Reviewerly/exHarmony huggingface dataset:
The Use of the Activity Approach in Teaching Foreign Languages in Higher Education Institutions

The article characterizes the essence of the activity approach in the aspect of learning foreign languages. An analysis of foreign scholars’ recommendations on the implementation of the principle of activity approach to learning was made. The essence of teaching in higher educational institutions, that is to help the teacher to acquire speech competencies for learning foreign languages. The essence of the activity approach principle, which consists of integral training with the active student, is found out. The teacher acts only as a guiding partner, providing advice on the way to achieve the goal. The disadvantages of the activity approach to teaching a foreign language are highlighted: time constraints, insufficient level of students’ basic knowledge and their unwillingness to study entirely student-oriented, during which the student is given full independence in choosing a way to effectively achieve the goal. During the experimental application of the activity approach, we used exercises in teaching vocabulary, grammar, phonetics and reading foreign texts. The essence of teaching methods is revealed, that is divided into three main phases of a foreign language lesson, based on the activity approach: the initial phase, the implementation phase and the analysis phase. Based on the results of the empirical research, a training program that will help to increase the level of knowledge and skills of the respondents of the experimental group was developed, based on educational group and frontal consultation and training for both students and teachers in higher educational institutions.
\({\mathbb{T}}\)-Proper Hypercomplex Centralized Fusion Estimation for Randomly Multiple Sensor Delays Systems with Correlated Noises

The centralized fusion estimation problem for discrete-time vectorial tessarine signals in multiple sensor stochastic systems with random one-step delays and correlated noises is analyzed under different T-properness conditions. Based on Tk, k=1,2, linear processing, new centralized fusion filtering, prediction, and fixed-point smoothing algorithms are devised. These algorithms have the advantage of providing optimal estimators with a significant reduction in computational cost compared to that obtained through a real or a widely linear processing approach. Simulation examples illustrate the effectiveness and applicability of the algorithms proposed, in which the superiority of the Tk linear estimators over their counterparts in the quaternion domain is apparent.
20 NIMEs: Twenty Years of New Interfaces for Musical Expression

This paper provides figures and metrics over twenty years of New Interfaces for Musical Expression conferences, which are derived by analyzing the publicly available paper proceedings.Besides presenting statistical information and a bibliometric study, we aim at identifying trends and patterns.The analysis shows the growth and heterogeneity of the NIME demographic, as well the increase in research output.The data presented in this paper allows the community to reflect on several issues such as diversity and sustainability, and it provides insights to address challenges and set future directions.
An implementation of real-time detection of cross-site scripting attacks on cloud-based web applications using deep learning

Cross-site scripting has caused considerable harm to the economy and individual privacy. Deep learning consists of three primary learning approaches, and it is made up of numerous strata of artificial neural networks. Triggering functions that can be used for the production of non-linear outputs are contained within each layer. This study proposes a secure framework that can be used to achieve real-time detection and prevention of cross-site scripting attacks in cloud-based web applications, using deep learning, with a high level of accuracy. This project work utilized five phases cross-site scripting payloads and Benign user inputs extraction, feature engineering, generation of datasets, deep learning modeling, and classification filter for Malicious cross-site scripting queries. A web application was then developed with the deep learning model embedded on the backend and hosted on the cloud. In this work, a model was developed to detect cross-site scripting attacks using multi-layer perceptron deep learning model, after a comparative analysis of its performance in contrast to three other deep learning models deep belief network, ensemble, and long short-term memory. A multi-layer perceptron based performance evaluation of the proposed model obtained an accuracy of 99.47%, which shows a high level of accuracy in detecting cross-site scripting attacks.
Heuristic-Based Usability Evaluation Support

Heuristic evaluation is a popular inspection method principally used for the assessment of usability according to expert-based criteria. Since its (practical) emergence in the early 90s, it has become one of the most widely-used method, mainly utilized for formative usability assessments at large. In this paper, an overview of the different approaches for heuristic-based usability evaluation is presented. To this end, a Systematic Literature Review has been carried out, together with a comparative study to analyze different existing tools for supporting heuristic evaluation. In this way, different research questions have been proposed, which have been answered by means of the evidences found, providing an extensive analysis of different typologies, classifications, outcomes and tools related to heuristic evaluation. In general, the study concludes that there is a lack of supporting tools with advanced software architectures, featuring client/server and database facilities for further analytics that encourage comparison and benchmarking of heuristic evaluations, lacking also advanced statistical processing for helping in decision-making. According to that, this study represents a starting point for the construction of a complete supporting tool intended to provide support for usability evaluators and usability engineers overall to carry out heuristic evaluation.
Environmental Protection - Blockchain Solutions for Intelligent Passenger Transportation of Persons

Blockchain is a technology that has become more and more common lately. Due to the possibility to record transactions between two parties securely in the transport chain, without the intervention of a third party, it can be used successfully. The number of people who will use public transport will increase, while the number of personal vehicles will be found in traffic in smaller numbers. We can use this technology at the level of crowded localities: –to streamline transport;–to protect the environment against pollution;–and last but not least to convince the residents of the locality, but not only, to use the means of public transport. They will be stimulated with virtual coins, if they use this type of transport, made with ecological cars. First of all, the local authorities may be the ones that need to make the transport in the territory more efficient, by reducing the noxious substances, the agglomeration and the safety of the citizen. Blockchain technology will be the one to support this research.
THE GROWING MORAL CHALLENGE IN THE FACE OF TECHNOLOGIES: INTERNET, SOCIAL NETWORKS, IOT, BLOCKCHAIN AND ARTIFICIAL INTELLIGENCE

The study evaluates five technologies or technological arrangements: internet, social networks, internet of things, blockchain and artificial intelligence. These technologies are in different degrees of maturity and make up the new wave of technological innovation, the effects of which will cause severe changes in the economy, in labor relations, in employment and in society in general. The objective of this research is to carry out a basic assessment of these technologies in terms of their characteristics, implications and impacts on human and social relations, expanding the analysis to understand the ethical issues and human values involved, with an emphasis on the Brazilian reality. The method used is an exploratory, multidisciplinary research, which uses as references, among others, articles from the social sciences and philosophy, the basis for ethics. The first topic discusses the ethical helplessness in which we live today, followed by the analysis of the contexts of technologies and the moral dilemmas involved. The final topic presents the assessment of human values and virtues present in the interaction with the technologies analyzed.
LayerPipe: Accelerating Deep Neural Network Training by Intra-Layer and Inter-Layer Gradient Pipelining and Multiprocessor Scheduling

The time required for training the neural networks increases with size, complexity, and depth. Training model parameters by backpropagation inherently creates feedback loops. These loops hinder efficient pipelining and scheduling of the tasks within the layer and between consecutive layers. Prior approaches, such as PipeDream, have exploited the use of delayed gradient to achieve inter-layer pipelining. However, these approaches treat the entire backpropagation as a single task; this leads to an increase in computation time and processor underutilization. This paper presents novel optimization approaches where the gradient computations with respect to the weights and the activation functions are considered independently; therefore, these can be computed in parallel. This is referred to as intra-layer optimization. Additionally, the gradient computation with respect to the activation function is further divided into two parts and distributed to two consecutive layers. This leads to balanced scheduling where the computation time of each layer is the same. This is referred to as inter-layer optimization. The proposed system, referred to as LayerPipe, reduces the number of clock cycles required for training while maximizing processor utilization with minimal inter-processor communication overhead. LayerPipe achieves an average speedup of 25% and upwards of 80% with 7 to 9 processors with less communication overhead when compared to PipeDream.
Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments

The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.
Formation of Future Specialists’ Information Competence

In the thesis the formation of information competence of future interpreters’ in professional training as a pedagogical problem has been considered; the psychological and pedagogical aspects of formation of information competence of future interpreters’ have been clarified; the tasks and problems of forming the information competence of future interpreters in the professional training have been determined and substantiated. It was found that the creation of an interactive educational environment is a transition from static information websites to dynamic, involving the use of multimedia simulators for learning, simple learning modules, complex distribution learning systems, e-course courses, open portal boards for members of the learning community and parents, interuniversity training courses. This interactive approach is relevant for the formation of information and communication competence of future specialists of Donetsk National University named after Vasyl Stus, Zhytomyr State University named after Ivan Franko, National Pedagogical University named after MP Drahomanov, Khmelnytsky National University, which provides inclusion of all departments of the university in partner relations with the administration, students, teaching staff, teachers of schools in cities and regions, public organizations with the help of ICT. The results of this study are directly relevant in establishing international cooperation in the study of programs and projects in the context of transforming the education system in accordance with international partnership standards and implementing a cultural exchange program in education and culture between countries.
Research on the Decision Mechanism of University-Enterprise Collaborative Innovation Based on Quantum Cognition

The relationship between quantum cognition and decision-making is an important field in the direction of human behavior research. The uncertain and entangled state of quantum cognition has largely explained the decision-making process with limited information and ambiguity. University-enterprise collaborative innovation, a systematic project of joint action by both parties, aims to integrate the resources of enterprises and universities and enhance their innovation capabilities. As the main decision-making parties in collaborative innovation decision-making, the university and enterprise have a state of uncertainty and entanglement in their cognitive preferences before the final decision is formed. A university-enterprise collaborative innovation decision-making model should be constructed from the perspective of enterprise decision-making, taking different innovation schemes as the final choice, and the method of quantum probability theory can be used to perform quantum cognition on the independent and joint decision-making models in the university-enterprise collaborative innovation decision-making. Research shows that the internal mechanism of university-enterprise collaborative decision-making is based on its quantum attributes, university-enterprise collaborative decision-making is more inclined to joint decision-making, joint university-enterprise collaborative decision-making is more inclined to quantum cognition incompatibility expression, and the opinions of innovative members will have sequential effects and interference effects on decision makers.
The Application of Padlet in Teaching and Learning of Writing Recount Text at Senior High School in Palu City

Writing skill is one of the essential skill in English language skills that should be mastered by Senior High school students.Meanwhile, writing is perceived as one of the most challenging and less attractive skills to be learned.The application of Padlet as a learning media is an alternative strategy for motivating and promoting students to write.Therefore, this study attempted to provide transparent information of the integration technology in teaching and learning English writing skills, particularly in writing recount text using Padlet, and how the student's response and attitude toward the use of Padlet in the writing process.This research applied a case study and conducted at Grade Eleventh, Madani Senior High School.The data were collected through observation, interviews obtained from the teacher, and questionnaires for the students.The researcher employed a Qualitative approach in analyzing the data.The findings show that students accepted the application of Padlet in conducting the teaching and learning process, it gives a positive effect on the students learning performance: 1. it was about 85% student can do the task collaboratively and `cooperatively, 2. students were motivating and participating actively in-class activities.These findings suggest that the use of Padlet can be implemented at other schools in Palu City.
Semantics Graph Mining for Topic Discovery and Word Associations

Big Data creates many challenges for data mining experts, in particular in getting meanings of text data. It is beneficial for text mining to build a bridge between word embedding process and graph capacity to connect the dots and represent complex correlations between entities. In this study we examine processes of building a semantic graph model to determine word associations and discover document topics. We introduce a novel Word2Vec2Graph model that is built on top of Word2Vec word embedding model. We demonstrate how this model can be used to analyze long documents, get unexpected word associations and uncover document topics. To validate topic discovery method we transfer words to vectors and vectors to images and use CNN deep learning image classification.
Aspect Based Online Sentiment Analysis Product Review and Feature Using Machine Learning

Today people, exchanging their thoughts through online web forums, blogs, and different platforms for social media.In online shopping, they are giving reviews and opinions on other various products, brands, and services.Their thoughts towards a product are do not only purchase decisions of the consumers but also improves the product quality about their requirements and find out the product's particular problem and get an excellent solution on that product.The present system concentrate on the peer-reviewed review model (User-generated review) and global qualification i.e., rating and, tries to classify the semantic aspect and emotions at the time aspect level from the data to investigate general sense feel of the reviews.SJASM represents each review document in the format of opinion pairs and, along with simulating the terms of appearance and the corresponding opinion words of the study, consideration for the hidden aspect and the sentiment detection.The current system is designed as a recommendation system Physiological Language Processing (NLP) Technique to read reviews and using Naïve Baye's Classification automatically.We have also extracted the thoughts of the product characteristics.Here admin can analyze the opinion pair that actually what is defect in the finished product so in future the market of that product will increase.This system to extract product aspects and corresponding opinions from consumer ratings on the internet.Different machine learning algorithms are discussed in Naïve Bayes is considered in order to classify of sentiments, and variables such as precision, recall, F-score, and accuracy are used to assess a classifier's performance.
Design and Implementation of a Low Power Consumption of ASK, FSK PSK, and QSK Modulators Based on FPAA Technology

Wireless communication techniques have become more popular due to their characteristics. These systems adopt the digital modification application, which provides more information for transmission and compatibility with digital data services as well as high information security and quality improvement. Digital wave signal-based communication techniques such as PSK, FSK, ASK, and QPSK considered among the most significant digital modulation techniques. These technologies are a crucial part of digital communication systems. Digital communication devices designed by FPAAs can provide multiple communication without arranging new devices and can support new within seconds. FPAA is extremely easy to reconfigure and program concerning FPGA or DSP-based applications. This article offers real-time implementation of ASK, FSK PSK, and QPSK modulator techniques with low power consumption ”Žbased on the ”Žcapability for dynamic reprogramming of the FPAA AN231E04 device ”ŽAnadigm. FPAA techniques provide growing application and high pliability in the implementation of analog circuits. The measurements achieved from the proposed modulated circuits show that the form and properties of the theoretical outcomes agree perfectly with the practical outcomes. FPAA techniques provide growing application and high pliability in the implementation of analog circuits for minimizing the time of design and testing. Another feature is the minimum power exhaustion (165 mw) due to the switched capacitor method used by the FPAA Chip, which gives a high accuracy process.
Locality Sensitive Hashing with Extended Differential Privacy

Extended differential privacy, a generalization of standard differential privacy (DP) using a general metric, has been widely studied to provide rigorous privacy guarantees while keeping high utility. However, existing works on extended DP are limited to few metrics, such as the Euclidean metric. Consequently, they have only a small number of applications, such as location-based services and document processing.In this paper, we propose a couple of mechanisms providing extended DP with a different metric: angular distance (or cosine distance). Our mechanisms are based on locality sensitive hashing (LSH), which can be applied to the angular distance and work well for personal data in a high-dimensional space. We theoretically analyze the privacy properties of our mechanisms, and prove extended DP for input data by taking into account that LSH preserves the original metric only approximately. We apply our mechanisms to friend matching based on high-dimensional personal data with angular distance in the local model, and evaluate our mechanisms using two real datasets. We show that LDP requires a very large privacy budget and that RAPPOR does not work in this application. Then we show that our mechanisms enable friend matching with high utility and rigorous privacy guarantees based on extended DP.
SUMBT+LaRL: Effective Multi-Domain End-to-End Neural Task-Oriented Dialog System

The recent advent of neural approaches for developing each dialog component in task-oriented dialog systems has remarkably improved, yet optimizing the overall system performance remains a challenge. Besides, previous research on modeling complicated multi-domain goal-oriented dialogs in end-to-end fashion has been limited. In this paper, we present an effective multi-domain end-to-end trainable neural dialog system <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SUMBT+LaRL</i> that incorporates two previous strong models and facilitates them to be fully differentiable. Specifically, the SUMBT+ estimates user-acts as well as dialog belief states, and the LaRL models latent system action spaces and generates responses given the estimated contexts. We emphasize that the training framework of three steps significantly and stably increase dialog success rates: separately pretraining the SUMBT+ and LaRL, fine-tuning the entire system, and then reinforcement learning of dialog policy. We also introduce new reward criteria of reinforcement learning for dialog policy training. Then, we discuss experimental results depending on the reward criteria and different dialog evaluation methods. Consequently, our model achieved the new state-of-the-art success rate of 85.4% on corpus-based evaluation, and a comparable success rate of 81.40% on simulator-based evaluation provided by the DSTC8 challenge. To our best knowledge, our work is the first comprehensive study of a modularized E2E multi-domain dialog system that learning from each component to the entire dialog policy for task success.