Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (3441)
- Software Engineering (2143)
- Artificial Intelligence and Robotics (1664)
- Information Security (1056)
- Numerical Analysis and Scientific Computing (1024)
-
- Graphics and Human Computer Interfaces (921)
- Engineering (857)
- Social and Behavioral Sciences (661)
- Business (625)
- Theory and Algorithms (493)
- Computer Engineering (431)
- Operations Research, Systems Engineering and Industrial Engineering (399)
- Programming Languages and Compilers (379)
- OS and Networks (322)
- Communication (297)
- Social Media (240)
- Public Affairs, Public Policy and Public Administration (207)
- Transportation (185)
- Medicine and Health Sciences (178)
- Education (164)
- Management Information Systems (164)
- Data Storage Systems (160)
- E-Commerce (146)
- Health Information Technology (107)
- International and Area Studies (107)
- Asian Studies (106)
- Technology and Innovation (100)
- Digital Communications and Networking (96)
- Keyword
-
- Deep learning (122)
- Machine learning (121)
- Social media (74)
- Artificial intelligence (70)
- Reinforcement learning (69)
-
- Data mining (64)
- Privacy (61)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (54)
- Optimization (53)
- Security (53)
- Visualization (51)
- Software engineering (49)
- Training (49)
- Neural networks (48)
- Online learning (48)
- Task analysis (48)
- Anomaly detection (47)
- Singapore (47)
- Twitter (46)
- Feature extraction (45)
- Blockchain (44)
- Collaboration (44)
- Large Language Models (43)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Machine Learning (38)
- Publication Year
- File Type
Articles 6751 - 6780 of 8479
Full-Text Articles in Computer Sciences
Decentralized Decision Support For An Agent Population In Dynamic And Uncertain Domains, Pradeep Reddy Varakantham, Shih-Fen Cheng, Thi Duong Nguyen
Decentralized Decision Support For An Agent Population In Dynamic And Uncertain Domains, Pradeep Reddy Varakantham, Shih-Fen Cheng, Thi Duong Nguyen
Research Collection School Of Computing and Information Systems
This research is motivated by problems in urban transportation and labor mobility, where the agent flow is dynamic, non-deterministic and on a large scale. In such domains, even though the individual agents do not have an identity of their own and do not explicitly impact other agents, they have implicit interactions with other agents. While there has been much research in handling such implicit effects, it has primarily assumed controlled movements of agents in static environments. We address the issue of decision support for individual agents having involuntary movements in dynamic environments . For instance, in a taxi fleet serving …
Sandcanvas: A Multi-Touch Art Medium Inspired By Sand Animation, Rubaiat Habib Kazi, Kien-Chuan Chua, Shengdong Zhao, Richard Christopher Davis, Kok-Lim Low
Sandcanvas: A Multi-Touch Art Medium Inspired By Sand Animation, Rubaiat Habib Kazi, Kien-Chuan Chua, Shengdong Zhao, Richard Christopher Davis, Kok-Lim Low
Research Collection School Of Computing and Information Systems
Sand animation is a performance art technique in which an artist tells stories by creating animated images with sand. Inspired by this medium, we have developed a new multitouch digital artistic medium named SandCanvas that simplifies the creation of sand animations. SandCanvas also goes beyond traditional sand animation with tools for mixing sand animation with video and replicating recorded free-form hand gestures. In this paper, we analyze common sand animation hand gestures, present SandCanvas’s intuitive UI, and describe implementation challenges we encountered. We also present an evaluation with professional and novice artists that shows the importance and unique affordances of …
Sandcanvas: New Possibilities In Sand Animation, Rubaiat Habib Kazi, Kien-Chuan Chua, Shengdong Zhao, Richard Christopher Davis, Kok-Lim Low
Sandcanvas: New Possibilities In Sand Animation, Rubaiat Habib Kazi, Kien-Chuan Chua, Shengdong Zhao, Richard Christopher Davis, Kok-Lim Low
Research Collection School Of Computing and Information Systems
Sand animation is a performance art technique in which an artist tells stories by creating animated images with sand. This video demonstrates the creative possibilities of SandCanvas, a new multi-touch digital artistic medium inspired by sand animation that simplifies the creation of sand animations. SandCanvas’s color and texture features enable faster, more dramatic transitions, while its mixed media and gesture recording features make it possible to create entirely new experiences. Session recording and frame capture complement these capabilities by simplifying postproduction of sand animation performances.
Adaptive Decision Support For Structured Organizations: A Case For Orgpomdps, Pradeep Reddy Varakantham, Nathan Schurr, Alan Carlin, Christopher Amato
Adaptive Decision Support For Structured Organizations: A Case For Orgpomdps, Pradeep Reddy Varakantham, Nathan Schurr, Alan Carlin, Christopher Amato
Research Collection School Of Computing and Information Systems
In today's world, organizations are faced with increasingly large and complex problems that require decision-making under uncertainty. Current methods for optimizing such decisions fall short of handling the problem scale and time constraints. We argue that this is due to existing methods not exploiting the inherent structure of the organizations which solve these problems. We propose a new model called the OrgPOMDP (Organizational POMDP), which is based on the partially observable Markov decision process (POMDP). This new model combines two powerful representations for modeling large scale problems: hierarchical modeling and factored representations. In this paper we make three key contributions: …
Leveraging Complex Event Processing For Smart Hospitals Using Rfid, Wen Yao, Chao-Hsien Chu, Zang Li
Leveraging Complex Event Processing For Smart Hospitals Using Rfid, Wen Yao, Chao-Hsien Chu, Zang Li
Research Collection School Of Computing and Information Systems
RFID technology has been examined in healthcare to support a variety of applications such as patient identification and monitoring, asset tracking, and patient–drug compliance. However, managing the large volume of RFID data and understanding them in the medical context present new challenges. One effective solution for dealing with these challenges is complex event processing (CEP), which can extract meaningful events for context-aware applications. In this paper, we propose a CEP framework to model surgical events and critical situations in an RFID-enabled hospital. We have implemented a prototype system with the proposed approach for surgical management and conducted performance evaluations to …
Design And Performance Analysis Of Mac Schemes For Wireless Sensor Networks Powered By Ambient Energy Harvesting, Zhi Ang Eu, Hwee-Pink Tan, Winston K. G. Seah
Design And Performance Analysis Of Mac Schemes For Wireless Sensor Networks Powered By Ambient Energy Harvesting, Zhi Ang Eu, Hwee-Pink Tan, Winston K. G. Seah
Research Collection School Of Computing and Information Systems
Energy consumption is a perennial issue in the design of wireless sensor networks (WSNs) which typically rely on portable sources like batteries for power. Recent advances in ambient energy harvesting technology have made it a potential and promising alternative source of energy for powering WSNs. By using energy harvesters with supercapacitors, WSNs are able to operate perpetually until hardware failure and in places where batteries are hard or impossible to replace. In this paper, we study the performance of different medium access control (MAC) schemes based on CSMA and polling techniques for WSNs which are solely powered by ambient energy …
A Simple Curious Agent To Help People Be Curious, Han Yu, Zhiqi Shen, Chunyan Miao, Ah-Hwee Tan
A Simple Curious Agent To Help People Be Curious, Han Yu, Zhiqi Shen, Chunyan Miao, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Curiosity is an innately rewarding state of mind that, over the millennia, has driven the human race to explore and discover. Many researches in pedagogical science have confirmed the importance of being curious to the students' cognitive development. However, in the newly popular virtual world-based learning environments (VLEs), there is currently a lack of attention being paid to enhancing the learning experience by stimulating the learners' curiosity. In this paper, we propose a simple model for curious agents (CAs) which can be used to stimulate learners' curiosity in VLEs. Potential future research directions will be discussed.
How Do Programmers Ask And Answer Questions On The Web? (Nier Track), Christoph Treude, Ohad Barzilay, Margaret-Anne Storey
How Do Programmers Ask And Answer Questions On The Web? (Nier Track), Christoph Treude, Ohad Barzilay, Margaret-Anne Storey
Research Collection School Of Computing and Information Systems
Question and Answer (Q&A) websites, such as Stack Overflow, use social media to facilitate knowledge exchange between programmers and fill archives with millions of entries that contribute to the body of knowledge in software development. Understanding the role of Q&A websites in the documentation landscape will enable us to make recommendations on how individuals and companies can leverage this knowledge effectively. In this paper, we analyze data from Stack Overflow to categorize the kinds of questions that are asked, and to explore which questions are answered well and which ones remain unanswered. Our preliminary findings indicate that Q&A websites are …
Interactivity-Constrained Server Provisioning In Large-Scale Distributed Virtual Environments, Nguyen Binh Duong Ta, Thang Nguyen, Suiping Zhou, Xueyan Tang, Wentong Cai, Rassul Ayani
Interactivity-Constrained Server Provisioning In Large-Scale Distributed Virtual Environments, Nguyen Binh Duong Ta, Thang Nguyen, Suiping Zhou, Xueyan Tang, Wentong Cai, Rassul Ayani
Research Collection School Of Computing and Information Systems
Maintaining interactivity is one of the key challenges in distributed virtual environments (DVE), e.g., online games, distributed simulations, etc., due to the large, heterogeneous Internet latencies; and the fact that clients in a DVE are usually geographically separated. In this paper, we consider a new problem, termed the interactivity-constrained server provisioning problem, whose goal is to minimize the number of distributed servers needed to achieve a pre-determined level of interactivity. We identify and formulate two variants of this new problem and show that they are both NP-hard via reductions to the set covering problem. We then propose several computationally efficient …
Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li
Comparing Twitter And Traditional Media Using Topic Models, Wayne Xin Zhao, Jing Jiang, Jianshu Weng, Jing He, Ee Peng Lim, Hongfei Yan, Xiaoming Li
Research Collection School Of Computing and Information Systems
Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to …
Two-Layer Multiple Kernel Learning, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
Two-Layer Multiple Kernel Learning, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple Kernel Learning (MKL) aims to learn kernel machines for solving a real machine learning problem (e.g. classification) by exploring the combinations of multiple kernels. The traditional MKL approach is in general “shallow” in the sense that the target kernel is simply a linear (or convex) combination of some base kernels. In this paper, we investigate a framework of Multi-Layer Multiple Kernel Learning (MLMKL) that aims to learn “deep” kernel machines by exploring the combinations of multiple kernels in a multi-layer structure, which goes beyond the conventional MKL approach. Through a multiple layer mapping, the proposed MLMKL framework offers higher …
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
Research Collection School Of Computing and Information Systems
We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “T-OLAP”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these …
Heterogeneous Signcryption With Key Privacy, Qiong Huang, Duncan S. Wong, Guomin Yang
Heterogeneous Signcryption With Key Privacy, Qiong Huang, Duncan S. Wong, Guomin Yang
Research Collection School Of Computing and Information Systems
A signcryption scheme allows a sender to produce a ciphertext for a receiver so that both confidentiality and non-repudiation can be ensured. It is built to be more efficient and secure, for example, supporting insider security, when compared with the conventional sign-then-encrypt approach. In this paper, we propose a new notion called heterogeneous signcryption in which the sender has an identity-based secret key while the receiver is holding a certificate-based public key pair. Heterogeneous signcryption is suitable for practical scenarios where an identity-based user, who does not have a personal certificate or a public key, wants to communicate securely with …
Learning Feature Dependencies For Noise Correction In Biomedical Prediction, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Learning Feature Dependencies For Noise Correction In Biomedical Prediction, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
The presence of noise or errors in the stated feature values of biomedical data can lead to incorrect prediction. We introduce a Bayesian Network-based Noise Correction framework named BN-NC. After data preprocessing, a Bayesian Network (BN) is learned to capture the feature dependencies. Using the BN to predict each feature in turn, BN-NC estimates a feature's error rate as the deviation between its predicted and stated values in the training data, and allocates the appropriate uncertainty to its subsequent findings during prediction. BN-NC automatically generates a probabilistic rule to explain BN prediction on the class variable using the feature values …
Utility-Oriented K-Anonymization On Social Networks, Yazhe Wang, Long Xie, Baihua Zheng, Ken C. K. Lee
Utility-Oriented K-Anonymization On Social Networks, Yazhe Wang, Long Xie, Baihua Zheng, Ken C. K. Lee
Research Collection School Of Computing and Information Systems
"Identity disclosure" problem on publishing social network data has gained intensive focus from academia. Existing k-anonymization algorithms on social network may result in nontrivial utility loss. The reason is that the number of the edges modified when anonymizing the social network is the only metric to evaluate utility loss, not considering the fact that different edge modifications have different impact on the network structure. To tackle this issue, we propose a novel utility-oriented social network anonymization scheme to achieve privacy protection with relatively low utility loss. First, a proper utility evaluation model is proposed. It focuses on the changes on …
Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection, Bin Li, Steven C. H. Hoi, Vivekanand Gopalkrishnan
Corn: Correlation-Driven Nonparametric Learning Approach For Portfolio Selection, Bin Li, Steven C. H. Hoi, Vivekanand Gopalkrishnan
Research Collection School Of Computing and Information Systems
Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this article, we propose a novel learning-to-trade algorithm termed CO Relation-driven Nonparametric learning strategy (CORN) for actively trading stocks. CORN effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, and show that it can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpass a variety …
A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
A Family Of Simple Non-Parametric Kernel Learning Algorithms From Pairwise Constraints, Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Previous studies of Non-Parametric Kernel Learning (NPKL) usually formulate the learning task as a Semi-Definite Programming (SDP) problem that is often solved by some general purpose SDP solvers. However, for N data examples, the time complexity of NPKL using a standard interior-point SDP solver could be as high as O(N6.5), which prohibits NPKL methods applicable to real applications, even for data sets of moderate size. In this paper, we present a family of efficient NPKL algorithms, termed "SimpleNPKL", which can learn non-parametric kernels from a large set of pairwise constraints efficiently. In particular, we propose two efficient SimpleNPKL algorithms. One …
Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivek Gopalkrishnan
Confidence Weighted Mean Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivek Gopalkrishnan
Research Collection School Of Computing and Information Systems
On-line portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing on-line portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidences show that the stock price relatives may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel on-line portfolio selection strategy named ``Confidence Weighted Mean Reversion'' (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, …
Ir-Tree: An Efficient Index For Geographic Document Search, Zhisheng Li, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee, Xufa Wang
Ir-Tree: An Efficient Index For Geographic Document Search, Zhisheng Li, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee, Dik Lun Lee, Xufa Wang
Research Collection School Of Computing and Information Systems
Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four …
A High-Throughput Routing Metric For Reliable Multicast In Multi-Rate Wireless Mesh Networks, Xin Zhao, Jun Guo, Chun Tung Chou, Archan Misra, Sanjay Jha
A High-Throughput Routing Metric For Reliable Multicast In Multi-Rate Wireless Mesh Networks, Xin Zhao, Jun Guo, Chun Tung Chou, Archan Misra, Sanjay Jha
Research Collection School Of Computing and Information Systems
We propose a routing metric for enabling highthroughput reliable multicast in multi-rate wireless mesh networks. This new multicast routing metric, called expected multicast transmission time (EMTT), captures the combined effects of 1) MAC-layer retransmission-based reliability, 2) transmission rate diversity, 3) wireless broadcast advantage, and 4) link quality awareness. The EMTT of one-hop transmission of a multicast packet minimizes the amount of expected transmission time (including that required for retransmissions). This is achieved by allowing the sender to adapt its bit-rate for each ongoing transmission/retransmission, optimized exclusively for its nexthop receivers that have not yet received the multicast packet. We model …
Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim
Predicting Item Adoption Using Social Correlation, Freddy Chong-Tat Chua, Hady W. Lauw, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Users face a dazzling array of choices on the Web when it comes to choosing which product to buy, which video to watch, etc. The trend of social information processing means users increasingly rely not only on their own preferences, but also on friends when making various adoption decisions. In this paper, we investigate the effects of social correlation on users’ adoption of items. Given a user-user social graph and an item-user adoption graph, we seek to answer the following questions: 1) whether the items adopted by a user correlate to items adopted by her friends, and 2) how to …
Abstracting Events For Data Mining, David Lo, Ganesan Ramalingam, Venkatesh-Prasad Ranganath, Kapil Vaswani
Abstracting Events For Data Mining, David Lo, Ganesan Ramalingam, Venkatesh-Prasad Ranganath, Kapil Vaswani
Research Collection School Of Computing and Information Systems
An event is described herein as being representable by a quantified abstraction of the event. The event includes at least one predicate, and the at least one predicate has at least one constant symbol corresponding thereto. An instance of the constant symbol corresponding to the event is identified, and the instance of the constant symbol is replaced by a free variable to obtain an abstracted predicate. Thus, a quantified abstraction of the event is composed as a pair: the abstracted predicate and a mapping between the free variable and an instance of the constant symbol that corresponds to the predicate. …
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Mkboost: A Framework Of Multiple Kernel Boosting, Hao Xia, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Multiple kernel learning (MKL) has been shown as a promising machine learning technique for data mining tasks by integrating with multiple diverse kernel functions. Traditional MKL methods often formulate the problem as an optimization task of learning both optimal combination of kernels and classifiers, and attempt to resolve the challenging optimization task by various techniques. Unlike the existing MKL methods, in this paper, we investigate a boosting framework of exploring multiple kernel learning for classification tasks. In particular, we present a novel framework of Multiple Kernel Boosting (MKBoost), which applies boosting techniques for learning kernel-based classifiers with multiple kernels. Based …
Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou
Weight-Based Boosting Model For Cross-Domain Relevance Ranking Adaptation, Peng Cai, Wei Gao, Kam-Fai Wong, Aoying Zhou
Research Collection School Of Computing and Information Systems
Adaptation techniques based on importance weighting were shown effective for RankSVM and RankNet, viz., each training instance is assigned a target weight denoting its importance to the target domain and incorporated into loss functions. In this work, we extend RankBoost using importance weighting framework for ranking adaptation. We find it non-trivial to incorporate the target weight into the boosting-based ranking algorithms because it plays a contradictory role against the innate weight of boosting, namely source weight that focuses on adjusting source-domain ranking accuracy. Our experiments show that among three variants, the additive weight-based RankBoost, which dynamically balances the two types …
Peercast: Improving Link Layer Multicast Through Cooperative Relaying, Jie Xiong, Romit Roy Choudhury
Peercast: Improving Link Layer Multicast Through Cooperative Relaying, Jie Xiong, Romit Roy Choudhury
Research Collection School Of Computing and Information Systems
Wireless multicast applications, such as MobiTV, web telecast, and multimedia classrooms, are gaining rapid popularity. The broadcast nature of the wireless channel is amenable to such multicasts because a single packet transmission can be received by all clients. Unfortunately, the rate of this transmission is bottlenecked by data rate of the weakest client, degrading system performance. Attempts to increase the data rate results in lower reliability and higher unfairness. This paper presents PeerCast, a wireless multicast protocol that engages clients in cooperative relaying. The main idea is simple. Instead of multicasting at the bottleneck rate, the access point transmits at …
Fusing Heterogeneous Modalities For Video And Image Re-Ranking, Hung-Khoon Tan, Chong-Wah Ngo
Fusing Heterogeneous Modalities For Video And Image Re-Ranking, Hung-Khoon Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Multimedia documents in popular image and video sharing websites such as Flickr and Youtube are heterogeneous documents with diverse ways of representations and rich user-supplied information. In this paper, we investigate how the agreement among heterogeneous modalities can be exploited to guide data fusion. The problem of fusion is cast as the simultaneous mining of agreement from different modalities and adaptation of fusion weights to construct a fused graph from these modalities. An iterative framework based on agreement-fusion optimization is thus proposed. We plug in two well-known algorithms: random walk and semi-supervised learning to this framework to illustrate the idea …
Certificateless Public Key Encryption: A New Generic Construction And Two Pairing-Free Schemes, Guomin Yang, Chik How Tan
Certificateless Public Key Encryption: A New Generic Construction And Two Pairing-Free Schemes, Guomin Yang, Chik How Tan
Research Collection School Of Computing and Information Systems
The certificateless encryption (CLE) scheme proposed by Baek, Safavi-Naini and Susilo is computation-friendly since it does not require any pairing operation. Unfortunately, an error was later discovered in their security proof and so far the provable security of the scheme remains unknown. Recently, Fiore, Gennaro and Smart showed a generic way (referred to as the FGS transformation) to transform identity-based key agreement protocols to certificateless key encapsulation mechanisms (CL-KEMs). As a typical example, they showed that the pairing-free CL-KEM underlying Baek et al.’s CLE can be “generated” by applying their transformation to the Fiore–Gennaro (FG) identity-based key agreement (IB-KA) protocol.In …
Authenticated Key Exchange Under Bad Randomness, Guomin Yang, Shanshan Duan, Duncan S. Wong, Chik How Tan, Huaxiong Wang
Authenticated Key Exchange Under Bad Randomness, Guomin Yang, Shanshan Duan, Duncan S. Wong, Chik How Tan, Huaxiong Wang
Research Collection School Of Computing and Information Systems
We initiate the formal study on authenticated key exchange (AKE) under bad randomness. This could happen when (1) an adversary compromises the randomness source and hence directly controls the randomness of each AKE session; and (2) the randomness repeats in different AKE sessions due to reset attacks. We construct two formal security models, Reset-1 and Reset-2, to capture these two bad randomness situations respectively, and investigate the security of some widely used AKE protocols in these models by showing that they become insecure when the adversary is able to manipulate the randomness. On the positive side, we propose simple but …
A Virtualization-Based Approach For Zone Migration In Distributed Virtual Environments, Nguyen Binh Duong Ta, Thang Nguyen, Tran Nguyen, Do Nguyen, Xueyan Tang, Wentong Cai, Suiping Zhou
A Virtualization-Based Approach For Zone Migration In Distributed Virtual Environments, Nguyen Binh Duong Ta, Thang Nguyen, Tran Nguyen, Do Nguyen, Xueyan Tang, Wentong Cai, Suiping Zhou
Research Collection School Of Computing and Information Systems
This paper deals with the zone migration problem in large-scale distributed virtual environments (DVEs), e.g., massively multi-player online games, distributed military simulations, etc. To support real-time interactions among thousands of concurrent, geographically separated clients, a distributed server architecture is generally needed. In such architecture, the large virtual world can be partitioned into multiple smaller zones, enabling load distributions or zone-to-server mappings to improve interactivity. For example, a zone might be mapped (assigned) to a server location near most of its clients to reduce network latency. In this paper, we consider the problem of live zone migration over wide area networks …
How Information Management Capability Influences Firm Performance, Sunil Mithas, Narayan Ramasubbu, V. Sambamurthy
How Information Management Capability Influences Firm Performance, Sunil Mithas, Narayan Ramasubbu, V. Sambamurthy
Research Collection School Of Computing and Information Systems
How do information technology capabilities contribute to firm performance? This study develops a conceptual model linking IT-enabled information management capability with three important organizational capabilities (customer management capability, process management capability, and performance management capability). We argue that these three capabilities mediate the relationship between information management capability and firm performance. To test our conceptual model, we use a rare archival data set that contains actual scores from multidimensional and high-quality assessments of firms and intraorganizational units of a conglomerate business group that had adopted a model of performance excellence for organizational transformation based on the Baldrige criteria. This research …