Reviewerly/exHarmony huggingface dataset:
Numerical Features and Embeddings in CTR Prediction

Click-Through Rate (CTR) prediction is a challenging task in recommendation systems. Nowadays, creating embeddings for numerical features is a trending topic in CTR domain. However, most deep CTR methods concentrate on model design to effectively create embeddings for numerical features. But they use the same number of embeddings for each numerical feature which means that they treat all the numerical features as they have the same characteristics. In this study, we propose Automatic Logarithmic Discretization (AutoLogDis) method that finds the proper number of embeddings for each numeric feature separately. Our approach is integrated into three different models and tested on Criteo and AutoML datasets. The results show that the AutoLogDis method improve the AUC results of models by %0.06, %0.08, %0.11 ve %1.03, %1.57, %1.03 on Criteo and AutoML datasets, respectively.
Racial Bias Mitigation with Federated Learning Approach

The use of Machine Learning models as decision support has brought about an increase in the reliability of these models. Especially in cases where data sharing is not desired due to confidentiality, the imbalance in the number of samples belonging to different demographic groups such as age, gender and race in the classrooms may cause bias in the models trained with these data. In our study, it has been shown that racial bias caused by the imbalance of racial information in classrooms can be reduced with the Federated Learning approach, which allows different data sources to contribute to model training without the need for data sharing. In addition, in the study, the results obtained with Federated Learning were compared with Central Learning and it was examined whether reliable models could be obtained without the need for data sharing. In order to compare the model results, metrics such as Accuracy and F1 score, as well as bias metrics such as Disparate Impact and Statistical Parity, which measure the fairness of the model, were also used. When the results were examined, it was seen that the local bias decreased with Federated Learning. The state of NH, where the greatest improvement was seen, decreased from 6.42 in the Disparate Impact results to 1.66 in Federated Learning, and from -0.33 to -0.12 in the Statistical Parity results.
Development of New Information Processing Technologies Based on Lexical and Grammatical Models of Agglutinative Languages

In this paper, we propose a new structure to model agglutinative natural languages (ANLs) on the basis of their lex-ical and grammatical features, namely recursion, morphological ellipsis, functional diversity and semantic multivalence of affixes (in particular, the affixes encoding underdefined information and fuzzy commands). This way we try to overcome the root cause of low efficiency of modern tools to accumulate and process knowledge in such languages. In our opinion, it lies in complexity of intellectualization of such tools created on the basis of primitive artificial programming languages that practically represent a subset of inflectional-analytical languages or artificial structures based on them. To reduce severity of the identified problem, it is proposed to build systems for modeling ANLs based on a decentralized system of technological means for ver-balization and meaning recognition, which consider the above-listed peculiarities of such languages. To evaluate and exemplify the proposed ideas, Tatar language will be used since authors have a fair experience in investigating this language.
The Problem of Pos Tagging and Stemming for Agglutinative Languages (Turkish, Uyghur, Uzbek Languages)

The number of possible word forms in agglutinative languages is theoretically unlimited. This, in turn, creates the problem of POS tagging (part-of-speech) of out-of-vocabulary (OOV) words in agglutinative languages. In agglutinative languages, words are formed by adding suffixes to the stem. Due to the occurrence of phonetic harmony and disharmony while adding suffixes to the stem, it is necessary to analyze both phonetic and morphological changes. When solving many NLP tasks, it is necessary to reduce word forms to the stem (stemming). Removing all inflectional affixes from a word and lemmatizing the rest of the word is considered one of the important tasks of natural language processing (NLP), and this process is called stemming. The stemming process is important in information retrieval (IR) systems.
A Comparative Analysis of Synthetic Data Generation with VAE and CTGAN Models on Financial Credit Loan Offer Data

Creating synthetic data is a practical approach to provide a solution for privacy and scalability issues of data in machine learning applications. Data science in finance is encountering an increasing need for anonymized data for the same reasons: strict privacy regulations, and need for balanced data for many modeling tasks. In this work, we address three problems in machine learning applications for financial application and offer solutions. First, we successfully generate synthetic data using a set of actual credit loan offer data by training a custom variational autoencoder and a GAN model. Second, we present a comparative analysis of these models using statistical methods. As far as we know, there are no golden standards for the assessment of synthetically generated data for finance applications. Lastly, we introduce a performance comparison method to evaluate synthetically generated data. Our experimental analysis has shown that the proposed methods achieve a satisfactory performance with the generated data in various machine learning models.
Challenges and Opportunities Related to Data Drift Problem in Sentiment

Transformer-based BERT Models can show great accuracy and success performance in sentiment analysis. However, these models also face some problems. Within the scope of this study, the problem of data drift in sentiment analysis is specifically examined. Data drift is caused by the fact that training data and test data having different properties. Within the scope of the study, new datasets consisting of Tweets and literary texts were created and the performances of different Bert models in these new datasets is examined. A BERT model trained on educational tweets has been shown to underperform on political/commercial tweets. It has been shown that the same BERT model trained on educational tweets can perform on literary texts just as well as a random model. For better results, we recommend using a combination of industry/domain specific small imitative BERT models.
Analysis of Kazakh Language Abbreviations Based on Machine Learning Approach

This research aimed to analyze the use of abbreviations in the Kazakh language using a machine learning approach. Studies of the most commonly used words-abbreviations in Kazakh texts, as well as analysis and classification were carried out. Several machine learning models, tc, including naive Bayes, neural networks and support vector machines (SVMs) were tested for the study. The linguistic corpus was used for the experimental part Abbreviations-abb.xml and abbreviations.csv. The results showed that SVM outperformed other models with an accuracy of 0.85. The unique features of the Kazakh language were also discussed, such as the use of the Cyrillic alphabet and complex word forms. The implications of this study for natural language processing and computational linguistics were presented, and the limitations of the study were discussed. This study contributes to understanding the use of abbreviations in the Kazakh language and demonstrates the potential of machine learning approaches to analyze languages with complex characteristics.
Anomaly Detection for ETL Packages Runtime: A Machine Learning Approach

In today's dynamic business environment., financial institutions and banks use ETL (Extract, Transform, Load) packages to perform data integration and optimize business processes. However, sudden changes and anomalies in the runtimes of these packages can negatively affect the workflow and cause delays and customer dissatisfaction. In this study, it is aimed to improve the service quality of enterprises, to provide operational efficiency, and to better manage business processes with the detection of anomalies in the runtime of ETL packages. In this project, the Interquartile Range (IQR) method for outlier detection and data labeling, and K-Nearest Neighbors (KNN) and eXtreme Gradient Boosting (XGBoost) algorithms for machine learning are used. The dataset includes approximately 2 thousand ETL packages runtime data in the last years. The results show that KNN and XGBoost models provide a weighted-averaged F1 score of 99.9%. This study provides significant benefits in areas such as customer satisfaction and cost reduction. Future works may address subjects such as using larger and more feature-rich datasets and different anomaly detection methods.
Automatic Dataset Generation from 3D Object Models for Object Detection Applications

Object detection techniques based on deep learning achieve remarkable success. In order to train deep learning-based object detection models, large amounts of labeled data are required. The study was motivated by the fact that the required 3D data can be generated completely in the simulation environment. Within the scope of the study, an algorithm is presented for systematic data collection. The presented algorithm is implemented using ROS and Gazebo with Real Sense D435 camera configurations. Consequently, R GB image, depth map, and point cloud data aligned with the RGB image were captured. In addition, 2D/3D labels are automatically generated for the objects in the recorded data. The obtained data were visualized and thus validated. Sample deep-learning models were trained using the generated data, and the results were presented within the scope of the study. Initial models that are to be trained with actual data can be trained using synthetic data generated based on the obtained results.
Sentiment Analysis from Turkish News Texts with BERT-Based Language Models and Machine Learning Algorithms

Sentiment analysis is defined as text analysis and is defined as identifying the class that the text wants to express emotionally. In this study, sentiment analysis was performed with BERT-based language models and machine learning algorithms on the data obtained from Turkish news texts. ALBERT, DistilBERT, and RoBERTa were used as BERT-based language models, and Naive Bayes, Support Vector Machine, and Random Forest methods were used as machine learning algorithms. Our dataset contains 5000 two-class (positive-negative) sentences, with 90% of the data used for training and 10% for testing. When the results of the experimental studies are examined, the accuracy values of the studies performed with language models have reached higher values than machine learning algorithms. The success rates of the language models are DistilBERT, RoBERTa, and ALBERT and the values obtained are 80%, 80%, and 77% respectively. The ranking of machine learning algorithms is Naive Bayes, Support Vector Machine, and Random Forest and the values obtained are 71%, 68%, and 68%.
Software Product Line Testing based on Event Sequence Graphs with Feature Expressions

Software Product Line testing is by its nature challenging, especially due to the exponential rise in the number of assets that need to be verified. Scalability and efficient verification, two challenges that model-based SPL testing must deal with, are discussed in this paper. An approach to automatically obtaining test suites for software product lines is proposed as a solution to these challenges. By exploiting Event Sequence Graphs with Feature Expressions, which concisely depict the Software Product Line behavior, the proposed approach automatically generates test sequences for different product configurations. The presented approach is applied to the illustrative case studies from the literature.
Privacy-Oriented Architecture for Building Automatic Voice Interaction Systems in Smart Environments in Disaster Recovery Scenarios

In the wake of increasing natural disasters and emergency situations, the deployment of Smart Environments with automatic voice interaction systems has become vital for efficient disaster recovery efforts. However, the widespread adoption of such systems raises significant concerns about user privacy and data security. This paper proposes a novel privacy-oriented architecture tailored explicitly for building automatic voice interaction systems in Smart Environments, designed to address the privacy challenges inherent in disaster recovery scenarios. The proposed architecture emphasizes the implementation of robust privacy-preserving mechanisms, ensuring that sensitive user data remains secure and inaccessible to unauthorized entities. To demonstrate the practicality and effectiveness of the proposed architecture, a proto-type voice interaction system was developed and deployed in a simulated environment. The system's performance is evaluated against traditional architectures, showcasing its superior ability to safeguard user privacy while delivering accurate and responsive voice-based services.
Neural Network Calibration for CTR Prediction

After a machine learning model has been trained, calibration techniques correct its prediction errors to produce predictions that are more robust and confident. As calibration techniques, we implement isotonic regression, Platt's scaling, neural networks, spline regression, and temperature scaling and test them on the prediction of click-through rate (CTR), which is an unbalanced task. We use 3 neural network based CTR prediction models on a publicly available CTR dataset and measure the improvements. Our findings show that isotonic regression is the fastest method whereas isotonic regression and spline regression are the two techniques that improves the performance the most.
Handcrafted Features Based Analysis of Social Media Images for Disaster Response

During a disaster images shared on social media enable concerned authorities to obtain rapid situational awareness and to estimate incurred damages. As the volume and velocity of such content are typically high, real-time filtering of irrelevant images and damage assessment has become an urgent need for faster disaster response. However, research in this field has yet to receive sufficient attention and extracting useful information is still challenging. This paper proposes a framework for filtering out irrelevant images and estimating the damages incurred due to a disaster. The framework relies on the fusion of handcrafted feature descriptors to analyze the social media imagery data. The performance of the proposed framework is compared with the existing handcrafted and learned feature descriptors. The results reveal that the proposed framework is able to filter out the irrelevant images with an accuracy of 87.8% (precision 89.6%, recall 90.4% and F1 score 90.0%) and estimate the disaster severity with an accuracy of 76.9% (precision 86.8%, recall 83.8% and F1 score 84.8%). In order to assess the robustness and generalization capability of the proposed framework, we compared it with the state-of-the-art techniques. Moreover, the proposed framework results in less computational cost than the deep learning models while achieving the comparable accuracy. We believe that the proposed framework can be integrated with the social media platform to filter out irrelevant images and assess the fatal impact of a disaster.
Image Retrieval with Segment Anything and CNN Techniques

In many fields, t he i mage r etrieval t echnique is crucial to efficiently s earch a l arge a mount o f i mage d ata and find similar images. Today, people use large amounts of big data produced in digital environments. It is a complicated process for users to search for the desired image or text. With the development of artificial i ntelligence, m any s tudies h ave been carried out on image retrieval in the literature. Image retrieval is crucial to obtaining an effective image representation. In this study, research was carried out on obtaining the desired image from the database containing the images belonging to the clothing category we created. The images obtained in the data preprocessing step were passed through the U2Net network and divided into three groups: upper body, lower body, and full body. The Segment Anything Model (SAM), the latest artificial intelligence tool developed by Meta, is used to segment the target image clearly. Then, similar image discovery was performed with the pre-trained ResNet-50 deep learning model. As a result of our study, we found that the rate of finding t he p air o f the recommendation model in the search among 100,000 images is 92%.
Potential for Improvement of Sentence Translations

Natural language processing tasks like machine translation can be parallelized, corrected, and repeated at the sentence level since they are data-centric and highly parallel. Each sentence's translation quality can vary, thus any problems must be fixed on an individual basis. At the same time, translation quality should be predicted before further processing and shipment since we cannot be sure without knowing the target language. We identify the potential for improvement (pfi) of sentence translations, whether a translation has potential to be improved by further processing or retranslation, and achieve overall performance improvements. We describe a method for ranking the obtained translations to fix translation errors by retranslating the sentences using a computationally more demanding translation model. In the first step we identify the potential regardless of prediction errors and later we use triangular uninorm combination to sort instances in our decision making process about sorting sentences for retranslation. By retranslating the top 200 sentences that have the most pfi we achieve up to 2.31 BLEU improvements.
Optimizing Load Balancing and Task Scheduling Problems in Cloud Computing

The task scheduling problem is an important problem that needs to be optimized in cloud computing in terms of meeting the expectations of both the user and the provider. A poorly optimized scheduling method harms the customer and the provider due to non-fulfillment of quality of service (QoS) and violation of the service level agreement (SLA). It is possible to meet customer and provider demands in the best way with a well-optimized task scheduling algorithm. The cloud task scheduling problem is an NP-Hard problem. Metaheuristic algorithms produce good outcomes in solving these problems. In this study, clonal selection algorithm (CSA) and parallel CSA (PCSA) are proposed to optimize load balancing and task scheduling in cloud computing environments. In addition to the results of the CSA and PCSA, a similar parallelization method to improve searching performance is applied to the genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO), and comparative results are presented. The results showed that the CSA algorithm, like other compared metaheuristic algorithms, was successful in the cloud task scheduling problem. Parallelization improved the performance of all tested algorithms, and the proposed PCSA algorithm reached better results than other compared algorithms.
A Quantitative Performance Benchmark of Different Navigation Patterns and User Interface Design Frameworks for an Enhanced iOS Experience

Native mobile application development is commonly utilized to create robust and high-performance applications. Even the abundance of resources to introduce different native application development approaches and user interface (UI) design frameworks, there is a deficit of quantitative analysis on them. Therefore, in this study, we present a quantitative performance benchmark model for an enhanced iOS experience. For this purpose, two major UI design frameworks i.e. UIKit and SwiftUI are examined along with various navigation patterns utilized for screen transitions such as tab bar transition, push transition and modal presentation. A combination of each navigation pattern - UI design framework pair is implemented by considering a variety of UI elements to be shown. Eventually, a performance evaluation is provided with respect the load time of screens and the memory consumption of the view controllers (VCs). The evaluation findings have demonstrated that SwiftUI results in high performance applications with lower load time and less memory consumption when collaboratively used with modal presentation as the navigation pattern.
Improving the Methodology of Teaching Specialized Subjects in The Preparation of Future Computer Engineering on The Basis of Innovative Technologies

In the preparation of future computer engineers, the issues of improving the methodology of teaching specialized subjects on the basis of innovative technologies are widely discussed in the article. The main focus of the article is on the issues of education of students in the direction of computer engineering using innovative technologies in the development of digital and software-technological competencies. Also theoretical and practical information on improvement on the basis of such features as content and essence of the disciplines of expertise, psychological characteristics that provide interthematic continuity, harmony of illustrations, materials encouraging activity, interdisciplinary connection, logical structure, connection with practice is presented. “dismath.uz”, author software based on mobile technologies in the teaching the course “Discrete structures” in the field of computer engineering, its practice-oriented teaching, “Agile” innovative technology, are improved on the basis of prioritizing the classification of live and online collaborations of the innovative technology of collaborative learning.
Bias Mitigation in Click-Through Rate Prediction Using Mixup Strategies

Recommendation systems are important for the companies to match the right products that match with the users' interests. Click-through rate (CTR) prediction has become a crucial task in today's recommendation systems, because it is an effective approach for the companies to increase their revenues. Deep learning based recommender models show upstanding performance in today's CTR prediction tasks. These models should be accurate and have a low bias with less data dependency for the companies to increase their profits. Thus, generalization capability is important for deep learning based recommender models. It indicates robustness in situations such as adversarial data or temporal data distribution changes. Mixup is a data augmentation method which is used in the recent studies of the computer vision domain. It is being incorporated in the model training phase to improve the generalization ability of the model by reducing the memorization of training data. In this study, mixup data augmentation and its variants are investigated in CTR prediction domain. Moreover, a novel vectoral projection mixup is presented. Extensive experiments are conducted with five different mixup variants using hard and soft labeled targets. Two public datasets, Avazu and Criteo, and a company dataset are used in the experiments.
Pos Taging of Uzbek Text Using Hidden Markov Model

Markov models are one of the most widely used machine learning methods for natural language processing. Markov chain and hidden Markov model is a stochastic (random) method used to model dynamic systems, and the current state of the system is predicted based on previous states. The Markov chain, which correctly generates a sequence of words in the generation of sentences, is widely used in NLP tasks. It is also used for identifying NERs in a sentence and POS tagging based on a hidden Markov model. Based on the Markov model, hidden tags are predicted based on the tagged words in the language corpus. This article presents methods and algorithms for automatic POS tagging of a given sentence based on the tagged Uzbek corpus using a hidden Markov model.