Miracle-dz/UniFAR-FTUs huggingface dataset:
Upsurging of Quality: Antencendent Framework for EServices Quality Measurement

Amidst the rapid development of information technology and communication, there is a demand for fast and reliable information that results in increasingly competitive competition. To the extent of carrying out and fulfillment of public policy and governmental output, public e-services are essential. In return, public e-service becomes a matter of obtaining access, not only to the IT artifact or the service process but also to governments and governmental output in general. The main goal of this study is intended to design an initial framework for measuring the quality of e-services services. The results of the research conducted produced an initial framework for measuring the service quality of e-services. The initial framework resulting from the stages of this study consists of five main aspects: security and trust, quality of service, information quality, system quality, system support. Twenty-two factors are obtained from the results of a literature review. This study is a part of our research in designing the e-Service quality framework. Hopefully, this research can add knowledge within the field of measuring e-Services service quality and collude with other existing frameworks to enhance the quality of e-services evaluations.
Pattern Separation Network Based on the Hippocampus Activity for Handwritten Recognition

Reaching high accuracy in handwritten character recognition is an essential challenge since it is widely used in many fields such as signature analysis and forgery detection. Recently, deep learning has demonstrated efficiency in this field. The problem with deep learning is that it uses a vast number of parameters that require a large dataset for training. To overcome this problem, an intelligent network is proposed in this study, based on the computational function of the dentate gyrus of the brain’s hippocampus. The ability to separate patterns with high overlapping is a task that is referred to as the dentate gyrus. Handwritten character images have high overlapping due to various writers’ styles, or even one writer’s style under different conditions. Therefore, proposing a network based on the dentate gyrus’ functional computation can be useful in this field. One of the prominent features of the proposed network is employing two excitation steps and two inhibition steps, augmenting the accuracy of recognizing handwritten characters. The proposed network was evaluated with six datasets of digits and characters from five languages. Experiments on all of the used datasets showed promising results. Moreover, a comparative and detailed analysis of the proposed network with other SOM-based and deep learning methods is provided. Experimental results show a significant boost in accuracy. While the character error rate (CER) was smaller than 1.85% for all the experiments, the smallest CER of 0.6% was achieved by the MNIST dataset. Moreover, in recognizing patterns with high noise, the proposed network showed satisfactory results.
A Deep Learning Based Distributed Compressive Video Sensing Reconstruction Algorithm for Small Reconnaissance UAV

Distributed compressive video sensing (DCVS) is an effective method for small reconnaissance Unmanned Aerial Vehicle(UAV) to obtain high-quality videos on the battlefield. However, the existing reconstruction algorithms based on deep learning fail to make full use of the temporal correlation of videos, resulting in low reconstruction quality. In this paper, a measurement information compensation network called MCINet is used to compensate for the information in non-key frame measurements with the help of key frame measurements before initial recovery. At joint reconstruction stage, a neural network with autoencoder mix with recurrent neural network (RNN) structure called ECLDNet which makes full use of high-quality key frames is adopted, the encoder extracts temporal-spatial features from key and non-key frames, the RNN uses features of key frame to compensate for missing details in non-key frame features, the decoder reconstructs images in a symmetrical way with encoder. Experimental results indicate that our model can get an additional performance gain of more than 1.5 dB peak signal-noise ratio (PSNR) without any changes at the encoding end. The reconstruction runtime of our model increases slightly, but is still much less than iterative reconstruction algorithms due to the non-iterative nature of deep learning.
Stateless Cloud Auditing Scheme for Non-Manager Dynamic Group Data With Privacy Preservation

As one of the core services of cloud computing, cloud storage could satisfy various storage and management requirements caused by the growth of data. Considering the complexity and uncontrollability of the cloud storage environment, many cloud auditing schemes were presented to assure the integrity of data in the cloud. However, most existing schemes have security risks, such as identity privacy and data privacy disclosure, authority abuse of group managers and collusion attacks during user revocation. To solve these problems, we propose a stateless cloud auditing scheme for non-manager dynamic group data with privacy preservation. The proposed scheme not only realizes user identity privacy preservation but also preserves data privacy security with the random masking technique. Unlike other solutions, our scheme allows $t$ group users to trace the user’s identity cooperatively without group managers, which eliminates authority abuse of group managers and provides non-frameability. Meanwhile, utilizing the concept of Shamir Secret Sharing, our scheme divides the re-signing process into several parts to resist collusion attacks during user revocation. By the designed binary tree, group users could trace dynamic data changes and recover the latest data when existing data are damaged. Besides, both users and the third-party auditor (TPA) are stateless in our scheme; that is, they no need to maintain data index information during cloud auditing. Our scheme also achieves mutual supervision between users and cloud service providers (CSPs), which ensures data are non-repudiation on both parties. Furthermore, we construct an efficient incentive for data visitors by using the blockchain technology and design a secure data sharing model to guarantee that data owners control their data ownership. Certificateless cryptography assures that the proposed scheme avoids certificate management and key escrow problems. Finally, security analysis and performance evaluation show that our scheme is secure and efficient.
A Time Window based Two-Dimensional PCA for Process Monitoring and Its Application to Tennessee Eastman Process

Multivariate statistical analysis methods like PCA have been widely utilized for fault diagnosis and quality control. Nevertheless, the traditional PCA based methods have some limitations in dealing with the dynamic data information which extensively exists in modern process industry. This paper developed a time window based 2D-PCA model to strengthen the capability of extracting dynamic features for process monitoring. First, this model uses the time window to construct 2-dimensional data matrices piece by piece to keep as much dynamic information as possible between samples. Second, 2D-PCA is applied to these two-dimensional data samples for dynamic feature learning. Finally, general statistics are calculated for online monitoring to detect abnormal states. Finally, Tennessee Eastman (TE) process is used to verify the effectiveness of the developed 2D-PCA monitoring strategy.
Dynamic Response of Gold-coated Optical Fiber Sensors Subjected to Voltage Variations

In this work, fiber-optic voltage sensors based on the deposition of gold nanocoatings, either in form of nanoparticles or planar thin-films are reported. A first optrode is manufactured by using in-situ synthesis of gold nanoparticles within a polymer matrix, leading to localized surface plasmon resonances. The second probe is manufactured by sputtering a planar gold thin-film onto the optical fiber. The obtained results confirm the possibility of detecting electrical events by means of plasmonic optical fiber sensors with sensitivities ranging from 0.05dB/V in the case of LSPRs to 0.12dB/V in the case of SPRs.
Birds of a Feather Rumor Together? Exploring Homogeneity and Conversation Structure in Social Media for Rumor Detection

Rumors in social media represent a severe problem prevailing in today’s society. Previous studies on automated rumor detection have shown that the topological information specific to social media is a vital clue for debunking rumors. However, existing automatic rumor detection approaches either oversimplify the graph structure or ignore this crucial clue. To address this issue, we propose a model that explores homogeneity and conversation structure to identify rumors. Our model learns more comprehensive and precise representations by modeling follower-following relationships of users, simulating the propagation layout of tweets, and connecting responders’ behavior. The experimental results on two public Twitter datasets show that our model’s performance outperforms other state-of-the-art baseline models. Furthermore, the experimental results prove our hypothesis that birds of a feather rumor together. The results demonstrate that both the conversation structure and the friend network’s homogeneity are significant for checking the veracity of a suspicious tweet.
3D Printed Wearable Exoskeleton Human-Machine Interfacing Device

This paper presents a wearable, flexible, 3D-printed exoskeleton human-machine interfacing device with embedded sensors and actuators for real-time haptic feedback. The benefits of this system compared with other similar designs are its biaxial flexibility, its stretching capabilities and the real-time vibrotactile interface, linking a sensorised robotic hand with a user remotely controlling it. The novelty of this work lies in the breathability of the device due to its intelligent design. We have validated our design through simulation and experimentation and also tested the device on a 3D printed robotic hand. This work proved a suitable platform for novel emerging sensor applications.
Determination of parameters of thermal model of automobile alternator

The increase in the number and capacity of the installed electric consumers onboard the vehicles leads to the growth of heat load of the alternator as the main source of electric power. A thermal model has been developed to analyze the thermal state of an automobile alternator, which allows determining the contribution to the general temperature background of individual elements, as well as its change in case of malfunction. The thermal model includes the main sources of heat emission and thermal resistances of the automobile alternator elements, as well as analytical expressions to determine its parameters. As a result of experiments, the influence of the type of current, rotor speed and stator winding current on the parameters of the thermal model of an operative alternator was established. Further researches will be directed on definition of parameters of thermal model at modeling of characteristic faults and development of practical recommendations on diagnostics of the automobile alternators on parameters of thermal condition.
Simultaneous Measurement of Refractive Index and Temperature using LMR on planar waveguide

In this work a simultaneous refractive index and temperature sensor is presented. The optical structure consists of a soda-lime planar waveguide coated with CuO thin film for the generation of two separated Lossy Mode Resonances (LMRs). One section of the CuO thin film is in contact with the external medium while the other part is coated with a thick layer of PDMS. As result of this, two different resonances are obtained, centered at 600 nm and 1000 nm respectively. The first resonance wavelength is modified by the external medium refractive index, while the second one is only affected by the PDMS refractive index, that due to the thermo-optic effect can be used to measure the ambient temperature. Using this simple structure, a dual sensor has been obtained, showing an RI sensitivity of 1600 nm/RIU and a temperature sensitivity of 1.12 nm/0C.
Piezoelectric MEMS Vibration Sensor Module for Machining Quality Prediction

In this work, a piezoelectric MEMS based vibration sensor with a unique gimbal shaped guard ring in differential electrical configuration has been designed for enhanced acceleration sensitivity in out-of-plane direction and reduced in-plane crosstalk. The Thin-film Piezo on Silicon (TPoS) accelerometer was fabricated using a GlobalMEMS Inc. PZT platform. This method makes use of the excellent piezoelectric property of the Lead Zirconate Titanate (PZT) thin-film along with low crystal defect property of single crystal silicon, thus ensuring a good combination of sensitivity, resolution, bandwidth, reliability, and lifetime. A system-level approach was adopted to integrate the MEMS device with a charge amplifier and a microcontroller. The device performance was compared with the standard reference accelerometer for 1-g load. The developed MEMS sensor module was further implemented for surface roughness prediction in a quartz polishing system using machine learning algorithm incorporating both the acceleration-time data and the feed rate of the polishing spindle.
Development of an Application for Recognizing Automobile Vehicles

In modern cities, a busy traffic is an acute problem. Therefore, road monitoring should become an essential feature of smart cities. The possible approach to car traffic control is the usage of neural networks. In this article, we consider the usability of this approach for the detection and classification of city traffic. The developed application is based on the convolutional neural network of the YOLOv3 system. With the testing data set of 750 images, the trained neural network showed an accuracy of 76%.
Performance Analysis and Evaluation of Implementing the MVDR Beamformer for the Circular Antenna Array

In the area of pattern array synthesis, highly-directional radiation pattern guarantees accuracy and resolution, [1], which are accompanied by a drastic reduction in SLL. Pattern synthesis techniques are characterized by the two distinct scenarios; (1) the design of antenna array geometries for steering beampattern in an arbitrary direction in the space, and (2) the implementation of beamforming techniques for steering beampattern in the direction of interest in the space. This study has a major contribution for fully evaluating the performance of implementing the minimum variance distortionless response (MVDR) beamformer for the circular antenna array geometry in comparison with the different linear array geometries. A full and quantitative comparison between the circular antenna array and the different available linear antenna array geometries have been rigorously fulfilled for highlighting differences and advantages of the performance of the circular antenna array geometry over the different linear array antenna geometries using the four following concepts of (1) spatial correlation function (SCF), (2) antenna efficiency, (3) signal to interference ratio (SIR), and (4) propagation time delay.
Development and Research of a Model Using IEC Protocols for 110/6 kV Digital Substations

The emergence of new international standards and information technologies opens opportunities for modern approaches to creating a digital substation. The use of digital substation technology based on the IEC 61850 standard should significantly reduce the cost of design, commissioning, operation and maintenance of power facilities in the future. The article presents the modeling process of transmitting and receiving digital signals using various protocols IEC 61850, reducing the load on the information network, providing an increase in speed of signal transmission between devices of relay protection and automatics equipment and supplies primary substation, as well as reducing the number of communication lines by switching on the Protocol IEC 61850, which will allow to lower expenses for construction and modernization of objects. Based on the developed model, it is possible to perform tasks of dispatching control and digital signal transmission between relay protection and automation devices for engineers and designers of the oil and gas complex. The resulting topology of the digital network of the substation increases the speed and reduces the response time of the Executive part by reducing the time of emergency mode.
Work-Based Learning Conceptual Framework for Effective Incorporation of Soft Skills Among Students of Vocational and Technical Institutions

The importance of soft skills has been recognized in most fields, hence, various companies make judgments on work and success evaluation based largely on employees’ soft skills. Work-Based Learning (WBL), as an educational approach, can harness students’ potential in soft skills. This investigation aimed to generate a conceptual framework for WBL to effectively integrate soft skills among students in technical colleges. A sample of 268 technical teachers was drawn from a population of 302 technical teachers from 22 public technical colleges within the study area using a stratified random sampling methodology based on the complex heterogeneity of the population. The study adopted structural equation modeling (SEM) to determine the nexus underlying the model, using analysis of moment of structures (AMOS) version 20. The study results discovered WBL preparation (planning and designing), supervision, and evaluation each have a positive impact on soft skill development among technical college students. By way of recommendation based on the study findings, the government should formulate and implement suitable policies to enhance planning and designing in WBL programmes, particularly, to address the needs of students for adequate incorporation of soft skills at technical colleges. Technical teachers could employ the WBL-recognized elements in integrating workplace experiences to successfully promote students’ soft skills at technical colleges.
Improved Convolutional Neural Network Based on Multi-head Attention Mechanism for Industrial Process Fault Classification

Deep learning theory demonstrates its great power in the complicated data processing field. As a prevalent deep learning technology, convolutional neural network (CNN) has achieved some successful applications in the field of fault detection and classification in the complicated industrial systems because of its strong nonlinear feature extraction capability. However, the basic CNN views all the features equally and can not highlight these features which play a more important role in the fault classification procedure. To deal with this problem, this paper proposes an improved CNN model, called multi-head attention CNN (MACNN), to distinguish the importance of different features for better fault classification performance. In the MACNN framework, the raw training data are firstly analyzed by the multiple convolutional layers for feature extraction. Then, the features obtained by the convolution layers are used as the input of the multi-head attention (MA) layers. The introduction of the MA mechanism can help exploit more meaningful information by emphasizing the important features. Lastly, the softmax classifier is utilized to classify the fault patterns. To test the method performance, one case study on the benchmark Tennessee Eastman industrial system is performed and the results show that MACNN gives the better fault classification results than the traditional feedforward neural network (FNN) and CNN models.
Real-Time Optimal Power Flow Using Twin Delayed Deep Deterministic Policy Gradient Algorithm

The general concept of AC Optimal Power Flow (ACOPF) refers to the economic dispatch planning under electric network constraints. Moreover, each instance with the entire network must be solved in real-time (i.e., every five minutes) to ensure cost-effective power system operation while satisfying power balance equation. As the operation of power systems penetrated with intermittent renewable energy becomes more complicated, this article proposes Deep Neural Network (DNN) and Levenberg-Marquardt backpropagation-based Twin Delayed Deep Deterministic Policy Gradient (TD3) approach to improve computational performance of ACOPF. Specifically, because the ACOPF model shall consider prevailing constraints of the power system, including power balance equation, we set the appropriate reward vector in the training process to build our own policy. Furthermore, we add random Gaussian noise to individual net loads for representing uncertainty characteristics introduced by renewable energy sources. Finally, the proposed model is compared with the MAT-POWER solution on the IEEE 118-bus system to demonstrate its efficacy and robustness.
Prefrontal Brain Electrical Activity and Cognitive Load Analysis Using a Non-linear and Non-Stationary Approach

This study presents an instantaneous spectrum analysis for electroencephalograph data processing that would facilitate the practice of learning and instruction through real-time measurements of the learner’s cognitive load. The instantaneous spectrum analysis is derived from the ensemble empirical mode decomposition which decomposes signals into a gathering of intrinsic mode functions without mode mixing. The multi-marginal Hilbert-Huang spectrum is introduced to estimate frequency contents. As a result, the amplitude of brain rhythms related to the cognitive load can be determined accurately. A model study was performed at first to test the efficacy of the proposed algorithm by comparing with the Fourier based technique, then a prefrontal experiment was conducted to show the advantages of the proposed method. With the higher resolution and more realistic of the proposed method relative to conventional spectrum analysis, more significant features of the signal can be extracted. We believe that the proposed method has the potential to be a substantial technique in electroencephalograph data analysis.
A New Method of Assigning Trust to User Addresses in Bitcoin

Trust of user address (i.e., wallet address) in Bitcoin is dependent on not who user is but the amount of Bitcoin currency in user’s wallet address. So we cannot necessarily trust a Bitcoin user with wallet address. Actually, there is no method for assigning trust to user addresses in Bitcoin. Although we can provide trust to user address by inserting certificate into Bitcoin transaction using OP_RETURN, it still has some problems about wastefulness. In this paper, we propose a new method that efficiently assigns trust to user address using Bitcoin transfer only, which does not need to use OP_RETURN. Our proposed method can realize a trust providing mechanism for “Blockchain 1.0” without adding any new function. Main idea of our method is that “token coin” (Bitcoin transfer itself) can be used as a trust providing mechanism. For feasibility study, we experimentally show the trust assigning in the Bitcoin testnet. Furthermore, our method can be easily adopted into other cryptocurrencies.
Sigmoids superposition for signals approximation with a deadband for sweep in a sequence of quasi-rectangular pulses

In this article, we show how a specially constructed combination of smooth, monotonic non-linear functions and affine transformations can approximate dead-zone-looking one-dimensional signals, previously represented, as a rule, in a piecewise-linear form. Our results can help solve some open questions in analyzing systems with dead-zone-looking nonlinearities. In particular, in the study of such systems in which there are mechanical gaps, or the development of pulse-phase control systems. They can be well approximated by superposition of two symmetric or asymmetrical sigmoidal functions. Finally, numerical experiments show that the resulting formulas describe deadband-looking signals with a high degree of accuracy. This method is simple, because requires a minimum set of input parameters.