Reviewerly/exHarmony huggingface dataset:
How Covid-19 changed the way we visit rivers? Applications of big data for sentiment analysis

Rivers lay the societal foundations to connect people with various activities around them. While the literature has overlooked the river, emotional values, and wellbeing linkage, how the rivers are perceived by visitors and how the use of rivers has changed during the pandemic can assist in evaluation and decision support in land use policies and plans. On the other hand, social media, assisting in articulating visitation patterns and moods proximate to the river, provides unprecedented insight to better macro- manage these areas. In this study, we employed Machine Learning to conduct a content analysis for rivers of Poland to expose User-Generated Content (UGC) through the visitors’ lens. We aim at understanding an essential cultural hegemony, the patterns of visits, and the moods of visitors. We further compared the results with the Covid-19 daily infections. The findings suggest an increased pressure on rivers during the pandemic, specifically at the time of the lowest sentiments. Our results may help in articulating patterns and moods proximate to the river that provide unprecedented practical insight and illuminate the path for further research proposals.
Fuzzy Comprehensive Evaluation of Students’ Classroom Experience in Online Teaching

Under the COVID-19 outbreak, the traditional teaching mode in universities is limited, and online teaching is in full swing. However, various factors that affect students’ online classroom experience in teaching have characteristics of fuzziness and uncertainty. Therefore, using the course of human resource management as an example, this paper employs the fuzzy comprehensive evaluation (FCE) method to conduct a comprehensive evaluation of the classroom experience of online teaching students. This paper aims to minimize the impact of human or subjective factors by developing a scientific, rational, and practical multilevel fuzzy comprehensive evaluation model for assessing students’ classroom experience in online teaching courses.
Distillery Plant Upflow Anaerobic Sludge Blanket Reactor comparision of Energy regime in Mesophilic and Thermophilic conditions

In this study, comparison of energy regime analysis of mesophilic and thermophilic condition upflow anaerobic sludge blanket (UASB) reactor treating distillery effluent is studied. Percentage COD reduction in thermophilic condition was more by 6.96% than mesophilic condition due to which biogas production increased by 0.1 m3/kg COD removed. In thermophilic temperature condition, methenogenesis was more than acidification than in mesophilic temperature condition; effluent VFA concentration in mesophilic condition was more in thermophilic condition. Stability of thermophilic reactor was more than mesophilic reactor from VFA/ Alkalinity ratio. Waste water treatment plant consumes 2.10 kWh/m3 total energy which comprises of electrical, mechanical and manual energy in mesophilic temperature condition. Input given in the form of electrical energy was 2.07 kWh/m3 and energy recovered in the form of biogas was 185 kWh/m3. Total energy input in thermophilic temperature condition was 1.82 kWh/ m3 and biogas recovery was 211 kWh/m3 in the form of energy. Net saving in energy input was 0.28 kWh/m3 (12.98%) and biogas production increased by 25.9 kWh/m3 (14%) in the form of energy in thermophic temperature condition. Treatment of distillery wastewater in thermophilic temperature condition can be a good option as compared to mesophilic temperature conditions considering the aspect of energy and it seems feasible due to the availability of elevated temperature of the feed for UASB.
ENABLING FLEXIBLE AND ADAPTABLE NAVIGATION OF GROUND ROBOTS IN DYNAMIC ENVIRONMENTS WITH LIVE LEARNING

Federated learning is utilized for automated ground robot navigation, enabling decentralized training and continuous model adaptation. Strategies include hardware selection, ML model design, and hyperparameter fine-tuning. Real-world application involves optimizing communication protocols and evaluating performance with diverse network conditions. Federated learning shows promise for machine learning-based life learning systems in ground robot navigation. Research objective: to explore the use of federated learning in automated ground robot navigation and optimize the system for improved performance in dynamic environments. Materials and methods. The research utilizes federated learning to train machine learning models for ground robot navigation. Hardware selection, ML model design, and hyperparameter fine-tuning are employed. Communication protocols are optimized, and performance is evaluated using multiple gaming machine algorithms. Results. The results show that decreasing the learning rate and increasing hidden units improve model accuracy, while batch size has no significant impact. Communication protocols are evaluated, with Protocol A providing high efficiency but low security, Protocol B offering a balance, and Protocol C prioritizing security. Conclusion. The proposed approach using federated learning enables ground robots to navigate dynamic environments effectively. Optimizing the system involves selecting efficient communication protocols and fine-tuning hyperparameters. Future work includes integrating additional sensors, advanced ML models, and optimizing communication protocols for improved performance and integration with the control system. Overall, this approach enhances ground robot mobility in dynamic environments.
AdaDSR: Adaptive Configuration Optimization for Neural Enhanced Video Analytics Streaming

Neural-based super-resolution (SR) has achieved great success in enhancing image or video quality, creating new opportunities for building bandwidth-efficient and high-accuracy video analytics (VA) systems. Intuitively, with the help of SR techniques, cameras only need to send downsampled low-quality frames to the server in a canonical edge-assisted video analytics framework. The server-side SR model then upscales the quality of received frames for the subsequent video analytics tasks, incurring thus substantially reduced bandwidth consumption. Nonetheless, as revealed by our measurement results on real-world video clips, higher delivery quality does not necessarily lead to higher analysis accuracy. This motivates us to study the content-adaptive downsampling and upscaling ratio selection problem for video analytics streaming. We propose a SR-based video analytics framework, named AdaDSR that can dynamically select the optimal downsampling and upscaling ratios so that the system utility can be maximized. AdaSDR is configured to balance the tradeoffs among accuracy, network cost, and computational cost. It further leverages the temporal consistency of videos to skip trivial decisions so that the camera’s processing overhead can be reduced. Experiments on real-world video datasets demonstrate that AdaDSR can improve the average utility by 7.2%-18.4% when compared with state-of-the-art approaches under diverse video scenes.
From Multi-Source Virtual to Real: Effective Virtual Data Search for Vehicle Re-Identification

Without tedious and time-consuming labeling processes, virtual datasets have recently shown their superiority for vehicle re-identification (re-ID). Existing virtual to real vehicle re-ID methods employ only a single virtual dataset for model training, while datasets from different generative sources are not jointly exploited. Multiple source virtual datasets contain more data diversity that can boost model performance. We thus propose a multi-source virtual to real vehicle re-ID pipeline, where multiple source virtual datasets are used during training. However, the multi-source virtual dataset suffers from more data redundancy than the single virtual dataset, which can affect the training efficiency. Intuitively, it can be mitigated by virtual data search. Unlike a single virtual dataset, a performance gap exists between multiple source virtual datasets, indicating their different contributions to model learning. Accordingly, we propose to split the multi-source virtual dataset into the main training set and the auxiliary training set, and then design the sampling strategy separately. For the main training set, the Consistent Attribute Distribution-FEature distance Trade-off (CAD-FET) strategy is designed to search for representative data. For the auxiliary training set, a cluster-based sampling strategy is further proposed to search for the most diverse subset. Besides, a simple yet effective two-stage training strategy is proposed to utilize these subsets reasonably. Extensive virtual-to-real vehicle re-ID experiments show that our data sampling method can reduce the volume of the multi-source virtual dataset by around 77%/96% and boost the model performance when tested on the VeRi776/VehicleID.
An Ising-based Model for Qubit Mapping

A crucial step in compiling a quantum algorithm involves addressing a layout problem to meet the device’s layout constraints. The Qubit Mapping and Routing (QMR) problem aims to minimize the number of SWAP gates added to the circuit to fulfill NISQ hardware’s connectivity constraints. Although this problem is NP-hard, finding solutions quickly is vital as it is part of the compilation process.
Summary and Future Works

This chapter gives a brief summary of the research works from Chaps. 2 , 3 , 4 and also discusses the potential future directions for applying deep learning in the field of power systems, including the most up-to-date deep learning techniques such as physics-informed deep learning, transfer learning, and meta-learning.
MENGEMBANGKAN KEMAMPUAN KETERAMPILAN MINAT MEMBACA PA-DA PESRTA DIDIK DI SEKOLAH DASAR MELALUI GAMBAR ANIMASI SE-BAGAI MEDIA PEMBELAJARAN

Animited cartoon film media is one of the media that can be used in the learning process to improve reading skills in elementary schools. The learning media can increase students’ anthusiasm for learning so that student can stimulus material quickly. Through animated videos it is hoped that it can help studeents in the learning process. Based on the data found the animated media is very effective in learning, especially in reading skills in elementary schools, so that students do not experience difficulties in the learning process.
Deep Metric Learning for Abnormal Rotation Detection of Satellites from Irregularly Sampled Light Curve

In recent years, satellites have become an indispensable infrastructure in our lives. The number of satellites is increasing yearly and becoming increasingly active. To use satellites safely, it is crucial to manage them and detect the anomaly of satellites as much as possible. However, it currently takes skilled operators to detect an anomaly, and it is difficult for even skilled operators to detect the anomaly early without the telemetry data in cases such as an abnormal rotation. To address these challenges, we tested the feasibility of using deep metric learning for early anomaly detection from the irregularly sampled light curve. One of the characteristics of a light curve is unequally spaced because the optical sensor on the ground can only observe the subject at night and not when the weather is terrible. Given an irregularly sampled light curve, our model employs a long short-term memory (LSTM) unit of encoding the temporal dynamics and learns the embedding on the feature space using triplet loss. Then, an anomaly score is calculated based on pairwise distances between segments from the learned embedding in the feature space. With actual data from the satellite being operated, we showed the effectiveness of our model and the feasibility of early anomaly detection. Also, by exploring learned embedding in the feature space, we show that our model could capture the continuous state of the satellite.
Teachers' Strategies in Teaching Speaking in Post-Pandemic

To develop students' speaking skills in English class, teachers require a variety of strategies based on needs, purpose, and situation. Therefore, this study aims to examine the types of teaching strategies are applied by teacher in teaching speaking after the Covid-19 pandemic at SMPN 264 Jakarta as well they way teachers implement them. Data was collected by applying observation and interview with three English teachers. The method implemented in this research is qualitative with a case study approach, data analysis was conducted in three stages: data reduction, data display, and conclusion drawing. The result of this study reveals that there are five strategies implemented by teachers in teaching speaking skills after the Covid-19 pandemic, they are: discussion, role-play, picture describing, storytelling, and drill. It is also shown that the teachers implement those strategy in various ways and learning topics. The results of this study are expected to be the basis for subsequent research on teacher teaching strategies in teaching speaking
The Rise of Digital Governmentality in the Era of Covid-19

A decisive effect of the worldwide spread of the corona virus is the transformation of digital lifestyle media into state-used recording, storage and distribution media. With the pandemic spread of the virus, the tectonics of digital power may have shifted the way forward. The global threat posed by the corona virus is transforming mobile media and their software applications into state-organized surveillance technologies. Mobility tracking is regarded by health authorities and government officials as a reliable data basis for enforcing political decisions as legitimate. Seen in this way, digital media take over the empirical basis of political action. The disciplinary techniques of state surveillance and punishment are migrating into all areas of digital communication and affect mobile media (geo-tracking), stop corona apps (monitoring), social media (blaming) and selfies (self-evidence). In a global comparison, this paper will compare different national strategies and also include non-western countries. In this context, this investigation is investigating the biosurveillance in China, Singapore and Hong Kong (SenseTime, TikTok etc.), the secret service control in Israel and the use of robotics in Tunisia.
Information Epistemology in Digital Learning

The research explores the transformative epistemological potentials of digital education fostered with information systems. It takes the current developments of Massive Open Online Courses (MOOCs) as case baseline, for a cross-disciplinary discussion in the phenomenological trends. With existing threats and risks in the digital realm, the chapter focuses on the education-centered vantage point in the discussions. The methodology proposed focuses on the cognitive advantages for learning efficacy with digital facilitation. Elements of institutional management have been considered in intellectual properties and scientific assets.&amp;nbsp;Digitization as intermediate artefacts in education serves both for in-person and digital classroom settings. Case studies are reported based on an astronomy MOOC, and the case effects have demonstrated the educational outreach efficacy of digital facilitation. The chapter concludes that infrastructural upgrades in the education sector’s information system is healthy investment. Education equality through digitization is limited for underdeveloped low-income population, and governmental and humanitarian supports are needed. Scientific paradigm shift in the information and telecommunication age calls for the overdue paradigm shift in education, and tautological paradigm shifts are needed with the changing media. Education innovation involves deep social and governmental topics, and digital education’s potentials in fostering critical thinking skills of the learners largely determine the future directions.Keywords: education equality; information infrastructure; media psychology; security; visual ontology.JEL Classification: E02; F01; H44; H83; I25; I28.Cite this chapter:Pachankis, Y. I. (2023). Information Epistemology in Digital Learning. In L., Nicola-Gavrilă (Ed), Digital Future in Education: Paradoxes, Hopes and Realities, 217pp., ISBN: 978-606-95516-1-5. In Book Series Socio-Economics, Research, Innovation and Technologies (SERITHA), ISSN: 3008-4237, p. 32-41. https://doi.org/10.57017/SERITHA.2023.DFE.ch2Chapter’s history:Received 14th of February, 2023; Revised 7th of April, 2023; Accepted for publication 20th of May, 2023; Published 30th of September, 2023.&amp;nbsp;
Education and Technology in a Data Driven Society. Turning Data into Education Intelligence

In a data-driven society, education and technology have become closely intertwined, transforming the way we learn, teach, and acquire knowledge. The availability of vast amounts of data and the advancements in technology have brought about significant changes in the education sector, enabling personalized learning experiences, improved access to information, and more effective educational outcomes. By embracing a data-driven approach, education systems can make informed decisions, tailor instruction to student needs, identify areas for improvement, and optimize resources, ultimately leading to improved student outcomes and enhanced educational experiences.In this chapter, we'll explore how data-driven technologies are unlocking the power of data for transforming education in an intelligent resource. Connecting data and leveraging technological tools we aim to foster collaboration, and contribute to the continuous improvement in teaching and learning outcomes.Keywords: data-driven; technology; education intelligence.JEL Classification: I20; O33.Cite this chapter:Nicola-Gavrilă, L. (2023). Education and Technology in a Data Driven Society. Turning Data into Education Intelligence. In L., Nicola-Gavrilă (Ed), Digital Future in Education: Paradoxes, Hopes and Realities, 217pp., ISBN 978-606-95516-1-5. In Book Series Socio-Economics, Research, Innovation and Technologies (SERITHA), ISSN: 3008-4237, p. 12-31. https://doi.org/10.57017/SERITHA.2023.DFE.ch1&amp;nbsp;Chapter’s history:&amp;nbsp;Received 3rd of July, 2023; Revised 7th of August, 2023; Accepted for publication 19th of August, 2023; Published 30th of September, 2023.&amp;nbsp;
Student Performance in E-learning Systems: An Empirical Study

This research paper focuses on using a convolutional neural network to assess student performance and addresses the impact of the COVID-19 pandemic on education. It introduces a two-step system that combines robust Bayesian model averaging with a frequentist approach for estimating parameters in a multinomial logistic regression model. The authors provide an empirical example illustrating the application of this system in analysing student performance. They also explore strategies to improve e-learning tools by addressing technological factors. The paper contributes to educational evaluation and policy analysis by incorporating deep learning systems and addressing the challenges posed by the pandemic.Keywords: machine learning; student performance; Bayesian inference; e-learning platforms; logistic regression; variable selection procedure.JEL Classification: I25.Cite this chapter:Pacifico, A and Giraldi, L., and Cedrila, E.&amp;nbsp; (2023). Student Performance in E-learning Systems: An Empirical Study. In L., Nicola-Gavrilă (Ed), Digital Future in Education: Paradoxes, Hopes and Realities, 213pp., ISBN: 978-606-95516-1-5. In Book Series Socio-Economics, Research, Innovation and Technologies (SERITHA), ISSN: 3008-4237, p.162-187. https://doi.org/10.57017/SERITHA.2023.DFE.ch8Chapter’s history:&amp;nbsp;Received 11th of May, 2023; Revised 17th of June, 2023; Accepted for publication 20th of July, 2023; Published 30th of September, 2023.&amp;nbsp;
Real-Time Object Detection Overview: Advancements, Challenges, and Applications

Real-time object detection is a crucial aspect of computer vision with applications spanning autonomous vehicles, surveillance, robotics, and augmented reality. This study examines real-time object detection techniques, highlighting their significance in artificial intelligence. The primary goal is swift and accurate object identification in images or video streams. Traditional methods like sliding windows and region-based approaches had limitations in computational efficiency. Deep learning, particularly Convolutional Neural Networks (CNNs), revolutionized object detection. Models like SSD, YOLO, and Faster R-CNN excel in accuracy and speed. They employ anchor boxes, feature pyramid networks, and non-maximum suppression to balance precision and processing speed. Hardware accelerators like GPUs, TPUs, and FPGAs facilitate real-time inference.&#x0D; Challenges in real-time object detection include occlusion, scale variations, and cluttered environments. Researchers must navigate the trade-offs between accuracy and speed. Real-time object detection is pivotal in computer vision, enabling intelligent systems across diverse applications. The continuous evolution of deep learning algorithms and hardware capabilities pushes the boundaries of this field, making it a dynamic research domain in artificial intelligence.
The Effect Of Reading Aloud Strategy To The Students’ Pronunciation Abiity For Grade Seven Of SMP Negeri 3 Pematangsiantar

The purpose of this research is to find out the effectiveness of students’ pronunciation abiity that was taught by using reading-aloud strategy. The research method that used in this research was a quantitative method, and the research design was quasi-experimental. There are two groups that involve in this research, the experimental class and the control class where each class has given a pre-test and post-test. The sampling technique used is purposivey sampling technique, the sample used in this research consisted of two classes, namely VII-1 as the experimental class were consist of 32 students and VII-2 as the control class were consist of 32 students at SMP Negeri 3 Pematangsiantar. Thus, the total of the sample used by researcher was 64 students. The sample was choose as experimental class has a reading – aloud strategy as a treatment in their class. Thus, control class was taught by applying the conventional method. In the data analysis, the data was obtained through students’ prinunciation in reading a text that contains 25 (twenty five) regular verb in past tense. It was taken after the researcher taught the reading skills to the experimental group by using the Reading Aloud Strategy and the conventional teaching to the control group. The students mean score in pretest at experimental class was 62.96 and the student mean score in the pos-test was 81.13. whie, the mean students in pre-test at control class was 62 and the post -test was 77.7. the researcher found after calculating the t-test formula, t-test is higher than t-table (t-test 2.2 &gt; t-table 1.672). It can be concluded that Ha was accepted and H0 was rejected. For the conclusion Reading-aloud strategy had a significant effect on students’ pronunciation for grade seven students at SMP Negeri 3 Pematang Siantar. Therefore.reading-aloud strategycan be used as teaching strategi on student pronunciation on grade seven students.
Tiling through Typography

In this paper, we provide a concise introduction to Sebastien Truchet's 17th-century combinatorial approach and Dominique Douat's notation, with the aim of modernising it for computer-based applications and contemporary interpretation.