Reviewerly/exHarmony huggingface dataset:
Subsequent Frame Prophecy

Abstract: Future frame prediction project aims at predicting future frames of a video given previous frames. If ‘n’ frames are given into the model (n being 19 in our case) our model predicts the n+1th frame in the sequence (i.e., 20th frame). The model used for prediction is a deep learning model - GAN (Generative Adversarial Model). The Generative adversarial model has 2 components: the generator which generates the 20th frame and the adversary (Critic) which compares the outputs of the generator with real outputs. The purpose of this comparison is to train both the generator and the critic in a cyclic fashion to the point where the generator can create almost real-looking outputs. Complex systems like aiming machines,self-driving cars, etc require a good level of correct future prediction to make their outputs correct. We aim at creating a common prediction model which can be generalized for any task and can be used in any such machine.
On the Current Situation and Problems of Investment in High-tech Industry in Anhui Province

Through the analysis of high-tech industry investment in Anhui Province, it is found that in 2021, the total amount of high-tech industry investment in Anhui province continues to grow and the scale of investment continues to expand, which plays a more and more obvious role in stimulating the economy. However, the development of various cities in the province is not coordinated. Most of the investment resources flow to Hefei, Wuhu and Ma'anshan. The investment in high-tech industries in other cities accounts for a relatively low proportion. In terms of industries, the investment in high-tech industries in Anhui Province is mainly concentrated in the pharmaceutical manufacturing industry, ignoring the development of information chemicals manufacturing industry. Compared with the surrounding provinces and cities, Anhui Province has some problems, such as small investment scale, low R&amp;D investment and insufficient innovation ability. In view of the problems existing in high-tech industry investment, this paper puts forward relevant suggestions from four aspects: improving the investment environment, adjusting the investment scale, increasing R&amp;D investment and improving innovation, so as to promote the development of high-tech industry investment in Anhui Province.
Analysis of the Effectiveness of Personality Education Subject for College Freshman

The study attempted to identify the effectiveness of the personality education subject at S University to help college freshmen to have a successful college life..The personality education subject is composed of contents to cultivate understanding of others and effective communication skills based on self-examination.A total of 15 weeks of online classes were offered to freshman college students in related liberal arts courses.The results of the study are as follows.In other words, after taking the personality education course, which was run as a compulsory liberal arts course for college freshmen, as online class for a total of 15 weeks, psychological maturity improved, but social maturity did not improve except for self-management ability.That is, positive thinking, emotional stability, self-identity, self-efficacy, self-esteem, and emotional sensitivity were improved, but interpersonal relationships, social affinity, school life adaptation, communication, self-management ability, social competence, social life adaptability, social efficacy, The sense of community did not improve significantly.Based on the results of this study, educational suggestions for the operation of personality education at universities are as follows.First, in recognition of the importance and necessity of character education, universities need to operate various character-related subjects and develop character education programs.Second, the university should systematically build a character education course for university students and provide systematic education on character from the moment they enter the university, helping them to live a life of self-confidence and understanding and consideration for others in society as well as in a successful university life.Third, when a personality course is operated as online class, it is necessary to recognize the need for a separate educational activity that enables university students to establish social relationships and practice community consciousness and values, and to devise a plan to include socialityrelated activities in the class.
Experimental Study of Language Models of "Transformer" in the Problem of Finding the Answer to a Question in a Russian-Language Text

The aim of the study is to obtain a more lightweight language model that is comparable in terms of EM and F1 with the best modern language models in the task of finding the answer to a question in a text in Russian. The results of the work can be used in various question-and-answer systems for which response time is important. Since the lighter model has fewer parameters than the original one, it can be used on less powerful computing devices, including mobile devices. In this paper, methods of natural language processing, machine learning, and the theory of artificial neural networks are used. The neural network is configured and trained using the Torch and Hugging face machine learning libraries. In the work, the DistilBERT model was trained on the SberQUAD dataset with and without distillation. The work of the received models is compared. The distilled DistilBERT model (EM 58,57 and F1 78,42) was able to outperform the results of the larger ruGPT-3-medium generative network (EM 57,60 and F1 77,73), despite the fact that ruGPT-3-medium had 6,5 times more parameters. The model also showed better EM and F1 metrics than the same model, but to which only conventional training without distillation was applied (EM 55,65, F1 76,51). Unfortunately, the resulting model lags further behind the larger robert discriminative model (EM 66,83, F1 84,95), which has 3,2 times more parameters. The application of the DistilBERT model in question-and-answer systems in Russian is substantiated. Directions for further research are proposed.
“An Analysis on Email Classification on Hindi Language using Bayesian Classifier”

Spam messages are one of the most important problems on the Internet today, costing businesses money and causing frustration to individual users. Spam filtering can assist with the issue in a variety of ways. The classifier-related challenges have been the focus of several spam filtering studies. Machine learning for a spam classification is now a significant research topic. The application of various machine learning techniques for categorizing spam messages from e-mail is investigated and identified in this research. Finally, with spam categorization, a comparative study of the algorithms has been presented.
Penerapan Model Project Based Learning Meningkatkan Kreativitas dan Hasil Belajar Muatan Pelajaran IPS

This research is motivated by observational data which shows that the results of observations and the acquisition of social studies learning outcomes are low. Student creativity in social studies learning is channeled as it should be. The average value of social studies learning outcomes has not reached the KKM. One of the efforts to overcome this is by applying a problem-based learning model. This study aims to determine the application of the Project-Based Learning model in increasing creativity and learning outcomes of social studies lessons for fourth-grade students at SD Negeri Melikan. The methodology used in this research is Classroom Action Research (CAR). The subjects were 11 students from the fourth-level SD Negeri Melikan. Data were collected utilizing observation, document analysis, and tests. The data analysis used is a descriptive analysis technique with qualitative and quantitative approaches. The research process is carried out in two cycles and each cycle includes four stages, namely 1) planning, 2) implementation, 3) observation, and 4) reflection. The results show that creativity and learning outcomes can be increased through the Project-Based Learning model. Students with creativity good category from pre-cycle 5 students (45.45%) to 7 students (63.63%) in the first cycle and 10 students (90.9%) in the second cycle. Social studies learning outcomes increased from the average an average of 68.64 in the pre-cycle to an average of 74.54 in the first cycle and an average of 80.9 in the second cycle. This study concludes that the application of the Project-Based Learning Model can improve creativity and learning outcomes for social studies lessons for grade IV at SDN Melikan Rongkop Gunungkidul in the 2021/2022 academic year.
Techniques, Applications and Issues of Text Mining

The Tremendous advancements in digital data collecting techniques have resulted in massive large datasets. unstructured data makes up well over 80% of today's data. The identification of wonderful ways and characteristics to learn text - based files from a massive amount of datasets are a major issue. Text mining /Data analytics is the process for identifying interesting and difficult problem patterns in vast quantities of text information. For mining textual material and uncovering useful facts for making predictions and decision making, there are various ways and methods available. Choosing an acceptable and wonderful text/word - based analysis approach improves speed and saves both time required to obtain useful data from huge amount of unstructured data . This research will highlight &amp; study all primary sources carefully and helps in understand few methods/technique’s for text mining. Keywords—Classification; Knowledge Discovery; Applications; Information Extraction; Patterns
Dataset Condensation via Efficient Synthetic-Data Parameterization

The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. However, the existing approaches have fundamental limitations in optimization due to the limited representability of synthetic datasets without considering any data regularity characteristics. To this end, we propose a novel condensation framework that generates multiple synthetic data with a limited storage budget via efficient parameterization considering data regularity. We further analyze the shortcomings of the existing gradient matching-based condensation methods and develop an effective optimization technique for improving the condensation of training data information. We propose a unified algorithm that drastically improves the quality of condensed data against the current state-of-the-art on CIFAR-10, ImageNet, and Speech Commands.
PENGARUH MANAJEMEN RANTAI PASOK BERBASIS SISTEM ERP DALAM MENINGKATKAN KINERJA PT UNILEVER

Supply chain management is an efficient method for preserving a company's competitive advantage and maximizing its production. ERP systems, in relation with Supply Chain Management, are useful tools for enhancing organizational performance and preserving competitive advantage. ERP systems are concerned with enhancing work operations and data utilization. This study aims to determine how ERP systems can assist distribution organizations in enhancing their supply chain management. This is a literature review that collects data from a variety of textbooks, scholarly articles, and credible information from companies and other reputable sources. The findings revealed that adopting a supply chain management system in order to become such a digital corporation involves and connects the entire company, outside of marketing, to the digital and physical worlds of consumers, and synchronizes activities across the organization. Unilever used real-time digital enterprises successfully to unleash value and facilitate corporate growth.
Methods of Formation of Professional Motivation of Students of Pedagogical Direction

The chapter is devoted to the formation of professional motivation of future teachers. The process of formation of professional motivation of a future teacher using the provided methods is also considered. The psychological and pedagogical features of motivating students-future teachers for professional growth are indicated, ways of improving the professionalism of future teachers, aimed at self-development and self-determination in the process of passing pedagogical practice, are revealed. The principles of pedagogical practice organization and factors influencing its effectiveness are revealed. The connection between the motivation of professional activity and the professional orientation of students and their personal trajectory of the development of professional skills and abilities is revealed. The relevance of the research is due to the state and social need to prepare a new generation that contributes to the development and prosperity of the state. The Sustainable Development Goals (SDGs) proclaimed around the world, which are reflected in the sustainable development program adopted by the world leaders of 193 countries under the auspices of the United Nations, are aimed at promoting sustainable economic growth, full and productive employment and decent work for all. The orientation of the UN Member States in creating their programs and developing political strategies for the next years (until 2030) on 17 goals, one of the first of which is SDG4: ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all. The realization of this goal requires the creation of certain social and economic conditions that provide an opportunity for everyone to receive a quality education, to be included in the system of continuing education, to study throughout life.
EVAC+: Multi-scale V-net with Deep Feature CRF Layers for Brain Extraction

Brain extraction is one of the first steps of pre-processing 3D brain MRI data and a prerequisite for any forthcoming brain imaging analyses. However, it is not a simple segmentation problem due to the complex structure of the brain and human head. Although multiple solutions have been proposed in the literature, we are still far from having truly robust methods. While previous methods have used machine learning with structural/geometric priors, with the development of Deep Learning (DL), there has been an increase in proposed Neural Network architectures. Most models focus on improving the training data and loss functions with little change in the architecture. However, the amount of accessible training data with expert-labelled ground truth vary between groups. Moreover, the labels are created not from scratch but from outputs of non-DL methods. Thus, most DL method's performance depend on the amount and quality of data one has. In this paper, we propose a novel architecture we call EVAC+ to work around this issue. We show that EVAC+ has 3 major advantages compared to other networks: (1) Multi-scale input with limited random augmentation for efficient learning, (2) a unique way of using Conditional Random Fields Recurrent Layer and (3) a loss function specifically created to enhance this architecture. We compare our model to state-of-the-art non-DL and DL methods. Results show that even with little change in the traditional architecture and limited training resources, EVAC+ achieves a high and stable Dice Coefficient and Jaccard Index along with a desirable lower surface distance. Ultimately, our model provides a robust way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of brain.
2-step Gradient Boosting approach to selectivity bias correction in tax audit: an application to the VAT gap in Italy

Tax evasion is the illegal evasion of taxes by individuals, corporations, and trusts. The revenue loss from tax avoidance can undermine the effectiveness and equity of the government policies. A standard measure of tax evasion is the tax gap, that can be estimated as the difference between the total amounts of tax theoretically collectable and the total amounts of tax actually collected in a given period. This paper presents an original contribution to bottom-up approach, based on results from fiscal audits, through the use of Machine Learning. The major disadvantage of bottom-up approaches is represented by selection bias when audited taxpayers are not randomly selected, as in the case of audits performed by the Italian Revenue Agency. Our proposal, based on a 2-steps Gradient Boosting model, produces a robust tax gap estimate and, embeds a solution to correct for the selection bias which do not require any assumptions on the underlying data distribution. The 2-steps Gradient Boosting approach is used to estimate the Italian Value-added tax (VAT) gap on individual firms on the basis of fiscal and administrative data income tax returns gathered from Tax Administration Data Base, for the fiscal year 2011. The proposed method significantly boost the performance in predicting with respect to the classical parametric approaches.
Working memory control predicts fixation duration in scene-viewing

When viewing scenes, observers differ in how long they linger at each fixation location and how far they move their eyes between fixations. What factors drive these differences in eye-movement behaviors? Previous work suggests individual differences in working memory capacity may influence fixation durations and saccade amplitudes. In the present study, participants (N = 98) performed two scene-viewing tasks, aesthetic judgement and memorization, while viewing 100 photographs of real-world scenes. Working memory capacity, working memory processing ability, and fluid intelligence were assessed with an operation span task, a memory updating task, and Raven’s Advanced Progressive Matrices respectively. Across participants, we found significant effects of task on both fixation durations and saccade amplitudes. At the level of each individual participant, we also found a significant relationship between memory updating task performance and participants’ fixation duration distributions. However, we found no effect of fluid intelligence and no effect of working memory capacity on fixation duration or saccade amplitude distributions, inconsistent with previous findings. These results suggest that the ability to flexibly maintain and update working memory is strongly related to fixation duration behavior.
Pengembangan Sistem Pemantauan Aktivitas Pengawasan Satpam dengan Proses Validasi Dinamis QR-Code pada Aplikasi Patrolee

Internet of Things (IoT) technology provides system integration to accelerate wireless remote data acquisition. This paper presents the implementation of IoT based applications to monitor security guard surveillance activities in the campus environment. The traditional reporting system still has many limitations. The security guard should make a written report to his supervisor and send the surveillance images using a third-party instant messaging application. The reporting results are not integrated. Implementation of IoT system which consists of Dynamic QR-code Display, Android Application, Web Application, and Cloud Database, enables the integration and real-time reporting system. Security guard reports can be seen directly, both written reports and the surveillance image, using a web application. Each security guard on duty has its unique QR code for each location and current surveillance schedule. It makes the security guard cannot ask their duties to other people. The results of this research show that this system has been able to meet the needs of security officers. The success rate of security guards when doing the task using this application is 90%.
Students’ self-educational activity actualization based on the mixed learning technology implementation

The phenomenon of self-educational activity as a factor of ensuring prospective specialists quality of education and successful training is actualized in this paper. The need for innovative technologies development contributing to the self-educational activities actualization and meeting both patterns of students development, their needs, and modern trends in the educational process organization at the university is indicated here. Noting the advantages of traditional and distance learning forms, their disadvantages are also identified when applied separately. Students needs in terms of choosing the format of study at the university are analyzed. The necessity to use the students digital potential with benefit and for the benefit, which consists in the fact that modern youth easily own various gadgets and spend most of their time on the Internet is shown in the paper. Based on the theoretical analysis carried out, the essence and content of the mixed learning technology is presented as an integration of traditional and electronic formats, organically combining pedagogical components, collectively ensuring the actualization of students self-educational activities. The content of the mixed learning technology is revealed as a series of consistently implemented stages (motivational, integration-activity, reflexive-evaluative). Reflection and formative assessment are of great importance at all stages of its implementation. A high degree of flexibility and adaptability of this technology in the educational space of the university is indicated in the paper.
WORD STRESS ERRORS MADE BY ENGLISH EDUCATION MASTER’S PROGRAM STUDENTS

This study investigates English Master’s Program students in producing suprasegmental features, namely word stress. The focus is on analysing disyllabic noun-verb words in six video presentations and what are the factors that affect word stress errors from 19 English master program students. The data were obtained from transcribing the videos and selecting the disyllabic noun-verb words from the transcriptions. Furthermore, a questionnaire was used to gather information about students’ background knowledge in learning English, especially linguistics. Qualitative content analysis was employed in this study. From those videos, there were 1,089 disyllabic noun-verb words produced by the students. The stress errors made were 15 words. The stress errors mostly occurred in the first syllable and no word stress. The findings showed the factors that affected the wordstress errors were students’ native language, Bahasa Indonesia. Bahasa Indonesia did nothave word-stress to differ a meaning, only for emphasis. The questionnaire showed that all students were Indonesian and only two of them used English as their native language. The background knowledge and gender had some effects on stress errors, but not vital. The results indicated that students needed to have a deeper understanding of word stress.