Reviewerly/exHarmony huggingface dataset:
WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking

We present our work on aligning the Unified Medical Language System (UMLS) to Wikipedia, to facilitate manual alignment of the two resources.We propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1 of 72%, a substantial improvement of 20% over word-and char-level BM25, enabling manual alignment with minimal effort.We release our resources, including ranked Wikipedia pages for 700k UMLS concepts, and WikiUMLS, a dataset for training and evaluation of alignment models between UMLS and Wikipedia collected from Wikidata.This will provide easier access to Wikipedia for health professionals, patients, and NLP systems, including in multilingual settings.
Application of Web-based Customer Relationship Management in Marketing Information System: PT. BPR Bali Dananiaga

Business competition in the current era requires companies to be careful in marketing strategies, one of which is by utilizing information technology. Many social networks have been used to improve business products, one of which is a website. Why is it better to use website technology in marketing information systems, website-based technology can be easily accessed by many people. This study aims to establish relationships with companies through special website technology about complaints through social media in addition to introducing the company to the outside community to increase the database. This study uses descriptive qualitative methods and in-depth interviews with informants. The informants in this study were employees of PT BPR Bali Dananiaga and customers from BPR BDN. The determination of the informant is done by purposive sampling technique. Based on the research results, the research assumes with the help of this technology the company can carry out all marketing activities that are not only efficient but also effective. Besides, the website provides some information that makes it easy to receive Fast and accurate information.
DETERMINING THE LENGMUR COEFFICIENT OF THE FILTRATION PROBLEM

Filtration of suspension in a porous medium is actual in the construction of tunnels and underground structures. A model of deep bed filtration with size-exclusion mechanism of particle capture is considered. The inverse filtration problem - finding the Langmuir coefficient from a given concentration of suspended particles at the porous medium outlet is solved using the asymptotic solution near the concentrations front. The Langmuir coefficient constants are obtained by the least squares method from the condition of best approximation of the asymptotics to exact solution. It is shown that the calculated parameters are close to the coefficients of the model, and the asymptotics well approximates the exact solution
Dense-JANet for Monocular 3D Object Detection

3D object detection is one of the vital tasks in many fields, especially for autonomous driving. Among all technology paths for this task, the detection based on monocular images has been proven an efficient way with low cost. However, the performance of most current algorithms is far from satisfactory. The current leading systems can't achieve the accuracy which is comparable with LiDAR-based algorithms, while the real-time problems also exist. In this paper, we propose a novel anchor-free model for monocular 3D object detection. We choose the effective modified DenseNet as the feature extraction part. The Joint Pyramid Upsampling is applied to fuse features maps for multiply scales, and the Atrous Spatial Pyramid Pooling is used to maximize the context information. Finally, 5 convolution layers are connected with the feature map to predict the information. We call the model Dense-JANet and train it on the large autonomous driving dataset, called nuScenes, which has more scenes and data than KITTI. Experiments show that Dense-JANet's performance exceeds SOTA model on small object prediction and orientation prediction, while the proposed model can fully meet the real-time requirements.
CiTAR - Preserving Software-based Research

&#x0D; In contrast to books or published articles, pure digital output of research projects is more fragile and, thus, more difficult to preserve and more difficult to be made available and to be reused by a wider research community. Not only does a fast-growing format diversity in research data sets require additional software preservation but also todayâ€™s computer assisted research disciplines increasingly devote significant resources into creating new digital resources and software-based methods.&#x0D; In order to adapt FAIR data principles, especially to ensure re-usability of a wide variety of research outputs, novel ways for preservation of software and additional digital resources are required as well as their integration into existing research data management strategies.&#x0D; This article addresses preservation challenges and preservation options of containers and virtual machines to encapsulate software-based research methods as portable and preservable software-based research resources, provides a preservation plan as well as an implementation.&#x0D; &#x0D;
On the Contribution of Specific Entity Detection in Comparative Constructions to Automatic Spin Detection in Biomedical Scientific Publications

In this article, we address the problem of providing automated aid for the detection of misrepresentation (“spin”) of research results in scientific publications from the biomedical domain. Our goal is to identify automatically inadequate claims in medical articles, i.e. claims that present the beneficial effect of the experimental treatment to be greater than it is actually proven by the research results. To this end, we propose a Natural Language Processing (NLP) approach. We first make a review of related work and an NLP analysis of the problem; then we present our first results obtained on the articles that report results of Randomized Controlled Trials (RCTs), i.e. clinical trials comparing two or more interventions by randomly assigning them to patients. Our first experiments concern the identification of entities specific to RCTs (outcomes and patient groups), obtained with basic methods (local grammars) on a corpus extracted from the PubMed open archive. We explore the possibility to extract outcomes from comparative constructions that are commonly used to report results of clinical trials. Our second set of experiments consists in extracting outcomes from a manually annotated corpus using deep learning methods.
IMPLEMENTATION OF THE TRAINING PROGRAM «PROFESSIONAL EDUCATION»: EXPERIENCE, PROBLEMS, PROSPECTS FOR DEVELOPMENT

The article describes the process of training future specialists under the program "Professional education", its features, conditions for implementation, aspects of further development, as well as some of the difficulties that arise during its organization. The focus of the department on how to create a new architecture of the educational process within the program "Professional education" is set by the leading principles: modularity, integrability, flexibility and dynamism, and methodological approaches - competence, activity-based, subject-oriented, environmental – were the basis for its development. As a result the authors presented a model for training a modern specialist with formed professional skills and personal and professional qualities, capable of being in demand in the labor market. The research results can be used in the educational process in the context of how to implement relevant programs in accordance with the new requirements in the educational services market. The rapid technologization of the socio-economic development of our society was reflected in the system of vocational education, designed to prepare highly qualified and competent specialists for the modern economy, science and industry. The transformation of the educational sphere is taking place under the influence of new technologies and human practices that change the social order. The determined infrastructural spheres have the greatest impact on this transformation, we are talking about "Communication Infrastructure": the sphere of information and communication technologies that affect all processes of accumulation and transmission of information; "Infrastructure of production and consumption": a financial and investment sphere, which sets the general rules for interaction in the economic and educational system. The above said made it possible to determine the further vector of educational activities of the Department of Pedagogy and Psychology of Vocational Education in training the specialists in "Professional Education" field both at the bachelor and master degrees.
An Invariable to Rotation Data Hiding Scheme for Semi-Fragile Blind Watermark

An invariant to rotation watermarking approach is proposed for the purpose of improving the security of digital images. The proposed digital watermark approach is invariable to rotation and guarantees the integrity of the inserted mark. The size of the resulting image is identical to the original image and the process guarantees acceptable transparency. This approach evolves both in the frequency and the spatial domain. Specifically, the proposed scheme processes the JPEG images by adapting its structure to the frequency domain by modifying the DCT coefficients of the image. This scheme is also adaptable to BMP images by adjusting these parameters for an LSB modification of the colorimetric values of the image. Moreover, during the extraction, the structure of our scheme organizes itself according to the orientation of the processed image. Based on the experiment conducting with eight substitution processes, we concluded that the substitution of four bits gives a great capacity of insertion, while guaranteeing an acceptable transparency for the insertion of text. However, a substitution of one bit only is recommended for the insertion of an image. This reduces the capacity, but guarantees transparency of the watermarking process.
Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining

Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user’s behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIPBSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIPBSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods.
LCR-SMPL: Toward Real-time Human Detection and 3D Reconstruction from a Single RGB Image

This paper presents a novel method for simultaneous human detection and 3D shape reconstruction from a single RGB image. It offers a low-cost alternative to existing motion capture solutions, allowing to reconstruct realistic human 3D shapes and poses by leveraging the speed of an object-detection based architecture and the extended applicability of a parametric human mesh model. Evaluation results using a synthetic dataset show that our approach is on-par with conventional 3D reconstruction methods in terms of accuracy, and outperforms them in terms of inference speed, particularly in the case of multi-person images.
Naive Bayes Classifier Optimization on Sentiment Analysis of Hotel Reviews

&lt;p class="JGI-AbstractIsi"&gt;Feature extraction plays an important role in the sentiment analysis process, especially of text data. The Naive Bayes Classifier performs well on low feature dimensions. However, the accuracy provided is not optimal. To acquire optimal machine learning model, information gain method, evolutionary algorithm, and swarm intelligent algorithm are applied. The objective of this study is to determine the performance of the Particle Swarm Optimization (PSO) to optimize the Naive Bayes Classifier. Vectorization of words is carried out using TF-IDF. In order to produce high PSO performance, the PSO-NBC model is tested with several parameters, namely the number of particles (k = 3), setting of the number of iterations and inertia weight, individual intelligence coefficient (c1 = 1), and social intelligence coefficient (c2 = 2). Inert weight is calculated using the formulation (w = 0.5+ Rand ([- 1,1])). In conclusion, PSO is able to solve the problem space of text-based sentiment analysis. PSO is able to optimize the accuracy of Naive Bayes at a value of 89% to 91.76%. PSO performance is determined by the parameters used, especially the number of particles, the number of iterations, and the weight of inertia. A large number of particles accompanied by an increase in inertia weight can increase accuracy. The number of particles 20-30 has reached the optimal accuracy.&lt;/p&gt;
On the History of the Spread of Protestantism on the Korean Peninsula at the End of the 19th – the Beginning of the 20th Centuries

This paper presents a multilateral analysis of the penetration process of Protestantism on the Korean Peninsula. For the first time in Russian historiography, the methods of missionary activity in the late 19th and early 20th centuries were studied. Using these techniques, Western missionaries instilled their teachings not only in China and Japan, but also with even greater success in Korea. Protestant and especially Presbyterian missionary centers, created during the period of the political and economic turmoil, became a shelter for the lower strata of Korean society. Thanks to them, numerous schools and hospitals appeared. Korean youth, regardless of gender and origin, had the opportunity to receive elementary general education and English lessons along with Bible study
Detection of Anonymised Traffic: Tor as Case Study

This work studies Tor, an anonymous overlay network used to browse the Internet. Apart from its main purpose, this open-source project has gained popularity mainly because it does not hide its implementation. In this way, researchers and security experts can fully examine and confirm its security requirements. Its ease of use has attracted all kinds of people, including ordinary citizens who want to avoid being profiled for targeted advertisements or circumvent censorship, corporations who do not want to reveal information to their competitors, and government intelligence agencies who need to do operations on the Internet without being noticed. In opposition, an anonymous system like this represents a good testbed for attackers, because their actions are naturally untraceable. In this work, the characteristics of Tor traffic are studied in detail in order to devise an inspection methodology able to improve Tor detection. In particular, this methodology considers as new inputs the observer position in the network, the portion of traffic it can monitor, and particularities of the Tor browser for helping in the detection process. In addition, a set of Snort rules were developed as a proof-of-concept for the proposed Tor detection approach.
Is Technology Based Note-Taking More Preferable For Millenial Students? Exploration Of English Students' Note-Taking Habit

Note-taking is one of important skills students need to practice in order to understand the content of both printed and unprinted texts effectively. To do note-taking, readers can do either manually or digitally. However, with the advancement of technology nowadays especially in this pandemic era, readers can practice note-taking more easily with the use of technological tools, such as laptop and mobile. This descriptive qualitative research aims to discover how university students practice note-taking particularly to explore their note-taking strategy and preference for refining literature review in English academic writing. This included identifying the strategy they apply in note-taking practices to understand the content of the articles they read and identifying their note-taking. To explore the issue, the researcher collected data through questionnaires and interview with 62 English students taking academic writing who have intense note-taking practices as part of the course activities. The results of this study showed the majority of millennial students prefer using digital note-taking with 66.12% responses. They decided to use it as it is faster and easier in doing note-taking. The digital technology that the students used commonly was mobile phone with 54.84% responses. They chose this tool to practice note-taking as it is handy.
Using Some Wavelet Shrinkage Techniques and Robust Methods to Estimate the Generalized Additive Model Parameters in Non-Linear Models

In this paper, the method of estimating the Generalized Additive Models (GAM) was highlighted, and a proposed robust weighted composition was found by combining the robust M method with the smoothing splines to estimate the Robust Generalized Additive Model and its notation is (RGAM). This estimator is used to deal with the effect of the presence of outliers in the data that do not fit into the overall data pattern by relying on some of the weight functions of the robust M method. Wavelet Shrinkage technique is used as well, which has been proposed as a smoothing of data using several types of wavelet filters in calculating the discrete wavelet transformation and relying on it in estimating the wavelet generalized additive model symbolized by (WGAM). In the case of using the simulation method, when data is contaminated with distributions ((t) Dis., Exp. Dis.) And with contamination rates (5%, 15%, 35%) and with sample sizes (50,150,300) it is noted that the smoothing method is with the Bisequare weight (BRGAM). It had a better performance compared to the rest of the methods for the simulated scenarios covered. The GCV criterion showed a marked advantage over other criteria, especially when estimating the model in the proposed robust M (RGAM) model. Some statistical criteria have been adopted. These criteria of the Generalized Additive Model (GAM) is used to compare estimation methods, the proposed methods were tested on simulation experiments as well as on real data collected from Ibn Sina Learning Hospital on cases of short stature, and the RGAM method gave the best results compared to the ordinary GAM and WGAM methods, and that by obtaining the smallest GCV value, this is because it is responsible for selecting the most suitable smoothing parameter for the smoothing spline estimator.
Morphologically Aware Word-Level Translation

We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way.Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectional morphology provides additional syntactic information.This approach leads to substantial performance improvements-19% average improvement in accuracy across 6 language pairs over the state of the art in the supervised setting and 16% in the weakly supervised setting.As another contribution, we highlight issues associated with modern BLI that stem from ignoring inflectional morphology, and propose three suggestions for improving the task.
The Practical Research of Project Teaching Method in the Teaching of Computer Specialty Courses in Universities

With the rapid development of computer technology, the society has higher and higher requirements for computer professionals. There are many problems in the existing computer teaching methods. The more typical one is the separation of theory and practice, which does not meet the current society's requirements for computer talents. . The application of project teaching method to computer teaching can effectively make up for the shortcomings of traditional teaching methods, effectively improve students' comprehensive ability, and greatly improve the efficiency and quality of college computer talent training. This article discusses the role of project teaching method in computer teaching in colleges and universities from multiple angles, and proposes the practical strategy of project teaching method in computer teaching.
ELSI-COVID-19 initiative: methodology of the telephone survey on coronavirus in the Brazilian Longitudinal Study of Aging/ Iniciativa ELSI-COVID-19: metodología para la encuesta telefónica sobre coronavirus en el Estudio Brasileño Longitudinal del Envejecimiento/ Iniciativa ELSI-COVID-19: metodologia do inquérito telefônico sobre coronavírus entre participantes do Estudo Longitudinal da Saúde dos Idosos Brasileiros

The COVID-19 pandemic (caused by the SARS-CoV-2) is a public health emergency of international concern that particularly affects older people. Brazil is one of the countries most affected by the pandemic, ranking second with the highest number of confirmed cases and deaths worldwide as of mid-June 2020. The ELSI-COVID-19 initiative is based on telephone interviews with participants of the Brazilian Longitudinal Study of Aging (ELSI-Brazil), conducted on a nationally representative sample of the population aged 50 or older. This initiative aims to provide information on adherence to preventive measures (social distancing, wearing masks, and handwashing/hygiene); reasons for leaving the house, when that was the case; difficulties obtaining medications, medical diagnosis of COVID-19, and receipt of confirmatory results; use of health-care services (recent care-seeking, care-seeking location, care receipt, among other aspects); and mental health (sleep, depression, and loneliness). The first round of telephone interviews was conducted between May 26 and June 8, 2020. The second and third rounds are expected to occur within the coming months. This article presents this initiative methodology and some sociodemographic characteristics of the 6,149 participants in the survey first round, relative the Brazilian population within the same age group.
Arabic Document Image Classification Using Neural Networks.(Dept.E)

The Neural Network Arabic Document Image Classification System (NNADICS) is an adaptive Arabic document classifier. By training NNADICS on a number of different document image types, NNADICS behaves as a multiple classifier, since it is capable for distinguishing between multiple document image types. NNADICS is designed, built, tested and evaluated. After training NNADICS a document image is applied to the system for classification. Before that the document image is scanned, pre-processed and binarized, and then applied to NNADICS to classify its contents to text, geometric, or photographic image type. NNADICS achieved an average of a 86% recognition rate as it is clearly demonstrated.