Reviewerly/exHarmony huggingface dataset:
Principles of Politety in Indonesian Language in Speech of Students of Class 5 Madrasah Ibtidaiyah Negeri 1 Dairi Sidikalang Sub-District Dairi District Academic Year 2022-2023

If children are not educated to be able to speak politely, then the tradition of polite language will fade in social life and then a generation that is arrogant, rude, and dry from the ethical values ​​of society and religion is born. These expressions of rude and arrogant language often cause disputes and fights among students. The objectives of this study are 1) to describe politeness in Indonesian between students in the classroom, 2) to describe politeness in Indonesian between students and their teachers in the classroom. and 3) to describe politeness in Indonesian between fellow students outside the classroom. The research method used in this study is a qualitative descriptive method. The data in this study are the speeches of the fifth grade students of MIN 1 Dairi, Sidikalang District, Dairi Regency spoken by students, both students' speeches with students and students' speeches with teachers. In qualitative research, the researcher himself or the help of others is the main data collection tool. The results of this study concluded that 1) politeness in Indonesian language between fellow students in the class, namely politeness in the maxim of wisdom as many as 7 utterances, politeness in the maxim of agreement as many as 29 utterances and politeness in the maxim of sympathy as many as 1 utterance, 2) politeness in Indonesian language between students with the teacher in the class, namely politeness in the maxim of wisdom as many as 4 utterances, politeness in the maxim of humility as many as 26 utterances and politeness in the maxim of agreement as many as 4 utterances and 3) politeness in Indonesian language between fellow students outside the class, namely politeness in the maxim of agreement as many as 8 speech and politeness in the maxim of sympathy as much as 1 utterance&#x0D;
Influence evaluation of academic groups based on multi-attribute decision method

Based on the articles published on journal Physical Review E (PRE), the influence of academic groups is evaluated by social networks analysis and the multi-attribute decision making method TOPSIS. At first, co-citing network established by considering the citing relationship among articles, and then the academic groups are detected by using community detection algorithms on co-cited network. After that, the index system for evaluating the influence of academic group is proposed, and finally a multi-attribute decision making method TOPSIS is used to evaluate the influence. The proposed method provides a new insight into analyzing the influences of academic groups, and can comprehensively describe academic groups’ influence.
Formal verification of smart contracts from the perspective of logic

Blockchain is becoming more popular with a combination of blockchain and the Internet of Things. The combination system is called IoT-blockchain system. Smart contracts play a critical role in IoT-blockchain systems. It can enable a wholly trusted transaction with no third parties. Therefore, the security of smart contracts needs to be guaranteed. To detect vulnerabilities in smart contracts, we use formal verification methods. We use Programming in logic (Prolog) theory to model contract programs. Then SWI-Prolog is utilized to check the model. In our paper, we use this formal method to check the vulnerability of smart contracts in Town Crier. The results show that Town Crier’s contracts satisfy the key security properties.
Quantum non-Markovianity: Overview and recent developments

In the current era of noisy intermediate-scale quantum (NISQ) devices, research on the theory of open system dynamics has a crucial role to play. In particular, understanding and quantifying memory effects in quantum systems is critical to gain a better handle on the effects of noise in quantum devices. The main focus of this review is to address the fundamental question of defining and characterizing such memory effects—broadly referred to as quantum non-Markovianity—utilizing various approaches. We first discuss the two-time-parameter maps approach to open system dynamics and review the various notions of quantum non-Markovianity that arise in this paradigm. We then discuss an alternate approach to quantum stochastic processes based on the quantum combs framework, which accounts for multi-time correlations. We discuss the interconnections and differences between these two paradigms and conclude with a discussion on the necessary and sufficient conditions for quantum non-Markovianity.
An Order Reduction Design Framework for Higher-Order Binary Markov Random Fields

The order reduction method is an important approach to optimize higher-order binary Markov random fields (HoMRFs), which are widely used in information theory, machine learning and image analysis. It transforms an HoMRF into an equivalent and easier reduced first-order binary Markov random field (RMRF) by elaborately setting the coefficients and auxiliary variables of RMRF. However, designing order reduction methods is difficult, and no previous study has investigated this design issue. In this paper, we propose an order reduction design framework to study this problem for the first time. Through study, we find that the design difficulty mainly lies in that the coefficients and variables of RMRF must be set simultaneously. Therefore, the proposed framework decomposes the design difficulty into two processes, and each process mainly considers the coefficients or auxiliary variables of RMRF. Some valuable properties are also proven. Based on our framework, a new family of 14 order reduction methods is provided. Experiments, such as synthetic data and image denoising, demonstrate the superiority of our method.
Comparing the Effects of Nuclear and Electron Spins on the Formation of Neutral Hydrogen Molecule

We introduce the association-dissociation model of neutral hydrogen molecule, which is a finite-dimensional cavity quantum electrodynamics model of chemistry with two two-level artificial atoms on quantum dots placed in optical cavities, based on the Tavis-Cummings-Hubbard model. The motion of the nuclei can be represented in quantum form. Electron spin transition and spin-spin interaction between electron and nucleus are both considered. Consideration is also given to the effects of nuclear and electron spins on the formation of neutral hydrogen molecule.
Research on noise suppression method of brain information perception system

As a typical physiological electrical signal, EEG signal has several characteristics include weak at amplitude, low SNR(Signal-Noise Ratio), susceptible to interference, low frequency, narrow frequency range, strong randomness, etc. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . The interference of external electromagnetic signal may cause to a situation of degradation in signal quality, even leads to the failure of brain information perception system’s operation. Therefore, we need to analyze the electromagnetic environment’s characteristics during the process of brain information perception system acquisition and circumvent system’s disadvantages. In this paper, we analyzed the spectral characteristics of noise sources that affect brain information perception system, given several effective noise suppression methods.
K-Means Clustering of Cloud Data using Weka and R Language

From the literature, it is observed that the there is tremendous growth of cloud data which is increasing day by day in an exponential manner.The cloud data contains large files in the form of text, audio and video formats.Therefore, for optimizing the search timings for said files, there is a need of clustering of data.In the present work, K-means clustering is applied for the large data of banking sector and for this purpose, Weka and R language are used which give optimize results to search the desired information.Computed results are depicted through figures and tables.
The Use of Artificial Intelligence in Digital Forensics

Digital forensics is a vital field in the IT industry that deals with the identification, collection, preservation, and analysis of electronic data.This paper explores the fundamental aspects of digital forensics in the IT industry, including its importance, key challenges, and various techniques used in the investigation process.Furthermore, the paper provides insights into the various tools and technologies that have been developed to aid in digital forensics investigations.Finally, the paper concludes by highlighting the significance of digital forensics in the IT industry and the need for professionals to stay up-to-date with the latest trends and practices in the field.
Cyber Pandemics

The focus of this special issue is on studying the consequences of pandemics and cyber pandemics on privacy and trust both in the digital and the real worlds. In the aftermath of the recent COVID-19 pandemic that has shaken several aspects of our lives for almost two years of time, preliminary research indicates that the technological capabilities and the data that have been deployed and exploited to contain the spread of the disease may have affected people's privacy, sense of security, as well as their trust in their governing institutions. The enclosed articles explore both the effectiveness and the impacts of some of the deployed technologies in the handling of the COVID-19 pandemic on people's privacy as well as on their security and related risks.
TransNet: Parallel encoder architecture for human pose estimation

Recently self-attention mechanisms have become increasingly popular for computer vision applications following the success of transformer in natural language processing. Yet, transformer remains under-appreciated compared to the dominant role of convolutional neural networks in the field of computer vision. In this study, we present various approaches for transformers and their application to human pose estimation. We propose a novel model (TransNet) using a convolutional neural network design with a parallel transformer encoder branch to capture the long-range spatial dependency simultaneously while fusing it with the local features extracted from the input images. Experiments results show that TransNet achieves the exceptional performance for human pose estimation on the COCO dataset. Our proposed model outperforms the competitors and achieves the Average Precision (AP) score of 78.3 on COCO val set. Specifically, there is a significant improvement in the average score between the proposed model and the advanced convolutional neural networks. We believe this research can contribute to a better understanding of transformers within computer vision models.
Evaluation of Block-chain Transaction Accuracy using Neural Network Model

Dubbed as distributed ledger technology, the based on blockchain (DLT). The fundamental features of blockchains include a shared ledger, updates that happen in almost real time, are time- and date-stamped, are secured, and support for programming programs. Human brains use nodes that are connected, which are similar to cells in the brain, to accomplish deep learning. As a result, human brains with many Deep learning is accomplished by using hidden layers.The confluence of block chain technology and artificial intelligence has been the focus of extensive recent research in payment information (AI). It is covered to use these artificial neural networks. The usage of blockchain technology can cut humankind’s carbon emissions. AI. If AI robots take the role of human workers, mining companies will dramatically drop in energy and cost use.
'If I Could Turn Back Time': Occupational Dynamics, Technology Trajectories, and the Reemergence of the Analog Music Synthesizer

There are numerous examples of the reemergence of old technology, such as vinyl records and film cameras. Yet, the literature on technology trajectories has focused almost exclusively on linear models of technology progression, and we have little understanding of the processes that may instead drive reemergence. In fact, no prior research has examined how users’ occupational considerations may shape technology trajectories, despite a large literature on how occupations condition interactions with technological tools. This article sheds light on these processes through an inductive study of the music synthesizer industry’s shifts from analog to digital and back to analog technology. Leveraging more than 40 years of data, we trace the relationship between technological developments and synthesizer players’ occupational meaning. While synthesists initially embraced the ease of use and novelty of digital’s black- boxed preset sounds, widespread adoption of digital sounds ultimately undermined musicians’ occupational goal of distinctive creative expression. In response, synthesists articulated preferences for technology that afforded control, enabling them to use their expertise to create sounds, and that provided an embodied connection with the tool. Synthesists associated these affordances with analog rather than digital instruments, leading to renewed demand for analog and the reemergence of a formerly displaced technology. Our work integrates occupational considerations into the literature on technology trajectories, uncovers new mechanisms that can underlie technology reemergence, and extends the literature on occupations and technology.
METHOD OF TEACHING CONTINUOUS MAPPING OF TOPOLOGICAL SPACE AND ITS APPLICATION BY THE CONCEPT OF SOCIETY AND EDUCATION

In this paper, we will show how to teach general topology by using traditional methods with examples and show some applications of the rules of topology, especially in the concept of continuity between topological spaces. The application will be given as a definition of society and education through the continuity of topological spaces.
The Effect of Missing Data Mechanisms on Deep Learning in Binary Classification: A Simulation Study

decreased data quality and may produce bias depending on the proportion of the missing.There are several sources which can cause the missing, such as database integration issues or system failure in large databases, software-produced errors in research data or
Penggunaan Teknik Pembelajaran Menarasi Grafik terhadap Keterampilan Menulis Teks Eksplanasi

This study aims to describe the effect of graphic narration learning techniques on writing explanatory texts for class XI students of SMK Negeri 2 Guguak. The method used in this study is a quantitative experiment. The research design used a pretest-posttest control group. Sampling is done using probability sampling (random sample). The results showed that the mean value obtained was 93 with tcount of 4.023 (&gt; ttable = 1.684). In conclusion, there is a significant difference between the explanatory text writing skills using graphic narration learning techniques compared to students taught using conventional methods at SMK Negeri 2 Guguak.&#x0D; Keywords: Writing Skills, Graph Narration Learning Techniques
Modernization of higher education: a philosophical analysis

The article deals with the main problems of modernizing modern higher education. The key directions of reforming the higher education system are investigated. The main ways of overcoming the crisis of higher education in the Russian Federation are analyzed. Measures to improve the efficiency of the higher education system and conditions for increasing the credibility of the teaching staff are considered.
A Survey on challenges of Blockchain Technology and incorporating Cloud Computing Technologies

Block Chain Technologies and Cloud computing are on demand and emerging technologies which is used by the various applications, enterprises, Business , Medical and Health, Banking services.Block chain technologies are the one disruptive core technology and it creates impact on technological society due to decentralization, transparency and secure data transaction.BT plays major role in the application of crypto currency it serve as digital immutable ledger and transaction done in decentralized manner, Cloud computing provides data storage in cloud and it provides several online services such as infrastructure as a services, platform as services and software as a services in cloud computing data stored in the cloud is mutable Finally this paper elaborated on key future directions, novel use cases and block chain meets cloud computing and provide secure environment of data transmission based on consensus or validation protocol.
Classifying Message Application Using Kotlin

Over the past few years, short message service (SMS) usage has significantly increased. This service is used to deliver text messages by billions of people. Service providers have launched a number of popular applications, including mobile banking, summons checkpoints, SMS chat, and others. This chapter explores the numerous SMS applications that are available to users and provides an outline of how this service is provided. We examine the causes of its success and the problems that need to be solved. We also look at upcoming trends and the difficulties that to improve this service, certain obstacles must be overcome. This chapter should help you understand how SMS applications work and what to expect from them going forwards given the improvements to current SMS and technological advancement. We propose a privacy-preserving Naive Bayes classifier and apply it to the problem of private text classification. In this setting, a party (Alice) holds a text message, while another party (Bob) holds a classifier. At the end of the protocol, Alice will only learn the result of the classifier applied to her text input and Bob learns nothing. Our solution is based on Secure Multiparty Computation (SMC). Our Rust implementation provides a fast and secure solution for the classification of unstructured text. Applying our solution to the case of spam detection (the solution is generic, and can be used in any other scenario in which the Naive Bayes classifier can be employed), we can classify an SMS as spam or ham in less than 340 ms in the case where the dictionary size of Bob’s model includes all words (n 5200) and Alice’s SMS has at most m 160 unigrams. In the case with n 369 and m 8 (the average of a spam SMS in the database), our solution takes only 21 ms.
Quarantine in Motion: A Graph Learning Framework to Reduce Disease Transmission Without Lockdown

Exposure notification applications are developed to increase the scale and speed of disease contact tracing. Indeed, by taking advantage of Bluetooth technology, they track the infected population's mobility and then inform close contacts to get tested. In this paper, we ask whether these applications can extend from reactive to preemptive risk management tools? To this end, we propose a new framework that utilizes graph neural networks (GNN) and real-world Foursquare mobility data to predict high risk locations on an hourly basis. As a proof of concept, we then simulate a risk-informed Foursquare population of over 36,000 people in Austin TX after the peak of an outbreak. We find that even after 50% of the population has been infected with COVID-19, they can still maintain their mobility, while reducing the new infections by 13%. Consequently, these results are a first step towards achieving what we call Quarantine in Motion.
Prognostic-Personal Approach to the Organization of Art Lessons in the System of Primary Education

The study presents the forms of fine arts lessons based on a prognostic-personal approach in the primary education system. The article actualizes the problem of optimization of art education and cognitive development by means of fine arts lessons. The purpose of the article is to highlight the results of theoretical research and their practical approbation of various forms of teaching the arts to primary school pupils on the basis of a personal approach and foretelling the child's activities. In the process of scientific research, it has been found that the most effective form of innovative teaching of fine arts in primary school classes is a lesson- problem-based learning , a lesson-fairy tale, a lessonjourney, a lesson the perception of painting and its description, prediction and forecasting of events in a painting. It has been proven that the process of a fine arts lesson and prognostic tasks for primary school pupils are better implemented in the form of a game. The formation of prognostic experience in primary schoolchildren has its own specifics - mastering the ability to predict their own actions based on educational and social experience is associated with the assimilation of the following operations: ability to establish cause-and-effect relationships; ability to apply and reconstruct the acquired knowledge in different conditions; make an independent forecast of their own actions, set goals, foresee ways to achieve them; choose the most effective means and ways of implementing the plan; establish a chronological sequence of actions required to achieve the objectives. The number of children simultaneously participating in art classes based on a prognostic-personal approach should not exceed 10 people. It has been established that all forms of fine arts lessons based on forecasting and planning, relying on a personal approach should contain individual, group and joint forms of organization of children. It is proved that the use of a prognostic personal approach in the process of teaching primary school students in fine arts lessons contributes to the optimization of primary art education, the prognostic abilities of children and their learning competence in accordance with the requirements of the New Ukrainian School.
High Performance and Power Efficient Accelerator for Cloud Inference

Facing the growing complexity of Deep Neural Networks (DNNs), high-performance and power-efficient AI accelerators are desired to provide effective and affordable cloud inference services. We introduce our flagship product, i.e., the Cloudblazer i20 accelerator, which integrates the innovated Deep Thinking Unit (DTU 2.0). The design is driven by requests drawn from various AI inference applications and insights learned from our previous products. With careful tradeoffs in hardware-software co-design, Cloudblazer i20 delivers impressive performance and energy efficiency while maintaining acceptable hardware costs and software complexity/flexibility. To tackle computation- and data-intensive workloads, DTU 2.0 integrates powerful vector/matrix engines and a large-capacity multi-level memory hierarchy with high bandwidth. It supports comprehensive data flow and synchronization patterns to fully exploit parallelism in computation/memory access within or among concurrent tasks. Moreover, it enables sparse data compression/decompression, data broadcasting, repeated data transfer, and kernel code prefetching to optimize bandwidth utilization and reduce data access overheads. To utilize the underlying hardware and simplify the development of customized DNNs/operators, the software stack enables automatic optimizations (such as operator fusion and data flow tuning) and provides diverse programming interfaces for developers. Lastly, the energy consumption is optimized through dynamic power integrity and efficiency management, eliminating integrity risks and energy wastes. Based on the performance requirement, developers also can assign their workloads with the entire or partial hardware resources accordingly. Evaluated with 10 representative DNN models widely adopted in various domains, Cloudblazer i20 outperforms Nvidia T4 and A10 GPUs with a geometric mean of 2.22x and 1.16x in performance and 1.04x and 1.17x in energy efficiency, respectively. The improvements demonstrate the effectiveness of Cloudblazer i20’s design that emphasizes performance, efficiency, and flexibility.