Publications

@inproceedings{yeshpanov-varol-2024-kazsandra-kazakh, title = "{K}az{SA}n{DRA}: {K}azakh Sentiment Analysis Dataset of Reviews and Attitudes", author = "Yeshpanov, Rustem and Varol, Huseyin Atakan", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.844", pages = "9657--9667", abstract = "This paper presents KazSAnDRA, a dataset developed for Kazakh sentiment analysis that is the first and largest publicly available dataset of its kind. KazSAnDRA comprises an extensive collection of 180,064 reviews obtained from various sources and includes numerical ratings ranging from 1 to 5, providing a quantitative representation of customer attitudes. The study also pursued the automation of Kazakh sentiment classification through the development and evaluation of four machine learning models trained for both polarity classification and score classification. Experimental analysis included evaluation of the results considering both balanced and imbalanced scenarios. The most successful model attained an F1-score of 0.81 for polarity classification and 0.39 for score classification on the test sets. The dataset and fine-tuned models are open access and available for download under the Creative Commons Attribution 4.0 International License (CC BY 4.0) through our GitHub repository.", }

Bissarinova U, Rakhimzhanova T, Kenzhebalin D, Varol HA. Faces in Event Streams (FES): An Annotated Face Dataset for Event Cameras. Sensors. 2024; 24(5):1409. https://doi.org/10.3390/s24051409

@INPROCEEDINGS{10463419, author={Mussakhojayeva, Saida and Gilmullin, Rinat and Khakimov, Bulat and Galimov, Mansur and Orel, Daniil and Adilbekov, Adal and Varol, Huseyin Atakan}, booktitle={2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)}, title={Noise-Robust Multilingual Speech Recognition and the Tatar Speech Corpus}, year={2024}, volume={}, number={}, pages={732-737}, keywords={Codes;Buildings;Focusing;Speech recognition;Noise robustness;Artificial intelligence;Automatic speech recognition}, doi={10.1109/ICAIIC60209.2024.10463419}}

@INPROCEEDINGS{10463261, author={Orel, Daniil and Kuzdeuov, Askat and Gilmullin, Rinat and Khakimov, Bulat and Varol, Huseyin Atakan}, booktitle={2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)}, title={TatarTTS: An Open-Source Text-to-Speech Synthesis Dataset for the Tatar Language}, year={2024}, volume={}, number={}, pages={717-721}, keywords={Training;Globalization;Training data;Coherence;Speech enhancement;Linguistics;Audio recording;Text- to-speech;speech synthesis;low-resource languages;Turkic languages}, doi={10.1109/ICAIIC60209.2024.10463261}}

@INPROCEEDINGS{10463430, author={Kuzdeuov, Askat and Mukayev, Olzhas and Nurgaliyev, Shakhizat and Kunbolsyn, Alisher and Varol, Huseyin Atakan}, booktitle={2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)}, title={ChatGPT for Visually Impaired and Blind}, year={2024}, volume={}, number={}, pages={722-727}, keywords={Voice activity detection;Visualization;Source coding;Oral communication;Blindness;User interfaces;Chatbots}, doi={10.1109/ICAIIC60209.2024.10463430}}

@Article{s24185993, AUTHOR = {Rakhimzhanova, Tomiris and Kuzdeuov, Askat and Varol, Huseyin Atakan}, TITLE = {AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI}, JOURNAL = {Sensors}, VOLUME = {24}, YEAR = {2024}, NUMBER = {18}, ARTICLE-NUMBER = {5993}, URL = {https://www.mdpi.com/1424-8220/24/18/5993}, PubMedID = {39338738}, ISSN = {1424-8220}, ABSTRACT = {Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.}, DOI = {10.3390/s24185993} }

@InProceedings{10.1007/978-3-031-70058-3_36, author="Ziat, Mounia and Kabdsyhev, Nurlan and Topp, Sven and Duvernoy, Basil and Milroy, Jeraldine and Kappassov, Zhanat", editor="Kajimoto, Hiroyuki and Lopes, Pedro and Pacchierotti, Claudio and Basdogan, Cagatay and Gori, Monica and Lemaire-Semail, Betty and Marchal, Maud", title="Evaluation of HaptiComm-S for Replicating Tactile ASL Numbers: A Comparative Analysis of Direct and Mediated Modalities", booktitle="Haptics: Understanding Touch; Technology and Systems; Applications and Interaction", year="2025", publisher="Springer Nature Switzerland", address="Cham", pages="442--448", abstract="This research investigates the efficacy of HaptiComm-S, a haptic communication device designed to facilitate tactile communication for Deafblind individuals. The primary focus is on evaluating the device's capability to replicate the tactile American Sign Language (ASL) numbers 0 to 10. Participants performed under two distinct conditions: direct ASL signing and mediated ASL signing through two modalities (Tap and Tap-and-Hold). Our findings demonstrate significant differences in performance between the Direct and Mediated ASL modes. Direct ASL consistently exhibited higher accuracy compared to mediated conditions. Mediated ASL conditions were prone to perceptual errors in number identification. Notably, specific numbers, such as 4, 7, 8, and 9, posed challenges in the mediated conditions, often resulting in confusion among participants. These findings contribute valuable insights for the ongoing refinement in the design of haptic communication devices tailored to the needs of the Deafblind community.", isbn="978-3-031-70058-3" }

@ARTICLE{10509679, author={Keutayeva, Aigerim and Abibullaev, Berdakh}, journal={IEEE Access}, title={Data Constraints and Performance Optimization for Transformer-Based Models in EEG-Based Brain-Computer Interfaces: A Survey}, year={2024}, volume={12}, number={}, pages={62628-62647}, keywords={Brain modeling;Electroencephalography;Transformers;Data models;Decoding;Task analysis;Training;Brain-computer interfaces;data scarcity;survey;deep learning;transformer;EEG;self-attention mechanism;motor imagery;BCI}, doi={10.1109/ACCESS.2024.3394696}}

@Inbook{Keutayeva2024, author="Keutayeva, Aigerim and Zollanvari, Amin and Abibullaev, Berdakh", editor="Vinjamuri, Ramana", title="Evolving Trends and Future Prospects of Transformer Models in EEG-Based Motor-Imagery BCI Systems", bookTitle="Discovering the Frontiers of Human-Robot Interaction: Insights and Innovations in Collaboration, Communication, and Control", year="2024", publisher="Springer Nature Switzerland", address="Cham", pages="233--256", abstract="This chapter explores the transformative impact of transformer models on EEG-based motor imagery brain-computer interfaces (BCIs)---systems that are pushing the boundaries of human-machine interaction. Transformers, renowned for their self-attention mechanisms, excel at handling sequential data, making them uniquely suited for decoding intricate EEG patterns. We offer a comprehensive review of transformer applications in BCIs, showcasing how they significantly improve signal interpretation accuracy, efficiency, and robustness. The chapter examines the technical foundations, including the inherent complexities of EEG signals---noise, non-stationarity, and intersubject variability---and how transformers tackle them through superior feature extraction and denoising capabilities. We trace the evolution of these models from traditional machine-learning approaches to sophisticated architectures that capture both temporal and spatial dependencies in EEG data. The chapter then delves into practical applications of these models in real-world BCI systems, discussing how they translate into tangible benefits for users. We explore prospects and ongoing research aimed at overcoming limitations like computational demands and the need for personalized models. By analyzing emerging trends and envisioning future directions, this chapter provides a roadmap for the BCI research community, ultimately leading to more intuitive, versatile, and effective human-computer interactions.", isbn="978-3-031-66656-8", doi="10.1007/978-3-031-66656-8_10", url="https://doi.org/10.1007/978-3-031-66656-8_10" }

@INPROCEEDINGS{10780705, author={Mukashev, Dinmukhammed and Lach, Agnieszka and Hammill, Chet W and Kappassov, Zhanat and Sharma, Adwait and Nittala, Aditya Shekhar and Kohli, Luv and Razzaque, Sharif}, booktitle={2024 IEEE 20th International Conference on Body Sensor Networks (BSN)}, title={Initial Exploration into Electrotactile Tongue Stimulation for Providing Force Feedback for Robot-Assisted Surgery}, year={2024}, volume={}, number={}, pages={1-4}, keywords={Electric potential;Tongue;Force measurement;Force feedback;Surgery;Particle measurements;Safety;Interviews;Grippers;Robots;haptics;force feedback;robotic-assisted surgery;displays;tongue;sensory substitution;electrotactile stimulation}, doi={10.1109/BSN63547.2024.10780705}}

@INPROCEEDINGS{10417155, author={Mussin, Tleukhan and Kassym, Yermakhan and Kabdyshev, Nurlan and Kaikanov, Marat and Kappassov, Zhanat}, booktitle={2024 IEEE/SICE International Symposium on System Integration (SII)}, title={Action-Driven Tactile Object Exploration for Shape Reconstruction via Silver Nanowire Injected Sensors}, year={2024}, volume={}, number={}, pages={532-538}, keywords={Resistance;Point cloud compression;Silver;Shape;Robot kinematics;Tactile sensors;Sensors}, doi={10.1109/SII58957.2024.10417155}}