This paper presents an innovative Transformer-based deep learning strategy for optimizing the placement of sensors aiming at structural health monitoring of semiconductor probe cards. Failures in probe cards, including substrate cracks and loosened screws, would critically affect semiconductor manufacturing yield and reliability. Some failure modes could be detected by equipping a probe card with adequate sensors. Frequency response functions from simulated failure scenarios are adopted within a finite element model of a probe card. A comprehensive dataset, enriched by physics-informed scenario expansion and physics-aware statistical data augmentation, is exploited to train a hybrid Convolutional Neural Network and Transformer model. The model achieves high accuracy (99.83%) in classifying the probe card health states (baseline, loose screw, crack) and an excellent crack detection recall (99.73%). Model robustness is confirmed through a rigorous framework of 3 repetitions of 10-fold stratified cross-validation. The attention mechanism also pinpoints critical sensor locations: an analysis of the attention weights offers actionable insights for designing efficient, cost-effective monitoring systems by optimizing sensor configurations. This research highlights the capability of attention-based deep learning to advance proactive maintenance, enhancing operational reliability and yield in semiconductor manufacturing.

Cheating in online video games compromises the integrity of gaming experiences. Anti-cheat systems, such as VAC (Valve Anti-Cheat), face significant challenges in keeping pace with evolving cheating methods without imposing invasive measures on users' systems. This paper presents AntiCheatPT\_256, a transformer-based machine learning model designed to detect cheating behaviour in Counter-Strike 2 using gameplay data. To support this, we introduce and publicly release CS2CD: A labelled dataset of 795 matches. Using this dataset, 90,707 context windows were created and subsequently augmented to address class imbalance. The transformer model, trained on these windows, achieved an accuracy of 89.17\% and an AUC of 93.36\% on an unaugmented test set. This approach emphasizes reproducibility and real-world applicability, offering a robust baseline for future research in data-driven cheat detection.

--A transformer-based deep learning approach is presented that enables the diagnosis of fault cases in optical fiber amplifiers using condition-based monitoring time series data. The model, Inverse Triple-Aspect Self-Attention Transformer (ITST), uses an encoder-decoder architecture, utilizing three feature extraction paths in the encoder, feature-engineered data for the decoder and a self-attention mechanism. The results show that ITST outperforms state-of-the-art models in terms of classification accuracy, which enables predictive maintenance for optical fiber amplifiers, reducing network downtimes and maintenance costs. In present optical transmission links, optical fiber amplifiers are key components in long-haul and metro fiber optical networks. Aging of these devices can result in slowly but permanently increasing performance degradation, but also complete outage of the affected link, resulting in cost-intensive maintenance and high financial loss of income.

In recent years, social media platforms have become prominent spaces for individuals to express their opinions on ongoing events, including criminal incidents. As a result, public sentiment can shift dynamically over time. This study investigates the evolving public perception of crime-related news by classifying user-generated comments into three categories: positive, negative, and neutral. A newly curated dataset comprising 28,528 Bangla-language social media comments was developed for this purpose. We propose a transformer-based model utilizing the XLM-RoBERTa Base architecture, which achieves a classification accuracy of 97%, outperforming existing state-of-the-art methods in Bangla sentiment analysis. To enhance model interpretability, explainable AI technique is employed to identify the most influential features driving sentiment classification. The results underscore the effectiveness of transformer-based models in processing low-resource languages such as Bengali and demonstrate their potential to extract actionable insights that can support public policy formulation and crime prevention strategies.

The rise of generative AI search engines is disrupting traditional SEO, with Gartner predicting 25% reduction in conventional search usage by 2026. This necessitates new approaches for web content visibility in AI-driven search environments. We present a domain-specific fine-tuning approach for Generative Engine Optimization (GEO) that transforms web content to improve discoverability in large language model outputs. Our method fine-tunes a BART-base transformer on synthetically generated training data comprising 1,905 cleaned travel website content pairs. Each pair consists of raw website text and its GEO-optimized counterpart incorporating credible citations, statistical evidence, and improved linguistic fluency. We evaluate using intrinsic metrics (ROUGE-L, BLEU) and extrinsic visibility assessments through controlled experiments with Llama-3.3-70B. The fine-tuned model achieves significant improvements over baseline BART: ROUGE-L scores of 0.249 (vs. 0.226) and BLEU scores of 0.200 (vs. 0.173). Most importantly, optimized content demonstrates substantial visibility gains in generative search responses with 15.63% improvement in absolute word count and 30.96% improvement in position-adjusted word count metrics. This work provides the first empirical demonstration that targeted transformer fine-tuning can effectively enhance web content visibility in generative search engines with modest computational resources. Our results suggest GEO represents a tractable approach for content optimization in the AI-driven search landscape, offering concrete evidence that small-scale, domain-focused fine-tuning yields meaningful improvements in content discoverability.

This study addresses the critical gap in Arabic natural language processing by developing an effective Arabic Reverse Dictionary (RD) system that enables users to find words based on their descriptions or meanings. We present a novel transformer-based approach with a semi-encoder neural network architecture featuring geometrically decreasing layers that achieves state-of-the-art results for Arabic RD tasks. Our methodology incorporates a comprehensive dataset construction process and establishes formal quality standards for Arabic lexicographic definitions. Experiments with various pre-trained models demonstrate that Arabic-specific models significantly outperform general multilingual embeddings, with ARBERTv2 achieving the best ranking score (0.0644). Additionally, we provide a formal abstraction of the reverse dictionary task that enhances theoretical understanding and develop a modular, extensible Python library (RDTL) with configurable training pipelines. Our analysis of dataset quality reveals important insights for improving Arabic definition construction, leading to eight specific standards for building high-quality reverse dictionary resources. This work contributes significantly to Arabic computational linguistics and provides valuable tools for language learning, academic writing, and professional communication in Arabic.

Transformers have excelled in natural language processing and computer vision, paving their way to sensor-based Human Activity Recognition (HAR). Previous studies show that transformers outperform their counterparts exclusively when they harness abundant data or employ compute-intensive optimization algorithms. However, neither of these scenarios is viable in sensor-based HAR due to the scarcity of data in this field and the frequent need to perform training and inference on resource-constrained devices. Our extensive investigation into various implementations of transformer-based versus non-transformer-based HAR using wearable sensors, encompassing more than 500 experiments, corroborates these concerns. We observe that transformer-based solutions pose higher computational demands, consistently yield inferior performance, and experience significant performance degradation when quantized to accommodate resource-constrained devices. Additionally, transformers demonstrate lower robustness to adversarial attacks, posing a potential threat to user trust in HAR.

Defined by Entmann (1993) as a process of selecting and making more salient the specific aspects of social reality, framing is among the most extensively used concepts in the field of communication science (Olsson & Ihlen, 2018). The abundant body of research utilising the concept of framing highlights the versatility of the concept: it has been used for examining the representation of armed conflict (Tschirky & Makhortykh, 2024), climate change (Vu et al., 2021), politics (Ogan et al., 2018), and racial injustice (Lane et al., 2020). The diversity of areas in which the concept of framing is applied and the vagueness of its operationalisation are, however, occasionally viewed as the concept's weakness: Cacciatore et al. (2016) note that it results in the unnecessarily broad understanding of framing that overlaps with other concepts, such as agenda-setting, and diminishes its explanatory potential. Despite the above-mentioned criticism, we suggest that framing remains an essential tool for understanding how certain interpretations of important societal issues become more visible and in which ways individuals are exposed to these interpretations. The importance of such an understanding increases under the conditions of the high-choice media environment (van Aelst et al., 2017) in which we are consuming information. With more available information sources and, consequently, more possibilities for being exposed to them -- both selectively (Messing & Westwood, 2014) and incidentally (Lee & Kim, 2014) -- it is crucial to be able to distinguish between frames coming from these sources, especially regarding the salience of epistemically contested issues which can easily amplify polarisation in the society. The ability to detect the presence or absence of specific frames in this context also becomes paramount for detecting attempts to manipulate public opinion. Another reason why frame detection is highly relevant is the growing reliance on artificial intelligence (AI)-powered systems for organising and generating information regarding societally relevant issues. The adoption of systems such as search engines and recommendations systems and, recently, generative AI-powered chatbots has profound implications for how individuals are exposed to information as these systems decide what information sources and interpretations are prioritised in response to the user input (e.g.

Detecting Machine-Generated Text (MGT) has emerged as a significant area of study within Natural Language Processing. While language models generate text, they often leave discernible traces, which can be scrutinized using either traditional feature-based methods or more advanced neural language models. In this research, we explore the effectiveness of fine-tuning a RoBERTa-base transformer, a powerful neural architecture, to address MGT detection as a binary classification task. Focusing specifically on Subtask A (Monolingual-English) within the SemEval-2024 competition framework, our proposed system achieves an accuracy of 78.9% on the test dataset, positioning us at 57th among participants. Our study addresses this challenge while considering the limited hardware resources, resulting in a system that excels at identifying human-written texts but encounters challenges in accurately discerning MGTs.

Transformer-based language models are highly effective for code completion, with much research dedicated to enhancing the content of these completions. Despite their effectiveness, these models come with high operational costs and can be intrusive, especially when they suggest too often and interrupt developers who are concentrating on their work. Current research largely overlooks how these models interact with developers in practice and neglects to address when a developer should receive completion suggestions. To tackle this issue, we developed a machine learning model that can accurately predict when to invoke a code completion tool given the code context and available telemetry data. To do so, we collect a dataset of 200k developer interactions with our cross-IDE code completion plugin and train several invocation filtering models. Our results indicate that our small-scale transformer model significantly outperforms the baseline while maintaining low enough latency. We further explore the search space for integrating additional telemetry data into a pre-trained transformer directly and obtain promising results. To further demonstrate our approach's practical potential, we deployed the model in an online environment with 34 developers and provided real-world insights based on 74k actual invocations.