Reviewerly/exHarmony huggingface dataset:
PROFESSIONAL SELF-DETERMINATION OF HIGHER SCHOOL STUDENTS

The article is devoted to the problem of professional self-determination, which is urgent for today's youth. The role of professional choice in a person's life is great, and the possibilities with which a young man approaches this process plays a decisive role. In their article, the authors present the results of an empirical study of thepersonal characteristics of students in grades 9, 10, 11 and their readiness to choose a profession. We identified important and significant connections between the level of development of self-esteem and reflexivity with such indicators of professional self-determination as autonomy and emotional attitude. This result indicates that the level of development of his self-awareness plays a decisive role in the professional self-determination of a young man. It is the teacher's task to help high school students in professional self-determination.
Pengaruh Tayangan Net Soccer Di Net Tv Terhadap Tingkat Pengetahuan Sepak Bola Remaja (Survei di Wilayah Inpres RW 04 Kelurahan Gaga Ciledug, Kota Tangerang)

Nielsen Consumer Media View (CMV) which shows that television penetration reaches 96%. In Indonesia there are several national and private television stations. For the national TVRI, there are also private television stations in Indonesia that are airing like Trans 7, Trans TV, Indosiar, MNC TV, Kompas TV, NET TV, and so on. The researcher wanted to know the effect of 'NET Soccer' on NET TV on the level of knowledge of football in teenage housing Inpres RW 04, Gaga village. Researchers use a quantitative approach, with an associative explanative type survey method. And use the S-O-R theory. The variable X of this study uses elements of program success and Y variable uses the concept of mass media effectiveness. In this study researchers used a simple random sampling technique. The population in this study amounted to 103 people with a sample of 82 people. The regression test has a value of 0.588 indicating the moderate relationship between NET Soccer on NET TV shows and the level of youth football knowledge. The coefficient of determination or R Square is 0.346, it can be concluded that the effect of NET Soccer on NET TV shows on the level of knowledge of teen soccer in the presidential housing is 34.6%. After conducting research, it can be proven that the correlation value of variables X and Y is 0.588 and the value of Sig. 0,000 <0,05, what happens is that Ho is rejected Ha is accepted, meaning that there is a moderate influence between Impressions of NET Soccer on NET TV (X) on the level of knowledge of teen soccer in Inpres housing (Y).
From Discovery to UX and UI: Tools of Voice Design

You’ve reached the final chapter of Part 2. After this, you move from the planning phase to the core building phase: detailed design and development. In this chapter, we look at how and where to find data that helps answer your discovery questions, data that can guide your design before you have users or when user access is limited. Fortunately, there’s a lot you can do quickly and cheaply if you know what questions to ask and how to ask them. You’ll be introduced to some common VUI design documentation approaches you can use for any platform. This isn’t a how-to lesson on tools, but an overview of what some experienced VUI designers use and why. If you’re in charge of VUI design in your organization, it gives you some options to choose from. If you enter an existing organization, of course you’ll use what’s standard. But who knows: maybe you can bring change! The chapter wraps up with an introduction to some powerful methods of testing your UI assumptions and what the results can tell you. You’ll learn more about how to test in Chapter 16.
Learning de-identified representations of prosody from raw audio

We propose a method for learning de-identified prosody representations from raw audio using a contrastive self-supervised signal. Whereas prior work has relied on conditioning models on bottlenecks, we introduce a set of inductive biases that exploit the natural structure of prosody to minimize timbral information and decouple prosody from speaker representations. Despite aggressive downsampling of the input and having no access to linguistic information, our model performs comparably to state-of-the-art speech representations on DAMMP, a new benchmark we introduce for spoken language understanding. We use minimum description length probing to show that our representations have selectively learned the subcomponents of non-timbral prosody, and that the product quantizer naturally disentangles them without using bottlenecks. We derive an information-theoretic definition of speech de-identifiability and use it to demonstrate that our prosody representations are less identifiable than other speech representations.
Sentiment Analysis on Indonesia Twitter Data Using Naïve Bayes and K-Means Method

This study focuses on the analysis of sentiments on Indonesian twitter data. Twitter data on Indonesian simultaneous pilkada used to get its sentiments using Naïve Bayes Classifier method as a method of classification and K-means method to get Label on the data train process. Combining the two methods is expected to get high accuracy results. The results obtained from the research shows a pretty good accuracy of 74.5%.
Computer State Monitoring and Fault Intelligent Identification of Monitoring Electronic Equipment in Highway Environment

ABSTRACTThe monitoring system can improve the effective use of the expressway. It is a necessary part of ensuring the expressway to achieve high-speed, safety and comfort functions. It is also a necessary means to ensure the normal operation of the expressway. The construction of the monitoring system directly affects the operation management system and operation management mode. This article analyses the reasonable construction and function of the monitoring management system, expounds the reasonable selection and layout of vehicle detectors and cameras, and conducts in-depth research and analysis on the layout of variable information boards and variable speed limit signs. Aiming at the problem of the difference in the distribution of monitoring data caused by the fluctuation of the equipment working condition, which leads to the failure of the original diagnosis model and the reduction of the fault intelligent recognition rate, a deep domain adaptive method is proposed. Through deep feature mapping, interference factors of working conditions are eliminated, and insensitive features of working conditions are excavated. In addition, a method for determining the parameters of a deep mapping network is proposed to achieve the best domain adaptation effect. The test results show that, compared with artificial features, the representation features of deep network mining are more robust to data fluctuations; compared with shallow transfer learning, deep networks have stronger domain adaptability and cross-working fault diagnosis correct rate is also higher.
Co-Evolution of Human Capabilities and Intelligent Technologies for Digital Education

There is much interest to advance digital technologies supporting teaching, learning and education. Yet, many ideas, e.g., implementing data and artificial intelligence in education, still lack systematic understanding of human learning process. Also, new kind of capabilities are needed that are necessary to succeed in a rapidly changing world. In my talk I introduce recent advancements in research on socially shared regulation in learning which provides a framework for developing these competences. I discuss the role of technology in understanding and supporting socially shared regulation and conclude with future perspective how co-evolution of human capabilities and technologies can be enhanced for digital education.
Standards of practice as indicator of forensic handwriting opinion value

The aim of the article is to provide a review of the standards regarding the work of experts performing examinations in order to elaborate opinions for the court, basing on an example of handwriting examiners. Our paper refers also to the admissibility of expert evidence in the light of rulings of US courts and the regulations on evidence. The article also makes references to the leading American and international organisations involved in the development of standards to improve the quality of handwriting comparative examinations. The question of attestation is related to this issue, as well, and certification of forensic laboratories, which is presented on the examples from American and Polish practice. In this respect, efforts are being made to harmonise and improve the quality of research to increase the value of expert evidence.
A New Clustering Algorithm Based on Non-Negative Matrix Factorization Approach

This paper presents a new clustering algorithm that is based on non-negative matrix factorization approach. The proposed algorithm is executed in two steps. The first step uses non-negative matrix factorization approach for dimensionality reduction to scale-back the computational burden and noise. The second step performs clustering by using the matrix with reduced dimensions obtained during the step 1.The algorithm is compared with two well-known clustering algorithms namely K-means algorithm and hierarchical clustering algorithm. IRIS dataset is used to compare the three algorithms. The algorithms are compared for the different initial values of parameters associated with clustering algorithms, and by presenting dataset with different order to clustering algorithms. The results indicate that the proposed algorithm produces good clusters while addressing some of the issues related to clustering.
PEMANFAATAN LITERASI MEDIA GAMBAR SERI DAN VIDEO UNTUK MENINGKATKAN KETERAMPILAN MENULIS CERITA

The research objective to be achieved is to improve children's story writing skills by using media literacy of series and video images for VA grade students of SDN Mojosongo III. The form of this research is classroom action research using 2 cycles. Each cycle consists of 4 stages: planning, implementing actions, observing, and reflecting. As the subject of the study was the VA class students of SDN Mojosongo III, amounting to 32 students. Researchers in taking the subject not individually, but classically. Data collection is done by observation, tests, documents. The data analysis technique used is a descriptive and interactive analysis model that has three components, namely data reduction, data display, and withdrawal or verification. Based on the results of the study it can be seen that in terms of cognitive students during the first cycle showed a figure of 62.5% (20 students), and in the second cycle of 81.25% (26 students). Thus there is an increase in terms of cognitive students from cycle I, to cycle II. From this study it can be concluded that the use of media literacy of series images and videos can improve children's story writing skills in VA grade students of SD Negeri Mojosongo III, District of Jebres, Surakarta in the academic year 2017/2018.
Building a Knowledge Society: Learnings from a Digital Literacy Program.

The advancements in digital technology are redefining society. In the digital transformation era, lack of digital literacy can result in social exclusion and leave people bereft of employment opportunities. Realizing the significance of a digitally literate society, the Government of India had launched a nationwide initiative, known as Digital Sakshatra Abhiyan (DISHA)/National Digital Literacy Mission (NDLM). The objective of DISHA was to impart basic digital literacy to participants to empower them and improve their lifestyle. In this paper, we build a research model using the Kirkpatrick model to evaluate the key components contributing to the effectiveness of DISHA. We conduct a survey with 2031 participants and use structured equation modelling to validate the research model. We also examine the influence of demographic factors on training. Our findings contribute to the digital literacy literature and inform the adoption and planning of similar digital literacy training initiatives in the future.
A cross-sectional study of facial index in Western Uttar Pradesh population between 18-25 years of age

Background: Facial anthropometry is the scientific study of measurements and proportions of the human face. There is huge paucity in the metric measurements of face in Indian population. This study is involved in collecting facial anthropometric data of Western Uttar Pradesh population of age group between 18-25 years.&#x0D; Aims and Objectives: This study aims at evaluating predominant facial phenotype, using facial height and facial width of Western Uttar Pradesh population as study subjects.&#x0D; Materials and Methods: The study was conducted on 200 medical and paramedical students of Shri Ram Murti Smarak Institute of Medical Sciences (SRMS-IMS), Bareilly, Uttar Pradesh. The study subjects were of 18-25 year’s age, belonging to Western Uttar Pradesh region. Digital Vernier Caliper was used to measure the facial parameters. The data was analyzed using statistical tool SPSS 16.0 version.&#x0D; Results: In the present study it is found that facial height and width are more in males compared to females. This difference is statistically significant. On analyzing facial index, hyperleptoprosopic face is the predominant phenotype in males as well as in females, followed by leptoprosopic type in males and mesoprosopic type in females. However, gender differences in facial index are not statistically significant at p&lt;0.05. &#x0D; Conclusion: The current study reveals that facial parameters can be used to determine the gender of an individual on the basis of height and width.
Improved Grasshopper Optimization Algorithm based Feature Selection with Evolutionary Outlay-Aware Deep Belief Network Classifier (IGOA-EOA-DBNC) for High Dimensional Datasets

Background: High dimensional datasets contain the curse of dimensionality, and hence data mining becomes a more difficult task. Feature selection in the knowledge data and discovery process provides a solution for this curse of dimensionality issue and helps the classification task reduce the time complexity and improve the accuracy. Objectives: This paper aims to recognize a bio-inspired algorithm that best suits feature selection and utilizes optimized feature selection techniques. This algorithm is used to design machine learning classifiers that are suitable for multiple datasets and for both high dimensional datasets, moreover to carry out performance analysis with regards to the accuracy of a classification and the processing time for classification. Methods: This study employs an improved form of grasshopper optimization algorithm to perform feature selection task. Evolutionary outlay aware deep belief network is used to perform the classification task. Findings: In this research, 20 UCI benchmark data sets are taken with full 60 features and 30000 instances. The datasets are Mammography, Monks-1, Bupa, Credit, Parkinson's, Monk-2, Sonar, Ecoli, Prognostic, Ionosphere, Monk-3, Yeast, Car, Blood, Pima, Spect, Vert, Prognostic, Contraceptive, and Tic-Tac-Toe endgame. Table 1 describes the dataset details, number of instances, datasets and features. The overall performance is performed using MATLAB 6.0 tool, which runs on Microsoft Windows 8, and the configuration is Core 13 processor with 1 TB hard disk and 8GB RAM. Performance standards, like classification accuracy and the processing time for classification, is achieved. Novelty: Interestingly, the Improved Grasshopper Optimization Algorithm uses error rate and classification accuracy of the Evolutionary Outlay Aware –Deep Belief Network Classifier as fitness function values. This combined work of classification and feature selection is briefly represented as IGOA-EOA-DBNC. Twenty datasets are selected for testing the performance regarding elapsed time and accuracy, which gives better results.
Modeling the crashes count using finite mixture models

"This paper aims at the modeling the crashes count in Al Muthanna governance using finite mixture model. We use one of the most common MCMC method which is called the Gibbs sampler to implement the Bayesian inference for estimating the model parameters. We perform a simulation study, based on synthetic data, to check the ability of the sampler to find the best estimates of the model. We use the two well-known criteria, which are the AIC and BIC, to determine the best model fitted to the data. Finally, we apply our sampler to model the crashes count in Al Muthanna governance.
The active participation of students in the formation of the educational process is a key to efficiency

The article discusses the issues of attracting students in the process of forming educational materials, as well as the problems of increasing the effectiveness of educational and cognitive activities of students. In the "Learn" option of the test, immediately after selecting the correct answer, a message will appear stating that the answer is correct or incorrect. It contains the messages "correct", "incorrect" and "partially correct".
Development of Intelligent Public Trash Can Based on Machine Vision and Learning

At present, with the maturity of machine vision technology and the continuous expansion of application fields, there have been many intelligent trash cans based on machine vision, which can realize certain garbage identification and automatic classification.However, due to certain technical limitations of machine vision, it is impossible to identify all garbage.In this paper, a smart public trash can based on machine vision and auxiliary sensors is proposed.In addition to realizing machine vision to identify and automatically classify garbage, sensors will also be used to assist in identifying garbage to solve problems such as the same garbage classification of different shapes.At the same time, enhanced learning will be added to realize the self-learning of the trash can, so as to achieve the goal of continuously increasing identifiable types.
Analysis of Trustworthiness in Machine Learning and Deep Learning

Trustworthy Machine Learning (TML) represents a set of mechanisms and explainable layers, which enrich the learning model in order to be clear, understood, thus trusted by users. A literature review has been conducted in this paper to provide a comprehensive analysis on TML perception. A quantitative study accompanied with qualitative observations have been discussed by categorizing machine learning algorithms and emphasising deep learning ones, the latter models have achieved very high performance as real-world function approximators (e.g., natural language and signal processing, robotics, etc.). However, to be fully adapted by humans, a level of transparency needs to be guaranteed which makes the task harder regarding recent techniques (e.g., fully connected layers in neural net-works, dynamic bias, parallelism, etc.). The paper covered both academics and practitioners works, some promising results have been covered, the goal is a high trade-off transparency/accuracy achievement towards a reliable learning approach.
Learning to Search for Fast Maximum Common Subgraph Detection

Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in biomedical analysis, malware detection, cloud computing, etc. This is especially important in the task of drug design, where the successful extraction of common substructures in compounds can reduce the number of experiments needed to be conducted by humans. However, MCS computation is NP-hard, and state-of-the-art MCS solvers rely on heuristics in search which in practice cannot find good solution for large graph pairs under a limited search budget. Here we propose GLSearch, a Graph Neural Network based model for MCS detection, which learns to search. Our model uses a state-of-the-art branch and bound algorithm as the backbone search algorithm to extract subgraphs by selecting one node pair at a time. In order to make better node selection decision at each step, we replace the node selection heuristics with a novel task-specific Deep Q-Network (DQN), allowing the search process to find larger common subgraphs faster. To enhance the training of DQN, we leverage the search process to provide supervision in a pre-training stage and guide our agent during an imitation learning stage. Therefore, our framework allows search and reinforcement learning to mutually benefit each other. Experiments on synthetic and real-world large graph pairs demonstrate that our model outperforms state-of-the-art MCS solvers and neural graph matching network models.
PENINGKATAN KEMAMPUAN MEMBACA PEMAHAMAN MELALUI MODEL PEMBELAJARAN KONTEKSTUAL KOMPONEN PEMODELAN DAN BERTANYA PADA SISWA KELAS III SDN. 08 PEMATANG RAMBAI

The aim of this research is to imporove students’ reading ability on reading comprehension at the grade III students of SDN 08 Pematang Rambai through the using of contextual learning with components modelling and questioning. Classroom action research is the kind of research method that used by the writer. Subjects of this research belong to the grade III students of SDN 08 Pematang Rambai with 31 students. To get data of this research, observation and test were taken by the writer. In data analysis, the writer used qualitative data analysis and quantitative data analysis. The result of this research showed that the using of contextual learning with components modelling and questioning was improving students’ reading ability on reading comprehension at the grade III students of SDN 08 Pematang Rambai. This improvement took from cycle I until cycle II. In this case, the writer gained that the average of the students was improving from 67.9 to 74.4 or there was improvement from 61.2% to 80.6%.
Branding Fresh Fruits and Vegetables through Blockchain Technology: First Mover Advantage Towards Safe Produce

Food, health and education are the major sectors in any economy. The blockchain technology is entering healthcare sector and it takes time to implement in food sector but it has to be invested in education too with more online learning experience particularly by taking the learning out of campus for continuous learning and skilling.Among these major sectors food dominates being the basic and fundamental need of the economy and business. In this regard an effort is made to depict the application of blockchain for creating strong brands with safe produce. First mover advantage can be taken by any organisation that applies this technology to build trust among buyers.