Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Intrusion Detection Model Based on Bagged Tree

Intrusion detection is an important step to ensure network security. Under today’s network environment, because of the increasing complexity of network security issues and the increasing amount of network security data, intrusion detection systems require higher recognition accuracy. The accuracy and effectiveness of traditional methods of intrusion detection no longer meet the need of today’s big data era. In the past ten years, the performance of the classifier is further improved with the development of machine learning, so we chose to apply machine learning on intrusion detection. In this paper, we propose a new intrusion detection method, which is based on Bagged Tree, and the data set UNSW_NB15 is used to verify the model. Experimental verification proves that the model designed in this paper has higher detection accuracy than previous classic intrusion detection algorithms.
A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography and Covid-19 Detection

This paper presents COVID-Net, an Artificial intelligent system that can detect COVID-19 from chest X-rays based on machine learning. COVID-Net is a 3-stage machine learning (ML) system. COVID-Net is a system built on a convolutional neural network trained on over 10,000 frontal view X-ray images. The merit of this system is that it detects COVID-19 from other kinds of diseases and can be used to diagnose a new type of viral or bacterial pneumonia.
Orchestrating Intelligent Network Operations in Programmable Networks

Our communication networks have grown in size and have witnessed an influx of heterogeneous devices and their specialized device controllers. These devices include traditional computers, light-weight Internet-of- Things devices and their con-trollers, and also programmable switches and routers. Managing and protecting networks used by these devices is non-trivial, especially since the devices either use different standards or no standards at all for control and management. In this article, we discuss PraNA, an SDN-based architecture for managing and orchestrating such heterogeneous networks. At the heart of PraNA is the PraNA Orchestrator which offers a comprehensive network-wide view of the devices by using the services of a wide range of specialized controllers.
Deep Convolution Neural Network for Thai Classical Music Instruments Sound Recognition

Identifying Thai classical music instruments from recorded sounds is challenge research in the area of Thai music analysis such as Thai music retrieval and Thai automatic transcription. Feature extraction and recognition are the two fundamental steps in sound recognition. This study presents the comparison Mel-Frequency Cepstral Coefficient (MFCC) and Mel-Spectrogram features extraction whereas a deep learning model based on a convolutional neural network (CNN) model is used for Thai Classical Music Instruments Sound Recognition. Thai classical music instruments sound dataset is collected by recording 13 Thai classical music instruments sound. The results of this study show that MFCC outperforms Mel-Spectrogram and the convolutional neural networks model outperforms other methods such as Long Short-Term Memory (LSTM) and Convolutional Recurrent Neural Network (CRNN). The experiment shows that the MFCC with CNN performed the best in the experiment with 99.44 percent of accuracy and 0.99 of F1-score.
Augmenting Differentiable Neural Computer with Read Network and Key-Value Memory

A Differential Neural Computer (DNC) is a type of neural network architecture that can make use of external memory. A DNC model can represent complex data sequences and reason about them. However, training a complex DNC with large memory matrices was slow, hence this study focused on improving a DNC model to learn faster and perform bAbi question-answering tasks more accurately. The attempted improvements were to use key-value pairs for memory locations instead of arrays and to obtain a read vector from the memory matrix with a neural network. Evaluation of the improved DNC models on the bAbi dataset showed that their compute time was 13% shorter and their error rate was 6.6% lower. To conclude, the attempted improvements were successful, and the training speed of the key-value memory model also became shorter.
Screening TB Using Deep Transfer Learning

Tuberculosis is a major public health problem and has to be proactive screening for quarantine by means of developing machine learning model to screen suspected case. This can be mutually beneficial to healthcare providers and patients. The application of deep learning technique for medical image classification has been developed and grown exponentially over the past few years. We propose Convolution Neural Network (CNN) model because it is one of several well-known and high performance models for image classification. This research presents neural network to classify chest imaging into 2 classes: normal and tuberculosis. We collect 3 datasets of chest X-ray image: Montgomery, Shenzen and Bureau of tuberculosis. The researchers compared 4 CNN classification models to find out the best model that is suitable for chest X-ray. Performance was measured by using metrics: accuracy, precision, recall and AUC. The result of this study shows that DenseNet model is more accurate than others and we tune the model for the best threshold and train it with Thai Bureau of tuberculosis chest image for screening TB for Thai people. The accuracy for discrimination normal lung and TB-infected lung in the best model is 91% and AUC is 95%. This model would aided healthcare providers for TB screening large population in Thailand.
Infrared and ISAR Image Fusion Based on Quadtree Decomposition and Bézier Interpolation

Inverse Synthetic Aperture Radar (ISAR) image obtained by stationary radar is the result of longitudinal and lateral two-dimensional high-resolution imaging of moving target. Infrared image is a thermal image corresponding to the temperature distribution of the scene, which formed by collecting infrared radiation emitted from the target and the surrounding. In order to make full use of the complementary information of infrared image and ISAR image, an algorithm that combines infrared image with ISAR image is researched. Firstly, this paper applies quadtree decomposition and Bézier interpolation to reconstruct the background of the ISAR image. Secondly, the salient features of the ISAR image are extracted by subtracting the reconstructed background from the original ISAR image. Finally, this algorithm fuses the salient features with the infrared image using the fusion rule proposed in this paper, through which to get the final fused image. Experimental results show that the algorithm can better preserve both the clear target and detailed information of infrared image and ISAR image in subjective evaluation. Compared with other fusion algorithms, experiments also verify the superiority of the algorithm in objective evaluation.
An Overview of Finite/Fixed-Time Control and its Application in Engineering Systems

The finite/fixed-time stabilization and tracking control is currently a hot field in various systems since the faster convergence can be obtained. By contrast to the asymptotic stability, the finite-time stability possesses the better control performance and disturbance rejection property. Different from the finite-time stability, the fixed-time stability has a faster convergence speed and the upper bound of the settling time can be estimated. Moreover, the convergent time does not rely on the initial information. This work aims at presenting an overview of the finite/fixed-time stabilization and tracking control and its applications in engineering systems. Firstly, several fundamental definitions on the finite/fixed-time stability are recalled. Then, the research results on the finite/fixed-time stabilization and tracking control are reviewed in detail and categorized via diverse input signal structures and engineering applications. Finally, some challenging problems needed to be solved are presented.
Distributed Architecture for Intelligent Robotic Assembly Part III: Design of the Invariant Object Recognition System

In previous chapter it has been described the overall architecture for multimodal learning in the robotic assembly domain (Lopez-Juarez & Rios Cabrera, 2006). The acquisition of assembly skills by robots is greatly supported by the effective use of contact force sensing and object recognition. In this chapter we will describe the robot’s ability to invariantly recognise assembly parts at different scale, rotation and orientation within the work space. The chapter shows a methodology for on-line recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques. In this sense, the described technique for object recognition is accomplished using an Artificial Neural Network (ANN) architecture which receives a descriptive vector called CFDP vision systems as a sensorial mode for robots have a growing demand requiring more complex and faster image processing functions in order to implement more sophisticated industrial applications, like assembly automation.
Direction of Arrival Estimation Based on Deep Neural Network in MIMO System

Multiple-input multiple-output (MIMO) is the key technology for the fifth-generation (5G) mobile communication system. However, to reduce the hardware cost of the MIMO system, a low-resolution analog to digital converter (ADC) is usually used at the receiver, which reduces the data resolution and males the data recovery more difficult. To address this problem, in this paper, we focus on direction-of-arrival (DOA) estimation and propose a novel deep neural network (DNN) based framework to recover received data effectively. We use the multiple signal classification (MUSIC) algorithm to estimate the DOA of the recovery data and analyze the effect of different SNRs and quantization bits on the performance. The simulation results show that the proposed DNN based framework can achieve much better performance on DOA estimation compared with traditional methods.
An Automatic Information Extraction Method for the Combustion Flame of Chain Furnace Based on U-net

The combustion flame of chain furnace is the key indication for evaluating the coal combustion status and serve as the key to adjusting the combustion condition. However, it can’t avoid human errors caused by fatigue after technician worked for a long time, for the traditional method is solely rely on the experience of firemen to judge through manual observation through furnance’s observation port. In this study, photos of furnace combustion flame at the observation port is obtained by industrial camera, segmentation research from the inner boiler area, flame position and color image of coal combustion. Using these data, we employed a deep learning exercise with U-net network. The recognition accuracy reaches 92% by using 1000 original images as sample training data set, which meet requirement of the coal combustion regulation. Our study showed the speed of automatic acquisition of combustion information of chain furnace using U-net is less than 1 second. The method is highly stable, and it could reduce labor intensity and potentially replace manual observation. These combustion information parameters are deeply integrated with other process parameters such as boiler working temperature, pressure, flow and load, and the boiler combustion control strategy has been considered, it could improve the boiler coal combustion efficiency, reduce emission and pollution, and conserve energy.
MDSDroid: A Multi-level Detection System for Android Repackaged Applications

The repackaged Android applications released in the app markets have posed huge threats to users' privacy and security. Prior detection algorithms based on code similarity can hardly detect obfuscated and encrypted applications. UI features are resistant to code reinforcement, but they may lack scalability if dynamically obtained when an application is executing, therefore they can be affected by noise through static methods. In this paper, MDSDroid (Multi-level Detection System for Android Apps) is proposed, which combines the fast scalability of static methods with the accuracy of dynamic methods into the extraction of layout features. A novel tree structure similarity comparison algorithm is presented to measure applications' similarity on the multi-dimensional sequence. Our method is evaluated on the AndroZoo dataset. Experiments show that it has better performance than the well-known repackaging detection tool FSquaDRA, in terms of accuracy, scalability, and resistance to code reinforcement.
Localization Method of Optic Disc in Retinal Image Based on Texture Feature and Shape Feature

The most important work of retinal image processing is blood vessel segmentation and optic disc location. The fovea in the retina image, as well as the bright spots and blood oozing caused by the disease will hinder the detection of optic disk. It is important for ophthalmology to locate the optic disc accurately. This method makes use of standard deviation in texture features and circular in shape feature. In this paper, we have screened the optic disc area, and then the angle information is used to detect the circle. The accuracy of our method to locate the optic disc can reach 100% on DRIVE dataset.
Study on Mechanical Behavior and Controlling Assembling Modes of Universal Segment Lining for Shield Tunnel

Simulating segment lining with the beam-spring model and calculating loads acting on segment lining with the load-structure model,the mechanical behavior of universal segment lining under different assembling modes for the metro section of the shield tunnel is analyzed with the finite element method.The results show that: The mechanical behavior of universal segment lining is different under different assembling modes,which relates to the assembling types,places of transverse and longitudinal joints and position of the seal roof block of the objective segment ring;design values of positive and negative moments,shearing force of segment liming and shearing forces of longitudinal bolts of universal segment lining are controlled with staggered joint assembling,and design values of deformation and surrounding rock resistances are controlled with straight joint assembling.The assembling modes with biggish internal forces should be avoided in construction.In order to reduce the quantity of reinforcing bars and bolt diameters,reinforcement is calculated and designed according to the general internal forces.Consequently the project cost may be decreased.
IoT and Big Data for ECG Signal Classification - A Quick Decision System

Internet of Things (IoT) based Electrocardiogram(ECG) monitoring system is a non-surgical tool to detect cardiac arrhythmias. Recently, IoT plays a major part in monitoring heart disease patients. Data collection using IoT is reliable, faster, easier to store and view on the remote server. The vast quantity of information produced by IoT sensors is stored and processed using big data tools like Hadoop Distributed File System (HDFS) and is available to doctors through cloud services. In the present work, for reducing the risk of heart attacks, a Quick Decision System (QDS) is presented for finding cardiac arrhythmias at the earliest. The presented QDS involves Data Collection, Data Preprocessing, Feature Extraction, and Classification of ECG signals into Normal or Arrhythmia. It uses Principal Component Analysis(PCA) for feature extraction, and Rule Induced Sequential Covering Algorithm termed as RI-SCA for classification of heart disease. The performance of the introduced model is experimented with using benchmark PTB-XL dataset and the results show that the presented QDS achieved an accuracy of 89% compared to the other machine learning classifiers.
Encrypted Traffic characterization using None Zero payload and Payload Ratio Characteristics

Traffic characterization has been the backbone of network maintenance procedures, such as limiting bandwidth for specific services. However, with today's network, traditional techniques fall short. Traffic characterization using machining learning has been intensively researched to alleviate the shortcomings of traditional methods. This paper proposed a Bi-direction Flow Non-zero Payload Flow (BIF-NZPF) data extraction scheme and Bi-direction Flow Payload Ratio feature (BIF-PR) for supervised traditional machine learning. Our approach is measured on a publicly available ISCX VPN-NonVPN dataset to classify 12 types of traffic using precision, recall, and accuracy. BIF-NZPF reduced the obscurity of application characteristics by filtering out TCP configuration packets. BIF-PR further detailed traffic characteristics using payload size distribution characteristics through local and global traffic flow while being lightly coupled with the duration of traffic flow. Lastly, the ISCX-VPN-NonVPN imbalance class issue is alleviated using a boosting ensemble algorithm, which improves performance.
SSLP-Based Micro-Application Terminal Access Authority Authentication Method

In today's society, consumer online consumption, online payment between merchants and other business operation models are developing rapidly, and are gradually changing people's lives and work. However, due to the openness of the Internet itself, the security problems brought by it have become a serious problem for Internet-based e-commerce activities and become an important factor hindering e-commerce. At present, the main solution to the security problems of e-commerce activities is based on SSLP and SETP micro-application terminals. This article is mainly based on the analysis of SSLProtocol to design a solution for micro-application terminal access authorization authentication. The mainstream security technology architecture is mainly divided into five levels: network services, encryption technology, security certification, security protocol and system applications. The network service layer is at the first layer. It is mainly responsible for network monitoring and scanning, virus prevention, network intrusion detection and acting as a firewall. The second layer is the encryption layer, and the main encryption methods are symmetric encryption and asymmetric encryption. Each of these five-layer structures is related, and the security protocol is mainly SSLProtocol. Experimental conclusion: This experiment is mainly to use the sslProtocol plan to improve the security performance of e-commerce by more than 50%, make full use of the advantages of Protocol, ensure consumer safety, and improve a healthy and safe network environment.
Cross-spectrum Thermal Face Pattern Generator

Conversion of a visible face image into a thermal face image (V2T), or one thermal face image into another one given a different target temperature (T2T), is required in applications such as thermography, human body thermal pattern analysis, and surveillance using cross-spectral imaging. In this work, we propose to use conditional generative adversarial networks (cGAN) with cGAN loss, perceptual loss and temperature loss to solve the conversion tasks. In our experiment, we used Carl and SpeakingFaces Databases. Frèchet Inception Distance (FID) is used to evaluate the generated images. As well, face recognition was applied to assess the performance of our models. For the V2T task, the FID of the generated thermal images reached quite low value of 57.3. For the T2T task, we achieved a rank-1 face recognition rate of 91.0 % which indicates that the generated thermal images preserves the majority of the identity information.
Energy Optimization by Using Last Level Cache Partitioning for Multicore Platforms

Last Level Cache (LLC) partitioning has been ex-tensively examined to optimize energy and improve performance. Cache partitioning strategies proposed to reduce inter-core in-terference for the shared cache in multicore platforms. However, these approaches start focusing on performance in multicores and energy consummation. Current energy-efficient solutions in this field minimize power consumption by employing dynamic voltage and frequency scaling (DVFS). This paper has proposed an algorithm for improvements to existing models for energy consummation in multicore platforms. The proposed algorithm enhances the energy consummation resulting from DVFS by using LLC partitioning. The algorithm consists of two phases. The first phase sets the level of DVFS to the task, while the second phase gets the optimum partition factor of LLC to this task. Extensive simulations illustrate the efficiency and influence of the proposed method on the total energy consumption of multicore platforms by each stage of the suggested algorithm. Comparison by DVFS only and DVFS with cache partitioning show that the proposed algorithm achieves 15% improvements in energy-saving and reduces 25% L2 cache misses rate.
Single Channel Speech Enhancement Based on Temporal Convolutional Network

Aiming at the problem of high-frequency artifacts in the speech waveform estimation, a single channel enhancement algorithm based on temporal convolutional network (TCN) model with the modulation domain auxiliary features (Mod-TCN) is proposed. Specifically, the deep learning model is composed of the encoder, the TCN module, the decoder, and the auxiliary feature extractor of modulation domain. Among them, both the encoder and decoder are used to transform the potential representation of speech waveform. The causal and dilated convolutional mechanism of the TCN module is equipped with skip-connection and residual-connection to accelerate the convergence speed of the model. Furthermore, the auxiliary feature extractor of modulation domain is composed of multiple depth separable convolutions, which is mainly used for automatic learning of modulation domain features. Experimental results show that the proposed Mod-TCN model can give consistently better enhancement results than the existing TCN model by combining the features of modulation domain and time domain.