Reviewerly/exHarmony huggingface dataset:
The problem of intentional consciousness in the formation of meaning in the contemporary theatrical text

Addressing the problem of awareness in the theatrical text is manifested through the cultural interaction that relates to the characteristics of the theater, and therefore this problem is considered one of the thorny concepts because it is based on the contemporary philosophical lesson to show through it the nature of the intentions that the theatrical text sees, and given the multiple connotations of the term (intentional awareness) and its overlap at times. And its contrast sometimes, it is one of the ingredients that contribute to directing the parties to the communicative process and controlling the general framework in which the various actors and meaning-forming elements move in the theatrical discourse, and contribute to establishing a continuous dynamic relationship between the linguistic elements and the non-linguistic elements, and in order to clearly define the impact of a standard Intentionality in directing theatrical discourse, we must investigate its types related to the poles of the communicative process (author, text, recipient), and identify their functions that contribute to revealing the criteria of awareness and monitoring transformations of intent in shaping meaning in the text. Intentional awareness - as a contemporary critical term - refers to the implicit and underlying meaning behind a text. It is the goal of the undeclared text producer who seeks to create his own world governed by a comprehensive vision related to the inside of the text to contribute to identifying the semantics and directing them in the proper manner, to gather the intentionality in this way. Between the contradictions, the inside and the outside, the awareness and the unconscious, and therefore we try in this research to highlight the path and implications of intentional awareness in directing theatrical discourse, as no one can deny that this concept has its features and foundations clear in modern studies, especially contemporary philosophical and critical studies in which the concept of intent was taken. Another dimension in the direction of awareness of receiving to reveal the nature of the meaning that is formed in the theatrical text.
Emotional Context Detection on Conversation Text with Deep Learning Method Using Long Short-Term Memory and Attention Networks

The conversation in the text is an interesting research on Natural Language Processing. One of the text conversation tasks is to know the emotions of the people involved in the conversation. The conversation in social media like Twitter, Instagram, short message service, WhatsApp, and so on, often involves emotion. Somehow the comments are impulsive sentences that can stimulate emotions. Expressing emotions using text are rarely done and uncomfortable. However, with natural language technology development, expressing emotions using text can be succeeded with specific symbols. We call the specific symbols emojis. So many emojis can express emotions. This research proposes the emoji symbol as a character feature. We introduce the Emoji2Vec method and Long Short-term Memory with Attention. The Attention that is used has a complex topology. We compared the results of this study with the baseline model. The method we propose is better than the baseline model.
Achieving significant cost reduction in solar through the targeted use of AI and Machine Learning

The current DOE targets for solar are to achieve 50 year lifetimes, and 50% cost reductions (50/50) by 2030. These goals are challenging from both a fundamental physics mode as well as from an implementation aspect. To achieve these goals it will be necessary to address not only the hardware and BoS types of costs, but also the execution costs. In this regard, it will be essential to have AI and Machine Learning as key components for development and for optimization. This paper will outline the critical role that AI/ML will play in the various segments of the value chain and indicate the modes where the targeted use will lead to achieving the challenging goals.This paper will describe the following: (1) the key advantages of AI/ML, along with the current issues, (2) the need for AI/ML to achieve the DOE goals of 50-year lifetimes, and 50% cost reductions, and (3) a potential AI/ML approach which can support some of the necessary developments.
Digital Technology Design of Innovative HHO Algorithm and Experiment

In order to solve the problem that the basic Harris hawks optimization (HHO) is easy to fall into local optimum and the convergence accuracy is low, an innovative digital technology design of HHO (improved Harris hawks optimization, IHHO) is proposed. In the initial population stage, elite reverse learning is integrated into the design to improve the quality of the population and improve the global optimization performance of the algorithm; in the mining stage, the adaptive weight factor is added to update the local position to improve the local mining ability. By solving high-dimensional test functions to conduct digital technology inspection, the experimental results show that the IHHO algorithm combining the two strategies has better optimization accuracy and stability.
2.5D Pose Guided Human Image Generation

In this paper, we propose a 2.5D pose guided human image generation method that integrates depth information with 2D poses. Given a target 2.5D pose and an image of a person, our method generates a new image of that person with the target pose. To incorporate depth information into the pose structure, we design a three-layer pose space that allows accurate pose transfer compared with regular 2D pose structure. Specifically, our pose space enables the generative models to address the occlusion problems commonly happened in human image generation and also helps recognize spatial front-back relations of limbs. Extensive quantitative and qualitative results on the DeepFashion and Human 3.6M datasets demonstrate the effectiveness of our method.
A family of multivariate discrete distributions

In this short paper, the multivariate Poisson-Gamma, the multinomial N-mixture and the negative multinomial distributions are shown to have probability mass functions of the same form and thus to share, broadly, the same distributional properties. The three distributions are, however, fundamentally very different in nature, that is, in terms of genesis, interpretation and model building, and these differences are highlighted and discussed.
Application for Selling Food and Beverage Products in CSDW Restaurants Based on Android

CSDW is one restaurant that sells various kinds of fish dishes. As the development of the mobile application finally impacts on various areas of our lives. One of them is the trade sector. Considering that many customers are lazy to go out and buy food, researchers take the initiative to build online ordering applications at CSDW restaurants online with smartphones to make it easier for customers. The aim of this study is to produce an Android-based sales application for ordering food and transactions that can be done anywhere without having to come and queue, accompanied by images to be easily understood.Data collection techniques used are by observing and studying literature. Researchers hope to be able to help customers to be able to facilitate food and transactions easily without having to queue and come to the place.The results of the study are that it makes it easier for customers to buy food quickly and efficiently without having to wait in line or come to the CSDW restaurant.
Machine Learning Basics

The previous chapters covered some of the key concepts of classical statistics. These include point estimation, confidence intervals, hypothesis testing, and regression analysis. With such tasks, the focus is often on the model, its properties, and interpretation of inference. However, paradigms arising incomputer science introduce additional data focused tasks, some of which are related to classical statistical tasks and others involving automated analysis ofbig data. We now focus on such methods which are generally calledmachine learning.
The University of Panic

A pandemic crushes assumptions and inherited narratives of higher education. This chapter explores how COVID-19 tested the parameters of teaching and learning and how universities failed this test. Through the panic of shutdowns, lockdowns, economic restructures, social distancing, and closures, the speed of change and decision making was profound and under public scrutiny. Online learning has been a panacea for economic and social problems for 20 years. To manage a crisis the scale of COVID-19, online learning would be the obvious solution. However, the pandemic showed the flaws in this strategy and the toxic reality of quick fixes to higher education. Students were short changed and academics pushed to exhaustion. After COVID-19, higher education is in shreds. The visions and futures of universities are blurred. Using the theories of Paul Virilio, particularly his University of Disaster, this chapter probes how higher education unravels and dissociates teaching and research. When time is short and risks are high, what mode of leadership will survive in the post-pandemic university?
Quality Management and Evaluation of UHV Transformer Based on Cloud Matter Element

As the critical component of power control and transformation in UHV transmission link, the effective management of UHV Transformer's whole life quality cycle is an important guarantee for the safe and stable operation of power grid. In this paper, through the analysis of UHV Transformer quality management mode, on the basis of summarizing the existing management shortcomings, the cloud matter-element model of UHV Transformer quality statistics is constructed. This model realizes the uncertain transformation of qualitative concept and quantitative value through specific algorithm, and uses shorter time and better scheme to deal with the existing or potential quality problems of substation equipment. In this study, the scientific selection of quality statistical model is used to build the quality management system, carry out the operation status evaluation of UHV Transformer, and optimize and improve the quality control process and control measures.
RDMA Based Performance Optimization on Distributed Database Systems: A Case Study with GoldenX

The performance of distributed database system heavily relies on network to assist collaboration among nodes, while the traditional TCP/IP network has become the major bottleneck of the distributed database. Meanwhile, the emerging technology RDMA, featured with CPU offloading, bypassing the operating system kernel and zero copy, is envisioned to achieve low latency and high throughput data transmission between nodes. For this purpose, in this paper, we design and implement a high performance data transmission scheme based on RDMA and apply it to the distributed database system. Firstly, the paper analyzes the application scenarios and internal communication requirements of distributed database systems, combined with RDMA hardware characteristics. Secondly, based on the analysis results, we perform targeted design optimizations in three aspects, RDMA data transmission and memory region management, data placement, and congestion control. We propose efficient variable length data transmission mechanism based on the sliding window, application friendly data placement mechanism, and priority based adaptive congestion control mechanism to optimize the three aspects respectively. We further implement our idea with the typical distributed database GoldenX, and perform comprehensive experiments. Results shows that, compared with using RDMA network directly, the performance of GoldenX integrated with the optimizations is increased to 2.95 times, and the throughput is up to 2.61 times.
Game technologies in education as a way to enhance the cognitive activity of students

A specific feature of the modern educational services market is a high degree of global competition. In this regard, the use of the most advanced technologies and teaching methods is becoming one of the main competitive advantages of an educational organization. Games are one of the modern interactive teaching methods. The feasibility of using educational games in the educational process is analyzed in the article. The lack of application of an exclusively traditional teaching model in modern conditions has been substantiated. The need to apply new approaches is determined both in terms of enhancing the cognitive activity of students, and from the standpoint of updating the human capital of the academic environment, professional growth of the teaching staff. This fully applies to business education, where the ability to respond to various challenges and new trends is imperative for successful development. The study aimed to study the possibility of using educational games in the preparation of students studying economic disciplines. The task was to develop a version of the game. The methods of abstraction, observation, comparison, experiment were used in the course of research. This article describes an educational game developed by the authors for students majoring in “Microeconomics”, analyzes a number of characteristics of the developed game by examining it through “lenses”. Highlighted competencies, the formation of which is facilitated by participation in this game. The mphasis is made on a number of limitations inherent in the game method of teaching. The main results of the developed game with students majoring in “Economics” and “Management” are described. The prospect of using the game approach in the practice of education is grounded.
Chess Is Primitive Recursive

A two-player deterministic board game is primitive recursive if there exists a primitive recursive function that returns a sequence of optimal moves for a given player and a given epoch number. The game of chess is shown to be primitive recursive.
Semantic Private Information Retrieval From MDS-Coded Databases

We investigate the problem of semantic private information retrieval (PIR) from coded databases, where a user requires to download a message out of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> independent messages, without revealing its identity to the databases. These messages are coded using an (N, K) MDS code and stored in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> non-colluding databases. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> messages are allowed to have different semantics, e.g., different sizes and different probabilities of retrieval. We characterize the exact capacity of semantic PIR with coded databases, and provide an achievable scheme with non-uniform subpacketization. We show that the retrieval rate of semantic PIR with coded databases outperforms that of classical PIR with coded databases when the effects of zero padding shorter messages are taken into account.
Few-shot Aircraft Detection Based on Deep Reinforcement Learning

Data dependence is one of the challenging problems in aircraft object detection tasks in the field of remote sensing. Compared with traditional machine learning methods, deep learning relies heavily on large amounts of training data to understand potential data patterns. Specifically, due to the few number of samples, the generalization performance of the model is limited. To deal with this issue, this paper combines deep learning with perceptive ability and reinforcement learning with decision-making ability to achieve an effective object detection method. Firstly, we adopt an improved LSTM network to generate the strategy of data augmentation; Secondly, the strategy is applied to train and test of the object detection network to get F1-score as reward; Then, the policy gradient in reinforcement learning is employed to update the LSTM network, so that the network can generate profitable augmentation strategies that are more conducive to improving the accuracy of object detection. Finally, these processes are looped until the network reaches convergence. Compared with the existing object detection algorithm based on deep learning, the performance of our deep reinforcement learning (DRL) method is superior to the prevalent algorithms in aircraft detection on the public data sets DIOR and RSOD.
Determining Optimal Frame Processing Strategies for Real-Time Document Recognition Systems

Mobile document analysis technologies became widespread and important, and growing reliance on the performance of critical processes, such as identity document data extraction and verification, lead to increasing speed and accuracy requirements. Camera-based documents recognition on mobile devices using video stream allows to achieve higher accuracy, however in real time systems the actual time of individual image processing needs to be taken into account, which it rarely is in the works on this subject. In this paper, a model of real-time document recognition system is described, and three frame processing strategies are evaluated, each consisting of a per-frame recognition results combination method and a dynamic stopping rule. The experimental evaluation shows that while full combination of all input results is preferable if the frame recognition time is comparable with frame acquisition time, the selection of one best frame based on an input quality predictor, or a combination of several best frames, with the corresponding stopping rule, allows to achieve higher mean recognition results accuracy if the cost of recognizing a frame is significantly higher than skipping it.
Using an interactive climbing wall to develop motivation for physical education

The article presents the experience of working with students with intellectual disabilities. The process of psychological support at physical education lessons, the development of mental abilities and physical abilities and skills necessary for the socialization of students is described.
Information-Theoretic Bounds for Integral Estimation

In this paper, we consider a zero-order stochastic oracle model of estimating definite integrals. In this model, integral estimation methods may query an oracle function for a fixed number of noisy values of the integrand function and use these values to produce an estimate of the integral. We first show that the information-theoretic error lower bound for estimating the integral of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> -dimensional function over a region with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{\infty}$</tex> radius <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r$</tex> using at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> queries to the oracle function is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega\left(2^{d}r^{d+1}\sqrt{d/T}\right)$</tex> . Additionally, we find that the Gaussian Quadrature method under the same model achieves a rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O\left(2^{d}r^{d}/\sqrt{T}\right)$</tex> for functions with zero fourth and higherorder derivatives with respect to individual dimensions, and for Gaussian oracles, this rate is tight. For functions with nonzero fourth derivatives, the Gaussian Quadrature method achieves an upper bound which is not tight with the information-theoretic lower bound. Therefore, it is not minimax optimal, so there is space for the development of better integral estimation methods for such functions.
Fast Application Activity Recognition with Encrypted Traffic

With the popularity and development of mobile devices, the types and number of mobile applications are increasing, and people will perform different activities in the same application (e.g., refreshing the page, sending messages, browsing). Obtaining network traffic in real-time is an important foundation for network content supervision. Identifying application activity based on network traffic helps network managers understand user behaviors better and improve the quality of service. The widespread use of encrypted traffic in Mobile Applications presents a challenge to accurately identify application activities. Due to the low recognition speed of the existing application action recognition work for encrypted traffic, it is difficult to meet the real-time requirements. Therefore, we propose a fast application activity recognition method based on encrypted traffic. We extract the trend characteristics of the traffic generated by the application activity and use machine learning to identify the activity. The experimental results show that our method can identify user activities effectively. In addition, we have improved the existing method in real-time, and experiments show that our method is two orders of magnitude faster than the existing method when the recognition rate is similar to that of the existing methods.
LABORATORY MODEL OF MOBILE ROBOT WITH SECTIONAL MOVER

A laboratory model and simulation of the movement of a mobile robot with a sectional propeller are considered. The results of mathematical modeling of motion dynamics are presented.
A Novel Approach for Mining Team Leaders in Community Question Answering

Expert finding is the task of ranking experts for a given topic. This task can be utilized for finding suitable people for a job. The problem of finding a team leader for coaching a team is a practical and industrial issue that can be addressed by employing the expert finding task. On the other hand, this is a challenging problem because for the position of team leader, having knowledge in the required skills solely is not enough. A suitable leader should have abilities, including communication, mentoring, solving problems, etc. In this paper, we proposed a model to address this problem. Our proposed model ranks candidates for a given team. In this model, team members vote for candidates based on the centrality metrics. Our experiments on two real-world and large datasets collected from Stackoverflow indicated that our proposed method outperforms the baseline.
Satellite Image Enhancement and Restoration Using RLS Adaptive Filter

The main objective of this paper is to find and analyze the performance of various image restoration techniques to enhance the retrieved satellite image. Satellite pictures in course of being caught and communicated are corrupted because of station influence. These impacts contain noise in the form of additive White Gaussian noise, salt and pepper noise and mixed noise. Subsequently, acquired images are exceptionally noisy tainted with the fact that the picture substance is weakened or strengthened. Here, recursive least square adaptive algorithm is used for performing image restoration from noise-corrupted satellite images. The exhibition is estimated by methods for human visual system, quantitative measures as far as MSE, RMSE, SNR and PSNR. The RLS filter gave MSE value of 385.86, RMSE value of 19.64, SNR value of 2.42 and PSNR value of 22.25. When we compare RLS filter performance with NLMS filter (MSE value of 444.29, RMSE value of 21.08 and PSNR value of 21.65) and SSLMS filter (MSE value of 5052.99, RMSE value of 71.08 and PSNR value of 11.09), we observe that the MSE value of RLS filter is the least, and PSNR value of RLS filter is the highest. Hence, the RLS adaptive algorithm is preferred.