Demo Data from EDBT 2026 / reviewers in the wild / expert
Andrea Tocchetti
dblp:281/3807
· DBLP profile ↗
5ranked-venue papers
2as first author
5since 2021 · last 2024
0000-0002-1681-5859ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 2 · 1 first-author · 2 since 2021Databases, data management, data science and information retrieval · 2 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 100% | |
| Computer graphics and multimedia
1 paper |
Visualization and visual analytics · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
interpretability |
0.8 | 1 | 2024 | Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification · IJCAI 2024 |
Machine learning › Trustworthy machine learning › interpretability
post-hoc explanation |
0.8 | 1 | 2024 | Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification · IJCAI 2024 |
Visualization and visual analytics
graph visualization |
0.8 | 1 | 2024 | Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification · IJCAI 2024 |
Visualization and visual analytics
human-in-the-loop |
0.8 | 1 | 2024 | Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification · IJCAI 2024 |
Methods — techniques the papers use, named apart from their topics
human-in-the-loop · 1.5CNN · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Unveiling Human-AI Interaction and Subjective Perceptions About Artificial Intelligent Agents
Mathyas Giudici, Federica Liguori, Andrea Tocchetti, Marco Brambilla 0001 |
ICWE | 3 |
| 2024 | Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification
Antonio De Santis, Andrea Tocchetti, Marco Brambilla 0001 |
IJCAI | 3 |
| 2024 | Detecting Social InteractionabstractIn this paper, we introduce a system designed to track the social interactions of individuals seated around a table, such as during work sessions or breaks. The system employs sensor technologies and data processing algorithms to monitor the seating arrangement and identify patterns of social engagement among participants. This represents an initial step towards developing a comprehensive system that provides deeper insights into collaborative behaviours and interpersonal connections within organizational settings. Andrea Tocchetti, Stefano Brusadelli, Maria Grazia Fugini |
WETICE | 1 |
| 2024 | Policy Sandboxing: Empathy As An Enabler Towards Inclusive Policy-MakingabstractDigitally-supported participatory methods are often used in policy-making to develop inclusive policies by collecting and integrating citizen's opinions. However, these methods fail to capture the complexity and nuances in citizen's needs, i.e., citizens are generally unaware of other's needs, perspectives, and experiences. Consequently, policies developed with this underlying gap tend to overlook the alignment of multistakeholder perspectives, and design policies based on the optimization of high-level demographic features. In our contribution, we propose a method to enable citizens understand other's perspectives and calibrate their positions. First, we collected requirements and design principles to develop our approach by involving stakeholders and experts in policymaking in a series of workshops. Then, we conducted a crowdsourcing study with 420 participants to compare the effect of different text and images, on people's initial and final motivations and their willingness to change opinions. We observed that both influence participant's opinion change, however, the effect is more pronounced for textual modality. Finally, we discuss overarching implications of designing with empathy to mediate alignment of citizen's perspectives. Andrea Mauri 0001, Yen-Chia Hsu, Himanshu Verma 0001, Andrea Tocchetti, Marco Brambilla 0001, Alessandro Bozzon |
Proc. ACM Hum. Comput. Interact. | 4 |
| 2021 | A Web-Based Co-Creation and User Engagement Method and PlatformabstractAbstract In recent years, new methods to engage citizens in deliberative processes of governments and institutions have been studied. Such methodologies have become a necessity to assure the efficacy and longevity of policies. Several tools and solutions have been proposed while trying to achieve such a goal. The dual problem to citizen engagement is how to provide policy-makers with useful and actionable insights stemming from those processes. In this paper, we propose a research featuring a method and implementation of a crowdsourcing and co-creation technique that can provide value to both citizens and policy-makers engaged in the policy-making process. Thanks to our methodology, policy-makers can design challenges for citizens to partake, cooperate and provide their input. We also propose a web-based tool that allow citizens to participate and produce content to support the policy-making processes through a gamified interface that focuses on emotional and vision-oriented content. Andrea Tocchetti, Lorenzo Corti, Marco Brambilla 0001, Diletta Di Marco |
ICWE | 1 |