Reviewerly/exHarmony huggingface dataset:
Flame

Distributed machine learning approaches, including a broad class of federated learning (FL) techniques, present a number of benefits when deploying machine learning applications over widely distributed infrastructures. The benefits are highly dependent on the details of the underlying machine learning topology, which specifies the functionality executed by the participating nodes, their dependencies and interconnections. Current systems lack the flexibility and extensibility necessary to customize the topology of a machine learning deployment. We present Flame, a new system that provides flexibility of the topology configuration of distributed FL applications around the specifics of a particular deployment context, and is easily extensible to support new FL architectures. Flame achieves this via a new high-level abstraction Topology Abstraction Graphs (TAGs). TAGs decouple the ML application logic from the underlying deployment details, making it possible to specialize the application deployment with reduced development effort. Flame is released as an open source project, and its flexibility and extensibility support a variety of topologies and mechanisms, and can facilitate the development of new FL methodologies.
Experimental Investigation of Spatial-temporal Graph Convolutional Networks and Attentional Mechanism in Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted much attention in recent years as skeleton data is robust to body scales, dynamic camera images, illumination changes and complex background situations. The natural structure of the human skeleton is well suited to structuring it as a graph. Therefore, many researchers have been working on graph convolution networks for skeleton-based action recognition task. In particular, spatial-temporal graph convolutional networks (STGCN) have proven effective in learning both spatial and temporal dependencies on skeleton graph data. Although it has proven to perform well on skeleton data, the topology of the graph representing the human body in ST-GCN models is manually adjusted and fixed on all layers. This limits the ability to obtain more complex and richer representations. In contrast, spatial-temporal attention graph convolutional networks (STA-GCN) operates by taking into account the critical connections between joints and the importance of joints in each frame, according to actions. In this way, it can identify important joint relations specific to actions. In this study, four network models with different layer depths were designed, based on the ST-GCN and STA-GCN architectures. Subsequently, these models were trained with various numbers of epochs, and the effects of layer depth and the number of epochs on the performance of skeleton-based action recognition were experimentally investigated. According to the experimental results, the proposed 3xSTA-GCN block structured model achieved an accuracy rate of 86.71% on the CS test set and 92.76% on the CV test set.
Determining the Positions and Orientations of Multiple Robots in Dynamic Environments by Using Real-Time Visual Imagery

RoboCup, which emerged by combining the fields of artificial intelligence and robotics, has been one of the most interesting areas in the world in recent years. Real-time and accurate position detection is one of the most important issues for RoboCup, which operates in a dynamic environment. This project explores the utilization of deep learning and image processing approaches for robot localization. It is aimed to determine the position and angle information of the robots by using Convolutional Neural Networks (CNN) and object detection algorithms. In the first stage of the study, the position information of each robot and ball, represented by different colors on the playing field will be obtained by using image processing algorithms. In addition, the robot orientation will be determined by capturing images of the robots from different angles and positions, and then training the CNN using deep learning algorithms in the MATLAB environment. This study aims to find the location and angle of the robots during the game from a camera on the football field as quickly and accurately as possible.
Semantic Segmentation Based Copy Paste Forgery Detection

Semantic segmentation refers to the process of finding meaningful sections in a picture. Semantic segmentation is employed in a variety of domains, including the military, robotics, and biomedicine. A new copy-paste forgery approach based on semantic segmentation is provided in this research. Forgery zones were found with improved accuracy and in less time using discrete cosine transforms and semantic segmentation. Because the suggested technique uses semantic segmentation, each region is compared inside its own class, resulting in both time savings and increased performance.
Resume Information Extraction via Post-OCR Text Processing

Information extraction (IE), one of the main tasks of natural language processing (NLP), has recently increased importance in the use of resumes. In studies on the text to extract information from the CV, sentence classification was generally made using NLP models. In this study, it is aimed to extract information by classifying all of the text groups after preprocessing such as Optical Character Recognition (OCT) and object recognition with the YOLOv8 model of the resumes. The text dataset consists of 286 resumes collected for 5 different (education, experience, talent, personal, and language) job descriptions in the IT industry. The dataset created for object recognition consists of 1198 resumes, which were collected from the open-source datasets and labeled as sets of text. BERT, BERT-t, DistilBERT, RoBERTa, and XLNet were used as models. F1 score variances were used to compare the model results. In addition, the YOLOv8 model has also been reported comparatively in itself. As a result of the comparison, DistilBERT showed better results despite having a lower number of parameters than other models.
Essential Components for Online Health Support and Reliable Data Storage

The spread of online transactions is accelerating in the health sector as well as in many other sectors. Therefore, it is important to securely store and protect personal data in existing health systems. In this study, an architectural structure that utilizes IPFS and asymmetric encryption techniques is proposed for reliable storage of data in online health systems. In the proposed architecture, the advantages of IPFS technology against possible data loss and data manipulation have been utilized. A new data model has been proposed so that patient data can be stored encrypted, unalterable and signed. In this data model, signatures and stakeholders can be verified publicly and private data of the patient is protected. Different functions such as the data in the system being unchangeable, reliable against data loss, and inability to access the data by third parties are explained through an example scenario. As a result, a reliable, access-controlled architecture has been obtained that can be proven anonymously, cannot be changed, is resistant to crashes and attacks, can be transmitted securely.
Özgeçmişlerde varlık isimlerinin tanınması Named entity recognition in resumes

Named entity recognition (NER) is used to extract information from various documents and texts such as names and dates. It is important to extract education and work experience information from resumes in order to filter them. Considering the fact that all information in a resume has to be entered to the company’s system manually, automatizing this process will save time of the companies. In this study, a deep learning-based semi-automatic named entity recognition system has been implemented with a focus on resumes in the field of IT. Firstly, resumes of employees from five different IT related fields have been annotated. Six transformer based pre-trained models have been adapted to named entity recognition problem using the annotated data. These models have been selected among popular models in the natural language processing field. The obtained system can recognize eight different entity types which are city, date, degree, diploma major, job title, language, country and skill. Models used in the experiments are compared using micro, macro and weighted F1 scores and the performance of the methods was evaluated. Taking these scores into account for test set the best micro and weighted F1 score is obtained by RoBERTa and the best macro F1 score is obtained by Electra model.
Effect of Number of Filters on Convolutional Neural Networks’ Performance

Convolutional Neural Networks are one of the most successful methods used in image classification. In convolutional neural networks, the image is processed by passing it through successive layers of convolution and pooling. For feature extraction, the convolution process is applied to the image using filters in convolution layers. In this study, the effect of the number of filters in the VGG16 model on classification performance was examined. CIFAR10 and Fashion-MNIST datasets were used in the experiments which shown that increasing the number of filters, especially in the first layers, increased the classification accuracy. In the experiments performed with the CIFAR10 dataset the accuracy is increased from 85.20% to 88.58 when the number of filters is increased from minimum to maximum. The improvement with Fashion- MNIST database is from 91.74% to % 92.62, with all the remaining parameters the same. Depending on the increasing number of filters, it is seen that the number of transactions, and consequently the duration, is increased in training.
A New Parallel Artificial Bee Colony Algorithm for Path Planning of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAV) and their military specialization powered by air-to-air or air-to-ground weapon systems and called unmanned combat aerial vehicles (UCAV) are used frequently for the critical operations due to the relatively low production and maintenance costs. The success of a task performed by these mentioned modern aerial vehicles has a direct relationship with the planned path. However, path planning is an NP-hard problem that should be solved optimally by considering the enemy threats, fuel consumption and some constraints defined for a UAV or UCAV. In this study, a new multi-colony based implementation of ABC algorithm in which onlooker bee phase is also modified was introduced for solving UAV or UCAV path planning problem. The paths calculated by the newly introduced multi-colony based implementation of ABC algorithm were compared with the paths found by other meta-heuristic algorithms. Comparative studies showed that the colonies assigned to different cores of a processor working simultaneously improve the solving performance of the ABC algorithm and help proposed path planner for outperforming other tested techniques.
Leveraging Non-Hermitian Systems for Hardware Security in the Internet of Things

The rapid growth of the Internet of Things (IoT) has resulted in an increasing number of information interactions among smart devices, including sensitive data that may be vulnerable to cyberattacks. Consequently, the need for robust cryptographic technologies with strong encryption capabilities has become critical. In this work, we propose a novel approach to address this demand by utilizing non-Hermitian systems as a hardware security primitive.
Single-Shot, Pixel-Encoded Strip Patterns for High-Resolution 3D Measurement

In this research, we combined two distinct, structured light methods, the single-shot pseudo-random sequence-based approach and the time-multiplexing stripe indexing method. As a result, the measurement resolution of the single-shot, spatially encoded, pseudo-random sequence-based method improved significantly. Since the time-multiplexed stripe-indexed-based techniques have a higher measurement resolution, we used varying stripes to enhance the measurement resolution of the pseudo-random sequence-based approaches. We suggested a multi-resolution 3D measurement system that consisted of horizontal and vertical stripes with pixel sizes ranging from 8 × 8 to 16 × 16. We used robust pseudo-random sequences (M-arrays) to controllably distribute various stripes in a pattern. Since single-shape primitive characters only contribute one feature point to the projection pattern, we used multiple stripes instead of single-shape primitive symbols. However, numerous stripes will contribute multiple feature points. The single character-based design transforms into an increased featured size pattern when several stripes are employed. Hence, the projection pattern contains a much higher number of feature points. So, we obtained a high-resolution measurement. Each stripe in the captured image is located using adaptive grid adjustment and stripe indexing techniques. The triangulation principle is used to measure 3D.
The Block Landweber Iterative Method for Light Field Reconstruction from a Focal Stack

Light field imaging involves reconstructing a 4D light field from a 3D focal stack, which makes it challenging to reconstruct the light field from incomplete projection data. To address this problem, a linear projection system is established to model the focal stack imaging process using discrete refocusing equations. Based on this system, we propose the block Landweber iterative method to find the least-squares solution. This method computes the sparse matrix while iterating, which overcomes the problem of data storage. The 2-norm of the block matrix is utilized as the weighted matrix to normalize every block matrix on an identical scale, delivering an effective relaxation strategy under the convergence condition in the inconsistent case, which yields better reconstruction results and accelerates the convergence speed. The experimental results based on the image quality assessments of reference and non-reference images show that our method achieved better reconstruction results compared to other relevant common methods, even with fewer focal stacks and higher angle resolution.
Investigation of the Gender-Specific Discourse about Online Learning during COVID-19 on Twitter Using Sentiment Analysis, Subjectivity Analysis, and Toxicity Analysis

This paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between 9 November 2021 and 13 July 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these Tweets were positive. The results of gender-specific sentiment analysis indicate that for positive Tweets, negative Tweets, and neutral Tweets, between males and females, males posted a higher percentage of the Tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated Tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that females posted a higher percentage of highly opinionated Tweets as compared to males. However, males posted a higher percentage of least opinionated and neutral opinionated Tweets as compared to females. Third, toxicity detection was performed on the Tweets to detect different categories of toxic content—toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of Tweets posted by each gender for each of these categories of toxic content revealed several novel insights related to the degree, type, variations, and trends of toxic content posted by males and females related to online learning. Fourth, the average activity of males and females per month in this context was calculated. The findings indicate that the average activity of females was higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India, a higher percentage of the Tweets about online learning during COVID-19 were posted by males as compared to females.
Multi-Cat Monitoring System Based on Concept Drift Adaptive Machine Learning Architecture

In multi-cat households, monitoring individual cats' various behaviors is essential for diagnosing their health and ensuring their well-being. This study focuses on the defecation and urination activities of cats, and introduces an adaptive cat identification architecture based on deep learning (DL) and machine learning (ML) methods. The architecture comprises an object detector and a classification module, with the primary focus on the design of the classification component. The DL object detection algorithm, YOLOv4, is used for the cat object detector, with the convolutional neural network, EfficientNetV2, serving as the backbone for our feature extractor in identity classification with several ML classifiers. Additionally, to address changes in cat composition and individual cat appearances in multi-cat households, we propose an adaptive concept drift approach involving retraining the classification module. To support our research, we compile a comprehensive cat body dataset comprising 8934 images of 36 cats. After a rigorous evaluation of different combinations of DL models and classifiers, we find that the support vector machine (SVM) classifier yields the best performance, achieving an impressive identification accuracy of 94.53%. This outstanding result underscores the effectiveness of the system in accurately identifying cats.
A Phase Retrieval Method for 3D Shape Measurement of High-Reflectivity Surface Based on π Phase-Shifting Fringes

Fringe projection profilometry (FPP) has been widely used for 3D reconstruction, surface measurement, and reverse engineering. However, if the surface of an object has a high reflectivity, overexposure can easily occur. Image saturation caused by overexposure can lead to an incorrect intensity of the captured pattern images, resulting in phase and measurement errors of FPP. To address this issue, we propose a phase retrieval method for the 3D shape measurement of high-reflectivity surfaces based on π phase-shifting fringes. Our method only requires eight images to be projected, including three single-frequency three-step phase-shifting patterns and one pattern used to provide phase unwrapping constraints, called conventional patterns, as well as the π phase-shifting patterns corresponding to the four conventional patterns, called supplemental patterns. Saturated pixels of conventional fringes are replaced by unsaturated pixels in supplemental fringes to suppress phase retrieval errors. We analyzed all 16 replacement cases of fringe patterns and provided calculation methods for unwrapped phases. The main advantages of our method are as follows: (1) By combining the advantages of the stereo phase unwrapping (SPU) algorithm, the number of projected fringes is reduced. (2) By utilizing the phase unwrapping constraint provided by the fourth fringe pattern, the accuracy of SPU is improved. For highly reflective surfaces, the experimental results demonstrate the performance of the proposed method.
Modelling a machine learning based multivariate content grading system for YouTube Tamil-post analysis

Writing is a crucial component of the language requirement and is an effective method for correctly reflecting language proficiency. Manually evaluating Tamil language exams becomes time-consuming and costly for standardized language administrators as they grow in popularity. Numerous studies on computerized English assessment systems have been conducted in recent years. Due to Tamil text’s complicated grammatical structures, less research has been done on computerized evaluation methods. In this research, we present a Tamil review comment analysis system using a novel multivariate naïve Bayes classifier (mv - NB) where the comments are acquired from an online social network and performed training using the database for further analysis. Experiments show that the graded Kappa of 0.4239, error rate of 2.55 and precision of 85% was achieved on the online dataset by our contents grading system, which is superior in grading compared to the other widely used machine learning algorithms training on big datasets. Our findings are promising. Additionally, our contents analysis may provide beneficial criticism on Tamil writing on YouTube posts including comments, spelling errors and morphological issues that help to analyze thelanguage correlation.
APNet: Adaptive projection network for medical image denoising

In clinical medicine, low-dose radiographic image noise reduces the quality of the detected image features and may have a negative impact on disease diagnosis.In this study, Adaptive Projection Network (APNet) is proposed to reduce noise from low-dose medical images.APNet is developed based on an architecture of the U-shaped network to capture multi-scale data and achieve end-to-end image denoising. To adaptively calibrate important features during information transmission, a residual block of the dual attention method throughout the encoding and decoding phases is integrated. A non-local attention module to separate the noise and texture of the image details by using image adaptive projection during the feature fusion.To verify the effectiveness of APNet, experiments on lung CT images with synthetic noise are performed, and the results demonstrate that the proposed approach outperforms recent methods in both quantitative index and visual quality. In addition, the denoising experiment on the dental CT image is also carried out and it verifies that the network has a certain generalization.The proposed APNet is an effective method that can reduce image noise and preserve the required image details in low-dose radiographic images.
Causality in extremes of time series

Abstract Consider two stationary time series with heavy-tailed marginal distributions. We aim to detect whether they have a causal relation, that is, if a change in one causes a change in the other. Usual methods for causal discovery are not well suited if the causal mechanisms only appear during extreme events. We propose a framework to detect a causal structure from the extremes of time series, providing a new tool to extract causal information from extreme events. We introduce the causal tail coefficient for time series, which can identify asymmetrical causal relations between extreme events under certain assumptions. This method can handle nonlinear relations and latent variables. Moreover, we mention how our method can help estimate a typical time difference between extreme events. Our methodology is especially well suited for large sample sizes, and we show the performance on the simulations. Finally, we apply our method to real-world space-weather and hydro-meteorological datasets.
The Utilization of Chain Word Games to Improve Senior High School Students’ English Vocabulary Mastery

Vocabulary is very important to be mastered by who are learning foreign language, especially for students at Senior High School. This research was conducted at MA Miftahul Qulub Polagan. Most of the students at MA Miftahul Qulub Polagan get difficulties in mastering English vocabulary. It was caused by the method in teaching vocabulary. So, the students felt boring and did not have motivation in learning vocabulary. Therefore, learning innovation is needed to build their motivation in learning vocabulary. This research conducted chain word games to improve students’ vocabulary mastery. The research design is classroom action research which has two cycles implementation. Test and observation were utilized to gather the data. The research performed that the students’ vocabulary mastery increased in each cycle such as in cycle 1 was 34% students who reached the comulative score and cycle II was 49% students who reached comulative score. Cycle I showed the average score of students' mastery was 67, and cycle II showed the average score 76. It can be concluded that Word Chain Game can improve students' vocabulary mastery.