Reviewerly/exHarmony huggingface dataset:
Social Distancing Violation Detection in Video Using ChessBoard and Bird’s-eye Perspective

Thousands of infections, hundreds of deaths every day - these are numbers that speak the current serious status, numbers that each of us is no longer unfamiliar with in the current context, the context of the raging epidemic - Coronavirus disease epidemic. Therefore, we need solutions and technologies to fight the epidemic promptly and quickly to prevent or reduce the effect of the epidemic. Numerous studies have warned that if we contact an infected person within a distance of fewer than two meters, it can be considered a high risk of infecting Coronavirus. To detect a contact distance shorter than two meters and provides warnings to violations in monitoring systems based on a camera, we present an approach to solving two problems, including detecting objects - here are humans and calculating the distance between objects using Chessboard and bird’s eye perspective. We have leveraged the pre-trained InceptionV2 model, a famous convolutional neural network for object detection, to detect people in the video. Also, we propose to use a perspective transformation algorithm for the distance calculation converting pixels from the camera perspective to a bird’s eye view. Then, we choose the minimum distance from the distance in the determined field to the distance in pixels and calculate the distance violation based on the bird’s eye view, with camera calibration and minimum distance selection process based on field distance. The proposed method is tested in some scenarios to provide warnings of social distancing violations. The work is expected to generate a safe area providing warnings to protect employees in administrative environments with a high risk of contacting numerous people.
The Classification of Multiple-domain Samples based on Multiple Deep Learning Models

This paper proposed a classification method that can classify the samples of multiple domains based on the cooperation of multiple models. On a sample, our method firstly predicts the domain that may contains this sample based on the output of trained models. Then, our method uses the models that are trained on this domain to classify the label of this sample. Our method can reduce the difficulty of training high performance model while can ensure high accuracy on the samples of multiple domains. Experiments show that our method achieved higher accuracy than the existing methods.
STUDENTS’ PERCEPTION OF SPADA UWGM AS LEARNING MANAGEMENT SYSTEM (LMS) IN WIDYA GAMA MAHAKAM SAMARINDA UNIVERSITY

Lecturers and students must be prepared to have technical knowledge and skills about easy applications and features that can accommodate online learning needs. It has been notified based on the researcher in pre research with the students. During the learning activities, the students find particular problems in learning online through SPADA as the LMS. However, they should know the system characteristic and functions to use the system effectively. Therefore, based on the background above, the researcher examined EFL studentsâ€™ perceptions of the SPADA as a learning management system. The objectives of this research study are to find out what are studentsâ€™ perception of SPADA UWGM as Learning Management System (LMS) in Widya Gama Mahakam Samarinda University. This study used qualitative research with case study approaches. The result showed that perception of using SPADA as the learning management system in the fifth semester of Class a English Education Department in Widya Gama Mahakam Samarinda University divided into three indicators of perception that are acceptance, understanding, and evaluation. Furthermore, from the studentâ€™s perception of acceptance the students stated that online learning can be said to be an effective learning resource when supported by adequate infrastructure such as good networks and the SPADA as the Learning Management System but the students still have difficulties in acceptance the material with SPADA as the Learning Management System
Heisenberg Limit

The uncertainty in photon number of a coherent source leads to the shot noise limit. We could eliminate this uncertainty by using a Fock state, which has a definite photon number. This approach produces the “Heisenberg limit,” which is the ultimate limit on measurement uncertainty. Multiphoton entangled states (called NOON states) in an interferometer are introduced as a means of achieving the Heisenberg limit. Super-sensitivity and super-resolution are introduced as applications. Methods of producing NOON states are described, and we introduce the Hong–Ou–Mandel (HOM) effect and linear optical quantum state engineering.
The correlation between students' frequency of listening English songs and students vocabulary mastery

This research was aimed to know : "The Correlation Between Students Frequency of Listening English Songs And Students Vocabulary Mastery". SMAN 10 Bandar Lampung in academic year 2020/2021. The research method of this research was a quantitative. The population of the research was the eleven grade students of SMAN 10 Bandar Lampung in academic year 2020/2021, the number of students was 35 students. The researcher gave a questionnaire to know students frequency of listening English songs, and test to know the students vocabulary mastery. The data that has been gathered was analyzed by Pearson Product Moment Correlation. Data Analysis shows that there is significant correlation between two variable since p&lt;0.05 (p=0.342). It can be happened because most of students use their time to listening English songs and it can enrich their vocabulary. Keywords: Frequency of Listening English Songs, Vocabulary Mastery
Quality Assessment of Screen Content Images

With the rapid development and popularization of smartphones and cloud computing, computer-generated screen content images (SCIs) are important mediums that represent enormous Internet information, having penetrated into our daily lives. There are many natural scene image quality assessment (QA) methods proposed in the past decades, but these methods are improper for SCIs since SCIs include three complex contents, i.e., texts, graphics, and illustrations, where distortion results in varying degrees of degradation. For the QA of SCIs, this chapter first presents the full-reference QA methods based on structural similarity to estimate structural changes and different statistical properties of regions. Second, for the sake of tackling the issue of unsatisfactory prediction monotonicity, it introduces the reduced-reference QA method based on the fusion of macroscopic and microscopic features. Third, to deal with the difficulties of monotonous color and simple shape in SCIs, it shows the no-reference QA methods based on adaptive multi-scale weighting and big data learning. In the end, the future research trends of SCI QA are discussed, and the necessity of constructing accurate and efficient objective QA models of SCIs is indicated.
Computer Assisted Language Learning and Speaking Proficiency of Second Language Learners

This study aimed to determine the speaking proficiency of L2 learners undergoing a computer-assisted language learning program in Southern Christian College in the school year 2015-2016. It specifically aimed to find the weighted study score and speech recognition score of the students in the Job Enabling English Proficiency or DynEd class, to identify their speaking proficiency, and to determine whether their scores in JEEP Start are significantly related to their speaking proficiency.&#x0D; Fifty students were identified to participate in the lottery method. Their scores in the JEEP Start were summarized through descriptive statistical tools like percentage, frequency count and mean, and their speaking proficiency was classified and encoded in a spreadsheet. The relationship between their scores and speaking proficiency was determined through regression analysis.&#x0D; Results revealed that the majority of the students had excellent WSS, and more than half of them had high speech recognition performance and were competent speakers of English. Further, the study found that speech recognition was a significant predictor of the students’ speaking proficiency. Based on the results, the researcher concluded that CALL is an excellent venue for L2 learners to enhance their speaking proficiency.
Pengoptimalan Kreativitas Guru Bahasa Indonesia di Masa Pandemi Covid-19

&#x0D; The Covid-19 pandemic has changed the learning system from face-to-face methods to distance learning methods. The distance learning system has more or less changed the learning patterns that have been implemented so far, starting from curriculum changes, teaching and learning processes and also learning evaluation. In its implementation, distance learning leaves a number of problems that make the implementation of learning not optimal. Therefore, optimizing the creativity of Indonesian language teachers needs to be done to support learning during the Covid-19 pandemic so that the implementation of Indonesian language learning can still run optimally. This study aims to describe 1) the Indonesian language learning system during the Covid-19 pandemic, 2) the role of Indonesian language teachers in learning during covid-19, 3) the creativity of Indonesian language teachers in learning during covid-19. The method used is descriptive qualitative method through theoretical studies. Based on the results of this study, it can be concluded that the Indonesian language learning system during the pandemic tends to focus on giving assignments only without any interaction between teachers and students so that the role of the teacher as a teaching staff is increasingly invisible. The covid-19 pandemic indirectly challenges Indonesian language teachers to be more creative in devising a learning method. Learning methods that can increase the creativity of Indonesian language teachers in learning during the Covid-19 pandemic are implementing learning using current learning applications, making learning videos, making home visits by creating small-scale learning groups. That way the role of Indonesian language teachers as teaching staff will be more visible than just giving assignments.
GMEKT: A Novel Graph Attention-Based Memory-Enhanced Knowledge Tracing

Knowledge Tracking (KT) attempts to predict students’ learning performance by tracking their changing knowledge state based on their past performance on exercises. Existing KT models primarily use knowledge concepts while ignoring the latent information of exercises, particularly for possible changes in students’ learning processes. This paper proposes a novel Graph attention-based Memory-Enhanced Knowledge Tracing (GMEKT) model to address these issues, employing the Graph Attention Network (GAT) and a novel fusion method to extract additional rich information from the exercises. In detail, we first create a dynamic adjacency matrix as the input of the graph attention network to alleviate the sparsity. Then a memory module based on self-attention and gating network is developed to trace the evolution of the knowledge state and memory unit when students do the exercises. Finally, we conduct extensive experiments on public datasets to validate the proposed model’s effectiveness. Compared to the state-of-the-art KT models, GMEKT outperforms them by more than 2.67% in AUC.
The administrative capacity to manage the effects of the COVID-19 pandemic in schools: legality of personal data processing concerning the staff vaccination status in order to resume courses with physical presence or online*

The administrative capacity of public authorities and bodies, as well as of the institutions under their subordination/in their coordination, includes, not only all material, institutional and human resources, but also the actions they carry out for the exercise of the competencies established by law. In the context of the COVID-19 pandemic, the Minister of Education together with the Minister of Health issued Joint Order no. 5558/2389/2021 which provided that, starting with 8 November 2021, the resumption of courses with physical presence is to take place in all pre-university education units in which a minimum percentage of 60% of the staff is vaccinated, otherwise the resumption of courses will be done online, and the Boards of Directors of the pre-university education units issue the decision on how to resume the courses. Therefore, this study will analyse, in the light of data protection legislation, if, in view of the competencies established by law, the pre-university education units had the necessary administrative capacity to issue an informed decision on how to resume the courses. The main conclusion after analysing the legal framework is that the legitimate basis for personal data processing concerning the staff vaccination status can only be point (c) of Article 6 (1) and point (g) of Article 9 (2) of Regulation (EU) 2016/679, but the Joint Order no. 5558/2389/2021 does not fall into either of these two legal basis for the processing, with the consequence that the Boards of Directors were in fact deprived of the necessary administrative capacity to issue the decision on how to resume the courses.
The impact of entrepreneurial competencies on firms' performance

The main objective of this study is to examine the impact of entrepreneurial competencies (opportunity, organising, strategic, relationship, commitment and learning) on firm performance, in addition, to examining the effect of firm capabilities as a moderating variable on the impact of entrepreneurial competencies on firm performance. The population of the study consists of Jordanian entrepreneurs who are the owners of entrepreneurial firms. Convenience sampling technique was used to collect the responses of the participants which were composed of 91 respondents. Further, simple, multiple, and hierarchical regression were applied to analyse the collected data. The findings of this study revealed that opportunity competency, strategic competency and commitment competency have an impact on the performance of the firm. Additionally, firm capabilities were found to have a moderating effect on the impact of entrepreneurial competencies on firm performance.
The use of Instagram as media to improve student's writing achievement in Descriptive texts

This research aimed to find out whether or not there is any significant improvement in students’ writing descriptive text after using Instagram and which aspect of writing improves the most after they are taught by using Instagram. The sample of the research were 26 students, and the population is class VIII at SMPN. This research was quantitative. One group of pre-test and post-test designs was used in this research. The instruments used in this research were writing test. The collected data were analyzed statistically through a Paired Samples T-test. The result of the research showed that the mean score of pre-tests is 55.77 and the post-test is 70.12. The results of t-value (15.179) is higher than t-table (2.0595) and the value of two-tailed significance is 0.000 &lt; 0.05. It showed that the hypothesis is accepted that is, there is a significant improvement of students’ writing achievement after the use of Instagram. The result also shows that the most improving aspect of writing is language use with the mean score of pre-tests is 10.90 and the mean score of post-tests is 16.42. The improvement of the language use is 5.52 with the percentage is 38%. On the other hand, the lowest improvement aspect of writing is mechanics. It showed that the mean score of pre-tests is 2.12 and the mean score of post-tests is 3.02. The gain is 0.90 with the percentage is 6 %. The results indicate that 1.) There was a significant improvement in their writing achievement after being taught by using Instagram. 2.) Language use has a significant increase because this aspect has a higher score of 38% compared to other aspects.. Based on the result, it is concluded that there was a significant improvement in their writing achievement after being taught by using Instagram.
Design and Implementation of Wake-on-Voice and Command Recognition Algorithm

This paper analyzes methods of speech recognition and lays an emphasize on acoustic model, language model and decoding algorithm based on the output of these two models. Hidden Markov Model is applied to acoustic model to build a state graph of acoustic features of input speech sample. After testing, the arousing rate is above 95%, the mis-arousing-rate is below 5% and response time of the wake-on-voice model is about 0.2s.
T Daydream

T Daydream TR Brady (bio) I was so lonely in the real dream. I washedall of Lindsey’s forks and none of mine.There was no meal to be had, dream hunger turningme inside out, dream water running all over the counter.I am so not rare there’s only an incompletenumber of me. The coin behind my ear,up my sleeve. The coin I’m referencingboth sides of, the pentacles playing out poorly. I daydream the real dream, dream Kate holding my shouldersin the waterless pool, dream James holding my facein an unfamiliar city. The dream where I’m in lovewith each facsimile of me. Where I’m my ownlittle happy house husband, something that feels gigantic,a tiny cut in the join of a mouth. [End Page 132] T Daydream At the house party I was a depressed convert. In the inaccurate hourI take the house apart. A keystroke to a dream. At the house partyI coveted your leather cap and you in it. I had no hereafterbody. Outside, I stood beyond the square of yellow light coming frominside the house. I mapped my desire for desire. I strayin the dream of the house party. I take myself apart. Not a goodfight. It was Thursday and fall and I didn’t go to the reading,but I went to the afterparty. I copy paste you to every room.I try to not notice you. In real time I walk up near youand change course. I put no clues to you. I wrong myselfto a slight shape and slip out every crack. [End Page 133] TR Brady TR Brady is a poet and fiber artist. Their work has appeared in Poetry Daily, Denver Quarterly, Bennington Review, and Copper Nickel. TR holds an MFA from the Iowa Writers’ Workshop and is the cofounder/co-editor of Afternoon Visitor, a new journal of poetry and hybrid text. Copyright © 2022 Middlebury College
Design for Better Ambient Temperature Experiences

Nowadays, most people still rely on weather reports to access information about the temperature. However, the weather report is less useful when people care more about the temperature of the immediate surrounding areas. With people’s well-being in ambient temperatures in mind, we designed and developed ConnecT, a family-shared mobile application connecting with multiple temperature sensors. It could provide information in two parts: loved ones’ surrounding temperature and saved places’ environmental information. The target users of our design are homemakers, as they plan the clothing and meals for their families. We believe this design can contribute to smart cities in the future, as the application could provide a platform to share temperature information for public places.
Quality Assessment of Light-Field Image

Light-field (LF) imaging technology is widely applied to the fields like 3D reconstruction, virtual reality and image rendering, and has attracted the focused attention of researchers in academia and industry. Unlike traditional imaging technique, the LF image records the intensity of light rays of scene points in different directions at the sensor plane, containing not only the color information but also the underlying depth information, which has a significant role in the next generation imaging technology researches. Yet, it is prone to loss of visual details in phases such as acquisition, encoding, and transmission of LF images. For the QA of LF images, this chapter first illustrates the full-reference QA methods on account of single- and multi-scale Gabor feature extraction to overcome the difficulty of ignoring the human visual system perceived characteristic. Second, to better evaluate different sizes of LF images, it introduces the reduced-reference QA method based on depth map distortion measurement. Third, for the consideration of the high-dimensional characteristics of LF images, it shows the no-reference QA methods that are based on spatial-angular measurement and oriented to tensor. In the end, the authors have validated the aforementioned QA methods on related databases and noted that it is essential to develop efficient LF image QA methods.
Analysing Reviewer’s Credibility and Sentiment to Build A Profile Model for Product Recommendation of User

Understanding a particular customers product needs, likes, and dislikes and to make an automation based on it is a very convolute job. This project augments heuristic-driven user interest profiling with reviewer credibility analysis and fine-grained feature sentiment analysis to conceive a vigorous recommendation methodology. The proposed credibility, interest and sentiment enhanced recommendation (CISER) model has five modules: candidate, feature extraction, reviewer credibility analysis, user interest mining, candidate feature sentiment assignment, and recommendation module. Review corpus is given as an input. The first module uses context and sentiment confidence to procure useful, crucial features.To detect the untrustworthy reviews and reviewers, reviewer credibility analysis proffers an approach to weigh reviews according to the parameters of credibility. The user interest mining module, uses fairness of review writing as heuristics for interest-pattern mining. The candidate feature sentiment assignment module comparesexisting features in review based on their fast Text sentiment polarity. The final module uses credible sentiment scoring for purchase recommendations. The proposed recommendation modelutilizes not only numeric reviewsbut also uses sentiment expressions connected with components, customer preference profiles, and reviewer credibility for quantitative analysis of various alternative products. The mean average precision (MAP@1) for CISER is 93%, and MAP@3 is 49%, which is better than current state-of-the-art systems.
Laying Out the Framework

To this point, we have focused entirely on the theoretical aspects of outlier and anomaly detection. We will still need to delve into theory on several other occasions in later chapters, but we have enough to get started on developing a proper anomaly detection service.
An efficient approach towards building CBIR-based search engine for embedded computing board

Investigating a picture gives us more information than how it is expressed through words. Image processing is such a field which is ever booming and handles 'n' number of images. Thanks to technology we are able to store and retrieve such massive dataset of image-based data from anywhere. Search engines provide a way to link images and queries. They are searched using various factors like keyword, image dimension, texture etc. which is called as content-based image retrieval. In this search methodology, the input query image is analysed, and its properties or features are saved. Using the recorded features, other images are retrieved which match with the input image. But then again, searching by just name, colour or texture is not very efficient and so we have proposed a novel algorithm for the same. The proposed algorithm takes features like colour, texture, SURF, entropy, etc. and finds out how differently they work and what distinct results they produce when combined. Implementation of CBIR on Beagle board led us to some satisfactory results, which encouraged us to do further research.