Miracle-dz/UniFAR-FTUs huggingface dataset:
A Progress on the Personality Measurement Model using Ontology based on Social Media Text

Personality measurement (PM) has evolved from taking a personality test or expert assessment into utilizing user generated content to measure personality. Several attempts to create a model for PM has been done by the various researcher using English as the main language. While the PM model in Bahasa Indonesia is limited. Further research is required due to the unavailability of PM model in Bahasa Indonesia, lack of corpus to gain high accuracy, and the urgency of automating the measurement process. The progress of PM Ontology model is 1) we are enriching the corpus in Bahasa Indonesia in the ontology model 2) design and prototyping the PM ontology platform using Ruby programming language 3) evaluate the PM ontology model. Our proposed platform offers a fast and affordable tool to analyze large textual data to measures human personality based on big five personality traits. Extensively, the platform is beneficial to have express analysis process and utilized the insight into various areas such as human resources, CRM, marketing or another process that requires personality measurement.
Type-2 Fuzzy Neural Network Synchronization of Teleoperation Systems with Delay and Uncertainties

This paper concerns the problem of uncertainties and time-delays in teleoperation systems. Human operators at the master side and partially unknown environments in the remote workspace introduces extreme uncertainties to the teleoperation process. Outstanding capability of type-2 fuzzy methodologies in dealing with uncertainties motivated us to apply this method in the design of a controller for teleoperation systems. Moreover, employing artificial neural networks, we propose an online learning approach that adaptively tunes the type-2 fuzzy-based control strategy. Asymptotic convergence of the learning methodology, and subsequently, the stability of the system is verified by Lyapunov-Krasovskii approach. Furthermore, experimental evaluations justify the performance of the developed control scheme.
Remaining Useful Life Estimation Based on a New Convolutional and Recurrent Neural Network

Remaining useful life (RUL) estimation is an important part of prognostic health management (PHM) technology. Traditional RUL estimation methods need to define thresholds with the help of experience, and the thresholds affect the precision of the test results. In this paper, a hybrid method of convolutional and recurrent neural network (CNN-RNN) is proposed for the RUL estimation. This method can accurately predict the RUL by using a trained hybrid network without setting a threshold. The prediction accuracy of the model is further improved by processing, clustering, and classifying the data. The proposed CNN-RNN hybrid model combines CNN and RNN, it can extract the local features and capture the degradation process. In order to show the effectiveness of the proposed approach, tests on the NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset of turbofan engine. The experimental results show that the proposed CNN-RNN hybrid model achieves better score values than the Multilayer Perceptron (MLP), Support Vector Regression (SVR) and Convolutional Neural Network (CNN) on FDOOI, FD003 and FD004 data sets.
Investigating Human Skin Using Deep Learning Enhanced Multiphoton Microscopy

Histopathological image analysis of stained tissue slides is routinely performed by a pathologist to diagnose diseases, such as cancers. Although the approach is effective, it is labor-intensive, time-consuming and risks being biased. Therefore, it would be beneficial to develop faster and more cost-effective approaches. Multiphoton microscopy can alleviate these problems by allowing label-free imaging with high contrast. When label-free multiphoton microscopy is combined with deep learning based image analysis, a wide variety of possibilities arise for the real-time characterization and diagnosis of tissues. Here, we overview our recent work on this topic focusing on automated classification of tissue images taken from human skin near the dermoepidermal junction.
A High Spectral Efficiency Receiver at 57-66 GHz Using 65-nm CMOS in LTCC Package With Polarization MIMO

This paper presents a dual-polarization antenna with high spectral efficiency for transmission application distance at 60 GHz. The dual-polarization antenna is integrated with the receivers through flip chip on the LTCC substrate at 60 GHz. The receiver front-end composes of a low noise amplifier and demodulator fabricated in 65-nm CMOS process. The conversion gain is higher than 14 dB and the noise figure is lower than 6 dB from 57 to 66 GHz. The total data rate of the proposed system’s spectral efficiency is up to 12 bit/s/Hz for wireless measurement and the EVM is below 5.5 %.
Joint Resource Allocation for Temporal Artificial Noise Assisted Multiuser Wiretap OFDM Channels with Finite-Alphabet Inputs

The security issue on the physical layer is of significant challenge yet paramount importance for 5G communications. In some previous works, transmit power allocation has already been studied for orthogonal frequency division multiplexing (OFDM) secure communication with Gaussian channel inputs for both a single user and multiple users. Facing with peak transmission power constraints, we adopt discrete channel inputs (e.g., QPSK) in practical communication system, instead of Gaussian channel inputs. Discrete channel inputs impose a more significant challenge as compared with conventional Gaussian channel inputs for the multiuser wiretap OFDM system. This paper considers the joint resource allocation for time-domain artificial noise (AN) aided multiuser wiretap orthogonal frequency-division multiplexing (OFDM) channels with discrete channel inputs, providing secure 5G communications. This security problem is formulated as nonconvex sum secrecy rate optimization by jointly optimizing the subcarrier allocation, information-bearing power and AN-bearing power. To this end, with suboptimal subcarrier allocation scheme, we develop an efficient iterative algorithm to allocate the power between the signal and the AN via the Lagrange duality method. Finally, we carry out some numerical simulation to demonstrate the performance of the proposed algorithms.
Automatic Visual Leakage Inspection by Using Thermographic Video and Image Analysis

Pipeline leakages are a critical issue in large-scale process plants, because leaks increase maintenance costs and create unsafe conditions. Therefore, detection of leakages is a crucial task for maintenance and condition monitoring. Recently, using IR cameras to detect leakages in large-scale plants was found to be a promising approach since IR cameras can capture images of leaking fluid if the fluid has a higher (or lower) temperature than its surroundings. In this paper, an approach based on thermographic data analysis using IR videos is proposed to detect leakage. In this approach, subsequent frames are subtracted to eliminate the background and reveal the motion of the leaking drops. Then, Principle Component Analysis is performed to extract features from the subtracted images. All subtracted images are individually transferred to feature vectors, which are considered as a basis for classifying the videos. Then, the K-Nearest Neighbor algorithm is used to classify the videos as normal (non-leakage) or anomalous (leakage). In order to evaluate the approach, a data set, consisting of video footage of a laboratory demonstrator plant captured by an IR camera, is considered. Leakages are simulated in the pipelines and the video data is used for image analysis. The results show that the proposed method is a promising approach to detect leakages from pipelines using IR video analysis.
Exploiting Oriented Gradient Histogram for Dorsal Vein Recognition

This paper discusses the combination of histogram of oriented gradients and dorsal vein images. Histogram of oriented gradients (HOG) is a feature descriptor used for dorsal hand vein images. Raw images of dorsal hand vein are taken from Bosphorus dataset and after pre-processing, region of interest is extracted. Interesting features are calculated by following the steps of HOG algorithm, which captures edge information inside the images. Edges are the important features containing valuable information even from the raw images. Nearest neighbor is used as a classifier. Bosphorus data is a collection of vein data from 100 subjects with lots of variations. By experimental work, our algorithm is proved to be better than all existing feature descriptors.
Color-Tunable Upconversion Luminescence in Er3+-Yb3+ Co-Doped Sodium Lutetium Fluoride Glass-Ceramics

In this work, the study of the near infrared to visible upconversion (UC) emission in transparent sodium lutetium fluoride glass-ceramics doped with ErF<inf>3</inf> and co-doped with YbF<inf>3</inf> is reported. Intense UC green and red emissions due to (<sup>2</sup>H<inf>11/2</inf>,<sup>4</sup>S<inf>3/2</inf>)<sup>→4</sup>I<inf>15/2</inf> and <sup>4</sup>F<inf>9/2</inf><sup>→4</sup>I<inf>15/2</inf> transitions respectively together with a more weak blue emission due to the <sup>2</sup>H<inf>9/2</inf><sup>→4</sup>I<inf>15/2</inf> transition have been observed under excitation at 980 nm. The influence of Yb<sup>3+</sup> co-doping and excitation power density on the upconversion emissions is investigated. The addition of Yb<sup>3+</sup> ions greatly enhances the overall UC emission intensity and can be used to tune the UC emission from green to yellow due to an increase of the red-to-green ratio.
Design of Reconfigurable Planar Inverted F Antenna for 5G Implementation

5G technology is expected to be the future of wireless communication to replace its predecessors. Some countries already did some researches to find suitable frequency bands for the 5G. Frequency around 3.5GHz and 4.8GHz are considered to implement 5G. To support the technology, a suitable antenna is needed. The antenna needs to work at 5G frequency bands and need to be as small as possible to be mounted at the rear of the mobile handset. Planar inverted F Antenna is a common type of antenna for mobile handset because of its compactness and low profile. The objective of this paper is to design a planar inverted F antenna that works at 5G frequency bands. It is designed on FR4 substrate with h = 1.6mm and dielectric constant εr = 4.3. The patch is placed 5mm above the ground plane and connected by the shorting plate at the edge of the patch and ground plane. An extended V patch is added to get the lower frequency band. The main patch and extended patch is connected by a lumped element which represents the PIN diode which will work as a switch to achieve the reconfigurable state of the antenna. After simulated, the antenna is fabricated and measured. The results show that the antenna works fine at both 3.5GHz and 4.8GHz while radiation pattern shows a unidirectional pattern that mainly aimed at the top of the patch. The antenna design, return loss, radiation pattern, and gain are presented in this paper.
Cognitive Risk Control for Mitigating Cyber-Attack in Smart Grid

In this paper, we extend our previous research on uniting the Cognitive Dynamic Systems (CDS) and the Smart Grid (SG) by introducing Cognitive Risk Control (CRC). The CDS is a structured physical model and research tool inspired by certain features of the brain. The CRC is an advanced feature of the CDS that embodies the concept of predictive adaptation allowing it to bring risk under control in situations involving unexpected or abnormal uncertainty such as a cyber-attack. The False Data Injection (FDI) attack is a special class of cyber-attack targeting the SG that is able to bypass the traditional bad data detection techniques. Here we will demonstrate how the entropic state, which is the objective function of the CDS, is able to detect and bring FDI attacks under control under the action of CRC. Through Task-Switch control, the CDS is able to switch on a new executive with different set of actions that affects the system configuration to bring the risk under control during an attack. With the CDS acting as the supervisor of the SG, simulations are carried out on a 4 bus-system and IEEE 14-bus system to demonstrate the capability of CRC when faced with FDI attacks. The results show that this system has great potential for future SG systems.
Towards Data-Driven Approximate Circuit Design

Approximate computing provides a paradigm shift to reduce the hardware cost or improve the speed of logic circuits by relaxing the quality of computation. This paper presents a novel approximate logic design methodology that integrates input data distribution to optimize the performance for specific applications. Furthermore, the proposed approach also provides sufficient scalability as only the circuit netlist and input data are needed in designing the approximate circuit. Our experimental results show that the data-driven designs generated by the proposed method achieve significantly better accuracy than the data-independent ones, while maintaining minimal hardware overheads. For example, in the design of an approximate finite impulse response (FIR) filter, our method reduces nearly 40% of the error metrics while still achieving a 29% area reduction, compared to the original exact circuit, which further validating the robustness of the proposed method beyond arithmetic elements.
Synthesis of Gallium Oxide from the Elements at RF Plasma Discharge in the Argon-Oxygen Mixture

Oxide semiconductor research is presently one of the most challenging topics [1] . We are developed a novel PECVD-based method for growth of Ga 2 O 3 epitaxial layers from elemental Ga precursor, avoiding complicated metal-organic reactions typically used in CVD, to reach the best purity and eventually the largest electron mobility of the material [2] , [3] . The envisaged method should be cost-effective and industry-relevant. The fundamental properties of doped with different species Ga 2 O 3 are not completely understood yet. This topic is therefore an interesting scientific challenge.
Collaborative Learning Strategies for Online Knowledge Sharing Tool Within Organizations: A Systematic Literature Review

In spite of there are some methods for applying collaborative learning (CL) to boost participation and motivation to exchange and share knowledge, there has been no clear strategy that briefly described to support sharing processes in collaborative-based knowledge sharing tools. Thus, we conduct a systematic literature review to identify recent studies on collaborative-based systems from 2010 to 2018 by using PRISMA method. This study is objected to point out applied-specific strategic approaches in the CL environment for building an online KS tool in a concept of computer based systems. By doing a comprehensive search through Science Direct, Springer Link. And IEEE Xplore using keywords; ‘knowledge sharing tools’, ‘collaborative learning strategy,’ ‘online knowledge sharing,’ ‘collaboration learning,’ the report results from an analysis of some strategic approaches in CL environment for online knowledge sharing tools. Findings of this study offer an understanding of features, user collaboration-based technique, and technology based approach as a strategy for CL to create a knowledge sharing tool within an organization.
Poor Data Throughput Root Cause Analysis in Mobile Networks using Deep Neural Network

As the mobile data usage is increasing with the complexity of new services, ensuring proper quality of experience (QoE) becomes challenging. While traditional machine learning techniques have been intensively applied in mobile networks, new training models are also mushrooming and are to some extent explored to handle large amount of data and complexity of new services in mobile data networks. Lately, deep learning algorithms have gained popularity since they scale efficiently with large data input and make use of the computing power to train the models. This paper proposes a deep learning model based on a deep neural network (DNN) architecture to train and evaluate the root cause analysis (RCA) for poor throughput in mobile networks. The proposed approach considered both the radio and the core network performance indicators of a mobile data network as inputs to ensure end-to-end correlation. Furthermore, the local interpretable model-agnostic explanations (LIME) method was used to provide features importance both for the global system and for individual subscribers. The prediction accuracy on the unseen data was 98.9% with an area under the receiver operating characteristic (ROC) curve of 99.87% and an F1_score of 99.23%
Enhanced Interferometric Weak Value Amplification With Multiple Reflection

A multiple-reflection scheme was proposed to improve the sensitivity and resolution of interferometric weak measurement by introducing a cuboid prism device in the optical path. Increasing the number of reflection in a total internal reflection weak value amplification (WVA) system resulted in a 3.8 fold improvement in the sensitivity of the central wavelength shift to refractive index unit (RIU) (from 1788 nm/RIU to 6821 nm/RIU). Using the multiple reflection device, a RIU resolution of <inline-formula> <tex-math notation="LaTeX">$2.7\times 10$ </tex-math></inline-formula>-7 RIU was obtained. The real-time deposition process of dopamine is monitored.
An Approximate Multiply-Accumulate Unit with Low Power and Reduced Area

Approximate computing benefits applications that are, to some extent, error tolerant with regard to accuracy by trading power for accuracy. In this paper, a low-power approximate multiply-accumulate (MAC) unit with reduced area is proposed. In the proposed MAC unit, a multiplier and an adder are merged in an approximate multiplier by distributing all accumulated sums to partial product rows. Experimental results demonstrate that, compared to a conventional MAC unit, the unsigned design of the proposed unit reduces power consumption and circuit area by 42.6% and 46.1%, respectively, without impacting accuracy significantly. While most approximate circuits do not handle signed numbers represented by two's complement effectively, evaluations using handwritten digit recognition on LeNet-5 indicate that the signed design of the proposed unit is sufficient for practical use.
Modeling Socially Normative Navigation Behaviors from Demonstrations with Inverse Reinforcement Learning

Navigation in an efficient and socially normative manner is essential for the robot to operate in human populated environments. Traditional methods treat the pedestrians as dynamic obstacles and design a manual cost function for collision avoidance, but neglect social norms in navigation and do not generalize well to new environments. In this paper, we propose a mixture model to capture the human navigation behaviors in terms of the features of the continuous trajectories and discrete navigation decisions, such as passing on the left or right. The lower level of the model aims to generate socially normative trajectories. To this end, we extend inverse reinforcement learning (IRL) framework to a motion planner called Timed Elastic Band to learn from demonstrations. The upper level comprises a discrete distribution over the homotopy classes of the trajectories. IRL algorithm is employed to find the parameters of distribution that match demonstrations best. Experiments demonstrate that our learning algorithm has the capacity to recover the human navigation behaviors that respect social norms, which makes our approach outperform state-of-the-art methods in social navigation scenarios.
Effective Defect Features Extraction for Laser Ultrasonic Signal Processing by Using Time–Frequency Analysis

The time-frequency analysis (TFA) by wavelet transform is adopted for the laser ultrasonic signal processing, and the effective features extraction of the material defect is obtained. The TFA is adopted here to analyze the laser-generated surface acoustic wave (SAW) signal which contains the defect features, the echo wave features are extracted significantly, especially under the condition of low signal-to-noise rate (SNR). The simulation model by using finite element method (FEM) is set up in an aluminum plate with different surface defect depths in detail, and the defect depths prediction with TFA is also considered. It shows that, without extra denoising process, the echo SAW is extracted significantly in case of defect depths ranging from 0.1mm to 0.9mm at SNR of −3dB by TFA. The TFA for processing the laser ultrasonic signal provides a promising way to get the defect information, with the accuracy increased by 7.9dB in this work, which is extremely meaningful for the ultrasonic signal processing and material evaluation.
Model predictive control of interleaved DC-DC boost converter with current compensation

A cascade control scheme is constructed combing a model predictive control (MPC) scheme as a current-mode controller with an Extended Kalman observer to control an interleaved DC-DC boost converter under load and input voltage step change conditions. A state-space average model is established and linearized near the stable point for predicting the future dynamic behavior. The process of the solving optimization problems with constraints of cost function is converted to the quadratic programming problem. An Extended Kalman observer is designed to obtain the load resistance variation and the integrated voltage error which is used to dynamically adjust the current reference. The algorithm has been tested in simulation. The results show that compared with PI controller a good transient and steady-state responses in the presence of load variations and input voltage disturbances.
Nutrition education resources used by renal dietitians for the education of the outpatient dialysis population in Michigan

iv Chapter 1: Introduction 1 Purpose of Study 4 Study Objectives and Justification 4 Chapter 2: Review of Literature 6 Nutrition and Kidney Disease 6 Clinical Guidelines and Current Practice 7 Causes and Metabolic Consequences of Kidney Failure 10 Nutrition Assessment 12 Education and Adult Learning in the Clinical Setting 14 Factors Regarding Compliance 15 Nutritional Outcome Markers 17 Chapter 3: Methodology 19 Subjects 19 Research Design 19 Survey 20 Statistical Analysis 20 Chapter 4: Results 22
An Effective Recursive Technique for Multi-Class Classification and Regression for Imbalanced Data

In machine learning, classification and regression are two of the most noteworthy key topics since they occur extensively in numerous real-world applications. However, real life data is hardly ever found balanced, rather skewed data is the common occurrence. This poses some serious challenges to the standard techniques of classification and regression. The performance and effectiveness of these techniques are substantially affected by overfitting, creating a bias towards the majority class. In recent years, quite a few number of methods have been introduced for classification of imbalanced data. But most of them are designed for binary classes and it is difficult or inefficient to extend them for multiple classes. Moreover, data imbalance problem occurs frequently in regression analysis too, with only a handful of algorithms robust enough to tackle to this problem. In this paper, we propose an effective recursive method for multi-class classification with imbalanced data. Our proposed algorithm partitions and balances the data, and is applied recursively coupled with ensemble techniques. Furthermore, we also extend our proposed method to solve the data imbalance problem in regression analysis. Experimental results demonstrate that the proposed recursive technique is effective and improves the performance when compared to existing methods for classification and regression with imbalanced distribution.