Reviewerly/exHarmony huggingface dataset:
TOP MANAGEMENT TEAM DIVERSITY IN FINANCIAL SERVICES: THE INFLUENCE OF FUNCTIONAL AND DEMOGRAPHIC DIVERSITY ON FIRM FINANCIAL PERFORMANCE

There has been exhaustive discussion on whether diversity in the TMT (Top Management Team) has an impact on the firm's financial performance.This research will analyze data from 59 Fortune 500 Financial Services firms' TMTs referencing Hambrick and Mason's Upper Echelon theory and review the TMT members' demographic composition as well as functional experience background attributes.We controlled for industry effects by focusing on the financial services sector, within which companies related to the banking sector are systemically essential to society.Another rationale for the focus on financial services is the degree with which this industry has been digitized, which we argue creates a more complex management environment.This research will compare the type of diversity within the TMT (functional vs. demographic) and the related impact on the firm's financial performance in terms of P/E (Price / Earnings) Ratio, ROA (Return on Assets) and ROE (Return on Equity).This is in the context of a dynamic digital marketplace that we hypothesize now rewards functional diversity in the TMT as this attribute in the team composition is critical to organizing the firm's competitive repertoires and strategy.The findings from the research suggest that functional diversity (TMT specialties such as marketing, finance, operations, etc.) is more significant than demographic diversity (gender, ethnicity, nationality).This study indicates that functional diversity had a positive correlation with financial performance in terms of the P/E Ratio and ROA, while demographic diversity had slightly negative correlation, not having a significant impact on the firm's financial performance.
PENURUNAN KADAR COD MENGGUNAKAN SARINGAN PASIR DUAL MEDIA PADA PENGOLAHAN AIR MINUM

Allowance for COD content in raw water can use a dual media sand filter. Sand filters have various advantages, namely low-cost and environmentally friendly operational costs and are able to produce quality processed water that meets quality standards. This study uses a dual media sand filter with dimensions of sand and gravel depth of 40 cm and 20 cm respectively, running the reactor for 15 days (sampling every 2 days) with 14 days of acclimation. The purpose of this research is to obtain the optimum flow rate from the sand filter reactor in setting aside COD in raw water. The results showed a dual media sand filter reactor unit with a flow rate of 0.3 m3 / m2.hour has a higher efficiency in setting aside COD compared to a reactor with a flow rate of 0.2 m3 / m2.hours which is 52.3%, where the magnitude This removal efficiency is influenced by biological processes in the form of bacterial activity in the schmutzdecke layer and chemical processes in the form of adsorption.
Multimodal Representations for Teacher-Guided Compositional Visual Reasoning

Neural Module Networks (NMN) are a compelling method for visual question answering, enabling the translation of a question into a program consisting of a series of reasoning sub-tasks that are sequentially executed on the image to produce an answer. NMNs provide enhanced explainability compared to integrated models, allowing for a better understanding of the underlying reasoning process. To improve the effectiveness of NMNs we propose to exploit features obtained by a large-scale cross-modal encoder. Also, the current training approach of NMNs relies on the propagation of module outputs to subsequent modules, leading to the accumulation of prediction errors and the generation of false answers. To mitigate this, we introduce an NMN learning strategy involving scheduled teacher guidance. Initially, the model is fully guided by the ground-truth intermediate outputs, but gradually transitions to an autonomous behavior as training progresses. This reduces error accumulation, thus improving training efficiency and final performance. We demonstrate that by incorporating cross-modal features and employing more effective training techniques for NMN, we achieve a favorable balance between performance and transparency in the reasoning process.
The application of machine learning algorithms in speech recognition error detection

The field of speech recognition technology has experienced a significant progress in recent years, with error detection being a crucial research domain. The present study offers a comprehensive overview of this area. Prior to the emergence of neural networks, Hidden Markov models (HMM) have been widely employed as a primary framework for speech recognition. However, this model only achieves local optimality, leading to its gradual replacement by neural network models, which have attracted considerable attention from researchers aiming to enhance their recognition performance. Various models, such as variant RNNs, bidirectional recurrent neural networks, and Fastcorrect2 have been developed. This paper introduces HMM, followed by a presentation of several neural network models, which entail a detailed description of their respective framework, principle, and idea. The variant RNN model is designed to enhance the recursive connection between input and output layers, while the deep bidirectional recurrent neural network model simulates the nonlinear relationship between input feature vectors and output labels using two models, namely bidirectional and deep. Additionally, the FastCorrect2 model enhances the voting effect of candidate words and the alignment algorithm. Finally, the study highlights the application of speech recognition error detection in everyday life, emphasizing the importance of speech recognition.
Exploring and envisioning the application of blockchain technology for privacy data protection

Addressing the issue of data leakage in social networks, this paper presents a classification of users' privacy information and introduces various personal data protection schemes utilizing blockchain technology. These schemes employ timestamp data storage within the blockchain, hash function anonymization techniques, and the Rivest-Shamir-Adleman (RSA) asymmetric encryption algorithm for encrypting and digitally signing transmitted data files. This innovative blockchain-based approach effectively tackles privacy leakage concerns in social networks and sets a benchmark for research in data security and social network safety. In this article, we propose a unique blockchain-based data protection scheme, specifically designed for different types of privacy leaks. This innovative solution addresses the pervasive problem of privacy leaks in social networks. However, current methods require more computational power during data interaction, which may hinder performance. Future research will concentrate on optimizing blockchain computational efficiency, aiming to develop a more robust data privacy protection system for blockchain-based social networks. By enhancing the efficiency and effectiveness of these privacy protection schemes, we hope to create a more secure environment for users to interact and share information on social platforms, ultimately fostering trust and confidence in digital social networks.
A research on quantum digital signatures

This paper provides an overview of the basic principles, types, recent works, and applications of quantum digital signatures. The security of traditional digital signature schemes is compromised by the rise of quantum computing, leading to a need for post-quantum cryptography. Quantum digital signatures, which rely on the principles of quantum mechanics, offer a potential solution to this problem. This paper aims to provide a comprehensive overview of quantum digital signatures and post-quantum digital signatures. The paper introduces the basic principles of quantum mechanics, then explains key distribution in quantum digital signatures. The paper then provides a detailed description of both quantum and post-quantum digital signatures, including their differences and applications. Finally, the paper summarizes the main findings in the field, highlights potential future directions, and discusses challenges that humans must address. In addition, the paper examines the widespread applications of quantum digital signatures and post-quantum digital signatures in various fields such as Bitcoin, smart city blockchain, and finance. Finally, the paper summarizes the key findings in the field, highlighting potential future directions and discussing challenges that humans must address. Overall, this paper aims to provide readers with a comprehensive understanding of quantum digital signatures and post-quantum digital signatures and their applications in various domains.
Cross-data recognition on sign language letters based on convolutional neural networks

With an estimated 1.5 billion hearing-impaired people globally, sign language is a vital means of communication among them. Meanwhile, the complexity and diversity of sign languages across different regions bring challenges to researchers. As existing studies in this field usually lack the implementation of recognition models with multiple datasets, which limits their practical value in real-life scenarios, this study intends to get around this constraint. This work constructs a baseline American Sign Language Letter Recognition model using Convolutional Neural Network (CNN) and then optimizes it to enhance its ability. Finally, cross-data recognition is carried out. In order to train and evaluate the model, this study gathers data from several sources, including the MNIST sign language set and real-life photos. It also investigates how data augmentation affects recognition ability. Consequently, the CNN model is able to recognize hand gestures for different alphabetic letters in solid backgrounds. Its accurate rate of it reaches about 99.83%. For the extra real-life dataset, which contains 96 images, the model could still detect the majority of the images with sign language equivalents, despite some accuracy loss in more crowded backgrounds. This work, in general, concentrates on the potential of CNNs for sign language identification and highlights the significance of cross-data recognition in creating useful recognition models.
The Fourth Industrial Revolution and Humanistic Reflection

Modern society is rapidly changing into a new technological environment based on cutting-edge science and technology and automation systems, and also the demand for awareness and responsiveness to the fourth industrial revolution is increasing not only in science and industry but also in various academic fields including the humanities. However, it will be difficult to be optimistic that the side effects of the operation of automated systems that have not yet been resolved and the legal and ethical problems that arise from them can be easily overcome. Therefore, rather than overly optimistic expectations for the progress of civilization according to the fourth industrial revolution, there should be parallel humanistic discussion and deep reflection on human consciousness and philosophy in the process of application and operation of science and technology.현대 사회는 최첨단 과학 기술과 자동화 시스템을 기반으로 하는 새로운 기술환경으로 빠르게 변화하고 있으며, 또한 과학이나 산업 분야 뿐만이 아니라 인문학을 비롯한 다양한 학계에서 4차 산업에 대한 인식과 대응력에 대한 요구는 갈수록 높아지고 있다. 그러나 아직 해결되지 않은 자동화 시스템의 운용에서 드러나는 부작용과 그로 인해 발생하는 법률적, 윤리적 문제들이 쉽게 극복될 수 있을 것인지 낙관하기는 어려울 것이다. 따라서 4차 산업혁명에 따른문명의 진보에 대한 지나친 낙관적 기대보다는, 그 적용과 운영하는 과정에 있어서 인문학적 논의와 인간의 의식 및 철학에 관한 깊은 성찰이 병행되어야한다.
The effectiveness of e-booklet and lectures to increase overweight adolescents’ nutritional knowledge

Overweight adolescents’ low knowledge of balanced nutrition is a problem that needs attention. If such a problem is left unchecked, overweight adolescents have a higher risk of suffering from various health problems. Efforts are needed to increase overweight adolescents’ nutrition knowledge, including through the provision of nutrition education using educational media. The purpose of this study was to analyze differences in knowledge after being given nutrition education using e-booklets and lectures. This study was conducted at SMAN 4 and SMAN 7 Banjarmasin in September 2022. This study was an experimental research with a quasi-experimental design with pre-test – post-test control group. The sample in this study was selected by purposive sampling technique, namely 64 overweight students divided into two groups, namely nutrition education with e-booklets accompanied by lectures and nutrition education with lectures only. The intervention was carried out 4 times in one month and each meeting took 55 minutes. Data on differences between pretest and posstest were analysed by paired T Test and data on differences between education groups were analysed by Independent T Test. Results, there were differences in pretest and posttest nutritional knowledge in groups given nutrition education with e-booklet media and lectures (p=0,000) and there are differences in groups given nutrition education with lectures only (p=0,001). There was a significant difference effect between the provision of nutrition education in both education groups (p=0,002). In conclusion, providing nutrition education with e-booklet media combined with lectures can improve knowledge about balanced nutrition for overweight adolescents more effectively than lectures.
7 Methods of cross-validation and bootstrapping

A statistical method called cross-validation is used to determine a machine learning model’s parameters and gauge its correctness. It is a type of resampling method where the dataset is arbitrarily split into training and test sets. Using different subsets of the same data, the model is tested and trained. Comparing the projected values from the testing data with the actual values of the same data allows one to evaluate the model’s performance.
8 Meta-learning through ensemble approach: bagging, boosting, and random forest strategies

Meta-learning through ensemble approaches is an intriguing subfield of machine learning research. With this method, a more comprehensive learning model is created by combining many machine learning methods, including neural networks and support vector machines. By using an ensemble of models, meta-learning techniques are able to produce more robust results than individual algorithms alone. In addition, ensemble techniques are advantageous because they can easily be expanded to accommodate additional data sources or algorithms. This approach can also embed more knowledge from the data into a more powerful meta-model, which allows the system to generalize better and discover patterns more accurately. In short, metalearning through ensemble approaches is an effective and useful technique for tackling challenging problems in machine learning.
PENGEMBANGAN INSTRUMEN DAN EVALUASI PEMAHAMAN PRODUK BAGI DIVISI PENJUALAN MENGGUNAKAN QUIZIZZ PADA PT. ACTiV

The development of the industrial revolution 4.0 make technology is improvement, because of that make so many things activities all fields become very simple, especially in companies engaged in the IT sector. Seeing these conditions need to upgrade the capabilities of new and old personnel, specifically for the sales force. By seeing this the team became very interested making an activity that could improve the quality and performance of the personnel by providing evaluation and training, which is expected in the future to be able to help in carrying out their duties. This community service aims to be able to provide good utilization for the company in the future. The team will use Quizizz an e-learning media delivery because attractive and interactive and for the research method used the Quasi Experimental Design. For the evaluating of a product and for the medium purposive sampling we are using documentation, observasion, pretest and posttest, for data used in making these questions category volume, profitability, product and other relevant measures. The final results from material carried out by the team the results quite 85% - 95% in very good category. The conclusion from this study is that e-learning using Quizizz is very effective in improving the ability, which previously got enough marks very good at the assessment and Quizizz can be used as a data bank, which can be used continuously as a media e-learning for other staff at PT. Alfa Cipta Technology Virtual (ACTiV).
PELATIHAN DESIGN UI/UX DALAM MEMBANGUN WEBSITE PADA RW.013 CIPINANG MELAYU JAKARTA TIMUR

Rukun Warga is a community institution that was formed through deliberation of neighborhood association administrators in order to provide government services to the community that are recognized by the local government. However, along with the rapid development of information, the services provided by RW administrators. 013 Cipinang Melayu is still not optimal because it still uses a manual system. There is no information system to find out any information available in the RW area. 013 Cipinang Melayu is one of the obstacles in the field. Therefore, it is hoped that this web-based information system can reduce the risks faced by manual systems so that the services provided can be more effective and efficient. The results of this community service activity are in the form of a website-based community information system. By determining an attractive user interface/appearance of a website and the UX designer is tasked with determining how the website operates so as to produce an information system with a user interface that is effective for use. With this information system, it is hoped that it will make it easier for RW administrators and the public to collect data or obtain information.
Analisa Usability pada Website Disdukcapil Kabupaten Ketapang

The importance of website-based e-SIAK at the Ketapang Regency Population and Civil Registry Service as a public information medium that can provide information effectively and efficiently. This research aims to evaluate the level of usability of the Ketapang Regency Dukcapil website, identify existing usability problems, and provide recommendations for improvements to improve user experience. The method used in this research is usability testing involving a group of participants from the community who frequently use population services. Data obtained from usability testing is analyzed using qualitative and quantitative techniques to identify problems faced by users when interacting with the website. The results of this analysis are then used as a basis for developing recommendations for improvements that can increase the level of user satisfaction and efficiency in using the Ketapang Regency Disdukcapil website. The research results showed that, (1) The Usability Testing method, which involves user participation in system testing, has proven to be an effective approach for evaluating the usability and performance of a website or application. (2) The use of the System Usability Scale (SUS) in this research provides a consistent and tested way to measure user satisfaction with the system being tested. (3) This research shows that although Usability Testing methods provide valuable insights, there remain several challenges in optimizing system usability.Keywords: Usability Testing, Usability, Disdukcapil
Nonparametric estimation of the number of classes with different average brightness in thermal images

When there is no information about the number of brightness classes, synthesizing algorithms for automatic image threshold segmentation involves a problem of determining the number of thresholds. The solution to the problem of estimating the number of classes in an image can be based on representing its distribution as a mixture of distributions of brightness classes when priori probabilities are unknown, or estimating the number of histogram modes. At the same time, it is known that the mixture splitting problem has a solution only for certain types of distributions and the histogram modes are not always distinguishable. In the general case, when the distributions of brightness classes are unknown, there are difficulties in applying these methods. The article proposes a non-parametric approach to determining the number of classes that differ in average brightness, based on rank histograms and using the property of local spatial grouping of elements of each brightness class in the image.
Three de-intuitionistic fuzzification procedures over circular intuitionistic fuzzy sets

Circular Intuitionistic Fuzzy Sets (CIFS) are one of the newest extensions of the intuitionistic fuzzy sets. In the present paper, three procedures for de-intuitionistic fuzzification of CIFS are discussed.
DEVELOPMENT OF A PLATFORM TO TEST THE SOLAR PHOTOCELL UNDER THE INFLUENCE OF INCLINATION, TEMPERATURE AND HUMIDITY: USE OF EMBEDDED SYSTEMS

This work presents the development of an experimental platform of the solar photocell under the influence of inclination, humidity temperature with the use of embedded systems. This system consists of 8 resistive loads with different values each connected to an NPN type transistor coupled to an instrumentation chain. A digital control programmed by an ATmega electronic board allows the automation of the variations of the resistive load fed by a solar panel of 19V 5 W of power. A computer program developed on the basis of an algorithm according to the defined operation, drives the Arduino electronic board. The movement of the sun during the day leads to the consideration of angle of incidence. During experiments this fundamental parameter is set electronically for automatic acquisition with the parameters: of the solar panel temperature, ambient temperature and humidity in the environment. The automatic variation of the resistive load with the combinations of the 8 electrical resistors to obtain 256 measurements in a few milliseconds. This speed of measurement makes possible the assumption of constant sunshine during the characterization, with a high number of points, of current-voltage curves of the photovoltaic solar cell with good precision.
How Positional Information Affect Convolutional Neural Networks?

The success of Transformers, including in the field of image processing, has recently attracted the attention of researchers. Some of the researchers tried to design Transformer- based or Convolutional Neural Network-based models separately, while others tried to combine them to produce hybrid models. A significant amount of hybrid model studies have examined the sub-model design and/or the applicability of self-attention in Convolutional Neural Networks. However, position embedding, another contribution from Transformers, has received much less attention. In this study, the effect of position information on Convolutional Neural Networks was analyzed. As a result of the experiments, it has been observed that the use of position information affects performance. In the AgeDB-30, CALFW, and LFW test sets, models with different position information usage have been able to surpass the performance of the model without position information by achieving 95.12%, 93.95%, and 99.52% accuracy, respectively.