Miracle-dz/UniFAR-FTUs huggingface dataset:
Virtual reality training platform for flexible ureterorenoscopy interventions with a minimally invasive surgical robot

The total number of ureteroscopy (URS) interventions during the past years has dramatically increased due to the ongoing technological advances and the benefits associated with these techniques. However, the current URS procedure presents some drawbacks to urologic surgeons. The LITHOS project was created with the main objective of developing a surgical robotic system for flexible ureterorenoscopic lithotripsy interventions, offering a technological solution that meets the real needs of both patients and surgeons in this type of procedures. In this paper, a virtual reality environment for flexible ureterorenoscopy interventions is presented. The proposed environment provides a suitable training platform for surgeons manipulating the surgical robotic system.
Deep Inductive Graph Representation Learning

This paper presents a general inductive graph representation learning framework called <inline-formula><tex-math notation="LaTeX">$\text{DeepGL}$</tex-math><alternatives><mml:math><mml:mtext>DeepGL</mml:mtext></mml:math><inline-graphic xlink:href="rossi-ieq1-2878247.gif"/></alternatives></inline-formula> for learning deep node <italic>and</italic> edge features that generalize across-networks. In particular, <inline-formula><tex-math notation="LaTeX">$\text{DeepGL}$</tex-math><alternatives><mml:math><mml:mtext>DeepGL</mml:mtext></mml:math><inline-graphic xlink:href="rossi-ieq2-2878247.gif"/></alternatives></inline-formula> begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order. Contrary to previous work, <inline-formula><tex-math notation="LaTeX">$\text{DeepGL}$</tex-math><alternatives><mml:math><mml:mtext>DeepGL</mml:mtext></mml:math><inline-graphic xlink:href="rossi-ieq3-2878247.gif"/></alternatives></inline-formula> learns <italic>relational functions</italic> (each representing a feature) that naturally generalize across-networks and are therefore useful for graph-based transfer learning tasks. Moreover, <inline-formula><tex-math notation="LaTeX">$\text{DeepGL}$</tex-math><alternatives><mml:math><mml:mtext>DeepGL</mml:mtext></mml:math><inline-graphic xlink:href="rossi-ieq4-2878247.gif"/></alternatives></inline-formula> naturally supports attributed graphs, learns interpretable inductive graph representations, and is space-efficient (by learning sparse feature vectors). In addition, <inline-formula><tex-math notation="LaTeX">$\text{DeepGL}$</tex-math><alternatives><mml:math><mml:mtext>DeepGL</mml:mtext></mml:math><inline-graphic xlink:href="rossi-ieq5-2878247.gif"/></alternatives></inline-formula> is expressive, flexible with many interchangeable components, efficient with a time complexity of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(|E|)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:mo>|</mml:mo><mml:mi>E</mml:mi><mml:mo>|</mml:mo><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="rossi-ieq6-2878247.gif"/></alternatives></inline-formula>, and scalable for large networks via an efficient parallel implementation. Compared with recent methods, <inline-formula><tex-math notation="LaTeX">$\text{DeepGL}$</tex-math><alternatives><mml:math><mml:mtext>DeepGL</mml:mtext></mml:math><inline-graphic xlink:href="rossi-ieq7-2878247.gif"/></alternatives></inline-formula> is (1) <italic>effective</italic> for across-network transfer learning tasks <italic>and</italic> large (attributed) graphs, (2) <italic>space-efficient</italic> requiring up to 6x less memory, (3) <italic>fast</italic> with up to 106x speedup in runtime performance, and (4) <italic>accurate</italic> with an average improvement in AUC of 20 percent or more on many learning tasks and across a wide variety of networks.
Joint UAV Hovering Altitude and Power Control for Space-Air-Ground IoT Networks

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites and base stations constitute a space-air-ground three-tier heterogeneous network, which are beneficial in terms of both providing the seamless coverage as well as of improving the capacity for increasingly prosperous Internet of Things networks. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks when sharing the same spectrum. The power association problem in satellite, UAV and macrocell three-tier networks becomes a critical issue. In this paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concave-convex procedure method are used to solve this problem, followed by a low-complexity greedy search algorithm. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network’s total throughput.
Metamorphic Relations for Enhancing System Understanding and Use

Modern information technology paradigms, such as online services and off-the-shelf products, often involve a wide variety of users with different or even conflicting objectives. Every software output may satisfy some users, but may also fail to satisfy others. Furthermore, users often do not know the internal working mechanisms of the systems. This situation is quite different from bespoke software, where developers and users typically know each other. This paper proposes an approach to help users to better understand the software that they use, and thereby more easily achieve their objectives—even when they do not fully understand how the system is implemented. Our approach borrows the concept of metamorphic relations from the field of metamorphic testing (MT), using it in an innovative way that extends beyond MT. We also propose a “symmetry” metamorphic relation pattern and a “change direction” metamorphic relation input pattern that can be used to derive multiple concrete metamorphic relations. Empirical studies reveal previously unknown failures in some of the most popular applications in the world, and show how our approach can help users to better understand and better use the systems. The empirical results provide strong evidence of the simplicity, applicability, and effectiveness of our methodology.
A Primal-Dual Interior-Point Method for Nonconvex Optimization with Multiple Logarithmic Barrier Parameters and with Strong Convergence Properties

It is observed that an algorithm proposed in the 1980s for the solution of nonconvex constrained optimization problems is in fact a primal-dual logarithmic barrier interior-point method closely related to methods under current investigation in the research community. Its main distinguishing features are judicious selection and update of the multiple barrier parameters (one per constraint), use of the objective function as merit function, and careful bending of the search direction. As a pay-o , global convergence and fast local convergence ensue. The purpose of the present note is to describe the algorithm in the interior-point framework and language and to provide a preliminary numerical evaluation. The latter shows that the method compares well with algorithms recently proposed by other research groups.
Modeling and multi-objective optimization of inductive power components

This thesis deals with the modeling and multi-objective optimization of inductive power components, in order to improve the efficiency and/or power density of power electronic systems. The first part of the thesis introduces how to model magnetic circuits, i.e. how to set up an accurate reluctance model of an inductive component. A novel approach to accurately determine the reluctances of air gaps is introduced. The approach is easy to handle as it is based on a modular concept where a simple basic geometry is used as a building block to describe different three dimensional air gap shapes. The second part of the thesis deals with core loss modeling. The applied core loss approach can be seen as a hybrid of an improved version of the empirical Steinmetz equation and an approach based on a material loss database (loss map). In order to build the material loss database, core loss measurements must be made. Therefore, special focus is placed on how core losses can be measured and what measurements are necessary for an accurate core loss modeling. Relaxation effects in magnetic materials are discussed. In modern power electronic systems, voltages across inductors or transformers generally show rectangular shapes, including periods of zero voltage. In most core loss models, the phase where the voltage across the magnetic component is zero (i.e. the flux remains constant) is not considered. It is implicitly assumed that no losses occur when the flux remains constant. However, as measurements show, this is not a valid simplification. In phases of constant flux, losses still occur in the material. This is due to relaxation processes. A new core loss modeling approach that takes such relaxation effects into consideration is given. Another aspect to be considered is the fact that core losses are influenced by a DC premagnetization. The Steinmetz Premagnetization
Speech Recognition in Noisy Environments using the NOise Spectrum Estimation based on the Histogram Technique

Spectral subtraction is widely-used preprocessing technique for speech recognition in additive noise environments, but it requires a good estimate of the noise power spectrum. In this paper, we employ the histogram technique for the estimation of noise spectrum. This technique has advantages over other noise estimation methods in that it does not requires speech/non-speech detection and can estimate slowly-varying noise spectra. According to the speaker-independent isolated word recognition in both colored Gaussian and car noise environments under various SNR conditions. Histogram-technique-based spectral subtraction method yields superier performance to the one with conventional noise estimation method using the spectral average of initial frames during non-speech period.
Preconsonantal palatal segments. A cross-dialectal analysis

The main purpose of this paper is to provide an Optimality-theoretical account of the phonological behavior of word-final palatal segments followed by a word beginning with a consonant in the different dialects of insular Catalan. In these varieties, internal palatal segments undergo a series of processes (depalatalization, gliding and splitting) that result in a simplification of the original consonant. Interdialectal variation is explained by means of different rankings of a set of faithfulness constraints and contextual markedness constraints on coda complexity and syllable structure. The analysis, framed in Correspondence Theory (McCarthy & Prince 1995), shows the suitability of some formal OT refinements, such as the Transderivational Correspondence Theory (Benua 1995, 1997), the Optimal Paradigms Model (McCarthy [2001] 2005) and Comparative Markedness (McCarthy [2002] 2003), to account for some puzzling opacity phenomena related to these data. The paper further investigates previous analyses framed in autosegmental phonology devoted to palatal segments in insular Catalan; and discusses some aspects related to the status of features in Optimality Theory.
Integrated Method For Designing Complex Cyber-Physical Systems

The design of a Cyber-Physical System (CPS) is defined as a complex activity, being composed of a set of design phases devoted to model application characteristics. In this sense, detailing the phase characteristics is required in order to help the design teams during the project design, and to aim the support of the correct application characteristics representation. However, despite some of these phases, such as the control systems design, being discussed at length by the engineering community, other phases have less detailed studies, i.e., the design activities that compose these phases are not so well documented. In these sense, it is required more experience from the design teams to perform activities such as, the sensing and actuation subsystems representation, and the integration of formal verification methods on the design process, among others. Despite the lack of information related to them, these phases are essential to the CPS design, by the fact that they support application characteristics representation and the properties validation, as well as, they also provide the integration between designed system and their environment. Regarding the CPS design process, different methods are available in the literature, aiming to guide the designers to perform the modeling tasks. However, these approaches do not provide enough information related to those described activities. In this context, this thesis proposes an integrated method applied to CPS design, more specifically devoted to the Unmanned Aerial Vehicles (UAV) design. That proposed method aims to integrate different modeling processes such as functional, architectural, sensor and actuator integrations, and formal verification design processes. Based on the proposed activities this method aims to support the UAV embedded system design, and allow the integration between the embedded platform and the set of system devices. The Model Driven Engineering (MDE) is used as basis to the proposed approach, and aims to support the automated model generation based on the application characteristics. It is intended to ensure the maintainability of the system information over all the design steps and provide the property evaluation and validation, considering the model transformation principles. Two different tools are designed with the proposed method, the ECPSModeling and the ECPSVerifier, for supporting the design activities. The ECPSModeling provides the transformation process from functional model to architectural model, in order to integrate sensor and actuator characteristics. On the other hand, the ECPSVerifier provides the CPS behavior representation, based on the architectural model, by using timed automatas, which allows the formal verification evaluation by performing model checking. The proposed method and the designed tools are applied on the project of a tilt-rotor UAV design. The details of the method proposed in this thesis are demonstrated by performing the UAV project, described as a case study.
Interactivity of Neural Representations for Perceiving Shared Social Memory

Although the concept of “common sense” is often taken for granted, judging whether behavior or knowledge is common sense requires a complex series of mental processes. Additionally, different perceptions of common sense can lead to social conflicts. Thus, it is important to understand how we perceive common sense and make relevant judgments. The present study investigated the dynamics of neural representations underlying judgments of what common sense is. During functional magnetic resonance imaging, participants indicated the extent to which they thought that a given sentence corresponded to common sense under the given perspective. We incorporated two different decision contexts involving different cultural perspectives to account for social variability of the judgments, an important feature of common sense judgments apart from logical true/false judgments. Our findings demonstrated that common sense versus non-common sense perceptions involve the amygdala and a brain network for episodic memory recollection, including the hippocampus, angular gyrus, posterior cingulate cortex, and ventromedial prefrontal cortex, suggesting integrated affective, mnemonic, and social functioning in common sense processing. Furthermore, functional connectivity multivariate pattern analysis revealed that interactivity among the amygdala, angular gyrus, and parahippocampal cortex reflected representational features of common sense perception and not those of non-common sense perception. Our study demonstrated that the social memory network is exclusively involved in processing common sense and not non-common sense. These results suggest that intergroup exclusion and misunderstanding can be reduced by experiencing and encoding long-term social memories about behavioral norms and knowledge that act as common sense of the outgroup
Contact-free Breathing Rate Monitoring with Smartphones: A Sonar Phase Approach

Vital sign (e.g., respiration rate) monitoring has become increasingly more important because it offers useful clues of medical conditions such as sleep disorders. There is a compelling need for technologies that enable contact-free and easy deployment vital sign monitoring over extended period of time for healthcare. In this paper, we present a SonarBeat system to leverage a phase based active sonar to monitor respiration rates with smartphones. We provide a sonar phase analysis and discuss the technical challenges for respiration rate estimation utilizing an inaudible sound signal. Moreover, we design and implement the SonarBeat system, with components including signal generation, data extraction, received signal preprocessing, and breathing rate estimation, with Android smartphones. Our extensive experimental results validate the superior performance of SonarBeat in different indoor environment settings.
Auxiliary Classifier Generative Adversarial Network With Soft Labels in Imbalanced Acoustic Event Detection

In acoustic event detection, the training data size of some acoustic events is often small and imbalanced. To deal with this, this paper proposes generating the virtual training data categorically using the auxiliary classifier generative adversarial networks. Soft labels of acoustic events are first calculated to represent the acoustic event localization information. The closer the current frame is to the middle of the manually labeled acoustic event, the higher the soft label will be, which makes the soft labels positively correlated with the acoustic event localization. Then, the acoustic event class and the quantized soft labels are used as the input condition to the auxiliary classifier generative adversarial networks to generate an arbitrary number of training samples. Experimental results on the TUT Sound Event 2016 under the home environment and TUT Sound Event 2017 under the street environment demonstrate the improved performance of the proposed technique compared to existing acoustic event detection systems.
Spectral redistribution of energy and the origin of inverse cascade for gyrokinetics in the sub-Larmor range

It is known that an inverse cascade of energy occurs in two-dimensional neutral fluid turbulence and also, under certain conditions, in magnetized plasma turbulence. The reason for this phenomenon in both cases is due to the existence of two quadratic invariants. The crucial feature of these invariants is that they are mutually-constraining in the sense that the spectral redistribution of one is constrained by the other. The gyrokinetic equation, a kinetic equation for magnetized plasma dynamics, has two collisionless quadratic invariants when restricted to two dimensions (in position-space). In this paper, we consider the consequences of this fact for scales smaller than the thermal Larmor radius, where turbulent fluctuations exist, with equal importance, in the position and velocity space dependence of the kinetic distribution function. Using a spectral formalism for position and velocity space, we find that the gyrokinetic invariants are mutually constraining with respect to spectral redistribution of energy. We adapt the analysis of [1] to investigate this in detail. We find that inverse cascade is possible, but depends on parameters in a different way than the conventional inverse cascade of fluid turbulence. For certain parameters, we argue that a dramatic non-local inverse cascade will occur, whereby fluctuations at scales much smaller than the Larmor radius can transfer energy directly to the Larmor scale. In the other extreme, we argue that inverse cascade is replaced by a forward cascade which is can be local or nonlocal.
Fault Diagnosis of an Induction Motor based on Fuzzy Logic, Artificial Neural Network and Hybrid System

This paper presents the fault diagnosis of a three-phase induction motor using fuzzy logic, neural network and hybrid system. Detailed analysis during voltage unbalance, open phase, low voltage and overload motor fault using these strategies are presented. Stator currents were measured and their root mean square were derived. These values were used to train data. Each output of these diagnosis tools is used to determine the motor conditions whether it is in a healthy state or in a faulty one. A novel hybrid system is design and used in fault diagnosis. Simulation results show that hybrid systems give the best estimation of faults and can be therefore used in monitoring of induction motors with greater efficiency.
An Empirical Study of the Extreme Learning Machine for Twitter Sentiment Analysis

Extreme Learning Machine (ELM) method is proposed for single hidden layer feed-forward networks (SLFNs). The ELM employs feed-forward neural network architecture and works with randomly determined input weights. In this aspect, ELM depends on principle that enables to determine weights and biases in the network. In the first phase of ELM that can be named as feature mapping, the usage of random values differs the ELM from other methods that employ a kernel function for feature mapping such as Support Vector Machines (SVM) and Deep Neural Networks. After the feature mapping, the main goal of the ELM is to learn weights between hidden and output layers by minimizing the error. The ELM has gained much more popularity recently; and can be utilized for classification, regression, and dimension reduction. In literature, Twitter sentiment analysis is generally considered as a classification task. Therefore, in this study, the basic ELM is utilized for Twitter sentiment analysis and compared with the SVM which is one of the most successful machine learning algorithms used for sentiment analysis. Experiments are conducted on two different Turkish datasets. Experimental results show that the performance of the two methods are slightly different, but SVM outperforms basic ELM.