Reviewerly/exHarmony huggingface dataset:
The Influence of Attendance and Exam Monitoring Card on Academic Achievement of Students

This study aimed to determine the effects of Science ATM (Attendance and Test Monitoring) Card on the performance of Grade 8 students of Calamba Bayside Integrated School in Science.Quasi-experimental, particularly the non-equivalent dependent variable was utilized in the study to determine the effectiveness of Science ATM Card as a motivational tool in teaching Science.The study also used mixed method which includes both qualitative and quantitative analyses.For two consecutive years, results of the study show that there is a significant difference in the achievement of students before and after the used of Science ATM Card in teaching and learning Science 8.The use of the motivational tool boosts the interest of the students.It encouraged students to regularly attend classes.A sense of fulfillment was obviously seen from the students.They were able to show ownership of the cards.The study is limited only to Grade 8 students enrolled on School Year 2016 -2017 and School Year 2017 -2018 and their Science teacher.Total enumeration was used as a sampling method.It is recommended that Science ATM Card be used in all academic subjects to help students developed intrinsic motivation in learning.Teachers, on the other hand are encouraged to use the card in order to monitor the attendance of the students.
Individual Re-Identification in Blurred Image using SVM/PCA Technique

Individual re-identification (Re-ID) is the task of matching a target person across different cameras, which has drawn extensive attention in computer vision and has become an essential component in the video surveillance system. Pre-id can be considered as a problem of image retrieval. Existing person re-identification methods depend mostly on single-scale appearance information. In this work, to address issues, we demonstrate the benefits of a techniques using Support vector machine or Principal component analysis proposed for pre-id in this study. However, great challenges are being faced in the pre-id task. First, in different scenarios, appearance of the same pedestrian changes dramatically by reason of the body misalignment frequently, various background clutters, large variations of camera views and occlusion. Second, in a public space, different pedestrians wear the same or similar clothes. Therefore, the distinctions between different pedestrian images are subtle. These make the topic of pre-id a huge challenge. The proposed methods are only performed in the training phase and discarded in the testing phase, thus, enhancing the effectiveness of the model. Our model achieves the state-of-theart on the popular benchmark datasets including Market-1501, duke mtmc -re-id and CUHK03. Besides, we conduct a set of ablation experiments to verify the effectiveness of the proposed methods
Study and Implementation of Convolutional Neural Network based Image Denoising

Image denoising is considered as the most important topic in the field of image processing. The primary objective is to refine and improve the image quality. Many photographs result in very high noise and grain levels as well as low sharpness due to the low sensor resolution and power. It’s very costly and always impossible to increase sensor resolution in cameras. In revolutionary neural networks, image shape and form are used as evolving characteristics in CNN. A literature review and implementation of CNN-based image denoising techniques are discussed in this paper. We first introduce different types of noise, filters to remove the noise, and then different methodology for image denoising using CNN.
Benefits of 3D Printing as A Sustainable Building Technology for Post-Disaster Housing

In previous years, many countries were exposed to many natural and artificial disasters.Iraq is considered one of the countries most affected in the recent period by wars and natural disasters.These disasters were accompanied by many losses in lives, equipment, and infrastructure.One of the most prominent losses resulting from disasters is the loss of many residents of their homes because they were destroyed and forced to flee or seek refuge in safer and more habitable areas.As a result, the demand for housing increases during disaster recovery, whether temporary or permanent, to meet the housing needs of IDPs or refugees.Therefore, the research examines the prospects of using 3D printing (3DP) as a sustainable building technology to meet the needs of temporary or permanent housing for those affected by disasters.The research aims to clarify the benefits of using this technology, compare it with traditional construction methods, and study the obstacles that prevent its use to form a reliable knowledge base to meet the housing needs of those affected by future disasters.The research found that 3D printing technology during disaster recovery has many advantages, such as providing housing at the required level and speed, reducing construction waste, increasing construction safety, achieving sustainability goals, and reducing construction errors and costs.It also includes some obstacles related to his condition, economic conditions, and availability.Workers with experience in dealing with these modern technologies in construction.
POSITION AS MAIN FACTOR OF FORMING AND DEVELOPMENT OF CREATIVE PERSONALITY

The features of forming and development of composition capabilities (special composition abilities and skills) as determining criteria of effectiveness in preparation of future teacher of fine art are considered in the article. An author attaches important significance to “Composition” as educational object which directly influences on development of creative capabilities of student.
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies

Reinforcement Learning has drawn huge interest as a tool for solving optimal control problems. Solving a given problem (task or environment) involves converging towards an optimal policy. However, there might exist multiple optimal policies that can dramatically differ in their behaviour; for example, some may be faster than the others but at the expense of greater risk. We consider and study a distribution of optimal policies. We design a curiosity-augmented Metropolis algorithm (CAMEO), such that we can sample optimal policies, and such that these policies effectively adopt diverse behaviours, since this implies greater coverage of the different possible optimal policies. In experimental simulations we show that CAMEO indeed obtains policies that all solve classic control problems, and even in the challenging case of environments that provide sparse rewards. We further show that the different policies we sample present different risk profiles, corresponding to interesting practical applications in interpretability, and represents a first step towards learning the distribution of optimal policies itself.
SensoryX ’22 Workshop on Multisensory Experiences at ACM IMX ’22

Our interactions with the world are multisensory in nature - the senses move us through spaces, mix with our memories and are constantly connected by our brains. Focused only on vision for a long time, the field of human computer interaction (HCI) started to meaningfully bring together all our senses in designing interactions for a variety of media. With this workshop, we look at different aspects of multisensory design - from authoring tools to the evaluation of multisensory experiences with the aim of identifying the current challenges and opportunities of mulsemedia.
Transitory cross entropy for model training on unbalanced datasets

The proposed transitory cross entropy loss function performs a weighted average of the cross entropy using both the truth labels and the predicted labels; this is a variation of the weighted cross entropy loss function that performs a weighted average using just the truth labels. We tested the transitory cross entropy loss function by training ICNet on the CityScapes dataset and saw an increase in the mean-intersection-over-union relative to the model trained using the standard weighted cross entropy loss function. We further propose modifying the weights based on dynamic performance metrics rather than just static distribution metrics.
A conditional random field framework for language process in product review mining

Abstract The Part-Of-Speech tagging is widely used in the natural language process. There are many statistical approaches in this area. The most popular one is Hidden Markov Model. In this paper, an alternative approach, linear-chain Conditional Random Fields, is introduced. The Conditional Random Fields is a factor graph approach that can naturally incorporate arbitrary, non-independent features of the input without conditional independence among the features or distributional assumptions of inputs. This paper applied the Conditional Random Fields for the car review word Part-Of-Speech tagging and then the feature extraction, which can be used as an input to an opinion mining system. To reduce the computational time, we also proposed applying the Limited-memory BFGS algorithm to train the Conditional Random Fields. Furthermore, this paper evaluated the Conditional Random Fields and the classical graph approach using the car review dataset to demonstrate that the Conditional Random Fields have a more robust result with a smaller training dataset.
Quantum Meta-Photonics

We discuss how various ideas and notions from the field of metamaterials can benefit the emerging area of quantum science and technology. Specifically, we show that the enhancement and speedup enabled by plasmonic metamaterials open up a means to outpace dephasing and thus make quantum systems immune to decoherence. We also discuss new opportunities for SiN quantum photonic circuitry enabled by our recent discovery of single-photon sources in this technologically important material platform. Our findings spark further studies of quantum emitters toward deeper understanding of their nature, deterministic formation, and scalable integration with on-chip quantum photonic circuitry. We also demonstrate how hybrid quantum sensors with record-high sensitivity can be developed, by employing spin qubits controlled by light and coupled via magnons. The important role of machine-learning designs for quantum photonic circuitry will be also discussed.
Investigation on Enhancing Students' Learning Positivity in the Course of Physical Chemistry

Physical chemistry is the main course of chemistry and chemical industry majors, and it is in the connecting position of the curriculum system.According to the characteristics of physical chemistry course, this paper tried to investigate on enhancing students' learning positivity through employing interactive teaching methods, building the overall framework of physical chemistry course, using multimedia to assist in teaching, and connecting theory with experiment and practice.On this basis, inspire students' thinking, develop students' innovation ability, enhance learning positivity, and then improve the quality of teaching.
The Effect of Emotional Intelligence and Self-Control on Aggressive Behavior and PAI Learning Achievement of Students in State Vocational High School 6 Malang

The study aimed to know: 1) the effect of emotional intelligence on aggressive behavior, 2) the effect of self-control on aggressive behavior, 3) the influence of emotional intelligence on Islamic education’s learning achievement, 4) the effect of self-control on Islamic education’s learning achievement, 5) the influence of behavior aggressive towards Islamic education’s learning achievement, 6) the influence of emotional intelligence on Islamic education’s learning achievement through aggressive behavior, 7) the effect of self-control on Islamic education’s learning achievement through aggressive behavior. This study used quantitative approach with the correlational method. Data was collected using a questionnaire to measure emotional intelligence, self-control, and aggressive behavior. Meanwhile, the researcher used the documentation of the final score of the report to measure the Islamic education’s learning achievement of students. The sample was 100 respondents. The data analysis technique used the Partial Least Square (PLS) test with smartPLS software. The results showed that: (1) significant negative effect of emotional intelligence on students' aggressive behavior (p-value 0.001 &lt;0.05). (2) significant negative effect of self-control on students' aggressive behavior (p-value 0.000 &lt;0.05). (3) significant positive effect of emotional intelligence on students' learning achievement (p-value 0.030 &lt;0.05). (4) significant positive effect of self-control on students' learning achievement (p-value 0.037 &lt;0.05). (5) significant negative effect of aggressive behavior on students' learning achievement (p-value 0.004 &lt;0.05). (6) significant indirect effect of emotional intelligence on learning achievement through student aggressive behavior (p-value 0.028 &lt;0.05). (7) significant indirect effect of self-control on learning achievement through student aggressive behavior (p-value 0.011 &lt;0.05).
KEY RESEARCH AND APPLICATION OF THE REAL-TIME SERVICE OF REMOTE SENSING BIG DATA

Abstract. With the rapid development of earth observation technology, the remote sensing data gradually becomes the significant data resources for spatiotemporal data analysis. However, to realize the efficient management and quick real-time service of the multi-source, multi-scale and multi-spectral is the key problem for the data management units. This paper brings up a novel technology system and resolution for online and real-time service of the massive remote sensing images by innovatively developing a data management model based on an extensible discrete global grid, inventing the grid-based search and dispatch method for remote sensing big data, firstly building a parameter-driven dynamic map service mode for multi -source, multi-scale and multi-spectral remote sensing big data. This method has greatly improved the efficiency for data management units, such as the Chinese government, corporations etc. and has produced a lot of social and economic benefits.
Proximal interpolation, tone mapping and pseudo-coloring for intra-frame high dynamic range infrared imaging

Intra-frame high dynamic range (HDR) infrared imaging is accomplished in a 1280 x 720 format, 8 um pixel pitch digital readout integrated circuit (DROIC) by spatially combining neighboring pixels with different integration times to obtain HDR pixels. Intra-frame HDR imaging achieves the same level of dynamic range improvement as traditional inter-frame HDR imaging without compromising temporal resolution. Proximal interpolation to retain the spatial resolution of the HDR infrared frame, tone mapping to effectively display HDR infrared content on limited dynamic range displays, and pseudo-coloring to better visualize HDR infrared imagery are discussed.
The Influence of Institutional Quality on Human Development: Evidence from Developing Countries

The paper evaluates the influence of institutional quality on human development in 14 developing countries using data over 1991-2019.We employed the Dynamic Common Correlated Effect method that accounts for heterogeneity and cross-sectional dependency associated with panel data, due to unobserved common factors.The findings revealed the evidence of positive and statistically significant long run effect of institutional quality on human development.In addition, financial development was found to promote human development whereas higher military expenditure negatively affected it in the long run.The results suggest that institutional quality promotes long run human development.Policymakers should nurture and develop institutions that have good quality such as deterring corruption, improving quality regulation and the application of the rule of law.
Students’ Perception of using Weekly Quizzes in EFL Classroom

The purpose of this study is to discuss and discover how students feel about using weekly quizzes in an EFL classroom. This research used mixed methods (QUAL-Quan). The population of in this study was the 3rd year students of the English Education Study Program at Universitas Negeri Makassar. Cluster random sampling was used to choose the 45 students for this study. The research used observation, interview, and a questionnaire as instruments. As a result, the students' perceptions of the utilization of weekly quizzes reflect this research. The result of the research showed that the students showed that students' perception of using weekly quizzes in EFL classroom in a positive category. According to the results of the research, it can be concluded that the use of weekly quizzes in EFL classroom can be a tool to determine and measure students' understanding of the material that has been taught previously.
Angle-uniform parallel coordinates

Abstract We present angle-uniform parallel coordinates, a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot. Despite being a common method for visualizing multidimensional data, parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations. To address this issue, we introduce a transformation that bounds all points horizontally using an angle-uniform mapping and shrinks them vertically in a structure-preserving fashion; polygonal lines become smooth curves and a symmetric representation of data correlations is achieved. We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing. Our method enables accurate visual pattern interpretation of data correlations, and its data-independent nature makes it applicable to all multidimensional datasets. The usefulness of our method is demonstrated using examples of synthetic and real-world datasets.
Application of iLauncher interfaces to the abstraction of complex deep learning workflows across a diversity of computing resources

We present some of the latest advancements in the development of the Interface Launcher (iLauncher), along with the application of this technology to the development of plugins that support distributed PyTorch deep learning workflows across a diversity of computing resources including Amazon Web Services (AWS) GovCloud, distributed clusters of heterogeneous nodes with multiple graphics processing units (GPUs) per node running the Slurm batch queuing software, and Department of Defense (DoD) high performance computing (HPC) supercomputers running the Portable Batch Scheduling (PBS) software. The iLauncher technology automates the submission of HPC jobs and provides a mechanism for rapidly prototyping web interfaces from the user’s desktop to powerful capabilities running on the HPC nodes. We describe the extension of previous work to show the development of the client-side plugin JavaScript Object Notation (JSON) description, the underlying server-side scripts for running distributed PyTorch deep learning models on various platforms with different queuing systems, and the recipes for the software along with all dependencies in an all-inclusive software packaging technology called a container. Finally, we show a representative use case running distributed PyTorch in a Jupyter Notebook through iLauncher on the various backend platforms along with some guidance on when each one may be beneficial for a range of scenarios based on models and data.
Opinion Mining on Twitter Data using Machine Learning: A Case Study

Sentimental analysis, also referred to as opinion mining or emotion extraction is the classification of emotions within a textual data. This technique has been widely used over the years in order to determine the sentiments, emotions within a particular textual data. Twitter is a social media platform that has been mostly used by the people to express emotions for particular events. In this paper we have collected the tweets automatically in a csv file for a number of events, analyzed them using a number of machine learning algorithms like Multinomial Naïve Bayes, Logistic regression, TF-ID vectorizer, Decision tree classifier and Support vector machine(SVM) and then compared the results. And also the exploratory Data analysis for positive, negative and neutral results are displayed by Bar graph, heat map, scatter plot, word cloud and a line graph.
ECOPRINT COLOR FASTNESS RESISTANCE ON GOATSKIN MEDIA AGAINST WET RUB AND SUNLIGHT

Many artisans prefer to use artificial (synthetic) dyes because they are faster and easier. However, chemical substances from synthetic dye waste are harmful to the environment. Eco-print technique minimizes environmental losses due to natural coloring techniques on printing or transferring colors and motifs from plants directly to the medium. This study aimed to determine the color fastness of eco-print on goatskin material against wet rubbing and sunlight using mordant and tunjung. This research was descriptive research with a quantitative approach. The results showed that the average evaluation value of color fastness to sunlight was 4.75, better than wet rubbing, which got an average evaluation value of 4.42. The bivariate analysis showed the value of sig. (0.043) or H0 was rejected. In conclusion, there was a difference in color fastness from eco-print on goatskin material against wet rubbing and sunlight.&#x0D; Keywords: eco-print, fastness resistance, goatskin.
Research on Multi-party Data Secure Interaction Technology for Energy Internet

Abstract Aiming at the problems of the energy Internet interaction process, which involves more users and intermediate links, and each participant is independent of each other, information asymmetry, and participants do not trust each other, this paper proposes a blockchain-based energy Internet multiparty data interaction model to build a data circulation architecture, including four layers, which are data sensing layer, network transmission layer, data processing layer and application service layer, to realize identity association, cross-domain access, It can realize the basic functions such as identity association, cross-domain access, trust evaluation and privacy protection, and effectively guarantee the secure interaction of multi-party data in the energy Internet.
FORMATION OF MUSIC AND AESTHETIC ORIENTATIONS OF FUTURE TEACHERS IN EXTRACURRICULAR ACTIVITIES ON MATERIALS OF INTEGRATION OF ARTS

In the paper the art integration is considered as a necessary condition offormation of musical aesthetic value.
Analysis of Multipoint Grounding Abnormality of Main Transformer

The health condition of transformer is related to the safe and stable operation of power grid. This paper introduces the reason of abnormal grounding current of iron core and clamp of 750kV main transformer. Firstly, the on-line monitoring device is used for real-time monitoring, and then the current limiting resistance is added at the iron core grounding lead down line to reduce the grounding current. Then the multipoint grounding fault of the main transformer is confirmed by outage diagnosis and eliminated by the welder's high current shock. Finally, the abnormal grounding current of similar transformer core or clip is dealt with. The research results of this paper have certain reference and guiding significance for the fault diagnosis of multipoint grounding of main transformer. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>