Miracle-dz/UniFAR-FTUs huggingface dataset:
RECONOCIMIENTO DE FORMAS EN VISIÓN ARTIFICIAL: APLICACIÓN DE LA TRANSFORMADA WAVELET

The Wavelet transform is a mathematical tool that is booming since the mid-80s of the twentieth century, because it is more efficient than the classical Fourier transform in the field of investigation in digital signal processing and imaging. Its applications are many, among which are: Analysis of noise, signal compression, extraction of edges (details diagonals, vertical and horizontal digital images), and key features for optimal extraction of contours. This article analyzes and describes the computational implementation, using models and processes (signatures and counting pixels) of the computer visualization of the degree of coincidence of contours in images, from the comparison of parameters such as area, perimeter, compactness, and diameter contours. The implementation presented is done by programming functions for the development of the wavelet transform included in the softwareMatlab 7.1 ®.
Motivation in software engineering: A systematic review update

Background/Aim - Given the relevance and importance that the understanding of motivation has gained in the field of software engineering, this work was carried out in order to update the results of a literature review carried out in 2006 on motivation in software engineering. Method - Based on guidelines for this specific type of study, we replicated the original study protocol. Results - The combination of manual and automatic searches retrieved 6,534 papers, of which 53 relevant papers were selected for data extraction and analysis. Conclusions - Studies address motivation using several viewpoints and approaches and, even though the number of researches increased in this area, the overall understanding of what actually motivates software engineers does not seem to have significantly advanced in the last five years.
Pre-crash test capabilities at VeHIL

-- In the last few years, the developments in the field of ADA systems shifted from comfort oriented systems to safety oriented systems. Consequently, the complexity of the systems, and thus the complexity of testing those systems, increased. This leads to a demand for reproducible, efficient and safe testing methods for ADA systems in general, and pre-crash systems in particular. A new method for evaluation of pre-crash systems is presented, consisting of TNO’s Vehicle Hardware-in-the-Loop facility (VeHIL) and TNO’s Crash Lab. This method, called VeHIL/Pre-crash, aims to support the development and verification of pre-crash systems, increasing the level of reproducibility, effectiveness and safety of the testing process. To this end, a test vehicle, positioned on a roller bench, is placed in a simulated traffic environment. In order to provide the test vehicle’s environment sensor input, one of the simulated traffic participants is represented by a rail guided target vehicle, which is stopped by crumple tubes.
An fMRI study of fluent and nonfluent beginning readers

Although reading fluency has been identified as an important component of skilled reading, few studies have examined the underlying neural processes. The purpose of the current study was to compare the neural systems for reading in fluent and nonfluent beginning readers. The Goldberg and Costa (1981) theory of hemisphere differences provided a theoretical framework for conceptualizing the development of reading fluency. This theory proposes that the right hemisphere processes novel stimuli and assembles new descriptive systems while the left hemisphere utilizes fully formed and well-routinized codes, and that a right-to-left shift in hemisphere superiority occurs during skill development. Children between 6 and 7 years of age participated in an fMRI experiment. Low and high fluency groups were based on level of fluency in grapheme-phoneme mapping. fMRI reading tasks were modeled on curriculum based measurement tests of reading fluency. Three different tasks involved letter-phoneme, word-spoken word, or picture-spoken word matching. In high fluency as compared to low fluency beginning readers, there was greater activation in the left parietotemporal area during letter and word reading tasks, an area involved in phonological processing, grapheme-phoneme mapping, and word decoding. Also, in the high fluency as compared to the low fluency group, there was greater activation in the left inferior frontal area during the word reading task, another area involved in phonological processing. Within the framework of the Goldberg and Costa theory, the greater left hemisphere involvement in the high fluency group may reflect the utilization of more routinized descriptive codes for phonological processing skills. There was greater activation in high fluency as compared to low fluency beginning readers in bilateral occipitotemporal areas during
Analytical least squares Hough transform with an implementation on a transputer network

Computer vision for real-world imagery normally consists of three stages: acquisition/low-level feature extraction (e.g. capture followed by edge detection), medium-level feature extraction (typically into a geometric and/or topological space) and task-oriented scene understanding (e.g. aggregation of geometric features to characterise objects of interest). The Hough transform (HT) is an efficient medium level method to extract geometric features from an image which works fairly well for images that contain noise and occlusion. However, its performance decreases with image and parameter space quantisation noise. This paper describes two HT variants based on an analytical least squares refinement procedure that helps overcome some of these difficulties. A parallel implementation on a transputer based system is also discussed and evaluated. <<ETX>>
Game-Based Learning with a Dialogic Teaching Approach: A Case of Deep Learning and the Use of SporeTM in A-Level Biology Lessons

This chapter is an instance of the deployment of game-based learning (GBL) in an upper secondary school in the UK. A retrospective case study was conducted with sixth-form students (aged 17) and their teacher. The study examines their perceptions after the use of Spore™ in their biology lessons. The teacher integrated GBL with a dialogic teaching approach to promote deep learning among the students. A surface–deep learning matrix was created to extract the students’ attributes as deep learners and to identify the teacher’s characteristics. A comparison is presented between the perceived advantages of GBL, the normal learning approach
A Standards-Based Approach to Team-Based Student Projects in an Information Technology Curriculum.

Team-based student projects have become an intrinsic part of coursework in information technology courses. The rationale is that once students enter the work environment they will be required to work in teams. Challenges of teamwork are reviewed and factors influencing student team performance are identified. An approach is described which deals with four categories of concern when incorporating a team-based project in information technology courses, namely planning, execution, evaluation and team process improvement. The approach stresses the importance of utilizing best practices, as available in international standards, for team projects of a full term duration that is based on a case study. It is argued that such an approach contributes to bridging the gap between student project teamwork based on limited case studies and real-world projects conducted in the competitive business environment of today. The best practices and standards used in 34 classes for eight courses of a master's program in computer and information systems are summarized in the paper. A total of 150 teams participated in these courses. Includes five tables. (Contains 27 references.) (Author) Reproductions supplied by EDRS are the best that can be made from the original document. A STANDARDS-BASED APPROACH TO TEAM-BASED STUDENT PROJECTS IN AN INFORMATION TECHNOLOGY CURRICULUM PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
An efficient algorithm for measurement of retinal vessel diameter from fundus images based on directional filtering

Automatic measurement of vessels from fundus images is a crucial step for assessing vessel anomalies in ophthalmological community, where the change in retinal vessel diameters is believed to be indicative of the risk level of diabetic retinopathy. In this paper, a new retinal vessel diameter measurement method by combining vessel orientation estimation and filter response is proposed. Its interesting characteristics include: (1) different from the methods that only fit the vessel profiles, the proposed method extracts more stable and accurate vessel diameter by casting this problem as a maximal response problem of a variation of Gabor filter; (2) the proposed method can directly and efficiently estimate the vessel's orientation, which is usually captured by time-consuming multi-orientation fitting techniques in many existing methods. Experimental results shows that the proposed method both retains the computational simplicity and achieves stable and accurate estimation results.
A 3 V, 10 bit, 6.4 MHz switched-current CMOS A/D converter design

An experimental 6.4 MS/s CMOS A/D converter design is presented. Fully-differential first-generation switched-current circuits with common-mode feedforward were used to realize a 1.5-b/stage pipelined architecture. High sampling rate and large input bandwidth were the main design objectives. The complete ADC was simulated using a SPICE level simulator. Performance is verified by analyzing the FFT of 2048 simulated samples. With f/sub in/=3.05 MHz, the simulated SFDR=62.7 dB and SNDR=57 dB. Simulations also indicate more than 6.7 effective number of bits at f/sub in/=15.85 MHz. Thus a high input signal bandwidth is demonstrated. Power dissipation is estimated to be less than 90 mW from a 3.0 V supply. Die area is 2.7 mm/sup 2/ when implemented in a 0.8 /spl mu/m digital CMOS process.
Behaviors of transform domain backpropagation (BP) algorithm

Several discrete orthogonal transforms have been used to study the behaviors of transform-domain backpropagation (BP) algorithms. Two examples of computer simulation show that, on selecting the appropriate parameters and the suitable structures of a neural network, the performance of the transform-domain BP algorithm is somewhat better than that of the original time-domain BP algorithm, regardless of which discrete orthogonal transform is applied. Among the transforms that have been used, the behaviors of the discrete cosine transform (DCT) and an alternative version of it are believed to be the best. <<ETX>>
Tear it down, build it up: the Research Output Team, or the library-as-publisher

Academic publishing is in an unstable period of transition. There is a growing degree of anger, especially from early-career academics in a time of austerity, at perceived publisher extortion. The current models of open access do not address all these problems, especially with regard to library budgets. While some of this anger is misplaced, publishers must either self-discipline their practices or find themselves displaced by alternate models. Libraries, likewise, face a dangerous time in which the threat of third-party tender for their services brings extinction ever closer. This article begins to outline a system dubbed the ‘Research Output Team’ (ROT) that moves publishing in-house to institutional libraries, thereby safeguarding publishing jobs and expertise while defeating the problematic aspects of the existing set-up.
Internet flow blocking probability calculation

A queuing analysis based on Internet traffic measurements is provided. First a finite-size Pareto queuing model for the heavy-tailed behavior of Internet flow traffic is presented. The derivation of the flow blocking probability with a finite buffer based on Internet flow traffic is provided. The flow blocking probability is given as a function of the buffer size, system load and parameter /spl alpha/ of the measured flow duration distribution. These results are very useful for design trade-off of admission control algorithm or general network engineering where heavy tail phenomenon exists. In addition the behavior of the Internet consecutive packet loss duration (outage duration) is studied. The implicit relationship between heavy tail flow duration and heavy tail outage duration is identified in the queuing aspect.
Trap-driven memory simulation

The use of trace-driven simulation in the evaluation of computer memory systems has been popular for at least 30 years. Despite considerable progress in the development of this method, the best trace-driven simulators still run at least one to two orders of magnitude slower than actual hardware and it is unlikely that they will exhibit further substantial improvements in speed. Trap-driven simulation is a new method for memory-system simulation that overcomes the bottlenecks inherent in trace-driven simulation speed. By invoking a memory-system simulator only on references that cause a change in the simulated memory state, trap-driven simulators can potentially reduce memory-system slowdowns to zero. Although promising in principle, little is known about how trap-driven simulation compares, in practice, to trace-driven simulation. Through a literature survey of trace-driven simulation and the implementation of a prototype trap-driven simulator, Tapeworm II, this dissertation compares the trace-driven and trap-driven methods on the basis of their flexibility, portability, speed, and accuracy. We show that trap-driven simulation offers clear advantages over trace-driven simulation with respect to speed and accuracy. With some exceptions, trap-driven simulation is nearly as flexible as trace-driven simulation, but suffers from certain problems of portability. We suggest methods for alleviating these problems, through simulation-host hardware support in existing and future systems.
Normative and Descriptive Models of Military Decisions to Deploy Precision Strike Capabilities

Precision strike capabilities represent a significant and highly controversial part of present day military operations. And yet, there is a surprising dearth of empirical research on military decision making in this domain. In this article, we therefore review different psychological perspectives on how these decisions can be made. Specifically, we compare the application of normative models of judgment and choice against the empirical research on human decision making, which suggests that people are more likely to employ heuristic strategies. We suggest that several features of decision tasks in the precision strike domain evoke the use of intuitive (heuristic) decision making whereas other features such as the sometimes unfamiliar (or novel) nature of the decision task requires analytic strategies to generate good solutions. Therefore, decisions about precision strike capabilities are best made with a mixture of intuitive and analytic thought, a mode of thinking known as quasirationality.
Competing with Knowledge: The Information Professional in the Knowledge Management Age

Introducing a new hobby for other people may inspire them to join with you. Reading, as one of mutual hobby, is considered as the very easy hobby to do. But, many people are not interested in this hobby. Why? Boring is the reason of why. However, this feel actually can deal with the book and time of you reading. Yeah, one that we will refer to break the boredom in reading is choosing competing with knowledge the information professional in the knowledge management as the reading material.
software to Aid Collaboration: Focus on Collaborative Authoring

Crossovers are fabricated by forming a conductive element on a carrier member and deforming selected portions of the conductive element into arches. The arches are formed either prior to or during bonding so that after bonding the arches cross over any intervening circuit elements. The carrier member is associated with a backing member having slots at those portions of the conductive element which are to be formed into arches. Deformation of the conductive elements into the slots is employed to form the arches. An intermediate material can be used to separate the conductive element from the circuit patterns at selected crossover points so that the conductive element is deformed into the slots during bonding. Or, the conductive elements can be deformed into the slots prior to bonding.
Interaction between fast lexical access and word verification in large vocabulary continuous speech recognition

Recently a two step strategy for large vocabulary isolated word recognition has been successfully experimented. The first step consists in the hypothesization of a reduced set of word candidates on the basis of broad bottom-up features, while the second one is the verification of the hypotheses using more detailed phonetic knowledge. This paper deals with its extension to continuous speech. A tight integration between the two steps rather than a hierarchical approach has been investigated. The hypothesization and the verification modules are implemented as processes running in parallel. Both processes represent lexical knowledge by a tree. Each node of the hypothesization tree is labeled by one of 6 broad phonetic classes. The nodes of the verification tree are, instead, the states of sub-word HMMs. The two processes cooperate to detect word hypotheses along the sentence. <<ETX>>
Encoding and Reconstruction about Video Image via Compressed Sensing

A new method for encoding and reconstruction high quality video image is given in this paper which uses the theory of compressed sensing. First the image frame of video is transformed into DCT domain. Then Image coding and decoding process using CS theory is given, frame I in image sequences is coded by frame coding mode after doing CS sampling to the DCT coefficients and the difference vector dv of the t-th fame for fame P. CS reconstruction and IDCT are done during decoding. Finally, the high quality reconstructed image is obtained. The experimental results shows that for images with sparseness, the image coding and decoding system integrated with CS theory and its methods can be used to obtain reconstructed images with high quality, and comparing with DCT and IDCT method, the method has some improvement in the term of PSNR for general images.
A smart camera with gesture recognition and SLAM capabilities for mobile robots

This paper presents a smart camera for a real-time gesture recognition system. The smart camera has been designed and implemented as a system-on-a-chip (SoC), using reconfigurable computing technology. In this system the gesture images are captured by means of a CMOS digital camera. After some pre-processing steps, those are sent to a fault tolerant module (FTM) for the actual recognition process. The FTM implements a RAM-based neural network, using three knowledge bases. In addition, a majority voting technique is used to improve the confidence level in the recognition step. A number of experiments using a prototype implementation and selected gestures resulted in a rate of 100% true recognition. The system also showed to be robust, and flexible as new gesture patterns can be easily included by means of the on-chip training capabilities. An application for this systems is also presented, consisting of four smart cameras used in simultaneous localization and mapping (SLAM) tasks for robotics.
Scaling Roll Call Votes with wnominate in R

This paper presents a software package designed to estimate Poole and Rosenthal W-NOMINATE scores in R. The package uses a logistic regression model to analyze political choice data, usually (though not exclusively) from a legislative setting. In contrast to other scaling methods, W-NOMINATE explicitly assumes probabilistic voting based on a spatial utility function, where the parameters of the utility function and the spatial coordinates of the legislators and the votes can all be estimated on the basis of observed voting behavior. Building on software written by Poole in Fortran, the new wnominate package in R facilitates easier data input and manipulation, generates bootstrapped standard errors, and includes a new suite of graphics functions to display the results. We demonstrate the functionality of this package by conducting a natural experiment using roll calls -- an experiment which is greatly simplified by the data manipulation capabilities of the wnominate package in R.
NLTK-Lite: Efficient Scripting for Natural Language Processing

The Natural Language Toolkit is a suite of program modules, data sets, tutorials and exercises covering symbolic and statistical natural language processing. NLTK is popular in teaching and research, and has been adopted in dozens of NLP courses. NLTK is written in Python and distributed under the GPL open source license. Over the past year the toolkit has been completely rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python language. This paper reports on the resulting, simplified toolkit, NLTK-Lite, and shows how it is used to support efficient scripting for natural language processing.
Scheduling Rules for Parallel Processors

Abstract This paper investigates various criteria under which simple scheduling rules generate optimum schedules for m machine scheduling problems. The problems are considered under a “job-splitting” assumption. Some of the rules which generate optimum schedules with job splitting for the multi-machine problems, do not split jobs for the single machine problems. The relationship between the job splitting and no job splitting problem in these cases, is analogous to the situation when an optimum solution to a linear program has all the variables integer and hence is optimum to the corresponding integer program.
Effectiveness of the backoff hierarchical class n-gram language models to model unseen events in speech recognition

Backoff hierarchical class n-gram language models use a class hierarchy to define an appropriate context. Each node in the hierarchy is a class containing all the words of the descendant nodes (classes). The closer a node is to the root, the more general the corresponding class, and consequently the context, is. We demonstrate experimentally the effectiveness of the backoff hierarchical class n-gram language modeling approach to model unseen events in speech recognition: improvement is achieved over regular backoff n-gram models. We also study the performance of this approach on vocabularies of different sizes and we investigate the impact of the hierarchy depth on the performance of the model. Performance is presented on several databases such as switchboard, call-home and Wall Street Journal (WSJ). Experiments on switchboard and call-home databases, which contain a few unseen events in the test set, show up to 6% improvement on unseen events perplexity with a vocabulary of 16,800 words. With a relatively large number of unseen events on the WSJ test corpus and using two vocabulary sets of 5,000 and 20,000 words, we obtain up to 26% improvement on unseen events perplexity and up to 12% improvement in WER when a backoff hierarchical class trigram language model is used on an ASR test set. Results confirm that improvement is achieved when the number of unseen events increases.
The architecture design for a terabit IP switch router

We propose an architecture for scalable, high capacity IP switch routers in which we apply fixed-length packet switching technology to support high speed IP forwarding. The proposed IP switch router consists of routing controllers, routing modules and a switch plane. The architectures of routing modules and switch plane whose aggregate capacity can be as large as ten terabit are proposed. The throughput of the IP switch router is guaranteed by a fixed-length packet switch plane which is able to emulate the ideal output queueing (OQ) switch in terms of throughput and average internal queueing delay. The performance of the switch plane is presented and the link list operations of routing modules are investigated.