Reviewerly/exHarmony huggingface dataset:
Joint Detection and Tracking of Occluded Target Using Lidar

Target tracking in transportation vehicles is important from both civilian and military perspectives. Objects that obstruct the sensor's field of view create occlusion regions in target tracking. The literature has developed dynamic solutions to address the problems arising from the target entering the sensor's occlusion spot behind an obstacle and detecting occlusion regions. In this study, target tracking using lidar is performed with a stationary vehicle obstacle and a moving vehicle in the Gazebo simulation environment. A solution is proposed for velocity disturbances during entry into the occlusion region in this environment. After comparing the performance of target tracking filters in the simulation environment, the algorithms are tested in a real-world scenario where a similar simulation environment is set up. Two different Bernoulli filters are implemented based on the nearest neighbor and probabilistic data association mechanisms. The results are evaluated using the Optimal SubPattern Assignment metrics.
Federated Learning in Predicting Heart Disease

Heart diseases are a significant health problem worldwide, and early diagnosis and prediction are of great im-portance. Artificial intelligence and machine learning techniques have become effective tools in predicting heart diseases. However, when traditional centralized approaches are used, challenges arise in terms of data privacy and security. The federated learning approach is presented as an important solution to address these issues. In this study, our aim is to predict heart diseases while preserving patient privacy and confidentiality using the federated learning approach. Experiments were conducted using the open-source dataset "PTB Diagnostic ECG Database" to compare the results of centralized and federated learning. Through this comparison, it has been shown that federated learning can achieve results close to centralized learning in cases where sharing sensitive data such as health data is problematic. Additionally, in the experiments conducted using different feature selection and machine learning models, both logistic regression and support vector machines showed the best accuracy results with mRMR feature selection in both centralized (86%) and federated training (85%). When examining the F1-Score results, applying feature selection in both classification models allowed for the best results (federated learning: 91%, centralized learning: 92%).
A Metric Learning Based System for Retail Product Recognition and Novel Class Discovery

Developing a retail product recognition system presents different challenges compared to traditional object recognition systems. Retail products contain classes that are very similar to each other but differ in small details. The current product classes in the system must be frequently expanded with new product classes or updated with packaging changes. For these reasons, we developed a metric learning-based product recognition system that provides superior product recognition performance and the ability to add new products and update existing ones. The key components of our system are the ConvNext_nano feature extractor, ArcFace loss, feature vector database, similarity search, and clustering algorithm. Through tests performed on a custom dataset consisting of 775,000 samples from 994 classes, we achieved an accuracy of 93.7% in product recognition and an average clustering accuracy of 67.58% in discovering new products.
A Turkish Text Classification Based Feature Selection and Density Peaks Clustering

Text classification, a well-known Natural Language Processing (NLP) task, can be defined as the process of categorizing documents according to their content. In this process, the selection of classification algorithms and the determination of the correct variables for classification are very important for an efficient classification. The texts to be classified in this study are first preprocessed using the IG (Information gain) method, taking into account the Tf (Term frequency) and Idf (Reverse document frequency) values, and then they are divided into different categories using the DPC (Clustering Density Peaks) algorithm which is a semi-supervised algorithm. In the study, TTC-3600 dataset, which includes texts obtained from 6 well-known Turkish news portals and 6 different fields, was used. The study performed better than the previous results in the selected dataset.
A Novel Occlusion Index

Recovery of 3D human pose and shape under realistic conditions is a challenging task. Despite the recent advances in this field, methods suffer from performance degradation due to naturally occurring occlusions. Benchmark datasets employed to compare the performance of methods under occlusion contain different amounts of challenges. Therefore, assessing the quantitative performance of the benchmarks turns into an equally important task. In this study, we propose a novel metric called the Occlusion Index (OI) to evaluate the severity of occlusion for benchmarks. OI enables the evaluation of the amount of occlusion and partitioning of the benchmark images into subsets according to the severity of the occlusion. We provide occlusion benchmarks obtained from widely-utilized datasets, OCHuman and AGORA, and show that they enable better evaluation of the occlusion robust techniques. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Blind Domain Adaptation For Semantic Segmentation via Edge Attention

Domain adaptation is a special type of transfer learning that aims to train machine learning architectures trained on a dataset to work on data created for the same task but with a different distribution. Blind domain adaptation is when only the source domain data is accessible during training and the target domain is unknown. In this study, we propose an edge attention module for the semantic segmentation problem to enable the model trained on synthetic datasets to work on real images in the target domain. Since there is no access to the target domain's data distribution in the blind domain adaptation, it is aimed to let the network focus the edges that will be common to both domains through the attention mechanism. Experiments show that the proposed method improves the segmentation performance of the fully convolutional network up to %27.1.
Performance Comparison of GM-PHD and JPDA for an Autonomous Vehicle Using Only Radar Data

Extended object tracking is an important component of autonomous driving systems, as it enables the vehicle to accurately perceive and respond to the surrounding environment. Unlike point tracking, which treats objects as single points in space, extended object tracking takes into account the shape and size of objects, as well as their motion over time. Joint Probabilistic Data Association (JPDA) and Gaussian Mixture Probability Hypothesis Density (GM-PHD) are two popular extended object tracking methods that are being used in many different applications. These two algorithms have been compared and analyzed for their performance in autonomous vehicle which uses only radar data. In this study, these two methods are being investigated and the performance of these methods are compared with each other for autonomous driving that is using only radar data. Based on the results, it is shown that both methods are good at keeping track of the multiple extended objects. However, comparison of these methods show that GM-PHD is more advantageous than JPDA in terms of Generalized Optimal Sub-Pattern Assignment (GOSPA) metric
Conformal Interpolations of Locally Defined Rotation, Scaling, and Translation Operations

In image morphing applications, deformation is not desired since it looks strange. To avoid deformation, the angles must be locally preserved. By deforming features, we can obtain funny-looking images, however, the shapes of the features will not look preserved if angles are not preserved. The angle-preserving operations are called conformal maps and locally they expect to be reduced into a concatenation of rotation, uniform scaling, and translation operations. This conceptual framework appears to give us a straightforward framework to construct general conformal maps. We can simply define desired rotation, uniform scaling, and translation in some select points and interpolate them by preserving their conformal property. Unfortunately, this does not work if these local operations are defined by using 3x3 matrices. Our approach is based on the well-known conformal property of maps in complex domains. Namely, any map in a complex domain is angle-preserving. Based on this well-known property of complex numbers, we show that if the local operations are defined as affine operations on complex numbers then any interpolation on complex numbers will give us conformal mapping of the whole domain. This is very useful since we can make extreme exaggeration of some local features and the resulting maps are still guaranteed to be conformal. Using this property, we have implemented an extended and conformal version of feature-based image metamorphosis.
Who Follows Turkish Presidential Candidates in 2023 Elections?

Social media serves as an important platform for political discussions, especially during election periods. While politicians use the platform to reach out to voters and share their campaigns, it also creates a debate environment for different voter groups. It is possible to see supportive and critical speeches simultaneously. Particularly as the election date approaches, undecided voters follow politicians on online platforms to learn about their campaigns. Changes in politicians' followers can be observed in detail by analyzing women and young voters who may be decisive in the results. In this study, we analyzed candidates for the 2023 Presidential elections of Türkiye and their followers on the Twitter platform. We examined how the demographic of these followers change over time. Prior to analyzing demographics such as gender and age group, we identified bot accounts and isolated them from the dataset. Significant events are reflected in the number of followers and the relationship between the observed anomalies and changes in the demographics have also been studied in this work.
Analysis of Subword Tokenization Approaches for Turkish Language

Various tokenization approaches have been proposed in natural language processing research. These approaches have further evolved from character- and word-level representations to subword-level representations. However, the impact of tokenizations on model performance has not been thoroughly discussed, especially for morphologically rich languages. In this paper, we comprehensively analyze subword tokenizers for Turkish which is a highly inflected and morphologically rich language and we propose a morphologically-based approach. Also, we examine how the tokenizer parameters like vocabulary and corpus sizes change the characteristics of tokenizers.
Histopathological Classification of Colon Tissue Images with Self-Supervised Models

Deep learning techniques have demonstrated their ability to facilitate medical image diagnostics by offering more precise and accurate predictions. Convolutional neural network (CNN) architectures have been employed for a decade as the primary approach to enable automated diagnosis. On the other hand, recently proposed vision transformers (ViTs) based architectures have shown success in various computer vision tasks. However, their efficacy in medical image classification tasks remains largely unexplored due to their requirement for large datasets. Nevertheless, significant performance gains can be achieved by leveraging self-supervised learning techniques through pretraining. This paper analyzes performance of self- supervised pretrained networks in medical image classification tasks. Results on colon histopathology images revealed that CNN based architectures are more effective when trained from scratch, while pretrained models could achieve similar levels of performance with limited data.
Band-Based Interpretability with SHAP for Hyperspectral Classification

Hyperspectral data enable high classification accuracies due to the large amount of spectral information they contain. However, the complete black box or at least partially opaque nature of machine learning approaches often make classification processes challenging to interpret and low in explainability. In this work, SHAP, which is an explainable artificial intelligence (XAI) method, is used for the interpretability of hyperspectral classification. For each class, band-based SHAP values are obtained over the trained classification model, providing a quantitative evaluation of the contribution of the spectral bands to the classification process. Preliminary experimental results are provided in this paper, and shed light on the proposed method's potential.
Anomaly Detection with False Alarm Rate Controllable Classifiers

Automatic anomaly detection gains attention as the number of surveillance cameras increase. Research on the field concentrates on the development of features modeling the overall behavior in a scene, and with each iteration drives the performance to its limit. However, there is a lack of research for detecting anomalous behavior not only based on extracted features, but also using its spatial properties. In this paper, we propose a framework that utilizes Neyman-Pearson classification for detecting anomalies and also providing a true local anomaly detection method, which is capable of detecting same action as normal or anomalous depending on its spatial properties. Our method is a context tree based, competitive ensemble NP classifier, containing multiple piece-wise linear NP models trained on different partitions of a video frame. Even though the number of partitions increases doubly exponentially with the increasing tree size, efficient tree framework provides training in linear time complexity. Competitive nature of the learning of the ensemble model ensures convergence to the optimal space partitioning depending on the performances of individual NP classifiers.
Split-Chain based Efficient Blockchain-Assisted Cross-Domain Authentication for IoT

The rapid development of network and manufacturing technologies has enabled a growing number of IoT devices to connect to the network. Like humans, these devices from different domains of the world also require interconnection, which necessitates identity authentication to ensure communication security and prevent cyber-attacks. To avoid the problem of single-point failure, there are existing cross-domain device authentication methods, many of which are based on blockchain. However, most schemes use a pre-existing blockchain architecture, and few people design customized blockchain solutions for cross-domain authentication scenes to improve the efficiency of authentication. In this paper, we present a split-chain based efficient authentication mechanism, SCBA, for cross-domain IoT. To improve the efficiency of authentication, we have designed an efficient blockchain structure that achieves high authentication. Additionally, we have designed a consensus algorithm that supports the above blockchain structure to ensure the security and fairness of the authentication system. Extensive experiments have been conducted to show that our scheme has significant advantages in terms of efficiency.
PEMODELAN M-ADAPTIVE GENERALIZED POISSON REGRESSION SPLINE PADA KASUS MDR-TB DI KALIMANTAN BARAT

Tuberculosis is a disease caused by the Mycobacterium tuberculosis. Multi-Drug Resistant Tuberculosis (MDR-TB) is the term used to describe Mycobacterium tuberculosis that is resistant to one or more Anti-TB drugs. This study aims to determine the factors that affect the number of patients recovering from MDR-TB, by modeling the number of MDR-TB cured patients using Multivariate Adaptive Generalized Poisson Regression Spline (MAGPRS) method. The predictor variables are the average age (X1), percentage of patients who fail category 2 treatment (X2), percentage of patients who fail category 1 treatment (X3), percentage of patients relapsed (X4), percentage of patients neglecting treatment (X5), and percentage history of close contact with other patients (X6). A combination of BF (Basis function), MI (Maximum interaction), and MO (Minimum observation), the BF value is two to four times of predictor variables, MI has value of 1,2, and 3, and MO has value of 0,1,2, and 3. From the result, the best model was obtained from the combination of BF=24, MI=3, and MO=1, with GCV values of 0,3504 and R2 of 88,3%, and there are 14 BF that affect the response variable . The most influential predictors variables in a row, are X6, X3, X5, and X2.
Learning-Based Calibration of Flux Crosstalk in Transmon Qubit Arrays

Superconducting quantum processors comprising flux-tunable data and coupler qubits are a promising platform for quantum computation. However, magnetic flux crosstalk between the flux-control lines and the constituent qubits impedes precision control of qubit frequencies, presenting a challenge to scaling this platform. In order to implement high-fidelity digital and analog quantum operations, one must characterize the flux crosstalk and compensate for it. In this work, we introduce a learning-based calibration protocol and demonstrate its experimental performance by calibrating an array of 16 flux-tunable transmon qubits. To demonstrate the extensibility of our protocol, we simulate the crosstalk matrix learning procedure for larger arrays of transmon qubits. We observe an empirically linear scaling in calibration time with system size while maintaining a median qubit frequency error below 300 kHz.
Information literacy profile of biology education students in different batch

Information literacy is one of the basic skills that students must have in today's digital era. This research was conducted to analyze the information literacy profile of students in different batch years. This quantitative study compared three groups of data based on the student year. The research population was all biology education students at UIN Walisongo Semarang. The research sample was determined using a stratified random sampling technique to obtain 94 students divided into three grades. The data were obtained using the information literacy test instrument and analyzed using the ANOVA test. Information literacy indicators consist of identifying information, accessing information, evaluating information, using information effectively, and understanding the ethics of using information. The study results show that students in all classes have information literacy skills in the sufficient category, but significant differences exist between batch years. The lowest information literacy ability was in first-year students, followed by second-year students, and the highest in third-year students. The study results indicate the need to increase students’ information literacy.
The effect of android-based augmented reality lab coats on students with different academic abilities on understanding of skeletal system

The understanding of students' concepts of SMP Negeri 1 Kandangan on the material of the skeletal system is still low. This study aims to determine the effect of applying Android-based Augmented Reality laboratory coats to students with different academic abilities on understanding the concept of skeletal system material for students of SMP Negeri 1 Kandangan. This research is a quasi-experiment or quasi-experiment with a pretest-posttest control group design. The population used in this study were all seventh-grade students of SMP Negeri 1 Kandangan. In contrast, the samples in this study were class VII A as the control class and class VII B as the experimental class, which would be treated as applying an Augmented Reality Android-based. The sample was determined using a purposive side technique. The ANCOVA test results show a significant difference in student's conceptual understanding of the skeletal system material between the control and the experimental, with a significance value or p of 0.006, which is &lt;0.05. Meanwhile, the follow-up test results showed that the use of android-based Augmented Reality Laboratory Coat learning media significantly differed in understanding the concepts of seventh-grade students of SMP Negeri 1 Kandangan. So it can be concluded that the android-based Augmented Reality Laboratory Coat affects increasing the understanding of the concepts of students with different academic abilities at SMP Negeri 1 Kandangan as a learning medium on skeletal system material to provide a better visualization effect in observing bony structures in the passive motion system (bones).
Evaluation of learning methods similar to deep learning and device using deep learning for the diagnosis of thyroid nodules

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
LEARNING STYLES, TECHNOSTRESS &amp; BLENDED LEARNING – IMPLICATIONS FOR THE EDUCATIONAL MODEL OF THE FUTURE

Although the blended learning teaching format has been existing for several years, the COVID-19 pandemic has shown us once again how important digitization is in the education industry.The aim of this study is therefore to find out what opportunities and risks blended learning has as a teaching format from the perspective of students with the learning style "pragmatist" and to what extent technostress will be relevant in this context.A qualitative study was conducted in which 11 undergraduate students with the learning style "pragmatist" were asked about their personal experience and opinion regarding the three teaching formats face-to-face, distance and blended learning in order to find out the individual advantages and disadvantages.The results have shown that blended learning is suitable as a teaching model from the students` perspective.However, attention must be paid to good organization and communication in order to be able to take advantage of both teaching formats, namely face-to-face and distance learning.The effects of technostress could not be determined after the COVID-19 pandemic as an external influencing factor strongly affected the students' perceptions of distance learning.
MOBILE TECHNOLOGIES – A KEY ELEMENT IN CONTEMPORARY APPROACHES TO LEARNING

Mobile technologies catalyse radical transformations in contemporary society's perceptions and needs.In terms of information and education services modern users require major changes in the approaches of provision of access to knowledge.This paper presents the results of a survey concerning mobile digital libraries and mobile learning conducted in June -July 2021 among information users, namely students at the University of Library Studies and Information Technologies -BULGARIA.The study was realised in accordance with the research project "Information portal for mobile learning and mobile access to library services and resources".The main aim is to analyse to what extent information users benefit from mobile devices for reading, learning and access to information resources, what their opinion on the inclusion of mobile digital services and learning in library services is and whether they support international policies on mobile learning and mobile access to library services and resources.For the achievement of this goal a questionnaire with three main sections has been devised: Part 1. Mobile devices, applications and mobile digital libraries; Part 2. Mobile learning and lifelong learning; Part 3. Policies for mobile information provision and mobile learning.The study has been conducted via an online questionnaire form and the application Google Forms.The empirical data has been inserted and treated with the statistics software IBM SPSS Statistics 21.The results of the survey prove that the inclusion of mobile technologies in the contemporary university environment is widely welcomed by students and effectively satisfies the individual needs of every learner.