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18th International Conferences IADIS Information Systems 2025 and 23rd International Conference on e-Society 2025

作者： Jacucci, Gianni Department of Information Engineering and Computer Science - DISI University of Trento Via Sommarive TN Povo9I-38123 Italy

ISBN: (纸本)9789898704658

This focused and dedicated short research paper examines the architectural design work of a Trustworthy Governable Platform (TGP), that redefines the information paradigm for institutional communication platforms-particularly in relational enterprises-where complex social dynamics are at play. Examples include addressing accountability and ensuring contextual integrity. We adopt a design-oriented sociological perspective to uncover how the platform's creator ensured that its social use and meaning align with the specific situational needs from the outset. The study begins by examining the key features of social interactions that characterise relational enterprises, as identified and articulated by the author of the design process through selected theoretical approaches. Next, we analyse the communicative affordances of technology in action that are necessary to realise these social interaction features. Finally, we explore the design elements of the supporting information and communication infrastructure that the author deemed essential to enable the appropriate options for action. These elements ensure users experience the intended social usage and meaning of the technology in the context of relational enterprises. The purpose of the essay is to stimulate discussion on what we deem a most promising enterprise. TGP has been under active technical development for a number of years and, at the time of writing, is undergoing initial trials for deployment and appropriation. © 2025 Proceedings of the International Conferences IADIS Information Systems 2025 and e-Society 2025. All rights reserved.

关键词： Communications on Information Complex Adaptive System Entrepreneurial Management & Organisation Cybernetics Relational Enterprises Socio-Cybernetics Theory

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Blending BDI Agents with Object-Oriented and Functional Programming with JaKtA

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SN computer science 2024年第8期5卷 1003页

作者： Baiardi, Martina Burattini, Samuele Ciatto, Giovanni Pianini, Danilo Department of Computer Science and Engineering (DISI) Alma Mater Studiorum Universitá di Bologna Via dell’Universitá 50 FC Cesena 47522 Italy

The popularity of multi-paradigm languages is on the rise, enabling developers to select the most appropriate paradigm for each task. While object-oriented and functional programming are commonly combined, other paradigms can also be hybridized. This paper introduces JaKtA, an internal Domain-Specific Language designed to support the definition of Belief-Desire-Intention agents in Kotlin. Our work represents an initial exploration into blending Agent-Oriented Programming with other prevalent paradigms, emphasizing the potential benefits of using internal Domain-Specific Languages. We demonstrate, through JaKtA, how this approach facilitates the creation of compact and expressive Belief-Desire-Intention agents that seamlessly integrate with the host language, its libraries, and tooling. © The Author(s) 2024.

关键词： AgentSpeak(L) BDI DSL JaKtA Kotlin

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Multimodal Industrial Anomaly Detection by Crossmodal Feature Mapping

Multimodal Industrial Anomaly Detection by Crossmodal Featur...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Alex Costanzino Pierluigi Zama Ramirez Giuseppe Lisanti Luigi Di Stefano Department of Computer Science and Engineering (DISI) CVLAB University of Bologna Italy

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements have shown the potential of lever-aging both point clouds and images to localize anomalies. Nevertheless, their applicability in industrial manufacturing is often constrained by significant drawbacks, such as the use of memory banks, which leads to a substantial in-crease in terms of memory footprint and inference times. We propose a novel light and fast framework that learns to map features from one modality to the other on nominal samples and detect anomalies by pinpointing inconsisten-cies between observed and mapped features. Extensive ex-periments show that our approach achieves state-of-the-art detection and segmentation performance in both the stan-dard and few-shot settings on the MVTec 3D-AD dataset while achieving faster inference and occupying less memory than previous multimodal AD methods. Furthermore, we propose a layer pruning technique to improve memory and time efficiency with a marginal sacrifice in performance.

关键词： Point cloud compression computer vision Memory management Feature extraction Pattern recognition Manufacturing Anomaly detection

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Test Time Training for Industrial Anomaly Segmentation

Test Time Training for Industrial Anomaly Segmentation

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Alex Costanzino Pierluigi Zama Ramirez Mirko Del Moro Agostino Aiezzo Giuseppe Lisanti Samuele Salti Luigi Di Stefano Department of Computer Science and Engineering (DISI) CVLAB University of Bologna Italy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Anomaly Detection and Segmentation (AD&S) is crucial for industrial quality control. While existing methods excel in generating anomaly scores for each pixel, practical applications require producing a binary segmentation to identify anomalies. Due to the absence of labeled anomalies in many real scenarios, standard practices binarize these maps based on some statistics derived from a validation set containing only nominal samples, resulting in poor segmentation performance. This paper addresses this problem by proposing a test time training strategy to improve the segmentation performance. Indeed, at test time, we can extract rich features directly from anomalous samples to train a classifier that can discriminate defects effectively. Our general approach can work downstream to any AD&S method that provides an anomaly score map as output, even in mul-timodal settings. We demonstrate the effectiveness of our approach over baselines through extensive experimentation and evaluation on MVTec AD and MVTec 3D-AD.

关键词： Training computer vision Conferences Quality control Feature extraction Pattern recognition Standards

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Accessibility of Graphic Scores: Design and Evaluation of Tactile Supports for Blind People

Accessibility of Graphic Scores: Design and Evaluation of Ta...

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International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Manenti, Francesco Dal Rí, Francesco Ardan Conservatory of Music Giuseppe Verdi Via Conservatorio 12 Milan20122 Italy DISI-Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 Trento38123 Italy

In this paper, we present the outcome of a musical workshop on accessibility of graphic scores for blind people, held in Brescia, Italy with the support of Unione Italiana dei Ciechi e degli Ipovedenti ETS-APS1 (UIC). Through a series of meetings and side-by-side study sessions, four blind musicians were involved in a collective musical experiment, culminating in a final concert based on three pieces from the historical repertoire. During these meetings, a series of semi-structured interviews with the participants allowed us to collect a series of guidelines and suggestions regarding the design and creation of tactile scores and to discuss performance strategies, in order to facilitate accessibility of this peculiar repertoire to visually impaired people. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Music

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TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network

TemporalStereo: Efficient Spatial-Temporal Stereo Matching N...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Youmin Zhang Matteo Poggi Stefano Mattoccia Department of Computer Science and Engineering (DISI) University of Bologna

We present TemporalStereo, a coarse-to-fine stereo matching network that is highly efficient, and able to effectively exploit the past geometry and context information to boost matching accuracy. Our network leverages sparse cost volume and proves to be effective when a single stereo pair is given. However, its peculiar ability to use spatio-temporal information across stereo sequences allows TemporalStereo to alleviate problems such as occlusions and reflective regions while enjoying high efficiency also in this latter case. Notably, our model - trained once with stereo videos - can run in both single-pair and temporal modes seamlessly. Experiments show that our network relying on camera motion is robust even to dynamic objects when running on videos. We validate TemporalStereo through extensive experiments on synthetic (SceneFlow, TartanAir) and real (KITTI 2012, KITTI 2015) datasets. Our model achieves state-of-the-art performance on any of these datasets.

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Ski Pose Estimation

Ski Pose Estimation

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Sport, Technology and Research (STAR), IEEE International Workshop on

作者： Giulia Martinelli Federico Diprima Niccoló Bisagno Nicola Conci DISI - Department of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350351453

ISBN: (纸本)9798350351460

Human Pose Estimation (HPE) aims at retrieving the position of human joints from images or videos. In many winter sports, the human pose captures only part of the action, as practitioners use skis and poles for propulsion and balance on the snow. In this work we propose a novel architecture that estimates the position of skis and poles as an integral part of the human skeleton. We leverage available algorithms to estimate the human body pose, which is then used as a prior for a novel self-supervised estimation approach for ski poses to retrieve the position of skis and poles using a lightweight transformer. The architecture learns to reconstruct randomly missing joints, that are masked at training time. During testing, the network the whole skeleton, which now includes also skis and poles. To overcome the lack of annotated data of ski position, we employ a variety of techniques to help our architecture establishing a link between the human body pose prior and the ski position, such as domain adaptation, data annotation and synthetic data augmentation. Our results show how our method can successfully retrieve the ski position for ski-jumping scenarios.

关键词： Training Visualization Pose estimation Transformers Skeleton Image reconstruction Sports

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Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation

Exploiting the Complementarity of 2D and 3D Networks to Addr...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Adriano Cardace Pierluigi Zama Ramirez Samuele Salti Luigi Di Stefano Department of Computer Science and Engineering (DISI) University of Bologna Italy

3D semantic segmentation is a critical task in many real-world applications, such as autonomous driving, robotics, and mixed reality. However, the task is extremely challenging due to ambiguities coming from the unstructured, sparse, and uncolored nature of the 3D point clouds. A possible solution is to combine the 3D information with others coming from sensors featuring a different modality, such as RGB cameras. Recent multi-modal 3D semantic segmentation networks exploit these modalities relying on two branches that process the 2D and 3D information independently, striving to maintain the strength of each modality. In this work, we first explain why this design choice is effective and then show how it can be improved to make the multi-modal semantic segmentation more robust to domain shift. Our surprisingly simple contribution achieves state-of-the-art performances on four popular multi-modal unsupervised domain adaptation benchmarks, as well as better results in a domain generalization scenario.

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GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction

GO-SLAM: Global Optimization for Consistent 3D Instant Recon...

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International Conference on computer Vision (ICCV)

作者： Youmin Zhang Fabio Tosi Stefano Mattoccia Matteo Poggi Department of Computer Science and Engineering (DISI) University of Bologna Italy

Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction. Purposely, we present GO-SLAM, a deep-learning-based dense visual SLAM framework globally optimizing poses and 3D reconstruction in real-time. Robust pose estimation is at its core, supported by efficient loop closing and online full bundle adjustment, which optimize per frame by utilizing the learned global geometry of the complete history of input frames. Simultaneously, we update the implicit and continuous surface representation on-the-fly to ensure global consistency of 3D reconstruction. Results on various synthetic and real-world datasets demonstrate that GO-SLAM outperforms state-of-the-art approaches at tracking robustness and reconstruction accuracy. Furthermore, GO-SLAM is versatile and can run with monocular, stereo, and RGB-D input.

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Looking at words and points with attention: a benchmark for text-to-shape coherence

Looking at words and points with attention: a benchmark for ...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Andrea Amaduzzi Giuseppe Lisanti Samuele Salti Luigi Di Stefano Department of Computer Science and Engineering (DISI) CVLab University of Bologna Italy

While text-conditional 3D object generation and manipulation have seen rapid progress, the evaluation of coherence between generated 3D shapes and input textual descriptions lacks a clear benchmark. The reason is twofold: a) the low quality of the textual descriptions in the only publicly available dataset of text-shape pairs; b) the limited effectiveness of the metrics used to quantitatively assess such coherence. In this paper, we propose a comprehensive solution that addresses both weaknesses. Firstly, we employ large language models to automatically refine textual descriptions associated with shapes. Secondly, we propose a quantitative metric to assess text-to-shape coherence, through cross-attention mechanisms. To validate our approach, we conduct a user study and compare quantitatively our metric with existing ones. The refined dataset, the new metric and a set of text-shape pairs validated by the user study comprise a novel, fine-grained benchmark that we publicly release to foster research on text-to-shape coherence of text-conditioned 3D generative models. Benchmark available at https://***/CrossCoherence-Web/.

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