Miracle-dz/UniFAR-FTUs huggingface dataset:
Kernel-Based Decomposition Model With Total Variation and Sparsity Regularizations via Union Dictionary for Nonlinear Hyperspectral Anomaly Detection

Many linear approaches have been extensively proposed for the anomaly detection problem in hyperspectral images (HSIs), while nonlinear approaches have been rarely studied although most practical cases are nonlinear. Moreover, these existing nonlinear methods simply nonlinearly map each pixel into a high-dimensional space, which does not describe complex light scattering effects between endmembers. To address the above issues, this article proposes an endmember-kernel-based decomposition model with total variation (TV) and sparsity regularizations via a union dictionary for the nonlinear anomaly detection in HSIs. The proposed decomposition model utilizes endmember-kernel theory to handle nonlinear interactions between atoms in the dictionary, allowing for the effective characterization of complex light scattering effects. By using this endmember-kernel-based decomposition model, hyperspectral imagery can be decomposed into three components: anomaly, background, and noise. To separate these components effectively, the TV and sparsity regularizations are incorporated into the decomposition model to characterize the spatial properties of the background and the anomaly, respectively. Besides, we present a novel construction framework of union dictionary that combines superpixel segmentation and clustering methods sequentially to achieve more accurate dictionary representation capabilities. Finally, the anomalous level of a tested pixel is calculated by the abundances associated with the anomaly dictionary. The experimental results on both synthetic and real hyperspectral datasets demonstrate that the proposed method outperforms several linear and nonlinear state-of-the-art anomaly detectors.
Immunity Study: Port Impedance Measurement of PMU and PCI testing under EMP

Phasor Measurement Units (PMUs) are critical for the Wide Area Measurement System (WAMS), especially for real-time grid monitoring, disturbance location, and situation awareness. Once they are damaged, the stable operation of the power system will be affected directly and greatly. Therefore, it is urgent and necessary to study their immunity to electromagnetic pulse (EMP) events. In this paper, an effective impedance measurement scheme is proposed for port impedance measurement of PMU. The equivalent non-uniform transmission line model is established to eliminate the impact of the fixture in the de-embedding process. Then circuit of pulsed current generator is established to generate damping sinusoid and double exponential wave applied to the port. Finally, by using measured impedance as a load of the generator, the voltage and current responses of different ports are calculated in the pulsed current injection (PCI) testing simulation. Results show that common-mode impedance is large and differential mode impedance is small. The critical condition will appear in the common mode for voltage and differential mode for current.
Analysis and Optimization of an Analog MOSFET with a Slit Well at Channel Center Towards Higher Output Resistance

This paper reports on a novel approach to improve and optimize an output resistance (Rout) which is critical to a long-channel analog MOSFET. The Rout degraded by halo doping can be overcompensated by the slit well inserted along the channel center, reaching the target value of 10Mohm*µm at the channel length of 0. 5µm. This improvement is brought by the pinch-off generation at the channel center which makes the drain-side half channel act as a buffer layer for the source-side half channel potential against the drain voltage. The increased fitting parameters can be precisely regressed and optimized by the machine-learning based TCAD scheme, maximizing the overall electrical performance including the Rout.
Implementation of Weakly Supervised Vitiligo Treatment Evaluation System

Vitiligo is the most common pigmentation disorder. In this research, we developed an automatic and effective evaluation system for lesion areas. We use Wood's lamp to collect and take pictures of the affected part and the normal part of the patient to build an image library. A weakly supervised system is implemented to gradually build a reliable and highly automated evaluation system through three stages. The first stage uses the activation map, a by-product of CNN deep learning, to preliminarily cut out the affected part from large-area images such as the head and face. In the second stage, a small number of doctor-marked and certified images of the affected area are used, and a large number of images of the affected area photographed by the Wood's lamp are used for self-learning and classification to generate accurate boundaries of the affected area and facilitate the evaluation of the curative effect. The third stage uses face recognition technology to correct the shooting angle of the camera to solve the image distortion caused by different shooting angles, which affects the calculation of the affected area.
Some consequences of shear on galactic dynamos with helicity fluxes

Galactic dynamo models sustained by supernova (SN) driven turbulence and differential rotation have revealed that the sustenance of large scale fields requires a flux of small scale magnetic helicity to be viable. Here we generalize a minimalist analytic version of such galactic dynamos to explore some heretofore unincluded contributions from shear on the total turbulent energy and turbulent correlation time, with the helicity fluxes maintained by either winds, diffusion, or magnetic buoyancy. We construct an analytic framework for modeling the turbulent energy and correlation time as functions of SN rate and shear. We compare our prescription with previous approaches that only include rotation. The solutions depend separately on the rotation period and the eddy turnover time and not just on their ratio (the Rossby number). We consider models in which these two time scales are allowed to be independent and also a case in which they are mutually dependent on radius when a radial dependent SN rate model is invoked. For the case of a fixed rotation period (or fixed radius) we show that the influence of shear is dramatic for low Rossby numbers, reducing the correlation time of the turbulence, which in turn, strongly reduces the saturation value of the dynamo compared to the case when the shear is ignored. We also show that even in the absence of winds or diffusive fluxes, magnetic buoyancy may be able to sustain sufficient helicity fluxes to avoid quenching.
An EEGgram-based Neural Network Enhancing the Decoding Performance of Visual Imagery EEG Signals to Control the Drone Swarm

Brain-computer interface (BCI) is a technology that controls computers by reflecting users’ intentions. Especially the electroencephalogram (EEG)-based BCI systems have been developed because of their potential utility. In BCI studies, controlling the drone swarm is one of the important issues since it improves work efficiency and safety. Also, current research has investigated how the drone swarms are controlled by imagining their formations using visual imagery (VI)-based EEG signals. The raw EEG signals and the spectrogram are widely used as input representations for decoding EEG signals. However, the decoding performance of the VI-based EEG signals is low to control the drone swarm due to noise in the raw EEG signals and information loss problems that may arise in the spectrogram. In this paper, we develop the EEGgram generator that extracts spectrogram-like features from the raw EEG signals minimizing information loss problems. Also, we propose the EEGgramNet, which could extract the significant information from VI-based EEG signals using both the spectrogram and the EEGgram as inputs. The proposed method outperforms an accuracy of 0.643, which is 8.4 % higher than that of the best conventional method. Hence, we demonstrate the possibility of constructing a VI-based BCI system to control the drone swarm by imagining its formations.
Detection of Android Malware Behavior in Browser Downloads

Hypertext transfer protocol has become one of the most widely used Internet or industrial control systems, so protecting Web services is critical. Many information security research institutions deploy honeypots to collect network packets and analyze the software services and methods for the attack to understand the hacker's attack behavior. However, in analyzing the log, the analyst may face the problem of massive data volume and repeated inspection. Therefore, the analyst needs a tool to detect whether many newly captured packets are new types to reduce the analysis log time. We propose a new exception detection method named 'Detect new exceptions for Web-server‘. It overcomes the characteristics of abnormal packets captured by honeypots, such as Diverse, Unlabeled, and Imbalanced, and learns historical strange packet behavior in a semi-supervised manner. Historical exception behavior models are built to detect whether newly captured packets are new-type exceptions. The discovery approach incorporated with a feature-based can accomplish the result of low false positives and typical false downsides. It is feasible to rapidly discover whether the recently caught packages are a new type of irregularity and determine the new sort of problem index, minimizing the moment and price of the evaluation procedure for analysts.
Prediction of PM2.5 in Hefei Based on a Hybrid CNN-GRU Model

The world has been affected by air quality problems, especially PM2.5 pollution for a long time. The existing prediction methods based on deep learning often pay attention to the influence of time series information on the prediction of PM2.5 concentration, but ignore the importance of spatial information, the prediction effect is unstable and the prediction accuracy of PM2.5 peak value is not high. In this paper, a method of combining Convolution Neural Network with Gated Recurrent Unit (CNN-GRU) is proposed to predict PM2.5 concentration by using current and past PM2.5 concentration data, meteorological data and time stamp data, considering the comprehensive influence of time series information and spatial information. The model consists of three parts: CNN structure, GRU network and fully connected network. Among them, the CNN structure can automatically extract and integrate the local trends and spatial correlation features of multi-site multimodal air quality data, and the GRU network can capture the long-term dependent features of time series. The combination of the first two parts can automatically extract the spatial and temporal features of air quality data and improve the accuracy of PM2.5 prediction. Finally, the fully connected network outputs the final prediction result of PM2.5. This study selects three monitoring stations from Hefei area as the research object. The prediction effect of hybrid CNN-GRU model is compared with that of traditional RNN, LSTM and GRU models, and the prediction performance of different models under different prediction steps is evaluated. The results show that the hybrid CNN-GRU model proposed in this paper has better prediction performance and stability than other models due to the introduction of spatial features.
On the Detection of Performance Regression Introducing Code Changes: Experience from the Git Project

For many software applications, performance is a critical Non-Functional requirement. Different software testing techniques are associated with various types of software testing, often related to performance regressions. Detecting code changes responsible for performance regression, for a rapidly evolving software with an increasing number of daily commits, is becoming arduous due to performance tests being time-consuming. The expense of running performance benchmarks, for all committed changes, has evolved to the bottleneck of detecting performance regression. Therefore, a recent technique called Perphecy was proposed to help, with quickly identifying performance regression introducing code changes, supporting the selection of performance tests, and reducing their execution time. However, Perphecy was not thoroughly tested on a large system, and so, its performance is still unknown in a real-world scenario. In this paper, we perform an in-depth analysis of Perphecy’s ability to identify performance regression introducing code changes on the open-source Git project. Our work challenges the ability of the model to sustain its performance when increasing the sample under test from 201 commits, to 8596 commits. In addition to verifying the scalability of the previous findings, we also test the efficiency of the proposed approach against a wider variety of performance regression introducing code changes. We provide insights into its advantages, limitations, and practical value.
Using ALBERT and Multi-modal Circulant Fusion for Fake News Detection

Fake news that combines text and images has a better story-telling ability than text-only fake news, making it more deceptive and easier to spread maliciously. Therefore, multi-modal fake news detection has become a new hot topic. There are two main challenges in this task. First, the traditional pre-training model BERT has more parameters and a relatively slow training speed, which limits the level of extracted text features. Second, in the multi-modal form, the fusion process is only a simple splicing of visual and textual features of news, and the obtained multi-modal features are insufficient to express the complementarity between multi-modal data and may have redundant information, potentially leading to biased detection results.In order to solve the above issues, we propose ALB-MCF, using ALBERT and Multi-modal Circulant Fusion(MCF) for fake news detection. ALB-MCF consists of four main modules: a multi-modal feature extractor, a multi-modal feature fusion, a fake news detection and a domain classifier. Specifically, the multi-modal feature extractor innovatively uses a pre-trained ALBERT model to extract text features and a pre-trained VGG19 model to extract visual features. Then, the text features and the visual features are fused into a multi-modal feature representation by MCF, which improves the fusion capability while avoiding an increase in parameters and computational cost. Finally, the multi-modal features are fed into the detector to detect fake news. The role of the domain classifier is mainly to remove event-specific features while retaining shared features between events, thus providing effective detection of emerging events. We have conducted extensive experiments on two real-world datasets. The results demonstrated that our model can handle multi-modal data more effectively, thus improving the accuracy of fake news detection.
Machine Learning-Based Heart Disease Prediction: A Study for Home Personalized Care

This study develops a framework for personalized care to tackle heart disease risk using an at-home system. The machine learning models used to predict heart disease are Logistic Regression, K - Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest and XG Boost. Timely and efficient detection of heart disease plays an important role in health care. It is essential to detect cardiovascular disease (CVD) at the earliest, consult a specialist doctor before the severity of the disease and start medication. The performance of the proposed model was assessed using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Compared to all machine learning algorithms, the Random Forest algorithm shows a better performance accuracy score of 90.16%. The best model may evaluate patient fitness rather than routine hospital visits. The proposed work will reduce the burden on hospitals and help hospitals reach only critical patients.
Optimal Improvement of Post-Disturbance Dynamic Response in Power Grids

Any disturbance in power grids can lead to electromechanical oscillations leading to fluctuations in frequency and rotor angle of generators. If proper control actions are not taken, severe disturbances could lead to instability of the system. However, the dynamic response of the system depends on the set of control actions. Therefore, we propose to optimally enhance the dynamic response of multi-machine power systems following disturbances by controlling the reference set-point of the governors. To achieve this, we combine the famous DC power flow model of the grid with linearized dynamics of generators/governors to achieve scalable linear programming (LP) based optimization model that provides a set-point of the governors to achieve an optimal dynamic response of the generators. We validated the accuracy of the proposed models by comparing them with an off-the-shelf commercial dynamic modeling tool using the IEEE 39-bus test system. Thereafter, we use the proposed optimization model to dampen the frequency oscillations resulting from load disturbances. The results show that the proposed model can sufficiently capture the dynamic behavior of power grids and the comparison of frequency and rotor angle response obtained from the optimization-based approach show that the proposed method can greatly reduce the settling time of frequency and rotor angle responses compared to the non-optimized cases.
Detecting DNS hijacking by using NetFlow data

DNS hijacking represents a security threat to users because it enables bypassing existing DNS security measures. Several malware families exploit this by changing the client DNS configuration to point to a malicious DNS resolver. Following the assumption that users will never actively choose to use a resolver that is not well-known, our paper introduces the idea of detecting client-based DNS hijacking by classifying public resolvers based on whether they are well-known or not. Furthermore, we propose to use NetFlow-based features to classify a resolver as well-known or malicious. By characterizing and manually labelling the 405 resolvers seen in four weeks of NetFlow data from a national ISP, we show that classification of both well-known and malicious servers can be made with an AUROC of 0.85.
Bi-level Optimization Based Electricity Selling Strategy for an Inter-Provincial Trader Considering the Uncertainty of Renewable Energy

In order to promote the optimal allocation of power resources nationwide, China is building a unified national power market gradually. In the initial stage of market construction, provincial power companies, as inter-provincial traders, participate in the two-level operation market on behalf of market members in the province. However, the uncertainty of renewable energy output brings difficulties to the formulation of inter-provincial trading strategies. For the provinces with surplus new energy, how to formulate the optimal electricity sales strategy under the two-level market environment, so as to reduce the market operation cost and increase the consumption of new energy, is an urgent problem to be solved at present. Therefore, this paper discusses the trading framework of grid companies participating in the inter-provincial market under the two-level market environment firstly. Then a two-level optimization model considering the uncertainty of new energy output in the province is established. The proposed model also optimizes the market operation cost under the risk scenario by using the conditional value at risk (CVaR) method and formulates the optimal inter-provincial electricity sales strategy. An example including one power selling province and four power buying provinces is given to verify the effectiveness of the proposed model. Finally, the IEEE30-bus system is used to simulate the intra-provincial topology for power flow calculation, and the applicability of the proposed model is verified.
Influence of Metal on Schottky Barrier Inhomogeneity in Ga2O3 Schottky Barrier Diodes

A systematic study of Schottky barriers fabricated on Ga2O3 film is reported. The Schottky barrier heights (SBHs) and current transport mechanisms were estimated through the analysis of current-voltage (I-V), capacitance-voltage (C-V), current-voltage-temperature (I-V-T), and x-ray photoelectron spectroscopy (XPS) measurements. The estimated values of the SBH of Ni varied from 1.01 eV to 1.13 eV depending on the method of analysis. These values are close to the theoretical value derived from the Schottky-Mott relationship. In contrast, for Au, there is a lack of general agreement between SBH values determined by different methods. This appears to hint at the presence of spatial inhomogeneities affecting the estimated SBH values to varying degrees depending on the method of analysis. The dependence of the SBH and carrier transport mechanism on metals suggests that metals/Ga2O3 interfaces are not fully pinned, and this fact can affect device reliability.
SHEARING FLOWS IN A GOODMAN-COWIN TYPE GRANULAR MATERIAL—THEORY AND NUMERICAL RESULTS

Goodman and Cowin (1972) proposed a continuum theory of a dry cohesionless granular material in which the solid volume fraction ” is treated as an independent kinematic fleld for which an additional balance law of equilibrated forces is postulated. By adopting the M˜ uller-Liu approach to the exploitation of the entropy inequality we show that in a constitutive model containing ”, _ ” and grad” as independent variables results agree with the classical Coleman-Noll approach only provided the Helmholtz free energy does not depend on _ ”, for which the Goodman-Cowin equations are reproduced. This reduced theory is then applied to analyses of steady fully-developed horizontal shearing ∞ow and gravity ∞ows of granular materials down an inclined plane and between vertical parallel plates. It is demonstrated that the equations and numerical results, presented by Passman et al. (1980) are false, and they are corrected. The results show that the dynamical behaviour of these materials is quite difierent from that of a viscous ∞uid. In some cases the dilatant shearing layers exist only in the narrow zones near the boundaries.
AutoDefense: Reinforcement Learning Based Autoreactive Defense Against Network Attacks

Attributed to the programmability and visibility provided by Software Defined Network (SDN) technologies, more flexible and complex functions can be performed on network at-tacks. However, identifying the attack traffic accurately for attack mitigation is a challenge. Most existing solutions leverage traffic characteristics to achieve this goal. Recent attacks characteristics have become more complex and indistinguishable from legitimate traffic. In this paper, we propose AutoDefense, a novel frame-work that leverages reinforcement learning techniques to deploy defense policies dynamically and adaptively based on the signals collected from the data plane. While we seek to achieve the same goal with the existing efforts where the network/server resources the attackers control should be limited, we allow more legitimate flows to enter the network, rather than relinquish bandwidth when attacks happen. Through evaluations, we demonstrate that AutoDefense could reduce 39% of the attack traffic and allow 48.6 % more legitimate flows in the network. AutoDefense also improves the average flow completion time by 42.7% for the flows with a long tail latency.