Open Access. Powered by Scholars. Published by Universities.®
Computer and Systems Architecture Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Computer Sciences (70)
- Physical Sciences and Mathematics (70)
- Software Engineering (39)
- Databases and Information Systems (21)
- Artificial Intelligence and Robotics (7)
-
- OS and Networks (7)
- Systems Architecture (5)
- Information Security (4)
- Data Storage Systems (3)
- Digital Communications and Networking (2)
- Graphics and Human Computer Interfaces (2)
- Numerical Analysis and Scientific Computing (2)
- Programming Languages and Compilers (2)
- Business (1)
- Digital Circuits (1)
- Education (1)
- Higher Education (1)
- Management Information Systems (1)
- Operations Research, Systems Engineering and Industrial Engineering (1)
- Public Affairs, Public Policy and Public Administration (1)
- Social and Behavioral Sciences (1)
- Theory and Algorithms (1)
- Transportation (1)
- Keyword
-
- Plan learning (3)
- Reinforcement learning (3)
- Self-organizing neural networks (3)
- Software architecture (3)
- Wireless sensor network (3)
-
- Attribute-based encryption (2)
- BDI (2)
- Minefield navigation (2)
- Reinforcement learning (RL) (2)
- Wireless sensor networks (2)
- (re)delegation (1)
- ACT-R (1)
- Abstract concept (1)
- Accelerometers (1)
- Acoustic attack (1)
- Action representation (1)
- Active learning (1)
- Adaptive Resonance Theory (1)
- Adaptive large neighborhood search (1)
- Adaptive parameter tuning (1)
- Adaptive resonance theory (1)
- Adaptive resonance theory (ART) (1)
- Adaptive wireless scheduling QoS Heterogenous channel Buffer requirements Fairness (1)
- Adult learners (1)
- Adversarial reinforcement learning (1)
- Amazon Mechanical Turk (1)
- Analytical tool (1)
- Angles of arrival (1)
- AoA (1)
- Application (1)
Articles 31 - 60 of 89
Full-Text Articles in Computer and Systems Architecture
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 …
Entagrec(++): An Enhanced Tag Recommendation System For Software Information Sites, Shawei Wang, David Lo, Bogdan Vasilescu, Alexander Serebrenik
Entagrec(++): An Enhanced Tag Recommendation System For Software Information Sites, Shawei Wang, David Lo, Bogdan Vasilescu, Alexander Serebrenik
Research Collection School Of Computing and Information Systems
Software engineers share experiences with modern technologies using software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. Tags help to improve the organization of questions and simplify the browsing of questions for users. However, tags assigned to objects tend to be noisy and some objects are not well tagged. For instance, 14.7% of the questions that were posted in 2015 on Stack Overflow needed tag re-editing after the initial assignment. To improve the quality of tags in software information sites, we propose EnTagRec …
A New Revocable And Re-Delegable Proxy Signature And Its Application, Shengmin Xu, Guomin Yang, Yi Mu
A New Revocable And Re-Delegable Proxy Signature And Its Application, Shengmin Xu, Guomin Yang, Yi Mu
Research Collection School Of Computing and Information Systems
With the popularity of cloud computing and mobile Apps, on-demand services such as on-line music or audio streaming and vehicle booking are widely available nowadays. In order to allow efficient delivery and management of the services, for large-scale on-demand systems, there is usually a hierarchy where the service provider can delegate its service to a top-tier (e.g., countrywide) proxy who can then further delegate the service to lower level (e.g., region-wide) proxies. Secure (re-)delegation and revocation are among the most crucial factors for such systems. In this paper, we investigate the practical solutions for achieving re-delegation and revocation utilizing proxy …
Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak
Fixation And Confusion: Investigating Eye-Tracking Participants' Exposure To Information In Personas, Joni Salminen, Bernard J. Jansen, Jisun An, Soon-Gyo Jung, Lene Nielsen, Haewoon Kwak
Research Collection School Of Computing and Information Systems
To more effectively convey relevant information to end users of persona profiles, we conducted a user study consisting of 29 participants engaging with three persona layout treatments. We were interested in confusion engendered by the treatments on the participants, and conducted a within-subjects study in the actual work environment, using eye-tracking and talk-aloud data collection. We coded the verbal data into classes of informativeness and confusion and correlated it with fixations and durations on the Areas of Interests recorded by the eye-tracking device. We used various analysis techniques, including Mann-Whitney, regression, and Levenshtein distance, to investigate how confused users differed …
Integrated Reward Scheme And Surge Pricing In A Ride Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye
Integrated Reward Scheme And Surge Pricing In A Ride Sourcing Market, Hai Yang, Chaoyi Shao, Hai Wang, Jieping Ye
Research Collection School Of Computing and Information Systems
Surge pricing is commonly used in on-demand ride-sourcing platforms (e.g., Uber, Lyft and Didi) to dynamically balance demand and supply. However, since the price for ride service cannot be unlimited, there is usually a reasonable or legitimate range of prices in practice. Such a constrained surge pricing strategy fails to balance demand and supply in certain cases, e.g., even adopting the maximum allowed price cannot reduce the demand to an affordable level during peak hours. In addition, the practice of surge pricing is controversial and has stimulated long debate regarding its pros and cons. In this paper, to address the …
Graphmp: An Efficient Semi-External-Memory Big Graph Processing System On A Single Machine, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Xiaokui Xiao
Graphmp: An Efficient Semi-External-Memory Big Graph Processing System On A Single Machine, Peng Sun, Yonggang Wen, Nguyen Binh Duong Ta, Xiaokui Xiao
Research Collection School Of Computing and Information Systems
Recent studies showed that single-machine graph processing systems can be as highly competitive as clusterbased approaches on large-scale problems. While several outof-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge …
Audiosense: Sound-Based Shopper Behavior Analysis System, Amit Sharma, Youngki Lee
Audiosense: Sound-Based Shopper Behavior Analysis System, Amit Sharma, Youngki Lee
Research Collection School Of Computing and Information Systems
This paper presents AudioSense, the system to monitor user-item interactions inside a store hence enabling precisely customized promotions. A shopper's smartwatch emits sound every time the shopper picks up or touches an item inside a store. This sound is then localized, in 2D space, by calculating the angles of arrival captured by multiple microphones deployed on the racks. Lastly, the 2D location is mapped to specific items on the rack based on the rack layout information. In our initial experiments conducted with a single rack with 16 compartments, we could localize the shopper's smartwatch with a median estimation error of …
Sugarmate: Non-Intrusive Blood Glucose Monitoring With Smartphones, Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang, Costas J. Spanos, Lin Zhang
Sugarmate: Non-Intrusive Blood Glucose Monitoring With Smartphones, Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang, Costas J. Spanos, Lin Zhang
Research Collection School Of Computing and Information Systems
Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference …
Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jiawang Bian, Wen-Yan Lin, Matsushita Yasuyuki, Sai-Kit Yeung, Tan-Dat Nguyen, Ming-Ming Cheng
Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jiawang Bian, Wen-Yan Lin, Matsushita Yasuyuki, Sai-Kit Yeung, Tan-Dat Nguyen, Ming-Ming Cheng
Research Collection School Of Computing and Information Systems
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching. However, such formulations are both complex and slow, making them unsuitable for video applications. This paper proposes GMS (Grid-based Motion Statistics), a simple means of encapsulating motion smoothness as the statistical likelihood of a certain number of matches in a region. GMS enables translation of high match numbers into high match quality. This provides a real-time, ultra-robust correspondence system. Evaluation on videos, with low textures, blurs and wide-baselines show GMS consistently out-performs other real-time matchers and can achieve parity with more sophisticated, much slower techniques.
Webapirec: Recommending Web Apis To Software Projects Via Personalized Ranking, Ferdian Thung, Richard J. Oentaryo, David Lo, Yuan Tian
Webapirec: Recommending Web Apis To Software Projects Via Personalized Ranking, Ferdian Thung, Richard J. Oentaryo, David Lo, Yuan Tian
Research Collection School Of Computing and Information Systems
Application programming interfaces (APIs) offer a plethora of functionalities for developers to reuse without reinventing the wheel. Identifying the appropriate APIs given a project requirement is critical for the success of a project, as many functionalities can be reused to achieve faster development. However, the massive number of APIs would often hinder the developers' ability to quickly find the right APIs. In this light, we propose a new, automated approach called WebAPIRec that takes as input a project profile and outputs a ranked list of web APIs that can be used to implement the project. At its heart, WebAPIRec employs …
Deepmon: Mobile Gpu-Based Deep Learning Framework For Continuous Vision Applications, Nguyen Loc Huynh, Youngki Lee, Rajesh Krishna Balan
Deepmon: Mobile Gpu-Based Deep Learning Framework For Continuous Vision Applications, Nguyen Loc Huynh, Youngki Lee, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
The rapid emergence of head-mounted devices such as the Microsoft Holo-lens enables a wide variety of continuous vision applications. Such applications often adopt deep-learning algorithms such as CNN and RNN to extract rich contextual information from the first-person-view video streams. Despite the high accuracy, use of deep learning algorithms in mobile devices raises critical challenges, i.e., high processing latency and power consumption. In this paper, we propose DeepMon, a mobile deep learning inference system to run a variety of deep learning inferences purely on a mobile device in a fast and energy-efficient manner. For this, we designed a suite of …
Sensor Placement With Time-To-Detection Guarantees, Saif Eddin Jabari, Laura Wynter
Sensor Placement With Time-To-Detection Guarantees, Saif Eddin Jabari, Laura Wynter
Research Collection School Of Computing and Information Systems
We present a novel and effective method for determining the placement of sensors so as to be able to satisfy probabilistic constraints on the time-to-detection of an incident. Indeed, with the wealth of real-time traffic data available today, an important new goal of intelligent traffic management systems is incident detection with time-to-detection guarantees, in particular on expressways with large distances between sensors. This goal drives investment decisions in new sensor deployment, hence making the topic a pressing need for traffic management. The method we provide makes use of a probabilistic formulation of traffic behavior and incident localization to determine the …
Towards Learning And Verifying Invariants Of Cyber-Physical Systems By Code Mutation, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Towards Learning And Verifying Invariants Of Cyber-Physical Systems By Code Mutation, Yuqi Chen, Christopher M. Poskitt, Jun Sun
Research Collection School Of Computing and Information Systems
Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network. A malfunctioning or compromised component in such a CPS can lead to costly consequences, especially in the context of public infrastructure. In this short paper, we argue for the importance of constructing invariants (or models) of the physical behaviour exhibited by CPS, motivated by their applications to the control, monitoring, and attestation of components. To achieve this despite the inherent complexity of CPS, we propose a new technique for learning invariants that combines machine learning with ideas from mutation testing. …
Towards Concolic Testing For Hybrid Systems, Pingfan Kong, Yi Li, Xiaohong Chen, Jun Sun, Meng Sun, Jingyi Wang
Towards Concolic Testing For Hybrid Systems, Pingfan Kong, Yi Li, Xiaohong Chen, Jun Sun, Meng Sun, Jingyi Wang
Research Collection School Of Computing and Information Systems
Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate whether we can combine random sampling and symbolic execution in order to effectively verify hybrid systems. We identify a sufficient condition under which such a combination is more effective than random sampling. Furthermore, we analyze different strategies of combining random sampling and symbolic execution and propose an algorithm which allows us to dynamically switch between them so as to reduce the overall cost. Our method has been implemented as a web-based checker named …
Designing Minimal Effective Normative Systems With The Help Of Lightweight Formal Methods, Jianye Hao, Eunsuk Kang, Jun Sun, Daniel Jackson
Designing Minimal Effective Normative Systems With The Help Of Lightweight Formal Methods, Jianye Hao, Eunsuk Kang, Jun Sun, Daniel Jackson
Research Collection School Of Computing and Information Systems
Normative systems are an important approach to achieving effective coordination among (often an arbitrary number of) agents in multiagent systems. A normative system should be effective in ensuring the satisfaction of a desirable system property, and minimal (i.e., not containing norms that unnecessarily over-constrain the behaviors of agents). Designing or even automatically synthesizing minimal effective normative systems is highly non-trivial. Previous attempts on synthesizing such systems through simulations often fail to generate normative systems which are both minimal and effective. In this work, we propose a framework that facilitates designing of minimal effective normative systems using lightweight formal methods. Given …
Repmatch: Robust Feature Matching And Pose For Reconstructing Modern Cities, Wen-Yan Lin, Siying Liu, Minh N. Do, Ping Tan, Jiangbo Lu
Repmatch: Robust Feature Matching And Pose For Reconstructing Modern Cities, Wen-Yan Lin, Siying Liu, Minh N. Do, Ping Tan, Jiangbo Lu
Research Collection School Of Computing and Information Systems
A perennial problem in recovering 3-D models from images is repeated structures common in modern cities. The problem can be traced to the feature matcher which needs to match less distinctive features (permitting wide-baselines and avoiding broken sequences), while simultaneously avoiding incorrect matching of ambiguous repeated features. To meet this need, we develop RepMatch, an epipolar guided (assumes predominately camera motion) feature matcher that accommodates both wide-baselines and repeated structures. RepMatch is based on using RANSAC to guide the training of match consistency curves for differentiating true and false matches. By considering the set of all nearest-neighbor matches, RepMatch can …
Developers’ Perceptions On Object-Oriented Design And Architectural Roles, Maurício Aniche, Marco Aurélio Gerosa, Christoph Treude
Developers’ Perceptions On Object-Oriented Design And Architectural Roles, Maurício Aniche, Marco Aurélio Gerosa, Christoph Treude
Research Collection School Of Computing and Information Systems
Software developers commonly rely on well-known software architecture patterns, such as MVC, to build their applications. In many of these patterns, classes play specific roles in the system, such as Controllers or Entities, which means that each of these classes has specific characteristics in terms of object-oriented class design and implementation. Indeed, as we have shown in a previous study, architectural roles are different from each other in terms of code metrics. In this paper, we present a study in a software development company in which we captured developers’ perceptions on object-oriented design aspects of the architectural roles in their …
An Adaptability-Driven Model And Tool For Analysis Of Service Profitability, Eng Lieh Ouh, Jarzabek Stan
An Adaptability-Driven Model And Tool For Analysis Of Service Profitability, Eng Lieh Ouh, Jarzabek Stan
Research Collection School Of Computing and Information Systems
Profitability of adopting Software-as-a-Service (SaaS) solutions forexisting applications is currently analyzed mostly in informal way. Informalanalysis is unreliable because of the many conflicting factors that affect costs andbenefits of offering applications on the cloud. We propose a quantitative economicmodel for evaluating profitability of migrating to SaaS that enables potentialservice providers to evaluate costs and benefits of various migration strategiesand choices of target service architectures. In previous work, we presented arudimentary conceptual SaaS economic model enumerating factors that have todo with service profitability, and defining qualitative relations among them. Aquantitative economic model presented in this paper extends the conceptualmodel with equations …
Interactive Teachable Cognitive Agents: Smart Building Blocks For Multiagent Systems, Budhitama Subagdja, Ah-Hwee Tan
Interactive Teachable Cognitive Agents: Smart Building Blocks For Multiagent Systems, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Developing a complex intelligent system by abstracting their behaviors, functionalities, and reasoning mechanisms can be tedious and time consuming. In this paper, we present a framework for developing an application or software system based on smart autonomous components that collaborate with the developer or user to realize the entire system. Inspired by teachable approaches and programming-by-demonstration methods in robotics and end-user development, we treat intelligent agents as teachable components that make up the system to be built. Each agent serves different functionalities and may have prebuilt operations to accomplish its own design objectives. However, each agent may also be equipped …
A Novel Hierarchical Bag-Of-Words Model For Compact Action Representation, Qianru Sun, Qianru, Hong Liu, Hong Liu, Liqian Ma, Tianwei Zhang
A Novel Hierarchical Bag-Of-Words Model For Compact Action Representation, Qianru Sun, Qianru, Hong Liu, Hong Liu, Liqian Ma, Tianwei Zhang
Research Collection School Of Computing and Information Systems
Bag-of-Words (BOW) histogram of local space-time features is very popular for action representation due to its high compactness and robustness. However, its discriminant ability is limited since it only depends on the occurrence statistics of local features. Alternative models such as Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV) include more information by aggregating high-dimensional residual vectors, but they suffer from the problem of high dimensionality for final representation. To solve this problem, we novelly propose to compress residual vectors into low-dimensional residual histograms by the simple but efficient BoW quantization. To compensate the information loss of this …
Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan
Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan
Research Collection School Of Computing and Information Systems
We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate joint mining of sensor & wearable data to capture a shopper’s product level interactions, and (b) judicious triggering of power-hungry wearable sensors (e.g., camera) to capture only relevant portions of a shopper’s in-store activities. To explore the feasibility of our vision, we conducted experiments with 5 smartwatch-wearing users who interacted with objects placed on cupboard racks in our lab (to …
All Your Sessions Are Belong To Us: Investigating Authenticator Leakage Through Backup Channels On Android, Guangdong Bai, Jun Sun, Jianliang Wu, Quanqi Ye, Li Li, Jin Song Dong, Shanqing Guo
All Your Sessions Are Belong To Us: Investigating Authenticator Leakage Through Backup Channels On Android, Guangdong Bai, Jun Sun, Jianliang Wu, Quanqi Ye, Li Li, Jin Song Dong, Shanqing Guo
Research Collection School Of Computing and Information Systems
Security of authentication protocols heavily relies on the confidentiality of credentials (or authenticators) like passwords and session IDs. However, unlike browser-based web applications for which highly evolved browsers manage the authenticators, Android apps have to construct their own management. We find that most apps simply locate their authenticators into the persistent storage and entrust underlying Android OS for mediation. Consequently, these authenticators can be leaked through compromised backup channels. In this work, we conduct the first systematic investigation on this previously overlooked attack vector. We find that nearly all backup apps on Google Play inadvertently expose backup data to any …
Gpu Accelerated On-The-Fly Reachability Checking, Zhimin Wu, Yang Liu, Jun Sun, Jianqi Shi, Shengchao Qin
Gpu Accelerated On-The-Fly Reachability Checking, Zhimin Wu, Yang Liu, Jun Sun, Jianqi Shi, Shengchao Qin
Research Collection School Of Computing and Information Systems
Model checking suffers from the infamous state space explosion problem. In this paper, we propose an approach, named GPURC, to utilize the Graphics Processing Units (GPUs) to speed up the reachability verification. The key idea is to achieve a dynamic load balancing so that the many cores in GPUs are fully utilized during the state space exploration.To this end, we firstly construct a compact data encoding of the input transition systems to reduce the memory cost and fit the calculation in GPUs. To support a large number of concurrent components, we propose a multi-integer encoding with conflict-release accessing approach. We …
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 …
Direct Structure Estimation For 3d Reconstruction, Nianjuan Jiang, Wen-Yan Lin, Minh N. Do, Jiangbo Lu
Direct Structure Estimation For 3d Reconstruction, Nianjuan Jiang, Wen-Yan Lin, Minh N. Do, Jiangbo Lu
Research Collection School Of Computing and Information Systems
Most conventional structure-from-motion (SFM) techniques require camera pose estimation before computing any scene structure. In this work we show that when combined with single/multiple homography estimation, the general Euclidean rigidity constraint provides a simple formulation for scene structure recovery without explicit camera pose computation. This direct structure estimation (DSE) opens a new way to design a SFM system that reverses the order of structure and motion estimation. We show that this alternative approach works well for recovering scene structure and camera poses from sideway motion given planar or general man-made scenes.
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 …
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 …
Cama: Efficient Modeling Of The Capture Effect For Low Power Wireless Networks, Behnam Dezfouli, Marjan Radi, Kamin Whitehouse, Shukor Abd Razak, Hwee-Pink Tan
Cama: Efficient Modeling Of The Capture Effect For Low Power Wireless Networks, Behnam Dezfouli, Marjan Radi, Kamin Whitehouse, Shukor Abd Razak, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Network simulation is an essential tool for the design and evaluation of wireless network protocols, and realistic channel modeling is essential for meaningful analysis. Recently, several network protocols have demonstrated substantial network performance improvements by exploiting the capture effect, but existing models of the capture effect are still not adequate for protocol simulation and analysis. Physical-level models that calculate the signal-to-interference-plus-noise ratio (SINR) for every incoming bit are too slow to be used for large-scale or long-term networking experiments, and link-level models such as those currently used by the NS2 simulator do not accurately predict protocol performance. In this article, …
Robust Tracking With Discriminative Ranking Middle-Level Patches, Hong Liu, Zilin Liang, Qianru Sun
Robust Tracking With Discriminative Ranking Middle-Level Patches, Hong Liu, Zilin Liang, Qianru Sun
Research Collection School Of Computing and Information Systems
The appearance model has been shown to be essential for robust visual tracking since it is the basic criterion to locating targets in video sequences. Though existing tracking-by-detection algorithms have shown to be greatly promising, they still suffer from the drift problem, which is caused by updating appearance models. In this paper, we propose a new appearance model composed of ranking middle-level patches to capture more object distinctiveness than traditional tracking-by-detection models. Targets and backgrounds are represented by both low-level bottom-up features and high-level top-down patches, which can compensate each other. Bottom-up features are defined at the pixel level, and …
Los And Nlos Classification For Underwater Acoustic Localization, Roee Diamant, Hwee-Pink Tan, Lutz Lampe
Los And Nlos Classification For Underwater Acoustic Localization, Roee Diamant, Hwee-Pink Tan, Lutz Lampe
Research Collection School Of Computing and Information Systems
The low sound speed in water makes propagation delay (PD)-based range estimation attractive for underwater acoustic localization (UWAL). However, due to the long channel impulse response and the existence of reflectors, PD-based UWAL suffers from significant degradation when PD measurements of nonline-of-sight (NLOS) communication links are falsely identified as line-of-sight (LOS). In this paper, we utilize expected variation of PD measurements due to mobility of nodes and present an algorithm to classify the former into LOS and NLOS links. First, by comparing signal strength-based and PD-based range measurements, we identify object-related NLOS (ONLOS) links, where signals are reflected from objects …