Reviewerly/exHarmony huggingface dataset:
Combination Classifier-Based Off-line Arabic Handwritten Word Recognition System

Offline text recognition is very important in a variety of applications, such as the automatic sorting of mail, altering ancient documents, and historical document analysis. This paper aims to develop an ensemble of various classifiers to enhance the recognition of Arabic handwritten words. To represent the handwritten words, features are generated by a histogram of oriented gradients and local binary patterns. where each of its feature dimensions is reduced using principal component analysis. The support vector machine classifier is given for each feature set separately. Two independent classifiers are to be produced. The outputs of the two classifiers are integrated using the Bayesian method. The AHDB database was used to test the suggested strategy. When the classifiers' outputs are combined, recognition rates improve and, in some circumstances, surpass those of cutting-edge recognition systems. The suggested strategy has produced noteworthy accomplishments compared with other studies.
Assessing Virtual Reality Sickness in Highly Immersive Virtual Laboratory Environments : Simulator Sickness Questionnaire and Mitigation Strategies

Virtual Reality (VR) sickness, a common challenge among VR users, has increasingly gained attention in academic research due to its adverse side effects. The Simulator Sickness Questionnaire (SSQ) has been traditionally employed to measure simulator motion sickness; however, the extent to which it measures symptoms specifically attributable to VR sickness remains underexplored. This study investigates the applicability of SSQ in assessing VR sickness in a virtual semiconductor laboratory environment, with a focus on reducing motion sickness in VR devices. A total of 97 participants performed target selection tasks using HP Reverb G2 and Oculus Quest 2 headsets in a virtual laboratory setting. The original SSQ was revised to create a VR Sickness Questionnaire, which served as the measurement index for VR-induced symptoms. The primary objective of this research was to determine the prevalence of VR sickness and explore ways to mitigate motion sickness in VR devices. The Results indicate that the majority of VR-related side effects pertain to nausea dysfunction, with a limited number of symptoms linked to cybersickness arising from various factors. These findings hold significant implications for future studies aiming to measure and design interventions for simulator sickness in VR environments. By shedding light on the effectiveness of the SSQ for evaluating VR sickness, this study contributes to the development of improved VR experiences and technologies.
A Hybrid Learning Model for Breast Cancer Diagnosis

Mammography is a technique for detecting breast cancer early and is able to reduce specific mortality rates. However, it is often difficult to accurately identify breast cancer through mammograms due to the difficulty of interpreting it, Therefore, early identification of breast cancer with mammography is an effective strategy for patients' survival rate. In this article, Based on the principles of deep neural network and machicne learning (ML) models will propose a new technique to precisely detect breast cancer. Firstly, Data collection and preprocessing of Images; second, Feature extraction by Visual Geometry Group (VGG-19); convolution neural network. In designing the proposed breast cancer detection system, Support Vector Machine (SVM), Neural Autoregressive Distribution Estimation (NADE) models, and hybrid of SVM and NADE models of ML are used for classification process. The experimental results showed that the above proposed method is very efficient for Binary classification and provides better accuracy.
Design and Implementation of Encryption and Decryption Algorithm Based on Encrypted Hidden Private Key with Logical Shift and XOR Operations

In this paper a new encryption and decryption algorithm using hidden encrypted private key with logical shift and XOR operations is introduced. The strength of this algorithm is depending on the use of encrypted and hidden key and the two logical operations. The main objective of using the encrypted hidden key in the cipher-text is to solve the problem of the private key distribution and to increase the security level provided by the proposed algorithm. The proposed algorithm is investigated on six text files of various sizes and compared with three other encryption algorithms. The obtained simulation results show that the main two advantages of the proposed method are represented in the computation simplicity and security efficiency compared with AES , DES and Triple DES algorithms.
The impact of blended learning on the adaptation processes of foreign students

The object of the study are foreign students of ASMU. The purpose of the work is to assess the impact of blended learning on the adaptation of foreign students in Russian universities on the example of ASMU. The subject of the study is the adaptation to the learning process of foreign students in the university environment. An online survey developed based on a set of questions was used to collect data from 80 first-year international students. The analysis of the survey results conducted using the SPSS Statistics program empirically confirms the fact that students' perception of their university learning experience and adaptation were beneficially influenced by the mixed learning environment. The scientific novelty consists in the fact that during the research a questionnaire was developed that allows to determine the general level of adaptation of foreign students to the learning process and to identify the problems that foreign students face more often during their studies in Russia. Based on the results of the survey, practical recommendations are proposed to improve work with foreign students for more successful adaptation in the university environment. Main conclusions 1. The studies conducted at ASMU indicate the positive impact of blended learning (thanks to digital online communication) on the interaction not only of the student and the teacher, but also on the interaction between each other. 2. The results obtained show that blended learning is the means that creates the necessary prerequisites for the emergence of internal motivation of a person's activity, especially when it is able to adapt to the characteristics of the trainees, the level of available knowledge, the pace of assimilation of new knowledge. In this case, students enjoy the learning process, their self-confidence and activity in learning increases. 3. Elements of online learning are organically combined with classroom classes, complementing them and developing self-study skills and providing accessible feedback from the teacher.
Cultural Communication-Oriented Construction of Innovative Teaching Model for English Majors

English grammar is a compulsory course for English majors in colleges and universities.English grammar is an independent basic subject and a branch of linguistics.It is the organizational law of language and gives language a structural system.The learning of grammar must be organically combined with the use of language.The aim is to explore how to construct a culture-transfer-oriented grammar teaching model for English majors, in order to explore new ideas for English major grammar teaching, build a new teaching system for English basic education, run through the new content of humanistic quality education, and use information technology to improve English teaching methods, and carry out the second classroom activities that reflect the characteristics of professional humanities.Integrate vocational humanistic quality education into the teaching mode of higher vocational English classrooms to comprehensively improve students' comprehensive quality.
Renewable Energy Potential Estimates Based on High-Resolution Regional Atmospheric Modeling over Southern Africa

Three-quarters of the global population that lacks reliable electricity supply lives in Africa, despite the fact of large untapped wind and solar energy potentials on the African continent. Using renewable energy to bridge the power supply gap in Africa also means a possibility to address climate change mitigation at the same time. Reliable, highly resolved spatio-temporal information on renewable energy potentials (REP) is albeit imperative for developing strategies for solar and wind energy expansion. Applying atmospheric datasets over Africa to REP estimations face challenges like data gaps in space and time, relatively coarse spatial resolution, and data quality. With the aim to produce a reliable atmospheric dataset for REP simulations at high spatial and temporal resolution, we conducted dedicated convection-permitting atmospheric simulations for a study domain in southern Africa, which features favorable meteorological conditions for solar and wind energy generation. Based on an evaluation study, further investigations on the added value of km-scale resolution for REP estimations will be shown. In a dynamical downscaling setup, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON-NWP) model v2.6.4 is run in limited area mode (ICON-LAM) with a weather forecasting configuration, driven by the operational 13km ICON global analysis from the German Weather Service (DWD). Our simulations cover the three years 2017 to 2019 at 3.3km resolution over a southern African model domain. To ensure a good agreement of the ICON-LAM simulations with observed weather over the large model domain, the atmosphere is reinitialized every five days with one preceding spin-up day, and the land surface as well as subsurface are run transiently. Variables of 10m wind speed (sfcwind), surface solar irradiance (rsds), 2m air temperature (tas), and precipitation (pr) are extensively validated using satellite data, composite data products, and in-situ data from meteorological stations. Results show that ICON-LAM is capable of reproducing observations on temporally aggregated and hourly time scales. Typical seasonal meteorological features are well reproduced during austral summer and winter. The average mean error (ME) for simulated hourly sfcwind is 1.12 (&amp;#177; 0.83) m s-1, and for 69% of the considered sites the correlation coefficients between observed and simulated hourly sfcwind are above 0.6. Simulated daytime rsds has an average ME of 50.8 (&amp;#177; 42.21) W m-2 and the mean daytime rsds correlation between observations and simulations is 0.87 (&amp;#177; 0.05); this indicates a well-represented daytime rsds variation in ICON-LAM simulations. A small bias is in the tas simulation with an average ME of 0.23 (&amp;#177; 0.99) &amp;#176;C. We also found the simulated monthly pr biases increasing from the West to the East of the model domain, following precipitation gradients associated with the general atmospheric circulation.
ROBOTIC ARMS PASSING THROUGH OBSTACLE NODES: USING AN IMPROVED INTELLIGENT ALGORITHM

In the working process, how efficiently the robotic arm passes the obstacle nodes has a great impact on its working efficiency.This paper briefly introduced the rapidly exploring random tree (RRT) algorithm and optimized it with the gravitational potential function based on its shortcomings.The B-spline curve was used to smooth the path.Experimental analyses were carried out in two-dimensional and three-dimensional environments.It was found that the improved RRT algorithm had an average running time of 0.39 s and an average path length of 93.96 mm in the two-dimensional environment; taking environment 1 as an example, the average running time and path length of the improved RRT algorithm was 1.03 s and 147.63 mm, respectively, in the threedimensional environment, after passing through seven path nodes, which were significantly better than the standard RRT algorithm.The results prove the effectiveness of the improved intelligent algorithm for robotic arm path optimization selection.The improved RRT algorithm can be further promoted and applied in practice.
Quantum algorithm for unsupervised anomaly detection

Anomaly detection, an important branch of machine learning, plays a critical role in fraud detection, health care, intrusion detection, military surveillance, etc. As one of the most commonly used unsupervised anomaly detection algorithms, the Local Outlier Factor algorithm (LOF algorithm) has been extensively studied. This algorithm contains three steps, i.e., determining the k-distance neighborhood for each data point x, computing the local reachability density of x, and calculating the local outlier factor of x to judge whether x is abnormal. The LOF algorithm is computationally expensive when processing big data sets. Here we present a quantum LOF algorithm consisting of three parts corresponding to the classical algorithm. Specifically, the k-distance neighborhood of x is determined by amplitude estimation and minimum search; the local reachability density of each data point is calculated in parallel based on the quantum multiply-adder; the local outlier factor of each data point is obtained in parallel using amplitude estimation. It is shown that our quantum algorithm achieves exponential speedup on the dimension of the data points and polynomial speedup on the number of data points compared to its classical counterpart. This work demonstrates the advantage of quantum computing in unsupervised anomaly detection.
Research Trends in Library and Information Science in India during 2016-2021: A Bibliometric Analysis

Research trends in Library and Information Science Ph.D. level carried out in universities of India during the six years (2016-2021) have been analysed to find out the current Research trends and areas of research, growth pattern and productivity of universities along with broad and narrow subject areas in the discipline in Library and Information Science. The present study helps the Library Science research community current research scenario and direction of research in this era of information and communication technology.
Surface Solar Radiation based on Satellite Observations – the new CM SAF SARAH-3 Climate Data Record

The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available. In 2023, finally the new edition of the &amp;#8220;Surface Solar Radiation data set &amp;#8211; Heliosat&amp;#8221; has been released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. The SARAH-3 climate data records are accompanied by a corresponding near-realtime processing (so-called interim climate data records). With that, SARAH-3 covers the time period since 1983 with a timeliness of 1 day and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05&amp;#176; x 0.05&amp;#176; lon/ lat grid. Compared to previous versions, the data quality of SARAH-3 has been substantially improved over snow-covered surfaces, e.g, in the Alpine region, by the use of an internally derived snow information. SARAH-3 enables a wide range of applications &amp;#8211; from model verification and renewable energy assessments to operational climate monitoring. In this presentation, an overview of the SARAH climate data record and example applications will be given. After presenting the SARAH-3 developments and validation results, SARAH-3 will be used for the analysis of climate variability and potential trends in Europe during the last decades. The data record reveals that there is a positive trend of surface solar radiation in Europe during the last decades, which is superimposed by decadal and regional variability.&amp;#160;
Advanced Night time Object Detection in Driver-Assistance Systems using Thermal Vision and YOLOv5

Driver-assistance systems have become an indispensable component of modern vehicles, serving as a crucial element in enhancing safety for both drivers and passengers.Among the fundamental aspects of these systems, object detection stands out, posing significant challenges in low-light scenarios, particularly during nighttime.In this research paper, we propose an innovative and advanced approach for detecting objects during nighttime in driver-assistance systems.Our proposed method leverages thermal vision and incorporates You Only Look Once version 5 (YOLOv5), which demonstrates promising results.The primary objective of this study is to comprehensively evaluate the performance of our model, which utilizes a combination of stochastic gradient descent (SGD) and Adam optimizer.Moreover, we explore the impact of different activation functions, including SiLU, ReLU, Tanh, LeakyReLU, and Hardswish, on the efficiency of nighttime object detection within a driver assistance system that utilizes thermal imaging.To assess the effectiveness of our model, we employ standard evaluation metrics including precision, recall, and mean average precision (mAP), commonly used in object detection systems.
Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge

Mixed-precision quantization, where a deep neural network’s layers are quantized to different precisions, offers the opportunity to optimize the trade-offs between model size, latency, and statistical accuracy beyond what can be achieved with homogeneous-bit-width quantization. To navigate the in-tractable search space of mixed-precision configurations for a given network, this paper proposes a hybrid search methodology. It consists of a hardware-agnostic differentiable search algorithm followed by a hardware-aware heuristic optimization to find mixed-precision configurations latency-optimized for a specific hardware target. We evaluate our algorithm on MobileNetV1 and MobileNetV2 and deploy the resulting networks on a family of multi-core RISC-V microcontroller platforms with different hardware characteristics. We achieve up to 28.6 % reduction of end-to-end latency compared to an 8-bit model at a negligible accuracy drop from a full-precision baseline on the 1000-class ImageNet dataset. We demonstrate speedups relative to an 8-bit baseline, even on systems with no hardware support for sub-byte arithmetic at negligible accuracy drop. Furthermore, we show the superiority of our approach with respect to differentiable search targeting reduced binary operation counts as a proxy for latency.
An Explainable Machine Learning Model to Analyze and Detect Fake Currency

The advent of state-of-the-art techniques and technologies has exposed human beings to some threats as well, apart from providing several advantages. Fake currency is such a major threat to society which in turn obstructs the country’s economic growth and financial status and causes an impact on human life. In addition to it, detecting fake currency is very challenging for the human eye. Detection of such currency is an important task and also consumes a lot of time if done manually. To address this critical issue, our paper contributes to identifying fake currency using Machine learning (ML) algorithms. In our article, we experimented with multiple ML algorithms to detect fake currencies and analyze their performance. Among the six algorithms experimented with for our work, Random Forest has shown the highest accuracy with 99.27% and a precision of 0.99 compared to the other algorithms. Additionally, we have demonstrated the interpretability of the ML model using LIME and SHAP which would be beneficial in understanding the important factors behind the particular prediction. We also performed hyperparameter tuning using GridSearchCV and manually too which showed us an improved accuracy performance of 99.99% from 99.27% for Random Forest. Feature Importance analysis and Explainability concepts are also demonstrated to provide an in-depth analysis of model prediction.
A 3D Hand Joint Detection Network for Real-Time Hand Capture and Its Application in a Game of Moving Mountains

Virtua1 reality (VR) technology has become one of the research hotspots of technology to promote teaching because of its high immersion and good interactivity. Putting VR games into education can make learning more participatory and incentive. Nevertheless, the majority of current technologies and efforts to accomplish human-computer interaction necessitate additional hardware, which has certain limits. To solve these problems, realize real-time operation to provide direct feedback, and enhance the user’s sense of immersion and experience, we have developed a real-time hand capture algorithm using a monocular camera to perform more accurate user gesture recognition. In the algorithm, we use the backbone architecture of deep residual network ResNet-50 as a feature extractor. Through the combined training of 2D and 3D annotation data, we are able to effectively predict 2D posture and 3D spatial information and implement virtual content interaction. This method achieves real-time performance (90fps) and accuracy (95.6%) on existing datasets and outperforms existing methods in hand mesh/pose accuracy and hand image alignment. We built and implemented a virtual reality game based on the method proposed in this research, and then transplanted it onto the VR platform with an ecological setting. While providing users with an immersive experience, we also want to use virtual reality technology to teach, play, and promote traditional Chinese culture.
Tackling the Imbalance Biases in the Code Cloze Test

Code automation aims to semi-automatically or automatically generate source code of software. However, previous methods fail to tackle the imbalance bias problem. To solve this problem, We propose a model that uses Gradient Harmonizing Mechanism(GHM) technology to alleviate the difference in the amount of data of different categories in the data. The proposed model first calculates the gradient of each sample, that is, the difference between the classification result and the label value, which is used to indicate the difficulty of classification, and then calculates the gradient density. Finally, take the reciprocal of the gradient density as the weight of the loss value calculation function, reconstruct the loss value calculation function, so as to reduce the influence of the data with too large gradient density on model training, so as to alleviating the data imbalance in the data set. The proposed method is evaluated through four experiments on our dataset. The results show that the results obtained by the Code Cloze model(CCM) outperforms the traditional model and the recurrent neural network. Compared with RNN, NBM and SVM models, the average accuracy in all programming languages is improved by 32.9%, 224.7% and 517.9%, respectively.
Special Agents Policy Gradient In Value Decomposition-based Approach

In many real-world environments, such as soldiers and general in a battlefield, or teammates and goalkeeper in a soccer field, the “general” has a significantly stronger role than the “soldier”, so that it is logical to assign higher “intelligence” and “flexibility” to the “general”, we define it as special agent. Here, we propose a multi-agent reinforcement learning algorithm that provides stronger intelligence to special agent in a fully cooperative heterogeneous multi-agent environment. Similar to QMIX, we design a common monotonicity critic for all agents, but a separate actor network to improve its “intelligence” for the special agent. In this way we can improve the group's ability to cooperate by giving special agent greater ability, while ensuring that the group remains cooperative. We evaluate the above algorithm on two sets of StarCraft 2 micromanagement tasks, and the experimental results show that the algorithm has a significant advantage over baseline algorithms for tasks with significant heterogeneity.