Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating the response of a neuro-musculoskeletal model to assistive forces: implications for the design of wearables compensating for motor control deficits

Models of the human arm may help to estimate design parameters like peak torque and power of wearable assistive devices by predicting required forces to compensate for motor control impairments. This work focuses on the idea of compensating hypermetria (overshoot)—a motor control deficit that may occur in neurodegenerative diseases—by a simple assistive device. As musculoskeletal dynamics play an important role in the interaction between an assistive device and the neuro-musculoskeletal system, we hypothesized that their consideration in the model might influence the predicted design parameters. To test this, we simulated two-degree-of-freedom point-to-point arm movements. By introducing inconsistent neuronal control parameters, we induced hypermetria. We implemented mechanical and low-level assistive torque strategies in simulation which lead to a reduction of hypermetria. We found that–depending on the type of assistance–the predicted torques and powers can differ by more than a factor of 10 between musculoskeletal and torque-driven arm models. We conclude that the magnitude of torque and power required to reduce hypermetria by simple wearable assistive devices may be significantly underestimated if muscle-tendon characteristics are not considered.
A Biometric Identification System with Kernel SVM and Feature-level Fusion

The paper presents a biometric system with optimization for identification. The design combines 2 biometrics (fingerprint and palmprint) with feature-level functional fusion, avoiding the concatenation. Data classification is done with a kernel SVM (Support Vector Machine) model and a multi-class extension. The experimental achievements show that the performance improvements are provided by the feature-level fusion together with an optimized design of the biometric data classifier. The model can be applied in use-cases in which the identity of the individuals should be guessed only based on the biometric credential.
A combination of FractalNet and CNN for Lung Nodule Classification

Lung Cancer is the fastest growing cancer around the world and is mostly diagnosed at an advanced stage. In order to prevent the delay in diagnosis, several medical imaging modalities like CT (Computed Tomography) has been used. So far researchers have used several machine learning architectures to classify the lung nodule captured in CT scans into benign or cancerous. In this paper, we use a deep learning architecture which combines fractalnet and CNN to classify the lung nodule into benign and malignant. The lung nodule classification was validated on LUNA dataset on which it achieved an accuracy of 94.06%, sensitivity of 97.52%, specificity of 86.76% and area under receiver operating characteristic (AUC) score of. 98, using the proposed architecture.
PHPIL: Fuzzing the PHP Interpreter with Custom Bytecode

We aim to fuzz the PHP interpreter to search for bugs which may or may not be able to compromise the security of the interpreter and the system it is running on. In our research we propose to implement a fuzzing framework for the standard implementation of the PHP interpreter. What makes our fuzzer different from other PHP interpreter fuzzers is the ability to create syntactically and semantically correct code samples. We found in our research that most of the available PHP interpreter fuzzers, although able to create syntactically correct code samples, are unable to produce semantic correctness. We created our own intermediate language composed of custom opcodes, which is used by the code generator to generate the code samples. Code generator is governed by the rules which make sure that the resulting code follows the PHP syntax and symmetric conventions. The mutator is driven by the code generator and it performs the mutation on the generated intermediate language. We created a corpus which is used to store the desired code samples on which further mutations can be performed. Thus new inputs are generated by performing mutations to the code which increases the coverage, thus maximizing the chances of finding vulnerabilities. The lifter lifts the mutated intermediate language sample to the php code before feeding to the interpreter. The execution of the php code sample is monitored for any unexpected behaviour of the interpreter. A report is formed in case of any unexpected behaviour.
Implementing a Virtual Network on the SDN Data Plane

The paper investigates the implementation of virtual networks on the SDN data plane, modeled by a graph of physical connections between network nodes. A virtual network is defined as a set of ordered host pairs (sender, receiver), and it is implemented by a set of host-host paths that uniquely determine the switch settings. It is shown that any set of host pairs can be implemented on a connected graph without the occurrence of an infinite transfer of packets in a loop and without duplicate paths when the host receives the same packet several times. However, undesired paths may occur when a host receives a packet that is not intended for this host. On the other hand, it is shown that in some cases, implementation without undesired paths inevitably leads to duplication or looping of packets. The question is posed: on which graph can any set of host pairs be implemented without looping, duplication and undesired paths? A sufficient condition is proposed and a hypothesis is put that this condition is also the necessary condition.
Embedded FBG Sensors in Carbon Fiber for Vibration and Temperature Measurement in Power Transformer Iron Core

This paper presents a new methodology for measuring vibration and temperature using fiber Bragg grating (FBG) sensors embedded in carbon fiber polymer attached directly on the iron core of a power transformer. The device has a quick-coupling clip design and is fabricated from a carbon fiber reinforced composite (CFRP) for robustness and flexibility. Temperature and vibration FBG sensors are embedded in the carbon fiber clip, which is easily and quickly coupled directly to the transformer iron core. In the low frequency ranges, from 0 to 500 Hz, vibrations are mainly generated in the core. Rigidity is controlled by clip thickness and geometry, since the system is designed to have a wide operating frequency range. The embedded strain FBG sensor sensitivity has been improved due to its spring-shaped design static and dynamic simulations applying the Finite Element Method (FEM) were performed to determine the optimal dimensions and structure of the part. The clip’s frequency response function (FRF) was assessed and tested by using three techniques: FEM harmonic analysis, mechanical impact testing, and optical FBG sensing. The results demonstrate a 3.94 % differential among the methods. A comparison was made of the power transformer’s FRF when fitted with a glued free FBG sensor and an embedded FBG sensor. In both cases, the transformer was subjected to varying electrical load conditions (no-load, linear, and nonlinear load). The results showed a 19 % average sensitivity improvement when the embedded sensor was compared to the free sensor.
Vision-based Guidance and Navigation for Autonomous MAV in Indoor Environment

The paper presents an autonomous vision-based guidance and mapping algorithm for navigation of drones in a GPS-denied environment. We propose a novel algorithm that accurately uses OpenCV ArUco markers as a reference for path detection and guidance using a stereo camera. It enables the drone to navigate and map an environment using vision-based path planning. Special attention has been given towards the robustness of guidance and controlling strategy, accuracy in the vehicle pose estimation and real-time operation. The proposed algorithm is evaluated in a 3D simulated environment using ROS and Gazebo. The results have been presented for drone navigation in a maze pattern indoor scenario. Evaluation of the given guidance system in the simulated environment suggests that the proposed system can be used for generating a 2D/3D occupancy grid map autonomously without the use of high-level algorithms and expensive sensors such as lidars.
An Efficient Wide Tuning Range −0.4 dBm 65 GHz NMOS VCO on 22 nm FD-SOI CMOS

This paper presents a voltage-controlled oscillator implemented on a 22-nm FD-SOI CMOS technology. Optimized for wide tuning range and high output power, the oscillator uses a transmission line and an MOS varactor in the resonator, achieving a tuning range of 56.0-73.3 GHz. Output powers up to −0.4 and −2.5 dBm have been measured, while drawing 12.8 mW and 20.0 mW power from supply voltages of 0.8 V and 1 V, respectively. The oscillator core and buffer require 6300μm2 of silicon area, which is almost entirely devoted to the resonator. The presented circuit exhibits a great combination of performance metrics, it has the highest output power, smallest area, second highest tuning range and efficiency among CMOS oscillators in similar frequency ranges reported to date.
Transfer Learning Based Equivalent Magnetization Hysteresis Recognition Algorithm for Transformer Protection

Power transformer is a key equipment of power system, and transformer protection is still an important research hotspot for the safe operation of the electric power system. Kinds of transform protection algorithms have been proposed including many artificial intelligence (AI) algorithms. But the requirement of big data limits the generalization of AI algorithms. Different types of ferromagnetic material have the same shape of magnetization hysteresis which is an effective indicator for transformer protection. We propose transfer learning based equivalent magnetization hysteresis recognition algorithm for transformer protection which use little trial/real data with simulation data to solve the few-shot problem. In our experiments, we validate that the proposed algorithm can reach an improved accuracy compared with other two mentioned in the article.
Estimation Models for the Refractive Index Response Curve of EFBGs

Estimation models for the surrounding refractive index response of EFBGs were developed from computer simulations and tested on experimental data. Numerical simulations were used to investigate the fundamental core mode’s effective refractive index dependence with the fiber radius and the surrounding refractive index. An empirical mathematical model was developed upon the simulation results, and it was used to derive the estimation models. The response curve is estimated from a reference calibration curve using two different approaches: interpolation and extrapolation. The interpolation is performed using the wavelength response of the EFBGs at two different surrounding refractive index values. Then, the response curve is obtained solving a simple pair of linear equations. The extrapolation is performed using the wavelength shift observed during the etching process, which is used to derive a multiplicative factor for the calibration curve. The response curves estimated by the model were compared with the experimental results, showing good agreement within the experimental uncertainties. Considering the surrounding refractive index ranging from 1.333RIU to 1.458RIU, with the sensors’ response ranging from 1544.84nm to 1552.70nm, the maximum deviation between the estimation and the experimental results were 0.26nm and 0.41nm, and the maximum standard deviation were 0.03nm and 0.05nm, respectively for interpolation and extrapolation.
SpecEdit: Projectional Editing for TLA+ Specifications

High quality requirements and specifications are the premises of efficient software system engineering. Formal approaches propose precise and unambiguous requirements amendable to automated reasoning. TLA+, for instance, is used by major companies, such as Microsoft and Amazon, to specify high-profile business critical systems. However, despite its undeniable strengths for the specification of complex distributed systems, TLA+ suffers from the duality of its syntax, which hinders its large-scale industrial adoption. A system engineer can easily read mathematical specifications in TLA+, produced through LaTeX. However, for writing TLA+ specifications, he must learn the discommoding ASCII syntax, which requires unnecessary effort and dedicated learning time. This paper introduces SpecEdit, an IDE for TLA+ with a projectional editor that solves this issue. SpecEdit exposes the mathematical syntax of TLA+ for both reading and writing specifications, without requiring external transformations. This approach minimizes the cognitive effort and streamlines the formal system specification process. We illustrate the benefits of our approach using the specification of the Elasticsearch cluster coordination module. We furthermore emphasize the complementarity with the existing TLA+ tools. Through SpecEdit, TLA+ gains the specification editor that was missing without compromising compatibility with the existing tools.
Application of Machine Learning to Stomatology: A Comprehensive Review

In recent years, machine learning methods has been widely used in various fields, such as finance, spatial sciences, smart grid, intelligent transportation, renewable energy, agriculture, especially medicine. In the era of big medical data, the advantage of machine learning is that it can predict and diagnose through the analysis of a large number of clinical data, and its performance is very close and competitive to or even better than the performance of clinicians. This article focuses on the application of machine learning techniques in the field of stomatology and detailedly describes application cases involving oral cancer, dental caries, periodontitis, dental pulp diseases, periapical lesions, oral implants, and orthodontics. Finally, the research obstacles and future work are discussed.
Online Micro-Modules Library Production for Fundamental Programming Courses with Active Learning

Engineering faculties encounter many challenges when teaching first-year programming courses. One of challenges is that the first-year students have grossly different levels of programming experience. We would also like to standardize the online programming training across different programming languages. As a result, Online Micro-Modules Library (OML) for fundamental programming courses are being developed in our university. This paper outlines the production process and preliminary outputs for the OML development, based on the Analyze, Design, Develop, Implementation and Evaluation (ADDIE) model. Various online learning tools, as well as active learning components, are employed in the OML. The development process reveals interesting insights and offers possibilities of applying the system design to other engineering disciplines.
Motion-Based Separation and Imaging of Closely Spaced Extended Targets

Separating images of multiple moving extended-targets by means of ultra-wideband (UWB) frequency-modulated-continuous-wave (FMCW) radar is investigated. The difference in motion of closely-spaced extended targets is used to separate and image them when their radar signatures overlap in the range-azimuth domain. A novel auto-focusing criterion based on the entropy of the eigenspectrum and the Fourier spectrum is proposed for target imaging using inverse synthetic aperture radar (ISAR). It allows separating closely spaced targets and estimating their motion parameters. The criterion proposed overcomes the low Doppler resolution of closely-spaced extended targets of conventional techniques. The bearing information is extracted by signal reconstruction for every target separately. With the estimated position and the motion parameters of multiple targets, their images are reconstructed using standard imaging processing algorithms. The performance of the proposed method is validated via numerical simulations.
Assessing Students’ Behavior in Error Finding Programming Tests: An Eye-Tracking Based Approach

Programming debugging is one of the most challenge part in the programming course, which is currently assessed by the teachers manually. With the development of eye-tracking technology, the student’s cognitive process can be estimated and researched based on the eye movement data. However, most of the existing eye-tracking measurement in programming focus on the difference among different person group or different mission, and can not be directly utilized for programming assessment. In this paper, we propose an assessing framework for debugging, providing quantified eye movement measurement on given errorfinding tasks. We focus on the task of finding errors in the C-language source code. An eye-tracking based measurement system is implemented to segmenting the students’ programming process in error-finding tasks. By dividing the source code AoIs and assigning the relevant exam point AoIs, the eye movement on specific exam point can be measured and analyzed. A procedural evaluation scheme is proposed to analyze the details of the testing process, including every error-finding activity and every eye movement. By checking the eye movement data on the related blocks, we estimate the students’ sensitivity to the specified examination point, and produce a reference classification on his performance. Experiment results show that, compared with the results of traditional error-finding question (only absolute true or false), our assessment method provides procedural evaluations of students according to his behavioral characteristics.
Breast Cancer Risk Prediction using XGBoost and Random Forest Algorithm

Breast cancer is as one of the common and serious cause of death among women globally. This is a disease where the cells grow out of control inside the breast. Family History of cancer disease, physical inactivity, psychological stress, increase in breast size are the risk factors of breast cancer. In this research paper, breast cancer dataset was analyzed to predict breast cancer using popular two ensemble machine learning algorithms. Random Forest and Extreme Gradient Boosting (XGBoost) were used to predict breast cancer. A total of 275 instances with 12 features were used for this analysis. With Random forest algorithm 74.73% accuracy and 73.63% using XGBoost had obtained in this analysis.
Toward an Enhanced Bengali Text Classification Using Saint and Common Form

Language processing tool has been strengthened by the measurements of text classification. Due to this concern many approaches investigate through the text documentation problem. Behalf of this conception we have focused on our bengali text written format. A long period of time bengali people are familiar with two bengali accents about saint and common form. With concern text document processing becomes easier to translate. Most well-known supervised six classifiers we have used to classify these two bengali forms of saint and common. Classifiers prediction will determine whether it is saint or common form. Collection of text documents more than 1200 mix sentences grabbed from bengali written sources. Each text needs preprocess to classify the text into a solid form of output. Before applying algorithms there has been some prerequisite ability to split the sentences, stemming, remove stop words, construct contraction. Ending with preprocess, the processed bengali text had been taken as input on machine learning classifiers that have raised very spontaneous outcomes over the accent of bengali data. Foremost output produced by NB classifier to identify the actual form about 77% on bengali text of saint and common form. Apart from that, other ML classifiers XGB, RB, DT, SVC, KNN showed nearly prediction upto 77% −64% accuracy which we have proposed in different segments of this paper.
Creating a 4D Photoreal VR Environment to Teach Civil Engineering

Motivated by recent developments in AR/VR technology and accessibility, an interdisciplinary team at a Sino-foreign Higher Education Institution in China have been engaged in the creation of a virtual reality learning resource that will be offered to students and educators in the institution’s Civil Engineering department. In Civil Engineering education, it is difficult to explain to students how classroom theories translate into real life solutions and field trips can help resolve this problem. However, these must overcome fundamental concerns associated with student site visits, which include safety, logistics and accessibility for disadvantaged students. The presented case study details the development of a virtual field trip that is accessible from simple phone-based virtual reality headsets. The photoreal virtual field trip was constructed through multiple site visits to capture data and the experience now provides students with an opportunity to view the entire construction process of a university library. This paper contributes to current research by exploring the challenges and conjugate solutions encountered whilst building the virtual reality environment, and by presenting the first known photoreal virtual field trip that allows students to autonomously move both spatially and chronologically around a construction site. This will benefit other educational practitioners who are contemplating similar initiatives.
Development and Testing of Approach for Separated Training of Branches of Multi-Task Convolutional Neural Networks

This paper explores the possibility of building a new model on the basis of an already trained neural network to perform an extended number of tasks, using the knowledge available in the original model. Possible areas of application are determined, the influence of such an approach on the accuracy and speed of the obtained multi-task model is investigated in comparison with a set of single-task models each of which fulfills specific task.
Evaluation of thermoelectric properties of Ga-Sn-O thin film formed by Mist CVD method

We fabricated Ga-Sn-O (GTO) thin film thermoelectric conversion devices at atmospheric pressure by mist chemical vapor deposition (Mist CVD) method and evaluated the power factor (PF). The best PF was 0.050 mW / mK2, with a Seebeck coefficient of -75 µV / K and a conductivity of 89 S / cm.
Malware Injection in Operational Technology Networks

Security Issues of Industrial Control System (ICS) increasing day by day because of this, it gets more and more attention. Hackers finding different methods or approaches to finding vulnerability of Operational Technology (OT) Networks. Some of the methods used by Attacker are injection of malware and backdoor. Programmable logic controller (PLC) and Supervisory Control and Data Acquisition (SCADA) both are important part of industry. Usually, Industrial Control System network is completely isolated from external network, but the administrative computer which is connected to the network are vulnerable, one of the reasons will be use of internet are increasing day by day. Stuxnet Attack is example of this type of vulnerability. The proposed work consist of are performing a Denial of service (DoS) attack on Allen Bradley PLC which is used to control Waste-Water Treatment Plant (WTP). To performing this attack we first, inject a malware into an administrative computer, which is connected to Waste-Water Treatment Plant network. The malware we inject into the network is capable of collecting all information of network which includes all IP address, its vendor information, MAC address etc. The data is stored into a text file which will downloaded from Victim Computer into Hacker computer by using Backdoor. And after finding target IP address, we will perform DoS attack. Also, this paper discussed about process of creating malware and backdoor as well as it includes analysis of network traffic before and after performing an attack.