Reviewerly/exHarmony huggingface dataset:
New approaches to formulation and solution of the problem of consciousness

Background. In scientific and theoretical terms, the theme of consciousness as a subject of psychological research is regularly updated. Since psychology established its scientific status, at each historical transition from one economic structure to another, general scientific knowledge has been updated. At the same time, the central issue of consciousness is being permanently changed: from methodological issues (introspection) to structural-functional and objectified knowledge (analytical philosophy of consciousness, cognitive science, neuroscience). In this process, the so-called “hard problem of consciousness” comes to the fore today (D. Chalmers, А. Revonsuo). Objective. The article seeks to outline the necessity for and explain new methodological approaches to the formulation of and conceptual solution to the problem of consciousness (G. Akopov), moving from the contexts of interdisciplinary discourse to psychological discourse. Methods. On the basis of previous author's reviews and research (G. Akopov) on the problems of consciousness, a certain range of philosophical, neuroscientific and psychological definitions and theories of consciousness is presented. The collected material, but for all its limitations in comparison with the hard-to-observe set of ideas, approaches and concepts that exist today in modern English-language literature on the problems of consciousness, allows us to identify a number of key points in an excessively multiple picture, more or less logically compatible. The typological approach we proposed in 2015 (an expanded version in 2021) allows us to streamline the entire set of ideas about consciousness. In accordance with the typology on the identification criterion, the concept of categories and categorical spaces of consciousness in various dimensions is specified. Results. A new methodology in the formulation and solution of the problem of consciousness has been formalized: categorical and typological approaches. The possibility of constructing categorical systems of various dimensions based on the typological identification of consciousness is demonstrated. The communicative nature of consciousness, implicitly represented in certain definitions of consciousness, is shown on the material of a number of existing approaches to identifying consciousness. Conclusion. Typological and categorical approaches make it possible to streamline and systematize the existing set of statements and solutions to the problem of consciousness. The relevant system of categorical spaces of various dimensions defines new opportunities for promising research, amplified by the thesis about the existence of non-verbal languages of consciousness in the socio-communicative, interpersonal and intrapersonal communicative dimensions. The new methodology, coupled with instrumental developments, makes it possible to pose and solve the topical problem of diversity and unity in human consciousness.
Towards a GUI Gesture Control Using the Leap Motion Controller

The Leap Motion Controller is a small hand motion capture device that has been the subject of numerous studies since it was launched in 2013.This sensor is particularly used in the field of disability, diagnosis or functional rehabilitation.This article presents two preliminary studies on the use of this device as a gesture mouse and then as a gesture controller.If the Leap Motion Controller is simple to implement, its lack of precision in some gesture configurations and of reliability (outliers) is a barrier which could certainly be lifted in the context of a multimodal interaction.
A Study of Smart Presence System by using Machine Learning Approach

In large organizations, the time and effort spent verifying whether or not employees have met their attendance quotas may become a substantial burden. In order to solve this issue, a presence mechanism that is both automated and efficient is being built. However, this technique relies heavily on verification. Smart Presence System deployments often make use of real-time facial recognition and identification technologies. In this investigation, we use two different kinds of algorithms. It uses the Convolutional Neural Network (CNN) technique as well as the Haar Cascade Classifier. The Haar Cascade Classifier technique was used during development of this function. We used the CNN algorithm to compare the results. When a user's face is detected, the system will generate a new spreadsheet for each day of the week. This real-time face recognition and identification technology is restricted to authorized corporate personnel only. Those who have not yet registered may have their information confirmed using a QR code verification mechanism. This system might make use of selected pieces of the user's data. Users with and without accounts may cohabit without incident within the parameters of this system. We got 99% accuracy after using the Haar Cascade Classifier.
Machine Learning Technique for Facial Datasets to Detect Examination Fraudulent Activities in the Online Examination: A Systematic Review Approach

Online Examination (OE) is the most challenging part of E-Learning (EL) since there is no proper mechanism provided to reduce the OE's fraudulent activities by the students.All the previous research provided different methods to avoid this issue, but those techniques could not be applied due to a few drawbacks of those methods.Recent studies suggested using facial recognition with Machine learning (ML) applications to reduce OE malpractices.This systematic review confirmed that Convolutional Neural Networks (CNN) can be applied to identify students' facial recognition with the help of the CK+ dataset compared to other ML techniques and datasets.Furthermore, future research can be conducted to develop an automated OE proctoring system in real-time.It is noted that this study could not be included a few more recent study results due to no funding.Also, there are no studies found related to this study for comparison of ML techniques and datasets.
Sign Language Detection using LSTM

Sign language is used by speech and hearing-impaired people as a method of communication. There are thousands of sign languages used all around the globe. Understanding sign language is a challenging task for a normal person. At present, speech and hearing-impaired people depend on human translators to make this task easier, but it is not always feasible to have a human translator. The objective of the present work is to design and implement an algorithm which detects real-time sign language using deep learning. The proposed system uses a Long Short-term memory model to train the dataset. The LSTM model works similarly as compared to recurrent neural networks. LSTM neural networks are used for the classification of sign language actions. A deep learning neural network is similar to the human brain in that it uses a combination of factors such as inputs, weights, and biases to perform various tasks, such as identifying and classifying objects. Deep learning algorithms work better when they are trained with a large amount of data. The performance of the proposed system has been evaluated based on accuracy, precision, and recall. The dataset consists of a Marathi sign which are daily used. The output of sign gesture is in text as well as in audio format. For audio we used google text to speech library. The proposed system could classify seven gestures with the highest training accuracy of 90-96%.
Challenges and Opportunities for Online Learning in India

Today's digital world demands more effective learning models that allow students to actively participate/enrol in their education. Digital technology is influencing the way the instruction is delivered as well as the way the information is discovered and shared. Recently, the adoption of internet and mobile learning technologies have changed the traditional perspective of education. In India, e-learning could be the viable solution to provide education in the context of 21st century. Effective mobile and e-learning applications can be developed by conducting an analysis on the use of mobile devices and applications in online education. Despite numerous initiatives to promote the use of mobile learning for education, there are some issues that must be addressed. By considering the sudden shift towards online teaching in the rise of COVID-19 pandemic in India, this study summarizes the research findings of mobile and e-learning technologies to avoid potential complications with the implementation of this technology during any future pandemic situations in India. The proposed study also assists in understanding the current state of mobile learning technology adoption in India.
A Comparative Study of Deep Learning based MODWT-LSTM and Machine Learning Model to Predict Environmental Parameters of Polyhouse

The accurate prediction of environmental parameters inside the polyhouse is prerequisite for reducing the potential yield and financial losses which might occur due to variations in weather conditions. A precise predictive model to forecast the internal climate of the polyhouse several hours beforehand on the basis of present and past weather conditions is needed for an hour. In this study, a new hybrid approach has been proposed which coupled artificial intelligence based deep learning model (Long-Short Term Memory (LSTM)) with Maximum Overlap Discrete Wavelet Transform (MODWT) to forecast the polyhouse environment. The wavelet transform method improved 10 to 20% accuracy of deep leaning model (LSTM) by filtering input data and extracting significant information. A comparative performance analysis of three machine learning models (MLP, SVM, GPR) with deep learning model (Long Short Term Memory (LSTM)) have been evaluated for predicting the temperature and relative humidity of polyhouse. This study also explores the ability of newly proposed hybrid model MODWT-LSTM and compares accuracy with LSTM. The findings of this study shows that MODWT-LSTM performs better than other models for predicting temperature (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.95, RMSE=0.64°C) and relative humidity (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.97, RMSE=2.43%) of polyhouse. The outcome of proposed work also states that deep learning can be applied more precisely for monitoring and controlling the environment of polyhouse.
Research on Electricity Retail Transaction Process Based on Block Chain Technology

As a distributed accounting technology, block chain technology has the characteristics of distributed storage and cannot be tampered with. Through the research on the electricity retail transaction process of block chain technology, it can effectively solve the problem of trust between transaction parties, optimize and reconstruct retail market transactions. Models and processes to achieve traceability of all links in the retail trading market, greatly improving transaction compliance. Firstly, this paper introduces the composition and technical characteristics of block chain, and proposes the electricity retail transaction process under the user-side open electricity market environment based on block chain technology. The entire transaction process is divided into 4 steps, namely the initiation stage, the transaction stage, the result authentication and calculation, and the data storage. Then, a detailed analysis of the electricity retail transaction process based on block chain technology is carried out.
Standardization Efforts for Blockchain in Energy Domain and Power Grid Applications

Blockchain is becoming one of the most promising emerging technologies in energy and power grid applications. Indeed, Blockchains have already been applied to these domains to provide transparent and secure energy transactions between multiple parties without the involvement of intermediaries. However, standardization guidelines and roadmaps for the application of Blockchains to power grids and energy domains are urgently required. Standardization is one of the most important factors for the success of an emerging technology and requires the support of multiple groups including industry, academia, governments, and other interest communities to boost its sustainable development, interoperability, and ease of use. Several international standards organizations (IEEE, ISO, and ITU-T) have led the Blockchain technology and application standards development in recent years. This paper provides a comprehensive review of applications of Blockchains to the energy and power grid domains, and the standard development activities by the IEEE Standards Association (SA). In addition, up-to-date industrial reflections are presented on the Energy Blockchain landscape collected via dedicated online surveys and meetings with industrial and academic experts.
Practical self-supervised continual learning with continual fine-tuning

Self-supervised learning (SSL) has shown remarkable performance in computer vision tasks when trained offline. However, in a Continual Learning (CL) scenario where new data is introduced progressively, models still suffer from catastrophic forgetting. Retraining a model from scratch to adapt to newly generated data is time-consuming and inefficient. Previous approaches suggested re-purposing self-supervised objectives with knowledge distillation to mitigate forgetting across tasks, assuming that labels from all tasks are available during fine-tuning. In this paper, we generalize self-supervised continual learning in a practical setting where available labels can be leveraged in any step of the SSL process. With an increasing number of continual tasks, this offers more flexibility in the pre-training and fine-tuning phases. With Kaizen, we introduce a training architecture that is able to mitigate catastrophic forgetting for both the feature extractor and classifier with a carefully designed loss function. By using a set of comprehensive evaluation metrics reflecting different aspects of continual learning, we demonstrated that Kaizen significantly outperforms previous SSL models in competitive vision benchmarks, with up to 16.5% accuracy improvement on split CIFAR-100. Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.
Methodology for the development of spatial thinking in young climbers 7-10 years old

The article substantiates the need for the development of spatial thinking of young athletes-climbers aged 7-10 years. The purpose of the study was to develop the spatial thinking of young climbers. The study involved young climbers 7-10 years old, who were included in the control and experimental groups. In the control group, the author's methodology for the development of spatial thinking of young climbers was used. The results of the study prove an increase in the level of spatial thinking development among young climbers in the experimental group. The study proves the need for the introduction of new techniques and tools for the development of spatial thinking of young climbers and the study of the relationship between the development of spatial thinking and other physical qualities.
Application of Quality Function Deployment for the Design &amp; Development of Consumer Cosmetic PackagingBottle

The purpose of this research is to be able to provide alternative product design updates that are acceptable to consumers. The method to be used is the Quality Function Deployment (QFD) approach. QFD is a structured product planning and development method, to clearly determine the desired consumer needs and then carry out systematic calculations, ultimately produce products that can satisfy consumers. The results of this study obtained the highest rating value was the technical response of the packaging design with a value of 400.00, then in second place with a value of 162.96 namely the strength of the packaging material, the selection of packaging materials and the quality of packaging.
Limbic Resonances

In an information-rich world, the information architecture of the world — from songs or movies to houses and cities, from products and services all the way to superstructures such as education or political representation — is the critical component we keep overlooking.
Development Of Archives Using Sequential Searching Algorithms In Design Department Politeknik Negeri Media Kreatif

The quality of service in each organization is one indicator of the success of a business entity or organization. In meeting these indicators, information systems or information applications are a mainstay in a business strategy. In several departments or faculties at various universities, the business process that is often carried out is archive management. Archiving activities are very important because they involve official documents and are important for every institution. Increased technological innovation has empowered institutions to monitor archives in a digital way. The purpose of this research is to create a website-based archive application using sequential searching algorithms that can be used to record and store easily and safely, and be managering every archive for essier access. The system development methodology uses the waterfall method and the PHP and MySQL programming languages. This application can be used to record, store, display archives by category and can be used to display archives by category, preview archives, download archives and archive details in the Design Department of the Polytechnic Media Creative.
How Team Culture Affects Corporate Performance: A Literature Review

This study examined the relationship between team culture and corporate performance. The objectives of the study are to examine how dimensions of team culture, such as collaboration, interdependence, and shared goals, affect the dimensions of corporate performance, such as product quality, time minimization, and waste minimization. A survey of literature was used as a source of data collection. The following were the findings: Collaboration improves product quality, Interdependence enhances time minimization, and Shared goals enhance waste minimization The study concluded that team culture improves the unity of employees toward achieving corporate performance in organizations. Sequel to the findings, the study recommends that organizations should create teams to achieve stipulated targets.
Pendampingan Kegiatan Exstrakurikuler English Public Speaking pada Madrasah Ibtidaiyah di Banyuwangi

This activity aims to increase achievement in English public speaking which is carried out at Madrasah Ibtidayyah or elementary schools to introduce the potential of human resources possessed by students. This activity is an extra-curricular activity that is very helpful for students so that in the future they can communicate and interact with the outside world using an international language, namely English.
Pelatihan Membangun Website Desa Dan Sistem Informasi Di Desa Sragi Kabupaten Ponorogo

Currently, technology is developing increasingly sophisticated, making it easier for users who are marked by the use of the internet which can convey, disseminate, and obtain information easily. The information provided is also diverse, there are aspects of education, business, news, entertainment, and much more. Sragi Village is a village located in the middle of the city, precisely in Sukorejo District, Ponorogo Regency. The problem that arises and is faced is that not many people know this village and the lack of skills of village officials in the conventional management system. To make it easier to process village archives, the conventional management system must be changed to digital by creating or building a village website which will later be used to publish the potential and all assets owned so that the wider community can find out information about Sragi village and get to know the Sragi village better. In addition, this can make village officials skills in managing village archives using a digital system. Villagers can also disseminate information about the business they run so that many are aware of and may be able to get consumers from inside and outside the village. This is an advantage for the village of Sragi itself which is increasingly being known by the wider community.
A Secured Cloud-Based Electronic Document Management System

Documents stored and managed on paper are characterized by lots of inefficiencies and work-related issues such as high time consumption, errors, and redundancy during information collection, storage, and retrieval. Producing documents using computer-supported methods has become common practice in the institution but the management of documents is still done with manual methods. It is also worth noting that the increased volume of document production and distribution through e-mail systems and workstation-based file managers has aggravated problems in document security, control, tracking, and retrieval. In this work, a secured cloud-based electronic document management system was designed (SECEDOMAS). The system consists of three processes, namely: The document entry process, Electronic document management system (EDMS), and Document retrieval and sharing process The system was implemented using HTML, CSS, JavaScript, Laravel (PHP framework), and MySQL. An Advanced Encryption Standard (AES) algorithm was used to encrypt the documents stored and shared to prevent the documents from unauthorized users. The system was tested using the document of exams and records unit of Olusegun Agagu University of Science and Technology, Okitipupa. The performance shows that it is effective in storing, retrieving, and sharing processes. SECEDOMAS is well-structured, more efficient, secure, and durable.
GRAMMATICAL ERROR ANALYSIS IN ENGLISH WRITING A CASE STUDY AT REKTOR PRIVATE INSTITUTE ENGLISH COURSE

This research aimed to investigate and reveal the grammatical error in English Writing made by the students in one of English Course in Makassar and how those errors appeared in a sentence. The writer also focused on the most frequent type of grammatical error and some other possible errors in English Writing. The writer used descriptive quantitative method to analyze the data which were found in the sentences. The writer used random sampling technique to choose which samples were to be analyzed. The data were determined according to the needs and purposes of this research. The writer found thirty-five samples. In this case, the writer used two methods of collecting data, library research and field research. After doing the analysis, the research result indicates that the most frequent error made by the students in English Writing in case of four types which occurs in this study. Omission, addition, malformation, and disordering are in the use of simple present tense. This thesis reveals that nearly 50% students of all subjects make many errors in it.