Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Role of Information System Quality and Managerial Competency on the Organization's Performance

This research intends to provide insight on the latest literature review that dealt with the role of the quality of IS and their impact on the performance of organizations. A systematic review approach using content analysis was used. The review includes articles obtained from various journals' databases, namely; Science Direct, Springer, and Emerald. The search techniques specified by the selected database were used to obtain related research papers within the period frame of the study. Based on fifty-eight empirical studies, the researchers found that information system quality is a variable used in many studies and highly contributes to the enhancement of organizational performance. Moreover, previous studies relating the information system quality to the improvement of the organization's management functions, activities, problem-solving and decision-making, which in turn impact performance. The results of this review provide a roadmap for researchers on organizational performance. It also highlights useful variables and directions for future research on the impact of information system quality and management competencies on the organizations' performance. The researchers concluded the study with a conceptual model, which provides a justifiable snapshot of the relationship between information system quality, managerial competency and their impact on the organization performance.
Theoretical CSPR Analysis and Performance Comparison for Four Single-Sideband Modulation Schemes With Kramers-Kronig Receiver

Recently, high-speed optical transmission using single-side-band (SSB) modulation with Kramers-Kronig (KK) scheme has gained great attention due to its high spectral efficiency (SE) and capability of electronic chromatic dispersion compensation (CDC), which can be widely employed in optical metro networks as a promising transmission scheme. To our knowledge, the carrier-signal power ratio (CSPR) is the key parameter to SSB-based systems and the optimum CSPRs can vary distinctly in different SSB modulation schemes. In this paper, the SSB-based KK systems with four kinds of SSB signal generation schemes are investigated, i.e. optical carrier-assisted SSB (OCA-SSB), virtual carrier-assisted SSB (VCA-SSB), SSB subcarrier modulation (SSB SCM) with IQ modulator (IQM) and with dual-drive Mach–Zehnder modulator (DDMZM) respectively. Here, the detailed theoretical derivations of these modulation processing are given and the theoretical relationship between device parameters and the CSPR has been further studied for the different SSB schemes. In order to verify those theoretical analyses, a 224-Gb/s direct detection (DD) transmission with SSB 16-ary quadrature amplitude modulation (16-QAM) signal has been demonstrated based on four SSB generation methods. The results show that theoretical derivations are closely consist with numerical analysis, such as $V_{bias}$ in the scheme of SSB SCM with IQM and optical modulation index (OMI) in SSB SCM with DDMZM. Therefore, our research can provide a theoretical guidance of appropriate device parameter configuration so as to achieve the optimum performance for SSB systems.
Stochastic Momentum Method With Double Acceleration for Regularized Empirical Risk Minimization

Momentum acceleration technique is famously known for building gradient-based algorithms with fast convergence in large-scale optimization. Recently, Nesterov ’s momentum and Katyusha momentum have significantly improved the convergence for stochastic optimization problems. However, the practical gain of acceleration with Nesterov’s momentum is mainly a by-product of mini-batching, while acceleration merely with Katyusha momentum in stochastic steps would make the optimization unstable. In this paper, we build a stochastic and doubly accelerated momentum method (SDAMM) which incorporates the Nesterov’s momentum and Katyusha momentum in the framework of variance reduction, to stabilize the accelerated algorithm and reduce the dependence on the mini-batching. Theoretically, SDAMM achieves the best-known convergence rates for convex objectives. The experimental results demonstrate that our SDAMM is competitive with state-of-the-art methods for the optimization problems in machine learning.
Nonlinear Variational Bayesian Factor Regression for Inferential Sensor Modeling

Inferential sensors are mathematical methods to describe the dependence of quality-related variables called primary variables on those easy-to-measure variables called secondary variables. For majority complex cases in biological and chemical industrial processes with especially nonlinear characteristics, traditional VBFR-based inferential sensor method may not function well because of its linearity assumption on the process data. To tackle the issue that nonlinear relationships exist between input and output variables, an enhanced nonlinear variational Bayesian factor regression (NVBFR) approach is proposed for inferential sensing. On the basis of the probabilistic modeling method, with incorporation of nonlinear mapping, this paper aims to extend the linear probabilistic inferential sensor into the nonlinear form. To evaluate the feasibility and efficiency of the developed Inferential sensors, a real industrail process example is demonstrated.
Gene Identification for Small Cell Lung Cancer via Combining Affinity Propagation Clustering and Conditional Mutual Information

Small cell lung cancer (SCLC) accounts for a small proportion of lung cancer types, but its mortality rate is the highest owing to the rapidly early development. Identifying the key genes of SCLC will be of great significance for targeted therapy. In this paper, a new gene identification method is proposed by combining affinity propagation (AP) clustering and conditional mutual information (CMI). AP clustering is firstly presented to divide genes of SCLC into 49 groups. Then gene significance in each group is evaluated by CMI. Eight genes with highest significance in corresponding groups and four exemplars whose significance is larger than two-thirds of the maximum significance index are identified as key genes of SCLC after literature search in NCBI database.
A Demand Response Scheme in Smart Grid with Clustering of Residential Customers

Demand response (DR) is one of the crucial technologies in smart grid for its potential benefits to lower the peak load and smooth the residential demand profiles. The existing DR schemes in the literature mainly focus on optimizing customer’s load profiles but not enough attentions are paid to important factors such as energy consumption patterns of residential appliances, electricity cost, users’ satisfactory level, fairness and energy consumption habits. This paper proposes a flexible DR scheme in smart grid with clustering of residential customers and comprehensively considering the aforementioned factors. New features are extracted from historical data to depict customers’ characteristics and clustering methods are applied to explore their electricity consumption habits. Then such information is further utilized to help schedule the residential appliances in a more flexible but effective manner. Numerical results based on real-world traces demonstrate that the proposed DR scheme performs well in reducing the system expenditure and lowering peak to average ratio (PAR). Our research further analyzes the impacts of various factors, including customers’ preferences and energy consumption patterns, which sheds some illuminations on how to devise efficient DR strategies.
Deep Face Image Retrieval for Cancelable Biometric Authentication

This paper presents a cancelable biometric system for face authentication by exploiting the convolutional neural network (CNN)-based face image retrieval system. For the cancelable biometrics we must build a template that achieves good performance while maintaining some essential conditions. First the same template should not be used in different applications. Second if the compromise event occurs original biometric data should not be retrieved from the template. Last the template should be easily discarded and recreated. Hence we propose a Deep Table-based Hashing (DTH) framework that encodes CNN-based features into a binary code by utilizing the index of the hashing table. We employ noise embedding and intra-normalization that distorts biometric data which enhances the non-invertibility. For training we propose a new segment-clustering loss and pairwise Hamming loss with two classification losses. The final authentication results are obtained by voting on the outcome of the retrieval system. Experiments conducted on two large scale face image datasets demonstrate that the proposed method works as a proper cancelable biometric system.
Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering

As a typical variation of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) is capable of exploiting information of the cluster embedded in the matrix of similarity. The traditional SNMF-based methods for clustering first performs the techniques of the similarity learning on input data to learn a matrix of similarity, which is subsequently factorized by SNMF or one of its variants to learn information from the cluster. While these methods have led to satisfactory clustering results, it is suboptimal, since they do not explicitly exploit the fact that processes of the similarity learning and the clustering are depend on each other. In this paper, we describe a new SNMF model, termed similarity learning-induced SNMF (SLSNMF). SLSNMF can be considered as a unified framework that jointly considers these two processes. SLSNMF improves the clustering performance of SNMF by thoroughly exploring the mutual reinforcement between the process of similarity learning and the process of clustering until convergence. We incorporate a constraint into the standard SNMF model to learn the matrices of similarity and cluster simultaneously. Meanwhile, for solving this new model, we use the strategy of alternating iterative and derive an efficient algorithm, whose convergence is theoretically guaranteed. Experimental results over three benchmark image data sets demonstrate that SLSNMF outperforms the state-of-the-art methods for clustering.
NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

Coronary artery disease (CAD) is one of the main causes of cardiac death around the world. Due to its significant impact on the society, early and accurate detection of CAD is essential. This study proposes a novel nested ensemble nu-Support Vector Classification (NE-nu-SVC) model which combines several traditional machine learning methods and ensemble learning techniques for effective diagnosis of CAD. We validated our model using two well-known CAD datasets (Z-Alizadeh Sani and Cleveland). To improve the performance of the model, we selected clinically significant features from the datasets using a genetic search algorithm. To further improve our results, we applied a multi-level filtering technique to balance the data using the ClassBlancer and Resample methods. Our base algorithm, nu-SVC, is performed using four well-known kernel functions (linear, polynomial, radial basis (RBF) and sigmoid). The proposed NE-nu-SVC model provided the highest accuracy of 94.66% and 98.60% to predict CAD entities in the Z-Alizadeh Sani and Cleveland CAD datasets, respectively. Our system can aid the clinicians to diagnose CAD accurately and may probably replace other invasive diagnostic techniques.
Disturbance Observer-Based Inverse Optimal Tracking Control of the Unmanned Aerial Helicopter

The attitude and altitude tracking control problem of an unmanned aerial helicopter (UAH) with unknown external disturbances is solved by employing an optimal control method in this paper. An inverse optimal control approach is adopted which avoids solving the associated Hamilton-Jacobi-Bellman (HJB) equation. A disturbance observer-based control (DOBC) method is utilized to deal with the unknown disturbances. To ensure the convergence of all tracking errors, a particular control Lyapunov function is selected via inverse optimal method. The performance of this developed control method is illustrated by simulation results.
Cost Minimization Energy Storage Sharing Management

In this paper, energy storage sharing among a group of households with integrated renewable generations in a grid-connected microgrid is studied. In such a microgrid, a shared energy storage management (SESM) system is operated by an aggregator aiming to minimize the long term time-averaged costs of all households, by jointly taking into account the operational constraints of the shared energy storage, the stochastic solar power generations and the time-varying load requests from all households, as well as the fluctuating electricity prices. We formulate this energy management problem, which comprises storage management and load control, as a constrained stochastic programming problem. A centralized real-time sharing control algorithm is designed based on the Lyapunov optimization theory. The performance of the proposed real-time sharing control algorithm is evaluated. By comparing with a greedy sharing algorithm, it is shown that the proposed sharing algorithm outperforms in terms of both cost saving and renewable energy generation utilization.
Dual-Functional MIMO Antenna Array With High Isolation for 5G/WLAN Applications in Smartphones

A dual-functional antenna array operating in the long term evolution (LTE) band 42 (3.4–3.6 GHz) and 5.8-GHz wireless wide area network (WLAN) band (5.725–5.875 GHz) for multiple-input multiple-output (MIMO) applications in 5G smartphones is proposed. The proposed 8-antenna array is composed of two different antenna elements, namely, the folded L-shaped antenna and couple-fed U-shaped antenna. It can be applied in the <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> (by using 8 antenna elements) and <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> (by using 4 antenna elements) MIMO system across the LTE band 42 and 5.8-GHz WLAN band, respectively. The quasi-orthogonal polarization, balanced mode excitation and pattern diversity are properly used to achieve high isolation within the operation bands. A prototype of the proposed antenna array was fabricated and measured. The measured isolations in the LTE band 42 (6-dB impedance matching) and 5.8-GHz WLAN band (10-dB impedance matching) are better than 17.1 dB and 34.6 dB, respectively. The total efficiencies are 45%–62% and 52%–59% in the two operation bands, respectively. Besides, the measured envelope correlation coefficients (ECCs) are less than 0.045 and 0.0001, the calculated peak ergodic channel capacities are 37.9 bps/Hz and 19.3 bps/Hz in the <inline-formula> <tex-math notation="LaTeX">$8\times8$ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">$4\times4$ </tex-math></inline-formula> MIMO system across the operation bands. Furthermore, user’s hand and head effects are investigated and desirable results are obtained. The above results show that this proposed antenna array is a good candidate for MIMO applications in smartphones.
GaN HEMT Model with Enhanced Accuracy under Back-off Operation

In this paper, a modification of the Angelov DC I/V model is proposed with the aim of improving its accuracy in the back-off condition under Class-B operation yet preserving its excellent prediction capabilities under high output power and saturated operations. Dispersion effects and capacitances are modelled by well-established techniques. As validation, the predictions of the model on S-parameters and load-pull circles at different bias conditions and different input power levels are shown.
EEG-Based Emotion Recognition Using Temporal Convolutional Network

Emotion recognition based on physiological signal can be used in many applications such as, intelligent human-computer interface design, emotional disorder diagnoses. For traditional approaches, the prior knowledge is required to design and extract a range of features from physiological signal. The generalization ability of traditional methods is poor because of the lack of high-level features. Using deep-learning methodologies to analyze physiological signal, i.e. eeg, becomes increasingly attractive for recognizing emotions. In this paper, we design a sequence model based on deep-learning that uses Temporal Convolutional Network(TCN) to extract high-level features in consideration of the time dependence of physiological signals for EEG emotion recognition. Specifically, we extract the differential entropy feature in seconds and construct a time series with fixed-length time window data as the input to TCN, and then using softmax to classify. Furthermore, in order to get reliable results, we divide the samples according to the trials, avoiding the testing set samples and training set samples from the same trial. Specifically, we first divide the samples according to the trials as the testing set and the training set, and then segment the trials in the testing set and training set with fixed time window length to obtain more samples respectively. To evaluate the performance of the proposed model, we conduct the emotion classification experiments on DEAP database. The experimental results show the effectiveness of our proposed model for EEG emotion recognition.
A Broadband Continuous Class-FGaN MMIC PA Using Multi-Resonance Matching Network

In this paper, we present a design technique for broadband harmonic-tuned monolithic microwave integrated circuit (MMIC) power amplifiers (PAs). A multi-resonance harmonic matching network is proposed for the continuous class -F mode operation, featuring low loss and compact chip area for integrated PA realization. A design procedure is developed for this network, considering low quality factor and electromigration current density limitation of on-chip inductors. A proof-of-concept GaN MMIC PA, implemented in a 0.25-$\mu$ m GaN-on-SiC technology, provides 33. 9-36.ldBm output power and 38-48% power-added efficiency (PAE) in the frequency band 4-6GHz. For a 64-QAM signal with 100 MHz modulation bandwidth and 8 dB peak-to-average power ratio (PAPR), at 5 GHz, the average output power of 30.2 dBm and average PAE of 32% are achieved, while the error vector magnitude (EVM) is -32dB.
Scale and Gradient Aware Image Smoothing

This paper presents a novel scale and gradient aware image smoothing method, particularly effective for removing high-contrast detailed textures while preserving boundary sharpness and fine structures. The core idea of the proposed method is based on an observation that small-scale textures can be removed only depending on a down-then-up scaling(DTUS) operation. Accordingly, we present a selective edge smoothing framework by jointly considering scale and gradient measurements. Specifically, we first compute the structural similarity(SSIM) of the DTUS image and the input image to distinguish the textures and structures from the input image. Then we use the SSIM map as weights to fuse the two images together to achieve a scale aware smoothing result. Furthermore, we use the fusion image as guidance to confine the number of non-zero gradients and perform a guided L0 gradient minimization to achieve gradient aware smoothing. Since our method makes full use of image scale and gradient, it outperforms state-of-the-art image smoothing algorithm, especially in removing high-contrast textures. Since our proposed method can remove the insignificant details while preserving sharp and undistorted structural edges, it is also adaptable to many application scenarios, such as boundary extraction, image abstraction, JPEG artifact removal, and layer decomposition-based editing.
An Experimental Study for the Design of Dual Input Load Modulated Wideband GaN Amplifier

In this paper, we present an experimental study focused on the characterization of a dual input Doherty Power Amplifier (DPA). A calibrated set-up is used for an accurate characterization of a DPA demonstrator and the study of several input driving conditions. A demonstrator PA which uses two packaged 10-W GaN HEMT from Wolfspeed was fabricated. Measurements obtained for several driving conditions (power levels and relative phase differences) show Drain Efficiency (DE) performances of DE≈55% at 6-dB Output Back Off (OBO) and DE≈60% at 20-W saturated power over a 2.1-2.6 GHz bandwidth. The main focus of the paper concerns the trade-off between efficiency and power gain shape versus output power that can be obtained with dual input architectures and input signal driving conditions.
Binary Symbiotic Organism Search Algorithm for Feature Selection and Analysis

Feature selection is a challenging step in the field of data mining, because there are many local optimal solutions in a feature space. Feature selection can be considered an optimization problem, which requires as few feature combinations as possible and high accuracy. The binary symbiotic organism search (BSOS) algorithm is proposed in this paper. It maps the symbiotic organism search algorithm from a continuous space to a discrete space using an adaptive S-shaped transfer function and can be used to search for the optimal feature subset in a feature selection space. The proposed BSOS algorithm is evaluated using 19 datasets from the UCI repository. First, the results of four basic S-shaped transfer functions are compared with those of the adaptive S-shaped transfer function. Additionally, the experimental results are compared with the results obtained by the popular binary grasshopper optimization, binary gray wolf optimization, traditional binary particle swarm optimization, and binary differential evolution algorithms, which are also employed for feature selection in the existing literature. The experimental results show that the BSOS algorithm can find the fewest number of features in most datasets and achieve a high classification accuracy. Moreover, the experiments also show that the BSOS algorithm is still at a disadvantage in handling low-dimensional datasets and attains low sensitivity in hyperdimensional datasets.
Computation of the Axial and Radial forces in the Windings of the Power Transformer

Transformer is one of the major components in the electrical power system. Safety and efficiency of the power system are also affected by the working of the power transformer. In this paper transformer's internal electromagnetic forces during the normal working and short-circuit conditions are investigated. Finite element analysis is used to simulate the three-phase transformer model and results are also verified by using the traditional analytical method. Axial and radial forces are calculated during the normal and short-circuit conditions. Transformer’s each high and low voltage windings are divided into 12 different parts. The main aim of dividing the transformer windings into different part is to investigate the radial and axial force into that particular part and to see where the radial and axial forces are higher.
Distributed Switching Learning Control for Nonlinear Multi-agent Systems with Unknown Control Directions and Input Disturbances

In this paper, we studies the leader-following consensus for a class of uncertain nonlinear multi-agent systems (N-MASs) via switching learning control method. The novelties are reflected in the unknown input disturbances, unknown Lipschitz rate and unknown control directions. These unknowns make the consensus problem different from the existing related literature. To capture these unknowns, we propose a distributed consensus protocol based on a switching logic scheme, which guarantees the leader-following consensus.