Reviewerly/exHarmony huggingface dataset:
An Analysis of Quantum Computing Spanning IoT and Image Processing

The significance of using quantum computing in a variety of applications is illustrated by qubits, coherence time, and gate error rate, respectively, in all of the measurements. In quantum information, the fundamental units are called qubits, which are comparable to bits in classical information. The complicated computation can be simplified by employing qubits, which can be used in a wide variety of contexts. Data can be analyzed without producing any decoherence errors, which is particularly useful for image processing and real-time applications. The concept of “noisy intermediate scale quantum” (NISA) can be addressed and used in real-time data collecting through the Internet of Things as well as in picture processing. The NISA has a primary emphasis on factorization and optimization techniques for the purpose of data analysis. Therefore, this progress will be essential in unlocking the full potential of quantum computing and enabling it to tackle a wider variety of difficult challenges like the Internet of Things (IoT) and Image Processing
Software Modeling and Design Process

Modeling is the designing of software applications before coding according to the object-oriented groups where under the model-based software design and development, software modeling and design are used as an important and mandatory part of the software development process. Software models are methods of expressing a software design. Basically, some sort of abstract language or pictures are used to express the software design under modeling. For object-oriented software, an object modeling language such as UML and many more are used to develop and express the software design. Also, the design model is based on the analysis and architectural essentials of the system. It denotes the application components and determines their appropriate placement and use within the overall architecture.
Playing Rock-Paper-Scissors Using AI Through OpenCV

Day-to-day life has become smarter and more intertwined with technology in the modern era. We can chat with AI chatbots, and we can play with AI machines. In many cases, AI machines are defeating human players. All age groups are likely to play these games. The computer gaming industry has found Artificial intelligence as a necessary element to make more entertaining and challenging games. This work involves the integration of rock-paper-scissors (RPS) game with artificial intelligence (AI) using OpenCV and MediaPipe. Through collaboration with designers and animators, the framework is designed to be computationally light while also entertaining and visually appealing. To evaluate kinematic hand data in Python, the basic gesture recognition pipeline emp1oys a 1eap motion device and two distinct machine 1earning architectures. This proposed system will provide a powerful application for future research into social human-machine interaction.
MagicCloth: Protect User Privacy in AR Streaming

With the growing number of users, Augmented Reality (AR) privacy issues have aroused researchers' concerns. Our work focuses on the protection of privacy-sensitive objects in indoor settings. We proposed a novel privacy protection mechanism MagicCloth for AR live streaming. When the user covers the privacy-sensitive object with a specially designed cloth, MagicCloth can accurately track it in real time, and replace it with a virtual object based on its shape and size seamlessly. By wisely integrating lightweight 0--1 segmentation, pattern detection, and AR plane tracking techniques, our MagicCloth system achieves real-time performance on Android smartphones. We extensively analyze the challenges involved in object replacement and have put forth corresponding solutions. In our future work, we aim to implement and refine these proposed solutions to effectively address the identified challenges and achieve seamless and privacy-preserving object replacement in AR scenarios.
Predicting Human Body Measurements from a Front Image

Quite a few scientists have recently performed research on modeling human body and shape. The current paper describes a solution to predict human body girth measurement and weight from a front image taken with a modern smartphone camera. In order to achieve this, a set of 3D body scans and manually taken body measurements have been used as an input to build several statistical PCA models. Seven linear measurements are extracted from the human front image. The body height is entered by the user and together with the seven measurements used as an input to the statistical models. These models are then applied to predict body measures and weight. The results of the experiments are promising but more tests with pilot users have to be performed to get a stable solution.
THEORETICAL FOUNDATIONS OF RESEARCH OF INFORMATION TECHNOLOGY AS A TOOL FOR FORMING PUBLIC OPINION

The problem of forming public opinion is important in the life of most countries today, especially with the development of new information technologies. The phenomenon of the latter is used in all spheres of society, a trend of which is rapidly increasing in the context of the continuous development of scientific and technological progress. This article presents some of the leading scientific developments that formed the theoretical basis for the study of the phenomenon of information technology as a power mechanism. Certain scientific approaches to defining the concept of «information technologies», identifying their essence and significance in the development of social consciousness are considered. The place and role of this phenomenon in the interaction of the political sector with civil society, as well as it’s influence on the formation of public opinion, are analyzed. The author also characterizes the place of IT in the political structure of modern society, the peculiarities of its manipulative nature, and its thematic and content content. In particular, the role of IT in the process of informing members of society, creating public awareness, and establishing communication between political leaders and ordinary citizens is established. At the same time, it is emphasized that the use of the spectrum of information technologies to influence public opinion is accompanied by various manipulation techniques used by certain political actors and implemented through information resources. The author emphasizes the dichotomous nature of information technologies due to the multifaceted and complex nature of the analyzed phenomenon. On the one hand, they play the role of a rational and multidimensional communication and management tool of global importance, while, on the other hand, they are used to implement destructive motives with destabilizing effects through appropriate communication and behavioral techniques and methods formulated in the scientific research of foreign thinkers of the twentieth century.
Quantum-Enabled Machine Learning With a Challenge in Clothing Classification With a QSVM Approach

Quantum information encoding also introduces the concept of quantum entanglement, where multiple qubits can become correlated in such a way that the state of one qubit cannot be described independently of the state of the other qubits. This property has important implications for quantum communication and quantum computing, as it allows for the creation of secure quantum communication channels and for the development of quantum algorithms that can solve certain problems much faster than classical algorithms. Quantum-enabled machine learning (QEML) is an emerging field that seeks to combine the power of quantum computing with classical machine learning techniques. One approach to QEML is the use of quantum support vector machines (QSVMs), which are quantum versions of classical support vector machines (SVMs).
IMPROVING READING COMPREHENSION OF THE EIGHTH-GRADE STUDENTS OF SMP (SLUB) SARASWATI 1 DENPASAR THROUGH KWL

Reading comprehension is the process of acquiring knowledge and comprehending the meaning of printed items. It is a necessary language ability that must be mastered. Initial research revealed that the majority of the eighth-grade students of SMP (SLUB) Saraswati 1 Denpasar struggled with reading comprehension, particularly identifying general and specific information, textual meaning, and textual reference. Moreover, the purpose of the study was to determine whether the reading comprehension of the eighth-grade students of SMP (SLUB) Saraswati 1 Denpasar could be enhanced using KWL (Know Want Learn). To achieve the objective, the researcher performed classroom action research in two cycles. Research instruments, including tests and a questionnaire, were used to obtain the necessary data. Afterwards, the data were examined by comparing pre- and post-test findings. On the basis of the findings, it was evident that the subjects’ mean scores improved significantly between the pre- and post-tests. As supporting data, the questionnaire was also completed at the end of the last cycle; the results indicated good responses to the implementation of KWL. Therefore, it could be concluded that implementing KWL could increase reading comprehension of the eighth-grade students of SMP (SLUB) Saraswati 1 Denpasar.
Software Development With UML Modelling and Software Testing Techniques

This chapter focuses on software development principles and discusses each principle thoroughly with diagrammatic representation. It also includes the definition of UML (unified modeling language) modelling with an explanation regarding how UML modelling takes place and a detailed example. It also focuses on software testing methods, with each method definition and diagrams well explained. A simple case study situation is taken to discuss the example of UML model. This chapter's main objective is to focus on all key points of software development testing and model design techniques precisely.
Spatio-Temporal dependency preserving Cognitive-assisted Continuous Chinese Sign Language Recognition

Sign Language recognition system plays a vital role for the hearing and visually impaired people to make normal communication with the other common people. But deaf and dumb people signs are not understandable to the common person, leading to a communication barrier. Also, there were only 50 certified sign language interpreters in India for a deaf population of around 7 million. To come this communication barrier, an intelligent translator system for isolated and continuous sign language recognition is proposed. In our proposed work, the continuous sign language recognition is dealt as a multi-class classification problem. A Video-based custom Long-Short Term Memory (VidcuLSTM) model was designed and configured with different hyperparameter tuning and optimizers to avoid memorization and overfitting of the translator system. We evaluated the proposed system using Chinese Isolated and Continuous SLR dataset and it is evident from the results that the proposed system outperforms existing state-of-art systems with improved performance in recognition rate by around 5% with reduced WER of around 2.67.
The Software Principles of Design for Data Modeling

The method for researchers to ascertain clients' software needs is requirement gathering. Requirement gathering rarely occurs successfully, and numerous software programs have failed as a result of incorrect or partial knowledge of the needs of users. The requirement-gathering technique is widely considered to be an essential part of development. The chapter will critically discuss why many developments fail due to poor requirements gathering. It is a challenging task to elicit requirements. When requirements are elicited, errors are most common. Addressing the system's needs presents several challenges, and the chapter intends to study these challenges and provide a solution. Professionals face challenges in gathering requirements due to the unavailability of stakeholders, unclear requirements, frequent changes in demand, and lack of skills for analysts. In a variety of contexts and areas, interviews with a preference for framework proved to be among the best gathering approaches.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI GOOGLE PLAY MENGGUNAKAN NAÏVE BAYES

In today's digital era, mobile applications are increasingly popular and their use is increasing, so it is important to understand user satisfaction and dissatisfaction in using these applications. One of the most popular mobile applications in Indonesia is the Dana application, which provides various financial services such as bill payments, money transfers, investments, and so on. Sentiment analysis of Dana application reviews on the Google Play Store was carried out using the Naive Bayes algorithm classification method. The purpose of this analysis is to understand user opinions and views on the Dana application, as well as provide input for developers in improving the quality of services and features provided. The results of the analysis show that the majority of users give positive reviews of the Dana application with a percentage of around 60%, while only around 12% have negative reviews, and around 28% have neutral reviews. From the results of this analysis, it can be concluded that the Dana application has received a positive response from users on the Google Play Store. However, there are still a small number of negative reviews which can be input for developers to improve the quality of services and features provided by the Dana application.
ANALISIS SEGMENTASI PELANGGAN BERBASIS MODEL RECENCY FREQUENCY DAN MONETARY (RFM) MENGGUNAKAN ALGORITMA K-MEANS

Business development is currently growing very rapidly, with the development of internet technology that can facilitate all business activities. Increasing business development has an impact on presenting new business competitors, so companies need strategies that are able to maintain customer quality. This study aims to segment customers from the company's sales transaction data, with a large number of transactions, technology is needed to group a data so that the method used in this study is a data mining method and uses the K-Means algorithm. With the K-Means Algorithm, it can help in grouping customers to make it easier for companies to strategize each customer group. This customer grouping uses an initial model of Recency, Frequency and Monetary (RFM) to help calculate customer groups. Data mining evaluation was carried out using Silhouette Coefficient with test results using Visual Studio Code software python programming language, The results of this study selected 3 clusters consisting of Low Loyalty totaling 137 customers, Medium Loyalty totaling 1636 customers and Highest Loyalty totaling 2395 customers.
Individual subject evaluated difficulty of adjustable mazes generated using quantum annealing

In this paper, the maze generation using quantum annealing is proposed. We reformulate a standard algorithm to generate a maze into a specific form of a quadratic unconstrained binary optimization problem suitable for the input of the quantum annealer. To generate more difficult mazes, we introduce an additional cost function $Q_{update}$ to increase the difficulty. The difficulty of the mazes was evaluated by the time to solve the maze of 12 human subjects. To check the efficiency of our scheme to create the maze, we investigated the time-to-solution of a quantum processing unit, classical computer, and hybrid solver.
A study of dynamic emoji emotional responses based on rhythms and motion effects

Dynamic emojis are a form of nonverbal communication used in social programs to express emotions during conversations. Studies have shown that different dynamic effects can influence users' emotional perceptions. Previous studies have focused on the emotional responses elicited by static emojis, while the emotional responses to dynamic emojis have not been thoroughly explored. In this study, we examined the impact of 128 different dynamic effects, categorized into emotional types (HAHV, LAHV, HALV, and LALV), on users' arousal and valence, and conducted semi-structured interviews to identify users' preferred dynamic effects. The results revealed significant and positive correlations between the arousal levels of all dynamic emojis and the effects of rhythms. However, the impact of rhythms on the valence of dynamic emojis varied depending on the emotion types of emojis. Specifically, the effects of motion on the valence of dynamic high-valence emojis were found to be significant, whereas they were not significant for dynamic low-valence emojis. Based on these findings, we recommend considering following factors in the design of dynamic emojis, including rhythms, motion effects, motion range, emotional metaphors, and the creation of contrast.
Assessing network-based earthquake early warning systems in low-seismicity areas

Earthquake early warning (EEW) technology, designed to alert the public of earthquake risks after initial P-wave detection but before the onset of strong tremors, has developed rapidly. Methodologies from various fields are combined in EEW systems to estimate earthquake locations, magnitudes, and expected intensities based on the initial P-wave data. These systems operate automatically because prompt responses are required. However, as no common evaluation framework for EEW system verification exists, potentially divergent evaluations from reviewers or countries could ensue. Moreover, evaluating EEW systems is more complicated when the target area does not experience frequent earthquakes. We aimed to establish a guidance review process for low-seismicity areas to ensure reliable and stable integrated EEW system operation. We incorporated management aspects through actual system operator surveillance and designed an EEW assessment process based on feedback from our surveys. Using this approach, we created a comprehensive and well-informed evaluation process that considers the diverse perspectives of experts involved in EEWs. Our proposed assessment method allows for a uniform and consistent evaluation process, regardless of changes in the methods or technologies used by EEW systems. The method aims to guide EEW system assessments in low-seismicity areas.
INTERPRETATION OF THE 3D GEOELECTRICAL MODEL OF THE INTERSECTION AREA OF THE ZVIZDAL-ZALISKA AND NEMYRIVSK FAULT ZONES

The interpretation of the deep three-dimensional geoelectricalal model of the central part of the Zvizdal-Zaliska and Brusyliv fault zones of the Ukrainian shield, which was built with the involvement of data from experimental observations of the Earth's low-frequency electromagnetic field conducted in 2019 by the Institute of Geophysics named after S.I. Subotin of the National Academy of Sciences of Ukraine, within the Ordyntsi-Lobachiv and Zoziv-Stryzhavka profiles was done. Experimental materials were processed with the PRC_MTMV software package, tipper estimates were obtained for periods of geomagnetic variations from 50 to 3400 s and curves of apparent resistivity (amplitude values and impedance phases) from 10 to 10000 s. Based on the qualitative analysis of magnetovariational profiling parameters, magnetotelluric sounding curves and pseudo-sections, several local near-surface structures of complex orientation in space were identified, which are confined to the Pogrebyshchensky and Ogiivskyi faults, areas of separate parts of the Zvizdal-Zaliska, Brusyliv and Nemyriv fault zones, as well as their convergence , and outside the fault zones in the crystalline massive of the Ukrainian shield. The main result of the geological-geoelectrical interpretation of the 3D model is the discovery of new anomalies of low resistivity in the near-surface part of the earth's crust (from the surface to 0.5-2 km), which correspond to the structural and metallogenic features of the study area; confirmation and detailing of the anomalously heterogeneous distribution of resistivity in the tops of the upper mantle at depths from 70 to 120 km west of the Ukrainian shield. The area of intersection of the Zvizdal-Zaliska and Nemyrivs fault zones is characterized by both a galvanically connected spatially diverse system and local separate near-surface anomalies with low resistivity from 5 to 100 Ohm·m, most of which dip to 0.5-1 km. It is shown that there are connections between conductivity and structural features of the Zvizdal-Zaliska, Brusyliv and Nemyriv fault zones. In addition, some anomalies are found in the granite migmatites of the Ros' structural-formation zone, outside the tectono-metasomatic mineragenic zones. As a rule, anomalies are confined to zones of disintegration of crystalline basement rocks, elongated zones of metasomatization, and areas of distribution of graphitized rocks; moreover, some of the surface anomalies correspond to regions of weathering crusts. The conducted geological-geoelectrical analysis of the model showed that several areas require further study, namely the sub-latitude anomaly west of the Zoziv-Stryzhavka profile and the complex-shaped anomaly in the eastern part of the study area. As promising for the search for minerals, which meets the geoelectrical criteria, the area of the anomaly within the Brusyliv fault zone with a depth of up to 2 km is highlighted.
A Novel Approach to the GQR Algorithm for Neural Networks Training

In this paper, a novel approach to the GQR algorithm is presented. The idea revolves around batch training for the feedforward neural networks. The core of this paper contains a mathematical explanation for the batch approach, which can be utilized in the GQR algorithm. The final section of the article contains several simulations. They prove the novel approach to be superior to the original GQR algorithm.
Application of Monte Carlo Algorithms with Neural Network-Based Intermediate Area to the Thousand Card Game

Modern techniques of artificial intelligence used in computer games allow to obtain very realistic and effective artificial agents controlling the game on the part of computer players. The main goal of this study is to create modern artificial intelligence algorithms based on neural networks and algorithms based on the Monte Carlo method to control players in the popular card game called Thousand. We propose two approaches based on neural networks trained on the basis of the Monte Carlo algorithm and the recursive Monte Carlo algorithm. Statistical approaches are characterized by high response times. Hence, an attempt is made to implement solutions that maintain high efficiency with a shorter operating time and, consequently, reduced requirements for computing complexity. The research showed no significant differences in the Monte Carlo approaches and the corresponding neural network methods. In the case of recursive method invocation, the effectiveness increased compared to the base methods.
Training Neural Tensor Networks with Corrupted Relations

We present two new objective functions through which neural tensor networks – machine learning models designed to learn relations between entities – might be trained, and hypothesize that our novel objectives may bolster the neural tensor network’s performance through so-called negative learning. We illustrate that our new training objectives can show more stable training behaviour than the original training objective, and that they can result in better behaviour from the model on a selected problem. We also show that our training objectives may be less extensible into more complex problem domains than the original, however.
Nonstandard Unitary Transformations of Quantum States

In quantum optics, unitary transformations of arbitrary states are evaluated by using the Taylor series expansion. However, this traditional approach can become cumbersome for the transformations involving non-commuting operators. Addressing this issue, a nonstandard unitary transformation technique is highlighted here with new perspective. In a spirit of “quantum” series expansions, the transition probabilities between initial and final states, such as displaced, squeezed and other nonlinearly transformed coherent states are obtained both numerically and analytically. This paper concludes that, although this technique is novel, its implementations for more extended systems are needed.