Reviewerly/exHarmony huggingface dataset:
Design Method of Component-Based Party Building Visualization Large-screen System

Aiming at the problems of long development period, tedious development of multiple views, high update and maintenance costs, the paper proposes the design method of party construction visualization large screen based on componentization and builds a party construction data visualization system. In this paper, we take the party construction data of a university in Hebei Province as an example, firstly, we describe the basic process of data processing and visualization, then we describe the componentized way to build the chart components applicable to the party construction scenario, and finally we propose a “V-C” (Views-Components) component interaction method for to improve the interactivity of the multi-view visualization screen. The interactivity of the large visualization screen. Through testing, the system has a friendly interface, easy to operate, and convenient to build a large visualization screen for party building data, which is convenient to understand the dynamics of daily party building work in time and play a role in reminding and warning.
Experience of Online Teaching the Course “Lasers and Laser Technology” during the COVID-19 Pandemic

The coronavirus pandemic has shaken the global education system. Universities canceled face-to-face study. In a very short time, the traditional teaching-learning process was replaced by online education around the world. Although e-learning, distance education had been used before, but only as part of general education. The circumstances of COVID-19 forced a complete shift of learning from the classroom to Zoom. Teachers had to develop new online courses to make distance education effective and relevant to 21st century technology. In this paper, we consider our experience developing and implementing a new online course “Lasers and Laser technology” in the face of the urgent demand to reorganize engineering student education due to the pandemic in Russia.
An Algorithm Model of Power Grid Maintenance Ticket Based on Bert

A variety of semantic instructions are needed to implement operations in the daily operation of the grid. Among them, maintenance ticket language is the most important one among all grid semantics. However, because the semantic recognition of maintenance ticket is implemented in a strong scenario like power distribution network, there are problems such as low recognition efficiency and misclassification of recognition results. In this paper, we propose to use BERT algorithm model with Bi-LSTM and CRF to form a new type of algorithm model, and on this basis, we propose to improve the structure of the algorithm model. Finally, it is experimentally shown that not only can the algorithm improve the recognition effect of each classification, but also can give better overall recognition effect.
Visual Analysis of the Application Research of Extended Reality in Education Based on CiteSpace

Extended reality (XR), including augmented reality (AR), virtual reality (VR) and mixed reality (MR), has been widely used in the field of education and has promising research prospects. In recent years, there has been a proliferation of theoretical and empirical studies on the application of XR in education, and it is necessary to review the relevant studies. With the help of CiteSpace, this study makes a visual analysis of 1613 documents published in this field in the last ten years (2012–2022). The specific contents of the study include revealing research trends, finding influential authors, institutions and countries, identifying highly cited literature, and detecting research hotspots and frontiers.
Hoax COVID-19 News Detection Based on Sentiment Analysis in Indonesian using Support Vector Machine (SVM) Method

The increasing use of technology makes it easier for information media such as news to be disseminated and does not demand possibilities, there is a lot of hoax news spreading. Twitter is one of the media most frequently used by the public to access and disseminate information. This research will focus on detecting Indonesian language COVID-19 news taken from Twitter. Detection of hoax news can be assisted by using sentiment analysis, one of the uses of classification text. Support Vector Machine (SVM) can be used to perform sentiment analysis tasks. After getting the sentiment analysis results, the hoax detection process will use the Bag of Words. Bag of Words is a collection of word dictionaries for weighting words to determine specific labels. The built SVM model succeeded in classifying tweet repliessentiment with an average accuracy of 83.17% with a threshold of 35%. At the same time, the hoax detection process gets the best accuracy of 62.5% with a threshold of -5 or -6.
Abstract Multivariate Neural Network Approximation Based on Richard’s Curve

Here we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We examine also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high order Fréchet derivatives.
Conservation Principles of A.M. and Energy

In any theory, conservation principles are regarded as “god’s principles”. First of all they make calculation simple. Secondly, in Newtonian rigid body dynamics, physicists regard conservation principle of angular momentum and conservation principle of energy more fundamental than Newton’s laws of motion itself.
Deep Learning for the Analysis of Solar Radiation Prediction with Different Time Horizons and Data Acquisition Frequencies

This study aims to develop and compare different AI systems for predicting solar radiation and evaluate their performance across different prediction horizons. Predicting solar radiation is of crucial importance for harnessing renewable energy sources. The models were designed to predict radiation over 6-h and 15-min horizons with the lowest possible error. The impact of prediction horizon and data acquisition frequency on prediction accuracy is discussed, emphasizing the need to consider the number of parameters and training time when comparing models. To improve the accuracy of short-term solar radiation predictions, five deep learning models, including classical, convolutional, and recurrent neural networks, were analyzed. The accuracy of the predictions was compared using two error metrics: root mean square error and mean absolute error.
Advanced Home-Based Diabetes Monitoring System: Initial Real-World Experiences

Diabetes is a disease that requires monitoring of healthy habits for its treatment. A recent study suggests that sensor-based activity recognition approaches are a suitable tool for monitoring such habits that are established between clinical personnel and the patient through a therapeutic contract. To date, there is no fully described system architecture for implementing a sensor-based activity recognition approach to monitor healthy habits. In this paper, we present the advanced architecture of a system for monitoring healthy habits of multiple diabetes patients in their homes. The presented system, called AI2EPD, features a complex architecture that encompasses from the sensor devices deployed in each patient’s home to persistence in the central server and visualisation in the technical and clinical interface. The system has been deployed in the municipality of Cabra in Córdoba (Spain) in collaboration with the Cabra Health Centre. The approach for sensor installation in the patient’s home, as well as the issues and challenges encountered during system deployment, are presented in this section.
Analysis of the publication activity of world researchers on the problem of forming a digital educational ecosystem for university students

Relevance. We consider the publication activity on the problem of forming a digital educational ecosystem for university students. The period of appearance of publications on the problem and the dynamics of the publication activity of the scientific community are revealed. Based on the use of Dimensions.ai, Lens.org, Scopus, Web of Science, VOSviewer built charts, bibliometric maps of the publication activity of authors by keywords, activity of countries. The purpose of the study is to show that the digital educational ecosystem is a promising tool for pedagogical communication and contains significant potential for enhancing the research and innovation activities of students. Research methods. To achieve the goals of the study, various methods were used, including the analysis of psychological and pedagogical literature, cluster analysis, generalization and systema-tization of data. Research results. An analysis of the distribution map of publications made it possible to identify the main directions of research on the problem. According to the data, the most significant areas are computer science, business, sociology, knowledge management and the environment. In the period from 2010 to 2019, there has been an active growth in publications on related topics. Pre-sumably, the increase in activity is associated with attempts to understand not so much the essence of the digital educational ecosystem as to understand the concept of a digital educational ecosystem, which was discussed in foreign publications during this period. But the results of the analysis based on these publications may indicate a shift in the focus of research in this area. Perhaps this is due to a change in trends that affect the activity of researchers in this topic. This downward trend may indicate a shift in researchers’ interest to problems related to the pandemic. It is concluded that studies related to “digital educational ecosystems” (digital educational ecosystems) are characterized by fragmentation and fragmentation, which represents the need for a systematic approach and a deeper study of this subject area, which will allow us to analyze and understand the complex interactions and relationships between the various components of these ecosystems. It is substantiated that publications on the research problem in the field of pedagogy are fragmented, and publication activity lags behind the information technology industry, which slows down research in the field of realizing the pedagogical potential of the digital educational ecosystem for university students. Conclusions. The digital educational ecosystem is a promising tool for pedagogical communica-tion and contains significant potential for enhancing the research and innovation activities of stu-dents. But this potential has not yet been realized due to the undeveloped methodological frame-work. A systematic understanding of the methodological basis of the digital educational ecosystem is required.
Implementation of Anticipated Technique in Digital Mammography for Piloting Classification of Breast Cancer Using CNN

Breast cancer is the other most protuberant reason for mortality among females in the whole world, surpassing lung cancer. The existence of lumps, masses, and microcalcification are the main symptoms of breast cancer. But if detected timely, it increases the chances of proper treatment and the probability of cure. Detection of breast cancer is a significant social obligation as it has been a prominent cause of death among women due to cancer. The anticipated approach made use of the adaptive median filter for the elimination of the noise from the mammogram images and the Gaussian mixture model has been used for accomplishing the segmentation before classification These papers represents the use of convolutional neural networks (CNN) for classification of breast cancer in three distinct categories, i.e., benign, malignant, and normal. The images are taken from the MIAS dataset. A sum of 322 images have been studied for classification. The research made use of CNN for conducting training and testing of mammogram images from the standard dataset which extracted 65 benign, 51 malignant, and 206 normal images. The achieved training set accuracy for the trained dataset is 96.67%. A GUI has been designed to conduct the research work in a user-friendly manner. The conducted research work classified the tested mammogram images precisely. It can be concluded that the higher the degree of precision, the better will be the classification results.
Minimal Optimal Region Generation for Enhanced Object Detection in Aerial Images Using Super-Resolution and Convolutional Neural Networks

In recent years, object detection has experienced impressive progress in several fields. However, identifying objects in aerial images remains a complex undertaking due to specific challenges, including the presence of small objects or tightly clustered objects. This paper proposes a novel methodology for enhancing object detection in aerial imagery by combining super-resolution and convolutional neural networks (CNNs). We begin by binarizing the grey zone of the image to detect roads and other regions of interest using the You Only Look Once (YOLO) model. Next, we generate minimal optimal regions. For every one of them, we apply Super-resolution (SR) to improve the number of pixels, generating a new image on which to re-infer. We then apply a CNN to these regions to detect objects more accurately. Our results show that the proposed methodology increases mean average precision in the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking Dataset (UAVDT).
STANN – Synthesis Templates for Artificial Neural Network Inference and Training

While Deep Learning accelerators have been a research area of high interest, the focus was usually on monolithic accelerators for the inference of large CNNs. Only recently have accelerators for neural network training started to gain more attention. STANN is a template library that enables quick and efficient FPGA-based implementations of neural networks via high-level synthesis. It supports both inference and training to be applicable to domains such as deep reinforcement learning. Its templates are highly configurable and can be composed in different ways to create different hardware architectures. The evaluation compares different accelerator architectures implemented with STANN to showcase STANN’s flexibility. A Xilinx Alveo U50 and a Xilinx Versal ACAP development board are used as the hardware platforms for the evaluation. The results show that the new Versal architecture is very promising for neural network training due to its improved support for floating-point calculations.
A Scalable Binary Neural Associative Memory on FPGA

The human brain and its ability to associate is one of the most fascinating things in nature. The long-known concept of binary neural associative memory offers the possibility to build a very simple hardware architecture, that allows direct association. In a BINAM the presented input is associated with the stored content of the memory, without the need for addressing. Hereby the BINAM is a fault-tolerant concept, which allows that erroneous input vectors will usually result in a correct output. In this work, we propose a modern hardware architecture of a BINAM on the VCU1525 FPGA board. We implemented the architecture in VHDL as a scalable, modular, generic, and easy to use design. For the evaluation designs in the range of 8,000 to 740,000 neurons, with equal input and output vector size, have been generated and tested on the FPGA board. Currently, a maximum clock frequency of $$\sim $$ 200 MHz with a resource utilization of only $$\sim $$ 33% CLBs $$, \sim $$ 22% LUTs, and $$\sim $$ 10% registers can be achieved. Maximum times for the storage and association process for all designs are stated in this paper.
A Deep Transfer Learning Approach to Support Opportunistic Wearable Activity Recognition

Most wearable activity recognition systems are defined to be used for a specific sensor setup. However, changes in the body sensor network are sometimes experienced due to sensor failures or upgrades. In such cases, the default activity recognition models are no longer applicable and a complete retraining of the system is normally needed, which is both time and resource-consuming. In this work, we present a deep transfer learning approach to automatically instruct new unseen wearable sensors by using the recognition capabilities of the existing activity recognition models used for the default sensor setup. The proposed approach is validated in a popular wearable activity recognition dataset, yielding quite promising results.
An Insight to Bitcoin Price Using Weber’s Law

Bitcoins and cryptocurrencies are in great debate these days. With its great market capitalization in 2020, after the COVID-19 outbreak, the attention of numerous investors has been attracted toward this new form of asset. So, we have been attracted toward the flair of this unforeseeable asset. In this paper, we have worked on the price OHLC (Opening, High, Low, and Closing) in a long span of time windows starting from its origin and have performed a comprehensive statistical analysis regarding the market value of Bitcoins in different time frames of last five years. Alongside that, we have validated its price using Weber’s law and have later supported our analysis using Zipf’s law. Though Weber’s law is well equipped to work in the branch of psychophysics, yet we have tried to extend its definition and have applied the law with a statistical essence in the well-fledged Bitcoin worth dataset. Finally, to support our analysis, we have also validated the price of Bitcoins using Zipf’s law and discussed some advantages of applying Weber’s law over Zipf’s law.
Fairness-Enhancing Ensemble Classification in Water Distribution Networks

As relevant examples such as the future criminal detection software [1] show, fairness of AI-based and social domain affecting decision support tools constitutes an important area of research. In this contribution, we investigate the applications of AI to socioeconomically relevant infrastructures such as those of water distribution networks (WDNs), where fairness issues have yet to gain a foothold. To establish the notion of fairness in this domain, we propose an appropriate definition of protected groups and group fairness in WDNs as an extension of existing definitions. We demonstrate that typical methods for the detection of leakages in WDNs are unfair in this sense. Further, we thus propose a remedy to increase the fairness which can be applied even to non-differentiable ensemble classification methods as used in this context.
Extending Drift Detection Methods to Identify When Exactly the Change Happened

Data changing, or drifting, over time is a major problem when using classical machine learning on data streams. One approach to deal with this is to detect changes and react accordingly, for example by retraining the model. Most existing drift detection methods only report that a drift has happened between two time windows, but not when exactly. In this paper, we present extensions for three popular methods, MMDDDM, HDDDM, and D3, to determine precisely when the drift happened, i.e. between which samples. One major advantage of our extensions is that no additional hyperparameters are required. In experiments, with an emphasis on high-dimensional, real-world datasets, we show that they successfully identify when the drifts happen, and in some cases even lead to fewer false positives and false negatives (undetected drifts), while making the methods only negligibly slower. In general, our extensions may enable a faster, more robust adaptation to changes in data streams.
Pedestrian Multi-object Tracking Algorithm Based on Attention Feature Fusion

Multi-Object Tracking (MOT) is a challenging research area in computer vision with significant practical applications. With the advent of deep neural networks, significant progress has been made in MOT, and Qdtrack has become a widely used algorithm due to its relatively simple structure and high performance. However, accurate target tracking in complex scenes with mutual occlusion, motion blur and complex backgrounds is still a significant challenge. To address the problem of low target tracking accuracy in complex scenes, this paper proposes an end-to-end multi-object tracking algorithm based on attention feature fusion. First, a new lightweight attention module is introduced into the backbone network, which enhances the ability of the network to capture key information and locate targets without increasing the computational complexity. Second, the feature pyramid structure is improved to reduce the loss of features in the fusion process and improve the feature expression ability of the model. Finally, the Intersection over Union (IoU) of the original model is optimised and the regression ability of the bounding box is improved using polyloss to optimise the cross-entropy loss. Experimental results on the MOT16 and MOT17 benchmarks show that the proposed algorithm effectively improves the accuracy and robustness of multi-object pedestrian tracking compared to other algorithms, and has better tracking performance.
Observe Locally, Classify Globally: Using GNNs to Identify Sparse Matrix Structure

The performance of sparse matrix computation highly depends on the matching of the matrix format with the underlying structure of the data being computed on. Different sparse matrix formats are suitable for different structures of data. Therefore, the first challenge is identifying the matrix structure before the computation to match it with an appropriate data format. The second challenge is to avoid reading the entire dataset before classifying it. This can be done by identifying the matrix structure through samples and their features. Yet, it is possible that global features cannot be determined from a sampling set and must instead be inferred from local features. To address these challenges, we develop a framework that generates sparse matrix structure classifiers using graph convolutional networks. The framework can also be extended to other matrix structures using user-provided generators. The approach achieves 97% classification accuracy on a set of representative sparse matrix shapes.
Meme-Text Analysis: Identifying Sentiment of Memes

This chapter discusses sentiment analysis of meme-texts. Sentiment analysis of a text is general these days but meme-text is very rare. In this era of the Internet, we see many Internet memes on almost all social media platforms. These are unimodal content; we cannot ignore the multimodal content as for the last five years, memes have become a source for sharing information. In this paper, a memotion analysis task is performed, which is the sentiment analysis of memes organized by the SemEval-2020 Task 8 (Subtask a). Pretrained models BERT, Bi-LSTM, GRU, XLnet are used to model the meme-text, and also a simple feed-forward CNN model is used. GloVe and fastText models are used as embeddings. The highest F1 score in this contest was 0.35 which is 63% more than the baseline score of 0.21. The Bi-LSTM model with fastText embeddings outperformed it with a score of 0.70 (macro F1-score).