Reviewerly/exHarmony huggingface dataset:
Relationship between Risk Factors of Water Conservancy Project Based on Machine Learning Algorithm

In recent years, with the continuous progress of science and technology, water conservancy projects are playing an increasingly important role in social economy.However, the risk accidents caused by the lack of traditional engineering experience and human factors are also gradually increasing.As one of the national infrastructure construction projects, the importance of water conservancy is self-evident.Therefore, how to improve the quality, reliability and operation efficiency of water conservancy projects through computer technology is very necessary and has practical significance.This paper first introduces the commonly used mathematical statistics methods and the application status of machine learning algorithm, and then uses machine learning method to build probabilistic neural network technology to study the role of artificial neural network in risk prediction of water conservancy projects.Finally, the numerical simulation software is used to convert the risk factor relationship analysis results into actual data.The test results show that the impact between natural disasters and engineering geological conditions is the greatest.Therefore, in the risk prediction of water conservancy projects, special attention should be paid to the concurrent impact results of these two.
ASSET INVENTORY APPLICATION DESIGN FOR GEDE PANGRANGO VILLAGE LOCAL GOVERNMENT OF SUKABUMI REGENCY

Gede Pangrango Village, Kadudampit District, Sukabumi Regency is a government agency engaged in the community and village empowerment sector such as village development and village fund allocation. This community service activity is to design a local government asset inventory application so that the system running in this section can run more computerized. The development method uses the waterfall method, and system modeling uses a flow mapand Unified Modeling Language (UML) using the Dreamweaver programming language while the software tools used MySQL as the database. With the local government asset inventory application that we made, namely by maximizing more automated and more efficient computerization is used. So that officers or admins will be facilitated in the process of inputting data and in making reports and systems that are made using automation and a good data storage system. And it would be even better if the system created was developed to be even better in terms of appearance and the addition of other features, so that it would be able to cover all existing activities in Gede Pangrango Village, Kadudampit District, Sukabumi Regency.
Public News Archive: A Searchable Sub-archive to Portuguese Past News Articles

Over the past few decades, the amount of information generated turned the Web into the largest knowledge infrastructure existing to date. Web archives have been at the forefront of data preservation, preventing the losses of significant data to humankind. Different snapshots of the web are saved everyday enabling users to surf the past web and to travel through this overtime. Despite these efforts, many people are not aware that the web is being preserved, often finding these infrastructures to be unattractive or difficult to use, when compared to common search engines. In this paper, we give a step towards making use of this preserved information to develop “Public Archive” an intuitive interface that enables end-users to search and analyze a large-scale of 67,242 past preserved news articles belonging to a Portuguese reference newspaper (“Jornal Público”). The referred collection was obtained by scraping 10,976 versions of the homepage of the “Jornal Público” preserved by the Portuguese web archive infrastructure (Arquivo.pt) during the time-period of 2010 to 2021. By doing this, we aim, not only to mark a stand in what respects to make use of this preserved information, but also to come up with an easy-to-follow solution, the Public Archive python package, which creates the roots to be used (with minor adaptations) by other news source providers interested in offering their readers access to past news articles.
Research on Mask Wearing Recognition Based on Deep Learning

Since the outbreak of COVID-19 epidemic, research results have shown that the COVID-19 transmitted by droplets, and the most effective means of epidemic prevention is to wear masks. In public places where crowds gather, it is particularly important to use technical means to detect the situation of wearing masks, and remind people to wear masks in time to prevent cross-infection. This paper mainly starts with the target detection and tracking technology in the field of computer vision, and takes the recognition of whether to wear a mask as the entry point. Using python as the development tool, based on the convolutional neural network, the YOLOv2 algorithm is used as the core algorithm, and the ResNet50 network structure is built. Compared with other existing system test experiments, we can see that the system we built has better detection performance.
Sentiment analysis from Bangladeshi food delivery startup based on user reviews using machine learning and deep learning

Food delivery methods are at the top of the list in today's world. People's attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM has the highest accuracy rate of the three DL algorithms, with an accuracy of 91.07%. Among ML and DL models, LSTM DL takes the lead to predict the sentiment.
Using time-energy entangled photons to probe biological samples (Conference Presentation)

Quantum optical approaches to biological measurement could enable unprecedented sensitivity, specificity, and resolution with minimal sample perturbation. Additionally, quantum optics provides new “knobs to turn” in imaging and spectroscopy that are not approachable with classical means. One such example is through entangled photons, in which groups of photons have properties that are intrinsically and inseparably linked. In this presentation, I will provide an introduction to entangled light applications, our experimental work in two-photon absorption with entangled light, and our experimental work establishing that even post-traversal through micrometers and millimeters of biological tissue/media, time-energy entangled light can maintain this unique linkage.
Social environment as a factor of personality formation

In the course of the research, analytical and bibliographic method was employed to study the scientific literature on the influence of the social environment on the process of personality formation. The questionnaire survey conducted by the authors of the study online served to clarify the most important issues related to the research topic in practice. According to the results of the study, the main important theoretical components of the process of influence of the social environment on the formation of personality have been identified. The point of view of practical psychologists and researchers-teachers of humanities universities on the key aspects of this issue has been investigated.
Preparation of students for studying in crisis situations

The primary directions of the higher school’s educational activity are professional training, education seekers’ personal growth and self-affirmation, development of all participants’ intellectual potential in the educational process, spiritual enrichment, and moral, aesthetic and physical improvement. The process of socialization of the student’s personality continues even during crisis situations. The purpose of the academic paper is to establish the main tendencies in preparing students for the educational process in crisis situations in the scientific literature and to clarify certain practical aspects of preparing higher education seekers to study in crisis situations. In the course of the research, the analytical-bibliographic method was used to study the scientific literature on the issues of training higher education seekers in crisis situations. Induction, deduction, analysis, synthesis of information, logical-linguistic methods, abstraction, and idealization were applied to study and process data. A questionnaire was conducted by the research authors in online mode to clarify certain aspects of training practically. Based on the research results, the primary theoretical aspects of the process of students’ training in crisis situations were studied, as well as the standpoints of education seekers and teachers of higher educational institutions regarding the key aspects of this issue were clarified.
Lightweight intelligent vehicle target detection algorithm based on Yolov4

Aiming at the complex and changeable driving scenarios of intelligent vehicles and the need to quickly and accurately identify obstacles, an improved YOLOV4 algorithm is proposed. To limit the number of neural network parameters, the CSP-darknet53 backbone of the original YOLOV4 was replaced with the Ghostnet backbone. In addition, to improve the neural network's accuracy, a lightweight attention mechanism ECA is added to the three effective feature layers generated by the backbone using residual block connections. Experiments have shown that the improved YOLOV4 has a 2.8% increase in mAP compared to the original YOLOV4. Without changing the accuracy, The network model's memory size is lowered by 39%, as well as a 50% improvement in detecting speed. Therefore, the improved YOLOV4 accuracy and real-time performance are better than the original network detection, providing a strong guarantee for intelligent vehicle obstacle avoidance.
Micro-grid power transaction management platform based on blockchain technology

To improve the efficiency and stability of power transaction management, a micro-grid power transaction management platform is studied. Block chain Internet technology is used to build the basic architecture of the electric energy transaction management platform. Based on the principle of low cost and high efficiency, the mathematical model of electric energy transaction is established. Combined with the distributed consensus mechanism of block chain technology, the intelligent contract of electric energy transaction is designed to realize the execution and management functions of electric energy block chain transaction. The test results show that the average time delay of the platform is about 0. 36 s for 10 groups of the random electricity trading instructions, which is real-time and efficient. For 5 random groups of users, the average electricity cost of the transaction results is 20. 42 yuan, which is feasible and economical, and lays a good foundation for the sustainable development of the micro-grid electricity trading management platform.
Detecting Fake News on Social Media Combined with the CNN Methods

Social media platforms are created to facilitate human social life as technology develops. Twitter is one of the most popular and frequently used social media for exchanging information. This social media platform disseminates real-time and complete information. Unfortunately, there are not a few tweets that contain false information or are often referred to as hoaxes. Those hoaxes that existed on Twitter are very troubling for society. Fake news or hoaxes can cause misunderstandings in receiving information. Therefore, this research aimed at developing a system that can detect hoaxes on Twitter to anticipate their spread, which can be detrimental to related parties. The system being developed uses a deep learning approach with the Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and Global Vectors (GloVe). The results of this study display the fake news detected by the system using the CNN method with baseline, BERT, and GloVe. The data have been adjusted to the keywords related to fake news and spread on online media, such as Hoax or Not from Detik.com, CekFakta from Kompas.com, etc. The results show the highest accuracy of 98.57% using CNN with a split ratio of 90:10, baseline unigram-bigram, BERT, and Top10 corpus tweet+IndoNews with an increase of 4.7%.
Unmasking Deepfakes: Using Resnext and LSTM to Detect Deepfake Videos

This paper proposes an approach for detecting deepfake videos using Resnext CNN and LSTM. The proposed approach involves training a Resnext CNN on a dataset of real and deepfake videos to classify them accurately. The Resnext CNN takes video frames as input and outputs a probability score for each frame, which is then fed into an LSTM to model the temporal dynamics of the video. The approach was evaluated on a dataset of real and deepfake videos and achieved promising results. The proposed approach can be used to detect deepfake videos, which can help in preventing the spread of misinformation and safeguarding our society.
Rethinking telepresence: post- and pre-COVID-19

Following the marked increase in the use of digital technologies during the recent pandemic, the article reconsiders the concept of social telepresence, in the sense of interpersonal connection at a distance, locating it in the longue durée and within media studies. It reminds the reader that, for centuries, when people were separated from one another by the force of various circumstances, including pandemics, they resorted to technologies at their disposal to experience telepresence, long before the term itself was coined by scholars. Foremost among these has been the epistolary, a vitally important interpersonal media largely overlooked by media and telepresence researchers. Rather than competitively evaluating the performance of various technologies, the article proposes a framework to compare them, along with the practices of social telepresence, in the course of history. This comparative program employs the following criteria: embodiment, synchronicity, the space of the encounter, the ontology of entities other than humans actuated by telepresence and the social preferences for different forms of telepresence.
Anomaly detection of multivariate industrial sensing data based on graph attention network

Recent development of Industrial Internet produces a large number of sensor data that record the production status of equipment in the field of industrial intelligence. This paper focuses on how to detect abnormal event from multivariate time series data. We propose an anomaly detection model DWGAT, that first uses DeepWalk method to generate an embedding vector for each sensor node that can represent sensor's behavior characteristics according to prior information, and then establishes a graph describing the initial relationships between sensor nodes. The graph attention network is used to mine and aggregate the data features to predict the multivariate data at the next moment. Finally, the predicted value is compared with the actual observed value, and a calculation method of anomaly score is designed to judge whether there are anomalies in the system according to whether the anomaly score is larger than the threshold value learned by the model. Experiments are established on two real-world public industrial datasets and compared with two baseline models. The results show that the F1 scores of the proposed anomaly detection model on the two data sets are higher than those of the current optimal model.
Federated Ensemble Algorithm Based on Deep Neural Network

In the realm of multi-source privacy data protection, federated learning is now one of the most popular study topics. When the data being used is not local, its architecture has the ability to train a common model that can satisfy the needs of many parties. On the other hand, there are circumstances in which local model parameters are challenging to incorporate and cannot be used for security purposes. As a result, a federated ensemble algorithm that is based on deep learning has been presented, and both deep learning and ensemble learning have been used within the context of federated learning. Using the various integrated algorithms that integrate local model parameters, which improve the accuracy of the model and take into account the security of multi-source data, the accuracy of the local model can be improved by optimizing the parameters of the local model. This in turn improves the accuracy of the local model. The results of the experiments show that, in comparison to conventional multi-source data processing technology, the accuracy of the algorithm in the training model for the MNIST dataset, the digits dataset, the letter dataset, and the wine dataset is improved by 1%, 8%, 1%, and 1%, respectively, and the accuracy is guaranteed. Additionally, accuracy is guaranteed. It also improves the security of data and models that come from more than one source, which is a very useful feature.
English Text Similarity Detection Algorithm Based on Animal Algorithm

The text similarity detection algorithm has a wide range of applications in the processing of massive natural language text information.Unlike simple and complete repetitive search, the complexity of natural language has caused great difficulties in the calculation of text semantic similarity.The Simhash algorithm does not involve the semantic information of the text, and cannot support the semantic problems of natural language processing such as synonyms and polysemes.Therefore, using the "dimensionality reduction" advantage of animal algorithms in English text processing and the efficiency of the retrieval process, aiming at its inability to recognize semantically similar text content, this paper studies the English text similarity detection algorithm of animal algorithms.Aiming at the shortcomings of simhash in the semantic similarity of text, this paper proposes a semantic code design based on the synonym word forest and context through the study of existing synonym expansion schemes.Based on the comprehensive improvement scheme, a semantic fingerprint generation algorithm incorporating synonym information is proposed, which solves the problem of similar texts that cannot identify replacement synonyms.The experiments in this paper show that after testing the sample data of the algorithm in this paper, under the condition of using k = 3 parameter determination, the accuracy and recall of correct identification are over 77%.In contrast, the two indexes of the traditional simhash algorithm and the word frequency statistical algorithm are only about 70%.This proves that the improved algorithm proposed in this paper has achieved relatively good results for identifying multiple similar modification situations, especially the problem of synonym replacement.
Research on the path of college teaching management team building under the background of the new era

With the rapid development of China's higher education, higher requirements have been put forward for the teaching management team of colleges, which are mainly reflected in whether it can meet the basic requirements of college education and teaching reform and innovation.Judging from the current situation, most colleges still have some problems in the construction of teaching management teams, such as low theoretical literacy of teaching managers, weak professional ability, lack of innovation ability, etc., which makes it difficult to effectively guarantee the quality of college education and teaching.To this end, this paper first briefly expounds the importance of colleges teaching management team building, then focuses on analyzing the existing problems in the current colleges teaching management team building, and puts forward some scientific construction ideas for reference.
Research on Rhythm Training of Double Piano Playing

In the process of double piano performance, mastering the sense of rhythm is the basic skill of double piano performance, but it is also a skill that tests the performer's ability.By strengthening the training of double piano rhythm, the player can continuously improve his own control of the playing rhythm, so as to play the double piano better.Based on this, the article first explores the artistic characteristics of double piano performance, then explores the rhythm combination forms in double piano music, and finally proposes targeted rhythm training methods for various rhythm organization forms, with a view to providing some reference for performers.
AutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment

Given a deep learning model trained on data from a source hospital, how to deploy the model to a target hospital automatically? How to accommodate heterogeneous medical coding systems across different hospitals? Standard approaches rely on existing medical code mapping tools, which have several practical limitations. To tackle this problem, we propose AutoMap to automatically map the medical codes across different EHR systems in a coarse-to-fine manner: (1) Ontology-level Alignment: We leverage the ontology structure to learn a coarse alignment between the source and target medical coding systems; (2) Code-level Refinement: We refine the alignment at a fine-grained code level for the downstream tasks using a teacher-student framework. We evaluate AutoMap using several deep learning models with two real-world EHR datasets: eICU and MIMIC-III. Results show that AutoMap achieves relative improvements up to 3.9% (AUC-ROC) and 8.7% (AUC-PR) for mortality prediction, and up to 4.7% (AUC-ROC) and 3.7% (F1) for length-of-stay estimation. Further, we show that AutoMap can provide accurate mapping across coding systems. Lastly, we demonstrate that AutoMap can adapt to two challenging scenarios: (1) mapping between completely different coding systems and (2) between completely different hospitals.