Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence and Natural Language Processing Inspired Chabot Technologies

Abstract: Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possible, chatbots emulate individuals and serve as virtual assistants. They easily interface and respond to customers' requests. In the modern technical environment, these conversation agents or chatbots are considered the next-generation invention. Chatbot has become more popular in the business field right now as it can reduce customer service cost and handle multiple users at a time. There are many techniques used to involve such intelligent experts in daily business. A comprehensive analysis of the methods is needed to determine the viability of the different strategies. This paper tracks the progress of this invention and further clarifies the influence of chatbots on numerous businesses. Besides, a survey of the multiple chatbot methodologies suggested by various researchers is provided. Along with the survey, a chatbot e-commerce customer service is designed to provide an efficient and accurate answer for any query based on the dataset of frequently asked questions. This chatbot can reduce customer service costs and can handle multiple customers at the same time.
Human Part Semantic Segmentation Using CDGNET Architecture for Human Activity Recognition

The segmentation of human body parts is a task that entails assigning labels to pixels in an image to identify the corresponding body part classes. To enhance accuracy, a technique known as sample class distribution was developed, considering the hierarchical structure of the human body and the unique positioning of each part. This technique involves gathering and applying primary human parsing labels in both vertical and horizontal dimensions to exploit the distribution of classes. By combining these guided features, a spatial guidance map is generated and incorporated into the backbone network. These semantic-guided features contribute to the effective recognition of human activity through semantic segmentation-enabled human pose. To assess the effectiveness of this approach, extensive experiments were performed on a large dataset called CIHP, using metrics such as mean IOU, pixel accuracy, and mean accuracy.
COMO O MOVIMENTO SUFRAGISTA FEMININO INFLUENCIOU NO ENFRENTAMENTO DA PANDEMIA DO CORONAVÍRUS (COVID-19) EM 2020: UMA ANÁLISE COMPARATIVA

There are recurrent points of flooding and lack of sanitation in the city of Santa Inês-MA.In this way, the objective of this work is, through a bibliographical and documentary research, to analyze the responsibility of the Public Administration in relation to the floods that occur in the city of Santa Inês-MA, focusing on Rua do Comércio, one of the main streets in the city., in order to find possible alternatives for urban drainage, in order to minimize the environmental impacts of urbanization related to rainwater, as well as to identify the main legal and legal applications regarding the right to seek the environment, the administrative principles and the responsibility of the Power Public in guaranteeing urban mobility and free access for the population in safety.
Positive unlabeled learning with tensor networks

Positive unlabeled learning is a binary classification problem with positive and unlabeled data. It is common in domains where negative labels are costly or impossible to obtain, e.g., medicine and personalized advertising. Most approaches to positive unlabeled learning apply to specific data types (e.g., images, categorical data) and can not generate new positive and negative samples. This work introduces a feature-space distance-based tensor network approach to the positive unlabeled learning problem. The presented method is not domain specific and significantly improves the state-of-the-art results on the MNIST image and 15 categorical/mixed datasets. The trained tensor network model is also a generative model and enables the generation of new positive and negative instances.
The Impact of Organizational Culture in ICT Companies on Corporate Social Responsibility (CSR) and Employee Job Satisfaction

This study aims to to analyze the influence of organizational culture in ICT companies on social responsibility initiatives (CSR) and member job satisfaction, in order to provide insights for sustainable management in the ICT industry. As a result of empirical analysis of questionnaires collected from employees working in ICT companies using SPSS 23.0, the organizational culture of ICT companies has a significant impact on CSR activities, especially innovative culture, rational culture, and relational culture. In addition, as the culture of the organization affects external activities, it can be seen that the culture inside the company also appears externally. In addition, as a result of examining the impact of organizational culture on members’ job satisfaction, it was found that hierarchical culture did not affect job satisfaction. Accordingly, ICT companies can improve their job satisfaction and organizational performance through external activities through open and innovative organizational culture rather than control, obedience, and formal organizational culture. Therefore, in order to improve the performance of ICT companies, it is suggested that an organizational culture that cooperates with each other through the creation of a working environment and information sharing among members is important.
Academic Society Scientific Paper Writing Training Poltekkes Kemenkes Palu

Poltekkes Kemenkes Palu has 131 authors on the Sinta Kemendikbud website. There are still many authors who have not had a sinta score in the last three years. The aim of the training in writing scientific papers for the academic community at the Poltekkes Kemenkes Palu is to improve the ability to write quality scientific papers. The activity was carried out on January 4-5 2023 in the meeting room of the Center for Research and Community Service, the Ministry of Health, Palu Health Polytechnic. There were 4 resource persons and 41 participants, Lecturers of the Polytechnic of the Ministry of Health, Palu. The implementation method is through lectures, writing practice, practice using publish or perish and practice using Mendeley. The activity was held in the PPM Central Hall. The material is preparing scientific article manuscripts, practical writing of scientific articles, practice of citation of journal articles through publish or perish and publication using a letter of editor” is given. The results of the community service activities showed an increase in the publication of letters to editors by 14 articles published from 17 January to 5 May 2023. The conclusion of the activity was that participants were able to publish letter to editor articles. It is recommended that participants continue to practice writing skills and try to submit or publish manuscripts independently.
Application of the Commercialization Index Model for Korea Government-Funded Science and Technology Research Institutes

Research institutes(RIs) have been performing research and development (R&D) based on the unique objectives of each field, aiming to generate research outcomes and achievements, and leading the development of the national economy. However, there is an opinion that the utilization rate of research outcomes based on these achievements is low, particularly in terms of technology transfer to other institutions, technology royalty income, and patent utilization rate (private transfer/owned patents×100(%)). This study aims to apply the commercialization index model to evaluate the commercialization index of RIs in terms of research outcomes and utilization. Specifically, first, we collected data on commercialization data and research funding of RIs and conducted basic statistical analysis. Secondly, we developed scenarios based on the index within the research outcomes and utilization commercialization index model. We categorized the scenarios according to the total research funding and actual research funding of RIs and assigned weights based on industrial research, basic research, and public research to construct the scenarios. Thirdly, we applied the research outcomes and utilization commercialization index model to analyze the differences in research outcome levels among the scenarios and aimed to find the most appropriate model. Based on the results of this study, we provide criteria to determine whether the level of research outcome and utilization is suitable for the unique characteristics of RIs, which can be used to assess whether the commercialization performance of RIs is appropriate for smaller-scale research funding or research institutes with a focus on basic research.
&lt;strong&gt;Geoscientists Make a Difference&lt;/strong&gt;

A new special report demonstrates that geoscientists’ knowledge, skills, and competencies can be used to advance every one of the United Nations Sustainable Development Goals.
Study and Analysis of Deep Learning Models for the Recognition of Sign Language

A population of 430 million people and above, or over a population of 5% of the world’s population, needs therapy to treat their “disabled” hearing and speaking condition. These people have the option to learn sign language to communicate with others. Hence, our project mainly targets the deaf and mute community. Around 5000 images of hand gestures have been used and divided into 10 categories for live detection. The categories are mainly American Sign Language (ASL) and are consisted of the first 10 numbers. Our model can detect these ten hand motions and categorize them correctly. We used the You Only Look Once Version 5 algorithm. The algorithm consists of a backbone namely CSPDarknet53, in which an SPP block is accustomed to accelerating the speed of the receptive field responsible to set apart prime traits and confirming that network operation speed is inclining in speed. The neck of the algorithm, PAN, is added to aggregate the parameters from different backbone levels. This model is very easy to use and understand and gives an accuracy above 98%. That is why we chose YoloV5 as our model for object detection due to its simplicity in usage. Therefore, an artificial sign language detection system has been suggested in this study which incorporates deep learning and image processing method. This study also gives a comparison between the two models to give a better understating of why we marked YoloV5 as a better algorithm even though both models gave an accuracy of above 98%. We believe that making a hand gesture detection system will encourage individuals to communicate with people who cannot hear or speak. That being the case, we aim to make the lives of the disabled better.
Underwater Image Denoising and 3D Modelling of Poompuhar Site

This paper proposes a method to digitally recreate an underwater heritage site, Poompuhar using a 3D model to help the ICPS division of the Department of Science and Technology (DST) of India to digitally reconstruct the underwater heritage site which is now a submerged port located 30 km away from the present Poompuhar town. To achieve 3D modeling the underwater images must be preprocessed, then mesh points, scanning process, and 3D mapping techniques are used to reconstruct a 3D model. For preprocessing one of the key factors is to reduce the noise generated by the image capture and transmission process. The existing systems use filters depending on the noise as the noises cannot be predicted because the Poompuhar images are taken at a greater depth than the normal underwater images. In this study, a combination of all the existing filters is used to nullify their individual disadvantages and to achieve a better SNR ratio.
An Analysis Employing Various Machine Learning Algorithms for Detection of Malicious URLs

Currently, there are millions and trillions of webpages online. Therefore, in order to protect their data, users must be able to distinguish between trustworthy and dangerous websites. Since they are on the rise, malicious URLs (Uniform Resource Locators) are one of the most common cybersecurity concerns nowadays. They host free content (like malware, ransomware, trojans, password sniffers, etc.), and by clicking on this free content, especially the links found in emails, messages, and other formats, unwary users become the targets of these scams, have their personal information compromised, or have their system infected with malware, resulting in the loss of billions of rupees. There are more threats to cybersecurity than ever before, so a quick fix is required. In this study, we investigate how well different machine learning methods work in spotting potentially dangerous URLs. To identify both dangerous and benign URLs, this study employs a variety of machine learning methods. Decision Tree classifier, Random Forest classifier, AdaBoost classifier, K-Nearest Neighbors classifier, Gaussian Nave Bayes classifier, and XGB classifier are just a few of the machine learning methods that are used. Finding the most effective algorithm for accurately detecting fraudulent URLs is the main objective of this project. The accuracy metric was utilised as the evaluation tool to determine the accuracy of the various algorithms, with Random Forest having the highest accuracy (92.6%) of all of them.
Vision Transformer and Bidirectional RoBERTa: A Hybrid Image Captioning Model Between VirTex and CPTR

Image captioning neural networks are trained simultaneously on image recognition sub-models and natural language processing sub-models to generate description sentences for images. This paper presents several image captioning models based on the encoder-decoder framework. We change the neural sub-models used for the encoder as well as the decoder, and make comparisons. First, we experiment with several ResNet architectures (viz., ResNet-50, ResNet-101, and ResNet-152) as encoders, and Transformer or bidirectional Transformer models as decoders. Second, we use the combination of the Vision Transformer as a visual encoder, and the standard Transformer or RoBERTa as the language decoder. Finally, we propose an image captioning model using Vision Transformer for encoding images and bidirectional Transformer for predicting image captions. The models are trained on the Flickr8k dataset in English and Vietnamese and evaluated using the BLEU metric. The combination model between the Vision Transformer and the bidirectional RoBERTa model outperforms the existing image captioning models, including VirTex and CPTR models. The BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of our best image captioning model are 0.870, 0.661, 0.443, and 0.331 on the English dataset, and 0.829, 0.647, 0.483, and 0.387 on the Vietnamese dataset.
A Survey of Statistical, Machine Learning, and Deep Learning-Based Anomaly Detection Techniques for Time Series

Anomaly detection, also known as outlier detection, has been a persistent yet active subject of study in a number of research areas for several decades. This study provides a complete taxonomy of the research on anomaly detection in time series related to types of time series data, types of anomalies, detection methods, and the dataset of time series. This taxonomy simplifies the understanding of the procedures related to each group. In addition, we detail the benefits and drawbacks of each type of approach under each respective category. Recent work based on deep learning has made significant strides in this sector. They are capable of discovering data abnormalities and learning unsupervised representations of large-scaled sequences. This survey aims to facilitate a better understanding of the many research approaches taken on this topic since the research community lacks a comprehensive comparison of statistics, machine learning, and deep learning techniques. Despite substantial progress in this area of research, there is no one anomaly detector that has been demonstrated to be effective across several datasets. Current anomaly detection methods struggle to detect anomalies related to system actions or context.
PSYCHOLOGICAL AND PEDAGOGICAL CONDITIONS OF FUNCTIONING OF A HYBRID INTELLECTUAL SYSTEM OF TEACHING MATHEMATICS IN HIGH SCHOOL

The article examines the characteristics of the psychological and pedagogical conditions required for the functioning of the hybrid intellectual system of teaching (HIST) mathematics in high school. The article describes such internal conditions related to the development and primary trial of the HIST as the methodological basis of the intellectual management of educational and cognitive activity of students in general and the research and project activities in particular, the identification of the personalized parameters of the high school students’ scientific potential which determines the teaching content, the development of such diagnostic tools that can take the students’ individual psychological characteristics into account and serve as a component of intellectual management in the mathematical modelling of research activities, the development of the procedures and the methods of creating individual educational routes that are based on the students’ types of thinking. These conditions determined the architecture and functions of the hybrid neural network. The trial experiment on the internal conditions of the functioning of HIST involved 711 high school students. The results of the trial study indicate a steady annual increase in the number of students in experimental groups who get high scores, which is connected with an in-depth study of mathematics within the framework of the student's educational and research activities aimed at acquiring complex mathematical knowledge with the help of a hybrid intellectual system. The obtained results confirmed the effectiveness of the intelligent management system of the students' research and project activities. It was found that the external conditions, i.e. the readiness of the participants of the research and project activities to use the HIST, is not unimportant for its successful implementation. Consequently, the effectiveness of the hybrid intelligent learning system is determined not only by the readiness of the teachers, but also by the motivational and technological readiness of high school students to carry out research and project activities in a hybrid learning environment.
The Theory of TWC

TWC theory consists of several components. This chapter reviews the key elements that make up the theory and the constituent algorithms that are used in TWC. There are theoretical components of TWC, among them: (1) pseudo-distances, (2) attraction strengthAttraction strength, (3) entropyEntropy, and (3) free energy, that are used in various TWC algorithms.
PENGARUH MANAJEMEN WAKTU TERHADAP PRESTASI BELAJAR MAHASISWA PENDIDIKAN EKONOMI STAMBUK 2018 UNIMED

This studi aims to determine the effect of time management on student achievment in economics Education at the State University f Medan. The type of research is quantiative research using IBM SPSS version 22. Data collection techniques are through questionnaries, observation and documentation. The result of the study show that there is a positive and significant effect of time manjement on learning achievment with t acount of 5,264 which is greater than t table of 1,990.
Adaptive k-Nearest Centroid Neighbor Classifier for Detecting Drifted Twitter Spam

With the growth of Internet and its related technologies have resulted in increased usage of smart and Internet connected devices and large amount of time is spent on Social Network. Nonetheless, because of increase in attractiveness of Social Network, cyber offenders are spreading spam on these networks to exploit possible targets. The spammers trap users to malware downloads or external phishing URLs, which has been an enormous problem for online safety and user quality of exposure. However, the existing research fails to detect spam in Twitter and has become a key issue in recent times. Recent work [14], focused on using Machine Learning (ML) approach for detecting spam in Twitter, by making use of the statistical features of Twitter data. However, adoption of such method affects the classification accuracy of ML algorithm. Because the Statistical Feature characteristics of spam tweets vary with respect to time. This problem is known as “Twitter Spam Drift”. To address this problem, we present a novel non-parametric Adaptive K-Nearest Centroid Neighbor (AKNCN) Classifier. Further, for meeting real-time requirement the AKNCN is trained using one million spam tweets and one million non-spam tweets data. The AKNCN model can discover spam more efficiently than the state-of-the-art model. Experiment outcome shows the AKNCN attains significant performance with reference to Accuracy (A), F-Measure (F) and Detection Rate (DR) in real-world scenarios.
News Rank: Ranking News Topics based on Social Media Factors

All media sources, particularly the news media have educated the everyday news. These days, internet based media like twitter gives us an immense amount of information that is created by client, which potentially contains news related information. These sources to be helpful we should remove undesirable information and concentrate just the information which is like the news media. Indeed, even though the unwanted information can still exist, so which is vital to give need to its usage. For this prioritization, information must be positioned utilizing three components. First Media Focus(MF) of the Topic which principally centers around both internet based life and news media, Next User Attention(UA) which depends on clients’ interests and User Interaction(UI), which is on how client responds to that specific topic. This is an unsupervised framework News Rank--- which find the news topics which is applicable in both news media and internet based life and after that ranking the news topics utilizing degree of three elements.
Real Time Solution for Prosopagnosia

The paper aims at building a prototype for solving the problem of a rare neurological disorder ‘Prosopagnosia’. It is also called face blindness / facial agnosia. This is a behavioral disorder of face perception that impairs the ability to detect familiar faces, which include one's own face, while other forms of visual processing and intellectual functioning remain intact. The Extensive research has indicated 1 out of 50 people may have this neurological disorder. In order to help the significant number of affected people overcome this difficulty, we have built a prototype which uses a camera to capture the image and an appropriate face recognition code using the Histogram of Oriented Gradients (HOG) approach is implemented for face detection. After detection, SVM classifier is used for classification and the name of the identified person will be displayed. Simultaneously, the conversation is being recorded and text mining is performed to extract the keywords of the conversation. The result is displayed on a suitable interface. The hardware module Raspberry Pi is used as a processor for processing the incoming image and audio data.