Reviewerly/exHarmony huggingface dataset:
Adaptive myriad filtering algorithms for removal of nonstationary noise in electrooculograms

Suppressing nonstationary noise present in biomedical signals is important to provide high-quality diagnoses. Nonstationary noise is difficult for removing due to its time-varying and previously unknown characteristics. The application of linear filtering to the electrooculograph (EOG) signals leads to the smoothing of diagnostically important rapid changes in a signal caused by saccadic eye movements. In this respect, for processing edges and other discontinuous transitions, nonlinear filters based on robust estimators are more appropriate. The paper introduces novel adaptive algorithms for real-time nonlinear filtering of nonstationary noise in EOG signal with a noise- and signal-dependent filter switching, which is more appropriate for processing a local vicinity of the current input signal sample. One of the algorithms is based on myriad filters and sub-filter weighted FIR (which inite Impulse Responce) myriad hybrid filters. It suggests replacing the median with a myriad operation, calculated by Newton¢s numerical technique with adaptive switching of window length and linearity parameter settings. The other algorithm adaptively switches sub-filter weighted FIR median hybrid and averaging filters with different window lengths, offering simpler calculations and high-speed performance. These algorithms do not require time for filter parameters modification and their exact tuning during real-time signal processing and a prior knowledge of the signal model and noise variance. Numerical simulations were conducted to evaluate the filtering quality based on criteria of mean-square error and signal-to-noise ratio for a model signal under different levels of Gaussian noise. The achieved results show good performance and algorithm high quality for suppression of nonstationary noise in EOG. The myriad type adaptive algorithm prevails over the median in effectiveness but requires a numerical technique for cost function minimization, however, myriad filtering real-time implementation is possible with utilization of high-speed computers. Suggested adaptive algorithms significantly improve the efficiency of nonadaptive filters.
CONTRIBUTION OF INFORMATION DESIGN FOR THE AUTONOMY OF THE POPULATION IN A HEALTH CONTEXT

This poster presents part of the study developed by the author in the context of the doctoral study, where the importance of Information Design for the autonomy of the population in health context was analyzed.Currently, despite the existence of governmental measures aiming at a preventive health model, it is often verified that the population still uses health services in a consumer perspective or in a reactive way, not taking into account their own health as a life goal to achieve.This behavior triggers a set of negative consequences, not only in personal terms, but also in terms of the inability of the services to respond effectively.After an in-depth bibliographic review of specialist authors in the field of health and in the field of information design and taking as object of study the clinical analysis report model currently used in Portuguese National Health Service, it was concluded that the current educational health messages are not adjusted to the user literacy, not taking into account either the cognitive needs (at the cultural and social level), nor to their emotional needs.This presentation leads to the recognition of the fundamental role of Information Design tools in this specific context, due to their integrating character.Through the construction of messages with a strong sense of ownership and commitment, an increase in the individual's awareness and autonomy regarding the decisions to be taken within the scope of his/her health is promoted.We conclude, however, that in order to enhance these results, a review of the design paradigm is necessary, in which the user cannot be understood as a mere recipient of contents built based on assumptions, but rather integrated in a process of co-creation during the construction of educational health messages, right from an early stage.
APLIKASI MARKET BASE ANALITIK UNTUK MEMPELAJARI POLA BELANJA MASYARAKAT PADA TOKO UD GARAM KUSTONO MENGGUNAKAN ALGORITMA APRIORI

At this time the transaction data at the UD Garam Kustono store has decreased, this shows that thereis an increase in sales competition followed by an increase in applying information technology. Toovercome these problems, a very appropriate sales strategy is needed to use the availability of usefuldata and information. In order for data from sales transactions to be more efficient, it is necessary toprocess data by applying data mining techniques. The method used by researchers is the a priorialgorithm. The aim of this study is to apply a data mining system to find information oninterrelationships between products that can support sales strategies from transaction data. Thisinformation will be used to support sales strategy decisions by adjusting the products to be offeredto buyers. The results of this study will show that this a priori algorithm is used to process salestransaction data and become information in the form of links between products that are most oftenpurchased by the public.
Comparative analysis of the performance of investment instruments in the fourth industrial revolution (a comparative study of risk and return between stocks, gold, and cryptocurrency)

This research is done to determine the best-performing investment instruments in the Fourth Industrial Revolution by analyzing the performance of stocks, gold, and cryptocurrencies. In this era of information, investing is a well-known strategy used to allocate wealth. However, the knowledge of investment still needs to be improved. Thus, this research is done to provide comprehensive information about investment instruments performances. The sample used in this research is the monthly closing price of IDX30 stocks, gold, and cryptocurrency from January 2018 – December 2021. The analytical method used in this research is a comparative method which is done by using secondary data. Microsoft Excel was used to calculate the formula of each variable. Afterward, the data obtained were processed using the IBM SPSS application, the Kruskall-Wallis’s test. Our research indicates no significant difference between IDX30 stocks, gold, and cryptocurrency when measured from return. However, our research finds significant differences between IDX30 stocks, gold, and cryptocurrency when measured from risk. Therefore, in conclusion, it is advised for investors to be wary of the risks presented by each investment instrument and invest according to each investor's risk tolerance. This research has several limitations, such as the short research period, namely from January 2018 – December 2022. For future research, it would be advised to widen the period of data that is going to be researched, and a wider variety of investment instruments would also be a welcome improvement as they can provide research results that would be more beneficial and extensive.
Software Effort Estimation using Machine Learning Techniques

Success of the software development companies is mostly dependent on the best effort prediction. If the predictedeffort is somewhat correct, then the company can find relief from the great tension of hurrying up the employees to getthe job done within targeted time. There are many estimation methods, techniques and tools that are available. But itis very difficult to select the best one for a particular project. Each method has its own advantages and disadvantages.And also the effort estimation depends on various parameters. It is the responsibility of the project manager to select thebest tool for his project. Based on the historical data, the project manager can find effort value of the new project afterapplying some statistical methods and data mining techniques on that data. The main aim of this work is to reveal howmuch accurate are data mining-classification techniques on software project effort prediction datasets when we performanalogy based effort estimation.
Cryptoactifs et appel public à l’épargne en zone CEMAC

Abstract Public offering (IPO) is a key mechanism in the functioning of the financial market. In the CEMAC zone, the related rules are plural and challenged by technological and financial developments such as crypto-assets. The present paper aims to demonstrate that, before the July 2022 reform of the financial market regulation, COSUMAF had a questionable legitimacy to regulate, and therefore to prohibit, fundraising involving crypto-assets. However, after this reform, the new provisions bring to the perimeter of the IPO some correctives that must nevertheless be well adjusted.
Security Assessment of an Enterprise’ E-mail and Domain Name System: A Case Study

The domain name system (DNS) is a critical protocol in today's Internet operation.It establishes a standard naming convention for human-readable, understandable, and memorable domain names and IP addresses of Internet services.The abundance of study on DNS in general and security and privacy suggests that all issues in this area have been resolved.The reality, however, is that despite the substantial amount of literature on different elements of DNS, there are still many unresolved issues.The researchers offered a standards-based security assessment using Cyber Security Evaluation Tool (CSET) to help the staffing agency understand and assess the many hazards associated with using the domain name system.Furthermore, the researchers investigate a range of potential countermeasures to such threats.
Risk management application in a Baja SAE team

Management techniques can be beneficial for projects and it can optimize human, financial and time resources if applied to adapt to their contexts. Risk management is essential for good budget planning, given the remarkable need to prevent possible eventualities that may arise and deviate the project from its objective. Due to financial difficulties that hindered its development, the Komiketo Baja UFSJ team noticed the necessity to plan that would manage unforeseen events, since the planned budget was lesser than the actual expenditure, creating a lack of cash and preventing the purchase of essential materials for the team. This study presents the application of risk management in the team during the period 2019-2020, based mainly on the book PMBOK. In addition, it will discuss concepts such as identification, qualitative analysis, quantitative analysis, action plans, control and monitoring. Through this study, it is possible to compare before and after application, which resulted in a decrease of 128% (2018-19) to 7% (2019-20) of the difference between the amount spent and the planned amount. Several recommendations were obtained for the team's future and similar applications.
Fall Detection Algorithm Using a Smart Wearable System for Remote Health Monitoring

Nowadays more people prefer to live independently, especially the elderly, leaving them prone to incidents that they might not be able to report.Falls, for instance, are responsible for over 3 million emergency hospitalizations for head injuries and hip fractures each year in the U.S. In addition, other cases often go unreported, leading to further complications including chronic disabilities and even fatality.Therefore, the detection of such incidents has become of urgent necessity.The purpose of this paper is to develop and propose a machine learning support vector classification (SVC) algorithm for fall detection using accelerometer, gyroscope, and magnetometer sensors embedded in a smart wearable system for remote health monitoring.The device is placed on the subject's wrist to collect data on various motion activities in real-time, such as walking, running, jogging, waving, and stair-climbing in addition to other static postures like standing, lying, and sitting.The constructed dataset comprises 30 subjects with over 1200 data frames.The model achieved an overall accuracy of 98.3% and a specificity of 98.2% in separating falls from other daily-life activities.
Effects of Coffee Consumption on the Psychological Wellbeing of Undergraduate Students during Online Learning

This study evaluates undergraduate students' coffee consumption levels and investigates how it affects their psychological well-being during online learning.Caffeine is a psychostimulant that boosts body energy levels, and coffee is its primary medium in beverages, so it is popular among students due to their need for help studying, especially during online classes.However, caffeine also has adverse effects.The researchers gathered data from a sample of 100 college students through a digital survey that aimed to know their level of coffee consumption and general mental health status using the GHQ-12 questionnaire.The GHQ-12 generates social dysfunction, anxiety and depression, and loss of confidence.The data obtained from the respondents were analyzed using descriptive and inferential statistics, and measurements of this study have determined that age and social dysfunction are associated with coffee consumption.It was concluded that age is directly proportional to coffee consumption, and students with high social dysfunction tend to consume a high amount of coffee.The researchers recommend that educational institutions must hold seminars for their students to discuss this awareness and encourage dosage limits of coffee to students.
Breast Cancer Detection Using Six Different Algorithms

Cancer is among the most prevalent causes of mortality worldwide.Breast cancer is one of most often diagnosed cancers, with over 200 different varieties to choose from.Given the high prevalence of morbidity and death, early and accurate diagnosis are critical.For this purpose, symptoms must be carefully assessed and classified and this can be done by Machine Learning (ML) and Data Analytics techniques.The main goal of this research is to examine several ML and Deep Learning (DL) approaches for breast cancer diagnosis and accuracy prediction.The primary dataset being used for research purposes is the Wisconsin Breast Cancer Diagnosis dataset.The following are the findings of the algorithms used: 93.08 percent accuracy for Linear Regression, 93.61 percent accuracy for the K Nearest Neighbor, 96.50 percent accuracy for Support Vector Machine, and 95.10 percent accuracy for Multilayer Perceptron approaches.
Corporate E-Training Tracking Mechanism Using a Combined Application of Power Query and Power Automate

Corporate training is essential for employee upskilling and reskilling.Companies invest huge amounts in the development of training pathways.It has a direct impact on employee productivity and efficiency.Many organizations have a set of mandatory compliance-related training for new employees to avoid compliance issues.Tracking this training status is a tedious task.The Human Resource Learning & Development team makes available many courses for employee upskilling and reskilling but whether the employees are using these training for their upskilling is a challenging task to track.Organizations also like to know many insights like -How much progress the employee has made on assigned training?What portion of total employees have completed the particular section of assigned training?Etc.In this situation, soft automation using modern Microsoft tools, mainly Power Query and Power Automate are useful tools that simplify the problem.In this paper, it is demonstrated that the use of automation techniques using these tools can help to track corporate training and improve the training completion rate of the employees thereby creating good opportunities for their growth.This automaton will also save an enormous amount of time as compared to manual tracking.This research paper is applicable to all sectors since training is part of all corporate learning programs.Corporate companies can leverage this automated training tracking mechanism to upskill their employees.
Self-Powered Dual Axis Solar Tracking System

A simple yet precise solar position measuring system is required to maximize the output power of a solar panel in order to boost panel efficiency while lowering system costs.Solar position may be monitored using either an active/passive sensor or the sun position monitoring algorithm.Sensor-based sun position measurement devices do not accurately estimate the solar position on a cloudy or intermittent day, and they need precise installation and periodic calibrations.A dual axis solar tracking system is used in this project to gather solar energy.Solar trackers are used to significantly increase the electric output of a photovoltaic panel.The solar tracking system consists of a primary solar panel and four guided panels behind it, two of which are linked to opposing polarities of a horizontal motor and two further panels are connected to opposing polarities of a vertical motor.When the angle of the sun's rays changes, the rays fall on the guided panel, allowing the motor to adjust the angle to maximize intensity.Hence, the solar tracking efficiency is improvised.
Refocusing Indian education system in the perspective of ancient Indian education system

Ancient or Traditional Indian Education System has always been a topic of interest among researchers as well as general educators. The New Education Policy rolled out by Government of India in 2020 has also focused upon this ancient Indian educational pedagogy. This article is an attempt to highlight the relevance of reorienting our present educational structure towards the ancient educational system, which was considered to be the best system of imparting value based and morally structured education among common masses. This article elaborates the relevance of knowledge acquisition, knowledge discrimination and knowledge mining from the ancient education system point of view as well as it’s relevance in the present day education system. This article is an attempt to elaborate the philosophy of Ancient Indian education system through the insight of the knowledge and use of this knowledge which helps in restoring and restructuring of human moral values for society and nation building rather than just gaining it without a defined purpose. In this article, it has been emphasized that for rejuvenating and restructuring the present Indian Education System and provide a national moral value to the education system, we need to understand the outstanding characteristics of Indian Ancient philosophy of life, that revolved around the concept of Karma. And as such it is need of the hour to have a holistic approach in the Present Education System based on our Ancient Education System of India, which provides both physical as well as spiritual solitariness. This article has emphasized on the fact that in order to bring back ancient glory that existed in terms of knowledge acquisition, knowledge discrimination and knowledge mining in our ancient education system we need to re-orient the present education system with Ancient Indian Value based Education.
Strategies for National Transformation: From Developing to Developed

The process of national transformation from developing to developed status is a complex endeavor that requires addressing multidimensional challenges while capitalizing on strategic opportunities. This research investigates the challenges faced by developing countries in their transformation journey, identifies effective strategies for achieving successful transformation, and explores the implications for policy formulation. Multidimensional challenges encompassing political instability, economic disparities, inadequate education and healthcare, environmental degradation, and gender inequality are analyzed in their interconnectedness. Effective transformation strategies encompass human capital development, economic diversification, governance reforms, sustainability integration, trade promotion, social inclusion, innovation, and international collaboration. The research draws insights from successful transformation case studies, including South Korea, Singapore, China, and others, highlighting the role of visionary leadership, strategic investments, and adaptability to global dynamics. Policy implications emphasize holistic approaches, good governance, investment in education and infrastructure, economic diversification, social equity, and climate resilience. The research identifies areas for further exploration, including long-term impact assessments, context-specific strategies, sustainable development metrics, gender equality, and behavioral economics. In conclusion, this research contributes a comprehensive understanding of the challenges, strategies, and implications involved in national transformation, guiding governments, international organizations, and stakeholders toward sustainable and equitable development paths
Browser Forensics on Web-based Tiktok Applications using Digital Forensic Research Workshop Method

The rapid development of the current age of information technology has brought many advantages and disadvantages, one of which is TikTok.However, it still often happens that people misuse this TikTok videos where the content harms certain decent things for public consumption.TikTok also affects society today as a place to spread hoax content.This research aims to obtain digital evidence of cybercrime cases using the current method, namely the Digital Forensic Research Workshop (DFRWS).The method used in this research uses the Digital Forensic Research Workshop (DFRWS) which has 6 stages of research, namely identification, maintenance, collection, examination, analysis, and presentation.The tools used in this research are FTK Imager, Browser History Examiner, Browser History Capture, and Video Cache View.The results of this study are that the FTK Imager obtained results in the form of usernames and Passwords The Browser History Examiner tool obtains evidence in the form of the date and time of uploading the tiktok video.Tools Browser History Viewer obtained 1 proof of the TikTok video thumbnail and the perpetrator's profile photo.Video Cache View tools get 1 proof that deleted videos can be extracted back into a complete video.