Miracle-dz/UniFAR-FTUs huggingface dataset:
A method to diagnose changes in Thevenin parameters using the average inductor current of a modular power converter

More than a billion people still lack access to clean and safe electricity. One of the main obstacles to Energy Access by these populations is the lack of a low-cost and ultra-flexible power electronics technology. This technology should not only evolve with the needs of the user but also adapt its operation to changes in the parameters of the sources or loads it is connected to.In this work a method is proposed to use a modular power electronics module to diagnose the parameters of its input and output. The power converter used in this work is a synchronous buck for its current bi-directionality and voltage flexibility. The model of the synchronous buck is revisited and an equation describing its average inductor current calculated.From the average inductor current, a set of three diagnosis parameters are proposed. These parameters allows the diagnosis of variations in the voltage and resistance of the Thevenin model of either the low-voltage or high-voltage side of the power module. A theoretical study of these parameters is proposed and validated through measurements for one of the three diagnosis parameters. Future work will focus on validating these findings in experimental conditions and defining the impact of the converter parameters in the diagnosis process.
Distributed Wireless Power Transfer Based on Bayesian-Optimized Control

Bayesian optimization is applied to antenna subset selection and transmit phase adjustment in distributed microwave power transfer. Both of the optimization controls are iteratively carried out based on energy feedback to maximize the received power in the low-complexity hardware configuration of massive antenna deployment. In addition, the computational complexity is reduced by exploiting the cyclic property of received power with respect to the transmit phase rotation. Our simulation results demonstrate that 95 percent of the maximum achievable received signal power was attained in 97 percent of the total trials by using 34 samples in both controls.
Prevalence of and Risk Factors for Work-Related Musculoskeletal Disorders in Informal Occupational Groups in Cha-Om Sub-District, Khangkoi District, Saraburi Province, Thailand

The research was a cross-sectional study with the objectives of finding the prevalence of and the risk factors for work-related musculoskeletal disorders (WMSDs) in informal occupational groups with majority (over 90%) of bamboo-thread production group at Cha-om Sub-district, Khangkoi District, Saraburi Province, Thailand. The work process of the bamboo-thread production group includes raw material acquisition, preparation, production, drying-up of the finished products, packaging, and product uploading onto the truck, most of which tend to cause the micro-trauma, especially the work postures while producing. Through the funding by the Thai Health and Bann Nee Mee Suk Project (Happy Home Project) initiated by Chulalongkorn University, data collection was conducted by a purposive sampling of 52 volunteered-villagers who mostly produced bamboo-thread in 2011 with informed consent. The standardized NORDIC Musculoskeletal Questionnaire (NMQ) was employed for face-to-face interview. SPSS version 17 was used to analyze the measurement on socio-demographic data, exposures (current health status and work postures), including pain regions. Descriptive statistics and Chi-square test/Fisher’s Exact test with p-value of <0.05 considered statistically significant were employed. The results revealed that 90.4% (n = 47) indicated their WMSDs annual prevalence (12-month before the interview) for the top three body areas: low back (72.3%, n = 34); shoulders (53.2%, n =25), and knees (48.9%, n =23). The past 7 days of WMSDs complaints were made by 45 respondents (86.5%) with the same order of low back (55.5%, n =25); shoulders (42.2%, n =19), and knees (33.3%, n = 15), respectively. Main factor related to WMSDs past 7-day prevalence was the respondents’ educational background (p-value=0.041). However, no relation was found for annual WMSDs prevalence. As ergonomics intervention can reduce the risk of musculoskeletal injuries, based on the current WMSDs prevalence data, tailor-made exercise regimen intervention program was recommended.
Continuous Analysis of Assignment Evaluation Results from Automated Testing Platform in Iterative-Style Programming Courses

This paper presents a proposal of usage continuous analysis of assignment evaluation results in a real programming course with 585 students to improve syllabus and teacher adaptation. The use of Elastic Stack has proven to be a convenient solution in terms of both efficiency and cost. At the same time the article answers important questions about the attitude of students in such courses. It was found that the structure and compilation issues persist during the whole course, the failure of the first assignment demotivates the student in solving following tasks and the number of students with the highest grades decreases. An important result is that students have only used 10% of the evaluation possibilities on average and work mostly on weekends and in the evenings. At the same time an increased number of above-average results was observed while using the same assignments as in previous course runs.
Modeling Language Design and Mapping Rules for REST Interface

In the process of software development, the codes for accessing the database have great similarity, and they involve multiple layers, such as the persistent layer, business logic layer, and controller layer. Developers always have to spend a lot of time to create and maintain those codes. In this paper, a modeling language and the mapping rules for REST interfaces are designed, including basic data access services, associative queries, fuzzy queries, and batch operations. Developers can design REST interfaces using the proposed language, and then the design can be parsed according to the mapping rules. The parsing result, together with the UML’s parsing result of database modeling, will be used as input to generate YAML files that guide the automatic codes generation for the SSM framework. Finally, the REST interface document and its codes will be generated. Developers can develop REST and SSM based applications quickly and efficiently with the help of our work.
ATCS: Auto-Tuning Configurations of Big Data Frameworks Based on Generative Adversarial Nets

Big data processing frameworks (e.g., Spark, Storm) have been extensively used for massive data processing in the industry. To improve the performance and robustness of these frameworks, developers provide users with highly-configurable parameters. Due to the high-dimensional parameter space and complicated interactions of parameters, manual tuning of parameters is time-consuming and ineffective. Building performance-predicting models for big data frameworks is challenging for several reasons: (1) the significant time required to collect training data and (2) the poor accuracy of the prediction model when training data are limited. To meet this challenge, we proposes an <italic>auto-tuning configuration parameters system</italic> (ATCS), a new auto-tuning approach based on <italic>Generative Adversarial Nets</italic> (GAN). ATCS can build a performance prediction model with less training data and without sacrificing model accuracy. Moreover, an optimized <italic>Genetic Algorithm</italic> (GA) is used in ATCS to explore the parameter space for optimum solutions. To prove the effectiveness of ATCS, we select five frequently-used workloads in Spark, each of which runs on five different sized data sets. The results demonstrate that ATCS improves the performance of five frequently-used Spark workloads compared to the default configurations. We achieved a performance increase of <inline-formula> <tex-math notation="LaTeX">$3.5\times $ </tex-math></inline-formula> on average, with a maximum of <inline-formula> <tex-math notation="LaTeX">$6.9\times $ </tex-math></inline-formula>. To obtain similar model accuracy, experiment results also demonstrate that the quantity of ATCS training data is only 6% of <italic>Deep Neural Network</italic> (DNN) data, 13% of <italic>Support Vector Machine</italic> (SVM) data, 18% of <italic>Decision Tree</italic> (DT) data. Moreover, compared to other machine learning models, the average performance increase of ATCS is <inline-formula> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> that of DNN, <inline-formula> <tex-math notation="LaTeX">$1.6\times $ </tex-math></inline-formula> that of SVM, <inline-formula> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> that of DT on the five typical Spark programs.
Application of Deep Reinforcement Learning on Automated Stock Trading

How to make right decisions in stock trading is a vital and challenging task for investors. Since deep reinforcement learning (DRL) has outperformed human beings in many fields such as playing Atari Games, can a DRL agent automatically make trading decisions and achieve long-term stable profits? In this paper, we try to solve this challenge by applying Deep Q-network (DQN) and Deep Recurrent Q-network (DRQN) in stock trading and try to build an end-to-end daily stock trading system which can decide to buy or to sell automatically at each trading day. The S…P500 ETF is selected as our trading asset and its daily trading data are used as the state of the trading environment. The agent’s performance is evaluated by comparing with benchmarks of Buy and Hold (BH) and Random action-selected DQN trader. Experiment results show that our DQN trader outperforms the two benchmarks and DRQN trader is even better than DQN trader mainly because the recurrence framework can discover and exploit profitable patterns hidden in time-related sequence.
Exploring Context’s Diversity to Improve Neural Language Model

The neural language models (NLMs), such as long short term memery networks (LSTMs), have achieved great success over the years. However the NLMs usually only minimize a loss between the prediction results and the target words. In fact, the context has natural diversity, i.e. there are few words that could occur more than once in a certain length of word sequence. We report the natural diversity as context’s diversity in this paper. The context’s diversity, in our model, means there is a high probability that the target words predicted by any two contexts are different given a fixed input sequence. Namely the softmax results of any two contexts should be diverse. Based on this observation, we propose a new cross-entropy loss function which is used to calculate the cross-entropy loss of the softmax outputs for any two different given contexts. Adding the new cross-entropy loss, our approach could explicitly consider the context’s diversity, therefore improving the model’s sensitivity of prediction for every context. Based on two typical LSTM models, one is regularized by dropout while the other is not, the results of our experiment show its effectiveness on the benchmark dataset.
A Quad-Band 4 × 4 MIMO Antenna Array for Millimeter-Wave 5G Communications

In this research, a novel design of quad-band (28/38/60/73 GHz) multiple-input multiple-output (MIMO) antenna for fifth-generation (5G) communications. The proposed antenna has the dimension of 22$\times$ 22 $\times$ 0.787${mm}^{3}$. The single-element antenna consists of a modified T-shaped patch with slit an elliptical-slot on the elliptical-patch of the circular-slot bottom ground. The results demonstrate that the proposed design has high isolation with mutual coupling $(\lt -20$ dB) and has an omnidirectional radiation pattern. Also, the proposed antenna provides high diversity performance in terms of envelope correlation coefficient (ECC $\lt 0.003$ dB), diversity gain (DG $\gt 9.98$ dB), mean effective gain (MEG $\lt 3$ dB), and channel capacity loss (CCL $\lt 0.4$ bits/s/Hz) which ensure its effectiveness MIMO performance for 5G wireless applications.
An Efficient Automated Corneal Ulcer Detection Method using Convolutional Neural Network

In this paper, we propose an automated system to detect corneal ulcer disease of eye from the facial image that is taken by general-purpose digital camera using convolutional neural network (CNN). Our proposed method first detects and segments the eye part of face using Haar Cascade Classifiers. Then the method detects whether the corneal disease exists or not by CNN. If corneal ulcer disease exists, our method automatically segments the ulcer area. At first, the outer skin area of the eye is removed using the GrabCut method and segment the iris and sclera region by applying Hough gradient and active contour techniques. Finally to localize and segment ulcer area active contour technique is used and measure the ratio of ulcer size. Here erode and dilate operations are applied on iris image to polish the corneal ulcer segmentation results. For training, the available dataset is limited in size. Thus we apply several augmentation techniques such as random rotation, flip, shift, brightness, noise, and random zoom. From the result we see augmented result is superior to the results that is applied in original images. The experimental results of CNN model achieved an accuracy rate of 99.43% with sensitivity 98.78% and specificity 98.60% by augmentation techniques.
Multimedia Application for Object-oriented Programming Education Developed by Unity Engine

Considering high complexity and abstraction of object-oriented programming concepts, there is a need for innovative solution, which may serve as mean for learning these concepts. The aim of this paper is the description of developed interactive educational application for object-oriented-programming. We decided to use Unity, which is a cross-platform 3D game engine. We made our interactive part of the application as a drag and drop game, in which the user creates solution for given problem using knowledge, which were presented to him in the introduction part of the application. After the user finishes the challenge, he needs to pass the quiz. In each level there are several questions for particular concepts, on which the user has to answer. In this part the user can verify his gained knowledge. We have collected feedback from the questionnaire.
Incorporating Temporal Information for Recommendation in Heterogeneous Information Network

Heterogeneous information network is a better representation of various types of data, which has been successively applied to many fields. It provides a promising way to deal with data sparsity problem and improves the quality of recommendation. But current HIN-based recommendations ignore the time attributes on the relationships and fail to capture the dynamic changes of users’ preference. In this paper, we use the temporal information to enhance the recommendation quality in HIN. Firstly, we propose the concepts of time-weighted HIN and extend the relevance measurement of time-weighted meta-path by designing a time deviation matrix. Then, SVD matrix factorization is applied to generate the latent features of each meta-path. To better assemble recommendations from different meta-paths, we propose a new weight learning approach called complementary combination (CC). Experiments on Movielens100K dataset illustrate the improvement of our work.
Investigation on a System for Positioning of Industrial Robots Based on Ultra-Broadband Millimeter Wave FMCW Radar

Industrial robots are the key components of most automated manufacturing systems, but exhibit a low intrinsic accuracy and precision. To overcome this disadvantage, a measurement system to estimate the 3D position of the tool center point (TCP) is presented. The measurement system is based on four FMCW radar systems operating from 68 GHz to 93 GHz, each measuring the 1D distance to a recognizable target mounted at the TCP, which is 3D printed and coated with copper spray paint. To estimate the 3D position of the target from the 1D measurements, two algorithms based on the Taylor series method and the two-stage maximum likelihood method are implemented. After testing the algorithms in a simulator, the measurement system was demonstrated and verified in an experimental setup. The system could successfully estimate the 3D position of the target by means of both algorithms.
Investigation of the Electrophysical and Thermoelectric Properties of Films Fabricated by Screen-printing

The development of flexible thermoelectric generators for energy harvesting from the human body has attracted considerable interest in recent years. This paper presents a fabrication technique of thermoelectric legs by screen-printing of suspensions on the basis of Bi<inf>2</inf>Te<inf>2.8</inf>Se<inf>0.2</inf> (n-type) and Bi<inf>0.5</inf>Sb<inf>1.5</inf>Te<inf>3</inf> (p-type) and binders. Zinc phosphate cement and an aqueous alkaline solution of sodium silicate were used as binders. The thermal, electrophysical, thermoelectric properties of the screen-printed films were investigated. The results of the calculation of ZT are presented.
Structure of the Singular Manifold of Protein Backbone and Its Applications

This paper aims to study the structure of the singular manifold of a fragment of protein backbone using the method of robot kinematics. By modeling chemical bonds as rigid links, and torsional motion around chemical bonds as revolute joints, a fragment of protein backbone turns into a kinematic linkage. The singular manifold of the forward kinematic map of this linkage contains important information about the structure of its inverse kinematics map, and the results also apply to the study of the conformation space of protein loops. By splitting the forward kinematic map into an orientation and position map, we derive equations of the singular manifolds for both maps and analyze their geometric and combinatorial structures. We discuss the application of singular manifolds in several problems of computational biology, including minimal parametrization of protein conformation space and conformation space sampling of protein loops.
Neural Network Development Based on Knowledge about Environmental Influence

The article proposes an approach to the development of the original neural network, as well as the implementation of such a network. The approach is to extract the effects that the environment has on any object. Further, the proposed neural network receives an image of the object without any impact. To train the proposed neural network, which eliminates environmental influences and compares objects without impacts, only a few training images for each recognizable class are needed, which will eliminate the lack of convolutional neural networks - the need for a large training set for accurate recognition.
DeepDroid: Feature Selection approach to detect Android malware using Deep Learning

Smartphones are now able to use for various purposes such as online banking, social networking, web browsing, ubiquitous services, MMS, and more daily essential needs through various apps. However, these apps are highly vulnerable to various types of malware attacks attributed to their open nature and high popularity in the market. The fault lies in the underneath permission model of Android apps. These apps need several sensitive permissions during their installation and runtime, which enables possible security breaches by malware. Hence, there is a requirement to develop a malware detection that can provide an effective solution to defense the mobile user from any malicious threat. In this paper, we proposed a framework which works on the principals of feature selection methods and Deep Neural Network (DNN) as a classifier. In this study, we empirically evaluate 1,20,000 Android apps and applied five different feature selection techniques. Out of which by using a set of features formed by Principal component analysis (PCA)can able to detect 94% Android malware from real-world apps.
Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network

In chest and abdomen robotic radiosurgery, due to the motion delay of the robotic manipulator, the tumor position tracking process has a period of delay. This delay ultimately affects the accuracy of radiosurgery treatment. To address the influence of the delay in robotic radiosurgery, a Long-and-Short-Term Memory (LSTM) network as a deep Recurrent Neural Network (RNN) has been applied in a prediction network model for respiratory motion tracking in recent years. However, patients’ respiratory state may change in the process of treatment, which may influence the accuracy of prediction. Therefore, it is necessary to update the prediction network through additional data, such as the actual position of the tumor obtained by X-ray imaging. However, the LSTM network has a long update time, and it may not be able to complete the prediction model update in a cycle of X-ray acquisition. To solve this problem, a fast prediction model based on Bidirectional Gated Recurrent Unit (Bi-GRU), is proposed in this paper. This method can reduce the average updating time of the network model by 30%.
A W-band Frequency Doubler with Differential Outputs in 90-nm CMOS

A W -band frequency doubler is proposed and demonstrated in a 90-nm CMOS technology. Differential output signals are obtained at the drain and source terminals with validated phase balance, which is seldom demonstrated in high frequency operations above 67 GHz. The input network of the circuit exhibits diverse reflections for the fundamental and second harmonic frequencies with respect to the doubler, enhancing the first harmonic rejection ratio better than 30 dB. The proposed frequency doubler shows a differential conversion gain of −8.6 dB with a 3-dB bandwidth from 91 to 109 GHz. A maximum output power larger than −5 dBm is measured to provide a sufficient power level as the local frequency signal in the transceiver design.
Photodiode with Low Dark Current Built in Silicon-on-Insulator by Electrostatic Doping

In this work, we demonstrate experimentally a novel photodiode built in the silicon-on-insulator (SOI) substrate. Compared to conventional photodiode, where the p-n junction is formed by diffusion or ion implantation, the p-n junction in our work is formed by electrostatic doping induced by top and bottom gates. The fabricated device used for photodetection at λ = 520 nm, shows a dark current 3 decades lower than a conventional photodiode. Furthermore, the device can be switched between photodiode mode with low reverse current and resistor mode with much higher current. This unique property can be exploited in the operation of a photodetector array.
Towards a New Paradigm for Programming Scientific Workflows

Applications and infrastructures are increasingly becoming more complex. Infrastructures have several layers of virtualisation, programmability and management while scientific applications are diverse in their computing model archetypes. Mapping these two opposing ends of the stack is a non-trivial task. Here we propose a new programming paradigm and architecture that takes into account the different layers of the stack in an effort to create isolated, portable and scalable application micro-infrastructures.