Reviewerly/exHarmony huggingface dataset:
Comparative Analysis Of Different Deep Learning CNN Models To Recognize And Detect The Masquerade’s Face

The significance of wearing masks in mitigating the transmission of infectious diseases has been underscored by the persistent COVID-19 pandemic.The utilisation of machine learning algorithms for mask detection has emerged as a promising solution for detecting whether individuals are wearing masks in public spaces.This paper presents an analysis of recent studies on the detection of masks using machine learning techniques.The focus is on the utilisation of deep learning algorithms, including convolutional neural networks (CNNs), transfer learning, and object detection models such as YOLO and Faster R-CNN.This study involves a comparative analysis of model performance, an examination of the influence of training datasets on model accuracy, and an investigation of ensemble techniques as a means of enhancing model performance.The results of our study indicate that the employment of CNNbased models and transfer learning methods that utilise pre-existing networks have exhibited the most elevated levels of precision.Nevertheless, additional investigation is required to enhance these models for pragmatic applications and to assess their efficacy in authentic environments.In general, the utilisation of machine learning techniques for mask detection holds promise as a useful strategy for mitigating the transmission of contagious illnesses.The present review offers valuable perspectives for future investigations in this domain
Analysis of Product Reviews based on sentiment analysis by using Machine Learning and Deep Learning

In today's digital era, the landscape of business platforms has undergone a significant shift towards online stores on the internet.This transition has allowed consumers to conveniently order goods using their smartphones and have them delivered to their doorstep, eliminating the need to visit physical shopping malls.However, a challenge of this online business platform is that consumers often lack direct knowledge about the quality of the products they are ordering.To address this issue, many platforms provide a review section where previous consumers can share their experiences and opinions about the products they received.These consumer reviews serve as a valuable source for analyzing consumer satisfaction.However, manually analyzing this vast amount of data can be time-consuming.In this article, we propose the development of computational models using deep learning approaches which are the subset of machine learning techniques to categorize the bad or good reviews by using the method emotion or sentiment exploration.We take a very famous shopping app to collect the datasets and use the BOW approach to convert the context into number for better results.
Sentimen Analysis of Twitter Reviews by Using Machine Learning Classifiers

Sentiment analysis of social media reviews has become a popular research topic in recent years.In this paper, we explore the effectiveness of machine learning classifiers for sentiment analysis of Twitter reviews.We collected a dataset of 10,000 tweets containing product reviews, and manually annotated them with their sentiment labels (positive, negative, or neutral).We then trained and evaluated six different machine learning classifiers, including Naive Bayes, Support Vector Machines (SVM), Random Forests, K-Nearest Neighbors (KNN), Decision Trees, and Logistic Regression.We used a variety of feature extraction techniques, including bag-of-words, n-grams, and word embeddings.Our results show that SVM achieved the highest accuracy of 85.4%, followed by Random Forests with 83.7% accuracy.Naive Bayes and Logistic Regression achieved similar accuracies of 80.2% and 80.1%, respectively.KNN and Decision Trees had the lowest accuracies of 73.5% and 71.8%, respectively.We also performed a detailed analysis of the classification results, including precision, recall, and F1score for each sentiment label.Our findings suggest that SVM is the most effective classifier for sentiment analysis of Twitter reviews, and that using word embeddings as features can improve classification performance.Overall, our study provides valuable insights into the performance of machine learning classifiers for sentiment analysis of Twitter reviews, and can be useful for researchers and practitioners in the field of natural language processing and social media analysis.
Performance Analysis of Extensional Deep Learning And Boosted Intensional Deep Learning Method

The paper described an intensional learning approach that aims to address the challenges of limited annotated data and the lack of explainability in machine learning algorithms, particularly in deep learning neural networks.The discussed method utilizes boosting and unsupervised learning preprocessing through clustering to enhance prediction performance.By incorporating unsupervised learning, the algorithm can derive intensional information from the data without relying solely on annotated data.Clustering is used to identify patterns and group similar data points together, providing valuable insights into the underlying structure of the data.This preprocessing step helps in capturing relevant features and reducing the dimensionality of the data, which can be beneficial for subsequent learning steps.Boosting, a technique that combines multiple weak learners to create a strong learner, is then employed to improve prediction performance.Boosting iteratively trains a series of models, with each subsequent model focusing on the samples that the previous models struggled to classify correctly.This process effectively learns from the mistakes of the previous models, resulting in a more accurate and robust final prediction model.The combination of boosting, unsupervised learning preprocessing, and the use of smaller annotated datasets offers potential benefits in scenarios where limited annotated data is available or when explainability is crucial.
Extraction of SIFT Image Features for Efficient Image Registration

This paper presents an image registration algorithm based on Scale Invariant&nbsp;&nbsp; Feature&nbsp;&nbsp; Transform (SIFT). SIFT is an image registration algorithm based on local features in an image. Compared to the previous registration algorithms, SIFT is more robust to variations caused by changes in size, illumination, rotation, and viewpoint of the images. The descriptors and key points that SIFT was able to gather demonstrate how resistant the algorithm is to scaling, noise, translation, and rotation. The image's most important details are first taken out. Later, dot products between the unit vectors are calculated to match the obtained points.&nbsp; Finally, transformation matrix is obtained by applying RANSAC algorithm. Experimental results shows that the algorithm extracts the better key points, which can be used for used for image registration applications. Further work includes implementation of affine transform and image fusion techniques to obtain the registered image.
Applications of Blockchain Techniques in Pet Adoption Industry

The rescue rate of homeless animals in the shelter is only at about 30% nowadays, which implies that the others are mostly put to death by the shelter in order to save the resource for the newcomers. This article has combined the blockchain technology and pet adoption to save stray animals from this situation. By designing smart contracts for adoption and deploying them in the blockchain, an efficient and convenient way is provided to adopt pets. Moreover, privacy of users will be well protected due to the property of blockchain. It is also possible that owners who wants to send their pets away can receive an estimate date of adoption calculated using machine learning algorithm. By providing these features and eliminating privacy violation risks, people would likely to trust and be fond of this new way for adoption, therefore increase the probability for stray animals to find a new home. These results shed light on guiding further exploration of pet adoption related blockchain technology.
Image Augmentation Strategies to Train GANs with Limited Data

Training modern generative adversarial networks (GANs) to produce high-quality images requires massive datasets, which are challenging to obtain in many real-world scenarios, like healthcare. Training GANs on a limited dataset overfits the discriminator on the data to the extent that it cannot correctly distinguish between real and fake images. This paper proposes an augmentation mechanism to improve the dataset’s size, quality, and diversity using a set of different augmentations, namely flipping of images, rotations, shear, affine transformations, translations, and a combination of these to form some hybrid augmentation. Fretchet distance has been used as the evaluation metric to analyze the performance of different augmentations on the dataset. It is observed that as the number of augmentations increase, the quality of generated images improves, and the Fretchet distance reduces. The proposed augmentations successfully improve the quality of generated images by the GAN when trained with limited data.
Predicting COVID-19 Occurrences from MDL-based Segmented Comorbidities and Logistic Regression

In this work, a method based on Minimum Description Language (MDL) is proposed to perform the preprocessing of Electronic Health Record (EHR) data and predict COVID-19 occurrences based on the comorbidities of patients with symptoms of the disease. In the proposed method, openEHR archetypes are used to standardize the database records and MDL is applied to segment of the text that indicates the comorbidities. From the resulting database, instances are extracted and used with Logistic Regression to predict the influence of comorbidities on the COVID-19 test result. The method was validated using data from COVID-19 patients who are from the State of Pernambuco – Brazil. The prediction based on MDL obtained 100% of accuracy and facilitated the verification of the influence of comorbidities on the patient’s diagnosis for COVID-19. In addition to making it difficult to interpret the results, the non-segmented model obtained an accuracy of 67.4%.
Solving Diagrammatic Reasoning Problems Using Deep Learning

Diagrammatic Reasoning (DR) questions are very common in competitive examinations. However, construction of interesting and fresh DR questions can be a tedious job even for the experts. We explore the possibility of using Artificial Intelligence (AI) and computer vision (CV) for construction and solving DR problems. In this paper, we have proposed a new deep learning-based framework that can be used to solve certain types of DR problems. The research also shows that a similar framework can be used to generate new DR problems of similar characteristics. We formulate the DR problem with an extension of conventional 4 $$\,\times \,$$ 1 Raven’s Progressive Matrix (RPM) by keeping 4 outputs. Thus, each problem sample has eight images, where the first four images are part of the input in a sequence and the last four images are options for the correct output. The first four images create a valid sequence and the target is to choose the fifth image from the next four images. To find the correct option, we have proposed a deep learning framework that consists of an LSTM, an Encoder and a fully connected classifier unit. The framework has also been used to generate new DR problems. We have tested our framework on Rotational DR problems. A new DR dataset has been generated using automated scripts to train the framework. The framework performs better as compared to SOTA deep learning frameworks.
SRTGAN: Triplet Loss Based Generative Adversarial Network for Real-World Super-Resolution

Many applications such as forensics, surveillance, satellite imaging, medical imaging, etc., demand High-Resolution (HR) images. However, obtaining an HR image is not always possible due to the limitations of optical sensors and their costs. An alternative solution called Single Image Super-Resolution (SISR) is a software-driven approach that aims to take a Low-Resolution (LR) image and obtain the HR image. Most supervised SISR solutions use ground truth HR image as a target and do not include the information provided in the LR image, which could be valuable. In this work, we introduce Triplet Loss-based Generative Adversarial Network hereafter referred as SRTGAN for Image Super-Resolution problem on real-world degradation. We introduce a new triplet-based adversarial loss function that exploits the information provided in the LR image by using it as a negative sample. Allowing the patch-based discriminator with access to both HR and LR images optimizes to better differentiate between HR and LR images; hence, improving the adversary. Further, we propose to fuse the adversarial loss, content loss, perceptual loss, and quality loss to obtain Super-Resolution (SR) image with high perceptual fidelity. We validate the superior performance of the proposed method over the other existing methods on the RealSR dataset in terms of quantitative and qualitative metrics.
Rancang Bangun Sistem Informasi Penjadwalan Mata Pelajaran dengan Metode Waterfall

Advances in technology that continue to develop make things easier. One application of information technology in the field of education is the existence of a subject scheduling information system. However, until now, the scheduling system at MTsN 03 Madiun still uses the manual method by the curriculum staff. Previously, a joint task distribution meeting was held with the teachers, which then the curriculum staff would confirm the schedule with the instructor, then the admin would supervise one by one the schedules of the teachers who were still empty. Then the schedule needs to be confirmed back to the students. The result of these conditions has an impact on the lack of timely publication of subject schedules which reduces the effectiveness of the learning system. It is not uncommon for errors to occur in scheduling, such as the large number of teaching hours on an inappropriate schedule or subject schedules that clash with other subjects. The schedule that has been made must guarantee that there will be no conflicting schedules, both in hours and instructors. In addition, it takes a long time to make a schedule because the scheduling department needs to adjust the availability of space and teachers. Therefore we need a subject scheduling system that can simplify and minimize errors that occur when preparing schedules. In this study, the method used when collecting data was the observation method, interview method and literature study. In addition, the software development method used was the waterfall method. This system was designed and built using MySQL and PHP as database storage. This subject scheduling system can facilitate curriculum staff and teachers in managing subject schedule information.
A Novel Approach on Detecting Phishing Attacks on URLS using ML Techniques

The simplest way to obtain sensitive information from unwitting users is through a phishing attack. The phishers' goal is to obtain sensitive information such as usernames, passwords, and bank account information. The proposed system is primarily concerned with detecting and preventing phishing websites. The websites are discovered using a web crawler. Phishing sites that are detected are added to the blacklist. The blacklist only contains fake websites. Web crawling is concerned with obtaining the web page's links. A phishing website can usually be identified by its URL and HTML code. The check website page alerts users to phishing websites and helps them avoid becoming victims of such attacks. This software is extremely useful in identifying and preventing the PHISHING.
The Impact of Ambidextrous Leadership on Firm Performance: An Empirical Analysis of Syrian SMEs

This study investigates the impact of ambidextrous leadership on firm performance among Syrian small and medium-sized enterprises (SMEs). Ambidextrous leadership, which entails the ability to adapt and balance between competing demands, has been identified as a key driver of organizational success. Using a sample of 381 SME business owners in Syria, the study employs multiple linear regression analysis to test the relationship between ambidextrous leadership and firm performance. The results indicate a positive and significant relationship between ambidextrous leadership and firm performance, thus suggesting that business owners who effectively balance both exploratory and exploitative activities are better positioned to enhance their firms' performance. The findings contribute to the existing literature on ambidextrous leadership and provide practical recommendations for business owners and policymakers in the Syrian context.
Research on Plate Shape Measurement System and Calibration Method of Wide and Thick Plate

Abstract：Aiming at the current demand for online intelligent detection of wide and thick plate shape, a wide and thick plate shape measurement system with single laser and multi-camera is designed, and a attitude calibration method of multi-camera coordinate system is proposed based on the unique laser plane.The plate shape measurement system is composed of two identical subsystems located before and after the wide and thick plate levelling machine.The subsystem adopts the method of multi-camera measurement vision splicing to realize the detection of wide and thick plate shape.First, using the uniqueness of the world coordinate system of the same attitude calibration plate in the common view of adjacent cameras, the coordinate systems of multiple cameras are mapped to the reference coordinate system.Then the multi-camera coordinate systems are set to the same posture according to the uniqueness of the laser plane.At last, fast stitching of point cloud data with multi-camera system is implemented according to position relation of adjacent cameras point cloud data which unified coordinate.The multi-camera structured light measurement system calibration has been finished.The experimental results show that the method is accurate and effective, which provides an effective calibration method for 3D measurement of large field of view structured light.
An Analysis of Making process of Youth Policy from the Historical Institutionalism Perspective : Focusing on the Introduction of the framework Act on Youth

This study aims to derive policy implications by analyzing the making process of youth policy from the perspective of historical institutionalism. In particular, the integrative approach of structure-institution-action can explain institutional change through external and internal factors. The results of this study are as follows. First, in the structural context, various socio-economic crises surrounding youth have been accumulated as potential problems. In addition, there was a change of power in some local governments and central government at an initiative trigger for institutional change. Secondly, in the institutional context, there were institutional constraints such as policy ideology emphasizing individual responsibility and self-help, employment policy and law. On the other hand, cooperative governance with youth groups was formed in some local governments, which led to the introduction of policies to guarantee the fundamental rights of youth. Third, in the action factor, there were youth organizations that emphasized guaranteeing the fundamental rights of all youth, the national assembly that insisted introduction of effective policies through coordination and management, the government that asserted comprehensive policy formation, and experts. Even though institutional constraints, they tried to change the policy by communicating in various ways.
Theoretical line loss calculation method based on feature clustering and residual fully connected network for low-voltage station area containing PV

In view of the inaccurate calculation of theoretical line loss in low-voltage areas containing PV due to the change of tidal direction caused by the access of distributed photovoltaic (PV) users in low-voltage (LV) areas and the complexity of urban low-voltage distribution network lines, the theoretical line loss calculation method based on Feature Aggregation (FA) and Residual Fully Connected Network (RFCNN) for low voltage stations containing PV is proposed. First, a feature clustering model based on hierarchical clustering is constructed to reorganize the similar features to achieve feature extraction and dimensionality reduction of the input data. Then, the best feature clustering number is selected according to the model performance advantages and disadvantages of different clustering feature numbers. Finally, the clustering features are fed into the residual fully connected neural network regression model for training to obtain the daily line loss value calculation model. Experimental results show that the proposed model has better generalization ability and accuracy compared with models such as multilayer perceptron (MLP), extreme gradient enhancement (XGBoost), shallow and deep residual fully connected networks.
Job running time prediction algorithm based on classification and ensemble learning

Backfill scheduling is a common scheduling strategy in high-performance computing systems that allows priority execution of low-priority jobs to make better use of available resources. Job running time is an important parameter that affects the performance of backfill scheduling algorithm. However, in order to avoid job killing due to lack of time, the running time requested by users is often several times higher than the actual running time, resulting in a certain degree of resource waste. In order to improve resource utilization, a new job running time prediction algorithm is proposed by combining classification and ensemble learning methods. The algorithm first classifies the historical job set according to the application type, then uses Jaccard coefficient to calculate the similarity between the jobs, and further classifies the jobs. At the same time, different integration models are constructed for the jobs of different application types. New jobs are categorized, and the class's integration model is used to predict the running time of the new job. The algorithm was tested on the historical job data of the National Supercomputing Center Kunshan, Hefei Advanced Computing Center and "Wuzhen Light" supercomputing Center and compared with GA-sim algorithm and IRPA algorithm. The experimental results show that compared with the IRPA algorithm, the average absolute error of the algorithm is improved by 60% on the three data sets on average. Compared with the GA-sim algorithm, the average prediction accuracy of the algorithm is improved by 20% on the three data sets on average. Through the in-depth analysis of the experimental results, the amplification method for the low estimation of long and short jobs is given.