Reviewerly/exHarmony huggingface dataset:
De-hazing CCTV Images using Dark Channel Prior for Improved Vehicle Detection

The recent advancements in artificial intelligence have led to significant improvements in object detection. Researchers have focused on enhancing the performance of object detection in challenging environments, as this has the potential to enhance the practical applications. Deep learning has been successful in image classification and object detection and has a wide range of applications including vehicle detection. However, vehicle detection models trained on high-quality images often struggle to perform well under adverse weather conditions, such as fog and rain. In this paper, we propose an improved vehicle detection method using a faster R-CNN with a dark channel prior (DCP). The proposed method first preprocesses the image using DCP and then performs vehicle detection on the preprocessed image using faster R-CNN. This method has been shown to improve the effectiveness of vehicle detection.
Advantages of Oversampling Techniques: A Case Study in Risk Factors for Fall Prediction

The evaluation of risk factors for falls (RFF) is a key point in fall prevention for the elderly. Since the information of the main actionable RFF can not always be regularly re-evaluated by medical factors, their automatic prediction would allow providing useful recommendations to reduce the risk of falls. This article explores the advantages of three oversampling methods to improve the quality of the prediction of 12 target RFF on the basis of a real imbalanced data set. We first present the data set, together with the selection of 45 variables and 12 target variables and other pre-processing steps. Second, we present the three oversampling methods, SMOTE, SMOTE-SVM, and ADASYN, the classifiers (Logistic Regression, Random Forest, Bayesian Network, Artificial Neural Network, and Naive Bayes), and the quality measures that we use in this study (balanced accuracy, area under ROC curve, area under Precision-Recall curve, F1 and F2 score). Each target is successively evaluated from all other variables. Results are presented by the classifier (averaging over targets) and by target (averaging over classifiers), for each oversampling method and quality measure. Finally, statistical tests validate the interest of using oversampling methods. The three methods demonstrate a clear advantage in comparison with the imbalanced data set, and SVM-SMOTE provides the best increment.
Self Supervised Learning for Classifying the Rotated Images

As the applications of image classification models have grown, there has emerged a need to develop classifier models which are invariant to the orientation of the image. Traditionally, image augmentation is applied during training of classification models to encourage the quality of rotation invariance. In our work, we propose an alternate method of classifying rotated images in which the 2d rotation transformation applied to an image is identified and reverted before being passed to an image classifier, thus increasing classification accuracy. The rotation correction is achieved by feeding the input images through a Rotnet, a deep convolutional model which has been trained to identify the orientation of images, and rotating the images back to the original orientation. We find that our method achieves an increase in image classification accuracy over the baseline model on the CIFAR10 dataset. Specifically, when images are rotated and passed to an image classifier with an accuracy of 94% on the CIFAR10 classification task, the accuracy drops to 59.1%. However when the randomly rotated input images were corrected for rotation using our method before being fed to the classifier, the accuracy increases to 75.8%.
ACDNet: Abusive Content Detection on Social Media with an Effective Deep Neural Network Using Code-Mixed Hinglish Data

In linguistically open geographic regions around the world, the amazing growth of social media platforms like Twitter, Facebook, and Instagram has led to the blending of native languages or regional tongues with English for the purpose of improving communication. Holocaust denial is a significant social problem that has the potential to escalate violence in a number of ways, from violent attacks to compassionate purging. A fundamental challenge in the categorization and tracking of extremely toxic lexical features is differentiating between language that incites hatred and language that is disparaging. Our study concentrates on locating abusive tweets written in Hinglish, a combination of Hindi and Roman Script. In this paper, we propose an approach for classifying tweets into three categories: hate speech, non-offensive speech, and abusive speech. Using the Hindi-English offensive tweet dataset, which includes tweets in the Hindi-English code transferred language and is divided into three categories: non-offensive, abusive, and hate speech. We utilized transfer learning to conduct research on the abusive and hate speech datasets and pre-trained the proposed model on English tweets that have been pre-processed using Hinglish Tweets. We were able to achieve 98.92% accuracy using the proposed model.
Study of Bitcoin Price Prediction Based on Sentiment Scores of Influencer Tweets with Experimental Validation

Although Bitcoin is the most popular cryptocurrency, it is very unstable even today. As the number of newsfeeds, articles, comments, trends, and opinions of influencers on popular social media platforms like Twitter is exponentially growing, they have a significant impact on the price of Bitcoin. People are often influenced by people who inspire them, events that impact their lives, or products and services they use. Therefore, an analysis of sentiments of people or investors triggered by social events becomes important for predicting the future of Bitcoin and its value. This paper forecasts Bitcoin prices by analyzing the sentiment scores of tweets and newsfeeds and their history. A large number of opinions on Twitter and other social media have drawn researchers to study public sentiment. We applied sentiment analysis to extract tweets from Twitter and analyzed the news blogs and historical bitcoin price data to improve our results’ accuracy. The presented work have achieved 82.87% accuracy to predict the sentiment score.
Configuring Participatory Design of ICT for Aging Well as Matters of Care

For good reason, a culture of user-centered and participatory design of technology has been established in the context of aging well in addition to the problem-solving approach for recent decades. We owe this shift in thinking not only to the collaboration of gerontologists, physicians, sociologists, and technology developers, but also to funding programs that explicitly call for user involvement and user studies in real life contexts. This is expected to lead to a better adaptation of the system requirements to the actual needs of the users and thus ensure a higher acceptance of the final product of technology. Finally, the approach should help to increase the subjective well-being of users and other stakeholders. This paper reports our own experiences and research on user-centered design and participation in the development of technology. From both an internal perspective as designers/developers and an external perspective as ethnographic researchers, we critically examine current practices of user integration in this paper. In doing so, we highlight and contextualize crucial aspects of reflexivity, reciprocity, and empowerment and provide the notion of configuring participation in the design process as matters of care. Moreover, we are convinced that participatory user-centered approaches should be at the center of future research on for ICT design in healthcare and thus also for the aging society.
Improving Signer-Independence Using Pose Estimation and Transfer Learning for Sign Language Recognition

Automated Sign Language Recognition (SLR) aims to bridge the communication gap between the hearing and the hearing disabled. Computer vision and deep learning lie at the forefront in working toward these systems. Most SLR research focuses on signer-dependent SLR and fails to account for variations in varying signers who gesticulate naturally. This paper investigates signer-independent SLR on the LSA64 dataset, focusing on different feature extraction approaches. Two approaches are proposed an InceptionV3-GRU architecture, which uses raw images as input, and a pose estimation LSTM architecture. MediaPipe Holistic is implemented to extract pose estimation landmark coordinates. A final third model applies augmentation and transfer learning using the pose estimation LSTM model. The research found that the pose estimation LSTM approach achieved the best performance with an accuracy of 80.22%. MediaPipe Holistic struggled with the augmentations introduced in the final experiment. Thus, looking into introducing more subtle augmentations may improve the model. Overall, the system shows significant promise toward addressing the real-world signer-independence issue in SLR.
Critical Review on Machine Reading Comprehension (MRC) Developments: From High Resource to Low Resource Languages

In Natural Language Processing (NLP), the significant role of Machine Reading Comprehension (MRC) is introduced the different dimension of task by asking the machine to asking a question on given context. This is one of the revolutionize way of thinking by the human and machine interactions with each other’s. The MRC technique can directly return the correct answer to the questions which are posed by the human in a natural language manner. The MRC system can read taking support s from the documents and provide users with high quality communication services. In this research paper the researcher trying to review different phases of machine learning comprehension model (MRC) to analyze that critical thinking by the human and machine interactions are with each other’s or not. The researcher focused on the different published and unpublished research articles from the different research portal and summarizes a review report based on MRC and its predictive model with high accuracy. The machine reading comprehension (MRC) is the advance technological approach based on machine learning approach to communicate to the real word and solve specific problem that are unaware of the technology and subject issues in the real world.
An Effective Approach for Identification of Multivariate Global Outlier Using Min, Average and Max Linkage Methods of Agglomerative

Outliers are normally defined as piece of data which is extremely faraway as of the remaining of dataset. Presently no unbending or any of mathematical description which describe an outlier. Defining for a data point whether it belongs to an outlier category or not, is at last the matter of subject. Data points could be outlier, can be defined as piece of dataset which departs extremely faraway with y the remaining data set. Present of Outliers indicates irregularities in a measurement or in experimental. Take a real work example, the average height of a person is between 5 to 6 feet tall. However, if it has been that a person has height 8 feet. Then the person with height 8 feet would be considered as outliers in comparison to the height of other persons. There are several methods are available to detect outliers present in a data set, but the main problem is Difficult to decide the method or tools for founding outliers in the data set, outlier affects the mean, variance and standard deviation, outliers are reducing the power of statistical tests, they can decrease normality of data set. In this paper we used hierarchical clustering technique to find out the global outlier. In hierarchical clustering there are three method Min, Max and Average linkage used to create clusters. In this paper we used real life data set of car sales in which we have taken 29 models of four famous brand Maruti Suzuki, Hyundai. Tata and Land Rover. There is a total of 9 attributes are taken are and we want to find that how the clusters are formed using these three methods and is there any outlier is present in the dataset based on these attributes. We have implemented these three-approach using R language. We found that the clusters forming is different, but the outlier is detected by three approaches are same.
Digital Technology and Practices of Humanities Research

Our world began with an image. A leaf falls from a tree and lands on the surface of the water, carried by ripples and whirlpools to a new place far removed from ins origin. There is he able to form new streams in calm water. This image is a metaphor, representing the interweaving of technology with the practices and values of humanities research, being both a point of intersection and a torrent of unexpected and predictable contingencies. Logical methods in the digital humanities lend themselves well to theory and documentation. At a level of a distinct set of defined statistical topics that can be generated using a software tool such as MALLET (Machine Learning for Language toolkit, a cross – platform topic modeling tool) rather that a set equivalent retrieved a linear normal reading process, representing the difference in degree. The methodological stake and consequences of the choice for the knowledge it creates are questioned, but taken into account and understood in scientific discussions. The falling leaf, getting into the wave, continues to move down, and the open frame of its fall can be lost, changing the path of the leaf. However, the leaf may later grow together, thus forming a barrier that affects further flows. Technology is sublimated into the fabric of scientific methods and the organization of the work of scientists is marked by a certain inevitability because of the methodological forces at work and because of the technologies that enable communication and interaction in the wide society.
Formation of Ecological Consciousness of Students on the Basis of Reflective Technologies

The article studies the process of formation of students’ ecological consciousness on the basis of reflective technologies. The content of the concepts of “reflection” and “nvironmental consciousness” is clarified and concretized. The main theoretical approaches to the formation of ecological consciousness of university students are revealed. The views of domestic and foreign teachers on the problem of using reflective technologies in the process of forming the ecological consciousness of a person are analyzed. Reflection is considered as one of the main components of creativity, as a result of which the student becomes the object of creative action for himself. On the basis of reflection, his self-development, the formation of his life path and the development of his consciousness take place. Based on this, when working with students with a low level of reflection, it is necessary to apply tasks based on co-creative reflective technologies of an ecological orientation, which will lead to an increase in the level of reflection and environmental awareness.
Mammogram Based Breast Cancer Detection with Transfer Learning

One of the primary and most prevalent causes of cancer in women is breast cancer. The occurrence of this condition has lately grown, and it is currently the most prevalent health problem. Breast cancer poses a major hazard to women, as it is highly morbid and lethal. Doctors find it challenging to develop a treatment strategy that might increase patient survival time due to the absence of reliable prognostic models. The greatest method for managing breast cancer outcomes is early detection. By facilitating suitable treatment and therapies, early detection of breast cancer increases the likelihood of survival. The digital mammography test aids in the early detection of breast cancer. A computer based automatic mammogram classification between benign and stages of cancers helps radiologist to prepare a diagnosis. For this purpose, deep learning based Convolutional Neural Network (CNN) models on Transfer Learning algorithms such as Dense Convolution Network (DenseNet201), Visual Geometry Group (VGG19), and Residual Network (ResNet50) is proposed in this paper. These models were applied on the dataset obtained from public shared data repository consist of about 7600 mammograms images. These models have performed accuracies of 96.77%, 97.99%, and 91.18% in DenseNet201, VGG19, and ResNet50, respectively. VGG19 model performs better in comparison with the other two models at epoch 40 in mammogram dataset.
The Development of Creative Industries as a Promising Tool for Popularizing the Culture of the Peoples of the Khanty and Mansi in the Younger Generation

The article addresses the issues of preserving and popularizing the culture of the Khanty and Mansi peoples through the development of creative industries. An analysis is made of organizations engaged in traditional economic activities and the number of employees in the field of small and medium-sized businesses, including individual entrepreneurs and self-employed in the Khanty-Mansiysk Autonomous Okrug Yugra. In addition, this article discusses the experience of implementing a student satrap affecting both the area of popularization of the culture of the peoples of the Khanty and Mansi, and the area of development of creative industries.
Attacks and Defenses of Smart Speakers: Voice Command Fingerprinting on Alexa

Smart speakers, such as Amazon Alexa and Google Home, are a form of voice-based interactive technology that helps consumers solve queries with real-time responses. Smart speaker adoption has increased significantly over the last few years, and this growth is expected to continue. Voice AI, specifically voice commands, have made people’s lives more convenient by helping them save time while avoiding physical contact. However, smart speakers also have security risks, including listening to and recording private conversations. In this paper, we review and analyze attacks on Amazon Alexa, their implementation, consequences, and defenses. We also dive into voice command fingerprinting and take inspiration from websites and video streaming fingerprinting to propose new potential defenses. This paper aims to highlight the attacks and protection in the newly emerging field of smart speakers.
Modern Technologies as Important Tools for Environmental Education During the Environmental Crisis

This article discusses the experience and problems of environmental education, education and upbringing. The author emphasizes that environmental education, education and upbringing play an important role in preserving the environment and creating a sustainable future for our planet. The article presents an overview of the experience of environmental education, education and upbringing in various countries, as well as an analysis of the main problems associated with these processes. The author discusses the shortcomings in the existing environmental education programs and suggests ways to eliminate them.
The Use of Information and Communication Technologies in the Environmental Education of Primary Schoolchildren

The article deals with the actual problem of environmental education and upbringing of children of primary school age. The results of a study conducted with the aim of studying the influence of information and communication technologies on the effective assimilation of knowledge about the world around by younger students, the education of respect for nature are presented. In the course of the pedagogical experiment, the hypothesis was tested that the use of various elements of information and communication technologies in the lessons of knowledge of the surrounding world will contribute to a better assimilation of natural history knowledge, skills and abilities and the formation of qualities of respect for nature in younger students. The results of testing conducted in the experimental and control groups at the ascertaining stage showed: there are certain gaps in the natural history knowledge of students; the degree of their interest in the subject “World around” is not high enough. The results of the questioning of teachers showed that information and communication technologies are rarely used at the lessons of knowledge of the surrounding world in the third grade.
The Use of Information Technologies in the Practice of the Educational Process as a Means of Improving the Quality of Education

The article substantiates the relevance of the need to create an online platform for distance learning, which allows to support students with special educational needs. The problematic issues voiced by school teachers during the transition to distance learning during the period of the threat of the spread of a new infection in the Russian Federation are presented. The main documents on the basis of which the children of this group receive education in schools, including the use of distance technologies, are named. The educational online resources recommended by the Ministry of Education of the Russian Federation are analyzed in order to identify the material necessary for the psychological and pedagogical support of children with specific educational needs. Examples of various sites and Internet portals used by teachers and psychologists are given. The article implements the developed and tested educational standards of primary general education for students with disabilities, which allow each student with different starting opportunities to create conditions for success in obtaining an education.
Communication in Social Networks as a Negative Factor Affecting the Performance of Schoolchildren

This article explores how social media has evolved into an online discussion category where people create content, share it, bookmark it, and chat at incredible speed. With ease of use, speed and reach, social media is rapidly changing public discourse in society and setting trends and agendas in areas ranging from environment and politics to technology and entertainment. Thanks to the invention of social media, people are now sharing ideas, feelings, personal information, photos and videos at an amazing rate. Over the past few years, the increased use of social networking sites has become an international phenomenon. What started as a hobby for some computer literate people has become a social norm and lifestyle for people all over the world. Teens and young adults use these sites as a way to connect with their peers, share information, reinvent their identity, and showcase their social lives. But in fact, this method of communication, as two sides of the coin, brings with it both positive and negative impacts. Social networks are distracting and time-consuming. Schoolchildren sit online for hours, completely ignoring their studies, which leads to a decrease in their academic performance.