Reviewerly/exHarmony huggingface dataset:
Research on the Related Problems of Network Teaching

The development of science and technology has a significant and profound impact on education, which is not only reflected in the continuous enrichment of teaching content, but also changes the form of education itself. With the development of modern science and technology, "network classroom" has become an important form of education. For educators, new educational ideas, new educational methods and new educational means emerge frequently. In the face of new changes, in order to effectively improve the efficiency of teaching, to better and more widely serve learners, how to effectively use the advantages of the network, how to share high-quality network teaching resources, is the problem that we must think and discuss.
“Best dentist ever” — evaluation strategies in the headlines of Danish and German positive online consumer reviews

Over the last few decades, computer-mediated communication (CMC) has become a significant element of our reality and, subsequently, an item of interest for researchers within many disciplines. CMC’s rapid development has resulted in the emergence of new, multimodal means of expression, text genres, and communication styles. Among the new genres are online consumer reviews, which impact the behavior of online consumers to a large extent, but are also valuable for linguistic researchers due to their structure, content, and functions. The headlines of online consumer reviews seem to be of particular importance, although they have not received much attention in linguistic research so far. This paper offers a contrastive analysis of 200 headlines of positive online customer reviews for Danish and German dental care providers. Because of the way consumer reviews are displayed and consumed online, their headlines can be treated as separate texts and are analyzed as such throughout the paper. The investigation’s overall goal is twofold: (i) to identify the basic communicative functions of the headlines, and (ii) to describe the linguistic means employed to express those functions. The analysis seeks to find potential similarities and differences between the headlines of positive reviews written in Danish and German. The findings show that there is little variation between the two subsets of the analyzed corpus as far as communicative functions and employed linguistic means are concerned. This may indicate that the identified features could be treated as characteristic of the genre rather than language-specific.
Investigation of Horizontal Solar Radiation Data Source in the World

Photovoltaic (PV) power is the third global power capacity used after hydro and wind. Even though solar energy is abundant and within reach, this energy assessment confronts a big challenge in terms of obtaining measured data due to the limited and unevenly distributed ground-based meteorological stations. To overcome this issue, many studies resort to other sources of radiometric data. Some of the sources are based on satellites. As a result, there is a disagreement of readings compared with ground-based meteorological stations. Therefore, this paper attempts to mitigate the differences by proposing the nearest source compared to measured data. Four different sources (NASA POWER, PVGIS 5, Meteonorm software, and hybrid-based average data) are investigated. The measured data of stations is used as a reference in this study which has been collected from the World Radiation Data Centre (WRDC). The comparative study is made based on monthly average horizontal solar radiation using statistical methods for five continents. Twenty-eight distributed locations are examined. Based on the Global Performance Indicator (GPI), Meteonorm is a suitable data source for Asia and South American continents. In Africa, Europe, and North American continents, the solar radiation source depends on the target location.
P50/P90 Analysis of a Solar Photovoltaic Plant in METU NCC Using the Empirical Method

Large investments in renewable energy systems have some financial risks, and energy exceedance probabilities are required to find the expected range of energy yield values. In this study, P50/P90 analysis was done for a 1 MWp solar photovoltaic (PV) power plant in METU NCC using the System Advisor Model (SAM) software. Three different transposition models were used, which are the isotropic sky-diffuse, HDKR, and Perez models. Photovoltaic Geographical Information System-Surface Solar Radiation Dataset Heliosat (PVGISSARAH) database was used as the long-term satellite-based data, which includes the time period 2005-2016 with hourly time resolution, and three different typical meteorological year (TMY) datasets were used to compare the results with P50 values. Actual energy yield data of the years 2016 and 2017 were also compared. Interannual variability of global horizontal irradiation (GHI) was found as about 2.21%. According to the comparison of TMY and P50 values, it was found that average TMY underpredicted the annual GHI by 5.15%, and the energy yield values by 6.77%, 6.86%, and 6.83% for the isotropic, HDKR, and Perez models, respectively. Normal distribution assumption was also compared with the empirical method, and P50 value was underpredicted by 0.43% and 0.93% on average for GHI and energy yield, respectively. On the other hand, Normal CDF overpredicted P90 values than the empirical CDF by 2.61% and 3.93% for GHI and the energy yield, respectively. The overall uncertainty of energy yield was found as about 7.08%. The levelized cost of electricity (LCOE) also varied by 3.50% on average, and the non-exceedance probability values were obtained with respect to the energy yield.
Driven Wheelchair Design and Implementation Using EOG Signals

In the last decades were developed a multitude of robotic systems with the main purpose of assisting in the mobility of the people with disabilities. The main objective of this paper is the implementation of a robust mobility assistance system based on the human eyes movement interpretation.The paper is focused on the development of a software and hardware system model for the human eye high precision movement detection, by analyzing the electrooculography (EOG) signals, for assisting people with disabilities, with special features for eliminating fixation problems, when the user is exhausted or when is using the system for long time intervals.
佛教、社會學及數位分析：「當代佛教資訊分析平台」的架設框架 (Buddhism, Sociology, and Digital Analysis: On the Design Framework for the 'Platform for the Analytics of Information on Contemporary Buddhism')

Chinese Abstract: 聖嚴法師對佛教的現代化貢獻甚多，注意佛學與社會學等學科的相互交流， 同時支持將科技應用於教學及學術研究。為了繼承及推揚聖嚴法師的弘法精神， 促進佛法、社會學研究和資訊科技的融合，本論文提出討論建構「當代佛教資訊 分析平台」 （The Platform for the Analytics of Information on Contemporary Buddhism） 的框架。該平台有三大特點：（一）讓用戶查找中華地區乃至世界不同地區的最新佛 教新聞和網絡資訊；（二）採用資料視像化的技術，透過三維地球模型，以互動 形式展示各地與佛教相關的社會信息；（三）具備人工智能數據分析功能，協助 探討世界各地佛教組織的聯繫，研究當代中外佛教的社會網絡及特徵。此外，架設上述分析平台有兩大意義：一方面提供新式教育工具，學校老師 講授佛教與現代社會發展的課題時，可以利用平台上的虛擬地球儀系統，讓學生 們一覽環球佛教社會新聞，加深認知，開闊眼界。另一方面提供一個全新網絡資 料庫，方便探討現當代佛教組織或社會狀況的研究人士初步查找資料，促進佛教 社會學的研討。English Abstract: Inspired by Master Sheng Yen’s philosophy of teaching and learning, this research aims to develop a digital database called "The Platform for the Analytics of Information on Contemporary Buddhism".The Platform has three distinctive features: Firstly, it allows users to obtain the latest and authentic information and news about international Buddhist societies. Secondly, with innovative, state-of-the-art technology for data visualisation and 3D computer graphics, the system can automatically interpret and visualise the collected data in an interactive way. Thirdly, taking advantage of artificial intelligence in data analysis, the Platform helps researchers carry out preliminary studies on social networks of Buddhist communities.The web-based application will not only provide teachers with an effective educational tool for the in-class exploration of social and religious issues in contemporary Buddhism, but also help expand cultural horizons for both undergraduate and postgraduate students. More importantly, it will serve as an indispensable online knowledge hub for investigation into the sociology of global Buddhism.
Measuring the Effectiveness of Static Maps to Communicate Changes Over Time

Both in digital and print media, it is common to use static maps to show the evolution of values in various regions over time. The ability to communicate local or global trends, while reducing the cognitive load on readers, is of vital importance for an audience that is not always well versed in map interpretation. This study aims to measure the efficiency of four static maps (choropleth, tile grid map and their banded versions) to test their usefulness in presenting changes over time from a user experience perspective. We first evaluate the effectiveness of these map types by quantitative performance analysis (time and success rates). In a second phase, we gather qualitative data to detect which type of map favors decision-making. On a quantitative level, our results show that certain types of maps work better to show global trends, while other types are more useful when analyzing regional trends or detecting the regions that fit a specific pattern. On a qualitative level, those representations which are already familiar to the user are often better valued despite having lower measured success rates.
Using Virtual Reality to Measure Working Memory Performance and Evaluate the Effect of the Degree of Immersion on Working Memory Performance

Today Virtual Reality is widely researched and applied in non-conventional, informal educational settings, psychology, and memory-based applications. The popular definition of working memory is a system that works to provide temporary access to a select set of representations allowing for manipulation [1]. However, there is a consensus that this system has a limited capacity. This limitation of capacity explains the systematic drop in performance with an increase in task complexity. There is increasing attention to developing applications that measure working memory. However, these applications are relatively low validity. Working memory performance (WMP) is typically measured by computerizing a WMP task and calculating the participant’s score, but these applications neglect aspects of the computer-user interface that may affect user’s WMP (e.g., immersion, presence, satisfaction, and usability). To build a valid measuring system, all these conditions need to be considered, and their impact on participant WMP must be thoroughly understood. We plan to investigate the impact of the degree of immersion on working memory performance. First, we will evaluate the effect of the immersion on the user’s feeling of being in the environment (i.e., presence), usability, and satisfaction, and then we will investigate the correlation between WMP and presence, usability, and satisfaction. This work will measure the WMP in levels of immersion that are provided through desktop VR (DVR), immersive VR (IVR), and immersive embodied VR (IEVR). We predict that these factors will impact participant WMP, with WMP increasing with higher levels of immersion.
Classification of Letter Images from Scanned Invoices Using Cnn

Data analytics helps companies to analyse customer trends, make better business decisions and optimize their performances. Scanned document analysis is an important step in data analytics. Automatically extracting information from a scanned receipt have potential application in industries. Both printed and handwritten letters are present in a receipt. Often these receipt documents are of low resolution due to paper damages and poor scanning quality. So correctly recognizing each letter is a challenge. This work focuses on building an improved Convolutional Neural Network (CNN) model with regularization technique for classifying all English characters (both uppercase and lowercase) and numbers from 0 to 9. The training data contains about 60000 images of letters (English alphabets and numbers) .This training data is made from letter images from windows true type (.ttf) files and from different scanned receipts. We developed different CNN models for this 62 class classification problem, with different regularization and dropout techniques. Hyperparameters of Convolutional Neural Network are adjusted to obtain the optimum accuracy. Considered different optimization methods to obtain better accuracy.Performance of each CNN model is analysed in terms of accuracy, precision value, recall value, F1 score and Confusion matrix to find out the best model.
Preparation and Curation of Multiyear, Multilocation, Multitrait Datasets

Genome-wide association studies (GWAS) are a powerful approach to dissect genotype-phenotype associations and identify causative regions. However, this power is highly influenced by the accuracy of the phenotypic data. To obtain accurate phenotypic values, the phenotyping should be achieved through multienvironment trials (METs). In order to avoid any technical errors, the required time needs to be spent on exploring, understanding, curating and adjusting the phenotypic data in each trial before combining them using an appropriate linear mixed model (LMM). The LMM is chosen to minimize as much as possible any effect that can lead to misestimation of the phenotypic values. The purpose of this chapter is to explain a series of important steps to explore and analyze data from METs used to characterize an association panel. Two datasets are used to illustrate two different scenarios.
Non-Gaussian Entanglement of Magnetically Coupled Modes: A Dynamical Analysis

The quantum entanglement of two connected harmonic oscillators via angular momentum yielding a magnetic coupling $\omega_{c}$ is discussed in this study. The corresponding Hamiltonian is diagonalized by using three canonical transformations, and then the stationary wave function is obtained. We explicitly determine the modes $\lambda_{k}$ based on the Schmidt decomposition, with with $k\in\left\lbrace 0,1,\cdots,n+m\right\rbrace$, $n$ and $m$ being two quantum numbers associated with the two oscillators. We summarize our findings by looking at the effects of anisotropy $R=\omega_{1}^{2}/\omega_{2}^{2} $, $\omega_{c}$, asymmetry $ |n-m| $, and dynamics on entanglement. (i)- With increasing $ (n,m) $, the entanglement grows exceedingly large. (ii)- The sensitivity to $\omega_c$ is determined by $ (n,m) $ and $R$. The physical parameters and quantum numbers play a big role in the periodic resuscitation of entanglement.
Meet Masks: A Novel Multi-Classifier's Verification Approach

In this study, a new ensemble approach for classifiers is introduced. A verification method for better error elimination is developed through the integration of multiple classifiers.A multi-agent system comprised of multiple classifiers is designed to verify the satisfaction of the safety property. In order to examine the reasoning concerning the aggregation of the distributed knowledge, a logical model has been proposed. To verify predefined properties, a Multi-Agent Systems' Knowledge-Sharing algorithm (MASKS) has been formulated and developed. As a rigorous evaluation, we applied this model to the Fashion-MNIST, MNIST, and Fruit-360 datasets, where it reduced the error rate to approximately one-tenth of the individual classifiers.
A Discussion of Optimization about Stereo Image Depth Estimation Based on Multi-baseline Trinocular Camera Model

The huge computational complexity and occlusion problems make stereo matching a major challenge. In this work, we use multi-baseline trinocular camera model to accelerate the stereo matching algorithms and improve the accuracy of disparity estimation. We propose a special scheme named the trinocular flexible disparity searching range (FDSR) to accelerate the stereo matching algorithms. In this scheme, we optimize stereo matching by reducing the disparity searching range. Based on FDSR, we proposed the FDSR-MCCNN for trinocular stereo matching. According to the evaluation results, the FDSR-MCCNN could not only reduce the computational complexity but also improve the accuracy. Moreover, the optimization schemes we designed can be extended to other stereo matching algorithms that possess pixel-wise matching cost calculations and aggregation steps. We proved that the proposed optimization methods for trinocular stereo matching are effective and that trinocular stereo matching is useful for either improving accuracy or reducing computational complexity.
Accordion AutoEncoders (A<sup>2</sup>E) for Generative Classification with Low Complexity Network

Deep learning technologies are popularly used in many areas including recognition, identification, anomaly detection, classification, etc. Large network complexities requiring more computational resources and response time is a popular challenge. Generative models used in modern applications have lower dimensional latent spaces which can be manipulated to change features of the output. Recently, autoencoders have been used in dimension reduction, data reconstruction, etc. One of the disadvantages is the network size in which the architecture of the autoencoder has a double workload due to the encoding and decoding processes. In this paper, we explore a scheme to reduce the network complexity of the autoencoder. The motivation behind exploring different autoencoder architectures lies in their practical uses for the applications such as anomaly detection, classification, and their usages in generative models. Additionally, a deeply understanding of the mechanism to improve dimensionality reduction may lead to a better understanding of how the human brain finds meanings in data. We come up with an Accordion Autoencoder (A<sup>2</sup>E) architecture, as an effective performing solution to anomaly detection and classification problems, which is not necessarily a solution to dimensionality reduction, but rather a performance improvement for the problems using several sets of lower-dimensional space to generate more meaningful features of the data. Based on our experiments, the proposed solution provides the network size reduction (85.1%~94.5%) with maintaining the accuracy (4.9%~13.6% accuracy drop) in fraud detection and MNIST classification.
Book Review

This research explores the relationship between customers' emotions and sentiments generated by the interaction with robots in hotels and the potential effect on the hotel's rating. To this end, text mining techniques are applied to TripAdvisor reviews by using Python 3.9.4. The results indicate a relationship between the emotions and sentiments detected in the reviews, the robots' functional typologies and traveller categories. The originality of this research is mainly found in the quantification of the relationship between robot functionality, traveller type and rating given to the hotel considering the emotions and sentiments that emerge from the functional dimension of robots implemented in hotels.
Prototyping and Prototype use in Participatory Design with older adults

This systematic literature review examines the role of prototyping and prototype use in Participatory Design (PD) with older adults. Through text analysis of 68 studies, ten descriptive categories of forms of prototyping and prototype use were constructed to characterize the application in PD with older adults: Refine, Evaluate, Motivate, Generate, Explore, Teach, Decide, Communicate, Understand, and Contribute. Further analysis examines the prevalence of each category, as well as who participates in these activities. With principal component analysis, we further examine the interrelationships of these forms of prototyping and prototype use to explore traditions within PD studies with older adults. We conclude by urging PD practitioners to explicitly describe who they include in prototyping and prototype activities, how they are involved, and why. Finally, we invite further discourse on the role of the use of prototypes and prototyping in PD, particularly in PD with older adults, as well as further examination of the concrete context of involvement.
A probabilistic Proof-of-Stake protocol with fast confirmation

Proof-of-Stake (PoS) protocol is one of the most promising blockchain consensus protocols in the permissionless setting. However, there is a general problem of slow confirmation in PoS protocol. To address the issue, a small-sized committee in PoS protocol is used to reduce the costs in terms of communication and computation, and accelerate confirmation. Following this idea, a committee-based PoS protocol called LaKSA (NDSS’2021) is proposed recently. In this paper, we present a balance attack on LaKSA, and show LaKSA cannot satisfy the liveness property even in the synchronous network. To ensure security and improve efficiency, we propose a self-healing probabilistic PoS protocol with a small-sized committee and a fast confirmation. Specifically, we propose a three-phase broadcast finality gadget and two compatible confirmation rules. The participants can invoke the three-phase broadcast periodically and commit a block according to different confirmation rules. By our approaches, on the one hand, we can prevent the PoS protocol from the balance attack and keep it committing new blocks over time. On the other hand, it empowers the protocol an ability of self-healing, by which nodes can commit a block as quickly as possible instead of waiting for a long time. Furthermore, we prove that our protocol can achieve the security properties of probabilistic safety and liveness. Finally, we conduct extensive experiments to evaluate the performance of the self-healing protocol. The experimental results show that, compared with LaKSA protocol, our protocol improves the commit rate by about 30% with a high throughput under a poor network condition.
Augmentation of Blockchain and 5G in Green Computing

Abstract: Green Computing (GC) is a step toward environmental sustainability. More precisely, it is entirely about utilizing power on the way to carry out processes in the maximum green manner. With increasing energy costs &amp; rising ecological apprehensions, green computing is getting additional consideration. The Blockchain (BC) has emerged as a favourable implementation for decentralized, Peer-to-Peer (P2P) transparent ledger systems. 5G is the latest development in an exceedingly wireless communication network. The flexibility of 5G with BC allows many applications to become fast, clear, significant &amp; harmless for transmission of data in this smart era. Both technologies can give green solutions to the environment. In this very research paper, convergence of BC and 5G is and integrating it with Green Computing Approaches is shown via proposed model. With use of Network Slicing approach of 5G and Sharding approach of BC, their significance in decreasing computational power and energy usage is shown. Also, major focus is on the “Green Use” approach of green computing. A hypothetical model is proposed by converging Blockchain and Fifth Generation with Green Computing. Objective is on the physical hardware services layer of green computing which will directly impact physical environment in order to give ecofriendly solutions to sustainable projects
Standalone Chatbot Application in Python

Abstract: A chatbot is an artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. This software is used to do duties such as replying swiftly to users, informing them, assisting with product purchases, and delivering better customer support. Business groups are increasingly using chatbots because they may minimize customer support costs and handle several consumers at once. However, in order to complete various jobs, chatbots must be as efficient as possible. In this project, we provide the architecture of a chatbot, which provides a human like and accurate response for any questions raised by users using Natural Language ToolKit(NLTK) and PyTorch with python language.