Reviewerly/exHarmony huggingface dataset:
HEVC Compatible Multiple Description Coding for Robust Video Transmission over Lossy Networks

In this paper, we propose a novel multiple description coding (MDC) method, which offers benefits of the new H.265/HEVC video coding standard combined with path diversity systems for robust video transmissions. In the proposed method, two descriptions including odd and even video subsequences are encoded using H.265/HEVC coder and then transmitted over two distinct channels of a path diversity system. At the receiver, the proposed MDC decoder is designed using a novel concept of distributed video coding (DVC) to provide a high image quality for the reconstructed description. Experimental results show that the proposed method can achieve a wide range of tradeoffs between coding efficiency and error resilience, and provide much better H.265/HEVC quality of experiences (QoEs) for users than other conventional MDC methods results
ViMRC - VLSP 2021: Using XLM-RoBERTa and Filter Output for Vietnamese Machine Reading Comprehension

Machine Reading Comprehension (MRC) has recently made significant progress. This paper is the result of our participation in building an MRC system specifically for Vietnamese on Vietnamese Machine Reading Comprehension at the 8th International Workshop on Vietnamese Language and Speech Processing (VLSP 2021). Based on SQuAD2.0, the organizing committee developed the Vietnamese Question Answering Dataset UIT-ViQuAD2.0, a reading comprehension dataset consisting of questions posed by crowd-workers on a set of Wikipedia Vietnamese articles. The UIT-ViQuAD2.0 dataset evolved from version 1.0 with the difference that version 2.0 contained answerable and unanswerable questions. The challenge of this problem is to distinguish between answerable and unanswerable questions. The answer to every question is a span of text, from the corresponding reading passage, or the question might be unanswerable. Our system employs simple yet highly effective methods. The system uses a pre-trained language model called XLM-RoBERTa (XLM-R), combined with filtering results from multiple output files to produce the final result. We created about 5-7 output files and select the answers with the most repetitions as the final prediction answer. After filtering, our system increased from 75.172% to 76.386% at the F1 measure and achieved 65,329% in the EM measure on the Private Test set.
Impact of Feature Selection and Data Augmentation for Pregnancy Risk Detection in Indonesia

This paper aims to develop an automatic system for pregnancy risk detection in Indonesia.The system requires a sophisticated approach to achieve the required performance as a sensitive field.Existing works are developed using small-sized datasets and limited classification features.Moreover, all features treated equally make the detection results hard to interpret which features contribute more.To address these issues, we propose to combine more complex features, data augmentation methods, and feature selection techniques.We prefer to use all 118 pregnancy indicators and 400 instances from Puskesmas as an original dataset.Next, the new datasets are used to build two data augmentation methods, i.e., GMM and CTGAN.Each data augmentation method generates 2,000 new synthetic instances.Following this, five machine learning methods combined with three feature selection approaches, i.e., RFE, Random Forest, and Chi-Square, are implemented in all datasets.Through experiments, we observed that feature selection techniques play an essential role in improving classification accuracies.While the GMM-based augmentation demonstrated performance improvement, the CTGAN-based synthetic dataset depicted low performances.The best accuracy on all experiment settings reached 95%.By using Random Forest combined with RFE on a GMM-based dataset, the highest accuracy was achieved using only five features.Another notable result is that both XGBoost and Decision Tree reached the same 95% accuracy on the GMM-based dataset on only nine features.The overall results show that appropriate data augmentation and feature selection are a matter for achieving better performance in this research.
Optimization method for segmentation of concrete components in digital images of test sample sections

The application of optimization method of image segmentation to estimate the percentage content of concrete components is considered. Non-destructive testing methods (ultrasonic, magnetic, radiographic, image processing) are actively used to assess the condition of concrete structures and structures of long-term use. Recently, the share of studies of the mechanical properties of concrete based on the processing of images of sections of its samples has increased significantly. The relationship between the parameters obtained by digital image processing methods and the compressive strength of concrete is established on the basis of regression analysis. A method of segmentation of color images of test sample sections of concrete based on the Gaussian mixture method and level-sets model is developed. Based on the analysis of the differences in the color characteristics of the background and the object, it is concluded that they can be divided into two classes in the RGB color space. For this purpose appropriate training samples are created, which contain image pixel samples with typical features of the respective classes. The training sample consists of a set of feature vectors of image pixels. The parameters of the segmentation model have been adjusted. The experimental results of the segmentation of color images of sections of test concrete samples by the proposed method are presented. An analysis of the obtained results is carried out.
Résumés en Français

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Résumés en Français. Am J Intellect Dev Disabil 1 January 2023; 128 (1): 96–97. doi: https://doi.org/10.1352/1944-7558-128.1.96 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
Training Set Optimization with Uncertainty Quantification for Machine Learning Models of Electromagnetic Structures

Neural Networks surrogate modeling for EM simulations saves computational and design time. Introducing uncertainty estimates into deterministic prediction models provides insight into the reliability and confidence of the model. However, gathering training data to train models is a very time-consuming and resource-consuming task. In this paper, we introduce a method to harness useful insights from confidence bounds to reduce the training set size required to train a model with reasonable accuracy and latency. Using a high-speed differential via structure, we show that the training samples required are 35% less with a slight trade-off in accuracy using the proposed method.
Records and Objects

In this chapter, we’ll learn about two more composite data types: records and objects. They are useful for representing structures with named fields, and have many similarities with objects in JavaScript. We’ll also discuss two different ways of thinking about types: structural and nominal typing.
Collections

In this chapter, we’ll discuss other common data structures for representing collections of values: sets, maps, stacks, and queues. ReScript’s standard libraries provide several options for these collections, with similar APIs but different use cases. We’ll break down these collections into two categories: immutable collections and mutable collections.
PROFESSOR HERBERT PÖLLMANN ENGAGED ME EARLY IN MANGANESE OXIDES

When I was invited to write a tribute to Professor Herbert Pöllmann to be published as special issue of BOMGEAM dedicated to him, the first thought that came to my mind was how to fit such tremendous influence in my academic and professional life into a short text. My most vivid memory of this unmatched master was when we met for the first time at the Geoscience Institute at UFPA (Brazil) in early 2005, thanks to Prof. Marcondes and Romulo. I was very unsure about my research topic of my master’s degree. At first I thought about studying a common topic then when I asked him about the theme, he only told me: why not study Mn oxides?!
Evaluation of Indexation Consistency in Publisher Subject Metadata

This work aims at evaluating the indexing of subject metadata published on the MercadoEditorial.org platform. The goal: to verify if the indexing is consistent. First, analysis was done of the tools available on the platform for assigning keywords, and, afterwards, publishers on the platform were documented and verified by running intrinsic evaluation for interconsistency. This was all done to compare the indexing of one work by four authors. The authors were Ciranda Cultural, IBEP, Excelsior Editora, and Via Leitura. The chosen book was “The Alienist” by Machado de Assis, a classic in Brazilian literature. The result of the indexing analysis was that publishers gave keywords such as title, author’s name, characters’ names, names of other books, and excessively used and repeated words. The last category was further broken down being with or without accentuation, being in the singular or plural form. Other terms were assigned that were related to university entrance exams. Thus, it can be concluded that an absence of vocabulary control can make retrieval of a work difficult, simply by assigning terms that inadequately define the subject of the book, and by lacking semantic, syntactic, and morphological standardization among the terms.
Related Rights in the Digital Economy

In the early 21st century, intellectual property has become the most crucial intangible asset. In the digital economy and Industry 4.0, the key drivers of intellectual property law will shift from classical institutions to new legal systems. Related rights are perhaps the most fast-paced new institution of intellectual property law. The purpose of this paper is to determine the role that related rights play in the digital economy. Related rights are a flexible and convenient mechanism protecting intangible property included in the digital economy through organizational and financial investments. In the digital economy, the development of related rights is focused within the framework of the so-called new production rights. We believe that such rights are: 1) the right to investment databases; 2) the right of media publishers to use their publications by online news aggregators; 3) the right to inform about sports events (especially in the digital media).
Scalable and Socially Inspired Blockchain Architecture for the Organic Food Supply Chain

Organic food supply chains are faced with heavy pressure to increase their output to meet the global demand. This confronts various challenges including scandals, adulteration, contamination, and growing regulations. As an alternative to third-party certification, Participatory Guarantee Systems (PGS) are popular community-based quality assurance system that integrates the social verification context into the organic certification process. As PGS is a local community-driven system, it has inherent limitations in the scalability of reaching consensus as the size of participants increases. The organic food industry has the potential to grow globally therefore, an appropriate scalable consensus mechanism is needed to deal with community-level consensus as an alternative to the existing PGS system. Blockchain architecture with hybrid consensus mechanisms seems to be the potential solution to address the trust and scalability issues in the organic food supply chain. This paper proposes a socially inspired hybrid blockchain architecture for the organic food supply chain to address the scalability issues via hybridizing two consensuses’ mechanisms with the combined advantages of Proof of Authority (PoA) and Federated Byzantine Agreement (FBA). In the proposed architecture, much eminent aspect of community-level trust is integrated into the consensus process. Furthermore, this paper presents a concept-level validation as a qualitative analysis of the proposed architecture based on experts’ opinions. Concept-level validation of the proposed model acknowledged that, in the context of social verification, the credibility of the organic products would be enhanced, and hybridization of the consensuses would mitigate the scalability issues.
Population aging and medical robotics

Subject. The population aging is becoming one of the most significant social challenges of the 21st century. It increases the demand for medicines, diagnostic equipment, and medical staff. An alternative to the growth of medical personnel is the robotization of medical services. However, the robotics is economically justified when the costs of medical robots are lower than the construction of additional medical facilities and training of additional healthcare professionals. Objectives. I focus on analyzing the medical robots connected with population aging, the link with the global robotics process, and the impact on the medical labor market. Methods. The study employs general scientific methods to analyze open data sources. Results. The paper demonstrates the economic feasibility and efficiency of using robots in general and orthopedic surgery, rehabilitation and maintenance of elderly patients. Conclusions. The medical robotics industry appeared later than the industrial and military segments. However, in recent years, it has been developing at a high rate. Russia lags behind in this area, including in servicing the country's elderly population. To solve this problem, fundamental reforms are required, starting with the system of engineering robotic education, accelerating the development of microelectronics and sensor instrumentation, and reforming the labor market.
Quantitative Ontology and Russian Preposition Database

Automatic natural language processing that goes beyond the bag-of-words model requires an understanding of semantic and syntactic relationships between language components. This work attempts to address the following issues: 1) describe a set of Russian prepositional structures as an interconnected system; 2) collect corpus-based statistics reflecting preposition meanings and their hierarchy; and 3) describe meanings of prepositions acting as grammatical connectors for content words. Preposition meanings are described using categories based on G. A. Zolotova’s concept of syntaxeme.&#x0D; The results obtained are based on word frequencies extracted from corpora of contemporary Russian texts. This work describes the key objectives, approaches, methods, novelty, and findings of the research. The key findings include grammar and ontology of the main Russian prepositions, research into secondary prepositions (prepositions derived from content words), preposition database with a network interface, and a set of automated procedures aiding in ‘working out’ the meaning of a preposition.
Artificial Intelligence Methods to Solve Energy Transmission Problems Through Data Analysis from Different Data Sources

This article proposes a discovery methodology of potential artificial intelligence sources that allow problem solving for the industry, specifically electricity transmission. Different data sources used as a reference for academic investigations such as thesis work project and patents were considered to observe the different investigation focus of a particular topic based on a study case.
Code-based CRUD analysis for prioritising test cases

When software is modified, an impact analysis is conducted to determine the effect of these modifications on other functions. However, the current impact-analysis techniques cannot identify such impact analysis. To compensate for this, comprehensive test cases are created. Therefore, impact analysis faces problems when increasing the true-positive ratio, which denotes the impacted implementations, and when reducing the false-positive ratio, which denotes the non-impacted implementations. To address this, Impact-Data-All-Used (IDAU) can be used to create and prioritise test cases based on CRUD information contained in design documents. We herein propose a code-based-IDAU (CB-IDAU) that applies IDAU to the source code using the control-graph and call-graph analysis. Based on a performance comparison of CB-IDAU to that of its previously proposed version, CB-IDAU, we observed an increase in the true-positive value by 157% and a reduction in the false-positive value by 60% when the full-coverage-test performance was used as the benchmark.
Perspectives on e-books and digital textbooks and the way ahead

This article presents a range of perspectives on current issues around e-book and textbook supply and consumption in libraries and universities. It is an attempt to provide an analysis of the often-contentious issues arising and also offers an insight into the positions of all the various parties involved. Whilst there might not be agreement or consensus on the causes of issues and the way to proceed, the article attempts to coalesce various perspectives, in the hope of achieving a greater understanding of different stakeholders. Much of the debate in recent years has focused on the situation in the United Kingdom, but similar issues exist in many other countries and an insight into the international perspective is provided. We also offer some commentary on ways forward for both the short and longer term.
DOMAIN ADAPTATION IN IMAGE DEHAZING: EXPLORING THE USAGE OF IMAGES FROM VIRTUAL SCENARIOS

This work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image haze removal problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator; these synthetic images
MR Image Denoising Based on Improved Multipath Matching Pursuit Algorithm

Magnetic resonance images are accompanied by random Rician noise due to the influence of uncertain factors in the process of imaging, storage, which brings a lot of inconvenience to the subsequent processing of the image and clinical diagnosis. This paper proposes an improved multipath matching pursuit algorithm based on learning Gabor pattern dictionary atom for image reconstruction and denoising. Firstly, Gabor wavelet transform based on neurophysiological constraints is used to generate dictionary atoms that match the local features of the image; Then this paper introduces adaptive differential evolution algorithm optimization to the process of solving multiple candidate atoms matching the local image features in each iteration of the multipath matching pursuit. It combines the advantages of adaptive differential evolution and multipath matching pursuit algorithm, not only avoids the genetic falling into the local optimal defect, but also obtains the best matching parameters with higher accuracy, and effectively reduces the computational complexity of the multipath matching pursuit. In the reconstruction experiment of the simulated MR images, compared with state-of-the-art denoising algorithms, our algorithm not only shows better denoising performance, but also retains more detailed information, and the running time is reduced nearly 50% than multipath matching pursuit; which fully shows the clinical application value.
Optimization Of Wsn Deployment Using Pso Algorithm For Forest Fire Detection

Abstract&#x0D; Forest fires are disasters that have often occurred in recent years. This has a huge impact on both the environment and society itself. Delayed handling of fires is one of the triggering factors for the large losses caused by the disaster. The use of a Wireless Sensor Network is one solution so that information related to fires is conveyed to the authorities quickly so that the handling can be done more quickly. In this study, a simulation was made to determine the optimal position of a node to detect fires optimally. This simulation is run on NS3 Software on Ubuntu 18.04 Linux Operating System. In the optimization process, the PSO algorithm is run with Google Colab. The results of each iteration on the PSO will be simulated in NS3 and the communication between nodes will be seen. There are 12 iterations of the maximum 30 iterations specified, and there are 12 simulations according to the number of iterations. From 12 simulations that have been carried out, it is known that in the last iteration of the 10 nodes installed, all nodes communicate. Communication between nodes can be seen through .pcap files and graphs on NetAnim, the communication is characterized by sending fire messages to each installed node. In the last iteration, 10 nodes received a fire message.&#x0D; Keywords: Wireless Sensor Network, Forest fires, Particle swarm optimization
Pengaruh Model Pembelajaran Kooperatif Tipe Teams Game Tournament Berbantuan Media Monopoli Terhadap Hasil Belajar Siswa pada Mata Pelajaran Ekonomi

This study aims to determine the effect of applying the TGT type of cooperative learning model with the help of monopoly media on student learning outcomes. Applied to Baiturrohmah Islamic High School students for the 2020/2021 semester 2 academic year, opening 36 people. Collecting data using experimental techniques through pretest and posttest. The results of data analysis showed that the application of the monopoly-assisted TGT cooperative learning model significantly improved student learning outcomes in economics subjects. This learning model is very effective in improving student learning outcomes.
Environmental Geography: Nature and Human Interaction

In Uzbekistan, differences in human occupation are dependent on access to the main resources needed to survive in an arid climate. Water is unevenly distributed across the country, resulting in significant contrasts in conditions for life between eastern and western Uzbekistan, and between the plains and the mountains. The natural conditions determine the variety of the histories, economic life, and social life of the people. Human activity and global geological processes have significantly changed the landscape within the country.
Riemannian Classification and Regression for EEGEyeNet

In this paper, we use emerging Riemannian geometry based classifiers and regressors to perform eye-tracking tasks over a 2021 dataset: EEGEyeNet. The classification task we attempt is determining Left/Right eye movement, and the regression task we attempt is determining absolute eye position on a Cartesian plane. We find that Riemannian methods are not more accurate than traditional ML techniques, and offer suggestions for future improvement.