Miracle-dz/UniFAR-FTUs huggingface dataset:
GraphMatcher: a graph representation learning approach for ontology matching

Ontology matching is defined as finding a relationship or correspondence between two or more entities in two or more ontologies. To solve the interoperability problem of the domain ontologies, semantically similar entities in these ontologies must be found and aligned before merging them. GraphMatcher, developed in this study, is an ontology matching system using a graph attention approach to compute higher-level representation of a class together with its surrounding terms. The GraphMatcher has obtained remarkable results in in the Ontology Alignment Evaluation Initiative (OAEI) 2022 conference track. Its codes are available at https://github.com/sefeoglu/gat_ontology_matching.
Self-learning ontological concept representation for searching and matching tasks

Ontology searching and matching can be achieved by learning vector-based representation of ontology concepts. Choosing appropriate features is a key step to learn good concept representations. Lexical annotations and structural features are complementary to the concept representation, but how to combine them to work together effectively is still an open question. To handle this problem, we propose a self-generated data method from input ontologies and corresponding deep neural network models to encode ontological concepts as embedding vectors. Our experiments on biomedical ontologies (SNOMED CT vs. HPO) showed that using semantic embedding features can increase searching effectiveness by over 20% from baseline methods. Our approach provides a generic method for both ontology searching and matching.
Logics for Binary-input Classifiers and Their Explanations (short paper)

We present our work [1, 2] on modal logics for binary-input classifiers and their explanations. They are able to represent classifiers that propositional logic cannot. In particular, black box classifier is understood as uncertainty among admissible classifiers which are coherent with an agent’s partial knowledge, and represented in a product modal logic framework. We also briefly show the logics’ application to XAI.
Identification of the Highest Wrinkle Grasping Point of a Folded Hospital Gown

There are already more than one billion people over the age of 60, and the World Health Organization predicts that number will increase to 1.4 billion by the year 2030. As a result, the need for caretakers is increasing, which could make society in the future unable to provide it. In this scenario, the need for automated assistance increases as the global population ages. One area of robotics where robots have demonstrated tremendous promise in closely collaborating with people is service robotics. Hospitals, residences, and facilities for the elderly will all require the deployment of intelligent robotic agents to carry out regular tasks. Cloth manipulation is one such daily activity and represents a challenging area for a robot. The research goal of this paper focused on finding the grasping points of the highest wrinkle (from a later point of view) of a folded hospital gown to then unfold it and help dressing a patient. The wrinkle is detected using the Generative Grasping Convolutional Neural Network (GG-CNN2), while the approach to the cloth by a manipulator is obtained by designing a visual servoing algorithm that considers the input of the GG-CNN2. In conclusion, the results described in this paper tend to study by deep some AI-based approaches for cloth manipulation capabilities; in particular, we concentrated on studying how to identify the first wrinkle of a cloth by combining the visual servoing approach with a neural network.
LamAPI: a comprehensive tool for string-based entity retrieval with type-base filters

When information available in unstructured or semi-structured formats, e . g . , tables or texts, comes in, finding links between strings appearing in these sources and the entities they refer to in some background Knowledge Graphs (KGs) is a key step to integrate, enrich and extend the data and/or KGs. This Entity Linking task is usually decomposed into Entity Retrieval and Entity Disambiguation because of the large entity search space. This paper presents an Entity Retrieval service (LamAPI) and discusses the impact of different retrieval configurations, i . e . , query and filtering strategies, on the retrieval of entities. The approach is to augment the search activity with extra information, like types, associated with the strings in the original datasets. The results have been empirically validated against public datasets.
The impact of imbalanced class distribution on knowledge graphs matching

Mapping large Knowledge Graphs (KGs) has been a fundamental problem in the semantic web community. Many state-of-the-art methods are not suitable for matching cross-domain, large, and automatically constructed KGs that often suffer from highly imbalanced class distribution. Therefore, recent studies have revisited instance-based matching techniques in addressing this task. This is because such large KGs often lack a well-defined structure and descriptive metadata about their classes, but contain numerous class instances. In this work, we study the problem of imbalanced class distribution in large KG schema matching using instance-based methods. Building on a state-of-the-art method reported in the 2021 OAEI common knowledge graphs track, we study different resampling techniques and propose a new method to address class imbalance in the matching task. We show that our method improves state-of-the-art by up to 11% of recall and 4% in terms of recall. In addition, this work also produces a new public gold standard dataset for mapping large KG classes with over 300 class links, and is by far the largest domain-independent dataset for KG schema matching.
The formation of compact groups of galaxies. I: Optical properties

The small crossing time of compact groups of galaxies (t(sub cr)H(sub 0) approximately less than 0.02) makes it hard to understand why they are observable at all. Our dissipationless N-body simulations show that within a single rich collapsing group compact groups of galaxies continually form. The mean lifetime of a particular compact configuration if approximately 1 Gyr. On this time scale, members may merge and/or other galaxies in the loose group may join the compact configuration. In other words, compact configurations are continually replaced by new systems. The frequency of this process explains the observability of compact groups. Our model produces compact configurations (compact groups (CG's) with optical properties remarkably similar to Hickson's (1982) compact groups (HCG's): (1) CG's have a frequency distribution of members similar to that of HCG's; (2) CG's are approximately equals 10 times as dense as loose groups; (3) CG's have dynamical properties remarkably similar to those of HCG's; (4) most of the galaxy members of CG's are not merger remnants. The crucial aspect of the model is the relationship between CG's and the surrounding rich loose group. Our model predicts the frequency of occurrence of CG's. A preliminary analysis of 18 rich loose groups is consistent with the model prediction. We suggest further observational tests of the model.
Evaluation of Human Performance in Driving Scenarios

As technology is evolving, the number of car accidents is decreasing. In the automotive industry, there are several systems that help the driver to avoid car accidents. The paper proposes to create a Human Performance Envelope for drivers. We use a device composed of an Nvidia Jetson Nano attached camera and NASA TLX questionnaire, while subjects are doing specified tasks. A face detection algorithm, implemented on a microprocessor, is developed to evaluate human emotions while they are solving their tasks. At the end of each microtask, the questionnaire is filled in by each participant and the results are compared in order to evaluate the subject's performance. Results show a direct link between the NASA TLX questionnaire and the microexpressions of the participant as registered by the developed system.
Using Solar Energy Harvesters in Engineering Education

This article presents our experience in integrating solar harvester systems into engineering training. The increased capacities of supercapacitors, as well as their advantages over ordinary batteries, together with harvester systems, make them extremely sought after and used by researchers and engineers to implement various projects. Following this trend, we have integrated into the students' practical classes laboratory exercises in the engineering disciplines “Microcontroller Design” and “Microprocessor Technology”, software and hardware methods for reducing the consumption of wireless sensor nodes and using solar energy harvesters as energy sources for sensor nodes. The article focuses on experimental studies of different harvesters - LTC 3588 and BQ25570, collecting energy from different photovoltaic panels. Students can design and implement autonomous wireless sensor nodes to be powered by solar energy harvesters. In this way, they acquire skills and knowledge to implement low-energy wireless sensor nodes without harmful chemical batteries. A good knowledge of solar harvester systems leads to an increase in the quality of engineering education.
Robust eigenstructure assignment with minimum subspace separation

A computation method for delivering a state feedback controller that assigns closed‐loop eigenstructure with a minimum subspace separation and is robust towards unstructured perturbations is presented. The subspace separation is measured via the difference between two subspace projections, while the eigenstructure robustness is achieved by maximizing an upper bound on the allowable perturbation size such that the closed‐loop system remains stable. By exploiting the Sylvester equation parametrization, the constraint of pole assignment can be satisfied intrinsically and the robust eigenstructure assignment design essentially becomes an unconstrained optimization task. Analytical gradient formulas of the objective functions to be handled are developed. The design approach is demonstrated through numerical examples. Copyright © 2004 John Wiley & Sons, Ltd.
D-term Chaotic Braneworld in Supergravity Constrained by WMAP9 Data

We study a supergravity D-term chaotic inflationary model, in the context of the braneworld scenario, in particular we consider the Randall—Sundrum model type 2. Using the latest release from the combination of WMAP9, eCMB, BAO, and H0, we show that the inflation observables depend only on the number of e-folds N. We also derive all known spectrum inflationary parameters, which are widely consistent with WMAP9 data for a particular choice of values N specially for the scalar spectral index ns and the ratio r. However, the running of the scalar spectral index dns/dlnk is now excluded from the range given by the latest observational measurements.
Received Path Power Prediction for Millimeter-wave Using LSTM under Indoor Environments

This paper discusses the received path power prediction technique by machine learning techniques. The technique is assumed that a user moves simply like walking straight, then the received power is predicted after the current position. In this paper, the LSTM (Long Short Term Memory) is used for the prediction. The LSTM is suited for prediction using the time sequence data. To increase the data used for learning, the technique that the wider channel transfer function is divided into narrower functions, and the power of the arrival path is obtained from the narrower channels is adopted for the LSTM network. The errors between the received path power obtained from the experiment and predicted data are evaluated to show the effectiveness of the proposed techniques.
Designing children-oriented educational interfaces: proposal of a new interface for Moodle

Conventional learning management systems are not usually designed for children-based audiences. The recent COVID-19 pandemics brought the urgency about thinking on dedicated design of learning environments for this kind of public, considering the impact that remote teaching brought to children in scholar ages all around the world. The present work aims to understand the usability of the child audience under systems and interfaces aimed at remote education, through research, bibliographic references, and practical examples. Its main objective is the development of a children-oriented Moodle interface that has been tested by teachers and pedagogical experts.
Adaptive Collaborative Relaying in High Mobility Environment

This paper proposes adaptive collaborative relaying in high mobility environment. The proposed adaptive collaborative applies filtering and forward relaying where all the signals received at the relays are filtered and simultaneously forwarded to the destination terminal for minimizing the relaying overhead and attaining the signal to noise power radio (SNR) gain at the destination terminal. Moreover, the proposed adaptive collaborative relaying selects the relays among all the relays based on the theoretical SNR during a packet where the Doppler shift is taken into account. The performance is evaluated by computer simulation where three relays are moving. The proposed relaying is shown to achieve SNR gain in fading channels.
Effectiveness Analysis of Using The Vehicle Tools Feature on ERP Systems with Delone & McLean Approach at PT. Putra Mulia Telecommunication

PT. Putra Mulia Telecommunication (PMT) developed Enterprise Resource Planning (ERP) to simplify business processes. The company finds that the use of the feature is not effective based on the results of the data recap generated by the ERP system. The company expect these features can be more useful cause this feature is one of core features. PT. PMT never analyzed the use of the system to find out the satisfaction and benefits. This study aims to analyze the level of effectiveness of the system, especially the Vehicle Tools feature. The measurement and evaluation method uses the DeLone & McLean approach with 6 variables: Information Quality (IQ), System Quality (SQ), Service Quality (SEQ), Use (U), User Satisfaction (US) and Net Benefit (NB). Data sourced from observations, interviews, questionnaires, and literature studies. In the questionnaire, digital questions were asked to 154 samples of the employee on the use of the Vehicle Tools feature. In conclusion, that customer satisfaction is influenced by information quality, service quality, system use, and net benefits. Based on the results, our suggestions is to socialization and retraining related to the vehicle tools feature. In addition, it is also necessary to pay attention to the Standard Operating Procedure (SOP) and the appropriate instructions. The company also needs to pay attention to the system maintenance schedule on a regular basis to anticipate possible errors and needs to conduct periodic surveys regarding the use of the system to all employees to find out if there are suggestions or other criticisms.
A New Multiport DC-DC Converter for DC Microgrid Applications

In this paper, a new multiport DC-DC converter is proposed for DC Microgrid applications. Besides, the proposed configuration has several benefits for the design and operation of DC microgrids such as reducing multiple power conversions, reducing number of elements and voltage boosting capability. The bidirectional buck-boost structure of the proposed topology allows for enhanced flexibility to connect sources and loads with different voltage and power levels. The control strategy is developed to achieve power control for renewable sources such as MPPT for PV, in addition to a certain degree of resilience for DC sources availability while maintaining boosted DC link voltage. The key feature of the proposed configuration is that it offers full control over DC link voltage regardless of resource availability (PV). A detailed steady-state analysis is conducted to derive the voltage relations between all ports. Additionally, the state-space averaging followed by small-signal modeling and frequency response-based controller design for the proposed topology is discussed. A MATLAB/Simulink-based simulation study is conducted to demonstrate the performance of the proposed multiport converter topology under different operating conditions. Finally, the performance of the proposed multiport converter is validated through an experimental study.
Performance Evaluation of Polarization Sensitive Array Based DOA Estimation

This paper presents the performance evaluation of antenna array-based direction of arrival (DOA) estimation. To illuminate the capability of polarization sensitive array (PSA) for localization, this paper studies its performance bound in contrast to the scalar array (SA). We formulate the signal models of PSA and SA in a unified format and describe the expression of the Cramer-Rao lower bounds (CRLB) for multi-variables. Then the CRLB of the azimuth and elevation angles are derived for them by varying array formation and direction of signal arrival. Simulations in various cases validate the benefits of polarization information for localization and provide guidance for array design.
Community Detection Approach Via Graph Regularized Non-Negative Matrix Factorization

One of the most important problems towards studying complicated networks is community detection. The methodology of non-negative matrix factorization (NMF) has lately emerged as among the hottest research issues within community detection because of its ability to reveal natural structures and trends in high-dimensional information. The primary difficulty is that most community detection methods are hampered by the issue of nodes belonging to several communities. We will use the NMF technique in this work to tackle this issue by creating a novel mathematical function. In addition, we will include a regularized factor for simulating latent embedding space and a correlation factor to prevent overlap inside nodes that belong to various communities. Following that, the entire objective function will employ an optimization approach to arrive at the variable values that are ideal. Finally, we assess the effectiveness of different methodologies on real networks. According to experimental findings, the introduced approach is better among the other state-of-the-art methodology.
The Effect of Graph Frequencies on Dynamic Structures in Graph Signal Processing

Multivariate signals are signals consisting of multiple signals measured simultaneously over time and are most commonly acquired by sensor networks. The emerging field of graph signal processing (GSP) promises to analyse dynamic characteristics of multivariate signals, while at the same time taking the network, or spatial structure between the signals into account. To do so, GSP decomposes the multivariate signals into graph frequency signals, which are ordered by their magnitude. However, the meaning of the graph frequencies in terms of this ordering remains poorly understood. Here, we investigate the role the ordering plays in preserving valuable dynamic structures in the signals, with neuroimaging applications in mind. In order to overcome the limitations in sample size common to neurophysiological data sets, we introduce a minimalist simulation framework to generate arbitrary amounts of data. Using this artificial data, we find that lower graph frequency signals are less suitable for classifying neurophysiological data than higher graph frequency signals. We further introduce a baseline testing framework for GSP. Using this framework, we conclude that dynamic, or spectral structures are poorly preserved in GSP, high-lighting current limitations of GSP for neuroimaging.
The ‘piston problem’ with thermal radiation

The classical problem of the motion of a one-dimensional unsteady shock generated by a piston moving with velocity vp = ctn is extended to take into account thermal radiation effects by the similarity method of Taylor and Sedov. Gray gas and local thermodynamic equilibrium are assumed and a modification of the Schuster-Schwarzschild differential equation for the heat flux is adopted. The optical thickness is not restricted to be thin or thick, and the absorption coefficient is assumed to vary with the density and temperature. Numerical results indicate that the pressure and velocity are not affected much by the radiation, but the density, temperature and radiant heat flux are changed considerably.
Comparison of Machine Learning Techniques on Snort for Predicting Realtime DoS and Probe Attack

Due to the rapid development of the cyber world and the many diverse cybercrime activities, it is necessary to have an intrusion detection system (IDS) capable of detecting anomalies beyond IDS which are spread both open-source and commercially. This research focuses on the development side of Snort which will be integrated with machine learning to be able to detect deeper DoS attacks and Probe attacks. This study also compares three types of machine learning algorithm models, including Neural Network, K-Means, and Support Vector Machine to predict NSL-KDD datasets, also detect DoS attacks and Probe attacks in real-time. The comparison of the three types of algorithms results in a conclusion where the Support Vector Machine is the best and most stable algorithm based on dataset analysis and detection of DoS and Probe attacks in real time. This research has succeeded in proving that the use of machine learning can detect deeper types of Probe attacks that can lead to TCP SYN Flooding DoS attacks and is also able to prove the Support Vector Machine algorithm model to be the best algorithm model for this type of application.
Automated Stitching of Noisy Scanning Electron Microscopy Images for Integrated Circuit Reverse Engineering

A scanning electron microscope (SEM) usually creates images in the range of megapixel resolutions, but analyzing an IC layer requires resolutions in the gigapixel range. To create such large images, many individual images must be taken and then fused into one large image, which poses unique challenges: SEM images are affected by distortion due to charging effects and often exhibit high levels of noise and low contrast. One way of reducing the entry barrier to IC reverse engineering is to develop algorithms that can provide good results even in the case of suboptimal image quality, as can be produced by comparatively older, pre-owned SEMs. The main contribution of this work is the introduction and evaluation of four new algorithms, capable of composing high noise and low contrast SEM images into fused images. While the problem of stitching small images into one fused image is not new, the application of stitching algorithms for noisy IC images poses challenges that have not been addressed in the literature.
Evidence for transonic flows in the solar granulation

High-resolution observations of the solar granulation are interpreted in the light of recent numerical simulations of compressible convection. The observations show a negative correlation between the width of suitably chosen, nonmagnetic lines and the continuum intensity. This result is consistent with a model of granular convection where regions of supersonic horizontal flow form intermittently in the vicinity of the downflow lanes. We conjecture that the observed line broadening in the regions of low intensity is caused by enhanced turbulent fluctuations generated by the passage of shock fronts bounding the regions of supersonic motion.