Miracle-dz/UniFAR-FTUs huggingface dataset:
Missing traffic data: comparison of imputation methods

Many traffic management and control applications require highly complete and accurate data of traffic flow. However, because of various reasons such as sensor failure or transmission error, it is common that some traffic flow data are lost. As a result, various methods were proposed by using a wide spectrum of techniques to estimate missing traffic data in the last two decades. Generally, these missing data imputation methods can be categorised into three kinds: prediction methods, interpolation methods and statistical learning methods. To assess their performance, these methods are compared from different aspects in this paper, including reconstruction errors, statistical behaviours and running speeds. Results show that statistical learning methods are more effective than the other two kinds of imputation methods when data of a single detector is utilised. Among various methods, the probabilistic principal component analysis (PPCA) yields best performance in all aspects. Numerical tests demonstrate that PPCA can be used to impute data online before making further analysis (e.g. make traffic prediction) and is robust to weather changes.
Networks: An Introduction

The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas. Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.
A segment model based approach to speech recognition

Proposes a global acoustic segment model for characterizing fundamental speech sound units and their interactions based upon a general framework of hidden Markov models (HMM). Each segment model represents a class of acoustically similar sounds. The intra-segment variability of each sound class is modeled by an HMM, and the sound-to-sound transition rules are characterized by a probabilistic intersegment transition matrix. An acoustically-derived lexicon is used to construct word models based upon subword segment models. The proposed segment model was tested on a speaker-trained, isolated word, speech recognition task with a vocabulary of 1109 basic English words. In the current study, only 128 segment models were used, and recognition was performed by optimally aligning the test utterance with all acoustic lexicon entries using a maximum likelihood Viterbi decoding algorithm. Based upon a database of three male speakers, the average word recognition accuracy for the top candidate was 85% and increased to 96% and 98% for the top 3 and top 5 candidates, respectively. <<ETX>>
The impact of technology on teaching and learning in an elementary science classroom

This dissertation documents a case study o f a 4th-5th grade science classroom, equipped with eight desktop computers, in which the classroom teacher and her students used technology tools to enhance instruction and learning; specifically in the areas of astronomy and space exploration. My research questions were: 1. How can the imaginative integration of technology tools extend the practices of a teacher and her students in an elementary science classroom? How do these teacher and student practices interact? 2. What conditions/structures were present in this case to nurture the development of technology as an imaginative extension of the complex learning environment? How may these conditions be considered as ‘enabling constraints’? I employed case study methodology and used complexity theory as an interpretive lens for better understanding the dynamic features of technology use in the classroom. The research environment exhibited many of the characteristics of a complex entity; thriving in the fertile space at the edge of chaos. To capture the complex nature of the interactions in a collective classroom setting, I became a member of the community, and employed the methods of participant research. Easily accessible computers enabled a series of student science projects of an expanded and open nature; within the context of an adaptive learning environment. The teacher and her students made significant modifications to their existing teaching and learning practices - with changes occurring in the teacher’s instructional role and assigned tasks, and the students becoming much more engaged with the subject matter through extensive research projects. In this open learning system, complex adaptation and change were continually occurring in all members; teacher, students, curriculum materials, and technology tools. The computers, with their continuing flow of information and experience, provided for a great deal of the open nature of the emergent classroom community. In summary, the way in which this classroom was structured allowed for new and unique ideas, practices, and learning to emerge, thus providing a rich, diverse and adaptive experience for all participants.
Space-time block coded interleaved orthogonal frequency division multiplexing system

A new communication technique called interleaved orthogonal frequency division multiplexing (IOFDM), which has higher code rate than the conventional OFDM, has been proposed by Prasad and Hari (see Proc. IEEE ICASSP, Orlando, Florida, May 2002, p.2745-48) for data transmission over the frequency selective channels. IOFDM enhances the code rate without bandwidth expansion and without increasing the number of OFDM tones.We study space-time block coded transmitter diversity for IOFDM systems. Space-time block coded IOFDM (STBC-IOFDM) achieves full spatial diversity along with code rate improvement. Based on numerical simulation study, the average bit error rate (BER) performance of the proposed STBC-IOFDM system is shown to be slightly better than that of the previously reported space-time block coded OFDM (STBC-OFDM) system.
Algorithms for software implementations of RSA

Two new algorithms that facilitate the implementation of RSA in software are described. Both algorithms are essentially concerned with performing modular arithmetic operations on very large numbers, which could be of potential use to applications other than RSA. One algorithm performs modular reduction and the other performs modular multiplication. Both algorithms are based on the use of look-up tables to enable the arithmetic computations to be done on a byte by byte basis. >
Application of the model inversion method for LAI estimation to the airborne multi-spectral and multi-angle data

Toward accurate leaf area index (LAI) estimation for the regional scales in topographically complicated vegetation surfaces, a practical model inversion method is developed. This method is applied to the airborne multi-angle and multi-spectral data acquired over needle forests for LAI estimation. Although some inversion results are quite good, all inversion results tend to rather underestimate the LAI values. In spite of the lack of sufficient bidirectional reflectance factor data in the case of the Zushi area, Kanagawa Prefecture, Japan, the inversion method for LAI estimation gives a reasonable accuracy.
Segmentation and Recognition of Arabic Printed Script

In this work we present a method for the recognition of Arabic printed script. The major problem of the automatic reading of cursive writing is a segmentation of script to isolate characters. The recognition process consists of four phases: Preprocessing, segmentation, feature extraction and the recognition. In the preprocessing, the image is scanned and smoothed. The correction of skew lines is done by using Hough transform . In the second phase, the text is segmented into lines, words or parts of words and each word into characters based on the principle of projection of the histogram. Features such as: density, profile, Hu moments and histogram are used to classifier the characters based on the Neural network.
Integration of speaker and speech recognition systems

A combination of a high-performance speaker identification system and an isolated word recognizer is presented. The front-end text-independent speaker identification system determines the most likely speaker for an input word. The speaker identity is then used to choose the reference word models for the speech recognizer. When used with a closed set of speakers, the combination is capable of automatically producing speech and speaker identification. For an open set of speakers, the speaker recognition system acts as speaker quantizer which associates the unknown speaker with an acoustically similar speaker. The matching speaker's word models are used in the speech recognizer. The application of this front-end speaker recognizer is described for a DTW and HMM speech recognizer. Results on a combination using a DTW word recognizer are 100% for closed set experiments. <<ETX>>
The evolution of GIPSY - or the survival of an image processing system

Since its introduction in the early seventies, GIPSY has constantly evolved. We present an overview of the developments over the last few years. These include the introduction of event-driven user interaction and the addition of a set of highly interactive graphical user interface (GUI) components. The GUI has been built on top of the. existing user interface with which it is completely compatible. We also present examples of applications based on these developments.
The KAOS Project: Knowledge Acquisition in Automated Specification of Software

A dry semipermeable membrane, which is adapted to separate solutes from solutions by reverse osmosis methods, is prepared by a process which comprises casting a film from a casting solution containing a cellulose derivative, a first additive, and a suitable amount of an organic compound with an organic solvent as the balance of the casting solution; evaporating a portion of said organic solvent from said cast film; dipping the partially evaporated film in water to extract the remainder of said organic solvent and drying the extracted film.
Directions, Methods and Metrics for Mapping Human to Robot Motion with Functional Anthropomorphism:

In this paper we provide directions, methods and metrics that can be used to synthesize a complete framework for mapping human to robot motion with Functional Anthropomorphism. Such a mapping can be used in order to perform skill transfer from humans to robot arm hand systems with arbitrary kinematics. The mapping schemes proposed, first guarantees the execution of specific functionalities by the robotic artifact in 3D space (task space functional constraints), and then optimizes anthropomorphism of robot motion. Human-likeness of robot motion is achieved through minimization of structural dissimilarity between human and robot arm hand system configurations. The proposed methodology is suitable for a wide range of applications ranging from learn by demonstration for autonomous grasp planning, to real time teleoperation and telemanipulation with robot arm hand systems in remote or dangerous environments.
An Ensemble Method for Multi-class and Multi-label Text Categorization

A method for multi-class and multi-label automated text categorization based on twin-SVM with naive Bayes ensemble is proposed. Twin-SVM classifiers give a solution to the multi-label problem. For multiclass situation, naive Bayes classifier constrains the belonging scope of a testing sample within a few most likely classes and greatly reduces the number of binary classifiers needed to make the final prediction. The benefits of the ensemble method are described and preliminary results with Reuters-21578 data set are also presented.
Etude de la biomécanique cellulaire à l'aide de MEMS piézoélectriques organiques

Ces travaux de these sont le fruit d’un constat : le developpement des materiaux organiques dans les MEMS ne cesse de croitre. Cela est du a leurs procedes de fabrication a moindre cout et a leurs proprietes qui different de celles des materiaux inorganiques. D’un point de vue biologique,les proprietes physiques et chimiques des materiaux organiques sont egalement plus proches des proprietes de l’environnement extra-cellulaire. Les MEMS sont des systemes tres polyvalents permettant de mesurer de nombreuses grandeurs physiques. Leur utilisation dans le domaine biologique n’est donc pas etonnante et il est interessant de combiner les MEMS avec des materiaux organique pour l’etude de cellules. L’objectif de ces travaux est de fabriquer et d’utiliser des resonateurs MEMS organiques piezoelectriques dans le but d’etudier la biomecanique et l’adhesion de cellules. En effet,la biomecanique des cellules est un domaine d’etude qui renseigne sur de nombreux processus operes par les cellules, comme l’adhesion cellulaire, ainsi que sur leur bien etre. Dans un premier temps, le procede de fabrication des resonateurs a ete etabli et optimise afin d’obtenir une efficacite d’actionnement piezoelectrique maximale. Dans un deuxieme temps, un systeme d’actionnement et de mesure electrique a ete realise, comportant une carte electronique ainsi qu’une enceinte etanche. L’influence de la temperature, de la densite et de la viscosite du milieu sur la resonance des MEMS a egalement ete etudiee. Finalement, les resonateurs crees ont ete utilises dans le suivi en temps reel de l’adhesion de cellules souches mesenchymateuses. D’autres applications ont ete realisees avec les resonateurs piezoelectriques comme l’etude de la position d’une masse sur les resonateurs, la determination de la rigidite d’un materiau ainsi que de la viscosite d’un liquide.
Flexible unparsing in a structure editing environment

Generators of structure editing-based programming environments require some form of unparse specification language with which an implementor can describe mappings between objects in the programming environment and concrete, visual representations of them. They must also provide an unparser to execute those mappings in a running programming environment. We descr ibe one such unparse specification language, called VIZ , and its unparser, called UAL . V IZ combines in a uniform descriptive framework a variety of capabilities to descr ibe flexible views of a programming database using a library of high-level formatting routines that can be customized and extended by the implementor. T h e U A L unparser allows the highly conditional unparse mappings of V IZ to be executed efficiently. Its implementation is based on the automatic generation of explicit display views, together with a scheme for efficient incremental updating of them in response to arbitrary changes to objects in the programming environment. Th is research is supported in part by the Software Engineering Division of C E N T A C S / C O R A D C O M , Fort Monmouth, N J . University Libraries Cf\r r University Table of
Introducing the roadmap concept to inexperienced users: a case study.

If an organisation introduces new working methods, a lot of effort is required to introduce the concept to the parties involved. This is true for the introduction of e.g. design methods, systems for quality control or new software. Usually, the roll-out, training and gaining understanding and experience require investments that –by farexceed the initial acquisition. Introduction of the roadmap concept should require less effort, as the concept more closely relates to logical reasoning. This is because it is possible to focus the introduction of roadmaps in an organisation on relevant topics, instead of on organisational and managerial topics. This publication describes a case study in which the roadmap concept and the EDENTM framework are introduced to a group of 35 students Industrial Design Engineering that were formerly unfamiliar with both topics. The students work in small groups; addressing the development, management and evaluation of roadmaps, and simultaneously using these roadmaps as a guidance for a design project.
Evolutionary Game Theory

This text introduces current evolutionary game theory--where ideas from evolutionary biology and rationalistic economics meet--emphasizing the links between static and dynamic approaches and noncooperative game theory. The author provides an overview of the developments that have taken place in this branch of game theory, discusses the mathematical tools needed to understand the area, describes both the motivation and intuition for the concepts involved, and explains why and how the theory is relevant to economics.
Beyond the Clinical Use of Neurofeedback

Neurofeedback (NFB) is thought to be a direct training of custom brainwaves in order to make the brain’s functionality more efficient. In a typical NFB session, the brain activity is measured with electroencephalography (EEG), from one or more sites according to the used protocol. The subject is rewarded, with acoustic or visual feedback, when his/her brain activity meets the protocol’s goals, increasing with that way the probability of this cerebral behavior [1]. Until now the NFB has been successfully used in several domains of psychiatry and neurology [2], but there is a huge gap in the use of NFB in healthy conditions. The NFB for the brain can be considered as the gymnastics for the body, and it can be used to make the brain more efficient in terms of attention, memory and emotions. Until now there is a limited literature to this direction, revealing that there is a free space for research with both clinical and social impact.
Adaptive volume rendering of cardiac 3D ultrasound images: utilizing blood pool statistics

In this paper we introduce and investigate an adaptive direct volume rendering (DVR) method for real-time visualization of cardiac 3D ultrasound. DVR is commonly used in cardiac ultrasound to visualize interfaces between tissue and blood. However, this is particularly challenging with ultrasound images due to variability of the signal within tissue as well as variability of noise signal within the blood pool. Standard DVR involves a global mapping of sample values to opacity by an opacity transfer function (OTF). While a global OTF may represent the interface correctly in one part of the image, it may result in tissue dropouts, or even artificial interfaces within the blood pool in other parts of the image. In order to increase correctness of the rendered image, the presented method utilizes blood pool statistics to do regional adjustments of the OTF. The regional adaptive OTF was compared with a global OTF in a dataset of apical recordings from 18 subjects. For each recording, three renderings from standard views (apical 4-chamber (A4C), inverted A4C (IA4C) and mitral valve (MV)) were generated for both methods, and each rendering was tuned to the best visual appearance by a physician echocardiographer. For each rendering we measured the mean absolute error (MAE) between the rendering depth buffer and a validated left ventricular segmentation. The difference d in MAE between the global and regional method was calculated and t-test results are reported with significant improvements for the regional adaptive method (dA4C = 1.5 ± 0.3 mm, dIA4C = 2.5 ± 0.4 mm, dMV = 1.7 ± 0.2 mm, d.f. = 17, all p < 0.001). This improvement by the regional adaptive method was confirmed through qualitative visual assessment by an experienced physician echocardiographer who concluded that the regional adaptive method produced rendered images with fewer tissue dropouts and less spurious structures inside the blood pool in the vast majority of the renderings. The algorithm has been implemented on a GPU, running an average of 16 fps with a resolution of 512x512x100 samples (Nvidia GTX460).
Staff line detection and revision algorithm based on subsection projection and correlation algorithm

Staff line detection plays a key role in OMR technology, and is the precon-ditions of subsequent segmentation 1& recognition of music sheets. For the phenomena of horizontal inclination & curvature of staff lines and vertical inclination of image, which often occur in music scores, an improved approach based on subsection projection is put forward to realize the detection of original staff lines and revision in an effect to implement staff line detection more successfully. Experimental results show the presented algorithm can detect and revise staff lines fast and effectively.
A Computational Theory of Metaphor

Metaphor is a conventional and ordinary part of language. A theory attempting to explain metaphor must account for the ease with which conventional metaphors are understood, and with the ability to understand novel metaphors as they are encountered. An approach to metaphor, based on the explicit representation of knowledge about metaphors, has been developed to address these issues. This approach asserts that the interpretation of conventional metaphoric language should proceed through the direct application of specific knowledge about the metaphors in the language. Correspondingly, the interpretation of novel metaphors can be accomplished through the systematic extension, elaboration, and combination of knowledge about already well-understood metaphors. MIDAS (Metaphor Interpretation, Denotation and Acquisition System) is a computer program that embodies this approach. MIDAS can be used to perform the following tasks: represent knowledge about conventional metaphors, interpret metaphoric language by applying this knowledge, and dynamically learn new metaphors as they are encountered during normal processing. Knowledge about conventional metaphors is represented in the form of coherent sets of associations between disparate conceptual domains. the representation captures both the details of individual metaphors, and the systematics exhibited by the set of metaphors in the language as a whole. These systematic sets of associations were implemented using the KODIAK knowledge representation language. MIDAS is capable of using this metaphoric knowledge to interpret conventional metaphoric language. The main thrust of this approach is that normal processing of metaphoric language proceeds through the direct application of specific knowledge about the metaphors in the language. This approach gives equal status to all conventional metaphoric and literal interpretations. Moreover, the mechanisms used to arrive at metaphoric and literal interpretations are fundamentally the same. When a metaphor is encountered for which MIDAS has no applicable knowledge, MIDAS calls upon its learning component - the Metaphor Extension System (MES). The approach embodies in the MES asserts that a novel metaphor can best be understood through the systematic extension of an already well-understood metaphor. MIDAS has been integrated as a part of the UNIX Consultant system. UC is a natural language consultant system that provides naive computer users with advice on how to use the UNIX operating system. By calling MIDAS, UC can successfully interpret and learn conventional UNIX domain metaphors, as they are encountered during the course of UC''s normal processing.