ppxscal/embedding_network huggingface dataset:
Design strategies for an artificial neural network based algorithm for automatic incident detection on major arterial streets
Authors: Albert Gan
Venue:  (2008)

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our nation&rsquo;s highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing.This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR).The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.
EVALPSN-Based Process Control in Brewery Plants
Authors: Sheng-Luen Chung, Yen-Hung Lai
Venue: Proceeding of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005 (2005)

Process control in a brewery plant deals with the open/close decisions of valves for pipelines in the brewery to meet the service requests of filtration and CIP (Clean in pipe) processes. In order to maximize concurrency among different process requests, it is desired that non-conflicting processes be enabled as much as possible. By exploring its similarity to that of railway interlocking policy, this paper adopts an EVALPSN-based concurrency control approach proposed by Nakamatsu et al. In doing so, system configuration of the system in terms of sub-process and valves for all the processes involved is first tabulated. EVALPSN-statements that reflect the pipeline configuration and imposed safety constraints of mutual exclusive usage of sub-processes are then systematically constructed. In deriving a decision as to either granting or denying a service request, these EVALPSN statements are executed in a PLC-based implementation that is connected to both human operator's input requests as well as sensor status updates. Successfully implemented for a local brewery plant in Taiwan, the EVALPSN-based decision approach is shown to have the advantage in general pipeline control applications.
Parallel Calculating of the Goal Function in Metaheuristics Using GPU
Authors: Wojciech Bożejko, Czesław Smutnicki, Mariusz Uchroński
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

We consider a metaheuristic optimization algorithm which uses single process (thread) to guide the search through the solution space. Thread performs in the cyclic way (iteratively) two main tasks: the goal function evaluation for a single solution or a set of solutions and management (solution filtering and selection, collection of history, updating). The latter task takes statistically 1-3% total iteration time, therefore we skip its acceleration as useless. The former task can be accelerated in parallel environments in various manners. We propose certain parallel small-grain calculation model providing the cost optimal method. Then, we carry out an experiment using Graphics Processing Unit (GPU) to confirm our theoretical results. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Microsoft Visual C# 2008 Step by Step
Authors: John Sharp
Venue:  (2007)

Get the hands-on, step-by-step guide to learning the latest enhancements in Microsoft Visual C# 2008. Visual C#, one of the tools in Microsoft Visual Studio 2008, is a modern programming language designed to deliver a productive environment for creating business frameworks and reusable object-oriented components. Whether you re a beginning programmer or new to the Visual C# programming language, you ll learn how to use the fundamental features of Visual Studio 2008 and gain a basic understanding of the latest enhancement of the Visual C# language. You ll work at your own pace through hands-on, learn-by-doing exercises, get started creating components and working Windows applications, and build your knowledge as you start creating your first Visual C# based applications. You ll also explore how to create data management and Web-based applications. In each chapter, work through learn-by-doing exercises that demonstrate how, when, and why to use the many features of the Visual C# rapid application development environment. Includes a companion CD with code samples, data sets, and a fully searchable eBook.Features easy-to-follow, logically planned lessons to help you learn at your own paceHelps you gain a solid understanding of how to use the fundamental features of Visual C# 2008Includes a companion CD with code samples, data sets, and a fully searchable eBook
Decision tree and instance-based learning for label ranking
Authors: Weiwei Cheng, Jens Hühn, Eyke Hüllermeier
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings.
Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong
Authors: Liming Jiang, Mingsheng Liao, Hui Lin, Limin Yang
Venue: International Journal of Remote Sensing (2009)

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up-to-date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub-pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS-2 SAR data. Validated by reference data derived from high-resolution colour-infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud-prone and rainy areas.
On joint coding and combinatorial optimization in communication networks
Authors: Micah Adler, Donald Towsley
Venue:  (2008)

Traditionally, information flows in communication networks are treated as commodity flows. Network coding is a new technique that allows nodes to manipulate the information content arbitrarily inside the network. Essentially, it raises a new joint coding and routing optimization question that needs to be addressed by exploiting both information theory and network combinatorics. This work focuses on the joint coding/routing optimization for throughput, energy consumption, and robustness of wired and wireless networks. We study three sub-problems within this general setup: minimize communication cost of collecting correlated data through a network at a sink; maximize the throughput of multi-pair independent unicast traffic in wireless networks; and maximize the data utility in random, unreliable wireless data collection networks. For the correlated data gathering problem, we introduce an original concept called distance entropy that characterizes the spatial distribution of information at networked sources and shows that it is a meaningful metric as the tight lower bound of the minimum communication cost for collecting the sources at a single sink. We also propose a practical coding/routing algorithm called Hierarchical Difference Broadcasting (HDB) that achieves an order optimal performance yet simple to implement. For the second multi-pair unicast problem, we characterize the throughput capacity order and show coding combined with wireless broadcasting provides no order difference improvement. We also derive tighter bounds for the constant factor. For the third problem, we model the scheduling problem into a Multi-Armed Bandit (MAB) problem and derive the optimal solution using Gittins Indices.
Designing hardware for finite synchronous state machines using quantum inspired evolution
Authors: Marcos Paulo Mello Araujo, Nadia Nedjah, Luiza De Macedo Mourelle
Venue: International Journal of Innovative Computing and Applications (2008)

Synchronous finite state machines are very important for digital sequential systems. Among other important aspects, they represent a powerful way for synchronising hardware components so that these components may cooperate adequately in the fulfilment of the main objective. In this paper, we propose to use an evolutionary methodology inspired from quantum computation to yield a concise and efficient evolvable hardware that implements the state machine control logic. The evolved circuits are promising.
Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems
Authors: Rafael Alcalá, Jesús Alcalá-Fdez, María José Gacto, Francisco Herrera
Venue: Applied Intelligence (2009)

One important Artificial Intelligence tool for automatic control is the use of fuzzy logic controllers, which are fuzzy rule-based systems comprising expert knowledge in form of linguistic rules. These rules are usually constructed by an expert in the field of interest who can link the facts with the conclusions. However, this way to work sometimes fails to obtain an optimal behaviour. To solve this problem, within the framework of Machine Learning, some Artificial Intelligence techniques could be successfully applied to enhance the controller behaviour. Rule selection methods directly obtain a subset of rules from a given fuzzy rule set, removing inefficient and redundant rules and, thereby, enhancing the controller interpretability, robustness, flexibility and control capability. Besides, different parameter optimization techniques could be applied to improve the system accuracy by inducing a better cooperation among the rules composing the final rule base. This work presents a study of how two new tuning approaches can be applied to improve FLCs obtained from the expert's experience in non trivial problems. Additionally, we analyze the positive synergy between rule selection and tuning techniques as a way to enhance the capability of these methods to obtain more accurate and compact FLCs. Finally, in order to show the good performance of these approaches, we solve a real-world problem for the control of a heating, ventilating and air conditioning system.
Effect of geoacoustic parameters uncertainties on acoustic transmission loss prediction
Authors: Gao Wei
Venue: WSEAS Transactions on Signal Processing (2009)

Geoacoustic parameters inverted from reverberation vertical correlation (RVC) are often directly used to predict the acoustic transmission loss (ATL) in shallow water. However, little work has been applied to the problem of quantifying uncertainty in predicted ATL produced by geoacoustic parameters uncertainties. In this paper, a posterior predictive probability analysis method (PPPAM) is first employed to evaluate the effects of geoacoustic parameters uncertainties inverted from RVC data on both coherent and incoherent ATL predictions. Where, the geoacoustic parameters uncertainties are characterized by their posterior probability distributions (PPD). And then the uncertainties of ATL prediction are analyzed quantitatively based on the posterior predictive probability distributions of ATL, which are the function of the PPDs of geoacoustic parameters and can be estimated using a Markov Chain Monte Carlo sampling method. Finally, the Yellow Sea Reverberation experimental results illustrate the PPPAM and show that: (1) in the range from 1km to 5 km, the mean values of 90% posterior credibility intervals (PCI) of coherent and incoherent ATL in frequency range of 500∼800Hz exceed 6dB and 3dB, respectively; (2) the coherent ATL are more difficult to predict near the positions of destructive interference of the normal modes. These results derived in this paper are helpful to evaluate and improve the detection and localization performance of sonar system.
Analyzing the performance of randomized information sharing
Authors: Prasanna Velagapudi, Oleg Prokopyev, Katia Sycara, Paul Scerri
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

In large, collaborative, heterogeneous teams, team members often collect information that is useful to other members of the team. Recognizing the utility of such information and delivering it efficiently across a team has been the focus of much research, with proposed approaches ranging from flooding to complex filters and matchmakers. Interestingly, random forwarding of information has been found to be a surprisingly effective information sharing approach in some domains. In this paper, we investigate this phenomenon in detail and show that in certain systems, random forwarding of information performs almost half as well as a globally optimal approach. We present analytic and empirical results comparing random methods with theoretically optimal sharing in small-worlds, scale-free, and random networks. In addition, we demonstrate a method for modeling real domains that allows our results to be applied toward estimating information sharing performance.
Signal manipulation based on perceptual thresholds enhances surface experience with common haptic device
Authors: Marco Vicentini, Debora Botturi
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

This paper presents evidences of the performance improvement due to perceptual-based force signal manipulation in an interaction task with an haptic device. We hypothesize that sub-threshold forces become perceivable when a vibration waveform is superimposed onto the force signal. We study the human capabilities in perception of harmonic forces, and we observe lower thresholds for sinusoidal waves than for onset force. Starting from these perceptual findings, we involve a frequency signal instead of a signal amplification to allow the tactile human sensors to sharpen the perception of sub-thresholds forces in a teleoperation task. We report on the results obtained from our experiments and on the method used to reach such goal.
An English Letter Recognition Algorithm Based Artificial Immune
Authors: Chunlin Liang, Lingxi Peng, Yindie Hong, Jing Wang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

In the study of letter recognition, the recognition accuracy is impacted by fonts and styles, which is the main bottleneck that the technology is applied. In order to enhance the accuracy, a letter recognition algorithm based artificial immune, referred to as LEBAI, is presented. Inspired by nature immune system, antibody cell (B-cell) population is evolved until the B-cell population is convergent through the learning of each training antigen and the memory cells pool is updated by the optimal B-cell. Finally, recognition is accomplished by memory cells. It is tested by the well-known letter recognition data set of UCI (University of California at Irvine). Compared with HSAC (Letter Recognition Using Holland-Style Adaptive Classifiers), LEBAI showed that recognition accuracy is increased from 82.7% to 95.58%. LEBAI achieves the same recognition accuracy for the letters of different fonts and styles, or stretched and distorted randomly.
ViewpointFace the inevitable, embrace parallelism
Authors: Anwar Ghuloum
Venue: Communications of the ACM (2009)

Hardware, software, and applications must all evolve in anticipation of the proliferation of parallelism.
Modeling the Behavior of Paraconsistent Robots
Authors: José Pacheco de Almeida Prado, Jair Minoro Abe, Alexandre Scalzitti
Venue: Proceeding of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005 (2005)

It can be observed that the number and the complexity of the application domains, where the Paraconsistent Annotated Logic has been used, have grown a lot in the last decade. This increase in the complexity of the application domain is an extra challenge for the designers of such systems, once there are not suitable computerized models for the representation and abstraction of the paraconsistent systems. This work proposes a new model to Paraconsistent Systems called Paraconsistent Finite Automata.
Thread and execution-context specific barriers via dynamic method versioning
Authors: Simon Wilkinson, Ian Watson
Venue: Proceedings of the 4th workshop on the Implementation, Compilation, Optimization of Object-Oriented Languages and Programming Systems (2009)

The insertion of read and write barriers into managed code is a typical runtime compilation task of a Virtual Machine. As part of our current work in applying Thread-Level Speculation (TLS) to Java, we insert a high density of barriers that are conditionally executed based on the identity of the running thread and current execution context. Rather than perform runtime tests, it is more profitable for our TLS system to maintain thread and execution-context specific versions of methods that are compiled with unconditional barriers, and then rely on modified dispatch semantics to ensure conditional execution. In this paper, we extract the method versioning system from our TLS implementation and present it in a general form, which we call Dynamic Method Versioning (DMV). DMV allows thread and execution-context specific versions of Java methods to be dynamically generated and compiled, with inter-version dispatch managed by a runtime policy. We describe our technique via its implementation within the Jikes Research Virtual Machine, and present initial measurements of its runtime overheads.
An efficient data dissemination scheme for spatial query processing
Authors: Kwangjin Park, Hyunseung Choo, Chong-Sun Hwang
Venue: Journal of Computer Science and Technology (2007)

Due to the personal portable devices and advances in wireless communication technologies, Location Dependent Information Services (LDISs) have received a lot of attention from both the industrial and academic communities. In LDISs, it is important to reduce the query response time, since a late query response may contain out-of-date information. In this paper, we study the issue of LDISs using a Voronoi Diagram. We introduce a new NN search method, called the Exponential Sequence Scheme (ESS), to support NN query processing in periodic broadcast environment. This paper aims to provide research directions towards minimizing both the access latency and energy consumption for the NN-query processing.
Technical Section: Persistence-based handle and tunnel loops computation revisited for speed up
Authors: Tamal K. Dey, Kuiyu Li
Venue: Computers and Graphics (2009)

Loops in surfaces associated with topological features such as handles and tunnels are important entities in many applications including surface parameterization, feature identification, and topological simplification. Recently, a persistent homology based algorithm has been proposed to compute them. The algorithm has several advantages including its simplicity, combinatorial nature and independence from computing other extra structures. In this paper, we propose changes to this loop computation algorithm based on some novel observations. These changes reduce the computation time of the algorithm dramatically. In particular, our experimental results show that the suggested changes achieve considerable speed up for large data sets without sacrificing loop qualities.
Simple versus optimal mechanisms
Authors: Jason D. Hartline, Tim Roughgarden
Venue: Proceedings of the tenth ACM conference on Electronic commerce (2009)

The monopolist's theory of optimal single-item auctions for agents with independent private values can be summarized by two statements. The first is from Myerson [8]: the optimal auction is Vickrey with a reserve price. The second is from Bulow and Klemperer [1]: it is better to recruit one more bidder and run the Vickrey auction than to run the optimal auction. These results hold for single-item auctions under the assumption that the agents' valuations are independently and identically drawn from a distribution that satisfies a natural (and prevalent) regularity condition. These fundamental guarantees for the Vickrey auction fail to hold in general single-parameter agent mechanism design problems. We give precise (and weak) conditions under which approximate analogs of these two results hold, thereby demonstrating that simple mechanisms remain almost optimal in quite general single-parameter agent settings.
Research on Multi-time Period Production Plan of Supply Chain under Demands Uncertainty
Authors: Hali Pang, Yongjun Wei, Shan Wang, Xiaobin Liu
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

Considering capacity constraints of manufacturer, a problem of multi-time period production planning of supply chain under customers' demands uncertainty was studied in this paper. According to characteristics of the problem, an optimal stochastic chance constraint programming model with objective of minimum costs was presented, and a real number matrix encoding and stochastic sampling constraints verifying based PSO algorithm was proposed. A numeric example is performed, and the results illustrate the feasibility and the effectiveness of proposed model and algorithm.