Reviewerly/exHarmony huggingface dataset:
Maximum Decision Support Regression-Based Advance Secure Data Encrypt Transmission for Healthcare Data Sharing in the Cloud Computing

The recent growth of cloud computing has led to most companies storing their data in the cloud and sharing it efficiently with authorized users. Health care is one of the initiatives to adopt cloud computing for services. Both patients and healthcare providers need to have access to patient health information. Healthcare data must be shared and maintained more securely. While transmitting health data from sender to receiver through intermediate nodes, intruders can create falsified data at intermediate nodes. Therefore, security is a primary concern when sharing sensitive medical data. It is thus challenging to share sensitive data in the cloud because of limitations in resource availability and concerns about data privacy. Healthcare records struggle to meet the needs of security, privacy, and other regulatory constraints. To address these difficulties, this novel proposes a machine learning-based Maximum Decision Support Regression (MDSR)-based Advanced Secure Data Encrypt Transmission (ASDET) approach for efficient data communication in cloud storage. Initially, the proposed method analyzed the node's trust, energy, delay, and mobility using Node Efficiency Hit Rate (NEHR) method. Then identify the efficient route using an Efficient Spider Optimization Scheme (ESOS) for healthcare data sharing. After that, MDSR analyzes the malicious node for efficient data transmission in the cloud. The proposed Advanced Secure Data Encrypt Transmission (ASDET) algorithm is used to encrypt the data. ASDET achieved 92% in security performance. The proposed simulation result produces better performance compared with PPDT and FAHP methods.
Faster R-CNN Approach for Detecting Smaller Jewels in Yakshagana Image

Applications on video analysis and image processing, object recognition has rapidly improved in recent years.Finding a desired object has become crucial, leading to the development of several methods for object detection.Due to its high-level processing, Deep Learning is quickly advancing in this area, ability to extract deeper characteristics, and ability to be more adaptable and dependable.The author of this paper advises employing convolutional neural networks and quicker area deep neural networks for object detection to create a straightforward approach with higher precision and mean average accuracy.
The Triangle Differential Cryptanalysis

In this paper, a new variant of differential cryptanalysis is developed by applying the idea of the boomerang attack on the truncated differential. We call this variant a triangle differential cryptanalysis since it utilizes the difference of every pair in an input and output triple. Similar to the boomerang attack, the triangle differential cryptanalysis combines two independent truncated differential distinguishers of two parts of a cryptosystem into a distinguisher of the whole cryptosystem. It provides a new perspective on the differential propagation, and so it is possible to break the limit of the traditional truncated differential. An MILP modeling technique is also provided for the triangle differential distinguisher search against general SPN ciphers. To demonstrate the power of this new type of differential distinguishers, we apply it to SKINNY-64 and CRAFT. For SKINNY-64, an 11-round triangle differential distinguisher is obtained, while the previous longest truncated differential distinguisher is 10-round. For CRAFT, a 13-round triangle differential distinguisher is obtained, while the previous longest truncated differential distinguisher is 12-round. Besides, compared with the best distinguishers other than the truncated differential distinguishers, there are still some improvements on the probabilities for both SKINNY-64 and CRAFT.
Classification and comparison of human activities by machine learning

The current study of human activity recognition and classification has been an important part of promoting the development of science and technology in society.Human activity recognition and classification are in several fields, such as competitive sports, criminal investigation field, etc.As the field of micro-electromechanics continues to evolve, more accurate human recognition is becoming possible, with wearable multi-axis inertial sensors that allow us to visually detect the desired data.In this paper, the data of 19 human activities for 8 testers are feature extracted and normalized.The data are divided into training and test sets by machine learning models: support vector machine (SVR) classification, XGBoost classification, and logistic regression.The experiment was repeated 10 times to take the average value.The models were then scored, and by comparing integrated machine learning with traditional machine learning, it was found that integrated learning improved by 5%-29% in terms of accuracy compared to traditional machine learning.
PME: pruning-based multi-size embedding for recommender systems

Embedding is widely used in recommendation models to learn feature representations. However, the traditional embedding technique that assigns a fixed size to all categorical features may be suboptimal due to the following reasons. In recommendation domain, the majority of categorical features' embeddings can be trained with less capacity without impacting model performance, thereby storing embeddings with equal length may incur unnecessary memory usage. Existing work that tries to allocate customized sizes for each feature usually either simply scales the embedding size with feature's popularity or formulates this size allocation problem as an architecture selection problem. Unfortunately, most of these methods either have large performance drop or incur significant extra time cost for searching proper embedding sizes. In this article, instead of formulating the size allocation problem as an architecture selection problem, we approach the problem from a pruning perspective and propose Pruning-based Multi-size Embedding (PME) framework. During the search phase, we prune the dimensions that have the least impact on model performance in the embedding to reduce its capacity. Then, we show that the customized size of each token can be obtained by transferring the capacity of its pruned embedding with significant less search cost. Experimental results validate that PME can efficiently find proper sizes and hence achieve strong performance while significantly reducing the number of parameters in the embedding layer.
Analysis on the Management and Maintenance of Computer Labs in Higher Vocational Colleges

With the continuous development of society, computers are playing an increasingly important role in people's lives, and the application of computer technology is becoming more and more widespread in various industries.The "Education Informatization 2.0 Action Plan" issued by the Ministry of Education points out that educational informatization is the basic connotation and significant feature of education modernization, and it is a key content and important sign of "Education Modernization 2035."During the "13th Five-Year Plan" period, schools, teachers, and students have built three major educational basic databases.The "14th Five-Year Plan" for National Informatization also explicitly proposes the requirements to improve the level of educational informatization infrastructure construction and build a high-quality educational support system.Therefore, the development of networks and computer technology has become the foundation for adhering to the strategy of building a strong country through science and technology and prioritizing educational development.In order to meet the needs of teaching and scientific researching, higher vocational colleges often establish computer labs to meet the demands of teaching and scientific researching when offering computer-related courses.As the number and scale of computer labs continue to increase, if the management and maintenance of computer labs are not strengthened, it will seriously affect the quality of teaching and scientific researching.Therefore, this paper starts with the current situation of the management and maintenance of computer labs in higher vocational colleges, analyzing the existing problems, and proposes solutions, in the hope of providing some reference for the management and maintenance of computer labs in higher vocational colleges.
Design of asynchronous federated learning incentive mechanism

Federated learning has been widely paid attention as a new type of distributed machine learning that can protect data privacy and ensure data security.Asynchronous federation learning, a variant of traditional federation learning, can effectively improve model training efficiency.The introduction of incentive mechanism can help asynchronous federation learning to improve model training utility effectively.A federated learning incentive mechanism is constructed using the Stackelberg game, which optimizes the central server and data owner utilities, respectively, of the Stackelberg game.Based on this, we derive the equilibrium solution of the whole game, and finally analyze the feasibility of the model by arithmetic examples to obtain the optimal incentive effect.
Human behavior recognition based on machine learning

At present, human action recognition technology has been widely used in intelligent monitoring, fatigue driving warning, fall detection, family rehabilitation training and other fields.In order to accurately identify human actions, this paper uses a variety of machine learning models.At the same time, in order to improve the accuracy of recognition, this paper uses a variety of feature data sets to train the model.Through experiments, it is found that the model trained by the feature data set after PCA dimensionality reduction has the best comprehensive effect.The prediction accuracy of logistic regression algorithm, KNN algorithm and LightGBM algorithm has been significantly improved.Compared with the models trained by other feature data sets, the recognition accuracy has been improved by 6% -20%, reaching 0.89, 0.87 and 0.83 respectively.
English Word Difficulty Classifier Based on Random Forest Model

Recently, Wordle has become popular worldwide as a daily puzzle game launched by the New York Times.Players try to solve the puzzle by guessing a five-letter word in six tries or less.According to Wordle's statistical data, this paper first uses the K-means algorithm to cluster the difficulty of solution words to quantify the difficulty of English words and analyzes the accuracy and scientificity of the clustering results.Then, the paper uses the Random Forest model to classify the difficulty of words into three categories: 'easy', 'normal' and 'hard'.The results show that the classification accuracy on the training set and the test set reaches 0.972 and 0.978 respectively.
Incremental innovations in operation theaters

The field of ophthalmology has been built upon continuous innovations. COVID-19 pandemic has been an important driving force behind many innovations in ophthalmology and other branches of medicine. Innovations in ophthalmology has been a key to surgical progress. The process of promoting innovation in surgery is imperative in the evolving practice of ophthalmology.In this video we demonstrate some incremental innovations in operation theaters which help in increasing the efficiency and improving the performance of a surgeon. These innovations also provide a more comfortable environment for the patient undergoing the surgery.A few incremental innovations that are described in our video also help in preventing the spread of COVID infection during surgery. This video also showcases a few wet lab innovations that help train residents in their surgical skills.Use and reuse of simple materials make it cost effective and ecofriendly. These incremental innovations help in the smooth running of operation theaters. Thus, they are small improvements in the existing setup and help in creating a smooth and error free OT flow.https://youtu.be/thFLvC-XykE.
Do The Non-Verbal Behaviors of Sprinters Before The Competition Affect Their Performance

In athletics, the athlete is expected to get their concentration and control his negative emotions before the start. Body language of athletes can give important clues about their "real" internal states. On account of, it is important to analyze the nonverbal behaviors of the athletes before the start. In line with this importance, the aim of this study is to analyze the pre-start nonverbal behaviors of the athletes by coding them with the "Body Action Posture Coding System" and to examine the effect on the performance of the athletes. As a result, with this thesis study, it has been revealed that some nonverbal behaviors of the athletes, affect their performance. Nonverbal behaviors can give clues about the mental state of the athlete as well as give clues about the result of the competition has been opened to discussion with this study.
Easy money? Examining the trustworthiness of HYIP

Deciding the right ways of investing money is complicated with the trade-offs of risks and returns. High Yield Investment Programs (HYIPs) have extremely high promised return rates, sometimes more than 100% daily, that can attract many investors. As a result, investing in most cryptocurrencies like bitcoins, HYIPs, and their websites is booming. Prior researchers have already classified HYIPs as scams. However, some websites still last for many years or constantly pay investors as promised. Therefore, we propose that trustworthiness of HYIP websites is questionable, with different affecting factors. Using a large secondary dataset, we empirically tested the relationships between response variables (trustworthiness) two groups of explanatory variables: stability, and media/platform factors. Overall, the effects of the media/platform group are much less than the stability group toward trustworthiness. This research provides theoretical and practical insights into the HYIP investment.
AUSOME: authenticating social media images using frequency analysis

Ever since human society entered the age of social media, every user has had a considerable amount of visual content stored online and shared in variant virtual communities. As an efficient information circulation measure, disastrous consequences are possible if the contents of images are tampered with by malicious actors. Specifically, we are witnessing the rapid development of machine learning (ML) based tools like DeepFake apps. They are capable of exploiting images on social media platforms to mimic a potential victim without their knowledge or consent. These content manipulation attacks can lead to the rapid spread of misinformation that may not only mislead friends or family members but also has the potential to cause chaos in public domains. Therefore, robust image authentication is critical to detect and filter off manipulated images. In this paper, we introduce a system that accurately AUthenticates SOcial MEdia images (AUSOME) uploaded to online platforms leveraging spectral analysis and ML. Images from DALL-E 2 are compared with genuine images from the Stanford image dataset. Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) are used to perform a spectral comparison. Additionally, based on the differences in their frequency response, an ML model is proposed to classify social media images as genuine or AI-generated. Using real-world scenarios, the AUSOME system is evaluated on its detection accuracy. The experimental results are encouraging and they verified the potential of the AUSOME scheme in social media image authentications.
DEMA: decentralized electrical network frequency map for social media authentication

The information era has gained a lot of traction due to the abundant digital media contents through technological broadcasting resources. Among the information providers, the social media platform has remained a popular platform for the widespread reach of digital content. Along with accessibility and reach, social media platforms are also a huge venue for spreading misinformation since the data is not curated by trusted authorities. With many malicious participants involved, artificially generated media or strategically altered content could potentially result in affecting the integrity of targeted organizations. Popular content generation tools like DeepFake have allowed perpetrators to create realistic media content by manipulating the targeted subject with a fake identity or actions. Media metadata like time and location-based information are altered to create a false perception of real events. In this work, we propose a Decentralized Electrical Network Frequency (ENF)-based Media Authentication (DEMA) system to verify the metadata information and the digital multimedia integrity. Leveraging the environmental ENF fingerprint captured by digital media recorders, altered media content is detected by exploiting the ENF consistency based on its time and location of recording along with its spatial consistency throughout the captured frames. A decentralized and hierarchical ENF map is created as a reference database for time and location verification. For digital media uploaded to a broadcasting service, the proposed DEMA system correlates the underlying ENF fingerprint with the stored ENF map to authenticate the media metadata. With the media metadata intact, the embedded ENF in the recording is compared with a reference ENF based on the time of recording, and a correlation-based metric is used to evaluate the media authenticity. In case of missing metadata, the frames are divided spatially to compare the ENF consistency throughout the recording.
SiamDF: Tracking training data-free siamese tracker

Much progress has been made in siamese tracking, primarily benefiting from increasing huge training data. However, very little attention has been really paid to the role of huge training data in learning an effective siamese tracker. In this study, we undertake an in-depth analysis of this issue from a novel optimization perspective, and observe that training data is particularly adept at background suppression, thereby refining target representation. Inspired by this insight, we present a data-free siamese tracking algorithm named SiamDF, which requires only a pre-trained backbone and no further fine-tuning on additional training data. Particularly, to suppress background distractors, we separately improve two branches of siamese tracking by retaining the pure target region as target input with the removal of template background, and by exploring an efficient inverse transformation to maintain the constant aspect ratio of target state in search region. Besides, we further promote the center displacement prediction of the entire backbone by eliminating its spatial stride deviations caused by convolution-like quantification operations. Our experimental results on several popular benchmarks demonstrate that SiamDF, free from both offline fine-tuning and online update, achieves impressive performance compared to well-established unsupervised and supervised tracking methods.
Research on the Improvement of College Students' Professional Ability under the Background of Academic Guidance

In the new era, the cultivation of college students' professional abilities plays an important role in promoting employment.Starting from promoting the cultivation of college students' vocational abilities, this article points out the importance of cultivating vocational abilities in the context of academic guidance, including external environmental factors, vocational cognitive ability, vocational competence, vocational positioning, vocational psychological quality, and ideological and political education.This article analyzes and discusses the improvement of college students' professional abilities, and specifies targeted measures to improve them.
Research on the Problems and Countermeasures in the Implementation of the 1+X Certificate System— Taking the Precision Intelligent Manufacturing Professional Group as an Example

Currently, the 1+X certificate system has become one of the important contents of higher vocational education reform.However, in the professional group of precision intelligent manufacturing, the implementation of the 1+X certificate system faces some problems, such as single certificate types, unclear evaluation standards, and low student participation enthusiasm.In order to solve these problems, this article proposes some countermeasures, such as increasing certificate types, establishing evaluation standard systems, and strengthening student participation, in order to provide some reference for other professional groups to implement the 1+X certificate system.
Integration and Innovative Development of English Teaching Resources in Open Education Based on Network Environment

With the continuous development and popularization of network technology, the integration and innovative development of open education English teaching resources has become a research hotspot in the field of English teaching. This study will explore how to use network technology to promote the integration and innovative development of open education English teaching resources. By analyzing and comparing the current situation of integration and innovative development of open education English teaching resources at home and abroad, and conducting empirical research on the current situation of integration and innovation of open education English teaching resources in China,the quality and effectiveness of English teaching will be improved, and the comprehensive quality and innovative ability of English talents will be cultivated, To effectively implement the teaching objectives of open education and play a good driving role.
Research on Higher Education Services and Student Satisfaction

Higher education is of great significance for the cultivation of higher talents in China.With the continuous improvement and development of the education industry and the education industry in the current stage, universities should change their teaching concepts, put students first, fully respond to the various needs of students in the learning process, strive to improve and improve quality, and further improve student satisfaction.To achieve this goal, universities should strengthen the analysis and research of students' learning needs, optimize the structure of disciplines and majors, improve teaching quality, innovate teaching methods and teaching, establish a sound logistics service guarantee system, in order to improve the overall level of higher education services and further enhance students' satisfaction in the subsequent learning process, Thus laying the corresponding educational foundation for promoting the development of agricultural education services in China.
Negative review detection model based on LightGBM

With the development of the Internet, online comments can be seen everywhere on major social platforms, and their content also involves all aspects of people’s life, such as clothing, food, housing, and transportation. However, it contains a large number of illegal negative comments released by Internet navy, these negative comments are often issued by Internet navy hired by Internet public relations companies, disrupting the order of the Internet, creating Internet panic, and seriously affecting public opinion. In this paper, the LightGBM algorithm is used, and the bag of words and the IF-IDF model are used for feature extraction to construct an illegal negative comment recognition model. By training with the IDMB training set, the results show that our model is more than 90% accurate. And compared to the popular classification models, our model has higher performance.