Reviewerly/exHarmony huggingface dataset:
A Data Authentication Method Based on Vector Projection for Industrial Edge Computing System

In this paper, we consider a success rate of data authentication for industrial edge computing system. We propose a data authentication method based on vector projection, which is a novel secure authentication program. The proposed authentication method is divided into initial authentication, calculating the vector projection of channel state information (CSI), and checking the validity of data frames. Experimental results show that the proposed scheme can significantly achieve a high success rate of data authentication.
Automated system for career guidance testing in educational organizations

Problem and goals. In article the problem of carrying out professional orientation activity in the general education organizations is considered. The role of early career guidance for the conscious choice by graduates of schools of the future profession is shown. Need of use of the automated systems for conducting professional orientation testing of pupils for the purpose of determination of tendencies and interests in one or another ﬁeld of professional activity is shown. Methodology. The research is directed to design and development of system “Psychodiagnostics of educational institution”, which allows to hold professional orientation testing in various options. Unlike the existing developments, the created system allows to carry out in a computer class and far oﬀ both group and individual testing. Results of testing for all the time of carrying out are kept in a system. The system gives an opportunity to carry out analytics of results of a research on various indicators and criteria. Results. Work has practical value for the educational institutions, which are engaged in career guidance of school students. Results of approbation of this system on the basis of Moscow State Budgetary Educational Institution “School No. 1357 ‘Na Bratislavskoi’ ” are shown. Conclusion. The system allowing to increase eﬃciency of professional orientation ac- tivity of the educational organization and to automate process of testing in various options with a possibility of analytics of results of testing is for the ﬁrst time oﬀered.
A Child Language Acquisition in Indonesian and English Language: A Longitudinal case study

Learning a dual language at the same time during early childhood has caused the wrong perception. Its false perception makes the parents delay the start of learning a second /foreign language. Therefore, This study aims at analyzing the first and second language acquisition of a child in the Indonesian Language and English. The issues concentrated on the spoken utterances in Indonesian and English and its affecting factors. The research adopted a qualitative approach by using a case study, as the unit analysis is a child of 3 years old. For data collection, I employed The naturalistic approach, which I kept daily notes on the progress of a child’s linguistic and recorded the dialogues. Since it is a longitudinal study, the research was conducted for two years, from 2014 to 2015. The research has revealed that utterances spoken have primarily increased and the child has already understood the commands by following the instructions. Some concepts such as feeling, taste, color, and numbers as well as short phrases and simple words in Indonesian and English at the same time can be understood and uttered. She also can change the affirmative sentence to question in term of objects she sees in her surrounding. Factors such as linguistic inputs, imitation, environment, and significant others as exposure, and language acquisition device affect a child utterance. Thus, it indicates that two languages can be taught at the same time at an early age, and it doesn’t cause misunderstanding or confusion utterance, language acquisition, exposure, L1 and L2
Medical documents classification using topic modeling

The number of digital medical documents is increasing continuously; several medical websites share a lot of unclassified articles. These articles have very long texts that should be read to determine the topic of each document. The classification of these documents is important so researchers can use these documents easily and the effort and time in reading and searching for a specific topic will be reduced. Therefore, an automatic way to extract latent topics from these text documents is needed. Topic modeling is one of the techniques used to deal with this problem. In this paper, a medical collection of documents is used; this collection contains documents from three types of widespread diseases (Heart Diseases, Blood Pressure and Cholesterol). LDA topic modeling technique is applied to classify these documents into the previous mentioned topics. An evaluation of the algorithm’s results is done and the LDA shows a good level of classification accuracy.
Realization of Natural User Interface for Computer Control With KNN Classifier Enhanced Smart Glove

Abstract Gesture interaction is a commonly used solution when introducing Natural User Interface (NUI), a kind of user interface where the interaction is direct and consistent with natural heuristic behaviors. In this paper, a smart glove with efficient real-time hand orientation calculation and accurate static hand gesture prediction is proposed. This custom-built wireless glove consists of flex sensors, an Inertial Measurement Unit (IMU) sensor, a microcontroller with multi-channel ADC/AMP (analog to digital converter and amplifier), a Bluetooth module, and an Arduino Micro Pro. K-Nearest-Neighbor (KNN) classifier is implemented to assist static hand gesture prediction with the validated accuracy exceeding 97%. This supervised machine learning algorithm allows a highly customizable smart glove which the input gestures, input number of gestures, and the associated activating functions are all easily changeable by the users any time. To show the benefits of combining the NUI and supervised machine learning, a validation experiments, computer control, were conducted.
Detection and Classification of Urban Actors Through TensorFlow with an Android Device

With the advancement of Artificial Intelligence, now it is possible to perform Neural Networks for detection and classification inside a device with limited hardware. We compare the performance of Deep Neural Networks (DNN). Model selected is Single Shot Detector (SSD) by default and re-trained, and frameworks are TensorFlow mobile versus TensorFlow lite. Default model contains 80 different classes of objects and the second one is our re-trained model with only 6 different classes based on urban actors (car, bus, truck, bicycle, motorcycle, person). The main goal is to build an object tracker for urban mobility with an Android device. Results are based in two metrics for object detection: mean Average Precision (mAP) and log-average miss rate. And for classification we report two metrics: precision (PRE) and recall (REC). We report inference time as an additional metric which is strongly related to hardware used. TensorFlow mobile is much slower than TensorFlow lite in terms of time of inference. Finally, re-trained model allows the integration of new scenarios, improving the detection rate.
Entity Extraction with Knowledge from Web Scale Corpora

Entity extraction is an important task in text mining and natural language processing. A popular method for entity extraction is by comparing substrings from free text against a dictionary of entities. In this paper, we present several techniques as a post-processing step for improving the effectiveness of the existing entity extraction technique. These techniques utilise models trained with the web-scale corpora which makes our techniques robust and versatile. Experiments show that our techniques bring a notable improvement on efficiency and effectiveness.
Grammar Translation Method: Current Practice In EFL Context

This paper reports the findings of a case study investigating Grammar Translation Method (GTM) through mother tongue, much-isolated words, and putting words together in grammar class to scaffold students’ learning. Mixed methods, classroom observations, field-notes and videotape recording, were employed to collect the data. The data gained, then, transcribed and classified by using code. The selected data is displayed into table and analyzed descriptively. While validating data is done by crosschecking the result of data analysis of each methods. The data is reported in regard with the result of findings and discussion to answer the research questions of this study. Findings indicate that among three types of GTM, the use of mother tongues was frequently used to give instruction.Other findings show that much-isolated words were used to introduce new vocabulary, and putting words together were used to explain topic discussion. Some changes to create effective GTM through other types of GTM was also discussed.
MARKET BASE ANALYSIS IN DROPSHIP BUSINESS WITH APRIORI ALGORITHM IN DETERMINING R-BASED PRODUCT BUNDLING

The association method will associate data using a priori (experience) rules that meet the minimum support requirements, namely the combination of each item in the database and the minimum confidence requirement, namely the strength of the relationship between items in the association rules. In this study, the sales transaction data for three months will be analyzed from the dropshipper in the field of sales of beauty and fashion products using the association method with a priori algorithm to look for product bundling data patterns. The minimum value of support is 10% and 20% confidence, and with % ain function where Ordinary Shampoo items become the main products in product bundling, obtained 15 packages of the strongest products with lift values more than 1.1.
Beliefs About Language Learning, Role of Gender and Students’ Achievement in Relation to Second Language Anxiety

The study has given a myriad of insights and a deeper understanding of the issue of second language anxiety, especially in Malaysia. This study encompassed several aspects of second language learning. This study focused on the relationship between second language anxiety and beliefs about language learning. Besides that, the role of gender in second language anxiety has also been one of the main focuses of the study. On top of that, this study also emphasized the effect of second language anxiety on second language achievement. To achieve the purpose, a study was conducted quantitatively among 334 respondents from a local public institution, which is a polytechnic in Negeri Sembilan. The respondents of the study were from Commerce Department which is the largest department in the institution. The respondents were the students from the third semester in the second year. This study used the modified version of the Foreign Language Classroom Anxiety Scale (FLCAS) and Beliefs About Language Learning Inventory (BALLI). From the study, it is found that the second language anxiety of the students is moderately related to their beliefs about language learning. Besides, the second language achievement is negatively correlated with the second language anxiety, thus explains the low performance while experiencing high level of anxiety. This study also reveals that there is no significant difference found between male and female students in second language anxiety. It is anticipated for researchers to continue and expand the field of study in order to continue contributing to the body of knowledge on second language anxiety.&#x0D; &#x0D; KEYWORDS: Achievement, Beliefs, Gender influence, Second language anxiety
Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems

Self-attentional models are a new paradigm for sequence modelling tasks which differ from common sequence modelling methods, such as recurrencebased and convolution-based sequence learning, in the way that their architecture is only based on the attention mechanism.Self-attentional models have been used in the creation of the state-of-the-art models in many NLP tasks such as neural machine translation, but their usage has not been explored for the task of training end-toend task-oriented dialogue generation systems yet.In this study, we apply these models on the three different datasets for training task-oriented chatbots.Our finding shows that self-attentional models can be exploited to create end-to-end taskoriented chatbots which not only achieve higher evaluation scores compared to recurrence-based models, but also do so more efficiently.
Unconstrained numerical optimization using real-coded genetic algorithms: a study case using benchmark functions in R from Scratch

Unconstrained numerical problems are common in solving practical applications that, due to its nature, are usually devised by several design variables, narrowing the kind of technique or algorithm that can deal with them. An interesting way of tackling this kind of issue is to use an evolutionary algorithm named Genetic Algorithm. In this context, this work is a tutorial on using real-coded genetic algorithms for solving unconstrained numerical optimization problems. We present the theory and the implementation in R language. Five benchmarks functions (Rosenbrock, Griewank, Ackley, Schwefel, and Alpine) are used as a study case. Further, four different crossover operators (simple, arithmetical, non-uniform arithmetical, and Linear), two selection mechanisms (roulette wheel and tournament), and two mutation operators (uniform and non-uniform) are shown. Results indicate that non-uniform mutation and tournament selection tend to present better outcomes.
University-business cooperation: directions of interaction

The article is devoted to the analysis of promising areas of university-business cooperation (UBC) based on the study of modern Russian and foreign experience. It justifies the relevance of the broad approach to the interpretation of UBC within which it is proposed to consider cooperation between universities and business not as a set of isolated transactions within a limited range of areas of interaction, but as a system of long-term mutually beneficial relations with a wide range of possible types of cooperation that can also include the state and community organizations. The article analyzes 14 types of joint activities of universities and business within four main blocks - education, research, valorization (knowledge and technology commercialization) and management, which can serve as a basis for developing a system of indicators for assessing the effectiveness of UBC. The conclusion is made about the need to adjust the state policy in the field of science and education in order to reduce the imbalance in the development of these areas of cooperation both in Russia and in Europe.
Psychological Health Restoration as a Way of Socio-Psychological Rehabilitation of a Person

The purpose of the investigation is to develop the technologies for psychological health restoring after the war-related trauma
Sparse Annotations with Random Walks for U-Net Segmentation of Biodegradable Bone Implants in Synchrotron Microtomograms

Currently, most bone implants used in orthopedics and traumatology are non-degradable and may need to be surgically removed later on e.g. in the case of children. This removal is associated with health risks which could be minimized by using biodegradable implants. Therefore, research on magnesium-based implants is ongoing, which can be objectively quantified through synchrotron radiation microtomography and subsequent image analysis. In order to evaluate the suitability of these materials, e.g. their stability over time, accurate pixelwise segmentations of these high-resolution scans are necessary. The fully-convolutional U-Net architecture achieves a Dice coefficient of 0.750 +/- 0.102 when trained with a small dataset with dense expert annotations. However, extending the learning to larger databases would require prohibitive annotation efforts. Hence, in this work we implemented and compared new training methods that require only a small fraction of manually annotated pixels. While directly training on these scribble annotation deteriorates the segmentation quality by 26.8 percentage points, our new random walk-based semi-automatic target achieves the same Dice overlap as a dense supervision, and thus offers a more promising approach for sparse annotations.
COMPARATIVE EVALUATION OF THE RULE BASED APPROACH TO REPRESENTATION OF ADAPTATION LOGICS

Due to the rapid growth of business processes digitalization, enterprise applications cover more and more business and daily life functions thus becoming more complex. Complex enterprise applications often deal with low users’ satisfaction of usability. This problem can be solved by implementing adaptation algorithms in enterprise applications, so they can be adjusted for specific context situations and specific users’ needs. Some adaptation logics representation techniques are complex and require specific knowledge and skills to manage and modify adaptation process. In this paper rule based adaptation approach is introduced where rules are used as means to manage and modify adaptation process. Rules are easy to read and understand, thereby rule based adaptation should ensure elastic, transparent and easy administrable adaptation process. The goal of this paper is to test this statement by carrying out a comparative adaptation logics representation evaluation experiment. During the experiment participants are required to complete tasks which include different forms of adaptation logics representation (code, rules and models). Experiment results are analyzed by qualitative and quantitative measures such as users’ understandability of applications behavior when adaptation case occurs and users’ satisfaction with adaptation logics representation. Experiment results are summarized and are to be used for further development of the study.
How Thomas A. Edison shaped today's singing ideal: Tracking his ambiguous concept of tremolo by analysing archival documents and sound recordings

We linked an analysis of vocal vibratos in early recordings with Edison's dismissive attitude towards singers' tremolos in the same recordings. We conclude that there are at least two different factors contributing to Edison's concept of tremolo, which include a) technical limitations and artefacts related to the recording process, and b) aesthetic judgements based on his taste for a good singing voice, but which are not necessarily related to the vibrato as such. In some cases, his comments on the tremolo even contradict the vibratos found in the recordings. Yet, Edison's concept of tremolo was as ambiguous as it was influential, for it was the tremolo on which Edison frequently based his decision to employ singers for commercial recordings. Since many of today's vocal artists still view early recordings as the gold standard of interpretation, we should consider the possibility that early recordings are by no means a representative sample of the artists of that time, but rather a selection due to the constraints of the technology at that time.
Convolutional Neural Network to Extract the Best Treatment Way of Warts Based on Data Mining

Cryotherapy and immunotherapy are two common methods to treat warts.This paper aims to determine which treatment methods is more effective in curing common types of warts.For this purpose, the author designed a convolutional neural network (CNN) to predict the effectiveness of wart treatment methods.The cryotherapy and immunotherapy datasets were randomly amplified by data mining rules, and represented as 2D grayscale pseudo images.The results show that the CNN with data mining can effectively predict the wart treatment effects; cryotherapy treatment is better than immunotherapy in curing common types of warts.This research is the first to combine CNN and data mining in predicting the treatment effects of warts.
Video Transcript Indexing and Retrieval Procedure

Finding appropriate e-Learning resources within a repository of videos represents a critical aspect for students. Given that transcripts are available for the entire set of videos, the problem reduces to obtaining a ranked list of video transcripts for a particular query. The paper presents a custom approach for searching the 16.012 available video transcripts from Media Website at Universitat Politecnica de Valencia. The proposed solution builds a bag-of-words, computes TF-IDF scores, clusters the transcripts and builds a Latent Dirichlet Allocation (LDA) model for each cluster. An inherent difficulty of the problem comes from the fact that transcripts are in the Spanish language. The experimental results are satisfactory, especially in terms of comparing with currently existing search mechanism.
Resolution verification sensing device for a stereo digital image correlation system based on dual-frequency laser interference

Digital image correlation (DIC) is widely used in materials mechanics, nondestructive testing, and other fields due to its advantages of noncontact, full-field measurement, and simple experimental setup. As an optical measurement method to measure the three-dimensional shape and deformation of an object, measurement accuracy is one of the most important technical indicators of DIC. If DIC is made into a measuring instrument, the resolution is an important parameter that must be provided. In general, the higher the measurement accuracy of the instrument, the higher the resolution of the instrument. At present, the research on DIC focuses on the analysis of factors affecting measurement accuracy, the noise reduction of measurement results, and the improvement of correlation algorithms. There are few reports on the verification of the resolution of DIC measurement instruments. However, accuracy analysis and resolution verification of the measurement instrument is a vital technical task to ensure the credibility of the measurement data. In this paper, a high-precision dual-frequency laser interferometer principle is used to design a sensing device to verify the measurement resolution of the DIC instrument. The accuracy and resolution of the self-made stereo DIC instrument were tested and evaluated using this sensing device.
MINAT DAN KUALITAS PELAYANAN TERHADAP TINGKAT KUNJUNGAN KE PERPUSTAKAAN

This study aims to determine the interest and quality of service to visit level in the library IPDN Jatinangor. The result of the research shows that the interest and the quality of service have partial and simultaneous effect on visit level in the library of IPDN Jatinangor. Where, member interest variable and service quality together have influence equal to 51,80% to visit level of library of IPDN Jatinangor.
Exploiting Open IE for Deriving Multiple Premises Entailment Corpus

Natural language inference (NLI) is a key part of natural language understanding.The NLI task is defined as a decision problem whether a given sentence -hypothesis -can be inferred from a given text.Typically, we deal with a text consisting of just a single premise/single sentence, which is called a single premise entailment (SPE) task.Recently, a derived task of NLI from multiple premises (MPE) was introduced together with the first annotated corpus and corresponding several strong baselines.Nevertheless, the further development in MPE field requires accessibility of huge amounts of annotated data.In this paper we introduce a novel method for rapid deriving of MPE corpora from an existing NLI (SPE) annotated data that does not require any additional annotation work.This proposed approach is based on using an open information extraction system.We demonstrate the application of the method on a well known SNLI corpus.Over the obtained corpus, we provide the first evaluations as well as we state a strong baseline.