Miracle-dz/UniFAR-FTUs huggingface dataset:
TrafficPSSF: A Fast and An Effective Malware Detection Under Online and Offline

The use of Android phones is becoming more and more widespread, and Android malware is also entering everyone’s field of vision. In this paper, we propose TrafficPSSF as a fast and an effective method for traffic detection and classification under online and offline detection. The traffic collection platform collects traffic data of application. Especially, we design an online detection and offline detection. One of the features of TCP session is the packet size, which is used for online detection. We can detect malicious traffic without waiting for all traffic packets to arrive, which can improve efficiency. What’s more, we use combination classifier model for our server to increase the accuracy of malicious detection. In the offline detection, we use seven statistical features of TCP as our model input and random forest algorithm for model training. The experiment shows that the online detection has 98.35% of the malicious detection rate, and the offline detection and classification accuracy rate reach 99.98%.
Conditional Generative Adversarial Network for Defect Classification with Class Imbalance

Automated Optical Inspection (AOI) is used for defect inspection during industrial manufacturing process. It uses optical instrument to snap the surface of products and identify defects through technique of machine vision processing. Deep learning and convolution neural network automatically produce the feature which are useful for identify the defect correctly. However, the class imbalance for number of defect samples and normal samples is typically in industrial process, which will lead to poor accuracy of deep learning model. This paper integrate pix2pix, a Conditional Generative Adversarial Network (GAN), which can generate synthetic image automatically, to generate more defect images to adjust the data distribution for class imbalance. Eventually, this paper uses Dense Convolutional Network (DenseNet) to get better result of defect data classification with manipulated data than with original data.
Effective Distance based Low Rank Representation for Image Classification

Low-rank representation (LRR) has recently attracted a great deal of attention due to its pleasing efficacy in exploring low-dimensional subspace structures embedded in data. However, conventional LRR methods simply use Euclidean distance to measure the similarity of samples, ignoring the dynamic structure information of data. Meanwhile, recent studies have shown that a probabilistically motivated distance measurement (called effective distance) can model the dynamic structure information of data. In this paper, we propose an effective distance based LRR (EDLRR)method for representation learning. The proposed EDLRR method can not only represent the dynamic structure of data, but also capture the geometric information in the inherent nonlinear data. Our method mainly uses Effective Distance Computation and Effective Distance based Low-Rank Representation. We evaluate our method on datasets in the task of image classification, with results demonstrating the effectiveness of the method.
Fog Service in Space Information Network: Architecture, Use Case, Security and Challenges

As a large heterogeneous information infrastructure, space terrestrial coinformation network provides a reliable and effective services for all types of space-based users, aviation users, marine users and land-based users through satellite networks. However, the precious computing and storage resources of satellite nodes during the service life is not fully exploited, and the enormous potential of the space information network (SIN) needs to be tapped to provide personalized services to end users. In this paper, to improve the utilization of satellite node resources and better meet the real-time requirements of compute-intensive and delay-sensitive users, we propose the SIN fog service. SIN fog service is an extension of fog service paradigm. The visionary concept is to integrate the computing power of satellite network edge nodes, enabling a wide range of benefits, including enhanced computing power, decreased bandwidth, reduced latency and without the need to lay complex and expensive ground networks, which can be widely developed in location navigation, environmental detection, traffic management, anti-terrorism, etc. We present the architecture and a potential use case of the SIN fog service. Then we discuss several related key security and challenges and finally the anti-quantum and efficient mutual-authentication protocol is introduced.
Android Malware Familial Classification Based on DEX File Section Features

The rapid proliferation of Android malware is challenging the classification of the Android malware family. The traditional static method for classification is easily affected by the confusion and reinforcement, while the dynamic method is expensive in computation. To solve these problems, this paper proposes an Android malware familial classification method based on Dalvik Executable (DEX) file section features. First, the DEX file is converted into RGB (Red/Green/Blue) image and plain text respectively, and then, the color and texture of image and text are extracted as features. Finally, a feature fusion algorithm based on multiple kernel learning is used for classification. In this experiment, the Android Malware Dataset (AMD) was selected as the sample set. Two different comparative experiments were set up, and the method in this paper was compared with the common visualization method and feature fusion method. The results show that our method has a better classification effect with precision, recall and F1 score reaching 0.96. Besides, the time of feature extraction in this paper is reduced by 2.999 seconds compared with the method of frequent subsequence. In conclusion, the method proposed in this paper is efficient and precise in the classification of the Android malware family.
Deep convolutional neural networks for detection and classification of tumors in mammograms

Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD). System based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO), in this work we used YOLO version three (YOLOv3). YOLO based CAD system can handle detection and classification simultaneously in one framework. It’s a little bigger than last time but more accurate. The proposed CAD system contains four steps : preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using fully connected neural networks (FC-NNs).
Resource sharing in CMOS SPAD arrays: application requirements and design solutions

The great potential of Single-Photon Avalanche Diodes (SPAD) co-integrated with CMOS electronics is still far to be fully exploited, mainly due to technological constraints. Since many applications require power-hungry time-tagging circuits with picosecond resolution, sharing readout channels among many SPADs is compulsory to improve sensor efficiency and reduce problems related to power consumption. This paper summarizes the techniques proposed so far to share resources in SPAD arrays, describing several key circuit blocks used in advanced architectures designed for different application domains. The current state of the art, together with future promising directions fostered by 3D integration technologies, is reviewed.
Iris Recognition based on Displacement Information using a Sparse Matching Technique

Iris recognition is one of the most promising fields in biometrics. During the image acquisition, images are affected by surrounding parts of the eye such as eyelids or eyelashes. Besides, deformations of the iris image are produced due to pre-processing procedures. In this work, a new iris recognition method is presented based on the displacement information. This approach is based on the fact that deformations produced in the iris images can be used as discriminative information. Our proposal introduces the use of image matching based on optical flow to the iris recognition area. The proposed methodology uses the DAISY descriptor combined with the algorithm CPM, for feature extraction and matching processes, respectively. The experimental results on the CASIA-Iris-Interval dataset demonstrate the high accuracy of the proposed method. Moreover, comparison with other approaches based on seed points shows that it improves their results and it is competitive with other techniques.
Data Driven Quality Control of Cast Iron Based on Adaptive PSO-LWLR Thermal Analysis Model

Rapid detection of the chemical compositions is of great significance to stabilize the quality of cast iron production, which is usually achieved by thermal analysis in front of the furnace. However, the traditional thermal analyzer usually adopts a fixed parametric mathematical model. Under complex and changeable working conditions (such as fluctuation of furnace burden, changes in process conditions and sample cup batches, etc.), the fixed model often causes large deviations in chemical composition detection, and its parameters are not easy to adjust, which greatly limits the popularization and application of thermal analyzers in front of the furnace. To this end, this study is devoted to exploring a more adaptive thermal analysis model. The temperature eigenvalues of cooling curves are taken as input parameters, local weighted linear regression (LWLR) algorithm is used for local fitting, and particle swarm optimization (PSO) algorithm is used to optimize the bandwidth parameter of LWLR. Finally, a PSO-LWLR hybrid model is formed. In a large foundry, the thermal analysis temperature data and corresponding chemical composition data of more than 200 furnaces of hot metal were collected from actual production. By using the above PSO-LWLR model, and comparing with the multiple linear regression (MLR) model and BP artificial neural network (BP-ANN) model, it is proved that the proposed model can achieve higher prediction accuracy. Unlike the global model with fixed parameters, PSO-LWLR is a locally optimized Just-In-Time (JIT) learning model. It can build different optimized local models for different query samples, so it has stronger adaptability and robustness under time-varying working conditions.
A Statistical Analysis for Wavelength-Resolution SAR Image Stacks

This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59% of the tested samples passed the Anderson–Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.
Big Data Analytics for Tool Health Monitoring in Panel Industry

Tool health monitoring and maintenance has become a more challenge issue for big data recently and is extremely essential in today's highly competitive environment in industry. Good monitoring approach and right maintenance strategy will be a great benefit to the company for reducing the cost and prolong the useful life of machines. Related researches have been studied based on different methods and approaches for investigating the health status of machines in many fields. In this study, we provide a data mining framework for monitoring the tool heal status under the partial least squares approach as we as the control chart construction. A real data from panel industry is applied to demonstrate our proposed research.
Broadband Folded Reflectarray Fed by a Dielectric Resonator Antenna

In this letter, a broadband folded reflectarray (FRA) is designed and fabricated. The FRA consists of a main reflectarray, the polarizing grid, and a feeding dielectric resonator antenna embedded in its structure. With the help of the multilayered reflecting element, the main reflectarray is capable to provide 90°of polarization rotation and proper phase compensation for a linearly polarized incident wave. By altering the microstrip line length, a wide phase coverage can be obtained with a good phase linearity. To validate the FRA design, both simulations and experiments are conducted, and good agreements are obtained. The FRA accomplishes 31% matching bandwidth from 9 to 12.1 GHz. In addition, a high aperture efficiency of 50.3% and the peak gain of 28.08 dBi at 10.8 GHz is achieved. Compared with the published works, the proposed FRA is especially advantageous for low profile and good efficiency.
Finding Relations Between Requirements for Healthcare Information Systems Use in Hospitals: A Study on EMRAM and JCI

EMRAM is a maturity model which goal is to measure the adoption and utilization of HIS functions in hospitals. However, maturity models regarding HIS in healthcare settings are not comprehensive and lack detail. Therefore, it is important for EMRAM to learn from other sources of HIS evaluation in healthcare organizations, such as JCI. In addition, it is important to understand how to adapt processes and implement technologies that can ensure compliance with the requirements established by both bodies. In this paper, we carry out an evaluation to identify relations between JCI and EMRAM requirements. We extracted EMRAM and JCI requirements and identified relations between them, with further validation by specialist. We identified 127 relations between JCI requirements and EMRAM and/or HIS. Six JCI requirements specifically related to IT are not currently required by EMRAM and could be used to promote the evolution of the maturity model. We also identified the JCI requirements that can be supported by HIS, which can be used by healthcare organizations to facilitate the management of JCI and EMRAM conformance.
IPSNN: Identification of Protein Structure based Neural Network

Protein structures play key roles in many fields of biology. However, identification of protein structural types from protein sequences seems to be a challenge issue. In this work, protein structure prediction issue can be regarded as the peptide segments. The neural network, whose parameters is optimized by the PSO algorithm. And then, the sample can be described by the amino acid energy interaction, which is a novel feature, in this work. The results show that the IPSNN algorithm has better performances than other art-of-the-state methods.
Deep quad path quality evaluator combined aesthetic estimation for Multi-exposure image fusion

There are limitations in existing multi-exposure fused image quality assessment (MEF-IQA) approaches. The first is that the existing approaches are hard to estimate the consistency of luminance distribution in fused images, which can be easily destroyed in fusion process. The second is that the aesthetics estimation of fused images are neglected in existing approaches, which should be considered for the images produced by fusion algorithms to give people an aesthetic experience. In order to solve these problems, a MEF-IQA approach based on the deep network was proposed. First, we adapted a quad path network as luminance error quality evaluator (LE) to extract the features, which can capture the luminance distribution patterns, from fused images. Then, we trained an aesthetic evaluator (AE) to obtain the feature which can describe the aesthetic attributes of fused image. Finally, the two extracted features are combined for predicting the perceptual quality scores. Experimental result clearly shows that the proposed approach can quantify the image quality reliably, and it is highly correlated with subjective quality scores.
Improved Mask R-CNN for Lung Nodule Segmentation

With more and more people suffer from lung cancer, computer-aided diagnosis plays a more and more important role in lung cancer diagnosis. CNN has achieved state-of-the-art performance in image processing, and Mask R-CNN outperforms most other methods on instance segmentation. However, the target is extraordinarily small, and the background is very large in images, which results in a large number of negative examples and most of them are easy negatives. They will contribute a large part of the loss value in smooth loss function. The class imbalance problem leads to inefficient training, which makes model degenerated. In this paper, we propose a method based on Mask R-CNN to segment lung nodules. Due to the non-uniformity of CT values, we use the Laplacian operator to do feature dimensionality reduction for filtering out part of the noise. In our model, the novel function Focal Loss is used to suppress well-classified examples. The model is tested on LIDC-IDRI dataset and the results showed that the average precision of lung nodules reaches 78%. Compared with the smooth loss function in Mask R-CNN it improves by 7%.
Fully-Synthesizable Current-Input ADCs for Ultra-Low Area and Minimal Design Effort

A fully synthesizable ADC architecture is proposed for low-end current sensing applications. Being based on standard cells and designed with a fully-automated flow, the proposed ADC allows very low area, digital-like scaling across CMOS technology generations, technology and design portability, minimal design effort, and immersed-in logic design (i.e., low integration effort), compared to traditional analog-intensive designs. In addition, it allows direct current readout without requiring a transresistance stage. Testchip measurements show a 5-nA to $1-\boldsymbol{\mu} \mathbf{A}$ input range, 6.7-bit ENOB and 2.2-kS/s sample rate, at 940-nW power and $4,580-\boldsymbol{\mu} \mathbf{m}^{2}$ area. To the best of the authors' knowledge, this testchip is the first demonstration of a fully-synthesizable input-current ADC. Along with the analysis of the specific limitations of the presented demonstration, this work aims to pave the way for a new class of current-input ADCs that can be designed and integrated with logic within hours, and occupy a silicon area in the order of 10kgates.
Active Fault Tolerant Control of Adaptive Cruise Control System Considering Vehicle-Borne Millimeter Wave Radar Sensor Failure

Vehicle-borne millimeter wave radar sensor may malfunction during signal acquisition and transmission process, which will affect the decision of adaptive cruise control (ACC) system and the safe driving of vehicles. However, adding other redundant environment-aware devices will increases costs. Therefore, in this paper, an active fault tolerant control of ACC system considering vehicle-borne millimeter wave radar sensor failure is proposed. Sensor faults are taken as discrete events, and the mixed logical dynamical (MLD) model of ACC upper control system is built which includes both the fault-free dynamics and the fault dynamics of the system. Then, the active fault tolerant control model of ACC system is established based on model predictive control (MPC) framework. Compared with the existing researches, this work emphasizes on the active fault tolerant control without adding other redundant environment-aware devices, which has not been fully revealed by the existing researches and is important to the industrial application. Moreover, the active fault tolerant control method can be easily ported to other driver assistance system besides ACC. The simulation results show that the vehicle equipped with the active fault tolerant ACC system can still drive safely and smoothly without being affected by radar sensor failures, which demonstrates its great significance to improve vehicle’s own intelligence and ensure the safe driving instead of relying entirely on sensors.
Calibration-Free Gaze Zone Estimation Using Convolutional Neural Network

In this paper we propose a gaze zone estimation method using deep learning. Compared with traditional method, our method does not need the procedure of calibration. In the proposed method, a Kinect is used to capture the video of a computer user, which is pre-processed to suppress illumination variations. After that, haar cascade classifier is adopted to detect the face region and eye region. Then, the eye region is used to estimate the gaze zone on the monitor via a trained CNN (Convolution Neural Network). Experimental results show that the proposed method has a high accuracy, which can be applied in human-computer interaction.
High Precision Classification of Hyperspectral Image Based on A Hierarchical Localized Multiple Kernel Learning Method

Aiming at the high precision classification of hyperspectral image in the case of small-size samples, a hierarchical localized multiple kernel learning (HLMKL) method for hyperspectral image classification is presented in this paper. By using a non-uniform sampling, the hyperspectral image can be hierarchically represented as several data sub-sets with different scales. On this basis, the dimension-reduced features are obtained through the KPCA method, and the hierarchical features and the multiple kernel function can be fused after the respectively mapping by introducing the localized multiple kernel model (LMKL). Ultimately, an effective classifier for hyperspectral image is trained by global optimization learning. Experiment results show that the proposed classification method has better universal practicability and robustness, which can give a higher overall accuracy (OA) and can greatly improve the average accuracy (AA) comparing to the single spectral classification algorithms and the non-hierarchical LMKL method. Furthermore, the presented hierarchical method with nonuniform sampling can improve the final classification precision more greatly for the small-size samples of the field objects.
The Effect of Implementing Resonator-Interdigital Capacitor and Complementary Split Ring Resonator (CSRR) on MIMO Antenna

Indoor Wi-Fi networks with 4G and 5G technology can work properly on wide coverage for high data rate services. Existing Wi-Fi devices need to implement Multiple-Input Multiple-Output (MIMO) antenna technology to provide high data rate. However, the antenna bandwidth and gain are relatively small. On previous research, the MIMO antennas with Resonator and Interdigital Capacitor have wide bandwidth, but gain of the antennas is low. In previous research, Complementary Split Ring Resonator (CSRR) on MIMO antennas provides high gain but the bandwidth decreases. This paper proposes an 2x2 MIMO monopole antenna design at 2.4 GHz for indoor Wi-Fi that implementing two combined methods, i.e., Resonator-Interdigital Capacitor with CSRR to improve the antenna performances. The idea is the addition of Resonators and Interdigital Capacitors is to increase the bandwidth, while CSRR is added to increase gain. The results show the proposed antenna has better return loss and decoupling values of 0.323 dB and 2.359 dB, respectively. The bandwidth and gain of the proposed antenna increase by 73.7 MHz and 0.361 dB, respectively when compared to Resonator Interdigital Capacitor MIMO antenna. The proposed antenna has return loss of −17.784 dB and decoupling value of −12.871 dB at 2.4 GHz, with the bandwidth of 644.3 MHz and gain of 1.996 dB.