Reviewerly/exHarmony huggingface dataset:
Visual Data Science for Industrial Applications

Advances in sensor and data acquisition technology and in methods of data analysis pose many research challenges but also promising application opportunities in many domains. The need to cope with and leverage large sensor data streams is particularly urgent for industrial applications due to strong business competition and innovation pressure. In maintenance, for example, sensor readings of machinery or products may allow to predict at which point in time maintenance will be required and allow to schedule service operations respectively. Another application is the discovery of the relationships between production input parameters on the quality of the output products. Analysis of respective industrial data typically cannot be done in an out-of-the-box manner but requires to incorporate background knowledge from fields such as engineering, operation research, and business to be effective. Hence, approaches for interactive and visual data analysis can be particularly useful for analyzing complex industrial data, combining the advantages of modern automatic data analysis with domain knowledge and hypothesis generation capabilities of domain experts. In this chapter, we introduce some of the main principles of visual data analysis. We discuss how techniques for data visualization, data analysis, and user interaction can be combined to analyze data, generate and verify hypotheses about patterns in data, and present the findings. We discuss this in the light of important requirements and applications in the analysis of industrial data and based on current research in the area. We provide examples for visual data analysis approaches, including condition monitoring, quality control, and production planning.
Analysis of Ghana’s Gross Domestic Product from 1960 - 2019

The most popular metric for assessing or predicting global economic progress is the GDP.The objective of this report was to analyze the GDP of Ghana from the period 1960 -2019.Secondary data was obtained, and trend analysis was done using the linear, quadratic, and exponential trend models to determine which model best fits the dataset.Trend analysis is a technique for examining and forecasting the movements of an item based on current and historical data.The results from the trend analysis showed that the exponential trend model had the lowest Mean Absolute Percent Error (MAPE), Mean Absolute Deviation (MAD), and Mean Square Deviation (MSD).When compared to the other models, the exponential trend model fits the dataset better, which is why it was chosen to forecast Ghana's GDP.The forecast showed that Ghana's GDP is expected to grow in the coming years to about $77 billion by 2026.Agriculture is considered as the backbone of Ghana and the country imports majority if its fertilizer from Russia.The challenges the Ghanaian economy is currently facing due to inflation, the global pandemic (COVID-19), and the Russia-Ukraine conflict could have an impact on the country' economic growth.Moreover, a change in leadership in the coming 2024 presidential election, could also have an impact on the projection.There are both positive and negative effects due to changes in leadership on economic growth.
The Degree of Practicing Transformational Leadership by Education Managers in Karak Governorate from the Point of View of Educational Supervisors

The study aimed to find out the degree of practice of transformational leadership by education managers in Karak governorate from the point of view of educational supervisors.The study sample consisted of educational supervisors in Karak Governorate; With (207) male and female supervisors in the first semester of the academic year (2021-2022), the study used the descriptive survey method.To achieve the objectives of the study, the study tool was developed and its validity and reliability were confirmed by scientific methods.To analyze the results, the arithmetic means and standard deviations were used, and the researcher used the "T" test to test the significance of the differences for the study variables.The results of the study showed that the degree of practice of transformational leadership by education managers in Karak governorate from the point of view of educational supervisors was low, and that there were no statistically significant differences in the answers of the study sample due to the practical experience variable, and there were statistically significant differences attributed to the educational qualification variable in favor of postgraduate studies. .In the light of the results of the study, the researcher recommended a number of recommendations, the most important of which are: the necessity of conducting similar studies on transformational leadership on other educational institutions.
Exploration on Teaching Reform of Comprehensive Chemistry Experiment in Normal Universities

Comprehensive chemistry experiment is a compulsory course for college chemistry majors, which is in a connecting position in the curriculum system.In view of the shortcomings in the traditional comprehensive chemistry experiment teaching, this paper discusses the reform from the aspects of the selection of teaching content and the evaluation method of the innovative experiment course of teaching methods.On this basis, the students' comprehensive chemistry experiment literacy, innovation consciousness and comprehensive experiment ability are comprehensively cultivated.Cultivating normal college students with both applied ability and practical teaching ability.
Teacher Perceptions and Professional Experience of E-Learning and Practical Subjects: Views of Selected Teachers in the Shiselweni Region of Eswatini

Several institutions amplected the tremendous variants technology offers in the advent of the covid-19 outbreak, bringing a paradigm shift in various fields regardless of digital competence and availability of resources.Many sectors, education inclusive had to adjust to the "new normal" transforming education drastically with a distinctive rise of e-learning as a pandemic management tool for tumultuous circumstances.E-learning was globally promoted to replace the conventional approach whereby teaching was undertaken remotely on digital platforms (Dhawan, 2020;Li & Lalani, 2020).The study evaluated primary teacher perceptions of the effectiveness of e-learning on practical subjects in 10 selected primary schools in the Shiselweni region of Eswatini.Practical subjects focus on two parts, theory and practice but practical sessions have huge significance in the learning process.Practical subject teaching posed a challenge to teachers during the covid-19 outbreak.The study was investigated by the baseline approach because it flags concerns by showing positive and negative impacts created during and after the study.Twenty-five (25) teachers were surveyed by convenience, snowballing, and random sampling procedures.Data interpretations epitomize that although the outbreak led to the sudden shutdown of schools, which was unprecedented, teachers managed to disseminate lessons by exploring different communication channels to connect learners, teachers, and parents.These were tailored toward the schedules and needs of families to ensure educational continuity.Engagement tools considered and utilized were messaging groups like WhatsApp, online conferences, e-mails, media, and other social media platforms to cater to all-inclusive instruction (UNICEF Romania, 2020).Teachers had fairly good knowledge of their subject content but no mastery over electronic tools for e-learning.E-learning demands acquired or learned skills, determination, strong self-motivation, and time management from both the teacher and learners.Amplecting newer educational technologies take time to implement effectually hence, a combined effort from the ministry of education, institutional management, teachers, and learners' inclusiveness is of paramount importance.The study recommends that teachers must grow and learn along with their learners for added benefits as the world advances.
Off-policy and on-policy reinforcement learning with the Tsetlin machine

Abstract The Tsetlin Machine is a recent supervised learning algorithm that has obtained competitive accuracy- and resource usage results across several benchmarks. It has been used for convolution, classification, and regression, producing interpretable rules in propositional logic. In this paper, we introduce the first framework for reinforcement learning based on the Tsetlin Machine. Our framework integrates the value iteration algorithm with the regression Tsetlin Machine as the value function approximator. To obtain accurate off-policy state-value estimation, we propose a modified Tsetlin Machine feedback mechanism that adapts to the dynamic nature of value iteration. In particular, we show that the Tsetlin Machine is able to unlearn and recover from the misleading experiences that often occur at the beginning of training. A key challenge that we address is mapping the intrinsically continuous nature of state-value learning to the propositional Tsetlin Machine architecture, leveraging probabilistic updates. While accurate off-policy, this mechanism learns significantly slower than neural networks on-policy. However, by introducing multi-step temporal-difference learning in combination with high-frequency propositional logic patterns, we are able to close the performance gap. Several gridworld instances document that our framework can outperform comparable neural network models, despite being based on simple one-level AND-rules in propositional logic. Finally, we propose how the class of models learnt by our Tsetlin Machine for the gridworld problem can be translated into a more understandable graph structure. The graph structure captures the state-value function approximation and the corresponding policy found by the Tsetlin Machine.
PII-PSM: A New Targeted Password Strength Meter Using Personally Identifiable Information

In recent years, unending breaches of users’ personally identifiable information (PII) have become increasingly severe, making targeted password guessing using PII a practical threat. However, to our knowledge, most password strength meters (PSMs) only consider the traditional trawling password guessing threat, and no PSM has taken into account the more severe targeted guessing threat using PII (e.g., name, birthday, and phone number). To fill this gap, in this paper, we mainly focus on targeted password strength evaluation in the scenario where users’ PII is available to the attacker. First, to capture more fine-grained password structures, we introduce the high-frequency substring as a new grammar tag into leading targeted password probabilistic models TarGuess-I and TarMarkov, and propose TarGuess-I-H and TarMarkov-H. Then, we weight and combine our two improved models to devise PII-PSM, the first practical targeted PSM resistant to common PII-accessible attackers. By using the weighted Spearman (WSpearman) metric recommended at CCS’18, we evaluate the accuracy of our PII-PSM and its counterparts (i.e., our TarGuess-I-H and TarMarkov-H, as well as two benchmarks of Optimal and Min_auto). We conduct evaluation experiments on password datasets leaked from eight high-profile English and Chinese services. Results show that our PII-PSM is more accurate than TarGuess-I-H and TarMarkov-H, and is closer to Optimal and Min_auto, with WSpearman differences of only 0.014 $$\sim $$ 0.023 and 0.012 $$\sim $$ 0.031, respectively. This establishes the accuracy of PII-PSM, facilitating to nudge users to select stronger passwords.
eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity.
Multi-Channel Expression Recognition Network Based on Channel Weighting

Accurate expression interpretation occupies a huge proportion of human-to-human communication. The control of expressions can facilitate more convenient communication between people. Expression recognition technology has also been transformed from relatively mature laboratory-controlled research to natural scenes research. In this paper, we design a multi-channel attention network based on channel weighting for expression analysis in natural scenes. The network mainly consists of three parts: Multi-branch expression recognition feature extraction network, which combines residual network ResNet18 and ConvNeXt network ideas to improve feature extraction and uses adaptive feature fusion to build a complete network; Adaptive Channel Weighting, which designs adaptive weights in the auxiliary network for feature extraction, performs channel weighting, and highlights key information areas; and Attention module, which designs and modifies the spatial attention mechanism and increases the proportion of feature information to accelerate the acquisition of important expression feature information areas. The experimental results show that the proposed method achieves better recognition efficiency than existing algorithms on the dataset FER2013 under uncontrolled conditions, reaching 73.81%, and also achieves good recognition accuracy of 89.65% and 85.24% on the Oulu_CASIA and RAF-DB datasets, respectively.
Information Teaching Mode of Mechanical Drawing in Higher Vocational College Based on Big Data Technology

Mechanical drawing is one of the core courses of mechanical specialty in higher vocational and technical schools.Students learn the basic knowledge of mechanical drawing.Higher vocational students can basically achieve the basic ability of reading paper and using Auto CAD to draw graphics skillfully.In higher vocational and technical schools, the teaching of traditional mechanical drawing course generally adopts mechanical exercises to improve the reading ability and drawing ability of drawings.Students in higher vocational and technical schools lack the initiative to learn theoretical knowledge.In order to improve students' initiative in learning knowledge and improve teachers' teaching effect, the information teaching mode of mechanical drawing in higher vocational colleges based on big data technology is applied to the classroom.By using the video teaching of-micro-class under big data technology, the text content of the textbook is static and boring, which is presented through wonderful pictures, moving pictures or animation.Through the test and evaluation of the effect of mechanical students using micro-class teaching, this paper studies the method of using test questions and questionnaires to compare the results before (60 points) and after use (72 points).250questionnaires were received to evaluate the effect of micro-class teaching and interview teachers.Finally, it is found that through the use of micro-class teaching, the students generally do not want to think, passive learning, waiting for teachers to learn habits, so that they are willing to learn, active learning, and finally make the quality of teaching and learning in secondary vocational schools can be substantially improved.
The psychological approach to the teaching and learning of mathematics in early childhood education

The present work aims to determine the importance that teachers give to the precepts of psychological approaches in the process of teaching and learning Mathematics in children of primary education, of a Public Educational Institution in the city of Guayaquil, province of Guayas, Ecuador. The methodology is descriptive, with a field design. The population under study was made up of twenty-five teachers (25) of the primary education stage. The information was collected through an interview and through a Likert scale questionnaire. The instrument was validated through expert judgment and reliability through the Alpha Cronbach coefficient. A quantitative analysis of the data was performed using descriptive statistics. Results: regarding the starting diagnosis; 36% always indicated; on the realization of the planning based on the postulates of the theories that exist on the teaching-learning process; 44% said always; Regarding activities for the strategic development of logical or operational thinking in the child, 60% always said; Regarding the use of recreational activities as a strategy for teaching and learning mathematics in primary school children, 80% always stated that psychological approaches favor the teaching and learning of mathematics in primary school children in a world in constant transformation, 0% always responded. In short, it is evident that the intention of the study of Mathematics from childhood must be aimed at providing the student with a positive vision of this science, in such a way that they acquire the skills of abstraction, logic and critical thinking, with the necessary support of psychological approaches for the adequate teaching and learning of mathematics in children of primary education.
Sikap Bahasa Mahasiswa Multikultural di Fakultas Brahma Widya Universitas Hindu Negeri I Gusti Bagus Sugriwa Denpasar Terhadap Mata Kuliah Bahasa Bali

The existence of Balinese language courses at a university received various responses from students at the university. Moreover, a university has students who come from different cultures, such as multicultural students at the Brahma Widya Faculty of UHN IGB Sugriwa Denpasar. The responses that come out cause different language attitudes between students from one another. Based on this background, the problems discussed in this study are how the language attitudes of multicultural students at the Brahma Widya Faculty of UHN IGB Sugriwa Denpasar towards Balinese language courses. This study aims to describe the language attitudes of multicultural students at the Brahma Widya Faculty of UHN IGB Sugriwa Denpasar towards Balinese language courses. The population of this research is the students of UHN IGB Sugriwa Denpasar, precisely at the Brahma Widya Faculty, Department of Hindu Theology in the 2021/2022 academic year with a total of 29 students selected through the total sampling technique. The method of data collection was carried out using a language attitude questionnaire/questionnaire using three characteristics of language attitudes according to Gavin and Mathiot. The data analysis technique used is descriptive qualitative which is then followed by qualitative verification. The results showed that the language attitude of the students of the Brahma Widya Faculty of UHN IGB Sugriwa Denpasar showed a tendency for positive language attitudes in Balinese language courses. This can be seen from the three characteristics of students' positive language attitudes, namely language loyalty, language pride, and awareness of norms in the main language of Balinese. Even though they come from areas with different cultures, these multicultural students have great motivation in learning languages, especially Balinese language is included in the courses they get during their education at UHN IGB Sugriwa Denpasar.
Encrypting Controllers via Input-Output Representation by Ring-LWE based Cryptosystem

Since security issues have emerged significantly with sophisticated attacks against cyber-physical systems, the notion of encrypted control using homomorphic encryption was proposed, which enables controllers to compute over encrypted data without decryption on the cyber layer. There have been several results providing methods to encrypt various controllers and systems, however, encrypting dynamic controllers has remained challenging due to the limited number of repeated homomorphic operations available on ciphertexts. This paper proposes a method to encrypt linear dynamic controllers using Ring-LWE (Learning With Errors) based cryptosystem, by transforming them into input-output representation, where the controller output is expressed as a linear combination of previous inputs and outputs. This approach avoids recursive homomorphic operations at the encrypted controller, with the additional help of re-encryption, so that the controller is able to operate for infinite-time horizon without growing encryption errors. Furthermore, we utilize properties of Ring-LWE based cryptosystem in order to reduce the number of homomorphic operations inside the controller compared to previous results, without involving any additional algorithms such as key switching, besides homomorphic addition and multiplication.
Donor Advocacy: the Need for an Ethically, Legally, and Morally Informed Framework for Adult Donors for Allogeneic Hematopoietic Cell Transplant

In this paper, we propose a hybrid and exploratory knowledge discovery approach for analyzing metabolomic complex data based on a combination of supervised classifiers, pattern mining and Formal Concept Analysis (FCA). The approach is based on three main operations, preprocessing, classification, and postprocessing. Classifiers are applied to datasets of the form individuals × features and produce sets of ranked features which are further analyzed. Pattern mining and FCA are used to provide a complementary analysis and support for visualization. A practical application of this framework is presented in the context of metabolomic data, where two interrelated problems are considered, discrimination and prediction of class membership. The dataset is characterized by a small set of individuals and a large set of features, in which predictive biomarkers of clinical outcomes should be identified. The problems of combining numerical and symbolic data mining methods, as well as discrimination and prediction, are detailed and discussed. Moreover, it appears that visualization based on FCA can be used both for guiding knowledge discovery and for interpretation by domain analysts.
Comparison between Generative Model and Discriminant Model Based on Support Vector Nachine Algorithm

Video target tracking is an important research direction in the field of computer vision, and plays an important role in artificial intelligence and big data applications.This paper mainly studies the comparison between generative model and discriminant model based on support vector machine (SVM) algorithm.This paper constructs MSA model and Structured SVM algorithm model according to the generation model and discriminant model classical target tracking algorithm respectively.This paper mainly uses OTB and TC-128 data sets as test data sets to conduct performance comparison experiments on target tracking algorithms of generative model and discriminant model.It can be seen from the experimental results that the Structured SVM model is superior to the MSA model in all aspects.