Reviewerly/exHarmony huggingface dataset:
On the image of higher education in the post-Covid world: Formation and development of the new type of thinking

Introduction. The article presents a study of the significant problems of higher education in the post-Covid world and the urgent need to transform education in accordance with the requirements of digitalization. The purpose of the article is to describe the image of modern higher education in the post-Covid world, which presupposes the formation and development of the new type of thinking. Materials and Methods. Analysis of higher education in the post-Covid world involves the use of a number of general philosophical principles and research methods, such as comparison, analysis and synthesis, induction and deduction, analogy, dialectical, systemic, structural-functional methods, as well as value-semantic, competence-based and other approaches, including the global cultural concepts of B. Holmberg, S. Jarmon and others, which make it possible to construct the image of modern higher education in the post-Covid world. To achieve the goal, the authors have reviewed a number of international and Russian studies on the problems of digitalization of education. Results. The authors clarify the role and place of higher education in the context of the coronavirus pandemic, which spread widely around the world in early 2020, and has had a serious impact on the education sector. It has led to almost complete paralysis of schools, colleges and universities. The process, which, only at first glance, seems to be temporary, in fact causes serious fears of deep socio-economic consequences and a new round of the world educational crisis. The COVID-19 pandemic has shaken the very foundations of education: its ability to exist. It seems that today it is important not only to rethink the problems of new digital, online and pedagogical possibilities of modern education, but also the very essence of education, its main goals and new mission. Reviewing recent research literature enables the authors to conclude that the education that was before the pandemic will never exist. The new type of education is being formed. Its nature, image, foundations, values and meaning depend on the efforts of modern researchers and teachers. This article has summarized the main trends in the field of education in the post-Covid world. Conclusions. The article concludes that the COVID-19 pandemic has had a significant impact on the development of the higher education system and highlights the need for its radical modernization. The authors emphasize that in the post-Covid world higher education will radically change its nature, content and forms. At the same time, it is necessary to adhere to a flexible synthesis in the development of innovative and traditional educational technologies and methods.
Intelligent Guidance Programming of Welding Robot for 3D Curved Welding Seam

Due to the limitations of welding complexity and machining error, traditional manual teaching and offline programming are not intelligent enough and have weak adaptability to workpiece.At present, the 2D perception visual welding guidance programming method is commonly used which cannot accurately locate and model the complicated 3D spatial curve welding seam.The 2D perception method can hardly meet the requirements of welding process.In this paper, a welding seam perception method based on a line structure-light sensor was proposed, which optimizes the generation of the weld seam trajectory, and builds a highly adaptable intelligent guidance programming system for welding robots.Firstly, 3D modeling perception of welding parts is realized through eye-in-hand system of robot, which solves the problem that offline programming system cannot be applied when the welding model is not precise enough or partly miss.Secondly, aiming at solving extraction problem of common types of 3D space curve welding seam, corresponding types of weld extraction algorithms are proposed according to the characteristics of different types of welds under the general process of weld extraction.These methods not only can directly extract the weld from the ordered point cloud with high precision and resolution, but also are less affected by the welding environment, which solve the problem of low perception accuracy of complex curved welds in 3D space.Then, on the basis of welding seam extraction, NURBS curves are used to realize the optimal generation of weld trajectory.Finally, the feasibility and effectiveness of all the methods proposed in this paper are verified by a large number of experiments.
RETRACTED: Multimodal discourse analysis in the blended teaching of college English flipped class

With the development of the new curriculum reform in China, the traditional college English teaching model is no longer suitable for the needs of college English teaching tasks in the new era. The multimodal literacy teaching in the college English multimodal teaching mode is to help students internalize their English language skills after learning and understanding English knowledge, thus improving the efficiency of English learning. The hot spots and topics in the information age are always inseparable from technological innovation. Wearable technology is becoming more and more popular, the scope of research is expanding, and innovations are constantly emerging, showing interdisciplinary and integrated features. This paper introduces the wearable technology in detail, and takes the college English teaching as an opportunity. Through the questionnaire survey of teachers and students, this paper analyzes the survey data. The results of the survey show that wearable technology can stimulate academic interest in college English. This research attempts to conduct interdisciplinary research on multi-modal semiotics and humanistic teaching methods in the following areas, which expands the scope of research and, to a certain extent, enhances the vitality of multi-modal discourse analysis.
Enabling Comparable Search Over Encrypted Data for IoT with Privacy-Preserving

With the rapid development of cloud computing and Internet of Things (IoT) technology, massive data raises and shuttles on the network every day. To ensure the confidentiality and utilization of these data, industries and com... | Find, read and cite all the research you need on Tech Science Press
Blockchain-Enabled Adaptive-Learning-Based Resource-Sharing Framework for IIoT Environment

The industrial Internet of Things (IIoT) has emerged as an essential paradigm to enhance industrial operations and productivity through efficient utilization of available resources. The paradigm allows industrial devices to share computing resources based on locality constraints to support innovative services. In this article, we propose a trusted multihop collaborative computing model for the efficient utilization of nearby devices in an IIoT environment. To establish trust, we explore a social-aware incentive scheme managed by network edge, using distributed ledgers. The extensive simulation results demonstrate the effectiveness of the proposed model in the presence of malicious nodes in the industrial environment.
Superconducting nanostrip single-photon detectors some fundamental aspects in detection mechanism, technology and performance

Abstract The paper is devoted to several recent rather fundamental achievements in the field of superconducting nanostrip single-photon detectors which make an impact on understanding the detection mechanism, technological challenges and performance metrics important for applications. Special attention is given to static and temporal fluctuations of different origin affecting key metrics of these detectors. Some salient points of older models such as detection criteria or real-time evolution of an electro-thermal domain are also highlighted. Recent technical and instrumental advances are intentionally left beyond the scope of this paper.
An integrated approach for spatial distribution of potentially toxic elements (Cu, Pb and Zn) in topsoil

Abstract In this study, statistical analysis and spatial distribution were performed to compare raw data and centred log-ratio ( clr ) transformed data of three copper (Cu), lead (Pb), and zinc (Zn) potentially toxic elements (PTEs) concentration for 550 surface soil samples in Khuzestan plain. The results of both approaches showed that classical univariate analysis and compositional data analysis are essential to find the real structure of data and clarify its different aspects. Results also indicated that spatial distributions of raw data and clr -transformed data were completely different in three studied metals. Raw data necessarily shows the effects of anthropogenic activities and needs an additional evaluation of human health risk assessment for these three studied elements. Data obtained from clr -coefficient maps also demonstrated the role of geological processes in the distribution pattern of potentially toxic elements (PTEs). To improve the understanding of the implications for PTE pollution and consequences for human health, a RGB colour composite map was produce to identify the potential origin of PTEs from areas with higher than typical baseline concentrations.
A Novel Scene Text Recognition Method Based on Deep Learning

Scene text recognition is one of the most important techniques in pattern recognition and machine intelligence due to its numerous practical applications. Scene text recognition is also a sequence model task. Recurrent neural network (RNN) is commonly regarded as the default starting point for sequential models. Due to the non-parallel prediction and the gradient disappearance problem, the performance of the RNN is difficult to improve substantially. In this paper, a new TRDD network architecture which base on dilated convolution and residual block is proposed, using Convolutional Neural Networks (CNN) instead of RNN realizes the recognition task of sequence texts. Our model has the following three advantages in comparison to existing scene text recognition methods: First, the text recognition speed of the TRDD network is much fast than the state-of-the-art scene text recognition network based recurrent neural networks (RNN). Second, TRDD is easier to train, avoiding the problem of exploding and vanishing, which is major issue for RNN. Third, both using larger dilated factors and increasing the filter size are all viable ways to change receptive field size. We benchmark the TRDD on four standard datasets, it has higher recognition accuracy and faster recognition speed based on the smaller model. It is hopefully used in the real-time application.
Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection

Multi-orientation scene text detection has recently gained significant research attention. Previous methods directly predict words or text lines, typically by using quadrilateral shapes. However, many of these methods neglect the significance of consistent labeling, which is important for maintaining a stable training process, especially when it comprises a large amount of data. Here we solve this problem by proposing a new method, Orderless Box Discretization (OBD), which first discretizes the quadrilateral box into several key edges containing all potential horizontal and vertical positions. To decode accurate vertex positions, a simple yet effective matching procedure is proposed for reconstructing the quadrilateral bounding boxes. Our method solves the ambiguity issue, which has a significant impact on the learning process. Extensive ablation studies are conducted to validate the effectiveness of our proposed method quantitatively. More importantly, based on OBD, we provide a detailed analysis of the impact of a collection of refinements, which may inspire others to build state-of-the-art text detectors. Combining both OBD and these useful refinements, we achieve state-of-the-art performance on various benchmarks, including ICDAR 2015 and MLT. Our method also won the first place in the text detection task at the recent ICDAR2019 Robust Reading Challenge for Reading Chinese Text on Signboards, further demonstrating its superior performance. The code is available at https://git.io/TextDet .
Total Quality Management And Hotel Employee Creative Performance: The Mediation Role Of Job Embeddedment

Underpinned by social exchange and JE theory, this study proposes and tests a research model that investigates the effect of TQM on FHE’s creative performance mediating by JE. The data come from FHE’s who work in five star hotels in northern Iraq. The structure equation modelling results revealed that JE is the underlying mechanism through which TQM manifested by the top-management, empowerment, training, reward and teamwork practice, is linked to the FHE’s CP. The empirical data support all hypothesis and research model we have proposed is practical. The results have important implications about FHE’s of TQM practice and aforesaid.
P3OI-MELSH: Privacy Protection Target Point of Interest Recommendation Algorithm Based on Multi-Exploring Locality Sensitive Hashing

With the rapid development of social network, intelligent terminal and automatic positioning technology, location-based social network (LBSN) service has become an important and valuable application. Point of interest (POI) recommendation is an important content in LBSN, which aims to recommend new locations of interest for users. It can not only alleviate the information overload problem faced by users in the era of big data, improve user experience, but also help merchants quickly find target users and achieve accurate marketing. Most of the works are based on users' check-in history and social network data to model users' personalized preferences for interest points, and recommend interest points through collaborative filtering and other recommendation technologies. However, in the check-in history, the multi-source heterogeneous information (including the position, category, popularity, social, reviews) describes user activity from different aspects which hides people's life style and personal preference. However, the above methods do not fully consider these factors' combined action. Considering the data privacy, it is difficult for individuals to share data with others with similar preferences. In this paper, we propose a privacy protection point of interest recommendation algorithm based on multi-exploring locality sensitive hashing (LSH). This algorithm studies the POI recommendation problem under distributed system. This paper introduces a multi-exploring method to improve the LSH algorithm. On the one hand, it reduces the number of hash tables to decrease the memory overhead; On the other hand, the retrieval range on each hash table is increased to reduce the time retrieval overhead. Meanwhile, the retrieval quality is similar to the original algorithm. The proposed method uses modified LSH and homomorphic encryption technology to assist POI recommendation which can ensure the accuracy, privacy and efficiency of the recommendation algorithm, and it verifies feasibility through experiments on real data sets. In terms of root mean square error (RMSE), mean absolute error (MAE) and running time, the proposed method has a competitive advantage.
Deepfake: An Overview

Recent advancements in digital technologies have significantly increased the quality and capability to produce realistic images and videos using highly advanced computer graphics and AI algorithms due to which it becomes difficult to distinguish between the real media and fake media. These computer-generated images or videos have useful applications in real life; however, these can also lead to various threats related to privacy and security. Deepfake is one of the ways which can lead to these threats. Term “Deepfake” is combination of two terms “deep learning” and “fake." Using deepfake, anyone can replace or mask someone else face on another person's face in an image or a video. Not only this, deepfake can change the original voice and facial expressions also in an image or a video. Nowadays, deepfake uses techniques like deep learning and AI to replace the original face, voice, or expressions. It is very hard for a human to detect that the content has been manipulated by deepfake techniques. This paper has attempted to introduce this concept of deepfake and has also discussed different types of deepfakes. The paper has also discussed methods to create and detect deepfake. The motivation behind this paper is to make the society aware about the deepfake tricks along with the treats offered by it.
Making Higher-Order Superposition Work

Abstract Superposition is among the most successful calculi for first-order logic. Its extension to higher-order logic introduces new challenges such as infinitely branching inference rules, new possibilities such as reasoning about formulas, and the need to curb the explosion of specific higher-order rules. We describe techniques that address these issues and extensively evaluate their implementation in the Zipperposition theorem prover. Largely thanks to their use, Zipperposition won the higher-order division of the CASC-J10 competition.
Learning to Synthesize Data for Semantic Parsing

Synthesizing data for semantic parsing has gained increasing attention recently.However, most methods require handcrafted (highprecision) rules in their generative process, hindering the exploration of diverse unseen data.In this work, we propose a generative model which features a (non-neural) PCFG that models the composition of programs (e.g., SQL), and a BART-based translation model that maps a program to an utterance.Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand.Moreover, explicitly modeling compositions using PCFG leads to a better exploration of unseen programs, thus generate more diverse data.We evaluate our method in both in-domain and out-ofdomain settings of text-to-SQL parsing on the standard benchmarks of GEOQUERY and SPI-DER, respectively.Our empirical results show that the synthesized data generated from our model can substantially help a semantic parser achieve better compositional and domain generalization.
A Novel Prediction Error-Based Power Forecasting Scheme for Real PV System Using PVUSA Model: A Grey Box-Based Neural Network Approach

This paper presents a prediction error-based power forecasting (PEBF) method for a Photovoltaic (PV) system, using Photovoltaics for Utility Scale Applications (PVUSA) model based grey box neural network (GBNN). First, the differential equation based PVUSA model is transformed into a neural network. In the proposed PEBF scheme, the neural network is set to train whenever the difference between predicted and output powers increases from a certain threshold defined based on system dynamics and requirements. The unique design of the PVUSA model based grey box neural network takes far less training time than usual black-box neural network based models. This gives the proposed prediction scheme an advantage of updating the prediction model parameters from frequent training of neural networks with the change in metrological variables. The effectiveness of the proposed prediction scheme is demonstrated by a real case study regarding a 20MW grid-connected PV system located in Dongying city of Shandong province China. To evaluate the efficiency of the developed scheme, different assessment metrics, mean absolute error (MAE), root mean square error (RMSE), weighted mean absolute error (WMAE) and coefficient of determination ( R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) are applied. The average values of MAE, RMSE, and WMAE were 0.12 %, 0.20% and 0.23% respectively for all cases. The results demonstrate that the proposed scheme predicts the PV power efficiently within the defined error tolerance level, which shows the effectiveness and feasibility of the proposed prediction scheme. The prediction accuracy of the proposed scheme has been compared with the conventional black box neural network models and reveals outperformed performance with respect to prediction accuracy improvement. The proposed prediction scheme will help to balance power production and demands across integrated networks through economic dispatch decisions between the power sources.
Learning to Copy Coherent Knowledge for Response Generation

Knowledge-driven dialog has shown remarkable performance to alleviate the problem of generating uninformative responses in the dialog system. However, incorporating knowledge coherently and accurately into response generation is still far from being solved. Previous works dropped into the paradigm of non-goal-oriented knowledge-driven dialog, they are prone to ignore the effect of dialog goal, which has potential impacts on knowledge exploitation and response generation. To address this problem, this paper proposes a Goal-Oriented Knowledge Copy network, GOKC. Specifically, a goal-oriented knowledge discernment mechanism is designed to help the model discern the knowledge facts that are highly correlated to the dialog goal and the dialog context. Besides, a context manager is devised to copy facts not only from the discerned knowledge but also from the dialog goal and the dialog context, which allows the model to accurately restate the facts in the generated response. The empirical studies are conducted on two benchmarks of goal-oriented knowledge-driven dialog generation. The results show that our model can significantly outperform several state-of-the-art models in terms of both automatic evaluation and human judgments.
A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies

The analysis of data in which multiple languages are represented has gained popularity among computational linguists in recent years.So far, much of this research focuses mainly on the improvement of computational methods and largely ignores linguistic and social aspects of C-S discussed across a wide range of languages within the long-established literature in linguistics.To fill this gap, we offer a survey of code-switching (C-S) covering the literature in linguistics with a reflection on the key issues in language technologies.From the linguistic perspective, we provide an overview of structural and functional patterns of C-S focusing on the literature from European and Indian contexts as highly multilingual areas.From the language technologies perspective, we discuss how massive language models fail to represent diverse C-S types due to lack of appropriate training data, lack of robust evaluation benchmarks for C-S (across multilingual situations and types of C-S) and lack of end-toend systems that cover sociolinguistic aspects of C-S as well.Our survey will be a step towards an outcome of mutual benefit for computational scientists and linguists with a shared interest in multilingualism and C-S.
Smart Bracelet System for Temperature Monitoring and Movement Tracking Analysis

In several epidemic diseases, one of the main symptoms exhibited by people is abnormal body temperature. Therefore, monitoring body temperature is crucial for preventing the spread of infectious diseases and facilitating timely responses. This study presents a wearable bracelet that can be used as a temperature monitoring and trajectory analysis system. The temperature sensor in the bracelet can effectively monitor the body temperature of the wearer in the target scene, and the data transmission between the bracelet and the data collector can effectively detect the movement range and trajectory of the wearer. Through these, the whole set can also detect the direct and indirect contact of the wearer in any period of time, which is very helpful for the prevention and control of infectious diseases and the isolation of potentially infected persons.