Reviewerly/exHarmony huggingface dataset:
Acquaintance with Addition &amp; Subtraction of Algebraic Expressions: Activity Oriented Learning

Introduction: Now, a challenging environment has appeared in the academic world due to Covid-19 Pandemic.But we are now sufficiently equipped with Information and Communication Technology through digital media.So, to overcome the present difficult situation, the 'on-line teaching-learning process' is being adopted as an alternative of regular schooling in various Boards in India.But these are facing various types of challenges to fulfil the students' need.The students face troubles to learn 'addition and subtraction of algebraic expressions' for nature of abstraction of it.So the activity is needed on it.Methodology: Collection of text books, analysis of text books/text book scanning, developing the activity, sequencing of steps of the activity and experts' opinions are the materials and methods which have been adopted for this study.These activities are presented step by step i.e. in an iterative sequential form.Result: In this paper, two activities on acquaintance with addition and subtraction of algebraic expressions have been developed and presented sequentially in a new look for the target group, i.e. the learners, considering their ages, grades and ability levels.This can be appropriate in preparing text material through computer-based learning. Conclusion: These activities are not introduced in the prescribed text books of mathematics in West Bengal Board of Secondary Education (W.B.B.S.E.) and National Council of Educational Research and Training (N.C.E.R.T.).All these activities will definitely help the learners to get the relish
Development of Picture Storybooks with TheoAnthropoEco Centric Approach for Elementary School Students

Learning media has a significant role in an effort to help abstract concepts so that students more easily understand these concepts. However, many teachers have difficulty in developing learning media. This study aims to develop learning media in the form of picture storybooks with a TheoAnthropoEco-centric approach that is feasible and practical for use by elementary school students. This study uses a research and development type that refers to the development stage of Borg Gall. The data was obtained using a product feasibility validation questionnaire and a teacher and student response questionnaire. The subjects of this research are media experts, material experts, linguists, class teachers and fourth-grade students of madrasah ibtidaiyah. The determination of research subjects is done by purposive sampling technique. Descriptive statistical techniques analyzed the data obtained in this study. The results showed that the picture book with the TheoAnthropoEco-centric approach developed in this study was feasible and practical for use by elementary school students. This is evidenced by the feasibility test results, which show the developed media is in the "Very Good" category. On the other hand, the results of the practicality test also show that the product developed in this study is in the "Very Good" category. Therefore, picture story books with a TheoAnthropoEco-centric approach can be used as a medium of learning at the elementary school level.
The Development of Networked-Based Teaching Materials Using Sevima Edlink in Elementary School

This research aims to develop a teaching material Based Networked using Sevima Edlink In Elementary School theme heat and its transfer. Data analysis shows that teaching materials based on Networked using Sevima Edlink in Grade V Elementary School theme heat and its transfer effective and attractive in learning. This research method is Research and Development with the ADDIE model. Validity results teaching materials based networked using application Sevima Edlinks, theme heat and its transfer and their displacement show criteria very valid after validation by material, media, and language validating with average value 87 percent with the category very valid. Practical results of teaching materials based networked using Sevima Edlinks theme heat and its transfer show practical criteria after evaluation practicality by students with an average value 95 percent with category very practical. The effectiveness test results showed the pretest value is 69.6, and at the time posttest increased to 80.6 percent after using product teaching materials based networked application Sevima Edlinks, theme heat and its transfer in learning thematic integrated. This shows presence enhancement results study students after using developed teaching materials.
Object Detection in Online Proctoring Through Two Camera Using Faster-RCNN

The COVID-19 pandemic has prompted changes in teaching methods from offline to online, including the implementation of exams. But many reports say that the potential for online exam cheating is very high which can compromise the credibility of the exam. The online exam monitoring system using one camera makes it difficult for officers to make decisions because of the lack of evidence and supporting data. In this study, we propose a monitoring approach using two cameras, namely a camera on a laptop to get a front view of the participant and a cellphone camera to get a side view of the examinee but because of the complexity of the problem, at this stage we only focus on the side camera. Implementation begins with the collection of video recording data, custom data sets for training and pretrained datasets from the zoo model. Training is carried out using a custom dataset to detect objects that are not recognized by the pretrained dataset. The evaluation of the training results using the COCO evaluator showed the average of the bbox-AP is 59,169. The fraud detection process is carried out using 6 exam videos with a total of 192,929 frames, producing two outputs, namely object detection videos and csv files. The csv file contains the frame number, time, object detected in each frame. The next process is to analyze the csv file and mark frames that have the potential to be fraudulent. The evaluation results show an accuracy of 0.884615385 and a recall of 0.821428571
“The playwright knows: the more productions, the more bad productions”

Dmitry Danilov is a writer, playwright, and poet. Author of the novels “Horizontal position” (2010), “Description of the city” (2012), “There are more important things than football” (2015), “Sasha, hello!” (2022), plays “The Man from Podolsk” (2016), “Seryozha is very stupid” (2017), “Witness testimony” (2018), “What did you do last night?” (2019). Winner of the Andrei Bely Award (2019), the Yasnaya Polyana Award (2022). In this issue of P&amp;amp;I the writer Dmitry Danilov talks about Mamleev and Dostoevsky, the news of theatrical Voronezh, network marketing, mistakes in Wikipedia, and the newspaper for Domodedovo Airport employees. Interview by Ekaterina Maksimova. Photo by Oleg Serdechnikov.
Quality Analysis of Service Load Balancing Using PCC, ECMP, and NTH Methods

One of the solutions to get a better quality of internet service is to utilize load balancing technology. We can use more than one different ISP (Internet Service Provider) which is then balanced with load balancing technology, where this technique is used to distribute the traffic load on two or more connection lines in a balanced way so that traffic can run optimally, maximizing throughput, minimizing response time. and avoid overload on any of the connection lines. This study aims to determine the comparison of Quality of Service load balancing with the PCC, ECMP and NTH methods on Mikrotik. The test method uses the web application www.speedtest.cbn.id, www.fast.com (1 connection), the results will show ping, download speed, upload speed, and monitoring from the Mikrotik router side. The research stages used the Network Development Life Cycle method, namely analysis, design, prototype simulation, implementation, monitoring and management. The results of the research are in the form of a performance comparison between load balancing on 2 internet lines using the PCC, ECMP and NTH methods on the proxy router. Based on the tests carried out as a whole the load balance using the ECMP method is superior and more reliable in terms of failover effects.
Decision Tree Method for Automation of Plant Sprinklers and Monitoring Based On Soil Moisture

Watering the plants is an important part of plant care, but due to our busy schedules, we frequently forget to do it. It can be caused by a number of variables, including the weather, in addition to human irresponsibility. The goal of this research is to develop a device that can automatically water plants and monitor plants based on soil moisture. Automatic sprinklers that use NodeMCU ESP8266 and Telegram bots employ the decision tree approach. The C 4.5 algorithm is used by the Decision Tree approach to choose the microcontroller's course of action. The value of various case studies utilized in this study is determined using algorithm C 4.5. The Arduino IDE, Fritzing, and Visual Studio Code were the tools used to design and produce this tool. Additionally, it configures Telegram bots using the Telegram universal bot library. The website setting for plant monitoring employs the native PHP language, but the configuration of the telegram bot uses the general telegraph bot library. The end result is an automatic plant sprinkler that operates based on the soil moisture value and activates the water pump when the soil moisture level drops below a threshold of 50%. When the soil moisture reading exceeds 75%, the water pump will then turn off. The outcomes of this investigation also comprise websites and Telegram bots for tracking soil moisture levels.
The role of educational marketing in managing the prolonged crisis of hgher education and improving its quality

The alarming decrease in the number of students in educational institutions can be considered a real crisis, which will not be resolved unless the necessary measures are taken to remove the underlying causes of this process. Currently, all national educational institutions are involved in the globalization of education, and this requires them to comply with the world standards of service provision in all fields of activity. In the current situation of inter-university competition, the issue of emigration of domestic students and attracting foreign students can be solved by using educational scoring tools, which can contribute to the increase of educational professionalism and will increase the image of local institutions in the world ranking. Universities will be competitive only if there is a strong brand that implies a positive image of an educational institution and a high level of individual culture. The implementation of European projects, including QFORTE, can contribute to increase the quality of university training and the development of a more efficient and effective education.
AIDA COMMUNICATION MODEL ON SOCIAL MEDIA PLATFORMS: ASSESSING THE HIERARCHICAL ORDER AND ITS APPLICABILITY ON PURCHASING BEHAVIOR OF STUDENTS IN HIGHER LEARNING INSTITUTIONS

The main aim of this research was to ascertain the influence of social media on promotion of universities in Tanzania, using the AIDA Communication Model. Second year students from 20 higher learning institutions were provided with questionnaires to respond on how social media platforms facilitated their choice in universities, based on indicators from AIDA Communication Model as stipulated by Girvu, (2013) and Abdelkader &amp; Rabie (2019), and modified by the researcher. The study employed an explanatory research design. Sampling was done through Cochran Sample formula, with a 95% confidence level of precision, where a total of 384 second year students out of 40,755 were sampled from 22 universities. A Generalized Structural Equation Model (GSEM) using the STATA software version16.0 was used to analyse the data. Findings revealed that while a hierarchical order may be followed in traditional face-to-face consumer decision making process, it is not necessarily the case when it comes to social media platforms. In addition to indicators for each element in the AIDA model appearing in multiple elements, what attracts one persons’ attention is the factor that causes another person to act.
A Comparison of Methods for Neural Network Aggregation

Deep learning has been successful in the theoretical aspect. For deep learning to succeed in industry, we need to have algorithms capable of handling many inconsistencies appearing in real data. These inconsistencies can have large effects on the implementation of a deep learning algorithm. Artificial Intelligence is currently changing the medical industry. However, receiving authorization to use medical data for training machine learning algorithms is a huge hurdle. A possible solution is sharing the data without sharing the patient information. We propose a multi-party computation protocol for the deep learning algorithm. The protocol enables to conserve both the privacy and the security of the training data. Three approaches of neural networks assembly are analyzed: transfer learning, average ensemble learning, and series network learning. The results are compared to approaches based on data-sharing in different experiments. We analyze the security issues of the proposed protocol. Although the analysis is based on medical data, the results of multi-party computation of machine learning training are theoretical and can be implemented in multiple research areas.
Digital watermarking scheme based on curvelet transform and multiple chaotic maps

The rapid development of digital products brings security issues. Digital watermarking technology is an important means to handle these problems. To enhance the imperceptibility of watermark and locate the possible tampering as well, a digital watermarking scheme based on curvelet transform is presented by combining with multiple chaotic maps. The host image is decomposed into three parts, i.e., coarse layer, detail layer and fine layer, with curvelet transform, and a robust watermark is embedded into the coarse layer for copyright protection of digital products. In addition, an authentication watermark is embedded into the fine layer to detect and locate the illegal changes. Simulation results show that the proposed digital watermarking scheme possesses acceptable robustness and security.
CAD2Render: A Synthetic Data Generator for Training Object Detection and Pose Estimation Models in Industrial Environments.

Computer vision systems become more wide spread in the manufacturing industry for automating tasks. As these vision systems use more and more machine learning opposed to the classic vision algorithms, streamlining the process of creating the training datasets become more important. Creating large labeled datasets is a tedious and time consuming process that makes it expensive. Especially in a low-volume high-variance manufacturing environment. To reduce the costs of creating training datasets we introduce CAD2Render, a GPUaccelerated synthetic data generator based on the Unity High Definition Render Pipeline
Can We Trust Chatbots for Now? Accuracy, Reproducibility, Traceability; A Case Study on Leonardo Da Vinci’s Contribution to Astronomy

Large Language Models (LLM) are studied. Applications to chatbots and education are considered. A case study on Leonardo’s contribution to astronomy is presented. Major problems with accuracy, reproducibility and traceability of answers are reported for ChatGPT, GPT-4, BLOOM and Google Bard. Possible reasons for problems are discussed and some solutions are proposed.
Identification of advanced optical modulation formatand estimation of signal-to-noise-ratio based on parallel-twin convolutional neural network

In this paper, we design a parallel-twin convolutional neural network (PT-CNN) deep learning model and use the signal constellation diagram to realize the identification of six advanced optical modulation formats (QPSK, 4QAM, 8PSK, 8QAM, 16PSK, 16QAM) and signal-to-noise-ratio (SNR) estimation. The influence of PT-CNN with different layers and kernel sizes is investigated and the optimal network model is chosen. Simulation results demonstrate that the proposed method has the advantages of not requiring manual feature extraction, having the ability to clearly distinguish the six modulation formats with 100% accuracy when SNR of the received signal sequences is higher than 12 dB. In addition, the high-accurate SNR estimation is realized simultaneously without increasing additional system complexity.
SEEN: Seen: Sharpening Explanations for Graph Neural Networks Using Explanations From Neighborhoods

Explaining the foundations for predictions obtained from graph neural networks (GNNs) is critical for credible use of GNN models for real-world problems. Owing to the rapid growth of GNN applications, recent progress in explaining predictions from GNNs, such as sensitivity analysis, perturbation methods, and attribution methods, showed great opportunities and possibilities for explaining GNN predictions. In this study, we propose a method to improve the explanation quality of node classification tasks that can be applied in a post hoc manner through aggregation of auxiliary explanations from important neighboring nodes, named SEEN. Applying SEEN does not require modification of a graph and can be used with diverse explainability techniques due to its independent mechanism. Experiments on matching motifparticipating nodes from a given graph show great improvement in explanation accuracy of up to 12.71% and demonstrate the correlation between the auxiliary explanations and the enhanced explanation accuracy through leveraging their contributions. SEEN provides a simple but effective method to enhance the explanation quality of GNN model outputs, and this method is applicable in combination with most explainability techniques.
An Enhancement of Long-Short Term Memory for the Implementation of Virtual Assistant for Pamantasan ng Lungsod ng Maynila Students

Purpose -Natural Language Processing is an aspect of Artificial Intelligence that focuses on how technology can understand words, derive meaning from them, and return a meaningful and correct output.Therefore, it is used in the making of Virtual Assistants today.Training virtual assistants require long temporal dependencies and sequence-tosequence classification.This study will be used to create a possible algorithm that will enhance the performance of a possible virtual assistant designed for PLM students and faculty members.Method -LSTM will be used to train the model to address these concerns.However.The LSTM algorithm faces the problem of slow computing speed and high computation costs.To address this the researchers implemented TensorFlow XLA to the model to optimize the computation costs in the problem.Results -Though the number of matrices exploded from 934 to 30000, the training can show slight improvement both in memory, CPU (Central Processing Unit) utilization, and time reduction.At 50 epochs, training the model with XLA has shown a time decrease of 8 minutes and can save at most 500 megabytes of memory.Conclusion -XLA has proven that it has helped the LSTM algorithm in terms of its usage in memory, utilization of CPU, and overall speed of training, especially in longer processes.Recommendations -The researchers recommend using XLA in the context of pruning and the effect of pruning paired with XLA to maximize the performance of the model.Practical Implication -This would allow a much more efficient and cost-friendly training of the model when feeding it new data to be used for virtual assistant designed for PLM students and faculty members.
SOCIAL MEDIA ALGORITHMS AND DATA MANAGEMENT

Although the audience in the digital media space has more power than in the traditional media environment, as indicated by their ability to create, reshape and share content, media users' behavior is shaped by the use of algorithms and big data management.Taking into consideration the fact that students use the internet and social media platforms daily, this paper aims to examine their perceptions and viewpoints on the operation of algorithms and data management on the Internet.According to a survey conducted by the authors, which consists of 200 respondents, twothirds of students notice the results of the algorithmic personalization, filtered selection of content and news, and the customized display of content on social media.Even though 70% of them realize that user activities are continually monitored and that control over personal data online is taken over by large companies and/or a third party, most respondents express only moderate concern for their data online (82%), which further confirms the fact that only a small percentage of students (18%) almost always read the terms of use on a website, application, or internet service.
Improved reweighting protocols for variationally enhanced sampling simulations with multiple walkers

We demonstrate that novel time-integration-based reweighting methods along with multiple walkers offer more rapid convergence of reweighted observables in variationally enhanced sampling (VES) simulations.
KIU (Kiswahili–Italiano-UniOr): The UniOr online Dictionary for Italian L1 Swahili Learners

KIU is an online bilingual Swahili–Italian dictionary with about 6000 entries aimed primarily at Italian L1 Swahili learners and which has been developed at the University of Naples 'L'Orientale'. The project was started in 2003 by M. Toscano and developed with the collaboration of lan­guage experts and young researchers until 2009 with the aim of offering online lexical resources from Swahili to Italian for learners of the language. After a long interruption, the work was resumed in 2019 by the authors of this article in cooperation with M. Toscano and a team of expert IT technicians. The current work consists of the development ex novo of the dictionary software, which had become obsolete, along with a redesign of some lexicographic features. In this report we will show how the upgraded version of the dictionary software has been implemented, with relevant learner-oriented features, by taking into consideration the standard lexicographic characteristics of Swahili–Italian bilingual dictionaries. This dictionary represents a valuable support for L2 learners and is the only on-line Swahili–Italian dictionary expressly built for university students and Italian users at large. Keywords: Swahili, Italian, online dictionary, learners, lexicography
Swarm UAV Defeat Modelling through Lifetime Distribution Analysis

The problem of defeating a swarm of unmanned aerial vehicles (UAVs) is of considerable importance to the modern warfighter.In recent studies high power radio frequency (HPRF) directed energy weapons (DEWs) have been shown to be suitable for this purpose.Hence there is a need to develop mathematical modelling frameworks to quantify HPRF DEW performance, especially when they are operating in a wideband or ultrawideband mode.Consequently, this paper introduces a novel mathematical model, based upon a new interpretation of UAV vulnerabilities to HPRF DEW, which permits performance assessment to be undertaken.The key to this is to view each UAV through its vulnerabilities to HPRF DEW energy at given frequencies and analyse its impact on the lifetime of each of the UAVs.This results in the definition of an appropriate stochastic process to count the number of UAVs still active in the swarm over a given time interval.Consequently, this permits the determination of minimum HPRF DEW power levels at given frequencies in order to guarantee likelihood of defeat of the swarm before it reaches the HPRF DEW source.Hence the results in this paper will provide a novel framework for determining the specifications of an HPRF DEW's required power distribution over target vulnerabilities to ensure a desired level of system performance.