Reviewerly/exHarmony huggingface dataset:
Machine Leaning Fundamentals

Machine Learning is one of the fastest-growing fields which has witnessed exponential growth in the technical world. But in this fast-growing field, the question is how to get started? Briefly this paper introduces various languages popular for machine learning and next it introduces a few math concepts that helps us understand what exactly is happening and to improve our model further we need to understand that. And finally, there is an overview of various IDEs that can be used to implement these languages for machine learning.
A conceptualization of digital bullying

This chapter investigates the phenomenon of digital bullying in schools through a study that compares interviews with teachers and pupils on digital Bildung. The study was conducted in four different Norwegian schools and part of a digital Bildung project. The comparison of interviews shows inequalities between the understanding of digital bullying between the pupils and the teachers. The findings correspond with the gap found in understanding the digital Bildung of the pupils and the teachers. This chapter aims to investigate the differences and similarities of the conceptions of digital bullying given by pupils and teachers.
The Modular Galvanization of the Nature of Self at the source of the university’s three missions

The modern requirements for the mission of the university are considered, the modular approach to learning in universities is analyzed, a methodological rationale for the use of modular technology in the Abkhaz State University is given, the modular architecture of the construction of the educational process is presented.
Machine Learning

Machine learning, as we have seen, is at the heart of the data science process as it is in this step that the actual models are built. This chapter is dedicated to the ML algorithms/techniques you can use to build models and libraries that implement these algorithms. Awareness of different ML algorithms and an intuitive understanding of the underlying concepts is crucial for the success of the entire data science process. We will start with a general categorization of ML algorithms and then look at a few popular algorithms. We will then discuss model performance evaluation that can help you evaluate the effectiveness of your models. This evaluation helps you choose the best model from multiple candidate models you might have built and also gives you an idea of how well the chosen model is likely to perform when deployed in production.
Mineral Wealth

In an inventory of Scotland’s natural resources one could quite reasonably include its scenery, its land, its birds and animals, its waters and their fishes, its minerals and its people. A country’s possession of mineral resources is a matter of geographical luck, combined perhaps with a degree of past political astuteness. But the findings and exploitation of mineral resources demands geological knowledge, technological skill and an economic climate. The different minerals are associated with specific rock assemblages and geologists know enough to be able to predict the occurrence of particular minerals in certain areas, but it is impossible to forecast the quantities that will be there. Knowledge of Scotland’s potential resources could prevent panic exploitation or expensive buying from overseas, and allow a proper evaluation of her position in the world context of mineral supplies. There are of course other metallic minerals in Scotland apart from those mentioned which require investigation but perhaps most important at the moment is uranium.
Where to limit the freedom of speech? Analysis in the context of the First Amendment to the US Constitution and the fighting words doctrine

This article is about the limits of the right to free speech. The first section provides a brief introduction to this topic, primarily in the context of the First Amendment to the U.S. Constitution. The second section describes the case of Chaplinsky v. New Hampshire, which was fundamental to the topic of this paper because the United States Supreme Court created the so-called "fighting words" doctrine based on it. In the next two sections, two court cases are presented that perfectly demonstrate the limits of the right to free speech in the United States: Snyder v. Phelps and Village of Skokie v. National Socialist Party of America. The fifth part shows the right to freedom of speech in the context of Polish civil, criminal, and constitutional law, as well as acts of international law binding on Poland. The last part is a short summary.
Strings

Strings are character sequences that offer a variety of methods. In this chapter, you will learn about this topic and practice through various exercises.
English-Chinese Corpus Information Collection and Quantum Translation Based on Big Data

With the continuous development of Big Data information resources, the resources that people can obtain through the Internet have also increased. At present, there are about 3,000 known languages in the world. Research on machine translation and automatic acquisition of machine translation knowledge has strong practical significance for people to break through language barriers and make use of Internet information. Based on the description of the whole process of bilingual corpus construction, this essay proposes the flexibility of the information change model and quantum translation platform of the English-Chinese corpus based on big data.
An Action Research of Online Writing Classes for Authority of College Students - Focusing on the online interaction -

This study revealed the nature of authority and theoretically examined the characteristics of online writing classes that can promote it with the aim of cultivating writers who can communicate with the discourse community based on authority in college writing. In addition, online writing classes were conducted for college students, and a detailed analysis was conducted on the interaction patterns between learners and how they affect the learner''s authority. As a result of examining the interaction patterns in online writing classes, focusing on actual learners'' critical comments, it was confirmed that there were five types of “reader-response as an ethos constructor, self-open reader-response, relational reader-response, practical reader-response, and reflective reader-response,” which differ from those in face-to-face writing classes. By internalizing these interactions with readers, the writer constructs a “self as an author” that responds sensitively to each voice and responds independently. This study is meaningful in that it substantially researched the meaning of the interaction between the writer and the reader in relation to authority education and confirmed the possibility and direction by designing an online writing class that can promote it.
Aubiome: A Collaborative Method for the Production of Interactive Electronic Music

Using a ‘performer-centric’ working method, artistic researchers Joel Diegert and Adrián Artacho investigate the potential of integrating the saxophone with real-time electronics. The musical work, 'aubiome', is used as a case study to demonstrate their collaborative co-creative approach. The six-stage, iterative working process that emerged during the aubiome project is broken down and described in detail.
Supervised Machine Learning: Classification

This chapter introduces supervised machine learning algorithms. In this chapter, the popular classification algorithms such as decision tree, random forest, knearest neighbor, Naïve Bayes classifier, and support vector machine are described in detail. Each algorithm is defined starting with its overview, followed by an algorithmic framework and a hands-on example. A detailed Python program is given at the end of each algorithm to support the precise understanding of the working behavior of the classifiers. The Python code is executed on a real dataset, which eventually gives the reader in-depth knowledge about the algorithm&#039;s applicability.
Sistem Pakar Menentukan Kelas Anak Berkebutuhan Khusus dengan Metode Certainty Factor pada SLB Negeri Serdang Bedagai

Parents need to understand the child's condition from an early age, pay attention to the behavior of children who are growing normally in general so that they can take various actions and also other things that are considered important related to the child's future. Choosing to enroll children in special schools is certainly supported by the daily lives of children who do require special behavior in studying school learning. The formulation of the research problem is to determine the class of children with special needs using the Certainty Factor method based on the diagnosis of symptom and characteristic data that can help teachers and parents get information about the types of characteristics of children with special needs students at the State Special School (SLBN) Serdang Bedagai. The characteristics of children with special needs according to the data on the number of symptoms and characteristics discussed are 52 symptoms and 5 characteristics. (Autism JK01, Tuna Grahita JK02, Deafblind JK03, Blind JK04, Tuna Daksa JK05. This application can help teachers and parents find out the characteristics of children with special needs quickly and accurately. This application can be used in every special school to determine early knowing the types of characteristics of children with special needs
Performance of Combined Recommendation Modeling Fused With Tagawareness In Mixed Traffic Scenarios

Abstract Due to the heterogeneous characteristics of vehicles and user terminals, information in mixed traffic scenarios can be interacted based on the Web protocol of different terminals. The recommendation system can dig users' travel preferences by analyzing historical travel information of different traffic participants, to publish accurate travel information and services for the terminals of traffic participants. The diversification of existing road network users and networking modes, as well as the dynamic changes of user interest distribution caused by high-speed movement of vehicles, traditional collaborative filtering algorithms have limitations in terms of effectiveness. This paper proposes a novel Hybrid Tag-aware Recommender Model (HTRM). The model embedding layer first employs the Word2vec model to represent the tags and ratings of projects and users, respectively. The feature layer then introduces the auto-encoder to extract self-similar features of the item, and a long short-term memory (LSTM) network is used to extract user behavior characteristics to provide higher-quality recommendations. The gating layer combines the features of users and projects and then makes score recommendations based on the Fully Connected Neural Network (FCNN). Finally, Web data sets of different service preferences of traffic participants during the trip are used to evaluate the model recommendation performance in different scenarios. The experimental results show that the HTRM model is reasonable in design and can achieve high recommendation accuracy.
Ensemble interaction in indeterminate music

Examining ensemble interaction in the music of experimental composer Christian Wolff, this chapter uses as a case study a professional recording session undertaken by the ensemble Apartment House of &lt;em&gt;Exercises&lt;/em&gt; (1973–2013), a series of pieces for (mostly) unspecified instrumentation and numbers of players. Wolff’s skeletal notation is deliberately under-determined, acting like something of a puzzle to be solved. Consequently, the players must negotiate a way of working with the notation and with each other, making decisions prior to, and during, the moment of performance. Drawing on interviews and observational studies, the chapter identifies three different forms of interaction in the musicians’ playing as they engaged with Wolff’s notation: working responsively, independently, and emergently. The chapter offers a view of ensemble interaction that is characterized by cooperation, but also uncertainty, surprise, or even complete breakdown.
Activation Function: Absolute Function, One Function Behaves more Individualized

&lt;p&gt;Inspire by nature world mode, a activation function is proposed. It is absolute function.Through test on mnist dataset and fully-connected neural network and convolutional neural network, some conclusions are put forward. The line of accuracy of absolute function is shaked around the training accuracy that is different from the line of accuracy of relu and leaky relu. The absolute function can keep the negative parts as equal as the positive parts, so the individualization is more active than relu and leaky relu function. The absolute function is less likely to be over-fitting. Through teat on mnist and autoencoder, It is that the leaky relu function can do classification task well, while the absolute function can do generation task well. Because the classification task need more universality and generation task need more individualization. The pleasure irritation and painful irritation is not only the magnitude differences, but also the sign differences, so the negative parts should keep as a part.&lt;b&gt;&lt;/b&gt;Stimulation which happens frequently is low value, it is showed around zero in figure 1 . Stimulation which happens accidentally is high value, it is showed far away from zero in figure 1. So the high value is the big stimulation, which is individualization.&lt;/p&gt;&lt;p&gt;&lt;b&gt;&lt;/b&gt;&lt;/p&gt;
Exploring Self-Supervised Learning for Retrieving Similar E-Commerce Images

Recent studies in self-supervised learning have shown encouraging results that fill the gap between supervised and self-supervised learning in computer vision. These methods are evaluated using linear evaluation protocol, which is a standard benchmark to measure the quality of representations inferred from self-supervised models. On the other hand, visual similarity search without supervision is very much less studied. With the amount of unlabeled data grows every day on the Internet (e.g., e-commerce platforms), it is only natural to embrace self-supervised methods for image retrieval task. In this paper, we present our evaluation of self-supervised methods for visual similarity search on e-commerce dataset. Our experiments show that while self-supervised learning have not yet matched the performance of supervised learning, it is capable to extract necessary features from images to perform similarity search. We present the results on both linear evaluation and semi-supervised settings. We find that the use of contrastive loss function during self-supervised pretraining creates better quality of representations than other methods.
Specificity of Multiethnic Students’ Interaction with Artificial Educational Environment in Foreign Language Classes

The research objective includes revealing the specificity of multiethnic students’ interaction with artificial educational environment in a foreign language classroom. The article examines the notions “artificial intelligence (AI)”, “educational information and communication technologies (ICT)”, defines the essence of educational interaction, justifies the necessity to study multiethnic students’ interaction with artificial intelligence. The research methodology is based on modeling. Scientific originality of the paper involves developing a model representing the specificity of multiethnic students’ interaction with artificial educational environment. The findings are as follows: the author proposes a model of multiethnic students’ interaction with artificial educational environment in foreign language classes, reveals systemic factors of interaction, identifies dynamic processes influencing the interaction itself and its subjects.
Algorithmic music generation by harmony recombination with genetic algorithm

Algorithmic music composition has recently become an area of prestigious research in projects such as Google’s Magenta, Aiva, and Sony’s CSL Lab aiming to increase the composers’ tools for creativity. There are advances in systems for music feature extraction and generation of harmonies with short-time and long-time patterns of music style, genre, and motif. However, there are still challenges in the creation of poly-instrumental and polyphonic music, pieces become repetitive and sometimes these systems copy the original files. The main contribution of this paper is related to the improvement of generating new non-plagiary harmonic developments constructed from the symbolic abstraction from MIDI music non-labeled data with controlled selection of rhythmic features based on evolutionary techniques. Particularly, a novel approach for generating new music compositions by replacing existing harmony descriptors in a MIDI file with new harmonic features from another MIDI file selected by a genetic algorithm. This allows combining newly created harmony with a rhythm of another composition guaranteeing the adjustment of a new music piece to a distinctive genre with regularity and consistency. The performance of the proposed approach has been assessed using artificial intelligent computational tests, which assure goodness of the extracted features and shows its quality and competitiveness.
VOICE BASED PRESCRIPTION USING MACHINE LEARNING

Medicine side effects are the major problem in the world, due to wrong prescriptions thousands of people die every year. Most of these mistakes are due to illegible handwriting which leads to taking the wrong medicine or dosage. To solve this issue, a voice-based prescription came into the picture where the prescription is taken as voice input, and a pdf file is generated which is then emailed to the patient. This method can save wealth and life throughout the world, particularly in developing countries where the prescriptions are generally paper-based. The system proposed in this paper is for those doctors and hospitals that are still using a paper-based handwritten prescription. Keywords: Healthcare, Voice-based, Python, Natural Language Processing (NLP), Electronic Prescription, Text Processing, Electronic Health Record (EHR).
Fraud in public grants: Piloting a data-driven risk model in Spain

This chapter presents a proof-of-concept for a risk model that the General Comptroller of the State Administration (Intervención General de la Administración del Estado, IGAE) of Spain can employ to assess fraud risks and detect likely fraud cases. The chapter presents an overview of the machine learning methodology that underlies the risk model, as well as a detailed account of how the model was built, based on data that are readily available to the IGAE. The chapter concludes with a discussion about the results of the model and recommendations for the IGAE to build on the proof-of-concept.
A fully homomorphism encryption scheme based on LWR

Abstract We believe that isomorphic encryption technology can provide strong technical support for users’ privacy protection in a distributed computing environment. There are three types of quasi-homomorphism encryption methods: partial homomorphism encryption, shallow homomorphism encryption, and full homomorphism encryption. homomorphism encryption methods have important applications for ciphertext data computing in distributed computing environments, such as secure cloud computing, fee computing, and remote file storage ciphertext retrieval. It is pointed out that the construction of the homomorphism encryption method is still in the theoretical stage and cannot be used for real high-density data calculation problems. How to design (natural) isomorphic encryption schemes according to algebraic systems is still a challenging research. This question discusses the problem of Learning With Rounding (LWR). Based on the difficulty of LWR, multiple IDs, and attribute categories, a fully homomorphism encryption method corresponding to an ID is proposed. In this paper, in order to reflect the effectiveness of the proposed method, we propose a homomorphism encryption technology based on the password search attribute.
Digital Image Encryption Using Double Crossover Approach for SARS-CoV-2 Infected Lungs in a Blockchain Framework

As the (Covid-19) pandemic spreads, the creativity of the scientific community is thriving while trying to control the situation. They are trying to treat patients viably and work with the almost exhausted medical equipment and staff, while growing new, successful antibodies. Successful screening of SARS-CoV-2 empowers fast and proficient determination of COVID-19 and can relieve the weight on medical care frameworks. Numerous forecast models are being created to comprehend and prognosticate the spread of the pandemic and to stay away from the following wave. But in the coming time, we can be sure that the models would experience the ill effects of a few issues, security being one of them. All the models need to be built in such a way that the investigation task gets successfully conducted without compromising the privacy and security of the patients. To take care of this, we propose a blockchain framework for sharing patients’ personal data or medical reports. A blockchain will take care of the integrity part, but we still need to worry about confidentiality. Therefore, combining a genetic approach with a blockchain seemed like a good idea. A twofold hybrid methodology is proposed in this paper to tackle the issue of confidentiality. The outcomes displayed high entropy accomplishment for the utilized dataset. The sensitivity of the plaintext and ciphertext is also checked and compared with existing approaches which thus demonstrates the security of the proposed approach in the given setting.
Electronic Medical Recordkeeping Application Using Blockchain Technology

This chapter is “hands-on” on blockchain-based Electronic Medical Records (EMRs). The blockchain EMR makes it faster and convenient for both the patients and doctors to interact for all sorts of medical assistance. The system uses the blockchain-embedded technology Ethereum and IPFS (InterPlanetary File System) and includes security-enforcing data encryption techniques.
Introduction to Tucson 2022: Celebrating Apatite-Supergroup Minerals &amp; Fluorescent Minerals

"Introduction to Tucson 2022: Celebrating Apatite-Supergroup Minerals & Fluorescent Minerals." Rocks & Minerals, 97(1), pp. 6–7
Robust object tracking using sparse color appearance model

Distinguishing the target from the background, judging target occlusion, and real-time processing are the problems that the visual tracking algorithm still needs to solve. Color information and position information of the target block are fused as new features to track the target under the framework of particle filtering. First, the hues, saturation, value space, and color integral graph of the image are constructed. The vector representation of the target is obtained on the color integral image by sparse matrix. Then, candidate particles are produced by a particle filter and the sampling mode of particles is adjusted by a uniform acceleration model. The difference of particles reflects the position and scale change of the target. Finally, the candidate with the smallest eigenvector projection error is taken as the tracking target and the feature template is updated based on the tracking results. The presented algorithm can be used to track a single target in the color image sequence and has some robustness to the scale change, occlusion, and morphological change of the target. Experiment results on public datasets show that the proposed algorithm performs favorably in both speed and tracking effect when compared with other conventional trackers.