Reviewerly/exHarmony huggingface dataset:
English Translation Model Based on Intelligent Recognition and Deep Learning

Aiming at the problems of low accuracy of English phrase part of speech recognition, poor English translation effect, and long translation time in the traditional English translation model, an English translation model based on intelligent recognition and deep learning is designed. An English phrase corpus was built, the phrase antecedent and postscript likelihood of the improved GLR algorithm by using the quaternion cluster were calculated, and the part of speech of the English phrase corpus was identified. According to the recognition results, the feature extraction algorithm is introduced to extract the best contextual features. On this basis, a neural machine translation model is constructed by integrating the traditional neural network in deep learning and combining the attention mechanism. It is used as a neural machine translation model for English translation. The simulation results show that the English translation model based on intelligent recognition and deep learning has high phrase recognition accuracy, good translation effect, and short translation time, which improves the quality of English translation.
ONLINE SHOPPING – AN OVERVIEW

Now days the life style of the people is different. People feel uncomfortable and time consuming for going crowded markets. So, E-Shopping is a boon as it saves lot of time. Online shopping is a process whereby consumers directly buy goods, services etc. from a seller without an intermediary service over the Internet. Online stores are usually available 24 hours a day and many consumers have internet access both at work and at home. So it is very convenient for them to shop Online. One of the most enticing factors about online shopping, particularly during holiday season is, it alleviates the need to wait in long lines or search from a store for a particular item. Variety of goods is available in online. So theresearcher wants to know the preference of the consumers. So fifty respondents were met and data were collected regarding their preference towards shopping online.
A Novel Object Detection Method in City Aerial Image Based on Deformable Convolutional Networks

Unmanned Aerial Vehicle (UAV) has been widely applied for aerial object detection.Recently, this issue has become a research hot spot in the field of computer vision.Currently, the performance of conventional methods to detect small objects has reached a breakthrough point.Moreover, the dense distribution and large-scale variation of objects in aerial images significantly affect the detection accuracy.In order to resolve this problem, a novel structure based on the YOLOv3 is proposed in the present study.To this end, the backbone network is replaced by ResNet_vd50 to prevent information loss in the downsampling process.Then the backbone network is modified by deformable convolution to improve the detection ability of deformed objects.In this regard, SE (Squeeze-and-Excitation) attention is embedded in the ResNet_vd block to improve the expression ability of features.Furthermore, the Soft-NMS (Soft Non Maximum Suppression) algorithm is introduced for bounding box fusion to resolve the occlusion problem.Finally, the MixUp method is used in the data augmentation stage to enrich the background information by fusing different images.Based on the obtained results, it is concluded that the proposed method has higher accuracy in aerial images than state of art object detection methods.
Social issues in the development and application of artificial intelligence

Today the world is going through a period of rapid development of artificial intelligence (AI). Advances in this field determine the creation of machines capable of learning and performing of previously purely human cognitive tasks. AI is one of the key elements in the era of convergence of technologies, which is associated with profound consequences for humans, culture, society and environment. Since AI is a cognitive technology, its various aspects are inextricably linked with the main forms of human activity: education, science, culture and communication. Algorithms of social networks and news sites can contribute to the spread of misinformation and affect the perception of the concepts facts and truth, affect political interaction and activity. Machine learning can reinforce and deepen bias, which, in turn, can increase inequality and alienation, pose a threat to cultural diversity. The scale and power of AI technologies increase the asymmetry between individuals, groups and countries, which is manifested in the digital divide within and between countries. Thus, although AI has the potential to change the future of humanity for the better, people are increasingly aware of the risks and challenges of this technology, especially in terms of aggravating existing inequalities and imbalances, and its consequences for human rights. To develop possible scenarios and to use the potential of AI for development aims under the control over risks, we need a more comprehensive understanding of how society is changing under the influence of such a revolutionary technology as AI.
A Privacy-Preserved Variational-Autoencoder for DGA Identification in the Education Industry and Distance Learning

One of the most insidious methods of bypassing security mechanisms in a modern information system is the domain generation algorithms (DGAs), which are used to disguise the identity of malware by periodically switching the domain name assigned to a command and control (C&C) server. Combating advanced techniques, such as DGAs, is an ongoing challenge that security organizations often need to work with and possibly share private data to train better and more up-to-date machine learning models. This logic raises serious concerns about data integrity, trade-related issues, and strict privacy protocols that must be adhered to. To address the concerns regarding the privacy and security of private data, we propose in this work a privacy-preserved variational-autoencoder to DGA combined with case studies from the education industry and distance learning, specifically because the recent pandemic has brought an explosive increase to remote learning. This is a system that, using the secured multi-party computation (SMPC) methodology, can successfully apply machine learning techniques, specifically the Siamese variational-autoencoder algorithm, on encrypted data and metadata. The method proposed for the first time in the literature facilitates learning specialized extraction functions of useful intermediate representations in complex deep learning architectures, producing improved training stability, high generalization performance, and remarkable categorization accuracy.
Alexa, What Day Is It Again?

Self-management is crucial for people living with dementia to maintain independence, capabilities, and participation in stimulating activities leading to greater quality of life (Craig, 2017). Although technology plays an important role in maintaining independence and ageing well (World Health Organization, 2008), the application of technology has largely focused on formalised care settings, rather than promoting self-management. As technology continues to evolve, innovative uses for self-management will expand along with the need for more evidence-based research (Øksnebjerg et al., 2020). Virtual assistants can provide valuable information to a person living with dementia, such as being reminded and guided through sequential steps of everyday tasks. However, technology for older adults is likely to fail unless it can be used effortlessly and be demonstrated to serve a clear and meaningful purpose (Lindley, Harper, and Sellen, 2009). For the technology to be successful, it must be tailored to address the specific needs of individuals living with dementia who encounter a range of issues affecting their cognitive performance, hearing, and vision. It must also adapt to the progressing stages of the disease.
Tacit knowledge elicitation process for industry 4.0

Abstract Manufacturers migrate their processes to Industry 4.0, which includes new technologies for improving productivity and efficiency of operations. One of the issues is capturing, recreating, and documenting the tacit knowledge of the aging workers. However, there are no systematic procedures to incorporate this knowledge into Enterprise Resource Planning systems and maintain a competitive advantage. This paper describes a solution proposal for a tacit knowledge elicitation process for capturing operational best practices of experienced workers in industrial domains based on a mix of algorithmic techniques and a cooperative game. We use domain ontologies for Industry 4.0 and reasoning techniques to discover and integrate new facts from textual sources into an Operational Knowledge Graph. We describe a concepts formation iterative process in a role game played by human and virtual agents through socialization and externalization for knowledge graph refinement. Ethical and societal concerns are discussed as well.
Testing quantum foundations with quantum computers

We present two complementary viewpoints for combining quantum computers and the foundations of quantum mechanics. On the one hand, ideal devices can be used as test beds for experimental tests of the foundations of quantum mechanics: We provide algorithms for the Peres test for complex numbers in quantum superpositions and the Sorkin test of Born's rule. On the other hand, noisy intermediate-scale quantum devices can be benchmarked using these same tests. These are deep quantum benchmarks based on the foundations of quantum theory itself. We present test data from Rigetti hardware.Received 28 November 2021Accepted 23 February 2022DOI:https://doi.org/10.1103/PhysRevResearch.4.L022001Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum computationQuantum foundationsQuantum simulationPhysical SystemsSuperconducting qubitsQuantum InformationGeneral Physics
College English Teaching Quality Monitoring and Intelligent Analysis Based on Internet of Things Technology

With the continuous development of network communication, modern control, information perception, artificial intelligence, and other technologies, the Internet of Things technology has also been more comprehensively developed and grown, and it is playing an increasingly important role in various fields of production and life. Due to the low efficiency and cumbersome procedures of the existing college English teaching quality assessment, this study intends to propose a new method to deal with this problem. Through this research, we get the following: (1) use static weight equalization method, dynamic weight adaptation method, and iterative weight method to filter and analyze the original data received by mobile phones and discard some obviously wrong values in the preprocessing. Analyzing the number requirements of the game model, the supply operation reference model, and the IoT technology upgrade model for the number of demand points, the results show that the IoT technology upgrade model requires the least number of demand points. (2) Optimize the related parameter information of the IoT system model, including candidate numbers, number of students in class, adjustment coefficient, Di, turnover rate, system cost, and total system consumption. The Internet of Things technology upgrade model performed best. <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>Adjustment</mtext> <mtext> </mtext> <mtext>coefficient</mtext> <mo>=</mo> <mo>−</mo> <mn>102</mn> </math> ; <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>Di</mtext> <mo>=</mo> <mo>−</mo> <mn>99.6</mn> </math> ; <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mtext>turnover</mtext> <mo>=</mo> <mo>−</mo> <mn>100</mn> </math> ; <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mtext>system</mtext> <mtext> </mtext> <mtext>cost</mtext> <mo>=</mo> <mo>−</mo> <mn>123.2</mn> </math> ; <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mtext>SL</mtext> <mo>=</mo> <mo>−</mo> <mn>123.5</mn> </math> ; <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mtext>SP</mtext> <mo>=</mo> <mo>−</mo> <mn>99.8</mn> </math> . (3) Passed the test of the number of questions and feedback rate, classroom number, classroom test scores, new vocabulary number, system scoring, and comprehensive scoring to judge the model. It is found that the number of questions, feedback rate, class number, and class test scores, number of new vocabularies, system scores, and comprehensive scores of the upgraded and transformed models of the Internet of Things are above the standard values, indicating that the model is highly efficient and intelligent.
Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques

Semantic segmentation is a significant research topic for decades and has been employed in several applications. In recent years, semantic segmentation has been focused on different deep learning approaches in the area of computer vision, which has aimed for getting superior efficiency while analyzing the aerial and remote-sensing images. The main aim of this review is to provide a clear algorithmic categorization and analysis of the diverse contribution of semantic segmentation of aerial images and expects to give the comprehensive details associated with the recent developments. In addition, the emerged deep learning methods demonstrated much improved performance measures on several public datasets and incredible efforts have been dedicated to advancing pixel-level accuracy. Hence, the analysis on diverse datasets of each contribution is studied, and also, the best performance measures achieved by the existing semantic segmentation models are evaluated. Thus, this survey can facilitate researchers in understanding the development of semantic segmentation in a shorter time, simplify understanding of its latest advancements, research gaps, and challenges to be used as a reference for developing the new semantic image segmentation models in the future.
FAFD: Fast and Accurate Face Detector

Deep Neural Networks (DNN) have contributed a significant performance improvement in face detection. However, since most models focus only on the improvement of detection accuracy with computationally expensive structures, it is still far from real-time applications with a fast face detector. The goal of this paper is to improve face detection performance from the speed-focusing point of view. To this end, we propose a novel Fast and Accurate Face Detector (FAFD) to achieve high performance on both speed and accuracy performance. Specifically, based on the YOLOv5 model, we add one prediction head to increase the detection performance, especially for small faces. In addition, to increase the detection performance of multi-scale faces, we propose to add a novel Multi-Scale Image Fusion (MSIF) layer to the backbone network. We also propose an improved Copy-Paste to augment the training images with face objects in various scales. Experimental results on the WiderFace dataset show that the proposed FAFD achieves the best performance among the existing methods in a Speed-Focusing group. On three sub-datasets of WiderFace (i.e., Easy, Medium, and Hard sub-datasets), our FAFD yields average precisions (AP) of 95.0%, 93.5%, and 87.0%, respectively. Also, the speed performance of the FAFD is fast enough to be included in the group of speed-focusing methods.
Health Datasets as Assets: Blockchain-Based Valuation and Transaction Methods

There is increasing recognition about health-oriented datasets that could be regarded as intangible assets: distinct assets with future economic benefits but without physical properties. While health-oriented datasets - particularly health records - are ascribed monetary value on the black market, there are few established methods for assessing the value for legitimate research and business purposes. The emergence of blockchain has created new commercial opportunities for transferring assets without intermediaries. Therefore, blockchain is proposed as a medium by which research datasets could be transacted to provide future value. For authorized individuals to verify their transactions, blockchain methodologies offer security, auditability, and transparency. The authors share data valuation methodologies consistent with accounting principles and include discussions of black market valuation of health data. Furthermore, this article describes blockchain-based methods of managing real-time payment/micropayment strategies.
Stimulating the Professional and Personal Self-Development of Future Teachers in the Context of Value-Semantic Orientation

The actualization of value-based approaches to vocational education is determined by the presence of several contradictions, among which it is necessary to name the following: the need for educational practice in more productive means; the implementation of a humanistic educational paradigm and the unpreparedness of teachers to use them; the need for teachers with formed attitudes towards value attitudes towards themselves, their professional activities, education, self-development, to the people around them, and the lack of an effective system of pedagogical tools for solving the abovementioned problem in the process of professional training of future teachers; and the particular importance of the axiological component of the content of vocational education and its insignificant component at the level of the educational process. The article deals with the essence of professional thinking of the teacher, ways and means to develop its value-semantic component in the process of learning pedagogy, problematic dialogue, analysis of texts, promotion, and other professional reflection.
Realization of real-time fault-tolerant quantum error correction

Correcting errors in real-time is essential for reliable large-scale quantum computations. Realizing this high-level function requires a system capable of several low-level primitives, including single-qubit and two-qubit operations, mid-circuit measurements of subsets of qubits, real-time processing of measurement outcomes, and the ability to condition subsequent gate operations on those measurements. In this work, we use a ten qubit QCCD (quantum charge-coupled device) trapped-ion quantum computer to encode a single logical qubit using the color code. The logical qubit is initialized into the eigenstates of three mutually unbiased bases using an encoding circuit, and we measure an average logical SPAM error of 1.7(2) 10^{-3}$, compared to the average physical SPAM error 2.4(4) 10^{-3} of our qubits. We then perform multiple syndrome measurements on the encoded qubit, using a real-time decoder to determine any necessary corrections, which are tracked software or applied physically.
Deep Learning-Based Channel Reciprocity Learning for Physical Layer Secret Key Generation

Using the physical layer channel information of wireless devices to establish the highly consistent secret keys is a promising technology for improving the security of wireless networks. Nevertheless, in the time division duplex system, the reciprocity of the wireless channel that is the basic principle of key generation is impaired by nonsimultaneous sampling and noise factors. Existing physical layer key generation approaches rely on hand-crafted feature extraction algorithms, which have high overhead or security issues and are impractical in real-world situations. This paper presents a novel physical layer key generation method to extract highly consistent keys from imperfect channel responses, which exploits channel reciprocity through deep learning. Specifically, we first design the Channel Reciprocity Learning Net (CRLNet), a neural network for efficiently learning channel reciprocity features from the wireless channel in TDD OFDM systems. Later, a new key generation scheme based on CRLNet is developed that can achieve a high key agreement rate. Experiments indicate that the CRLNet-based key generation scheme performs excellently in terms of key generation rate, key error rate, and randomness, confirming that our method has better performance and lower overhead than existing methods.
A CORRELATION BETWEEN VOCABULARY MASTERY AND EMOTIONAL INTELLIGENCE TOWARDS READING COMPREHENSION

This research aims to prove a correlation between Vocabulary Mastery and Emotional Intelligence towards Reading Comprehension (Survey of State Junior High School Students in Bekasi Regency). This research employs a survey of regression and correlation analysis using the SPSS program. There are three variables in this research: two independent variables (vocabulary mastery and emotional intelligence), and one dependent variable (reading comprehension). The data is gathered through vocabulary and reading comprehension tests, as well as a questionnaire on emotional intelligence. Following an analysis of the research variables, the following conclusion can be drawn: (1) There is a significant correlation of vocabulary mastery and emotional intelligence together on students' ability to understand English reading as evidenced by the acquisition of Sig scores. 0.000 &lt; 0.05 and Fh= 20.095 or 29.3%, (2) there is a significant correlation of vocabulary mastery on students' ability to understand English reading as evidenced by the acquisition of Sig scores. 0.002 &lt; 0.05 and t-test= 3.164 or 14.45%, (3) there is a significant correlation of emotional intelligence on the ability to understand students' English reading as evidenced by the acquisition of Sig scores. 0.002 &lt; 0.05 and t-test= 3.233 or 14.87%.
Introducing chaotic circuits in an undergraduate electronic course

For decades, the engineering undergraduate education in the area of circuit and design has been mainly focused in linear models.The reason is that linear system theory has been thoroughly developed and mathematical tools are available to analyze such systems.This philosophy has led to disregard many observed phenomena such as unusual complicated waveforms, commonly known as strange attractors.Therefore, it is important for today's students to be exposed to this complex chaos phenomenon.In this paper several chaotic circuits are presented.Electronics Workbench [1] is used to simulate circuits and show the presence of chaos.Complete circuits implementation and oscilloscope plots are all given.