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Articles 271 - 300 of 345
Full-Text Articles in OS and Networks
A Misspecification Test For Logit Based Route Choice Models, Tien Mai, Emma Frejinger, Fabian Bastin
A Misspecification Test For Logit Based Route Choice Models, Tien Mai, Emma Frejinger, Fabian Bastin
Research Collection School Of Computing and Information Systems
The multinomial logit (MNL) model is often used for analyzing route choices in real networks in spite of the fact that path utilities are believed to be correlated. Yet, statistical tests for model misspecification are rarely used. This paper shows how the information matrix test for model misspecification proposed byWhite (1982) can be applied to test path-based and link-based MNL route choice models.We present a Monte Carlo experiment using simulated data to assess the size and the power of the test and to compare its performance with the IIA (Hausman and McFadden, 1984) and McFadden–Train Lagrange multiplier (McFadden and Train, …
Adaptive Scaling Of Cluster Boundaries For Large-Scale Social Media Data Clustering, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch
Adaptive Scaling Of Cluster Boundaries For Large-Scale Social Media Data Clustering, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch
Research Collection School Of Computing and Information Systems
The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the …
Bring-Your-Own-Application (Byoa): Optimal Stochastic Application Migration In Mobile Cloud Computing, Jonathan David Chase, Dusit Niyato, Sivadon Chaisiri
Bring-Your-Own-Application (Byoa): Optimal Stochastic Application Migration In Mobile Cloud Computing, Jonathan David Chase, Dusit Niyato, Sivadon Chaisiri
Research Collection School Of Computing and Information Systems
The increasing popularity of using mobile devices in a work context, has led to the need to be able to support more powerful computation. Users no longer remain in an office or at home to conduct their activities, preferring libraries and cafes. In this paper, we consider a mobile cloud computing scenario in which users bring their own mobile devices and are offered a variety of equipment, e.g., desktop computer, smart- TV, or projector, to migrate their applications to, so as to save battery life, improve usability and performance. We formulate a stochastic optimization problem to optimize the allocation of …
Fast Reinforcement Learning Under Uncertainties With Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan
Fast Reinforcement Learning Under Uncertainties With Self-Organizing Neural Networks, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Using feedback signals from the environment, a reinforcement learning (RL) system typically discovers action policies that recommend actions effective to the states based on a Q-value function. However, uncertainties over the estimation of the Q-values can delay the convergence of RL. For fast RL convergence by accounting for such uncertainties, this paper proposes several enhancements to the estimation and learning of the Q-value using a self-organizing neural network. Specifically, a temporal difference method known as Q-learning is complemented by a Q-value Polarization procedure, which contrasts the Q-values using feedback signals on the effect of the recommended actions. The polarized Q-values …
Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng
Learning And Controlling Network Diffusion In Dependent Cascade Models, Jiali Du, Pradeep Varakantham, Akshat Kumar, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Diffusion processes have increasingly been used to represent flow of ideas, traffic and diseases in networks. Learning and controlling the diffusion dynamics through management actions has been studied extensively in the context of independent cascade models, where diffusion on outgoing edges from a node are independent of each other. Our work, in contrast, addresses (a) learning diffusion taking management actions to alter the diffusion dynamics to achieve a desired outcome in dependent cascade models. A key characteristic of such dependent cascade models is the flow preservation at all nodes in the network. For example, traffic and people flow is preserved …
Shopminer: Mining Customer Shopping Behavior In Physical Clothing Stores With Passive Rfids, Longfei Shangguan, Zimu Zhou, Xiaolong Zheng, Lei Yang, Yunhao Liu, Jinsong Han
Shopminer: Mining Customer Shopping Behavior In Physical Clothing Stores With Passive Rfids, Longfei Shangguan, Zimu Zhou, Xiaolong Zheng, Lei Yang, Yunhao Liu, Jinsong Han
Research Collection School Of Computing and Information Systems
Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to identify comprehensive shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which items of clothes they pay attention to, and which items of clothes they usually match with. The intuition is that the phase readings of tags attached on …
Endogenous Network Effects, Platform Pricing And Market Liquidity, Mei Lin, Ruhai Wu, Wen Zhou
Endogenous Network Effects, Platform Pricing And Market Liquidity, Mei Lin, Ruhai Wu, Wen Zhou
Research Collection School Of Computing and Information Systems
This paper examines a monopoly platform's two-sided pricing strategies in a setting with seller competition, which gives rise to not only positive cross-side network effects between buyers and sellers, but also a negative same-side network effect among sellers. We show that platform pricing depends crucially on the characteristics associated with market liquidity, which contrasts with the previous studies that point to the two sides' relative demand elasticities and/or network effects. A market is said to be more liquid when it has less friction, resulting in a larger total surplus for the platform economy and hence greater equilibrium entry on both …
Towards A Robust Sparse Data Representation In Wireless Sensor Networks, Abu Alsheik Mohammad, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato
Towards A Robust Sparse Data Representation In Wireless Sensor Networks, Abu Alsheik Mohammad, Shaowei Lin, Hwee-Pink Tan, Dusit Niyato
Research Collection School Of Computing and Information Systems
Compressive sensing has been successfully used for optimized operations in wireless sensor networks. However, raw data collected by sensors may be neither originally sparse nor easily transformed into a sparse data representation. This paper addresses the problem of transforming source data collected by sensor nodes into sparse representation with a few nonzero elements. Our contributions that address three major issues include: 1) an effective method that extracts population sparsity of the data, 2) a sparsity ratio guarantee scheme, and 3) a customized leaerning algorithm of the sparsifying dictionary. We introduce an unsupervised neural network to extract an intrinsic sparse coding …
Replica Placement For Availability In The Worst Case, Peng Li, Debin Gao, Mike Reiter
Replica Placement For Availability In The Worst Case, Peng Li, Debin Gao, Mike Reiter
Research Collection School Of Computing and Information Systems
We explore the problem of placing object replicas on nodes in a distributed system to maximize the number of objects that remain available when node failures occur. In our model, failing (the nodes hosting) a given threshold of replicas is sufficient to disable each object, and the adversary selects which nodes to fail to minimize the number of objects that remain available. We specifically explore placement strategies based on combinatorial structures called t-packings; provide a lower bound for the object availability they offer; show that these placements offer availability that is c-competitive with optimal; propose an efficient algorithm for computing …
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Self-Organizing Neural Networks Integrating Domain Knowledge And Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Jacek M. Zurada
Research Collection School Of Computing and Information Systems
The use of domain knowledge in learning systems is expected to improve learning efficiency and reduce model complexity. However, due to the incompatibility with knowledge structure of the learning systems and real-time exploratory nature of reinforcement learning (RL), domain knowledge cannot be inserted directly. In this paper, we show how self-organizing neural networks designed for online and incremental adaptation can integrate domain knowledge and RL. Specifically, symbol-based domain knowledge is translated into numeric patterns before inserting into the self-organizing neural networks. To ensure effective use of domain knowledge, we present an analysis of how the inserted knowledge is used by …
A Nested Recursive Logit Model For Route Choice Analysis, Tien Mai, Mogens Fosgerau, Emma Frejinger
A Nested Recursive Logit Model For Route Choice Analysis, Tien Mai, Mogens Fosgerau, Emma Frejinger
Research Collection School Of Computing and Information Systems
We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value …
Measuring Centralities For Transportation Networks Beyond Structures, Yew-Yih Cheng, Lee Ka Wei, Roy, Ee-Peng Lim, Feida Zhu
Measuring Centralities For Transportation Networks Beyond Structures, Yew-Yih Cheng, Lee Ka Wei, Roy, Ee-Peng Lim, Feida Zhu
Research Collection School Of Computing and Information Systems
In an urban city, its transportation network supports efficient flow of people between different parts of the city. Failures in the network can cause major disruptions to commuter and business activities which can result in both significant economic and time losses. In this paper, we investigate the use of centrality measures to determine critical nodes in a transportation network so as to improve the design of the network as well as to devise plans for coping with the network failures. Most centrality measures in social network analysis research unfortunately consider only topological structure of the network and are oblivious of …
Memory Dynamics In Attractor Networks, Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, Changyun Wen
Memory Dynamics In Attractor Networks, Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, Changyun Wen
Research Collection School Of Computing and Information Systems
As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented …
Modeling Neuromorphic Persistent Firing Networks, Ning Ning, Guoqi Li, Wei He, Kejie Huang, Li Pan, Kiruthika Ramanathan, Rong Zhao, Luping Shi
Modeling Neuromorphic Persistent Firing Networks, Ning Ning, Guoqi Li, Wei He, Kejie Huang, Li Pan, Kiruthika Ramanathan, Rong Zhao, Luping Shi
Research Collection School Of Computing and Information Systems
Neurons are believed to be the brain computational engines of the brain. A recent discovery in neurophysiology reveals that interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites, which has not been understood and could be very important for exploring the mechanism of human cognition. The conventional models are incapable of simulating the important newly-discovered phenomenon of persistent firing induced by axonal slow integration. In this paper, we propose a computationally efficient model of neurons through modeling the axon …
Stability Of Transportation Networks Under Adaptive Routing Policies, Sebastien Boyer, Sebastien Blandin, Laura Wynter
Stability Of Transportation Networks Under Adaptive Routing Policies, Sebastien Boyer, Sebastien Blandin, Laura Wynter
Research Collection School Of Computing and Information Systems
Growing concerns regarding urban congestion, and the recent explosion of mobile devices able to provide real-time information to traffic users have motivated increasing reliance on real-time route guidance for the online management of traffic networks. However, while the theory of traffic equilibria is very well-known, much fewer results exist on the stability of such equilibria, especially in the context of adaptive routing policy. In this work, we consider the problem of characterizing the stability properties of traffic equilibria in the context of online adaptive route choice induced by GPS-based decision making. We first extend the recent framework of “Markovian Traffic …
Efficient Data Compression With Error Bound Guarantee In Wireless Sensor Networks, Mohammad Abu Alsheikh, P. K. Poh, S. Lin, Hwee-Pink Tan, D. Niyato
Efficient Data Compression With Error Bound Guarantee In Wireless Sensor Networks, Mohammad Abu Alsheikh, P. K. Poh, S. Lin, Hwee-Pink Tan, D. Niyato
Research Collection School Of Computing and Information Systems
We present a data compression and dimensionality reduction scheme for data fusion and aggregation applications to prevent data congestion and reduce energy consumption at network connecting points such as cluster heads and gateways. Our in-network approach can be easily tuned to analyze the data temporal or spatial correlation using an unsupervised neural network scheme, namely the autoencoders. In particular, our algorithm extracts intrinsic data features from previously collected historical samples to transform the raw data into a low dimensional representation. Moreover, the proposed framework provides an error bound guarantee mechanism. We evaluate the proposed solution using real-world data sets and …
Multi-Cost And Upgradable Spatial Network Databases, Yimin Lin
Multi-Cost And Upgradable Spatial Network Databases, Yimin Lin
Dissertations and Theses Collection (Open Access)
In this dissertation, we first consider data processing problems in multi-cost networks and in upgradable networks. These network types are motivated by real-life situations, which do not fall under the standard spatial network formulation and have not received much attention from database researchers. In a multi-cost network (MCN), each edge is associated with more than one weight type that may affect the user-specific perception of distance. We study two query types on MCNs, namely, the MCN skyline and the MCN top-k query. In an upgradable network, a subset of the edges are amenable to weight reduction, at a cost (e.g., …
Integrating Self-Organizing Neural Network And Motivated Learning For Coordinated Multi-Agent Reinforcement Learning In Multi-Stage Stochastic Game, Teck-Hou Teng, Ah-Hwee Tan, Janusz A. Starzyk, Yuan-Sin Tan, Loo-Nin Teow
Integrating Self-Organizing Neural Network And Motivated Learning For Coordinated Multi-Agent Reinforcement Learning In Multi-Stage Stochastic Game, Teck-Hou Teng, Ah-Hwee Tan, Janusz A. Starzyk, Yuan-Sin Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Most non-trivial problems require the coordinated performance of multiple goal-oriented and time-critical tasks. Coordinating the performance of the tasks is required due to the dependencies among the tasks and the sharing of resources. In this work, an agent learns to perform a task using reinforcement learning with a self-organizing neural network as the function approximator. We propose a novel coordination strategy integrating Motivated Learning (ML) and a self-organizing neural network for multi-agent reinforcement learning (MARL). Specifically, we adapt the ML idea of using pain signal to overcome the resource competition issue. Dependency among the agents is resolved using domain knowledge …
Joint Virtual Machine And Bandwidth Allocation In Software Defined Network (Sdn) And Cloud Computing Environments, Jonathan David Chase, Rakpong Kaewpuang, Wen Yonggang, Dusit Niyato
Joint Virtual Machine And Bandwidth Allocation In Software Defined Network (Sdn) And Cloud Computing Environments, Jonathan David Chase, Rakpong Kaewpuang, Wen Yonggang, Dusit Niyato
Research Collection School Of Computing and Information Systems
Cloud computing provides users with great flexibility when provisioning resources, with cloud providers offering a choice of reservation and on-demand purchasing options. Reservation plans offer cheaper prices, but must be chosen in advance, and therefore must be appropriate to users' requirements. If demand is uncertain, the reservation plan may not be sufficient and on-demand resources have to be provisioned. Previous work focused on optimally placing virtual machines with cloud providers to minimize total cost. However, many applications require large amounts of network bandwidth. Therefore, considering only virtual machines offers an incomplete view of the system. Exploiting recent developments in software …
Optimal Performance Trade-Offs In Mac For Wireless Sensor Networks Powered By Heterogeneous Ambient Energy Harvesting, Jin Yunye, Hwee-Pink Tan
Optimal Performance Trade-Offs In Mac For Wireless Sensor Networks Powered By Heterogeneous Ambient Energy Harvesting, Jin Yunye, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
In wireless sensor networks powered by ambient energy harvesting (WSNs-HEAP), sensor nodes' energy harvesting rates are spatially heterogeneous and temporally variant, which impose difficulties for medium access control (MAC). In this paper, we first derive the necessary conditions under which channel utilization and fairness are optimal in a WSN-HEAP, respectively. Based on the analysis, we propose an earliest deadline first (EDF) polling MAC protocol, which regulates transmission sequence of the sensor nodes based on the spatially heterogeneous energy harvesting rates. It also mitigates temporal variations in energy harvesting rates by a prediction and update mechanism. Simulation results verify the performance …
Ubiquitous Data-Centric Sensor Networks, Ting Yang, Peng Yung Woo, Zhaoxia Wang, Javid Taheri, Hoong Chor Chin, Guaqiang Hu
Ubiquitous Data-Centric Sensor Networks, Ting Yang, Peng Yung Woo, Zhaoxia Wang, Javid Taheri, Hoong Chor Chin, Guaqiang Hu
Research Collection School Of Computing and Information Systems
Ubiquitous data-centric sensor networks (U-DCSN) are a new integrated science and technology, which focus on data instead of individual sensor nodes. The network, as a dynamic database system, can accurately acquire data, perform high-performance processing of big data, and effectively access data from different users/actuators. Due this characteristic, U-DCSN hold huge potentials on service improvement in a wide range of applications and have attracted significant attention in recent years, for example, mobile cloud and consumer electronics. The modern mobile cloud, comprised of mobile devices (smart phones, tablets, and embedded sensor nodes), provides unlimited information resources, putting “cloud into a pocket.” …
A Robust Smart Card-Based Anonymous User Authentication Protocol For Wireless Communications, Fengton Wen, Willy Susilo, Guomin Yang
A Robust Smart Card-Based Anonymous User Authentication Protocol For Wireless Communications, Fengton Wen, Willy Susilo, Guomin Yang
Research Collection School Of Computing and Information Systems
Anonymous user authentication is an important but challenging task for wireless communications. In a recent paper, Das proposed a smart cardï based anonymous user authentication protocol for wireless communications. The scheme can protect user privacy and is believed to be secure against a range of network attacks even if the secret information stored in the smart card is compromised. In this paper, we reanalyze the security of Das' scheme, and show that the scheme is in fact insecure against impersonation attacks. We then propose a new smart cardï based anonymous user authentication protocol for wireless communications. Compared with the existing …
A Secure And Effective Anonymous User Authentication Scheme For Roaming Service In Global Mobility Networks, Fengtong Wen, Willy Susilo, Guomin Yang
A Secure And Effective Anonymous User Authentication Scheme For Roaming Service In Global Mobility Networks, Fengtong Wen, Willy Susilo, Guomin Yang
Research Collection School Of Computing and Information Systems
In global mobility networks, anonymous user authentication is an essential task for enabling roaming service. In a recent paper, Jiang et al. proposed a smart card based anonymous user authentication scheme for roaming service in global mobility networks. This scheme can protect user privacy and is believed to have many abilities to resist a range of network attacks, even if the secret information stored in the smart card is compromised. In this paper, we analyze the security of Jiang et al.’s scheme, and show that the scheme is in fact insecure against the stolen-verifier attack and replay attack. Then, we …
Coupling Alignments With Recognition For Still-To-Video Face Recognition, Zhiwu Huang, X. Zhao, S. Shan, R. Wang, X. Chen
Coupling Alignments With Recognition For Still-To-Video Face Recognition, Zhiwu Huang, X. Zhao, S. Shan, R. Wang, X. Chen
Research Collection School Of Computing and Information Systems
The Still-to-Video (S2V) face recognition systems typically need to match faces in low-quality videos captured under unconstrained conditions against high quality still face images, which is very challenging because of noise, image blur, low face resolutions, varying head pose, complex lighting, and alignment difficulty. To address the problem, one solution is to select the frames of `best quality' from videos (hereinafter called quality alignment in this paper). Meanwhile, the faces in the selected frames should also be geometrically aligned to the still faces offline well-aligned in the gallery. In this paper, we discover that the interactions among the three tasks-quality …
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
No abstract provided.
An Agent-Based Network Analytic Perspective On The Evolution Of Complex Adaptive Supply Chain Networks, Loganathan Ponnanbalam, A. Tan, Xiuju Fu, Xiaofeng Yin, Zhaoxia Wang, Rick S. M. Goh
An Agent-Based Network Analytic Perspective On The Evolution Of Complex Adaptive Supply Chain Networks, Loganathan Ponnanbalam, A. Tan, Xiuju Fu, Xiaofeng Yin, Zhaoxia Wang, Rick S. M. Goh
Research Collection School Of Computing and Information Systems
Supply chain networks of modern era are complex adaptive systems that are dynamic and highly interdependent in nature. Business continuity of these complex systems depend vastly on understanding as to how the supply chain network evolves over time (based on the policies it adapts), and identifying the susceptibility of the evolved networks to external disruptions. The objective of this article is to illustrate as to how an agent-based network analytic perspective can aid this understanding on the network-evolution dynamics, and identification of disruption effects on the evolved networks. To this end, we developed a 4-tier agent based supply chain model …
An Agent-Based Network Analytic Perspective On The Evolution Of Complex Adaptive Supply Chain Networks, L. Ponnambalam, A. Tan, X. Fu, X. F. Yin, Zhaoxia Wang, R. S. Goh
An Agent-Based Network Analytic Perspective On The Evolution Of Complex Adaptive Supply Chain Networks, L. Ponnambalam, A. Tan, X. Fu, X. F. Yin, Zhaoxia Wang, R. S. Goh
Research Collection School Of Computing and Information Systems
Supply chain networks of modern era are complex adaptive systems that are dynamic and highly interdependent in nature. Business continuity of these complex systems depend vastly on understanding as to how the supply chain network evolves over time (based on the policies it adapts), and identifying the susceptibility of the evolved networks to external disruptions. The objective of this article is to illustrate as to how an agent-based network analytic perspective can aid this understanding on the network-evolution dynamics, and identification of disruption effects on the evolved networks. To this end, we developed a 4-tier agent based supply chain model …
Approximating The Performance Of A "Last Mile" Transportation System, Hai Wang, Amedeo Odoni
Approximating The Performance Of A "Last Mile" Transportation System, Hai Wang, Amedeo Odoni
Research Collection School Of Computing and Information Systems
The Last Mile Problem (LMP) refers to the provision of travel service from thenearest public transportation node to a home or office. We study the supply side of thisproblem in a stochastic setting, with batch demands resulting from the arrival of groupsof passengers at rail stations or bus stops who request last-mile service. Closed-formbounds and approximations are derived for the performance of Last Mile TransportationsSystems as a function of the fundamental design parameters of such systems. An initialset of results is obtained for the case in which a fleet of vehicles of unit capacity providesthe Last Mile service and each …
Behind The Magical Numbers: Hierarchical Chunking And The Human Working Memory Capacity, Guoqi Li, Ning Ning, Kiruthika Ramanathan, Wei He, Li Pan, Luping Shi
Behind The Magical Numbers: Hierarchical Chunking And The Human Working Memory Capacity, Guoqi Li, Ning Ning, Kiruthika Ramanathan, Wei He, Li Pan, Luping Shi
Research Collection School Of Computing and Information Systems
To explore the influence of chunking on the capacity limits of working memory, a model for chunking in sequential working memory is proposed, using hierarchical bidirectional inhibition-connected neural networks with winnerless competition. With the assumption of the existence of an upper bound to the inhibitory weights in neurobiological networks, it is shown that chunking increases the number of memorized items in working memory from the "magical number 7" to 16 items. The optimal number of chunks and the number of the memorized items in each chunk are the "magical number 4".
Network Structure Of Social Coding In Github, Ferdian Thung, David Lo, Lingxiao Jiang
Network Structure Of Social Coding In Github, Ferdian Thung, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
No abstract provided.