Reviewerly/exHarmony huggingface dataset:
STUDENT-CENTERED DISTANCE LEARNING IN COMPLICATED CONDITIONS (PANDEMIC COVID-19, MARITAL STATE IN UKRAINE)

The article updates the problems of student-centered learning, which is the core of modern higher education. The specificity arises during the implementation of student-centered distance learning in the complicated conditions of the global Covid-19 pandemic (2020-2021) and martial law in Ukraine (2022). The author included 1071, 443, and 834 university students in the anonymous online survey in 2020, 2021, and 2022, respectively. The computer program IBM SPSS Statistics 23 was used to process the results of the student survey. The obtained results indicate that the following means of distance learning were most widespread at the university at the beginning of the pandemic (listed in order of decreasing number of used educational disciplines, 2020): Moodle, Email, messengers. Google Meet, Google Classroom, Zoom are gaining priority as distance learning tools for students in 2021, 2022. The majority of students (about 70%) easily register in the Moodle system, but about 20% experience minor and about 10% significant difficulties. Some students express dissatisfaction with the quality of Internet connection (about 25-29%), their own technical support (about 17-20%), psychological comfort (about 22-27%). The calculation of the t-test indicates the absence of a statistically significant difference (p≤0.05) for independent samples of neighboring years during distance learning according to the following characteristics: ease/difficulty of registering students in Moodle (2020-2021, 2021-2022), psychological comfort (2020-2021), satisfaction with the quality of Internet connection (2020-2021, 2021-2022) and the quality of own technical support (2021-2022); in other cases of comparison, a statistically significant difference is observed. We used computer-oriented re-testing in Moodle, taking into account the complicated conditions of distance learning in Ukraine (2020-2022). This approach helps to improve the learning results of bachelor and master students by almost 16% and 13-17% for training, as well as by 4% and 3-20% for modular testing, respectively. We believe that the positive results of re-testing in Moodle should be taken into account and disseminated in the practice of higher education.
Reinforcement Learning and Action Space Shaping for a Humanoid Agent in a Highly Dynamic Environment

Reinforcement Learning (RL) is a powerful tool and has been increasingly used in continuous control tasks such as locomotion and balancing in robotics. In this paper, we tackle a balancing task in a highly dynamic environment, using a humanoid robot agent and a balancing board. This task requires complex continuous actuation in order for the agent to stay in a balanced state. In this work, we propose an RL algorithm structure based on the state-of-the-art Proximal Policy Optimization (PPO) using GPU-based implementation; the agent achieves successful balancing in under 40 min of real-time. We sought to examine the impact of action space shaping on sample efficiency and designed 6 distinct control modes. Our constrained parallel control modes outperform the naive baseline in both sample efficiency and variance to the starting seed. The best-performing control mode, using parallel configuration, including lower body and shoulder roll joints named (PLS-R), is 33% more sample efficient than all the other defined modes, indicating the impact of action space shaping on the sample efficiency of our approach.Our implementation is open-source and freely available at: https://github.com/NTNU-ERC/Robinion-Balance-Board-PPO .
A Study on the Influence of Smart Factory’s Intention to Continue to Use on the Management Effect of Enterprises

In South Korea, although companies are creating many business outcomes by building infrastructure for smart manufacturing through the smart factory construction projects, which has been promoted by the government since 2015, the levels of smartization of most small and medium-sized enterprises (SMEs) are still remaining at the basic level of basic steps. Therefore, an empirical study was conducted on the factors affecting the intention to continue to use smart factories and net impacts with large enterprises (including public enterprises), strong medium enterprises, and SMEs that have built and have been operating smart factories. This study prepared a questionnaire targeting executives and employees working at companies with a research model that combines the IS success model, the TOE model, and the UTAUT2 model on how companies that built and are using smart factories will be affected when they use the smart factories with an intention to continue to use smart factories and conducted empirical analyses. It is judged that the data from this study will contribute to studies on the establishment of smart factory strategies or implementation plans for the strategies conducted by companies or the government. However, the results of analyses were found to have limitations because differences in perceptions between large enterprises and SMEs in the positions of users that have built and are operating smart factories are large. Therefore, more studies are necessary from the viewpoints of net impacts according to the intention to continue to use and operation of smart factories by scale, business type, level of smart factories.
Color-SIFT Features for Histopathological Image Analysis

Histopathology is one of the most used practices in the bio-medical field for disease and cancer detection and grading. Following the digitalization of biological data and improvement of machine/deep learning methods, the challenges for developing Computer-Assisted Diagnosis (CAD) systems arose. In this work, we explore the use of a model based on Color-SIFT descriptors, Bag-of-Features (BoF) and Support Vector Machine (SVM) to analyse and classify tumoral histopathological tissus. We tested the system using a limited amount of data and compared its results with ResNet18 results. Our model obtained a 64.8% accuracy while ResNet18 obtained 61.8% classification accuracy. We performed the experiments using the CPU rather than the GPU. The training and validation of ResNet18 took 07 H and 29 min while the proposed model took 03 H and 14 min.
The Introduction and Application of Immersive Experience by Museum Exhibition in Space Reconstruction

Based on the necessity of museum exhibition with introduction of immersive experience in spatial reconstruction, this paper explores the ideas and application methods of museum exhibition with introduction of immersive experience in spatial reconstruction on the basis of analyzing the value, significance and demand of museum exhibition introducing immersive experience in spatial reconstruction.
Phanerozoic Geomorphology in Egypt

The current paper gives an outlook of some major lineaments of Egypt’s landforms during the Phanerozoic Eon. Egyptian lands have been exposed for all geological episodes that affected the global earth's crust. Phanerozoic geomorphology in Egypt is heavily affected by the succession stages of Tethys sea transgression and regression, the largest one was in Early Eocene, covering most of Egypt up to the White Mountain in Northern Sudan. In the Oligocene, a dramatic change has occurred in Egypt’s geological evolution with the formation of rifting and drifting system. This paper adopted the thesis concluded by Issawi et al. (The phanerozoic geology of Egypt: A geodynamic approach. Ministry of Petroleum, Cairo, 2009) concerning the development of Egyptian lands during the Phanerozoic. In this thesis, Egypt is composed of tectonic arcs (positive relief) separated in between by lowlands or huge depressions. In the current review, the author depends on his field trips across Egypt during the last three decades and summarizing, at the same time, the conclusions of the previous works. The paper consists of two principal items: (1) the physiographic regions which include the generic units of Nile valley, Delta, and the three deserts of the Eastern Desert, Sinai Desert and the Western Desert; and (2) the geomorphological evolution of paleo-fluvial systems.
A Real-Time Multistage False Data Detection Method Based on Deep Learning and Semisupervised Scoring Algorithms

This article proposes a real-time multistage data-driven method to detect false data injected by an intruder. The proposed method is based on an accurate load model constructed by deep neural networks (long short-term memory and gated recurrent unit) where historical demand along with extra weather features are employed to formulate the problem as a sequence of predictions and build an appropriate DNN model. A distinct feature of the proposed method that dramatically improves accuracy is hyperparameter optimization (e.g., dropout rate, batch size, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta _{1}$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta _{2}$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> ) based on cross-validation. Since hyperparameter optimization consumes enormous resources to analyze the different combinations of hyperparameters and causes out-of-memory problems, we have implemented our framework based on a parallel distributed computing technique on high-performance computer clusters. A variety of case studies are examined to find the best model. At the end of this step, the best model is employed as a base reference to a semisupervised scoring algorithm for the purpose of finding sequence of injected false data. To improve the detection efficiency, inter-range, pruning, and minimum score are added to the proposed methodology that suppress high false-positive rates. Several scenarios from a real-world dataset have been studied in this article, and the results demonstrate that the proposed method can be used to detect various types of attacks, including scaling, ramping, professional ramping, and random attacks, with a good performance score (i.e., recall, precision, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F1$</tex-math></inline-formula> score) for all of them. Furthermore, it can mitigate false data with the forecasted load values.
AR-DIC: A Novel Automatic Optical Strategy for Displacement Measurement of the Curved-in-Place Pipe Under Dynamic Fracturing

Curved-In-Place Pipe (CIPP) technology is widely used in urban underground pipeline repair nowadays. However, the lack of automatic global displacement acquisition method makes it still quite challenging to effectively obtain the detailed displacement characteristics of the CIPP pipes under dynamic loads. Herein, we propose a cascade strategy named automatic speckle region extraction digital image correlation (AR-DIC) to achieve the dynamic, efficient and accurate displacement estimation of the CIPP pipelines, so as to determine the variation characters of the CIPP pipelines under dynamic loads. In the first stage, an improved automatic DeepLabv3+ neural network (IA-DeepLabv3+) is meticulously designed to extract the region of interest (ROI) of the CIPP pipeline speckle automatically. A dataset containing the cluttered background and the real experimental speckle are also generated to train the model parameters. Note that this is the first time an automated ROI extraction method has been proposed in CIPP, even in the DIC field. In the second stage, the digital image correlation based on scale-invariant feature transform (SIFT-DIC) algorithm is adopted to calculate the extracted ROI and get the displacement field. Finally, the proposed method is tested on both the simulated and experimental datasets. The experimental results confirm that our method can not only extract the speckle ROI region accurately, but also achieve high-precision calculation of the displacement field.
Attacks on Recent DNN IP Protection Techniques and Their Mitigation

With the rapid increase in the development of deep learning methodologies, deep neural networks (DNNs) are now being commonly deployed in smart systems (e.g., autonomous vehicles) and high-end security applications (e.g., face recognition, biometric authentication, etc.). The training of such DNN models often requires exclusive valuable training datasets, enormous computational resources, and expert fine-tuning skills. Hence, a trained DNN model can be regarded as valuable proprietary intellectual property (IP). Piracy of such DNN IPs has emerged as a major concern, with increasing trends of illegal copying and redistribution. A number of mitigation approaches targeting DNN IP protection have been proposed in recent years. In this work, we target two recently proposed DNN IP protection schemes: 1) chaotic map theory-based encryption of the weight parameters and 2) traditional block cipher-based encryption of the weights. We demonstrate attacks on two recent DNN IP protection techniques, with one technique each belonging to the above-mentioned schemes, under a pragmatic attack model. We also propose a novel DNN IP protection technique based on selective encryption of the weight parameters, termed limited encryption of weights for IP protection (LEWIP) to mitigate the exposed weaknesses, while having low implementation and performance overheads. Finally, we demonstrate the effectiveness of the LEWIP technique against state-of-the-art DNN implementations.
Towards an Anticipatory Mechanism for Complex Decisions in a Bio-Hybrid Beehive

To a certain extent, humans and many other biological agents are able to anticipate the consequences of their actions and adapt their decisions based on available information on current and future states of their environment. The same principle can be applied to enable decision-making in artificial agents. In order to decide on an action, an agent could envision the consequences for each of the actions and then choose the one promising the best outcome. This anticipatory scheme can enable fast decisions in highly dynamic and complex situations, which has been demonstrated in humanoid robots playing soccer. We extend this principle to the scenario of bio-hybrid beehives augmented with robotic actuators, which allow to influence the foraging locations of the bees. We investigate how a bio-hybrid beehive can make decisions and direct the bees in a way which would benefit the the whole ecosystem enabling sustainable beekeeping. We explore the general principles of anticipation and discuss connections to cognitive science and developmental robotics. We present an implementation of a simulator for the behavior of the augmented beehive and present preliminary results demonstrating the feasibility of the anticipatory approach.
Security Enforcing

Formal definitions of state-based and language-based security with respect to timing attacks are proposed and studied. Then various ways how to secure systems with respect to such attacks are discussed. First, we investigate time insertion functions. Conditions, when such functions exist and could protect systems, are investigated. Then we discuss the concept of supervisor control which can be used if there is no appropriate time insertion function to protect the systems.
Use of SARIMAX Model for Solar PV Power Output Forecasting in Baguio City, Philippines

Over the past few decades, there has been a continuous increase in the public interest for solar energy as an alternative and cleaner source of energy. Therefore, it is not surprising that there is a similar interest in developing accurate models to forecast solar photovoltaic (PV) power production. Such models vary depending on the geographical location of PV sites and the seasons considered. The Philippines has yet to have a solar PV output forecasting model adapted to the country's local conditions. This study aims to evaluate the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) forecasting model as a tool for forecasting solar PV generation based on the seasonal characteristics of the country and identify which input parameters are significant for each season. This work used solar PV production data as an endogenous variable. Meanwhile, exogenous variables include in-situ solar irradiance data from solar power plants; and cloud cover, wind speed and direction, ambient temperature, precipitation, and relative humidity, which we extracted from ERA5 Reanalysis data. Datasets were divided based on the seasons as defined by the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA), namely, hot dry (HD), rainy (R), and cold dry (CD) seasons. Then, we performed a forecast on each season and one full year to assess the performance of the SARIMAX model. The best model per season was done based on the forecasting accuracy measured using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). This work analyzed seasonal and year-round SARIMAX models for Baguio City, Philippines. Results show that the cold dry season got the highest accuracy value in terms of 2.26 MAE and 4.06 RMSE. Meanwhile, the rainy season had the lowest accuracy of 12.91 MAE and 16.16 RMSE. We can infer from our findings that seasonal forecasting is better during hot dry and cold dry seasons. We also found that the year-round forecasting model performs better than the rainy season model. From the significant parameters identified in our best models, our analysis showed that wind direction can be removed from all models; irradiation and relative humidity were significant for all seasons.
Impact of attention mechanism on U-Net architecture for the Left Ventricle segmentation

Echocardiography is the most widely used medical imaging technique for diagnosing cardiovascular diseases. Further, delineation of left ventricle (LV) in 2D echocardiographic images by cardiologists is required for systolic function assessment. However, this daily task is tough to do, takes a lot of time and is not precise due to the mediocre quality of these images. Recently, deep learning methods, and more particularly encoder-decoder networks, have been widely proposed to solve these problems and to automate LV segmentation. A precise segmentation is very important for properly estimating clinical parameters that can be used in diagnostic aid and follow-up of cardivascular pathologies. Therefore, we examine the performance of attention mechanism into two U-Net architectures. These two models created from the original U-Net and adapted for the Cardiac Acquisitions for Multistructure Ultrasound Segmentation (CAMUS) dataset present better accuracies for segmenting echocardiographic images. Experiences demonstrate an improvement in performances when applying attention by achieving a Dice coefficient of 0.940 and a Hausdorff distance of 4.11 with attention U-Net 2.
Deep learning-based Cervical Cancer Classification

Cervical Cancer (CC) is one of the most preoccupied cancers in the medical midst, particularly affecting the vulnerable category of women in undeveloped and developing countries due to lack of screening programs and sensitization campaigns. The cervical cancer does not reveal any symptoms during its growth until it reaches advanced stages where by a simple visual diagnosis it can be detected. However, it is too late to be healing; early stage detection cannot be picked up. Thereby, there is a need of precise and reliable diagnosis will help radiologists to give proper and effective treatment. This paper contributes toward an achievement of a cervical cancer detection system based on Deep Learning (DL) dealing with axial and sagittal T2-weighted MRI images to determine the pathological stage of the tumor. Deep learning has presented significant potential in medical image analysis that allows gaining more importance in computer-aided detection system. In this paper, eight deep-learning-based approaches applied to cervical cancer are studied and compared. The proposed pipeline based on two pre-trained Convolutional Neural Networks (CNN) for automatic cervical classification consists of two main steps: Data Augmentation and Transfer Learning. The Sensitivity, Specificity and Accuracy of the best classification model to differentiate malign cases from benign ones were 100%, 100% and 100% respectively, with an AUC of 1.
Analysis of Influencing Factors of Economic Benefits of Metaverse Digital Collections

In recent years, the metaverse industry has gradually expanded, and the economic benefits brought by the metaverse blockchain industry, including digital collections, have become more obvious. For this economic benefit, the researchers took the Opensea platform, which is representative of digital collections, as an example. They took the economic benefit index of the platform as an explanatory variable, comprehensively used the multiple regression model to analyze and test, and used the stepwise regression method to adjust and correct the model. The analysis results show that the economic benefit index of digital collections shows a clear proportional relationship with the number of NFT digital collections sold and the scale of people’s consumption. From the perspective of economics, some economic law problems of the digital collection industry are explained, and some conclusions are made for the development of the digital collection industry and suggestions are given for reference.
Comparative Research of Chinese and American Game Mechanism Design

The video game industry is one of the most influential entertainment industries, which has been generating high revenue and developing rapidly in recent years. However, in recent years, social problems caused by video games have emerged in China. This article proposes that game mechanism is an important kernel of game design and has a crucial influence on the social significance of games. By comparing China and the United States, the article analyzes the history of game mechanism design in both sides, draws the reasons for the healthy development of the game industry in America and uncovers the problems of the game industry in China, and proposes optimization strategies for game mechanism design in China in combination with the current situation.
Novel Machine Learning–Based Approach for Arabic Text Classification Using Stylistic and Semantic Features

Text classification is an essential task for many applications related to the Natural Language Processing domain. It can be applied in many fields, such as Information Retrieval, Knowledge Extraction, and Knowledge modeling. Even though the importance of this task, Arabic Text Classification tools still suffer from many problems and remain incapable of responding to the increasing volume of Arabic content that circulates on the web or resides in large databases. This paper introduces a novel machine learning-based approach that exclusively uses hybrid (stylistic and semantic) features. First, we clean the Arabic documents and translate them to English using translation tools. Consequently, the semantic features are automatically extracted from the translated documents using an existing database of English topics. Besides, the model automatically extracts from the textual content a set of stylistic features such as word and character frequencies and punctuation. Therefore, we obtain 3 types of features: semantic, stylistic and hybrid. Using each time, a different type of feature, we performed an in-depth comparison study of nine well-known Machine Learning models to evaluate our approach and used a standard Arabic corpus. The obtained results show that Neural Network outperforms other models and provides good performances using hybrid features (F1-score = 0.88%).