Reviewerly/exHarmony huggingface dataset:
INTER-STUDENTS’ LANGUAGE POLITENESS OF SMP NEGERI 2 KELAYANG INDRAGIRI HULU

This paper describes and analyzes the language politeness among students of SMP Negeri 2 Kelayang Kelayang District in Indragiri Hulu Regency. It is qualitative research using an analytical descriptive method, namely analyzing and exposing data found in the form of words. Data analysis techniques begin with data reduction, presentation, and making conclusions and verification of the data. The data collection techniques carried out are recording, listening, and taking notes techniques. The source of the research data is inter-student speech. Based on the research results, it was found that the forms of language politeness compliance were the obedience of the maxim of wisdom, the obedience of the generosity maxim, the obedience of the praise maxim, the obedience of the humility maxim, the obedience of the agreement maxim, and the obedience of sympathy maxim. In addition to the obedience of language politeness, violations of language politeness were also found, namely violations of the maxim of wisdom, violations of the generosity maxim, violations of the praise maxim, violations of the humility maxim, violations of the agreement maxim, and violations of the sympathy maxim. The dominant maxim is the violation of the maxim of praise. In addition to the violation of the praise maxim, a dominant maxim refers to obedience to the generosity maxim.
RUSTY: Effective C to Rust Conversion via Unstructured Control Specialization

Rust is an emerging programming language designed for both performance and security, and thus many research efforts have been conducted recently to migrate legacy code bases in C/C++ to Rust to exploit Rust's safety benefits. Unfortunately, prior studies on C to Rust conversion still have three limitations: 1) complex structure; 2) code explosion; and 3) poor performance. These limitations greatly affect the effectiveness and usefulness of such conversions. This paper presents Rusty, the first system for effective C to Rust code conversion via unstructured control specialization. The key technical insight of Rusty is to implement C-style syntactic sugars on top of Rust, thus eliminating the discrepancies between the two languages. We have implemented a software prototype for Rusty and conducted experiments to evaluate the effectiveness and testify the usefulness of it by applying Rusty to micro-benchmarks, as well as 3 real-world C projects: 1) Vim; 2) cURL; and 3) the silver searcher. And experimental results demonstrated that Rusty is effective in eliminating unstructured controls, reducing the code size by 16% on average with acceptable overhead (less than 61 microseconds per line of C code).
PENGEMBANGAN MEDIA COOL APPLICATION POWERPOINT INTERACTIVE PADA TEMA 6 ENERGI DAN PERUBAHANNYA DI KELAS III SD

The limited availability of learning media and the lack of varied media in schools greatly affect the student learning process. Meanwhile, judging from the learning styles of third grade elementary school students tend to follow the audio-visual learning style, meaning that students can learn optimally if there is an audio-visual-based learning media. One of the media that can be developed is media cool application powerpoint interactive. The aims of this study were (1) to determine the design of the development of cool application powerpoint interactive media on the 6 energy theme and its changes in class III SD, (2) to determine the feasibility of the cool media application powerpoint interactive media for students on the 6 energy theme and its changes in class III SD, ( 3) find out the response of teachers and students to the cool application powerpoint interactive media on theme 6 energy and its changes in grade III SD. This type of research is Research and Development, which uses a 4D model with 4 stages, namely define, design, develop and disseminate. The research instruments were media expert validation sheets, material expert validation sheets, teacher response questionnaires and student response questionnaires. The results showed (1) the results of media validation obtained a score of 100% with a very feasible category (2) the results of material validation obtained a score of 80% with a decent category, (3) the results of media and material validation by the teacher obtained a score of 100% with a very decent category. and (4) the results of the teacher's response with a score of 100% with a very decent category and the results of student responses with a percentage of 96% with a very decent category. Based on the results of these data, it can be concluded that the development of cool media application powerpoint interactive on theme 6 energy and its changes in class III SD is very feasible to use.
Quantum Computing-Enabled Machine Learning for an Enhanced Model Training Approach

Machine learning is an exciting area where ever-growing problems such as anomaly detection using sensor data, natural language processing, image processing, etc., are solved using complex yet fascinating algorithms. Such algorithms learn the function that maps input to output from the training examples. The algorithms are evaluated with the validation data and then used to predict the output for an unknown dataset. For the past few years, scholars have been researching to improve such classical machine learning algorithms using quantum computing. Some of the latest research is the optimization of computationally expensive algorithms with quantum computing and transforming stochastic procedures into the semantics of quantum theory. The quest for the learning-based algorithm is aspiring: the discipline seeks to comprehend what learning is and studies how algorithms approximate learning. Quantum machine learning takes these aspirations further by looking at the subatomic level to aid learning. Machine learning-based algorithms minimize a constrained multivariate function. Different algorithms have different hyper-parameters that need to be tuned for the trained model to generalize well. Such optimization has high time and space complexity, which is central to learning theory. This contribution gives an organized overview of the evolving arena of quantum machine learning. It presents the methods as well as practical details. We start this chapter by introducing the major components of Quantum Computers, where we provide an overview of quantum computing with an in-depth explanation of the superposition of state, which will be crucial for all quantum algorithms. Next, we exploit a fascinating phenomenon called entanglement in quantum computations. Parallelism is the key to speeding up the training process of learning algorithms. One of the significant advantages of quantum computing is quantum parallelism which we will explore through Grover's search algorithm. Next, we go over the learning mechanism of a traditional machine learning algorithm, namely a Support Vector Machine (SVM) trained on classical computers. Furthermore, we exploit the Quantum SVM (QSVM) trained on quantum computers. Finally, we solve the famous malignant breast cancer classification problem using the quantum SVM algorithm. We study various simulations in Python language on the mentioned dataset to analyze their time complexity and performances on standard evaluation metrics, namely accuracy, precision, recall, and F1-score. Two simulations are conducted using classical machine learning algorithms using the Python library Scikit-learn. Finally, the last simulation is based on IBM's real quantum computer using its quantum machine learning library called Qiskit.
A Privacy-Preserving Hybrid Range Search Scheme Over Encrypted Electronic Medical Data in IoT Systems

Electronic wearable devices play an important role in the Internet of Things (IoT) systems for collecting medical data. Searching over such numerical medical data help to provide better service and treatment. However, user and data security is a major barrier to public adoption. Existing approaches designed to facilitate secure (range) searches over encrypted data generally incur expensive computational overhead suffer from unexpected information leakage, and/or have high false-positive results. Therefore, in this article, we design a hybrid searchable encryption scheme that supports efficient, secure, and accurate range searches over encrypted data sensed and collected from medical IoT devices. The designed graph structure helps to filter out most of the false data, and the batching processing on ciphertexts accelerates the removal of irrelevant data. Unlike most prior works, the proposed random index hides the distribution of data, and the probabilistic fixed-length trapdoor hides the range size and repetition of the query. If necessary, all the encrypted data can be refreshed by the cloud server after a range search. The scheme is proven to be secure in a simulation-based model. Then, we evaluate the performance of our proposed scheme on Microsoft Azure cloud servers and Azure IoT Central. The comparisons with several prior works demonstrate that our scheme supports more efficient secure range searches.
On Lattice Constructions D and D' from q-ary Linear Codes

Multilevel lattice codes, such as those associated to Constructions $C$, $\overline{D}$, D and D', have relevant applications in communications. In this paper, we investigate some properties of lattices obtained via Constructions D and D' from $q$-ary linear codes. Connections with Construction A, generator matrices, expressions and bounds for the lattice volume and minimum distances are derived. Extensions of previous results regarding construction and decoding of binary and $p$-ary linear codes ($p$ prime) are also presented.
Enhanced 4A Identity Authentication Center Based on Super SIM Technology

This paper explains how large-scale IT systems balance security and enabling under the pressure of supporting complex business models, constantly introducing new technologies, maintaining unified identity management, and meeting the business diversification of digital transformation. The paper introduces the necessity and basic idea of using China Mobile Super SIM technology to realize the enhanced Authentication, Authorization, Accounting, and Audit (4A) identity authentication center. We then describe the system’s design principles, references, and architecture and detail several key processes and capabilities. Finally, the implementation and evolution of the enhanced identity authentication system are described.
A Feature Extraction Algorithm Based on Blockchain Storage that Combines ORB Feature Points and Quadtree Division

In the process of 5G power grid inspection robot moving for a long time, the sensor constantly collects the feature information of the substation. Due to the limited memory capacity, this feature information must be stored on the network, which requires the storage network must have strong security. The storage network based on blockchain can better solve the problem of feature data encryption, and a good feature extraction method can also relieve the pressure of network storage. As the input information of the whole SLAM, feature points play a crucial role in the detection performance and accuracy of the whole SLAM. When the extracted feature points are few or evenly distributed, they cannot express the information of the whole environment, which will make the mapping and localization error of SLAM system larger, and seriously lead to the loss of tracking. In this paper, we first analyze the standard ORB algorithm and the Qtree_ORB algorithm. Aiming at the problems existing in the two algorithms, an improved ORB feature extraction algorithm is developed. For the problem that the feature points extracted by the Qtree_ORB algorithm are too uniform, the maximum division depth of the quadtree is limited according to the number of feature points required for each layer of the image pyramid, which improves the problem that the feature points are too uniform. Finally, we evaluate the performance of the improved algorithm, and analyze the uniformity of feature points to verify the performance and robustness of the improved algorithm.
To the question of transformation of education in a post-industrial society

Questions of innovative politics at higher school are extremely actual today, because without perfection of education there is no scientific and social progress. Currently, the transition from the University of the classical type to the University of the Third Generation is in progress, directly associated with a production.The separate aspects of this process are examined in this article.
Fundamental changes in the determinants of western ecopsychology at the end of the 20th and the beginning of the 21st century

The article reveals the theoretical aspects of the subject and the genesis of ecological psychology and ecopsychology as a branch of modern psychological knowledge. It is noted that the ecological crisis and the awareness of the limits of subduing nature have actualized the problem of both protecting nature and preserving psychological health. Different definitions of the subject of environmental psychology, which are available in scientific and educational literature, are considered. It is noted that they are tangential to Western environmental psychology. The fundamental difference between environmental psychology and ecopsychology is indicated. It was found that the first of them is a branch of behaviorist research, the second is a type of Jungian-oriented psychoanalysis. The article argues that ecopsychology arose on the basis of fundamental transformations of the determinants of Western self-awareness in the second half of the 20th century. Nine principles of ecopsychology by T. Roszak are given, which determine the foundations of this psychological direction. It is noted that ecopsychology pays the main attention to the ecological unconscious, which relates a person to the natural world. It is indicated that on the basis of the theoretical approaches of ecopsychology, whole complexes of psychotherapeutic methods of restoring the human connection with the surrounding world, overcoming alienation from nature, have been created. In the conclusions, it is stated that the domestic environmental psychology by subject and method gravitates more towards environmental psychology, but recently tries to take into account the achievements of ecopsychology, sharing its certain theoretical foundations. It is the synthesis of these two directions that will give a new impetus to the development of this field of psychological science and psychotherapeutic practices.
Pengaruh Model Problem Based Learning terhadap Keterampilan Menulis Teks Eksplanasi Siswa Kelas VII MTsS Baitul Ihza Sitapus

There are three objectives of this research. First, it describes the skills of writing explanatory texts for class VII students of MTsS Baitul Ihza Sitapus without using a problem based learning model. Second, it describes the skills of writing explanatory texts for class VII students of MTsS Baitul Ihza Sitapus using a problem based learning model. Third, analyzing the effect of the problem based learning model on the skills of writing explanatory texts for class VII MTsS Baitul Ihza Sitapus. This type of research is quantitative research with experimental methods. Based on the data analysis, the problem based learning model was used to influence the skills of writing explanatory texts for class VII MTsS Baitul Ihza Sitapus.
Diurnal Analysis of the Performance of Photovoltaic Systems under the Guinea Savannah Atmosphere in Ogoja, Cross Rivers State, Nigeria

A thorough experimental investigation was carried out to diurnally analyze the performance of photovoltaic systems deployed for domestic purposes under the guinea savannah atmosphere in Ogoja, Cross River State, Nigeria. In this study, the time of day to expect maximum efficiency from the photovoltaic (PV) module is shown, and the time of day that the panel temperature will exceed it maximum operating cell temperature is also shown. Instruments employed in the process include a digital solar power meter, digital hygrometer, digital infrared gun thermometer and a digital high precision photovoltaic panel maximum power point tracker (MPPT) to track and determine the maximum power, voltage and current produced by the photovoltaic module. The result shows that the highest level of efficiency was attained at 14:30 which reveals that the climatic condition which occurs at 14:30 is the most favorable for PV systems deployed for domestic purposes at the location. The study shows that 42% and 400C is the best relative humidity and PV temperature level for optimum PV efficiency at this location. In addition, the temperature of the study location appears encouraging as on average, the panel temperature will rarely exceed the maximum operating cell temperature for months besides January and march in which a cooling mechanism to regulate the panel temperature may be needed to avoid a massive loss in efficiency. An altitude of 85m above sea level makes the location relatively favorable for receiving intense solar radiation. With 90% efficiency assured, the location can be seen as suitable for generating electricity via PV technology and installing solar farms for commercial purposes.
Formal verification of Fuzzy-based XAI for Strategic Combat Game

Explainable AI is a topic at the forefront of the field currently for reasons involving human trust in AI, correctness, auditing, knowledge transfer, and regulation. AI that is developed with reinforcement learning (RL) is especially of interest due to the non-transparency of what was learned from the environment. RL AI systems have been shown to be "brittle" with respect to the conditions it can safely operate in, and therefore ways to show correctness regardless of input values are of key interest. One way to show correctness is to verify the system using Formal Methods, known as Formal Verification. These methods are valuable, but costly and difficult to implement, leading most to instead favor other methodologies for verification that may be less rigorous, but more easily implemented. In this work, we show methods for development of an RL AI system for aspects of the strategic combat game Starcraft 2 that is performant, explainable, and formally verifiable. The resulting system performs very well on example scenarios while retaining explainability of its actions to a human operator or designer. In addition, it is shown to adhere to formal safety specifications about its behavior.
Detection of Deep Fake in Face Images Based Machine Learning

Fake face images is a recent critical issue of artificial intelligent due to it has directly impacts on the social lives, and may be made to imply threats against privacy, fraud, and other issues. Currently, creating fake images has become relatively simple due to the powerful yet user-friendly mobile applications that navigate in the social media world and with the invention of the Generative Adversarial Network (GAN) that provides a good quality images that might be difficult for humans to differentiate with their eyes and makes image and video manipulation simple to do, quickly spread and hard to detect, Therefor, image processing and artificial intelligence are crucial in solving such problems. That is why scientists must create technologies or algorithms to control and to avoid these various negative impacts by different detection approaches can be applied. The proposed approach is more robust than current methods when propose a model based on support vector machine as a classifiers to detect fake human faces created by machines. The first stages includes a preprocessing that start with changing images from RGB to YCbCr and then applying the gamma correction. finalize the results show that the extracted edges using Canny filter were useful for detecting fakes in face images. After that, applying two distinct methods of detection by utilizing "Support Vector Machine" with "Principal Component Analysis" and "Support Vector Machine" without "Principal Component Analysis" as a classifiers. The findings show that the highest accuracy gained is 96.8% when using the SVM with PCA while the accuracy obtained is 72.2% when using the SVM alone.
Relationship between Emotional Intelligence and Motivation among Primary Pupils in Manjung District

Attaining good quality of education among pupils requires emotional intelligence and motivation that works perpetually together.Emotional intelligence is positively linked with motivation as it standardized the relationship between academic performance and cognitive ability.However, the emotional intelligence factor is often not acknowledged.This study determines the relationship between emotional intelligence and academic motivation.The sample of 120 pupils was selected from primary schools consisting of National School and Vernacular Schools in the Manjung district.The questionnaire was used as an instrument to collect data, and collected data are analysed in descriptive and inferential analysis using SPSS version 26 software.The analysis used mean value, frequency, standard deviation and Pearson correlation.The results showed primary schools' high mean regarding emotional intelligence and academic motivation.Their emotional intelligence greatly influences pupils' motivation to excel.In addition to improving academic performance, emotional intelligence also shapes pupils' social and personal lives.Hence, efforts should be taken to increase the emotional intelligence level, which affects academic motivation and provides insights for the relevant authorities who seek benefits from this research.
Spatial Distribution of Heavy Metals in the Soil of Different Area at Right Bank in Mosul City, Northern Iraq, Part 1

The present study focuses on how to assess the reality of soil pollution by heavy elements on the right bank of Mosul City after 2017. Seventy samples are collected from the right-bank soils depending on the random system. Chemical analysis is conducted using the ICP-MS technique to determine the concentrations of the heavy elements (As, Cd, Cr, Ni, Pb, Zn) in these samples, and then to calculate the pollution factors: Geoaccumulation Index, Enrichment Factor, and Contamination Factor. The average crust is adopted for the comparison and definition of the permissible limits for calculating the pollution factors. The results of the chemical analysis show that the concentrations of the elements (As, Cd, Cr, Ni, Pb, Zn) are higher than the average crust by a ratio of 100%, 85.3%, 44.29%, 100%, 37.14%, 24.28% respectively, which have an effect on the results of the pollution factors, and this refers to an addition of heavy elements to soil of the right bank of Mosul City as a result of human activity (anthropogenic). These activities are the military operations and the use of different types of weapons such as bombs, missiles, ammunition and the remnants thereof, which contain many heavy metals as well as fumes and gases emitted from burning waste, vehicle exhaust, electricity generators, liquid waste in urban and industrial areas, sewage water, fertilizers and pesticides. To know the role of physicochemical characteristics and their contribution to the distribution of heavy elements in the soil, pH and electrical conductivity are measured and the organic matter content is determined. The results show a rate of 7.8, 27.8, and 4.6, respectively.