Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlating Biomedical Image Fingerprints between GAN-generated and Real Images using a ResNet Backbone with ML-based Downstream Comparators and Clustering: ImageCLEFmed GANs, 2023

To address the challenge of ImageClef GAN 2023, this paper proposes a comprehensive approach that incorporates models with a similarity constraint in common.A relation network based on few-shot learning to capture the underlying similarities between real and artificial images is used. This model learns to differentiate between the two classes by leveraging the information from the limited labeled data. Secondly, Agglomerative clustering is used to group similar images together. By identifying clusters predominantly composed of real images, the authors enhance the ability to distinguish between real and artificial images effectively. Lastly, SVM is implemented for classification. The SVM model is trained using the combined feature representations obtained from the real and artificial images. The performance of the SVM on a test set containing 200 real images is evaluated, predicting which of these images were generated using real images. The experimental results demonstrate the effectiveness of the relational model in accurately identifying real images within the test dataset.
Evaluating Privacy on Synthetic Images Generated using GANs: Contributions of the VCMI Team to ImageCLEFmedical GANs 2023

This paper presents the main contributions of the VCMI Team to the ImageCLEFmedical GANs 2023 task. This task aims to evaluate whether synthetic medical images generated using Generative Adversarial Networks (GANs) contain identifiable characteristics of the training data. We propose various approaches to classify a set of real images as having been used or not used in the training of the model that generated a set of synthetic images. We use similarity-based approaches to classify the real images based on their similarity to the generated ones. We develop autoencoders to classify the images through outlier detection techniques. Finally, we develop patch-based methods that operate on patches extracted from real and generated images to measure their similarity. On the development dataset, we attained an F1-score of 0.846 and an accuracy of 0.850 using an autoencoder-based method. On the test dataset, a similarity-based approach achieved the best results, with an F1-score of 0.801 and an accuracy of 0.810. The empirical results support the hypothesis that medical data generated using deep generative models trained without privacy constraints threatens the privacy of patients in the training data.
Overview of the Multi-Author Writing Style Analysis Task at PAN 2023

The analysis of the writing style of multi-authored texts aims at identifying those places where authorship changes in a document. This task is an important step in reliably identifying the authors of a given text. This year, we ask participants to solve an intrinsic style change detection task: For a given text, find all places where the writing style changes at the paragraph-level. To control for topic information as a style change signal, we provide participants with data sets with three levels of difficulty and thus different thematic variants. This paper presents the multiple author writing style analysis task, the underlying data set, the approaches used by participants, and the results obtained.
Leveraging MiniLMv2 Pipelines for EXIST2023

This paper introduces our methodology for addressing the three EXIST2023 tasks focused on Sexism Identification in Social Networks. Our proposed solution leverages advanced multi-lingual transformers, specifically XLMR and MiniLMV2, which serve as state-of-the-art frameworks for handling the provided data. Additionally, we employ a data-processing pipeline and task-specific metrics to fine-tune the pre-trained model. The evaluation of our solution demonstrates promising outcomes on the testing set and attains a commendable overall ranking within the competition, particularly excelling in the Soft-Soft evaluation. Our proposed architecture successfully tackles all tasks with favorable outcomes, while also offering opportunities for further enhancements. While the Hard evaluation could benefit from improvements, it is worth noting that our models exhibited signs of overfitting when trained on the available data. In light of this, we made the decision to provide them with more flexibility in the classification process. This approach allowed for increased adaptability and potentially better generalization when handling unseen data instances.
The Meaning of Stromatolites

Stromatolites document microbial interactions with sediments and flowing water throughout recorded Earth history and have the potential to illuminate the long-term history of life and environments. Modern stromatolites, however, provide analogs to only a small subset of the structures preserved in Archean and Proterozoic carbonates. Thus, interpretations of secular trends in the shapes and textures of ancient columnar stromatolites require nonuniformitarian, scale-dependent models of microbial responses to nutrient availability, seawater chemistry, influx of sediment grains, shear, and burial. Models that integrate stromatolite scales, macroscopic organization, and shapes could also help test the biogenicity of the oldest stromatolites and other structures whose petrographic fabrics do not preserve direct evidence of microbial activity. An improved understanding of stromatolite morphogenesis in the presence of oxygenic and anoxygenic microbial mats may illuminate the diversity of microbial metabolisms tha.
Overview of the MEDIQA-Sum Task at ImageCLEF 2023: Summarization and Classification of Doctor-Patient Conversations

This paper presents the overview of the MEDIQA-Sum task at ImageCLEF 2023. MEDIQA-Sum 2023 includes three subtasks, in which a doctor-patient dialogue source is given, and participants were tasked with (A) dialogue2topic classification, e.g. classifying the conversation into one of twenty section header categories, (B) dialogue2note snippet generation, e.g. generating clinical note section text additionally given the clinical section header, and (C) dialogue2note full note summarization, e.g. generating a full clinical note. Twelve teams participated with a total of 48 runs. The best teams achieved 0.8 Accuracy on topic classification (subtask A) and ROUGE-1 scores of 0.43 and 0.49 F1, for subtasks B and C, respectively.
A Dual of Stacked Attention Networks (SAN's) and VGG-16 Model-Based Visual Question Answering Evaluation

The research aims to assess the number of open-source systems based on deep learning is growing, and various data preprocessing techniques are proposed. Considering the timeliness of Artificial Intelligence (AI) systems, particularly in terms of their immediate responsiveness and predictive capabilities, the evaluation of deep learning projects holds significant appeal for a diverse array of researchers, developers, and enthusiasts. The AI-based applications, like ChatGPT, draws interest due to their ability to rapidly generate informed responses and predictions, showcasing the potential for groundbreaking advancements in the field. The researchers can benefit from a wide selection of models and different data preprocessing methods without the need to start from scratch. Consequently, competitions like Medical Visual Question Answering for Gastrointestinal (MEDVQA-GI) Task, identifying Lesions in Colonoscopy images, organized by ImageCLEF Medical 2023, provide an opportunity for community-driven researchers to utilize multiple open-source algorithms such as Visual VGG-16 (Visual Geometry Group-16) Convolutional Neural Network model, and Long Short Term Memory (LSTM) models, enabling them to address complex challenges like identifying lesions in colonoscopy images effectively. Tasks like MEDVQA-GI allow participants to refine the literature and make the researchers work on new aspects of the field by adding multiple modalities to the picture. This study focuses on evaluating an open source system, namely Stacked Attention Networks for Image Question Answering, which utilizes a Task 1 approach, i.e., combines images and textual questions to generate textual answers, commonly applied in various research domains. The evaluation results, including assigned scores by the ImageClef MEDVQA-GI committee and other study observations, demonstrate that the selected system is highly suitable for Task 1. The system incorporates several preprocessing techniques, such as tokenization, word embedding using Word2Vec, preprocessing of questions and answers, question filtering, and feature extraction from images using the VGG16 model. Additionally, noteworthy observations were made regarding Task 2 (visual question generation) throughout the evaluation process. Overall, this research provides insights into the effectiveness of the Stacked Attention Networks for Image Question Answering open-source system for Task 1, highlighting the significance of the employed data preprocessing techniques and model selection. The findings contribute to the understanding of the capabilities of deep learning models and their applicability in addressing complex problems like identifying lesions in colonoscopy images. The results also offer valuable guidance to researchers, developers, and enthusiasts in choosing suitable open-source systems for their specific needs, saving them time and effort in model development.
Exploring Humor in Natural Language Processing: A Comprehensive Review of JOKER Tasks at CLEF Symposium 2023

As linguistic phenomena that showcase the richness and complexity of human language, puns pose significant challenges to natural language processing (NLP) systems. The significance of these tasks lies not only in the enhancement of humor recognition capabilities in AI but also in the improvement of machine translation systems, as puns often encapsulate cultural, idiomatic, and context-sensitive information [1]. We investigate a broad range of models, including traditional machine learning methods, such as Random Forests and Naive Bayes, and state-of-the-art deep learning architectures, like Transformer and BERT-based models. Experimental results show promising advancements in these areas, with specific models outperforming others depending on the task. The results contribute valuable insights towards the goal of improving the understanding, detection, and translation of puns in AI systems, ultimately promoting a more nuanced and culturally sensitive communication interface in AI technologies.
RADIATION HAZARD FROM NATURAL RADIOACTIVITY IN THE SEDIMENT OF THE EAST COAST PENINSULAR MALAYSIA EXCLUSIVE ECONOMIC ZONE (EEZ) (Hazard Sinaran Dari Radionuklid Tabii Dalam Sedimen Di Zon Ekslusif Ekonomi (ZEE) Perairan Pantai Timur Semenanjung Malaysia)

Sixteen marine sediment cores from selected locations within the EEZ were collected for determination of NORM concentrations. The samples were dried, finely ground, sealed in a container and stored for more than 30 days to establish secular equilibrium between 226 Ra and 228 Ra and their respective radioactive progenies. They were counted and quantified using highpurity germanium (HPGe) detector coupled to spectrometer at respective progeny energy peak. Three calculated parameters from NORM concentrations, i.e. the Radium equivalent (Raeq), Representative level index (I r), External hazard index (Hex) are ranged between 68.6 – 210.5 Bq/kg (mean 143.1 27.7 Bq/kg), 0.50 – 1.54 (mean 1.04 0.20) and 0.19 – 0.57 (mean 0.39 0.07 Bq/kg), respectively. This is well below the recommended limit value of 370 Bq/kg (for Raeq) and unity (for Hex). It is also slightly less than the background level radiation from soil in Peninsular Malaysia, I r ~ 1.5. Therefore, the additional radiation exposure to peoples handling the samples is small, when compared to the background radiation received by them. The data is discussed and compared with those given in the literature.
Concept Detection and Caption Prediction in ImageCLEFmedical Caption 2023 with Convolutional Neural Networks, Vision and Text-to-Text Transfer Transformers

This work discusses the participation of CS _ Morgan in the Concept Detection and Caption Prediction tasks of the ImageCLEFmedical 2023 Caption benchmark evaluation campaign. The goal of this task is to automatically identify relevant concepts and their locations in images, as well as generate coherent captions for the images. The dataset used for this task is a subset of the extended Radiology Objects in Context (ROCO) dataset. The implementation approach employed by us involved the use of pre-trained Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and Text-to-Text Transfer Transformer (T5) architectures. These models were leveraged to handle the different aspects of the tasks, such as concept detection and caption generation. In the Concept Detection task, the objective was to classify multiple concepts associated with each image. We utilized several deep learning architectures with ‘sigmoid’ activation to enable multilabel classification using the Keras framework. We submitted a total of five (5) runs for this task, and the best run achieved an F1 score of 0.4834, indicating its effectiveness in detecting relevant concepts in the images. For the Caption Prediction task, we successfully submitted eight (8) runs. Our approach involved combining the ViT and T5 models to generate captions for the images. For the caption prediction task, the ranking is based on the BERTScore, and our best run achieved a score of 0.5819 based on generating captions using the fine-tuned T5 model from keywords generated using the pre-trained ViT as the encoder.
An Evaluation of MUSS and T5 Models in Scientific Sentence Simplification: A Comparative Study

This paper discusses a study by the QH Research Group at the University of Zurich aimed at simplifying scientific text for SimpleText@CLEF-2023’s Task 3. Using the pre-trained MUSS and T5 models, we explored their effectiveness in reducing sentence complexity without loss of essential information. Performance comparison across various scientific fields was undertaken, using both quantitative and qualitative measures for assessing simplification quality and fluency. Results highlight the substantial potential of both models, yet revealing distinct strengths and weaknesses. Strategies for further enhancements are discussed.
Accenture at CheckThat!-2023: Identifying Claims with Societal Impact using NLP Data Augmentation

This paper discusses the results of the Accenture Team in the 2023 CheckThat Lab! focusing on check-worthiness classification on multigenre text. Check-worthiness classification is similar to misinformation detection or claim detection, but informed by the potential societal impact of the claims. We utilized high quality back-translation to augment the minority classes in labeled English, Arabic, and Spanish datasets and fine-tune pre-trained foundation models for each of the languages. This method placed 2nd in Arabic, 3rd in English, and 5th in Spanish. We further show that high-quality translation is preferable for data augmentation to translation with lower BLEU scores, and that using NLP data augmentation to increase the minority class in quantities over the minority class shows promise on this task.
Alternative Work Practices and Quit Rates: Methodological Issues and Empirical Evidence for Canada Analytical Studies Research Paper Series Alternative Work Practices and Quit Rates: Methodological Issues and Empirical Evidence for Canada

The Analytical Studies Research Paper Series provides for the circulation, on a pre-publication basis, of research conducted by Branch staff, visiting Fellows and academic associates. The Research Paper Series is intended to stimulate discussion on a variety of topics including labour, business firm dynamics, pensions, agriculture, mortality, language, immigration, statistical computing and simulation. Readers of the series are encouraged to contact the authors with comments, criticisms and suggestions. A list of titles appears inside the back cover of this paper. Papers in the series are distributed to Statistics Canada Regional Offices, provincial statistical focal points, research institutes, and specialty libraries. These papers can be downloaded from the Internet at www.statcan. ca. The authors wish to thank Richard Chaykowski and seminar participants at McMaster University for their helpful comments. Remaining errors and imprecisions are ours. This paper represents the views of the authors and does not necessarily reflect the opinions of Statistics Canada.
Protection of pregnant women at work in Switzerland: implementation and experiences of maternity protection legislation

alessia.zellweger@hesav.ch Abstract Objectives Like most industrialized countries, Switzerland has introduced legislation to protect the health of pregnant workers and their unborn children from workplace hazards. This study aims to assess legislation’s degree of implementation in the French-speaking part of Switzerland and understand the barriers to and resources supporting its implementation. Methods Data were collected using mixed methods: 1) an online questionnaire send to 333 gynecologist-obstetricians (GOs) and 637 midwives; 2) exploratory semi-structured interviews with 5 workers who had had a pregnancy in the last 5 years. Results Questionnaire response rates were 32% for GOs and 54% for midwives. Data showed that several aspects of the implementation of maternity protection policies could be improved. Where patients encounter workplace hazards, GOs and midwives estimated that they only received a risk assessment from the employer in about 5% and 2% of cases, respectively. Preventive leave is underprescribed: 32% of GOs reported that they “often” or “always” prescribed preventive leave in cases involving occupational hazards; 58% of GOs reported that they “often” or “always” prescribed sick leave instead. Interviews with workers identified several barriers to the implementation of protective policies in workplaces: a lack of information about protective measures and pregnancy rights; organizational problems triggered by job and schedule adjustments; and discrepancies between some safety measures and their personal needs. Conclusions Results demonstrate the need to improve the implementation and appropriateness of maternity protection legislation in Switzerland. More research is required to identify the factors affecting its
Corporate Income Tax Reform in Switzerland Corporate Income Tax Reform in Switzerland

This paper analyzes the likely economic consequences of a specific proposal for corporate i n-come tax reform in Switzerland that is based on the recent ERU (2001) report. The proposal includes a partial dividend tax relief, more effective taxation of capital gains, and a property tax reduction, all relating to qualified stakes in corporate firms. Based on an analytical and quantitative analysis, we find that the reform removes an important tax barrier against dividend payments , reduces the cost of equity capital, thereby reduces debt leverage and encourages i n-vestment in the corporate sector. In stimulating transitional growth towards higher long-run income levels, the reform expands tax bases and thereby becomes considerably less costly in the long-run. A sensitivity analysis shows that the quantitative results are rather robust. 1 The paper is based on a policy report to the Swiss Tax Administration (ESTV) which supplied essential empirical information.
A new algorithm for “the LCS problem” with application in compressing genome resequencing data

The longest common subsequence (LCS) problem is a classical problem in computer science, and forms the basis of the current best-performing reference-based compression schemes for genome resequencing data. First, we present a new algorithm for the LCS problem. Then, we introduce an LCS-motivated reference-based compression scheme using the components of the LCS, rather than the LCS itself. For the Homo sapiens genome (original size 3,080,436,051 bytes), our proposed scheme compressed the genome to 5,267,656 bytes. This can be compared with the previous best results of 19,666,791 bytes (Wang and Zhang, 2011) and 17,971,030 bytes (Pinho, Pratas, and Garcia, 2011). Thus, our compression ratio is about 3.73 to 3.41 times better than those from the state-of-the-art reference-based compression algorithms.
Studies on the yield components of Amaranthus sp in tropical plains of West Bengal (India)

The edible Amaranthus is one of the popular leafy vegetables in the South-East Asia and is increasingly its popularity in the Asia and elsewhere due to its attractive leaf color, taste and nutritional value. In some countries of South Africa, this crop is consumed as staple food. Indeed, this is an unparallel crop for producing leafy vegetable as well as Amaranthus grains, considering its adaptability round the year in any tropical countries in the globe. It is very common item for regular food/diets and medicinal uses. Fifteen genotypes of Amaranthus sp were collected from National Bureau of Plant Genetic Resources (NBPGR), New-Delhi in the beginning of the year 2011 for their location trial performance in this part of West Bengal. These genotypes were grown in the Crop Research Farm (CRF) following Randamized Block Design (RBD) lay-out having 4 replications. Various phenotypic characters viz. (i) Days to seed germination (ii) Plant height (cm) in 15 days (iii) Leaf plant -1 in 15 days (number) (iv) Leaf length in 15 days (cm) (v) Leaf width in 15 days (cm) (vi) Length of petiole(cm) in 15 days (vii) Plant height in 30 days (cm) (viii) Leaf plant -1 in 30 days (number) (ix) Leaf length in 30 days (cm) (x) Length of petiole in 30 days (cm) (xi) Leaf width in 30 days (cm) (xii) Basal diameter plant -1 in 30 days (cm) (xiii) Plant height in 60 days (cm) (xiv) Number of leaves plant -1 in 60 days (xv) Leaf length in 60 days (cm) (xvi) Leaf width in 60 days (cm) (xvii) Length of petiole in 60 days (cm) (xviii) Basal diameter plant -1 in 60 days (xix) Days to emergence of inflorescence (days) (xx) Inflorescence plant -1 (number) and all these data were recorded properly which have been exhibited
Building Characterization Using L-Band Polarimetric

This letter proposes a building characterization tech- nique for L-band polarimetric interferometric synthetic aperture radar (SAR) data. This characterization consists of building iden- tification and height estimation. Initially, a polarimetric interfer- ometric segmentation is performed to isolate buildings from their surroundings. This classification identifies three basic categories: single bounce, double bounce, and volume diffusion. In order to compensate for the misclassifications among the volume and the double-bounce classes, interferometric phases given by the high- resolution Estimation of Signal Parameters via Rotational Invari- ance Techniques (ESPRIT) method are analyzed. Once buildings are localized, a phase-to-height procedure is applied to retrieve building height information. The method is validated using E-SAR, German Aerospace Center (DLR) fully polarimetric SAR data, at L-band, repeat-pass mode, over the Oberpfaffenhofen, Germany, test site, with a spatial resolution of 1.5 m in range and azimuth. More than 80% of buildings are retrieved with acceptably accu- rate height estimates.