Multiple studies have investigated bibliometric features and uncategorized scholarly documents for the influential scholarly document prediction task. In this paper, we describe our work that attempts to go beyond bibliometric metadata to predict influential scholarly documents. Furthermore, this work also examines the influential scholarly document prediction task over categorized scholarly documents. We also introduce a new approach to enhance the document representation method with a domain-independent knowledge graph to find the influential scholarly document using categorized scholarly content. As the input collection, we use the WHO corpus with scholarly documents on the theme of COVID-19. This study examines different document representation methods for machine learning, including TF-IDF, BOW, and embedding-based language models (BERT). The TF-IDF document representation method works better than others. From various machine learning methods tested, logistic regression outperformed the other for scholarly document category classification, and the random forest algorithm obtained the best results for influential scholarly document prediction, with the help of a domain-independent knowledge graph, specifically DBpedia, to enhance the document representation method for predicting influential scholarly documents with categorical scholarly content. In this case, our study combines state-of-the-art machine learning methods with the BOW document representation method. We also enhance the BOW document representation with the direct type (RDF type) and unqualified relation from DBpedia. From this experiment, we did not find any impact of the enhanced document representation for the scholarly document category classification. We found an effect in the influential scholarly document prediction with categorical data.
- Klíčová slova
- COVID-19, Domain-independent knowledge graph, Influential scholarly document prediction, Machine learning algorithms, Text mining, World health organization,
- MeSH
- algoritmy MeSH
- COVID-19 * MeSH
- jazyk (prostředek komunikace) MeSH
- lidé MeSH
- rozpoznávání automatizované * MeSH
- strojové učení MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Face recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templates and improve recognition accuracy. Our research offers a three-fold contribution to the field. Firstly, (i) we strengthened the evidence that QIM outperforms other contemporary template creation approaches. For this reason, we investigate seven template creation methods, which include four cluster description-based methods and three estimation-based methods. Further, (ii) we extended testing; we use a nearly four times larger database compared to the previous study, which includes a new set, and we report the recognition performance on this extended database. Additionally, we distinguish between open- and closed-set identification. Thirdly, (iii) we perform an evaluation of the cluster estimation-based method (specifically QIM) with an in-depth analysis of its parameter setup in order to make its implementation feasible. We provide instructions and recommendations for the correct parameter setup. Our research confirms that QIM's application in template creation improves recognition performance. In the case of automatic application and optimization of QIM parameters, improvement recognition is about 4-10% depending on the dataset. In the case of a too general dataset, QIM also provides an improvement, but the incorporation of QIM into an automated algorithm is not possible, since QIM, in this case, requires manual setting of optimal parameters. This research contributes to the advancement of secure and accurate face recognition systems, paving the way for its adoption in various security applications.
New methods of securing the distribution of audio content have been widely deployed in the last twenty years. Their impact on perceptive quality has, however, only been seldomly the subject of recent extensive research. We review digital speech watermarking state of the art and provide subjective testing of watermarked speech samples. Latest speech watermarking techniques are listed, with their specifics and potential for further development. Their current and possible applications are evaluated. Open-source software designed to embed watermarking patterns in audio files is used to produce a set of samples that satisfies the requirements of modern speech-quality subjective assessments. The patchwork algorithm that is coded in the application is mainly considered in this analysis. Different watermark robustness levels are used, which allow determining the threshold of detection to human listeners. The subjective listening tests are conducted following ITU-T P.800 Recommendation, which precisely defines the conditions and requirements for subjective testing. Further analysis tries to determine the effects of noise and various disturbances on watermarked speech's perceived quality. A threshold of intelligibility is estimated to allow further openings on speech compression techniques with watermarking. The impact of language or social background is evaluated through an additional experiment involving two groups of listeners. Results show significant robustness of the watermarking implementation, retaining both a reasonable net subjective audio quality and security attributes, despite mild levels of distortion and noise. Extended experiments with Chinese listeners open the door to formulate a hypothesis on perception variations with geographical and social backgrounds.
- MeSH
- algoritmy MeSH
- jazyk (prostředek komunikace) MeSH
- komprese dat metody MeSH
- lidé MeSH
- percepce řeči MeSH
- rozpoznávání automatizované metody MeSH
- telekomunikace MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Čína MeSH
Precision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of fish, infrastructure, and the environment ideally by contactless methods. The identification of individual fish of the same species within the cultivated group is critical for individualized treatment, biomass estimation and fish state determination. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. We introduced a methodology for fully automatic Atlantic salmon (Salmo salar) individual identification according to the dot patterns on the skin. The method was tested for 328 individuals, with identification accuracy of 100%. We also studied the long-term stability of the patterns (aging) for individual identification over a period of 6 months. The identification accuracy was 100% for 30 fish (out of water images). The methodology can be adapted to any fish species with dot skin patterns. We proved that the methodology can be used as a non-invasive substitute for invasive fish tagging. The non-invasive fish identification opens new posiblities to maintain the fish individually and not as a fish school which is impossible with current invasive fish tagging.
This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine learning model in the black-box attack scenario. This way, we can find adversarial examples without access to model's parameters, only by querying the model at hand. We have tested a range of machine learning models including deep and shallow neural networks. Our experiments have shown that the vulnerability to adversarial examples is not only the problem of deep networks, but it spreads through various machine learning architectures. Rather, it depends on the type of computational units. Local units, such as Gaussian kernels, are less vulnerable to adversarial examples.
- Klíčová slova
- Adversarial examples, Genetic algorithms, Kernel methods, Neural networks, Supervised learning,
- MeSH
- algoritmy MeSH
- lidé MeSH
- neuronové sítě (počítačové) * MeSH
- řízené strojové učení * trendy MeSH
- rozpoznávání automatizované metody trendy MeSH
- strojové učení trendy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.
- Klíčová slova
- accelerometers, classification, computational intelligence, machine learning, motion monitoring, multimodal signal analysis,
- MeSH
- akcelerometrie metody MeSH
- algoritmy MeSH
- Bayesova věta MeSH
- cvičení MeSH
- cyklistika * MeSH
- fitness náramky * MeSH
- lidé MeSH
- mobilní telefon přístrojové vybavení MeSH
- neuronové sítě (počítačové) MeSH
- počítačové zpracování signálu MeSH
- pohyb těles MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované MeSH
- software MeSH
- srdeční frekvence * MeSH
- statistické modely MeSH
- support vector machine MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND AND OBJECTIVE: We present a fully automatic system based on learning approaches, which aims to localization and identification (labeling) of vertebrae in 3D computed tomography (CT) scans of possibly incomplete spines in patients with bone metastases and vertebral compressions. METHODS: The framework combines a set of 3D algorithms for i) spine detection using a convolution neural network (CNN) ii) spinal cord tracking based on combination of a CNN and a novel growing sphere method with a population optimization, iii) intervertebral discs localization using a novel approach of spatially variant filtering of intensity profiles and iv) vertebra labeling using a CNN-based classification combined with global dynamic optimization. RESULTS: The proposed algorithm has been validated in testing databases, including also a publicly available dataset. The mean error of intervertebral discs localization is 4.4 mm, and for vertebra labeling, the average rate of correctly identified vertebrae is 87.1%, which can be considered a good result with respect to the large share of highly distorted spines and incomplete spine scans. CONCLUSIONS: The proposed framework, which combines several advanced methods including also three CNNs, works fully automatically even with incomplete spine scans and with distorted pathological cases. The achieved results allow including the presented algorithms as the first phase to the fully automated computer-aided diagnosis (CAD) system for automatic spine-bone lesion analysis in oncological patients.
- Klíčová slova
- Convolution neural network, Learning-based approach, Pathological vertebrae, Vertebra detection,
- MeSH
- algoritmy MeSH
- databáze faktografické MeSH
- diagnóza počítačová MeSH
- lidé MeSH
- metastázy nádorů MeSH
- meziobratlová ploténka diagnostické zobrazování patologie MeSH
- nádory kostí diagnostické zobrazování patologie MeSH
- nemoci páteře diagnostické zobrazování MeSH
- neuronové sítě (počítačové) MeSH
- páteř diagnostické zobrazování patologie MeSH
- počítačová rentgenová tomografie * MeSH
- počítačové zpracování obrazu MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované MeSH
- software MeSH
- zobrazování trojrozměrné metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
This article presents a steganographic method StegoNN based on neural networks. The method is able to identify a photomontage from presented signed images. Unlike other academic approaches using neural networks primarily as classifiers, the StegoNN method uses the characteristics of neural networks to create suitable attributes which are then necessary for subsequent detection of modified photographs. This also results in a fact that if an image is signed by this technique, the detection of modifications does not need any external data (database of non-modified originals) and the quality of the signature in various parts of the image also serves to identify modified (corrupted) parts of the image. The experimental study was performed on photographs from CoMoFoD Database and its results were compared with other approaches using this database based on standard metrics. The performed study showed the ability of the StegoNN method to detect corrupted parts of an image and to mark places which have been most probably image-manipulated. The usage of this method is suitable for reportage photography, but in general, for all cases where verification (provability) of authenticity and veracity of the presented image are required.
- Klíčová slova
- CoMoFoD database, Neural network, Photomontage, Steganography, StegoNN,
- MeSH
- databáze faktografické normy MeSH
- fotografování metody normy MeSH
- lidé MeSH
- neuronové sítě (počítačové) * MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované metody normy MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Machine learning (ML) is a growing field that provides tools for automatic pattern recognition. The neuroimaging community currently tries to take advantage of ML in order to develop an auxiliary diagnostic tool for schizophrenia diagnostics. In this letter, we present a classification framework based on features extracted from magnetic resonance imaging (MRI) data using two automatic whole-brain morphometry methods: voxel-based (VBM) and deformation-based morphometry (DBM). The framework employs a random subspace ensemble-based artificial neural network classifier-in particular, a multilayer perceptron (MLP). The framework was tested on data from first-episode schizophrenia patients and healthy controls. The experiments differed in terms of feature extraction methods, using VBM, DBM, and a combination of both morphometry methods. Thus, features of different types were available for model adaptation. As we expected, the combination of features increased the MLP classification accuracy up to 73.12%-an improvement of 5% versus MLP-based only on VBM or DBM features. To further verify the findings, other comparisons using support vector machines in place of MLPs were made within the framework. However, it cannot be concluded that any classifier was better than another.
- MeSH
- diagnóza počítačová metody MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- mozek diagnostické zobrazování MeSH
- neuronové sítě (počítačové) * MeSH
- rozpoznávání automatizované metody MeSH
- schizofrenie diagnostické zobrazování MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
We present the ROCA (ROad Curvature Analyst) software, in the form of an ESRI ArcGIS Toolbox, intended for vector line data processing. The software segments road network data into tangents and horizontal curves. Horizontal curve radii and azimuth of tangents are then automatically computed. Simultaneously, additional frequently used road section characteristics are calculated, such as the sinuosity of a road section (detour ratio), the number of turns along an individual road section and the average cumulative angle for a road section. The identification of curves is based on the naïve Bayes classifier and users are allowed to prepare their own training data files. We applied ROCA software to secondary roads within the Czech road network (9,980 km). The data processing took less than ten minutes. Approximately 43% of the road network in question consists of 42,752 horizontal curves. The ROCA software outperforms other existing automatic methods by 26% with respect to the percentage of correctly identified curves. The segmented secondary roads within the Czech road network can be viewed on the roca.cdvgis.cz/czechia web-map application. We combined data on road geometry with road crashes database to develop the crash modification factors for horizontal curves with various radii. We determined that horizontal curves with radii of 50 m are approximately 3.7 times more hazardous than horizontal curves with radii accounting for 1000 m. ROCA software can be freely downloaded for noncommercial use from https://roca.cdvinfo.cz/ website.
- MeSH
- automobily normy MeSH
- bezpečnost MeSH
- dopravní nehody prevence a kontrola MeSH
- geografické informační systémy MeSH
- lidé MeSH
- pomůcky pro sebeobsluhu * MeSH
- řízení motorových vozidel * normy MeSH
- rotace * MeSH
- rozpoznávání automatizované metody MeSH
- software * normy MeSH
- životní prostředí - projekt MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
