Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Fraud Detection from Diverse Datasets to Algorithmic Performance

The growth of e-wallets and the ease with which credit card information may be obtained online have contributed to the rise of credit card fraud in the digital age. In order to detect and prevent fraudulent acts in these areas, advanced and effective fraud detection systems must be created. In this work, we describe the results of a deep dive into the field of fraud detection using electronic money and credit card transactions as case studies. We investigate the most recent advances in deep learning, to tackle the challenging problem of fraud detection in these advanced settings. We conduct comprehensive experiments, utilizing e-wallet and credit card transaction datasets, to experimentally assess the performance of the suggested models. In terms of detecting and preventing fraudulent transactions, the experimental results provide more evidence that deep learning models are superior to more traditional rule-based paradigms. Using carefully constructed confusion matrices and receiver operating characteristic (ROC) curves, we elaborate on the models' ability to accurately discern and appraise the fraudulent undertones permeating the complexities of transactional data, as well as a variety of performance metrics.
Risk Assessment of Linkedin Profiles

Fake or fraudulent accounts are a significant problem in social networking that is often addressed by the users of various social networking platforms involving Linkedin. Fraudulent accounts generally try to convince people about their identity that they are authentic. Adversaries use fake profiles or they impersonate a real identity on social networks to obtain more privileges. Therefore, it is critical to understand problems regarding fraudulent profiles. In this paper, we propose new sustainable framework to assess risks about profiles in social networking platforms. The framework is implemented as a web extension and it is analyzed with ITU alumni profiles in Linkedin. The analyses results show that there are different profiles within one community in Linkedin that may be significant threat for social network users.
Efficiently Sampling in Neural Network Training for Click-Through Rate Prediction

Finding efficient downsampling techniques has become more crucial as the training datasets for advertisement click-through rate (CTR) prediction models are growing to billions in size. We present efficient downsampling to sample CTR datasets with goals of faster training and limited decrease in the performance. We present encouraging results demonstrating the effectiveness of our approach on two publicly available CTR prediction datasets and compare efficient downsampling with stratified random downsampling.
Resume Matching Framework via Ranking and Sorting Using NLP and Deep Learning

Online job search through websites has been a remarkably advantageous tool for both job seekers and employers, effectively serving their needs for numerous years. The job-resume matching system uses natural language processing (NLP) techniques to analyze the content of resumes and job descriptions. In this study, we used AI models to analyze resumes and rank each resume based on its similarity to the job description. This proposed framework uses optical character recognition and object recognition models to detect text and sections from resumes. Afterward, the text preprocessing step is applied. The preprocessed section is classified with the BERT model. The section of resumes classified into 5 different categories (education, experience, skills, personal, and language) are sent to the information extraction module. By using the named entity recognition method, the module identifies 8 different categories in the text (title, degree, major, skill, language, city, country, and date). Based on extracted sections and named entities similarity between the job description and resume is calculated using cosine similarity. Each section's similarity score is calculated independently. This allows users to assign each section a weight based on the importance for the total score. Finally, in the developed web application, these weights are loaded into the system and resumes are compared with job applications to be sorted. The proposed resume matching framework accurately extracts relevant information from resumes, displays the similarity results of the candidates by ranking and sorting, and helps recruiters or managers to select the best-scored candidates.
Information Hiding in Images Using Neural Network

This research work is very relevant in the field of image processing using artificial intelligence techniques. In today's world where information security plays an important role, developing methods to detect and analyze hidden data in images is a necessity. The aim of the research is to investigate and develop image processing methods using artificial intelligence techniques to improve the accuracy and efficiency of image processing. This may include creating new neural network models, exploring different data preprocessing techniques, optimizing computational processes and exploring the use of generative models for image synthesis and enhancement. During the experiment, it was proved that, as a result of training the neural network, the values of the loss function decreases during the training process, which in turn means improvements in the model. The model was trained in the Kaggle service.
Enhancing Paraphrasing in Chatbots Through Prompt Engineering: A Comparative Study on ChatGPT, Bing, and Bard

Paraphrase generation, a crucial task in Natural Language Processing (NLP), is pivotal for the effectiveness of AI chatbots. However, generating high-quality paraphrases that are contextually relevant, semantically equivalent, and linguistically diverse remains a challenge. This paper explores the use of prompt engineering to enhance the paraphrasing capabilities of AI chatbots, specifically focusing on ChatGPT, Bing, and Bard. We introduce a new dataset of 5000 sentences generated by ChatGPT across diverse topics and propose two distinct prompts for paraphrase generation: a direct approach and an engineered prompt. The engineered prompt explicitly instructs the chatbot to generate paraphrases that exhibit lexical diversity, phrasal variations, syntactical differences, fluency, language acceptableness, and relevance, while preserving the original meaning. We conduct a comprehensive evaluation of the generated paraphrases using a range of metrics, including BERTScore, STS-B, METEOR for semantic similarity; ROUGE, BLEU, GLEU for diversity; and CoLA, Perplexity for language acceptableness or fluency. Our findings reveal that the use of the engineered prompt results in higher quality paraphrases across all three chatbots, demonstrating the potential of prompt engineering as a tool for improving chatbot communication.
Offensive Language Detection in Social Media for Turkish Language

It is known that the use of offensive language in social media posts has increased considerably in recent years. In this study, it is aimed to determine the use of offensive language in Turkish language. A comprehensive and original data set was obtained by combining two data sets previously used in the literature. There are 31277 data in the first data set (Offenseval-2020) and 35285 data in the second data set (A Corpus of Turkish Offensive Language-troff). The first data set, the second data set and the original data set created from them were preprocessed separately. Afterwards, three data sets were trained in the model created by the LSTM method. The model was tested with 20% of the data set. The experimental results of the study were measured using metrics such as accuracy, precision score, F1 score, and sensitivity score. With the new data set created, higher success has been achieved compared to other data sets. With these successful results, a contribution has been made to the literature in order to determine the use of offensive language in the Turkish language.
A New Approach for Human Body Orientation Detection Using Camera Footage

This study contributes to the field of orientation detection from camera footage, showcasing the advancements in computer vision techniques and their practical applications. The insights gained from this study can be leveraged in various domains, including surveillance, security, activity recognition, and pose estimation, fostering the development of more efficient and accurate orientation detection systems. Impressive results are obtained, with high AUROC values achieved for classifying different orientations: 0.91 for “right,” 0.91 for “left,” 0.95 for “fore,” and 0.93 for “back.” These results underline the models' ability to accurately estimate human body orientations as fore, back, right, and left from camera footages with a high degree of accuracy.
Capturing Dynamic User Behavior in Recommender Systems by Finetuning

The amount of digital content is increasing significantly. Thus, it is important for companies to show the most relevant digital advertisements to the users to maximize their profits and i ncrease user experience. For this purpose, click through rate (CTR) estimation tasks are used, which track whether the user clicks on the displayed content. The fact that deep learning algorithms work successfully in many areas has inevitably been valid in recommender systems to predict clickthrough rate prediction also. Deep learning algorithms need large amounts of data to work well. Therefore, user data is gathered at specific i ntervals a nd t rains a re r un w ith t his d ata t o m odel user behaviors. Training models for high-traffic o r g anizations c a n be quite costly due to the high volume of data and because the data is collected over a long period of time, user interactions cannot be generalized well due to the dynamic user behavior. To overcome this problem, we present the application of finetuning f o r the click-through rate prediction domain to reduce the computational cost of model training and increase the generalization capability of deep learning-based recommender models. Consequently, the model will be able to capture the latest trends in user behaviors and recommend more relevant content to the users. The results show the superiority of training the model using finetuning compared to conventional batch training.
TUR2SQL: A Cross-Domain Turkish Dataset For Text-to-SQL

The field of converting natural language into corresponding SQL queries using deep learning techniques has attracted significant attention in recent years. While existing Text-to-SQL datasets primarily focus on English and other languages such as Chinese, there is a lack of resources for the Turkish language. In this study, we introduce the first publicly available cross-domain Turkish Text-to-SQL dataset, named TUR2SQL. This dataset consists of 10,809 pairs of natural language statements and their corresponding SQL queries. We conducted experiments using SQLNet and ChatGPT on the TUR2SQL dataset. The experimental results show that SQLNet has limited performance and ChatGPT has superior performance on the dataset. We believe that TUR2SQL provides a foundation for further exploration and advancements in Turkish language-based Text-to-SQL research.
Texture and CNN Based Doppler Signal Classification

Background understanding and extraction from images becomes easy when different image descriptors capture the location and movement of different sub-elements in a given image. One main type of artificial neural network called Convolutional neural network (CNN) has attracted lot of interest in bio-medical sub-domain. Depending on the type and amount of data, its different architectures lead to its heterogeneous learning capability. Cerebral emboli detection is considered as one of the main problems in understanding the nature of normal and abnormal blood flow. Embolus detection using transcranial Doppler ultrasound is a useful method for the identification of active embolic sources in cerebrovascular diseases. Texture features represent the lower space of image data and encode the relationship of different pixel values. Local Derivative pattern (LDP) calculates the n-th order derivative directions of each pixel in an image. In this paper, we make use of labelled Doppler signal dataset collected from patients with carotid stenosis and make use of combination of CNN layers and LDP operators for Doppler signal classification. We convert Doppler signals into image data and add LDP feature extraction as a layer in CNN. In a single mode CNN-LDP, network on our Doppler image data achieved validation accuracy of 90% with a loss of 21.91% (Batch size of 64, epochs 10). In multi-mode CNN-LDP, network achieved validation accuracy of 91% with a loss of 20.64% (Batch-size 64 epochs 8).
Towards Turkish Word Embeddings: An Intrinsic Evaluation

Effective representation of textual data is a prereq-uisite for most of the downstream tasks, which increases the importance of word embedding evaluation methods. The intrinsic approach assesses the similarity between word representations and human judgements. In this paper, we present a compre-hensive intrinsic evaluation of Turkish word embedding models with different tasks using task-specific datasets such as SemEval-2017, MC-30, SimVerb-3500 for word similarity, MSR for word analogy and methods that have not been tested for Turkish before such as oncept categorization with BLESS and ESSLLI and outlier detection with 8-8-8 Dataset. While each of these datasets were originally in English, we translated them into Turkish and trained Wor2Vec, FastText and Glove language models with these datasets from scratch. The results suggest that while Word2Vec is generally more successful in word similarity and outlier detection tasks, fastText outperforms other models in word analogy and concept categorization.
Linguistic Knowledge Graphs of the “Turkic Morpheme” Portal

This paper presents a description of linguistic knowledge graphs, which form the basis of knowledge base of the portal “Turkic morpheme”. Peculiarity of these knowledge graphs is that ontological models that form the basis are developed using pragmatically oriented approach to linguistic models' development and are focused on the most complete description of structural and functional features of the Turkic languages. These knowledge graphs are a useful resource, both for creating information and reference systems for Turkic languages, and application programs for computer processing.
Constructing the Terminological Core of NLP Ontology

The basis of any intellectual resource is a knowledge base, which, based on the basic terms of the field under consideration, builds relationships between them. Therefore, the first task to building multilingual information system using Natural language processing (NLP)in scientific and educational activities will be the development of a multilingual dictionary on modern NLP methods, including terms in Kazakh, English and Russian. For its construction, linguistic models for semantic dictionaries and thesauruses will be used, as well as methods of automatic extraction of terms from the corpus of texts of a given subject area. In this paper, a system of concepts of the NLP domain will be formalized, which will form the terminological core of the NLP ontology. To systematize information and provide support for multilingualism and accessibility, we plan to apply ontological engineering methods to systematize information and build the upper levels of the NLP ontology (its terminological core) using the dictionary of terms obtained at the previous stage. The ontology developed by us can later become the conceptual basis for a multilingual information system used in scientific and educational activities using NLP. This system will provide systematization of all information, convenient navigation on it, integration into a single information space, as well as access to it.
Transformer Based Punctuation Restoration for Turkish

Mobile devices and social media platforms make communication faster than humans have had before, thanks to the technologies such as automatic speech recognition(ASR). However, the speed in text-based communication methods leads to several mistakes that could be solved. The two well-known mistakes are grammatical errors and forgotten punctuation usage. The punctuation restoration task is inherited from the automatic speech recognition domain. Understanding and restoring correct places of punctuation are challenging problems in speech recognition. However, no datasets exist to train a punctuation restoration model for the Turkish language. This paper focuses on restoring punctuations in Turkish texts and introduces a new Turkish dataset for punctuation restoration. Three transformer models: BERT, ELECTRA, and ConvBERT, are fine-tuned and tested on the newly created dataset for three distinct labels: PERIOD, COMMA, and QUESTION MARK. Benchmark results in the paper are reported regarding precision, recall, and F1 score due to imbalanced class distribution. Although each model shows similar performance scores, ELECTRA reaches 83.9% F1 score overall.
Evaluation of ChatGPT and BERT-based Models for Turkish Hate Speech Detection

The popularity of large language models (LLMs) is increasing day by day. ChatGPT is one of the most popular LLMs. It is known for its success in many areas of natural language processing (NLP). Most importantly, we have yet to find zero-shot performance on various NLP tasks for low-level languages such as Turkish. Detection of hate speech is among the most important problems in NLP. With the growing social media usage, the prevalence of hate speech has also increased. However, automatic detection of hate speech in Turkish is rare compared to studies conducted in English. In our work, we analyzed the performance of ChatGPT and various fine-tuned BERT-based transformer models in detecting hate speech in Turkish. We found that ChatGPT provides similar results to the BERT-based models in detecting Turkish hate speech; thus, it is promising. In this study, a dataset consisting of 1000 Turkish tweets labeled “hate,” “aggressor,” and “none” was used.
Residual Fusion Models with Neural Networks for CTR Prediction

No single prediction model achieves the best performance on all datasets and we are better off combining the strengths of different models for each task. Residual fusion learning is a two step combination method that trains a second model on the residual of the target from the first model's prediction. In the final phase, the predictions of both of these models are added. We use gradient boosting decision trees (GBDT) and neural networks for the initial model in residual fusion and compare three GBDT models and four neural network models. We introduce residual fusion with two different neural network models and show that we can achieve AUC gains that
A Supervised Approach With Transformer, CNN, Optical Flow, and Sliding Window for Temporal Video Action Segmentation

Temporal video action segmentation (TVAS) is a challenging problem in analyzing and understanding long-term video content. Action segmentation attracts significant interest from both researchers and practitioners to many applications such as video editing in social media, surveillance in security, robotics, daily activities in instructional videos, and sports analysis. This paper addresses the action segmentation problem in videos including procedural activities as a supervised approach. We proposed an end-to-end framework to segment video streams by applying the sliding window method and state-of-the-art deep models. First, proposed pipeline extracts spatiotemporal features from the input video. Second, with the supervised approach with the sliding window method, we applied domain-specific segmentation task which belongs to the text segmentation domain to video domain. Third, we performed action classification with Transformer and 3D ConvNet enabling us to combine consecutive frames and optical flow to predict each atomic action in the video. Inspired by the success of the sliding window approach in text segmentation, we assigned an atomic action class to each frame in the video sequence and implemented end-to-end temporal video action segmentation pipeline. We present state-of-the-art experimental results on two small-scale benchmarks with small datasets including 50Salads and magnetic wall chess. Our experimental results exhibit the efficiency of our methodology.
Designing a Prototype of a Fuzzy Expert System for a Dialectologist Using Geographic Information Systems and Technologies

The article presents the results of scientific research on the design of a prototype of a geoinformation system for processing knowledge in the field of dialectology, as well as subsequent data visualization using geoinformation technologies and systems. The geoinformation system includes the ability to process heterogeneous data (linguistic, semantic), presented in various forms (graph and relational), and to provide intelligent data retrieval in conditions of uncertainty by using fuzzy logic. To create a web application, the Django software library (framework) of the Python programming language was used. To increase the efficiency of knowledge processing, the Ne04j graph database was used. Visualization of the received and processed spatial data on the map was performed using software libraries Pandas, Neomodel, Fuzzy, etc. The developed prototype of the geoinformation system is intended for specialists working in the field of dialectology, linguistics, typology and comparative studies, as it allows collecting, processing and analyzing spatial data on linguistic units of various language levels.
Generating Image Captions Using Deep Neural Networks

The irrepressible increase in the images shared on the internet continues to keep the automatic caption generation as an active research field. Generating automated image captions using both computer vision and natural language processing techniques is a challenging field of artificial intelligence. We present a study to generate Turkish captions by using TasvirEt dataset, which is a Turkish dataset based on the Flickr8K. The feature extraction task of the images is performed by using the VGG-16 and DenseNet deep learning models. The caption generation task is realized by using the LSTM model. The use of deep neural networks allows us to improve upon the results obtained in previous studies and generate more accurate and meaningful captions. The experimental results confirm the effectiveness and performance of deep learning models in this field.
Segmentation, Detection and Tracking of Video Objects for Interactive Video Editing Application

Artificial intelligence applications have become important in various aspects of our lives, including the field of image processing. One prominent area in this regard is the detection and tracking of objects in videos, which has numerous application examples. Objects present in visual media elements are considered valuable data, particularly by the advertising sector. This study aims to detect objects from video images, segment these objects in the images, and track them seamlessly across consecutive frames. This enables the creation of interactive areas, positioning of multimedia content, overlaying visual layers such as advertisements onto objects, and covering the texture of the object area within the commercial application, Cinema8, a video editing application. To achieve this, the YOLO architecture is utilized to identify the classes of objects in the images and detect bounding boxes for these objects. Based on these bounding boxes, a plugin infrastructure is developed to edit multimedia images in a commercial product, offering users the ability to generate interactive videos.