Reviewerly/exHarmony huggingface dataset:
Differentially Private Kernel Support Vector Machines Based on the Exponential and Laplace Hybrid Mechanism

Support vector machines (SVMs) are among the most robust and accurate methods in all well-known machine learning algorithms, especially for classification. The SVMs train a classification model by solving an optimization problem to decide which instances in the training datasets are the support vectors (SVs). However, SVs are intact instances taken from the training datasets and directly releasing the classification model of the SVMs will carry significant risk to the privacy of individuals, when the training datasets contain sensitive information. In this paper, we study the problem of how to release the classification model of kernel SVMs while preventing privacy leakage of the SVs and satisfying the requirement of privacy protection. We propose a new differentially private algorithm for the kernel SVMs based on the exponential and Laplace hybrid mechanism named DPKSVMEL. The DPKSVMEL algorithm has two major advantages compared with existing private SVM algorithms. One is that it protects the privacy of the SVs by postprocessing and the training process of the non-private kernel SVMs does not change. Another is that the scoring function values are directly derived from the symmetric kernel matrix generated during the training process and does not require additional storage space and complex sensitivity analysis. In the DPKSVMEL algorithm, we define a similarity parameter to denote the correlation or distance between the non-SVs and every SV. And then, every non-SV is divided into a group with one of the SVs according to the maximal value of the similarity. Under some certain similarity parameter value, we replace every SV with a mean value of the top-k randomly selected most similar non-SVs within the group by the exponential mechanism if the number of non-SVs is greater than k. Otherwise, we add random noise to the SVs by the Laplace mechanism. We theoretically prove that the DPKSVMEL algorithm satisfies differential privacy. The extensive experiments show the effectiveness of the DPKSVMEL algorithm for kernel SVMs on real datasets; meanwhile, it achieves higher classification accuracy than existing private SVM algorithms.
Philosophical Discourse for the Development and Improvement of the Intellectualization of Human Nature

The principal goal of the study is to characterize the philosophical discourse of the development and improvement of human nature in the context of its intellectualization. The article uses a set of methods that make it possible to reveal a philosophical analysis of the development and improvement of human nature in the context of its intellectualization, in particular: general scientific methods; logical methods of theoret- ical analysis; technical analysis, clarification. Because of the study, the philosophical aspects of the devel- opment and improvement of human nature in the context of its intellectualization were characterized. The needs of the modern economic, political, spiritual development of society require further, deeper research of the actual problems of the doctrine of man. Therefore, interest in man and humanity is becoming espe- cially acute, prompting to justify social and cultural transformations.
THE SCHOOL AND THE TEACHER: CHANGES IN STUDENT PERCEPTION DURING THE PANDEMIC

The relationship between the learning environment and the learning behaviours has long been of interest in educational literature. When addressing the socioemotional stages, Erickson raises awareness of the psycho-social influence of school by way of diligence vs inferiority (Harwood et al., 2010), while Galos and Aldridge (2020) explore how designing a learning environment focused on student self-efficacy triggers statistically significant differences in 4 (out of 9) areas of analysis: fairness, task clarity, learning responsibility and task achievement. The aim of the present study is to highlight the significance and the differences in the main student psychosocial representations of school and teachers before and during the pandemic, the latter being characterised by government-imposed restrictions as well as changes in the student-teacher interaction, both during the second school term of 2019-2020 and the two school terms of the academic year 2020-2021. The areas we intend to explore are: overall attitude to school and student emotional states, the perception on teacher and peer relations, the perception on school as an organisation but also as a learning environment, the parents as a filter on schoolrelated perceptions, and the projective dimension on school life. The resulting statistical analysis (both nonparametric tests for independent groups and correlation) reveals major changes in the student perception on school and teachers, which will require systematic future intervention, as well as an upgrade of educational strategies, considering that the approaches designed and applied during the pandemic proved unable to compensate for the changes brought about by the restrictions on learning.
Inflation Rate Forecasting using Extreme Learning Machine and Improved Particle Swarm Optimization

Inflation is an important tool to assess the current condition of a nation economy.Uncontrolled inflation rate may have a lot of negative impacts on economic development.Forecasting can be used to control the inflation by making appropriate economic policies.However, uncertainty pattern of the inflation rate may make it hard to forecast.This study proposes an extreme learning machine (ELM) for the inflation rate forecasting.As part of the machine learning algorithm, the ELM has an ability to address uncertainty in data input pattern.However, ELM had weakness in determining initial weights that may produce inaccurate results.So, we propose particle swarm optimization (PSO) to determine good initial weights for the ELM.PSO is a metaheuristic method that gives good results in local searches but requires longer computation time to locate its particles on the global optimum point in the vast search space area.To overcome this problem, auto-speed acceleration algorithm is employed to drive particles of the PSO in searching of the global optimum with lower computation time.The performance of the proposed approach is evaluated using root mean square error (RMSE).A series of computational experiments prove that the proposed approach achieves better results with the average RMSE of 0.01926.This result is better than RMSE of 0.02020 achieved by the original version of ELM.
Teaching Software Engineering using Abstraction through Modeling

Teaching software engineering (SWE) as a core computer science course (ACM, 2013) is a challenging task.The challenge lies in the emphasis on what a large-scale software means, implementing teamwork, and teaching abstraction in software design while simultaneously engaging students into reasonable coding tasks.The abstraction of the system design is perhaps the most critical and theoretical part of the course and requires early engagement of the students with the necessary topics followed by implementation of the abstract model consistently.Normally, students would take such courses in the undergraduate curriculum sequence after data structures and/or object-oriented design/programming.Therefore, they would be able to learn about systematic modeling of software as a system.In this work, we address how to facilitate the teaching of SWE by introducing abstract modeling.Furthermore, functional decomposition is reviewed as a critical component which in turn, requires understanding of how different tasks are accomplished by enterprise software.Combining such pieces with concepts of architecture and design patterns of software provides foundational knowledge for students to be able to navigate around enterprise software in the real world.
A Blended Collaborative Teaching Mode in Language Learning Based on Recommendation Algorithm

With the continuous expansion of economic globalization, English, as the official language in the world, has a high popularity. It can be related to a better development in the future. Meanwhile, with the continuous progress of technology in modern society, multimedia online teaching has become the main means in colleges and universities, which especially brings great changes to foreign language teaching. However, foreign language teaching is facing many difficulties such as large number of contents, less lessons and low participation of students in class. Therefore, the reform of college foreign language teaching is imperative. Based on blended collaborative teaching mode, the paper has designed a “Three Classes” composed of “Communication Class”, “Famous Teacher Class” and “Elite Online Class”. Teaching process has been designed according to the cognitive process of “Previewing before class, consolidating in class and expanding after class”. In addition, recommendation algorithm has been used to collect data on four dimensions, namely, involvement in foreign language learning, content completion of foreign language learning, interaction in foreign language learning and effectiveness of foreign language learning. A performance evaluation system for foreign language learning supported by big data has been established. The results of teaching practice have proved that the blended collaborative teaching mode implemented in the study can be integrated into the whole teaching process through intelligent data analysis, information sharing and technology application, improving teaching quality, enhancing the interaction between teachers and students, which is worthy of promotion and application.
PENERAPAN MODEL PEMBELAJARAN FLIPPED CLASSROOM BERBANTUAN GOOGLE SITES UNTUK MENINGKATKAN KEAKTIFAN DAN HASIL BELAJAR IPS

The objectives to be achieved in this study are: (1) To find out whether the application of the Google Sites-Assisted Flipped Classroom learning model can increase student learning activity in the Social Sciences subject for class VIIIA semester 1 of Veteran 1 Manyaran Middle School in the 2021/2022 academic year. (2) To find out whether the Flipped Classroom Learning Model Assisted by Google Sites can improve the learning outcomes of SMP Class VIIIA students in social studies subjects. This research is expected to be useful: (1) For teachers as input on the use of the Google Sites-Assisted Flipped Classroom Learning Model in social studies learning to increase student learning activity, (2) For students, it is expected to increase activeness and learning outcomes in social studies subjects. The results showed that the application of the Google Sites-Assisted Flipped Classroom learning model was able to increase student activity from the pre-cycle learning activity percentage of 31%, increasing in cycle 1 to 75%, and in cycle 2 to 94%. That the Flipped Classroom Learning Model Assisted by Google Sites is able to improve student learning outcomes from an average of 72.50 in the pre-cycle to an average of 75.63 in cycle 1 and 80.63 in cycle 2. In the end, the researcher hopes that the results of this classroom action research can be used by teachers as an alternative in learning to increase student interest and learning outcomes in social studies in particular, and all subjects in general.&#x0D; ABSTRAKTujuan yang akan dicapai dalam penelitian ini adalah: (1) Untuk mengetahui apakah penerapan model pembelajaran Flipped Classroom Berbantuan Google Sites dapat meningkatkan keaktifan belajar siswa pada mata Pelajaran IPS kelas VIIIA semester 1 SMP Veteran 1 Manyaran Tahun Pelajaran 2021/2022. (2) Untuk mengetahui apakah Model Pembelajaran Flipped Classroom Berbantuan Google Sites dapat meningkatkan hasil belajar siswa SMP Kelas VIIIA dalam mata pelajaran IPS. Penelitian ini diharapkan dapat bermanfaat: (1) Bagi guru sebagai bahan masukan tentang penggunaan Model Pembelajaran Flipped Classroom Berbantuan Google Sites dalam pembelajaran IPS untuk meningkatkan keaktifan Belajar belajar siswa, (2) Bagi siswa diharapkan dapat meningkatkan keaktifan dan hasil belajar pada mata pelajaran IPS. Hasil Penelitian menunjukkan bahwa penerapan model pembelajaran Flipped Classroom Berbantuan Google Sites mampu meningkatkan keaktifan siswa dari prosentase keaktifan belajar pra siklus sebesar 31 % meningkat pada siklus 1 menjadi 75 %, dan pada siklus 2 menjadi 94 %. Bahwa Model pembelajaran Flipped Classroom Berbantuan Google Sites mampu meningkatkan hasil belajar siswa dari rata-rata 72,50 pada pra siklus menjadi rata-rata 75,63 pada siklus 1 dan 80,63 pada siklus 2. Pada akhirnya peneliti berharap hasil penelitian tindakan kelas ini dapat digunakan oleh para guru sebagai salah satu alternatif dalam pembelajaran untuk meningkatkan minat dan hasil belajar siswa dalam pelajaran IPS khususnya, dan semua mata pelajaran pada umumnya.
Cognitive Development of Children Aged 4-7 Years During The Covid-19 Pandemic

Cognitive development is one aspect of child development that is very important to get intervention during the COVID-19 pandemic, Cognitive development is a development that is closely related to thinking skills, such as reasoning, memory, and the ability to solve practical problems. The cognitive mindset of early childhood is very much different from people who are teenagers and adults or even children who are in middle school and above. Elementary school age children have limitations in thinking about abstract things, cognitive development has a correlation with other developments and will have an impact especially on children's academic abilities. The purpose of this paper is to examine how the cognitive development of early childhood during the covid-19 pandemic and what strategies are carried out by parents in developing cognitive development of early childhood during the covid-19 pandemic? Based on the formulation of the problem above, the data analysis technique used in this study is an interactive model of data analysis in analysing the data carried out in three ways, namely data reduction activities, data presentation and also data conclusions. as well as parents. These results and discussions describe various issues related to cognitive development problems such as several barriers to children's activities and activities that have an impact on children's cognitive during the COVID-19 pandemic in Pangkalan Kuras District, Pelalawan Regency, Riau Province. In conclusion, this study aims to describe the cognitive development of early childhood during the COVID-19 pandemic and provide views regarding several activities that can be internalized to stimulate children's cognitive development.
Virtual Mouse using OpenCV

Abstract: This research introduces a novel method for controlling mouse movement with a real-time camera. Adding more buttons or repositioning the mouse's tracking ball are two common ways. Instead, we recommend that the hardware be redesigned. Our idea is to employ a camera and computer vision technologies to manage mouse tasks (clicking and scrolling), and we demonstrate how it can do all that existing mouse devices can. This project demonstrates how to construct a mouse control system.
Entropy Based Monotonic Task Scheduling and Dynamic Resource Mapping in Federated Cloud Environment

Multiple cloud computing services are collectively managed as federated cloud.The growth of users into this federated cloud for accessing a variety of services has introduced challenges on resource utilization and load imbalance that consumes larger waiting time of users.To address these two issues, this paper proposes data center (DC) clustering, virtual machine (VM) clustering, resource mapping and task scheduling.Initially the DCs are clustered using region based fuzzy possibilistic C-means clustering (R-FPCM) algorithm.DC clustering is performed by gathering the information of data dependency, million instructions per second (MIPS), latency, storage, bandwidth and counts of VM.Depending on DC clustering, the VMs are clustered by multi-objective density-based spatial clustering based on the estimated capacity and bandwidth.In order to balance load, Markov chain is applied to predict future load and balance accordingly.An intermediate broker is employed to monitor the VM resources and map based on the user's task requirement.Service level agreement (SLA) is satisfied for every user by the broker and then fast 1 to N resource mapping algorithm is involved for mapping resources.This is followed by entropy-based monotonic scheduling algorithm that arranges user tasks in an order that is determined from task type, task size, task arrival time and deadline.The dynamic computation of entropy enables to improve scheduling with the current status of the system.The extended simulations of this proposed system are simulated in Cloudsim and the results are evaluated in terms of execution time, latency, resource utilization and response time and compared with the performance of Capacity based VM clustering algorithm.These simulation results show that the performance of the proposed system is much better than the existing approach.
Price Analysis and Forecasting for Bitcoin Using Auto Regressive Integrated Moving Average Model

Abstract This paper investigated Bitcoin daily closing price using time series approach to predict future values for financial managers and investors. Daily data were sourced from CoinDesk, with Bitcoin Price Index (BPI) for 5 years (January 1, 2016 to May 31, 2021) extracted. Data analysis and modelling of price trend using Autoregressive Integrated Moving Average (ARIMA) model was carried out, and a suitable model for forecasting was proposed. Results showed that ARIMA(6,1,12) model was the most suitable based on a combination of number of significant coefficients and values of volatility, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A two-month test window was used for forecasting and prediction. Results showed a decline in prediction accuracy as number of days of the test period increased; from 99.94% for the first 7 days, to 99.59 % for 14 days and 95.84% for 30 days. For the two-month test period, percentage accuracy was 84.75%. The study confirms that the ARIMA model is a veritable planning tool for financial managers, investors and other stakeholders; especially for short-term forecasting. It is however imperative that the influence of external factors, such as investors’/influencers’ comments and government intervention, that may affect forecasting be taken into consideration.
A Decision Support System for Power Components Based on Improved YOLOv4-Tiny

The traditional image object detection algorithm applied in power inspection cannot effectively position power components, and the accuracy of recognition is low in scenes with some interference. In this research, we proposed a data-driven power detection method based on the improved YOLOv4-tiny model, which combined the ResNet-D module and the adjusted Res-CBAM to the backbone network of the existing YOLOv4-tiny module. We replaced the CSPOSANet module in the YOLOv4-tiny backbone network with the ResNet-D module to reduce the FLOPS required by the model. At the same time, the adjusted Res-CBAM whose feature fusion ways were replaced with stacking in the channels was combined as an auxiliary classifier. Finally, the features of five different receptive scales were used for prediction, and the display of the results was optimized by merging the prediction boxes. In the experiment, 57134 images collected on the power inspection line were processed and labeled, and the default anchor boxes were re-clustered, and the speed and accuracy of the model were evaluated by video and validation set of 3459 images. Processing multiple pictures and videos collected from the power inspection projects, we re-clustered the default anchor box and tested the speed and accuracy of the model. The results show that compared with the original YOLOv4-tiny model, the accuracy of our method that can position objects under occlusion and complex lighting conditions is guaranteed while the detection speed is about 13% faster.
Black, White, and Grey

This study seeks to extend wicked problems analysis within the context of a library’s support for virtual reality (VR) and the related extended reality (XR) emerging technologies. The researchers conducted 11 interviews with 13 librarians, embedded IT staff, and/or faculty members who were involved in administering, managing, or planning a virtual reality lab or classroom in a library (or similar unit) in a higher education setting. The qualitative analysis of the interviews identified clusters of challenges, which are categorized as either emergent (but solvable) such as portability and training; complicated (but possible) such as licensing and ethics: and/or wicked (but tameable). The respondents framed their role in supporting the wickedness of VR/XR in three basic ways: library as gateway, library as learning partner, and library as maker. Five taming strategies were suggested from this research to help librarians wrestle with these challenges of advocating for a vision of VR/XR on their respective campuses. This research also hints at a larger role for librarians in the research of technology diffusion and what that might mean to their role in higher education in the future.
Recurrent Neural Network Techniques: Emphasis on Use in Neural Machine Translation

Natural Language Processing (NLP) is the processing and the representation of human language in a way that accommodate its use in modern computer technology. Several techniques including deep learning, graph-based, rule-based and word embedding can be used in variety of NLP application such as text summarization, question and answering and sentiment analysis. In this paper, machine translation techniques based on using recurrent neural networks are analyzed and discussed. The techniques are divided into three categories including recurrent neural network, recurrent neural network with phrase-based models and recurrent neural techniques with graph-based models. Several experiments are performed in several datasets to make translation between different languages. In addition, in most of techniques, BLEU is used in evaluating the performance of different translation models.
Internet of Things Supervision System and Supply Chain Financial Supervision Methods Based on Blockchain Technology

With the continuous progress of blockchain technology, how to use it for commodity traceability has become a concern. This study mainly discusses the Internet of Things supervision system and supply chain financial supervision method based on blockchain technology. The whole blockchain network is a decentralized system, which consists of six layers: data layer, network layer, and consensus layer. These three layers are the foundation layer; the other three layers are incentive layer, contract layer, and application layer. The interface of the platform needs to be conceived in combination with the business of building materials. According to the functional differentiation, the required interfaces mainly include information generation interface, information verification, information retrieval, and other information interfaces. Smart contracts can process data, operate asset transactions, manage smart assets, and expand the ability of blockchain to use data. In this way, the blockchain technology can trace the source of the data, so as to ensure the authenticity and security of the data. Blockchain technology can be used in data tracking. Specifically, in the building materials industry, with the help of blockchain technology, building material mixing enterprises can see the raw materials such as sand and gravel purchased under the order, the region where they are produced, the time point of transportation, the carrier, etc. In the process of supply chain financial supervision experiment, when the pledge rate of building materials is within the range of [0.4206,1], the bank income under the block chain mode is higher. The system designed in this study realizes the certification and traceability of building materials and has good use value.