Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech Command Classification System for Sinhala Language based on Automatic Speech Recognition

Conversational Artificial Intelligence is revolutionizing the world with its power of converting the conventional computer to a human-like-computer. Exploiting the speaker’s intention is one of the major aspects in the field of conversational Artificial Intelligence. A significant challenge that hinders the effectiveness of identifying the speaker’s intention is the lack of language resources. To address this issue, we present a domain-specific speech command classification system for Sinhala, a low-resourced language. It accomplishes intent detection for the spoken Sinhala language using Automatic Speech Recognition and Natural Language Understanding. The proposed system can be effectively utilized in value-added applications such as Sinhala speech dialog systems. The system consists of an Automatic Speech Recognition engine to convert continuous natural human voice in Sinhala language to its textual representation and a text classifier to accurately understand the user intention. We also present a novel dataset for this task, 4.15 hours of Sinhala speech corpus in the banking domain. Our new Sinhala speech command classification system provides an accuracy of 89.7% in predicting the intent of an utterance. It outperforms the state-of-the-art direct speech-to-intent classification systems developed for the Sinhala language. Moreover, the Automatic Speech Recognition engine shows the Word Error Rate as 12.04% and the Sentence Error Rate as 21.56%. In addition, our experiments provide useful insights on speech-to-intent classification to researchers in low resource spoken language understanding.
The objective comparison of recent video compression formats for visual weather prediction

This paper analyzes the coding efficiency of recent compression formats H.264, H.265 and AV1 for the visual weather prediction purpose. It compares the bitrate saving of these codecs using objective metrics. The analysis is done on 8 video sequences at Full HD and Ultra HD resolutions. We discovered, that bitstream saving of AV1 is approximately 50% higher than H.264 for both resolutions, 30% higher than H.265 encoded videos at Full HD and 20% higher than those at Ultra HD resolution. From the experimental results, we can also state that objective video quality is content dependent.
Application of Deep Learning in Stock Market Valuation Index Forecasting

Deep learning is the core technology of artificial intelligence, which has higher accuracy than traditional algorithms. The characteristics of high-risk and high-yield in stock market make investors hope to make predictions on it through scientific methods, so as to reduce investment risks. Long short-term memory (LSTM) model in deep learning can effectively describe the long memory of data and is suitable for predicting financial time series. Therefore, this paper uses LSTM model in deep learning to learn and forecast the stock market valuation indicator, price-earnings ratio (P/E ratio). Then the prediction bias is measured by forecast trend accuracy (FTA), average forecast deviation rate (AFDR), and root mean square error (RMSE). Empirical results show that LSTM model has a good predictive effect on P/E ratio sequence, indicating that there is practical research value for applying deep learning network algorithm to the field of stock market forecasting. At the same time, this paper also provides a reference for stock market investors.
Estimation method of permittivity and permeability for sheet materials using a strip-loop antenna

In this paper, an estimation method of relative permittivity and permeability for sheet materials using a strip-loop antenna is proposed. In this method, permittivity and permeability can be evaluated by 1-port measurement of reflection characteristics. The material constant is estimated by comparing to FDTD simulation results. It is shown that the effect of permittivity and permeability can be observed independently as matching frequency shift of reflection characteristics of the sample-loaded antenna. Experimental result using several dielectric materials is also shown to validate the effectiveness of the proposed method.
Unmatching Observer-based Controller Design for T-S Time-delay Systems

In this paper, the problem of stabilization for imperfect premise matching is studied, in which the T-S fuzzy observer model with time-delay and the fuzzy controller does not share the same membership functions. A sufficient condition for the stabilization via observer-based state feedback under imperfect premise matching is presented, and an unmatching observer-based state feedback controller is also constructed. The proposed control scheme enhances the design flexibility as the membership functions of the observer-based controller can be arbitrarily selected. A numerical example is given to illustrate the conservative of the proposed design method.
How to Answer Comparison Questions

“Which city has the larger population, Tokyo or New York? ”. To answer the question, in general, we necessarily obtain the prior knowledge about the populations of both cities, and accordingly determine the answer by numeric comparison. Using Machine Reading Comprehension (MRC) to answer such a question has become a popular research topic, which is referred to as a task of Comparison Question Answering (CQA). In this paper, we propose a novel neural CQA model which is trained to answer comparison question. The model is designed as a sophisticated neural network which performs inference in a step-by-step pipeline, including the steps of attentive entity detection (e.g., “city”), alignment of comparable attributes (e.g., “population” of the target “cities”), contrast calculation (larger or smaller), as well as binary classification of positive and negative answers. The experimentation on HotpotQA illustrates that the proposed method achieves an average F1 score of 63.09%, outperforming the baseline with about 10% F1 scores. In addition, it performs better than a series of competitive models, including DecompRC, BERT.
A Novel Bandstop Frequency Selective Surface Using Coupled Split Ring Resonators

In this paper, a bandstop frequency selective surface is devised using coupled split ring resonators. A four-SRR unit cell is the building block of the FSS. Split rings arranged in a round robin fashion gives polarization insensitive transmission characteristics. A modified structure is proposed to eliminate the dependence of the transmission characteristics on the angle of incidence. A considerably good performance was obtained for oblique incidence up to 75° and a 40% size reduction is also achieved. The structure can be further enhanced by improving the passband performance at oblique incidence.
Spectral Capacitively Coupled Electrical Resistivity Tomography for Breast Cancer Detection

Early detection of breast cancer is key for a better survival rate, so there are huge demands for a safe and reliable breast cancer screening method, which currently is not available. Earlier studies are showing that the impedance spectroscopy can be an effective discriminator for breast tumor against healthy tissues. Traditional electrical impedance tomography (EIT) has progressed well into clinical stages for such an application with some promising results. But EIT generally operates at low frequencies, ranging typically lower than 1 MHz. Previous research work has suggested that the important information on the tumor impedance spectra exists in the higher frequency domain. Thus, this work focuses on the introduction and the evaluation of a spectral capacitively-coupled electrical resistivity tomography (CCERT) for breast cancer diagnosis. Compared to the traditional EIT, CCERT can reach a much higher frequency domain producing the spectroscopic images in a broader range. This in turns offers improved discrimination of tumors against normal tissues via frequency difference imaging or spectral imaging as it is shown in this paper. Additionally, direct contact between the EIT electrodes and the surface of the body, which causes significant challenges and uncertainties in measured data, is avoided in CCERT inherently a contactless method. In this paper, a 3D in-silico model of the CCERT system is created and solved using the finite element method (FEM) to simulate the forward model for each frequency. The feasibility simulation analysis is conducted based on the existing clinical breast cancer data and a CCERT reconstruction. For the experimental verification, an 8-electrode laboratory phantom was tested with the excitation frequency ranging from 200 kHz to 13 MHz. The simulation and experimental results show the promising potential of this method for breast cancer imaging. The wide-range spectroscopic images provide better discrimination of tumors against healthy tissues through their spectral conductivity profile as well as the rate of changes in spectral conductivity images.
Spoken Dialogue-based Remote Control of Educational Mobile Robot with Mecanum Wheels

This paper describes a new platform of educational mobile robot with Mecanum wheels and voice enabled dialogue-based human robot interaction model was established. Moreover, new trends in using deep neural networks for cloud- and edge-based speech recognition was described in detail. Finally, the progress of both the cloudand edge-based speech recognition was reported. Especially, promising usability test result on resisting room reverberation and background noise had been achieved by the proposed convolutional neural network (CNN)-based wake-up word (WUW) spotting system.
Novel Deep Regression and Stump Tree-Based Ensemble Models for Real-Time Load Demand Planning and Management

The electrical power companies’ load consists of several kinds of customers, for example, agricultural, building, industrial, etc. Agricultural and industrial energy consumption is based on highly inductive loads, and it leads to the electricity shutdown because of highly inductive loads effect large spikes to the power curve. These spikes show their irregular disturbance in the electric power system due to shutdown and start-up of these large loads. It is considered irregular as well as a tough job to forecast the load with the existence of such kinds of spikes. Further, the heterogeneous and multi-source load consumption characteristics necessitate the integration of evolutionary models and the ensemble techniques render better robustness and forecast accuracy. For this purpose, we proposed the novel deep regression and stump tree-based ensembles models (DRTSEM) for accurate load planning and management with the use of highly inductive load data, buildings loads, etc. The hyperparameters e.g., network weight decay, network learning rate, the total number of hidden layers and momentum of the DRTSEM is optimized by using the deep regression and stump tree-based algorithms. For forecasting analysis, two different climate zones and seven model’s input model parameter selection (MPS) with new sets of the data samples are used. The forecasting performance is testified with the generalization network error and resubstituting loss, 10-fold cross-validated mean square error, four performance evaluation statistics and existing tree bagger (TB) model. The mean absolute percentage error is noted 47%, 64% and 41% better than the TB model at MPS-3 to MPS-5 respectively. The proposed DRTSEM can efficiently tackle the nonlinear characteristics of the heterogeneous and multi-source loads and moreover increase accuracy and reduce the error between actual and forecasted load demand. The modeling results proved that the use of DRTSEM renders more accurate utility energy planning and management compared with the existing ensemble approaches.
Phaseless Far-Field Gain Measurement of Circularly-Polarized Antennas Using Artificial Anisotropic Polarizers

A novel phaseless far-field gain measurement method of circularly-polarized (CP) antennas using artificial anisotropic polarizers (AAP) is proposed in this paper. This method utilizes two identical antennas of any type, a linearly-polarized (LP) standard-gain horn (SGH) antenna and two identical AAP to obtain the CP gain of the antenna under test (AUT). No phase measurement or probe rotation is required. The essential part of the method is to degenerate the gain characteristics of the probe antenna into two orthogonal CP components. Experimental results for a conical CP SGH operating between 50 to 67 GHz show gain error is within 1 dB. The measurement method is useful for gain measurements of CP antennas especially at millimeter-wave and terahertz range.
Risk Assessment Scheme for Mobile Applications Based on Tree Boosting

In the forthcoming era of IoT, where everything will be connected, mobile devices will play a key role in providing data sharing and user-centric services between devices. In such a service environment, if a mobile application is vulnerable to security threats and exposed to malicious behavior, malware can spread to hundreds of millions of connected devices. In particular, it is important to isolate and respond quickly to malicious mobile code. This requires the prediction of malicious behavior. Currently, security risk assessment schemes based on the permission use the description of the application or user review, but these schemes mostly offer a subjective evaluation, which inevitably reduces accuracy. In this paper, we thus propose a scheme for assessing security risk of Android mobile applications by analyzing their application programming interfaces (APIs) using machine learning. The key idea of the proposed scheme is to extract the APIs from the execution code of the application with reverse engineering analysis, such that each API can be compared with the malicious API database built from the existing malware dataset. Instead of simply judging the applications as malicious or benign, our scheme shows their risk as a score. To do this quantitative evaluation, we use an ensemble of tree boosting machine learning algorithms. To prove the practicality of the proposed scheme, we experiment with a set of benign and malicious real world samples, and compare our results with existing schemes. Experimental results show better performance and accuracy than conventional schemes based on Naive Bayes and simple ensemble algorithms. Our proposed scheme is expected to significantly contribute in responding rapidly to ever-more-intelligent malware of the future.
28 Gbps Bang-Bang CDR for 100G PSM4 with Independently Tunable Proportional and Integral Parameters of the Loop Filter in $0.25\ \mu \mathrm{m}$ Photonic BiCMOS Technology

A 28 Gbps NRZ bang-bang clock and data recovery (CDR) chip for 100G PSM4 is presented. It exhibits an adaptable loop filter transfer function with independently tunable proportional and integral parameters. This allows to optimize the jitter transfer, jitter tolerance, and locking range of the CDR according to system requirements. The CDR represents a key component for a single-chip 8-channel electronic-photonic PSM4 transceiver. A CDR chip was manufactured in a $0.25\ \mu \mathrm{m}$ monolithic photonic BiCMOS technology. The core chip area is 0.51 mm2 and it dissipates 330 mW from 2.5 V and 3.3 V power supplies.
Real-Time Network Intrusion Detection System Based on Deep Learning

Computer network is vulnerable to hackers, computer viruses, and other malicious attacks. As an active defense technology, intrusion detection plays an important role in the field of network security. Traditional intrusion detection technologies face problems such as low accuracy, low detection efficiency, high false positive rate, and inability to cope with new types of intrusions. To solve these problems, we propose a real-time network intrusion detection system based on deep learning, which uses big data technology, natural language processing technology and deep learning technology. Our main contributions are as follows: (1) Use Flume as the agent for log collection to realize real-time massive log collection, using Flink as real-time computation engine. (2) Aiming at the high dimensional problem of traffic data, a self-encoder-based intrusion detection dimension reduction method is proposed, and the intrusion detection data is preprocessed, including data cleaning, coding, extraction and integration, and normalization. (3) Propos a deep learning-based intrusion detection model, AE-AlexNet, which uses Auto-Encoder AlexNet neural network. The experimental results of the intrusion detection data set KDD 99 show that the accuracy of the AE-AlexNet, model is as high as 94.32%.
The Impact of Water Surface Albedo on Incident Solar Insolation of a Collector Surface

This paper presents the modelling of the total insolation on a collecting surface using a varying water surface albedo model to approximate the reflected radiation. The effect of the water surface on the reflected radiation for a solar collector inclined at a fixed tilt optimum slope is investigated. The annual insolation is approximated to demonstrate the extent to which the energy differences exist in using the varying water surface albedo when compared to the default albedo in floating solar modelling. The results show that for a floating solar system, the annual energy approximation from using the varying water surface albedo is lower in comparison to the usage of the default albedo.
Property Prediction of Molecules in Graph Convolutional Neural Network Expansion

The prediction of molecular properties is further studies of molecular structure similarity and plays an important role in the drug development process. Graph convolutional neural networks provide an effective method to study the relationship between molecular structure and properties. Many variants of graph convolutional networks that have emerged in recent years have improved network performance from different perspectives. The expansion of the graph convolution neural network (ExGCN) is proposed in this paper. The graph convolutional neural network is expanded by incorporating the core ideas of the latest variants including Graph Attention Network, Gated Graph Neutral Network, and construct new molecular graphs. Though experiments on the toxicity data set of tox21, the lipophilicity data set of Lip, and the solubility data set of ESOL. The proposed method performs better compared to other traditional methods.
Research on the Influence of Feed Speed Variation on Turning Chatter Stability

Turning operation is one of the most widely used machining process but its machining efficiency often subjected to the chatter phenomenon. In order to suppress the chatter, this paper investigated the influence of feed speed on the chatter stability and proposed a feed speed variation method. The regenerative chatter mechanisms of two different turning operations were analyzed in detail namely orthogonal and cylindrical turning operation. The efficiency of feed speed variation was studied, the optimal control parameters for chatter suppression were proposed and the improvement of stability limit was verified by the Simulink software.
Synchronization of Radiography for Newborns Heart Study Using Electrocardiogram Analysis

The paper considers the possibility of using electrocardiogram analysis to synchronize the X-ray examination of the heart of newborns. The determination of the moment of the diastolic phase on the electrocardiogram allows radiography to be performed at a time when the patient's heart is at rest, which can significantly improve the quality of X-ray images. The paper presents the method of diastolic phase recognition on the electrocardiogram and the synchronization algorithm.
A Ka-Band Class-C BiCMOS VCO with Temperature Compensation

This paper presents a Ka-band class-C Voltage-Controlled Oscillator (VCO) with a temperature compensation circuit. The VCO core circuit is implemented by class-C topology with 6-bit capacitor array to achieve a wide frequency tuning range, 4GHz. The temperature compensation circuit consists of temperature detection circuit, logic circuit and bias voltage generate circuit. The Ka-band VCO is designed using TowerJazz $0.18\ \mu\mathrm{m}$ SiGe BiCMOS technology with a supply voltage of 3.3 V, and the effectiveness of the temperature compensation circuit is verified by the simulations when temperature varies from −40°C to 125°C. The VCO has the operating frequency ranges from 23.5 to 29.6GHz, and the phase noise at 1MHz offset frequency is - 107dBc/Hz while consumes 10mA from a 3.3V supply voltage. Without compensation, the frequency varies 360MHz over temperature, while the variation reduced to 31MHz with compensation.
Constrained Multi-objective Robot’s Design Optimization

In this article, we consider an approach for the optimal multi-objective constrained design of the parallel robots. Optimization is carried out according to two key criteria: the workspace area and the global dexterity index (GDI) concerning constrain. The workspace is the area that the robot can serve. The value of the GDI correlates with the quality of this service. Both objectives are maximized. However, there is a tradeoff between these goals, and they can't achieve maximums at the same point. Therefore, the solution for this problem is a Pareto- optimal frontier (the set of points, which can’t be optimized in any criteria without worsening the other). The paper describes the robot’s kinematic and singularity analysis, workspace area approximation, the GDI calculation. The Pareto-frontier for the multi-objective constrained optimization problem under consideration is constructed. We also create representative visualization for robot workspace, singularity positions, and efficient Pareto-set.
Facial Expression Recognition Based on Convolutional Neural Network

Facial expression recognition is an important field of pattern recognition research. Traditional machine learning methods extract features manually. It has insufficient generalization ability and poor stability. Moreover, its accuracy is difficult to improve. In order to achieve better facial expression recognition, this paper designs a modular multi-channel deep convolutional neural network. To avoid overfitting, the network output uses a global average layer. Data enhancement on the dataset before training can improve the generalization ability of the model. Test the performance of network on the FER2013 emoticon dataset. The accuracy of expression recognition is 68.4%. It performs a prediction for about 0.12s. Compared to other recognition algorithms, network has certain advantages. Finally, a real-time facial expression recognition system is constructed by using the trained recognition model. The experimental results show that the system can effectively recognize facial expressions in real time.
Integration of Renewable Energy Resources in the Distribution Network of Industrial Enterprises

Distributed generation is understood to mean the totality of sources of electrical energy closest to consumers. Traditionally, the flow of electric energy is unidirectional and comes from high voltage networks to lower voltage networks and subsequently to the consumer. Integrating ever increasing renewable energy resources (RES) into the distribution system (DS) puts several demands on both the DS and the RES. Mains interference by the integration requires special protective devices and as the penetration becomes high, so also the demands on the grid become high. Therefore, interfacing techniques becomes very important. This article discusses the integration of renewable energy with management, protection and stability in mind for efficient power quality and system reliability.