Reviewerly/exHarmony huggingface dataset:
A new approach for data augmentation in a deep neural network to implement a monitoring system for detecting prostate cancer in MRI images1

Prostate cancer is one of the most common cancers in men, which takes many victims every year due to its latent symptoms. Thus, early diagnosis of the extent of the lesion can help the physician and the patient in the treatment process. Nowadays, detection and labeling of objects in medical images has become especially important. In this article, the prostate gland is first detected in T2 W MRI images by the Faster R-CNN network based on the AlexNet architecture and separated from the rest of the image. Using the Faster R-CNN network in the separation phase, the accuracy will increase as this network is a model of CNN-based target detection networks and is functionally coordinated with the subsequent CNN network. Meanwhile, the problem of insufficient data with the data augmentation method was corrected in the preprocessing stage, for which different filters were used. Use of different filters to increase the data instead of the usual augmentation methods would eliminate the preprocessing stage. Also, with the presence of raw images in the next steps, it was proven that there was no need for a preprocessing step and the main images could also be the input data. By eliminating the preprocessing step, the response speed increased. Then, in order to classify benign and malignant cancer images, two deep learning architectures were used under the supervision of ResNet18 and GoogleNet. Then, by calculating the Confusion Matrix parameters and drawing the ROC diagram, the capability of this process was measured. By obtaining Accuracy = 95.7%, DSC = 96.77% and AUC = 99.17%, The results revealed that this method could outperform other well-known methods in this field (DSC = 95%) and (AUC = 91%).
Toward Optimal Synthesis of Discrete-Time Hopfield Neural Network

In this research brief, the relationship between eigenvectors (with {+1, -1} components) of a synaptic weight matrix W and the stable/anti-stable states of discrete-time Hopfield associative memory (HAM) is established. Also, the synthesis of W with desired stable/anti-stable states using spectral representation of W in even/odd dimension is discussed when the threshold vector is a non-zero vector. Freedom in choice of eigenvalues is capitalized to improve the noise immunity of the Hopfield neural network (HNN). Also, the problem of optimal synthesis of Hopfield Associative memory is formulated.
Deep Learning Techniques for Aspect Based Sentiment Analysis

Sentiment analysis is an important tool, which is aiming to use the natural language process to detect and extract the information inside the text in order to understand the emotional states of human beings. Sentiment analysis has been used widely in the world to extract information from a large number of user-generated content. However, with the development of the user needs, sentiment analysis requires improvement with the granularity at the aspect level. It needs to be performed at a more fine-grained level. Aspect-based sentiment analysis (ABSA) is a technique that classifies data by aspect and identifies the sentiment attributed to each aspect. ABSA has been proposed to meet the needs at the aspect level. Aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets are two main distinct targets of the improvement. Deep learning approaches have the advantage that its ability to capture the syntactic and semantic features within the text, and it does not require the high-level feature engineering. Deep learning has become the prospect of the industry. More and more researchers have tried to apply the deep learning approaches in the ABSA tasks in recent years. Nevertheless, there is a lack of summary of this kind of application, so we review and summarize some of the work for recent years.
A Novel Method for Faster Video Defogging

Road accidents are prone to kill one person every four minutes in India. Handling high fog conditions and obstacles in high-speed driving, especially at night is an extremely difficult task and needs at most attention and alertness from the driver. In these challenging situations, a smart driving assistant that provides a defogged output with obstacle detection comes handy. The system developed defogs the road visual during foggy conditions in order to assist the driver in a better manner.
Residual reinforcement learning for logistics cart transportation

Autonomous logistics cart transportation is a challenging problem because of the complicated dynamics of the logistics cart. In this paper, we tackle the problem by using two robots system with reinforcement learning. We formulate the problem as the problem of making a logistics cart track an arc trajectory. Our reinforcement learning (RL) controller consists of a feedback controller and residual reinforcement learning. The feedback controller regards a logistics cart as a virtual leader and robots as followers, and the robots' position and velocity are controlled to maintain the formation between the logistics cart and the robots. Residual reinforcement learning is used to modify the other model's output. Simulation results showed that the residual reinforcement learning controller trained in a physical simulation environment performed better than other methods, especially under the condition with a large trajectory curvature. Moreover, the residual reinforcement learning controller can be transferred to a real-world robot without additional learning in a real-world environment.
Pose Estimation Based on Snake Model and Inverse Perspective Transform for Elliptical Ring Monocular Vision

In the pose estimation of the elliptical ring contour in monocular vision, it is difficult to achieve high precision midline contour extraction and pose estimation, because of the close distance between the inner and outer contours. To ensure measurement accuracy, a high-precision contour separation scheme is proposed in this paper based on Zernike moment detection and the Snake model. Specifically, the sub-pixel edge detection method based on Zernike moment is designed to achieve the rough extraction of internal and external contour, in which the adaptive binarization method is used to obtain the coordinates of the internal and external contours as maskers. Then, combined with mask operation to obtain the coordinates of the separated contours, an iterative contour finding algorithm based on the Snake model is designed to obtain the high-precision inner and outer contour. In addition, an ellipse correction method based on inverse perspective transformation is proposed to solve the problem of difficult matching of ellipse feature points when identifying pixel coordinates of feature points. Finally, the proposed method is applied to the static contact hole oval pose estimation of switchgear on a high voltage power system. The results show that depth measurement error is ±0.5 mm, to moderate repeated measurement precision of ±0.3 mm, indicating that this algorithm can achieve higher accuracy of elliptical ring pose estimation.
Cloud hardware and software

This chapter presents the computing and the software stack for cloud computing. An in-depth discussion of cloud hardware systems includes a presentation of Warehouse Scale Computers (WSCs) and their performance analysis. Then, the focus switches to the software as the presentation concentrates on virtual machines and hypervisors, and then on software frameworks such as Dryad, Mesos, Borg, Omega, and Quasar. Dryad is an execution engine for coarse-grained data parallel applications, while Mesos is used for fine-grained cluster resource sharing, Omega is based on state sharing, and Quasar supports QoS-aware cluster management. Next, the chapter analyzes resource isolation, in-memory cluster computing with Spark and Tachyon, and Docker containers and Kubernetes. Section 4.1 examines the challenges faced by cloud infrastructure and the benefits of virtualization and containerization. The next two sections, Section 4.2 and 4.3, analyze Warehouse Scale Computers (WSCs) and their performance. Then the focus switches to software and presents VMs and hypervisors in Section 4.4 and frameworks, such as Dryad, Mesos, and Borg in Sections 4.5, 4.6, and 4.7, respectively. Dryad is an execution engine for coarse-grained data parallel applications, Mesos is used for fine-grained cluster resource sharing, and Borg is a cluster management system. Section 4.8 presents the evolution of cluster management systems and Section 4.9 covers Omega, a system based on state sharing. Section 4.10 analyzes Quasar, a system supporting QoS-aware cluster management. Resource isolation discussed in Section 4.11 is followed by an analysis of in-memory cluster computing with Spark and Tachyon in Section 4.12. Docker containers and Kubernetes are covered in Sections 4.13 and 4.14. Further readings and exercises and problems are covered in Sections 4.15 and 4.16.
A Research on the Intelligent Recognition of Athletes’ Wrong Actions Based on Visual Data Technology

In recent years, China has made great achievements in sports culture, and its requirements for exercises and sports competitions are getting higher and higher. But, in the process of training and competition, athletes will inevitably have some irregular or even wrong movements. Only by artificially judging whether the action is accurate can easily lead to errors in the results of sports competitions. Therefore, misjudgment events occur from time to time. Therefore, how to effectively identify the athletes’ wrong actions is particularly important. At the same time, visual data technology continues to develop and begin to be widely used in various fields, and has played a greater role in the field of image recognition. Therefore, the application of visual data technology to identify the athletes’ wrong actions will effectively improve the detection rate of wrong actions. This article uses experimental analysis and data analysis to help sports athletes make correct judgments about their own mistakes before the competition, so as to avoid wrong actions and improve their competition performance. The research results show that the motion recognition system in this experiment has a high recognition rate and a better recognition effect. In addition to individual motions that need to improve the corresponding functions, the system can meet the basic function of motion recognition and wrong motion recognition.
Research on the Fusion Framework of Multi-Sensing and Aviation Metrology Technology

This paper takes aviation metrology technology as the main research aviation surveillance information, and the main research work is divided into the following aspects: First, it studies a variety of radar surveillance information formats, and proposes a BP neural network as an information fusion method to monitor information from multiple radars. Perform fusion, design the network structure and various component modules involved in the BP algorithm, and analyze the radar surveillance information in multiple formats; the relevant algorithms for the data decision part of the information fusion technology are limited by prior knowledge and can not effectively deal with the problem of uncertain information, and proposed an improved evidence theory algorithm for conflicts.
Signless Laplacian state transfer on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi mathvariant="script">Q</mml:mi></mml:math>-graphs

For a simple graph G, its Q-graph Q(G) is derived from G by adding one new point in every edge of G and linking two new vertices by edge if they are between two edges that having a common endpoint. In our work, we demonstrate that for a regular graph G, if all the signless Laplacian eigenvalues are integers, then the Q(G) exists no signless Laplacian perfect state transfer. We also present a sufficient restriction that the Q(G) admits signless Laplacian pretty good state transfer when G exhibits signless Laplacian perfect state transfer between two specific vertices for a regular graph G. In addition, in view of these results, we also present some new families of Q-graphs, which have no signless Laplacian perfect state transfer, but admit signless Laplacian pretty good state transfer.
Designing Accountable Health Care Algorithms: Lessons from Covid-19 Contact Tracing

SummaryAn academic team at Stanford University worked with the County of Santa Clara Public Health Department to develop a machine-learning system for language matching in Covid-19 contact tracing through a collaborative design process. Although developed for a specific public health activity, the setting is representative of a wide range of health care delivery contexts, and the experience lays out how health care organizations can develop accountable algorithms that improve care, mitigate risk, and enable evaluation by stakeholders. Key elements of the design process involved: (1) a partnership and stakeholder consultation to develop a common understanding of and iteration around algorithmic design; (2) the use of a model understandable to all stakeholders, which exhibited only modest performance degradation relative to more complex models; and (3) a randomized controlled trial and qualitative survey of how the algorithm impacted real-world contact tracing, providing an evaluation that goes beyond narrow technical measures of algorithmic performance.
Real-Time Traffic Response Processing Using Convolutional Neural Network and Edge Computing

The present state of art is static traffic signals in most cities, whereas some cities use approach based on dynamic traffic signaling using edge detection, vehicular count using object detection, and traffic density estimation which are less efficient. We propose an edge intelligence-based solution which is more efficient in dynamically adjusting the traffic signals. We begin by collecting data and presenting an approach to label them automatically using transfer learning and K-means clustering, followed by manual corrections. We then build and train a custom ResNet model which takes traffic images as input from cameras and determines the traffic density from the same, based on which the green signal time is adjusted in real-time using predetermined parameters. To build our edge computing system, we use Node-RED which is a low-code, flow-based tool that significantly speeds-up development time. This allows us to perform all the above computations in an edge device (say, Raspberry Pi) for low latency. It also removes the need for persistent Internet connection unlike cloud computing, thus enabling real-time processing. The test results for our ResNet model show an accuracy of 84% which is comparable to other existing solutions while being computationally much cheaper.
SingleNet: A Lightweight Convolutional Neural Network for Safety Detection of an Industrial Control System

The traditional industrial control security research mainly focuses on network intrusion detection or trapping system and lacks abnormal detection after intrusion, and the abnormal detection algorithm ability of the underlying operation data of industrial control is insufficient. The modern industrial control system is a side-cloud collaborative architecture that accesses the Internet, the edge side is usually an industrial computer with weak computing power, and the deep learning algorithm requires a lot of computing resources and is difficult to use directly on the edge side. In this paper, a lightweight convolutional neural network anomaly detection algorithm “SingleNet” suitable for the edge side of the industrial control system is proposed, which convolutes the data of each sensor for a period of time and calculates the feature correlation between points in the association calculation layer. Experimental results show that the accuracy rate on the oil depot dataset is increased from 73% to 99.4%, the training time is shortened from 2 hours to 3 minutes, and the model size is compressed from 101 MB to 1.6 MB. The accuracy rate is improved from 87% to 99.2% on the Mississippi dataset, the training time is shortened from 15 minutes to 3 minutes, and the model size is compressed from 10.6 MB to 1.63 MB. The accuracy rate is improved from 85% to 99.4% on the Batadal dataset, the training time is shortened from 18 minutes to 3 minutes, and the model size is compressed from 15.5 MB to 1.62 MB. Compared with several lightweight algorithms recently proposed, SqueezeNet, MobileNet, and ShuffleNet, the proposed algorithm has significantly improved the performance indicators of training speed, accuracy, model size, and iteration time on the industrial control datasets. Both the training and testing of the algorithm can be done on the CPU, making it possible to apply deep learning to the edge side of the industrial control system.
Theoretical analysis of retrace error in Fizeau interferometer for deformed optical flats

Laser Fizeau interferometers are widely used for surface form metrology for optics and silicon wafers. The reference beam and sample beam share a common optical path in Fizeau interferometer when measuring a non-tilted optical flat. However, in the process of measuring surface topography of a wafer, as the form of a wafer often deviates from an ideal flat, such as surface warping caused by the manufacturing process, the so-called retrace error may occur due to the violation of the common path condition. In this paper, we establish two ray tracing models that correspond to the home-built Fizeau interferometer in our lab, and find that the retrace error only occurs in the presence of optical aberrations in the interferometer. In addition, we study the effect of the variation of the wafer surface form on the retrace error by superposing different Zernike polynomials on the flat surface. Based on the simulation results, the tolerance for aberrations in the specific Fizeau interferometer is provided.
Application of Smart Contracts in Online Product Procurement

In this work, a platform for the acquisition of online products is presented, where companies finally offer merchandise, so that users can buy from the comfort of their homes, taking into account that each of the parties will ensure compliance with the service. For this, the concept of smart contract is applied, which guarantees the correct execution of the agreement during the process, this based on blockchain technology, which provides confidence in the assurance of information.
Improved DeblurGAN multi-dimensional feature fusion blind demotion blur algorithm

Demotion blur has always been a classic problem in computer vision. In the past ten years, algorithm research in the field of deblurring can be divided into two categories. One is the non-blind deblurring algorithm based on the calculation of the blur kernel, and the other is the use of neural networks in the absence of information about the blur kernel and cam-era movement under the condition that the blur kernel is unknown. Remove motion blur. For this reason, Kupyn et al. [2] proposed a blind deblurring algorithm based on DeblurGAN. The algorithm can achieve good results in most scenes, but the deblurring effect on blurred objects with smaller scales is not obvious. The details are not prominent enough, and the grid effect is easy to produce. For this reason, this paper modifies its network structure on the basis of DeblurGAN and adds a residual module [3] as its backbone network. This paper uses Inception-ResNet-v2 [9] to extract features at different scales. Then FPN [4] is used for feature fusion, the smaller-scale feature pictures are up-sampled, and then the larger-scale pictures are convolved with the 1*1 size convolution kernel, and finally feature fusion is performed. The traditional multiscale pyramid generates features of different scales on images of different scales, and then predicts the features of different scales separately. The advantage is that the features at different depths of the network are merged, which improves the accuracy. The disadvantage is that the calculation cost is high. The advantage of FPN is that it connects the feature map from top to bottom and reduces the output of feature calculation. The advantage of this is that it can obtain more semantic information of the high-level network without losing the detailed information of the picture. Speed up the training speed and ensure the richness of feature extraction.
Design and Implementation of Arduino Insect Robot for Real Time Applications

A robotic project that mimic the insect motions is the Arduino-based Insect Robot. This Arduino Insect robot&#x0027;s characteristics are part of the current advancement of Artificial Intelligence. The characteristics include obstacle avoidance, light attraction, and random resting abilities, which are quite like insect behavior. The project includes an Arduino product, the Arduino-Pro Mini, that serves as the project&#x0027;s principal controller. It has five servo motors that move its front right leg, front left leg, center, or shaft, which is coupled to the two back servos, back right leg and back left leg. The shaft servo used in this project aids robot operation by allowing the rear servos to move at a higher angle. The project also includes three ultrasonic sensors that measure obstacle distance and have configurable parameters that control how many steps the insect robot will take to escape the barrier. Photodiodes with pull-up resistors are also included in the project, which are used to detect light. Two USB connections are also included, with channel 1 rated at 1A, channel 2 rated at 2.1A, and a toggle switch for power supply. A solar panel is also utilized in this project, which absorbs the sun&#x0027;s rays and converts them to electricity. Simply said, they are utilized to produce power through the photovoltaic effect.
Assessment of patch-based mammogram denoising methods using virtual clinical trials and deep learning: trade-off between denoising strength and preservation of structural details

Noiseless digital mammograms (DM) are unobtainable in clinical screening environments, limiting the development of deep learning-based (DL) denoising applications. Virtual clinical trials (VCTs) allow the precise simulation of noise levels in DM images for controlled training of DL models. We evaluated a set of DL denoising models, trained using VCT data, that showcases the trade-offs between denoising strength and fine structure preservation. Our results show that metrics, such as peak signal-to-noise ratio (PSNR), are improved with the use of our trained residual convolutional neural network. This quantifiable improvement indicates that our proposed DL methodology can accurately denoise simulated mammograms.
Image Segmentation Using Matrix-Variate Lindley Distributions

The aim of this article is to study a statistical model obtained by the mixture of the Wishart probability distribution on symmetric matrices. We call it “Matrix-variate Lindley distributions”. We show that this distribution includes the matrix-variate Lindley distribution of the first kind and second kinds on the modern framework of symmetric cones. Its statistical properties and its relationship with the Wishart distribution is discussed. For estimating its parameters, an iterative hybrid Expectation-Maximization Fisher-Scoring (EM-FS) algorithm is created. Finally, in medical image segmentation the effectiveness and the applicability of the proposed distributions are proved with respect to the Wishart distribution.