Reviewerly/exHarmony huggingface dataset:
Secure Comparisons of Single Nucleotide Polymorphisms Using Secure Multiparty Computation: Method Development

Background While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. Objective In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. Methods Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. Results We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. Conclusions Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security.
Improving Trust in a (Trans)National Invoicing System: The Performance of Crash vs. Byzantine Fault Tolerance at Scale

Crash fault tolerance describes the capability of a distributed system to maintain its proper function despite the occurrence of crashes or failures in one or more of its components. When a distributed system possesses crash fault tolerance, it can be further fortified to achieve Byzantine fault tolerance. Byzantine fault tolerance empowers a distributed system to establish consensus among participants, even when faced with faulty or malicious behavior. Consensus plays a critical role in various tasks, including determining the accurate value of a shared variable, electing a leader, or validating the integrity of a business transaction. Compared to crash fault tolerance, Byzantine fault tolerance instills greater trust because it enables consensus even in the presence of malicious entities. This paper focuses on the performance evaluation of two blockchain solutions that exhibit Byzantine fault tolerance, in contrast to a blockchain solution that demonstrates crash fault tolerance. Specifically, the paper investigates the additional performance requirements associated with the enhanced trust resulting from Byzantine fault tolerance in e-business trading on both national and transnational scales. We analyze the resources needed to operate a business-to-business/business-to-government (B2B/B2G) compliance framework in two distinct geographic scenarios. The first examines the national scale, using Denmark as an example, which is the eleventh largest European country by GDP. The second scenario considers the scale of the European Union (EU) with its 27 member states (plus the United Kingdom).
K-Means Clustering Method for Determining Waste Transportation Routes to Landfill

Waste is worsening in Magelang City, especially in urban areas. As a result of poorly managed waste disposal, a landfill is needed. Magelang City has a landfill called TPA Banyuurip, located in Plumbon Hamlet, Banyuurip Village, Tegalrejo Subdistrict, Magelang City. From this case, the application of the kmeans clustering method to determine the efficiency of the waste transportation route to the landfill is needed. The research began by conducting direct observations at the Banyuurip landfill by interviewing the drivers of waste vehicles to find out information such as waste sources, transportation schedules, etc. In this study, the data used are the name and address of the supplier, sub-district, coordinate point, and distance from the supplier's place to the landfill. After data collection, data preprocessing is done by dividing and selecting data based on sub-districts. Then the data is processed using the kmeans clustering algorithm to divide the route efficiency and the haversine formula algorithm to determine the closest distance between clusters. After the data has been successfully processed, the number of clusters is 4 for north Magelang, where each cluster will become a corridor with four routes. For central Magelang, 2 clusters with two routes, while for south Magelang, the results are 4 clusters with four routes. From these results, the evaluation results using silhouette score for data clustering of 3 sub-districts are 0.632560 for North Magelang, 0.640667 for Central Magelang, and 0.630186 for South Magelang. This method is expected to help in grouping routes and mapping supplier areas effectively and efficiently in the waste transportation process in Magelang City.
A Structured Recognition Method for Invoices Based on StrucTexT Model

Invoice recognition has long been an active research direction in the field of image recognition. Existing invoice recognition methods suffer from a low recognition rate for structured invoices, a slow recognition speed, and difficulty in mobile deployment. To address these issues, we propose an invoice-structured recognition method based on the StrucTexT model. This method uses the idea of knowledge distillation to speed up the recognition speed and compress the model size without reducing the model recognition rate; this is achieved using the teacher model StrucTexT to guide the student model StrucTexT_slim. The method can effectively solve the problems of slow model recognition speed and large model size that make mobile deployment difficult with traditional methods. Experimental results show that the proposed model achieves an accuracy rate of over 94% on the SROIE and FUNSD public datasets and over 95% on the self-built structured invoice dataset. In addition, the method is 30% faster than other models (YOLOv4, LeNet-5, and Tesseract-OCR) in terms of recognition speed, while the model size is compressed by about 20%.
A Constant Elevation Search Method for Narrow Beam Radar to Capture Space Targets

Position prediction is the basis of guiding radar to seek, capture, and track space targets, although there’s a risk of insufficient accuracy for narrow beam radar due to the influence of orbit determination and propagation error. Since the along-track error is the main factor that affects the accuracy of position prediction, this paper proposed a Constant Elevation Search (CES) method for narrow beam radar, based on the idea of along-track error compensation, to seek and capture transit targets. With along-track error estimation, all possible positions on a specified elevation can be observed, thus the success rate of capturing transit targets will be improved. Simulation shows that when the position prediction method fails, the CES method still works.
Recommendation System: Technical Study

We present in this paper a study of the features and approaches of the recommender system. The use of recommendation techniques is very important for a system that will provide good recommendations to its users. This explains the importance given to the different recommendation approaches, such as based on collaborative filtering, content-based, hybrid approach, etc.
Analysis on Exposition of Speech Type Video Using SSD and CNN Techniques for Face Detection

With the advancement of the internet and multimedia, more and more videos are being generated. In this regard, storing, managing, and indexing many videos have become a major problem. As a result, a system that extracts the relevant data with mixed emotions from the original video is proposed. The main purpose of video exposition is to present a significant abstract vision with emotions of complete video in a brief amount of instance by using deep learning techniques. The following limitations has been croos over through a novel implementation of a one-time facial image job examination technique, named SSD, for detection expressions and accomplishment expression related classifications, including emotions. The proposed model has been used fully convolutional neural networks (CNN) that continually detect videos uploading from the internet, local server or cloud, using speech samples storing data frames are read out by application and finding the face in running video. This application is most suitable for current video surveillance and CCTV footage analysis. Face-SSD includes two parallel branches: one for expression recognition and the other for expression analysis, which is the part of low level filters. The proposed model haven't need following steps: face identification, facial area removal, size normalisation, and facial region processing since the productions of together modules are spatially associated images created in sequence. Usually existing models like Random Forest optimisation (RFO), Genetic algorithm (GA), Decision tree (DT), and X-boosting techniques cannot solve the issue of face detection in dynamic video. Therefore, the necessity of multiple and multi-task face recognition models is there with measure rates. In this research, CNN-based speech type video extraction and face detection were performed for storage estimation and reduced content indexing complexity. Finally, performance measures have been estimated, like the accuracy of 98.45%, sensitivity 97.34%, recall 94.23%, and throughput.
THEORETICAL AND PRACTICAL FOUNDATIONS OF THE FORMATION OF THE INFORMATION ENVIRONMENT OF THE EDUCATIONAL PROCESS USING COMPUTER TECHNOLOGY

The article is devoted to the analysis of computer technologies used in the professional training of future specialists; training of future specialists in the context of a competence-based approach to education and the didactic foundations of the formation of professional competence of students of the transport University using electronic learning tools.
A Comprehensive Review in Using the Advances of Deep Learning in the 3D Race Classification

Human faces can reveal not just the human identity, but even demographic characteristics such as ethnicity and gender. Recently, the researchers get the advantages of Deep Learning techniques in developing face recognition systems implemented on both 2D and 3D face datasets. However, the usefulness of Deep learning in analyzing facial features of 3D faces gender, and ethnicity are examined in literature with only three main perspectives: data representation, augmentation, and comparison using the several commonly used format of 3D face representation such as depth images, point clouds, normal maps, triangular mesh, and horizontal disparity images. Many algorithms are implemented by authors on popular 3D datasets including FRGC v2, 3D-Texas, and BU3D-FE. In this work, we highlight the advantages of using the deep learning 3D representation in “race recognition” approaches and refer the researchers to the important related works in this field by comparing them according to their distinguishing metrics and invariant conditions support and the used techniques and datasets.
A Deep Learning-Based Method for Drivers’ Shoe-Wearing Recognition

What types of shoes that the driver should be wear on driving is under the clear regulation. Non-standard shoe-wearing such as wearing high-heeled shoes, platform shoes, slippers or with bare feet will bring great safety risks and may lead to traffic accidents. According to statistics by traffic department, many traffic accidents are caused by irregular shoe-wearing. Although computer vision has been applied for monitoring driver’s behavior in many aspects, such as driver’s face, eye, and hand, there is still no computer vision-based method or hardware device on the vehicle that to monitor the driver’s shoe-wearing. Therefore, if the driver’s illegal shoe-wearing behavior can be identified before driving, it can play an important role in reducing the incidence of traffic accident. The main difficulties in drivers’ shoe-wearing detection lie in the diversity of shoe types, the variety of foot postures, and the complexity of the detection environment. Therefore, the traditional computer vision method will be high error detection rate in such scenes. In this paper, a deep learning-based method for detecting abnormal shoe-wearing of drivers before driving is proposed. Different models such as SVM, Fast-RCNN, Faster-RCNN, YOLO v2, YOLO v3, YOLO v4 are used to identified drivers’ shoe-wearing. To the best of our knowledge this is the first work that using the computer vision technology for automatic monitoring on drivers’ shoe-wearing. The experimental results show the proposed method can identify the drivers’ shoe wearing effectively and efficiently. It has high application value for improving traffic safety.
A Solution to Pose Change Challenge: Real-Time, Robust, and Adaptive Human Tracking Systems Using SURF

This chapter deals with an application of visual object tracking. The algorithm is detecting and following a human in real time, while overcoming all the practical challenges: brusque object motion, pose variation, non-rigid object structure, full/partial occlusion, and smooth/abrupt camera motion. A critical scrutiny of the efficacy of human tracking algorithms by analyzing assorted visual parameters has been done. This work proposes a novel implementation of the GRABCUT algorithm on interest point-based method. This region growing algorithm eliminates the background descriptors from the object model and this information is used by a SURF-based tracker. Later to differentiate between pose change and occlusion situation an auto-tuned classifier is implemented. The algorithm developed in this chapter is adaptive, robust, and computable in real time. The experiments are conducted in indoor/outdoor environments.
Formation of natural science competency concept based on PISA international research

The article presents an analysis of conceptual apparatus formation of science competency and a role in this process of PISA international study of education quality. The components and aspects of science competency are considered, presented in educational documents of the European Union and the Organisation for Economic Co-operation and Development. It is revealed that recommendations of the European Council and reference frameworks of the European Commission “Key competences for lifelong learning” distinguish such components of science competency as knowledge, skills and attitudes. It is noted that the Organisation for Economic Co-operation and Development framework documents for PISA international study of education quality formulates an equivalent concept of “science literacy”, consisting of a number of aspects – contexts, competencies, knowledge and attitudes. Changes in determination of science literacy in PISA study for the period 2000–2022 and characteristics of its aspects are analyzed. Significant attention is paid to specifics of PISA terminology, its complexity and versatility. Ukrainian definitions of science competency in national normative educational documents and concepts are investigated. It is determined that they are largely focused on foreign recommendations and framework documents of international research, and also distinguish certain components of competency – basic knowledge, skills and attitudes. The author emphasized the need to further borrowing by national education system of world experience in the field of competency approach implementation, in connection with transition of basic school to study by new educational standards on competent basis.
Research on China Academic Social Sciences and Humanities Library (Third Edition)

One of the most spectacular aspects of the changes to University Library Alliances (ULAs) in China over recent years is the establishment of several large-scale alliances covering the entire country, among which CASHL is one.CASHL is the largest ULA in the field of Humanities and Social Sciences (HSS), which has 900 member libraries now.CASHL has established China's largest and most comprehensive HSS document guarantee system.The value of CASHL lies in its ability to integrate literature resources in the HSS across China onto a common platform to serve readers.This is a case study on CASHL, aimed at introducing readers to China's literature resource guarantee in the fields of HSS.
Numerical Simulation of Flood Propagation in the Kelara River Flood Early Warning System

Flood historical data from the Kelara River in the last 10 years shows that the river has often overflowed, and the worst floods happened on January 22, 2019. One of the efforts to minimize the negative impact of a flood disaster is to conduct flood tracking. Flood tracking is an analysis of the flood along the river, or also known as flood propagation, which can be used as a reference in the preparation of a flood early warning system. This study aims to determine the propagation of the Kelara River flood which can be used to determine flood-prone areas and as a reference in the preparation of a flood early warning system. This research was carried out in 3 stages, namely flood hydrology analysis using the HEC-HMS program, numerical simulation of 2D floods using the HEC-RAS program, spatial modeling of flood-prone areas using the ArcGIS program, and preparation of a flood early warning system. The results of this study showed that the flood that occurred on January 22, 2019, was a 100-year return period flood, and determined that 10 points of residential areas/villages must be alerted when the intensity of rain is high, with the fastest time to be alerted being 52 minutes.
An Evaluation of RSA and a Modified SHA-3 for a New Design of Blockchain Technology

A blockchain is a decentralized database solution that contains a huge number of data records that are verified by the nodes that are part of the chain. The data is registered in a public directory with details about each completed transaction. Blockchain is a decentralized solution that requires no third-party organization. All the nodes are shared and accessible for any transaction that has ever been completed in Blockchain. This feature makes the system transparent rather than centralized third-party transactions. Furthermore, the nodes in Blockchain are all anonymous, making transactions safer for others. However, while Blockchain appears to be an appropriate solution for transactions by utilizing cryptocurrencies, some technological challenges and constraints remain to be examined and addressed. High transaction integrity, memory consumption, complexity, speed, protection, and node privacy are required to avoid attacks and attempts to distort transactions in Blockchain. This paper proposed an evaluation of the RSA algorithm used in the design of some blockchain technology by adding a new variable to improve memory consumption. In addition, a modified hash function (SHA-3) was suggested by replacing the operations (AND) and (NOT) with (ADD) arithmetic operation to improve hashing speed. The primarily results reduced the process of memory consumption and increased the complexity in amendment RSA algorithm and hashing speed. Finally, some compression between suggested algorithms (amendment RSA and modified SHA-3) among origin and similar algorithms to memory, security, and speed was demonstrated to improve the suggested algorithms' security, performance, and efficiency.
Estimation of the Extent of Vertical Head Movements From Sound Alone

Abstract Human-like agents are becoming more and more common. However, the usefulness of these agents depends to a large extent on the naturalness of their movements. The classification procedure presented in this article aims to increase the naturalness of the head movements of human-like agents. The method is capable of estimating the vertical range of head movement from the speech sound alone, and thus allows a final phase amplitude correction of the generated head movements of virtual talking heads in order to increase naturalness. The advantage of the method, is that it does not require visual information, works for general subjects, its precision and effectiveness can be improved by defining further classes, and it can improve the naturalness of any head movement generation method’s output by a posterior amplitude scaling.
Lithuanian Discourse Markers And Their Relations In A Multilingual Corpus

Summary The development and research of discourse-annotated corpora is a relatively new field, therefore Lithuanian researchers seek to supplement the existing corpora resources and look for ways to study discourse relations by linking and comparing them with their counterparts in other languages because in different languages discourse relations are realized by different linguistic means. The aim of the article is to present the developing available corpora resources drawing on the experience of foreign scholars and to discuss guidelines for translation research at the discourse level. Therefore, the article first deals with the possibilities of expressing discourse relations by using discourse markers as their linguistic realization in different languages, discussing possible choices of translators, taking into account discourse realtions in translation and the use of different linguistic means. The article presents the parallel multilingual corpus TED-MBD ( Multilingual discourse-annotated corpus ), which is annotated at the discourse level, in accordance with the objectives and principles of PDTB ( Penn Discourse Treebank ) discourse annotation. The article discusses in detail the annotation system of PDTB discourse markers, the reader is introduced to the hierarchy of senses of discourse relations, the principles of annotation and insights into the application of the PDTB scheme. It also describes the annotation principles of the Lithuanian part of the corpus in accordance with the PDTB discourse annotation rules; the first results related to the expression of discourse relations and the use of discourse markers are discussed. The article also presents the first research guidelines on how to compare Lithuanian and English discourse-annotated texts in order to understand translation tendencies at the discourse level.
A Quantitative Evaluation Method for Nonstationarity of Training Image Based on Pattern Tiles Distance

Abstract An a priori model for multipoint statistics (MPS) modeling approaches is a training image. Before using MPS modeling, it must be determined whether the training images satisfy the spatial statistical stationarity. Modeling can be performed using the regular MPS approach if a training image is stationary. Otherwise, an enhanced method of nonstationary modeling is required. For instance, partition-based nonstationary modeling is an option. This study proposes a nonstationary evaluation metric based on pattern tile distances. It is possible to more accurately quantify the characteristics of the various distributions of spatial structure features in the entire space and achieve the goal of quantitatively evaluating the nonstationary metrics of training images by quantifying the distances of lower-level subpatterns in the pattern. Furthermore, an automatic partitioning approach based on pattern tile discrepancy is proposed for nonstationary training images to avoid the subjective and inefficient issues of manual partitioning when the training images cannot meet the stationary requirement of MPS modeling.