Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration and control for range imaging in mobile robot navigation

This paper addresses some issues in the development of sensor-based systems for mobile robot navigation which use range imaging sensors as the primary source for geometric information about the environment. In particular, we describe a model of scanning laser range cameras which takes into account the properties of the mechanical system responsible for image formation and a calibration procedure which yields improved accuracy over previous models. In addition, we describe an algorithm which takes the limitations of these sensors into account in path planning and path execution. In particular, range imaging sensors are characterized by a limited field of view and a standoff distance -- a minimum distance nearer than which surfaces cannot be sensed. These limitations can be addressed by enriching the concept of configuration space to include information about what can be sensed from a given configuration, and using this information to guide path planning and path following.
A practical guide to instructional design

A Practical Guide to Instructional Design shows how various principles that underlie effective facilitation of learning can be put into practice when designing instructional systems of all types. The book is divided into two main sections plus three appendices. Section One provides a general introduction to the principles and practice of instructional design, showing how instructional systems such as courses, course units, instructional exercises and courseware packages can be developed using a highly practical algorithmic approach. Section Two consists of three detailed case studies that describe how this approach was used to develop an open–learning course, a role–playing simulation game and a multimedia courseware package. The three appendices contain further material relating to these three systems, including the full package described in Case Study 3 – in the form of a CD–ROM. A Practical Guide to Instructional Design should be of interest to an extremely wide readership, including educationalists, academics, tertiary–level teachers, trainers and instructional designers of all types. A Practical Guide to Instructional Design is a companion volume to Facilitating Student Learning by Henry Ellington & Shirley Earl also published by Penerbit Universiti Teknologi Malaysia in 1999.
Metrics for object-oriented software design

Object oriented programming promises to move software development and maintenance from the current 'craft' environment to something more closely resembling conventional engineering. In order to fulfill this promise, it will require measures or metrics of the process. While software metrics are a generally desirable feature in the software management function, they are of special importance with a new technology such as the object-oriented approach due to the need to train current and new software engineers in generally accepted object-oriented principles. This dissertation first presents theoretical work that builds a suite of metrics for object-oriented design (OOD). In particular, these metrics are based upon measurement theory and ontology. In addition, they incorporate the insights of experienced object-oriented software developers. The proposed metrics are mathematically evaluated against a widely-accepted list of seven software metric evaluation criteria and the formal results of this evaluation are presented. A second goal of this work is to make these metrics machinable, i.e. build a tool that extracts these metrics from commercially used class libraries. A metrics extractor tool is built to extract metrics from C++ class libraries. The metrics extractor is currently configured to collect metrics from C++ code in UNIX workstation environments. A third goal of this dissertation is to relate the metrics gathered on commercial projects to performance indicators of managerial relevance such as development effort, extent of reuse, and productivity in order to determine the degree to which the proposed metrics are useful predictors of typical economic considerations faced by MIS managers. In the final phase of this dissertation, the organizational considerations of using metrics as part of the development process are examined. The message to practicing managers is that without adequate measures for OOD, organizations will find themselves increasingly unable to achieve the full benefits of object-oriented technology. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
A Study on Hybrid Recommendation System Based on Usage frequency for Multimedia Contents

Recent advancements in information technology and the Internet have caused an explosive increase in the information available and the means to distribute it. However, such information overflow has made the efficient and accurate search of information a difficulty for most users. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Libraries and information centers have been in the forefront to provide customized services to satisfy the user`s information needs under the changing information environment of today. The aim of this study is to propose an efficient information service for libraries and information centers to provide a personalized recommendation system to the user. The proposed method overcomes the weaknesses of existing systems, by providing a personalized hybrid recommendation method for multimedia contents that works in a large-scaled data and user environment. The system based on the proposed hybrid method uses an effective framework to combine Association Rule with Collaborative Filtering Method.
Machine Learning Techniques For Detecting Untrusted Pages on the Web

The Web is both an excellent medium for sharing information, as well as an attractive platform for delivering products and services. This platform is, to some extent, mediated by search engines in order to meet the needs of users seeking information. Search engines are the “dragons” that keep a valuable treasure: information. Many web pages are unscrupulous and try to fool search engines to get to the top of ranking. The goal of this project is to detect such spam pages. We will particularly consider content spam and link spam, where untrusted pages use link structure to increase their importance. We pose this as a machine learning problem and build a classifier to classify pages into two category - trustworthy and untrusted . We use different link features, in other words structural characteristics of the web graph and content based features, as input to the classifier. We propose link-based techniques and context based techniques for automating the detection of Web spam, a term referring to pages which use deceptive techniques to obtain undeservedly high scores in search engines. We propose Naive Bayesian Classifier to detect the content Spam and PageRank and TrustRank to detect the link spam.
Clickers in the classroom : can the use of electronic response systems improve student learning?

Lectures can be an efficient means to introduce new material, however they do not allow for all students to actively participate. Some students are engaged in answering questions and asking them during lecture, but many are not. They passively sit and observe. Without their participation it is difficult for the instructor to gauge their understanding. The purpose of this study was to see if the use of student electronic response systems could help students increase their understanding and retention of the biology curriculum. An electronic student response system involving clickers was used over the course of a semester. Exam scores, student interviews, and surveys were used to evaluate their effectiveness. A comparison of exam scores did not show a statistically significant difference. Student and teacher opinions were very positive as a large majority of student respondents said that clickers help to engage them in the learning process.
An intelligent co-reference resolver for Winograd schema sentences containing resolved semantic entities

There has been a lot of research in the field of artificial intelligence about thinking machines. Alan Turing proposed a test to observe a machine’s intelligent behaviour with respect to natural language conversation. The Winograd schema challenge is suggested as an alternative, to the Turing test. It needs inferencing capabilities, reasoning abilities and background knowledge to get the answer right. It involves a coreference resolution task in which a machine is given a sentence containing a situation which involves two entities, one pronoun and some more information about the situation and the machine has to come up with the right resolution of a pronoun to one of the entities. The complexity of the task is increased with the fact that the Winograd sentences are not constrained by one domain or specific sentence structure and it also contains a lot of human proper names. This modification makes the task of association of entities, to one particular word in the sentence, to derive the answer, difficult. I have developed a pronoun resolver system for the confined domain Winograd sentences. I have developed a classifier or filter which takes input sentences and decides to accept or reject them based on a particular criteria. Once the sentence is accepted. I run parsers on it to obtain the detailed analysis. Furthermore I have developed four answering modules which use world knowledge and inferencing mechanisms to try and resolve the pronoun. The four techniques I use are : ConceptNet knowledgebase, Search engine pattern counts,Narrative event chains and sentiment analysis. I have developed a particular aggregation mechanism for the answers from these modules to arrive at a final answer. I have used caching technique for the association relations I obtain for different modules so as to boost the performance. I run my system on the standard ’nyu dataset’ of Winograd sentences and questions. This dataset is then restricted by my classifier to 90 sentences. I evaluate my system on the 90 sentences’ dataset. When I compare my results against the state of the art system on the same dataset, I get nearly 4.5 % improvement in the restricted domain.
Receptive Field Weighted Regression

Abstract We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model as well as the parameters of the locally linear model itself are learned independently, i.e., without the need for competition or any other kind of communication. This characteristic is accomplished by incrementally minimizing a weighted penalized local cross validation error. As a result, we obtain a learning system that can allocate resources as needed while dealing with the bias-variance dilemma in a principled way. The spatial localization of the linear models increases robustness towards negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained in isolation, and as a learning system which profits from combining independent expert knowledge on the same problem. It illustrates the potential learning capabilities of purely local learning and offers an interesting and powerful approach to learning with receptive fields.
Web-based instruction

State or Country________________________Zip___________________ In 59 chapters, this 480-page volume, 7 x 10 inches, provides users of the Web with online sources, case studies, references, and other forms of information regarding ways to use this new technology to improve opportunities for learning at all levels. Nearly one hundred authors, representing institutions situated throughout the world, participated in the writing of this timely volume—using the Web to coordinate their efforts, thus assuring a remarkably complete treatment of this important topic. E-mail and World Wide Web addresses are given for all chapter authors, most of whom have active Web sites that can provide additional information to readers of the book. The complete Table of
Optimizing multiple pronunciation dictionary based on a confusability measure for non-native speech recognition

This paper addresses issues associated with an efficient pronunciation variation modeling for non-native automatic speech recognition (ASR), where non-native speech is mostly characterized by different pronunciation from native speech. In order to improve the performance of non-native ASR, a multiple pronunciation dictionary using an indirect data-driven approach is first proposed. However, this approach results in an increased search space for ASR decoding due to the increase of the dictionary size. Therefore, we propose a method for optimizing the size of the multiple pronunciation dictionary by removing some confusable pronunciation variants in the dictionary. To this end, a confusability measure is also proposed here based on the Levenshtein distance between two different pronunciation variants. In addition, the number of phonemes for each pronunciation variant is used to optimize the dictionary size. To investigate the effect of the proposed approach on ASR performance, English is selected as a target language and English utterances spoken by Koreans are considered as non-native speech. It is shown from the continuous non-native ASR experiments that the ASR system using the optimized multiple pronunciation dictionary can achieve the average word error rate reduction by 13.53% with less computational complexity by 21.10% relatively, compared with that using the multiple pronunciation dictionary without optimization.
Histogram remapping as a preprocessing step for robust face recognition

Image preprocessing techniques represent an essential part of a face recognition systems, which has a great impact on the performance and robustness of the recognition procedure. Amongst the number of techniques already presented in the literature, histogram equalization has emerged as the dominant preprocessing technique and is regularly used for the task of face recognition. With the property of increasing the global contrast of the facial image while simultaneously compensating for the illumination conditions present at the image acquisition stage, it represents a useful preprocessing step, which can ensure enhanced and more robust recognition performance. Even though, more elaborate normalization techniques, such as the multiscale retinex technique, isotropic and anisotropic smoothing, have been introduced to field of face recognition, they have been found to be more of a complement than a real substitute for histogram equalization. However, by closer examining the characteristics of histogram equalization, one can quickly discover that it represents only a specific case of a more general concept of histogram remapping techniques (which may have similar characteristics as histogram equalization does). While histogram equalization remapps the histogram of a given facial image to a uniform distribution, the target distribution could easily be replaced with an arbitrary one. As there is no theoretical justification of why the uniform distribution should be preferred to other target distributions, the question arises: how do other (non-uniform) target distributions influence the face recognition process and are they better suited for the recognition task. To tackle this issues, we present in this paper an empirical assessment of the concept of histogram remapping with the following target distributions: the uniform, the normal, the lognormal and the exponential distribution. We perform comparative experiments on the publicly available XM2VTS and YaleB databases and conclude that similar or even better recognition results that those ensured by histogram equalization can be achieved when other (non-uniform) target distribution are considered for the histogram remapping. This enhanced performance, however, comes at a price, as the nonuniform distributions rely on some parameters which have to be trained or selected appropriately to achieve the optimal performance.
Medical Image Set Compression Using Wavelet and Lifting Combined With New Scanning Techniques.

Today, hospitals are desirous of better methods for replacing their traditional film-based medical imaging. A major problem associated with a “film-less hospital” is the amount of digital image data that is generated and stored. Image compression must be used to reduce the storage size. This dissertation presents several techniques involving wavelet analysis, lifting, image prediction and image scanning to achieve an efficient diagnostically lossless compression for sets of medical images. This dissertation experimentally determines the optimal wavelet basis for medical images. Then, presents a new wavelet based prediction method for prediction of the intermediate images in a similar set of medical images. The technique uses the correlation between coefficients in the wavelet transforms of the image set to produce a better image prediction compared to direct image prediction. New methods for scanning similar sets of medical images are introduced in this dissertation. These methods significantly reduce the image edges needed for compression with wavelet lifting. Lifting plus new scanning methods have the following advantages: a) images in the set do not have to be the same size, b) additional compression is obtained from the continuous image background, and c) lifting produces better compression. The scanning techniques, introduced in this dissertation, reduce the number of edges. These scanning techniques separate the diagnostic foreground from the continuous background of each image in the set.
Introduction to Realistic Neural Modeling

urn:nbn:de:0009-3-2189 Abstract. This document corresponds to the tutorial on realistic neural modeling given by David Beeman at WAM-BAMM*05, the first annual meeting of the World Association of Modelers (WAM) Biologically Accurate Modeling Meeting (BAMM) on March 31, 2005 in San Antonio, TX. Part I - Introduction to Realistic Neural Modeling for the Beginner: This is a general overview and introduction to compartmental cell modeling and realistic network simulation for the beginner. Although examples are drawn from GENESIS simulations, the tutorial emphasizes the general modeling approach, rather than the details of using any particular simulator. Part II - Getting Started with Modeling Using GENESIS: This builds upon the background of Part I to describe some details of how this approach is used to construct cell and network simulations in GENESIS. It serves as an introduction and roadmap to the extended hands-on GENESIS Modeling Tutorial.
Web Programming and Dataviz with Stata

This article presents web programming and datavisualization implementation using Stata (do file and/or ado files). Stata datasets or excel files, so as text files (.txt) are used to create, fill in and format HTML and Java content. Programming, loops and automation can be smartly implemented to increase proficiency and robustness. The resulting Stata programs can easily be updated and adapted by non-specialists (statisticians and/or webmasters). The strength of this technic is to get together statisticians using specific software (Stata, R) and web designers matching their field of competencies.
Models and algorithms for task allocation in a parallel environment

A task allocation algorithm in a parallel system assigns tasks to each processor with a goal of minimizing both execution and communication costs. Previous research shows the need for algorithms which can approximate this problem efficiently as well as the need for a model which incorporates more fully the communication costs between tasks. The bulk-synchronous parallel (BSP) model proposed by Valiant is shown as a viable model for the task allocation problem. It is shown that the problem is strongly NP-Complete and that no fully polynomial approximation scheme is possible for solving the general problem. Performance results are possible, however, for certain restricted problems. Using the BSP model, approximation algorithms are given for both restricted and the general task allocation problems. Algorithms and performance bounds are also given for the more practical problem of allocating tasks which form precedence graphs. By relaxing previous restrictions used in algorithms for this problem, a practical model was designed which can be implemented on the XPRAM BSP architecture.
The Archaeology Coursebook: An Introduction to Themes, Sites, Methods and Skills

Introduction Part 1: Understanding Archaeological Resources 1. Archaeological Reconnaissance 2. Excavation 3. Post-Excavation Analysis 4. Understanding Dating in Archaeology 5. Archaeological Interpretation Part 2: Studying Themes in Archaeology 6. Religion and Ritual 7. The Archaeology of Settlement 8. Material Culture and Economics 9. People and Society in the Past Part 3: Issues in World Archaeology 10. Managing the Past 11. Presenting the Past Part 4: Examination Success and Beyond 12. Studying for Archaeology Success in Archaeology Exams 13. Doing an Archaeological Project 14. Other Resources: Where to Next? Studying Archaeology in the UK. Appendix: Answers and Mark Schemes. Glossary of Terms and Abbreviations. Bibliography. Index
Converting Qualitative Assessments to Quantitative Assumptions: A Bayesian Approach

Methods and apparatus for blow-molding hollow articles from open-ended tubular bodies of thermoplastics material include dimensionally expanding the open end of the tubular body to accomodate a blow pin plate of preselected dimension and/or geometry. The expanded open end of the tubular member will thus be closed by the blow pin plate. Subsequent introduction of pressurized fluid (e.g., air) into the tubular body through the blow pin plate will cause the tubular body to conform to the female cavity which envelops the same. In such a manner, hollow articles having relatively large sized and/or irregular shaped openings may be fabricated utilizing blow-molding techniques.
L’Histoire brouillée. Musées et mémoriaux du génocide cambodgien

La memoire du genocide commis par les Khmers rouges de Pol Pot represente un enjeu crucial pour le Cambodge. Deux tendances contradictoires ont ete a l’œuvre : gene et mise a l’ecart du cote cambodgien, interet grandissant du cote occidental. Du fait des pressions venues de ce dernier (y compris des touristes), les lieux-temoins, et d’abord la prison de Tuol Sleng, sont parfois remis en etat et developpes. L’ampleur des crimes commis y frappe tout visiteur et les investigations des historiens y trouvent un support essentiel. Neanmoins, un double travestissement menace : la spectacularisation de l’horreur et son rejet du cote d’une monstruosite mi-individuelle, mi-abstraite. L’effet ne pourrait en etre que la dissimulation des chaines de responsabilite et des consequences du fanatisme ideologique. L’action des communautes locales au Cambodge et le travail memoriel des refugies de la diaspora pourraient participer d’un recours.