Reviewerly/exHarmony huggingface dataset:
Evaluating Winograd Algorithm for Convolution Neural Network using Verilog

Convolutional neural networks (CNNs) are very resource intensive and consume a lot of computational power. The convolution operation itself is a very complex process. Hence this work deals with optimizing the convolution operation by employing an efficient algorithm called the “Winograd algorithm”. The performance of the algorithm was evaluated and compared to that of the general matrix multiplication method (GeMM) of the convolution. The RTL models of GeMM and Winograd algorithm were synthesized on the Xilinx XCVU9P FPGA. The synthesis results show that Winograd algorithm was resource efficient and utilizes 42.98% lesser resources than the GeMM algorithm. The power consumption for Winograd algorithm is 39.47% lesser than that of GeMM.
The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights

Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency.
Analyzing Nelder-Mead Simplicial Heuristic Using DEMATEL Method

Nelder-Mead Simplicial Heuristic in DEMATEL Method. A simple volume mesh element patterns a parallel algorithm framework for optimization we describe. By using simultaneous Parallel and asymmetric multiprocessing in hardware, we achieve more than this ten times the speed of Current state-of-the-art sequencing methods. Alternative: Strength ’pedestrian avoidance, For intimate and personal spaces moderation in between, For intimate and personal spaces The intensity of the transition between Strength of obstacle avoidance, Governs the queue width, Evaluation Preference: The strength of ’pedestrian avoidance (A1), Moderation between intimate and personal space (A2), The intensity of transition between intimate and personal space (A3), and Strength of obstacle avoidance(A4), Governs the queue width(A5), From the result it is seen that Strength of ’pedestrian avoidance and is got the first rank whereas is the Strength of obstacle avoidance got is having the lowest rank. The value of the dataset for Freight Distribution Concept Selection in DEMATEL shows that it results in Strength of ’pedestrian avoidance and top ranking.
A comparison study on experimental characterization of unidirectional fiber reinforced composites using strain-gauges and virtual extensometers

Abstract The aim of this study is to characterize E-glass/epoxy unidirectional fiber reinforced composites using the digital image correlation method with virtual extensometer, which is a less laborious method than strain gauges, compare the results and investigate whether virtual extensometers can be used instead of strain gauges. Measurements in tensile and Iosipescu shear tests were made with both strain gauge and virtual extensometer. Unlike full-field strain measurements in literature, the strains were measured using virtual extensometers. Tensile test and in-plane shear test results gave very consistent results. The differences between the strain gauge and the virtual extensometer for the tensile and in-plane shear tests were less than 3% in the linear region. However, the out-of-plane shear test showed a larger difference of 8.6%. This study showed that the 2D digital image correlation method with virtual extensometers is highly sufficient to find the elasticity moduli and shear moduli in tensile and shear tests in the linear region. In addition, after the damage has started, more measurement data can be obtained with virtual extensometers than with strain gauges.
IOT Based secured Low-cost Visitor Tracker using smart Application

Since the end of 2019, the world has been reeling with the COVID-19 pandemic. The drastic and dramatic spread has affected human lives and livelihoods as well as businesses across the world. Organizations across the world are uniting and coming forward to minimize the seriousness of healthcare. The survival of the human community should be the top priority during this time. To control spread, respective higher authorities impose restrictions on public gatherings, with strict action taken against those who exceed the maximum allowed people in public gatherings. Our IOT-based project aims to limit the number of people entering the academic blocks during COVID-19 in order to monitor overcrowding in these buildings. Our electrical circuit or device will monitor the number of people entering the academic blocks, and once the maximum allowed number of people is reached, the next person will not be allowed to enter the building, and the electrical circuit will ring a siren. This will also ensure that social distancing is maintained. Our project is based on Arduino. Several electrical components, such as an Arduino Uno, a prototype, a breadboard, a piezo-buzzer, ultraviolet sensors, and jumper wires, were used. Software simulations were carried out in the well-known online electrical circuit compiler, Tinkercad. A hardware simulation of our project was also made.
Analysis of the Effect of Using E-Learning on the Learning Process Using the UTAUT Method

The purpose of this study is to conduct empirical testing of the influence of the use of the Moodle E-learning System in the Muhammadiyah Samarinda Junior High School environment in supporting the process of ICT-based activities in the teaching and learning process in order to develop ICT-based learning that is considered innovative under the standards of the 2013 Education Curriculum. This study's subjects were Muhammadiyah Junior High School students in Samarinda. The UTAUT model is used to determine the factors influencing E-Learning Behavior's purpose. The data collection survey was conducted by distributing questionnaires directly to 56 students, using Multiple Linear Regression Analysis by conducting a statistical T-test. The results of this study show the influence of expected variables in the performance of E-Learning (PE) technology, expectations on the business of using E-Learning (EE) technology, the influence of teachers and students (SI), the condition of school facilities (FC) and the suitability of learning (JF) with the behavioral intention of Muhammadiyah Junior High School students to use E-Learning learning technology by 83.4%.
A Method for 3D Human Pose Estimation and Similarity Calculation in Tai Chi Videos

Human pose estimation from video sequences has become a hot research topic in the domain of robotics and computer vision. However, existing three-dimensional (3D) pose estimation methods usually analyze individual frames, which have a low accuracy due to various human movement speed, limiting its practical application. In this paper, we propose a method for estimating 3D pose and calculating similarity from Tai Chi video sequences based on Seq2Seq network. Specifically, using 2D joint point coordinate sequence of the original image as input, our method constructs an encoder and a decoder to build a Seq2Seq network. Our method introduces an attention mechanism for weighing the input data to obtain an intermediate vector and decode it to estimate the 3D joint point sequence. Afterwards, using a template video and a target video as input, our method calculates the cost of passing through each point within the constraints to construct a cost matrix for video similarity. With the cost matrix, our method can determine the optimal path and use the correspondence of the video sequence to calculate the image similarity of the corresponding frame. The experimental data show that the proposed method can effectively improve the accuracy of 3D pose estimation, and increase the speed for video similarity calculation.
Visual Intelligent Recognition System based on Visual Thinking

Image semantic segmentation plays an important role and has application value in robot arm object capture, automatic driving, medical image analysis, geographic information system, etc. Aiming at the semantic segmentation method of deep learning(DL), this paper makes some attempts in the direction of weak supervised semantic segmentation, proposes AI and DL technology, and applies them to the Image segmentation technology(IST) in the construction machinery(CM) grasping task for analysis and exploration.The improvement of image weak location based on depth learning, network segmentation graph and image classification objective function are briefly analyzed.Finally, the experimental test analysis shows that a better initial attention map is not only helpful for direct segmentation, but also can promote the joint optimization performance, which verifies the effectiveness and feasibility of IST in the CM grasping task relying on AI and depth learning in this paper.
A Novel Metric for Password Security Risk Against Dictionary Attacks

Passwords are still the most used method of user authentication in the usage of information systems, and they have an important role in practical security. Despite the fact that researchers have discovered various vulnerabilities in the usage of passwords, this authentication method is still frequently used. The main issue with passwords is their quality or strength, i.e., how hard they can be guessed by an attacker, and there are various password strength metrics have been proposed so far. In this paper, we propose a new metric for password strength that takes into account the risk of dictionary attacks. We create datasets from leaked password lists and regard them as Markov information sources. Then we calculate the password self-information and compare it to the threshold value we specified to determine the password strength. With this numerical value, we can know how risky a password has against dictionary attacks, and can easily compare the strength of several passwords. Through experimental results, we show that our method is very effective, does not require huge computational resources, and can effectively help users create stronger passwords.
vkTracer: Vulnerable Kernel Code Tracing to Generate Profile of Kernel Vulnerability

Vulnerable kernel codes are a threat to an operating system kernel. An adversary’s user process can forcefully invoke a vulnerable kernel code to cause privilege escalation or denial of service (DoS). Although service providers or security operators have to determine the effect of kernel vulnerabilities on their environment to decide the kernel updating, the list of vulnerable kernel codes are not provided from the common vulnerabilities and exposures (CVE) report. It is difficult to identify the vulnerable kernel codes from the exploitation result of the kernel which indicates the account information or the kernel suspension. To identify the details of kernel vulnerabilities, this study proposes a vulnerable kernel code tracer (vkTracer), which employs an alternative viewpoint using proof-of-concept (PoC) code to create a profile of kernel vulnerability. vkTracer traces the user process of the PoC code and the running kernel to hook the invocation of the vulnerable kernel codes. Moreover, vkTracer extracts the whole kernel component’s information using the running and static kernel image and debug section. The evaluation results indicated that vkTracer could trace PoC code executions (e.g., privilege escalation and DoS), identify vulnerable kernel codes, and generate kernel vulnerability profiles. Furthermore, the implementation of vkTracer revealed that the identification overhead ranged from 5.2683 s to 5.2728 s on the PoC codes and the acceptable system call latency was 3.7197 $$\upmu $$ s.
Dual consistency semi-supervised nuclei detection via global regularization and local adversarial learning

Nuclei detection is a fundamental analytical step in digital histopathology image analysis. Since labeling the centroids for each nucleus in histopathology images is extremely time-consuming, researchers attempt to explore consistency-based approaches for efficient semi-supervised nuclei detection. However, existing methods can only be used for detection on small patches mostly containing one nucleus and contextual information among neighboring nuclei is not considered. On the contrary, using the whole image to achieve nuclei detection in a semi-supervised manner may suffer from a large amount of background noise and thus cannot yield optimal performance. To address these problems, we propose a novel semi-supervised learning method for nuclei detection on a full-size histopathology image via global consistency regularization and local consistency adversarial learning. Specifically, the proposed dual consistency semi-supervised learning can improve the efficiency in inference and learn the context-aware nuclei features by global consistency regularization. Meanwhile, local consistency adversarial learning is introduced to focus on nuclei regions and reinforce the local spatial contiguity of prediction maps. We have evaluated the proposed dual consistency semi-supervised method on public CRCHisto and collected SemiBCN datasets, and the results show that with the synergy of global consistency regularization and local consistency adversarial learning, our method delivers a significant improvement over the state-of-the-art methods.
Unsupervised Training for Neural TSP Solver

There has been a growing number of machine learning methods for approximately solving the travelling salesman problem. However, these methods often require solved instances for training or use complex reinforcement learning approaches that need a large amount of tuning. To avoid these problems, we introduce a novel unsupervised learning approach. We use a relaxation of an integer linear program for TSP to construct a loss function that does not require correct instance labels. With variable discretization, its minimum coincides with the optimal or near-optimal solution. Furthermore, this loss function is differentiable and thus can be used to train neural networks directly. We use our loss function with a Graph Neural Network and design controlled experiments on both Euclidean and asymmetric TSP. Our approach has the advantage over supervised learning of not requiring large labelled datasets. In addition, the performance of our approach surpasses reinforcement learning for asymmetric TSP and is comparable to reinforcement learning for Euclidean instances. Our approach is also more stable and easier to train than reinforcement learning.
Image Dehazing Using Improved Dark Channel and Vanherk Model

The various bad climatic whether conditions occur in our daily life are snow, sandstorm, haze, fog etc., which will effects the natural scene and the visibility and these will be restore by image dehazing. In this work, we compute the relativity of Gaussian for red, green, and black color spaces to produce guidance image and improved dark channel prior is used to improve the transmission depth map construction in our work. There are different dehazing methods used to restore the visual perception of the image, increase the natural view. It is a highlighted research area because of its real-time applications in surveillance systems, driver assistance system especially for the people who resides in hilly areas where mist and haze is prominent. The proposed dehazing method is based on improved dark channel prior and vanherk model which is concentrated on detailed enhancement of the transmission map and hence reconstructing the hazeless image. The different proposed dehazing methods results in both qualitative analysis and quantitative analysis. Quantitative analysis has more existing methods like structural similarity index and peak signal to noise ratio these are used for the evaluate when ground truth images are present and blind reference less image spatial quality evaluator and naturalness image quality evaluator are used to evaluate when ground truth images are not present. These existing methods give better results without any distorted images. This work provides good performance for the task of obtaining hazeless image both qualitative and quantitative manner. Experimental results show that the proposed method estimates haze more accurately, the reconstruct the images are more realistic and detailed information is restored.
Evaluation of Network Parameters in Cloud Environment

Virtualization is considered one of the green information technology (IT)s that will help lower the cost of technology and upkeep. Virtualization and multitasking operating systems have similar working capabilities. Virtualization techniques are now used in data centers to abstract physical networks, create large pools of logical resources consisting of central processing units (CPUs), memory, file storage, disks, networking and applications and distribute those resources to users or customers in the form of agile, modular and consolidated virtual machines. Cloud computing provides more convenience for any computing needs than conventional, offering custom virtual machine (VM). The proposed work focuses on two forms of tests in a preliminary analysis to assess the efficiency of cloud infrastructure. The first test is to assess network efficiency and the second to measure the performance of cloud computation. Here the focus is on the performance efficiency pillar of the Amazon web service (AWS) Well-Architected Framework. AWS is Amazon’s extensive and developing cloud computing network, including Platform as a Service (PaaS), and Software as a Service (SaaS) and Infrastructure as a Service (IaaS). The factors examined in this study include uptime, transfer rate, jitter and latency while transferring a file from the desktop to an Elastic Compute Cloud (EC2) instance. The findings demonstrate that VMs from a basic cloud computing infrastructure that are utilized to produce video have a significantly longer processing time than the same specification personal computer (PC). The network side has been considered a gateway to the success of degradation rendering in cloud computing.
Will Open Science Succeed in Higher Education?

Despite the current interest in Open Science in the scientific community, it is not clear if Open Science will succeed in higher education. Open Science is based on three basic ideas: open data, open results, and open publication. If these principles can be successfully implemented, it will allow for much greater participation in the scientific process, including, perhaps, broad public participation. In order for Open Science to be successful, it must be compatible with the practices of higher education. This will require significant adjustment. This article will discuss the pluses and minuses of Open Science in higher education.
Roundtable: Raising Empathy through Virtual Reality

Virtual reality (VR) has been described as the ultimate empathy machine; but does it deserve this reputation? Thanks to its features of embodied technology, VR can let users virtually walk in someone else’s shoes. In addition, multi-sensory VR experiences can present evocative and heart-wrenching stimuli. For these reasons, VR seems to be a likely candidate to foster empathy. However, the published literature indicates that the impact of VR on empathy is complex and depends both on the type of VR and also the type of empathy being evaluated. The present chapter compares two meta-analyses which suggest that VR can elicit empathy, but the theoretical factors on which the technology has more efficacies are in contrast. In this chapter, these discordant meta-analyses are discussed, and the reasons why they find different results are theorized. We attempt to answer when and how VR could be an empathy machine. We conclude that low-tech but evocative storytelling is most likely to yield emotional empathy, and embodied experiences that encourage perspective-taking will improve cognitive empathy. Although we attempt to present the latest empirical evidence about empathy and VR, we are aware that the scientific consensus around this topic is likely to evolve in the future.
Implementation of Crow Search Algorithm for Achieving Optimal Control of a Single-Link Flexible Manipulator

A metaheuristic optimizer based on the cognitive behavior of crows, called the Crow Search Algorithm (CSA), is suggested in this paper for the optimization of the performance of the single link flexible manipulator as a constrained optimal control problem, which comprises objective functions, constraints, and decision variables. The CSA outperformed other algorithms, such as Differential Evolution, Grey Wolf Optimization, and Particle Swarm Optimization. Parameter setting is another highlight of the current paper. In conclusion, CSA yields more promising results than other approaches.
Data collection of biomedical data and sensing information in smart rooms

This paper presents a new dataset, including behavioral, biometric, and environmental data, obtained from 23 subjects each spending 1 week to 2 months in smart rooms in Tokyo, Japan. The approximate duration of the experiment is 2 years. This dataset includes personal data, such as the use of home appliances, heartbeat rate, sleep status, temperature, and illumination. Although there are many datasets that publish these data individually, datasets that publish them all at once, tied to individual pseudo IDs, are valuable. The number of days for which data were obtained was 488, the number of records was 18,418,359, and the total size of the obtained data was 2.76 GB. This dataset can be used for machine learning and analysis for tips on getting a good night's sleep, for example.
A Comparative Study on Traditional and Modern Apprenticeship Education of Traditional Chinese Medicine

General Secretary Xi Jinping has a good grasp of "what to teach, whom to teach and how to teach" in education.From the three aspects, we compared traditional and modern TCM teacher-inheritance education.We found that the modern teacher-inheritance model has inherited the traditional advantages and abandoned the traditional disadvantages.We believe that with the further deepening of the modern apprenticeship system in the teaching of traditional Chinese medicine, the training of Chinese medicine talents will get better and better.