Reviewerly/exHarmony huggingface dataset:
More-than-Human Computer Interaction for Urban Food Governance

Digital technologies such as sensors, blockchain, and artificial intelligence are increasingly being used in global food production and consumption, including in urban contexts through the notion of the “smart city”. Food governance is addressed through logics of efficiency in supply chains, increasing profits for shareholders of large companies but doing little to address unsustainable social and environmental inequalities. Human–computer interaction (HCI) designers and researchers are increasingly interested in algorithmic governance of smart cities, raising concerns around issues of control, agency, access, and benefit. Against these concerns, some HCI researchers have also started to question a human-centred perspective to designing socio-technical systems, drawing on more-than-human perspectives to consider the interrelations and interdependencies between human and non-human others within the food web. In this chapter, these emerging perspectives within HCI are drawn together to consider the ways in which new technologies such as blockchain can be used in urban food governance. A case study of co-designing futures of algorithmic food governance with grassroots urban communities that account for multispecies actors, labours, and relationships is presented. The project surfaces new possibilities for computation to intervene in urban food governance in ways that are more sustainable and fairer.
A survey on human behavior analysis and actions recognition from videos

Human behavior analysis using action recognition remains an active research domain of computer vision. Action prediction using Artificial Intelligence (AI) by machine learning has attracted the attention of several researchers. The presentation of human action is usually considered an important challenge. An effective representative should be robust to noise, invariant to viewpoint changes and complex scenes involving high speeds. Two main challenges in this task include how to efficiently represent spatio–temporal patterns of skeletal movements and how to learn their discriminative human features for activity classification. In this survey, we present an overview of human presence and action recognition methods used in the last years. The results of the related works are compared with our results. The performance of methods is evaluated with different human action dataset such as KTH, UCF101, UCF Sport and CAD-60.
A New Gradient Approach Based on Reproducing Kernel Applied in Metaheuristic optimization

This work introduces the kernel based gradient evolution optimization method where the concept of a reproducing kernel is incorporated for the first time as an efficient alternative to estimate the numerical gradient as part of the approximating solution rule. The presented algorithm searches for a feasible solution by means of a swarm and involves three principal stages: the updating rule, exploration (jumping) and refreshing. The steepest descent direction is determined using the kernel gradient estimation with a local Taylor series expansion. Twelve test functions were tested as a means of evaluating the performance of the introduced optimization method. Then, a comparison among some state-of-art methods was carried out. According to numerical results, the proposed method significantly outperforms five other well-known algorithms in most test functions.
Prediction of Indian Currency for Visually Impaired People Using Machine Learning

Most of the time the people are finding it difficult to recognize the paper currencies due to their inability to see. Also in real life, it is not possible to trust the people to help a visual impaired person in recognizing the paper currency. But, in this regard, we can always trust an application that is easy to access and trust worthy. For building the application for the same, we need the help of machine learning algorithms. Several algorithms of machine learning such as supervised, unsupervised, semi-supervised, and reinforcement learning are available. Our paper mainly focuses on the recognition of different currency notes for the visually impaired people by using machine learning and taking the model as ResNet50 which is a 50 layers deep convolutional neural network. We have also used image processing by resizing and cropping the image to extract the important features of the notes so that the machine can recognize the currency notes efficiently. Our main contribution in this paper is through using various methods of image processing and feeding those processed image in our model, so that this model gets a fairly high accuracy.
Extreme Gradient Boosting for Toxic Comment Classification

Increasingly, online discussions have been subject to offensive comments and abuse from malintent users. The threat of abuse and online harassment often forces many people to withdraw from discussions or shut down essential conversations. The current explosion in the number of online communities poses a huge problem for online social media platforms in terms of effective moderation of discussions and prevention from abuse. This paper aims to present a scalable method for effectively classifying toxic comments. Recently, the XGBoost framework has become very popular among machine learning practitioners and data scientists due to its effectiveness, flexibility, and portability. It exploits the gradient boosting framework and adds several improvements that enhance the performance and accuracy of the model. Due to its desirability, we utilize XGBoost to create an efficient system for toxic comment classification and evaluate its performance using area under curve and receiver operating characteristic curve.
A Media Archeology of Education Innovation History and Pandemics

How do we as researchers and educators discern how past institutions of higher education responded to pandemics and other social stresses, and what practices we can adopt from them? Combining Jussi Parikka’s ideas of media archeology with the radical post-colonialism of Nonwestern Educational Traditions by Timothy Reagan, this research explores specific artifacts of western and nonwestern systems of education innovation at specific periods in history, how these institutions responded to crises and pandemics, and what this suggests to our contemporary post-literate networked system of higher education. The emerging field of Media Archeology suggests a literature review mash-up and remix to find under-represented historical ideas around pedagogy and higher education. The impacts of these education innovations are seen through the artifacts such as ‘open plan’ school architecture, educational broadcasting, and proto-internet distance learning classes. Each of these dyads of a western tradition of higher education contrasted with a non-western or resistance tradition of higher education has left artifacts and practices that have been able to escape intentional destruction or cultural appropriation into current western hegemonies. By standing outside the constraints and politics of corporate narratives, these artifacts can point the way toward reviving educational systems based on techniques and technologies that can address the systematic problems with higher education. This study of media, sociology, education, and history seeks to suggest workable practices for a post-COVID world, that resists hegemonic practices and corporatization of education and addresses new challenges in the future.
Wireless Communication Channels: Associated Vulnerabilities &amp; Possible Solutions

In modern communication scenarios, the use of wireless communication channels for data transmission becomes essential. Wireless communication channels are very prone to security attacks. In Denial of Service (DoS) attack, an intruder can intercept the network with targeted messages in order to make sure that the network will not be available for genuine users. Spoofing can be another problem where an intruder pretends to be a legitimate user and performs malicious activities. Eavesdropping is another area of concern where an intruder intercepts the ongoing data by capturing the wireless network. The list of wireless security attacks is even long. So in this context, in this paper, vulnerabilities of wireless communication channels along with the possible solutions are discussed. The up gradation of hardware and software is always a costly and slow process so new security protocols must be developed keeping existing hardware and software infrastructure in mind. The discussion related to security issues and possible solutions of wireless communication channels will be very helpful for the design and development of new concepts and methods.
Cryptocurrency: Concept and Associated Aspects

A cryptocurrency can be seen as an encoded data string which can be used as a unit of currency or its equivalent. In recent years, we have seen that cryptocurrency has emerged as a buzz word and everybody is talking about it. In this paper, an introductory view of cryptocurrency is given along with basic concepts and associated terms. The knowledge of the above-mentioned terms is very important in modern aspects as it is a buzz word today and many jobs are being created in this particular area.
Cryptography and Associated Aspects

In this fast transforming world finance and money is also transforming on a high note.Digitalized strength and other innovative systems and instruments are creating new platforms for financial traction who rectify and assume your transaction. Crypto is one such platform where transactions are secured and verified trustfully. Here in this paper, we will come to know how mathematics has enhanced the word of cryptography.In this paper we are going to study about role of mathematics in crypto text, encryption, symmetric key, stream, block cypher, asymmetric key and functions along with hash function,.Crypto key management andEfficient implementation. This paper provides a pure knowledge of application of mathematics in cryptography and how mathematics has enhanced in a truly different way.
Sequential multi-view subspace clustering

Self-representation based subspace learning has shown its effectiveness in many applications, but most existing methods do not consider the difference between different views. As a result, the learned self-representation matrix cannot well characterize the clustering structure. Moreover, some methods involve an undesired weighted vector of the tensor nuclear norm, which reduces the flexibility of the algorithm in practical applications. To handle these problems, we present a tensorized multi-view subspace clustering. Specifically, our method employs matrix factorization and decomposes the self-representation matrix to orthogonal projection matrix and affinity matrix. We also add ℓ1,2-norm regularization on affinity representation to characterize the cluster structure. Moreover, the proposed method uses weighted tensor Schatten p-norm to explore higher-order structure and complementary information embedded in multi-view data, which can allocate the ideal weight for each view automatically without additional weight and penalty parameters. We apply the adaptive loss function to the model to maintain the robustness to outliers and efficiently learn the data distribution. Extensive experimental results on different datasets reveal that our method is superior to other state-of-the-art multi-view subspace clustering methods.
Variational algorithm of quantum neural network based on quantum particle swarm

Most models of quantum neural networks are optimized based on gradient descent, and like classical neural networks, gradient descent suffers from the barren plateau phenomenon, which reduces the effectiveness of optimization. Therefore, this paper establishes a new QNN model, the optimization process adopts efficient quantum particle swarm optimization, and tentatively adds a quantum activation circuit to our QNN model. Our model will inherit the superposition property of quantum and the random search property of quantum particle swarm. Simulation experiments on some classification data show that the model proposed in this paper has higher classification performance than the gradient descent-based QNN.
INTERNAL MOTIVATION OF THE INDIVIDUAL TO LEARN ABOUT ART

One of the most important topics in art history remains the issue of motivating people to learn about art and the ability to encourage or arouse the desire to join the knowledge of art to a wide audience. Today’s information society provides an opportunity to analyze how powerful the system of knowledge in the field of art history is. The question of art practices and their impact on the development of emotional, intellectual and creative potential of the individual remains important. There is insufficient research on this issue in relation to the internal motivation of modern man to learn about art in the scientific field. This is mostly the case for research related to motivation in the pedagogical field in the teaching of schoolchildren or students. In the field of pedagogy, the problem of motivation was dealt with by В. Dodonov, A. Kolot, S. Serikov. However, in terms of motivating a wide audience, such research is lacking. This led to the analysis and research of this topic, to identify factors influencing the intrinsic motivation of people to learn about art, to analyze the results of the survey on what motivated participants to participate in artistic activities and track the effects of artistic knowledge. A survey of 50 participants was conducted to determine the motivational component of learning about art. The applied value of artistic knowledge for a wide audience is analyzed. Theories that can influence the formation of a person’s internal motivation to learn about art have been studied. The formation of intrinsic motivation to learn about art depends on various factors, among the main – the presence of a prerequisite for the formation of interest, understanding of the usefulness and practical application of artistic knowledge, regardless of type or profession. Also, an important factor for increasing motivation is to substantiate the importance of artistic knowledge as a tool for personal development and the possibility of transferring a person from a deficient state to a resource state.
ANTI-TOTALITARIAN TRENDS OF CONTEMPORARY UKRAINIAN ART

The aim of the article is to the research interaction of various methodological approaches and theoretical foundations on which the construction of visual works is based, highlighting current aspects of today. Recent artistic practices need to rethink their theoretical basis, based on the analysis of the previous historical period, which was largely a brake on art development. The threat of dogmatic thinking is due to incomplete reflection and, as a result, the inconspicuous influence of erroneous principles, in particular, some guidelines of Soviet and pro-russian ideology, in works of art. Some works of those artists who have a clear civic and political position, which does not contradict the basic aesthetic principles and even increases their artistic value, are considered. Thus, in today’s art discourse, moral and ethical principles prevail over aesthetic ones, although there is a connection between them. Considering the relativity of the mechanism of visual image formation, proved by neuroart theorists (R. Arnheim and other), a further step in this direction is proposed, namely the transition from the level of neuroaesthetics to neuroethics, if this term is widespread among scientists. At least such an explanation justifies the actualization of socio-political ethics in contemporary art, which displaces the hedonistic-aesthetic approach. Emphasis is placed on the need to expand the analytical apparatus of modern art discourse using concepts from the field of related scientific fields, such as the theory of relativity.
MODERN ART IN VIRTUAL AND AUGMENTED REALITY

The article examines the features of modern art in virtual and augmented reality. The technology of virtual reality and the essence of augmented reality art are described in detail. Artistic examples of the use of VR and AR at the national and global levels are given. The scientific article is aimed at studying the features and analysis of modern art in virtual and augmented reality. The research methods used in this article are: logical method, analysis, generalization. The scientific novelty lies in the distinction between the concepts of «virtual reality», «augmented reality» and in a clear and detailed selection of examples of implementation of these concepts in modern art. The world of art in the online space over the last thirty years has become extremely interesting, diverse and no less active than «offline» one. And if earlier artists even tried to resist this, urging viewers to «take their eyes off» the screens of gadgets, the current situation has shown how necessary the opportunities for digital technology, including the cultural sphere, are in this unpredictable time for public communication. Art is very important for the human race. From the very beginning of our existence, people have been creating some kind of art. From paintings on the walls to beautiful sculptures and graffiti. Now, more than ever, people are becoming more and more interested in art. Augmented reality and virtual reality (usually abbreviated as AR and VR, respectively) are reality technologies that enhance or replace the real environment with a simulated one. Augmented reality (AR) complements your environment by adding digital elements to live viewing, often with a smartphone camera. Virtual reality (VR) is a completely exciting experience that replaces the real environment with a simulated one. Virtual reality offers a complete immersion in another reality. However, AR shows the reality and the changed version side by side. VR replaces what the user sees with an alternative reality. AR adds what the user can already see. This means that it can be useful for annotating scenes and providing additional information. It is also used to place scenes in context and emphasize contrasts with current reality. Virtual reality requires special technologies such as headsets, controllers and sensors. All you need for augmented reality is a smartphone or tablet, and they can be downloaded as apps.
Line Following Robot Car Using Arduino

Asian Journal of Organic & Medicinal Chemistry includes communications, full papers (articles), short papers, green chemistry, reviews, and perspectives. Specifically, these include synthesis, synthetic methods and regents, mechanism of reactions, biologically active compounds with their possible mechanism of action, natural products, in vitro and in vivo tests of compounds, computational chemistry, physical-analytical-inorganic chemistry, nanotechnology, drug carriers, proteomics and genomics. Rapid synthesis of new compounds following eco-friendly methods and their functions as medicinally active agents is certainly the central part of chemical and medicinal research in modern times. On the basis of this fact, significant emphasis is placed to rapid publication and disseminates original discoveries in this new journal.
Hierarchical Decision Granules Optimization through the Principle of Justifiable Granularity

Interpretable Machine Learning (IML) aims to establish more transparent decision processes where the human can understand the reason behind the models’ decisions. In this work a methodology to create intrinsically interpretable models based on fuzzy rules is proposed. There is a selection to identify the rule structure by extracting the most significant elements from a decision tree by the principle of justifiable granularity. There are defined hierarchical decision granules and their quality metrics. The proposal is evaluated with ten publicly available datasets for classification tasks. It is shown that through the principle of justified granularity, rule-based models can be greatly compressed through their fuzzy representation, not only without significantly losing performance but even with compression of 40% it manages to exceed the performance of the initial model.
Detecting Offensive Language in Multi-Dialectal Arabic Social Media

Recently, reliance on social media has been steadily increasing from year to year.And as an anonymous medium of communication, people tend to share offensive comments which could be problematic and potentially cause a lot of harm to society.In order to find ways of addressing this issue, researching an automated method that detects offensive text within social media platforms has become important.Research in this field within the Arabic language is not as widely available as in other languages.Due to recent breakthroughs in Arabic Natural Language Processing, we were able to achieve results which are more accurate in detecting offensive content within social media.The Arabic language is in itself a different challenge compared to English, being a morphologically rich language.With the recent breakthrough in transformer based models such as BERT, which have been able to achieve state-of-the-art results in various tasks and building upon the AraBERT pre-training which has been proven to outperform multilingual BERT, as well as utilizing Arabic specific methods of pre-processing, we were able to achieve better results than established approaches for this task.Specifically, the BERT-base model achieved an F1-score of 84.88% on a multiplatform, multi-dialect dataset.
Responsive Innovation of Blockchain Technology in Traditional Supply Chains

As a form of distributed ledger technology, blockchain is being increasingly studied for renovating traditional supply chains both theoretically and practically with a few real-world applications. However, the adoption of this technology may also lead to socioeconomic and environmental side-effects that could deviate it from the goal of being a responsive innovation. The recognition and understanding of the potential negative impact and risks of blockchain technology is a relatively recent development, especially for applications to operations and supply chain domain. In this chapter, we will scan a few recent studies by focusing on several potential challenges and disruptions for responsive integration of the technology into supply chain systems. Though few new suggestions are given, we highlight a need for careful considerations of these challenges and disruptions as well as creative and proactive system and policy design. Instead of giving a comprehensive literature review or discussion of this broad research topic, the main purpose of this chapter is to extract a few good examples and points from representative recent literature, so as to trigger timely investigations and explorations into the subject.
Principle Analysis of Voiceprint Identification and Authentication System Based on LSTM

Recurrent neural networks, as an effective method to study the analysis and prediction of largescale time series, have existed in various applications on time series data, but the traditional RNN neural network has the problem of gradient disappearance and the problem of poor prediction accuracy has not been solved.This paper proposes a vocal recognition system based on LSTM neural network, whose core structure can be divided into four parts: forgetting gate, input gate, cell The core structure can be divided into four parts: forgetting gate, input gate, state and output gate, which are more suitable to analyze the characteristics of different human voice patterns, and can obtain higher recognition accuracy after the process of feature extraction, data enhancement, model training and voice pattern recognition.Finally, the performance of the LSTM neural network is tested on 300 speech data, and the test results prove that the LSTM neural network can obtain high recognition rate with less iterations.