Reviewerly/exHarmony huggingface dataset:
Anomaly detection and representation learning in an instrumented railway bridge

In this contribution, the strain measurements of a railway bridge are used for anomaly detection, in the context of Structural Health Monitoring (SHM).The methodology used is a combination of a sparse convolutional autoencoder (CSAE) and a Mahalanobis distance.Due to the lack of labeled anomalous data, a simulated fault is used to evaluate the performance of the algorithm.The proposed approach far outperforms the classical feature-based approach.Finally, the latent dimension of the autoencoder is studied and shown to be structured and representative of the underlying physics of the problem.
A Hybrid Density Network-Based Dance Movement Generation Algorithm and Internet of Things for Young Children

The music development matching model and the quantifiable planning model have an undesirable fit between the dance produced by the model and the music self in terms of music-driven dance development age (e.g., generated dance development is lacking, and long-distance dance arrangements are lacking in perfection and discernment). The traditional methodology cannot produce new dance moves or other associated concerns. To address these concerns, we are working on a dance age estimation based on technological developments and neural networks that will eliminate the need for voice and development planning. The first stage uses the prosody elements and sound beat highlights extracted from music as music highlights, while the second stage uses the directions of essential human body issues derived from dance recordings as movement highlights. The model’s generating module acknowledges the vital planning of music and dance advancements to build a smooth dance posture in the next stage; the discriminator module acknowledges the autoencoder module agent has improved sound characteristics and the consistency of dance and music. In the third and final step, the model’s transformed form changes the dance act succession into a good diversity of dance. Finally, a reasonable rendition of the dance that matches the music has been found (e.g., trial data is gathered from online dance recordings, and the exploratory outcomes are examined from five perspectives: poor work esteem, correlation of various baselines, assessment of grouping age influence, client examination, and genuine dance recording quality assessment). The proposed dance age model has a reasonable impact on converting into actual dance recordings, according to the results.
Determination of Vibration Object’ Coordinate on Surface of Earth

The problem of determination of vibrating object’s coordinates on Earth’s surface is considered for the case in which generated seismic waves are measured with several probes. Analytical dependencies of vibrating object’s coordinates as functions of the probes’ coordinates, relative delays of the wave front for different probes, and velocity of its propagation are obtained. Configurations involving three and four probes are being investigated, calculations’ algorithms and codes are described, and the accuracy of the coordinate’s determination is studied.
Tree-Based Adaptive Model Learning

We extend the Kearns–Vazirani learning algorithm to be able to handle systems that change over time. We present a new learning algorithm that can reuse and update previously learned behavior, implement it in the LearnLib library, and evaluate it on large examples, to which we make small adjustments between two runs of the algorithm. In these experiments our algorithm significantly outperforms both the classic Kearns–Vazirani learning algorithm and the current state-of-the-art adaptive algorithm.
Comparative Analysis of the Performance of Complex Texture Clustering Driven by Computational Intelligence Methods Using Multiple Clustering Models

Traditional texture cluster algorithms are frequently used in engineering; however, despite their widespread application, these algorithms continue to suffer from drawbacks including excessive complexity and limited universality. This study will focus primarily on the analysis of the performance of a number of different texture clustering algorithms. In addition, the performance of traditional texture classification algorithms will be compared in terms of image size, clustering number, running time, and accuracy. Finally, the performance boundaries of various algorithms will be determined in order to determine where future improvements to these algorithms should be concentrated. In the experiment, some traditional clustering algorithms are used as comparative tools for performance analysis. The qualitative and quantitative data both show that there is a significant difference in performance between the different algorithms. It is only possible to achieve better performance by selecting the appropriate algorithm based on the characteristics of the texture image.
Research on Athlete Detection Method Based on Visual Image and Artificial Intelligence System

Pedestrian detection and tracking based on computer vision has gradually become an international pattern recognition, which is one of the most active research topics in the field of computer vision and artificial intelligence. Using the theoretical results in the field of pattern recognition and computer vision technology, we are committed to detect and track pedestrians from video sequences. In addition to computer vision-based passer-by detection and tracking technology as the key, in the advanced computer vision action and analysis, it has a direct impact on the accuracy and robustness of its understanding. We analyzed various targets, such as subsequent recognition motion and pedestrian motion, and described them as high-level application processing, such as action understanding. In addition, because of the unique texture of human clothes compared with the surrounding natural landscape, they are highly "prominent" from the perspective of human visual system, and they are particularly prominent in the peripheral part of human contact with the background. In this paper, a binary function based on importance is proposed. As the space representation of image itself is not sensitive to noise and local signal, space representation is used. In addition, as an observation model, it can reduce the adverse effects of background noise and local noise on the tracking algorithm. Through the function block tracking, the pedestrian's body can be tracked in detail. At the same time, the color band learning method is used to update the target template online to deal with the changes of target appearance caused by sunshine, pedestrian posture, and other factors. According to the experimental results, even if the appearance and posture of pedestrians change greatly, it has a stable tracking effect.
An Evaluation Model of Urban-Rural Exchange Teachers in Elementary School Based on Optimal Control Neural Network

In this paper, an optimal control neural network algorithm is used to conduct an in-depth study and analysis of the evaluation of elementary school urban-rural exchange teachers, and an optimal control neural network evaluation model is designed and applied to the actual elementary school urban-rural exchange process. A tracking controller is designed to track the target trajectory for a typical second-order nonlinear system where the system model is partially unknown and the internal state of the system is unpredictable. The neural network observer is first designed using the input and output information of the system to approximate the drift dynamics of the system on the one hand and estimate the internal state information of the system on the other hand; then, based on the estimated system state, the sliding mode tracking controller is designed to achieve tracking of the established target trajectory. It is found that the policy implementation is not in place, teachers are not motivated to exchange and most of them aim at position promotion, their sense of responsibility is weak, and they cannot treat the matter of exchange seriously and correctly, thus cannot play their role in the exchange school. The ways to prevent and alleviate the conflict of interest among policy subjects are mainly divided into subjective and objective aspects: first, from the subjective level to arouse the value recognition of multiple interest subjects, including the government, should play the function of policy guidance and exhortation and clarify the value orientation of the policy; schools should cultivate the moral quality and professionalism of teachers and strengthen ideological mobilization; and teachers should improve their ideological awareness and consciously change their willingness to exchange. Establishing teachers’ status as civil servants, paying attention to teachers’ professional development and safeguarding the interests of rotating teachers to increase teachers’ motivation to exchange and rotate, creating a good social opinion environment, providing a good inflow school support environment for rotating teachers, and building a cloud platform for sharing high-quality educational resources create a good policy implementation environment to ensure that the teacher exchange and rotation policy is effectively implemented to achieve its goals of balancing teacher deployment. Although gradient-based methods can achieve high training accuracy, the performance of the backpropagation algorithm may be unsatisfactory when applied to test data, that is, if the size of the training data is not large enough.
An Analysis of Semantic Similarity Measures for Information Retrieval

One of the most significant difficulties in information retrieval is measuring semantic similarity. Semantically similar words, phrases, and concepts are measured in this field of study. It is the degree to which two concepts resemble one another depending on their meaning. Various measures of semantic similarity have been suggested several times throughout the years. This paper divides these methodologies into two groups: knowledge-based and corpus-based, to demonstrate how they evolved. On specified parameters, a study of similarity approaches is performed. This assessment, which analyzes each method for semantic similarity, provides an analysis, review of existing methods on semantic similarity to explore, and creates new ideas for researchers.
mu2e Track Quality Selection in Python/Sklearn [Poster]

is a critical step in the overall design of the dipoles and their reliable operation at such high frequencies over the life of the experiment. This choice, based on a series of the thermal and magnetic measurements of the ferrite samples, is discussed in the paper. Additionally, the first results from the testing at 300 kHz of a prototype AC dipole are presented.
Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning

Over the years, with the widespread use of computer technology and the dramatic increase in electronic medical data, data-driven approaches to medical data analysis have emerged. However, the analysis of medical data remains challenging due to the mixed nature of the data, the incompleteness of many records, and the high level of noise. This paper proposes an improved neural network DBN-LSTM that combines a deep belief network (DBN) with a long short-term memory (LSTM) network. The subset of feature attributes processed by CFS-EGA is used for training, and the optimal selection test of the number of hidden layers is performed on the upper DBN in the process of training DBN-LSTM. At the same time, the validation set is combined to determine the hyperparameters of the LSTM. Construct the DNN, CNN, and long short-term memory (LSTM) network for comparative analysis with DBN-LSTM. Use the classification method to compare the average of the final results of the two experiments. The results show that the prediction accuracy of DBN-LSTM for cardiovascular and cerebrovascular diseases reaches 95.61%, which is higher than the three traditional neural networks.
Construction and Computation of the College English Teaching Path in the Artificial Intelligence Teaching Environment

Today, English is the world’s main international language and is widely spoken. In this context, the learning of English has long been valued by all countries. In addition, English plays an essential role in the process of economic globalization, specifically in the majority of countries where English is not the native language. At the same time, as China’s integration into the world grows, the importance of English has become more pronounced, and the demand for English language skills has increased. At present, China’s ever-expanding opening to the outside world, its developing science and technology, and its rising international status have created an urgent need for English-speaking professionals. This will lay the foundation for China’s economic and social development and enable China to play a greater and more active role in international affairs, which has great practical and far-reaching historical significance. At present, the traditional English teaching mode has been criticized due to a great number of issues. At the same time, the new English language standards and the change of evaluation methods have created new demands on English teaching methods. As a result, with the development of artificial intelligence technology, the application of artificial intelligence technology in the field of education has brought new directions for the optimization of English classroom. In addition, diversified network technologies also provide new support for the realization of intelligent and collaborative English classroom teaching. To be specific, the maturity of English intelligent speech synthesis and intelligent speech evaluation technologies has enabled intelligent technologies to reach or even surpass the level of human teachers in certain areas. However, due to the limitations of the market environment and evaluation methods, the products that can focus on English classroom teaching are not highly intelligent and less collaborative. What is more, the process of English classroom intelligence has stagnated compared to other subjects. Obviously, from the perspective of integrating artificial intelligence technology with English classroom, it is quite necessary and feasible to design and develop an intelligent teaching assistant that can be applied to college English teaching. As information technology continues to evolve, both national and teacher levels are beginning to focus on how it can be better used in the teaching of subjects. Therefore, the use of online information technology in teaching and learning presents a great opportunity for research and practice in English language learning at the university level.
The Path Deduction from Folk Art to Stage Art in the Change of Geographical Environment: Take Anhui Flower Drum Lantern as an Example

Anhui flower drum lantern, as a folk dance in the Huaihe River Basin, exudes the local cultural atmosphere and expresses the cultural color of song and dance drama incisively and vividly. At the same time, it is a sacrificial activity with social belief. As a typical example of the development of folk activities in Anhui, it is an important intangible cultural heritage. With the accelerating construction of economic globalization and social innovation, the pursuit of "cultural confidence" has gradually become the belief of a country, a nation, and even everyone. More and more attention is being paid to the protection and dissemination of national intangible cultural heritage. The paper takes Anhui flower drum lamp as the research object, combined with the characteristics of the regional nature, performance form, content, and cultural connotation of Huagu Lantern, by reading the social activities of the people on both sides of the Huaihe River Basin and using literature data, field visits, interviews, logical analysis, and other research methods. The origin, living environment, development historical context, cultural value, existing confusion, and development countermeasures of Huaihe River are analyzed and discussed. This article helps to spread to the people the understanding of the evolution law of the traditional projects with the characteristics of generational attack. At the same time, it is also helpful to grasp the understanding of Hua Gu Deng and the social life, social beliefs, social relations, and other factors on both sides of the Huaihe River. This also make people further understand the social status and value function of the flower drum lantern along the Huai River and it is of great practical significance to the dissemination of the same type of intangible cultural heritage.
Nexus of 6G and Blockchain for Authentication of Aerial and IoT Devices

Internet of Things (IoT) is a system of interrelated sensors and computers to transfer data over a network. However, the sensors, Unmanned Aerial Vehicles (UAVs), and other IoT equipment used are susceptible to different security attacks. Dumb sensors are used to collect data in hostile environments. Dumb sensors are low powered sensors that lack computational power to perform cryptological operations. These sensors are preferred over high powered sensors due to their low electrical signature, but they have negligible computing power. To overcome the loss of authentication data due to node capture and lack of sensor location verification, we propose the Nexus of 6G and Blockchain for Authentication (NBA) system. The NBA system utilizes a permissioned blockchain-based network of UAVs and smart sensors to prevent code tampering. The system enables two-way trusted data transfer between UAVs and dumb sensors through a novel Hybrid Physical Unclonable Function Hashing (HPUFH) model. The system also utilizes a novel Pattern-based Signal Strength Correlation (PbSSC) algorithm to detect any unexpected location changes in the dumb sensor field. The extensive security and performance evaluation demonstrates that the proposed system is highly efficient and secure with a linear computational cost proportional to the number of challenge-response pairs.
Detect &amp; Protect-medical Device Cybersecurity

Today, cybersecurity incidents have become a global challenge in many industries, even healthcare. Unlike those other industries, the healthcare industry is quite reluctant in cybersecurity protection, putting the systems at risk of cyberattack. This is somehow due to the complexity of the healthcare system and cybersecurity priority variability in different hospitals across the United States. Many hospitals across the country have no resources to counter sophisticated cyberattacks. An attack to the healthcare industry means an attack on medical devices, big data loss, and theft, even infrastructure. These can be very costly to the hospital, resulting in millions of dollars, downtime pressures, a damaged reputation, and loss of vaccinations and research. Therefore, the healthcare industry should invest in detecting and protecting medical device cybersecurity to harden the systems and monitor intrusion. Cybersecurity detection and protection involve using some programs, technologies, and behaviors in the hospital to improve cyber resiliency. However, some of these programs and technologies are not long-lasting or self-sustaining. Nevertheless, if adopted and implemented, they can maintain the healthcare system's ability to be resilient to cyberattacks. The detection and protection process uses artificial intelligence, machine learning, and natural language processing, among other digital technologies.
Collaborative Filtering Hybrid Recommendation Algorithm based on Optimal Weight

A collaborative filtering hybrid recommendation algorithm based on the idea of optimal combination prediction (BEST-CF) is proposed, and the effectiveness of BEST-CF is verified on Movielens 100K data set using the optimal combination of the user-based collaborative filtering recommendation algorithm (User-CF) and the item-based collaborative filtering recommendation algorithm (Item-CF). Experiments results show that the BEST-CF algorithm significantly improves the rating prediction accuracy and can enhance the recommendation quality.
Vector-mode Multiplexing For Photonic Tensor Accelerator

We propose a coherent multi-dimensional (wavelength, spatial mode, polarization, etc.) photonic tensor accelerator capable of performing high-speed artificial neural network computation. High-speed matrix-vector and matrix-matrix multiplication were experimentally demonstrated.
Action Languages Based Actual Causality in Decision Making Contexts

Rationally understanding the evolution of the physical world is inherently linked with the idea of causality. It follows that agents based on automated planning have inevitably to deal with causality, especially when considering imputability. However, the many debates around causation in the last decades have shown how complex this notion is and thus, how difficult it is to integrate it with planning. This paper’s contribution is to link up two research topics—automated planning and causality—by proposing an actual causation definition suitable for action languages. This definition is a formalisation of Wright’s NESS test of causation.
Process Optimization and Die Design of Hydromechanical Calendering Based on Wireless Network Communication and IoT

Aiming at the problem of the study, of high error probability in the practical application of traditional hydromechanical calendering process, the hydromechanical calendering process is optimized based on wireless network communication. GPRS or LORaipot-IOT wireless communication mode is adopted to calculate the impact kinetic energy of hydromechanical calendering, and the simulation model of hydromechanical calendering is built based on AMESim. The characteristics of hydromechanical calendering are analyzed. The oil pressure is controlled by setting the pressure adjusting valve and adjusting the spring pressure to realize the optimization of the hydromechanical calendering process. On this basis, the grinding tool is designed, and the clearance between punch and die is reserved to prevent the blank from cracking. The experimental results show that the error probability of hydromechanical calendering with the optimized design process is significantly lower than that of the control group, which can solve the problem of high error probability of traditional hydromechanical calendering process in practical application and meet the application requirements. Conclusions. Hydraulic mechanical calendering may be used to calendar different plastic materials, with a blank thickness of 0.5-3 mm, according to the technology’s operating principle, test production, and foreign data. Thicker sheets may be calendered with adequate hydraulic pressure. For the form of parts, the optimum effect is that the projectile, cone, etc. section vary from small too big. For the section does not change or smaller parts also have an impact? Some pieces with an interior concave shape and bottom and side stiffeners are difficult to the calendar. Hydromechanical calendering technologies make it easier to create these pieces.