Data Augmentation with In-Context Learning and Comparative Evaluation in Math Word Problem Solving

Math Word Problem (MWP) solving presents a challenging task in Natural Language Processing (NLP). This study aims to provide MWP solvers with a more diverse training set, ultimately improving their ability to solve various math problems. We propose several methods for data augmentation by modifying the problem texts and equations, such as synonym replacement, rule-based: question replacement, and rule based: reversing question methodologies over two English MWP datasets. This study extends by introducing a new in-context learning augmentation method, employing the Llama-7b language model. This approach involves instruction-based prompting for rephrasing the math problem texts. Performance evaluations are conducted on 9 baseline models, revealing that augmentation methods outperform baseline models. Moreover, concatenating examples generated by various augmentation methods further improves performance.Comment: Accepted in SN Computer Scienc

Scalable curriculum learning for artificial neural networks

WOS: 000405294100008Learning process of people usually starts with easy samples and goes towards hard ones. Using this method for machine learning is called curriculum learning. Samples are given in an order related to their difficulty level, rather than in random order. The aim of this approach is to create models that have better generalization performance. In existing studies, difficulty levels of the samples were determined by prior knowledge and given to the system. However, this is not a scalable approach for every application. Because of that, such studies were usually carried out in very limited application areas. In this study, a new approach is proposed that automatically generates difficulty levels of the samples from data sets. In this way, it is possible to overcome mentioned constraint in the implementations. Thus, curriculum and anti-curriculum learning methods could be applied on many different application areas. In the experiments where artificial neural networks are used as learners, more successful results were obtained with curriculum and anti- curriculum learning compared with the models where samples were given in random order. After various methods have been tried for determining the difficulty ratings of the samples, this study showed that ensemble learning-based approach is more successful

Investigating Semi-Supervised Learning Algorithms in Text Datasets

Using large training datasets enhances the generalization capabilities of neural networks. Semi-supervised learning (SSL) is useful when there are few labeled data and a lot of unlabeled data. SSL methods that use data augmentation are most successful for image datasets. In contrast, texts do not have consistent augmentation methods as images. Consequently, methods that use augmentation are not as effective in text data as they are in image data. In this study, we compared SSL algorithms that do not require augmentation; these are self-training, co-training, tri-training, and tri-training with disagreement. In the experiments, we used 4 different text datasets for different tasks. We examined the algorithms from a variety of perspectives by asking experiment questions and suggested several improvements. Among the algorithms, tri-training with disagreement showed the closest performance to the Oracle; however, performance gap shows that new semi-supervised algorithms or improvements in existing methods are needed.Comment: Innovations in Intelligent Systems and Applications Conference (ASYU

Enhancing Retrieval-Augmented Generation Accuracy with Dynamic Chunking and Optimized Vector Search

Retrieval-Augmented Generation (RAG) architectures depend on the integration of efficient retrieval and ranking mechanisms to enhance response accuracy and relevance. This study investigates a novel approach to improving the response performance of RAG systems, leveraging dynamic chunking for contextual coherence, Sentence-Transformers (all-mpnet-base-v2) for high-quality embeddings, and cross-encoder-based re-ranking for retrieval refinement. Our evaluation utilizes RAGAS metrics to assess key performance metrics, including faithfulness, relevancy, correctness, and context precision. Empirical evaluations highlighted the significant impact of index choice on the performance. Our proposed approach integrates the FAISS HNSW index with re-ranking, resulting in a balanced architecture that improves response fidelity without compromising efficiency. These insights underscore the importance of advanced indexing and retrieval techniques in bridging the gap between large-scale language models and domain-specific information needs. The findings provide a robust framework for future research in optimizing RAG systems, particularly in scenarios requiring high-context preservation and precision

Machine learning for human-centered and value-sensitive building energy efficiency

Enhancing building energy efficiency is one of the best strategies to reduce energy consumption and associated CO2 emissions. Recent studies emphasized the importance of occupant behavior as a key means of enhancing building energy efficiency. However, it is also critical that while we strive to enhance the energy efficiency of buildings through improving occupant behavior, we still pay enough attention to occupant comfort and satisfaction. Towards this goal, this research proposes a data-driven machine-learning-based approach to behavioral building energy efficiency, which could help better understand and predict the impact of occupant behavior on building energy consumption and occupant comfort; and help optimize occupant behavior for both energy saving and occupant comfort. Three types of models were developed and tested – simulation-data-driven, real-data-driven, and hybrid. Accordingly, the research included five primary research tasks. First, the importance levels of energy-related human values (e.g., thermal comfort) to building occupants and their current satisfaction levels with these values were identified, in order to better understand the factors that are associated with higher/lower importance and/or satisfaction levels and identify the potential factors that could help predict occupant comfort. Second, a data sensing and occupant feedback collection plan was developed, in order to capture and monitor the indoor environmental conditions, energy consumption, energy-related occupant behavior, and occupant comfort in real buildings. Third, a set of buildings were simulated, in order to model the energy consumption of different buildings in different contexts – in terms of occupant behavior, building sizes, weather conditions, etc.; and a simulation-data-driven occupant-behavior-sensitive machine learning-based model, which learns from simulation data, was developed for predicting hourly cooling energy consumption. Fourth, a set of real-data-driven occupant-behavior-sensitive machine learning-based models, which learn from real data (data collected from real buildings and real occupants), were developed for predicting hourly cooling and lighting energy consumption and thermal and visual occupant comfort; and a genetic algorithm-based optimization model for determining the optimal occupant behavior that can simultaneously reduce energy consumption and improve occupant comfort was developed. Compared to the simulation-data-driven approach, the real-data-driven approach aims to better capture and model the real-life behavior and comfort of occupants and the real-life energy-consumption patterns of buildings. Although successful in this regard, the resulting models may not generalize well outside of their training range. Fifth, a hybrid, occupant-behavior-sensitive machine learning-based model, which learns from both simulation data and real data, was developed for predicting hourly cooling and lighting energy consumption. The hybrid approach aims to overcome the limitations of both simulation-data-driven and real-data-driven approaches – especially the limited ability to capture occupant behavior and real-life consumption patterns in simulation-data-driven approaches and the limited generalizability of real-data-driven approaches to different cases – by learning from both types of data simultaneously. The experimental results show the potential of the proposed approach. The energy consumption prediction models achieved high prediction performance, and the thermal and visual comfort models were able to accurately represent the individual and group comfort levels. The optimization results showed potential behavioral energy savings in the range of 11% and 22%, with significant improvement in occupant comfort

LegalTurk Optimized BERT for Multi-Label Text Classification and NER

The introduction of the Transformer neural network, along with techniques like self-supervised pre-training and transfer learning, has paved the way for advanced models like BERT. Despite BERT's impressive performance, opportunities for further enhancement exist. To our knowledge, most efforts are focusing on improving BERT's performance in English and in general domains, with no study specifically addressing the legal Turkish domain. Our study is primarily dedicated to enhancing the BERT model within the legal Turkish domain through modifications in the pre-training phase. In this work, we introduce our innovative modified pre-training approach by combining diverse masking strategies. In the fine-tuning task, we focus on two essential downstream tasks in the legal domain: name entity recognition and multi-label text classification. To evaluate our modified pre-training approach, we fine-tuned all customized models alongside the original BERT models to compare their performance. Our modified approach demonstrated significant improvements in both NER and multi-label text classification tasks compared to the original BERT model. Finally, to showcase the impact of our proposed models, we trained our best models with different corpus sizes and compared them with BERTurk models. The experimental results demonstrate that our innovative approach, despite being pre-trained on a smaller corpus, competes with BERTurk

Perceptions about the sexuality of women with fibromyalgia syndrome: a phenomenological study

Aims: The aim of this study was to explore and understand the perceptions and experiences of women with fibromyalgia syndrome regarding their sexuality. Background: Fibromyalgia syndrome is a chronic pathology, which compromises a woman’s physical, mental and emotional health. Although concerns related to sexuality are commonly reported, research has tended to focus on the physical symptoms. Design: An interpretive qualitative research methodology using Gadamer’s philosophical hermeneutics was carried out. Methods: This qualitative study explores the sexuality of women with fibromyalgia syndrome. A focus group and semi-structured interviews were conducted with 13 women with fibromyalgia syndrome. Data were collected between April - June 2014. Participants were recruited until findings reached saturation. Findings: Three themes define the perception of sexuality for these women: (i) Physical impact: don’t touch, don’t look; (ii) Sexuality and identity: fighting against their loss; (iii) Impact on the relationship: sexuality as a way of connecting the couple. Conclusion: Despite limitations, sexuality is important for the identity and quality of life of women with fibromyalgia syndrome. Together with the physical symptomology, guilt, fear and a lack of understanding compromise the coping process. Women need the support of their partner, their socio-family environment and health professionals. Nurses can aid the successful adjustment to sexual problems related to fibromyalgia syndrome

Lingual ortodonti

Currently, aesthetics of the anterior teeth is a significant issue in general dentistry and the most frequently cited reason for patients seeking orthodontic treatment. For many years, lingual orthodontics was perceived as complex and problematic treatment procedures and therefore not widely used internationally. However, during the last decade, the percentage of patients treated with lingual orthodontics has increased, appliance systems have renewed, and the technique has developed to such an extent that in some cases, it is easier, quicker, and more accurate than traditional buccal orthodontics. The aim of this review is to evaluate and generally consider the usage of lingual orthodontics which commonly preferred by patients in routine orthodontic procedures and look through current applications. Additionally, special considerations regarding the contemporary diagnosis and treatment planning in lingual orthodontics are presented in this study. ÖZET Günümüzde, ön dişlerin estetik olarak dizilimi ve görünümü genel diş hekimliğinde önemli bir konu olmuş ve bu önem hastalar için lingual ortodonti tercihinde birinci sebep halini almıştır. Lingual ortodonti yıllardır karmaşık ve uygulanması zor tedavi yöntemlerinden kabul edilmiş ve uluslar arası alanda yaygın bir kullanım alanı bulamamıştır. Ancak son dönemlerde, bu yöntem aracılığıyla tedavi edilen hasta yüzdesi artmış, vakalarda uygulanan aygıtlar yenilenmiş, tedaviler kolaylaştırılmış, hızlandırılmış ve her geçen gün geleneksel ortodontik tedavilerle elde edilen başarılara ulaşılmaya başlanmıştır. Bu derlemenin amacı, ortodontide her geçen gün daha fazla hastanın tercih ettiği bu uygulamayı genel olarak değerlendirmek ve güncel uygulamalara genel bir bakış yapmaktır. Ayrıca lingual ortodontik uygulamalardaki güncel muayene ve tedavi planlamalarından, karşılaşılan özel durumlardan da bu çalışmada bahsedilecektir. Anahtar Kelimeler: Lingual ortodonti, güncel uygulamala

Single dominant left coronary artery: An autopsy case report with review of literature

Coronary artery anomalous course is rare, reported incidence is approximately 0.3–1.3% of patients undergoing coronary angiography and approximately 1% of routine autopsy examinations. A single coronary artery is an unusual congenital anomaly where only one coronary artery arises from the aortic trunk from a single coronary ostium, supplying the entire heart. We describe here a rare case with an unusual dominant left circumflex artery and absent right coronary artery