Reviewerly/exHarmony huggingface dataset:
TaskCO: Android App for Task Management

With the increasing impact of mobile phones on our daily lives, we rely on the smallest things on our mobiles using various applications. Applications have become a daily routine in our lives. Attractive interfaces often ignite the spark to inculcate such good habits. In this hustle-bustle life, it is necessary to keep track of your day-to-day deadlines, commitments, and progress. This is where the task management app plays an important role. This paper contains the main motive for creating this app and its technologies and features to make it better and user-friendly. It also includes the thought process behind every feature which was thoughtfully curated and designed for the user. To make the app intriguing a splash screen is added to make the user feel pleasant as soon as the app is opened. In addition, various features like segregated folders, light and dark modes, one-time login and signup, an attractive interface, and notifications, make this app very engaging to the user.
Review and Comparison of Different Methods Involved in Process of Efficient Communication Between Disabled and Normal People

Sign language is a way which deaf and dumb people use for communication. These people face lots of difficulty in communication with normal people since normal people does not know the sign language. This paper describes the several methods for data collection, pre- processing, features extraction and classification for the translation of sign language to audio, along with the suggested system. The purpose of this paper is to outline several techniques for translation of sign language into audio and vice versa, as well as to develop a communication system to achieve efficient communication between disable and normal people. This project converts sign language into Audio for Dumb people and Audio into sign language for deaf people. It consists of two stages: the first stage comprises of CNN which translates an ASL hand gesture into audio, and the second stage comprises of speech recognition which translates audio from any language into sign language.
Visual Question Answering System for Indian Regional Languages

Questions are asked over the image within the Visual question answering system (VQAS). VQAS takes input as a user's natural language question and relative image. VQAS are mostly trained with a huge dataset with image, question and its correct answer. Many datasets like VQA, Visual7W and others have questions and answers over the image in English but there is scarcity of resources for many regional languages like Hindi and Marathi. In this paper we investigate different methods over the Hindi and Marathi version of the easy-VQA dataset.
Development and Design of English Vocabulary Learning APP on College English Blended Teaching

Mobile APP is network software generated under the support of information technology, which injects new impetus into people’s lives and development. The application of mobile APPs to teaching university English with their advantages can promote the development of blended teaching of college English. Thus, the English vocabulary APP is designed by adding industry English vocabulary and textbook-matched vocabulary and featuring the professionalism and richness of the thesaurus. The proposed personalized dictionary customization model provides a convenient English vocabulary learning and query platform for school students and college teachers.
Simple Quantum Communication Protocol Based on Bell Test

In this paper, we propose a robust quantum communication protocol which needs Bell test only with one round classical communication. Current quantum communication protocols such as quantum dense code and quantum direct communication protocol are vulnerable to interference, and quantum key distribution protocol which have already in large scale use needs multiple rounds classical communication to remove the effects of noise. Bell test is a well-known entanglement detector and as a powerful tool to verify the quantum non-locality that the quantum correlation between two space separated objects. It has been applied in several quantum communication protocols for security check and network relay. Moreover, we found that entanglement is a valuable quantum correlation resource itself and Bell test can verify it. In our protocol, the quantum entanglement is used to establish a shared code information space between sender and receiver via the peculiar quantum correlation. The information is encoded as the position of message sequence and the receiver decodes it by a Bell Test, the maximum correlation value occurs exactly at the encoded position. The protocol completes information transmission by only one round classical communication and work well under imperfect apparatus as well. Our protocol is simple and be implemented seamlessly in existing entanglement based quantum communication protocol.
A Robust Vehicle Detection Model Based on Attention and Multi-scale Feature Fusion

Vehicle detection plays a critical role in enhancing the environmental perception ability of intelligent vehicles and ensuring safety of vehicles in driving environments. Although many research has been conducted with innovative methods on vehicle detection, improving the robustness of the model in challenging scenes(such as vehicles with a large variance of scales, small-scale vehicles and occluded vehicles) is still an open research question. First, we propose the pyramid multi-scale information module (PMSIM) to collect multi-scale vehicle feature information from dense context, which helps our model adapt to violent changes in vehicle scales. Then, a new detection head for tiny vehicles is added to ameliorate the performance of the model for detecting small-scale vehicles. Finally, we introduce the Transformer encoder to extract global context information to enhance the inference ability of the model. With the help of this ability, the effect of the model to detect occluded vehicles get promoted. The qualitative and quantitative experimental results on KITTI dataset verify that our model improves the robustness and detection accuracy, and outperforming the state-of-the-art methods.
Joint Mining Offloading and Computing Resource Optimization in Mobile Blockchain System

Mobile edge computing is considered as a promising solution to mobile blockchain system, where mobile nodes with limited computing capability may participate in the mining process by offloading the computing intensive mining tasks to nearby edge service providers (ESPs). However, malicious ESP will dishonestly perform miners' mining tasks, which harms miners' interests. We consider blockchain mining and computing offloading services jointly, and model the interaction between the ESP and miner nodes as a two-stage Stackelberg game. Then, we obtain the optimal edge computing demand and the corresponding price of each miner by solving the Nash equilibrium of the game iteratively with gradient descent method. Simulation results show that the more computing power ESP has, the more computing services it will provide for miners. When the revenues coefficient of ESP on vehicular services increases, ESP will provide more computing resources for vehicular services to obtain maximum benefits.
Environment-aware Path Planning for UAV-assisted Search and Rescue

In this paper, we consider the UAV-assisted search and rescue (SAR) in a disaster area. The flying path for maximizing the expected number of detectable targets is designed under different amount of a-prior information on the environment. The target distribution is assumed to be modelled by the Neyman Scott Process (NSP). When the locations of target cluster centers are known, the probability map is constructed and the problem is formulated as a team orienteering problem (TOP) and solved by ant colony optimization (ACO) algorithm. When the locations of cluster centers are unknown, the correlation among targets can be utilized to optimize the UAV flying path in an online manner. The simulation results show that environment information is important for UAV path designing and the proposed online path planning by utilizing the spatial correlation among targets can greatly enhance the detection ratio, as compared with the random search.
UM-Net: An MLP-based Image Segmentation Network

In the field of image segmentation based on deep learning, U-shaped network has always been one of the most popular network frameworks because of its unique structure and wonderful segmentation effect. At the same time, the structure of MLP is simple and compared with CNN and transformer, it introduces less inductive bias and has stronger generalization performance. This paper aims at that the current U-shaped network does not make full use of the rich local information in the deep network structure, and the existing MLP model directly uses fixed weights for feature aggregation and ignores the information of different semantics in different images, focusing only on the structure of the encoder and ignoring the performance improvement brought by the decoder, in order to solve above problems, a new image segmentation network named UM-Net is proposed. It is a network based on convolution and MLP, we add Wave-MLP blocks to the deep structure of the network to make it fully utilize and dynamically aggregate the rich local features in the deep structure, using decoupled dynamic filters to the network upsampling to make it content adaptive. We test our UM-Net on the standard BUSI dataset, the experimental results show that compared with several current mainstream baseline networks, UM-Net achieves better segmentation performance, with F1 score and IoU reaching 80.77% and 68.66% respectively. At the same time, we also conducted different ablation experiments to explore the contribution of each module of the network to the performance.
TernaryNeRF: Quantizing Voxel Grid-based NeRF Models

Photo-realistic neural rendering, represented by neural radiance field (NeRF), is considered to be a key technology for AR/VR applications and has been actively studied in recent years. In order to enable widespread adoptions of AR/VR, it is critical to enable low-cost and high-quality rendering on mobile and server systems. In our work, we investigate the feasibility of low-precision representation on the two state-of-the-art NeRF models, InstantNeRF and TensoRF. Our proposed quantization is based on our observation on the characteristics of trained NeRF models. In order to reduce the model size while limiting the loss of rendering quality due to model compression, we propose quantizing the portion of model which dominates the total model size while being robust to aggressive quantization. In our experiments, we demonstrate our proposed ternary quantization can reduce by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7 \times \sim 15\times$</tex> the model sizes of state-of-the-art NeRF models at a negligible loss of rendering quality, which, we consider, will contribute to the AR/VR adoptions on mobile and server systems.
Malicious Models-based Federated Learning in Fog Computing Networks

In the fog computing network (FCN), due to the characteristics of user dispersion and real-time data, the user privacy is an urgent issue to be solved. Federated learning is considered as a potential solution to avoid data leakage between fog nodes and remote clouds. In this paper, a malicious models-based federated learning framework is proposed, in which the dual-layer blockchain contains the main-blockchain and the directed acyclic graph chain is enabled in the network structure to ensure the data security. Moreover, in federated learning, the reliability of local models cannot be guaranteed, when there are malicious models, the global model accuracy will be decreased. An isolation forest-based malicious model detection algorithm is proposed, which could filter malicious local models and perform global aggregation through the Stochastic Gradient Descent algorithm to ensure the security of the global model. Finally, the simulation results show the effectiveness of the proposed algorithm.
HISTORY OF THE DEVELOPMENT OF THE BGUT LIBRARY: TO THE 50TH ANNIVERSARY OF ITS FOUNDATION

The article describes the formation and development of the library of the Belarusian State University of Food and Chemical Technologies: from its foundation to the present day. The main historical milestones of the library in persons, events and dates are presented, and the current stage of the library's activities is described. The key areas of work and indicators for the library's development over a 50-year period were highlighted.
A LED Module Number Detection for LED Screen Calibration

In this paper, an LED (Light-Emitting Diode) screen number detection method is proposed to guide the autonomous navigation of UAV (Unmanned Aerial Vehicle) for further task implementation. Aiming at the LED module identification in the task of detecting bright and dark lines on the LED screens, an improved YOLOv5 object detection algorithm has been developed with a series of operations, i.e., the backbone network, activation function, optimization strategy and post-processing screening prediction box. The LED dataset is designed, along with data augmentation so as to achieve a high detection accuracy of the trained detection model in too strong or too dark light complex scenes. The trained model can be transplanted to the Android platform, which provides support for subsequent UAV automatic navigation and portable screen calibration. With extremely high detection accuracy (0.843 mAP@0.5), the detection speed of the proposed model is also quite fast, achieving almost real-time performance (85 FPS).
Research on semantic segmentation of unmanned aerial vehicle visual image based on deep learning—take the outdoor environment of Anhui University of Finance &amp; Economics as an example

In recent years, with the rapid development of unmanned aerial vehicle (UAV) technology, UAV processing visual information, especially image semantic segmentation technology, has developed rapidly.This paper proposes a semantic segmentation model, which has achieved high accuracy on CityScapes data set, and has been verified on the newly collected data set, and the verification results are in line with the actual situation.
Mastering “Gongzhu” with Self-play Deep Reinforcement Learning

“Gongzhu” is a card game popular in Chinese circles at home and abroad, which belongs to incomplete information game. The game process is highly reversible and has complex state space and action space. This paper proposes an algorithm that combines the Monte-Carlo (MC) method with deep neural networks, called the Deep Monte-Carlo (DMC) algorithm. Different from the traditional MC algorithm, this algorithm uses a Deep Q-Network (DQN) instead of the Q-table to update the Q-value and uses a distributed parallel training framework to build the model, which can effectively solve the problems of computational complexity and limited resources. After 24 h of training on a server with 1 GPU, the “Gongzhu” agent performed 10,000 games against the agent that uses a Convolutional Neural Network (CNN) to fit the strategies of human players. “Gongzhu” agent was able to achieve a 72.6% winning rate, and the average points per game was 63. The experimental results show that the model has better performance.
ON THE DEVELOPMENT OF INFORMATIZATION IN BELARUS: 20TH ANNIVERSARY OF THE «RINTI» CONFERENCE

The article presents an analysis of the results of the congresses and conferences in the field of information technologies and scientific and technical information (STI) held in Belarus over the past 20 years – the predecessors of the 20th anniversary international scientific and technical conference on the development of informatization and state system of STI (RINTI–2021).
TECHNOLOGY OF DIGITIZATION OF A LIBRARY СOLLECTION ON THE EXAMPLE OF CSAL

The article discusses the processes of digitization of a library collection, the necessary equipment and software. The necessity of pagination for the reader's convenience is justified. It is concluded that a presentable visual look of the resulting electronic document should be maintained. The practical steps of scanning and subsequent software processing of a file for further creation of an electronic document and replenishment of the electronic library are described.
MULTILINGUAL THESAURUS ON AGRICULTURE AND FOOD AGROVOC: METHODOLOGICAL FEATURES OF CREATING NATIONAL VERSIONS

The article discusses methodological features of creating national versions of the AGROVOC thesaurus. Research institutes, universities, libraries, and other organizations take part in editing the national versions of the thesaurus. The selection of national equivalents to the English terms of the thesaurus is carried out using an interdisciplinary approach based on the cooperation of specialists in various fields of knowledge. In order to provide a consistent, unified approach to the selection of concepts and terms, the Food and Agriculture Organization of the United Nations has created general guidelines for the AGROVOC editors. Comparative analysis of the guidelines published in 2008, 2015, 2020 and 2022 showed how the methodology for creating AGROVOC language versions has changed over the past 15 years.
LINGUISTIC SUPPORT OF THE SEMANTIC PROCESSING OF DOCUMENTS IN THE CENTRAL SCIENTIFIC LIBRARY OF THE NATIONAL ACADEMY OF SCIENCES OF BELARUS

The article presents the experience of creating and using of linguistic tools in the Yakub Kolas Central Scientific Library of the NAS of Belarus for supporting the semantic processing of documents including classification and coordinate indexing. The publication provides a brief description of the classification worksheets compiled on the basis of the Universal Decimal Classification tables. It also describes the technology used for creation of authority records and keywords needed for compilation of a document search description as well as the criteria for their appropriateness. The article considers the issues concerning maintenance of the library local subject headings list used for indexing of scientific publications.
Values semantics of the pedagogical profession by the eyes of students

In the article, on the basis of the analysis of theoretical sources, an analysis of the concepts of "value" and "meaning" is presented. On their basis, the definition of "value sense" is given. The results of the analysis of the questioning of students of different courses (first and final undergraduate, master's) and teachers on the question of determination by ranking the professional values of a preschool teacher are presented. Significant differences were found in the choice of value meanings of the pedagogical profession of different groups of respondents. The most similar election of students-masters and teachers. The greatest differences are observed in assessing such qualities of a teacher as initiative and mobility. In the first five, the value is communicative. It is common to all that education in the first place is made in each group, which testifies to the understanding by all participants of the survey of the role of vocational training for the teacher.
Humanistic paradigm as an Educational Innovation

The article focuses on the moral and spiritual values of a growing personality who acquire them consciously and freely. The author denies the methodological postulate, which declares that person’s freedom is incompatible with the organized educational effect. It is noted that concerning of personality’s own spirituality is a core motive that person should follow. Nevertheless, all others motives have to be subordinated. The system of personal experience that is adequate to the current educational goals of the individual has been analyzed. The essential characteristics of the humanistic paradigm have been defined as an educational innovation.
Federal journals need transparency policies

The US White House Office of Science and Technology Policy (OSTP) recently advised federal departments and agencies to promote free, immediate access to data that supports publications of federally funded research (1). However, the guidance did not address federal science journals’ policies regarding access to data and computer code used in peer-reviewed articles. Federal journals should adopt the free, immediate data access policy of OSTP as well as the code access policies of top independent journals.
Searching for answers

The move by Google, Microsoft and Baidu to add AI chatbots into search engines may bring advantages, but at what cost, asks Matthew Sparkes
Development and Validation of the General Learning Motivation Regulation Scale for Chinese High Middle School Students

Motivation regulation has a profound effect on the students’ learning and associated achievement. Yet, to date, there is a lack of a brief and valid approach for systematically assessing the regu­lation of learning motivation from the perspective of psychometrics, especially for the high middle school students. This study develops a general scale for quantitatively assessing the learning motiva­tion regulation of high middle students, based on the combination of literature analysis and open‑ended interviews in china General Learning Motivation Regulation Scale for Chinese High Middle School Students (GLMR‑CHMSS). A total of 2,304 questionna­ires of high middle school students were included for item analysis and exploratory factor analysis, and another 2,304 questionnaires of high middle school students were subjected to confirmatory fac­tor analyses and a validation and reliability test. The scale con­sisted of 24‑items that can be divided into four factors: intrinsic motivation regulation, extrinsic motivation regulation, volitional behavioural regulation, and self‑efficacy regulation. As indica­ted by the confirmatory factor analysis with the control group, the four‑factor structure model of the GLMR‑CHMSS fitted well (χ2 /df=7.839, RMSEA=0.054, CFI=0.918, TLI=0.908). In ad­ dition, using academic achievement as the empirical validity crite­rion, the scores of each factor of GLMR‑CHMSS and the scale sco­re were significantly correlated with academic achievement were the reliability indicators. This study show cases a novel and va­lid tool for mesuring the degree of learning motivation regulation of high middle school students in China.
Iterative Methods for Computing Eigenvectors of Nonlinear Operators

In this chapter we are examining several iterative methods for solving nonlinear eigenvalue problems. These arise in variational image processing, graph partition and classification, nonlinear physics, and more. The canonical eigenproblem we solve is T(u) = λu, where $$T:\mathbb {R}^n\to \mathbb {R}^n$$ is some bounded nonlinear operator. Other variations of eigenvalue problems are also discussed. We present a progression of five algorithms, coauthored in recent years by the author and colleagues. Each algorithm attempts to solve a unique problem or to improve the theoretical foundations. The algorithms can be understood as nonlinear PDEs which converge to an eigenfunction in the continuous time domain. This allows a unique view and understanding of the discrete iterative process. Finally, it is shown how to evaluate numerically the results, along with some examples and insights related to priors of nonlinear denoisers, both classical algorithms and ones based on deep networks.