Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization

To explore the advantages of adversarial learning and deep learning, we propose a novel network intrusion detection model called SAVAER-DNN, which can not only detect known and unknown attacks but also improve the detection rate of low-frequent attacks. SAVAER is a supervised variational auto-encoder with regularization, which uses WGAN-GP instead of the vanilla GAN to learn the latent distribution of the original data. SAVAER’s decoder is used to synthesize samples of low-frequent and unknown attacks, thereby increasing the diversity of training samples and balancing the training data set. SAVAER’s encoder is used to initialize the weights of the hidden layers of the DNN and explore high-level feature representations of the original samples. The benchmark NSL-KDD (KDDTest+), NSL-KDD (KDDTest-21) and UNSW-NB15 datasets are used to evaluate the performance of the proposed model. The experimental results show that the proposed SAVAER-DNN is more suitable for data augmentation than the other three well-known data oversampling methods. Moreover, the proposed SAVAER-DNN outperforms eight well-known classification models in detection performance and is more effective in detecting low-frequent and unknown attacks. Furthermore, compared with other state-of-the-art intrusion detection models reported in the IDS literature, the proposed SAVAER-DNN offers better performance in terms of overall accuracy, detection rate, F1 score, and false positive rate.
Epileptic Seizures Prediction Using Deep Learning Techniques

Epilepsy is a very common neurological disease that has affected more than 65 million people worldwide. In more than 30 % of the cases, people affected by this disease cannot be cured with medicines or surgery. However, predicting a seizure before it actually occurs can help in its prevention; through therapeutic intervention. Studies have observed that abnormal activity inside the brain begins a few minutes before the start of a seizure, which is known as preictal state. Many researchers have tried to find a way for predicting this preictal state of a seizure but an effective prediction in terms of high sensitivity and specificity still remains a challenge. The current study, proposes a seizure prediction system that employs deep learning methods. This method includes preprocessing of scalp EEG signals, automated features extraction; using convolution neural network and classification with the support of vector machines. The proposed method has been applied on 24 subjects of scalp EEG dataset of CHBMIT resulting in successfully achieving an average sensitivity and specificity of 92.7% and 90.8% respectively.
Audio Codec Simulation based Data Augmentation for Telephony Speech Recognition

Real telephony speech recognition task is challenging due to 1) diversified channel distortions and 2) limited access to the real data because of the data privacy consideration. In this paper, assuming no real telephony data are available, we employ diversified audio codecs simulation based data augmentation method to train telephony speech recognition system. Specifically, we assume only wide-band 16 kHz data are available, and we first down-sample the 16 kHz data to the 8 kHz data; we then pass the down-sampled data through various categories of audio codecs to simulate the real channel distortion. As a result, we train our speech recognition with such distorted data. To analyze the effectiveness of different audio codec simulation methods, we classify them into three main categories according to their distortion severity, in terms of their spectrogram analysis. We conduct experiments on various real telephony test sets to show the effectiveness of the proposed data augmentation method. The result shows that the real data is more close with highly distorted simulation data, since the model with highly distorted data reduce the Word-Error-Rate 7.28%–12.78% compared to the baseline.
Finite-Time Stability of Switched Linear Time-Delay Systems Based on Time-Dependent Lyapunov Functions

In this paper, finite-time stability of switched linear time-delay systems has been addressed. By constructing a class of time-dependent common (multiple) Lyapunov functions, new explicit conditions for finite-time stability of the system under arbitrary switching and average dwell-time switching are established respectively. Compared with most of existing results in the literature, our results are easily verifiable by solving several linear matrix inequalities rather than complex matrix Riccati differential equations. The effectiveness of the proposed method is demonstrated by numerical examples.
Reconstituted Wafer Deformation Analysis Through Whole Process Emulation

Wafer reconstitution is a vital process for serving as a buffer to decouple the processing developments between IC fabrication and packaging. Through this approach, the IC packaging is then independent from the chip processing. However, such a process brings numerous mechanical and thermal loadings during molding and curing phases and this directly causes serious wafer warpage on the reconstituted wafer and imposes additional difficulties in subsequent processing. In this work, a global investigation on both fluid and thermo-mechanical analysis issues is firstly presented for addressing the observed die-shift and wafer warpage for finding the dominated factors and some preliminary results are presented. Furthermore, the key factor for affecting the reconstituted wafer warpage is analyzed by performing solid thermo-mechanical analyses through emulating the entire process. Specifically, to have a deeper insight, essentially material characterizations are performed to serve as the foundation for subsequent thermo-mechanical analyses and simplified 2D and detailed 3D finite element analyses have been constructed to mimic the entire reconstitution process. Detail thermo-mechanical processing steps are then emulated by these finite element models. After validation, systematic parametric studies are then performed to investigate the controlling factors for dominating wafer warpages. The simulation results indicated that the thermal expansion coefficients and the Young’s modulus of molding compounds could be the dominated factor. It is estimated that by choosing compounds with more desirable above-mentioned mechanical properties, it is expected that a 20 to 30 percentage reduction of warpage can be achieved.
Multi-Resolution Space-Attended Residual Dense Network for Single Image Super-Resolution

With the help of deep convolutional neural networks, a vast majority of single image super-resolution (SISR) methods have been developed, and achieved promising performance. However, these methods suffer from over-smoothness in textured regions due to utilizing a single-resolution network to reconstruct both the low-frequency and high-frequency information simultaneously. To overcome this problem, we propose a Multi-resolution space-Attended Residual Dense Network (MARDN) to separate low-frequency and high-frequency information for reconstructing high-quality super-resolved images. Specifically, we start from a low-resolution sub-network, and add low-to-high resolution sub-networks step by step in several stages. These sub-networks with different depth and resolution are utilized to produce feature maps of different frequencies in parallel. For instance, the high-resolution sub-network with fewer stages is applied to local high-frequency textured information extraction, while the low-resolution one with more stages is devoted to generating global low-frequency information. Furthermore, the fusion block with channel-wise sub-network attention is proposed for adaptively fusing the feature maps from different sub-networks instead of applying concatenation and $1\times 1$ convolution. A series of ablation investigations and model analyses validate the effectiveness and efficiency of our MARDN. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed MARDN against the state-of-the-art methods. Our super-resolution results and the source code can be downloaded from https://github.com/Periter/MARDN.
A Deep Learning Framework with Spatial-Temporal Attention Mechanism for Cellular Traffic Prediction

Currently, traffic prediction in cellular communication has become an important way to relieve traffic congestion, and then guarantee users' quality of experience (QoE) in multimedia service. However, when concerning traffic prediction in the context of big data, traditional deep learning models only have limited memory lengths and thus do not satisfy high prediction accuracy demand. In this paper, we design a new deep learningbased framework, which is built on our proposed novel spatialtemporal attention mechanism, to further increase the prediction accuracy of cellular traffic resource. Specifically, we firstly design a big data hierarchical architecture with four levels to extract meaningful information. Then, considering that cellular traffic resource from base stations characterizes spatial-temporal dependencies, we integrate conventional temporal attention mechanism for target area with that from spatial neighbor areas, proposing a novel spatial-temporal attention mechanism. Finally, we utilize this spatial-temporal attention mechanism based LSTM (STaLSTMs) to predict cellular traffic resource from base stations. Experimental results demonstrate that our proposed framework has better performances in cellular traffic prediction than other competing models. Importantly, in some application scenarios, this framework can also maintain high accuracy but only with 60% data.
Fluorine in the Solar Neighborhood: The Need for Several Cosmic Sources

The cosmic origin of fluorine is still not well constrained. Several nucleosynthetic channels at different phases of stellar evolution have been suggested, but these must be constrained by observations. For this, the fluorine abundance trend with metallicity spanning a wide range is required. Our aim is to determine stellar abundances of fluorine for . We determine the abundances from HF lines in infrared K-band spectra ( ) of cool giants, observed with the IGRINS and Phoenix high-resolution spectrographs. We derive accurate stellar parameters for all our observed K giants, which is important as the HF lines are very temperature-sensitive. We find that [F/Fe] is flat as a function of metallicity at [F/Fe]∼0, but increases as the metallicity increases. The fluorine slope shows a clear secondary behavior in this metallicity range. We also find that the [F/Ce] ratio is relatively flat for , and that for two metal-poor ( ), s-process element-enhanced giants, we do not detect an elevated fluorine abundance. We interpret all of these observational constraints as indications that several major processes are at play for the cosmic budget of fluorine over time: from those in massive stars at low metallicities, through the asymptotic giant branch star contribution at , to processes with increasing yields with metallicity at supersolar metallicities. The origins of the latter, and whether or not Wolf–Rayet stars and/or novae could contribute at supersolar metallicities, is currently not known. To quantify these observational results, theoretical modeling is required. More observations in the metal-poor region are required to clarify the processes there.
Influence of the Load on the Impulse Frequency Response Approach Based Diagnosis of Transformer’s Inter-Turn Short-Circuit

Frequency Response Analysis (FRA) is an Off-line condition monitoring technique for the transformers and is successful, in diagnosing mechanical faults and inter-turn short-circuits. Only limited literature is available for On-line FRA works, and the effect of the transformer’s load on the fault diagnosing capability of FRA has not yet been analysed. Motivated by this research gap, an experimental investigation was done to analyze the effect of load on IFRA based inter-turn short-circuit diagnosis. A single-phase 1 kVA, 240V/240 V transformer supplying R and R-L loads and a three-phase 5 kVA, 440 V/440 V transformer supplying an induction motor at various load levels were tested. Inter-turn short-circuits were emulated in their windings and, the effectiveness of IFRA in diagnosing the same was investigated. Comparisons were made between the frequency response of the loaded transformers in their healthier conditions and emulated fault conditions, based on the transfer function plots and the statistical parameters. Investigations reveal that the loads alter the useful spectrum of IFRA. The frequency response of the healthier transformer itself varies based on the transformer’s load and necessitates a careful focus on the sensitive frequency spectra for effective inter-turn short-circuit diagnosis in loaded transformers.
Reliability Assessment of AlGaN/GaN Schottky Barrier Diodes Under ON-State Stress

This article aims to study the degradation of Schottky Barrier Diodes (SBDs) with a Gated Edge Termination (GET) under ON-state stress conditions. After all the stress experiments a recoverable behavior is observed, which indicates charge trapping in pre-existing defects and no creation of new traps. A broad statistical analysis demonstrates better reliability and a longer lifetime compared to previous works in a 200-V technology. Some systematic differences in parameter degradation are observed depending on wafer location, likely caused by process-related variations. By using matched pairs (MPs) technique, we have demonstrated that probability distributions characterized by single Weibull slopes can be obtained over the wafer that could allow better characterization of the intrinsic reliability of these devices.
A Multi-Level Optimization Framework for Simultaneous Grasping and Motion Planning

We present an optimization framework for grasp and motion planning in the context of robotic assembly. Typically, grasping locations are provided by higher level planners or as input parameters. In contrast, our mathematical model simultaneously optimizes motion trajectories, grasping locations, and other parameters such as the pose of an object during handover operations. The input to our framework consists of a set of objects placed in a known configuration, their target locations, and relative timing information describing when objects need to be picked up, optionally handed over, and dropped off. To allow robots to reason about the way in which grasping locations govern optimal motions, we formulate the problem using a multi-level optimization scheme: the top level optimizes grasping locations; the mid-layer level computes the configurations of the robot for pick, drop and handover states; and the bottom level computes optimal, collision-free motions. We leverage sensitivity analysis to compute derivatives analytically (how do grasping parameters affect IK solutions, and how these, in turn, affect motion trajectories etc.), and devise an efficient numerical solver to generate solutions to the resulting optimization problem. We demonstrate the efficacy of our approach on a variety of assembly and handover tasks performed by a dual-armed robot with parallel grippers.
A Privacy-Preserved E2E Authenticated Key Exchange Protocol for Multi-Server Architecture in Edge Computing Networks

Edge computing has played an important role in enabling 5G technology which supports a great number of connected narrow-band IoT devices. In an edge computing architecture enabled with global mobile network, edge or IoT devices are wirelessly connected to the edge of the network. Data acquisition and processing will be handled at or close to the edge of the network in a distributed way. Since edge computing is a heterogeneous distributed interactive system with multiple domains and entities, it might suffer from potential attacks and threats. To provide a trusted edge computing, there must have a robust scheme that allows all participants to mutually authenticate in a secure and privacy-preserved way. With the rapid development of IoT technologies, mobile networks and edge computing architecture, single server has been unable to meet the needs of users. In this paper, we propose a privacy-preserved end-to-end password-based authenticated key exchange protocol for multi-server architecture in edge computing networks. Our protocol allows an end user to use an easy-to-remember password to login to the server, then through foreign agent compute a shared key with another end user for specific use of services. The proposed protocol provides strong user anonymity during communication process. Besides, the proposed protocol is proved to be secure using BAN logic and AVISPA tool. Furthermore, performance analysis shows that the proposed protocol gains stronger security and better computational efficiency. Providing lightweight computation with short key size of ECC, our work is a solution to lower latency and improve efficiency in edge computing networks.
Recent progress on superconductors with time-reversal symmetry breaking

Superconductivity and magnetism are adversarial states of matter. The presence of spontaneous magnetic fields inside the superconducting state is, therefore, an intriguing phenomenon prompting extensive experimental and theoretical research. In this review, we discuss recent experimental discoveries of unconventional superconductors which spontaneously break time-reversal symmetry and theoretical efforts in understanding their properties. We discuss the main experimental probes and give an extensive account of theoretical approaches to understand the order parameter symmetries and the corresponding pairing mechanisms, including the importance of multiple bands.
Monitoring Buildings at Landslide Risk With SAR: A Methodology Based on the Use of Multipass Interferometric Data

Interferometric methods are drivers of the recent exponential growth in the use of synthetic aperture radar (SAR) for monitoring both natural and anthropogenic hazards. Since the first use of interferometric SAR (InSAR) in the late 1990s to detect deformations associated with earthquakes and volcanoes, important developments have improved sensor performance and data-processing capabilities for the extraction of information of interest in geophysical applications. Here, we describe the improvements that have enabled the 3D reconstruction and monitoring of buildings, with reference to their protection against and prevention of risks. We focus on buildings affected by systematic displacements related to natural hazards-specifically, landslides. We address the straightforward use of InSAR methods in the detection and characterization of the hazard as well as in higher-level exploitation for geotechnical and structural studies and, more generally, vulnerability analysis.
Defects Inspection in Polycrystalline Solar Cells Electroluminescence Images Using Deep Learning

Solar cells defects inspection plays an important role to ensure the efficiency and lifespan of photovoltaic modules. However, it is still an arduous task because of the diverse attributes of electroluminescence images, such as indiscriminative complex background with extremely unbalanced defects and various types of defects. In order to deal with these problems, this paper proposes a new precise and accurate defect inspection method for photovoltaic electroluminescence (EL) images. The proposed algorithm leverages the advantage of multi attention network to efficiently extract the most important features and neglect the nonessential features during training. Firstly, we designed a channel attention to exploit contextual representations and spatial attention to effectively suppress background noise. Secondly, we incorporate both attention networks into modified U-net architecture and named it multi attention U-net (MAU-net) to extract effective multiscale features for defects inspection. Finally, we propose a hybrid loss which combines focal loss and dice loss aiming to solve two problems: a) overcome the class imbalance problem, and b) allowing the network to train with irregular image labels for some complex defects. The proposed multi attention U-net is evaluated on real photovoltaic EL images datasets using 5-fold cross validation technique. Experimental results demonstrate that the proposed network can segment and detect various complex defects correctly. The proposed method achieved the mean intersection over-union (m-IOU) of 0.699 and F-measure of 0.799 which outperforms the previous methods.
Community Detection in Scientific Co-Authorship Networks using Neo4j

Community structure in scientific collaboration network has become an important research area. Co-author of a paper can be thought of as a collaborative document between more than one authors. Community detection in co-authorship network reveals characteristic patterns of scientific collaboration in computer science research and help to understand the identity-organization of the author community. Louvain algorithm is a simple, easy to implement and efficient to recognize community in huge networks. In this paper, it is used to examine the structure of community in Computer University’s coauthor network in Myanmar. Neo4j is also used to visualize the co-authorship network analysis results. Modularity is used to measure the quality of the cluster structure found by community discovery algorithms. In experiment, Louvain algorithm gives more effective qualitative community structures than other algorithms in co-authorship network.
Effective Analytics on Healthcare Big Data Using Ensemble Learning

Healthcare big data is a collection of record of patient, hospital, doctors and medical treatment and it is so large, complex, distributed and growing so fast that this data is difficult to maintain and analyze using some traditional data analytics tools. To solve this difficulties, some machine learning tools are applied on such big amount of data using big data analytics framework. In recent years, many researchers have proposed some machine learning approaches on healthcare data to improve the accuracy of analytics. These techniques were applied individually and compared their results. To get better accuracy, this paper proposes one machine learning approach called ensemble learning, in which the results of three machine learning algorithms are combined. Soft voting method is used for combining accuracies. From these results, it is observed that ensemble learning can obtain maximum accuracy.
Designing Discrete-Time Sliding Mode Controller With Mismatched Disturbances Compensation

The main objective of this article is to explore the issue of how to improve the performance of discrete-time sliding mode control (DSMC) law for a class of discrete-time dynamic systems with both matched and mismatched disturbances. By using tools from mismatched disturbance compensation technique, a new kind of discrete-time sliding surface is constructed and subsequently utilized in designing the desirable DSMC laws. Specifically, two different types of DSMC laws, i.e., the equivalent-control-based DSMC law and the reaching-law-based DSMC law, are respectively constructed based upon the developed sliding surface. Rigorous analysis on stability of the corresponding closed-loop system is performed where it is shown that the mismatched disturbances could be successfully attenuated from the output channel in steady state. The effectiveness of the analytic result is supported by the experimental studies as well as numerical simulations.