Reviewerly/exHarmony huggingface dataset:
Pelatihan Penggunaan Software Avogadro untuk Pemodelan Senyawa Hidrokarbon Sederhana kepada Siswa Kelas XI IPA SMA Negeri 5 Maluku Tengah

Learning Hydrocarbon at the High School requires qualified teaching aids. The limitations of teaching aids make students' knowledge of the spatial structure (3 dimensions) of hydrocarbon compounds not following the content being studied so that they do not achieve the expected competence. In the end, this impacts the difficulty of understanding the same material at a higher level, high school. For this reason, the training on the use of the Avogadro software is expected to provide knowledge to students on how to operate the software, and students can do modeling for simple hydrocarbon compounds. In the end, the students understand how the three-dimensional structure of hydrocarbon compounds looks. We need the help of software that students can use in studying molecular structures, especially hydrocarbon compounds. In this service process, a training method is carried out using demonstrations of the operating steps of the Avogadro software and is followed by all training participants. The results obtained are that the participants (Science class students of SMA Negeri 5 Maluku Tengah) can perform modeling with Avogadro software, drawing 3-dimensional structures, optimizing geometry and determining structural parameters of simple hydrocarbon molecules.
Coronavirus-themed malware is spreading. Here's how to avoid it

Hackers weaponized a legitimate novel coronavirus map application created by Johns Hopkins Health.
Corpus and Models for Lemmatisation and POS-tagging of Old French

Old French is a typical example of an under-resourced historic languages, that furtherly displays animportant amount of linguistic variation. In this paper, we present the current results of a long going project (2015-...) and describe how we broached the difficult question of providing lemmatisation andPOS models for Old French with the help of neural taggers and the progressive constitution of dedicated corpora.
Boilerplate Detection via Semantic Classification of TextBlocks

We present a hierarchical neural network model called SemText to detect HTML boilerplate based on a novel semantic representation of HTML tags, class names, and text blocks. We train SemText on three published datasets of news webpages and fine-tune it using a small number of development data in CleanEval and GoogleTrends-2017. We show that SemText achieves the state-of-the-art accuracy on these datasets. We then demonstrate the robustness of SemText by showing that it also detects boilerplate effectively on out-of-domain community-based question-answer webpages.
Phrase Retrieval Learns Passage Retrieval, Too

Dense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval-the most fine-grained retrieval unit-is appealing because phrases can be directly used as the output for question answering and slot filling tasks. In this work, we follow the intuition that retrieving phrases naturally entails retrieving larger text blocks and study whether phrase retrieval can serve as the basis for coarse-level retrieval including passages and documents. We first observe that a dense phrase-retrieval system, without any retraining, already achieves better passage retrieval accuracy (+3-5% in top-5 accuracy) compared to passage retrievers, which also helps achieve superior end-to-end QA performance with fewer passages. Then, we provide an interpretation for why phrase-level supervision helps learn better fine-grained entailment compared to passage-level supervision, and also show that phrase retrieval can be improved to achieve competitive performance in document-retrieval tasks such as entity linking and knowledge-grounded dialogue. Finally, we demonstrate how phrase filtering and vector quantization can reduce the size of our index by 4-10x, making dense phrase retrieval a practical and versatile solution in multi-granularity retrieval.
P vs NP: P is Equal to NP: Desired Proof

Computations and computational complexity are fundamental for mathematics and all computer science, including web load time, cryptography (cryptocurrency mining), cybersecurity, artificial intelligence, game theory, multimedia processing, computational physics, biology (for instance, in protein structure prediction), chemistry, and the P vs. NP problem that has been singled out as one of the most challenging open problems in computer science and has great importance as this would essentially solve all the algorithmic problems that we have today if the problem is solved, but the existing complexity is deprecated and does not solve complex computations of tasks that appear in the new digital age as efficiently as it needs. Therefore, we need to realize a new complexity to solve these tasks more rapidly and easily. This paper presents proof of the equality of P and NP complexity classes when the NP problem is not harder to compute than to verify in polynomial time if we forget recursion that takes exponential running time and goes to regress only (every problem in NP can be solved in exponential time, and so it is recursive, this is a key concept that exists, but recursion does not solve the NP problems efficiently). The paper’s goal is to prove the existence of an algorithm solving the NP task in polynomial running time. We get the desired reduction of the exponential problem to the polynomial problem that takes O(log n) complexity.
Non-invasive health prediction from visually observable features

<ns4:p><ns4:bold>Background: </ns4:bold>The unprecedented development of Artificial Intelligence has revolutionised the healthcare industry. In the next generation of healthcare systems, self-diagnosis will be pivotal to personalised healthcare services. During the COVID-19 pandemic, new screening and diagnostic approaches like mobile health are well-positioned to reduce disease spread and overcome geographical barriers. This paper presents a non-invasive screening approach to predict the health of a person from visually observable features using machine learning techniques. Images like face and skin surface of the patients are acquired using camera or mobile devices and analysed to derive clinical reasoning and prediction of the person’s health.</ns4:p><ns4:p> <ns4:bold>Methods: </ns4:bold>In specific, a two-level classification approach is presented. The proposed hierarchical model chooses a class by training a binary classifier at the node of the hierarchy. Prediction is then made using a set of class-specific reduced feature set.</ns4:p><ns4:p> <ns4:bold>Results:</ns4:bold> Testing accuracies of 86.87% and 76.84% are reported for the first and second-level classification. Empirical results demonstrate that the proposed approach yields favourable prediction results while greatly reduces the computational time.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>The study suggests that it is possible to predict the health condition of a person based on his/her face appearance using cost-effective machine learning approaches.</ns4:p>
Biomedicínske použitie 3D tlače

235 / 5000 Výsledky překladu A research article that deals with a summary of information and specific examples of the use of 3D technology in medicine, pharmacy and healthcare. In the second part, the materials that are most often used, their advantages and limitations are discussed.
Book Review (Reviewing Henning Hartwig Ed., 2021 Research Handbook on Design Law)

This review discusses a book that examines design protection by different types of intellectual property rights in multiple jurisdictions. The editor, Dr. Hartwig, a leading design law practitioner involved in the landmark decisions of the Court of Justice for European Union (CJEU), selected fundamental and advanced topics of product design protection; the world’s leading practitioners and scholars joined to write on the topics from the comparative law approach. The book mainly focuses on the EU and the US, but various chapters cover other major jurisdictions including Japan, China, Australia, Brazil, South Africa, and Turkey. The review includes the structure and topics of the book and discusses the valuable information available from the book for US IP lawyers. Many features of EU design protection are unique and not well-known to non-EU IP lawyers. The book discusses trademark, copyright and other neighboring rights which will function as fallback options for protecting product designs if the designs were first disclosed outside the EU without filing a design application at the EUIPO during the one-year grace period and thus cannot rely on unregistered community design rights. The review recommends the book to law students, IP lawyers, and academics. It offers a starting point to learn the basics of design protection and research on most current issues in product design protection in the global market.
Mouse Controlling with Hand Gestures

The recognition of gestures plays a key role in the interaction of computers. As we can see that so many new technological advancements are taking place, such as bioauthentication, which we often see in our smartphones, Mouse Controlling with hand gestures makes the perfect technology to use a few hand gestures today to control the system without a physical mouse (i.e. mouse with hardware).This can be controlled by showing hand gestures in front of the webcam that have already been recorded while programming. This paper will be structured on the basis of this idea. This paper provides a detailed explanation of how the mouse controls hand gesturemethodologies and results.
Avengers Ensemble! Improving Transferability of Authorship Obfuscation

Stylometric approaches have been shown to be quite effective for real-world authorship attribution. To mitigate the privacy threat posed by authorship attribution, researchers have proposed automated authorship obfuscation approaches that aim to conceal the stylometric artefacts that give away the identity of an anonymous document's author. Recent work has focused on authorship obfuscation approaches that rely on black-box access to an attribution classifier to evade attribution while preserving semantics. However, to be useful under a realistic threat model, it is important that these obfuscation approaches work well even when the adversary's attribution classifier is different from the one used internally by the obfuscator. Unfortunately, existing authorship obfuscation approaches do not transfer well to unseen attribution classifiers. In this paper, we propose an ensemble-based approach for transferable authorship obfuscation. Our experiments show that if an obfuscator can evade an ensemble attribution classifier, which is based on multiple base attribution classifiers, it is more likely to transfer to different attribution classifiers. Our analysis shows that ensemble-based authorship obfuscation achieves better transferability because it combines the knowledge from each of the base attribution classifiers by essentially averaging their decision boundaries.
Analysis on work engagement and its influencing factors among nurses in grade-A tertiary hospital of Neijiang City

Objective To understand the work engagement and its influencing factors among nurses in grade-A tertiary hospitals of Neijiang City. Methods From November to December, 2019, general information questionnaire and Utrecht work engagement scale-Chinese version(UWES) were used to conduct a questionnaire survey among 520 nurses from three grade-A tertiary hospitals of Neijiang City. And the data were analyzed. Results The score of work engagement in respondents was 4.92±1.15. The scores of each dimension from high to low were dedication(5.04±1.17), concentration(4.92±1.16) and vitality(4.83±121). The working years and the department were the factors that affect nurses’ work engagement(both P<0.05). Conclusion The work engagement of nurses in the grade-A tertiary hospitals in Neijiang City is at a high level, and the working years and departments are the factors influencing the work engagement of nurses.
A Comparison of the Best Fitness Functions for Software Defect Prediction in Object-Oriented Applications Using Particle Swarm Optimization

The quality of software can be assessed by studying how prone it is to defects. This paper introduces a defect prediction approach using particle swarm optimization (PSO) and assesses what impact a fitness function has on its performance. The fitness functions used in this study are G-mean, sensitivity, specificity, accuracy, and precision. This study is novel in the regard that it seeks to improve the quality of widely used software and applications by focusing solely on object-oriented software and metrics. The performance is measured for each fitness variant by calculating the G-mean for each predicted dataset. The results are validated using tenfold cross-validation. Much attention has also been paid that the results are proven to be significant by using the Friedman test. The final results contribute towards the selection of better fitness functions for PSO in future defect or change prediction studies.
BIOGRAPHICAL AND PEDAGOGICAL DIMENSION OF VIKTOR SYDORENKO AS A SCIENTIST IN THE FIELD TECHNOLOGY EDUCATION

The article analyzes Viktor Konstiantynovych Sydorenko’s life and scientific path. The authors have identified four stages of his life as a Doctor of Pedagogical Sciences, Full Professor, Corresponding Member of the Academy of Pedagogical Sciences of Ukraine: 1) the primary stage – 1951-1968; 2) the stage of scientific research – 1968-1977; 3) the stage of pedagogical formation – 1977-1997; 4) the stage of active scientific activity – 1997-2013. Every stage of his life is briefly described, the scientists’ scientific views, the results of his scientific research, opinions about the place of drawing in the curriculum are singled out and emphasized; the scientist’s concern about the removal of drawings from the curriculum and the lack of students’ basics of graphic literacy, which are necessary for further technical engineering and computer graphics. The scientist’s views on updating the content of labor training, the inconsistency of the subject title with the world’s needs are analyzed and concretized, and a modern teacher’s main qualities are determined. Moreover, in the course of the research, it has been revealed that among the main tasks that should be included in the content of future labor education (technology) teacher training in higher pedagogical educational institutions, there is students’ readiness to develop technical concepts, gain practical skills, technical and technological knowledge and creative attitude to work. The problems on which the scientist worked are outlined and systematized; V. Sydorenko’s scientific heritage is analyzed and further directions of research on this issue are determined, which are the following: the systematization of scientific works, analysis of achievements and issues of the scientific school of contradictions research, which cause the emergence of new requirements for teacher training and technologies.
Práctica académica en Grupo Bernier SAS: perceptrón multicapa en computación cuántica

This paper shows a perceptron alternative designed in quantum computing, which consists of two input neurons, two neurons in its hidden layer and a neuron in its output layer. The main objective of such work is the design of a point sorter located within a Cartesian plane, which depend on two input variables x, y; these are classified into two types (type 1 and type 0). This design is based on the term distance between quantum states, so the program determines whether the point in question is type 0 or type 1 depending on the probability of being of one type or another. The main idea of the work revolves around the quantum sphere, where values 1 and 0 are assigned to points stipulated by the user (Network Training). Then, depending on the distance between the training points and the test points, it is determined by quantum gates if it is more likely to be type zero or type one.
Cautious Random Forests: a new decision strategy and some experiments

Random forest is an accurate classification strategy, which estimates the posterior probabilities of the classes by averaging frequencies provided by trees. When data are scarce, this estimation becomes difficult. The Imprecise Dirichlet Model can be used to make the estimation robust, providing intervals of probabilities as outputs. Here, we propose a new aggregation strategy based on the theory of belief functions. We also propose to assign weights to the trees according to their amount of uncertainty when classifying a new instance. Our approach is compared experimentally to the baseline approach on several datasets.
Compact Binary Fingerprint for Image Copy Re-Ranking

Image copy detection is challenging and appealing topic in computer vision and signal processing. Recent advancements in multimedia have made distribution of image across the global easy and fast: that leads to many other issues such as forgery and image copy retrieval. Local keypoint descriptors such as SIFT are used to represent the images, and based on those descriptors matching, images are matched and retrieved. Features are quantized so that searching/matching may be made feasible for large databases at the cost of accuracy loss. In this paper, we propose binary feature that is obtained by quantizing the SIFT into binary, and rank list is re-examined to remove the false positives. Experiments on challenging dataset shows the gain in accuracy and time.
Difficulties of Post-editing on the Translation of Power Company Qualifications

With the burgeoning development of economic globalization, China has increased its ties with the world after proposing the going-global strategy, which has encouraged numerous companies to seek opportunities in the international arena. Electric companies are among them. Based on a project in translating the qualification of an electric company, founded in Baoding, Hebei province, this paper investigates the common errors faced by translators during post-editing. The limitation of Machine Translation requires translators’ to possess critical thinking in distinguishing errors of terminologies and collocations. The paper concludes that despite the helpful role Machine Translation plays in translating technical texts, translators still have to value day-to-day accumulation.
Culinary Tourism Trend and Its Correlation with Obesity and Diabetes in Indonesia: Google Trends Analysis

Pendahuluan: Wisata Kuliner, atau perjalanan untuk makanan telah menjadi tren di banyak negara seluruh dunia dan dapat menyebabkan masalah kesehatan. Kelebihan berat badan atau terus terjadi pada dewasa dan anak-anak. Dari tahun 1975 sampai 2016, prevalensi kelebihan berat badan atau pada anak dan remaja usia 5-19 tahun meningkat lebih dari empat kali lipat dari 4% menjadi 18% secara global. Metode: Peneletian ini menggunakan metode kuantitatif dan desain deskripsi dengan data sekunder dari Google Trends. Instrumen yang digunakan merupakan time-series data dari 1 Maret 2016 sampai dengan 1 Maret 2020. Unit analisis yaitu Provinsi dan Nasional. Hasil: Pola time series untuk wisata kuliner setidaknya terdapat dua kali puncak kenaikan data setiap tahunnya dan dua puncak penurunan pada kategori diabetes. Tren kecenderungan penurunan untuk kegemukan, gemuk, dan orang gemuk kata kunci sedangkan obesitas kata kunci cenderung meningkat selama periode penelitian. Korelasi negatif dengan kekuatan sedang untuk melitus (r = -0.4341, -0.4914, -0.4722); (r = -0.4470); penderita (r = -0.3605); penyakit (r = -0.3130). Secara signifikan berbeda (p-value 0.05) untuk kategori diabetes pada destinasi wisata. Kesimpulan: Google Trends dapat digunakan untuk meramalkan dan diabetes terkait dengan wisata kuliner. Wisata kuliner kategori lebih tinggi pada musim liburan dibandingkan musim biasa sedangkan kategori diabetes cenderung lebih rendah. Promosi kesehatan dapat diimplementasikan pada waktu yang tepat dan menggunakan kata kunci yang tepat terutama untuk pengguna internet. Kata kunci: Google Trends, Kuliner, Wisata, Obesitas, Diabetes Background: Culinary tourism, or travel for food, has become a trend in many countries in the world and it might be causing health problems. Overweight and obesity continue to grow in adults and children. From 1975 to 2016, the prevalence of overweight or obese children and adolescents aged 5-19 years increased more than four-fold from 4% to 18% globally. Method: This study is a quantitative research and descriptive design with secondary data from Google Trends. The instrument is time-series data from 1st March 2016 up to 1st March 2020. The analysis units are National and Provinces. Result: Time series pattern for culinary tourism with at least 2 times peaks every year and 2 times valleys for diabetes category. Decreasing tendency trend for kegemukan, gemuk, and orang gemuk keywords while obesitas keyword tend to increase during study period. Negative medium correlation for melitus (r = -0.4341, -0.4914, -0.4722); (r = -0.4470); penderita (r = -0.3605); penyakit (r = -0.3130). Significantly different (p-value 0.05) for diabetes category in tourist destination. Conclusion: Google Trends could be used forecasting obesity and diabetes related to culinary tourism. Culinary tourism category was higher in holiday season rather than regular season while diabetes category tends to be lower. Health promotion might be applied in the right time and using right keywords especially for internet user.
IZRADA APLIKACIJE ZA PRAĆENJE I KREIRANJE PERSONALIZIRANIH TRENINGA

For best results, the training of professional andrecreational athletes should be carefully planned,and their performance in training properlymonitored. For this purpose, in this paper wepresent a new software solution Fitweb. Bycombining web technologies and databases, usershave the opportunity to easily organize and plantraining, as well as display various statisticalanalyzes of their current progress. The goal of thissolution is to facilitate the process of preparationand selection of the best exercises for users, whichultimately achieves the best possible results in theshortest possible time.In this paper, we will describe the softwaresolution, its functionalities and implementation,and the technology used.
Adaptive Neural Message Passing for Inductive Learning on Hypergraphs

Graphs are the most ubiquitous data structures for representing relational datasets and performing inferences in them. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations. This drawback is mitigated by hypergraphs, in which an edge can connect an arbitrary number of nodes. Most hypergraph learning approaches convert the hypergraph structure to that of a graph and then deploy existing geometric deep learning methods. This transformation leads to information loss, and sub-optimal exploitation of the hypergraph's expressive power. We present HyperMSG, a novel hypergraph learning framework that uses a modular two-level neural message passing strategy to accurately and efficiently propagate information within each hyperedge and across the hyperedges. HyperMSG adapts to the data and task by learning an attention weight associated with each node's degree centrality. Such a mechanism quantifies both local and global importance of a node, capturing the structural properties of a hypergraph. HyperMSG is inductive, allowing inference on previously unseen nodes. Further, it is robust and outperforms state-of-the-art hypergraph learning methods on a wide range of tasks and datasets. Finally, we demonstrate the effectiveness of HyperMSG in learning multimodal relations through detailed experimentation on a challenging multimedia dataset.