Reviewerly/exHarmony huggingface dataset:
Facial Expressions Classification based on Broad Learning Network

In this paper we propose a new broad architecture network for facial expressions classification. We use Grad-CAM technique to observe which parts of the image are important for classification. We analyze the changes of the expanded width in the network structure which may cause Grad-CAM to shift their focus on the impact parts of various emotional images. They help to establish association between neuromorphic networks and emotion perception. At the same time, we analyze the similar patterns between Grad-Cam map for image of broad architecture network and the electroencephalogram (EEG) heat-map of brain network in facial expressions classification. In order to show the effectiveness of our broad architecture network we test the proposed method on three motion expression classification benchmark datasets: Extended Cohn-Kanade Dataset (CK+), Facial Expression Recognition (FER), JAFFE. The experimental results show the effectiveness of our broad network when compared with vision transformer (ViT).
A Survey of Meta-learning for Classification Tasks

The superior performance of deep learning is supported by massive data and powerful computing engines. Meta-learning is an imitation of human learning ability. Instead of relying on massive quantities of data or numerous trials to learn features of current tasks, general knowledge obtained from historical tasks will be applied to future unknown tasks during meta-learning. Thus, it is considered one of the keys to achieving general artificial intelligence. In conjunction with the classification problem, meta-learning has had a new advancement recently, which is reviewed in this paper. First, the general settings and current formal definition of meta-learning are described. Then, the current methods in this field are summarized. The latest directions─methods based on data augmentation, transfer-learning, and unsupervised or semi-supervised learning are described in detail. Additionally, the quantitative performance of the examined approaches is assessed using benchmark datasets for categorization tasks facing small samples. Finally, it is proposed that the potential of meta-learning can be thoroughly explored from three perspectives: cross-domain adaptability, breakthrough of task space, and cost reduction.
GASP: A New Feature Fusion Module Based on ASPP

By obtaining multiple-scale context information from the same feature maps, the feature aggregation modules can better express the semantic information, which are beneficial for improving the performance of semantic segmentation models. In addition, compared with small receptive fields, large receptive fields can obtain more comprehensive context information, improving the segmentation accuracy. The ASPP uses global pooling and atrous convolution, and the large receptive field can be obtained by global pooling. However, using the global pooling results in the reduction of feature resolution and features. We proposed the GASP feature aggregation module and demonstrated its performance. The ablation experiments on Cityscapes validation dataset showed that mIoU of the GASP module increased by 0.65% compared with the original ASPP module.
EOPSA-FACE: An Encoder-Only DETR for Classroom Face Detection

Face detection in classroom scenes has always been a challenging problem because of the diversity and difficulty in extracting face features caused by multi-face clustering and multi-scale face changes. The recently proposed end-to-end object detector called DETR uses tr899978ansformer architecture instead of hand-designed components to obtain global interactive attention information of images, which not only simplifies the model structure but also greatly improves the feature interaction ability. Inspired by these works, we proposed an encoder-only DETR for classroom face detection without reducing the accuracy of detection, dubbed EOPSA-FACE, considering the huge amount of computation caused by the encoding and decoding structure of transformer included in the DETR. Firstly, aiming at the deficiency of multi-scale feature fusion, an efficient pyramid squeeze attention block is used to improve the backbone Resnet, so that the model can learn richer multi-scale feature representation. Secondly, considering the decrease of detection accuracy caused by insufficient allocation of positive and negative samples in the DETR, a pseudo intersection over union (Pseudo-IOU) design was introduced to achieve more accurate sample allocation. Extensive experiments on self-built face datasets in real classroom scenarios demonstrate the superiority of EOPSA-FACE.
Master data management method based on Blockchain and CP-ABE

Aiming at the problems of data security and credibility in master data management, this paper proposes a master data management method and system design that takes advantage of the decentralized, tamper-proof and traceable features of blockchain. Aiming at the privacy and security problems of data sharing, this paper combines symmetric encryption algorithm with attribute-based encryption algorithm to achieve fine-grained access control of data. At the same time, the blockchain technology is combined with distributed storage, and only data, hash index, access control policy and other information are stored on the blockchain, which greatly reduces the storage pressure of blockchain nodes.
A Novel Low Light Image Enhancement Method Based on Multi-Attention Generative Adversarial Networks

Images taken in low light realistic conditions cannot capture enough illumination to show more details, so that the recovered images show color deviation and unclear edge textures along with much noise information in the experiments of low light image enhancement. In order to obtain high brightness and clear images, this paper designs a network based on multi-attention Generative Adversarial Networks for low light image enhancement. The network uses a low illumination enhancement module that incorporates the non-local illumination attention and edge aware attention proposed in this paper. Non-local illumination attention can combine global information to quickly capture the relationship of long-range features, solving the color deviation problem while reducing the number of convolution layers. Edge aware attention focuses on local information at the edges, highlighting the edge contours of the image while smoothing out the noise. These two attentions serve as a complement to each other. And Attentional Feature Fusion is used to fuse the features of different branches and retain more valid information. It is proved that our proposed low light enhancement network is superior to other networks in terms of color fidelity, edge clarity, and denoising.
Mentor and mentee matching

Pedagogic systems are gaining traction in the provision of training, learning, and continuing professional development (often required to maintain professional qualifications). An essential element in pedagogic systems is the matching of teachers (mentors) and students (mentees). In this paper we present an intelligent context-aware learning system based on profile criteria developed using big data analytic solutions. The proposed system is designed to provide systematic support for mentors based on student profiles. The goal of the proposed system is to match the mentor profiles with the type of pedagogic system, the student profile, the student requirements, and the student’s goals and expectations. The proposed system is predicated on the use of fuzzy logic definitions with a maximal length matching algorithm using expert knowledge. The proposed system implements a mentor (teacher) and mentee (student) matching algorithm based on their profile criteria. The proposed system has been successfully tested by matching mentor and mentee profiles and preferences. Experimental results show that the proposed system can access multi-factorial mentor and mentee profiles, effectively match suitable mentors (teachers) with appropriate mentees (students), and meet the mentee expectations.
Sequential Fusion Filter for State Estimation of Nonlinear Multi-Sensor Systems with Cross-Correlated Noise and Packet Dropout Compensation

This paper is concerned with the problem of state estimation for nonlinear multi-sensor systems with cross-correlated noise and packet loss compensation. In this case, the cross-correlated noise is modeled by the synchronous correlation of the observation noise of each sensor, and the observation noise of each sensor is correlated with the process noise at the previous moment. Meanwhile, in the process of state estimation, since the measurement data may be transmitted in an unreliable network, data packet dropout will inevitably occur, leading to a reduction in estimation accuracy. To address this undesirable situation, this paper proposes a state estimation method for nonlinear multi-sensor systems with cross-correlated noise and packet dropout compensation based on a sequential fusion framework. Firstly, a prediction compensation mechanism and a strategy based on observation noise estimation are used to update the measurement data while avoiding the noise decorrelation step. Secondly, a design step for a sequential fusion state estimation filter is derived based on an innovation analysis method. Then, a numerical implementation of the sequential fusion state estimator is given based on the third-degree spherical-radial cubature rule. Finally, the univariate nonstationary growth model (UNGM) is combined with simulation to verify the effectiveness and feasibility of the proposed algorithm.
Deep learning-based image watermarking technique with hybrid DWT-SVD

In digital media copyright protection, image watermarking topologies afford a promising solution. But the robustness of watermarking methods should be considered. Therefore, a robust image watermarking technique has been proposed to show better robustness against rotation attacks and other issues in watermarking. In this work, the Deep Belief Network (DBN) is trained by the Bear Smell Search Algorithm (BSSA) during the first phase. The second phase is the embedding phase, which utilizes the hybrid transform domain of Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). After embedding, the extraction is performed with the help of a Back Propagation Neural Network (BPNN). The experiments are conducted on four kinds of host images such as Barbara, Peppers, Person and Lena. Experimental results proved that the superiority of our proposed method achieving the state-of-the-art accuracy.
DS-GAU: Dual-sequences gated attention unit architecture for text-independent speaker verification

Text-independent speaker verification provides people identified from their voice characteristics. In this paper, we propose a new method, Dual-Sequences Gate Attention Unit to improve the accuracy of a massive speaker verification system. Dual-Sequences Gate Attention Unit is based on the Gated Dual Attention Unit and the Gated Recurrent Unit. Two different inputs from the same source are the state pooling layer in the x-vector and the frame layer information in the x-vector. It is developed by applying the attention mechanism to the traditional Gated Recurrent Unit to enhance the learning ability of the x-vector system. The whole system follows the statistics pooling from each time-delay neural network layer of the x-vector baseline. It passes through the Dual-Sequences Gate Attention Unit layer to aggregate more information from the variant temporal context of input features while training at the frame level. We train our model on the Voxceleb2 and then evaluate the accuracy of Voxceleb1 and the Speakers in the Wild dataset for simulation. Finally, the system is compared with the x-vector, L-vector, and ETDNN-OPGRUs x-vector. There is an obvious improvement to our proposed method. Compared with the x-vector system, it shows that at least 17.5% on Voxceleb1 and 0.5% on Speakers in the Wild equal error rate improvement is achieved in the fusion system.
Analysis of Airline ESG Activities using Big Data

This study examined the ESG cases of domestic airlines using big data as ESG has become an important issue through the pandemic and climate crisis. Frequency analysis, centrality analysis, and CONCOR analysis were conducted using Textom, a big data analysis solution, and the results were visualized using Ucinet. As a result of frequency analysis and centrality analysis, large airlines and eco-friendly activities were exposed frequently, also their connectivity with other keywords was high. The result of the concor analysis was classified into four groups which are Korean Air"s ESG activities, airlines" ESG organizational activities, Asiana Airlines" social contribution activities, and ESG evaluation organization. As a result of the study, the following implications were derived for Airlines’s ESG. Governments and associated organizations should provide support and policy for Low-Cost Airlines’s ESG while it will be also necessary for ESG evaluation organizations to establish systematic and equitable evaluation standards. Lastly, management and supervision system will be required for Airlines to perform authentic and sustainable ESG activities.
ResneSt-Transformer: Joint attention segmentation-free for end-to-end handwriting paragraph recognition model

Offline handwritten text recognition (HTR) typically relies on segmented text-line images for training and transcription. However, acquiring line-level position and transcript information can be challenging and time-consuming, while automatic line segmentation algorithms are prone to errors that impede the recognition phase. To address these issues, we introduce a state-of-the-art solution that integrates vision and language models using efficient split and multi-head attention neural networks, referred to as joint attention (ResneSt-Transformer), for end-to-end recognition of handwritten paragraphs. Our proposed novel one-stage, segmentation-free pipeline employs joint attention mechanisms to process paragraph images in an end-to-end trainable manner. This pipeline comprises three modules, with the output of one serving as the input for the next. Initially, a feature extraction module employing a CNN with a split attention mechanism (ResneSt50) is utilized. Subsequently, we develop an encoder module containing four transformer layers to generate robust representations of the entire paragraph image. Lastly, we designed a decoder module with six transformer layers to construct weighted masks. The encoder and decoder modules incorporate a multi-head self-attention mechanism and positional encoding, enabling the model to concentrate on specific feature maps at the current time step. By leveraging joint attention and a segmentation-free approach, our neural network calculates split attention weights on the visual representation, facilitating implicit line segmentation. This strategy signifies a substantial advancement toward achieving end-to-end transcription of entire paragraphs. Experiments conducted on paragraph-level benchmark datasets, including RIMES, IAM, and READ 2016 test datasets, demonstrate competitive results compared to recent paragraph-level models while maintaining reduced complexity. The code and pre-trained models are available on our GitHub repository here: HTTPS link.
Fractured in-group identity (re)negotiation in an online white nationalist forum

This study investigates the construction and (re)negotiation of the identity boundaries in the context of a white nationalist online forum. Using over three million words of data, a corpus linguistic approach is combined with elements of critical discourse analysis, namely social actor (van Leeuwen, 1996) and transitivity (Halliday and Matthiessen, 2004) analyses, to examine the positioning of social actors in relation to one another. The data shows that, despite an assumption among some scholars of a united and ideologically coherent in-group of extremists, forum members often disagree on the nature and boundaries of both their racial (white) and ideological (white nationalist) identities. This calls into question the value of the ‘in-group’ concept as we currently understand it. Instead, the ‘in-group’ in the far-right context should be seen as slippery and unfixed, comprising multiple overlapping but distinct identities.
Non-invasive Mechanical Fault Detection Device

With the increasing degree of industrial intelligence, it is necessary to ensure the normal operation of automation equipment. The detection device based on image recognition is too expensive and cannot identify the internal faults of the machine. In order to solve this problem, this paper proposes a novel method based on STM32F407 microprocessor and TDOA generalized cross-correlation (Referred to as GCC) algorithm for fast and non-invasive mechanical fault location. The device can determine whether there is mechanical failure and if yes, where the location of the failure is by analyzing the ambient sound. To test the feasibility of the theory and the superiority of the device, a large number of simulation experiments were carried out. In an open room, a recorded audio is used to simulate the sound produced by mechanical failure, and then the device is used to locate the sound source. After testing, the identification accuracy of this device is high, the positioning error is not more than 15cm, and the response time is not more than 5s,which can guarantee the normal operation of mechanical equipment with low cost and high efficiency.
Analysis of Preferred Teaching Types of Professors Perceived by Foreign Students in Tourism Major Using Q Methodology

Due to the recent decrease in the university-age population, foreign students are recognized as an important resource for university management. This study analyzed the preferred teaching types of professors perceived by foreign students majoring in tourism management, which had not been studied in the past, using the Q methodology. The study selected 27 statements as the Q sample derived from literature review and in-depth interviews with professors and foreign students majoring in tourism management. As a result of the factor analysis, four teaching types were derived: active knowledge transfer, task consideration, interest expression, and cultural exchange facilitating. The results suggest that different methods are needed for each teaching type when guiding foreign students. Based on the results, this study discussed the theoretical and practical implications of the study and suggested future research directions.
Analysis of Variance (ANOVA)

Chapter 4 presents methods of analysis of variance (ANOVA). Based on the GLM, it is shown that ANOVA differs from regression only in the nature of the independent variables. Whereas in regression, the independent variables are usually metric, in ANOVA, they are categorical. In univariate ANOVA, there is one factor (that is, independent variable). In factorial ANOVA, there are multiple factors. In multivariate ANOVA (MANOVA), there are multiple dependent variables. In addition, this chapter discusses ANOVA for repeated observations and ANOVA with metric covariates.
Real-time monitoring for sport and mental health prevention of college student based on wireless sensor network

This article uses wireless sensor network technology to freely combine multiple sensor nodes to achieve, but the scale and capacity of WSN internal nodes are limited, so it is easier to encounter a series of network attacks and security threats. When setting the access protocol, the protocol needs to identify the precautions for malicious attacks against the network, and at the same time try to achieve a balance between network weights to improve the life cycle of the network. This paper presents a multi-station basis for a wireless sensor connection system. The design uses elliptic curve encryption technology to ensure the security of identification between the base station and the cluster head, and uses blockchain technology to ensure the security and efficiency of the ID card between the base station and the terminal. And design a real-time monitoring system through wireless sensor network to monitor the sport and mental health of students. We have analyzed the system demand information of college students' sport and mental health, and analyzed the problems that the system needs to solve in detail. Through in-depth analysis of the sport and mental health information system, using wireless sensor network technology, we conducts a thorough investigation of the needs of the student sport and mental health system. Then we conduct an in-depth analysis of the system structure, architecture design, business model, available system, database system, to make the system more acceptable to people.
Analysis of Trends in Reliability Engineering Using Patent Information

Purpose: This study aims to grasp the trends in reliability engineering research in Korea based on quantitative data such as patent information.BRMethods: Trend analysis, time series analysis BRResults: Time series analysis revealed that the compound annual growth rate (CAGR) of reliability-related patents has steadily increased to 16%, with domestic corporations holding the majority of such patents. In addition, these patents are concentrated in the field of electrical and physical engineering; reliability engineering is widely employed in precision manufacturing. While hardware-related patents still hold many reliability-related patents, patents in the network reliability engineering (NRE) and software reliability engineering (SRE) sectors are relatively increasing.BRConclusion: Patent analysis showed that patents in the field of reliability engineering in Korea are steadily increasing and the scope of patent use is gradually expanding.
Study on Reliability Trend Analysis of Warship Weapon System Using Field Data RAM Analysis Results

Purpose: This study proposes a reliability trend analysis process of the warship weapon system using actual failure data for use in establishing a warship maintenance policy and consequently strengthening the utilization of field data RAM analysis result.BRMethods: The characteristics of the warship weapon system and the types of data that can be extracted from the field data RAM analysis results were reviewed. Based on this, methods such as annual analysis and non-dimensionalization were proposed.BRResults: The proposed method involved several processes, including data extraction from RAMVV, calculations of annual reliability, data calibration, non-dimensionalization, year calibration, and reliability trend analysis.BRConclusion: A case study was performed by applying the proposed method. The reliability of the warship weapon system continuously increased over the entire life cycle. Moreover, the trend was clearly observed in the section that included various ships; thus, it is expected that the reliability of the trend analysis will be improved through the accumulation of considerable amount of data.