Miracle-dz/UniFAR-FTUs huggingface dataset:
Peut-on vraiment reconstruire les mégapoles après une inondation généralisée ?

Plus de deux ans apres la catastrophe, la Nouvelle-Orleans est encore une cite fantome. Comment une telle difficulte a sortir de l’apres-crue est-elle possible au cœur de la premiere puissance economique du monde ? C’est le modele social, economique et politique des Etats-Unis que Katrina est venue mettre a nu, en revelant la vulnerabilite. Certaines de nos metropoles n’auront-elles pas un jour a faire face a des impacts et des difficultes de meme nature ? Ne devrions-nous pas nous y preparer ? La catastrophe n’est-elle pas l’occasion de repenser la protection dans une vision plus large de prevention et de projet urbain ? De comprendre que, face a des catastrophes d’une ampleur equivalente, les conditions de la reconstruction s’anticipent. La remise en cause d’un modele de developpement urbain, plus subi que reellement maitrise, serait alors radicale.
An Investigation of the Differential Effects of Visual Input Enhancement on the Vocabulary Learning of Iranian EFL Learners

This study investigated the effect of visual input enhancement on the vocabulary learning of Iranian EFL learners. One hundred and thirty-two EFL learners from elementary, intermediate and advanced proficiency levels were assigned to six groups, two groups at each proficiency level with one being an experimental and the other a control group. The study employed pretests, treatment reading texts, and posttests. T-test was used for the analysis of the data. The results revealed positive effects for visual input enhancement in the advanced level based on within group and between groups’ comparisons. However this positive effect was not found for the elementary and intermediate levels based on between groups’ comparisons. It was concluded that although visual input enhancement may have beneficial effects for elementary and intermediate levels, it is much more effective for the advanced EFL learners. This study may provide useful guiding principles for EFL teachers and syllabus designers.
Scalable distributed RDFS reasoning using MapReduce and Bigtable

The reasoning over massive RDF data has a great advancement in last few years. Many methods have been proposed in past several years, including the method with MapReduce. But the current MapReduce approach contains four reasoning steps and avoids data duplication by special data processing and partitioning. Our work is to propose an algorithm for RDFS reasoning with MapReduce and Bigtable. Through the optimization of RDFS rules’ applying sequence in map and reduce methods, our approach can complete RDFS closure reasoning without special data preprocessing and partitioning in only one MapReduce reasoning step. We have implemented our method on Hadoop and HBase with 3 nodes. We compute the RDFS closure over different datasets and our practice enjoys faster speed and better speedup, calculating RDFS closure of 260 million triples in 50 minutes, about 15 minutes faster than WebPIE.
Numerical methods for engineers

The fifth edition of "Numerical Methods for Engineers" continues its tradition of excellence. Instructors love this text because it is a comprehensive text that is easy to teach from. Students love it because it is written for them--with great pedagogy and clear explanations and examples throughout. The text features a broad array of applications, including all engineering disciplines. The revision retains the successful pedagogy of the prior editions. Chapra and Canale's unique approach opens each part of the text with sections called Motivation, Mathematical Background, and Orientation, preparing the student for what is to come in a motivating and engaging manner. Each part closes with an Epilogue containing sections called Trade-Offs, Important Relationships and Formulas, and Advanced Methods and Additional References. Much more than a summary, the Epilogue deepens understanding of what has been learned and provides a peek into more advanced methods. Approximately 80% of the end-of-chapter problems are revised or new to this edition. The expanded breadth of engineering disciplines covered is especially evident in the problems, which now cover such areas as biotechnology and biomedical engineering. Users will find use of software packages, specifically MATLAB and Excel with VBA. This includes material on developing MATLAB m-files and VBA macros.
Pengembangan Aplikasi Penyampaian Kearifan Lokal Melalui Cerita Rakyat Bali untuk Anak Sekolah Dasar Berbasis Mobile

Penelitian ini mencoba menjawab kebutuhan untuk menyampaikan nilai kearifan lokal Bali dengan menyertakan Bahasa Bali sebagai alternatif bahasa pengantar. Misi utamanya adalah melestarikan kearifan lokal Bali baik itu nilai-nilai etika atau tata susila, dan budaya (Bahasa Bali). Upaya pelestarian bisa dimulai dari mengembangkan metode dalam belajar bahasa Bali. Metode pembelajaran dan pendidikan untuk anak semakin berkembang seiring fasihnya anak-anak dalam menggunakan benda hasil teknologi. Penelitian ini mengupayakan penciptaan media penyampai nilai kearifan lokal melalui pendekatan storytelling. Cara-cara yang digunakan mengadaptasi kebiasaan anak-anak yang terbiasa dengan teknologi, khususnya teknologi mobile, dengan menyertakan Bahasa Bali sebagai pilihan bahasa pengantar aplikasi. Android dipilih sebagai sistem operasi dengan pertimbangan jumlah penggunanya paling banyak dibandingkan sistem operasi mobile yang populer lain seperti IOS atau Windows. Pengujian yang dilakukan dengan pengujian aplikasi model black box menghasilkan aplikasi yang siap digunakan dengan tiga pilihan bahasa pengantar yaitu bahasa Inggris, Indonesia dan bahasa daerah Bali. Evaluasi oleh pengguna dilakukan pada 30 anak umur 6-12 tahun di Denpasar Children Center, Sidakarya. 25 dari 30 anak (83%) menganggap aplikasi ini menarik. 25 dari 30 anak (83%) antusias untuk menantikan seri cerita selanjutnya, namun 16 dari 30 anak (53%) kurang paham mengenai bahasa Bali yang digunakan. 23 dari 25 (92%) anak dapat menangkap pesan yang disampaikan pada cerita. Aplikasi ini didistribusikan melalui situs aliansipedulibahasabali.org
The Relative Effectiveness of Audio, Video, and Static Visual Computer-Mediated Presentations

Research on non-computerized media such as television, slides, animation, and film has developed an information-processing framework that can be adapted to research on computerized media. We examined the relative effectiveness of three different forms of computer presentation: digital video presentations with text, audio-only presentations with text, and static visual presentations with text. ANOVA results showed that although the achievement scores of participants in different treatment groups did not differ significantly, there was a significant difference in the time participants took to complete the presentations. From an information-processing perspective, these differences may be attributed to bisensory interference or bisensory facilitation. We concluded that when computer-mediated presentations are being designed, the comparative efficiency of the learning medium might often be more pertinent than the effectiveness of media implementation. Les auteurs ont analyse l'efficacite relative de trois formes de presentation informatisee: la presentation video numerique avec texte, la presentation exclusivement audio et la prdsentation visuelle statique avec texte. Les resultats de l'analyse de la variance revelent une nette difference dans le temps pris par les participants pour effectuer les presentations. Du point de vue du traitement de l'information, ces diff6rences peuvent s'expliquer par l'interference bisensorielle ou la facilitation bisensorielle. Les auteurs concluent que, dans les presentations assistees par ordinateur, l'efficience relative du media d'apprentissage peut souvent etre consid6ree plus importante que l'efficacite reelle de l'utilisation du media. As integrated computer-mediated instruction enters the mainstream of instructional development at all levels of education and training, the implications of the use of multi-media techniques have become essential to program design and development. Exploration of the relative effectiveness of various multi-media techniques and their impact on design efficiency, as well as on learning efficiency, has the potential to provide programmers and instructional designers with a theoretical and practical basis from which to select the appropriate medium for individual learning objectives. The effectiveness of different multimedia formats in facilitating learning is a major consideration within this framework. An additional concern, however, must be the efficiency of the pedagogical program. In other words, although a specific
Rain attenuation measurements at 6.75 GHz in Malaysia

Attenuation of radio frequency signal by clouds, snow and in particular, rains have to be considered at frequencies above 10 GHz. Indeed, below 10 GHz, rain may cause severe degradation at certain threshold frequencies especially when operating in tropical regions. At high rain rates, it causes significant absorption, scattering and depolarization. Therefore, data and prediction models are required for establishing any satellite-Earth communication links. The successful development of predicted models calls for the knowledge of propagation characteristics along the signal path. Such knowledge is acquired both from theoretical studies of relevant propagation phenomena and statistical analysis of long-term propagation data bases. In view of the importance of such data the University has started to do measurements to model the slant path attenuation and the rain structure. In this paper, results of a year of measurement of radiometer and rain gauges are presented. The measured sky noise temperatures are analyzed in both event and statistical terms. The values of the sky temperature are converted into attenuation using the radiometer equation with medium temperature of 290 K. The cumulative distribution of attenuation was calculated. The rain rate measurements are made using two tipping bucket rain gauges employing one-minute integration time. Investigation of several events shows that the rain rate is not uniform over a short distance (3 km) especially at high rain rates. Comparison of rain rates and attenuation with the International Radio Consultative Committee (CCIR) proposed model shows discrepancy especially at low percentages of time. More data are required to develop a new model.
A learning system based on genetic adaptive algorithms

A learning system is a system that improves its performance with respect to a given task domain over time through its interactions with the task environment. The mechanisms by which such a system manipulates its knowledge about the task environment in response to these interactions constitute the system's "methods of learning". In constructing an artificial learning system, the particular methods employed determine, to a large extent, the ultimate generality of the system. A learning system capable of functioning in a variety of task domains necessarily requires the presence of domain independent methods of learning. This thesis is concerned with investigating the feasibility of constructing a general purpose learning system around a particular class of domain independent methods called genetic algorithms. To this end, a specific learning system organization, LS-1, is proposed. In further specifying the design, a production system language amenable to manipulation by a genetic algorithm is defined as the system's representation of knowledge, organized as a domain independent framework into which task specific primitives can be injected. The classical genetic algorithms are then modified to suit the specific characteristics of the defined knowledge structure representation. A formal analysis of the search conducted by such a revised genetic algorithm through the space of possible production system programs is performed, demonstrating that it possesses properties analogous to those exhibited by classical genetic algorithms and establishing a sound theoretical foundation for LS-1. Finally, a critic to judge the "relative worth" of a production system program as a potential solution to the task at hand is specified, incorporating both domain independent and task specific sources of judgmental information. As a demonstration of the feasibility of the design an LS-1 implementation is tested in two distinct and unrelated task domains, each the domain of a related effort in learning system construction. Specifically, the system is faced with (1) a simple maze walk problem and (2) the problem of making the bet decision in draw poker. Initialized in each test with randomly generated production system programs, the LS-1 implementation is shown to rapidly converge on high performance knowledge structures in both task domains, providing empirical evidence of the effectiveness of a genetic algorithm as a general purpose learning mechanism.
Variable scale reprojection for iterative reconstruction: application to PET reconstruction with OSEM

Iterative reconstruction algorithms require the repetitive application of reprojection and backprojection operations. Reprojection of an image at angle /spl theta/ can be decomposed into rotating the image by angle /spl theta/ and summing it at angle 0. Here, the authors show that this "rotate and sum" (RS) technique can be reduced to applying one-dimensional translations of the image rows, followed by an interpolation-free summation of the image columns. This sequence actually yields the proper reprojection magnified by a factor that depends on the angle. By pre-processing the data to match this angular variation in scale, most iterative reconstructions can be performed with this variable scale reprojection scheme (VS). The main routine is a "translate and sum" function that accesses the memory row by row. The OSEM algorithm was implemented with the VS, the RS and the line-length model, and OSEM reconstructions were performed on real PET data. The VS scheme was demonstrated to be significantly faster than RS and twice as fast as LL, without compromising image quality, and even slightly improving it.
Basit Doğrusal Regresyon Analizi İle Hiyerarşik Doğrusal Modeller Analizinin Karşılaştırılması

Hiyerarsik veya ic ice gecmis yapidaki verilere bircok arastirma alaninda rastlanmaktadir. Egitim alaninda ogrenciler, sinif, okul, sehir, ulke gibi hiyerarsik bir sosyal yapinin icinde yer alirlar. Bu yapidaki bir veride ayni siniftaki veya okuldaki ogrenciler, evrenden rasgele secilen ogrencilere gore birbirlerine daha benzer ozellikler gosterirler. O halde, ayni sosyal birimde yer alan bireylerden elde edilen gozlemlerin birbirinden tamamen bagimsiz oldugu soylenemez. Halbuki dogrusal regresyon analizi gibi geleneksel istatistiksel analiz yontemlerindeki en onemli varsayimlardan biri gozlemlerin birbirinden bagimsiz olmasidir. Hiyerarsik yapidaki verilerde ortaya cikan bu problem, hiyerarsik dogrusal modeller yontemi ile giderilebilir. Bu calismada iki duzeyli hiyerarsik dogrusal modellerin yapisi ve TIMSS 1999 Turkiye verisi uzerinde basit dogrusal regresyon analizi ile hiyerarsik dogrusal modeller analizinin karsilastirmali uygulamasi sunulmustur.
Neural network learning and generalization for performance improvement of industrial robots

In this article, we present an approach for improving the trajectory tracking performance of industrial robots using multilayer feedforward neural networks. The controller design based on this approach consists of a PID control and a neural network. The function of the neural network is to complement the PID controller for improving the performance of the system over time. The proposed approach has been implemented on an industrial robot-the CRS Robotics A460. Experiments are conducted to investigate the learning and generalization ability of neural networks in complementing the PID method in robot trajectory tracking. The results of this work suggest that neural networks could be added to existing PID-controlled industrial robots for performance improvement.
Real-time tracking of free-form objects by range and intensity image fusion

Range images are helpful in tracking the motion of objects defined by smooth curved surfaces. However, combined use of range images with intensity data such as texture data is desirable in order to improve measurement stability. In this paper, a method is proposed for model-based tracking of 6-axis object motion. Pose estimation based on intensity image is not merely added to a range-image-based tracking system but instead, a texture-mapped model is built through range and intensity measurement and then used for tracking. We first explain a method for direct acquisition of object motion by applying the object rigidity to the gradient method, and then this method is shown to be easily fused with range-image-based motion tracking. An algorithm is also offered for high-speed image generation and reflectance estimation using CG hardware. Finally, experiments are described that demonstrate the feasibility of real-time tracking of the 6-axis motion of an object and of acquisition of the shape and reflectance model. © 1998 Scripta Technica, Syst Comp Jpn, 29(8): 1927, 1998
Polysemy: Theoretical and Computational Approaches

1. Polysemy: An overview 2. Aspects of the Micro-Structure of Word Meanings 3. Autotroponomy 4. Lexical Shadowing and Argument Closure 5. Describing Polysemy: The case of 'Crawl' 6. 'The Garden Swarms with Bees' and the Fallacy of 'Argument Alternation' 7. Polysemy: A problem of definition 8. Lexical Representations for Sentence Processing 9. Large Vocabulary Word Sense Disambiguation 10. Polysemy in a Broad-Coverage Natural Language Processing System 11. Disambiguation and Connectionism
Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing

This book explores unintentional learning from an information-processing perspective. What do people learn when they do not know that they are learning? Until recently all of the work in the area of implicit learning focused on empirical questions and methods. In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. He introduces a theoretical framework that unifies existing data and models on implicit learning, along with a detailed computational model of human performance in sequence-learning situations. The model, based on a simple recurrent network (SRN), is able to predict perfectly the successive elements of sequences generated from finite-state, grammars. Human subjects are shown to exhibit a similar sensitivity to the temporal structure in a series of choice reaction time experiments of increasing complexity; yet their explicit knowledge of the sequence remains limited. Simulation experiments indicate that the SRN model is able to account for these data in great detail. Cleeremans' model is also useful in understanding the effects of a wide range of variables on sequence-learning performance such as attention, the availability of explicit information, or the complexity of the material. Other architectures that process sequential material are considered. These are contrasted with the SRN model, which they sometimes outperform. Considered together, the models show how complex knowledge may emerge through the operation of elementary mechanisms-a key aspect of implicit learning performance.
An Introduction to Cybernetics

Getting the books an introduction to cybernetics now is not type of inspiring means. You could not forlorn going like ebook addition or library or borrowing from your connections to door them. This is an certainly easy means to specifically get lead by on-line. This online revelation an introduction to cybernetics can be one of the options to accompany you taking into consideration having new time.
An improved approximate decoding with correlated sources

We consider ad hoc sensor network topologies that aim for distributed delivery of correlated delay-sensitive data. In order for efficient data delivery, network coding technique in conjunction with approximate decoding algorithm is deployed. The approximate decoding algorithm enables receivers to recover the original source data even when the number of received data packets is not sufficient for decoding. Therefore, it leads to significantly improved decoding performance and enhanced robustness for delay-sensitive data. In this paper, we further improve the approximate decoding algorithm by explicitly considering the characteristics of the correlation. Specifically, we study the case where the source data are correlated by a simple linear correlation, which is quantified by a similarity factor. We investigate several properties of the proposed algorithm and analyze the impact of the similarity factor on the decoding performance. Our experimental results confirm the properties of the proposed approximate decoding algorithm with linear correlation.
The acoustic role of supralaryngeal air sacs

This invention provides a method of inhibiting the transcription of a gene, which is activated by AP-1 or an AP-1 component, comprising binding AP-1 or the component with a nuclear receptor so as to prevent the binding of AP-1 to the gene. The nuclear receptor can be the retinoic acid receptor, glucocorticoid receptor, vitamin D3 receptor, thyroid receptor, or estrogen receptor. Also provided is a composition of matter comprising AP-1 or an AP-1 component bound to a nuclear receptor. These methods and compositions can be used to treat arthritis and cancer.
E-Commerce and Privacy: Conflict and Opportunity

Abstract As e-commerce expands, Web sites increasingly collect, process, and sometimes divulge personal information about their customers without the customers' consent. In light of the limited success of several e-commerce initiatives to guard privacy, the Federal Trade Commission has recommended that Congress consider legislation similar to that existing in the European Union to guarantee customers' privacy online. The authors discuss the conflict between the e-commerce community's need to collect data and customers' desire to protect their privacy, the privacy tools available to deal with these issues, the possible impact of privacy laws on the future profitability of e-commerce firms, and privacy as a component of any course involving e-commerce.
Object Detection and Recognition in Natural Settings

Much research as of late has focused on biologically inspired vision models that are based on our understanding of how the visual cortex processes information. One prominent example of such a system is HMAX [17]. HMAX attempts to simulate the biological process for object recognition in cortex based on the model proposed by Hubel & Wiesel [10]. This thesis investigates the ability of an HMAX-like system (GLIMPSE [20]) to perform object-detection in cluttered natural scenes. I evaluate these results using the StreetScenes database from MIT [1, 8]. This thesis addresses three questions: (1) Can the GLIMPSE-based object detection system replicate the results on object-detection reported by Bileschi using HMAX? (2) Which features computed by GLIMPSE lead to the best object-detection performance? (3) What effect does elimination of clutter in the training sets have on the performance of our system? As part of this thesis, I built an object detection and recognition system using GLIMPSE [20] and demonstrate that it approximately replicates the results reported in Bileschi’s thesis. In addition, I found that extracting and combining features from GLIMPSE using different layers of the HMAX model gives the best overall invariance to position, scale and translation for recognition tasks, but comes with a much higher computational overhead. Further contributions include the creation of modified training and test sets based on the StreetScenes database, with removed clutter in the training data and extending the annotations for the detection task to cover more objects of interest that were not in the original annotations of the database.