ppxscal/embedding_network huggingface dataset:
Agent modeling with individual human behaviors
Authors: Hiromitsu Hattori, Yuu Nakajima, Toru Ishida
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

The challenge presented in this paper is to obtain a human-like individual behavior model by using participatory modeling technology in the traffic domain. We show a methodology that can elicit prior knowledge for explaining human driving behavior in specific environments, and then construct a driving behavior model based on of a set of prior knowledge. Since, in the real world, human drivers often perform unintentional actions, and occasionally they have no logical reason for their driving actions, we are forced to construct a behavior model with an insufficient amount of prior knowledge. To construct an individual driving behavior model with insufficient knowledge, we take the approach of using knowledge from others to complement the lack of knowledge from oneself.
Principal component regression for data containing outliers and missing elements
Authors: Sven Serneels, Tim Verdonck
Venue: Computational Statistics Data Analysis (2009)

A methodology is presented to construct an expectation robust algorithm for principal component regression. The presented method is the first multivariate regression method which can resist outliers and which can cope with missing elements in the data simultaneously. Simulations and an example illustrate the good statistical properties of the method.
Resolution-Based Argumentation Semantics
Authors: Pietro Baroni, Massimiliano Giacomin
Venue: Proceeding of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008 (2008)

In a recent work we have proposed a comprehensive set of evaluation criteria for argumentation semantics and we have shown that none of a set of semantics including both traditional and recent proposals is able to meet all criteria. This naturally raises the question whether such criteria are actually satisfiable altogether: this paper provides a positive answer to this question by introducing a new family of argumentation semantics, called resolution-based and showing that all the desirable criteria are met by the resolution-based version of grounded semantics.
Mining statistical correlations with applications to software analysis
Authors: Inderjit S. Dhillon, Emmett Witchel
Venue:  (2008)

Machine learning, data mining, and statistical methods work by representing real-world objects in terms of feature sets that best describe them. This thesis addresses problems related to inferring and analyzing correlations among such features. The contributions of this thesis are two-fold: we develop formulations and algorithms for addressing correlation mining problems, and we also provide novel applications of our methods to statistical software analysis domains.We consider problems related to analyzing correlations via unsupervised approaches, as well as algorithms that infer correlations using fully-supervised or semi-supervised information. In the context of correlation analysis, we propose the problem of correlation matrix clustering which employs a k-means style algorithm to group sets of correlations in an unsupervised manner. Fundamental to this algorithm is a measure for comparing correlations called the log-determinant (LogDet) divergence, and a primary contribution of this thesis is that of interpreting and analyzing this measure in the context of information theory and statistics. Additionally based on the LogDet divergence, we present a metric learning problem called Information-Theoretic Metric Learning which uses semi-supervised or fully-supervised data to infer correlations for parametrization of a Mahalanobis distance metric. We also consider the problem of learning Mahalanobis correlation matrices in the presence of high dimensions when the number of pairwise correlations can grow very large.In validating our correlation mining methods, we consider two in-depth and real-world statistical software analysis problems: software error reporting and unit test prioritization. In the context of Clarify, we investigate two types of correlation mining applications: metric learning for nearest neighbor software support, and decision trees for error classification. We show that our metric learning algorithms can learn program-specific similarity models for more accurate nearest neighbor comparisons. In the context of decision tree learning, we address the problem of learning correlations with associated feature costs, in particular, the overhead costs of software instrumentation. As our second application, we present a unit test ordering algorithm which uses clustering and nearest neighbor algorithms, along with a metric learning component, to efficiently search and execute large unit test suites.
Determining the optimal percent of negative examples used in training the multilayer perceptron neural networks
Authors: Cosmin Cernazanu-Glavan, Stefan Holban
Venue: Proceedings of the 10th WSEAS international conference on Neural networks (2009)

Neural networks prove their usefulness mainly in solving difficult problems of estimation, identification, prediction or complex optimization. For finding a correct solution for the problem, the network must be trained with a comprehensive dataset, able to completely cover all the possible situations that may appear when using it. Most often, the training set includes only positive examples which may change the neural network into an over confident network in solving the problems. A simple solution for this problem is the introduction of negative examples in the training set. Through this procedure, the network will be prepared for the cases it has not been trained for. The present article aims at finding the exact percentage of negative examples that can successfully be used in training any multilayer perceptron neural network. For this purpose, two databases completely different in content and uniformity between examples have been chosen. There have been made successive trainings for exactly determining the percentage. Moreover, there has been suggested a new calculus formula for calculating a network performance based on its impact on a complex production set.
A Common Assessment Results Model for ILE
Authors: Guillaume Durand, Christian Martel
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

Many Interactive Learning Environments (ILE) produce assessment results. Norms exist to define how these results can be used, obtained and manipulated within ILE. AICC, certainly the most known, defines how to communicate assessment results and more widely, datas from a Learning Object (LO) to a Learning Management System (LMS). However, AICC and the other norms don't define what an assessment result has to look like. Moreover, assessment results are often semantically poor and expressed in a ILE-specific formalism. A common results model would improve the interoperability between ILE but also, if it is rich enough, clarify the meaning of assessment results. In this paper, such a common results model is introduced. At first its conceptual model is exposed, then its information model is described.
Learning styles: novices decide
Authors: Carol Zander, Lynda Thomas, Beth Simon, Laurie Murphy, Renée McCauley, Brian Hanks, Sue Fitzgerald
Venue: Proceedings of the 14th annual ACM SIGCSE conference on Innovation and technology in computer science education (2009)

We present what students say about their preferred learning style to succeed in introductory programming. Using the Felder-Silverman learning styles, students contrasted the 'best' learning style for programming with the 'best' for learning mathematics. Overall students believe that while they learn mathematics using a reflective style, learning to program is significantly more active. They also believe learning mathematics has a strong verbal component, whereas learning programming is primarily visual.
Using a Blended Learning Approach to Support Problem-Based Learning with First Year Students in Large Undergraduate Classes
Authors: Ron Oliver
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

This paper describes an exploration of the use of a technology-enabled problem-based learning approach undertaken with a group of first year students. The exploration sought to determine students' responses to the learning setting and to investigate its ability to successfully cater for the diverse needs and expectations of the subjects.
Optimal Auction Model Analysis and Mechanism Design of Indivisible Goods
Authors: Congjun Rao, Yong Zhao, Huiling Bao, Qing Wang
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this paper, an optimal auction model that maximizes the seller's expected utility is proposed for a kind of indivisible goods. Firstly, the correlation of bidders' valuations and the asymmetry of the private information are analyzed. Then, the properties of the optimal auction are given. Thirdly, the feasibility of optimal auction is discussed. Finally, an example of the optimal auction is given to show how to apply the discriminatory auction to realize the optimal auction mechanism. Therefore, this paper effectively generalizes the auction models with single-unit.
Practical Secure Evaluation of Semi-private Functions
Authors: Annika Paus, Ahmad-Reza Sadeghi, Thomas Schneider
Venue: Proceedings of the 7th International Conference on Applied Cryptography and Network Security (2009)

Two-party Secure Function Evaluation (SFE) is a very useful cryptographic tool which allows two parties to evaluate a function known to both parties on their private (secret) inputs. Some applications with sophisticated privacy needs require the function to be known only to one party and kept private (hidden) from the other one. However, existing solutions for SFE of private functions (PF-SFE) deploy Universal Circuits (UC) and are still very inefficient in practice.In this paper we bridge the gap between SFE and PF-SFE with SFE of what we call semi-private functions (SPF-SFE), i.e., one function out of a given class of functions is evaluated without revealing which one.We present a general framework for SPF-SFE allowing a fine-grained trade-off and tuning between SFE and PF-SFE covering both extremes. In our framework, semi-private functions can be composed from several privately programmable blocks (PPB) which can be programmed with one function out of a class of functions. The framework allows efficient and secure embedding of constants into the resulting circuit to improve performance. To show practicability of the framework we have implemented a compiler for SPF-SFE based on the Fairplay SFE framework.SPF-SFE is sufficient for many practically relevant privacy-preserving applications, such as privacy-preserving credit checking which can be implemented with our framework and compiler as described in the paper.
Fault Diagnosis of Analog IC Based on Wavelet Neural Network Ensemble
Authors: Lei Zuo, Ligang Hou, Wuchen Wu, Jinhui Wang, Shuqin Geng
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

A new method of analog IC fault diagnosis is proposed in this paper, which is based on wavelet neural network ensemble (WNNE) technique and Adaboost algorithm. This makes the way of the directory be of use in fault, and enhances the validity of the fault diagnosis. Using wavelet decomposition as a tool for extracting feature, Then, after training the WNNE by faulty feature vectors, the fault diagnosis of a radar scanning circuit is implemented with this new method. The simulation results show that the new method is more effective than the traditional wavelet neural network (WNN) method.
Non-modular-operation algorithms in residue number system based on mixed radix conversion
Authors: Pepe Siy
Venue:  (2008)

The Residue Number System (RNS) has attracted many researchers as basis for the computational hardware in the last 60 years with an increasing interest in the last decade, in particular for applications like Digital Signal Processing (DSP) and Fast Fourier Transform (FFT). This system has a very big advantage in the modular arithmetic operations like addition, subtraction and multiplication since this system provides the ability to add, subtract or multiply without the need to wait for the carry propagation as required by the weighted number systems. The non modular operation such as sign detection, division, conversion to weighted number system, and overflow detection presented a challenge to researchers. A lot of research has been done to address these issues. In this dissertation, we present an efficient and simplified algorithm for the RNS conversion to weighted number system. The algorithm is based on the Mixed Radix conversion (MRC). The new algorithm simplifies the hardware implementation and improves the speed of conversion by replacing a number of multiplication operations with small look-up tables. The algorithm requires less ROM size compared to those required by existing algorithms. Also, we present a generic sign detection algorithm based on Mixed Radix Conversion algorithm MRC-II and an optimum algorithm for sign detection based on a special moduli set. Finally, we present two new algorithms. One for RNS Scaling and the other is for RNS division. The new scaling algorithm is based on Mixed Radix Conversion MRC-II. The algorithm utilizes a simplified base extension process that works on smaller moduli set utilizing look-up tables. The algorithm works with a single moduli or a product of moduli as divisors or scaling factors. The new division algorithm presented in this paper addresses the long standing problem of RNS division by breaking the process into division remainder zero and a small correction process.
Design and implementation of the via (vision interactive architecture)
Authors: Pusik Park, Hangyun Jung, Daekyo Shin, Kitaeg Lim, Jongchan Choi
Venue: Proceedings of the 2nd conference on European computing conference (2008)

Recently, there is a trend of interacting with electronics by touchable or noncontact UIs which are different from the legacy UI such as mice and keyboards. In addition, a small display and a computer shaped like a dummy box never kindle users' emotion and, what is more, the appreciative users don't overlook a counterfeit world. There are several researches for well-appointed interaction. Merits and demerits of legacy researches had been analyzed and our platform was designed with large displays, multi-touchable screen controller based-on infra-red, hand tracking, gesture recognition, and standardized middleware protocol for distributed environment and various services and then was implemented on x86-based computing resources.
LCA and LCC Data Semantics Sharing across Product Lifecycle Processes
Authors: Q. Z. Yang, C. Y. Miao, Y. Zhang, R. Gay
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

Product sustainability approaches, such as life cycle assessment (LCA) and life cycle costing (LCC), are becoming increasingly critical to influence product decisions for all lifecycle stages. However, LCA and LCC methods must be integrated into the product lifecycle processes for sharing their data semantics with other applications in order to support decisions in the technical, ecological and economic aspects of products. Semantics sharing has raised big challenges in understanding and interpreting the intended meaning of LCA, LCC and other product data. This paper presents an ontology-driven, standard-based approach to the explicit capture and reuse of product data semantics for heterogeneous information sharing across lifecycle processes in computer aided design, quality assurance, LCA and LCC analysis, and product development process management. The approach incorporates object-based product data modeling, interoperability standardization, and ontology engineering to address the product data semantics sharing issues. A software implementation of the approach with a use scenario is also discussed in the paper.
A study of HMM-based bandwidth extension of speech signals
Authors: Geun-Bae Song, Pavel Martynovich
Venue: Signal Processing (2009)

In this paper, we investigate a representative statistical method for artificial bandwidth extension: the hidden Markov model (HMM) based method. In particular, we are interested in objectively quantifying its performance using both static and dynamic measures. The Gaussian mixture model (GMM) based method is presented as a reference method for the performance test under various HMM configurations. We also reasonably claim that a general approach using Baum-Welch re-estimation algorithm performs better than the existing training algorithm suggested by Jax. Accordingly, it is used as the basic algorithm for the training of HMM model. Test results show that the static performance of HMM-based method depends only on the total number of Gaussian components of HMM model, while its dynamic performance depends dominantly on the number of states of the model. More specifically, it is also observed that the GMM-based method is quite comparable with the HMM-based one in static performance, but, in dynamic performance, the latter outperforms the former even with higher computational complexities.
Postnominal prepositional phrase attachment in proteomics
Authors: Jonathan Schuman, Sabine Bergler
Venue: Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis (2006)

We present a small set of attachment heuristics for postnominal PPs occurring in full-text articles related to enzymes. A detailed analysis of the results suggests their utility for extraction of relations expressed by nominalizations (often with several attached PPs). The system achieves 82% accuracy on a manually annotated test corpus of over 3000 PPs from varied biomedical texts.
Neutral Differential Systems with Impulse and Delay
Authors: Junhao Hu, Huafeng Chen
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

In this paper, we discuss a class of impulsive partial neutral functional differential inclusions with infinite delay. The existence of mild solutions of these neutral differential systems with impulse and delay is obtained by using the new nonlinear alternatives of Leray-Schauder type.
Digging Into AutoCAD Map 3D 2008 - Level 1 Training
Authors: Rick Ellis, Russell Martin
Venue:  (2007)

Digging Into AutoCAD Map 3D 2008 is an introduction to the most useful features of this powerful, yet often overlooked Autodesk software. The authors are skilled instructors with direct industry and classroom experience. You will learn the best procedures for over a dozen common tasks, including: Repairing Incomplete or Inaccurate Geometry Collecting Data from Different Sources Attaching Data to AutoCAD Objects Automating Annotation of AutoCAD Objects Using Better COGO Tools Using Multiple Coordinate Systems Inserting Rectified Raster Images Using Contour Data from GIS Sources Learning the Basics of Queries Quickly and Easily Mapping your Data Exporting to DWF and other Software This book is suitable for anyone from general AutoCAD users to seasoned professionals in the Civil Engineering, Surveying, Mapping, Land Development or GIS industries. Perfect for users of stand-alone Map 3D, and since Map 3D is a standard part of both Land Desktop and Civil 3D, this book can also take users of these popular software platforms to a whole new level of productivity. Features and Benefits of All Cadapult Books: Cadapult Software Solutions is an Authorized Autodesk Publisher Lessons build upon one another using a contiguous dataset, providing consistency through the entire book Skills learned through easy-to-follow, step-by-step procedures Sequential exercises that follow one logical, real-world example project Flexible datasets allow you to skip ahead to a relevant lesson, or use as an on-demand reference guide Detailed explanations and chapter summaries facilitate learning and retention Written by instructors with decades of industry and teaching experience Tested and refined in classrooms with a broad cross-section of students
Microsoft Word 2007 Advanced Quick Source Guide
Authors: Quick Source
Venue:  (2007)

Do you need an easy-to-follow software reference right at your fingertips? Our 6 page, tri-fold guides are the answer. These quick reference guides include step by step instructions to help users to learn software features more quickly, as well as full color screen graphics to highlight important features. Quick Source reference guides also include the software program s NEW features and gives you quick access to time saving shortcuts. The Microsoft Word 2007 Advanced Quick Source guide is a clear and concise tool for learning the advanced features in Word 2007. Features contained in this guide include NEW features such as Changing Trust Center Settings, and Inspecting a Document. It also includes Creating Text Columns, Inserting a Watermark, Creating a New Text Style, Applying a Theme, Editing a Graphic, Inserting a Table, Creating a Chart, Creating a Master Document, Marking an Index Entry, Creating a Password, and much more. 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
Deciding Reachability in Mobile Ambients with Name Restriction
Authors: Giorgio Delzanno, Roberto Montagna
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

We investigate the reachability problem for fragments of the Mobile Ambients, a powerful model for distributed and mobile computation. By using a connection with associative-commutative term rewriting, we prove that reachability is decidable in the open-free fragment of pure Mobile Ambients with name restriction and weak reduction semantics. Processes in this model have three sources of infiniteness: depth of ambients, width of parallel composition, and number of restricted names. Our work extends similar results obtained for public fragments of Mobile Ambients.
Extraction of Coronary Vascular Tree and Myocardial Perfusion Data from Stacks of Cryomicrotome Images
Authors: Pepijn Horssen, Jeroen P. Wijngaard, Froukje Nolte, Imo Hoefer, Rene Haverslag, Jos A. Spaan, Maria Siebes
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

Computational models of myocardial perfusion in normal as well as pathological conditions have contributed to recent advances in the personalized diagnosis and treatment of cardiovascular disease.. For the coronary circulation we attempt to develop a vascular tree model based on realistic replicas from the intramural vessels in hearts from animals and man. These virtual replicas are obtained by means of a novel imaging cryomicrotome producing a stack of 4000 images of 2000x2000 pixels. This paper describes the methods developed for segmentation of the images resulting in a 3D virtual vascular model. Also the methods for fluorescent microsphere detection are described used to validate the models in prediction of flow distribution over the walls of the heart chambers. Special attention is provided to the detection of collaterals and collateral flow.