Reviewerly/exHarmony huggingface dataset:
La enseñanza de la comunicación en la escuela secundaria en Mendoza: propuestas para un abordaje crítico del diseño curricular

This article shows the results of a research conducted in two stages between 2013 and 2017. In the first stage, the main objective was to reconstruct the repre­sentations of Social Communication teachers from secondary schools in Mendoza (Argentina) around teaching of communication. In the second stage, as a continuation of the previous one, the contents of the Provincial Curricular Design of the same subject were analyzed and resignified, taking into account the perspective of the critical media education (EMC). The assumption is that no media education can be called “critical” if the starting points when designing the processes of teaching and learning, and understanding the universe of media and technologies are not so. On the basis of the insights offered by the theories of resistance in education, critical curriculum theories and EMC, the contents on communication offered to school students are analyzed and challenged. This article is aimed at taking up again some questions that were part of the research: What is the critical dimension of media education? What pedagogical positions make it possible? What approaches should be prioritized in a really critical media education? How to question the curricular designs and schedules from a critical perspective to avoid the reproduction of hegemonic forms of communication?
Effects of Financial Development and Institutional Quality on the Economic Growth in The Arabian Gulf states: A Panel Cointegration Analysis

Many studies on the link between finance and growth, in Arab gulf countries, have suggested however, without demonstrating it, that the relationship between the two spheres goes through the country's specific institutional structure. Nevertheless, this relation is neither well analyzed nor stated in the case of the Gulf States, which justifies the problem of this article. Different estimates were made by the method of panel data for the period from 1995 to 2012 for six Arab Gulf Countries. It appears from these estimates that the quality of the institutions is the transmission vector of the financial sphere towards the real sphere for the case of these countries. So, the financial system cannot stimulate economic growth if it is accompanied by a “solid institutional framework” presented by a better bureaucracy, a fight against corruption, a strong legal framework and better socio-economic conditions.
A secure and lightweight user authentication mechanism for wireless body area network

Wireless Body Area Network (WBAN) is an IoT-based healthcare system which enhances the medical services significantly by enabling remote monitoring of patients' health condition. As WBAN handles sensitive health data, the security and privacy issues should be resolved with upmost priority for the wide acceptance of WBAN. User authentication is used in WBAN to verify the identities of communicating parties and prevent unauthorized access to patients' data. Authentication needs to be performed between sensor nodes and patient's mobile devices and between mobile devices and the application provider. In literature, an end-to-end user authentication mechanism between sensor nodes and medical experts is missing and integration of existing protocols for both communication scenarios is difficult due to their diverse nature. We propose a lightweight user authentication and session-key agreement protocol between sensor nodes and medical experts which addresses both communication patterns. The security analysis depicts that required security properties are preserved.
Implementation of DES Algorithm in New Non-Synchronous Architecture Aiming DPA Robustness

This paper proposes a pipelined architecture of the Data Encryption Standard (DES) Algorithm robust to Differential Power Analysis (DPA) based on globally asynchronous locally synchronous (GALS) project methodology using random clocks. The architecture was prototyped in the Intel Altera© FPGA Cyclone IV and EP4CE115F29C7 device is used as target technology. Robustness is achieved at the cost of only 6.25% average increasing in the use of logic elements. The DPA robustness of the proposal is analyzed through the statistics of the processing time along to 50000 encryptions.
Design and Implementation of an Efficient Web Crawling Using Neural Network

The number of users the usage of internet is mounting day by day. Currently, researches on the information using the retrieval model neural networks have been actively progressed for the retrieval of information and the classification of documents. Various types of algorithms have been applied for identification and quantification of the words weights in documents. As information technologies accelerate, it is necessary to understand the exact meaning of documents through analyzing the words, using the advanced methods of technologies. In this paper, specific keywords were used by word2vec to identify naturally fused word frequencies, semantic relationships, and directional text-ranks. Therefore, the neural network is the advanced mechanism to verify the semantic relationship between words and texts in a particular document. Our approach uses the Word2vec to capture the semantic features between words in the selected text, and meanwhile naturally integrate the word frequency, semantic relation.
Semantic matching of cloud resource service for user product design requirement

Under the cloud manufacturing (CMfg) environment, a multilayer semantic matching model of design requirement and cloud service resource based on ontology was proposed to guarantee the rapid and accurate matching of resource services to meet the cloud user design requirement. First, the CMfg classification of demand and service was analyzed, and the ontology description was derived according to the service resource classification. From the aforementioned service resource classification, the multilayer matching model was established. The model included six matching stages, namely, base information matching, function information matching, state information matching, quality of service information matching, manufacturing capability information, and comprehensive matching. Then, the matching algorithm of each layer was quantitatively established and described to achieve intelligent matching of the service resource. Meanwhile, an improved analytic hierarchy process was presented to express the influence of each layer on its matching goal scientifically, ensuring that each affecting factor is reasonable. Finally, a case was used to prove the correctness and practicality of the proposed method.
Toward a Sustainable Handling of Interlinear-Glossed Text in Language Documentation

While the amount of digitally available data on the worlds’ languages is steadily increasing, with more and more languages being documented, only a small proportion of the language resources produced are sustainable. Data reuse is often difficult due to idiosyncratic formats and a negligence of standards that could help to increase the comparability of linguistic data. The sustainability problem is nicely reflected in the current practice of handling interlinear-glossed text, one of the crucial resources produced in language documentation. Although large collections of glossed texts have been produced so far, the current practice of data handling makes data reuse difficult. In order to address this problem, we propose a first framework for the computer-assisted, sustainable handling of interlinear-glossed text resources. Building on recent standardization proposals for word lists and structural datasets, combined with state-of-the-art methods for automated sequence comparison in historical linguistics, we show how our workflow can be used to lift a collection of interlinear-glossed Qiang texts (an endangered language spoken in Sichuan, China), and how the lifted data can assist linguists in their research.
ARKLib: An Augmented Reality Library for Applications using Kinect

In the course of the emergence of new technological solutions, ways of applying them must be studied and proposed. In this context, the objective of this work was to use the augmented reality to the merchandising, increasing the number of technological options for the improvement of conventional commercial establishments. We proposed and developed a library that makes use of Microsoft Kinect to help developers build augmented reality applications that can be used in physical retail commerce as marketing tools, allowing passers-by to interact with the application. The goal is for them to become shop customers during the interaction process. Finally, the application of an interactive virtual showroom was developed to show the practical utility of the tool for both developers and the commercial retail sector.
Classification of Eimeria species from digital micrographies using CNNs

This paper presents a model for the classification of the seven species of avian Eimeria, the protozoan parasite that causes avian coccidiosis. Digital micrographs dataset consists of 4485 isolated samples of the various species of oocytes (status of the Eimeria protozoon in which the internal structure is visually different in each species). The proposed solution applied a convolutional neural network architecture for the classification of the oocytes. Different experiments were developed to enhance the previous results of the literature, and with our proposal, we obtained a better average of correct classification for the seven species, reaching 90.42% of precision. Finally, with our strategy we used for the first time a CNN model over the Eimeria dataset, demonstrating that CNN is a robust technique for artificial vision problems.
La educación mediática y el uso de los recursos tecnológicos en el aula en el contexto iberoamericano

The present article aims to present a reflection, within the Ibero-American context, on the use of digital resources in the classroom and its relationship with media education to highlight common points and diverse strategies in addressing both aspects in education systems, as well as to establish the main challenges for a real media education that could take advantage of new technologies in the classroom. The methodology used for this purpose is mainly based on a systematic review of the litera­ture on the subject performed between the years 2012 and 2018. Moreover, the article addresses some of the results of the “Citizen’s media competencies in emerging digital media in university environments” project (2016-2019) from the National Research, Development and Innovation (I+D+I) Plan of the Spanish Government. Additionally, it presents reflections from experts in media competency of the Euro-American inter-university research network Alfamed. The main conclusions include specific initiatives and particular experiences, but they do not imply an integral and innovative use of information and communication technologies. Beyond the inequality in access oppor­tunities, Ibero-American countries make instrumental use of technological resources in the classroom and their potential is not used for a real media education based on innovation in methodologies that could address the challenges that current education system is facing. This article, more than providing new results, entails a systematiza­tion and a comparative framework on the evolution, innovative experiences and current situation of the use of technologies in education in different Ibero-American countries.
Parallel fast Fourier transform in SPMD style of Cilk

In this paper, we propose a parallel one-dimensional non-recursive fast Fourier transform (FFT) program based on conventional Cooley-Tukey's algorithm written in C using Cilk in single program multiple data (SPMD) style. As a highly compact designed code, this code is compared with a highly tuned parallel recursive fast Fourier transform (FFT) using Cilk, which is included in Cilk package of version 5.4.6. Both algorithms are executed on multicore servers, and experimental results show that the performance of the SPMD style of Cilk fast Fourier transform (FFT) parallel code is highly competitive and promising.
Software and hardware for a portable heart monitor

The portable cardiomonitor is developed for ensuring constant observation of a status of the patient. It allows to trace in real time a warm rhythm of the person. A possibility of data storage and transfers on Wi-Fi to the mobile device or the computer is provided in it. At emergence of emergency situations the cardiomonitor is capable to notify the patient in the form of a sound signal or a signal on the device monitor.
Generating Accurate Candidate Windows by Effective Receptive Field

Towards involving the convolutional neural networks into the object detection field, many computer vision tasks have achieved favorable successes. In order to adapt targets with various scales, deep feature pyramid is widely used, since the traditional object detection methods detect different objects in Gaussian image pyramid. However, due to the mismatching between the anchors and the feature distributions of targets, the accurate detection for targets with various scales is still a challenge. Considering the differences between the theoretical receptive field and effective receptive field, we propose a novel anchor generation method, which takes the effective receptive field as the standard. The proposed method is evaluated on the PASCAL VOC dataset and shows the favorable results.
The PCC and OCLC: Some Reflections on 45 Years of Cooperation

Both authors have served as OCLC’s representative to the Program for Cooperative Cataloging’s Policy Committee and Steering Committee and participated in the PCC’s creation. They offer some reminiscences of the early years of the PCC and of OCLC’s involvement in, and support of, the PCC.
FPGA Implementation of Optimal PN_Sequences by Time_Multiplexing Technique

In this paper, FPGA implementation of a LFSR and PSEUDO_NOISE(PN) sequences are designed and carried out for many purposes. The motivation for that is multifold: LFSR and related PN sequences are indispensable building blocks not only for cipher sequence used in cryptography, steganography but also for BIST (build in self test system), scrambler. There have been a lot of attempts to develop specific circuit and sequences for these applications. However, the interleaving approach renders the optimal products in term of sequences length, statistical properties (distributions, autocorrelation function(ACF) and linear complexity (LC). It will be shown that the Interleaving structure can be easily expressed in time-multiplexing technique (namely Delay operation-D-transform). The equivalence between the D-transform and other well-known methods will be also highlighted in this contribution. Furthermore, the interleaving structure with outstanding LC will be given.
Deep Autoencoder on Personalized Facet Selection

Information overloading leads to the need for an efficient search tool to eliminate a considerable amount of irrelevant or unimportant data and present the contents in an easy-browsing form. Personalized faceted search has been one of the potential tools to provide a hierarchical list of facets or categories that helps searchers to organize the information of the search results. Facet selection is one of the important steps to pursue a good faceted search. Collaborative-based personalization was introduced to facet selection. Previous studies have been performed on the use of Collaborative Filtering techniques for personalized facet selection. However, none of the study has investigated Artificial neural network techniques on personalized facet selection. Therefore, this study aims to investigate the possible use of deep Autoencoder on the prediction of facet interests. Autoencoder model was applied to address the association of collaborative interest in facets. The experiments were conducted on 100K and 1M rating records of Movielen dataset. Rating score was used to represent the explicit feedback on facet interests. The performance was reported by comparing the proposed technique and the state-of-the-art model-based Collaborative Filtering techniques in terms of prediction accuracy and computational time. The results showed that the proposed Autoencoder-based model achieved better performance and it was able to significantly improve the prediction of personal facet interests.
Development of marker recognition augmented reality

This article proposed an improved recognition system of augmented reality markers. Using ArUco markers, the system produces more correct to recognize, accurately produce the marker angle calculation. Presents the main components for the reconstruction and scope of this system.
Aproximaciones a la cultura digital de las familias venezolanas en torno al proyecto Canaima Educativo

The Canaima Educativo project supplied laptops (canaimitas) to Venezuela’s public schools. Although family integration was among the project’s objectives, there were not guidelines on how to achieve such objective. This research aimed to interpret how families used the canaimitas considering the importance to include digital techno­logies in daily life, since our society is living in a digital culture. Therefore, public policies should provide support from a broad and inclusive perspective. We used an ethnographic qualitative methodology with a hermeneutic approach. Fieldwork was performed at two high schools located in the state of Vargas. Four (4) students and their families were selected using online surveys and participated in focus group interviews. The informa­tion was organized through a textual discourse analysis using the ATLAS.ti software. The research results showed the lack of support to include the canaimitas to those families’ homes. In this sense, the members of such families approach to the digital culture with spontaneous and dynamic practices according to their own interests.
Recommender System: Towards Classification of Human Intentions in E-Shopping Using Machine Learning

Recommender systems were introduced in mid-1990 for assisting the users to choose a correct product from innumerable choices available. The basic concept of a recommender system is to advise a new item or product to the users instead of the manual search, because when user wants to buy a new item, he is confused about which item will suit him better and meet the intended requirements. From google news to netflix and from Instagram to LinkedIn, recommender systems have spread their roots in almost every application domain possible. Now a days, lots of recommender system are available for every field. In this paper, overview of recommender system, recommender approaches, application areas and the challenges of recommender system, is given. Further, we study conduct an experiment on online shoppers’ intention to predict the behavior of shoppers using Machine learning algorithms. Based on the results, it is observed that Random forest algorithm performs the best with 93% ROC value.
Intelligent Path-following Control of Unmanned Surface Vehicles Based on Improved Line-of-sight Guidance

Abstract For the way point-based path following control of under-actuated unmanned surface vehicle (USV), a new adaptive compensation line-of-sight (LOS) guidance law and speed solver are proposed, and the fuzzy heading control and integral S-plane speed control are designed. The path following control scheme is based on the separation control, and a speed control is added to improve the overall control structure. For adaptive compensation LOS guidance law, confirm the optimal circle radius of the adaptive LOS method first, and then the angle compensation of heading deviation of navigation is conducted for the LOS angle solved by adaptive LOS method, which is used to guide the USV to approach the desired path with the optimal improved LOS angle. Considering the rapidness of path following, a speed solver is designed according to the distance between the two path points, which can calculate the optimal desired speed of the USV in real time. The experimental results testify the effectiveness and superiority of the proposed guidance strategy, the speed solver and the control strategies.
Decomposition diversity with symmetric data and codata

The expression problem describes a fundamental trade-off in program design: Should a program's primary decomposition be determined by the way its domain objects are constructed ("functional" decomposition), or by the way they are destructed ("object-oriented" decomposition)? We argue that programming languages should not force one of these decompositions on the programmer; rather, a programming language should support both ways of decomposing a program in a symmetric way, with an easy translation between these decompositions. However, current programming languages are usually not symmetric and hence make it unnecessarily hard to switch the decomposition. We propose a language that is symmetric in this regard and allows a fully automatic translation between "functional" and "object-oriented" decomposition. We present a language with algebraic data types and pattern matching for "functional" decomposition and codata types and copattern matching for "object-oriented" decomposition, together with a bijective translation that turns a data type into a codata type ("destructorization") or vice versa ("constructorization"). We present the first symmetric programming language with support for local (co)pattern matching, which includes local anonymous function or object definitions, that allows an automatic translation as described above. We also present the first mechanical formalization of such a language and prove i) that the type system is sound, that the translations between data and codata types are ii) type-preserving, iii) behavior-preserving and iv) inverses of each other. We also extract a mechanically verified implementation from our formalization and have implemented an IDE with direct support for these translations.
LAMAL: LAnguage Modeling Is All You Need for Lifelong Language Learning

Most research on lifelong learning (LLL) applies to images or games, but not language. We present LAMAL, a simple yet effective method for LLL based on language modeling. LAMAL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity. Specifically, LAMAL is a language model that simultaneously learns to solve the task and generate training samples. When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task. The results show that LAMAL prevents catastrophic forgetting without any sign of intransigence and can perform up to five very different language tasks sequentially with only one model. Overall, LAMAL outperforms previous methods by a considerable margin and is only 2--3\% worse than multitasking, which is usually considered the LLL upper bound. The source code is available at https://github.com/xxx.
Optimization of noisy blackboxes with adaptive precision

In derivative-free and blackbox optimization, the objective function is often evaluated through the execution of a computer program seen as a blackbox. It can be noisy, in the sense that its outputs are contaminated by random errors. Sometimes, the source of these errors is identified and controllable, in the sense that it is possible to reduce the standard deviation of the stochastic noise it generates. A common strategy to deal with such a situation is to monotonically diminish this standard deviation, to asymptotically make it converge to zero and ensure convergence of algorithms because the noise is dismantled. This work presents MpMads, an algorithm which follows this approach. However, in practice a reduction of the standard deviation increases the computation time, and makes the optimization process long. Therefore, a second algorithm called DpMads is introduced to explore another strategy, which does not force the standard deviation to monotonically diminish. Although these strategies are proved to be theoretically equivalents, tests on analytical problems and an industrial blackbox are presented to illustrate practical differences.