Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Estimation of Human Weight From Body Silhouette Using Multiple Linear Regression

Estimating weight based on 2D image is advantageous especially for contactless and rapid measurement. Several researches used additional thermal camera or Kinect camera, required subjects to do front and side pose and manually extract body measures. This research propose an algorithm to estimate body weight automatically using 2D visual image where subject only do front pose. This research studied 4 features of body measures which are: (F1) height, and width of (F2) shoulder, (F3) abdomen/waist plus arm, (F4) feet. Each feature was simply subtracted based on body proportion where normal body has 8 equal segments. Shoulder is in 2nd segment, abdomen/waist is in 4th segment and feet is in the last segment. Multiple Linear Regression is used to determine weight estimation formula of all combination of 4 features, 15 in total. The highest significance R2 (0.80) and RMSE 2.68 Kg is given when using all 4 features in the estimation formula.
Feature Expansion for Sentiment Analysis in Twitter

The community's need for social media is increasing, since the media can be used to express their opinion, especially the Twitter. Sentiment analysis can be used to understand public opinion a topic where the accuracy can be measured and improved by several methods. In this paper, we introduce a hybrid method that combines: (a) basic features and feature expansion based on Term Frequency–Inverse Document Frequency (TF-IDF) and (b) basic features and feature expansion based on tweet-based features. We train three most common classifiers for this field, i.e., Support Vector Machine (SVM), Logistic Regression (Logit), and Naïve Bayes (NB). From those two feature expansions, we do notice a significant increase in feature expansion with tweet-based features rather than based on TF-IDF, where the highest accuracy of 98.81% is achieved in Logistic Regression Classifier.
An Effective Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

The performances of a model predictive control algorithm largely depend on the knowledge of the system model. A model-free predictive control approach skips all the effects of parameters variations or mismatches, as well as of model nonlinearity and uncertainties. A finite-set model-free current predictive control is proposed in this paper. The current variations predictions induced by the eight base inverter voltage vectors are estimated by means of the previous measurements stored into lookup tables. To keep the current variations information up to date, the three current measurements due to the three most recent feeding voltages are combined together to reconstruct all the others. The reconstruction is performed by taking advantage of the relationships between the three different base voltage vectors involved in the process. In particular, 210 possible combinations of three-state voltage vectors can be found, but they can be gathered together in six different groups. A light and computationally fast algorithm for the group identification is proposed in this paper. Finally, the current reconstruction for the prediction of future steps is thoroughly analyzed. A compensation of the motor rotation effect on the input voltages is proposed, too. The control scheme is evaluated by means of both simulation and experimental evidences on two different synchronous reluctance motors.
A Textural Deep Neural Network Combined With Handcrafted Features for Mechanical Failure Classification

Periodic fractographic analysis of fracture surfaces helps improve performance of mechanical pieces and avoids economical and security problems in many industries, such as the automotive industry. Classifying a fracture into a failure mode is necessary to determine the causes that generated the fracture in the first place. Experts in fracture classification of metallic materials usually use texture and surface marks to determine the type of fracture. Deep Learning is a machine learning technique that learns features directly from input data and has achieved outstanding results in object classification. However, when it comes to texture classification, the results of deep learning are not as good as in other classification tasks. This paper proposes to improve the performance of deep learning for texture analysis in the context of fractographic classification by extracting handcrafted features (Haralick, fractal dimension and local binary patterns) from the output of the convolutional layers of the VGG-19 model. Four datasets are used in this paper. Two common textural databases, KTH-TIPS and KTH-TIPS2-B, are used as benchmark for the texture recognition problem, and two datasets of fractures are used for evaluation of the proposed methods. One of the datasets for evaluation corresponds to real-scale images of ductile, brittle and fatigue fractures; and the other dataset corresponds to images acquired with a Scanning Electron Microscopy (SEM) of ductile, brittle, fatigue and corrosion fatigue fractures. The best performance for the KTH-TIPS and KTH-TIPS2-B datasets was attained with local binary patterns (LBP) extracted from the first feature map of the third convolutional layer with an F1-score of 1.0 and from the second feature map of the fifth convolutional layer group with an F1-score of 0.77, respectively. In the case of the fracture database of real-scale images, the best performance was attained with fractal dimension features extracted from the feature maps of the first convolutional layer with an F1-score of 0.72, and with LBP extracted from the first feature maps of the fourth convolutional layer for the SEM images.
Success Factors of Human Resource Information System Implementation: A Case of Ministry of State-owned Enterprise

This study aims to analyze factors influencing the successful implementation of Human Resources Information System (HRIS) at the Ministry of State-Owned Enterprises (MSOE). There are 22 factors influence the success of HRIS implementation that are categorized into 4 dimensions, namely human, organization, technology and environment based on DeLone & McLean information system success model, HOT (human-organization-technology) fit model and TOE (technology-organization-environment). This research use quantitative method approach and data collection is gathered using questionnaire. Method of data analysis using Entropy method to calculate the weight of success factors and dimensions, and rank factors and dimensions. There are 99 respondents of the HRIS users provided data through the questionnaires distributed to them. This study shows that the dimensions influencing the success of HRIS implementation at the MSOE in priority order are technology, human, environment and organization. In addition, there are 5 factors selected with the highest weights namely information quality, service quality, top management support, system quality and social influence.
Distributed Alternating Direction Method of Multipliers for Linearly Constrained Optimization Over a Network

In this letter we address the distributed optimization problem for a network of agents, which commonly occurs in several control engineering applications. Differently from the related literature, where only consensus constraints are typically addressed, we consider a challenging distributed optimization set-up where agents rely on local communication and computation to optimize a sum of local objective functions, each depending on individual variables subject to local constraints, while satisfying linear coupling constraints. Thanks to the distributed scheme, the resolution of the optimization problem turns into designing an iterative control procedure that steers the strategies of agents-whose dynamics is decoupled-not only to be convergent to the optimal value but also to satisfy the coupling constraints. Based on duality and consensus theory, we develop a proximal Jacobian alternating direction method of multipliers (ADMM) for solving such a kind of linearly constrained convex optimization problems over a network. Using the monotone operator and fixed point mapping, we analyze the optimality of the proposed algorithm and establish its ${o}$ (1/ ${t}$ ) convergence rate. Finally, through numerical simulations we show that the proposed algorithm offers higher computational performances than recent distributed ADMM variants.
IDEnet : Inception-Based Deep Convolutional Neural Network for Crowd Counting Estimation

In crowd counting task, our goals are to estimate density map and count of people from the given crowd image. From our analysis, there are two major problems that need to be solved in the crowd counting task, which are scale invariant problem and inhomogeneous density problem. Many methods have been developed to tackle these problems by designing a dense aware model, scale adaptive model, etc. Our approach is derived from scale invariant problem and inhomogeneous density problem and we propose a dense aware inception based neural network in order to tackle both problems. We introduce our novel inception based crowd counting model called Inception Dense Estimator network (IDEnet). Our IDEnet is divided into 2 modules, which are Inception Dense Block (IDB) and Dense Evaluator Unit (DEU). Some variations of IDEnet are evaluated and analysed in order to find out the best model. We evaluate our best model on UCF50 and ShanghaiTech dataset. Our IDEnet outperforms the current state-of-the-art method in ShanghaiTech part B dataset. We conclude our work with 6 key conclusions based on our experiments and error analysis.
Graphene synaptic transistor based on Ion-Gel dielectric

Artificial synapses is the basis for realizing the artificial neural network computing architecture. In recent years, the hardware implementation of biological synapse has been achieved based on CMOS circuits, memristors, carbon-based transistors and complex oxide transistors. Here, a synaptic device based on graphene transistor coupled with an Ion-Gel based dielectric layer is demonstrated. Inspired by the electrolyte function of the electric double layer capacitor (EDLC), the Ion-Gel can be used as the dielectric layer of the field effect transistor (FET) to implement a memory function. The effect of ions in the Ion-Gel dielectric layer on the graphene channel was explored. And the “learning” and“forgetting” processes of inhibitory synapse in organisms were successfully mimicked. The feasibility of realizing both behaviors of excitatory and inhibitory in a single device was explored. This work would offer a broad new vista for the application of artificial synapses in the flexible electronics.
Design of Processing-“Inside”-Memory Optimized for DRAM Behaviors

The computing domain of today’s computer systems is moving very fast from arithmetic to data processing as data volumes grow exponentially. As a result, processing-in-memory (PIM) studies have been actively conducted to support the data processing in or near memory devices to address the limited bandwidth and high power consumption due to data movement between CPU/GPU and memory. However, most PIM studies so far have been conducted in a way that the processing units are designed <italic>only</italic> as an accelerator on the base die of 3D-stacked DRAM, not involved inside memory while not servicing the standard DRAM requests during the PIM execution. Therefore, in this paper, we show how to design and operate the PIM computing units inside DRAM by effectively coordinating with standard DRAM operations while achieving the full computing performance and minimizing the implementation cost. To make our goals, we extend a standard DRAM state diagram to depict the PIM behaviors in the same way as standard DRAM commands are scheduled and operated on the DRAM devices and exploit several levels of parallelism to overlap memory and computing operations. Also, we present how the entire architecture layers from applications to operating systems, memory controllers, and PIM devices should work together for the effective execution by applying our approaches to our experiment platform. In our HBM2-based experimental platform to include 16-cycle MAC (Multiply-and-Add) units and 8-cycle reducers for a matrix-vector multiplication, we achieved 406% and 35.2% faster performance by the all-bank and the per-bank schedulings, respectively, at (<inline-formula> <tex-math notation="LaTeX">$1024\times1024$ </tex-math></inline-formula>) <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$1024\times1$ </tex-math></inline-formula>) 8-bit integer matrix-vector multiplication than the execution of only its operand burst reads assuming the external full DRAM bandwidth. It should be noted that the performance of the PIM on a base die of a 3D-stacked memory cannot be better than that provided by the full bandwidth in any case.
Appropriate Sets of Criteria for Innovation Adoption of IS Security in Organizations

Determining sets of criteria and alternatives becoming main priorities is essential to guarantee the success of innovation adoption of Information System (IS) security. The goal of this research was to select and determine important entities as representation of each criterion for managers in making decisions of innovation adoption of IS security. This research applied Technology-Organization-Environment (TOE) Framework, and Human-Organization-Technology-Fit (HOT-Fit) Model to map relative importance variables of criteria and alternatives. AHP Approach was applied for computation simulation to determine priorities of criteria and alternatives. Results show that a principal criterion is manpower of organizations. The eigen factor score is 4.398. Moreover, alternatives covering complexity, financial resources, intensity of competition, and CIO innovativeness have these respective eigen factors scores: 4.326, 9.307, 4.376, and 4.545.
Hybrid Overloading and Stochastic Analysis for Redundant Scheduling in Real-time Multiprocessor Systems*

In multiprocessor systems, redundant scheduling is a technique that trades processing power for increased reliability. One approach, called primary-backup task scheduling, is often used in real-time multiprocessor systems to ensure that deadlines are met in spite of faults. Briefly, it consists in scheduling a secondary task conditionally, in such a way that the secondary task actually gets executed only if the primary task (or the processor executing it) fails to terminate properly. Doing so avoids wasting CPU resources in the failure-free case, but primary and secondary tasks must then compete for resources in case of failure. To overcome this, overloading strategies, such as primary and backup overloading (PB) and backup-backup overloading (BB), aim at improving schedulability while retaining a certain level of reliability. In this paper, we propose a hybrid overloading technique based on extended PB overloading, which combines advantages of both PB and BB overloading. The three overloading strategies are then compared through a stochastic analysis, and by simulating them under diverse system conditions. The analysis shows that hybrid overloading provides an excellent tradeoff between schedulability and reliability.
A Novel Arrhythmia Classification Method Based On Convolutional Neural Networks Interpretation of Electrocardiogram Images

A new method for classifying cardiac abnormalities is here proposed based on the electrocardiogram (ECG). The ECG may manifest abnormal heart patterns, which are generally known as arrhythmias. MIT-BIH arrhythmia database and AAMI standards are used for machine learning purposes considering the patient-oriented scheme. Heartbeat time intervals and morphological features processed by a 2-D time-frequency wavelet transform of ECG signals are combined into an image, which carries relevant information from each heartbeat. These dataset images are used as input to train and evaluate the classifier, which is essentially a 6 layers convolutional neural network (CNN), resulting in powerful artifact discrimination. The training set is artificially augmented to reduce the imbalance of the five heartbeat classes, achieving better results. A significant achieved overall accuracy of 95.3% of the proposed method, compared to some of the most relevant published methods, permits to expect effective results when applied to real patients.
Template Matching Based on Geometric Invariance in Deep Neural Network

Machine learning models, especially deep neural networks (DNNs), have achieved state of the art in computer vision and speech recognition. However, with wide applications of DNNs, some problems have appeared, such as lack of interpretability and vulnerable to adversarial examples. Whether the judgment of the model is consistent with that of human is a key to the wide application and development of neural networks. In this paper, we propose a novel and interpretable method to enable the model to make the same judgment as humans in the adversarial examples, which is based on the geometric invariance between images of the same category. Template matching is combined with convolution neural network during the training and testing stage. Moreover, we manage to give a theorical proof. The geometric invariance features got from the template matching are fused with the features extracted by the convolutional layers. The experimental results demonstrate the temp_model (network added the template matching) has a higher test accuracy both on benchmark sequences and adversarial examples, and we use a visual method to explain the reason why adding template can make the network perform better. The generality and convergence of the network improve without increasing the model size and training time after adding the template as common sense.
Field Techniques in Remote Sensing: Learning by Doing

Abstract The paper summarizes one professor's observations and resulting approach to teaching a semester‐long course on the subject of field techniques as they relate to remote‐sensing campaigns. The text describes how a “learning‐by‐doing strategy” is applied to such a course. More specifically, the paper describes how the instructor requires students to design, construct, and actually implement “hardware solutions” to problems involving collection of spectral (and ancillary) data in the field. Pupils must test their developed methodologies and “technological creations” with regard to a focused research problem undertaken in a difficult field setting during a week‐long outdoor experience. The students are expected to evaluate, and possibly modify, their methods and procedures “on‐the‐fly. ” Individual group research problems, methods, and hardware solutions are briefly described, and selected student evaluations of the experience are provided.
Aspect-Based Sentiment Analysis Approach with CNN

Lots of research has been done on the domain of Sentiment Analysis, for example, research that conducted by Bing Liu's (2012) [1]. Other research conducted in a SemEval competition, the domain of sentiment analysis research has been developed further up to the aspect or commonly called Aspect Based Sentiment Analysis (ABSA) [2]. The domain problem of Aspect Based Sentiment Analysis (ABSA) from SemEval is quite diverse, all of those problems arise mostly from the real data provided. Some existing problems include Implicit, Multi-label, Out Of Vocabulary (OOV), Expression extraction, and the detection of aspects and polarities. This research only focuses on classification aspect and classification of sentiment. This study uses an existing method of Convolution Neural Network (CNN) method, which was introduced again by Alex K. The study by Alex K reduces the error rate by 15%, compared in the previous year the decrease was only 5%. This research would like to propose CNN methods that have been optimized, and use Threshold (CNN-T) to select the best data in training data. This method can produce more than one aspect using one data test. The average result of this experiment using CNN-T got better F-Measure compared to CNN and 3 classic Machine Learning method, i.e. SVM, Naive Bayes, and KNN. The overall F1 score of CNN-T is 0.71, which is greater than the other comparable methods.
Analysis and Design of Decision Support System Dashboard for Predicting Student Graduation Time

Information Systems is one of the existing study program at Telkom University that has produced many graduates since it was established in 2008. However, not all graduates produced successfully completed the study period during the four years of normal study. The percentage of graduates on time has some decline between the target and the achievement of the study program. From academic year 2014/2015 to 2016/2017 decrease annually about 1% every year, which is it becomes problems for the credibility and existence of study program and also for academic planners who may have an impact on accreditation assessment process of the study program when it is audited. One of the efforts that can be done by the study program to increase the students on time graduation rate is by making decision support system dashboard that giving early warning to the lecturer or the head of the study program if there are students who are predicted not to graduate on time. By using the C4.5 algorithm to perform the data analysis by looking at the causes of student’s graduation time and pureshare methodology to perform dashboard development method. The result of this study is a prototype of decision support system dashboard, because there are lack of analysis in decision making and the dashboard only showing information and temporary prediction. The data model that used on this research is labeling data that has been processed using C4.5 algorithm and data that has been through data cleansing process using Pentaho Data Integration. This prototype is expected to be used as a reference base to support academic planners in order to make this application run with real time data.
Exploiting Social Review-Enhanced Convolutional Matrix Factorization for Social Recommendation

To deal with the inherent data sparsity and cold-start problem, many recommender systems try to exploit the textual information for improving prediction accuracy. Due to the significant progress of deep learning techniques, neural network-based content modeling methods have been investigated in recent studies. However, most of these existing methods often assume that users are independent and identically distributed (i.i.d), and the social influence is not considered. However, in the real world, we always turn to our friends for recommendations, and the closer the friendship between the friends, the greater the social impact is. These methods only exploit the reviews from an item perspective and rarely consider the user’s reviews to capture the user’s interests, but in reality, users often express their preferences by posting different reviews to different items. Based on the above-mentioned observations, we propose a social-enhanced content-aware recommendation method by fusing the social network, item’s reviews, and user’s reviews in a unified framework. Specifically, to better model the item’s reviews, we first introduce the convolutional matrix factorization (ConvMF) as our basic recommendation framework, which utilizes convolutional neural network (CNN) to capture the deeper understanding of the content context. Then, to consider the user’s social influence, we integrate the user’s social network into ConvMF by a shared user latent factor, which can bridge the user’s social interests and user’s general preferences in the same latent space. To model the user’s reviews, similar to ConvMF, we exploit another CNN to learn a deeper understanding of the user’s posted contents. Finally, we conduct experiments on the real-world dataset Yelp to demonstrate the effectiveness of our method. The experimental results indicate that our proposed method can effectively model the social and the review information and outperforms other related methods in terms of root mean squared error (RMSE) and mean absolute error (MAE).