Reviewerly/exHarmony huggingface dataset:
Model Design of Teachers' Curriculum Teaching Happiness Improvement System in Higher Vocational Colleges

This paper systematically studies the influencing factors of teachers' curriculum teaching happiness in higher vocational colleges, and provides a theoretical model and implementation path for the government education department and higher vocational colleges to improve curriculum teaching level and teachers' happiness. Three dimensions and eight influencing factors of the improvement of teachers' curriculum teaching happiness in higher vocational colleges were established respectively. Eight influential factors were analyzed and evaluated by fuzzy AHP, and the weights of the influencing factors were sorted to design the model of teachers' curriculum teaching happiness improvement system. According to the weight of each influencing factor in the evaluation of curriculum teaching happiness, from the three dimensions of teaching environment, teaching subject and influence of teaching, the methods and measures for improving the happiness of curriculum teaching are proposed, which provides a useful reference for the construction of curriculums teaching quality and the improvement of teachers' happiness in government education departments and higher vocational colleges.
A research of action recognition based on attention mechanism

Abstract To solve the problem of inadequate expression of action behavior features, this paper proposes an action recognition method based on attention mechanism. Firstly, in the feature extraction part, a CSE module is designed to model action features spatio-temporally, and then this module is incorporated into the residual network to improve the feature extraction ability of the model; after that, the LSTM network is used to solve the problem of temporal association of features; finally, the actions are classified by Softmax. The experimental results show that the improved recognition rates of this method on UCF101, HMDB51 and Kinetics400 datasets are 96.23%, 92.03% and 75.65%, respectively.
Design an Environment Information Chatbots System for a Smart School Framework

research-article Share on Design an Environment Information Chatbots System for a Smart School Framework Authors: Napasorn Phaokla Rangsit University, ThaiLand Rangsit University, ThaiLandView Profile , Paniti Netinant Rangsit University, Thailand Rangsit University, ThailandView Profile Authors Info & Claims ICSIM 2021: 2021 The 4th International Conference on Software Engineering and Information ManagementJanuary 2021 Pages 152–157https://doi.org/10.1145/3451471.3451496Online:13 July 2021Publication History 0citation47DownloadsMetricsTotal Citations0Total Downloads47Last 12 Months47Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on Video Detection Method of Moving Target Oriented to Substation

Abstract With the promotion of unattended substations across the country, intrusion detection technology for people in important areas of substations has become an important research area. This article focuses on the problem of personnel entering important areas of substations for the purpose of man-made destruction, and proposes an improved two-frame difference method A moving target detection method combined with the background subtraction method. This method is based on the two-frame difference method, taking into account the impact of changes in illumination, and avoids the image sequence by combining the difference map of the background subtraction method and the two-frame difference method. The “double shadow” phenomenon caused by the difference method can also avoid the situation that the background subtraction method misjudges the sudden change of light as a moving target, improves the recognition accuracy, and can meet the real-time requirements of the substation intelligent monitoring system.
Credibility Assessment of Machine Learning in a Manufacturing Process Application

Abstract We present a framework for establishing credibility of a machine learning (ML) model used to predict a key process control variable setting to maximize product quality in a component manufacturing application. Our model coupled a purely data-based ML model with a physics-based adjustment that encoded subject matter expertise of the physical process. Establishing credibility of the resulting model provided the basis for eliminating a costly intermediate testing process that was previously used to determine the control variable setting.
A comparative study on facial recognition algorithms

Facial recognition methods were first explored in security systems to identify and compare human faces and is far superior compared to biometric and iris recognition, this technique has been implemented in iris recognition, image detection, etc.Recently these methods have been explored in other fields of study and have become a commercial identification and marketing tool.This paper describes the different algorithms of facial recognition and compared their recognition accuracies.The face is detected through Haar Cascades algorithm which is saved into a database, after that, the study intended to compare facial recognition accuracy of the well-known algorithms Eigen faces with Principal Component Analysis (PCA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN).The results showed out of the three algorithms we used CNN yielded the maximum accuracy.
Analysis of evolutionary algorithms on fitness function with time-linkage property (hot-off-the-press track at GECCO 2021)

In real-world applications, many optimization problems have the time-linkage property, that is, the objective function value relies on the current solution as well as the historical solutions. Although the rigorous theoretical analysis on evolutionary algorithms has rapidly developed in the last two decades, it remains an open problem to theoretically understand the behaviors of evolutionary algorithms on time-linkage problems. This paper takes the first step towards the rigorous analyses of evolutionary algorithms for time-linkage functions. Based on the basic OneMax function, we propose a time-linkage function where the first bit value of the last time step is integrated but has a different preference from the current first bit. We prove that with probability 1 - o(1), randomized local search and (1 + 1) EA cannot find the optimum, and with probability 1 - o(1), (μ + 1) EA is able to reach the optimum.
QoS Scheduling Algorithm for Videoclips Denoising

This article presents a general frame-work for scheduling videoclips denoising processes ensuring the quality of service (QoS). In general, a denoising algorithm has two phases which are run sequentially: the first one determines the noisy pixels in the videoclip frames and the second applies a median filtering over the each frame considering the only good pixels. In all such denoising algorithms, the first phase is run for multiple times depend on the noise power. The second phase also may be executed more than one time but this depends on the specific algorithm. The issue in such applications is the denoising process may not terminate within its deadline. The proposed solution adapts the execution time in such way so the deadline to be respected by determining the remaining time to the deadline before running each phase and reducing the number of runs in each phase in order to not exceed the deadline. The goals of the article are the following: presents the QoS scheduling algorithm and proposes an implementation solution of based on Blackfin microcomputer with support of Visual DSP kernel (VDK). The article is organized in 5 sections: a briefly introduction to set up the general context of quality of services in videoclips denoising applications and to present the original video processing algorithm, two sections that present the proposed solution and its VDK implementation, the performance evaluation and the conclusions.
The Mosaic Sustainable Marker Detection Method for Augmented Reality Systems

The paper proposes the mosaic sustainable marker detection method for augmented reality markers. The existing markers of augmented reality systems have been analyzed. The advantages and disadvantages of existing markers of augmented reality systems have been analyzed. The mosaic sustainable marker detection method for augmented reality systems has been developed. The block diagram of mosaic sustainable marker detection method for augmented reality systems is presented. The stages of the mosaic sustainable marker detection method for augmented reality systems are considered. Experimental studies of the mosaic sustainable marker detection method for augmented reality systems has been carried out.
DCDC-LSB: Double Cover Dark Channel Least Significant Bit Steganography

In this paper, we mainly discuss how to promote the robustness and invisibility of steganography. State-of-the-art steganography techniques have gained pretty good results on capacity, invisibility, and robustness. However, it is difficult to extract secret messages undamaged under lossy compression for these schemes, and no effective architectures have been proposed to implement a high capacity and invisibility steganography at the same time. In this work, we filled the gap and proposed the Double Cover Dark Channel Least Significant Bit (DCDC-LSB) steganography. Extensive experimental results demonstrate that the robustness of our method outperforms recent approaches in the Bit Error Ratio (BER), the invisibility of our method is better due to dark channel, and the capacity, as well as security, has good performance because we apply compression and encryption technology to our work.
Computing with Open Dynamical Systems

Computation is often thought of as a branch of discrete mathematics, using the Turing model. That model works well for conventional applications such as word processing, database transactions, and other discrete data processing applications. But much of the world's computer power resides in embedded devices, sensing and controlling complex physical processes in the real world. Other computational models and paradigms might be better suited to such tasks. For example, a computer can be regarded as a form of open dynamical system. One particular approach taking this view is reservoir computing, which can be instantiated in a range of different material substrates. This approach can support smart processing `at the edge', allow a close integration of sensing and computing in a single conceptual model and physical package, and provides a uniform approach to building hybrid architectures of heterogeneous devices.
Novel Quantum Random Number Generator for Cryptographical Applications

Random numbers are a fundamental resource in science and engineering, and are also widely used in many fields, such as simulation, cryptography, lotteries, fundamental science. There are algorithmically generated numbers that look like random numbers but are not truly random. These pseudo random numbers are generated from a computer, using deterministic algorithms. Because, we cannot use pseudo random generators in situations, where true randomness is necessary, we use true random number generators. In this case, we use unpredictable random events as a random source. Quantum random number generators (QRNG) output true random numbers based on inherent randomness in quantum measurements. Our goal is to generate fast random numbers at a lower cost. At the same time, a high level of randomness is essential. We present the improved novel quantum random number generator, which is based the on time of arrival QRNG. It is rather efficient, as it uses the simple version of the detectors with rather few requirements. The novel QRNG produces more than one random bit per each photon detection.
The Procedure for Implementing the Operation of Multiplying Two Matrices Using the Residual Number System

The report considers solution to the problem of improving the speed implementation of the operation of multiplying two square matrices of the same dimension. To carry out calculations and comparative analysis of the speed of the multiplication operation, we consider a computer system (CS) in the positional binary number system (PNS) and in the non-positional number system in the residual classes (the residual number system - RNS). A comparative analysis of the performance of the CS was carried out with the same characteristics of the computing system: equal lengths of bit grids, the same command systems, the same methods of addressing operands and instructions, the same clock speed of the processor, the equal number of program commands, etc. When calculating the speed of the matrix multiplication operation, the fastest data processing method in RNS was used, based on the tabular principle.
Distress-Level Detection Using Deep Learning and Transfer Learning Methods

Kalyan, Srijha Ravishankar, Hamsini Arunkumar, C.The mental health of young adults and teenagers proves to be vital for having a flourishing life. Neglecting the issue of mental health can lead to anxiety, stress, and depression. These problems need to be addressed in the early stages to ensure better mental health for young adults. This paper provides a comprehensive study by leveraging natural language processing and deep learning techniques to detect depression by examining the relationship between language usage and the psychological characteristics of the communicator. A private dataset DAIC-WOZ has been used to predict the levels of depression. It includes audio, video, and textual information from 189 interviews. This paper focuses on the textual analysis of detecting depression from transcripts of the interviews conducted by an animated virtual interviewer. Due to the disadvantages faced by the context-independent nature of GloVe embeddings, the proposed approach uses transfer learning techniques such as ELMo, ULMFit, and BERT for predicting depression severity from transcripts. Furthermore, a Python Web application has been deployed to identify negative sentiments and depression severity from sentences inputted by the user. The proposed approach uses an ensemble learning method for the application to provide better predictions for classifying the texts into levels of depression. Hence, the inferences made in this paper can be extrapolated to other all demographics around the world to help detect depression in textual data as the algorithms and techniques used are all-encompassing..
DDCNet-Multires: Effective Receptive Field Guided Multiresolution CNN for Dense Prediction

Dense optical flow estimation is challenging when there are large displacements in a scene with heterogeneous motion dynamics, occlusion, and scene homogeneity. Traditional approaches to handle these challenges include hierarchical and multiresolution processing methods. Learning-based optical flow methods typically use a multiresolution approach with image warping when a broad range of flow velocities and heterogeneous motion is present. Accuracy of such coarse-to-fine methods is affected by the ghosting artifacts when images are warped across multiple resolutions and by the vanishing problem in smaller scene extents with higher motion contrast. Previously, we devised strategies for building compact dense prediction networks guided by the effective receptive field (ERF) characteristics of the network (DDCNet). The DDCNet design was intentionally simple and compact allowing it to be used as a building block for designing more complex yet compact networks. In this work, we extend the DDCNet strategies to handle heterogeneous motion dynamics by cascading DDCNet based sub-nets with decreasing extents of their ERF. Our DDCNet with multiresolution capability (DDCNet-Multires) is compact without any specialized network layers. We evaluate the performance of the DDCNet-Multires network using standard optical flow benchmark datasets. Our experiments demonstrate that DDCNet-Multires improves over the DDCNet-B0 and -B1 and provides optical flow estimates with accuracy comparable to similar lightweight learning-based methods.
RA-RL: Reputation-Aware Edge Device Selection Method based on Reinforcement Learning

The development of smart technology and smart cities has solved the problem of data islands, but it has also brought about information security problems. Federated learning provides solutions to information security problems, which is a new machine learning method that effectively protects the local privacy of edge devices by distributing models to edge devices for training. However, due to malicious attacks from malicious edge devices, the accuracy and efficiency of federated learning are greatly compromised. Therefore, to solve the above problems, this paper proposes a reputation-aware method based on reinforcement learning (RA-RL) to select edge devices to ensure that the federated learning process is not attacked. Specifically, we introduce a reputation measurement scheme to evaluate the reputation of edge devices and use it as one of the features of edge devices. Then extract the feature matrix of candidate edge devices as the RL training environment to calculate the probability of each edge device is selected, and finally use the greedy algorithm to determine the devices that will eventually participate in the federated learning. Simulation experiments show that the RA-RL algorithm can effectively solve the training data security problem in federated learning, and is superior to other algorithms in terms of load balance, efficiency and accuracy.
Misclassification detection based on conditional VAE for rule evolution in learning classifier system

This paper focuses on the problem of Learning Classifier System (LCS) that is hard to guarantee to generate the "correct" output (i.e., the action in LCS) as the dimension size of data increases (which results in producing the "incorrect" output) and proposes the method that can detect the incorrect output of LCS. For this issue, this paper proposes the Misclassification Detection based on Conditional Variational Auto-Encoder (MD/C) which detects and rejects the incorrect output of LCS through a comparison between the original data and the restored data by CVAE (Conditional Variational Auto-Encoder) based on the output of LCS (as the condition to CVAE). The results of a ten-class classification problem using handwritten digits showed that MD/C properly rejects the incorrect output of LCS and achieves 99.0% correct rate.
WoTEdu: A Multimodal Interactive Storytelling System

This paper presents WoTEdu, a multimodal interactive storytelling system in the Nautical Cultural Heritage domain. The storytelling system is based on the Web of Things (WoT) paradigm and facilitates the museum visitors' interaction with museum artefacts. Multiple interaction modalities supported by the system allow people with motor impairment to experience the story through alternative input channels.
A Blockchain Framework for Healthcare Data Management Using Consensus Based Selective Mirror Test Approach

Blockchain technology is promptly gaining traction in the healthcare industry as one of the most exciting technological developments. Blockchain technology in the healthcare domain has primarily focused on the complex medical process like clinical trials and surgeries that suffers from drawbacks of permission attainment. Thus, there is a requirement for fault-tolerance and secure blockchain to suit the necessities of the healthcare domain. We recommend a consensus mechanism for healthcare data management that diminishes fault tolerance and yields better data management. In addition to fault tolerance, we propose a solution for managing and accessing a huge amount of medical-based data. The outcome of this work anticipates numerous stack-holders who have engaged themselves in the medical system to provide superior services for data management. From the results, it demonstrates that the proposed approach can provide better trade-off by analyzing different parameters like transaction rate, data encryption, and fault tolerance. This study illustrates the undertakings of utilizing blockchain technology in the health domain are expanding exponentially. There are areas inside the health domain that might be profoundly affected by blockchain technology.
Research and Implementation of Anomaly Detection Algorithm in Data Mining

When data mining, there will be a lot of abnormal data, abnormal data refers to data in the data set that is inconsistent with most data or deviates from the normal behavior pattern. In this paper, the KNN (k-Nearest Neighbor) algorithm, the Local Outlier Factor algorithm, and the Isolation Forest algorithm will be used to process the MIT-BIH arrhythmia data set. The KNN algorithm is an Anomaly detection algorithm based on distance but may divide normal data into abnormal data due to the deviation of parameter selection. The improvement proposed in this paper is to add weight to the distance to reduce the probability of division error. The Isolation Forest algorithm divides the data according to the characteristics of the data and then predicts the data to be abnormal or normal data. The improvement proposed in this paper is to first select the features of the data, so that the algorithm can be more accurate when dividing the data, thereby improving the detection. Effect. In terms of visual display of test results, this article selects the Receiver Operating Characteristic Curve graph, which can intuitively show the detection effect of the algorithm.
An Architecture to Define Cohorts over Medical Imaging Datasets

The DICOM standard has been widely adopted for the exchange and management of biomedical images. Its hierarchical structure allows representing data and metadata of medical imaging studies. However, other patient data not directly related with the study, such as prescriptions and treatments, are stored in independent Electronic Health Record (EHR) systems. With the increasing production of medical imaging studies, repositories responsible for storing DICOM images started to contain massive amounts of data. Therefore, retrieving a subset of images based on similar criteria as the ones used in EHR systems, is a complex task for a medical researcher. In this paper, we propose an architecture to define cohorts over medical imaging data sets. This proposal uses a DICOM archive to index and to retrieve images, while the studies' selection is performed through a web application, ATLAS, which is normally used on observational studies upon EHR data. The presented architecture was validated using a public data set with synthetic EHR data.
An Improved Dragonfly Algorithm Based on Angle Modulation Mechanism for Solving 0–1 Knapsack Problems

Dragonfly Algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous problems. To make DA work in the binary spaces, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems. Instead of running on the original high-dimensional binary spaces, the original AMDA utilizes the four-dimensional trigonometric function. However, the original AMDA has certain limitations, such as poor algorithm stability and slow convergence speed. Therefore, an improved AMDA called IAMDA is proposed. Based on the original generating function, a variable coefficient is added to control the vertical displacement of the cosine function. In this paper, seven high-dimensional zero-one knapsack problems are considered. Experimental results prove that IAMDA has superior convergence speed and quality of solution as compared to AMDA, BDA and BPSO.