The availability of metadata for scientific documents is pivotal in propelling knowledge forward and adhering to the FAIR principles (i.e. Findability, Accessibility, Interoperability, Reusability) research findings. However, lack sufficient published documents, particularly those from smaller mid-sized publishers, hinders their accessibility. This issue widespread some disciplines, such as German Social Sciences, where publications often employ diverse templates. To address this challenge,...

Edge detection is a fundamental technique in various computer vision tasks. Edges are indeed effectively delineated by pixel discontinuity and can offer reliable structural information even textureless areas. State-of-the-art heavily relies on pixel-wise annotations, which labor-intensive subject to inconsistencies when acquired manually. In this work, we propose novel self-supervised approach for edge that employs multi-level, multi-homography transfer annotations from synthetic real-world...

Our vision paper outlines a plan to improve the future of semantic interoperability in data spaces through application machine learning. The use spaces, where is exchanged among members self-regulated environment, becoming increasingly popular. However, current manual practices managing metadata and vocabularies these are time-consuming, prone errors, may not meet needs all stakeholders. By leveraging power learning, we believe that can be significantly improved. This involves automatically...

Vojta-therapy is a useful technique for the treatment of physical and mental impairments in humans, very effective children less than 6 months. During therapy, specific stimulation given to patient's body perform certain reflexive pattern movements. The repetition this ultimately makes previously blocked connections between spinal cord brain available, after few session, patients can these movements without any external stimulation. must be performed several times day or week last weeks...

This demo paper presents a generic toolchain to extract, segment and match literature references from full text PDF files in the project EXCITE. The aim of EXCITE is extracting matching citations social science publications making more citation data available researchers. Each single step pipeline open source tools used accomplish tasks are explained. public system which integrates all components under an user-friendly interface put forward illustrated. As final step, special component...

Topic modeling is a popular technique for clustering large collections of text documents. A variety different types regularization implemented in topic modeling. In this paper, we propose novel approach analyzing the influence on results Based Renyi entropy, inspired by concepts from statistical physics, where an inferred topical structure collection can be considered information system residing non-equilibrium state. By testing our four models-Probabilistic Latent Semantic Analysis (pLSA),...

This paper addresses the problem of extracting and segmenting references from PDF documents. The novelty presented approach lies in its capability to discover highly varying mainly terms content, length location document. Unlike existing works, proposed method does not follow classical pipeline that consists sequential phases. It rather learns different characteristics be used a coherent scheme reduces error accumulation by following probabilistic approach. Contrary conventional references,...

Abstract In the academic world, number of scientists grows every year and so does authors sharing same names. Consequently, it is challenging to assign newly published papers their respective authors. Therefore, author name ambiguity considered a critical open problem in digital libraries. This paper proposes an disambiguation approach that links names real-world entities by leveraging co-authors domain research. To this end, we use data collected from DBLP repository contains more than 5...

Due to the significant advancement of Natural Language Processing and Computer Vision-based models, Visual Question Answering (VQA) systems are becoming more intelligent advanced. However, they still error-prone when dealing with relatively complex questions. Therefore, it is important understand behaviour VQA models before adopting their results. In this paper, we introduce an interpretability approach for by generating counterfactual images. Specifically, generated image supposed have...

In this paper we apply multifractal formalism to the analysis of statistical behaviour topic models under condition varying number topics. Our reveals existence two self-similar regions and one transition region in function density-of-states depending on As earlier a that can be expressed through was successfully used determine optimal topics, test applicability for same purpose. We provide numerical results three (PLSA, ARTM, LDA Gibbs sampling) marked-up collections containing texts...

Abstract Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with popularity deep learning techniques. Furthermore, we see GTs used not only for training detectors Convolutional Neural Networks (CNN), but also evaluating skeleton-related pruning and matching algorithms. However, most existing shape image datasets suffer from lack GT inconsistency standards. As a result, it difficult evaluate reproduce CNN-based algorithms on fair basis....

For semantic analysis of activities and events in videos, it is important to capture the spatio-temporal relation among objects 3D space. In this paper, we present a probabilistic method that extracts trajectories from 2D captured monocular moving camera. Compared with existing methods rely on restrictive assumptions, propose can extract much less restriction by adopting new example-based techniques, which compensate lack information. Here, estimate focal length camera based similar...

Image research has shown substantial attention in deblurring networks recent years. Yet, their practical usage real-world deblurring, especially motion blur, remains limited due to the lack of pixel-aligned training triplets (background, blurred image, and blur heat map) restricted information inherent images. This paper presents a simple yet efficient framework synthetic restore images using Inertial Measurement Unit (IMU) data. Notably, includes strategy for triplet generation,...

Yawning detection is actively used in multimedia applications such as driver fatigue assessment and status monitoring. However, the accuracy robustness of existing yawning detectors are limited due to variations environments (especially lights), facial expressions, confusion behaviours (e.g., talking eating). This paper introduces a transformer-based method, YawnNet, for accurate by leveraging spatial-temporal encoding local cues. In particular, YawnNet contains data processing stage with...

Background Pneumonia and lung cancer have a mutually reinforcing relationship. Lung patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for patients. Developing an early diagnostic system pneumonia help improve the prognosis of infection. Method This study proposes neural network diagnosis based on non-enhanced CT scans, consisting two 3D convolutional networks (CNN) connected in series form modules. The first...

The principles of data spaces for sovereign exchange across trusted organizations have so far mainly been adopted in business-to-business settings, and recently scaled to cloud environments. Meanwhile, research established distributed infrastructures, respecting the principle that must be FAIR, i.e., findable, accessible, interoperable reusable. For mutual benefit these two communities, FAIR Data Spaces project aims connect them towards vision a common, cloud-based space industry research....

Since the birth of Bitcoin in 2009, cryptocurrencies have emerged to become a global phenomenon and an important decentralized financial asset. Due this decentralization, value these digital currencies against fiat is highly volatile over time. Therefore, forecasting crypto-fiat currency exchange rate extremely challenging task. For reliable forecasting, paper proposes multimodal AdaBoost-LSTM ensemble approach that employs all modalities which derive price fluctuation such as social media...

Traditional data monetization approaches face challenges related to protection and logistics. In response, digital marketplaces have emerged as intermediaries simplifying transactions. Despite the growing establishment acceptance of marketplaces, significant hinder efficient trading. As a result, few companies can derive tangible value from their data, leading missed opportunities in understanding customers, pricing decisions, fraud prevention. this paper, we explore both technical...