better security. For instance, critical credentials are often associated with expiry dates in system designs. However, using time correctly in protocol design is challenging, due to the lack of time related formal specification and verification techniques. Thus, we propose a comprehensive analysis framework to formally specify as well as automatically verify timed security protocols. A parameterized method is introduced in our framework to handle timing parameters whose values cannot be decided in the protocol design stage. In this work, we first propose timed applied p-calculus as a formal language for specifying timed security …
Offline Versus Online: A Meaningful Categorization Of Ties For Retweets, Felicia Natali, Feida Zhu
Offline Versus Online: A Meaningful Categorization Of Ties For Retweets, Felicia Natali, Feida Zhu
Research Collection School Of Computing and Information Systems
With the recent proliferation of news being shared through online social networks, it is crucial to determine how news is spread and what drives people to share certain stories. In this paper, we focus on the social networking site Twitter and analyse user’s retweets. We study retweeting patterns between offline and online friends, particularly, how tweet novelty and tweet topic differ between tweets retweeted by offline friends and those retweeted by online friends.
Esg And Corporate Financial Performance: Empirical Evidence From China's Listed Power Generation Companies, Changhong Zhao, Yu Guo, Jiahai Yuan, Mengya Wu, Daiyu Li, Yiou Zhou, Jiangang Kang
Esg And Corporate Financial Performance: Empirical Evidence From China's Listed Power Generation Companies, Changhong Zhao, Yu Guo, Jiahai Yuan, Mengya Wu, Daiyu Li, Yiou Zhou, Jiangang Kang
Research Collection School Of Computing and Information Systems
Nowadays, listed companies around the world are shifting from short-term goals of maximizing profits to long-term sustainable environmental, social, and governance (ESG) goals. People have come to realize that ESG has become an important source of the corporate risk and may affect the company's financial performance and profitability. Recent research shows that good ESG performance could improve the financial performance in some countries. Yet, the question of how does ESG affect financial performance has not been thoroughly discussed and studied in China. In this article, we study China's listed power generation groups to explore the relationship between ESG performance and …
Neural Collective Entity Linking, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu
Neural Collective Entity Linking, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu
Research Collection School Of Computing and Information Systems
Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of …
Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi
Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias, …
Customer Level Predictive Modeling For Accounts Receivable To Reduce Intervention Actions, Michelle L. F. Cheong, Wen Shi
Customer Level Predictive Modeling For Accounts Receivable To Reduce Intervention Actions, Michelle L. F. Cheong, Wen Shi
Research Collection School Of Computing and Information Systems
One of the main costs associated with Accounts receivable (AR) collection is related to the intervention actions taken to remind customers to pay their outstanding invoices. Apart from the cost, intervention actions may lead to poor customer satisfaction, which is undesirable in a competitive industry. In this paper, we studied the payment behavior of invoices for customers of a logistics company, and used predictive modeling to predict if a customer will pay the outstanding invoices with high probability, in an attempt to reduce intervention actions taken, thus reducing cost and improving customer relationship. We defined a pureness measure to classify …
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, …
Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Trajectory-Driven Influential Billboard Placement, Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, Zhiyong Peng
Research Collection School Of Computing and Information Systems
In this paper 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, 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 should be …
Server-Aided Attribute-Based Signature With Revocation For Resource-Constrained Industrial-Internet-Of-Things Devices, Hui Cui, Robert H. Deng, Joseph K. Liu, Xun Yi, Yingjiu Li
Server-Aided Attribute-Based Signature With Revocation For Resource-Constrained Industrial-Internet-Of-Things Devices, Hui Cui, Robert H. Deng, Joseph K. Liu, Xun Yi, Yingjiu Li
Research Collection School Of Computing and Information Systems
The industrial Internet-of-things (IIoT) can be seen as the usage of Internet-of-things technologies in industries, which provides a way to improve the operational efficiency. An attribute-based signature (ABS) has been a very useful technique for services requiring anonymous authentication in practice, where a signer can sign a message over a set of attributes without disclosing any information about his/her identity, and a signature only attests to the fact that it is created by a signer with several attributes satisfying some claim predicate. However, an ABS scheme requires exponentiation and/or pairing operations in the signature generation and verification algorithms, and hence, …
Lightweight Break-Glass Access Control System For Healthcare Internet-Of-Things, Yang Yang, Ximeng Liu, Robert H. Deng
Lightweight Break-Glass Access Control System For Healthcare Internet-Of-Things, Yang Yang, Ximeng Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
Healthcare Internet-of-things (IoT) has been proposed as a promising means to greatly improve the efficiency and quality of patient care. Medical devices in healthcare IoT measure patients' vital signs and aggregate these data into medical files which are uploaded to the cloud for storage and accessed by healthcare workers. To protect patients' privacy, encryption is normally used to enforce access control of medical files by authorized parties while preventing unauthorized access. In healthcare, it is crucial to enable timely access of patient files in emergency situations. In this paper, we propose a lightweight break-glass access control (LiBAC) system that supports …