Reviewerly/exHarmony huggingface dataset:
Construction Principles of Physical Fitness Training Objective System Based on Machine Learning and Data Mining

The objective is to address the issue of simplification of physical education classes offered by large colleges and universities. The evaluation standard of physical education curriculum is not unified. The physical education management system focuses on the functions of collecting information, sorting, and statistics and has low timeliness and guiding significance. This paper puts forward an analysis of the construction principle of physical fitness training target system based on machine learning and data mining. This paper uses informational analysis to statistically analyze the healthy behavior of college students, to guide the physical education of college students, and to propose a model for the analysis of healthy sports behavior of college students based on data mining technology. Create a decision tree template for students whose cardiovascular function does not meet the standard using the decision tree algorithm. The association rule algorithm is used to mine the association of five indexes of physical health, so as to judge the hidden law between students’ physical fitness and behavior habits and get the correlation information of various physical health indicators. The simulation results show that, through the prediction of college students’ healthy sports behavior data, when the sample point is 5, the original value data is 16, which is higher than the estimated value, the convergence of the overall data characteristic distribution is good, and the disturbance error is low. Therefore, using this method to analyze the application of college students’ healthy sports behavior has a high accuracy of sports-related data mining and can effectively guide college students’ sports management and training.
Analysis of Different Approaches for Automatic Text Summarization

In the NLP culture, text summarization is regarded as a difficult task. We now have access to a vast amount of information from offline and online resources. We may find many papers on a single topic, each offering a wealth of knowledge. Manually extracting valuable information from them is really tough. Automatic text summarizing techniques have been developed to address this issue. Text summarizing is the process of taking relevant information from huge papers and condensing it into a short summary that retains all of the important information. This survey study provides a general overview of the work that has been implemented with topic of automatic text summarizing in different languages and utilizing several text summarization methodologies. Text summary is the method of defining and compressing the most significant meaningful material in a document or collection of similar documents into a shorter version that retains its general meanings. This paper aims to discuss various techniques and approaches used by automated text summarization researchers.
Deterministic preparation of supersinglets with collective spin projections

We introduce a procedure to generate supersinglets, the multipartite generalization of angular momentum singlet states. A supersinglet is defined as a total spin zero state consisting of $ N $ spin-$ j $ particles. They are highly entangled and have zero spin variance in any direction, and as such are potentially useful for quantum metrology. Our scheme is based on projective measurements that measure the collective spin of the whole spin ensemble. A local unitary rotation is applied conditionally on the measurement outcome, such as to maximize the probability of obtaining spin zero on the subsequent measurement. The sequence is repeated in the $ z $- and $ x $-basis until convergence is obtained towards the supersinglet state. Our sequence works regardless of the initial state, and no postselection is required. Due to the use of strong projective measurements, very fast convergence towards zero spin variance is obtained. We discuss an example implementation using quantum nondemolition measurements in atomic ensembles, and perform numerical simulations to demonstrate the procedure.
Increasing Access to Cultural Heritage Objects from Multiple Museums through Semantically-Aware Maps

Geographical information is gaining new momentum as an analysis and visualization tool for collections of cultural objects. It provides all kinds of users with new opportunities to contextualize and understand these objects in ways that resemble our ordinary spatially-located experience and to do so better than textual narratives. The SeMap project has built an online resource that shows more than 200,000 cultural objects through spatiotemporal maps, thus enabling new experiences and perspectives around these objects. Data come from the CER.ES repository and were created by a network of more than 100 Spanish museums. This article explains the refinement of the data provided by the repository, mostly by adding a semantic structure thanks to the CIDOC-CRM ontology, and by simplifying the exceedingly complex terminologies employed in the original records. Particular attention is paid to the methods for geolocating the information, as well as adding temporal filters (among others) to user queries. The functionalities, interface, and technical requirements are also explored at length.
A Grey BP Neural Network-Based Model for Prediction of Court Decision Service Rate

The judgment service rate is an important index to reflect the fairness of the judgment of legal cases in a certain area, which is of great significance to verify the accuracy of a court judgment. In this paper, a grey neural network model combining grey system theory and BP neural network algorithm is proposed to predict the index. Analyze the judgment service rate of the court judgment system, and build a prediction system based on the completion rate, completion rate, plaintiff satisfaction, defendant satisfaction, litigation time, property preservation cycle, document delivery time, implementation information disclosure rate, and other key indicators. Through example analysis, it is proved that the combined model of the grey prediction model and BP neural network has a small error and good simulation effect on the prediction of court decision-making service rate, which can better promote the development of court and society.
Indoor Scene Classification Algorithm Based on an Object Vector for Robot Applications

At present, many indoor scene classification algorithms are based on abstract feature descriptions and lack semantic interpretation for classification results. For robot applications, these classification methods fail to reuse prior knowledge of environmental objects, resulting in a waste of computing resources, and are not conducive to interaction. In addition, a large number of existing methods have not been verified for robot applications because training and test are performed on the same dataset. Here, intermediate semantic structure, called object vector, is proposed to describe the scene. The recognition confidence, category, and quantity of typical objects, which are related to scene concepts, can be captured by this structure. Based on the object vector, the semantic prototype models of scene categories are obtained using the prototype learning method, and the corresponding prototype confidence vector is generated for scene prediction. The object vector and prototype confidence vector are concatenated to form a complete descriptor. Applying sparse random forest learning, scene conceptual models are formed and used in real scene classification. In the experiment, the training and test sets were selected from sets with different styles. The average accuracy on the MIT Indoor 67, VPC, and Tsotsos_home datasets is higher than that of fine-tuned VGG. In particular, the accuracy on the VPC dataset improved by more than 5%. Moreover, the algorithm performance is evaluated on an actual robot platform.
Newton Algorithm Based DELM for Enhancing Offline Tamil Handwritten Character Recognition

Numerous research based on offline Tamil recognition deals only with few Tamil characters since it becomes extremely complicated in distinguishing small variations in large handwritten document. The writer’s complexity affects the overall formation of the characters. Such types of complexities are due to discontinuation of structures, unnecessary over loops, variation in shapes as well as irregular curves. This complex issue results in enhanced error value rate. Therefore, to conquer such issues, this paper proposes a novel approach to enhance the offline Tamil handwritten character recognition by utilizing four principal steps: pre-processing, segmentation, feature extraction and classification. For optimal segmentation of Tamil characters, this paper utilizes the Tsallis entropy approach-based atom search (TEAS) optimization algorithm. Then a Newton algorithm based deep convolution extreme learning (DELM) approach is utilized for the extraction and classification of input images. Finally, experiments are carried out for numerous Tamil handwritten recognition-based approaches. The proposed Tamil character recognition utilizes the datasets of isolated Tamil handwritten characters established by HP lab India to evaluate the efficiency of the system.
Faster R-CNN railway foreign body detection algorithm combined with attention between channels

The intrusion of foreign objects such as pedestrians and vehicles into the boundary of the railway has seriously threatened the safety of pedestrians and the safety of railway traffic. Aiming at the low utilization rate of the classic Faster R-CNN model for inter-channel information, in the feature extraction stage of the model, combined with the inter-channel attention module of SENet, the SE-Faster R-CNN model is proposed and used on this basis A more balanced loss function and adjustment of the size of the anchor improve the detection accuracy of the model. When the number of railway foreign body images is insufficient, first use VOC2007 to train the model, and then use part of the KITTI data set and a small amount of high-speed rail platform monitoring data and railway foreign body data to fine-tune the model, making the model better in the railway scene Performance. The experimental results show that although the algorithm speed has dropped slightly, the mAP of the improved Faster R-CNN model on the VOC data set has reached 80.3%, which is an increase of 6.9% compared to the original model. The image data on the railway platform and the railway foreign body The average accuracy on the image data reached 87%, especially for pedestrians and trains in the railway, the detection accuracy reached 89.61% and 90.9%, respectively. Studies have shown that the introduction of the SEnet structure into Faster R-CNN improves the feature extraction capabilities of the original model, which makes the model's detection accuracy for targets greatly improved, and can well complete the task of railway foreign body detection.
Virtual Reality Based Escape Room

Escape rooms have become highly popular over the last few years which builds one&#x0027;s creativity by making puzzles or clues where the person can find the clues and reach a certain destination. The purpose of the escape room is to improve creative thinking and it also seems to be interesting for the users. Escape rooms can be designed in various interesting worlds in the field of gaming. The users can find the clues by navigating and solving riddles or puzzles that will unlock the doors and finally escape from the environment. These escape rooms use in virtual reality technology where the user can feel the real-time experience as he/she is there inside the environment. The user can also interact with the environment by picking objects, finding the clues, navigation, etc which provides real-time hands-on experience. Escape rooms can be designed as per the choice of the owner who creates the environment. The primary thing required for an escape room is a simple room and as per the assets used, it can be designed at a lower cost. If some changes/advancement is to be done inside the environment, the owner can directly change inside the project in unity software instead of completely changing the environment. The VR simulations can be viewed through many headsets namely HTC Vive, Oculus Rift, Oculus Quest, Google cardboard, etc. The Virtual Reality escape rooms can be created through various engines but this developed escape room is created using a unity engine. This VR escape room is designed for Oculus Quest 2 and it can be viewed through this VR headset.
Research on the Construction and Realization of Data Pipeline in Machine Learning Regression Prediction

The data set used by machine learning usually contains missing value and text type data, and sometimes, it is necessary to combine the attributes in the data set. The data set must be cleaned and converted before the machine learning model can be generated. This is frequently a chain of events. The entire processing procedure will be time-consuming and inconvenient. This article examines the data pipeline and recommends that it be used to process all data. We carry out automation and use k-fold cross-validation to evaluate the performance of the model. Experiments demonstrate that it can lower the regression prediction model’s root mean square error and enhance prediction accuracy.
Pre-processing of Multimodal MR Images using NLM and Histogram Equalization

Magnetic resonance imaging has emerged as one of the maxima broadly used and flexible scientific imaging modalities today. While at the beginning of this technique the research was fundamentally qualitative and relied exclusively on observation of images, currently several technological developments have driven it possible to derive quantitative measurements from these data. However, MR images are normally affected by several types of artifacts which must be minimized before a quantitative biomarker assessment pipeline is applied. This paper proposes preprocessing to the magnetic resonance (MR) images to enhance the standard of the image and to ease the next processing and analysis. The proposed method uses Non-Local Means (NLM) for noise removal and Histogram Equalization (HE) for enhancing the contrast of Magnetic resonance images for the preprocessing.
CP-ABE encryption over MQTT for an IoT system with Raspberry Pi

Nowadays, the increasing number of devices deployed in IoT systems implementation and the requirement of preserving the integrity of data transported over the Internet, demand the use of data encryption schemes. This paper aims to show the performance evaluation of CP-ABE (Ciphertext-Policy Attribute Based Encryption) type of encryption over MQTT (Message Queue Transport Telemetry) that focuses on execution time for an IoT system with Raspberry Pi. For the implementation, two Raspberry Pi 4 Computer Model B are used for both the publisher and the subscriber, and a computer with Ubuntu 20.04 LTS operating system is used for the Broker and the Key Authority. The result of the present work provides relevant information on the execution times required in the CP-ABE encryption scheme to provide data integrity and fine-grained access control policy in an IoT system. The work demonstrates that the CP-ABE encryption scheme is suitable for IoT systems.
Multimodal Behavioral Cues Analysis of the Sense of Presence and Social Presence During a Social Interaction With a Virtual Patient

User's experience evaluation is a key challenge when studying human-agent interaction. Besides user's satisfaction, this question is addressed in virtual reality through the sense of presence and social presence , generally assessed thanks to subjective post-experience questionnaires. We propose in this article a novel approach making it possible to evaluate automatically these notions by correlating objective multimodal cues produced by users to their subjective sense of presence and social presence. This study is based on a multimodal human-agent interaction corpus collected in a task-oriented context: a virtual environment aiming at training doctors to break bad news to a patient played by a virtual agent. Based on a corpus study, we applied machine learning approaches to build a model predicting the user's sense of presence and social presence thanks to specific multimodal behavioral cues. We explore different classification algorithms and machine learning techniques (oversampling and clustering) to cope with the dimensionality of the dataset and to optimize the prediction performance. We obtain models to automatically and accurately predict the level of presence and social presence. The results highlight the relevance of a multimodal model, based both on verbal and non-verbal cues as objective measures of (social) presence. The main contribution of the article is two-fold: 1/ proposing the first presence and social prediction presence models offering a way to automatically provide a user's experience evaluation and 2/ showing the importance of multimodal information for describing these notions.
Neural Language Modeling of Unstructured Clinical Notes for Automated Patient Phenotyping

The availability of huge volume and variety of healthcare data provides a wide scope for designing cutting-edge clinical decision support systems (CDSS) that can improve the quality of patient care. Identifying patients suffering from certain conditions/symptoms, commonly referred to as phenotyping, is a fundamental problem that can be addressed using the rich health-related data collected for generation of Electronic Health Records (EHRs). Phenotyping forms the foundation for translational research, effectiveness studies, and is used for analyzing population health using regularly collected EHR data. Also, determining if a patient has a particular medical condition is crucial for secondary analysis, such as in critical care situations to predict potential drug interactions and adverse events. In this paper, we consider all categories of unstructured clinical notes of patients, typically stored as part of EHRs in the raw form. The standard MIMIC-III dataset is considered for benchmark experiments for patient phenotyping. Experiments revealed that our proposed models outperformed state-of-the art works built on vanilla BERT & ClinicalBERT models on the patient cohort considered, measured in terms of standard multi-label classification metrics like AUROC score (improvement by 6%), F1-score (by 4%), and Hamming Loss (by 17%) when we considered only patient discharge summaries and radiology notes. Further experiments with other note categories showed that using discharge summaries and physician notes yields significant improvements on the entire dataset giving 0.8 AUROC score, 0.72 F1 score, 0.09 Hamming loss.
Oral Cancer Diagnosis using Deep Learning for Early Detection

Oral cancer's overall prognosis is still bad. Over half of those who are afflicted are in advanced stages. Screening testing results and earlier detection strategies for oral malignancies, as previously said, rely in great part on medical professionals' health centre experience and there is no established strategy. The goal of the proposed model is to provide a fast, non-invasive, cost-effective, and simple Deep learning approach to determine oral cavity squamous cell carcinoma (OCSCC) sufferers from photographs. Oral cancers are broad and complex malignancies with a high mortality rate. Oral cancer is the eighth most frequent cancer in India, accounting for 130,000 fatalities per year. The tumour can be found in the glands, tonsils, neck, face, and mouth. Oral cancer can be diagnosed using a variety of ways, including biopsy, which involves taking a small sample of tissue from a section of the body and examining it under a microscope, as well as some screening measures. However, because it is difficult to clearly distinguish cancer cells that have been impacted by cancer [1], cancer cells that have been affected in the oral area are detected and classified using digital processing technology in this study. For early diagnosis and categorization, modern technologies and a deep learning algorithm can be used. The fuzzy particle swarm optimization (FPSO) is used to select the optimal texture characteristic after extracting the texture characteristics. Finally, the Convolution Neural Network classifiers was used to classify the best qualities [3]. Recall Rate, Accuracy Rate, Precision Rate, and Failure Rate are used to compare the performance of the proposed method. The suggested model outperforms existing models in detecting oral cancer, according to the results of the evaluation.
MMSNet: Multi-modal scene recognition using multi-scale encoded features

Utilizing multi-level features has been proven to improve RGB-D scene recognition performance. However, simply fusing features after conducting RGB and depth data separately may not satisfy multi-modal integrity. In this work, we propose an effective multi-modal RGB-D scene recognition model that integrates global or local multi-scale/multi-semantic features. The proposed approach is built on two key components. In the first stage, multiple random recursive neural networks (RNNs) are employed on a baseline CNN model to obtain multi-scale encoded features from multi-level feature hierarchy. In the second stage, multi-layer perceptrons (MLPs) learn global/local features at multiple levels while encouraging the correlation of multi-modal mutual features. Our learning design is based on the insight that correlated multi-modal features provide the complementary relation between the two modalities that promotes better performance of RGB-D scene recognition. In addition, the network is trained using a decisive fusion based on modality prediction confidence weights to yield RGB-D multi-modal recognition. Experiments on three RGB-D scene datasets verify the effectiveness of the proposed approach by achieving superior or highly competitive results compared to other state-of-the-art counterpart methods. Evaluation code and models are available at https://github.com/acaglayan/MMSNet.
Applying Transformer Models for Disease Named Entity Recognition

Disease Named Entity Recognition (DNER) is an emerging field of research that identifies disease mentions in health-related texts. Downstream tasks of DNER includes exploring the cause of a given disease, analysis of relationships among diseases, prevention mechanisms and specialized treatment procedures. Various models have been proposed for DNER, including dictionary-based approaches, rule-based systems, machine learning models with handcrafted features and deep learning models. Though deep learning models perform better for DNER, they highly depend on large annotated datasets, which is often not feasible. Multi-task approaches that combine manually annotated datasets with similar domains boost the performance of DNER; however, it is often difficult to find the combination of datasets that give a performance gain. This study addresses such drawbacks by investigating the applicability of transfer learning via Bidirectional Encoder Representations from Transformers (BERT) and proposes a model using BERT and Conditional Random Fields (CRF), which performs better than similar state-of-the-art models based on general deep learning and multi-task learning methods.