Reviewerly/exHarmony huggingface dataset:
An Algorithm for Time Prediction Signal Interference Detection Based on the LSTM-SVM Model

Interference detection is an important part of the electronic defense system. It is difficult to detect interference with the traditional method of extracting characteristic parameters for interference generated at the same frequency as the original signal. Aiming at this special time-frequency overlapping interference signal, this paper proposes an interference detection algorithm based on the long short-term memory-support vector machines (LSTM-SVM) model. LSTM is used for the time series prediction of the received signal. The difference between the predicted signal and the received signal is used as the feature sample, and the SVM algorithm is used to classify the feature samples to obtain the recognition rate of whether the sample has interference. The LSTM-SVM model is compared with the gate recurrent unit-support vector machines (GRU-SVM) model, and the comparison results are visualized using a confusion matrix. The simulation results show that this LSTM-SVM model algorithm cannot only detect the existence of the interference signal but also can determine the specific position of the interference signal in the received waveform, and the detection performance is better than the GRU-SVM model.
A Set of Comprehensive Evaluation System for Different Data Augmentation Methods

Data augmentation is an effective method to prevent model overfitting in deep learning, especially in medical image classification where data samples are small and difficult to obtain. In recent years, different data augmentation methods, such as those based on single data transformation, multiple data mixing, and learning data distribution, have been proposed one after another, but there has never been a systematic system to evaluate various data augmentation methods. An impartial and comprehensive data augmentation evaluation system not only can assess the benefits and drawbacks of existing augmentation approaches in a specific medical image classification but also can provide an effective research direction for the subsequent proposal of new medical image data augmentation methods, thereby advancing the development of auxiliary diagnosis technology based on medical images. Therefore, this paper proposes an objective and universal evaluation system for different data augmentation methods. In this method, different augmented methods are evaluated objectively and comprehensively in terms of classification accuracy and data diversity by using existing large public data sets. The method is universal and easy to operate. To imitate the prevalent small-sized data sets in deep learning, an equal-interval sampling technique based on similarity ranking is presented to select samples from large public data sets and construct a subset that can fully reflect the original set. The augmented data sets are then created using various data augmentation approaches based on the small-sized data sets. Finally, different data augmentation strategies are objectively and fully evaluated based on the comprehensive scores of classification accuracy and data diversity following data augmentation. The validity and feasibility of the suggested sampling method and assessment system in this study are demonstrated by experimental findings on numerous data sets.
Seismicity analysis of Southern Ghana II: Updated crustal velocity model and hypocentral parameters

&amp;lt;p&amp;gt;A small network of six broadband seismic sensors operated in southern Ghana between October 2012 and April 2014 (GHDSN). During this period, no seismicity was reported by the global data centers, however application of the Deep Learning algorithm EQTransformer resulted in the detection and subsequent location of 73 earthquakes. Preliminary constant crustal velocity models with &amp;amp;#160;vp=5.55 km/s and vs=3.36 km/s have been utilized since 2002 to locate the local earthquakes in southern Ghana. Using this crude velocity model resulted in scattered seismicity, hinting into a likely inadequacy of these preliminary velocity parameters to represent the elastic properties of the area. In this study, we perform a joint-inversion for estimating an updated 1D crustal velocity model and the hypocentral parameters of the 73 recently detected local earthquakes. A grid search method is implemented and a 6-layer velocity model is defined, down to 45 km crustal depth. The space of velocity model parameters is searched by altering the upper depth of the layers (ud), P-wave velocity in each layer (vp), and the ratio of vp/vs. The optimized velocity model and hypocenteral parameters are evaluated by minimizing the RMS error function between the observed (533 picked phases consisting of 282 P and 251 S phases) and calculated arrival times. A two-step implementation is devised to increase the computational efficiency of the inversion process. Initially, the optimum &amp;amp;#160;vp/vs is estimated by implementing a coarse grid search on vp and ud values. Then, incorporating the optimum vp/vs a fine grid search on vp and ud is applied.&amp;lt;br&amp;gt;The results yields layers with 1, 13, 8, 13 and 10 km thickness, with vp = 5.9, 6.1, 6.3, 6.5, 6.9 and 7.2 km/s, respectively. The updated velocities for the first and last layers are 6% and 26% percent higher than the previously reported constant velocity models. Furthermore, the updated vp/vs=1.70 is 0.03% higher than the corresponding value vp/vs=1.65 of the constant velocity model. The updated hypocentral locations of the 73 earthquakes with &amp;amp;#160;2.5&amp;lt;ML&amp;lt;3.9 are concentrated on five major clusters. Two clusters are located on the AFZ, indicative of the active role of this structure in the seismicity of the region. Two other clusters, which have the highest rate of activity, &amp;amp;#160;are positioned in the intersection between the AFZ and CBF. The last cluster consists of scattered earthquakes that coincide with mapped segments of the AFZ. Incorporating the updated velocity model for estimating the hypocentral parameters resulted in enhanced seismogenic source delineation in southern Ghana. &amp;amp;#160;This research contributes to the FCT-funded projects SHAZAM (PTDC/CTA-GEO/31475/2017), RESTLESS (PTDC/CTA-GEF/6674/2020), SIGHT (PTDC/CTA-&amp;lt;br&amp;gt;GEF/30264/2017) and IDL (UIDB/50019/2020).&amp;lt;/p&amp;gt;
Biodigestion: self-sustaining process to obtain renewable energy

Anaerobic biodigestion is a process that is carried out in oxygen-free conditions since the microorganisms present in these bioprocesses do not require oxygen to develop, they feed on organic matter and can generate various gaseous components. Throughout the process it is necessary to control various temperatures because each phase of the process has different types of bacteria, each type of bacteria has its optimal processing parameters. The temperature range for psychrophilic bacteria is 50 to 65 °C, while for mesophilic and thermophilic bacteria it is 20 to 40 °C. Methanogenic microorganisms are affected by process conditions, temperature, redox inhibitors, and pH. The characteristic of biogas as renewable energy is undoubtedly a subject that requires constant research so that it can be used both at an industrial and domestic level.
Unsupervised machine learning driven Prospectivity analysis of REEs in NE India

&amp;lt;p&amp;gt;Traditional mineral prospectivity modelling for mineral exploration and targeting relies heavily on manual data filtering and processing to extract desirable geologic features based on expert knowledge. It involves the integration of geological predictor maps that are manually derived by time-consuming and labour-intensive pre-processing of primary geoscientific data to serve as spatial proxies of mineralisation processes. Moreover, the selection of these spatial proxies is guided by conceptual genetic modelling of the targeted deposit type, which may be biased by the subjective preference of an expert geologist. This study applies Self-Organising Maps (SOM), a neural network-based unsupervised machine learning clustering algorithm, to gridded geophysical and topographical datasets in order to identify and delineate regional-scale exploration targets for carbonatite-alkaline-complex-related REE deposits in northeast India. The study did not utilise interpreted and processed or manually generated data, such as surface or bed-rock geological maps, fault traces, etc., and relies on the algorithm to identify crucial features and delineate prospective areas. The obtained results were then compared with those obtained from a previous supervised knowledge-driven prospectivity analysis. The results were found to be comparable. Therefore, unsupervised machine learning algorithms are reliable tools to automate the manual process of mineral prospectivity modelling and are robust, time-saving alternatives to knowledge-driven or supervised data-driven prospectivity modelling. These methods would be instrumental in unexplored terrains for which there is little or no geological knowledge available.&amp;amp;#160;&amp;lt;/p&amp;gt;
IVIST: Interactive Video Search Tool in VBS 2022

This paper presents the details of the proposed video retrieval tool, named Interactive VIdeo Search Tool (IVIST) for the Video Browser Showdown (VBS) 2022. In order to retrieve desired videos from a multimedia database, it is necessary to match queries from humans and video shots in the database effectively. To boost such matching relationship, we propose a multi-modal-based retrieval scheme that can fully utilize various modal features of the multimedia data and synthetically consider the matching relationships between modalities. The proposed IVIST maps human-made queries (e.g., language) and features (e.g., visual and sound) from the database into a multi-modal matching latent space through deep neural networks. Based on the latent space, videos with high similarity to the query feature are suggested as candidate shots. Prior knowledge-based filtering can be further applied to refine the results of candidate shots. Moreover, the user interface of the tool is devised in a user-friendly way for interactive video searching.
Recognition of the MNIST-dataset with skeletonized images

Abstract For a long time there have been many neural networks that are able to recognize handwritten numbers. In our work, we will investigate a neural network according to a specific feature, indicating the number in the picture. We will try to achieve an important image skeleton for recognition.
An Exploration of Explainable Machine Learning Using Semantic Web Technology

The behavior of a Machine Learning (ML) algorithm is generally accepted to be a black box, i.e., it cannot be opened and understood. This paper reports on an effort to provide explanation to ML algorithms by using semantic background knowledge. A preliminary paper was found as a project seed, its experiment of ML explanation using the DL-Learner tool recreated and semi-automated. DL-Learner is a framework for supervised ML using background knowledge. DL-Learner induces class relationships that hold true for a positive example set. The work presented in this paper is a novel, semi-automated framework for testing the use of DL-Learner in ML explanation. A scene classifier pipeline was created to obtain test data. For the chosen dataset input to the ML, 32 trials were conducted, and explanations produced. Furthermore, this paper reports on the use of DL-Learner as a tool and the lessons learned from its use. DL-Learner, though temporally slow, may prove to be a novel means of ML explanation.
Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Aiming at the problems of low error recognition accuracy and low correlation of data matching in the existing dance action and music beat matching error recognition methods, a dance action and music beat matching error recognition method based on data mining is designed. The quaternion array is used to represent the joint coordinate points of the dance movement, and the coordinates are rotated to obtain the curve change of the dance movement. The dance movement characteristics are regarded as a set of positive and negative sample data, the initial weights of different sample data are calculated, the absolute value is processed for the music beat signal, the interference factors are filtered with the help of Gauss filter, and according to the rhythm law of the music beat signal, the characteristics of music beat signal are extracted and the feature extraction of dance action matching is completed with music beat. The corresponding relationship between dance action and music beat is regarded as the corresponding model, the pairwise occurrence probability between music beat and action is determined, the matching model between dance action and music beat is discretized, and the peak point of correlation data is introduced to complete the matching between dance action and music beat. The features of matching data are extracted and segmented by short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error identification model is established with the help of support vector mechanism, and the constraint conditions of error detection are set to complete the matching error identification. The experimental results show that the proposed method has high recognition accuracy and high data matching correlation.
Knowledge-Enhanced CO-Monitoring in Coal-Mines

Detection of underground fires is an important security task in hard-coal mining. Automated fire detection systems are usually based on the monitoring of carbon monoxide (CO).Systems using conventional technology based on threshold and tendency observations, however, generate a large number of false alarms. We show how CO-concentrations can be forecast by appropriate models of the physical and chemical processes. We furthermore describe a rule-based specification system utilizing forecasting for CO-monitoring. The improvement of this approach over the conventional is threefold. First, the number of false alarms is reduced by 50%, at least. Simultaneously, the thresholds for warnings and alarms can be reduced so that, second, the detection of real fires becomes both quicker and more reliable. Third, heuristic rules for fire detection and suppression of false alarms as well as the control of the forecasting can be described in a declarative way. While our system is still in a prototypic stage, the three major German hard-coal mining companies decided to use our approach in their CO-monitoring systems.
HD Camera-Equipped UAV Trajectory Planning for Gantry Crane Inspection

While Unmanned Aerial Vehicles (UAVs) can be a valuable solution for the damage inspection of port machinery infrastructures, their trajectories are still prone to collision risks, trajectory non-smoothness, and large deviations. This research introduces a trajectory optimization method for inspecting vulnerable parts of a gantry crane by a UAV fitted with a high-definition (HD) camera. We first analyze the vulnerable parts of a gantry crane, then use the A* algorithm to plan a path for the UAV. The trajectory optimization process is divided into two steps, the first is a trajectory correction method and the second is an objective function that applies a minimum snap method while taking into consideration flight corridor constraints. The experimental simulation results show that, compared with previous methods, our approach can not only generate a collision-free and smooth trajectory but also shorten the trajectory length significantly while substantially reducing the maximum deviation average deviation distances. The simulation results show that this modelling approach provides a valuable solution for UAV trajectory planning for gantry crane inspection.
Multi-Perspective Reasoning Using Adaptive Learning

An evaluation and decision of learning methodology is a huge locale in AI, man-made thinking, data mining, model affirmation, etc. There are a large number of learning calculations which exists, and new calculations are being included the AI writing; in any case, it is hard to choose the most reasonable learning calculation for a given dataset. With the data blast of various learning calculations and regularly changing information situations, there is a need of versatile learning framework. A specific learning strategy which is reasonable in a circumstance may not be fruitful for all the sorts of learning. The learning cycle is firmly identified with the learning issue or information within reach or what we are attempting to learn, and henceforth, while choosing learning techniques, it is important to comprehend the learning issue. There is a need to investigate the learning issue and afterward select the most appropriate methodology by versatile learning. It does not simply mean joining more than one learning calculations and it is not exchanging two unique techniques. It is tied in with discovering information attributes and suggesting the most suitable strategy. It is a test to devise an approach which yields the most exact outcomes and manage the evolving condition. Multi-viewpoint learning is fundamental in multi-viewpoint dynamic. The accentuation of the work is on choosing arrangement calculation (s) for a specific choice situation. As highlight determination strategy influences classifier execution, the mixture choice technique utilizing rotation forest is proposed for clinical conclusion. The goal is to build up a versatile learning philosophy with expanded exactness where the learning strategy can be managed, and it tends to be a solitary classifier or it very well may be an outfit of classifiers.
Low-cost system for sunlight incidence angle measurement using optical fiber

The development and optimization of renewable energy systems are some of the most necessary topics to advance towards secure and sustainable energy models. Photovoltaic energy is one of those sustainable options that could contribute to the reduction of greenhouse gas emissions. The optimal angle of solar incidence producing the highest absorption in a day is an important parameter to install photovoltaic systems. This value is often estimated using simulation models based on geographic location; however, those models ignore the influence of nearby obstruction objects, albedo, and local weather conditions. Such a problem is addressed in this work by designing a system to estimate the optimum angle of solar incidence for the photovoltaic panels. The system is based on an arrangement of 33 measurement points spaced in arcs every 45 degrees in azimuth and every 22.5 degrees in elevation, which provides a wide range for analysis. The light captured by each optical fiber is transmitted to a flat array where the power is measured using a single RGB camera.
A Study on the Preference for Class Methods and Satisfaction with Non-face-to-face Online Classes of University Students due to COVID-19: Focused on Practical Classes for Hotel and Tourism Majors

In this study, college students" preference for class methods due to COVID-19 and non-face-to-face online number of major subjects. The purpose of this study was to analyze job satisfaction. first. As a result of analyzing the class preference according to the characteristics of the study subjects, recorded video classes were overwhelmingly preferred, followed by hybrid classes. In the form of university, students at local universities had a relatively high preference for face-to-face classes and hybrid classes. Second, as a result of analyzing the preference of online classes for major practicum subjects according to the characteristics of the study subjects, the preference for face-to-face classes was overwhelmingly high in all characteristics, but the only significant difference was the average grades. Third, there was a partially significant difference in satisfaction by type of distance learning according to grade and type of university. In all items with significant differences, the satisfaction level of metropolitan universities was lower than that of regional universities. Based on the analysis results, it is necessary to develop lecture content suitable for the various characteristics of students and university types and to reinforce the system.
Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

For long-distance running tactics, a computer vision-based motion correction solution is presented. The depth information is merged into the KCF algorithm to enhance it, which overcomes the classic KCF method’s incapacity to tackle the tracking drift issue caused by occlusion and extracts the technical features of long-distance running motions. Based on computer vision, the posture area of long-distance runners is detected, and the technical movements of long-distance runners are recognized. Calculate the centroid coordinates of the wrong technical movement correction area in long-distance running, and generate a tracking image of the wrong technical movement in long-distance running. The foreground and background information of the image is separated by the optical flow feature of machine vision, and the motion trajectory of the long-distance running error technique is extracted, and the motion correction of the long-distance running technique based on computer vision is realized. The experimental results show that the method in this paper has better accuracy in extracting long-distance running motion features and can accurately identify and correct the technical movements of the long-distance running, the technical movements of the head, and the technical movements of the body balance, and the correction efficiency is high.
A Virtual Reality Reminiscence Interface for Personal Lifelogs

Letters, diaries, postcards, photo albums, home videos, and lifelogs! These are artefacts of our personal history, they represent how we cherish and preserve memories, re-engage with our past and share our experiences with others. In this demonstration paper, we explore an Virtual Reality (VR) approach to help people reminisce about the past through lifelogs. Our user study found that most participants enjoyed the experience, although for some, the VR environment was overwhelming.
Recognition of Human Body Feature Changes in Sports Health Based on Deep Learning

With the rapid development of social economy and the extensive and in-depth development of national fitness activities, national physical fitness monitoring and research work has achieved rapid development. In recent years, the application of deep learning technology has also achieved research breakthroughs in the field of computer vision. How deep learning technology can effectively capture motion information in sample data and use it to realize the recognition and classification of human actions is currently a research hot spot. Today's popularization of various shooting devices such as mobile phones and portable action cameras has contributed to the vigorous growth of image data. Therefore, through computer vision technology, image data is widely used in practical application scenarios of human feature recognition. This paper proposes a deep learning network based on the recognition of human body feature changes in sports, improves the recognition method, and compares the recognition accuracy with the original method. The experimental results of this paper show that the result of this paper is 1.68% higher than the original recognition method, the accuracy rate of the improved motion history image is increased by 14.8%, and the overall recognition rate is higher. It can be seen from the above experimental results that this method has achieved good results in human body action recognition.