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Author | Dawid Rymarczyk; Joost van de Weijer; Bartosz Zielinski; Bartlomiej Twardowski | ||||
Title | ICICLE: Interpretable Class Incremental Continual Learning. Dawid Rymarczyk | Type | Conference Article | ||
Year | 2023 | Publication | 20th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1887-1898 | ||
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Abstract | Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continual learning poses new challenges for interpretability, as the rationale behind model predictions may change over time, leading to interpretability concept drift. We address this problem by proposing Interpretable Class-InCremental LEarning (ICICLE), an exemplar-free approach that adopts a prototypical part-based approach. It consists of three crucial novelties: interpretability regularization that distills previously learned concepts while preserving user-friendly positive reasoning; proximity-based prototype initialization strategy dedicated to the fine-grained setting; and task-recency bias compensation devoted to prototypical parts. Our experimental results demonstrate that ICICLE reduces the interpretability concept drift and outperforms the existing exemplar-free methods of common class-incremental learning when applied to concept-based models. | ||||
Address | Paris; France; October 2023 | ||||
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Area | Expedition | Conference | ICCV | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ RWZ2023 | Serial | 3947 | ||
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Author | Jordy Van Landeghem; Ruben Tito; Lukasz Borchmann; Michal Pietruszka; Pawel Joziak; Rafal Powalski; Dawid Jurkiewicz; Mickael Coustaty; Bertrand Anckaert; Ernest Valveny; Matthew Blaschko; Sien Moens; Tomasz Stanislawek | ||||
Title | Document Understanding Dataset and Evaluation (DUDE) | Type | Conference Article | ||
Year | 2023 | Publication | 20th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 19528-19540 | ||
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Abstract | We call on the Document AI (DocAI) community to re-evaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI. | ||||
Address | Paris; France; October 2023 | ||||
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Area | Expedition | Conference | ICCV | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ LTB2023 | Serial | 3948 | ||
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Author | Yuyang Liu; Yang Cong; Dipam Goswami; Xialei Liu; Joost Van de Weijer | ||||
Title | Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection | Type | Conference Article | ||
Year | 2023 | Publication | 20th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 11367-11377 | ||
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Abstract | In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model. | ||||
Address | Paris; France; October 2023 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICCV | ||
Notes | LAMP | Approved | no | ||
Call Number | Admin @ si @ LCG2023 | Serial | 3949 | ||
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Author | Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez | ||||
Title | Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules | Type | Conference Article | ||
Year | 2023 | Publication | 37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery | Abbreviated Journal | |
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Abstract | Pòster | ||||
Address | Munich; Germany; June 2023 | ||||
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Area | Expedition | Conference | CARS | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ TGR2023a | Serial | 3950 | ||
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Author | Sonia Baeza; Debora Gil; Carles Sanchez; Guillermo Torres; Ignasi Garcia Olive; Ignasi Guasch; Samuel Garcia Reina; Felipe Andreo; Jose Luis Mate; Jose Luis Vercher; Antonio Rosell | ||||
Title | Biopsia virtual radiomica para el diagnóstico histológico de nódulos pulmonares – Resultados intermedios del proyecto Radiolung | Type | Conference Article | ||
Year | 2023 | Publication | SEPAR | Abbreviated Journal | |
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Abstract | Pòster | ||||
Address | Granada; Spain; June 2023 | ||||
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Area | Expedition | Conference | SEPAR | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ BGS2023 | Serial | 3951 | ||
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Author | Debora Gil; Guillermo Torres; Carles Sanchez | ||||
Title | Transforming radiomic features into radiological words | Type | Conference Article | ||
Year | 2023 | Publication | IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
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Abstract | Pòster | ||||
Address | Cartagena de Indias; Colombia; April 2023 | ||||
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Area | Expedition | Conference | ISBI | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ GTS2023 | Serial | 3952 | ||
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Author | Pau Cano; Debora Gil; Eva Musulen | ||||
Title | Towards automatic detection of helicobacter pylori in histological samples of gastric tissue | Type | Conference Article | ||
Year | 2023 | Publication | IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
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Address | Cartagena de Indias; Colombia; April 2023 | ||||
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Area | Expedition | Conference | ISBI | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ CGM2023 | Serial | 3953 | ||
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Author | Guillermo Torres; Debora Gil; Antonio Rosell; Sonia Baeza; Carles Sanchez | ||||
Title | A radiomic biopsy for virtual histology of pulmonary nodules | Type | Conference Article | ||
Year | 2023 | Publication | IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
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Abstract | Pòster | ||||
Address | Cartagena de Indias; Colombia; April 2023 | ||||
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Area | Expedition | Conference | ISBI | ||
Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ TGR2023b | Serial | 3954 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Advances in Face Presentation Attack Detection | Type | Book Whole | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023a | Serial | 3955 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Face Presentation Attack Detection (PAD) Challenges | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 17–35 | ||
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Abstract | In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges. | ||||
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Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023b | Serial | 3956 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Best Solutions Proposed in the Context of the Face Anti-spoofing Challenge Series | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 37–78 | ||
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Abstract | The PAD competitions we organized attracted more than 835 teams from home and abroad, most of them from the industry, which shows that the topic of face anti-spoofing is closely related to daily life, and there is an urgent need for advanced algorithms to solve its application needs. Specifically, the Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round; the Chalearn Face Anti-spoofing Attack Detection Challenge attracted 340 teams in the development stage, and finally, 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively; the 3D High-Fidelity Mask Face Presentation Attack Detection Challenge attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. In this chapter, we briefly the methods developed by the teams participating in each competition, and introduce the algorithm details of the top-three ranked teams in detail. | ||||
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Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023d | Serial | 3958 | ||
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Author | Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z Li | ||||
Title | Face Anti-spoofing Progress Driven by Academic Challenges | Type | Book Chapter | ||
Year | 2023 | Publication | Advances in Face Presentation Attack Detection | Abbreviated Journal | |
Volume | Issue | Pages | 1–15 | ||
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Abstract | With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges | ||||
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Series Editor | Series Title | Abbreviated Series Title | SLCV | ||
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Area | Expedition | Conference | |||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ WGE2023c | Serial | 3957 | ||
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Author | Jose Luis Gomez | ||||
Title | Synth-to-real semi-supervised learning for visual tasks | Type | Book Whole | ||
Year | 2023 | Publication | Going beyond Classification Problems for the Continual Learning of Deep Neural Networks | Abbreviated Journal | |
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Abstract | The curse of data labeling is a costly bottleneck in supervised deep learning, where large amounts of labeled data are needed to train intelligent systems. In onboard perception for autonomous driving, this cost corresponds to the labeling of raw data from sensors such as cameras, LiDARs, RADARs, etc. Therefore, synthetic data with automatically generated ground truth (labels) has aroused as a reliable alternative for training onboard perception models.
However, synthetic data commonly suffers from synth-to-real domain shift, i.e., models trained on the synthetic domain do not show their achievable accuracy when performing in the real world. This shift needs to be addressed by techniques falling in the realm of domain adaptation (DA). The semi-supervised learning (SSL) paradigm can be followed to address DA. In this case, a model is trained using source data with labels (here synthetic) and leverages minimal knowledge from target data (here the real world) to generate pseudo-labels. These pseudo-labels help the training process to reduce the gap between the source and the target domains. In general, we can assume accessing both, pseudo-labels and a few amounts of human-provided labels for the target-domain data. However, the most interesting and challenging setting consists in assuming that we do not have human-provided labels at all. This setting is known as unsupervised domain adaptation (UDA). This PhD focuses on applying SSL to the UDA setting, for onboard visual tasks related to autonomous driving. We start by addressing the synth-to-real UDA problem on onboard vision-based object detection (pedestrians and cars), a critical task for autonomous driving and driving assistance. In particular, we propose to apply an SSL technique known as co-training, which we adapt to work with deep models that process a multi-modal input. The multi-modality consists of the visual appearance of the images (RGB) and their monocular depth estimation. The synthetic data we use as the source domain contains both, object bounding boxes and depth information. This prior knowledge is the starting point for the co-training technique, which iteratively labels unlabeled real-world data and uses such pseudolabels (here bounding boxes with an assigned object class) to progressively improve the labeling results. Along this process, two models collaborate to automatically label the images, in a way that one model compensates for the errors of the other, so avoiding error drift. While this automatic labeling process is done offline, the resulting pseudolabels can be used to train object detection models that must perform in real-time onboard a vehicle. We show that multi-modal co-training improves the labeling results compared to single-modal co-training, remaining competitive compared to human labeling. Given the success of co-training in the context of object detection, we have also adapted this technique to a more crucial and challenging visual task, namely, onboard semantic segmentation. In fact, providing labels for a single image can take from 30 to 90 minutes for a human labeler, depending on the content of the image. Thus, developing automatic labeling techniques for this visual task is of great interest to the automotive industry. In particular, the new co-training framework addresses synth-to-real UDA by an initial stage of self-training. Intermediate models arising from this stage are used to start the co-training procedure, for which we have elaborated an accurate collaboration policy between the two models performing the automatic labeling. Moreover, our co-training seamlessly leverages datasets from different synthetic domains. In addition, the co-training procedure is agnostic to the loss function used to train the semantic segmentation models which perform the automatic labeling. We achieve state-of-the-art results on publicly available benchmark datasets, again, remaining competitive compared to human labeling. Finally, on the ground of our previous experience, we have designed and implemented a new SSL technique for UDA in the context of visual semantic segmentation. In this case, we mimic the labeling methodology followed by human labelers. In particular, rather than labeling full images at a time, categories of semantic classes are defined and only those are labeled in a labeling pass. In fact, different human labelers can become specialists in labeling different categories. Afterward, these per-category-labeled layers are combined to provide fully labeled images. Our technique is inspired by this methodology since we perform synth-to-real UDA per category, using the self-training stage previously developed as part of our co-training framework. The pseudo-labels obtained for each category are finally fused to obtain fully automatically labeled images. In this context, we have also contributed to the development of a new photo-realistic synthetic dataset based on path-tracing rendering. Our new SSL technique seamlessly leverages publicly available synthetic datasets as well as this new one to obtain state-of-the-art results on synth-to-real UDA for semantic segmentation. We show that the new dataset allows us to reach better labeling accuracy than previously existing datasets, at the same time that it complements well them when combined. Moreover, we also show that the new human-inspired SSL technique outperforms co-training. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Antonio Lopez | |
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Gom2023 | Serial | 3961 | ||
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Author | Jose Elias Yauri | ||||
Title | Deep Learning Based Data Fusion Approaches for the Assessment of Cognitive States on EEG Signals | Type | Book Whole | ||
Year | 2023 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | For millennia, the study of the couple brain-mind has fascinated the humanity in order to understand the complex nature of cognitive states. A cognitive state is the state of the mind at a specific time and involves cognition activities to acquire and process information for making a decision, solving a problem, or achieving a goal.
While normal cognitive states assist in the successful accomplishment of tasks; on the contrary, abnormal states of the mind can lead to task failures due to a reduced cognition capability. In this thesis, we focus on the assessment of cognitive states by means of the analysis of ElectroEncephaloGrams (EEG) signals using deep learning methods. EEG records the electrical activity of the brain using a set of electrodes placed on the scalp that output a set of spatiotemporal signals that are expected to be correlated to a specific mental process. From the point of view of artificial intelligence, any method for the assessment of cognitive states using EEG signals as input should face several challenges. On the one hand, one should determine which is the most suitable approach for the optimal combination of the multiple signals recorded by EEG electrodes. On the other hand, one should have a protocol for the collection of good quality unambiguous annotated data, and an experimental design for the assessment of the generalization and transfer of models. In order to tackle them, first, we propose several convolutional neural architectures to perform data fusion of the signals recorded by EEG electrodes, at raw signal and feature levels. Four channel fusion methods, easy to incorporate into any neural network architecture, are proposed and assessed. Second, we present a method to create an unambiguous dataset for the prediction of cognitive mental workload using serious games and an Airbus-320 flight simulator. Third, we present a validation protocol that takes into account the levels of generalization of models based on the source and amount of test data. Finally, the approaches for the assessment of cognitive states are applied to two use cases of high social impact: the assessment of mental workload for personalized support systems in the cockpit and the detection of epileptic seizures. The results obtained from the first use case show the feasibility of task transfer of models trained to detect workload in serious games to real flight scenarios. The results from the second use case show the generalization capability of our EEG channel fusion methods at k-fold cross-validation, patient-specific, and population levels. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Aura Hernandez;Debora Gil | |
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Notes | IAM | Approved | no | ||
Call Number | Admin @ si @ Yau2023 | Serial | 3962 | ||
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Author | Artur Xarles; Sergio Escalera; Thomas B. Moeslund; Albert Clapes | ||||
Title | ASTRA: An Action Spotting TRAnsformer for Soccer Videos | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports | Abbreviated Journal | |
Volume | Issue | Pages | 93–102 | ||
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Abstract | In this paper, we introduce ASTRA, a Transformer-based model designed for the task of Action Spotting in soccer matches. ASTRA addresses several challenges inherent in the task and dataset, including the requirement for precise action localization, the presence of a long-tail data distribution, non-visibility in certain actions, and inherent label noise. To do so, ASTRA incorporates (a) a Transformer encoder-decoder architecture to achieve the desired output temporal resolution and to produce precise predictions, (b) a balanced mixup strategy to handle the long-tail distribution of the data, (c) an uncertainty-aware displacement head to capture the label variability, and (d) input audio signal to enhance detection of non-visible actions. Results demonstrate the effectiveness of ASTRA, achieving a tight Average-mAP of 66.82 on the test set. Moreover, in the SoccerNet 2023 Action Spotting challenge, we secure the 3rd position with an Average-mAP of 70.21 on the challenge set. | ||||
Address | Otawa; Canada; October 2023 | ||||
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Area | Expedition | Conference | MMSports | ||
Notes | HUPBA | Approved | no | ||
Call Number | Admin @ si @ XEM2023 | Serial | 3970 | ||
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