Miracle-dz/UniFAR-FTUs huggingface dataset:
The Progress of the Helicopter-Borne Transient Electromagnetic Method and Technology in China

The central and western regions of China have appropriate conditions for mining development, but there are many deserts, swamps and forest-covered areas in this vast land. As a result, the traditional on-ground methods are difficult to carry out efficiently in these areas. The airborne transient electromagnetic method can implement detection over large areas rapidly and efficiently, and play an important role in terms of resource and environment detection. In recent years, two Helicopter-borne Transient electromagnetic (HTEM) systems, CHTEM-I and CAS-HTEM, were developed in China. In this paper, the difficult issues in the development of HTEM hardware and software will be analyzed, firstly. By tracing the specific technical paths to solve these difficult issues, the latest research achievements in the development process of CHTEM-I and CAS-HTEM will be expounded. Finally, we would like to briefly discuss the research and development direction of HTEM technology in China to promote further development of the research and application of this technology.
Mining top-up transactions and online classified ads to predict urban neighborhoods socioeconomic status

Quantifying income inequalities in developing countries faces challenges regarding data publicly available. Census data, collected every five or ten years, is the only source for socioeconomic indicators. Thus, local authorities need ways of producing more frequently updated indicators. Studies conducted for developed countries (Europe and USA) use Call Detail Records (CDRs) for such a purpose. In our study we propose to exploit patterns observed in developing countries, specifically in Latin America, where mobile phone usage is pervasive even among the poorest and the dominant modality for purchasing mobile airtime is the prepaid scheme (top-ups). We analyze more than 1M top-up transactions together with more than 5K online classified ads for housing sales to predict the socioeconomic status measured at an intra-urban level for 89 neighborhoods. Using a Linear Regression with Regularization L1 (Lasso), we can explain the economic status with a prediction rate up to 71% for urban neighborhoods in Guayaquil, Ecuador. Consequently, we show evidence that top-up transactions provide effective signals to characterize urban neighborhoods socioeconomic status.
Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset

In recent years, due to the extensive use of the Internet, the number of networked computers has been increasing in our daily lives. Weaknesses of the servers enable hackers to intrude on computers by using not only known but also new attack-types, which are more sophisticated and harder to detect. To protect the computers from them, Intrusion Detection System (IDS), which is trained with some machine learning techniques by using a pre-collected dataset, is one of the most preferred protection mechanisms. The used datasets were collected during a limited period in some specific networks and generally don’t contain up-to-date data. Additionally, they are imbalanced and cannot hold sufficient data for all types of attacks. These imbalanced and outdated datasets decrease the efficiency of current IDSs, especially for rarely encountered attack types. In this paper, we propose six machine-learning-based IDSs by using K Nearest Neighbor, Random Forest, Gradient Boosting, Adaboost, Decision Tree, and Linear Discriminant Analysis algorithms. To implement a more realistic IDS, an up-to-date security dataset, CSE-CIC-IDS2018, is used instead of older and mostly worked datasets. The selected dataset is also imbalanced. Therefore, to increase the efficiency of the system depending on attack types and to decrease missed intrusions and false alarms, the imbalance ratio is reduced by using a synthetic data generation model called Synthetic Minority Oversampling TEchnique (SMOTE). Data generation is performed for minor classes, and their numbers are increased to the average data size via this technique. Experimental results demonstrated that the proposed approach considerably increases the detection rate for rarely encountered intrusions.
Investigation of a Modified Flux-Coupling-Type SFCL for Low-Voltage Ride-Through Fulfillment of a Virtual Synchronous Generator

To solve the low-voltage ride-through (LVRT) issue of a virtual synchronous generator (VSG) under severe grid fault, our research group proposes to use a modified flux-coupling-type superconducting fault current limiter (SFCL). In this study, the configuration structure of the VSG with the SFCL at the point of common coupling (PCC) is designed. The fault analytical circuit of the VSG is presented, and the functions of the SFCL are stated from the aspects of current restraint, voltage compensation, and energy dissipation. Using MATLAB/Simulink, symmetrical and asymmetrical faults are both simulated to assess the transient behaviors of the VSG, and the SFCL parameters are changed to clarify the quantitative effects. From the comparison results, the SFCL parameters are properly determined to provide favorable performance. The SFCL can visibly decrease the fault current of each phase within the permissible level, and facilitate the VSG to have a preferable voltage profile. Furthermore, the SFCL can positively stabilize the VSG by enhancing the power delivery, mitigating the frequency fluctuation and consuming the excess energy. A satisfactory LVRT operation is gained, and the efficacy of the SFCL on reinforcing the VSG robustness is validated.
Gossip Learning: Off the Beaten Path

The growing computational demands of model training tasks and the increased privacy awareness of consumers call for the development of new techniques in the area of machine learning. Fully decentralized approaches have been proposed, but are still in early research stages. This study analyses gossip learning, one of these state-of-the-art decentralized machine learning protocols, which promises high scalability and privacy preservation, with the goal of assessing its applicability to real-world scenarios.Previous research on gossip learning presents strong and often unrealistic assumptions on the distribution of the data, the communication speeds of the devices and the connectivity among them. Our results show that lifting these requirements can, in certain scenarios, lead to slow convergence of the protocol or even unfair bias in the produced models. This paper identifies the conditions in which gossip learning can and cannot be applied, and introduces extensions that mitigate some of its limitations.
Design of a Chaotic Oscillator based Model Building Attack Resistant Arbiter PUF

Physical Unclonable Functions (PUFs) are vulnerable to various modelling attacks. The chaotic behaviour of oscillating systems can be leveraged to improve their security against these attacks. We have integrated an Arbiter PUF implemented on a FPGA with Chua's oscillator circuit to obtain robust final responses. These responses are tested against conventional Machine Learning and Deep Learning attacks for verifying security of the design. It has been found that such a design is robust with prediction accuracy of nearly 50%. Moreover, the quality of the PUF architecture is evaluated for uniformity and uniqueness metrics and Monte Carlo analysis at varying temperatures is performed for determining reliability.
Automated exchange transfusion in premature and newborn infants with hyperbilirubinemia using a peripheral arteriovenous vascular access device

: The technique of exchange transfusion using a plastic catheter in the umbilical vein developed by Diamond and modified by Allen was instrumental in decreasing the mortality and morbidity in newborn infants with jaundice. Allen et al. demonstrated that the development of kernicterus in infants with erythroblastosis with indirect hyperbilirubinemia could be prevented by this method. Since that time numerous modifications of this "single site, push pull technique" have been described to further reduce the risk of morbidity and mortality. On the basis of the successful use of percutaneous radial artery catheters for arterial blood gas monitoring, we developed a technique for exchange transfusion using a peripheral arterial catheter inserted in the radial artery for blood withdrawal and a peripheral venous catheter for replacement of heparinized compatible donor blood. For simultaneous withdrawal and replacement we used two volumetric infusion pumps, one aspirating patients' arterial blood, the other replacing equal volumes of donor blood at exactly the same time. We evaluated our new procedure in 19 newborn infants (group 1) with hyperbilirubinemia who required exchange transfusion. Retrospectively we analysed the data in 18 newborn infants (group 2) treated over a previous period by means of the conventional "one site, push pull" technique using the umbilical vein, and compared the data. Mean birth weights, gestational ages and Apgar scores were similar in both groups, as were volumes of blood used for exchange transfusions. The results were superior in group 1 infants, as reflected by the significantly greater percentage decreases in serum bilirubin concentration and fewer complications. No baby died. (ABSTRACT TRUNCATED AT 250 WORDS)
Emotion Recognition From Multi-Channel EEG Signals by Exploiting the Deep Belief-Conditional Random Field Framework

Recently, much attention has been attracted to automatic emotion recognition based on multi-channel electroencephalogram (EEG) signals, with the rapid development of machine learning methods. However, traditional methods ignore the correlation information between different channels, and cannot fully capture the long-term dependencies and contextual information of EEG signals. To address the problems, this paper proposes a deep belief-conditional random field (DBN-CRF) framework which integrates the improved deep belief networks with glia chains (DBN-GC) and conditional random field. In the framework, the raw feature vector sequence is firstly extracted from the multi-channel EEG signals by a sliding window. Then, parallel DBN-GC models are utilized to obtain the high-level feature sequence of the multi-channel EEG signals. And the conditional random field (CRF) model generates the predicted emotion label sequence according to the high-level feature sequence. Finally, the decision merge layer based on K-nearest neighbor algorithm is employed to estimate the emotion state. According to our best knowledge, this is the first attempt that applies the conditional random field methodology to deep belief networks for emotion recognition. Experiments are conducted on three publicly available emotional datasets which include AMIGOS, SEED and DEAP. The results demonstrate that the proposed framework can mine inter correlation information of multiple-channel by the glia chains and catch inter channel correlation information and contextual information of EEG signals for emotion recognition. In addition, the classification accuracy of the proposed method is compared with several classical techniques. The results indicate that the proposed method outperforms most of the other deep classifiers. Thus, potential of the proposed framework is demonstrated.
Salient View Selection for Visual Recognition of Industrial Components

We introduce a new method to find a salient viewpoint with a deep representation, according to ease of semantic segmentation. The main idea in our segmentation network is to utilize the multipath network with informative two views. In order to collect training samples, we assume all the information of designed components and even error tolerances are available. Before installing the actual camera layout, we simulate different model descriptions in a physically correct way and determine the best viewing parameters to retrieve a correct instance model from an established database. By selecting the salient viewpoint, we better understand fine-grained shape variations with specular materials. From the fixed top-view, our system initially predicts a 3-DoF pose of a testing object in a data-driven way, and precisely align the model with a refined semantic mask. Under various conditions of our system setup, the presented method is experimentally validated. A robotic assembly task with our vision solution is also successfully demonstrated.
Spectral–Spatial Exploration for Hyperspectral Image Classification via the Fusion of Fully Convolutional Networks

Due to its remarkable feature representation capability and high performance, convolutional neural networks (CNN) have emerged as a popular choice for hyperspectral image (HSI) analysis. However, the performances of traditional CNN-based patch-wise classification methods are limited by insufficient training samples, and the evaluation strategies tend to provide overoptimistic results due to training-test information leakage. To address these concerns, we propose a novel spectral–spatial 3-D fully convolutional network (SS3FCN) to jointly explore the spectral–spatial information and the semantic information. SS3FCN takes small patches of original HSI as inputs and produces the corresponding sized outputs, which enhances the utilization rate of the scarce labeled images and boosts the classification accuracy. In addition, to avoid the potential information leakage and make a fair comparison, we introduce a new principle to generate classification benchmarks. Experimental results on four popular benchmark datasets, including Salinas Valley, Pavia University, Indian Pines, and Houston University, demonstrate that the SS3FCN outperforms state-of-the-art methods and can be served as a baseline for future research on HSI classification.
Retrieving Snow Depth Information From AMSR2 Data for Qinghai–Tibet Plateau

Passive microwave data have been extensively used for snow depth (SD) inversion, but their accuracy has large error in the Qinghai–Tibet Plateau (QTP). Thus, there is still room for improvement in regional SD inversion. Ground-measured SD data combined with elevation, longitude, and land cover data were used to develop a multifactor SD inversion model based on Advanced Microwave Scanning Radiometer 2 (AMSR2) over the QTP. The SD inversion model and the AMSR2 SD product were validated and compared using the meteorological stations and ground-measured SD over the QTP at the same time. The results show that the root-mean-square error (RMSE) of the novel model developed in this study is approximately 5 cm, which is much better than the accuracy of the AMSR2 SD product (11–13 cm) released by the Japan Aerospace Exploration Agency. When the ground-measured SD is less than 30 cm, the RMSE and the mean absolute error of the developed model are below 7 and 6 cm, respectively, and the BIAS is approximately 1 cm. The SD inversion results are poor in the western part of the plateau due to the complex terrain and thick snow cover, and its RMSE is greater than 15 cm. In conclusion, this novel SD inversion model is more applicable and accurate than the AMSR2 SD products.
Size-Dependent Switching Properties of Spin-Orbit Torque MRAM With Manufacturing-Friendly 8-Inch Wafer-Level Uniformity

We have developed a manufacturing-friendly spin-orbit torque magnetic random access memory (SOT-MRAM) technology in CMOS compatible 8-inch fab process. The proposed SOT-MRAM process technology resolves etching non-uniformity and reduction of high resistivity heavy-metal nanowire resistance issues. Besides, we present device size-dependent switching current threshold in the proposed SOT-MRAM cell structure. To realize the potential of our fabricated SOT-MRAM, wafer-level uniformity, cycling and temperature dependence SOT switching have been comprehensively investigated. Furthermore, the thermal stability factor ( ${\Delta }$ ) was calculated from temperature-dependence SOT switching to fulfill the thermal stability criteria, i.e., > 10 years of this emerging SOT-MRAM technology.
Investigating Edge vs. Cloud Computing Trade-offs for Stream Processing

The recent spectacular rise of the Internet of Things and the associated augmentation of the data deluge motivated the emergence of Edge computing as a means to distribute processing from centralized Clouds towards decentralized processing units close to the data sources. This led to new challenges in ways to distribute processing across Cloud-based, Edge-based or hybrid Cloud/Edge-based infrastructures. In particular, a major question is: how much can one improve (or degrade) the performance of an application by performing computation closer to the data sources rather than in the Cloud? This paper proposes a methodology to understand such performance trade-offs and illustrates it through experimental evaluation with two real-life stream processing use-cases executed on fully-Cloud and hybrid Cloud-Edge testbeds using state-of-the-art processing engines for each environment. We derive a set of take-aways for the community, highlighting the limitations of each environment, the scenarios that could benefit from hybrid Edge-Cloud deployments, what relevant parameters impact performance and how.
RecANt: Network-based Recruitment for Active Fake News Correction

To improve the reliability of content shared on social media, effective strategies for mitigating the diffusion of fake news are increasingly necessary. Traditionally, to counter false belief a competing cascade approach is used. This approach assumes that the opposite belief is already known, and thus, not applicable to newly spreading fake news. Another approach is to block nodes and links of the network to impede the flow of fake news (rumor/influence blocking). However, a more active way to battle the dissemination of fake news is to propagate the corresponding real news, since people who receive the real news in tandem with the fake news are less likely to believe in fake news. Such a setting is especially useful on a messaging platform such as WhatsApp, where the news item flows as a private message and the correction of fake news and its propagation must be performed by the users within the network as they receive it. To achieve this goal, we propose network-based recruitment for active fake news correction (RecANt) to find a set of individuals of a pre-defined size to be incentivized for actively fact-checking and passing on the real news so as to reach the maximum number of nodes in the network. These individuals should be such that they are likely to receive the fake news so that they can test its credibility, and when they propagate the corresponding real news, it reaches a large number of individuals. We prove that RecANt is NP-Hard with a monotone and submodular objective, leading to a polynomial time greedy algorithm (AFC) which provides a (1 – 1/e – ϵ)-approximation. We further optimize the runtime of AFC by developing a fast graph-pruning heuristic (RAFC) that performs as well as AFC in checking the spread of fake news while reducing the runtime significantly. Simulations on several networks demonstrate that our approach outperforms popular social network centrality measures and state-of-the-art information diffusion algorithm.
Numerical Study of the Effect of Pre-Pulse on THz Generation by Two-Color Laser-Induced Microplasma

In this article, a photocurrent model is used to investigate terahertz (THz) generation in two-color laser-induced preformed air microplasma. Comprehensive 2-D numerical particle-in-cell (PIC) simulations are done to understand the most important mechanism of THz generation in laser-induced microplasma. The model used in this simulation involves both ionization current (transverse current) and ponderomotive forces (longitudinal current). The laser pulse is divided into two types, a pre-pulse and a delayed main pulse, which propagate in a parallel direction (i.e., collinear configuration). Our simulation shows that the THz amplitude significantly reduces in the presence of a pre-pulse compared to the case with no pre-pulse. Also, in the study of how a time delay between the pre-pulse and the main pulse affects THz radiation, it has been demonstrated that THz radiation decreases as the time delay increases. Finally, through the investigation of the main pulse and pre-pulse intensity ratio, it emerged that the THz amplitude decreased with an increase in that ratio. The results point to the major role of tunneling ionization in THz generation via laser-induced microplasma.
Understanding Multilingual Communities through Analysis of Code-switching Behaviors in Social Media Discussions

Currently, the enormous span of social media usage – while providing valuable resources for linguistic behavior analysis – makes tracking and understanding these multilingual discussions a challenging task. We have undertaken a multidisciplinary comprehensive study of multilingual discussions via the development of specialized data collection techniques that discover and track multilingual users of social media, and their associated discussions, within a defined geographical region. To facilitate automatic discussion analysis of large numbers of discussions we generated a machine learning model based on ground truth data obtained from Amazon Turk. Our approach goes beyond analyzing social media posts in isolation, by analyzing them in the context of the discussion in which they appear. We show a selection of example discussions found using our approach which reveals a number of interesting socio-linguistic interactions in the communities that we sampled, in support of approach as a general methodology for multilingual community analysis.
A Highly-Reliable Full-Automatic System for Analyzing ECG Waveforms in Real Time Applications

The ECG analysis system, presented in this paper, provides beat-to-beat localization, classification and measurements in real time. Besides, numbers of rhythm analysis events can be detected instantly by the system, including critical ventricular and atrial arrhythmia events. Using the system will increase the cardiac safety for patients in many cardiac applications, including home-monitoring, ambulatory monitoring and cardiac drug safety. The algorithms used by the system are validated and tested. Furthermore, the system can be deployed on different computing hardware targets and operation systems.
Noninvasive Electrocardiographic Imaging Diagnosis Of Brugada Syndrome

Brugada syndrome (BrS) is a well-known inherited heart disease with a high risk of a sudden cardiac death (SCD) in young male patients. Thy only way to make a diagnosis is standard ECG. The spectrum of repolarization changes between normal ECG and BrS diagnostic criteria, are called BrS-like ECGs. They occur due to nonspecific right bundle brunch conduction disorders. Patients with this abnormal ECG are always a challenge for a doctor - is there a potentially fatal diagnosis or variant of normal ECG.
Complex Event Analysis for Traffic Risk Prediction based on 3D-CNN with Multi-sources Urban Sensing Data

Predictive analytics are concerned as a type of complex event processing where a complex event can be predicted by utilizing insights extracted from a set of related events. This paper introduces a new complex event analysis for traffic risk prediction using 3D-CNN and a set of related events detected from multi-sources urban sensing data (e.g., congestion, traffic accident, precipitation). The contribution of this paper involves (1) the spatio-temporal information of multi-sources urban sensing data is reserved and wrapped into 3D raster images towards being able to leverage recent developments of 3D-CNN to conduct predictive analytics, (2) the imbalanced data problem which could severely affect the performance of deep learning models is tackled by straightening curved geographic chains, (3) traffic risks can be predicted well in both short-term and medium-term time horizons, and (4) The influence of related events detected from extra factors on a complex event can be explained explicitly. The proposed method is evaluated on the real dataset collected in Kobe, Japan during 2014 and 2015. The comparisons to baseline methods such as historical average and 2D-CNN show the advantage of the proposed method as well.