Miracle-dz/UniFAR-FTUs huggingface dataset:
A multitask multiview neural network for end-to-end aspect-based sentiment analysis

The aspect-based sentiment analysis (ABSA) consists of two subtasks—aspect term extraction and aspect sentiment prediction. Existing methods deal with both subtasks one by one in a pipeline manner, in which there lies some problems in performance and real application. This study investigates the end-to-end ABSA and proposes a novel multitask multiview network (MTMVN) architecture. Specifically, the architecture takes the unified ABSA as the main task with the two subtasks as auxiliary tasks. Meanwhile, the representation obtained from the branch network of the main task is regarded as the global view, whereas the representations of the two subtasks are considered two local views with different emphases. Through multitask learning, the main task can be facilitated by additional accurate aspect boundary information and sentiment polarity information. By enhancing the correlations between the views under the idea of multiview learning, the representation of the global view can be optimized to improve the overall performance of the model. The experimental results on three benchmark datasets show that the proposed method exceeds the existing pipeline methods and end-to-end methods, proving the superiority of our MTMVN
Summarizing Game Reviews: First Contact

In recent years the number of players that are willing to submit a video game review has increased drastically. This is due to a combination of factors such as the raw increase of video gamers and the wide use of gaming platforms that facilitate the review submission process. The vast data produced by reviewers make extracting actionable knowledge difficult, both for companies and other players, especially if the extraction is to be completed in a timely and efficient manner. In this paper we experiment with a game review summarization pipeline that aims to automatically produce review summaries through aspect identification and sentiment analysis. We build upon early experiments on the feasibility of evaluation for the task, designing and performing the first evaluation of its kind. Thus, we apply variants of a main analysis pipeline on an appropriate dataset, studying the results to better understand possible future directions. To this end, we propose and implement an evaluation procedure regarding the produced summaries, creating a benchmark setting for future works on game review summarization.
Continuous Rationale Identification in Issue Tracking and Version Control Systems

During the elicitation and implementation of requirements, the involved stakeholders make decisions and build up important decision knowledge, which they should make explicit and share. Issue tracking and version control systems offer opportunities for a lightweight capture of decision knowledge but currently lack techniques for making this knowledge explicit. In this paper, we present techniques to make decision knowledge explicit in the text of issue tracking and version control systems. We present features for its manual documentation as well as for its automatic identification using a text classifier. The text classifier identifies implicit decision knowledge in natural language texts, in particular, in the description and comments of tickets in the issue tracking system and commit messages.
A Model of Interpretation of Embedded Stories

Stories told by a character within a story are known as embedded stories. They occur frequently in narrative and they constitute an important challenge to models of narrative interpretation. Computational procedures for interpreting a story need to account for these embedded stories in terms of how to represent them and how to process them in the context of the story acting as frame for them. The present paper proposes a simpliﬁed computational model capable of representing discourses for embedded stories and interpret them onto a representation that captures their recursive structure. Then it tests this model over examples of stories from di ↵ erent domains and draws some conclusions on what embedding implies in terms of interpretation of narrative.
Feedback linearization with integrator of DCDC buck converter taking into account parasitic elements

This paper presents the feedback linearization based control of DC/DC buck converter, the output voltage controlled. Also, the presence of parasitic elements are not studied in detail in a previous article, therefore the applied state space model are taking into account of those. The high slew rate of the controller cause inrush current during start-up, which have been examined and reduced. The linearized system has been investigated with linear quadratic regulator (LQR) through an example and simulated via Matlab.
Schizophrenia Diagnosis from fMRI data Based on Deep Curvelet Transform

Combining neuroimaging data with machine learning has an important potential for the classification of Schizophrenia disease. This work presents a short deep-learning history and introduces one novel solution for fMRI image classification. Our deep convolutional Curvelet Transform Network (CTN) is divided into both levels: The feature learning level is to extract the deep features utilizing the deep curvelet transform depend on multiscale approximation MSA. The latter is utilized to determine the hidden inputs layer. Subsequently, those inputs are purified by utilizing the Real Adaboost technique to choose the best corresponding to each fMRI image. The second level involves building an AutoEncoder (AE) utilizing the best choice of the curvelet of all series of fMRI images. An intelligent pooling is applied after a sequence of stacked AEs for each hidden layer to obtain our deep convolutional CTN architecture of the classification level. Our experimental tests use three different datasets: COBRE, WUSTL, and UCLA. These datasets are given classification rates, using our deep architecture, of 99.9%, 99.2%, and 98.6%, respectively. Compared to those cited approaches, our results indicate that our architecture can achieve better performance for all datasets.
Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients

The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of recipients. Nevertheless, raw medical data often contain complexities such as missing values and class imbalance that reduce the reliability of the constructed model. This paper aims at eliminating the respective challenges to ensure the reliability of the decision-making process. To this aim, we first propose a novel deep learning method to simultaneously eliminate these challenges and predict the patients’ survival chance. Secondly, a hybrid framework is designed that contains three main modules for missing data imputation, class imbalance learning, and classification, each of which employing multiple advanced techniques for the given task. Furthermore, these two approaches are compared and evaluated using a real clinical case study. The experimental results indicate the robust and superior performance of the proposed deep learning method in terms of F-measure and area under the receiver operating characteristic curve (AUC).
Performance Evaluation of an Electric Vehicle Traction Drive using Si/SiC Hybrid Switches

The parallel connection of a Silicon (Si)-based IGBT and a Silicon Carbide (SiC)-based MOSFET forming a so called hybrid switch (HyS) can be used to exploit the advantageous features of both semiconductor and materials technologies. In this paper, a HyS-based inverter designed for the application of Electric Vehicle (EV) traction is compared to the conventional inverter assembled with Si-based IGBTs and SiC-based MOS-FETs. According to different standardized driving cycles, EVs operate in low partial load for a considerable amount of the time. Therefore, in this application, semiconductor conduction losses can be considerably reduced when unipolar switches such as MOSFETs are used. All in all, this work shows that the HyS configuration constitutes a good compromise between efficiency and cost when compared to the solution implementing only Si-based IGBT or solely SiC-based MOSFETs.
Towards HPC and Big Data Analytics Convergence: Design and Experimental Evaluation of a HPDA Framework for eScience at Scale

Over the last two decades, scientific discovery has increasingly been driven by the large availability of data from a multitude of sources, including high-resolution simulations, observations and instruments, as well as an enormous network of sensors and edge components. In such a dynamic and growing landscape where data continue to expand, advances in Science have become intertwined with the capacity of analysis tools to effectively handle and extract valuable information from this ocean of data. In view of the exascale era of supercomputers that is rapidly approaching, it is of the utmost importance to design novel solutions that can take full advantage of the upcoming computing infrastructures. The convergence of High Performance Computing (HPC) and data-intensive analytics is key to delivering scalable High Performance Data Analytics (HPDA) solutions for scientific and engineering applications. The aim of this paper is threefold: reviewing some of the most relevant challenges towards HPDA at scale, presenting a HPDA-enabled version of the Ophidia framework and validating the scalability of the proposed framework through an experimental performance evaluation carried out in the context of the Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE). The experimental results show that the proposed solution is capable of scaling over several thousand cores and hundreds of cluster nodes. The proposed work is a contribution in support of scientific large-scale applications along the wider convergence path of HPC and Big Data followed by the scientific research community.
Big Data Management in Smart Grids: Technologies and Challenges

Smart grids are re-engineering the electricity transmission and distribution system throughout the world. It is an amalgam of increased digital information with the electrical power grids. Managing the data generated from the grid efficiently is the key to successful knowledge extraction from the smart grid big data. Most of the scientific advancements are becoming data-driven and becoming an interesting area of research for data scientists. It is challenging the world computationally enough to develop new storage methods and data processing technologies. Managing big data involves data cleaning, integration of varied data sources, and decision-making applications. This paper focuses on the study of big data management and proposes a management process to help manage the data in the grid. Data management tools and techniques have been leveraged in understanding the sources and data types in the grid. The paper emphasizes the limitations of the existing solutions inclined towards applications of the smart grid big data.
Kinematic Analysis and Co-simulation of 3UPS/S Parallel Mechanis

In this paper, in order to improve the design accuracy and design efficiency of a three-DOF hip-assistive exoskeleton, a 3-UPS/S parallel mechanism is studies as the researching target, a particular description of the structure characteristics is presented. By using differentiation method which differ the constraint equations of parallel mechanism, direct parallel kinematical Jacobian matric denoting the velocities relationship between active joints and end-effector is obtained. the virtual prototype simulation was built through ADAMS software, the geometrical dimension parameters and mechanical performance, including running stability and feasibility, are verified by the motion simulation. Finally, using MATLAB/Adams joint co-simulation technology to compare end-effector outcome of ADAMS simulation with MATLAB parametric model, the inverse kinematics analysis of 3UPS/S parallel mechanism is verified, which provide a theoretic basis for the further workspace analysis and control system design.
Robust Array Beamforming via an Improved Chicken Swarm Optimization Approach

Robust array beamforming is a challenging task in radar, sonar and communications due to the influence of direction of arrival (DOA) mismatch and sensor position errors. However, how to enhance the robustness of beamforming is a key issue in antenna arrays. The current paper focuses on a novel approach called the improved chicken swarm optimization (ICSO) method to settle the optimization model of conventional linearly constrained minimum variance (LCMV) based on support vector machine (SVM) to against the mismatch problems as well as control the sidelobe level (SLL). As far as the ICSO method is concerned, considering that the particle swarm optimization (PSO) algorithm has outstanding convergence performance in the early iteration, the dominance of the alpha wolf in the grey wolf optimization (GWO) algorithm and the innovative mutual attraction mechanism in the firefly algorithm (FA), and we introduce these three strategies into the solution update method of conventional chicken swarm optimization (CSO) algorithm for achieving better optimization capability. Moreover, an operation of removing duplicate solutions is proposed to enhance the utilization of the population. In terms of the SVM-based LCMV beamforming algorithm, we adopt the so-called linear $\varepsilon $ - insensitive loss function to reconstruct the final cost function of LCMV by penalizing the errors between the actual and ideal array responses. Finally, we conduct simulations to evaluate the performance of the swarm intelligent optimization algorithms under an ideal scenario without mismatch and an actual scenario with the mismatch, respectively. And the results demonstrate that the developed ICSO algorithm obtains excellent robustness for different scenarios compared to PSO, FA, GWO and CSO optimization algorithms.
Gray-Box Loss Model for Induction Motor Drives

Both high-precision and high-efficient torque control of induction motor drives is an important research field due to the extensive use of these motors in torque-controlled applications, e.g. electric vehicles. To achieve high precision, a gray-box rotor flux observer and inverter model have been lately introduced and validated to be effective. Gray-box models (GBMs) combine first-order principles from physics with data-driven identification enabling accurate model performances at low model complexity. Since the effectiveness of an operating strategy significantly depends on the accuracy of the underlying loss model, in this paper, the aforementioned GBMs’ scope is extended to estimate also the power losses in the motor and inverter. Hence, the achieved universal drive model delivers flux, torque, and loss estimations which are substantial for various control tasks, like torque control, operating strategy, or thermal models. In comprehensive test bench investigations, which take into account the entire drive operating range, the measured power losses are compared to the model estimations. This analysis validates that the GBMs can estimate the losses in the motor with a root-mean-square error of 0.47 % and in the inverter with 0.87 %, both related to the nominal mechanical power of the motor.
Application of selected higher-order methods to detect rotor unbalance of drive system with PMSM

The paper presents the possibility of using higher-order transforms to detect unbalance in permanent magnet synchronous motors (PMSM). The phenomenon of unbalance and the method of its detection by means of mechanical vibration analysis are described. Bispectrum analysis and full spectrum transform were used to detect damage and their effectiveness was compared with those obtained using classical fast Fourier transform. The research methodology based on the signal of mechanical vibration acquisition is presented and the results obtained are discussed.
Book Announcements [Bookshelf]

Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, developments in this area have exploded (especially in clustering algorithms for big data and open-source software for cluster analysis). This second edition reflects these new developments, covers the basics of data clustering, includes a list of popular clustering algorithms, and provides program code that helps users implement clustering algorithms. This book will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.
A Comprehensive Steady-State Analysis for Modular Multi-Parallel Rectifiers (MMR) with Shared DC-Link

This paper proposes a comprehensive steady-state analysis for a front-end Modular Multi-Parallel Rectifiers (MMR) system with a shared DC-link. A generalised mathematical representation for n number of parallel rectifiers is proposed. The MMR is analysed for both Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). In addition, the CCM and DCM boundary of the MMR is identified mathematically. A 60kW Simulink model for the MMR is utilised to confirm the proposed steady-state mathematical modelling equations.
Effect of Structure on the Electrochemical Performance of Nitrogen- and Oxygen-Containing Carbon Micro/Nanospheres Prepared from Lignin-Based Composites

Nitrogen- and oxygen-containing carbon micro/nanospheres (CPAN and CPNA) were prepared by direct pyrolysis of lignosulfonate/polyaniline and lignosulfonate/poly(N-ethylaniline) composite spheres at the temperature of 700 °C. The two type lignin-based composite spheres were prepared by an in situ polymerization of aniline and N-ethylaniline in the presence of lignosulfonate, respectively. The carbon spheres were investigated by thermal gravimetric analysis, scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy and were used as anodes for lithium ion batteries. The two carbon spheres showed different electrochemical performances due to their different surface functional groups and structures. The CPAN carbon nanospheres had better electrochemical performance as anodes for lithium ion batteries than that of CPNA carbon microspheres. The first charge capacity and discharge capacity of the CPAN nanosphere.
MicroRNA expression classification for human disease prediction

Recent research has shown that microRNA plays an important role in human disease specification. Study of miRNA expression helps to accelerate the diagnosis of diseases and to anticipate treatment. However experimental identification of diseases from microRNA expression such as RPM poses difficulties. Nowadays, we haven't enough bioinformatics algorithm to predict the association between miRNA and diseases. Herein, we present a machine learning based approach for distinguishing patient from normal person based on miRNA expression. In this paper, we compare different machine learning algorithms such as SVM, KNN and logistic regression to predict infected gene from miRNAs RPM values.
Stereo Boron Nitride Nanoribbons with Junction-Dependent Electronic Structures from First-Principles

Using first-principles calculations, we investigate the structures and properties of stereo triwing boron nitride nanoribbons (TBNNRs). These triwing structures are constructed by three BN ribbon wings connected via the junctions, which have comparable thermodynamic stabilities to the planar nanoribbons. We find that the junctions play a primary role in the electronic structures of triwing nanoribbons, which cause versatile electronic and magnetic properties for the stereo structures. For the armchair edges, all the TBNNRs are semiconductors, for which the wide band gaps are opened between the occupied N pz orbitals and unoccupied B pz orbitals adjacent to the junctions, while for the zigzag ones, the TBNNRs can be metals or narrow-band gap semiconductors depending on the bonding characteristics of the junctions. The sp2-bonding junctions substantially reduce the band gaps, and the sp3-bonding junctions even induce the metallic behaviors. Moreover, due to the Stoner effect, an intrinsic spin-polarized phe.
A High-Gain and Pattern-Reconfigurable Patch Antenna Under Operation of TM₂₀ and TM₂₁ Modes

In this article, a high-gain square patch antenna with reconfigurable broadside and bidirectional beams under operation of TM20 and TM21 modes is proposed. Initially, two open slots are loaded to the edges in two opposite sides of the radiating patch so as to reshape the current distribution of TM21 mode, by which its four-beam radiation pattern is transformed into the desired broadside beam. With this arrangement, the resonant frequency of TM21 mode can additionally get reduced towards that of TM20 mode. By utilizing a simple feeding network with four p-i-n diode switchers, TM20 and TM21 modes can be further excited with common-mode and differential feeding schemes, respectively, thus bringing out switchable broadside and bidirectional patterns. The simulated and measured results are found in good agreement, both demonstrating that the antenna achieves an overlapping bandwidth of 7.3% in a range from 2.35 to 2.54 GHz, and the measured peak gains as high as 10.4 and 9.6 dBi for the broadside and bidirectional states, respectively.
Low-cost reconfigurable temperature sensor array on field programmable gate array

Digital temperature sensors based on a ring oscillator were used to monitor the temperature distribution of a FPGA chip during operation. The FPGA has a high requirement for real-time data, but high real-time data requires a large number of logic resources, which will have a negative impact on the FPGA’s capabilities. To address this problem, this paper proposes an online monitoring system based on parallel mode, using a linear feedback shift register (LFSR) to generate pseudo-random numbers as the frequency counter for the system, which solves the problem of excessive consumption of logic resources brought by the traditional binary frequency counter, while allowing counters to be configured independently for all digital sensors to achieve parallel measurement. Parallel mode gives a greatly improved measurement speed over serial mode. This system achieves a balance between real-time data and resource consumption and allows online monitoring of changes in the internal temperature distribution of the FPGA chip.
Potentials of Machine Learning in Vacuum Electronic Devices Demonstrated by the Design of a Magnetron Injection Gun

Great progress has been made on machine learning and its applications are expanding rapidly nowadays. Through the case study of optimizing a magnetron injection gun for gyrotron devices, the functions of machine learning were investigated by using two supervised learning algorithms, regression trees and artificial neural networks. They showed excellent performance in predicting the outputs, exploring the importance of the input parameters and the relationship with the output parameters. Machine learning can be a useful tool in the development of microwave vacuum electron devices.