Multilingual Domain Adaptation for Speech Recognition Using LLMs

Siemens Healthineers AGWe present a practical pipeline for multilingual domain adaptation in automatic speech recognition (ASR) that combines the Whisper model with large language models (LLMs). Using Aya-23-8B, Common Voice transcripts in 22 languages are automatically classified into the Law and Healthcare domains, producing high-quality domain labels at a fraction of the manual cost. These labels drive parameter-efficient (LoRA) fine-tuning of Whisper and deliver consistent relative Word Error Rate (WER) reductions of up to 14.3% for languages that contribute at least 800 in-domain utterances. A data-volume analysis reveals a clear breakpoint: gains become reliably large once that 800-utterance threshold is crossed, while monolingual tuning still rescues performance in truly low-resource settings. The workflow therefore shifts the key success factor from expensive hand labelling to scalable data acquisition, and can be replicated in new domains with minimal human intervention. © 2025 Elsevier B.V., All rights reserved

Improved Approximation via Hybrid Shepard-Lagrange Operators: Linear and Nonlinear Perspectives

This paper introduces a hybrid operator that combines Shepard operators with Lagrange polynomials, proving that the new operator exhibits superior approximation properties compared to the classical Shepard operator. In the linear case, our approach advances known results in the literature, providing a more effective framework for approximation. Building on this foundation, the method is also extended to nonlinear scenarios by employing max-product operations, demonstrating that the nonlinear operator achieves even better approximation characteristics than its linear counterpart. The theoretical findings are validated through numerical computations and graphical representations, strongly supporting the effectiveness of the hybrid operator in both linear and nonlinear contexts

Ethical Barriers to Artificial Intelligence Adoption in Vaccine Distribution: A Systematic Scoping Review

The rapid advancement of AI has opened new avenues for improving healthcare systems, particularly in a pandemic response. AI technologies can potentially affect the equitable distribution of vaccines. However, there are ethical concerns such as privacy, governance, data security, acceptance, access, affordability, prioritization among others that arise from such implementation. This article synthesizes literature to identify the ethical implications of utilizing AI in vaccine distribution, planning and scheduling during a pandemic, with a focus on ensuring equitable access to vaccines in LMICs using a combination of 20 search string-words. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guideline for scoping review was used. A full-text open access peer review journals in English addressing the research interest from PubMed, ScienceDirect, and the Directory of Open Access Journals (DOAJ) was included in the study. These search engines were chosen based on their comprehensive coverage, advanced search capabilities, reputation for academic quality, and efficient retrieval of relevant and diverse literature. Data from each search engine was screened for inclusion criteria and charted from 2019 to 2023 to cover the COVID-19 pandemic period. Bibliometric analysis was done on the Web of Science search engine using R-studio and Biblioshiny to identify trends. Out of 1,555 records, 358 articles relevant to the search query were found; after careful consideration, 28 articles met the inclusion criteria for analysis. Thematic analysis was done to identify the ethical considerations associated with using AI in planning and scheduling vaccine distribution, particularly in the context of a pandemic. The article emphasized the importance of integrating lessons learned from the COVID-19 pandemic into future actions to strengthen a fair and equitable pandemic preparedness plan ensuring the ethical compliance of AI-support system responses in LMICs during pandemics. © 2025 Elsevier B.V., All rights reserved

Comprehensive Analysis of Behavioral Hardware Impairments in Cell-Free Massive MIMO-OFDM Uplink: Centralized Operation

Isik UniversityCell-free massive MIMO is a key 6G technology, offering superior spectral and energy efficiency. However, its dense deployment of low-cost access points (APs) makes hardware impairments unavoidable. While narrowband impairments are well-studied, their impact in wideband systems remains unexplored. This paper provides the first comprehensive analysis of hardware impairments - such as nonlinear distortion in low-noise amplifiers, phase noise, in-phase/quadrature imbalance, and low-resolution analog-to-digital converters - on uplink spectral efficiency in cell-free massive MIMO. Using an OFDM waveform and centralized processing, APs share channel state information for joint uplink combining. Leveraging Bussgang decomposition, we derive a distortion-aware combining vector that optimizes spectral efficiency by modeling distortion as independent colored noise. © 2025 Elsevier B.V., All rights reserved

Opposition's Paradox of Victory: Electoral Success and Authoritarian Retrenchment in Turkey

This study examines the transformation of Turkey's opposition politics between the May 2023 presidential and parliamentary elections and the 2024 local elections. Following the ruling party's victory in 2023, political capital within the opposition shifted from formal party structures to influential metropolitan mayors operating outside party hierarchies. This trend, reinforced by the local election successes of Ekrem ; Idot;mamo ; gbreve;lu and Mansur Yava ; scedil;, challenged President Erdo ; gbreve;an's preferred model in which strong party institutions sidelined prominent mayors, as before the last presidential race. The opposition's 2023 defeat revealed the limitations of a party-centred strategy in confronting incumbents, weakening institutional control and enabling mayoral figures to emerge as potential presidential contenders. The paper analyzes this decline of party dominance, the rise of mayoral autonomy, and Erdo ; gbreve;an's strategic response - marked by judicial interventions against municipalities and the CHP, including ; Idot;mamo ; gbreve;lu's imprisonment - highlighting how electoral gains paradoxically triggered authoritarian recalibration rather than democratic consolidation

AIGCodeSet: Yapay Zeka Üretimli Kod Tespiti İçin Yeni Bir Veri Kümesi

Isik UniversityWhile large language models provide significant convenience for software development, they can lead to ethical issues in job interviews and student assignments. Therefore, determining whether a piece of code is written by a human or generated by an artificial intelligence (AI) model is a critical issue. In this study, we present AIGCodeSet, which consists of 2.828 AI-generated and 4.755 human-written Python codes, created using CodeLlama, Codestral, and Gemini. In addition, we share the results of our experiments conducted with baseline detection methods. Our experiments show that a Bayesian classifier outperforms the other models. © 2025 Elsevier B.V., All rights reserved

Elektronik Harp Geniş Bant Almaçlarda Enterferans Etki Analizi

Isik UniversityIn electronic warfare systems, wideband receivers are used to continuously monitor threat signals in the electromagnetic environment. In a modern threat environment, radar signals coexist with various communication signals within the same frequency band. These signals create interference in wideband receivers, hindering the accurate measurement of radar signal parameters. Interfering signals cause varying levels of distortion in digital and analog wideband receivers. In this study, the wideband FFT and instantaneous frequency measurement algorithms are utilized to analyze the impact of parameter estimation under different Signal-to-Interference Ratios (SIR), and their performance is analyzed with different FFT sizes under QPSK modulation. © 2025 Elsevier B.V., All rights reserved

Microstrip Stub Filter Design with Enhanced Performance Inspired by Siw Structures Operating at 1.93 Ghz GSM Band

This paper reports a microstrip stub filter design operating at 1.93 GHz GSM band with enhanced performance inspired by SIW structures. In the designed filter additional vias are placed around the microstrip lines to enhance the encasing of the electromagnetic fields while propagating through the filter to develop the filter performance. The filter was examined with electromagnetic simulations for various numbers of vias and different via to microstrip line distances. Results show that the maximum transmission coefficient (S21 parameter) magnitude value reached in the pass band of the filter increases with the number of the vias and as the vias get closer to the lines. On the other hand, when the via number increases and the space between them and the lines narrows, the frequency at which the maximum S21 value is attained shifts to lower frequencies. The designed filters were manufactured, too. Results obtained in the measurements agree well with the simulation results. Additionally, a receiver system operating at 1.93 GHz band was constructed. System experiments were carried out with the constructed prototype for the manufactured filters. Results show that a greater signal level in the filter pass band is achieved and unwanted signals outside the filter pass band are suppressed more in the system where the filter with vias is used instead of the filter without any additional via. The findings indicate that the designed filters inspired by SIW structures are promising for applications requiring high signal quality

Dynamic Behavior Analysis Through Novel Windows Event Logs with Machine Learning

Teknolojik gelişmelerin hız kazanmasıyla birlikte siber saldırılar ve zararlı yazılım tehditleri giderek karmaşıklaşmakta ve daha tehlikeli hale gelmektedir. Bu durum, bilgi güvenliği alanında yenilikçi yaklaşımların ve etkili çözümlerin geliştirilmesi gerekliliğini ortaya koymaktadır. Bu tez çalışması, zararlı yazılım analizinde dinamik yöntemlerin etkinliğini artırmayı amaçlayarak, özellikle Windows işletim sistemine ait olay kayıt verilerinin detaylı incelenmesine odaklanmaktadır. Çalışmanın temel hedefi, izole edilmiş sanal ortamlarda zararlı ve zararsız yazılımların çalışma anında oluşturdukları işletim sistemi kayıt verilerinden anlamlı öznitelikler çıkarıp, makine öğrenmesi teknikleri kullanılarak zararlı yazılım tespitine yönelik yenilikçi bir yaklaşım geliştirmektir. Bu amaç doğrultusunda, hem manuel hem de çevrimiçi doğrulama süreçleriyle zararlı veya zararsız olduğu belirlenen çalıştırılabilir dosyalar titizlikle toplanmıştır. Kontrollü sanal makine ortamlarında gerçekleştirilen deneysel uygulamalar sonucunda elde edilen veriler, öncelikle sayısal, bağlamsal, zaman tabanlı ve istatistiksel öznitelikler olarak sınıflandırılmıştır. Bu verilerden hangilerinin kullanılabilir olacağı belirlenirken, bazıları özellik mühendisliği teknikleriyle daha işlevsel hale getirilmiş ve model performansını artıracak kritik göstergeler oluşturulmuştur. İlgili öznitelikler, Gradient Boosting, Logistic Regression ve Destek Vektör Makineleri gibi çeşitli sınıflandırma algoritmaları kullanılarak işlenmiş; elde edilen modellerin performansı ise K-Fold çapraz doğrulama yöntemi ile titizlikle değerlendirilmiştir. Uygulanan veri normalizasyonu ve özellik mühendisliği teknikleri, modellerin genel doğruluğunu artırırken, zararlı ile zararsız yazılımlar arasındaki ayrımı daha net ortaya koymuştur. Bu kapsamlı analiz, dinamik analiz temelli yaklaşımların, geleneksel statik yöntemlere kıyasla daha yüksek doğruluk oranları ve düşük yanılma payları sağladığını göstermiştir. Ayrıca, bu çalışma literatürde sınırlı yer alan dinamik analiz yaklaşımlarına önemli katkılar sunmayı hedeflemektedir. Farklı veri setleri üzerinde gerçekleştirilen kapsamlı deneyler sonucunda, geliştirilen metodolojinin siber saldırılara karşı proaktif bir yaklaşım sağlayabileceği ortaya konulmuştur. Dinamik davranış analizi, hem eski hem de yeni zararlı yazılımların tespitinde geniş zaman aralıklarında etkili sonuçlar vermekte, böylece siber güvenlik stratejilerinin geliştirilmesinde kritik bir rol oynamaktadır. Sonuç olarak, bu tez çalışması, zararlı yazılım tespiti ve önlenmesinde dinamik analiz yöntemlerinin uygulanabilirliğini ve etkinliğini ortaya koyarak, siber güvenlik alanında yeni ufuklar açmaktadır.With the acceleration of technological developments, cyber attacks and malware threats are becoming increasingly complex and dangerous. This situation demonstrates the necessity of developing innovative approaches and effective solutions in the field of information security. This thesis aims to enhance the effectiveness of dynamic methods in malware analysis by focusing particularly on the detailed examination of Windows operating system event log data. The primary objective of the study is to extract meaningful features from the operating system log data generated during the runtime of malicious and benign software in isolated virtual environments and to develop an innovative approach for malware detection using machine learning techniques. To this end, executable files, determined to be either malicious or benign through both manual and online verification processes, have been meticulously collected. The data obtained from experimental applications conducted in controlled virtual machine environments were primarily classified as numerical, contextual, time-based, and statistical features. In determining which of these features could be utilized, some were further refined through feature engineering techniques, thereby creating critical indicators that enhance model performance. The relevant features were processed using various classification algorithms such as Gradient Boosting, Logistic Regression, and Support Vector Machines; the performance of the resulting models was rigorously evaluated using the K-Fold cross-validation method. The applied data normalization and feature engineering techniques not only increased the overall accuracy of the models but also clarified the distinction between malicious and benign software. This comprehensive analysis has demonstrated that dynamic analysis-based approaches provide higher accuracy rates and lower error margins compared to traditional static methods. Furthermore, this study aims to make a significant contribution to the field of dynamic analysis, an area that has been relatively underrepresented in the literature. Comprehensive experiments conducted on different datasets have revealed that the developed methodology can offer a proactive approach against cyber attacks. Dynamic behavior analysis yields effective results over extended time intervals in detecting both old and new malware, thereby playing a critical role in the development of cybersecurity strategies. In conclusion, this thesis illustrates the applicability and effectiveness of dynamic analysis methods in malware detection and prevention, opening new horizons in the field of cybersecurity

Robust Observer Design for the Longitudinal Dynamics of a Fixed-Wing Aircraft

This paper presents a novel control-based observer (CbO) framework for robust state and disturbance estimation in the longitudinal dynamics of fixed-wing aircraft. In this approach, the observer design problem is recast as an equivalent control problem, enabling the use of advanced control techniques for observer synthesis. Within the proposed framework, the estimation of both system states and unknown disturbance inputs is achieved by integrating disturbance rejection capabilities into the control sub-block of the observer. This integration ensures that the output mismatch between the plant and observer model is minimized, even in the presence of modeling uncertainties and external disturbances. Two observer designs are developed: (i) an H-infinity-CbO, formulated as an H infinity control problem around a linearized model at a nominal operating point, and (ii) a robust H-infinity-CbO, which extends the design to account for significant model nonlinearities and variations by incorporating multiple operating points and optimizing for the worst-case estimation error. The longitudinal dynamics of a fixed-wing aircraft are derived and linearized to provide the basis for observer design. The performance of the proposed observers is evaluated through comprehensive simulation studies under three scenarios: operation at the nominal point, operation around neighboring points, and comparison with conventional linear observers. Simulation results demonstrate that the proposed observer offers superior robustness and accuracy in estimating both states and external disturbances, particularly in the presence of model uncertainties and varying flight conditions