Reviewerly/exHarmony huggingface dataset:
Hand Gesture-Controlled Robotic Arm with All-Terrain Surveillance Car

Abstract: In this proposed work, a gesture-based all-terrain car and a gesture-based robotic arm are designed to perform various real-life tasks. The motion of the robotic arm is based on the gesture input provided by the user. The use of a gesture controller provides ease for the user to control it efficiently. To have a clear and proper vision around the car, First-person View (FPV) cameras have been brought into the picture with a featured mechanism of pan and tilt placed in a particular position per the user's requirement and operation. A setup of two FPV cameras placed side by side can be made so as to have a panoramic view of the front of the car. Basic hand gestures are required for the motion of the surveillance car. A conveyor belt and off-road sturdy tires can be used for the purpose of all-terrain. The user can also get a virtual view of the robot by using a Virtual Reality (VR) set.
Penerapan Restful API Pada Push Notofication Untuk Rekam Data Perwalian

Guardianship is counseling carried out by students with guardian lecturers, to provide direction for the tutoring process at the tertiary level. Currently the academic guardianship system has an indication of a problem, which is when the student has a guardianship record every semester. And in the existing guardianship academic system, they don't have that yet. Because it is caused by students who are on leave and students who are repeating courses. In addition, guardian lecturers cannot track courses taken by students, this can be said to be ineffective. Thus, data recording and Reminder Information were developed, so that guardian lecturers can carry out the teaching and learning process more effectively and students can carry out lectures properly, because of the technology used in the academic system. This research uses the RestFul API method on Push Notifications to Record Guardianship Data to be more effective. Then to Respond and Request trust history data, namely using JSON, so that it is more structured and easier to use. The methods applied are Get, Post, Put, and Delete. Each method has uses, namely displaying trusteeship data records using the Get method, then providing guardianship records using the Post method, as well as changing records using the Put method and deleting using the Delete method. With the implementation of the RestFul API in Push Notification for the trust data recording system, a feasibility test has been carried out and obtained a value of 81.05%, which means that the application built is very feasible so that it can help the process of recording trust data.
Improving the Students’ Ability in Mastering Vocabulary Through Brainstorming Game in Eight Grade at MTS Al-Wasliyah Lubuk Pakam 2021/2022

The study find out that teaching vocabulary through brainstorming game could develop the students' ability in using vocabulary, especially for students in Eighth grade MTs.Al-Wasliyah Lubuk Pakam in the extend 85.18% . After analyzing the data, it was found out the students' ability in using vocabulary got improvement through brainstorming game. The result of the analyzing the data showed that there was an improving on the students' ability in mastering vocabulary from each cycles. It was showed from the mean of pre-test was 39,62. Where, there were 9 students got success score criteria or it was only 33.33% and 18 students got unsuccess or it was 66,67 %. After doing cycle I through brainstorming game, there was an improving of the result of the students' mean was 58.14. Where, 15 students got success criteria score or it was only 55.56% and 12 students got unsuccess criteria score or it was 44.44%. Then, doing repairing for second cycle after reflection on the first cycle, there was improving of students' mean was 88.89. Where, 23 students' got success criteria score or it was 85,18%. In other words, the students' ability in mastering vocabulary was improved. And based on interviewed, observation sheet, diary notes and documentation, it showed that the expression and excitement of the students were also improved.
Support Vector Machine Classifier with Truncated Least Squares Loss

Support vector machine (SVM), as one of effective and popular classification tools, has shown highly advantageous performance on manyclassification problems. However, it may incur prohibitive computational costs for large-scale classification problems. To address this problem, we construct a new fast SVM with truncated least squares loss (dubbed as $L_{tl}$-SVM). We begin with developing the first-order optimality conditions for local minimizer of the non-convex and non-smooth $L_{tl}$-SVM, which is convenient for us to investigate the $L_{tl}$ support vectors and working set of $L_{tl}$-SVM. Based on which, we propose a new fast alternating direction method of multipliers with working set (dubbed as \ADMML) to deal with $L_{tl}$-SVM. This method is found to enjoy a tremendously low computational complexity when the number of working set is very small, which makes sufficiently decreasing the demand for extremely large-scale computation possible. Numerical comparisons with other nine solvers show that \ADMML~enjoys excellent performance for large-scale classification problems with regard to more desirable accuracy, shorter computational time, fewer support vectors and more robust to outliers.
Evaluation of the financial performance of companies using blockchain technology and with high brand value by TOPSIS method

Purpose- Nowadays, thanks to data security, consistency and full-time traceability of transactions, blockchain technology is widely used by companies with high brand value operating in technology, retail, commercial services, media, banking, logistics and defense sectors. Blockchain technology will contribute to these companies reducing their costs, increasing customer satisfaction and thus increasing their financial performance. The purpose of this study is to compare the financial performances of 15 companies that are both among the first Forbes Blockchain 50 companies using blockchain technology and announced in the Brand Finance Global 500 company list with the highest brand value in 2021. Firms in the banking and insurance sector were excluded from the scope of the study. Methodology- TOPSIS method, which is a multi-criteria decision-making method, was applied in the evaluation of the financial performance of these companies. Performance index scores were calculated by using the profitability ratios of the companies. Findings- The analysis reveals that the performance index scores of companies operating in the technology, logistics and commercial services sectors are higher than those of other companies. In addition, it was observed that the brand values of the companies that are in the top 5 according to the performance index score were also high. Conclusion-According to the results of the analysis, the performance index scores of especially technology companies that use blockchain technology and have high brand value were high. It was also observed that the brand values of these companies have increased compared to the previous year. Based upon the findings, it may be concluded that using blockchain technology has a positive effect on the financial performance and brand values of companies. Keywords: Blockchain, TOPSIS, financial performance, financial ratios, brand value JEL Codes: M41, M49, C44
Higher Education Challenges in the Era of COVID-19 from the Perspective of Educators and Students (Ghana, Georgia and Pakistan Cases): A Literature Review

For the last three years, the entire world has faced a colossal phenomenon due to the COVID-19 pandemic. All sectors and areas of life have been affected, which has forced rapid and radical changes towards adaptation in its wake. The unexpected pandemic’s mark and impact on education is more severe and longer lasting than imagined. It has evidently disrupted education provision at an unprecedented scale. This paper is a literature review that focuses on the experience of different countries and education systems during the COVID-19 pandemic. Based on the analysis of the existing literature and research on this issue, from the perspective of educators and students, including the experience of different countries around the world, the pandemic has had a great impact on higher education. This has resulted to digital transformation, which implies overcoming many challenges. The review uses particular examples of higher education in the era of COVID-19 in Georgia, Ghana, and Pakistan. The measures taken to continue education in spite of the pandemic are also highlighted. Although this phenomenon proved to be challenging, it has initiated enormous opportunities for creativity within progress. This paper further discussed barriers that students and academics faced during online teaching-learning, the pros and cons of online teaching-learning, the quality of teaching-learning, and the state of preparedness for future education.
Аnalysis of the psychosocial climate in the pharmacy workforce and prospects for its improvement

Aim. To analyze the state of the psychosocial climate in the workforce of a pharmacy institution and develop ways to improve it.&#x0D; Materials and methods. The methods used by the authors to measure the psychosocial climate in the pharmacy workforce include questionnaires, the method for determining value-orientational unity, the method of O. S. Mikhalyuk and A. Yu. Shalyto, the psychometric test of C. E. Seashore, the method for studying the job attractiveness, the method for studying the level of satisfaction of pharmacy employees with working conditions, the method “The schematic map of the psychological and pedagogical characteristics of the group”. In the expert survey 697 respondents took part. The study was conducted between March 2021 and January 2022.&#x0D; Results. Using the method of O. S. Mikhalyuk and A. Yu. Shalyto it was determined that 45.01 % of the respondents surveyed liked working in a pharmacy; 41.86 % of respondents expressed no desire to change their place of work. A negative psychosocial climate was observed in 11.38 % of the pharmacy institutions studied; 51.6 % of labor groups were characterized by a low level of organization.&#x0D; Conclusions. Using valid methods the psychosocial climate state in the workforce of the pharmacies under research, the needs of employees in material and social security, mutual relations in the team, communication and the level of group cohesion have been studied. Directions for improving the psychosocial climate in the pharmacy workforce have been proposed, namely: strict compliance with the rules of conduct adopted in the pharmacy institution; involvement of organizational psychologists; team cohesion based on joint projects, informal events; conducting social and psychological trainings; development of the corporate culture; the optimal placement of the personnel, etc.
Gurukul Education and Its Impact on Modern Education

Education has played important role in the expansion of civilization in the world today. Similarly, in the context of education, Gurukul education is known as the oldest system of education. It is mostly true that education in the world got its root in the Gurukul education system. Our ancient Vedas, Purans, and Upanishads were written through the skills obtained from the Gurukul education system which are sources of vast knowledge. So, Gurukul education has played a major role in the transformation of modern education systems. So, the purpose of this research study is to present the impact of Gurukul education in building modern education in this form. This research study is a qualitative study and data are collected through interview-based on fieldwork and document analysis. The interview is taken faceto-face with teachers and students of Gurukul schools located in Chitwan, Nepal.The finding of this study show that Gurukul education is the root of all knowledge created by our ancient saints (Munis) and it has certainly got an impact on the present modern education. In modern education systems, there are traces of the education systems to bring transformation as we find today. This study has got practical implications for the development of modern education showing the relation between Gurukul and modern education.
MULTIDIMENSIONAL SIGNALS COMPRESSION ALGORITHM DEVELOPMENT USING WAVELET TRANSFORMS

The paper considers algorithms for compressing multidimensional signals using wavelet transforms, aimed at increasing the degree of information compression, compared with existing compression methods.&#x0D; Software for compressing multidimensional signals has been developed and the general capabilities of a software product that allows image compression using wavelet transforms are presented.
Metacognition Analysis of 7th Grade Students Junior High School After Applying Li-Pro-GP Learning Design

Students' metacognitive abilities develop from the knowledge they have and the management of that knowledge. Knowledge management will lead to students' strategies and skills in solving problems and higher-order thinking skills. The students' metacognitive skills play a major role in activities involving cognitive abilities such as understanding, communicating, paying attention, remembering, and solving problems. This research was aimed to find out the metacognitive skill of 7th grade students of Junior High School 23 Malang after applying Li-Pro-GP (literasi berbasis proyek terintegrasi GLS dan PPK: Li-Pro-GP) Learning Design. This is a descriptive qualitative research. It takes time from August to October 2021. This research was carried out at Junior High School 23 Malang. The population were 7th grade students and the sample were students of class VII.2 and VII.4. The research procedures included; 1) compiling MAI questionnaires to measure metacognitive ability in the form of short questions using likert scale (1-4), 2) giving students an instrument via Google Form before and after giving the Li-Pro-GP learning model. The instrument used to collect data was MAI (Metacognitive Awareness Inventory) from Schraw and Dennison. The method used to collect data is giving questionnaires to students. Data analysis technique was using Independent T test. The results of this study indicated that students' metacognitive abilities increased after the implementation of Li-Pro-GP learning model (sig 0.000 &lt; 0.05). Apart from the research results obtained, further research can apply Li-Pro-GP model to teach other materials.
Web-Based Customer Services Management Implementation For The Sales Division

With the growing business at PT Mastersystem Infotama, more and more customers and orders have been obtained. As time goes by, many sales go in and out, and staff change, so the customer database cannot be maintained and updated correctly. All leads and opportunities cannot be monitored and appropriately managed, and the daily activity of the sales division is also not monitored. Implementation of Customer Relationship Management (CRM) helps maintain customer data and can be continuously updated to maintain good relations with all customers. Prospects and opportunities can be managed and monitored, the daily activities of the sales division are neatly scheduled, and customer observations can be gathered, all of which can be done in one application, CRM. With this implementation, sales at PT Mastersystem Infotama experienced a 20% increase in sales in 2018. This research uses the waterfall model, which has the advantage of being a gradual and more detailed method to minimize errors. This CRM implementation produces a web-based CRM application that can be accessed by all employees wherever they are connected to a LAN. Employees can access CRM according to each division's capacity to make work easier.
A Transfer learning model for high secure image steganography using ResNet-50

Cryptography is commonly employed in data security, but one of the main disadvantages of the scheme would be that it attracts attention to just the perpetrator, which results throughout the exposure of the data. In order to solve these issues, digital image steganography is among the several techniques used to provide protection during data transmission. Capacity embedding and distortion are indeed the two key factors that play a key role when hiding a message in image. Therefore, this paper explores strategies consisting of two steps leading to the creation of that very stable framework with a large embedding ability. The first step determines an arbitrary image by morphing through hybrid discrete wavelet transform (HDWT) in order to produce a cover image that relies on the characteristics of both the chosen image. That second step integrates a text message of adequate duration using the transfer learning based ResNet50 architecture, integrating multi-keys and compound operators into the algorithm to provide robust protection. Simulation results shows that the proposed method resulted in superior PSNR and SSIM performance as compared to state of art approaches.Finally, the robustness and imperceptibility of the proposed method is improved, which reduced the attacks on the system.
Why Smart Contracts Reported as Vulnerable were not Exploited?

&lt;p&gt;As smart contracts process digital assets, their security is essential for blockchain applications. Many approaches have been proposed to detect smart contract vulnerabilities. Studies show that few of the reported vulnerabilities are exploited and hypothesize that many of the reported vulnerabilities are false positives. However, no follow-up study is performed to confirm the hypothesis and understand why the reported vulnerabilities are not exploited. In this study, we first collect 136,969 unique real-world smart contracts and analyze them using four vulnerability detectors, namely Oyente, SmartCheck, Slither, and SolDetector. Then, we apply Strauss’ grounded theory approach to manually analyze the source code of the smart contracts reported as vulnerable to recognizing false positives and understand the reasons for false results. In addition, we analyze the transaction logs of the smart contracts reported as vulnerable to identifying and understanding their exploitations. Our results show that 75.37% of the 4,364 smart contracts reported as vulnerable are false positives, and eleven reasons are causing the false positives. After analyzing the 4,106,134 transaction logs of the contracts reported as vulnerable, we find that vulnerabilities of only 67 (0.015%) of the contracts have been exploited in history. We also identify six reasons for demotivating and preventing the attackers from exploiting the vulnerabilities. Our results reveal that state-of-the-art smart contract vulnerability detectors primarily treat the smart contracts as yet another application developed using Object Oriented (OO) languages when analyzing and reporting the smart contract vulnerabilities. Without considering the specific design principles of the Solidity programming language and the characteristics of smart contracts' application scenarios and execution environments, many of the reported vulnerabilities are not exploitable or not cost-effective to be exploited by adversaries. &lt;/p&gt;
Large Language Models Show Human Behavior

Neural networks can approximate any function given sufficiently many hidden units, which implies that they, in theory, can approximate human behavior. Recently, natural language processing has advanced rapidly due to increases in the amount of hidden units and in the size of the datasets. With these advances in natural language capabilities, we wondered whether state-of-the-art Large Language Models show human behavior. In this article, we demonstrate that these models show language comprehension and communication skills to solve problems, which are considered to be key features of human behavior. Moreover, the process by which such AI-based models encode information leads to errors which are also common in humans, such as being vulnerable to misleading questions, source amnesia, and being sensitive to small changes in wording. Given the similarities with human behavior, we discuss the potential applications of LLMs in social science research.We conclude that LLMs and their close alignment with human behavior may provide a valuable source of information that can be studied to gain a better understanding of human behavior.
Attention-Based Latent Features for Jointly Trained End-to-End Automatic Speech Recognition with Speech Enhancement

In this paper, we propose a joint training framework that efficiently combines time-domain speech enhancement (SE) with an end-to-end (E2E) automatic speech recognition (ASR) system utilizing attention-based latent features. Using the latent feature to train E2E ASR implies that various timedomain SE models can be applied for noise-robust ASR and our proposed framework is the first approach. We implement a fully E2E scheme pipelined from SE to ASR without domain knowledge and short-time Fourier transform (STFT) consistency constraints by applying a time-domain SE model. Therefore, using the latent feature of time-domain SE is the main approach in our framework. Furthermore, we apply an attention algorithm to the time-domain SE model to selectively concentrate on certain latent features to achieve the better relevant feature for the task. Detailed experiments are conducted on the hybrid CTC/attention architecture for E2E ASR, and we demonstrate the superiority of our approach compared to baseline ASR systems trained with Mel filter bank coefficients features as input.
Penerapan Algoritma C4.5 Untuk Rekomendasi Mentor Santri Baru

The acceptance of new students is something that is routinely carried out by pesantren every year. The very large number of students and the limited number of teaching staff which can have an impact on the ineffectiveness of the activities of the educational process in the pesantren environment. This is common and has been predicted to always occur at every new student admission. The pesantren has also taken steps to anticipate this phenomenon that usually occurs every year, namely by selecting mentors for new students. In the selection of mentors for new students, the pesantren chose seniors who had been studying at the pesantren for a long time. By choosing an accurate mentor, the mentor function can be more effective. These results were tested for accuracy using the confusion matrix formula with an accuracy of 87.5%.
Kenny, Dorothy (2022). Machine translation for everyone. Empowering users in the age of artificial intelligence

RESEÑA DE:Dorothy KennyMachine translation for everyone. Empowering users in the age of artificial intelligenceBerlin: Language Science Press, 2022, 224 p.ISBN: 978-3-96110-348-5 (pdf)ISBN: 978-3-98554-045-7 (hbk)https://doi.org/10.5281/zenodo.6653406
Adversarial Intensity Awareness for Robust Object Detection

In recent years, the deep neural network has achieved remarkable performance in object detection. However, same as other vision models, object detection models are demonstrated to be vulnerable to adversarial examples with imperceptible perturbation. One of the most effective ways to improve the robustness of object detectors is adversarial training (AT), which incorporates adversarial examples of multiple adversarial intensities into the training process. Although the previous AT-based approaches achieve improved results in resisting adversarial attacks, they perform poorly in maintaining robustness against adversarial attacks with multiple intensities. In this paper, we propose a novel robust object detection method based on adversarial intensity awareness, which exploits the attack intensity information neglected in the adversarial examples for training. Specifically, the proposed method exploits the intensity information by an intensity-aware discriminator and uses intensity-guide convolution to achieve robust detection against multi-intensity adversarial attacks. Extensive experiments on PASCAL VOC and MS-COCO substantially demonstrate the superior performance of our method over other defense methods against multi-intensity adversarial attacks.