Association for the Advancement of Artificial Intelligence: AAAI Publications
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News on TikTok: An Annotated Dataset of TikTok Videos from German-Speaking News Outlets in 2023
No full textTikTok has emerged as a leading social media platform with increasing relevance for the consumption and distribution of news, especially for younger age groups. Despite its growing relevance, analyses of how traditional news outlets produce content for the platform and maintain journalistic news values are limited. Moreover, there are few large-scale datasets that are suitable for tracing larger journalistic trends and developing approaches for the automated annotation of multimodal content. This paper addresses these gaps and introduces “News on TikTok,” an annotated dataset of 8,623 TikTok videos published by 18 major German-speaking news outlets in 2023. Combining metadata with human-annotated data of 25 variables (incl. the presence of visual, auditory, and interactive elements, and journalistic news values), our dataset makes three significant contributions: First, it enables extensive analyses of news characteristics on TikTok. Second, it provides ground truth data to develop and validate automated tools for multimodal content analyses. Third, it offers a comprehensive guide for generating datasets with similar research interests
Human-Centric Teaching at Scale in Online Education through Bidirectional Feedback in Human-AI Interaction
No full textIn online education, the nature of information presents a significant challenge. The crux of the problem is that for effective transfer of knowledge in the absence of context and non-verbal communication, meaning must be communicated in the language itself. In this work, we outline the roles that human and AI can fill to address this gap with social and technical approaches in the individual, classroom, and organizational levels of human-AI interaction. We aim to stimulate dialogue on roles in human-AI collaboration that prioritize human social norms in the design of sociotechnical systems which lead to the preservation of human-centric values
Towards Human-Compatible AI for Well-being by Integrating Physiological Viewpoint With Machine Learning Viewpoint
No full textThis paper focuses on “human-compatible AI” which aligns with human values and remains under human control to pre-vent unintended and harmful consequences, and discusses it to develop human-compatible AI for well-being. For this is-sue, this paper proposes the human-compatible AI for a sleep as one of the human-compatible AI for well-being, which is designed to have the functions of (1) checking how the esti-mated sleep stage (corresponding to suggestions to users) fol-lows the biological rhythms which determine their health conditions (corresponding to human values) and (2) modify-ing the estimated sleep stage according to their biological rhythms. To investigate an importance of the proposed ap-proach, this paper applies it into the sleep stage estimation based on the acceleration sensor data. Through the human subject experiment, the following implications have been re-vealed: (1) it is dangerous to simply employ machine learning (i.e., Random Forest in this research) for the sleep stage esti-mation because the sleep stage is artificially estimated without following the ultradian rhythm which are generally found in humans; and (2) it is important to integrate the physiological characteristic (i.e., the ultradian rhythm) with machine learning for the sleep stage estimation because such an inte-gration can estimate the sleep stage that follow the ultradian rhythm
Augmenting Exploration with Locally Greedy Probes
No full textEnhancing Greedy Best First Search (GBFS) with stochastic exploration will often greatly improve search performance. In this work, we show that one way exploration does so is by helping the search find states that are "easy" for standard GBFS without exploration. In particular, we show that in problems in which standard GBFS struggles and exploration helps, there are often many states that are reachable from the initial state that standard GBFS can quickly find solutions from. Many such states are actually outside the Bench Transition System (BTS) --- which is a structure that contains all states that standard GBFS may encounter --- meaning GBFS cannot reach them without using exploration. To allow exploration mechanisms to better exploit the existence of such states, we introduce a method called locally greedy probes. Upon a successor having an improved heuristic from its parent, locally greedy probes pause exploration and greedily hill-climb along a single path as long as heuristic improvements keep occurring. Our empirical evaluation shows that this approach is effective at enhancing several exploration mechanisms in a variety of classical planning domains
Potential Heuristics: Weakening Consistency Constraints
No full textIn classical planning, admissible potential heuristics are computed by solving linear programs (LPs) with constraints expressing consistency and goal-awareness of the heuristic. Potential heuristics can return negative estimates. So, given a potential heuristic h^P, the actual heuristic used in search is another heuristic defined as h^P_0+(s) = max(h^P(s),0) for every reachable state s. In this paper, we reformulate the LP constraints for consistency of h^P so that they ensure consistency of h^P_0+ instead. This leads to more informative heuristics with positive impact on the overall performance in exchange for a more time and memory demanding computation using mixed integer linear programs instead of LPs
Task and Motion Planning Using Infinite Completion Tree and Agnostic Skills
No full textThis work builds upon existing task and motion planning (TAMP) frameworks by integrating pre-trained Sequencing Task-Agnostic Policies (STAP) and Effort Level Search (ELS) to create a hierarchical approach that decouples high-level task decisions from low-level motion execution. The method enhances the planning process by incorporating a novel success rate estimator (P ), which provides more accurate task success predictions than traditional Q-value estimators. We formalize the problem of long-horizon manipulation tasks, where high-level decisions are made in discrete spaces and low-level actions are executed in continuous space. To guide the search process efficiently, we leverage the infinite completion tree structure of ELS, which dynamically adjusts computational resources based on task complexity. Empirical results demonstrate that our approach significantly improves planning efficiency and execution reliability, outperforming traditional methods by reducing the search space and computational overhead. Our work highlights the effectiveness of combining learned skills from STAP with ELS and P in a hierarchical structure, laying the foundation for scalable robotic planning in complex, real-world manipulation tasks
A Context-Aware IoT Security Framework for Elderly Care
No full textThe use of IoT networks in healthcare has led to the adoption of smart home environments for users in their place of abode and one such example is smart home for elderly care. Sensors and devices in the network capture data that can be stored or transmitted and this can lead to security issues. The security solutions for securing the IoT network are usually applied blanket-wise. On the other hand, context-aware security approaches consider contextual data when applying security solutions. In this paper, we identified critical weaknesses in popular data transmission protocols as well as the interactions of devices communicating with them in a simulated environment. To mitigate the identified vulnerabilities, a Context-Aware IoT Security Framework was developed that dynamically adjusts security measures based on environmental and contextual factors in smart home environments for elderly care. By adopting a user-centric approach, the proposed framework minimizes the risk of unauthorized access, data manipulation, and network-based attacks, and ensures that the solution aligns with regulatory standards such as GDPR and HIPAA. The evaluation of the proposed framework demonstrated encouraging improvements to the protection and robustness of the system
Digital Twin-Driven Innovations in SOFC Electrode Virtual Engineering
No full textThis study presents a digital twin-based virtual laboratory designed to optimize the manufacturing processes of electrode materials for Solid Oxide Fuel Cells (SOFC). Leveraging artificial intelligence models (R2=0.89, RMSE=0.03), the system accurately predicts critical material properties such as electrode thickness, dispersion stability, and polarization resistance. The virtual simulation of key processes—material selection, combining, coating, and sintering—significantly enhances process accuracy, efficiency, and product quality, effectively reducing experimental time and costs
I-TREES: A Context-Aware Framework for Energy-Efficient Tree Health Monitoring in Forest Internet of Trees (IoTr) Networks
No full textInternet of Trees (IoTr) technology enables a novel approach to real-time remote monitoring, particularly of trees and forest ecosystems. However, existing literature focuses on soil moisture monitoring of trees and lacks the contextual intelligence and adaptive sensing capabilities necessary for smart tree monitoring. This paper proposes a novel layered architecture for IoTr and the I-TREES (IoTr-based Tree Routing for Energy-Efficient Systems) framework for smart sensing and context-driven monitoring. I-TREES integrates embedded IoTr environmental sensors, LPWAN communication, edge processing, and cloud-based reasoning to monitor physiological and ecological stress parameters in vulnerable tree species. The proposed architecture enables selective sensing, where sensors adapt their behaviour based on real time environmental conditions to reduce energy consumption and avoid redundant transmissions. We evaluate I-TREES using Network Simulator 3 (NS-3) simulations across 700 node deployments under forest-like conditions and compare them with benchmark routing protocols. Results show that I-TREES achieves a superior packet delivery ratio approximately 97%, reduced latency, and lower energy consumption than existing schemes. I-TREES offers a robust solution for sustainable forest ecosystem monitoring and early disease detection by combining scalable sensing with intelligent context awareness
EchoScript: Enhancing AI Music Generation for Cinematic Scoring via Script-Aware Fine-Tuning
No full textRecent advancements in artificial intelligence (AI) have significantly transformed the landscape of music generation, enabling context-sensitive and emotionally expressive soundtracks for diverse media applications such as film, gaming, and therapeutic environments. However, existing AI models continue to face persistent challenges in maintaining melodic coherence, thematic continuity, and emotional depth—qualities essential for professional soundtrack production.
This research addresses these limitations by fine-tuning MusicGen, a transformer-based generative AI model, to create EchoScript—an optimized variant specifically tailored for cinematic soundtrack composition through script-driven conditioning. A curated dataset enriched with detailed metadata, including genre, mood, instrumentation, tempo, and narrative context, was employed to guide the fine-tuning process.
Evaluation results demonstrate substantial improvements over the baseline model. EchoScript achieved a lower Fréchet Audio Distance (FAD) score (4.3738 vs. 4.5492) and outperformed the baseline in structured listening tests, with participants consistently preferring EchoScript for musical quality and narrative alignment.
Beyond these empirical findings, the study critically examines technical constraints and outlines key future directions, including symbolic-audio integration, enhanced audio mixing, and the development of standardized evaluation metrics. Collectively, these contributions advance the pursuit of AI-generated music that closely approximates human-level expressiveness and narrative coherence, offering meaningful benefits for creative industries reliant on adaptive and emotionally resonant soundtracks