Miracle-dz/UniFAR-FTUs huggingface dataset:
The Syntax of American Sign Language: Functional Categories and Hierarchical Structure

Recent research on the syntax of signed language has revealed that, apart from some modality-specific differences, signed languages are organized according to the same underlying principles as spoken languages. This book addresses the organization and distribution of functional categories in American Sign Language (ASL), focusing on tense, agreement and "wh"-constructions. Signed languages provide illuminating evidence about functional projections of a kind unavailable in the study of spoken languages. Along with manual signing, crucial information is expressed by specific movements of the face and upper body. The authors argue that such nonmanual markings are often direct expressions of abstract syntactic features. The distribution and intensity of these markings provide information about the location of functional heads and the boundaries of functional projections. The authors show how evidence from ASL is useful for evaluating a number of recent theoretical proposals on, among other things, the status of syntactic agreement projections and constraints on phrase structure and the directionality of movement.
Cataloging and Classification: An Introduction

Preface PART ONE Introduction Recommended Reading 1 Information Resource Management: Description, Access, Organization PART TWO Description Standards and Tools Recommended Reading 2 Foundations, Principles, and Standards of Resource Description 3 Anglo-American Cataloguing Rules: Description 4 Dublin Core and Other Metadata Schemas PART THREE Access and Authority Control Standards and Tools Recommended Reading 5 Access Points: Name and Title 6 Authority Control: Forms of Name Headings and Uniform Titles PART FOUR Subject Access and Controlled Vocabularies Standards and Tools Recommended Reading 7 Principles of Controlled Vocabularies and Subject Analysis 8 Library of Congress Subject Headings 9 FAST (Faceted Application of Subject Terminology) 10 Sears List of Subject Headings 11 Medical Subject Headings PART FIVE Organization of Library Resources Standards and Tools Recommended Reading 12 Classification and Categorization 13 Dewey Decimal Classification 14 Library of Congress Classification 15 National Library of Medicine Classification and Other Modern Classification Schemes PART SIX Encoding Formats and Production of Cataloging Metadata Records Standards and Tools Recommended Reading 16 MARC Formats and Encoding Schemas 17 Producing and Processing Cataloging Records Appendix A: MARC Bibliographic Records Appendix B: MARC Authority Records Appendix C: Key to Exercises Glossary Bibliography Index About the Author
Time versus frequency domain channel estimation for OFDM systems with antenna arrays

We compare channel estimation schemes in the time and frequency domains in terms of the error performance of both the estimated channel impulse response (CIR) and transfer function (TF) as well as the resulting bit-error rate (BER) in an orthogonal frequency-division multiplexing (OFDM) system with a time-multiplexed preamble. If the total number of sub-carriers, K, exceeds the number of taps, L, in the CIR, as is the case in most practical OFDM systems, the TF estimate based on the time domain least squares (LS) scheme is more accurate than the one obtained from direct LS estimation in the frequency domain. An equivalent mean-squared error performance results for the case K=L.
Are we teaching the HCI that we want to teach rather than what they need to learn

In this position paper we discuss the motivations for our research in HCI education and practice. Our findings support the case for a living curriculum, sensitive and responsive to the contrasting cultures and cognitive styles of educators, professionals and students. Our data reflects a predominance of analytic over intuitive styles in HCI educators, which has implications for curriculum development and delivery.
Connections, Binding, Unification and Analogical Promiscuity

This paper claims that higher cognition implemented by a connectionist system will be essentially analogical, with analogical mapping by continuous systematic substitution as the core cognitive process. The centrality of analogy is argued to be necessary in order for a connectionist system to use representations that are effectively symbolic. In turn, these representations are argued to be a necessary consequence of a sequence of broad design decisions needed to address technical problems in adapting a connectionist system for higher cognition. The design decisions are driven by the demands of a paradigmatic cognitive task and the desire to remain faithful to the constraints of connectionist components. Thus, the argument explains the origin of symbolic representations and analogy as necessary consequences of task demands and connectionist processing capabilities.
Ant colony optimization approach for stacking configurations

Ant Colony Optimization Approach for Stacking Configurations by Chen Yijun Master of Philosophy In data mining, classifiers are generated to predict the class labels of the instances. An ensemble is a decision making system which applies certain strategies to combine the predictions of different classifiers and generate a collective decision. Previous research has empirically and theoretically demonstrated that an ensemble classifier can be more accurate and stable than its component classifiers in most cases. Stacking is a well-known ensemble which adopts a two-level structure: the base-level classifiers to generate predictions and the meta-level classifier to make collective decisions. A consequential problem is: what learning algorithms should be used to generate the base-level and meta-level classifiers in the Stacking configuration? It is not easy to find a suitable configuration for a specific dataset. In some early works, the selection of a meta classifier and its training data are the major concern. Recently, researchers have tried to apply metaheuristic methods to optimize the configuration of the base classifiers and the meta classifier. Ant Colony Optimization (ACO), which is inspired by the foraging behaviors of real ant colonies, is one of the most popular approaches among the metaheuristics. In this work, we propose a novel ACO-Stacking approach that uses ACO to tackle the Stacking configuration problem. This work is the first to apply ACO to the Stacking configuration problem. Different implementations of the ACO-Stacking approach are developed. The first version identifies the appropriate learning algorithms in generating the base-level classifiers while using a specific algorithm to create the meta-level classifier. The second version simultaneously finds the suitable learning algorithms to create the base-level classifiers and the meta-level classifier. Moreover, we study how different kinds on local information of classifiers will affect the classification results. Several pieces of local information collected from the initial phase of ACO-Stacking are considered, such as the precision, f-measure of each classifier and correlative differences of paired classifiers. A series of experiments are performed to compare the ACO-Stacking approach with other ensembles on a number of datasets of different domains and sizes. The experiments show that the new approach can achieve promising results and gain advantages over other ensembles. The correlative differences of the classifiers could be the best local information in this approach. Under the agile ACO-Stacking framework, an application to deal with a direct marketing problem is explored. A real world database from a US-based catalog company, containing more than 100,000 customer marketing records, is used in the experiments. The results indicate that our approach can gain more cumulative response lifts and cumulative profit lifts in the top deciles. In conclusion, it is competitive with some well-known conventional and ensemble data mining methods.
Fuzzy Logic Based Battery Charger For Inverter

In this paper Li-Ion battery charger techniques are reviewed and compared. The fuzzy logic battery charging method is proposed to optimize and improve the battery charger performance, reducing damage of battery during the charging process. Some recent designs are discussed and the results are compared. According to results of comparison, the fuzzy control charging system can shorten the charging time with higher efficiency and lower temperature rise. Additionally, we have used optimal Li-ion battery charging frequency by using AC impedance technique which means if the battery is charged by the optimal charging frequency fZmin, the charging time and charging efficiency will improve. The proposed charger is capable of charging the Li-ion batteries with higher efficiency 97.16%, lower temperature rise and life cycle of battery is improved.
Performance Analysis of Cascaded H-Bridge Multilevel Inverter Fed PMSM Drive

In this paper, 3 phase cascaded Half-Bridge (CHB) multilevel inverter topology is presented based on power cells connected in cascaded mode using two inverter legs in series. Half-Bridge cells are usually connected in cascaded mode on their ac side to achieve medium-voltage operation and reduction of harmonic effect. Both IGBT/Diode switches are used in same leg with input voltage as Vdc/2. The severity of explosion of power cell is reduced to half by this connection as half power is dissipated in switches. It improves reliability of system. Three phase cascaded Half-bridge multilevel inverter is designed by using phase shift multicarrier pulse width modulation fed permanent magnet synchronous motor (PMSM) drive for best results in high power high voltage applications. Simulation results were carried out using MATLAB/Simulink. Steady state and dynamic performance of drive is validated. Keywords— Cascaded System; Multilevel System Inverters; Pulse Width Modulation (PWM); permanent magnet synchronous motor (PMSM); Medium-voltage drives
Descoberta da topologia de redes dinâmicas e descentralizadas com agentes móveis inteligentes

This paper introduces a strategy based on mobile agents and swarm intelligence for topology discovery in dynamic and decentralized networks, such as unstructured peer-to-peer networks and wireless mobile ad hoc networks. The proposed strategy follows a metaphor inspired by ant colonies, employing simple agents that disseminate information about the topology and communicate through stigmergy — a scheme used by real-world ants. Experimental results show that the nodes obtain descriptions that are very close to the real network topology. It is also shown that the stigmergy-based method for the selection of agent destinations produces better results than a random method, and that the number of agents can be dynamically adjusted as the size of the network changes. Resumo. Neste trabalho, e proposta uma estrategia baseada em agentes moveis e swarm intelligence para a descoberta da topologia de redes dinâmicas e descentralizadas, tais como redes peer-to-peer nao-estruturadas e redes moveis sem fio ad hoc. A estrategia proposta segue uma metafora inspirada em colonias de formigas, empregando agentes simples que disseminam informacoes sobre a topologia e se comunicam atraves da estigmergia — forma de comunicacao usada por formigas reais. Resultados experimentais mostram que a estrategia produz descricoes bastante similares a topologia real. Tambem e mostrado que a selecao de destinos baseada em estigmergia produz resultados melhores que os obtidos com uma selecao aleatoria; e que a populacao de agentes pode ser ajustada dinamicamente as mudancas de tamanho da rede.
DESIGN AND IMPLEMENTATION OF A SOFTWARE SYSTEM FOR DETECTING ORTHOGRAPHICAL OR MORPHOLOGICAL ERRORS IN PERSIAN WORDS

This paper presents a new method for analyzing words in the Persian language context to find orthographical and structural errors regardless of the meaning. This technique tokenizes each word in a statement then tries to detect the kind of word, and analyses its correctness in terms of orthography and morphology by means of a lexicon. It should be noted that some words in the Persian language have the same stem, which are constructed by adding particles to them according to certain rules. For these words the researchers present a new method to reduce the size/volume of the lexicon and to quicken in searching.
EXTENSION OF MICROINSTRUCTION FORMAT IN COMPOSITIONAL MICROPROGRAM CON TROL UNIT WITH ELEMENTARIZATION OF OPERATIONAL LINEAR CHAINS

The modification of synthesis method of compositional microprogram control units is directed to decrease of hardware amount in scheme of control unit in FPGA basis. Reduction of complexity of block of microinstruction addressing is reached due to of field with pseudoequivalent operational linear chain class code. Conditions of proposed method usage possibility are given. The example of method implementation is shown.
MOSFET stair-shaped I-V circuit and applications

A new circuit block having a stair-shaped I-V curve is described. The proposed circuit is based on novel configuration of three cascode branches. Furthermore, 4-level quantizing inverter and 4-level latch utilizing the proposed circuit are also presented. Unlike the previous work, these works are compatible with a standard CMOS technology.
Binding of audio elements in the sound source segregation problem via a two-layered bio-inspired neural network

We use a two-layered bio-inspired neural network to segregate sound sources, i.e. double-vowels or intruding noises in speech. The architecture of the network consists of spiking neurons. The spiking neurons in both layers are modelized by relaxation oscillators. The first layer of the network is locally connected, while the second layer is a fully connected network. Our auditory image is based on the reassigned spectrum technique. No prior estimation or knowledge of pitch is necessary for the segregation.
Comparison of DC offset effects in four LMS adaptive algorithms

It is well known that DC offsets degrade the performance of analog adaptive filters. In this paper, the effects of DC offsets on four variations of the stochastic gradient algorithm are analyzed. Assuming a Gaussian probability distribution for the input signal and error signal, the output mean squared error (MSE) performance in the presence of DC offsets is evaluated for each of the algorithms. The theoretical work is compared with computer simulations and the results, together with convergence properties of each of the algorithms and their respective hardware requirements, are used in selecting the most appropriate algorithm. Although a Gaussian input distribution is assumed, it may reasonably be inferred that the critical results obtained should also hold for other input distributions. >
Editorial: Affective shared perception

COPYRIGHT © 2022 Sciutti, Barros, Castellano and Nagai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: A ective shared perception
Adding input and output to the transactional model

Many computing systems provide transactions to facilitate the creation of reliable software. Transactions allow programmers to specify that a sequence of operations are atomic; either they all occur, or none of them occur. Existing transaction processing systems have concentrated on supporting operations that access or update long-lived data objects. External operations, such as user input/output or controlling a real world device, are typically prohibited. This dissertation extends the transactional model to include many types of external operations, especially user interaction. First, I describe the transactional typescript, a fully implemented piece of system software which uses the techniques of tentative output and a recoverable queue of user keystrokes to implement transactional input and output operations. An additional feature of this system is that after a system or hardware failure, when the system is restarted the user's display is completely restored to the time of the failure, including the location and contents of multiple windows on a bit-map display. The second contribution is the creation of a taxonomy of external events, based on their salient properties (deferrability, compensability, testability, idempotence, and safety). The taxonomy places all transactional operations into one of six classes. Two of the classes are problematic, and operations in these classes cannot always be implemented with proper semantics. Based on the taxonomy, I have designed an external operation server for the Camelot transaction processing system under development at Carnegie Mellon. This work describes a transactional command shell, similar to the UNIX$\sp{\rm TM}$ shell, but including primitives to begin and end transactions. The design describes how to implement each of the operation classes from the taxonomy, and also provides mechanisms for handling the large number (tens of thousands) of physical devices that are often configured to use such a system. The work concludes by analyzing the server design in the context of ET1, a standard industry benchmark for transaction processing systems.
Linear and quadratic local models for ICE-Networks

ICE-Networks are hybrid Neural Networks that are capable of fast initial learning and continuous learning, They have been developed for predicting the development of technical processes. ICE-Networks have a dynamic structure, they are built up starting from empty networks during the training process. This construction process is continued as long as the network is in use thus the network can yield an actual prognosis at any time. An ICE-Network is a layered network consisting of four layers. The units of the first hidden layer are RBF-neurons, called prototypes, and combine subsets of input vectors into so called local models that are maintained in the units of the second hidden layer. The type of the local models can be predefined by the developer of the ICE-Network, they can be linear or of higher order. In this paper the preciseness of the prognosis made by linear and quadratic models and the efficiency of computing those models are compared.
Bridging semantic gap

Software interoperation by semantics ensures that the requestor and the provider have a common understanding of the requested information. An example of semantic heterogeneity is the use of synonyms, such as employees or staff, which are used to refer to the same concept in different information systems. This type of software interoperation includes the semantics of the user queries and of information sources. The bridging gap pattern intends to bridge the semantic gap between the requestor and the provider .
The plasticity of cardiac sympathetic nerves and its clinical implication in cardiovascular disease

The heart is electrically and mechanically controlled by the autonomic nervous system, which consists of both the sympathetic and parasympathetic systems. It has been considered that the sympathetic and parasympathetic nerves regulate the cardiomyocytes’ performance independently; however, recent molecular biology approaches have provided a new concept to our understanding of the mechanisms controlling the diseased heart through the plasticity of the autonomic nervous system. Studies have found that cardiac sympathetic nerve fibers in hypertrophic ventricles strongly express an immature neuron marker and simultaneously cause deterioration of neuronal cellular function. This phenomenon was explained by the rejuvenation of cardiac sympathetic nerves. Moreover, heart failure and myocardial infarction have been shown to cause cholinergic trans-differentiation of cardiac sympathetic nerve fibers via gp130-signaling cytokines secreted from the failing myocardium, affecting cardiac performance and prognosis. This phenomenon is thought to be one of the adaptations that prevent the progression of heart disease. Recently, the concept of using device-based neuromodulation therapies to attenuate sympathetic activity and increase parasympathetic (vagal) activity to treat cardiovascular disease, including heart failure, was developed. Although several promising preclinical and pilot clinical studies using these strategies have been conducted, the results of clinical efficacy vary. In this review, we summarize the current literature on the plasticity of cardiac sympathetic nerves and propose potential new therapeutic targets for heart disease.
Construction scheduling, cost optimization, and management : a new model based on neurocomputing and object technologies

1. Review of Construction Scheduling and Management Methods. 2. Neural Dynamics Model of Adeli and Park and its Application to Engineering Design. 3. A General Mathematical Formulation for Scheduling of Construction Projects with Construction Cost Optimization. 4. Solution of Construction Scheduling and Cost Optimization with Neural Dynamics. 5. An Object-Oriented Information Model for Construction Project Management. 6. CONSCOM: An Object-Orient Construction Scheduling and Change Management System. 7. Integrated Management Environment and User Interface. 8. Regularization Neural Network for Construction Cost Estimation. References. Appendix Nomenclature.
Fitness monitor system

According to U.S. Department of Health and Human Services, 26% of U.S. adults between the ages of 20 through 74 are overweight. Exercise is a consequential and rewarding strategy to lose body fat and improve one's health. Throughout a large portion of aerobic exercise, a linear relationship exists between heart rate intensity of the exercise and the oxygen consumption (and therefore caloric expenditure). Thus if the heart rate is known, caloric consumption can be reasonably estimated. This paper introduces a new piece of electronic device - fitness monitor system, specially designed for heart rate monitoring and weight management. Its output parameters prefigure one's fitness level and meanwhile give warning if there's health abnormality.
Neural network edge detectors for separation of particles in 2-D grey-scale images

A method is developed for edge detection in grey-scale images of particles using artificial neural network classifiers. The edge detection is used to separate the individual particles in the image especially where the particles are touching each other. Once the particles are separated individual measurements can be made to compile accurate size-distribution information. Images of eighty calibrated gravel stones are used to test the method. Gravel stones are adopted as abstracts for the general class of irregularly shaped particles. Comparisons are made between probabilistic neural network multilayer perceptron and Gaussian mixture model Bayesian classifiers for edge detection. Methods are developed to reduce the number of feature vectors and features in order to speed up the edge defection process without a loss in performance for the probabilistic neural network edge detector.