Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Network Analysis of the Pilkada Serentak 2018: Towards National Coalition in the 2019 Indonesia's General Election

The Indonesian general election in 2019 marks a new era for the country democratic evolution. This is the first time the Presidential election is being held at the same time as the legislative election. This creates new dynamics on how the political parties as the country legal proposer of the presidential candidates. Based on the current law, the legal threshold for the Indonesian presidential candidates is at least being proposed by party or coalition of parties with at least 20% of total popular votes in the previous general election. Parties are searching for optimum configuration to not only to improve their coalitional presidential candidate winning probability but also to enhance electability in the legislative election as coattail effect.
A traceability set up using Digitalization of Data and Accessibility

The main intention of this development is to build a smart outcome for managing and maintaining data digitally using radiofrequency digitalization (RFID) tags and Quick Response code (QR) codes. The component traceability system either requires significant human effort and supervision or outsized amounts of subsidy and capital. This task provides better inventory supervision through enhanced clarity and organization of resources. The RFID-QR employed in this project is to afford admittance to the Records 24×7 that are collected via RFID tags-QR codes. Automated data compilation facilitates to improve the precision of conclusion building processes. RFID tool facilitates to evade vast disasters and upset origin by short of fact concerning the methodological state of rail coaches. The QR code supervision system mitigates some of the human efforts. This supervision system works via one application openly reachable to certified users. The Real-Time Dynamics monitoring System (RTDMS) solution is designed to manipulate manifold facilities or allow numerous users to scrutiny fact at a solitary competence. This method is employed in the cloud server and has password fortification, which averts unauthorized users from screening or revises facts.
A Review on IGBT Module Failure Modes and Lifetime Testing

This article focuses on failure modes and lifetime testing of IGBT modules being one of the most vulnerable components in power electronic converters. IGBT modules have already located themselves in the heart of many critical applications, such as automotive, aerospace, transportation, and energy. They are required to work under harsh operational and environmental conditions for extended target lifetime that may reach 30 to 40 years in some applications. Therefore, addressing the reliability of IGBT modules is of paramount importance. The paper provides a comprehensive review on IGBT modules dominant failure modes, and long-term reliability. A detailed discussion on accelerated testing, and lifetime and degradation characterization considering thermo-mechanical stress is also presented in details.
A weightless regression system for predicting multi-modal empathy

This work takes into account the benefits of machine learning in order to estimate the valence of emotions on the OMG Empathy dataset, considering the information obtained from face expressions and dialogue of interlocutors. RegressionWiSARD and ClusRegressionWiSARD n-tuple regressors and its ensembles were employed to this end. The best performance achieved among all the combinations of weightless neural models considered (evaluated using the CCC metric) was 0.25 in validation set of the Personalized Track.
Hardware Trojan detection method based on the frequency domain characteristics of power consumption

Hardware security has long been an important issue in the current IC design. In this paper, a hardware Trojan detection method based on frequency domain characteristics of power consumption is proposed. For some HTs, it is difficult to detect based on the time domain characteristics, these types of hardware Trojan can be analyzed in the frequency domain, and Mahalanobis distance is used to classify designs with or without HTs. The experimental results demonstrate that taking 10% distance as the criterion, the hardware Trojan detection results in the frequency domain have almost no failure cases in all the tested designs.
Design a Microstrip Patch 5G Antenna with Modified Defective Ground Structure

The proposed design is of 5G Micro-Strip Patch Antennas with Defected Ground Structure on ground slots. Slots or defects merge on the ground plane of microwave planar circuits are defined as Defected Ground Structure. DGS is a very effective Partial Defected Ground Structure is used to improve all the antenna parameters and performance. DGS used to achieve high gain higher bandwidth. The proposed paper introduced the defective ground structure with a v slot in the partial ground. This will improve the parameter like return loss, bandwidth, VSWR, efficiency, and compactness of the antenna.
A Survey on Microaneurysms Detection in Color Fundus Images

Early Detection of Microaneurysms (MA) plays a vital role in preventing the blindness caused by diabetic retinopathy (DR). DR is preventable yet a serious diabetic problem. Treatment at an earlier stage reduces the risk of blindness. Microaneurysm is the first sign of DR found in fundus images while doing screening. Detection of MA is a challenging task mainly because of its size. MA appears as a tiny red spot ranging from 15µm to 60µm size. The most common way to detect the MA from a colour fundus image is by classification/segmentation through machine learning and deep learning approaches. The FROC-based performance evaluation shows that the existing methods can reach only up to 80% of sensitivity at 8 False Positive per Image on average. In recent researches, machine learning and deep learning approaches are equally competing each other to be better in detecting MA both in lesion level as well as pixel-level.
Data Analytics of Strategic Agility in Organizational Structure and Excellence: A Case of Bahrain Civil Service Sector

Organizational structure refers to how information, knowledge, and resources are distributed and coordinated across the organization and how members of an organization communicate and interact socially. Strategic agility is the ability to learn fast, develop the right products and services at the right time, and deliver them to the right place and to the right customers. Organization excellence is the capacity to maximize development opportunities, to generate conditions that encourage performance, and to effectively identify and correct performance-related problems.The objective of this study is to examine the mediating role of strategic agility between organizational structure and organizational excellence in the Bahrain Civil Sector. The sample for this study includes 393 civil servants drawn from the service sector. The 2-tailed Pearson Correlation results show that there is a correlation between organizational structure and organizational excellence. They also indicate that there is a correlation between organizational structure and strategic agility, as well as a correlation between strategic agility and organizational excellence. These correlations imply that strategic agility can mediate the relationship between organizational structure and organizational excellence. To attain the organizational excellence of the civil service sector, there is needs to encourage data analytics, quality assessment, open leadership, and idea-sharing to promote strategic agility.
Enhanced Iris Recognition Method by Generative Adversarial Network-Based Image Reconstruction

Iris recognition is one of the non-contact biometric identification methods that are hygienic and highly accurate. Iris recognition involves using iris images obtained by a near-infrared (NIR) camera or a visible light camera. A clear image of iris can be obtained when an NIR camera is used, but it requires an NIR illuminator in addition to the NIR camera. Iris recognition can be performed with a built-in camera device when a visible light camera is used, which also has the advantage of obtaining a three-channel image containing the color information. Accordingly, studies are being conducted on iris recognition by obtaining iris images from the face images taken by a high-resolution visible light camera in smartphones. However, when iris images have unconstrained conditions or are obtained without the cooperation of the subjects, the quality of iris images are reduced by noises such as optical and motion blur, off-angle view, specular reflection (SR), and other artifacts, thus ultimately deteriorating the recognition performance. Therefore, in this study, a method has been proposed for enhancing the quality of iris images by blurring the iris region and deep-learning-based deblurring. In addition, we propose the method for improving the recognition performance by integrating the recognition score in periocular regions and support vector machine (SVM). The method proposed in this study, which was experimented with noisy iris challenge evaluation-part II training database and MICHE database, exhibited an improved performance compared to the state-of-the-art methods.
Explainable Detection of Zero Day Web Attacks

The detection of malicious HTTP(S) requests is a pressing concern in cyber security, in particular given the proliferation of HTTP-based (micro-)service architectures. In addition to rule-based systems for known attacks, anomaly detection has been shown to be a promising approach for unknown (zero-day) attacks. This article extends existing work by integrating outlier explanations for individual requests into an end-to-end pipeline. These end-to-end explanations reflect the internal working of the pipeline. Empirically, we show that found explanations coincide with manually labelled explanations for identified outliers, allowing security professionals to quickly identify and understand malicious requests.
Fast Track Implementation of Energy Management System 2018 Using Sequential Approach

Energy management system ISO 50001:2011 standard was developed in 2011 to cater requirement of energy efficiency and saving culture in order to control carbon emissions and production cost. This version was adopted by many organizations and industries. The second version of energy management system was revised in 2018 that focused on leadership role, procurement, risk and opportunities, competence and managing operations. However industries and organizations are hesitant to adopt this standard due to complexity doubts. In this paper a fast track implementation plan is discussed that can be adopted by any organization to ease the implementation and reduce the efforts. This half yearly plan covers all the major aspects of standard that is to be fulfilled by the organization in order to get ISO 50001:2018 certified.
Lip Synchronization Modeling for Sinhala Speech

Lip synchronization, also known as visual speech animation, is the process of matching the speech with the lip movements. Visual speech animation has a great impact on the gaming and animation film industry, due to the reason that it provides a realistic experience to the users. Furthermore, this technology also supports better communication for deaf people.For most of the European languages, lip synchronizing models have been developed and used vastly in the entertainment industries. However, there are still no research experiments conducted towards the speech animation of the Sinhala language. Less contribution towards research development and unavailability of resources have been the issues for this.This research is focused on the problem of achieving a lip synchronization model for the Sinhala language. The project presents a study on how to map from acoustic speech to visual speech with the goal of generating perceptually natural speech animation. The experiments on developing a visemes alphabet is carried out using a static visemes approach on a video data set created by the author. The implemented lip synchronizing model was evaluated using a subjective evaluation based on six different categories. The generated model using the static visemes approach achieved 68.8% rating accuracy and a 70.8% ranking accuracy. This model performs well for individual words and short sentences rather than long sentences and sentences that are uttered with different speed levels.
Study of Effective Feature Combination for Fault Diagnosis of Bearings in Motors

Condition monitoring for bearing faults is remaining more vital in the present scenario. This study presents a simple approach for diagnosing various bearing faults of induction motor, which uses effective statistical time domain features. The statistical time domain features like mean absolute value, zero crossing, waveform length, slope sign changes, simple sign integral and Willison amplitude are considered. The performance of features is studied with 6 feature ensembles (FEs) to identify the effective FE for fault diagnosis in the induction motor. This identification is based on the classifications that are performed by using linear discriminant analysis (LDA), naïve Bayes (NB), support vector machine (SVM) and logistic model tree (LMT) classifiers. Each feature ensemble is investigated for 5 datasets derived from a bench mark database for a maximum of 48 faulty conditions. The results indicate the FE constituting six features with SVM classifier outperforms other fault diagnosis techniques.
Propositional Aspect between Apache Spark and Hadoop Map-Reduce for Stock Market Data

Big data analytics is becoming tremendously popular in every field today. Everyday lots of data are being generated and analyzed using big data analytics tools and technique. Here the technology used is apache spark and language used is Scala. So, in this paper study is being done on the behalf of research done in stock market data using apache spark technique. Here the nifty-50 data is taken to analyze the impact due to covid-19. As it is being seen that Covid-19 has affected almost everything around the globe, so the purpose is to analyze its effect on stock market. Thereafter comparison is done between the techniques used to analyze that massive volume of stock exchange data. Here the comparative analysis between Hadoop maps-reduce and apache spark on the behalf of some important parameter is being done. That concludes which technique is better for the analysis of the stock exchange data.
Attitude Tracking Control of Small-Scale Unmanned Helicopters Using Quaternion-Based Adaptive Dynamic Surface Control

In this paper, a quaternion-based adaptive dynamic surface control method is proposed for attitude tracking control for small-scale unmanned helicopters with external disturbance and uncertain dynamics. The quaternion formalism is introduced and a quaternion-based multi-input-multi-output nonlinear model is derived from the attitude dynamics of a small-scale helicopter. The low-complexity controllers are designed by the dynamic surface control method as it eliminates the problem of the explosion of items. The singularity problem is avoided by substituting Euler kinematic equations in the nonlinear model with quaternion expressions and integrating the quaternion expressions into the design process of the dynamic surface control. For improving the robustness of the control system, the radial basis function networks are applied to approximate the uncertain dynamics. The external disturbance is also compensated in the controllers’ design. This paper proves that the proposed method can guarantee the uniformly ultimate boundness of this attitude system. Simulation results are presented finally and show the effectiveness of this control approach.
Power System Inertia Estimation Based on Frequency Measurement

The magnitude of system inertia determines the rate of frequency change if there is any power deviation in the system. Thus, estimation of inertia magnitude is essential to be performed in order to obtain accurate defense scheme when the system is interrupted. In addition, the inertia magnitude can also be used to determine the magnitude of power deviation, so that defense scheme can perform load shedding more accurately. Therefore, this study will discuss about the method for estimating system inertia using Artificial Neural Network (ANN) only with frequency measurement data. The training and validation data for ANN are obtained from the simulation results of power swing equation. After that, the ANN network is used to estimate the inertia magnitude of isolated system from the real measurement data. The results show that the estimation of system inertia and power deviation only with frequency measurement data will have magnitude that close to the real measurement results. Furthermore, this method for knowing the system inertia will be used in the company to determine the proper defense scheme for different system inertia.
RFID-Based Dual-Chip Epidermal Sensing Platform for Human Skin Monitoring

Wireless epidermal devices based on Radio frequency Identification (RFID) enable a contactless and non-invasive monitoring of the human body by sampling health parameters directly on the skin. To achieve multi-parametric sensing, while preserving the intrinsic simplicity and the low cost of RFID tags, a dual-chip epidermal device is here proposed. At this purpose a polarization-diversity loop antenna is exploited so that two almost independent current modes are excited. The resulting radiation patterns are both broadside, thus enabling the simultaneous gathering of two independent dataset from the same maximum distance. A 3.5 by 3.5 cm battery-less, flexible and soft prototype provides −13 dBi embedded realized gain with read distances ranging from 0.6m to 1.5m depending on the microchip sensitivity. The electromagnetic performance of the two ports remain similar even when the tag is applied onto rather in-homogeneous body regions. With reference to body temperature monitoring, the device has been experimented in both controlled and real-life environments, demonstrating the possibility of doubling the sensing capabilities of RFID epidermal devices without affecting their size and radiation performances.
Tensor RPCA for Downward-Looking 3-D SAR Imaging with Sparse Linear Array

Downward-looking 3-D SAR with a sparse linear array alleviates the storage and transmission burden with the sacrifice of the imaging performance. Although many sparse reconstruction methods can improve the quality of 3-D image results, the vectorizing or matrixing of 3-D data makes unacceptable computation load. In this paper, a novel 3-D imaging algorithm based on tensor space is proposed. The 3-D echo data is represented as a 3-mode tensor. Then, the missing data can be recovered by tensor robust principal component analysis on the assumption that the echo tensor is essentially low rank. Finally, the resulting 3-D images can be well focused by any Fourier transform-based 3-D imaging algorithms with the recovered full-sampled data tensor. The proposed algorithm achieves not only high resolution and low-level side-lobes but also the ideal computational cost and memory consumption. The effectiveness of the proposed algorithm has been verified by several numerical simulations and multiple comparative studies.
Road Conditions and Obstacles Indication and Autonomous Braking System

Road conditions and obstacles indication system is a system that can be added to a vehicle to indicate possible events that can cause sudden deceleration on the road to avoid rear-end collisions and for improving road safety while driving. Normally every vehicle is equipped with a tail light to indicate the brakes, but this indication is dependent on the application of brakes. Due to this, they are not helpful in various cases that contain sudden deceleration. Most of the rear-end collisions occur when the driver of the vehicle is Unable to predict the behavior of the speeding of the preceding vehicle. The tail light is a manual option for this problem, but Road conditions and Obstacle Indication System provides an automatic option. This system contains simple architecture that includes the camera sensors, processing unit, and an indicating device. To prevent rear-end collisions, the indication system helps the following driver to make judgments based on Road conditions and the type of obstacle along with its intensity in front of the preceding driver. This system can use to increase the safety of road traffic by using more relevant signals for indications of obstacles. According to the invention, the indicator comprises an indication of obstacles like potholes, road bumps, and objects appearing in front of the vehicle including its intensity, here intensity is considered as distance between obstacle and vehicle. A significant purpose of the Road conditions and Obstacles Indication System (RCOIS) is to indicate that the preceding driver may slow down or stop the vehicle.
Multi-Scale Region with Local Relationship Learning for Facial Action Unit Detection

Recently, an effective method to solve the problem of complex facial expression recognition is to encode individual facial expressions through the action units (AUs) encoded by the Facial Action Coding System (FACS). A large number of methods have been proposed for AU detection, but the problem of AU detection is still very challenging due to the different sizes and shapes of facial AU. Therefore, to solve the problem of locating AU, many methods first detect the landmark of the face and then detect AU according to the location of the landmark. However, in this way, the accuracy of landmark detection greatly affects the detection result of AU, making this method less robust. In this paper, we use a multi-scale structure (MTL) to solve the problem of facial AU region distribution size varies while being independent of the facial landmark information. Besides, due to the strong correlation between facial AU regions, we propose the structure of region relationship learning (LRT) using rich local information to learn the relationship between facial local regions. The end-to-end Multi-Scale Region with Local Relationship Learning (MTLRT-Net) we proposed is a lite network with low hardware requirements, and extensive experiments on the BP4D and DISFA demonstrate that our network framework is better than the state-of-the-art methods.
SECURE AND ENHANCED DATA SHARING WITH IDENTITY BASED USER REVOCATION

Secure data storage and sharing in cloud storage suffers from several security issues and privilege problems. Data is stored securely using Message Locked Encryption in which the key the generated from the message itself. When this stored data is shared among group of users, it leads to issues related to data ownership, dynamic changes in group members and access control mechanism. To address the above problems, identity based user revocation scheme is proposed. We introduce a secure and reliable group data sharing system that stores the user attributes along with access control provided for each user. If a user is revoked from the group, the data is protected from further access by the revoked user. This proposed scheme achieves secure group data sharing with efficient backward secrecy. The efficiency analysis shows that this proposed work has achieved better performance in terms of computational cost and network bandwidth.