This paper presents an Artificial Intelligence (AI) integrated novel approach to Brain - Computer Interface (BCI) - based wheelchair development, utilizing a motor imagery r ight - l eft - h and m ovement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left - hand movements using electroencephalogram (EEG) data. A pre - filtered dataset, obtained from an open - source EEG repository, was seg mented into arrays of 19x200 to capture the onset of hand movements. Th e data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter - based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose a BiLSTM - BiGRU model that shows a superior test accuracy of 92. 26 % as compared with v arious machine learning baseline models, including XGBoost, EEGNet, and a transformer - based model . The Bi - LSTM - BiGRU attention - based model achieved a mean accuracy of 90.13 % through cross - validation, showcasing the potential of attention mechanisms in BCI applications. Keywords: Brain Computer Interface (BCI), BiLSTM - BiGRU, Raspberry Pi, E lectroencephalogram (EEG), Hybrid Deep learning 1. Introduction Brain - Computer Interfaces (BCIs) are advanced systems that establish direct communication between the human brain and external devices . In recent years, BCIs have been widely investigated for their potential to assist individuals with mobility impairments, offering novel pathways for restoring autonomy. This paper proposes a BCI - based wheelchair control system driven by electroencephalogra phy (EEG) signals associated with motor imagery. The proposed framework incorporates a variety of machine learning models with tailored hyperparameter optimization techniques, culminating in the deployment of a BiLSTM - BiGRU hybrid deep learning model for effective EEG signal classification.

This paper investigates how to extract objects-of-interest without relying on hand-craft features and sliding windows approaches, that aims to jointly solve two sub-tasks: (i) rapidly localizing salient objects from images, and (ii) accurately segmenting the objects based on the localizations. We present a general joint task learning framework, in which each task (either object localization or object segmentation) is tackled via a multi-layer convolutional neural network, and the two networks work collaboratively to boost performance. In particular, we propose to incorporate latent variables bridging the two networks in a joint optimization manner. The first network directly predicts the positions and scales of salient objects from raw images, and the latent variables adjust the object localizations to feed the second network that produces pixelwise object masks. An EM-type method is then studied for the joint optimization, iterating with two steps: (i) by using the two networks, it estimates the latent variables by employing an MCMC-based sampling method; (ii) it optimizes the parameters of the two networks unitedly via back propagation, with the fixed latent variables. Extensive experiments demonstrate that our joint learning framework significantly outperforms other state-of-the-art approaches in both accuracy and efficiency (e.g., 1000 times faster than competing approaches).

Visual Document Retrieval (VDR), the task of retrieving visually-rich document pages using queries that combine visual and textual cues, is crucial for numerous real-world applications. Recent state-of-the-art methods leverage Large Vision-Language Models (LVLMs) in a multi-vector paradigm, representing each document as patch-level embeddings to capture fine-grained details. While highly effective, this approach introduces a critical challenge: prohibitive storage overhead, as storing hundreds of vectors per page makes large-scale deployment costly and impractical. To address this, we introduce DocPruner, the first framework to employ adaptive patch-level embedding pruning for VDR to effectively reduce the storage overhead. DocPruner leverages the intra-document patch attention distribution to dynamically identify and discard redundant embeddings for each document. This adaptive mechanism enables a significant 50-60% reduction in storage for leading multi-vector VDR models with negligible degradation in document retrieval performance. Extensive experiments across more than ten representative datasets validate that DocPruner offers a robust, flexible, and effective solution for building storage-efficient, large-scale VDR systems.

Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.

Large Language Models (LLMs) are starting to be profiled as one of the most significant disruptions in the Software Testing field. Specifically, they have been successfully applied in software testing tasks such as generating test code, or summarizing documentation. This potential has attracted hundreds of researchers, resulting in dozens of new contributions every month, hardening researchers to stay at the forefront of the wave. Still, to the best of our knowledge, no prior work has provided a structured vision of the progress and most relevant research trends in LLM-based testing. In this article, we aim to provide a roadmap that illustrates its current state, grouping the contributions into different categories, and also sketching the most promising and active research directions for the field. To achieve this objective, we have conducted a semi-systematic literature review, collecting articles and mapping them into the most prominent categories, reviewing the current and ongoing status, and analyzing the open challenges of LLM-based software testing. Lastly, we have outlined several expected long-term impacts of LLMs over the whole software testing field.

This short paper presents first steps in the scientific part of the KIRETT project, which aims to improve first aid during rescue operations using a wearable device. The wearable is used for computer-aided situation recognition by means of artificial intelligence. It provides contextual recommendations for actions and operations to rescue personnel and is intended to minimize damage to patients due to incorrect treatment, as well as increase the probability of survival. The paper describes a first overview of research approaches within the project.

Extensive monitoring systems generate data that is usually compressed for network transmission. This compressed data might then be processed in the cloud for tasks such as anomaly detection. However, compression can potentially impair the detector's ability to distinguish between regular and irregular patterns due to information loss. Here we extend the information-theoretic framework introduced in [1] to simultaneously address the trade-off between the three features on which the effectiveness of the system depends: the effectiveness of compression, the amount of distortion it introduces, and the distinguishability between compressed normal signals and compressed anomalous signals. We leverage a Gaussian assumption to draw curves showing how moving on a Pareto surface helps administer such a trade-off better than simply relying on optimal rate-distortion compression and hoping that compressed signals can be distinguished from each other.

The very expressiveness of Bayesian networks can introduce fresh challenges due to the large number of relationships they often model. In many domains, it is thus often essential to supplement any available data with elicited expert judgements. This in turn leads to two key challenges: the cognitive burden of these judgements is often very high, and there are a very large number of judgements required to obtain a full probability model. We can mitigate both issues by introducing assumptions such as independence of causal influences (ICI) on the local structures throughout the network, restricting the parameter space of the model. However, the assumption of ICI is often unjustified and overly strong. In this paper, we introduce the surjective independence of causal influences (SICI) model which relaxes the ICI assumption and provides a more viable, practical alternative local structure model that facilitates efficient Bayesian network parameterisation.

In recent years, large language models (LLMs) have driven major advances in language understanding, marking a significant step toward artificial general intelligence (AGI). With increasing demands for higher-level semantics and cross-modal fusion, multimodal large language models (MLLMs) have emerged, integrating diverse information sources (e.g., text, vision, and audio) to enhance modeling and reasoning in complex scenarios. In AI for Science, multimodal emotion recognition and reasoning has become a rapidly growing frontier. While LLMs and MLLMs have achieved notable progress in this area, the field still lacks a systematic review that consolidates recent developments. To address this gap, this paper provides a comprehensive survey of LLMs and MLLMs for emotion recognition and reasoning, covering model architectures, datasets, and performance benchmarks. We further highlight key challenges and outline future research directions, aiming to offer researchers both an authoritative reference and practical insights for advancing this domain. To the best of our knowledge, this paper is the first attempt to comprehensively survey the intersection of MLLMs with multimodal emotion recognition and reasoning. The summary of existing methods mentioned is in our Github: \href{https://github.com/yuntaoshou/Awesome-Emotion-Reasoning}{https://github.com/yuntaoshou/Awesome-Emotion-Reasoning}.

We present an overview of the SCIDOCA 2025 Shared Task, which focuses on citation discovery and prediction in scientific documents. The task is divided into three subtasks: (1) Citation Discovery, where systems must identify relevant references for a given paragraph; (2) Masked Citation Prediction, which requires selecting the correct citation for masked citation slots; and (3) Citation Sentence Prediction, where systems must determine the correct reference for each cited sentence. We release a large-scale dataset constructed from the Semantic Scholar Open Research Corpus (S2ORC), containing over 60,000 annotated paragraphs and a curated reference set. The test set consists of 1,000 paragraphs from distinct papers, each annotated with ground-truth citations and distractor candidates. A total of seven teams registered, with three submitting results. We report performance metrics across all subtasks and analyze the effectiveness of submitted systems. This shared task provides a new benchmark for evaluating citation modeling and encourages future research in scientific document understanding.