Reviewerly/exHarmony huggingface dataset:
Nict-Tib1: A Public Speech Corpus Of Lhasa Dialect For Benchmarking Tibetan Language Speech Recognition Systems

The Lhasa dialect is the primary Tibetan dialect, with the most speakers in Tibet and the most extensive written scripts over its lengthy history. Studying speech recognition methods in the Lhasa dialect significantly conserves Tibet’s distinctive linguistic variety. Previous research on Tibetan speech recognition focused on academic research on non-public datasets, e.g., selecting phone-level acoustic modeling units and incorporating tonal information, but had less contribution to limited data for the community. To solve the low-resource data problem, we introduce the NICT-Tib1 (phase1) database, a new open-sourced database for the Lhasa dialect. We further update benchmark systems under the monolingual and multilingual settings, respectively. Experimental results show that the performances of these models are consistent with previous work. We believe our work will promote the existing speech recognition research on the Tibetan language, and other low-resource languages.
Comparative Analysis of Accuracy of Face Recognition System Using CNN and SVM

The aim of this work is to recognize the face images by comparing the performance of accuracy using Convolutional Neural Network (CNN) Support Vector Machine. Convolutional Neural Network and Support Vector Machine were implemented to recognize the face images and to analyze the accuracy using MATLAB. Group 1 with 20 samples of CNN and group 2 with 20 samples of SVM were collected to analyze with the help of 80% of G-power. From the MATLAB simulation result, Convolutional Neural Network achieves image accuracy rate of 95% and Support Vector Machine achieves image accuracy rate of 83%. The achieved significance value was 0.01 (“ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p} &lt; 0.05$</tex> ”). Conclusion - From this analysis, Novel Convolutional Neural Network achieves significantly better accuracy compared to Support Vector Machine.
Comparison of CNN-LSTM in Sentiment Analysis for Hindi Mix Language

Despite the fact that Hindi is spoken by over 490 million people globally and social media is producing a massive quantity of Hindi data on a daily basis, few research studies and initiatives to develop Hindi language resources and assess user sentiments have been accomplished. The study's major objectives are to (1) develop Hindi-English-Chhattisgarhi dataset for agriculturist's sentiment analysis and (2) assess multiple approaches of sentiment analysis through deep putting the deep learning classifiers into action (1D-CNN and LSTM).
Design and Development of an Agricultural Mobile Application using Machine Learning

Machine learning algorithms such as KNN and SVM can provide assistance with a variety of issues, including determining what crops should be planted when, as well as determining when the field requires additional water and fertilizer. The proposed system is intended to collect data on the current condition of the soil and make use of that data in order to establish the types of nutrients that are present in the soil. Farmers will be able to identify pest damage to their crops using camera sensor modules for the internet of things. They will be able to take the appropriate actions now that they have the ability to. Through the use of the app, the farmer is able to receive notifications and other information regarding crops based on the conditions of the soil and the weather. The types of soil, crops, nitrogen, potassium, and phosphorus are a few examples of the types of information that fall under this category. In addition to the characteristics of the soil and the weather, farmers can also base their decisions on the kind of crops they grow based on these elements. Because of this, the farmer is given the ability to take the appropriate measures to reduce crop loss and increase crop yield.
IRIS Data Classification using Genetic Algorithm Tuned Random Forest Classification

Optimising hyper-parameters in Random Forest is a time-consuming undertaking for several academics as well as professionals. To acquire greater performance hyper-parameters, specialists should explicitly customize a series of hyper-parameter settings. The best outcomes from this manual setting are then modelled and implemented in a random forest algorithm. Several datasets, on the other side, need various prototypes or hyper-parameter combinations, which may be time-consuming. To solve this, we offered various machine learning models and classifiers for correctly optimising hyper-parameters. Both genetic algorithm-based random forest and randomised CV random forest were assessed on performance measures such as sensitivity, accuracy, specificity, and F1-score. Finally, when compared to randomised CV random forest, our suggested model genetic algorithm-based random forest delivers more incredible accuracy.
Machine and Deep Learning Classifiers for Indic Scripts Recognition: Challenges and Research Perspective

The character recognition technique is based on electronic or mechanical transformation of handwritten or printed document images into machine interpretable form. The machine learning and deep learning based methods have been proven to be highly effective for the character recognition problems. In this work, the main objective is to review and explore different classification methods available for the recognition of handwritten Bangla and Devanagari scripts. The classification models are broadly categorized into three groups, viz., SVM, neural networks, and transfer learning based techniques.
Robustness Analysis of Zero Knowledge Proofs using Diffie Hellman Problem

By the means of asymptotic security of cryptographic security mechanism we can get knowledge about efficiency and tolerable features against various type of attacks compromised on it. Analytical study about how zero-knowledge proofs can be used with Diffie Hellman problem (DHP) are presented in this research. One of the better algorithms of discrete logarithm problem which is suggested by Henry for zero knowledge proofs is suitable for DHP problem for the robustness analysis of it. The efficiency of discrete logarithm algorithm for DHP problem and integer factorization problem are analyzed and made comparison between them and covariance and correlation between their asymptotic functions is obtained as final result which clearly give us idea about strong relationship between each other and correlation factor between them is high, so they are similar in nature.
Research on the Development and Application of English for Specific Purposes Corpora

The advancement of computer science benefits the development of corpora. The English for specific purposes (ESP) corpus is an interdisciplinary research field formed by the intersection of computer science and language disciplines, and it is also an important practice in response to the “Belt and Road Initiative” and the “Double World-class Project”. With exploring the role of intelligent language construction based on corpora in ESP, this paper researches on the development and application of ESP Corpora. The results can be directly applied in computer field and other related research to promote the development of computer science; can improve users' ability of independent retrieval to broaden the audience range of computer science; can enhance the intersection and integration between computer science and other disciplines.
A Secured Movie Recommendation System using Decentralized Blockchain Network

Recommender systems strongly affect a company’s profitability and regularly play a significant role in the success of companies’ sales policies. The companies are now collecting vast amounts of user information to make sure they can use recommender systems and related algorithms for recommending products and services according to customer needs, preferences, and likenesses. Unfortunately, privacy concerns prevent users from sharing data generously with interested companies, even if the quality of the recommendations would be improved. One way to overcome privacy-related issues is using a secured solution such as incorporating blockchain technologies for privacy-based applications. Blockchain is a distributed system and has a peer-to- peer linked structure. Integrating blockchain technology and the Internet of Things creates modern decentralized systems. The integration gives scalability and security to recommender systems. This research calls for innovative and advanced research on Blockchain and recommendation systems. We constructed a blockchain-based recommender system using “the Movielens” database. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recommendation system with acceptable performance. The results demonstrate that blockchain-based methods can be efficiently used for the privacy preservation of recommender systems.
A Medical Device Management System Using Smart Contract on Blockchain

With the increasing variety and quantity of medical devices, there is an urgent need for safe and efficient management of them. The traditional centralized management system has the problems of information asymmetry and low security. Blockchain technology can effectively guarantee the immutability and reliability of system information. However, the management of medical devices requires the participation of multiple parties and the process is complicated. The existing methods still can not simply realize the whole-process tracking management of medical devices. To address this problem, we design and implement a medical device management system using smart contract on blockchain. First, we design a complete medical device management process. Then, we design smart contracts to implement bidding purchase, contract communication, inventory management, and multi-department joint supervision of medical devices. Finally, we test the transaction processing performance of the system. The results show that our system is effective and feasible.
Modeling Sequential Representation with Interaction Dynamics for Social Prediction

Sequential prediction is of great value for resource allocation in our society, such as recommendation and urban planning. To train an effective sequential prediction model, it is a common practice to optimize the representation learning of historical item-item transitions. However, real-world sequential behaviors are constantly affected by dynamic environments, which makes the training network suffer from model uncertainty. It is a challenge task to incorporate such dynamic uncertainty into the learning of sequential patterns. In this paper, we rethink the dynamics in interaction sequences, and present a novel sequential representation learning framework DSeq which models dynamics from three levels: network architecture, training strategy and uncertainty estimation.
Linear Hashing with ℓ<sub>∞</sub> guarantees and two-sided Kakeya bounds

We show that a randomly chosen linear map over a finite field gives a good hash function in the $\ell_{\infty}$ sense. More concretely, consider a set $S\subset\mathbb{F}_{q}^{n}$ and a randomly chosen linear ${map}L:\mathbb{F}_{q}^{n}\rightarrow\mathbb{F}_{q}^{t}$ with q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> taken to be sufficiently smaller than $|S|$. Let U <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</inf> denote a random variable distributed uniformly on S. Our main theorem shows that, with high probability over the choice of L, the random variable $L(U_{S})$ is close to uniform in the $\ell_{\infty}$ norm. In other words, every element in the range $\mathbb{F}_{q}^{t}$ has about the same number of elements in S mapped to it. This complements the widely-used Leftover Hash Lemma (LHL) which proves the analog statement under the statistical, or $\ell_{1}$, distance (for a richer class of functions) as well as prior work on the expected largest ’bucket size’ in linear hash functions [1]. By known bounds from the load balancing literature [2], our results are tight and show that linear functions hash as well as truly random function up to a constant factor in the entropy loss. Our proof leverages a connection between linear hashing and the finite field Kakeya problem and extends some of the tools developed in this area, in particular the polynomial method.
Dealing with a small amount of data – developing Finnish sentiment analysis

Sentiment analysis has been more and more prominently visible among all natural language processing tasks. Sentiment analysis entails information extraction of opinions, emotions, and sentiments. In this paper, we aim to develop and test language models for low-resource language Finnish. We use the term “low-resource” to describe a language lacking in available resources for language modeling, especially annotated data. We investigate four models: the state-of-the-art FinBERT [1], and competitive alternative BERT models Finnish ConvBERT [2], Finnish Electra [3], and Finnish RoBERTa [4]. Having a comparative framework of multiple BERT variations is connected to our use of additional methods that are implemented to counteract the lack of annotated data. Basing our sentiment analysis on partly annotated survey data collected from eldercare workers, we supplement our training data with additional data sources. In addition to the non-annotated section of our survey data, additional data (external in-domain dataset and open-source news corpus) are focused on to determine how training data can be increased with the use of methods like pretraining (masked language modeling) and pseudo-labeling. Pretraining and pseudo-labeling, often defined as semi-supervised learning methods, make it possible to utilize unlabeled data either by initializing the model, or by labeling unlabeled data samples with seemingly real labels prior to actual model implementation. Our results suggest that out of all the single BERT models, FinBERT performs the best for our use case. Moreover, applying ensemble learning and combining multiple models further betters model performance and predictive power, and it outperforms a single FinBERT model. The use of both pseudo-labeling and ensemble learning proved to be valuable assets in the extension of training data for low-resource languages such as Finnish. However, with pseudo labeling, proper regularization methods should be considered to prevent confirmation bias from affecting the model performance.
CHILDHOOD MULTILINGUALISM: CONTRIBUTIONS OF VYGOTKY AND SHCHERBA

This work presents reflections on childhood multilingualism and child development. We reviewed the works of two Belarusian authors: Lev Semionovitch Vygotsky and Lev Vladimirovitch Shcherba. As a result, we indicate the relevance of the dialogue between psychology and linguistics. Vygotsky wrote the text K voprosu o mnogoiazitchii v detskom vozraste (The Question of Multilingual Children) between 1928 and 1929. Shcherba wrote Metodika: k voprossu o dvuiezitchii (Methodology: On the question of bilingualism) in 1930. Both texts became sources of our bibliographical research. By approaching Vygoysky and Shcherba’s works, we highlight the complexity and breadth of childhood multilingualism.
UNDERSTANDING BY STUDENTS OF INTERNET TEXTS IN THE SITUATION OF COVID-19 IN UKRAINE

In the article we determined four main psychological peculiarities of understanding by students Internet texts. These peculiarities are: (1) understanding the Internet text by its contextuality, the predictors of which are: reality, versatility, paradigm, descriptiveness, deepness, subjectiveness and personally centered qualities; (2) understanding the Internet text by its psychological context, which includes such predictors, as: psychological justification, compressiveness, interest, entertaining, satisfaction, harmoniousness, emotiveness, expressiveness; (3) understanding the Internet text by its emotional context and predictors, such as: comfort, stylistic expressiveness, convenience, expressiveness, lightness, colloquial features, journalistic style of writing; (4) understanding the Internet text by its multifaceted paradigm, which is characterized by such predictors: paradigmatic, space, being updated, its stimulating, dialogic content, comfort characteristics of perceiving, frivolousness.
An Ontology Towards Predicting Terrorism Events

Although there is an increasing amount of information for counter-terrorism operations freely available online, it is a complex process to extract relevant information and to detect useful patterns in the data in order for intelligence functionaries to identify threats and to predict possible terror attacks. Automation is required for intelligent decision-making. To assist with this, in this paper, the researchers propose an ontology-based data access system for counter-terrorism. The system will enable intelligence analysts to perform specialised semantic searches about terrorist events or groups for analysis using an ontology. In this paper, the researchers present the ontology that was created by following an existing methodology for ontology development, and an ontology-based data access system together with all the components used in development (i.e., databases, web-scraper tools, ontology-based data access software, and data sources). Lastly, the ontology is demonstrated by means of use cases with example queries for generating actionable intelligence for operations.
Automatic Bug Classification System to Improve the Software Organization Product Performance

Consistently, many bugs are raised, which are not completely settled, and countless designers are utilizing open sources or outsider assets, which prompts security issues. Bug-triage is the impending mechanized bug report framework to appoint individual security teams for a more than adequate pace of bug reports submitted from various IDEs inside the association (on-premises). We can lessen the time and cost of bug following and allocate it to the fitting group by foreseeing which division it has a place in within an association. In this paper, the authors are executing an automatic bug tracking system (ABTS) to allocate the group for the revealed bug involving the text examination for bug naming and characterization AI calculation for anticipating designer. Hybrid natural language processing and machine learning techniques are used for automatic bug identification to improve the performance of software organization products.
THE EVOLUTION OF HUMAN CAPITAL THEORY

The development of the leading countries of the world has led to the formation of a post-industrial economic system based on knowledge, innovation, global information systems, a new quality of education, and new technologies. One of the pillars of the new economy is human capital, which is an important factor in the socio-economic development of modern society. There are many definitions of the term "human capital". Summarizing the definitions existing in the economic literature, we can say that human capital is a stock of health, knowledge, skills, motivation accumulated as a result of investments, which is used in the labor process, ensuring productivity and income growth. The theory of human capital was formed within the framework of the conceptual foundations of the new classical theory and was further developed by various researchers who enriched the theory of human capital with the achievements of institutional, behavioral, evolutionary and synergetic theories. The origin of the theory of human capital was associated with a rethinking of the role of man in the post-industrial economy, since a person could no longer be considered as an "appendage" of capital. If in the pre-industrial period land was the most important factor of production, and capital in the industrial economy, then human capital becomes the most important factor in the post-industrial economy as the paradigm of development of economic growth, human intellectual and creative potential changes. abilities become a source of development.
FPGA-oriented lightweight multi-modal free-space detection network

For autonomous vehicles, free-space detection is an essential part of visual perception. With the development of multi-modal convolutional neural networks (CNNs) in recent years, the performance of driving scene semantic segmentation algorithms has been dramatically improved. Therefore most free-space detection algorithms are developed based on multiple sensors. However, multi-modal CNNs have high data throughput and contain a large number of computationally intensive convolution calculations, limiting their feasibility for real-time applications. Field Programmable Gate Arrays (FPGAs) provide a unique combination of flexibility, performance, and low power for these problems to accommodate multi-modal data and the computational acceleration of different compression algorithms. Network lightweight methods offer great assurance for facilitating the deployment of CNNs on such resource-constrained devices. In this paper, we propose a network lightweight method for a multi-modal free-space detection algorithm. We first propose an FPGA-friendly multi-modal free-space detection lightweight network. It comprises operators that FPGA prefers and achieves a 95.54% MaxF score on the test set of KITTI-Road free-space detection tasks and 81 ms runtime when running on 700 W GPU devices. Then we present a pruning approach for this network to reduce the number of parameters in case the complete model exceeds the FPGA chip memory. The pruning is in two parts. For the feature extractors, we propose a data-dependent filter pruner according to the principle that the low-rank feature map contains less information. To not compromise the integrity of the multi-modal information, the pruner is independent for each modality. For the segmentation decoder, we apply a channel pruning approach to remove redundant parameters. Finally, we implement our designs on an FPGA board using 8-bit quantisation, and the accelerator achieves outstanding performance. A real-time application of scene segmentation on KITTI-Road is used to evaluate our algorithm, and the model achieves a 94.39% MaxF score and minimum 14 ms runtime on 20W FPGA devices.
Hydrometeorological Disaster Assessment: Study of Risk and Loss Assessment of Disaster Events in Central Java

For years, Central Java has been hit by hydrometeorological disasters that have resulted in huge socioeconomic losses. Exposure and potential losses from hydrometeorological disasters increase as economic growth and disaster mitigation increase. Few studies have compared the potential risk to the actual loss it causes. Previous research has focused exclusively on risk assessment. This article describes a study that evaluates Central Java from a temporal and spatial perspective on a regional scale. In order to analyze flood risks and losses, a method based on flood data from the Central Java province disaster database (from 2008 to 2018) is utilized. Using a disaster system with the variables of hazard, exposure, and vulnerability, the influence of natural, economic, and social factors on flood risk is also studied. In practice, the findings of this study can be used to highlight disaster prevention priorities in disaster-prone areas, as well as specific measures that can help improve the efficiency of resource allocation.