Reviewerly/exHarmony huggingface dataset:
Enhancing Teamwork in First-year Science and Technology Majors through an&lt;i&gt; Employability Program&lt;/i&gt;

Enhancement of communication and teamwork skills are important themes in higher education. For example, the Accreditation Board for Engineering and Technology (ABET) and the Japan Accreditation Board for Engineering Education (JABEE) have adopted these skills as significant criteria in their accreditation program. They are also listed in generic skills as the significant competence of students. Gunma University’s School of Science and Technology implemented an Employability Program from 2010 as a requirement for all first-year students. Under the auspices of this program, coursework in career planning and career design are offered to enhance student abilities in these areas. This study focuses on the enhancement of communication and teamwork skills through group discussions and poster presentations based on problem surveys. Student perceptions of communication skills and teamwork were investigated through a questionnaire. Results showed that students perceived improvement in those vital skills.
Evaluation of Facebook Translation Service (FTS) in Translating Facebook Posts from English into Arabic in Terms of TAUS Adequacy and Fluency during Covid-19

The study aims to verify the capacity of Facebook Translation Service in translating English Facebook posts into Arabic in terms of two criteria: adequacy and fluency in line with the Translation Automation User Society (TAUS) scales.To ensure consistency and objectivity as recommended by TAUS, six evaluators, native speakers of Arabic and near-native speakers of English, rated the same data on each scale.The evaluators were acquainted with fluency and adequacy scales along with MT limitations and potentials.Once the corpus was uploaded and sent to the evaluators using TAUS tools, they had to assign scores online on 1-4 rating scales.Then, each report was displayed online on the TAUS reports tool.Evaluators' responses were combined in thematic categories and were calculated to obtain frequencies and percentages.The study found that FTS provided fluent output with highest percentage of the scale good equal to 3 on a scale from 1 to 4, where the output is assessed as flowing smoothly with minor linguistic errors.Moreover, FTS succeeded in generating an adequate output with the highest percentage of responses as 'most' equal to 3 on a scale from 1 to 4, where almost the full meaning of the source is deemed to be transferred in the target language.This study is useful since it highlights the role of Facebook Translation service in translating, educating the public and fighting COVID-19.Consequently, such research would encourage the use and research on the potentiality of MT and FTS in dealing with abrupt crises, such as
Bound Constructive Fraud and Implementing Dual Accessibility Approach on Biometric Credit/Debit Cards

Behavioural transition from cash transaction (CT) to credit card transaction (CCT) raises frauds sharply in an e-commerce environment. CCT is a multi-functional device in fund transaction pool. The unstable growth of luxury promotes the use of cashless transaction heavily. This scenario is totally fuelled by the customer and the service providers in an e-commerce environment. This paper proposes a new approach to prevent the banking cards (credit/debit) fraud with the privilege of withdrawal of limited amount to the entitled person by dual access biometric card. This takes place in two ways—firstly, it matches the biometric credentials of prime authorized person, and secondly, it provides (Europay Mastercard and Visa chip) EMV strip encryption technique for permitting shared access. The approach utilized both biometric image of owner and second holder for enhancing liberty in access and the security in card transactions. Biometric technique makes fraudster to stay on circumference of fraud rather than entering into the pool of transactions. The approach can also trap the fraudster if he/she makes another attempt, by keeping the track of the previous fraud attempts.
Using Gaussian based Bayesian Optimization in Recommender Systems

Recommender systems have evolved as a major component of many businesses across various domains. Recommender systems help users making quick decisions by providing relevant information based on the past history of the user. This research work is focussed to enhance the performance of a recommender system by doing hyper parameter tuning and optimization techniques using Grid Search and Bayesian Optimization. The dataset consists of ratings of various users on the product. The surprise package of Python has been used to build the recommender system. A Collaborative System type recommender system is used to predict the products for a user. Various classifiers like Baseline Only, KNN Basic, SVD, NMF, and Co-Clustering were used. RMS E is used as a metric to determine the efficacy of the recommender system. The results show that Bayesian Optimization performed better than grid search in terms of RMSE values and also in the number of iterations required. Tuning the parameters improved the RMSE values to a major extent as compared to default parameters.
Discussion on the Definition and Construction Principles of City Brain

This paper makes a preliminary discussion on the generation, composition, and construction principles of city brains, proposing that the Internet has changed significantly in the structure through 50 years' development, and it is evolving from a net-like structure to the brain model. The combination of the Internet brain model and the construction of smart cities is the root cause for the generation of city brains. By analyzing the features of the Internet brain model, three principles for the construction of city brains are presented. The first principle is to establish a unified technology framework for city neurons across the world; the second is to achieve the human-computer dual intelligent control over city neurons, and ensure the human has the priority of control; and the third is to set the information routing meeting the city's needs, realizing the cloud reflex arc of the city brain.
Enhanced Accuracy in Machine Learning Using Feature Set Bifurcation

In the diagnosis of deadly diseases, the use of machine learning is proving to be very much helpful and provides diagnosis with high performance. It has been observed that most of the deadly diseases have similar symptoms in patients. Machine learning advantage is being used in comparing the data of patients with similar symptoms, which eases decision making. Since the parameters related to the symptoms are prodigious and historical data of the millions of the patients cannot be compared with conventional processing techniques; hence, research is continuously being done to evolve techniques and algorithms for the same. This work provides an analysis of the machine learning techniques available and their applications (Sanyal and Agrawal in Int Res Anal J 14:348–353, 2018, [1]). In this work, a bifurcated feature set has been used to implement decision trees using a multithreaded environment. Paper elaborates the technique applied for bifurcation of dataset features, multithreaded decision trees implementation and combining process to obtain a final result. Implementation results depict that large feature sets are having considerable improvements in obtaining better accuracies. This improvement in the algorithm is not only enhancing the accuracy of disease prediction, but also performance.
Emerging Software Engineering Research Networks in (East) Africa

Software engineering (SE) researchers and research networks from emerging communities are often not visible in already established Software Engineering venues for a multitude of reasons. This limits the opportunities and mutual bene ts that can arise from collaborations between global and emerging Software Engineer- ing networks. This article focuses on a rst attempt to provide a map of the African software engineering research community with focus on the networks of two big East African Universities. We hope that this very initial mapping e ort will help to raise aware- ness in the international community about the variety of software engineering research in Africa. We formulate some suggestions for making our academic Software Engineering community more inclusive.
An Improved Method to Enhance Low Illumination Image Based on Retinex Theory

The computer vision technology is applied more and more extensively. In order to improve the effectiveness of various computer vision algorithms, the enhancement for low-illumination image is now playing an important role as image preprocessing. Aiming at the problems of image restoration and adaptive illumination adjustment, we proposed an improved low-illuminance image enhancement method based on convolutional neural network and Retinex theory. We first decompose the low-illuminance image into a reflection component and an illuminance component. Secondly, a convolutional neural network is designed for each of the two components. The reflection component branch restores the details of the image, and the illuminance component branch adaptively adjusts the image illuminance. Finally, the image is reconstructed. The experiments result show that our method has a better performance in SSIM and PSNR. Moreover, the image noise is well suppressed and the image details are also enhanced visually
Do Connections Pay Off in the Bitcoin Market?

This paper identifies the bitcoin investor network and studies the relationship between connections and returns. Using transaction data recorded in the bitcoin blockchain from 2015 to 2020, we reach three conclusions. First, connectedness is not strongly correlated with higher returns in the first four years. However, the correlation becomes strong and significant in 2019 and 2020. Second, returns also differ among those connected addresses. By dividing the connected addresses into ten decile groups based on their centrality, we find that the top 20% most-connected addresses earn higher returns than their peers during most of our sample period. Third, eigenvector centrality is more related to higher returns than degree centrality for the top 20% most-connected addresses, implying that the quality of connections may matter more than quantity among those highly connected addresses.
Compulsory Vaccination and the ECtHR: What to Expect

The multiple COVID-19 vaccines developed over the past months are typically thought of as the only means to meet the challenges posed by the current pandemic. Still, the public opinion on vaccines is heavily divided. And, of course, discussions about compulsory vaccination, oftentimes based on fundamental rights arguments, tend to become heated. This note analyses the issues at hand based on the case law of the European Court of Human Rights (ECtHR).See the peer reviewed paper: https://www.degruyter.com/document/doi/10.1515/icl-2021-0010/html
Multimedia system model for electrical circuits on android mobile devices

Abstract This article aims to design the multimedia system application model of electrical circuits to make it easier for everyone to learn and understand electrical circuits in support of completing the required electrical circuit learning practice activities. The learning material models are designed interactively, which includes basic knowledge about electrical circuits and electrical circuit simulations. The Multimedia Development Life Cycle (MDLC) method is used for the design of this multimedia system. The design phase consists of 6 stages, namely, concept, design, material collecting, assembly, testing, and distribution. The results of the study are the multimedia system application of electrical circuit that mainly can be applied on Android-based mobile devices.
Design and Development of Topic Identification Using Latent Dirichlet Allocation

Data storing and retrieving are the most important task in the present condition. Storing can be ended based on the topic that the document describes. Text mining generates documents from the collection of topics. To identify the topics, we have to categorize the documents; to classify, we are using topic modeling. Text mining technique is used for discovering latent semantic structure which is a fragment of topic modeling. Various research areas that make use of probabilistic modeling includes software engineering, political science, and medical science. A topic model is a probability-based model that discovers the major themes which are a group of documents. The main idea is to treat the documents as mixtures of topics in the topic model, and every topic is viewed as a probability distribution of the words. This research work aims to propose a model called topic modeling using LDA, and this model has been experimented on two datasets, where one is two news group dataset, and other is twenty news group dataset, and finally, all the results are tabulated.
Digitizing and Encoding a Multilingual Literary Review: Commerce Numérique

Commerce was an important literary review founded in Paris by Princess Margherita Caetani, Prince Roffredo Cae-tani's wife. Born in America, she was polyglot and maecenas. Between 1924 and 1932 she surrounded herself with three prestigious collaborators: Paul Valéry, Léon-Paul Fargue, Valéry Larbaud. The review promoted the translation of World and European literature in French, translating some of the most important authors like Joyce, T.S. Eliot, Pirandello, Ungaretti, Saint-John Perse, Rilke, Hofmannsthal. The aim of this project is to promote by digitizing the dissemination of the review, to develop studies and research concerning the Caetani family's cultural activities in Europe. All the volumes of the literary review Commerce have been scanned, acquired by OCR and encoded in TEI-XML. The cultural value of the operation is discussed and the work-flow to create the digital textual corpus is described in detail.
Three new large (n, r) arcs in PG(2,31)

A set of n points in PG(2,q) some r of which are collinear, but every r+1 are not collinear, forms an (n, r) arc. By m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> (2, q) the maximum size of a such arc is denoted. In this talk (n, r) arcs with parameters (699, 24), (769, 26), and (838, 28) in PG(2,31) will be presented. The constructed arcs improve the respective lower bounds on m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> (2, 31) in [8]. As a consequence there exist three new three-dimensional linear codes over GF(31).
Multi-Criteria-Based Selection of Fast Moving Consumer Goods Using Intelligent Techniques with Linguistic Inputs

This research paper concentrates on using intelligent techniques to recommend fast moving consumer goods (FMCGs) available in superstores with various brands, costs, discount or schemes, quantities, and reviews using linguistic inputs. Such linguistic inputs are managed through type 2 fuzzy logic and neural network techniques. The paper presents a literature survey, system architecture, and details about the experiment carried out using Google Colab along with fuzzy membership functions and other technical information. The result achieved with the investigation is 98% accurate. The approach is generic and can be used in many different domains. In the end, the paper presents applications and possible future enhancements on the work done.
Low differentially uniform permutations from the Dobbertin APN function over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

Substitution boxes (S-boxes) play a central role in block ciphers. In substitution-permutation networks, the S-boxes should be permutation functions over F2n to realize the invertibility of the encryption. More importantly, the S-boxes should have low differential uniformity, high nonlinearity, and high algebraic degree in order to resist differential attacks, linear attacks, and higher order differential attacks, respectively. In this paper, we construct new classes of differentially 4 and 6-uniform permutations by modifying the image of the Dobbertin APN function xd with d=24k+23k+22k+2k−1 over a subfield of F2n. In addition, the algebraic degree and the lower bound of the nonlinearity of the constructed functions are given.
Creating Arabic Lexical Resources in TEI: A Schema for Discontinuous Morphology Encoding

An Arabic word can be described according to its lexical and morphological information. Lexical analysis consists in gathering both semantic information (meaning and translation) and syntactic properties (parts of speech). Morphological analysis, instead, identifies word patterns that group the words having the same syntactic, inflectional and semantic behaviour. Such descriptions constitute two different but complementary levels of study. This paper illustrates our work, aimed at creating an exhaustive resource consisting of two levels: lexical and morphological. The lexical level collects information extracted from the dictionary <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$al=q\bar{a}m\bar{u}s\ al=m\underset{.}{h}\bar{\imath}\underset{.}{t}$</tex> . The morphological level describes the word patterns. The two levels are autonomous but complementary. Each word described at the lexical level is linked to its corresponding pattern. The formalization of the word pattern makes it possible to enrich word descriptions with additional morphosyntactic and inflectional information. To obtain a digital systematic resource, we followed the guidelines provided by the Text Encoding Initiative (TEI). We adopted the TEI module devoted to encoding digital dictionaries and lexicons in order to formally represent the medieval primary source <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$al=q\bar{a}m\bar{u}s\ \ al=mu\underset{.}{h}\bar{\imath}\underset{.}{t}$</tex> . We also used the TEI interpretation approach to encode the morphological word patterns keeping the two levels separate but at the same time allowing them to be linked.
Accelerating Stochastic Gradient Descent by Minibatching, Learning Rate Annealing and Momentum

In last five to ten years of history of machine learning, the prediction capacity of machine learning has been increases drastically. However, with this increase in data comes great responsibility. Online large scale Machine Learning requires dealing with algorithm complexity and architecture of system simultaneously. Stochastic gradient descent (SGD) does not use all data point of training sets in order to improve the present value of parameters. SGD in fact start changing the parameters value after reading the single training data point. Initialize the parameters at some value w0 ℇ Rd, and decrease the value of the empirical risk iteratively by sampling a random index ~ it uniformly from {1, , , n} and then updating it. In this paper we also discussed how Minibatching, Learning Rate Annealing and Momentum help to accelerate stochastic gradient descent to convergence.
Shaping duo binary turbo-coded BICM scheme for JPWL image transmission using a link adaptation strategy over wireless channels

Abstract In order to guarantee a robust transmission of JPWL (JPEG Wireless: Joint Photographic Experts Group Wireless) images through time and frequency selective wireless channels, an efficient adaptive communication strategy is proposed. It is based on an optimization of a closed-loop adaptive multiple-input multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) scheme associated with a shaping BICM (bit-interleaved coded modulation) technique composed of a duo binary turbo code (DBTC), high-order modulations such as 64–256 QAM (Quadrature Amplitude Modulation) and a shaping code. According to the CSI (channel state information) knowledge at the transmitter side, an algorithm based on unequal error protection (UEP) and unequal power allocation (UPA) is used to select the transmitter key parameters (source/channel encoder rate, modulation order, power, number of quality layers and number of iterations of the Turbo decoder) to achieve the target Quality of Service (QoS). The proposed DBTC-shaping BICM scheme reaches a shaping gain of 1.2 dB for a 256 QAM modulation over a SISO Gaussian channel, whereas only 0.7 dB of shaping gain can be achieved in a scheme that uses the LDPC shaping BICM scheme for the same modulation order. Based on a DBTC shaping BICM scheme and an adaptive algorithm, the proposed MIMO-OFDM strategy achieves better performance compared to a strategy using an iterative process between an RS (Reed-Solomon) and arithmetic decoders. As a result, and on the one hand, a gain of 5.38 dB can be achieved in terms of PSNR (peak signal-to-noise ratio). On the other hand, a gain of 78% in terms of power consumption is obtained for the same QoS level. Moreover, the adaptive number of iterations in the proposed strategy can minimize the computational complexity of the turbo decoding compared to a scheme using four iterations whatever the channel conditions.
Stop Hobby King Text Messages: 6 Most Effective Solutions

Has the sound of an incoming text message made you so excited that your heartbeat has been so fast that you cant help it? Have you ever reached your ...
Solving of Distinct Engineering Optimization Problems Using Metaheuristic Algorithms

In the current chapter, the metaheuristic techniques are utilized to solve different engineering optimization problems. To provide a wide insight into the issue, both unconstrained and constrained optimization cases are considered. Four mathematical functions as the general engineering problems, a time–cost trade-off problem about construction management engineering, an optimal design of a truss system regarding structural engineering and a pressure vessel cost minimization, and a gear system optimal design from mechanical engineering are selected as the test problems. The five well-established metaheuristic methods are described and applied to solve the aforementioned problems. The selected methods can be chronologically sorted as butterfly optimization algorithm (BOA), interactive search algorithm (ISA), Drosophila Food-Search algorithm (DSO), teaching and learning-based optimization algorithm (TLBO), and the firefly algorithm (FA). The acquired results (i.e., convergence rate, standard deviation, mean, and the best optimal solutions) are addressed and compared through the illustrative diagrams and tables.
Digital Documents as Data Carriers and a Method of Data Management Guaranteeing the Unambiguity of the Recorded Information

This study presents a method for the storage of data organized in digital documents, which is proven in practice. The discussed method does not bear any disadvantages of the relational model used for data organization, such as the loss of data context and complications evoked by the lack of data redundancy. The method presented here can be used for data organization into documents (digital and paper) as classified aggregates and for data classification. The study also describes a new metamodel for the data structure which assumes that documents, being data structures, form compact aggregates, classified as objects, or event descriptions, thus always assigning them a specific and unambiguous context. Furthermore, the study presents a design method for documents as context aggregates that allows leveling the disadvantages of the relational model and ensures efficient information management. The work also contains practical examples of the application of the described method.