Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic relational models: learning and evaluation

Statistical relational learning (SRL) appeared in the early 2000s as a new field of machine learning that enables effective and robust reasoning about relational data structures. Several conventional data mining methods have been adapted for direct application to relational data representation. Relational Bayesian Networks (RBNs) extend Bayesian networks (BNs) to a relational data mining context. To use this model, it is first necessary to build it: the structure and parameters of a RBN must be set manually or learned from a relational observational dataset. Learning the structure remains the most complicated issue as it is a NP-hard problem. Existing approaches for RBNs structure learning are inspired from classical methods of learning the structure of BNs. The evaluation of learning approaches requires testing datasets and evaluation measurements. For BNs, datasets are usually sampled from real known networks. Otherwise, processes to randomly generate the model and the data are already established. Both practices are almost absent for RBR. Moreover, metrics to evaluate a RBN structure learning algorithm are not yet proposed. This thesis provides two major contributions. I) A synthetic approach allowing to generate random RBNs from scratch. The proposed method allows to generate RBNs as well as synthetic relational data from a randomly generated relational schema and a random set of probabilistic dependencies. Also, we discuss the adaptation of the evaluation metrics of BNs structure learning algorithms to the relational context and we propose new relational evaluation measurements. II) A hybrid approach for RBNs structure learning. This approach presents an extension of the MMHC algorithm in the relational context. We present an experimental study to compare this new learning algorithm with the state-of-the-art approaches.
Frame Identification as Categorization: Exemplars vs Prototypes in Embeddingland

Categorization is a central capability of human cognition, and a number of theories have been developed to account for properties of categorization. Even though many tasks in semantics also involve categorization of some kind, theories of categorization do not play a major role in contemporary research in computational linguistics. This paper follows the idea that embedding-based models of semantics lend themselves well to being formulated in terms of classical categorization theories. The benefit is a space of model families that enables (a) the formulation of hypotheses about the impact of major design decisions, and (b) a transparent assessment of these decisions. We instantiate this idea on the task of frame-semantic frame identification. We define four models that cross two design variables: (a) the choice of prototype vs. exemplar categorization, corresponding to different degrees of generalization applied to the input; and (b) the presence vs. absence of a fine-tuning step, corresponding to generic vs. task-adaptive categorization. We find that for frame identification, generalization and task-adaptive categorization both yield substantial benefits. Our prototype-based, fine-tuned model, which combines the best choices for these variables, establishes a new state of the art in frame identification.
Blood Saturation Decreasing Level Based on the Features of a Spirogram Signal

Obstructive sleep apnea is a common disease associated with respiratory processes during sleep due to the narrowing of the upper respiratory airways, which leads to a decrease in the amount of oxygen in arterial blood flow - SpO2 – to human internal organs. Low blood saturation values can lead to serious consequences in the patient's health, and it can be monitored, for example, using pulsoximetry sensors. In this article a comparative analysis of various regression approaches – Support Vector Regression (SVR), Gradient Boosting, Lasso Regression, Ridge Regression and Sequential Neural Network-was carried out in order to discover an opportunity for predicting SpO2 level decreasing basing on the features of a spirogram for 4 different patients separately. The best results of R2 determination score were shown by models based on SVR and Neural Network: 0.6878 and 0.6697. The best correlation coefficient between the ground truth blood saturation decreasing and the predicted values was 0.9019 for the Support Vector Regressor model.
An Automated Reclassification Project at the University of Kentucky

ABSTRACT The University of Kentucky Libraries began use of the Library of Congress Classification System in 1978, leaving the bulk of their older material in the Dewey Classification System. In 1992 during the planning for the new Young Library, the opportunity arose to complete the reclassification of the collection from Dewey to Library of Congress. This paper outlines the process used to reclassify the collection. Unique features of this project were the reliance on automation to reduce the number of person hours needed for reclassifying the materials and the minimal disruption of services to users.
Exploring an Efficient Handwritten Manipuri Meetei-Mayek Character Recognition Using Gradient Feature Extractor and Cosine Distance Based Multiclass k-Nearest Neighbor Classifier

In this paper, a new approach for efﬁ-ciently extracting cognition out of a total of 56 different classes of handwritten Manipuri Meetei-Mayek (Indian language) is being described. Although character recognition algorithms has been re-searched and developed for other Indian scripts, no research work has been re-ported so far for recognising all the characters of the Manipuri Meetei-Mayek. The work begins with a thorough literature survey of existing works which high-lighted the need of a good feature extractor as a pre-requisite for training the classiﬁer. The limitations are experimentally removed using multiple sized cell grids using Histogram of Oriented Gradient (HOG) descriptors as feature extractor. HOG being a gradient based descriptor is very efﬁcient in data discrimination and very stable with illumination variation. For efﬁcient classﬁcation of the HOG features of the Manipuri Meetei-Mayek, the robust k-Nearest Neighbor was tweaked suitably to recognize all the 56 classes of the script. The proposed approach resulted in an overall accuracy of 94.29 % with a training time of about 540.81 seconds.
Developing Lexicon and Classifier for Personality Identification in Texts

Personality, an essential foundation of human behavior is difficult to identify and classify from texts because of the scarcity of explicit textual clues. Several works were attempted for personality identification by employing well-known lexicons like WordNet, Senti-WordNet, SenticNet etc. However, a lexicon solely devoted for identifying different types of personality is rare. Thus, in the present article, we have discussed the methodologies to develop a personality lexicon from the Essay dataset, a personality corpus based on Big Five model. We have used a frequency based N-gram approach to extract the unique words as well as phrases with respect to each of the Big Five personality classes. In addition to the words, we have added another feature, corpus based probability of occurrence into the lexicon. Finally, we have evaluated our lexicon on a small Youtube personality dataset and found satisfactory coverage. In addition, we have developed a LIWC based classification framework by employing several machine learning algorithms followed by feature selection using information gain and correlation techniques. SVM and Logistic Regression achieved the maximum accuracies of 78.52% and 62.26% with a reduced set of feature size 15 and 10 selected by information gain and correlation attribute evaluation, respectively.
Systolic FFT algorithms on Boolean cube networks

A description is given of a systolic Cooley-Tukey fast Fourier transform algorithm for Boolean n-cubes with a substantial amount of storage per cube node. In mapping a Cooley-Tukey type FFT to such a network, the main concerns are effective use of the high connectivity/bandwidth of the Boolean n-cube, the computational resources, the storage bandwidth, if there is a storage hierarchy, and the pipelines should the arithmetic units have such a feature. Another important consideration in a multiprocessor, distributed storage architecture is the allocation and access to coefficients, if they are precomputed. FFT algorithms are described that use both the storage bandwidth and the communication system optimally and require storage of P+nN coefficients for a transform on P>or=N data elements. A complex-to-complex FFT on 16 million points is predicted to require about 1.5 s on a Connection Machine model CM-2. <<ETX>>
Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping

Motor imagery-based brain-computer interface (BCI) has been widely used to translate user’s motor intentions in BCI applications. In general, experiment trial of motor imagery task is repeated to improve the accuracy of the motor imagery-based BCI application, but it is not well known whether the accuracy would converge from a certain number of trial repetition. This study identified that how many trials are required in the classification model for motor imagery task with hand-grasping to show reliable classification performance. Five participants equipped with an electroencephalography device were enrolled, and they were requested to perform the motor imagery tasks with hand-grasping and unfolding. Trials were classified into hand-grasping, unfolding and resting. We observed that the classification performance is converged when more than 40 trials are used in the model. This finding could be utilized to develop reliable motor imagery-based BCI application with increasing the efficiency of the experiment.
Towards concept based software engineering for intelligent agents

The development of AI and machine learning applications at an industry mature level while maintaining quality and productivity goals is one of today's major challenges. Research in the field of intelligent agents has achieved many successes in recent years, especially due to various reinforcement learning techniques, and promises a high benefit in times of automation and autonomous systems. Bringing them into production, however, requires optimization against many other criteria than just accuracy. This leads to the emerging field of machine teaching. We already know many of the objectives used there from software engineering research, which has led to many well-established principles in recent decades. One of them is the component-based development whose idea finds an interesting counterpart in hierarchical reinforcement learning. We show that both areas can benefit from each other and introduce our approach of concept based software engineering, which is focused on supporting productivity and quality goals during the development of such systems.
Automatic Cardiotocography Diagnostic System Based on Hilbert Transform and Adaptive Threshold Technique

The visual analysis of cardiotocographic examinations is a very subjective process. The accurate detection and segmentation of the fetal heart rate (FHR) features and their correlation with the uterine contractions in time allow a better diagnostic and the possibility of anticipation of many problems related to fetal distress. This paper presents a computerized diagnostic aid system based on digital signal processing techniques to detect and segment changes in the FHR and the uterine tone signals automatically. After a pre-processing phase, the FHR baseline detection is calculated. An auxiliary signal called detection line is proposed to support the detection and segmentation processes. Then, the Hilbert transform is used with an adaptive threshold for identifying fiducial points on the fetal and maternal signals. For an antepartum (before labor) database, the positive predictivity value (PPV) is 96.80% for the FHR decelerations, and 96.18% for the FHR accelerations. For an intrapartum (during labor) database, the PPV found was 91.31% for the uterine contractions, 94.01% for the FHR decelerations, and 100% for the FHR accelerations. For the whole set of exams, PPV and SE were both 100% for the identification of FHR DIP II and prolonged decelerations.
EvoSuite at the SBST 2019 Tool Competition

EvoSuite is a search-based tool that automatically generates executable unit tests for Java code (JUnit tests). This paper summarises the results and experiences of EvoSuite's participation at the seventh unit testing competition at SBST 2019, where EvoSuite achieved the highest overall score (255.43 points) for the sixth time in seven editions of the competition.
A Semantic Ontology of Danish Adjectives

This paper presents a semantic annotation scheme for Danish adjectives, focusing both on prototypical semantic content and semantic collocational restrictions on an adjective’s head noun. The core type set comprises about 110 categories ordered in a shallow hierarchy with 14 primary and 25 secondary umbrella categories. In addition, domain information and binary sentiment tags are provided, as well as VerbNet-derived frames and semantic roles for those adjectives governing arguments. The scheme has been almost fully implemented on the lexicon of the Danish VISL parser, DanGram, containing 14,000 adjectives. We discuss the annotation scheme and its applicational perspectives, and present a statistical breakdown and coverage evaluation for three Danish reference corpora.
Classification of Working Memory Performance from EEG with Deep Artificial Neural Networks

Individuals have different working memory performance and some studies investigated a relationship between working memory performance and electroencephalography (EEG) band power. In this paper, we study EEG features to classify low performance group and high performance group and find that the power ratio feature of alpha and beta is more separable than their absolute powers. We test a deep artificial neural network (ANN) using the power ratio feature to classify the low performance group and high performance group. Experimental results on the working memory tasks show that some subjects have quite low accuracies (<20%) and it results in a low average classification accuracy of 61%, but we can see a possibility in the estimation of working memory performance using EEG data.
5-Aminolevulinic Acid Fluorescence-Guided Resection of 18F-FET-PET Positive Tumor Beyond Gadolinium Enhancing Tumor Improves Survival in Glioblastoma

Abstract BACKGROUND The value of early postoperative 18F-FET-PET in patients with glioblastoma (GBM) is unclear. Five-aminolevulinic acid (5-ALA) is used for fluorescence-guided resections in these patients and previous data suggest that fluorescence and 18F-FET-PET both demarcate larger tumor volumes than gadolinium enhanced magnet resonance imaging (MRI). OBJECTIVE To correlate fluorescence with enhancing volumes on postoperative MRI and 18F-FET-PET tumor volumes, and determine the value of postoperative 18F-FET-PET for predicting survival through observational study. METHODS GBM patients underwent fluorescence-guided resection after administration of 5-ALA followed by early postoperative MRI and 18F-FET-PET for determination of residual tissue volumes. All patients were treated with standard temozolomide radiochemotherapy and monitored for progression-free and overall survival (PFS, OS). RESULTS A total of 31 patients were included. For functional reasons, residual 5-ALA derived fluorescent tissue was left unresected in 18 patients with a median 18F-FET-PET volume of 17.82 cm3 (interquartile range 6.50-29.19). In patients without residual fluorescence, median 18F-FET-PET volume was 1.20 cm3 (interquartile range 0.87-5.50) and complete resection of gadolinium enhancing tumor was observed in 100% of patients. A 18F-FET-PET volume of above 4.3 cm3 was associated with worse OS (logrank P-value ≤ .05), also in patients with no residual contrast enhancing tumor on MRI. More patients in whom fluorescencing tissue had been removed completely had postoperative 18F-FET-PET tumor volumes below 4.3 cm3. CONCLUSION Postoperative 18F-FET-PET volumes predict OS and PFS. Resection of 5-ALA derived fluorescence beyond gadolinium enhancing tumor tissue leads to lower postoperative 18F-FET-PET tumor volumes and improved OS and PFS without additional deficits.
Terrosamycins A and B, Bioactive Polyether Ionophores from Streptomyces sp. RKND004 from Prince Edward Island Sediment

Terrosamycins A (1) and B (2), two polycyclic polyether natural products, were purified from the fermentation broth of Streptomyces sp. RKND004 isolated from Prince Edward Island sediment. The one strain-many compounds (OSMAC) approach coupled with UPLC-HRMS-based metabolomics screening led to the identification of these compounds. The structure of 1 was determined from analysis of NMR, HRMS, and X-ray diffraction data. NMR experiments performed on 2 revealed the presence of two methoxy groups replacing two hydroxy groups in 1. Like other polyether ionophores, 1 and 2 exhibited excellent antibiotic activity against Gram-positive pathogens. Interestingly, the terrosamycins also exhibited activity against two breast cancer cell lines.
Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI

The EEG-based brain-computer interface (BCI) requires removal of irrelevant channels to improve performance. In this paper, we propose the optimal channel selection using EEG channel covariance matrix and cross-combining region. First, the discriminative H channels and target channel are selected by difference of EEG channel covariance matrix between two classes. Second, we configure several sub-channel regions to cover the H channels. Then, we extract FBCSP features from cross-combining regions which are combination of the sub-channel regions and target channel. We select the best one cross-combining region and the optimal channels which are included in selected cross-combining region are finally selected. The features of selected region are used as input of LS-SVM classifier. The simulation results show the performance improvement of proposed method for BCI competition III dataset IVa by comparing the conventional channel selection methods.
Selective Untargeted Evasion Attack: An Adversarial Example That Will Not Be Classified as Certain Avoided Classes

Deep neural networks (DNNs) have useful applications in machine learning tasks involving recognition and pattern analysis. Despite the favorable applications of DNNs, these systems can be exploited by adversarial examples. An adversarial example, which is created by adding a small amount of noise to an original sample, can cause misclassification by the DNN. Under specific circumstances, it may be necessary to create a selective untargeted adversarial example that will not be classified as certain avoided classes. Such is the case, for example, if a modified tank cover can cause misclassification by a DNN, but the bandit equipped with the DNN must misclassify the modified tank as a class other than certain avoided classes, such as a tank, armored vehicle, or self-propelled gun. That is, selective untargeted adversarial examples are needed that will not be perceived as certain classes, such as tanks, armored vehicles, or self-propelled guns. In this study, we propose a selective untargeted adversarial example that exhibits 100% attack success with minimum distortions. The proposed scheme creates a selective untargeted adversarial example that will not be classified as certain avoided classes while minimizing distortions in the original sample. To generate untargeted adversarial examples, a transformation is performed to minimize the probability of certain avoided classes and distortions in the original sample. As experimental datasets, we used MNIST and CIFAR-10, including the Tensorflow library. The experimental results demonstrate that the proposed scheme creates a selective untargeted adversarial example that exhibits 100% attack success with minimum distortions (1.325 and 34.762 for MNIST and CIFAR-10, respectively).
Proposal-Driven Segmentation for Videos

Effectively utilizing the common information in a set of video frames is a vital aspect in video segmentation. However, existing methods that transport the common information from a prior frame to the current frame do not make use of the common information effectively. In order to address this issue, we apply a new strategy that jointly segments object through a convolutional neural network (CNN) to build a proposal-driven framework for exploiting the common information between two video frames by processing two video frames simultaneously in this letter. Moreover, proposals from the video frames are found useful for refining the segmentation results through fusing their segmentation results with the ones of the video frames. In our framework, proposals with features are generated by a faster region-CNN, and the L2 loss function is used to establish proposal pairs among proposals from the two selected frames. A new trained ResNet then keeps proposal pairs, which contain the same content, and the PSPNet model for segmentation is utilized to generate the segmentation results belonging to the frames and proposals. Finally, the proposals’ segmentation results are refined using the video frames’ segmentation results. The VOT 2016 segmentation dataset, the DAVIS 2017 dataset, and the SegTrack v2 dataset were used for training and testing our framework. Experimental results show that our proposal-driven segmentation framework is able to achieve higher accuracies in video segmentation challenge compared to those of the existing video segmentation methods.
The Effect of Neurofeedback Training in Virtual and Real Environments based on BCI

In this study, we investigated the effect of real-time neurofeedback systems by adjusting the speed of a racing car and report the difference in effect between virtual and real environments. Thirty participants were divided into two conditions of the neurofeedback system (i.e., racing in real track and virtual game). For the performance evaluation, the band power of resting state EEG data and cognitive tests (Stroop and Digit span) were evaluated before and after the neurofeedback training. In the result, a significant increase of band power in the alpha frequency range (8–13Hz) as well as the test score were observed in both the virtual and real environments. Furthermore, neurofeedback in the virtual environment showed enhanced training effects compared to the real environment. We conclude that the performance of the neurofeedback training can be profoundly effected by the system environment as various factors (e.g., motivation, reward) are involved in the performance.
Design of a Novel Piezoelectric Energy Harvester Based on Integrated Multistage Force Amplification Frame

This paper proposes a novel integrated multistage energy harvester to scavenge the mechanical energy from human footstep during walking. The device involves a four-stage force amplification using wedge mechanism, leverage mechanism, half-bridge mechanism, and bridge-type force amplifier. It is devised to magnify the input force exerted on a piezoelectric stack so as to enhance the power output. By fully exploiting the characteristics of the force amplification frame, the harvester also provides the function of position protection, which enables the bearing of large load and thereby protecting the piezoelectric stack. By this, the maximum displacement input is restricted to a certain range to ensure the walking comfort. A static analytical model of the energy harvester is established through the force analysis based on the stiffness matrix method. The main architectural parameters of the device are optimized by adopting a multiobjective genetic algorithm with ANSYS software. Moreover, a prototype is fabricated for experimental investigation. Experimental results show that the harvester exhibits a large force amplification ratio of 17.9 and high-peak power output of 50.8 mW across a matched resistor.