Graph Neural Re-Ranking via Corpus Graph

Re-ranking systems aim to reorder an initial list of documents to satisfy better the information needs associated with a user-provided query. Modern re-rankers predominantly rely on neural network models, which have proven highly effective in representing samples from various modalities. However, these models typically evaluate query-document pairs in isolation, neglecting the underlying document distribution that could enhance the quality of the re-ranked list. To address this limitation, we propose Graph Neural Re-Ranking (GNRR), a pipeline based on Graph Neural Networks (GNNs), that enables each query to consider documents distribution during inference. Our approach models document relationships through corpus subgraphs and encodes their representations using GNNs. Through extensive experiments, we demonstrate that GNNs effectively capture cross-document interactions, improving performance on popular ranking metrics. In TREC-DL19, we observe a relative improvement of 5.8% in Average Precision compared to our baseline. These findings suggest that integrating the GNN segment offers significant advantages, especially in scenarios where understanding the broader context of documents is crucial.

Advancing Robot-Assisted Autism Therapy: A Novel Algorithm for Enhancing Joint Attention Interventions

Recent studies have revealed that using social robots can accelerate the learning process of several skills in areas where autistic children typically show deficits. However, most early research studies conducted interactions via free play. More recent research has demonstrated that robot-mediated autism therapies focusing on core impairments of autism spectrum disorder (e.g., joint attention) yield better results than unstructured interactions. This paper aims to systematically review the most relevant findings concerning the application of social robotics to joint attention tasks, a cardinal feature of autism spectrum disorder that significantly influences the neurodevelopmental trajectory of autistic children. Initially, we define autism spectrum disorder and explore its societal implications. Following this, we examine the need for technological aid and the potentialities of robot-assisted autism therapy. We then define joint attention and highlight its crucial role in children's social and cognitive development. Subsequently, we analyze the importance of structured interactions and the role of selecting the optimal robot for specific tasks. This is followed by a comparative analysis of the works reviewed earlier, presenting an in-depth examination of two distinct formal models employed to design the prompts and reward system that enables the robot to adapt to children's responses. These models are critically compared to highlight their strengths and limitations. Next, we introduce a novel algorithm to address the identified limitations, integrating interactive environmental factors and a more sophisticated prompting and reward system. Finally, we propose further research directions, discuss the most relevant open questions, and draw conclusions regarding the effectiveness of social robotics in the medical treatment of autism spectrum disorders.

A Novel Time Series-to-Image Encoding Approach for Weather Phenomena Classification

Rainfall estimation through the analysis of its impact on electromagnetic waves has sparked increasing interest in the research community. Recent studies have delved into its effects on cellular network performance, demonstrating the potential to forecast rainfall levels based on electromagnetic wave attenuation during precipitations. This paper aims to solve the problem of identifying the nature of specific weather phenomena from the received signal level (RSL) in 4G/LTE mobile terminals. Specifically, utilizing time-series data representing RSL, we propose a novel approach to encode time series as images and model the task as an image classification problem, which we finally address using convolutional neural networks (CNNs). The main benefit of the abovementioned procedure is the opportunity to utilize various data augmentation techniques simultaneously. This encompasses applying traditional approaches, such as moving averages, to the time series and enhancing the generated images. We have investigated various image data augmentation methods to identify the most effective combination for this scenario. In the upcoming sections, we will introduce the task of rainfall estimation and conduct a comprehensive analysis of the dataset used. Subsequently, we will formally propose a new approach for converting time series into images. To conclude, the paper's final section will present and discuss the experiments conducted, providing the reader with a brief yet comprehensive overview of the results.