Human-Robot Interaction (HRI)

Human-Object Interaction: Understanding and optimizing the complex relationships between humans and objects in various domains. Human-Object Interaction (HOI) is a multidisciplinary field that focuses on understanding and optimizing the complex relationships between humans and objects in various domains, such as e-commerce, online education, social networks, and interactive visualizations. By studying these interactions, researchers can develop more effective and user-friendly systems, products, and services. One of the key challenges in HOI is to synthesize information from different sources and connect themes across various domains. This requires a deep understanding of the nuances and complexities of human behavior, as well as the ability to model and predict interactions between humans and objects. Machine learning techniques, such as network embedding and graph attention networks, have been employed to mine information from temporal interaction networks and identify patterns in human-object interactions. Recent research in the field has explored various aspects of HOI, such as multi-relation aware temporal interaction network embedding (MRATE), which mines historical interaction relations, common interaction relations, and interaction sequence similarity relations to obtain neighbor-based embeddings of interacting nodes. Another study investigated the optimization of higher-order network topology for synchronization of coupled phase oscillators, revealing distinct properties of networks with 2-hyperlink interactions compared to 1-hyperlink (pairwise) interactions. Practical applications of HOI research can be found in numerous areas. For example, in e-commerce, understanding human-object interactions can help improve product recommendations and user experience. In online education, insights from HOI can be used to develop more engaging and effective learning materials. Additionally, in the field of interactive visualizations, incorporating data provenance can lead to the development of novel interactions and more intuitive user interfaces. A company case study that demonstrates the value of HOI research is the development of interactive furniture. By reimagining the ergonomics of interactive furniture and incorporating novel user experience design methods, companies can create products that better cater to the needs and preferences of users. In conclusion, Human-Object Interaction is a vital area of research that seeks to understand and optimize the complex relationships between humans and objects across various domains. By leveraging machine learning techniques and synthesizing information from different sources, researchers can gain valuable insights into the nuances and complexities of human-object interactions. These insights can then be applied to develop more effective and user-friendly systems, products, and services, ultimately benefiting both individuals and society as a whole.

Hurdle Models: A versatile approach for analyzing sparse and zero-inflated data. Hurdle models are a class of statistical models designed to handle data with an excess of zeros or other specific values, commonly found in fields such as economics, biology, and social sciences. These models are particularly useful for analyzing sparse data, where the presence of many zeros or other specific values can pose challenges for traditional statistical methods. The core idea behind hurdle models is to separate the data analysis process into two stages. In the first stage, the model focuses on the presence or absence of the specific value (e.g., zero) in the data. In the second stage, the model analyzes the non-zero or non-specific values, often using a different distribution or modeling approach. This two-stage process allows hurdle models to account for the unique characteristics of sparse data, providing more accurate and reliable results. Recent research has expanded the capabilities of hurdle models, integrating them with other statistical methods and machine learning techniques. For example, the low-rank hurdle model combines the hurdle approach with low-rank modeling to handle data with excess zeros or missing values. Another example is the ES Attack, a model stealing attack against deep neural networks that leverages hurdle models to overcome data hurdles and achieve functionally equivalent copies of victim models. Practical applications of hurdle models can be found in various domains. In manufacturing, they can be used for missing value imputation, improving the quality of data analysis. In the field of citation analysis, hurdle models can help researchers understand the factors that influence the chances of an article being highly cited. In the mining industry, hurdle models can be used to identify risk factors for workplace injuries, enabling the implementation of preventive measures. One company case study that demonstrates the value of hurdle models is the analysis of Italian tourism behavior during the Great Recession. Researchers used a multiple inflated negative binomial hurdle regression model to investigate the impact of the economic recession on the total number of overnight stays. The results provided valuable insights for policymakers seeking to support the tourism economy. In conclusion, hurdle models offer a versatile and powerful approach for analyzing sparse and zero-inflated data, addressing the challenges posed by traditional statistical methods. By integrating hurdle models with other techniques and applying them to various domains, researchers and practitioners can gain valuable insights and make more informed decisions.