Miracle-dz/UniFAR-FTUs huggingface dataset:
Redes neuronales artificiales

Una RNA (Red Neuronal Artificial) es un modelo matemático inspirado en el comportamiento biológico de las neuronas y en la estructura del cerebro. Esta también puede ser vista como un sistema inteligente que lleva a cabo tareas de manera distinta a como lo hacen las computadoras actuales. Si bien estas últimas son muy rápidas en el procesamiento de la información, existen tareas muy complejas, como el reconocimiento y clasificación de patrones, que demandan demasiado tiempo y esfuerzo aún en las computadoras más potentes de la actualidad, pero que el cerebro humano es más apto para resolverlas, muchas veces sin aparente esfuerzo (considere el lector como ejemplo el reconocimiento de un rostro familiar entre una multitud de otros rostros). El cerebro puede considerarse un sistema altamente complejo. Su unidad básica, la neurona, está masivamente distribuida con conexiones entre ellas (se calcula que hay aproximadamente 10 billones de neuronas en la corteza cerebral y 60 trillones de conexiones neuronales). Si bien hay distintos tipos de neuronas biológicas, en la figura 1 se muestra un esquema simplificado de un tipo particular que es muy común. Vemos que la misma está compuesta por:
Combining static and dynamic features using neural networks and edge fusion for video object extraction

Semantic object representation is an important step for digital multimedia applications such as object-based coding, content-based access and manipulations. The authors propose an image sequence segmentation scheme which provides region information for the semantic object representation of those applications. The objective is to develop a hardware-friendly segmentation algorithm by combining static and dynamic features simultaneously in one scheme. In the initial stage, a multiple feature space is transformed to one-dimensional label space by using self-organising feature map (SOFM) neural networks. The next stage is an edge fusion process in which edge information is incorporated into the neural network outputs to generate more precisely located boundaries of segmentation. The proposed algorithm differs from existing methods as follows: it can segment textured images with low-dimensional features; leads to more meaningful segmentation region boundaries; and is easier to map into hardware than existing methods. Experimental results are compared with an existing segmentation method using evaluation metrics to clarify the advantages of the proposed algorithm objectively.
The detective story pattern in the shona folktale

This article discusses an aspect of Shona literature that has not been given much scholarly attention, the detective story genre. Although an attempt has been made to explain why perhaps students of literature have not concerned themselves seriously with detective fiction as a genre the major focus of the researcher is on discussing the origin of the genre. An effort has been painstakingly made to argue that the modern written Shona detective story has its 'roots' in non-detective traditional shona folktales and not in reading the detective stories written by western authors or in watching western detective dramas on television. A study of the morphology of some folktales reveals the existence of a 'detective' story plot in them. It is argued that the shona traditional folktale is a kind of oral 'detective' story that reflects traditional methods pf solving mysteries or puzzles and that rudimentary forms of the written Shona detective story should therefore be regarded as an extension or expansion of the non-detective traditional folktale
THE EFFECTS OF CULTURE ON VOICE LIKABILTY

This study investigated the effects of culture on voice likability assessments. A total of 32 Finns and 32 Estonians rated the likability of 40 Finnish and 40 Estonian female and male voices. The voices represented two phonogenres: poetry and interview. The results showed that Finns and Estonians liked the same voices, but the listeners preferred Finnish voices reading poetry to Estonians, and Estonian voices in interviews to Finnish. The gender and age of the speaker only had a low impact on likability ratings. An analysis of acoustic correlates of voice likability was also conducted, which showed that likable and unlikable voices were differentiated by a set of frequency, energy and spectral parameters, but not tempo parameters.
Handbook of fiber optics : theory and applications

S. E. Miller and I. P. Kaminow, Eds., Optical Fiber Telecommunications II, San I. P. Kaminow and A. E. Siegman, Eds., Laser Devices and Applications, IEEE, 1973. I. P. Kaminow, "Foreword," in Handbook of Optical Constants of Solids, I. P. Kaminow, "Book Review: Integrated Optics: Theory and Technology, ed. If you want to get Handbook of Fiber Optics: Theory and Applications pdf eBook copy write by good author Wolf, Helmut F. , you can download the book copy. G. L. Cariolaro, "Error probability in digital fiber optic communication systems," IEEE Trans. M. G. Kendall and A. Stuart, The Advanced Theory of Statistics (Hafer, New detection with application to FSK fiber-optic communications," IEEE Trans. M. Abramowitz and I. E. Stegun, Handbook of Mathematical Functions (U.S.
Eliglustat tartrate, a prototypic glucosylceramide synthase inhibitor

Eliglustat tartrate is a highly specific inhibitor of glucosylceramide synthase, developed for the treatment glucosylceramide-based glycosphingolipidoses. Eliglustat is in late clinical development for Gaucher disease type 1. Phase II and III clinical trials have demonstrated clinical efficacy for eliglustat as a stand-alone agent for newly diagnosed patients that are naïve to prior therapy and for patients who have been previously treated with enzyme replacement therapy. Importantly, the reported toxicity of eliglustat has been limited. Eliglustat will be submitted for the US FDA and EMA review in late 2013. Several structurally unrelated glucosylceramide synthase inhibitors have been identified and are in various stages of development, some of which cross the blood–brain barrier. Targeting glucosylceramide synthesis is also a promising approach for the treatment of type 2 diabetes mellitus, autosomal dominant polycystic kidney disease and certain cancers.
State of the Art Reviews: Glycemic Index, Obesity, and Chronic Disease

There is increasing evidence that both the amount and type of carbohydrate play an important role in weight management and risk of chronic disease. Classifying carbohydrates according to their post-prandial glycemic effect (ie, the glycemic index of foods) has yielded more useful insights than the historical distinctions of simple versus complex chemical structure. Diets based on carbohydrate foods that are more slowly digested and absorbed (ie, low glycemic index diets) have been independently linked to reduced risk of type 2 diabetes, cardiovascular disease, and some types of cancer. In individuals with diabetes, intervention studies have shown improvements in insulin sensitivity and glycated hemoglobin concentration with low glycemic index diets. Research also suggests that low glycemic index diets may assist with weight management through effects on satiety and fuel partitioning. Although ongoing research is needed, the current findings, together with the fact that there are no demonstrated negative effects of a low glycemic index diet, suggest that the glycemic index should be an important consideration in the dietary management and prevention of obesity and chronic disease.
A Conscious AI System Based on Recurrent Neural Networks Applying Dynamic Information Equilibrium

A basic structure and behavior of a human-like AI system with conscious like functions is proposed. The system is constructed completely with artificial neural networks (ANN), and an optimal-design approach is applied. The proposed system using recurrent neural networks (RNN) which execute learning under dynamic equilibrium is a redesign of ANN in the previous system. The redesign using the RNNs allows the proposed brain-like autonomous adaptive system to be more plausible as a macroscopic model of the brain. By hypothesizing that the “conscious sensation” that constitutes the basis for phenomenal consciousness, is the same as “state of system level learning”, we can clearly explain consciousness from an information system perspective. This hypothesis can also comprehensively explain recurrent processing theory (RPT) and the global neuronal workspace theory (GNWT) of consciousness. The proposed structure and behavior are simple but scalable by design, and can be expanded to reproduce more complex features of the brain, leading to the realization of an AI system with functions equivalent to human-like consciousness.
Two-Dimensional Software Defect Models with Test Execution History

In general, the software-testing time may be measured by two kinds of time scales: calendar time and test execution time. In this paper, we develop two-dimensional software reliability models with two-time measures and incorporate both of them to assess the software reliability with higher accuracy. Since the resulting software defect models are based on the familiar nonhomogeneous Poisson processes with two time scales, which are the natural extensions of one-dimensional software defect models, it is possible to treat the time data both simultaneously and effectively. We investigate the dependence of test-execution time as a testing effort on the software reliability assessment and validate quantitatively the software defect models with two-time scales. We also consider an optimization problem when to stop the software testing in terms of two-time measurements.
"Music is Waiting For You:" The Lived Experience of Children's Musical Identity

“MUSIC IS WAITING FOR YOU:” THE LIVED EXPERIENCE OF CHILDREN’S MUSICAL IDENTITY by L. Michelle Mercier-De Shon This phenomenological study of lived experience (Van Manen, 1990) explored the perspectives of four 4 grade children as they live in and live through music to formulate their musical identities. Framed within perspectives of symbolic interaction theory (Blumer, 1969), communities of practice (Wenger, 1998), and figured worlds (Holland, et al., 1998), data were collected using methods consistent with qualitative inquiry. These included: observations of quasi-formal music learning settings, in musical playgroups and during professional musicians’ presentations; close observations of children’s daily school lives; and planned discussion group interviews (O’Reilly, 2005). Findings emerged from the data via a bricolage of existentialist (Morrisette, 1999; Holyroyd, 2001) and interpretative phenomenological analyses (Smith, 2003). Children in my study explored and expressed their musical identities through selfdirected engagement across multiple modalities of singing, listening, performing on instruments, and creating music. They engaged with these modalities in individualized and shared ways. Singing was situated, by context and in concert with social and gender comparisons. Listening, performing, and creating encompassed a trajectory from experimentation to intentionality, with continually embedded exploration and musical play. Findings indicated that children in middle childhood may actively shape their musical identities within a dynamic nexus of individualized and social continuums of music experience and learning. These continuums may be understood along three dimensions: development; components, i.e., music participation and learning; and processes. The developmental spectrum of children in middle childhood provides a fluid context for understanding musical identity, revealed not as a fixed entity, but through interweaving elements of their past, present, and future musical lives. Self-directed music participation and learning may shape musical identity and provide a context for its expression through both musical and social roles, as children enact musical behaviors through social interaction. Finally, children’s musical identity may be understood as a process, in which personal dialogue meets external discourses, as children continuously negotiate self-conceptions of musicality within and among their musical worlds. Findings indicate that music teachers may offer opportunities for exploration and musical play as a basis for concurrently nurturing the development of musical identities and fostering musical understanding. “MUSIC IS WAITING FOR YOU:” THE LIVED EXPERIENCE OF CHILDREN’S MUSICAL IDENTITY by L. Michelle Mercier-De Shon
CFOND: Consensus Factorization for Co-Clustering Networked Data

Networked data are common in domains where instances are characterized by both feature values and inter-dependency relationships. Finding cluster structures for networked instances and discovering representative features for each cluster represent a special co-clustering task usefully for many real-world applications, such as automatic categorization of scientific publications and finding representative key-words for each cluster. To date, although co-clustering has been commonly used for finding clusters for both instances and features, all existing methods are focused on instance-feature values, without leveraging valuable topology relationships between instances to help boost co-clustering performance. In this paper, we propose CFOND, a consensus factorization based framework for co-clustering networked data. We argue that feature values and linkages provide useful information from different perspectives, but they are not always consistent and therefore need to be carefully aligned for best clustering results. In the paper, we advocate a consensus factorization principle, which simultaneously factorizes information from three aspects: network topology structures, instance-feature content relationships, and feature-feature correlations. The consensus factorization ensures that the final cluster structures are consistent across information from the three aspects with minimum errors. Experiments on real-life networks validate the performance of our algorithm.
Evaluating WorldClim Version 1 (1961–1990) as the Baseline for Sustainable Use of Forest and Environmental Resources in a Changing Climate

WorldClim version 1 is a high-resolution, global climate gridded dataset covering 1961–1990; a “normal” climate. It has been widely used for ecological studies thanks to its free availability and global coverage. This study aims to evaluate the quality of WorldClim data by quantifying any discrepancies by comparison with an independent dataset of measured temperature and precipitation records across Europe. BIO1 (mean annual temperature, MAT) and BIO12 (mean total annual precipitation, MAP) were used as proxies to evaluate the spatial accuracy of the WorldClim grids. While good representativeness was detected for MAT, the study demonstrated a bias with respect to MAP. The average difference between WorldClim predictions and climate observations was around +0.2 °C for MAT and −48.7 mm for MAP, with large variability. The regression analysis revealed a good correlation and adequate proportion of explained variance for MAT (adjusted R2 = 0.856) but results for MAP were poor, with just 64% of the variance explained (adjusted R2 = 0.642). Moreover no spatial structure was found across Europe, nor any statistical relationship with elevation, latitude, or longitude, the environmental predictors used to generate climate surfaces. A detectable spatial autocorrelation was only detectable for the two most thoroughly sampled countries (Germany and Sweden). Although further adjustments might be evaluated by means of geostatistical methods (i.e., kriging), the huge environmental variability of the European environment deeply stressed the WorldClim database. Overall, these results show the importance of an adequate spatial structure of meteorological stations as fundamental to improve the reliability of climate surfaces and derived products of the research (i.e., statistical models, future projections).
Discovering mathematics: The challenges that deaf/hearing-impaired children encounter

This paper reports on difficulties that deaf/hearing-impaired children may encounter when learning mathematics in early childhood settings. The objective was to enhance teacher practice by identifying possible ways in which barriers to learning could be overcome. The key issue arising revealed that deaf/hearingimpaired children's limited language base may prevent them from understanding and developing mathematical language. Other related issues that influenced mathematical development were parental involvement and comprehensive support in the home environment, together with a greater emphasis on the use of mathematical resources and specific strategies for teaching mathematics to deaf/hearing-impaired children.
A COMBINED DEEP LEARNING MODEL FOR PERSIAN SENTIMENT ANALYSIS

With increasing members in social media sites today, people tend to share their views about everything online. It is a convenient way to convey their messages to end users on a specific subject. Sentiment Analysis is a subfield of Natural Language Processing (NLP) that refers to the identification of users’ opinions toward specific topics. It is used in several fields such as marketing, customer services, etc. However, limited works have been done on Persian Sentiment Analysis. On the other hand, deep learning has recently become popular because of its successful role in several Natural Language Processing tasks. The objective of this paper is to propose a novel hybrid deep learning architecture for Persian Sentiment Analysis. According to the proposed model, local features are extracted by Convolutional Neural Networks (CNN) and long-term dependencies are learned by Long Short Term Memory (LSTM). Therefore, the model can harness both CNN's and LSTM's abilities. Furthermore, Word2vec is used for word representation as an unsupervised learning step. To the best of our knowledge, this is the first attempt where a hybrid deep learning model is used for Persian Sentiment Analysis. We evaluate the model on a Persian dataset that is introduced in this study. The experimental results show the effectiveness of the proposed model with an accuracy of 85%. ABSTRAK: Hari ini dengan ahli yang semakin meningkat di laman media sosial, orang cenderung untuk berkongsi pandangan mereka tentang segala-galanya dalam talian. Ini adalah cara mudah untuk menyampaikan mesej mereka kepada pengguna akhir mengenai subjek tertentu. Analisis Sentimen adalah subfield Pemprosesan Bahasa Semula Jadi yang merujuk kepada pengenalan pendapat pengguna ke arah topik tertentu. Ia digunakan dalam beberapa bidang seperti pemasaran, perkhidmatan pelanggan, dan sebagainya. Walau bagaimanapun, kerja-kerja terhad telah dilakukan ke atas Analisis Sentimen Parsi. Sebaliknya, pembelajaran mendalam baru menjadi popular kerana peranannya yang berjaya dalam beberapa tugas Pemprosesan Bahasa Asli (NLP). Objektif makalah ini adalah mencadangkan senibina pembelajaran hibrid yang baru dalam Analisis Sentimen Parsi. Menurut model yang dicadangkan, ciri-ciri tempatan ditangkap oleh Rangkaian Neural Convolutional (CNN) dan ketergantungan jangka panjang dipelajari oleh Long Short Term Memory (LSTM). Oleh itu, model boleh memanfaatkan kebolehan CNN dan LSTM. Selain itu, Word2vec digunakan untuk perwakilan perkataan sebagai langkah pembelajaran tanpa pengawasan. Untuk pengetahuan yang terbaik, ini adalah percubaan pertama di mana model pembelajaran mendalam hibrid digunakan untuk Analisis Sentimen Persia. Kami menilai model pada dataset Persia yang memperkenalkan dalam kajian ini. Keputusan eksperimen menunjukkan keberkesanan model yang dicadangkan dengan ketepatan 85%.
Animating Z specifications in Haskell using a monad

This paper illustrates a method of implementing a wide range of systems, specified in Z, in the lazy functional programming language Haskell. Input, output and state – three basic elements of Z – are usually handled in a somewhat non-intuitive way in functional languages. This is now remedied by the creation, by a process of semi-formal calculation and reasoning, of a monad which incorporates these features. By using the monad, Haskell programs can be written which are much closer in spirit and style to the Z specifications which they fulfill than those obtained using previous methods. (Note: A better typeset version of this paper, set in Monotype Bembo and printed at a resolution of 600 d.p.i., is available by post from the author.)
A semantics "outside language": Operational Semantics A new semantic theory, based on the nature and structure of thought

In this article, the author briefly introduces a new theory in Semantics, Operational Semantics (OS). OS deals with the meanings of all the basic linguistic elements that are indispensable for any linguistic expression, that is, mainly all the “grammatical” words (conjunctions, prepositions, articles, most pronouns and corresponding adjectives, fundamental verbs like “to be”, “to have” etc, the main adverbs) and, in the large number of languages that have a more or less rich morphology, almost all morphemes (the ones which indicate cases, in languages that have cases, the number and gender of nouns and adjectives, moods and tenses of verbs etc). The fundamental presupposition of OS is that the meanings of such linguistic elements are essentially sequences of elemental mental operations, amongst which the ones of attention play a key role. The author proposes a list of these elemental mental operations and shows how it is possible, by basing ourselves on these, to identify the meanings of the aforesaid linguistic elements. A new linguistic theory, dealing with the “deep” structure of language, derives from this. This theory also allows us to define the fundamental concepts of Linguistics (such as “noun”, “subject”, “object” etc) in a simple and clear way, propose new solutions for some other problems in Linguistics and Psycholinguistics, and open new research perspectives. In the last part of the article, the author also mentions a possible short-term practical application of OS, i.e. a device to improve the quality of machine translation, and highlights the limits of OS.
DESIGN OF 64 BIT ERROR TOLERANT ADDER

Adders are the core element of complex arithmetic operations like addition, multiplication, division, exponentiation etc. In most of these systems adder lies in the critical path that affects the overall speed of the system. This project introduces the design of low power error tolerant adder which acquires less area with the lowest transistor count. Enhanced performance of the proposed Error Tolerant adder in terms of power makes it suitable for portable image processing applications where minimum percentage of error is tolerable. The low power error tolerant adder circuit design and the simulation have been carried out on T a n n e r EDA tool. The result shows that t h e proposed low power error tolerant adder is an efficient adder cell with less MOS transistor count that reduces the high power consumption and it considerably increases the speed.
Iterative space-time detection of TCM for broadband wireless channels

This paper presents a turbo (iterative) equalization method for complex trellis coded modulation (TCM) signals over broadband wireless channels based on receiver antenna array measurements. The channel is highly dispersive at high data rates causing a severe intersymbol interference (ISI) effect and making the direct application of any trellis-based equalization algorithm infeasible. The problem of reducing the excess interference is solved by receiver diversity combining, i.e., using a linear antenna array and a broadband beamformer in the receiver. The beamformer output that contains less ISI is viewed as the output of a serial concatenated coding system and optimum symbol detection is achieved by an iterative equalization and decoding scheme. The proposed receiver structure is simulated for two-dimensional TCM signals such as 8-16 PSK and 16-QAM and the results indicate an improved performance of the diversity receiver.