Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings, Part III
| Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings, Part III |
| Autore | Degen Helmut |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (498 pages) |
| Altri autori (Persone) | NtoaStavroula |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-031-60615-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- HCI International 2024 Thematic Areas and Affiliated Conferences -- List of Conference Proceedings Volumes Appearing Before the Conference -- Preface -- 5th International Conference on Artificial Intelligence in HCI (AI-HCI 2024) -- HCI International 2025 Conference -- Contents - Part III -- Large Language Models for Enhanced Interaction -- Enhancing Relation Extraction from Biomedical Texts by Large Language Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Relation Extraction via In-Context Few-Shot Learning with LLMs -- 3.2 Seq2seq-Based Relation Extraction Enhanced by LLMs -- 3.3 Classification-Based Relation Extraction Enhanced by LLMs -- 4 Experimental Settings -- 4.1 DDI Extraction Task Settings -- 4.2 LLMs and Prompts -- 4.3 PLMs for Seq2seq Methods -- 4.4 PLMs for Classification Methods -- 5 Results and Discussions -- 5.1 In-Context Few-Shot Learning-Based Relation Extraction by LLMs -- 5.2 Seq2seq-Based Relation Extraction Enhanced by LLMs -- 5.3 Classification-Based Relation Extraction Enhanced by LLMs -- 6 Conclusion -- References -- Using a LLM-Based Conversational Agent in the Social Robot Mini -- 1 Introduction -- 2 A Short History of Language Models -- 3 The Proposed System -- 3.1 Prompting -- 4 Integration into Mini -- 4.1 Design of the Conversational Agent Skill -- 5 Evaluation -- 6 Conclusions -- References -- A Proposal to Extend the Modeling Language for Interaction as Conversation for the Design of Conversational Agents -- 1 Introduction -- 2 Related Work -- 2.1 Conversational Agents -- 2.2 Modeling Interaction in Conversational Agents -- 3 MoLIC -- 4 MoLIC's Limitation to Represent Conversational Agents -- 4.1 Standardized Communication Snippets -- 4.2 Transfer of Responsibility / Interlocutor During Communication -- 4.3 Modeling Breakdown Recovery -- 4.4 Conversational Agents' Intelligence.
5 Extending MoLIC -- 5.1 Template Element -- 5.2 Allowing for the Interaction with a Third-Party System -- 5.3 Adaptations to MoLIC 2.0 Elements -- 6 Initial Evaluation of Proposal -- 7 Final Remarks and Future Works -- References -- Optimizing Conversational Commerce Involving Multilingual Consumers Through Large Language Models' Natural Language Understanding Abilities -- 1 Introduction -- 1.1 Objectives and Research Questions -- 2 Review of Related Literature -- 3 Method and Implementation -- 3.1 Technical Architecture -- 3.2 Knowledge Base -- 3.3 Synthetic Customer Data Preparation -- 3.4 Synthetic Seller Persona Creation -- 3.5 Synthetic Sales Conversation Creation -- 4 Results -- 4.1 General Applied CoT Approach -- 4.2 Presence of Necessary Conditions -- 4.3 Product Resolution -- 4.4 Database Insertions -- 4.5 Sample Case -- 4.6 Drawbacks and Limitations -- 5 Discussions -- 6 Conclusion and Future Work -- References -- A Map of Exploring Human Interaction Patterns with LLM: Insights into Collaboration and Creativity -- 1 Introduction -- 2 Related Work -- 2.1 The Undergoing Change in HAII Driven by Large Language Model -- 2.2 The Current Review of Human-AI Interaction -- 3 Method -- 3.1 Search and Selection -- 3.2 Mapping -- 4 Result -- 4.1 Processing Tool -- 4.2 Analysis Assistant -- 4.3 Creative Companion -- 4.4 Processing Agent -- 5 Discussion -- 5.1 Mapping Methodology Based on Human and Algorithmic Approaches -- 5.2 Differences Between Clusters -- 5.3 About the Vacancy in the Mapping -- 5.4 Future Directions -- 6 Limitation and Future Work -- 7 Conclusion -- References -- The Use of Large Language Model in Code Review Automation: An Examination of Enforcing SOLID Principles -- 1 Introduction -- 2 Background -- 2.1 Code Reviews -- 2.2 SOLID Principles -- 2.3 Large Language Model Technology -- 2.4 Mixtral LLM. 2.5 Role of Bots in Code Development and Review -- 2.6 Benefits for Large Global Development Teams -- 3 Related Works -- 3.1 A Systematic Evaluation of Large Language Models of Code -- 3.2 Effects of Adopting Code Review Bots on Pull Requests to OSS Projects -- 3.3 Reducing Human Effort and Improving Quality in Peer Code Reviews Using Automatic Static Analysis and Reviewer Recommendation -- 3.4 ChatGPT: A Study of Its Utility for Common Software Engineering Tasks -- 3.5 Insights and Implications for LLM-Based Code Review -- 4 Proposed Concept -- 4.1 Proposed Architecture and Integration -- 4.2 Usage of the Proposed Bot -- 5 Impact Analysis -- 5.1 Comparison with Existing Solutions -- 5.2 Potential Benefits -- 5.3 Challenges and Limitations -- 6 Conclusion -- References -- LLM Based Multi-agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain -- 1 Introduction -- 2 Related Work -- 2.1 LLMs in the PA Domain -- 3 Proposed Approach -- 3.1 Template Pre-processing -- 3.2 Multi-agent Interaction -- 3.3 Document Post-processing -- 4 Experimental Evaluation -- 4.1 Semantics Identification Agent -- 4.2 Information Retrieval Agent -- 4.3 Content Generation Agent -- 4.4 Prompt-Engineered Results -- 5 Conclusions -- References -- Enabling Human-Centered Machine Translation Using Concept-Based Large Language Model Prompting and Translation Memory -- 1 Introduction -- 1.1 Challenges in Traditional Machine Translation Within Human-Computer Interaction Contexts -- 1.2 Augmented Machine Translation via Large Language Model -- 2 Augmented Machine Translation via Concept-Driven Large Language Model Prompting -- 2.1 Motivation -- 2.2 Augmented Instruction for Discourse-Level Style -- 2.3 Augmented Instruction for Concept-Based Sentence-Level Post-editing -- 2.4 Performance Evaluation. 3 Assessing the Proficiency of Large Language Model in Applying Translation Concept -- 3.1 Motivation -- 3.2 The Capability of LLMs to Elucidate Translation Concepts -- 3.3 Assessing the LLM's Proficiency in Identifying When to Apply Translation Concepts -- 3.4 The Capability of LLM to Produce Target Translations that Reflect Relevant Concepts -- 4 Conclusions -- References -- Enhancing Large Language Models Through External Domain Knowledge -- 1 Introduction -- 2 Problem Identification and Objectives -- 3 Related Works -- 4 Design and Development of the Artifact -- 4.1 Expert Knowledge Acquisition -- 4.2 Metadata Provision -- 4.3 Prompt Generation -- 5 Demonstration -- 5.1 Implementation -- 5.2 Case Study -- 6 Discussion -- References -- ChatGPT and Language Translation -- 1 Introduction -- 1.1 Historical Background - PreGPT -- 1.2 Background - LLMs and ChatGPT -- 1.3 ChatGPT and Translation -- 2 Motivation and Methodology for This Study -- 2.1 Motivation -- 2.2 Methodology -- 2.3 Examples -- 3 Results -- 3.1 Classifying AI Generated Text -- 3.2 Human Ratings of Translation Quality -- 4 Conclusions -- References -- Large Language Models for Tracking Reliability of Information Sources -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- The Heuristic Design Innovation Approach for Data-Integrated Large Language Model -- 1 Introduction -- 2 Related Works -- 2.1 Domain-Specific LLMs -- 2.2 Expert System -- 2.3 Human-AI Collaboration Design -- 3 Method -- 3.1 Overview of DIABot -- 3.2 Prompt -- 3.3 Database -- 3.4 Workflow -- 4 Value Assessment -- 4.1 Experimental Design -- 4.2 Participants -- 4.3 Experimental Process -- 4.4 Experimental Result -- 5 Discussion and Conclusion -- 6 Limitation and Future Work -- A Prompt of DIAbot -- B Tools OpenAPI -- References -- Advancing Human-Robot Interaction Through AI. FER-Pep: A Deep Learning Based Facial Emotion Recognition Framework for Humanoid Robot Pepper -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Pepper -- 3.2 NAOqi Python API -- 3.3 EfficientNetV2 -- 4 Dataset Collection and Preprocessing -- 5 Experiments -- 5.1 Candidate Models for Facial Emotion Recognition -- 6 System Implementation -- 7 Result and Discussion -- 8 Conclusion -- References -- You Got the Feeling: Attributing Affective States to Dialogical Social Robots -- 1 Introduction -- 2 Empathy and Emotions Theories -- 3 The Experiment -- 3.1 Method and Interaction Steps in the Dialogues -- 4 Evaluation -- 5 Results and Future Works -- References -- Enhancing Usability of Voice Interfaces for Socially Assistive Robots Through Deep Learning: A German Case Study -- 1 Introduction -- 2 Related Work -- 2.1 Voice Interface Evaluations -- 2.2 Technical Construction of Voice Interfaces -- 3 Voice Interface -- 3.1 Design Goals -- 3.2 System Description -- 4 Evaluation -- 4.1 Methods and Material -- 4.2 Participants -- 4.3 Results -- 4.4 Discussion -- 5 Limitations -- 6 Conclusion -- References -- Enhancing User Experience: Designing Intuitive Interfaces for Sumo Robot Operations -- 1 Introduction -- 1.1 Intuitive Interface -- 1.2 Robotics -- 1.3 Sumo Robots -- 1.4 Designing Intuitive Interfaces for Sumo Robot Operations -- 2 Methodology -- 3 Result -- 3.1 Sumo Robot Performance -- 3.2 User Feedback -- 4 Discussion -- 4.1 Interpretation of Results -- 4.2 Comparison with Existing System -- 4.3 Implications and Future Works -- References -- Adaptive Robotics: Integrating Robotic Simulation, AI, Image Analysis, and Cloud-Based Digital Twin Simulation for Dynamic Task Completion -- 1 Introduction -- 1.1 Autonomous Robots -- 1.2 Robotics Simulation -- 1.3 AI in Robotics -- 1.4 Internet of Things -- 1.5 Isaac Simulation -- 1.6 Skydio and Sundt. 2 Theoretical Framework and Research Objectives. |
| Record Nr. | UNISA-996601562303316 |
Degen Helmut
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings, Part III
| Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings, Part III |
| Autore | Degen Helmut |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer, , 2024 |
| Descrizione fisica | 1 online resource (498 pages) |
| Altri autori (Persone) | NtoaStavroula |
| Collana | Lecture Notes in Computer Science Series |
| ISBN | 3-031-60615-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- HCI International 2024 Thematic Areas and Affiliated Conferences -- List of Conference Proceedings Volumes Appearing Before the Conference -- Preface -- 5th International Conference on Artificial Intelligence in HCI (AI-HCI 2024) -- HCI International 2025 Conference -- Contents - Part III -- Large Language Models for Enhanced Interaction -- Enhancing Relation Extraction from Biomedical Texts by Large Language Models -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Relation Extraction via In-Context Few-Shot Learning with LLMs -- 3.2 Seq2seq-Based Relation Extraction Enhanced by LLMs -- 3.3 Classification-Based Relation Extraction Enhanced by LLMs -- 4 Experimental Settings -- 4.1 DDI Extraction Task Settings -- 4.2 LLMs and Prompts -- 4.3 PLMs for Seq2seq Methods -- 4.4 PLMs for Classification Methods -- 5 Results and Discussions -- 5.1 In-Context Few-Shot Learning-Based Relation Extraction by LLMs -- 5.2 Seq2seq-Based Relation Extraction Enhanced by LLMs -- 5.3 Classification-Based Relation Extraction Enhanced by LLMs -- 6 Conclusion -- References -- Using a LLM-Based Conversational Agent in the Social Robot Mini -- 1 Introduction -- 2 A Short History of Language Models -- 3 The Proposed System -- 3.1 Prompting -- 4 Integration into Mini -- 4.1 Design of the Conversational Agent Skill -- 5 Evaluation -- 6 Conclusions -- References -- A Proposal to Extend the Modeling Language for Interaction as Conversation for the Design of Conversational Agents -- 1 Introduction -- 2 Related Work -- 2.1 Conversational Agents -- 2.2 Modeling Interaction in Conversational Agents -- 3 MoLIC -- 4 MoLIC's Limitation to Represent Conversational Agents -- 4.1 Standardized Communication Snippets -- 4.2 Transfer of Responsibility / Interlocutor During Communication -- 4.3 Modeling Breakdown Recovery -- 4.4 Conversational Agents' Intelligence.
5 Extending MoLIC -- 5.1 Template Element -- 5.2 Allowing for the Interaction with a Third-Party System -- 5.3 Adaptations to MoLIC 2.0 Elements -- 6 Initial Evaluation of Proposal -- 7 Final Remarks and Future Works -- References -- Optimizing Conversational Commerce Involving Multilingual Consumers Through Large Language Models' Natural Language Understanding Abilities -- 1 Introduction -- 1.1 Objectives and Research Questions -- 2 Review of Related Literature -- 3 Method and Implementation -- 3.1 Technical Architecture -- 3.2 Knowledge Base -- 3.3 Synthetic Customer Data Preparation -- 3.4 Synthetic Seller Persona Creation -- 3.5 Synthetic Sales Conversation Creation -- 4 Results -- 4.1 General Applied CoT Approach -- 4.2 Presence of Necessary Conditions -- 4.3 Product Resolution -- 4.4 Database Insertions -- 4.5 Sample Case -- 4.6 Drawbacks and Limitations -- 5 Discussions -- 6 Conclusion and Future Work -- References -- A Map of Exploring Human Interaction Patterns with LLM: Insights into Collaboration and Creativity -- 1 Introduction -- 2 Related Work -- 2.1 The Undergoing Change in HAII Driven by Large Language Model -- 2.2 The Current Review of Human-AI Interaction -- 3 Method -- 3.1 Search and Selection -- 3.2 Mapping -- 4 Result -- 4.1 Processing Tool -- 4.2 Analysis Assistant -- 4.3 Creative Companion -- 4.4 Processing Agent -- 5 Discussion -- 5.1 Mapping Methodology Based on Human and Algorithmic Approaches -- 5.2 Differences Between Clusters -- 5.3 About the Vacancy in the Mapping -- 5.4 Future Directions -- 6 Limitation and Future Work -- 7 Conclusion -- References -- The Use of Large Language Model in Code Review Automation: An Examination of Enforcing SOLID Principles -- 1 Introduction -- 2 Background -- 2.1 Code Reviews -- 2.2 SOLID Principles -- 2.3 Large Language Model Technology -- 2.4 Mixtral LLM. 2.5 Role of Bots in Code Development and Review -- 2.6 Benefits for Large Global Development Teams -- 3 Related Works -- 3.1 A Systematic Evaluation of Large Language Models of Code -- 3.2 Effects of Adopting Code Review Bots on Pull Requests to OSS Projects -- 3.3 Reducing Human Effort and Improving Quality in Peer Code Reviews Using Automatic Static Analysis and Reviewer Recommendation -- 3.4 ChatGPT: A Study of Its Utility for Common Software Engineering Tasks -- 3.5 Insights and Implications for LLM-Based Code Review -- 4 Proposed Concept -- 4.1 Proposed Architecture and Integration -- 4.2 Usage of the Proposed Bot -- 5 Impact Analysis -- 5.1 Comparison with Existing Solutions -- 5.2 Potential Benefits -- 5.3 Challenges and Limitations -- 6 Conclusion -- References -- LLM Based Multi-agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain -- 1 Introduction -- 2 Related Work -- 2.1 LLMs in the PA Domain -- 3 Proposed Approach -- 3.1 Template Pre-processing -- 3.2 Multi-agent Interaction -- 3.3 Document Post-processing -- 4 Experimental Evaluation -- 4.1 Semantics Identification Agent -- 4.2 Information Retrieval Agent -- 4.3 Content Generation Agent -- 4.4 Prompt-Engineered Results -- 5 Conclusions -- References -- Enabling Human-Centered Machine Translation Using Concept-Based Large Language Model Prompting and Translation Memory -- 1 Introduction -- 1.1 Challenges in Traditional Machine Translation Within Human-Computer Interaction Contexts -- 1.2 Augmented Machine Translation via Large Language Model -- 2 Augmented Machine Translation via Concept-Driven Large Language Model Prompting -- 2.1 Motivation -- 2.2 Augmented Instruction for Discourse-Level Style -- 2.3 Augmented Instruction for Concept-Based Sentence-Level Post-editing -- 2.4 Performance Evaluation. 3 Assessing the Proficiency of Large Language Model in Applying Translation Concept -- 3.1 Motivation -- 3.2 The Capability of LLMs to Elucidate Translation Concepts -- 3.3 Assessing the LLM's Proficiency in Identifying When to Apply Translation Concepts -- 3.4 The Capability of LLM to Produce Target Translations that Reflect Relevant Concepts -- 4 Conclusions -- References -- Enhancing Large Language Models Through External Domain Knowledge -- 1 Introduction -- 2 Problem Identification and Objectives -- 3 Related Works -- 4 Design and Development of the Artifact -- 4.1 Expert Knowledge Acquisition -- 4.2 Metadata Provision -- 4.3 Prompt Generation -- 5 Demonstration -- 5.1 Implementation -- 5.2 Case Study -- 6 Discussion -- References -- ChatGPT and Language Translation -- 1 Introduction -- 1.1 Historical Background - PreGPT -- 1.2 Background - LLMs and ChatGPT -- 1.3 ChatGPT and Translation -- 2 Motivation and Methodology for This Study -- 2.1 Motivation -- 2.2 Methodology -- 2.3 Examples -- 3 Results -- 3.1 Classifying AI Generated Text -- 3.2 Human Ratings of Translation Quality -- 4 Conclusions -- References -- Large Language Models for Tracking Reliability of Information Sources -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- The Heuristic Design Innovation Approach for Data-Integrated Large Language Model -- 1 Introduction -- 2 Related Works -- 2.1 Domain-Specific LLMs -- 2.2 Expert System -- 2.3 Human-AI Collaboration Design -- 3 Method -- 3.1 Overview of DIABot -- 3.2 Prompt -- 3.3 Database -- 3.4 Workflow -- 4 Value Assessment -- 4.1 Experimental Design -- 4.2 Participants -- 4.3 Experimental Process -- 4.4 Experimental Result -- 5 Discussion and Conclusion -- 6 Limitation and Future Work -- A Prompt of DIAbot -- B Tools OpenAPI -- References -- Advancing Human-Robot Interaction Through AI. FER-Pep: A Deep Learning Based Facial Emotion Recognition Framework for Humanoid Robot Pepper -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Pepper -- 3.2 NAOqi Python API -- 3.3 EfficientNetV2 -- 4 Dataset Collection and Preprocessing -- 5 Experiments -- 5.1 Candidate Models for Facial Emotion Recognition -- 6 System Implementation -- 7 Result and Discussion -- 8 Conclusion -- References -- You Got the Feeling: Attributing Affective States to Dialogical Social Robots -- 1 Introduction -- 2 Empathy and Emotions Theories -- 3 The Experiment -- 3.1 Method and Interaction Steps in the Dialogues -- 4 Evaluation -- 5 Results and Future Works -- References -- Enhancing Usability of Voice Interfaces for Socially Assistive Robots Through Deep Learning: A German Case Study -- 1 Introduction -- 2 Related Work -- 2.1 Voice Interface Evaluations -- 2.2 Technical Construction of Voice Interfaces -- 3 Voice Interface -- 3.1 Design Goals -- 3.2 System Description -- 4 Evaluation -- 4.1 Methods and Material -- 4.2 Participants -- 4.3 Results -- 4.4 Discussion -- 5 Limitations -- 6 Conclusion -- References -- Enhancing User Experience: Designing Intuitive Interfaces for Sumo Robot Operations -- 1 Introduction -- 1.1 Intuitive Interface -- 1.2 Robotics -- 1.3 Sumo Robots -- 1.4 Designing Intuitive Interfaces for Sumo Robot Operations -- 2 Methodology -- 3 Result -- 3.1 Sumo Robot Performance -- 3.2 User Feedback -- 4 Discussion -- 4.1 Interpretation of Results -- 4.2 Comparison with Existing System -- 4.3 Implications and Future Works -- References -- Adaptive Robotics: Integrating Robotic Simulation, AI, Image Analysis, and Cloud-Based Digital Twin Simulation for Dynamic Task Completion -- 1 Introduction -- 1.1 Autonomous Robots -- 1.2 Robotics Simulation -- 1.3 AI in Robotics -- 1.4 Internet of Things -- 1.5 Isaac Simulation -- 1.6 Skydio and Sundt. 2 Theoretical Framework and Research Objectives. |
| Record Nr. | UNINA-9910864182403321 |
|
Degen Helmut
|
||
| Cham : , : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings, Part II
| Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29-July 4, 2024, Proceedings, Part II |
| Autore | Degen Helmut |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (480 pages) |
| Altri autori (Persone) | NtoaStavroula |
| Collana | Lecture Notes in Computer Science Series |
| ISBN |
9783031606113
9783031606137 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- HCI International 2024 Thematic Areas and Affiliated Conferences -- List of Conference Proceedings Volumes Appearing Before the Conference -- Preface -- 5th International Conference on Artificial Intelligence in HCI (AI-HCI 2024) -- HCI International 2025 Conference -- Contents - Part II -- Ethical Considerations and Trust in AI -- Effects of Explanations by Robots on Trust Repair in Human-Robot Collaborations -- 1 Introduction -- 2 Method -- 2.1 Participants -- 2.2 Design and Procedure -- 2.3 Measures and Analyses -- 3 Results -- 3.1 Perceptions of Trustworthiness -- 3.2 Effects of Explanations on Trust Repair -- 4 Discussion -- 4.1 Limitations and Future Work -- 5 Conclusion and Implications -- References -- Do You Trust AI? Examining AI Trustworthiness Perceptions Among the General Public -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Results -- 4.1 The Most and Least Important Aspects that Influence Trust in AI -- 4.2 Changes in Trust at the Beginning and End of the Questionnaire -- 4.3 Results from Analysis of the Open-Ended Question -- 5 Discussion -- 6 Conclusion and Future Work -- References -- Factors of Trust Building in Conversational AI Systems: A Literature Review -- 1 Introduction -- 2 Background -- 2.1 Trust -- 2.2 Artificial Intelligence -- 2.3 Conversational AI Systems -- 3 Related Work -- 3.1 Concept of Trust -- 3.2 Key Challenges in Trusting an AI System -- 3.3 Factors Affecting Trust in Human-Computer Interaction -- 4 Methodology -- 5 Results -- 5.1 Trust-Building Factors in Conversational AI-Based Systems -- 5.2 How to Build and Improve Trust -- 5.3 Best Practices of Conversational AI Systems -- 6 Discussion -- 7 Conclusion -- 8 Limitations and Future Research -- References -- BlocklyBias: A Visual Programming Language for Bias Identification in AI Data -- 1 Introduction -- 2 Related Works.
2.1 Visual Languages in Data Analysis -- 3 BlocklyBias -- 3.1 Design -- 3.2 Development -- 3.3 Testing -- 4 Evaluation -- 4.1 Goals -- 4.2 Research Question -- 4.3 Study Design -- 4.4 Participants -- 4.5 Settings and Tasks -- 4.6 Procedure -- 4.7 Findings -- 5 Discussion -- 6 Conclusions -- References -- Comparing Socio-technical Design Principles with Guidelines for Human-Centered AI -- 1 Introduction -- 2 Background -- 3 Method -- 4 Findings -- 5 AI-Related Revision of Sociotechnical Heuristics -- 6 Discussion and Conclusion -- References -- Negative Emotions Towards Artificial Intelligence in the Workplace - Motivation and Method for Designing Demonstrators -- 1 Introduction -- 2 Negative Emotions Related to AI in the Workplace -- 3 Empirical Survey: Employee Reactions to AI in the Workplace in Manufacturing Industry -- 3.1 Research Design -- 3.2 Results of the Survey -- 3.3 Conclusions from the Survey -- 4 Approaches to Create Acceptance and Trust in AI in the Workplace -- 5 Method for Designing a Demonstrator to Reduce Negative Emotions About AI in the Workplace -- 6 Summary and Outlook -- References -- Why Designers Must Contribute to Responsible AI -- 1 The Need for Responsible AI -- 2 The Impact of the Interactional Approach -- 3 Case Studies -- 3.1 Design -- 3.2 Data Collection and Participants -- 3.3 Validating the Online Survey Design -- 4 Mental Models and Control -- 4.1 Case 1 Digital Learning: Mental Models -- 4.2 Case 2 Streaming Video: Design Affecting Control -- 5 Design Choices Affecting Trust -- 5.1 Case 3: Smart Sustainability Websites -- 5.2 Case 4 Professional Social Media -- 6 Discussion -- References -- Learning Fair Representations: Mitigating Statistical Dependencies -- 1 Introduction -- 2 Preliminaries -- 2.1 Fairness Metric -- 2.2 Fair Representation Learning -- 3 Methodology -- 4 Experiments -- 4.1 Dataset -- 4.2 Setting. 5 Results and Discussion -- 6 Conclusion -- References -- Model-Free Motion Planning of Complex Tasks Subject to Ethical Constraints -- 1 Introduction -- 2 Preliminary -- 2.1 Parially Observable Markov Decision Process (POMDP) -- 2.2 Linear Temporal Logic (LTL) -- 2.3 Limit-Deterministic Generalized Büchi Automaton (LDGBA) -- 3 Methodologies -- 3.1 Ethical Constraints -- 3.2 Product POMDP -- 3.3 Problem Definition -- 3.4 Q-Learning -- 4 Example -- 4.1 Scenario 1: Deontological Government and Company but Utilitarian Protesters -- 4.2 Scenario 2: Utilitarian Government and Company but Deontological Protesters -- 4.3 Scenario 3: Utilitarian Government, Company, and Protesters -- 5 Conclusion -- References -- Enhancing User Experience Through AI-Driven Technologies -- Incorporating Artificial Intelligence into Design Criteria Considerations -- 1 Introduction and Background -- 1.1 Improving User Interaction by Understanding User Attention -- 1.2 The Interdisciplinary Approach to the Design of Effective UIs -- 1.3 Assessing AI Integration into Design Criteria for UI Design -- 2 Method -- 2.1 Identifying Underlying Conditions -- 2.2 Defining Scope and Boundaries -- 2.3 Validation Process -- 3 Identifying Underlying Conditions -- 4 Scope and Boundaries -- 5 Literature Review (Review Process) -- 5.1 Conducting the Literature Review -- 5.2 Summary of the Results -- 6 Comparison (Analysis Process) -- 6.1 Pre-Considerations Incorporating AI into Design Criteria -- 6.2 Design Criteria to Consider When Integrating AI into UI Design -- 7 Conclusion and Outlook -- 7.1 Outlook -- 7.2 Limitations -- References -- Multimodal Interfaces for Emotion Recognition: Models, Challenges and Opportunities -- 1 Introduction -- 2 Modalities for Emotion Recognition -- 3 Multimodal Fusion -- 4 Applications of Multimodal Emotion Recognition -- 5 Open Challenges and Opportunities. 6 Conclusion -- References -- Co-creation with AI in Car Design: A Diffusion Model Approach -- 1 Introduction -- 2 Related Work -- 2.1 Car Design Semantic Corpus -- 2.2 Text-to-Image Diffusion Model -- 2.3 ControlNet -- 2.4 DreamBooth Training Approach -- 3 Method -- 3.1 Car Design Data Set -- 3.2 Workflow Framework -- 3.3 Training -- 3.4 Model Testing -- 4 Result -- 4.1 Generation Rounds and Time -- 4.2 Comparison -- 5 Discussion -- 5.1 Limitations -- 6 Conclusion -- References -- A Study on the Application of Generative AI Tools in Assisting the User Experience Design Process -- 1 Introduction -- 2 The Impact of AI on UX Design -- 3 Method -- 4 Results -- 4.1 Problem Distillation Stage -- 4.2 Prototype Design Stage -- 4.3 Design Validation Stage -- 4.4 Summary of Research Findings -- 5 Discussion -- 5.1 The Benefits and Limitations of Generative AI Assistance in the UX Design Process -- 5.2 Potential Applications of Generative AI Across Different Design Fields -- 5.3 How Generative AI May Reshape the Future of UX Designers -- 5.4 Research Limitations and Future Research Directions -- 6 Conclusions and Future Works -- 6.1 Generative AI Efficiently Streamlines Early-Stage Design Processes, Enabling a Greater Focus on Creativity and Strategy -- 6.2 Generative AI Should Offer Clear Decision-Making Processes to Facilitate Precise Questioning by Designers -- References -- Estimating Reliability of Speech Recognition Results Using Recognition Results Change Degree When Minute Changes to Speech Waveform -- 1 Introduction -- 2 Analysis of Speech Recognition Errors in Daily Conversation -- 2.1 Speech Recognition System -- 2.2 Conversation Database -- 2.3 Speech Recognition Results -- 3 The Reliability Estimation Method of Speech Recognition Result by Processing Slight Changes to Input Speech Data. 4 Relationship Between Speech Recognition Performance and Recognition Result Change Rate -- 5 Evaluation of Reliability Estimation Performance of Speech Recognition Results -- 6 Applications Examples Using the Recognition Result Reliability Estimation Method -- 7 Conclusion -- References -- Emotion Detection from Facial Expression in Online Learning Through Using Synthetic Image Generation -- 1 Introduction -- 2 Related Work -- 3 Framework for Educational Emotion Detection -- 3.1 Synthetic Image Generation -- 3.2 A Lightweight Model for Educational Emotion Detection -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Experiment Setup -- 4.3 Results and Discussion -- 4.4 Statistical Analysis -- 5 Conclusion and Future Work -- References -- Design Method of AI Composing Smart Speaker Based on Humming Behavior -- 1 Introduction -- 2 Related Concept -- 3 Method and Design Process -- 3.1 Behavior-Function Intersection Evaluation Model -- 3.2 Questionnaire Design -- 3.3 Questionnaire Reliability and Validity Test -- 4 AI Composing Smart Speaker Based on Humming Behavior -- 4.1 Design Style Orientation -- 4.2 AI Composition -- 4.3 Human-Computer Interaction Process -- 4.4 Product Appearance Design -- 4.5 Idea Sketch -- 4.6 3D Modeling -- 4.7 Product Introduction -- 4.8 App UI Design -- 4.9 Product Scene Renderings -- 5 Conclusion -- References -- Leveraging Generative AI Concepts with Uncertainty of Information Concept -- 1 Introduction -- 1.1 Generative AI, Large Language Models, Foundation Models -- 1.2 Type of Models -- 1.3 Data -- 2 Uncertainty of Information Concept -- 2.1 A Subsection Sample -- 3 Generative Uncertainty of Information -- 3.1 First GUoI Idea -- 3.2 Second GUoI Idea -- 3.3 Third GUoI Idea -- 3.4 Fourth GUoI Idea -- 3.5 Fifth GUoI Idea -- 3.6 Sixth GUoI Idea -- 4 Conclusion -- References. Cybersickness Detection Through Head Movement Patterns: A Promising Approach. |
| Record Nr. | UNINA-9910865281303321 |
|
Degen Helmut
|
||
| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29 - July 4, 2024, Proceedings, Part I
| Artificial Intelligence in HCI : 5th International Conference, AI-HCI 2024, Held As Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29 - July 4, 2024, Proceedings, Part I |
| Autore | Degen Helmut |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing AG, , 2024 |
| Descrizione fisica | 1 online resource (491 pages) |
| Altri autori (Persone) | NtoaStavroula |
| Collana | Lecture Notes in Computer Science Series |
| ISBN |
9783031606069
9783031606052 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- HCI International 2024 Thematic Areas and Affiliated Conferences -- List of Conference Proceedings Volumes Appearing Before the Conference -- Preface -- 5th International Conference on Artificial Intelligence in HCI (AI-HCI 2024) -- HCI International 2025 Conference -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Human-Centered Artificial Intelligence -- Qualitative User-Centered Requirements Analysis for a Recommender System for a Project Portfolio Platform in Higher Education Institutions -- 1 Introduction and Motivation -- 2 Related Work -- 3 Gaps in Literature -- 4 Methodology -- 5 Object of Investigation -- 6 Results -- 6.1 Project Overview and Detailed Information -- 6.2 Connecting People and Projects -- 6.3 Interaction -- 6.4 Personal Recommendation -- 7 Discussion and Critical Appraisal -- 8 Conclusion and Outlook -- References -- Examining User Perceptions to Vocal Interaction with AI Bots in Virtual Reality and Mobile Environments: A Focus on Foreign Language Learning and Communication Dynamics -- 1 Introduction -- 2 Related Works -- 3 Methods -- 3.1 Participants -- 3.2 Experimental Design -- 3.3 Tools -- 3.4 Procedures -- 4 Results -- 5 Conclusions -- References -- Evaluation of Generative AI-Assisted Software Design and Engineering: A User-Centered Approach -- 1 Introduction -- 2 Methodology -- 3 Background -- 3.1 Software Development -- 3.2 Software Development Cycle and Design Thinking Process -- 3.3 Generative Artificial Intelligence -- 4 Evaluation of Generative AI Tools -- 4.1 Ideate -- 4.2 Prototype -- 4.3 Validate -- 4.4 Define MVP -- 4.5 Implement -- 4.6 Document Code -- 4.7 Test -- 4.8 Deploy -- 5 Related Work -- 6 Discussion -- 7 Conclusion and Outlook -- References -- A Three-Year Analysis of Human Preferences in Delegating Tasks to AI -- 1 Introduction -- 2 Related Work.
2.1 Ethical Issues Regarding AI -- 2.2 Human-AI Collaboration -- 2.3 AI Task Delegation -- 3 Survey Design -- 4 Analysis Results -- 4.1 Comparison of Delegability -- 4.2 Task Category Clustering -- 4.3 Structural Equation Modeling -- 5 Discussion -- 6 Limitation -- 7 Conclusion -- A Task Category Clustering Result -- References -- Evaluating the Effectiveness of the Peer Data Labelling System (PDLS) -- 1 Introduction -- 1.1 The Peer Data Labelling System (PDLS) -- 1.2 Engagement -- 1.3 Methods of Emotion Recognition -- 1.4 Existing Data Sets for Machine Learning that Include Children -- 2 Assessing Accuracy Using iMotions -- 3 Assessing Accuracy Using Reviewers -- 3.1 Participants -- 3.2 Apparatus -- 3.3 Results -- 3.4 Normalised Results -- 3.5 Data Validation -- 4 PDLS Effectiveness -- 5 Conclusion and Further Work -- References -- Enhancing Historical Understanding in School Students: Designing a VR Application with AI-Animated Characters -- 1 Introduction -- 2 Literature Review -- 3 Contextual Analysis -- 3.1 Current Situation: Students Real-Life After School Scenario -- 3.2 Potential Requirements Throw Intelligence Centred Design Approach -- 3.3 Transformation Scenario -- 4 Conclusions and Future Works -- References -- A Multidisciplinary Heuristic Evaluation of AI-Enhanced Web Tools: Insights and Implications for Legal Contract Management Systems -- 1 Introduction -- 2 Methods and Challenges of HCI When Evaluating AI Tools -- 3 Heuristic Evaluation and Human-AI Interaction Guidelines -- 4 The Evaluated Tool -- 5 The Legal Field and AI -- 6 Methodology -- 6.1 Heuristic Evaluation -- 6.2 Participant Experience Survey -- 7 Results -- 7.1 Heuristic Evaluation Results -- 7.2 Survey Results -- 8 Discussion -- 9 Conclusion -- References -- PyFlowML: A Visual Language Framework to Foster Participation in ML-Based Decision Making -- 1 Introduction. 2 Related Works -- 2.1 Visual Programming Languages -- 2.2 End-User Development for Machine Learning -- 3 PyFlowML -- 3.1 PyFlow -- 3.2 PyFlowML Prototype -- 4 Expert Evaluation -- 4.1 Goals -- 4.2 Study Design -- 4.3 Settings -- 4.4 Procedure -- 5 Results -- 5.1 PyFlow Environment -- 5.2 PyFlowML Prototype -- 5.3 ML Analysis -- 5.4 Survey -- 6 Post-Hoc Analysis and Lessons Learnt -- 7 Conclusion -- References -- What Makes People Say Thanks to AI -- 1 Introduction -- 2 Related Work -- 2.1 Anthropomorphism in AI -- 2.2 Relationship Between AI and Human -- 2.3 Interaction Methods of AI Products -- 3 Concept -- 4 Pilot Study -- 4.1 Study Design -- 4.2 Conditions and Metrics -- 4.3 Participants, Apparatus and Procedure -- 5 Results -- 5.1 Intelligence -- 5.2 Interaction -- 5.3 Correlation Analysis -- 5.4 Findings -- 6 Discussion -- 6.1 Intelligence and Interaction -- 6.2 Polite Behaviour and User Reviews -- 6.3 Limitation and Future Work -- 7 Conclusion -- References -- Explainability and Transparency -- How to Explain It to System Testers? -- 1 Introduction -- 2 Related Work -- 2.1 Explainability -- 2.2 Explainability in SW Engineering and System Testing -- 2.3 Derivation of Mental Model of Explanations -- 3 System Domain: Software Testing -- 3.1 User Role: System Tester of ML-Based Systems -- 3.2 Intelligent System Testing Application for ML-Based Systems -- 3.3 Derived Mental Model of Explanations -- 4 Study Design -- 4.1 Study Participants -- 4.2 Study Approach -- 4.3 Data Collection -- 4.4 Data Analysis -- 5 Study Results -- 5.1 Participants -- 5.2 Results -- 6 Discussion, Limitations, and Future Work -- 6.1 Discussions -- 6.2 Limitations and Future Work -- 8 Appendix -- 8.1 User task specific explanation needs -- 8.2 Interview protocol -- References. WisCompanion: Integrating the Socratic Method with ChatGPT-Based AI for Enhanced Explainability in Emotional Support for Older Adults -- 1 Introduction -- 2 Background -- 2.1 Technology Adoption Concerns of Older Users -- 2.2 The Socratic Method and AI Interaction -- 2.3 Large Language Models (LLMs) -- 3 Approach -- 3.1 Sensemaking Framework -- 3.2 ChatGPT Architecture -- 3.3 Socratic Questions for Emotion Elicitations -- 3.4 WisCompanion Design Implementation -- 3.5 Datasets -- 4 Experimental Setup -- 4.1 User Personas and Journey Mapping -- 5 Results and Discussion -- 6 Evaluation of WisCompanion -- 7 Limitations -- 8 Conclusions -- 9 Future Work -- References -- Exploring the Impact of Explainability on Trust and Acceptance of Conversational Agents - A Wizard of Oz Study -- 1 Introduction -- 2 Related Work -- 2.1 Conversational Agents -- 2.2 Explainable AI -- 3 Chatbot: Design and Development -- 3.1 Prioritization of Explanations -- 3.2 Explanation-Aware System Design -- 3.3 Structuring Explanations -- 3.4 Technical Implementation of VacationBOT -- 4 Methodology -- 4.1 Study Design -- 4.2 Scenario and Tasks -- 4.3 Dependent Variables -- 4.4 Procedure -- 4.5 Sample -- 4.6 Data Analysis -- 5 Results -- 5.1 Perceived Causal Understanding -- 5.2 Trust -- 5.3 Acceptance -- 5.4 Correlation Between Trust, Acceptance and Perceived Causal Understanding -- 5.5 Correlation Between Previous Experience with Chatbots and Perceived Causal Understanding -- 6 Discussion -- 7 Conclusion -- References -- What Is the Focus of XAI in UI Design? Prioritizing UI Design Principles for Enhancing XAI User Experience -- 1 Introduction -- 2 Related Work -- 2.1 User Experience in XAI -- 2.2 UI Design for XAI -- 3 XAI User Experience Standards for Non-expert Users -- 3.1 Universal User Experience Level: Satisfaction. 3.2 Excellent Explanation Tool Level: Persuasiveness, Efficiency -- 3.3 Unique XAI User Experience Level: Understandability, Trust -- 4 Method -- 4.1 Design Principles for Enhancing XUI User Experience -- 4.2 Prototype Design -- 4.3 Participants -- 4.4 Data Collection and Analysis -- 4.5 Interviews -- 5 Result -- 6 Discussion -- 7 Limitation and Future Work -- 8 Conclusion -- References -- Navigating Transparency: The Influence of On-demand Explanations on Non-expert User Interaction with AI -- 1 Introduction -- 2 Related Literature -- 3 Experiment -- 3.1 Experimental Design -- 3.2 Data and Analysis -- 4 Results -- 4.1 Factors Influencing Verbal Explanation Demands -- 4.2 Effects of Demanding a Verbal Explanation -- 5 Discussion -- 6 Conclusion -- A Composition of the Experimental Groups -- B Between-Treatment Variation -- C Within-Treatment Variation -- References -- Ontology-Based Explanations of Neural Networks: A User Perspective -- 1 Introduction -- 2 Related Work -- 2.1 Ontology-Based Explanations -- 2.2 Experimental Evaluation of Explanations -- 3 Experiment Setup -- 3.1 Experiment Overview -- 3.2 Dataset -- 3.3 Used Software -- 3.4 Methodology -- 4 Experiment Results -- 5 Conclusion -- References -- Designing for Complementarity: A Conceptual Framework to Go Beyond the Current Paradigm of Using XAI in Healthcare -- 1 Introduction -- 2 Background -- 3 XAI: A Three Pillars Conceptual Framework -- 4 Designing with the Users: Methods, Data Collection and Analysis -- 5 The Case Study -- 5.1 Threats to Validity -- 6 Phase 1: Understanding the Design Space -- 7 Phase 2: Integrating the Theory into the Design -- 7.1 ML Model Development and Explainers -- 7.2 Prototype Interface and User Interaction -- 8 Phase 3: Co-designing the Prototype -- 9 Conclusion and Future Work -- References. Operationalizing AI Explainability Using Interpretability Cues in the Cockpit: Insights from User-Centered Development of the Intelligent Pilot Advisory System (IPAS). |
| Record Nr. | UNINA-9910865289003321 |
|
Degen Helmut
|
||
| Cham : , : Springer International Publishing AG, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence in HCI [[electronic resource] ] : 4th International Conference, AI-HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part I / / edited by Helmut Degen, Stavroula Ntoa
| Artificial Intelligence in HCI [[electronic resource] ] : 4th International Conference, AI-HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part I / / edited by Helmut Degen, Stavroula Ntoa |
| Autore | Degen Helmut |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (683 pages) |
| Disciplina | 004.019 |
| Altri autori (Persone) | NtoaStavroula |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Artificial Intelligence |
| ISBN | 3-031-35891-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Human-Centered Artificial Intelligence -- Explainability, Transparency, and Trustworthiness -- Ethics and Fairness in Artificial Intelligence -- AI-Supported User Experience Design -- Artificial Intelligence for Language, Text, and Speech-Related Tasks -- Human-AI Collaboration -- Artificial Intelligence for Decision-Support and Perception Analysis -- Innovations in AI-Enabled Systems. |
| Record Nr. | UNISA-996542668203316 |
|
Degen Helmut
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Intelligence in HCI : 4th International Conference, AI-HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part I / / edited by Helmut Degen, Stavroula Ntoa
| Artificial Intelligence in HCI : 4th International Conference, AI-HCI 2023, Held as Part of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, July 23–28, 2023, Proceedings, Part I / / edited by Helmut Degen, Stavroula Ntoa |
| Autore | Degen Helmut |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (683 pages) |
| Disciplina | 004.019 |
| Altri autori (Persone) | NtoaStavroula |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Artificial Intelligence |
| ISBN | 3-031-35891-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Human-Centered Artificial Intelligence -- Explainability, Transparency, and Trustworthiness -- Ethics and Fairness in Artificial Intelligence -- AI-Supported User Experience Design -- Artificial Intelligence for Language, Text, and Speech-Related Tasks -- Human-AI Collaboration -- Artificial Intelligence for Decision-Support and Perception Analysis -- Innovations in AI-Enabled Systems. |
| Record Nr. | UNINA-9910734862703321 |
|
Degen Helmut
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||