Reviewerly/exHarmony huggingface dataset:
Automatic processing technology based on nanomaterials

Nanotechnology refers to technology that studies the performance and usage of materials with a structure size of 0.1 to 100 nanometers. Due to the special size of this material, it determines that this material has many more special properties than ordinary materials, such as size effect, surface effect, etc., so its application market can be said to be very broad. At present, the application of nanomaterials in daily production and life has aroused widespread concern, especially the two major blocks of biology and medicine. How to combine different properties such as magnetism and catalytic properties to form multifunctional nanomaterials and how to use the material more efficiently in the field of biology has become a research direction of experts. Machine learning can realize the automatic identification of non-good wood, which effectively avoids the impact of visual fatigue or other subjective factors in the manual identification process, and machine learning has a more accurate positioning of the standard of non-good wood, thereby improving the wood's quality. Usage rate. In this paper, the fuzzy analytic hierarchy process is mainly used to study the catalytic performance of nanocomposites on glucose oxidation. Compared with glassy carbon bare electrode (GCE) and IL-GR / GCE, Ni3S2/ IL-GR / GCE showed significant electrocatalytic activity for glucose oxidation. The linear range is 0 ∼ 1000 μM, and the detection limit is 0.162 μM (S / N = 4). The results showed that the current response of glucose did not change significantly, proving that Ni3S2/ IL-GR / GCE has a strong anti-interference ability.
Improving the Quality of Information Support of the University Performance Monitoring System

High-quality and timely information ensures the effective functioning of the university’s internal control and the ability to achieve its goals. This paper formulates the basic requirements for information transparency of the university and ways to improve it by forming a single integrated external reporting of the university. The authors also pay attention to the formation of information support for risk-based control of university activities. The results of the study can serve as a tool to improve the quality of information support for the system of monitoring the activities of both Russian and foreign universities.
Improvement Joint Detection and Tracking of Small RCS Targets from Image Observations

Small targets tracking from image observations is a particularly difficult problem with unknown measurement variances. The Cardinality-Balanced Multi-target Multi-Bernoulli filter (CBMeMBer) is an optimal Bayes approach of multi-object filtering and it has outstanding performance over the opposite filters. Recently, CBMeMBer supported Variational Bayesian (VB) approximations have implemented for multi-target tracking with unknown measurement variances. However, VB-CBMeMBer filter implemented by known probability of detection, which is unsuitable for small targets tracking. Recently, the new approach known by track-before-detect (TBD) which is an efficient algorithm to track small objects. During this work, a completely unique VB-CBMeMBer-TBD filter presented to unravel the fluctuation problems of small target parameters like the probability of detection and therefore the variances of measurement. The simulation results confirm the robustness and effectiveness of the proposed filter.
Zero-Padding OFDM Receiver Using Machine Learning

Orthogonal frequency-division multiplexing (OFDM) systems have championed the elimination of inter-symbol interference (ISI) and inter-block interference (IBI) originated from multi-path fading. By introducing some redundant symbols at the transmitter such as zero padding (ZP), spectral efficiency is reduced. The amount of redundancy is related to the channel-model order, an information carrying some uncertainty in practical situations, particularly when one is willing to increase data transmission. The recent trend of utilizing neural networks to address some communication issues sparkled the idea of exploiting machine-learning (ML) to improve the performance of ZP-OFDM transceivers whenever the channel order is not known. This work presents a novel application of ML to address ZP-OFDM physical layer issues. The simulation results show that the ML ZP-OFDM brings about performance improvements, such as reduced bit-error-rate (BER), when the amount of redundancy is insufficient and some form of nonlinearity is present at the transmitter end.
Algebraic Decomposition Method Utilized in Optimized Zero Watermarking Technique

Mathematics is the backbone of most fields of computer science, especially image processing. Linear algebra achieves important results in image processing by the use of algebraic decomposition methods in different trends, the most important of which are the watermarking techniques. The main aim of this paper is to optimize the zero watermarking technique depending on an optimization algorithm and a decomposition method as an algebraic transformation without using any other popular transform such as discrete wavelet transform (DWT), discrete cosine transform (DCT), and lifting wavelet transform LWT. The singular value decomposition (SVD) is regarded as one of the algebraic methods used in this paper to transform the 8*8 blocks of the original grayscale image into the frequency domain to extract the features of the original image. The singular values in the position (1,1) of each diagonal matrix for each block are chosen to generate the feature bits matrix (master-share) to obtain the final zero-secret of the watermark image. The genetic algorithm (GA) is performed on zero-secret to obtain the zero secret sequence that represents the optimal feature bits matrix (master-share) to optimize the zero watermarking technique. The experimental results show that the technique is worked successfully and is robust and resistant against the attacks adopted depending on the test of the robustness and imperceptibility values.
Intuitionistic Fuzzy Characteristic of a Ring

The main aim of this paper is to investigate the relationship between the characteristic of a ring and the order of an element in the intuitionistic fuzzy environment. We also characterise the term divisor of zero in a ring and study their connection with the ideal of the ring in the intuitionistic fuzzy setting. Some related results are also analyse.
The Reconstruction of Enterprise Employee Management Paradigm in the Era of Artificial Intelligence

With the current high-tech development, the management of enterprises has gradually realized intelligent management. Especially for the management of enterprise employees, it is very necessary to realize artificial intelligence. There are many mistakes in the traditional employee management, which results in a waste of human resources. The artificial intelligence technology has exerted unprecedented advantages in employee management, but also has many challenges. The purpose of this article is to study the reconstruction of enterprise employee management paradigm in the era of artificial intelligence. This article first briefly describes the meaning of employee management paradigm, and then studies the impact of artificial intelligence technology on employee management paradigm. Then this article uses the fuzzy comprehensive evaluation method to establish a model for evaluating the basic qualities of business managers. The experimental part introduces the experimental process in combination with the quality evaluation index system. By evaluating the basic quality factors of the director of a state-owned enterprise, the quality factors of the factory director are calculated by using the weighted average operator. 0, the evaluation result is that the basic quality is relatively good.
Facebook, Yahoo most imitated by phishing scammers in fake emails

Scammers create fake emails that imitate legitimate brands to trick users into clicking on fraudulent links or giving out sensitive information.
Photogrammetry-Based Experimental Modal Analysis for Plate Structures

A photogrammetry-based vibration measurement method was first developed and applied to beam structures for modal parameter estimation and structural damage identification. In this work, the photogrammetry-based method is experimentally investigated for its application to modal parameter estimation of plate structures. A single-point laser equipped with a diffractive optical element projected a grid consisting of 100 laser spots to a plate structure in a fully non-contact, non-destructive manner. Deformations of the plate were measured by tracking the projected spots that served as external optical features to be tracked in the photogrammetry-based method. A scanning laser Doppler vibrometer was also used to measure deformations of the plate for comparison purposes. The accuracy of the photogrammetry-based method and its three-dimensional kinematic relation for plate structures were studied by comparing its measurements and modal parameter estimation results with those by the scanning laser Doppler vibrometer. The three-dimensional kinematic relation between a point on a field-of-view plane of the plate and that on an image plane of a camera was experimentally verified. Both rigid body and elastic modes of the plate were identified, and their modal parameters were estimated. Mode shapes of the plate estimated using the photogrammetry-based method were compared with those from a finite element model, and their modal assurance criterion values were all over 90%, which verified the accuracy of the EMA method.
A Comparison of Different Clustering Approaches for High-Dimensional Presence-Absence Data

Presence-absence data is defined by vectors or matrices of zeroes and ones, where the ones usually indicate a “presence” in a certain place. Presence-absence data occur, for example, when investigating geographical species distributions, genetic information, or the occurrence of certain terms in texts. There are many applications for clustering such data; one example is to find so-called biotic elements, i.e., groups of species that tend to occur together geographically. Presence-absence data can be clustered in various ways, namely, using a latent class mixture approach with local independence, distance-based hierarchical clustering with the Jaccard distance, K-modes, a density-based approach, or also using clustering methods for continuous data on a multidimensional scaling representation of the distances. These methods are conceptually very different from each other, and can therefore not easily be compared theoretically. We compare their performance with a comprehensive simulation study based on models for species distributions.
Application of Online and Offline Teaching Mode in the Course of “Embedded System” Under the Background of Internet

Abstract Since the Internet was proposed many years ago, the application of the Internet has become diverse. Now, with the importance of education, the Internet is gradually being used in teaching. Therefore, the purpose of this paper is to explore the application of online and offline teaching mode in embedded system courses under the background of the Internet. In this paper, after consulting the literature on embedded systems and online and offline education models, the data model is constructed by using BP neural network algorithm to process and analyze the relevant data, and the experimental results are drawn. Experimental results show that the Internet-based online and offline teaching model is of some help to the embedded system curriculum.
Dependency Graph Convolution and POS Tagging Transferring for Aspect-Based Sentiment Classification

Aspect-based sentiment classification (ABSC) task is a fine-grained task in natural language processing, which mainly recognizes the sentiment polarity of various aspects in a sentence. Most of the existing work ignores the syntactic constraints of the local context, and few studies use feature enhancement when dealing with ABSC problems. To solve these problems, this paper proposes a new transfer learning model based on aspect sentiment analysis, namely LCF-TDGCN. It is based on local context focus mechanism and self-attention mechanism, and uses Part-Of-Speech (POS) tagging as an auxiliary task to enhance sentiment polarity. Secondly, this method utilizes the dependency graph convolution (DGC) to analyze the syntactic constraints of local context and capture long-term word dependencies. In addition, this paper integrates the pre-trained BERT model, and improves the performance of ABSC tasks by using syntactic information and word dependence. The experimental results on five different datasets show that the LCF-TDGCN produces good results.
Privacy Preserving in Smart Cities Using Various Computing Technologies

IoT has influenced our daily lives through various applications. The high possibility of sensing and publishing sensitive data in the smart environment leads to significant issues: (1) privacy-preserving and (2) real-time services. Privacy is a complex and a subjective notion as its understanding and perception differ among individuals, hence the observation that current studies lack addressing these challenges. This chapter proposes a new privacy-preserving method for IoT devices in the smart city by leveraging ontology, a data model, at the edge of the network. Based on the simulation results using Protege and Visual Studio on a synthetic dataset, the authors find that the solution provides privacy at real-time while addressing heterogeneity issue so that many IoT devices can afford it. Thus, the proposed solution can be widely used for smart cities.
A Survey on Recent Techniques in Face Recognition for Various Databases

Face recognition is a significant biometric application in Image Processing. In the research zone, the Face recognition topic persistently continued due to the most significant challenge such that illumination effects. This may occur due to camera type, focus, resolutions, illumination directions, Face pose conditions, aging, expression type, and so forth. To improve the performance of Face Recognition System [FRS] different face detection, Illumination Pre-processing, feature extraction, classification techniques are needed to be utilized in a hybrid manner. This paper reviews various face recognition methods, and also their effective performance on different combinations.
Application of Generative Ideas in Chinese Language and Literature Teaching

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Harnessing Solar Energy from Wind Farms: Case Study of Four Wind Farms

In the renewable energy sector, power generation from solar photovoltaic and wind turbine systems are the most popular. The electricity production has to be increased day-by-day for meeting the ever-increasing demand for electricity. Large-scale wind and solar photovoltaic power plants require a large size of land. In the combined system, both solar photovoltaic modules and wind turbines are installed at the same site. It thus reduces the cost of separate land and transmission lines. In this regard, the paper presents power performance improvement by the integration of a wind power plant with a solar PV power plant in the available installation area. The wind power plants at four sites namely Pratapgarh in Rajasthan (45 MW), Davangere in Karnataka (29.70 MW), Tirunelveli in Tamil Nadu (33 MW), and Anantpur in Andhra Pradesh (50.40 MW) are considered for study and analysis. These sites are having enormous solar photovoltaic and wind energy potential. Wind farm array is considered of 5D × 7D configuration and the possibility of installing solar photovoltaic power plant is analyzed. The electricity generated by solar photovoltaic systems at wind farms sites is estimated by using PVsyst software. Six cases of wind farm area such as 5%, 10%, 15%, 20%, 25%, and 30% are taken into consideration for the installation of solar photovoltaic power plants. The annual average performance ratio and annual average capacity utilization factor of solar photovoltaic power plants are varying from 77%–79% to 18%–20%, respectively, for the four locations. The annual solar electricity generation is estimated the highest at Pratapgarh (Rajasthan) and annual wind electricity generation is estimated highest at Davangere (Karnataka). The combined annual electricity generation is estimated highest at Davangere (Karnataka) and is followed by Tirunelveli (Tamil Nadu), Anantpur (Andhra Pradesh), and Pratapgarh (Rajasthan).
Formal Methods for Robot Motion Planning with Time and Space Constraints (Extended Abstract)

Motion planning is one of the core problems in a wide range of robotic applications. We discuss the use of temporal logics to include complex objectives, constraints, and preferences in motion planning algorithms and focus on three topics: the first one addresses computational tractability of Linear Temporal Logic (LTL) motion planning in systems with uncertain non-holonomic dynamics, i.e. systems whose ability to move in space is constrained. We introduce feedback motion primitives and heuristics to guide motion planning and demonstrate its use on a rover in 2D and a fixed-wing drone in 3D. Second, we introduce combined motion planning and hybrid feedback control design in order to find and follow trajectories under Metric Interval Temporal Logic (MITL) specifications. Our solution creates a path to be tracked, a sequence of obstacle-free polytopes and time stamps, and a controller that tracks the path while staying in the polytopes. Third, we focus on motion planning with spatio-temporal preferences expressed in a fragment of Signal Temporal Logic (STL). We introduce a cost function for a of a path reflecting the satisfaction/violation of the preferences based on the notion of STL spatial and temporal robustness. We integrate the cost into anytime asymptotically optimal motion planning algorithm RRT $$^\star $$ and we show the use of the algorithm in integration with an autonomous exploration planner on a UAV.
An Interleaved Model for Chinese Socio-Economic Indicator Extraction

Automatically extracting socio-economic indicators from text allows us to know the real status of a country or a company. Most conventional methods rely on hand-crafted features or use pipeline or joint deep learning model. They are either effort intensive or do not utilize the dependency between indicator detection and element extraction. In this paper, we use an interleaved model to cope with the challenges in this task. After feature extraction, the input sequence is first fed into the preliminary detection module to test whether it mentions indicators. If the sequence contains indicator mentions, it will then be input into element extraction module to extract indicator elements. If there are no elements, the sequence will be judged containing no indicators. We conduct extensive experiments on the dataset we build, which shows that the proposed model performs better both on f1-score and efficiency.
Professional Training of Future Teachers for The Implementation of Interdisciplinary Educational Integration in Profile Training

The current problems of professional training of future teachers for the implementation of interdisciplinary educational integration in specialized education are considered; its essence and role in the process of training future teachers at a university are revealed. The ways of implementing the idea of integration are described.Since the most important component of the educational policy of any state, as evidenced by world experience, is pedagogical education, which requires a high degree of flexibility and accessibility at all its levels, this poses an urgent task for society to update its content and structure under the influence of comprehensive processes of internationalization and integration.A significant contradiction to this is the fact that in Kazakhstan the traditional system of training a future teacher in the context of the indicated processes has ceased to meet the needs of society and the school. Moreover, both in socio-cultural and organizational-pedagogical and scientific-content plans. It is obvious that this situation is explained by considering the main function of the professional training of a future teacher as only a repeater of the regulated volume of knowledge.
Multi-Task Fine-Tuning on BERT Using Spelling Errors Correction for Chinese Text Classification Robustness

Spelling errors are common in our daily life and the industrial application, caused by automatic speech recognition, optional character recognition and human writing. Because of lack of robustness, Text classification models trained on clean datasets tend to perform poorly on the datasets with spelling errors. We conduct experiments to find out the influence of spelling errors on the performance of Chinese text classification and solve the Chinese text classification task with spelling errors by multi-task fine-tuning on BERT. We use spelling errors correction task to assist the text classification task. The results on four Chinese text classification datasets show that our method can effectively improve the robustness of the classification model which decrease the influence of spelling errors and prove the effectiveness of multi-task fine-tuning on BERT.
Research on Application of Physical Education Teaching Based on Computer Digital Fast Feedback System

Abstract Physical education teaching should be based on students’ individual characteristics and basic situation to carry out targeted teaching, so as to improve students’ sports literacy, at the same time, it can help students relieve academic pressure, and lay a healthy foundation for their continuous professional course learning. With the iterative development of computer digitization, its application in physical education fully releases the vitality of the injected physical education teaching classroom. Based on this, this paper first analyzes the concept of rapid feedback system for physical education teaching, then studies the application design of the rapid feedback system for physical education teaching based on computer digital technology, and finally gives the value of rapid feedback system analysis for physical education teaching based on computer digitization.
Image Tracking and Detection of Standing Posture of Moving Human Body Based on The Recognition Algorithm of Human Body Structure

Abstract Aiming at the problem of human motion posture and recognition in low-quality deep images, the three-step search algorithm can be adopted by using the human body structure posture recognition algorithm. This paper firstly explains the significance of human standing posture image tracking detection, and at the same time carries out depth image acquisition and tracking calculation in combination with the experiment of moving human posture image. In the aspect of the tracking algorithm based on the depth image, the complex estimation problem is transformed into a simple classification problem by predicting the position of human bone points based on the image, which has made a breakthrough in the research on the tracking algorithm of human motion and posture.
Methodology for the Formation of a Set of Features for Describing Objects of a Social Network

The paper considers an approach to the formation of a set of features that describe objects of a social network - users, posts - and relations between them. Representation of objects in the form of a vector of values of selected features makes it possible to apply machine learning methods to analyze these objects.