Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision Tree Visualization for High-Dimensional Numerical Data

Big Data enforces the usage of data mining techniques to provide the user valuable insights. There is a broad range of data mining and machine learning techniques tackling different tasks. Generic approaches are classification algorithms, which label given data points by a pretrained model. Decision tree-based classification algorithms are often used, as they provide a human-explainable model, which can be represented by simple induced rules. In order to present the classification results and the concrete model to the user, there exist for both problems a set of different solutions. Current visualizations either project labeled data into the plane or three-dimensional space, or the visualizations illustrate the decision tree rules as e.g. graph structures. But they lack to provide a possibility to show both, data and the model, within a single plot. Therefore, we propose a projection strategy to present both decision tree model and data in a single plot. Furthermore, we developed an interactive visualization to showcase the proposed approach and evaluated the visualization on open-source datasets. The results show that the plots can be computed in short time and projection adjustments are reasonably low.
Image Pattern Classification Using MFCC and HMM

We propose a novel method for recognizing temporally or spatially varying patterns using MFCC (mel-frequency ceptral coefficient) and HMM (hidden Markov model). MFCC and HMM have been adopted as de factostandard for speech recognition. It is very useful in modeling time-domain signals with temporally varying characteristics. Most images have characteristical patterns, so HMM is expected to model them very efficiently. We suggest efficient pattern classification algorithm with MFCC and HMM, and showed its improved performance in MNISTand fashionMNIST databases.
Discrete Attacks and Submodular Optimization with Applications to Text Classification

Adversarial examples are carefully constructed modifications to an input that completely change the output of a classifier but are imperceptible to humans. Despite these successful attacks for continuous data (such as image and audio samples), generating adversarial examples for discrete structures such as text has proven significantly more challenging. In this paper we formulate the attacks with discrete input on a set function as an optimization task. We prove that this set function is submodular for some popular neural network text classifiers under simplifying assumption. This finding guarantees a $1-1/e$ approximation factor for attacks that use the greedy algorithm. Meanwhile, we show how to use the gradient of the attacked classifier to guide the greedy search. Empirical studies with our proposed optimization scheme show significantly improved attack ability and efficiency, on three different text classification tasks over various baselines. We also use a joint sentence and word paraphrasing technique to maintain the original semantics and syntax of the text. This is validated by a human subject evaluation in subjective metrics on the quality and semantic coherence of our generated adversarial text.
Continued Intention to Use UCOM: Four Factors for Integrating With a Technology Acceptance Model to Moderate the Satisfaction of Learning

This paper investigates whether the universities’ communication model and the technology acceptance model, which include four factors, namely, academic performance, student satisfaction, effectiveness, and support assessment, simultaneously enhance student satisfaction and improve the teaching method and academic performance level. This paper aims to adapt a model to improve the assessment method and improve the model of student satisfaction. This paper focuses on improving e-learning to activate the feedback of continued intention of using the model that helps to navigate the student understanding level and academic performance immediately. In this paper, surveys were distributed to (295) undergraduates in four universities in Oman to assess their feedback on e-learning. The partial least squares-structural equation model was used to calculate the measurement of all hypotheses proposed. The results of this paper prove that most of the hypotheses are a positive influence on the continuance of intention to use the proposed model.
Fuzzy MPPT Based Grid-Connected Photovoltaic System

This paper proposes grid-tied single-phase a dual-stage photovoltaic (PV) energy generation system and its control schemes. The control shames of the proposed system involve of a dual stage. The first stage is maximum power point tracking (MPPT) controller based on the fuzzy logic controller (FLC) to accomplish the objective of maximizing the output power from the PV array under irreg-ular atmospheric conditions. The second stage is the control of H-bridge voltage source inverter (VSI) with unipolar sinusoidal pulse width modulation (SPWM) technique which consists of an external loop voltage regulator and an internal loop current regulator. An external loop voltage regulator aims to stabilize the dc link voltage to a specific value. To limit the voltage ripple at the dc link, the capacitor of the dc link is well designed in this paper. In addition, the internal loop current regulator is designed to maintain the unity power factor operation. Moreover, voltage source inverter (VSI) regulator integrated to a phase-locked loop (PLL) based on transport delay in order to synchronize the VSI with utility grid is used in this paper. To validate the effective-ness, efficiency, and validity of the proposed model the simulation results have carried out through MATLABI Simulink simscope power system.
Robust Locality-Constrained Label Consistent K-SVD by Joint Sparse Embedding

We mainly propose a robust Embedded Locality-Constrained Label Consistent Dictionary Learning (ELC2DL) framework for discriminative classification. ELC2DL improves the representation and classification performance by performing DL in the noise-removed sparse embedding space, since most real data often contains noise and performing DL over noisy data for reconstruction may decrease performance potentially. To reduce the noise in data, our model computes a sparse projection jointly for noise reduction and then uses the noise-removed data for DL. By incorporating a noise-reduction term with a discriminative locality-constrained label consistent term that associates the label information with each dictionary atom to preserve local structure of training data, a noise-reduction projection, an over-complete dictionary and discriminative sparse codes are obtained jointly. Simulations on several image databases show that our algorithm can deliver enhanced performance over other state-of-the-arts.
Deep PCANet Framework for the Binary Categorization of Breast Histopathology Images

Breast Cancer is one of leading causes for high mortality among women across the world. A computer aided diagnosis tool for examining breast tumor histopathology images and classifying them is in much need. Principal Component Analysis Network (PCANet) is simple and efficient deep learning framework which uses linear approach for feature extraction. In this study, to effectively integrate the color information for learning the features, we propose a framework for analyzing color histopathological images using PCANet. In the presented framework a color histopathology image is split into LAB color space components and cascaded PCA is performed to give principal component images for each component from which color angular pattern and norm pattern images are generated. Simple binary hashing is performed on these color angular and norm pattern images to produce several binary images which are further encoded to extract block-wise histogram features. The extracted features are fed to the SVM classifier for further classification into benign or malignant. Experimental results on BreakHis dataset demonstrates that for breast tumor classification the proposed method shows a significant improvement in performance over the traditional methods. It achieved an accuracy of 97% on 100x magnification which shows its potential as a computer aided diagnosis tool for breast cancer diagnosis.
High-Power Long-Waveguide 1300-nm Directly Modulated DFB Laser for 45-Gb/s NRZ and 50-Gb/s PAM4

A 1300-nm high-speed high-power directly modulated distributed-feedback laser using floating feeding electrode has been demonstrated. A 5-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-thick benzocyclobutene was used for planarization to float a coplanar waveguide, connecting a reversed-mesa ridge waveguide of a laser. Low parasitic capacitance was then formed. A long waveguide can thus be fabricated with low roll-off at high-frequency modulation, further enabling high-output optical power. A 250-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-long waveguide was fabricated, leading to >28-mW optical power with linear operation and −3-dB bandwidth of 26 GHz. Furthermore, improvement in high-frequency regime and the reduction near resonance peak were found, attaining a broadband and flat frequency response at high current. A 45-Gb/s non-return-to-zero and 50-Gb/s four-level pulse amplitude modulation with 35-km fiber transmission were successfully shown through modulating high-output optical power.
Robust Beamforming Design for Power-Splitting SWIPT-based Distributed Antenna Systems

The paper focuses on a distributed antenna system which transport information and energy simultaneously in the presence of channel uncertainty of receivers. A mobile station decode information while harvesting energy via a power splitter, an antenna port equipped multiple antennas can support a series of mobile users. On the premise of guaranteeing distributed antenna port power constraint as well as individual energy harvesting constraint, we introduce worst case analysis for maximize signal-to-interference-plus-noise ratio (SINR). To reduce the computational complexity of the conventional algorithm, successive convex approximate treatment is adopted as well as the Lagrangian method. The simulation indicates the good effectiveness of our iterative scheme.
Impact of Uncertainty in IAM Measurement on Energy Predictions

In the context of an extremely competitive solar economic landscape, small differences in predicted power plant energy can have a large impact on project financing. This calls for precise and accurate module characterization for use in energy predictions, especially when performing competitive comparisons between module technologies. This study compares incidence angle modifier (IAM) results from five Tier-1 modules characterized outdoors simultaneously and the resulting net difference in modeled plant energy variation. IAM profiles from other sources are also compared for these same modules, and the resulting net variation in losses is reported. Overall, the difference in the profiles measured with the same procedure concurrently were very small, resulting in IAM loss differences of less than 0.2%. However, the difference in IAM profiles from various sources was larger; using lab quoted uncertainties, the variation can be over 1%, indicating that measurement uncertainty and/or test procedure may inflate differences between modules. This highlights the need for improved testing standards and round robin comparisons to reduce inter-lab variability.
Performance Analysis of Centralized and Distributed SDN Controllers for Load Balancing Application

Software Defined Networking (SDN) decuples data and control planes to simplify the management procedure. SDN control plane functions are to manage the overall network through a global view of the network, define policies for the incoming flows, and enforce these policies by using centralized or distributed controller environment. In this paper, we compare the performance of centralized and distributed controllers by using load balancing application with three algorithms namely Random, Round Robin, Weighted Round Robin algorithm. The results show that using distributed controllers is better than centralized in terms of response time and a number of transactions. Furthermore distributed controllers are better for scalability and reduces the bottleneck as well as support failover mechanism. Additionally, Weighted Round Robin algorithm proves its ability to perform better than other mentioned algorithms using centralized and distributed controllers.
On the NBTI of Junction-less Nanowire and Novel Operation Scheme to Minimize NBTI Degradation in Analog Circuits

Nano-wire (NW) transistor is expected to be used in sub-5nm technology nodes for its better electrostatic control. Junction-less (JL) NW is a feasible candidate, as steep source/drain junctions are not required. In this paper, Negative Bias-Temperature-Instability (NBTI) of JL-NW is studied through calibrated TCAD simulation. It is found that JL NW has 20 times less NBTI degradation (in terms of oxide/channel fixed charge generation) than regular NW because of40 times less hole carrier concentration at the oxide/channel interface and absence of field enhanced degradation. A novel operation scheme is then proposed to reduce NBTI degradation in analog circuit by switching the source and drain terminals periodically. The concept is verified through TCAD simulation of NW current mirror and it is found that NW NBTI degradation can befurther reduced by 25% to 35% by using the novel scheme.
Multi-Mode PWM Scheme for Locomotive Traction

In high power traction drive system, the switching frequency is low due to the limitation of switching loss and heat dissipation. To improve the performance of the control system, this paper introduce a new multi-mode PWM scheme for Locomotive traction. Asynchronous SVPWM, synchronous SVPWM, SHEPWM and square wave modulation are adopted in whole speed region, respectively. The advantage of this strategy is that it not only reduced the harmonics but also achieved smooth transition between different modulation schemes. On the traction experiment platform, the speed sensorless vector control algorithm was used to verify the effectiveness of the proposed modulation algorithm.
Half-Mode Substrate-Integrated Waveguides and Their Applications for Antenna Technology: A Review of the Possibilities for Antenna Design

Since its first introduction about a decade ago, the halfmode substrate-integrated waveguide (HMSIW) has gained enormous interest from researchers, with particular appeal in the antenna community. This is essentially due to the offered advantage of size reduction compared to a full SIW but more importantly to the intrinsic availability of an open aperture in the basic geometry. A great amount of effort has been spent on investigating this structure? s characteristics and its potential for integrated antenna designs, which can be efficient up to the millimeter-wave range. This article aims to provide a comprehensive snapshot of the literature on HMSIW antennas, including structure variations, analysis methods, and applications. A broad variety of HMSIW antennas covering a wide range of applications are systematically categorized, namely, into traveling-wave antennas, resonant cavity antennas, and HMSIW-fed antennas. Conclusively, the HMSIW is a versatile structure for numerous antenna applications.
Joint Identification and Localization of a Speaker in Adverse Conditions Using a Microphone Array

We discuss a joint identification and localization microphone array system based on diagonal unloading (DU) beamforming, which has been recently introduced for acoustic source localization. First, we propose a DU beamformer version for the signal enhancement problem. Then, we propose a enhanced DU steered response power (SRP), in which the first estimate of the source position is further refined with the information gathered from the speaker recognition module. The enhanced SRP-DU is obtained by weighting the frequency components with respect to the spectral characteristics of the speaker. The approach does not add significant computational load to the array processing. Experiments conducted in noisy and reverberant conditions show that the use of the DU beamformer provides better speaker recognition performance if compared to the conventional one since it reduces deleterious effects due to the spatially white noise and point-source interferences. Simulations also show that the speaker identification can improve the localization accuracy, and it is thus interesting for applications and systems which rely on Intearated localization and speaker identification.
Improving Classifier Performance Using Particle Swarm Optimization on Heart Disease Detection

Data mining is an evolving technology that aims to extract information from very large data to generate useful knowledge. Lots of datasets provided online can be beneficial in the context of research, such as medical datasets obtained from public and private repository developed to conduct a clinical diagnosis of diseases. Hence, we employ a public dataset from the UCI Machine Learning Repository on heart disease. in the current study, we proposed an improvement of the classifier, i.e. Naive Bayes, Decision tree, and k-Nearest Neighbor on detection of heart disease using the particle swarm optimization. additionally, the proposed approach runs to select some significant features as data entries for the classification phase in order to improve the performance of the classifiers. the Experimental results show that our built-model in selecting prime features increases the performance accuracy of heart disease detection. eventually, The main objective, as well as advantage of this study, are to provide a significant improvement of classifiers' performance, which then resulted in the correct diagnosis on time.
Incorporation of the Virtual Gate Effect and Consequences of its Neglect in the Simulation of ON-State $\pmb{I}_{\pmb{D}}-\pmb{V}_{\pmb{DS}}$ Curves of AlGaN/GaN HEMTs

We propose a simple method for incorporating the virtual gate effect present in Aluminum Gallium Nitride (AlGaN) / Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs) in numerical simulation of their ON-state current-voltage characteristics. Further, we show that the simulations which match the measured ON-state data neglecting the virtual gate effect end up employing an electron saturation velocity versus gate bias behavior that is qualitatively unphysical.
Cloud, Edge, or Both? Towards Decision Support for Designing IoT Applications

The rapidly evolving Internet of Things (IoT) includes applications which might generate a huge amount of data, this requires appropriate platforms and support methods. Cloud computing offers attractive computational and storage solutions to cope with these issues. However, sending to centralized servers all the data generated at the edge of the network causes latency, energy consumption, and high bandwidth demand. Performing some computations at the edge of the network, known as Edge computing, and using a hybrid Edge-Cloud architecture can help addressing these challenges. While such architecture may provide new opportunities to distribute IoT applications, making optimal decisions regarding where to deploy the different application components is not an easy and straightforward task for designers. Supporting designers' decisions by considering key quality attributes impacting them in an Edge-Cloud architecture has not been investigated yet. In this paper, we: explore the importance of decision support for the designers, discuss how different attributes impact the decisions, and describe the required steps toward a decision support framework for IoT application designers.
Optimization of Photovoltaic MPPT System Efficiency Based on Combined Algorithm

In order to realize the Maximum Power Point Tracking (MPPT) of photovoltaic system efficiently, an improved method of perturb and observe based on fuzzy logic control is proposed in this paper. A cost function describing the system efficiency is constructed and embedded into the proposed algorithm where the reference voltage can be obtained. Consequently, the optimal duty ratio can be achieved by PI regulation. Therefore, more accurate and efficient MPPT performance will be acquired. Simulation results are shown to validate the effectiveness of the proposed method.
Experience of a Gamified Spelling Solving by Students for Learning Spelling: Development of Kids Spell Dzongkha App.

In recent years a trend of gamification can be seen in every filed of education. However, there is a gap in research on the applicability of gamification in education, with few studies available. Therefore, this study seeks to investigate gamified spelling learning with focus on how Bhutanese kids experience learning English spelling through playing “EDUBUZZ kids” English spelling game app. By which study aims to develop similar kind of app for Dzongkha spelling learning for Bhutanese students to learn their national language easily. Study was conducted by allowing 10 participants to play the English spelling game and by performing the spelling test and interview before and after playing the game. The spelling test score before and after playing game was analyzed and found there was significant difference between two scores resulting to improvement of scores with game. Analysis from interview questions found that gamification of English spelling learning can be useful for rising interest and easy learning remembering it for longer duration than that of classroom learning. Through the above results authors of this study have found need of similar app for learning Dzongkha spelling. And the development of Kids Spell Dzongkha came in.
Aggregate-Compression-Aided Subcarrier IQ Index Modulation

In this paper, we propose aggregate-compressionaided subcarrier in-phase/quadrature-phase index modulation (AC-SIQIM). Conventional compressed-sensing aided SIM (CSSIM) compresses signals in each sub-block, so that the size of the compression matrix is small. In our proposal, signals based on subcarrier in-phase/quadrature-phase index modulation (SIQIM) in whole subcarriers are compressed by a large linear matrix. This relaxes the limitation of the size and enables to use discreteness-aware approximate message passing (DAMP) providing the superior reconstruction performance. We further propose two different construction methods for compression matrices based on random matrix theory and frame theory, respectively. Computer simulations demonstrate the superior performance of AC-SIQIM.
Dicyclohexylammonium 3,5-dinitrobenzoate

The asymmetric unit of the title salt, C12H24N+·C7H3N2O6 −, contains two cations and two anions. In the crystal, the cations and anions are connected by N—H⋯O hydrogen bonds, forming a 12-membered ring with an R 4 4(12) graph-set motif. The center of this 12-membered ring coincides with an inversion centre. π–π stacking is observed between parallel benzene rings [centroid–centriod distance = 3.771 (2) Å].