Institutions dependent on IT services and resources acknowledge the crucial significance of an IT help desk system, that act as a centralized hub connecting IT staff and users for service requests. Employing various Machine Learning models, these IT help desk systems allow access to corrective actions used in the past, but each model has different performance when applied to different datasets. This work compares eleven Information Retrieval techniques in a dataset of IT support tickets, with the goal of implementing a software that facilitates the work of Information Technology support analysts. The best results were obtained with the Sentence-BERT technique, in its multi-language variation distilluse-base-multilingual-cased-v1, where 78.7% of the recommendations made by the model were considered relevant. TF-IDF (69.0%), Word2vec (68.7%) and LDA (66.3%) techniques also had consistent results. Furthermore, the used datasets and essential parts of coding have been published and made open source. It also demonstrated the practicality of a support ticket recovery system by implementing a minimal viable prototype, and described in detail the implementation of the system. Finally, this work proposed a novel metric for comparing the techniques, whose aim is to closely reflect the perception of the IT analysts about the retrieval quality.

Teenager who lost his legs in crash will'never forgive' driver 38 minutes agoShareSaveKen Banks and Louise HosieBBC Scotland NewsShareSaveBBC Adam Golebiewski had a double amputation after the crash last year A teenager who lost his lower legs in a crash says he "will never forgive" the drink-driver at the wheel. Young footballer Adam Golebiewski, 18, had been a passenger in Arran Paterson's car in Macduff, Aberdeenshire, in September last year. Paterson, 19, admitted dangerous driving, being over the drink-drive limit and driving without insurance at Aberdeen Sheriff Court. Adam walked into court unaided on prosthetic legs following intensive rehabilitation. He said: "I want to try to enjoy life again and stay positive."

We present a collection of parallel corpora of 12 sign languages in video format, together with subtitles in the dominant spoken languages of the corresponding countries. The entire collection includes more than 1,300 hours in 4,381 video files, accompanied by 1,3~M subtitles containing 14~M tokens. Most notably, it includes the first consistent parallel corpora for 8 Latin American sign languages, whereas the size of the German Sign Language corpora is ten times the size of the previously available corpora. The collection was created by collecting and processing videos of multiple sign languages from various online sources, mainly broadcast material of news shows, governmental bodies and educational channels. The preparation involved several stages, including data collection, informing the content creators and seeking usage approvals, scraping, and cropping. The paper provides statistics on the collection and an overview of the methods used to collect the data.

In large-scale maintenance organizations, identifying subject matter experts and managing communications across complex entities relationships poses significant challenges -- including information overload and longer response times -- that traditional communication approaches fail to address effectively. We propose a novel framework that combines RDF graph databases with LLMs to process natural language queries for precise audience targeting, while providing transparent reasoning through a planning-orchestration architecture. Our solution enables communication owners to formulate intuitive queries combining concepts such as equipment, manufacturers, maintenance engineers, and facilities, delivering explainable results that maintain trust in the system while improving communication efficiency across the organization.

Large Language Models (LLMs) succeed in many natural language processing tasks. However, their tendency to hallucinate - generate plausible but inconsistent or factually incorrect text - can cause significant problems in certain tasks, including response generation in dialogue. To mitigate this issue, we propose two novel graph knowledge-augmented frameworks, Dialogue Response Generation via Textualised Graphs (TG-DRG) and Graph-Aware Dialogue Response Generation (GA-DRG), which combine reasoning-guided dialogue reformulation, dialogue sense knowledge selection, and graph-enhanced response generation to improve the factuality of dialogue responses. To evaluate the factuality of generated responses, we propose a dialogue fact score that addresses the limitations of existing fact-score methods in dialogue settings, providing a more reliable assessment of factual consistency. We evaluate our methods using different baselines on the OpendialKG and HybriDialogue datasets. Our methods noticeably improve factuality compared to other graph knowledge-augmentation baselines, including the state-of-the-art G-retriever, achieving improvements of 3.47% on OpendialKG and 3.12% on HybriDialogue in terms of dialogue fact score. The code will be released on GitHub.

This work presents a novel approach for \textit{bearing rigidity} analysis and control in multi-robot networks with sensing constraints and dynamic topology. By decomposing the system's framework into \textit{subframeworks}, we express bearing rigidity -- a global property -- as a set of \textit{local} properties, with rigidity eigenvalues serving as natural \textit{local rigidity measures}. We propose a decentralized gradient-based controller to execute mission-specific commands using only bearing measurements. The controller preserves bearing rigidity by keeping the rigidity eigenvalues above a threshold, using only information exchanged within subframeworks. Simulations evaluate the scheme's effectiveness, underscoring its scalability and practicality.

Large language models (LLMs) have revolutionized many natural language processing (NLP) tasks.

KI) platform, a new benchmark designed to address the unique challenges of integrating medical knowledge into foundation models under privacy constraints.

Multivariate time series forecasting (MTSF) seeks to model temporal dynamics among variables to predict future trends. Transformer-based models and large language models (LLMs) have shown promise due to their ability to capture long-range dependencies and patterns. However, current methods often rely on rigid inductive biases, ignore inter-variable interactions, or apply static fusion strategies that limit adaptability across forecast horizons. These limitations create bottlenecks in capturing nuanced, horizon-specific relationships in time-series data. To solve this problem, we propose T3Time, a novel trimodal framework consisting of time, spectral, and prompt branches, where the dedicated frequency encoding branch captures the periodic structures along with a gating mechanism that learns prioritization between temporal and spectral features based on the prediction horizon. We also proposed a mechanism which adaptively aggregates multiple cross-modal alignment heads by dynamically weighting the importance of each head based on the features. Extensive experiments on benchmark datasets demonstrate that our model consistently outperforms state-of-the-art baselines, achieving an average reduction of 3.28% in MSE and 2.29% in MAE. Furthermore, it shows strong generalization in few-shot learning settings: with 5% training data, we see a reduction in MSE and MAE by 4.13% and 1.91%, respectively; and with 10% data, by 3.62% and 1.98% on average.

Abstract--The accurate labeling of datasets is often both costly and time-consuming. Given an unlabeled dataset, programma tic weak supervision obtains probabilistic predictions for th e labels by leveraging multiple weak labeling functions (LFs) that p ro-vide rough guesses for labels. Weak LFs commonly provide guesses with assorted types and unknown interdependences that can result in unreliable predictions. This paper presents a methodology for programma tic weak supervision that can provide confidence intervals for l abel probabilities and obtain more reliable predictions. In par ticular, the methods proposed use uncertainty sets of distributions that encapsulate the information provided by LFs with unrestric ted behavior and typology. Experiments on multiple benchmark datasets show the improvement of the presented methods over the state-of-the-art and the practicality of the confidence intervals presented. OR many machine learning applications, the accurate labeling of datasets is both costly and time-consuming [1]-[4]. Given an unlabeled dataset, methods for programmatic weak supervision aim to leverage multiple wea k labeling functions (LFs) to provide accurate labels [5], [6 ]. Since common LFs only provide rough guesses for labels, programmatic weak supervision methods use the outputs of multiple LFs to obtain probabilistic predictions for the la bel of each instance [7]-[13]. These predictions can then be use d to create a fully supervised dataset composed by the instanc es corresponding to high-confidence predictions, e.g., a labe l with a large enough predicted probability is regarded as the actu al Manuscript received September 30, 2024; accepted August 4, 2025.