Reviewerly/exHarmony huggingface dataset:
PEMBELAJARAN MENULIS KEMBALI ISI TEKS BIOGRAFI BERBASIS FILM DENGAN MENGGUNAKAN METODE CLUSTERING YANG BERFOKUS PADA PENGEMBANGAN GAGASAN KE DALAM TEKS DESKRIPTIF PADA PESERTA DIDIK KELAS X SMK TAMANSISWA BANDUNG TAHUN PELAJARAN 2020/2021

This research is motivated by the low ability of students in learning to write biographical texts. This study aims to determine the ability of planning and implementing the researcher's learning; students' writing skills; and the effectiveness of the clustering method in learning to rewrite the contents of a film-based biographical text that focuses on developing ideas into descriptive texts; The research method used is an experiment with a quasi-experimental design. The results of this study are: first, the ability of researchers to plan learning gets a score of 3.78 in the very good category, and the ability of researchers to carry out learning gets a score of 3.67 with a good category. Second, the ability of students has increased based on the Paired Samples Test which proves the value of Asymp. Signature. (2-tailed) of 0.000 is less than 0.05, then Ha2 is accepted. Third, an effective grouping method is applied in learning to rewrite the contents of a film-based biographical text that focuses on developing ideas into descriptive text based on the Independent Samples Test which proves the value of Asymp. Signature. (2-tailed) of 0.020 is smaller than 0.05, then Ha3 is accepted. Based on the results of the study, the researcher concluded that learning to rewrite the contents of a film-based biographical text using a grouping method that focused on developing ideas into descriptive texts for class X students of SMK Tamansiswa Bandung in 2020/2021 was successful.
EFEKTIVITAS PEMBELAJARAN DARING DI PROGRAM STUDI PENDIDIKAN BAHASA DAN SASTRA INDONESIA MENGGUNAKAN APLIKASI MOODLE (ELEARNING FKIP UNPAS)

This research was motivated by learning affected by the Covid-19 pandemic at the Indonesian Language and Literature Education Study Program (PBSI), Faculty of Teacher Training and Education (FKIP), Pasundan University (Unpas) Bandung using the Moodle application (e-learning FKIP Unpas). This study aims to determine the mechanism for implementing the online learning process carried out at PBSI FKIP Unpas, to determine student satisfaction in carrying out online learning using the Moodle application, and to identify the effectiveness of online learning implemented at PBSI FKIP Unpas. The research method used is descriptive research with a qualitative approach that focuses on ongoing events and the results will be analyzed. The results of this study, namely the online learning mechanism applied at PBSI FKIP Unpas, were compiled and implemented systematically, starting from student registration as course participants to the actual learning process. After analyzing the survey results, most of the students were satisfied with the online learning mechanism of PBSI FKIP Unpas. Respondents assessed that online learning using the Moodle application (e-learning FKIP Unpas) was very effective at 23%, effective at 54%, and less effective at (16%). However, there were a small number of students who rated it as ineffective, namely 7%, while those who rated it very ineffective were 0%. Based on this, online learning using the Moodle application (E-Learning FKIP Unpas), is effectively applied in the Indonesian Language and Literature Education Study Program FKIP Unpas.
Analysis of Security Challenges in Cloud-Based SCADA Systems: A Survey

&lt;p&gt;Supervisory control and data acquisition (SCADA) systems allow industrial organizations to control and monitor real-time data and industrial processes. Moving SCADA systems to the cloud environments can improve the performance of traditional SCADA systems by enhancing features such as storage capacity, reliability and availability, security measures, as well as reducing technical and industrial costs. However, cloud cyberattacks are rapidly increasing, posing a major challenge to such type of systems. In addition, these cyberattacks still remains scattered across many research, and much of published research on cloud-based SCADA systems has been focused on narrow sets of attacks. Thus, this research provides a survey and an analysis of the most common cybersecurity attacks and challenges that cloud-based SCADA systems might experience. It also conducts a security risk assessment of the analyzed attacks. Finally, it proposes the existing suitable detection and prevention techniques to mitigate the impact of cyberattacks on such critical control systems.&lt;/p&gt;
Radar hand gesture recognition based on three-dimensional united attention network

Radar-based dynamic gesture recognition has great potential in human-computer interaction (HCI) applications. With the development of wideband radar, the radar signal of hand gestures is often represented by the three-dimensional (3D) range-Doppler-time cube, which is mainly processed by multichannel two-dimensional (2D) CNNs and 3D CNNs. However, the utilization and fusion of different kinds of features in existing networks is simple and not well optimized. In this paper, an efficient attention method named three-dimensional united attention (3D-UA) module in 3D space is proposed. The 3D-UA module applies a multi-scale pyramid convolution spatially, extracts channel attention weights on feature maps and captures the global temporal cues simultaneously. Furthermore, a network named 3D-UANet is proposed by replacing the 3x3x3 convolution with the 3D-UA module in the 3D-ResNet. 3D-UANet can efficiently extract the range-Doppler-time features of gestures. Experimental results show that the proposed method has good generalization performance on data from subjects in complex scenes.
Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

Conversion of one video bitstream to another video bitstream is a challenging task in the heterogeneous transcoder due to different video formats. In this paper, a region of interest (ROI) based super resolution technique is used to convert the lowresolution AVS (audio video standard) video to high definition HEVC (high efficiency video coding) video. Firstly, we classify a low-resolution video frame into small blocks by using visual characteristics, transform coefficients, and motion vector (MV) of a video. These blocks are further classified as blocks of most interest (BOMI), blocks of less interest (BOLI) and blocks of noninterest (BONI). The BONI blocks are considered as background blocks due to less interest in video and remains unchanged during SR process. Secondly, we apply deep learning based super resolution method on low resolution BOMI, and BOLI blocks to enhance the visual quality. The BOMI and BOLI blocks have high attention due to ROI that include some motion and contrast of the objects. The proposed method saves 20% to 30% computational time and obtained appreciable results as compared with full frame based super resolution method. We have tested our method on different official video sequences with resolution of 1K, 2K, and 4K. Our proposed method has an efficient visual performance in contrast to the full frame-based super resolution method.
Survey on Fully Homomorphic Encryption, Theory and Applications

This paper comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. The paper delves into the mathematical foundations required to understand fully homomorphic encryption FHE. It consequently covers design fundamentals and security properties of FHE, and describes the main FHE schemes based on various mathematical problems. On a more practical level, the paper presents a view on privacy-preserving Machine Learning using homomorphic encryption, then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing, and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.
Abordagem de Simulação de Ruídos para Avaliação do Reconhecimento de Locutores em Ambientes Ruidosos

There is frequent noise that affects the performance of voice recog-nition and speaker recognition algorithms in a real-world envi-ronment. For voice and speaker recognition systems to be more robust, these noises must not interfere in a harmful way, causingerrors in understanding commands. To evaluate signal degradationand speaker recognition when exposed to real-world environments,we explore a reverberation noise simulation environment usinga specific library in this work. We tested a speaker recognition model with i-vectors and Probabilistic Linear Discriminant Analy-sis (PLDA). We have analyzed the impact of noise in conjunction with reverberation on its error rate. Results based on Monte Carlosimulation showed that, for the tested cases, the noise set withreverberation worsened the recognition rate by up to 24,43%.
Predicting Bug-Fixing Time with Machine Learning - A Collaborative Filtering Approach

ABSTRACTPredicting bug-fixing time helps software managers and teams prioritizetasks, allocations and costs in software projects. In literature,machine learning (ML) models have been proposed to predict bugfixingtime. One of features highlighted by studies is the reporter(the person who open the bug) has positive influence in the timeto resolve a bug. In this way, this paper answers the following researchquestion: How does a collaborative filtering approach performin predicting bug-fixing time compared to the supervised machinelearning approaches? In order to answer this question we performedan experiment using collaborative filtering approach to recommendthe bugs that are fast to be resolved in two open software projects.We compare our proposed approach with the ML approach relatedto the literature. As a result, the collaborative filtering approachoutperforms the supervised ML achieving an F-measure of 74%while the supervised ML achieved 66%. The collaborative filteringapproach showed to be a new perspective to predict bug-fixing timein software projects focusing the prediction on the reporter.
What are the Top Used Modules in Python Open-Source Projects?

ABSTRACTWhen a team of developers are creating new software, they mostlikely will use libraries of code that can assist in a given requiredfeature. One source to find these libraries can be popular questionanswerwebsites, blogs, personal web pages and the usage of toolsthat can automatically suggest libraries. Popularity might be onecriterion that developers can use when choosing a library. In thiswork, we performed an empirical evaluation through mining Pythonprojects hosted in GitHub to identify the most popular used modules.We selected 129 projects based on specific criteria, one of thembeing the number of stars that reflects their popularity. To automatethe data extraction process, we developed the PySniffer, an opensourcetool that performs a static code analysis in Python scripts,checking which modules from both the standard library and externalmodules are used in a project. Our tool also has a front-end thatcan display the data more friendly with statistical information. Asa result, we generated a list with the top used modules in Pythonprojects hosted in GitHub, serving as complementary informationalongside the most popular libraries informed in personal blogs andwebsites.
A INTERVENÇÃO EDUCACIONAL DE ENFERMAGEM NA ADESÃO AO REGIME TERAPÊUTICO EM PACIENTES COM DIABETES MELLITUS TIPO 2

Objective: To discuss the role of nursing in encouraging breastfeeding in the pregnancy-puerperal cycle.Methodology: This is an integrative literature review carried out in the following electronic databases: National Library of Medicine (MEDLINE), Latin American and Caribbean Literature on Health Sciences (LILACS), Scientific Electronic Library Online (SciELO) and Database Nursing Data (BDENF).To define the inclusion criteria, complete original articles were selected, freely available online in Portuguese, English and Spanish and published in the last 10 years.As for the exclusion criteria, we defined: duplicate studies, review articles, monographs, theses, editorials and those that do not answer the guiding question of the study.Thus, the study obtained a total sample of 09 articles.Results: The study discussed the role of nursing in encouraging breastfeeding throughout the woman's pregnancy-puerperal cycle, which highlighted the relevance of the role of these professionals in the context of public health, since the literature has already shown that the Breastfeeding is an act of multiple benefits for the mother/baby binomial.Conclusion: Nursing plays a key role in the breastfeeding process, and therefore needs to be valued.Thus, it is believed that there is a need to invest in promoting the training of these professionals, aiming at better quality of care for women in the context of breastfeeding.
The recent progress and state-of-art applications in physics Quantum Communication

Quantum communication is an emerging interdisciplinary discipline that combines classical telecommunication and quantum mechanics. This paper discusses the recent progress of quantum communication in terms of the basic theoretical framework of quantum communication technology, optical devices, state-of-the-art applications. Based on the analysis, quantum communication technology research hotspots mainly involve quantum key distribution, quantum confidential communication, quantum invisible transmission, quantum entanglement, quantum cryptography, etc., which exhibits an increasingly rich trend. In addition, the limitations of the quantum communication are demonstrated from the perspective of practical applications. Afterwards, the future prospects of quantum communication technology are proposed accordingly. These results reveal that quantum communication applications are promising and are moving toward practicality.
The Progress and the state-of-art approaches of Quantum Teleportation and Communication

Quantum teleportation is a new technique utilized dispersed quantum entanglement to transmit information (i.e., the quantum states). In this paper, the principles and the protocol will be introduced systematically. Specifically, the experimental setups and the state-of-art applications will be discussed. According to the analysis, one can better understand the working principle and current development of quantum teleportation. However, the limitation of further applying the quantum teleportation is obvious the cost of building the QT infrastructure and the superconducting technology. Besides, it is also too expensive to achieve large-scale use and commercial promotion. In the future, QT will expand its industrial scale and build a national or even global quantum communication network. These results shed light on guiding further development for quantum teleportation important.
The Comparison of the computing ability of quantum and conventional computer

Contemporarily, whether quantum computing performs better than conventional computers remain an unresolved issue. In this paper, we compare the time complexity between quantum and conventional computers for a specific type of issue. Theoretically, quantum computation is better in solving nonlinear calculations, the quantitative evaluation of the computing ability should be investigated. In this paper, the Fourier transform was applied in both classical and quantum logical circuits to calculate the theoretical time complexities respectively and make a comparison after that. According to the analysis, the ideal quantum computer performed really fast as the number of tasks increased, but the gate-level accurate quantum circuit, which was a simulated circuit was running slower. The paper shows that the current technology cannot perform the perfect quantum computer, but the future of it would be very promising. Overall, these results shed light on guiding further exploration of quantum computing.
Issues on Sentencing of Network Service Providers in Cybercrime

With the continuous progress of science and technology, the problem of conviction and sentencing of network service providers in the field of cybercrime has become increasingly prominent. In judicial practice, there are often different judgments in the same case, ambiguous sentences in the judgment, and ambiguous qualitative nature of the subject of the crime, which shows that the path of conviction and sentencing of network service providers needs to be improved. Through analyzing the current laws and regulations concerning network service providers in China, the lack of classification of network service providers and the application of relevant sentencing laws are the main obstacles to the conviction and sentencing of network service providers. It is suggested to solve the practical obstacles of conviction and sentencing of network service providers by classifying network service providers, limiting the crime of helping information network crimes, strictly controlling the information network security management obligations of network service providers, and perfecting the relevant legal provisions on the obligations of network service providers.
Research on the restriction of "new" pocket crime under the background of the Internet- from the Amendment of Criminal Law (IX)

With the release of the criminal law amendment (9), refusing to fulfill the obligation of network security crime and helping information network crime, as two important new charges, have improved the criminal law because the responsibility of "network service provider" was introduced for the first time in the field of criminal responsibility. However, in the implementation of relevant charges, due to its limitations in legislation and the similar problems in judicial practice, the scope of related crimes is improperly expanded forming the so-called "new pocket crime". Based on this situation and the legislation in Germany, combined with our own judicial practice, the systematization of related charges should be promoted in network service providers on the legislative level of special legislation. In the judicial level, specification of "knowing" standard and expanding the application of interpretation should be adopted. These two ways can realize the reduction of pocket crime and produce positive effect on the disadvantages of the rule of law construction in China. This paper proposes the method to solve the restriction of pocket crime from two aspects. At the legislative level there are three initiatives. Firstly, special legislation network service providers should be implemented. Secondly, the criminal law charges should be set systematically based on the typing of network service providers. Lastly, the criminal responsibility should be apportioned hierarchically based on the typing of network service providers. As for the judicial level, the identification standard of "knowing" should be clarified in judicial practice. In the process of applying the extended interpretation, it is important to adhere to the same kind of interpretation. In addition, the judicial authorities should organically combine the crackdown on crime with the development of the Internet in the process of cracking down on cybercrimes.
Semantics of quantum programming languages: Classical control, quantum control

This chapter is devoted to a discussion on the semantics of quantum programming languages. We will discuss two main paradigms: the usual, standard co-processor model, and more exotic models based on quantum superposition of executions. We will discuss how these two systems can –or cannot– be unified, and how this relates to languages.
Integrating anisotropic filtering, level set methods and convolutional neural networks for fully automatic segmentation of brain tumors in magnetic resonance imaging

An accurate, fully automatic detection and segmentation technique for brain tumors in magnetic resonance images (MRI) is introduced. The approach basically combines geometric active contours segmentation with a deep learning-based initialization. As a pre-processing step, an anisotropic filter is used to smooth the image; afterwards, the segmentation process takes place in two phases: the first one is based on the concept of transfer learning, where a pre-trained convolutional neural network coupled with a detector is fine-tuned using a training set of 388 T1-weighted contrast enhanced MRI images that contain a brain tumor (Meningioma); this trained network is able to automatically detect the location of the tumor by generating a bounding box with certain coordinates. The second phase takes place by using the coordinates of the bounding box to initialize the geometric active contour that iteratively evolves towards the tumor's boundaries. While most of the ingredients of this processing chain are more or less well known, the main contribution of this work is in integrating the various techniques in a novel and hopefully clever form, which could take the best of both geometric segmentation algorithms and neural networks, with a relatively light training phase. The performance of such a processing network is evaluated using a separate testing set of 97 MRI images containing the same type of brain tumor. The technique proves to be remarkably effective, with a precision of 97.92%, recall of 96.91%, F-measure of 97.41% and an average Dice similarity coefficient (DSC) for segmented images above 0.95.
Prototyping NLP Non-Word Detection System for Dinka using Dictionary Lookup Approach

Spellcheckers are computer software used for non-word or real word error detection. The Dinka text editors have been developed, however, no one has developed their spellcheckers. The research entitled Prototyping NLP Non-Word Detection System for Dinka Using Dictionary Lookup Approach was a solution to Dinka spellchecking. The study objectives were: requirements gathering and analysis. The computer keyboard was customized to accept the Dinka characters. Dinka lexicon was created with 6,976 words. The prototype was implemented using java programming language and dictionary lookup approach was used for non-word detection. The accuracy of detection (detecting real words and non-words) gave 98.10%, and the accuracy of non-word detection (detection of non-words only) was 91.36%. The True Positive Rate (TPR) was 99.10% and the True Negative Rate (TNR) was 91.36 %. The speed of non-word detection which was found as1, 044 Hz was slow.
Machine-Readable Entailments with the Italian Prendere Construction Expressing Hitting and Insulting Events

The Italian language features a little debated transitive construction with prendere ‘to take/to catch’ in which a prepositional phrase (PP) with an adverbial value occurs mandatorily (e.g. Lui prese a pugni Leo ‘He punched Leo’). Semantically, this construction often implies the use of physical force or verbal offence. In the hitting or insulting event, the notional subject generally is a [+ Human] Agent, whilst the notional direct object generally is a [+ Animate] Affectee ([1]: 4). It can be contended that prendere, which carries no literal meaning, is zero-valent and that the predicate assigning semantic roles is the PP. A computational tool will be illustrated, which automatically performs the following NLP / NLU tasks: it provides a reliable syntactic and semantic representation of the clause type, and it produces machinereadable entailments with three clause types, i.e. sentences with ordinary verbs, support verbs, and the causative verb fare ‘to make/to have’.