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[null,"1.","Record Nr.",null,"UNINA990002657090403321"]
[null,null,"Autore",null,"Carnevale, G.F."]
[null,null,"Titolo",null,"Internal auditing / by Carnevale e Ferandin i"]
[null,null,"Pubbl/distr/stampa",null,"Milano : Kompass, 1970"]
[null,null,"Locazione",null,"ECA"]
[null,null,"Collocazione",null,"6-9-1-TI"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996601562303316"]
[null,null,"Autore",null,"Degen Helmut"]
[null,null,"Titolo",null,"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"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"3-031-60615-9"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (498 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14736"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"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."]
[{"$nulls":4},"5 Extending MoLIC -- 5.1 Template Element -- 5.2 Allowing for the Interaction with a Third-Party System -- 5.3 Adaptations to MoLIC 2.0 Elements -- 6 Initial Evaluation of Proposal -- 7 Final Remarks and Future Works -- References -- Optimizing Conversational Commerce Involving Multilingual Consumers Through Large Language Models' Natural Language Understanding Abilities -- 1 Introduction -- 1.1 Objectives and Research Questions -- 2 Review of Related Literature -- 3 Method and Implementation -- 3.1 Technical Architecture -- 3.2 Knowledge Base -- 3.3 Synthetic Customer Data Preparation -- 3.4 Synthetic Seller Persona Creation -- 3.5 Synthetic Sales Conversation Creation -- 4 Results -- 4.1 General Applied CoT Approach -- 4.2 Presence of Necessary Conditions -- 4.3 Product Resolution -- 4.4 Database Insertions -- 4.5 Sample Case -- 4.6 Drawbacks and Limitations -- 5 Discussions -- 6 Conclusion and Future Work -- References -- A Map of Exploring Human Interaction Patterns with LLM: Insights into Collaboration and Creativity -- 1 Introduction -- 2 Related Work -- 2.1 The Undergoing Change in HAII Driven by Large Language Model -- 2.2 The Current Review of Human-AI Interaction -- 3 Method -- 3.1 Search and Selection -- 3.2 Mapping -- 4 Result -- 4.1 Processing Tool -- 4.2 Analysis Assistant -- 4.3 Creative Companion -- 4.4 Processing Agent -- 5 Discussion -- 5.1 Mapping Methodology Based on Human and Algorithmic Approaches -- 5.2 Differences Between Clusters -- 5.3 About the Vacancy in the Mapping -- 5.4 Future Directions -- 6 Limitation and Future Work -- 7 Conclusion -- References -- The Use of Large Language Model in Code Review Automation: An Examination of Enforcing SOLID Principles -- 1 Introduction -- 2 Background -- 2.1 Code Reviews -- 2.2 SOLID Principles -- 2.3 Large Language Model Technology -- 2.4 Mixtral LLM."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"for Discourse-Level Style -- 2.3 Augmented Instruction for Concept-Based Sentence-Level Post-editing -- 2.4 Performance Evaluation."]
[{"$nulls":4},"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."]
[{"$nulls":4},"FER-Pep: A Deep Learning Based Facial Emotion Recognition Framework for Humanoid Robot Pepper -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Pepper -- 3.2 NAOqi Python API -- 3.3 EfficientNetV2 -- 4 Dataset Collection and Preprocessing -- 5 Experiments -- 5.1 Candidate Models for Facial Emotion Recognition -- 6 System Implementation -- 7 Result and Discussion -- 8 Conclusion -- References -- You Got the Feeling: Attributing Affective States to Dialogical Social Robots -- 1 Introduction -- 2 Empathy and Emotions Theories -- 3 The Experiment -- 3.1 Method and Interaction Steps in the Dialogues -- 4 Evaluation -- 5 Results and Future Works -- References -- Enhancing Usability of Voice Interfaces for Socially Assistive Robots Through Deep Learning: A German Case Study -- 1 Introduction -- 2 Related Work -- 2.1 Voice Interface Evaluations -- 2.2 Technical Construction of Voice Interfaces -- 3 Voice Interface -- 3.1 Design Goals -- 3.2 System Description -- 4 Evaluation -- 4.1 Methods and Material -- 4.2 Participants -- 4.3 Results -- 4.4 Discussion -- 5 Limitations -- 6 Conclusion -- References -- Enhancing User Experience: Designing Intuitive Interfaces for Sumo Robot Operations -- 1 Introduction -- 1.1 Intuitive Interface -- 1.2 Robotics -- 1.3 Sumo Robots -- 1.4 Designing Intuitive Interfaces for Sumo Robot Operations -- 2 Methodology -- 3 Result -- 3.1 Sumo Robot Performance -- 3.2 User Feedback -- 4 Discussion -- 4.1 Interpretation of Results -- 4.2 Comparison with Existing System -- 4.3 Implications and Future Works -- References -- Adaptive Robotics: Integrating Robotic Simulation, AI, Image Analysis, and Cloud-Based Digital Twin Simulation for Dynamic Task Completion -- 1 Introduction -- 1.1 Autonomous Robots -- 1.2 Robotics Simulation -- 1.3 AI "]
[{"$nulls":4},"in Robotics -- 1.4 Internet of Things -- 1.5 Isaac Simulation -- 1.6 Skydio and Sundt."]
[{"$nulls":4},"2 Theoretical Framework and Research Objectives."]
[null,"3.","Record Nr.",null,"UNINA9910349283703321"]
[null,null,"Titolo",null,"Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I / / edited by Igor V. Tetko, Věra Kůrková, Pavel Karpov, Fabian Theis"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019"]
[null,null,"ISBN",null,"3-030-30487-6"]
[null,null,"Edizione",null,"[1st ed. 2019.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XXX, 839 p. 372 illus., 242 illus. in color.)"]
[null,null,"Collana",null,"Theoretical Computer Science and General Issues, , 2512-2029 ; ; 11727"]
[null,null,"Disciplina",null,"006.31"]
[{"$nulls":4},"006.32"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Computer engineering"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Algorithms"]
[{"$nulls":4},"Data protection"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Computer Engineering and Networks"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Data and Information Security"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Bidirectional associative memory with block coding: A comparison of iterative retrieval methods -- Stability analysis of a generalised class of BAM neural networks with mixed delays -- Dissipativity Analysis of a Class of Competitive Neural Networks with Proportional Delays -- A Nonlinear Fokker-Planck Description of Continuous Neural Network Dynamics -- Multi-modal associative storage and retrieval using Hopfield auto-associative memory network -- Chaotic Complex-Valued Associative Memory with Adaptive Scaling Factor Independent of Multi-Values -- A Comparative Analysis of Preprocessing Methods for Single-"]
[{"$nulls":4},"Trial Event Related Potential Detection -- Sleep State Analysis using Calcium Imaging Data by Non-negative Matrix Factorization -- Detection of directional information flow induced by TMS based on symbolic transfer entropy -- Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network -- Distinguishing Violinists and Pianists based on their Brain Signals -- Research on Image-to-Image Translation with Capsule Network -- Multi-View Capsule Network -- Advanced Capsule Networks via Context Awareness -- DDRM-CapsNet: Capsule Network based on Deep Dynamic Routing Mechanism for complex data -- Squeezed Very Deep Convolutional Neural Networks for Text Classification -- NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems -- Swap kernel regression -- Model-Agnostic Explanations for Decisions using Minimal Patterns -- NARPCA: Neural Accumulate-Retract PCA for Low-latency High-throughput Processing on Datastreams -- An Evaluation of Various Regression Models for the Prediction of Two-Terminal Network Reliability -- Capsule Generative Models -- Evaluating CNNs on the Gestalt Principle of Closure -- Recovering Localized Adversarial Attacks -- On the Interpretation of Recurrent Neural Networks as Finite State Machines -- Neural field model for measuring and reproducing time intervals -- Widely Linear Complex-valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models -- NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images -- Deep Semantic Asymmetric Hashing -- A Neural Network for Semi-Supervised Learning on Manifolds -- Counting with Analog Neurons -- On the Bounds of Function Approximations -- Probabilistic Bounds for Approximation by Neural Networks -- Tree Memory Networks for Sequence Processing -- On Deep Set Learning and the Choice of Aggregations -- Hilbert Vector Convolutional Neural Network : 2D Neural Network on 1D Data -- The Same Size Dilated Attention Network for Keypoint Detection -- Gradient-Based Learning of Compositional Dynamics with Modular RNNs -- Transfer Learning with Sparse Associative Memories -- Linear Memory Networks -- A Multi-Armed Bandit Algorithm Available in Stationary or Non-Stationary Environments Using Self-Organizing Maps -- Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions -- Boosting Reinforcement Learning with Unsupervised Feature Extraction -- A multi-objective Reinforcement Learning algorithm for JSSP -- A Reinforcement Learning Approach for Sequential Spatial Transformer Networks -- Deep Recurrent Policy Networks for Planning under Partial Observability -- Mixed-Reality Deep Reinforcement Learning for a Reach-to-grasp Task -- FMNet: Multi-Agent Cooperation by Communicating with Featured Message Network -- Inferring Event-Predictive Goal-Directed Object Manipulations in REPRISE -- On Unsupervised Learning of Traversal Cost and Terrain Types Identification using Self-Organizing Maps -- Scaffolding Haptic Attention with Controller Gating -- Benchmarking Incremental Regressors in Traversal Cost Assessment -- CPG driven RBF Network Control with Reinforcement Learning for Gait Optimization of a Dung Beetle-like Robot -- Training Delays in Spiking Neural Networks -- An Izhikevich Model Neuron MOS Circuit for Low Voltage Operation -- UAV Detection: A STDP trained Deep Convolutional Spiking Neural Network Retina-Neuromorphic Approach -- Autonoumous Learning Paradigm for Spiking Neural Networks -- Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm -- The Importance of Self-excitation in Spiking Neural Networks Evolved to Recognize Temporal Patterns -- Estimating "]
[{"$nulls":4},"and factoring the dropout induced distribution with Gaussian mixture model -- Sequence disambiguation with synaptic traces in associative neural networks -- Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks -- A Neural Circuit Model of Adaptive Robust Tracking Control for Continuous-Time Nonlinear Systems."]
[null,null,null,"Sommario/riassunto","The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. ."]