Reviewerly/exHarmony huggingface dataset:
Filter pruning via feature map clustering

With the help of network compression algorithms, deep neural networks can be applied on low-power embedded systems and mobile devices such as drones, satellites, and smartphones. Filter pruning is a sub-direction of network compression research, which reduces memory and computational consumption by reducing the number of parameters of model filters. Previous works utilized the “more-simple-less-important” criterion for pruning filters. That is, filters with the smaller norm or more sparse weights in the network are preferentially pruned. In this paper, we found that feature maps are not fully positively correlated with the sparsity of filter weights by observing the visualization of feature maps and the corresponding filters. Hence, we came up with the idea that the priority of filter pruning should be determined by redundancy rather than sparsity. The redundancy of a filter is the measure of whether the output of the filter is repeated with other filters. Based on this, we defined a criterion called redundancy index to rank the filters and introduced it into our filter pruning strategy. Extensive experiments demonstrate the effectiveness of our approach on different model architectures, including VGGNet, GoogleNet, DenseNet, and ResNet. The models compressed with our strategy surpass the state-of-the-art in terms of Floating Point Operations Per Second (FLOPs), parameters reduction, and classification accuracy.
Explainable Natural Language Processing, by Anders Søgaard. San Rafael, CA: Morgan &amp; Claypool, 2021. ISBN 978-1-636-39213-4. XV+107 pages.

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RESPIRATORY DISEASES DETECTION USING MACHINE LEARNING

Worldwide, lung illness is a prevalent occurrence.They include pneumonia, asthma, TB, fibrosis, chronic obstructive pulmonary disease, and others.The early detection of lung illness is highly important.Misdiagnosis in respiratory disorders is still very common.Given that many lung diseases affect children, there is an urgent need to broaden the field of accurate diagnosis.is A problem of over diagnosis has to be addressed.Early abnormality detection is difficult, but it aims to be a crucial component of automated screening systems.For examination of human body modalities in medical imaging are employed.The most crucial stage of image classification is feature extraction.With the help of this best features of images are extracted.The feature that would be helpful in classifying and identifying the photos is obtained using feature extraction techniques.SVM techniques for diagnosing asthma and CNN approaches for detecting pneumonia are used in the proposed system.
Promotion prediction using machine learning

Promotions play a vital role in organizations, as they provide employees with recognition, career advancement, and increased responsibilities.Predicting which employees are likely to be promoted can greatly benefit both employees and organizations by optimizing talent management strategies and enhancing workforce planning.This paper presents a comprehensive study on promotion prediction using machine learning techniques, aiming to develop accurate and reliable models for predicting employee promotions.The study begins by exploring various data sources typically available in organizations, such as employee profiles, performance evaluations, educational qualifications, work experience, and past promotion history.These data sources are used to extract relevant features that are indicative of an employee's potential for promotion.Feature engineering techniques are applied to transform raw data into meaningful representations, considering factors such as performance metrics, leadership skills, and job-related accomplishments.A wide range of machine learning algorithms are then applied to develop predictive models for promotion.These algorithms include but are not limited to decision trees, random forests, logistic regression, and support vector machines.The models are trained using historical promotion data and evaluated using appropriate performance metrics such as accuracy, precision, recall, and F1-score.The performance of different algorithms is compared to identify the most effective approach for promotion prediction.Furthermore, the study investigates the impact of different factors on promotion decisions.This includes analyzing the significance of individual features and their contribution to the prediction models.Additionally, the study explores the potential biases and fairness issues that may arise in promotion prediction, aiming to develop techniques that ensure transparency, accountability, and equality in the promotion process.The proposed machine learning models are validated using real-world data obtained from a large organization.The results demonstrate that the predictive models achieve high accuracy in predicting employee promotions, thereby providing valuable insights to organizations for making informed promotion decisions.The findings of this study have the potential to revolutionize talent management practices and contribute to the development of fair and efficient promotion processes.In conclusion, this paper presents a comprehensive study on promotion prediction using machine learning techniques.The results highlight the effectiveness of machine learning algorithms in accurately predicting employee promotions, enabling organizations to optimize their talent management strategies and enhance workforce planning.The study also addresses the challenges of bias and fairness, ensuring transparency and equality in the promotion process.The findings contribute to the advancement of HR analytics and provide valuable insights for organizations seeking to leverage data-driven approaches for promotion decisions.
Human Fall Detection using Machine Learning, IoT &amp; Mobile Computing

Falls are one of the leading causes of death and serious injury for elderly people living alone at home.Fall detection is the most essential component of elderly healthcare.For more than five years, the discipline of computer vision has seen significant advancements, including deep learning.An algorithm may learn on its own using training data thanks to a form of computer method called deep learning.Convolutional neural networks (CNNs), a specific type of deep learning, have established the benchmark for picture classification performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC).In this paper, we show how convolutional neural networks may be used to detect falls in a surveillance environment using video.To learn human shape deformation characteristics that define various human postures and ascertain if a fall occurred, CNN is immediately applied to each frame picture in the movie.According to experimental findings, our suggested method operates in real-time and has a 10-fold cross-validation accuracy for fall detection that averages 93.98%.It has been demonstrated that the employed CNN-based strategy to fall detection can be promising.An overview of machine learning-based fall detection systems is given.These devices are designed to alert others when a fall is detected so that the person who has fallen can receive emergency treatment without delay.
Optimal input states for quantifying the performance of continuous-variable unidirectional and bidirectional teleportation

Continuous-variable (CV) teleportation is a fundamental protocol in quantum information science. A number of experiments have been designed to simulate ideal teleportation under realistic conditions. In this paper, we detail an analytical approach for determining optimal input states for quantifying the performance of CV unidirectional and bidirectional teleportation. The metric that we consider for quantifying performance is the energy-constrained channel fidelity between ideal teleportation and its experimental implementation, and along with this, our focus is on determining optimal input states for distinguishing the ideal process from the experimental one. We prove that, under certain energy constraints, the optimal input state in unidirectional as well as bidirectional teleportation is a finite entangled superposition of twin-Fock states saturating the energy constraint. Moreover, we also prove that, under the same constraints, the optimal states are unique; that is, there is no other optimal finite entangled superposition of twin-Fock states.
Role of Knowledge Management in Firm Performance: A Quantitative Study

There is an examination of the role of knowledge management (KM) in firm behaviour and performance.The following literature review also provides insights for managers and policymakers on the importance of implementing effective KM practices to enhance firm performance.The previous literature for this study finds that effective KM practices positively impact several aspects of firm performance, including innovation, productivity, and profitability.Additionally, it shows that the impact of KM on performance is stronger in firms with a culture that values and encourages knowledge sharing and collaboration.Overall, this review contributes to the existing literature on KM and firm performance by providing empirical evidence of the positive impact of KM on both financial and innovation performance.It also highlights the importance of considering the level of KM implementation, the extent of KM practices, OL, and OC when examining the relationship between KM and firm performance.
Enhanced post-quantum key escrow system for supervised data conflict of interest based on consortium blockchain

Consortium blockchains offer privacy for members while allowing supervision peers access to on-chain data under certain circumstances. However, current key escrow schemes rely on vulnerable traditional asymmetric encryption/decryption algorithms. To address this issue, we have designed and implemented an enhanced post-quantum key escrow system for consortium blockchains. Our system integrates NIST post-quantum public-key encryption/KEM algorithms and various post-quantum cryptographic tools to provide a fine-grained, single-point-of-dishonest-resistant, collusion-proof and privacy-preserving solution. We also offer chaincodes, related APIs, and invoking command lines for development. Finally, we perform detailed security analysis and performance evaluation, including the consumed time of chaincode execution and the needed on-chain storage space, and we also highlight the security and performance of related post-quantum KEM algorithms on consortium blockchain.
Self-organization skills and their relationship to school integration among high school students

The study aimed to reveal the relationship between self-organization skills with school integration among high school students, and the study sample consisted of (710) high school students, the male sample was (330) and the female sample was (380) in secondary schools in the southern governorates of Palestine, and the study used the self-organization scale.And the school integration scale and the study followed the descriptive approach, and the study concluded that the relative weight of the axis of self-organization skills was (74%) high, while the relative weight of the axis of school integration reached (76.8%), and there is a statistically significant relationship at the level of significance ( α ≥ 0.05) between self-organization skills and school integration, and there are statistically significant differences at the significance level (α ≤ 0.05) between the average grades of high school students on the self-organization skills scale, and the school integration scale for study variables (type -grade in the previous grade) .
Evaluating the Student’s Misbehavior in the Class (In Case of Hidase Fire High School 10th Grade)

The purpose of this research is to evaluate the misbehavior of the student in the class using action methodology techniques.An Evaluate the student misbehavior in the class was used to draw data from focused on selected students in school.The findings reveal that leaving class in middle, late coming, and not doing homework is common among students in selected secondary schools of Hidase Fire.In this study, the teacher and student interventions are undertaken to evaluate the misbehavior of the student in the class.As the result obtained 60 % of the misbehavior student is leaving the class in the middle of class this misbehavior activity act 53.33 % during the morning time.The punishment for the student that acts misbehavior is 40 % sending to the director and assigning extra work respectively.The main causes of misbehavior in class 73.33 % are lack of interest of students and the teacher observed misbehavior in the class 33.33% is coming to late class and not doing homework respectively.Overall, the major causes of misbehavior in class are lack of the implementation of the rule and laws of the school.To solve those behavioral problems in school action strategies and interventions have been taken then the student behavior has improved.
Assessing Writing Abilities using AI-Powered Writing Evaluations

This study aims to investigate the effectiveness of AI software, Automated Writing Evaluation (AWE), in identifying grammatical errors and increasing writing abilities among Malaysian public university students, as well as their perspectives on AI software in enhancing writing skills. The outcomes of this study reveal that students have good attitudes toward the implementation of AWE. The findings have implications for using artificial intelligence (AI) software (AWE) in ESL writing classes. Moreover, future scholars are anticipated to do similar research to increase the effect and relevance of this work.
Hotspots and Smoke Detection from Forest and Land Fires Using the YOLO Algorithm ( You Only Look Once )

The term forest and land fires is used to refer to unplanned, controlled and unwanted fires that destroy vegetated areas and their ecosystems triggered by natural or human causes . Early detection of hotspots can reduce the risk of wider forest and land fires. The use of the Deep Learning YOLO ( You Only Look Once ) algorithm is carried out to detect fire and also the smoke it produces. This study tested in 3 ways, 1) 1341 after data augmentation (496 original data), 2) 608 after data augmentation (253 original data), and 3) 1790 after data augmentation (746 original data). Detection of fire and smoke objects in the form of design, implementation and testing resulted in the YOLOv4 framework successfully producing high confidence of up to 97% in the second test. Based on the test results in this study, it is known that the image datasets used for training data greatly affect object detection and affect the confidence value. The more diverse the shape of the object from the image datasets, the lower the confidence value obtained.
The Use of Recurrent Nets for the Prediction of e-Commerce Sales

The increase in e-commerce sales and profits has been a source of much anxiety over the years. Due to the advances in Internet technology, more and more people choose to shop online. Online retailers can improve customer satisfaction using sentiment analysis in comments and reviews to gain higher profits. This study used Recurrent Neural Networks (RNNs) to predict future sales from previous using the Kaggle dataset. A Bidirectional Long Short Term Memory (BLTSM) RNN was employed by tuning various hyperparameters to improve accuracy. The results showed that this BLTSM model of the RNN was quite accurate at predicting future sales performance.
Tech Book (A Web-Application which Connects you through a Technology)

Abstract: As the name specifies “TechBook” is a Web Application developed for managing the search of technologies by the individual users. Identification of the drawbacks of the existing system leads to the designing of computerized system that will be compatible to the existing system with the system Which is more user friendly. We can improve the efficiency of the system, thus overcome the drawbacks of the existing systems i.e. Less human error , Strength and strain of manual labour can be reduced , High security , Data redundancy can be avoided to some extent ,Data consistency , Easy to handle , Easy data updating , Easy record keeping ,Backup data can be easily generated. To manage all these, we can design a full-fledged software which can take care of all the above operations with the help of technologies like: HTML, CSS, JAVA, JSP, AJAX, MySQL, JDBC etc. It is a more closed network, where members usually only connect with people they already know or have been introduced to by someone in their network. This makes TechBook ideal for building professional relationships, as opposed to personal ones.
Research on personalized Japanese language teaching model based on big data technology

Abstract In order to better improve students’ Japanese language performance and help them progress, this paper proposes an analysis of personalized Japanese language teaching in a big data technology environment. We build a model of learner characteristics and use collaborative filtering techniques to push learning information from learners with the same or similar interest and preference characteristics. Adjust teaching strategies based on visual user information in the information panel. Build a personalized teaching model based on big data, extract and visualize the data of each student’s learning behavior, find out the gap between the teaching objectives and the preset ones, and record the students’ learning methods. Based on data to dynamically update the teaching content, assign the most appropriate learning tasks, determine the distance of teaching objectives, and improve students’ learning ability and learning effectiveness. Through inductive learning inference on the training dataset, the probabilities of all subsets are calculated, the relevant data subset intervals are merged, and the business sensitivity index is introduced to measure the whole source, which makes the improved decision tree gradually reduce the uncertainty of the division and decrease the weight degree of the attributes, which makes the personalized Japanese teaching better. The analysis results showed that the improved decision tree model reached 71% accuracy in 80 cases and stayed above 73% afterwards, and the ratio of students’ mastered knowledge to the number of knowledges learned was 0.4. Personalized Japanese language teaching under this method led to a significant improvement in students’ performance.
Offbeat physics: machines, meditations and misconceptions

"Offbeat physics: machines, meditations and misconceptions." Contemporary Physics, ahead-of-print(ahead-of-print), p. 1
Exploration of ecological environmental protection and sustainable development path in the new era

In recent years, China's economic level has developed rapidly, and its attention to the ecological environment has been continuously improved, and it is committed to strengthening the protection of the ecological environment and achieving the basic goal of sustainable development.In the process of economic construction in the past, ecological protection was neglected, which caused serious impacts, and at the same time the protection was weak, the protection methods were improper, and it was difficult to achieve the ideal goal.Relevant departments should attach great importance to it, take effective measures, carry out continuous supervision and management and optimization, maintain the balance between ecology and economic development, and achieve long-term development.This paper is mainly based on the exploration of ecological environmental protection and sustainable development path in the new era.
The construction of blockchain platform for engineering museums based on vectorized image processing technology

Abstract The characteristics of blockchain such as tamper-evident and distributed can effectively solve the increasingly prominent security and privacy issues in engineering pavilions, however, the current throughput of mainstream blockchain platforms is far from meeting the demand for rapid chain-up of massive data in engineering pavilions, and the high redundancy storage mechanism adopted by traditional blockchain cannot be applied to engineering pavilion scenarios. To address the above problems, based on vectorized image processing technology, a hierarchical blockchain architecture is first proposed to store block data in multiple distributed cloud servers and edge servers in a hierarchical manner to cope with the growing data volume in the industrial Internet. Then based on the topology between edge servers, a blockchain network slicing algorithm is designed based on an improved complex network association partitioning algorithm, which improves the blockchain network throughput while shortening the slicing time. Finally, the impact of block broadcast time on throughput is demonstrated by formalizing the block broadcast process as a spanning tree, based on which a spanning tree-based intra-slice master node selection algorithm is proposed to further improve the throughput of the blockchain network. The analysis and experimental results show that the proposed scheme effectively reduces the partitioning time of the blockchain network and the broadcast time of the blocks within each partition. Compared with the classical association partitioning algorithm, this paper's partitioning algorithm can reduce the partitioning time by about 36% without sacrificing the quality of partitioning when partitioning a large-scale network.
Intellectual capital, knowledge management, innovation, and organizational performance: A literature review study

In recent years, knowledge or more broadly intellectual capital (IC) has been considered an organization's most important resource, and its effective management is a key factor to an organization's success. One of the widely recognized approaches is knowledge management (KM), as KM is the systematic and organized utilization of the intellectual power of an organization to achieve efficiency, ensure competitive advantage, and encourage innovation. KM as a science has become a topic of increasing interest to many people as a tool to help enterprises work more effective, especially to improve innovation capability and organizational performance (OP). Although there have been many studies and publications on KM, however, there are still many unanswered questions. Specifically, how to transform IC resource into innovation capability and OP through KM operation is still an issue to be explored and controversial, especially in the context of small and medium enterprises. This study was carried out through reading and analyzing the content of 372 academic articles related to the concepts of IC, KM, innovation, and OP, with the aim of systematizing the concepts and discussing research results on the relationship between concepts to identify research gaps. From that analysis result, the article proposes a research framework and model.