Reviewerly/exHarmony huggingface dataset:
The Analysis of The Lesson Plan Based 2013 Curriculum Designed by The English Teacher At MTS Al Hidayah Makassar

This research discusses Analysis of the lesson plan based 2013 curriculum designed by the English teacher at MTS. Al – Hidayah” This research will cover how the English teacher designs learning activities relate with the basic competence to basic competence, and how English teacher do assessment to the students based on 2013 curriculum objectives. This research applied a qualitative research design. The subjects of this research were English teachers in Mts Alhidayah Makassar. Sample of the researcher took two English teachers in Mts Alhidayah Makassar, since there are two English teachers in the school. In collecting the data, the researcher used two kinds of instruments. In collecting the data through interview. Research results show dealing with finding out the basic competence and learning activities, it was that found some of learning activities are related and non related with the basic competences in 2013 curriculum for Junior High School which designed by the English teachers. In learning assessment, the teacher applied various kinds of assessment for cognitive and psychomotor domain. Teachers’ way in assessing students’ achievement in terms of cognitive and psychomotoric based on 2013 curriculum is applying instrument assessment
A Weighted Markov Chain Application to Predict Consumer Price Index When Facing Pandemic Covid-19

Covid-19 is an infectious disease caused by acute respiratory syndrome coronavirus 2 (severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2). It makes the decrease of people's purchasing power. Meanwhile, the economic growth indicates the success of a country's economic development. Therefore, the Consumer Price Index occurs Inflation and Deflation, which is commonly referred to in the economy as the Consumer Price Index. This study aimed to apply a weighted Markov chain method to predict the consumer price index in the future. The satisfactory results obtained by researchers in predicting the consumer price index are in, the chance is 84.34% and the 12th month has a 78.54% chance.
Minimizing The Maximum Distance Traveled To Form Patterns With Systems of Mobile Robots

In the pattern formation problem, robots in a system must self-coordinate to form a given pattern, regardless of translation, rotation, uniform-scaling, and/or reflection. In other words, a valid final configuration of the system is a formation that is \textit{similar} to the desired pattern. While there has been no shortage of research in the pattern formation problem under a variety of assumptions, models, and contexts, we consider the additional constraint that the maximum distance traveled among all robots in the system is minimum. Existing work in pattern formation and closely related problems are typically application-specific or not concerned with optimality (but rather feasibility). We show the necessary conditions any optimal solution must satisfy and present a solution for systems of three robots. Our work also led to an interesting result that has applications beyond pattern formation. Namely, a metric for comparing two triangles where a distance of $0$ indicates the triangles are similar, and $1$ indicates they are \emph{fully dissimilar}.
Obstacles of using the science lab from the point of view of the science teachers of the high basic stag in Mafraq directorate: معيقات استخدام مختبر العلوم من وجهة نظر معلمي العلوم للمرحلة الأساسية العليا في قصبة المفرق

The study was designed to identify the obstacles of using the science lab from the point of view of the science teachers of the high basic stage in Al- Mafraq directorate. The study followed the descriptive method and the questionnaire was applied as a data collection tool. The study community was formed from all teachers of science for the high primary stage in the province of Mafraq during the first semester 2018/2019, The sample of the study was randomly selected by 40% of the original study population. The results showed that the obstacles of using the science lab came in a medium degree from the point of view of the sample of the study and there were no statistically significant differences at the significance level of 0.05 = α attributable to the type or qualification or years of experience. And the study recommended the provision of schools by laboratories that suit the numbers of students in the schools, the need to provide the laboratory with specialized teacher in each school laboratory, to provide the school laboratories with the appropriate tools and methods for the science curriculum, periodic maintenance of laboratories, training of teachers on scientific experiments and safety procedures and general maintenance in the laboratory
Investigation of wind properties at MUTSU Bay and TSUGARU Strait JAPAN

Abstract Present investigation was conducted to confirm the wind properties at Mutsu Bay and Tsugaru Strait by measurement. The wind distribution along the pathway of ferry ship was investigated. Wind speed and wind direction were measured on one of the four ships operated between Aomori Harbour and Hakodate Harbour during 2018. This paper describes the wind properties measured by ferry anemometer.
Analisis kesalahan dalam keterampilan menulis tegak bersambung pada peserta didik kelas ii sekolah dasar

This research is a descriptive qualitative study with a single case study approach. This research was conducted because there are many errors in handwriting skills. This study aims is to analyze errors in handwriting skills in 2nd grade students of SD Negeri Tunggulsari 1 No. 72 Surakarta academic year 2018/2019. The sample is determined by purposive sampling technique with certain considerations and objectives. The research data was collected through interviews, observation and documentation which were then analyzed based on indicators. Error analysis is carried out based on indicators of writing error and the aspect. The results of this study found that writing errors consist of letter formation errors, spacing errors between letters and words, slant consistency errors, size and proportion, legibility, and neatness of handwriting by 2nd grade students. The most mistake is found in the formation, size and propotion, and also the error of spacing between letters and words. While errors in legibility, neatness and consistency of writing slant are rarely found in the work of students.
Fake News: Legal Analysis of False and Misleading News and Cyber Propaganda

Today ‘Fake News’ has become a menace in the society causing misunderstanding, hatred and even violence. It is not easy to deal with false news and cyber propaganda as it may come in conflict with the right to freedom of speech and expression. Because of losing trust in mainstream media and the availability of choice of media to receive news, most of the readers have shifted to social media. It is very easy to create fake news and as consumers are not aware so often they fail to differentiate between fake news and news that is real. This paper discuss the role of Fake news in spreading violence; cyber propaganda; role of Facebook as a social media platform in spreading false news; and the legal position in different countries like France, UK, Egypt and India.
Convergence Analysis and System Design for Federated Learning over Wireless Networks

Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. However, as the training data in FL is not collected and stored centrally, FL training requires frequent model exchange, which is largely affected by the wireless communication network. Therein, limited bandwidth and random package loss restrict interactions in training. Meanwhile, the insufficient message synchronization among distributed clients could also affect FL convergence. In this paper, we analyze the convergence rate of FL training considering the joint impact of communication network and training settings. Further by considering the training costs in terms of time and power, the optimal scheduling problems for communication networks are formulated. The developed theoretical results can be used to assist the system parameter selections and explain the principle of how the wireless communication system could influence the distributed training process and network scheduling.
Performance Analysis of Machine Learning Algorithms Used for Web Based Phishing Detection

Phishing is a cybercrime technique in which the attacker creates a copy of genuine websites with the same color pattern, layout, font, and logo and with a domain name that matches with the real one. Then, broadcast this fake website through various online modes like emails and social media. The attacker creates lucrative offers or discounts to lure in people to click on the phishing link. Once the user clicks on this phishing link, they a re directed to the duplicate website that the attacker had created. The user believes that it is the real website and enters his/her login details and other confidential data. This data is stored on the attacker’s server thus giving him full access to the victim’s data. The phishing attack is mainly targeted to collect confidential data of the victim. This data includes Username, Passwords, Bank details, security Credit card numbers etc. Machine Learning algorithms are being used widely in detecting phishing websites. This paper shows performance analysis of three Machine learning algorithms used for URL phishing detection. These algorithms are Extreme Learning Machine, Support Vector Machine and Naïve Bayes algorithm. The paper analyses these algorithms on the parameters of Accuracy, Precision, Recall, F1 score and Confusion matrix. The dataset includes 11,000 entries and 30 features from UC Irvine dataset repository. The literature survey shows how only importance is given to only one parameter i.e., Accuracy parameter when analyzing performance of the URL phishing detection algorithms. This paper concludes on how Accuracy parameter does not show full picture on the overall performance of the URL phishing detection algorithms and also how Precision and Recall parameters are very important in understanding the working of these algorithms.
Incentive systems for research data sharing in funded projects

Sharing research data has shown to improve the findability of data and prevent duplication of effort.  However, common cultures and practices in many research disciplines do not support data sharing. A ‘collective action problem’ means that there are commonly misalignments in incentives to cooperate around data – a mismatch of individual, institutional and societal wants and needs. Many researchers have explored what the common constraints to data sharing are and what potential incentives might be. This report provides a literature review on these results to provide a framework of incentives that may be applied to organizations working on funded program. Within the framework, incentives are categorized by ‘value systems’ – the reasons why researchers or other organizations are working with data. By delving into value systems, the report shows how organizations with different values may respond differently to various incentives. A mixture of reward and penalty incentives offered in the context of strong funder and peer support and pressure is likely to be key in influencing behavior change for improved sharing. The report demonstrates two incentive systems based on this framework which may help to conceptualize implementation of incentives based on values. These systems specifically emphasize how the funder may support implementation of incentives for system change. Prototype flow and incentive choice diagrams are presented to support decision-making, helping guide directors of research systems and grantmakers to better structure investments to better incentivize data sharing.
Self-Supervised Multimodal Opinion Summarization

Recently, opinion summarization, which is the generation of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled review as a pseudo summary. However, non-text data such as image and metadata related to reviews have been considered less often. To use the abundant information contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate encoder for each modality, and the text decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a multimodal training pipeline. We first pretrain the text encoder--decoder based solely on text modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained text decoder as a pivot for the homogeneous representation of multimodal data. Finally, to fuse multimodal representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by conducting experiments on Yelp and Amazon datasets.
LibGuides: Library Policies : Circulation

Writing library policies and procedures is an important task, but is often at the bottom of a busy library board's to-do list. This guide is designed to provide some sample policies, along with resources to make the process a little faster and easier. Thi
Smart Glove for Blind

To assist visually impaired individuals, a study that helps those individuals to walk additional with confidence is planned. The study hypothesizes a sensible walking glove that alerts visually-impaired individuals over obstacles, pits therefore this device might facilitate them in walking with less accident. It outlines a far better guidance tool for the visually impaired. It consists of straightforward walking equipped with sensors to provide data concerning the environment. GPS technology is integrated with a microcontroller which can facilitate their favorite ones to stay an eye on them. during this system, supersonic sensing elements, GPS receiver, buzzer, vibrator, microcontroller, fall detector, NFC communication, and battery are used. This project will be enforced by victimization Arduino UNO. It will be interfaced with higher than mentioned sensors. The ultrasonic sensing element helps distinguish obstacles and guide the impaired in their manner. If an ultrasonic detector finds any inconvenience in their manner, then Buzzer/Vibrator is employed to send the acknowledgment to the person. All of the unexpected, if the person met with an associate accident or any excess factor happen then fall detector senses that and it'll send the acknowledgment to their guardian. additionally, to all or any of these options, NFC will be used as a backup choice. the device aims to supply a convenient and safe technique for the blind to beat their difficulties in lifestyle.
Intercomparisons of liquid water path based on SEVIRI images and gradient boosting regression trees with in-situ observations and satellite-derived products

&amp;lt;div&amp;gt;This contribution presents a technique for the machine-learning-based retrieval of cloud liquid&amp;amp;#160;water path. Cloud effects are among the major uncertainties in climate models for estimating&amp;amp;#160;and predicting the Earth&amp;amp;#8217;s energy budget. The study of cloud processes requires information&amp;amp;#160;on cloud physical properties, such as the liquid water path (LWP), which is commonly&amp;amp;#160;retrieved from satellite sensors using look-up table approaches. However, the accuracy of&amp;amp;#160;LWP varies temporally and spatially, also due to assumptions inherent in any physical&amp;amp;#160;retrieval. The aim of this study is to improve the accuracy of LWP and analyze quantitatively&amp;amp;#160;the accuracy and its errors. To this end, a statistical LWP retrieval was developed using&amp;amp;#160;spectral information from geostationary satellite channels (Meteosat Spinning-Enhanced&amp;amp;#160;Visible and Infrared Imager, SEVIRI), and satellite viewing geometry. The machine-learning&amp;amp;#160;method chosen is gradient-boosted regression trees (GBRTs), which is an ensemble of&amp;amp;#160;decision trees but more effective than traditional tree-based models. This study reports on&amp;amp;#160;first results, as well as a comparison between the GBRT-derived LWP estimates and those&amp;amp;#160;from the SEVIRI-based products of the Climate Monitoring Satellite Application Facility&amp;amp;#160;(CM-SAF, CLAAS-A2), as well as MODIS products. We use case studies for individual&amp;amp;#160;in-situ measurement sites in Europe under varying meteorological conditions to determine&amp;amp;#160;the factors influencing LWP retrieval quality.&amp;lt;/div&amp;gt;
An Accelerator-based Logistic Map Image Cryptosystems for Grayscale Images

The logistic map is a class of chaotic maps. It is still in use in image cryptography. The logistic map cryptosystem has two stages, namely permutation, and diffusion. These two stages being computationally intensive, the permutation relocates the pixels, whereas the diffusion rescales them. The research on refining the logistic map is progressing to make the encryption more secure. Now there is a need to improve its efficiency to enable such models to fit for high-speed applications. The new invention of accelerators offers efficiency. But the inherent data dependencies hinder the use of accelerators. This paper discusses the novelty of identifying independent data-parallel tasks in a logistic map, handing them over to the accelerators, and improving their efficiency. Among the two accelerator models proposed, the first one achieves peak efficiency using coalesced memory access. The other cryptosystem further improves performance at the cost of more execution resources. In this investigation, it is noteworthy that the parallelly accelerated logistic map achieved a significant speedup to the larger grayscale image used. The objective security estimates proved that the two stages of the proposed systems progressively ensure security.
Exploiting Transductive Property of Graph Convolutional Neural Networks with Less Labeling Effort

Recently, machine learning approaches on Graph data have become very popular. It was observed that significant results were obtained by including implicit or explicit logical connections between data samples that make up the data to the model. In this context, the developing GCN model has made significant experimental contributions with Convolution filters applied to graph data. This model follows Transductive and Semi-Supervised Learning approach. Due to its transductive property, all of the data samples, which is partially labeled, are given as input to the model. Labeling, which is a cost, is very important. Within the scope of this study, the following research question is tried to be answered: If at least how many samples are labeled, the optimum model success is achieved? In addition, some experimental contributions have been made on the accuracy of the model, whichever sampling approach is used with fixed labeling effort. According to the experiments, the success of the model can be increased by using the local centrality metric.
RESEACHING THE ASPECTS OF INTERACTION BETWEEN EDUCATORS AND STUDENTS IN THE DISTANCE LEARNING SYSTEM

The research studies the inquiries of students and educators in Institute of Journalism of Borys Grinchenko Kyiv University due to the change of an activity format during COVID-19 quarantine measures. 62 professors, 302 bachelors and masters have been inquired, as well as the participants’ activity of internal distance learning system and the level of technical possibilities of access to it, utilization rate of platforms and services have been under analysis to receive the data. 1/3 of the respondents consider distance learning to be a critical need of that period, and more than 2/3 admit that it as a perspective learning form. 40% of educators regularly use distant technologies in their work and believe that the main problem is absence of face-to-face communication with students. More than 90% of students have an easy access to distance learning; moreover, poor Internet connection was specified as a problem for its implementation. Up to 70% of respondents consider the use of different learning platforms to be productive, a significant amount of them are video conference services. The monitoring provides grounds for further study of psychological needs for face-to-face communication, since the data received from the respondents demonstrate different approaches to this issue.
IMPLEMENTATION OF PRACTICE-ORIENTED APPROACH IN THE TRAINING OF FUTURE BACHELORS IN AGRICULTURAL ENGINEERING

Modern trends in the labour market of Ukrainian agricultural sector place high demands on the professional expertise and training of future Agricultural Engineers. Agricultural sector of Ukraine is currently experiencing an acute shortage of human resources capable of adapting rapidly to new social and economic conditions, mobile and competitive in the world labour market. In this case, the development of professional competence is an important requirement risen for future Agricultural Engineering bachelors’ training. The article discusses the essence of the practice-oriented approach used for future Agricultural Engineering bachelors’ training and identifies effective practice-oriented technologies for their training future. The experiment was conducted at Hlukhiv Agrotechnical institute named after S. A. Kovpak, Sumy National Agrarian university. 90 undergraduate students, 15 university teachers participated in the study and 18 representatives of employers. The study was aimed at identifying effective training technologies targeted at developing Agricultural Engineers’ skills necessary in the labour market. In order to conduct experiment a questionnaire was developed to identify teaching and learning problems as well as students’ requests to improve the educational process. The survey also identified the most effective training technologies and those that didn’t find their widespread practical use.Practice-oriented technologies facilitate future Agricultural Engineering bachelors’ professional activities during their training in higher education institutions. These include contextual learning technologies, design technologies, case-study technologies, interactive technologies, problem-based technologies, portfolio-based technologies, master class technologies.
Domain Adaptive Egocentric Person Re-identification

Person re-identification (re-ID) in first-person (egocentric) vision is a fairly new and unexplored problem. With the increase of wearable video recording devices, egocentric data becomes readily available, and person re-identification has the potential to benefit greatly from this. However, there is a significant lack of large scale structured egocentric datasets for person re-identification, due to the poor video quality and lack of individuals in most of the recorded content. Although a lot of research has been done in person re-identification based on fixed surveillance cameras, these do not directly benefit egocentric re-ID. Machine learning models trained on the publicly available large scale re-ID datasets cannot be applied to egocentric re-ID due to the dataset bias problem. The proposed algorithm makes use of neural style transfer (NST) that incorporates a variant of Convolutional Neural Network (CNN) to utilize the benefits of both fixed camera vision and first-person vision. NST generates images having features from both egocentric datasets and fixed camera datasets, that are fed through a VGG-16 network trained on a fixed-camera dataset for feature extraction. These extracted features are then used to re-identify individuals. The fixed camera dataset Market-1501 [20] and the first-person dataset EGO Re-ID [3] are applied for this work and the results are on par with the present re-identification models in the egocentric domain.