Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Fabrication of a low-cost fluorescent-based hydrocarbon detector

This work describes an efficient low cost and portable method that can be used for the detection of pollutants in water such as hydrocarbons in oceans, rivers, aquifers, and other sites. According to the detection behavior of the portable system gives the opportunity to obtain real time measurements of pollutants with high resolution and, low cost. A strong absorbance band was found for different hydrocarbons in the wavelength range of 375-380 nm subsequently, fluorescence response was found in the wavelength range of 400-500 nm. High Power UV LEDs (HP UV-LED) were used to stimulate the fluorescence and a wide range (400-1100 nm) photodetector for the detection embedded in the portable device. Moreover, the paper includes experimental results about the minimal hydrocarbon thickness on water that can be detected, as well as a bandpass filter to optimize the electrical measurement. The low cost of this system implies the use of discrete electronics such as a photodetector, UV light-emitting diode and a circuit transimpedance amplifier.
A Group Analysis of Oscillatory Phase and Phase Synchronization in Cortical Networks

Neuronal oscillatory phase and phase synchronization are two main aspects of neuronal oscillation. Neurophysiological and computational studies have demonstrated that oscillatory phase for individual neurons has quantifiable relationships with neuronal excitation and input stimulus. In order to investigate the issue for neuronal groups, we constructed orientation columns by means of a spiking neural network and introduced six network activity states, pre-stimulus and stimulus periods for comparison. We proposed a new method of spike-LFP (Local Field Potential) phase based on vector addition of point spike-LFP phases to represent oscillatory phase. We also proposed a PPCG (Pairwise Phase Consistency for Group) method to quantify phase synchronization for neuronal groups. As illustrated in the simulation, the characteristics of oscillatory phase and phase synchronization for neuronal groups were consistent with the ones for individual neurons. Preferred orientations and stronger external inputs tended to result in smaller and more concentrated oscillatory phases. No matter individual neurons or neuronal groups, the oscillatory phase decreased monotonically as a function of neuronal excitation and input strength. More importantly, neuronal groups had a competitive advantage over individual neurons, because they can achieve reliable relationship quantification of oscillatory phase for all network activity states, even in weak oscillatory or non-oscillatory states.
Asian Female Facial Beauty Prediction Using Deep Neural Networks via Transfer Learning and Multi-Channel Feature Fusion

Facial beauty plays an important role in many fields today, such as digital entertainment, facial beautification surgery and etc. However, the facial beauty prediction task has the challenges of insufficient training datasets, low performance of traditional methods, and rarely takes advantage of the feature learning of Convolutional Neural Networks. In this paper, a transfer learning based CNN method that integrates multiple channel features is utilized for Asian female facial beauty prediction tasks. Firstly, a Large-Scale Asian Female Beauty Dataset (LSAFBD) with a more reasonable distribution has been established. Secondly, in order to improve CNN’s self-learning ability of facial beauty prediction task, an effective CNN using a novel Softmax-MSE loss function and a double activation layer has been proposed. Then, a data augmentation method and transfer learning strategy were also utilized to mitigate the impact of insufficient data on proposed CNN performance. Finally, a multi-channel feature fusion method was explored to further optimize the proposed CNN model. Experimental results show that the proposed method is superior to traditional learning method combating the Asian female FBP task. Compared with other state-of-the-art CNN models, the proposed CNN model can improve the rank-1 recognition rate from 60.40% to 64.85%, and the pearson correlation coefficient from 0.8594 to 0.8829 on the LSAFBD and obtained 0.9200 regression prediction results on the SCUT dataset.
Multi-Adversarial Partial Transfer Learning With Object-Level Attention Mechanism for Unsupervised Remote Sensing Scene Classification

In recent years, deep learning methods have been widely applied in remote sensing image classification tasks, providing valuable information for natural monitoring and spatial planning. In an actual application like this, acquiring massive labeled data for deep convolutional networks is costly and difficult especially in the situation that the data sources are diverse and the requirements are changing. Transfer learning methods have already shown superior performance on exploiting domain invariance features in existing data for deep network-based categorization tasks. However, the data imbalance between source and target domains may bring negative transfer and weaken the classifier’s ability. Moreover, it is still a difficult problem to extract object-level visual features among easy-mixed categories. In this context, Multi-adversarial Object-level Attention Network (MOAN) is proposed for partial transfer learning and selecting useful features. On the one hand, we present an improved object-level attention proposal network (OANet) for perceiving structural features of the main object in the picture, and weakening the unrelated regions. On the other hand, the extracted features are further enhanced by multi-adversarial framework in order to promote positive transfer, selecting and mapping valuable cross domain features from shared categories and suppressing others. This adversarial learning module can also generate pseudo tags for the samples in target domain so as to perceive integral visual signals, similar to the process in source domain. In addition, virtual adversarial training method is introduced in MOAN so as to regularize the model and maintain stability. Experimental analyses show that our MOAN can significantly promote positive transfer and restrain negative transfer in unsupervised classification problems. MOAN has good performances such as higher accuracies and lower loss values on several benchmark data sets.
Optimal Preventive Control of Transient Instabilities of Rotor Angles and Nodal Voltage Magnitudes

This paper addresses the fundamental issue regarding how to perform the preventive control of transient dynamics associated with rotor angles and nodal voltage magnitudes to obtain a transiently stable operating point for a given contingency scenario. The problem is formulated as a single contingency transient stability-constrained optimal power flow (TSC-OPF) model, in which the optimization and the transient stability problems are sequentially solved until a suitable generation dispatch that provides a transiently stable equilibrium point is assessed. A relevant feature of the proposed approach is its formulation in the form of a conventional OPF problem, which includes two power re-dispatch constraints that are only a function of the steady state active power output of generators. Hence, the control objective is achieved by constraining the direction and magnitude of the steady-state active power re-dispatch of the generators. The effectiveness of the proposed approach is demonstrated on the WSCC 3machine-9 bus test system.
An Algorithm to Measure the Stress Level from EEG, EMG and HRV Signals

This work proposes the analysis in time and frequency of EEG and EMG waves with the purpose of obtaining stress states in 5 levels. Due to the advance and evolution of technology, it is possible to obtain low-cost brain-computer interfaces with greater ease in neurofeedback sessions, which, in turn, helps to reduce stress levels in individuals and this requires a deeper analysis due to a scarce investigation. Different studies are limited to only obtaining binary results, that is to say, if an individual is in a state of stress or not, but they do not present results in a scale of levels. We analyzed 6 EEG channels with a sampling frequency of 250 Hz following the 10-20 standard and 1 EMG channel decomposed in the time and frequency domain obtaining parameters with the discrete wavelet transform and energy per band. The parameters obtained from each signal were entered into a k-NN classifier. In the same way, for the validation, the stress level was established by the graphic analysis of the heart rate variability following Baevsky's method, following with the measurement of the relaxation and stress of differents students while subjecting them to psychotechnical tests. The proposed algorithm was able to differentiate in most cases and quantify the states, reaching an accuracy level of 92%.
Design and Characterization of a Low Loss Polarization Maintaining Photonic Crystal Fiber for THz Regime

In this article a hexagonal core structured photonic crystal fiber (PCF) is demonstrated for Terahertz (THz) wave propagation where the cladding is suspended by a number of rectangles. The geometrical design and wave guiding properties characterization are done by Finite Element Method (FEM) based comsol multiphysics software v.5. 3a. The numerical outcomes of this proposed geometry exhibits an impressive performance in effective material loss (EML) reduction with a value of 0.01557cm−1 in x-polarization mode and 0.023858 cm−1 in y-polarization mode at 1THz operating frequency. Additionally, a significant difference is noticed in effective refractive index between x and y polarization mode which indicates that, this presented PCF structure carries polarization maintaining fiber (PMF) characteristics. This designed PCF also shows a flattened dispersion of ± 0.03 ps/THz/cm and high core power fraction of 47%. The existing extrusion fabrication process is very much suitable for hexagonal core structured PCF fabrication.
Enabling Predictable, Simultaneous and Coherent Data Sharing in Mixed Criticality Systems

Emerging embedded systems deployed in the automotive and avionics domains execute applications with different criticalities, comprising what is known as Mixed Criticality Systems (MCS). Applications in MCS often share data between tasks (coming from sensors for instance). Data sharing is challenging because it can lead to increased response times or even unpredictable behaviors if not carefully addressed. Therefore, several prior works in MCS either assumed that tasks do not share data or disallowed it by design. Recent solutions attempt to mitigate the effects of data sharing, albeit by introducing new restrictions on the system either by prohibiting applications from caching shared data or prohibiting the operating system from running tasks with shared data in parallel. We find these solutions also to have limited applicability as they deteriorate system schedulability and prohibit simultaneous access to shared data. In this paper, we propose PENDULUM: a time-based cache coherence protocol to enable simultaneous and predictable access to shared data in MCS. Our evaluation shows that PENDULUM, achieves flexibility and better performance compared to existing solutions, while maintaining system predictability.
Simulating the Effect of Test Flakiness on Fault Localization Effectiveness

Test flakiness (non-deterministic behavior of test cases) is an increasingly serious concern in industrial practice. However, there are relatively little research results available that systematically address the analysis and mitigation of this phenomena. The dominant approach to handle flaky tests is still detecting and removing them from automated test executions. However, some reports showed that the amount of flaky test is in many cases so high that we should rather start working on approaches that operate in the presence of flaky tests. In this work, we investigate how test flakiness affects the effectiveness of Spectrum Based Fault Localization (SBFL), a popular class of software Fault Localization (FL), which heavily relies on test case execution outcomes. We performed a simulation based experiment to find out what is the relationship between the level of test flakiness and fault localization effectiveness. Our results could help the users of automated FL methods to understand the implications of flaky tests in this area and to design novel FL algorithms that take into account test flakiness.
Computational Prediction of Critical Temperatures of Superconductors Based on Convolutional Gradient Boosting Decision Trees

Superconductors have been one of the most intriguing materials since they were discovered more than a century ago. However, superconductors at room temperature have yet to be discovered. On the other hand, machine learning and especially deep learning has been increasingly used in material properties prediction and discovery in recent years. In this paper, we propose to combine the deep convolutional neural network (CNN) model with fully convolutional layers for feature extraction with gradient boosting decision tree (GBDT) for superconductors critical temperature ( $T\text{c}$ ) prediction. Our prediction model only uses the elemental property statistics of the materials as original input and learns a hierarchical representation of superconductors using convolutional layers. Computational experiments showed that our convolutional gradient boosting decision tree (ConvGBDT) model achieved the state-of-the-art results on three superconductor data sets: DataS, DataH, and DataK. By visually comparing the raw elemental feature distribution and the learned feature distribution, it is found that the convolutional layers of our ConvGBDT can learn features that can more effectively distinguish cuprate and iron-based superconductors. On the other hand, the GBDT part of our ConvGBDT model can learn the sophisticated mapping relationship between extracted features and the critical temperatures to obtain good prediction performance.
Fault Localization for Power Line Communications with Topology Inference

Power line communications (PLC) is one of the communication methods which have been deployed and attracted considerable attention on supporting smart grid applications. Furthermore, link failures will have a great impact on the normal operation of PLC network and the causes of link failures are diverse. Therefore, rapid fault location is extremely important for PLC. Fault localization generally requires complete knowledge of the network topology which is usually unknown and needs to be learned by topology inference algorithms. However, it is difficult for routers to collect complete topology information for topology inference. We propose an algorithm based on partial information for topology inference to help the PLC to detect and locate faults normally. The inference algorithm first builds a virtual topology, overestimates the number of network components, and then repeatedly merges the links in this topology to infer the structure of the real network. We conducted simulations and compared our algorithm with the most advanced inference algorithms. The results show that compared with other methods, the topology structure of our inference algorithm significantly improves the performance of the fault location.
A Long Short-Term Memory Autoencoder Approach for EEG Motor Imagery Classification

Motor imagery represents one Brain-Computer Interface (BCI) paradigm that has been utilized in developing applications to assist subjects with motor disability. Such paradigm relies on analyzing brain electroencephalography (EEG) activity to identify the intended movement direction. Existing motor imagery feature extraction techniques are focused on utilizing traditional signal processing and machine learning techniques. Recent advances in the deep learning field has inspired the development of few methods for motor imagery classification that achieved further performance improvement. This paper proposes a deep neural network approach for motor imagery classification using Long Short-Term Memory (LSTM) combined with Autoencoders based on a sequence-to-sequence architecture. The proposed network extracts features from the frequency-domain representation of EEG signals. This network is trained to obtain low-dimensional representation of EEG features that are then fed into multilayer perceptron of 3 layers for classification. Systematic and extensive examinations have been carried out by applying the approach to public benchmark EEG datasets. The obtained results outperformed classical state-of-the-art methods employing standard frequency-domain features and common spatial patterns, and comparative results to methods such as filter bank common spatial pattern and its variants. Our results indicate the efficacy of the proposed LSTM autoencoder approach in EEG motor imagery classification.
Relationship between the Effectiveness of Spectrum-Based Fault Localization and Bug-Fix Types in JavaScript Programs

Spectrum-Based Fault Localization (SBFL) is a well-understood statistical approach to software fault localization, and there have been numerous studies performed that tackle its effectiveness. However, mostly Java and C/C++ programs have been addressed to date. We performed an empirical study on SBFL for JavaScript programs using a recent bug benchmark, BugsJS. In particular, we examined (1) how well some of the most popular SBFL algorithms, Tarantula, Ochiai and DStar, can predict the faulty source code elements in these JavaScript programs, (2) whether there is a significant difference between the effectiveness of the different SBFL algorithms, and (3) whether there is any relationship between the bug-fix types and the performance of SBFL methods. For the latter, we performed a manual classification of each benchmark bug according to an existing classification scheme. Results show that the performance of the SBFL algorithms is similar but there are some notable differences among them as well, and that certain bug-fix types can be significantly differentiated from the others (in both positive and negative direction) based on the fault localization effectiveness of the investigated algorithms.
An RF-Powered Wireless Micro-Heater Integrated with Acrylate-Composite-Based Temperature Regulator for Hyperthermia Treatment

This work presents a wireless micro-heater with an integrated temperature regulator for the application of endohyperthermia treatment of restenosis. The proposed device uses a micromachined $L-C$ tank circuit for achieving a wireless resonant RF heating capability. An acrylate-composite-based circuit breaker integrated with a polymer film with sharp resistivity-temperature dependence characteristic was proposed for providing the temperature regulation feature. Wireless heating tests demonstrate the functionality of the breaker, which regulates the heater temperature rise below 8 °C with an RF excitation power of 100 mW. The preliminary measured results also showed that the proposed device potentially enables a reliable and safeguarded hyperthermia treatment through wireless microheaters.
Data Adaptive Compressed Sensing using deep neural network for Image recognition

Compressive sensing (CS) using deep learning for recovery of images from measurements has been well explored in recent years. Instead of sensing/sampling full image, block or patch based compressive sensing is chosen to overcome memory and computation limitations. The drawback of this block based CS sampling and recovery is that it does not capture global context and focuses only on the local context. This results in artifacts at the boundary of two consecutive image blocks. Random Gaussian or random Bernoulli matrix are commonly used as sensing matrices to sample an image block and generate corresponding linear measurements. Although, random Gaussian or random Bernoulli matrices exhibits Restricted Isometry property (RIP), which is a guarantee for good quality reconstructed image, its two main disadvantages are: 1) large memory and computational requirements and 2) their encoded measurements doesn't generalize well to a large-scale dataset. In this paper, we propose a data adaptive CS based on deep learning framework for image recognition where 1) sampling is done considering the global context and 2) encoding to obtain measurements is learned from data, so as to achieve the generalization over large-scale dataset.
Comparison between PID-Fuzzy and Numerical Methods Based on Linear Algebra Controllers for Glucose Control in Type 1 Diabetes Treatment

The main problem of treatment in Type 1 Diabetes Mellitus is to maintain glycemia between Normal levels in order to reduce possible long-term nervous and vascular complications. A possible solution for this is an Artificial Pancreas because it automatically provide enough insulin amount during the day. In recent years, a variety of PIDs and MPC controllers have been developed for the proper functioning of the artificial pancreas. This paper presents the design and evaluation of two controllers, a Numerical Methods based on Linear Algebra and a PID-Fuzzy. The simulation is based on 3 meals per day and different parametric variations. The simulation analysis shows that PIDFuzzy maintains blood glucose levels better than the NMLA. The controllers are compared using several performance indexes such as: settling time, ISE and TVu. Finally, a noise source has been added to the glucose sensor in order to see how the controllers are affected.
Artificial Intelligence for Vehicle Behavior Anticipation: Hybrid Approach Based on Maneuver Classification and Trajectory Prediction

Innovative technologies and naturalistic driving data sources provide a great potential to develop reliable autonomous driving systems. Understanding the behaviors of surrounding vehicles is essential for improving safety and mobility of autonomous vehicles. Onboard sensors like Radar, Lidar and Camera are able to track surrounding vehicles motion and to get different features like position, velocity and yaw. This paper proposes a hybrid approach to integrate maneuver classification using neural networks and trajectory prediction using Long Short-term Memory (LSTM) networks to get the future positions of adjacent vehicles. In this study we use the Next Generation Simulation (NGSIM) public dataset that provides a real driving data. The proposed approach is validated experimentally using VEDECOM demonstrator data. The results demonstrate that the proposed approach is able to predict driver intention to change lanes on average 2.2 seconds in advance. The Root Mean Square (RMS) errors of lateral and longitudinal positions are 0.30 m and 3.1 m respectively. The results demonstrate a high performance compared to various existing methods.