Reviewerly/exHarmony huggingface dataset:
A Correct Face Mask Usage Detection Using YOLOv4 Framework

In this research, we built a system to detect the use of facial masks in images, especially in public places. Our system used YOLOv4 for detecting the facial masks usage. This research was conducted using dataset with outdoor and indoor test scenarios. In previous research that has been conducted in 2020, images of faces with masks and for the rest of the faces without masks, the resulting accuracy is 96% with more than 4000 training times. We used three labels for the dataset, namely correct mask use, incorrect mask use, and not using mask. We used 1984 images for training set and 992 images for testing set. From our experiment, the best results were obtained when making adjustments to the IoU value of 0.5 which has an F1-Score value of 0.9850 when testing for all classes.
Face Mask Wearing Detection: A Comparative Analysis

The COVID-19 pandemic has had a tremendous influence around the globe, impacting nearly every element of daily life. It has resulted in widespread illness and death, economic disruption, and changes in societal norms. Governments and organizations have applied various measures to slow the spread of the virus and mitigate its impacts. Among the most important mechanisms is the use of face masks to prevent the transmission and infection of COVID-19. This paper investigates and analyzes different machine learning (ML) methods to execute the classification task of categorizing faces into three classes: wearing masks, not wearing masks, or wearing masks improperly. The preprocessed and augmented dataset used in the study contains 4801 images with the dimension (50, 50, 3) and there are approximately 1500 faces for each class. According to the experimental results, convolutional neural networks (CNNs) can achieve 87% accuracy in classifying faces. These results indicate that CNNs outperform other ML methods, such as random forest, Naïve Bayes, and support vector machine.
Analyzing Adversary’s Attack on Ethereum Collected from Honeypots

With the increasing reliance on digital infrastructure and the transmission of sensitive information online, the necessity for robust cybersecurity measures has become urgent. This research paper explores the effectiveness of employing honeypots and the MITRE ATT&CK framework to detect adversary behaviors in the context of email and cryptocurrency attacks. Through the deployment of honeypots, a diverse range of attack data was captured, enabling the identification of recurring patterns such as phishing and scamming in email, as well as account hijacking in cryptocurrency. By mapping this data to the MITRE ATT&CK framework, we were able to identify the Tactics, Techniques, and Procedures (TTP) utilized by adversaries, which can guide security strategies and mitigate future attacks. Our analysis underscores the value of honeypots in detecting and analyzing adversaries’ TTP, emphasizing the need for ongoing research to enhance our comprehension of emerging threats.
Customer Perception of AI Presence and Intention Toward AI-Recommended Products

Artificial intelligence (AI) has been widely employed in many fields in recent years. In marketing and business, AIs have been incorporated into product design and production to create environmentally friendly robots to remove waste and pollutants and track invasive species. AIs can also collect and analyze customer data and provide personalized information to customers. However, theoretical discussions about the opportunities and challenges of AI-based marketing and business dominate the literature. Empirical research on AI-related products and customers’ perceptions of AI technology and AI-related products still is limited. In addition, how customers perceive the presence of AIs and the information generated by AIs and how these perceptions can affect their intention toward the products or services recommended by AIs have been largely unaddressed. As a result, the appropriateness and effectiveness of using AIs in information provision and promotion cannot be confirmed. Therefore, this study examined potential customers’ opinions concerning AI presence, AI-generated information, and AI-recommended products. Preliminary data was gathered from a small young customer sample in Japan (n = 84) in June 2022. Later analysis in SmartPLS suggested a linear correlation among the variables measured: perceived AI presence – thoughts about AI-generated information (creative and customized) – feelings about AI-generated information (amazed, amused, and surprised) – purchasing intention. Implications for further adopting AI in marketing were drawn from these findings.
Robot and Human Interaction using Natural Language Understanding in Indonesian based on Mental Image Based Understanding

Robots, in order to understand human commands, could be possible if there is an interaction between them. Possible interaction including the Natural Language, that humans have used to communicate with each other. The method used in this research for robots to understand Natural Language is Mental Image Directed Semantic Theory (MIDST) which uses unique Lmd formula as a basis for computing the Natural Language Understanding (NLU) processes. Equipped with support modules such as Speech to Text, Text to Speech, and Synonym Search Algorithm the robot successfully to receive, understand, and issue responses according to the Natural Language expressions given in Indonesian Language. The movement of the robot is also simulated in a two-dimensional plane to demonstrate its ability to serve humans.
A Face Recognition Security Alarm Method Based on Embedded Artificial Intelligence Chip

In the aspect of intelligence, we try to use computer vision technology to analyze the face recognition information collected by the camera, so as to achieve the functions of early warning, prevention and active monitoring, so as to achieve the goal of completing the monitoring task instead of people. Therefore, this paper has carried out further research on this, and the experimental test results show that the recognition rate of this algorithm in ORL face database can be as high as 94.7%, and the recognition rate in Yale face database can also be as high as 85.3%. It is better than the other two algorithms. It can be seen that the recognition rate of the algorithm in this paper is the highest in the test results of two face databases. It can be seen that the embedded face recognition system has a certain robustness to the light changes in real life while accurately recognizing the face image. The face recognition stage based on embedded AI(Artificial Intelligence) chip extracts the features of the face image to be recognized, and compares them with the features of the trained samples stored on the disk, ensuring the user’s autonomy in use. At the same time, the embedded AI chip can realize timely awareness and judgment of the recognition results, increasing the security privacy and reliability.
A locality capacity enhancement attention network for semantic segmentation

MaskFormer[1] is an effective approach to simplify the semantic and panoptic segmentation tasks based on mask classification, where the Transformer module allows it to effectively capture the long-range dependencies, demonstrating excellent results on several datasets. However, there are limitations to the self-attentive mechanism in Transformer, leading to a tendency to overlook some details and attentional deficiencies, which may cause incomplete segmentation of large categories of objects, or misclassification of small categories of objects. In CNNs, the inherent rectangular box property of the convolutional kernel makes it good at extracting features from local regions and more perceptive on details. FLA is a CNN-based Non-Local module that is not only able to explore interdependencies along channels and spatial dimensions, but also has a strong ability to extract detailed features and effectively retain attention in all dimensions. To address the problem of MaskFormer that missing attention to detail, we propose a new approach, namely, a Locality capacity Enhancement Attention Network (LEAN) for semantic segmentation. Specifically, we concatenate the self-attention module in the decoder layer of the Transformer with a fully attentional block, which is CNN-based. It serves to preserving more details when capturing information over long distances. We tested on two challenging semantic segmentation datasets, i.e. on the test set of Cityscapes and the validation set of ADE20k. The experimental results prove that the novel method LEAN proposed in this paper shows outstanding performance level, as well as greater detail capture and locality capacity.
Contrastive Learning for Improving the Robustness in Temporal Action Localization

This paper presents introducing contrastive learning to the model for temporal action localization (TAL) tasks, inspired by its extremely wide applications, including natural language processing (NLP), computer vision (CV), automatic speech recognition (ASR) and so on. While the applications in CV domain mainly focus on image representations learning tasks such as object recognition and image retrieval, we propose to apply the contrastive learning to TAL. We have primarily done two parts, data augmentation and contrastive loss, which are both crucial to contrastive learning. For data augmentation,we adopt cropping and color distortion to generate positive and negative pairs which are essential components for training of contrastive learning model. In addition, we build a formula Lc representing the contrastive loss, through which the model can learn how to map similar samples to nearly the same embedding space while mapping dissimilar samples to distant embedding spaces. What we do contributes to improving the accuracy and generalized ability of the model.
A Novel Hybrid Encryption Scheme Using RSA-Co-CP-ABE for Ciphertext Transmission in Cloud Environment

With the development of modern communication technology, an increasing number of people are choosing to communicate end-to-end over the network. However, the risk of information leakage and data tampering has also increased, and the issue of information confidentiality has become an increasing concern. Among the existing encryption algorithms, symmetric encryption algorithms cannot securely manage the key and share the key between communication parties; ECC and other public key encryption algorithms have problems such as slow encryption and decryption speed, low encryption strength, etc. In addition, key management is still not guaranteed to be very secure; conventional hybrid encryption algorithms need to manage two different types of keys, and key management may be very complicated and prone to key leakage or theft. Based on the problems of the above algorithms, this paper proposes RSA-Co-CP-ABE Architecture, which uses socket communication, encrypts the RSA public key passed from Client to Server with CP-ABE, encrypts the confidential message with the public key passed from Client at Server, and finally sends the encrypted message to Client. This scheme has higher flexibility than the traditional symmetric encryption and public key encryption schemes, and by using CP-ABE encryption, it can realize attribute-based access control. And RSA encryption can make the scheme make a better balance between encryption and decryption efficiency and encryption strength. In addition, because the communication is transmitted in full cipher text during the communication, the scheme can effectively resist tampering or stealing by man-in-the-middle attacks.
Research on load data classification method based on the characteristics of electricity consumption behavior by extreme learning machine

In the background of the era of grid information intelligence, with the comprehensive development of power sales enterprises, the data mining work of electric power customers has been emphasized and grown rapidly. Therefore, this paper proposes an Extreme Learning Machine (ELM)-based algorithm for electricity customer behavior analysis. Initially, we conduct an analysis of typical load curves using multiple indicators. Firstly, we employ a feature selection strategy to extract the most relevant features from load curves, which enables us to classify and analyze customers' electricity consumption data effectively. Subsequently, we utilize the feature preference set as input and evaluate the accuracy of the training and test sets using various activation functions and different numbers of nodes in the hidden layer. This optimization process aims to determine the best input parameters for the ELM algorithm in customer behavior analysis. Finally, the arithmetic simulation experiments are carried out to compare and analyze with BP neural network. The results show that the load data classification method based on the electricity consumption behavior features of the extreme learning machine can meet the goal of flexible and interactive intelligent electricity consumption, and improve the efficiency of abnormal electricity consumption detection and identify the patterns of users' electricity consumption behavior through data mining and electricity consumption behavior analysis of electricity users' daily electricity consumption.
A Super-Resolution Attention Feedback Network

Since the Internet and social media grow rapidly in recent years. Image processing is playing an increasingly important role in daily life. A key task for image processing is super-resolution. In a super-resolution task, an image of a high-resolution rate would be built on the basis of a low-resolution picture. Super-resolution technologies could help recover damaged images and recognize the detail of the images. As neutral networks develop quickly, many industries have used deep learning to improve their efficiency. Since neutral networks have a good performance in image processing, they quickly become the dominant methods in super-resolution tasks. Unlike traditional methods, deep learning could work better and more effectively. Based on the super-resolution feedback network (SFBEN), a channel attention mechanism has been added to the feedback block of the network. It is all known that attention mechanisms and feedback are two important ways for a human to deal with an image. This paper combines these mechanisms in the network. The new channel attentional layer uses a convolutional layer to generate a weight matrix and then adjust the input based on the matrix. The application of this channel attentional block reduces the training loss of the whole network and increases the peak signal-to-noise ratio.
A lightweight off-line handwritten Chinese character recognition algorithm based on gradient feature and channel attention

Handwritten Chinese character recognition has a broad application prospect in the field of optical character recognition system and handwritten character input device. In order to solve the problem that there are a large number of Chinese characters with various handwriting styles and plenty of similar characters, this paper proposes a lightweight off-line handwritten Chinese character recognition algorithm— Handwritten Chinese Character Recognition Depthwise Seperable Convolutional Neural Network (HCCR-DSCNN) based on gradient features and channel attention. Firstly, HCCR-DSCNN combines the traditional gradient feature extraction method to process the original image, and takes the eight-direction gradient feature as the input of the network, and uses the prior knowledge of Chinese character field to express the Chinese character image more effectively; then, the importance of each channel is obtained by introducing the channel attention mechanism to improve the recognition accuracy of the network. Finally, the traditional convolution is replaced by the depth-separable convolution to reduce the network parameters and computation. Experimental results show that the proposed algorithm can improve the recognition performance significantly on CASIA-HWDB dataset, and the accuracy of off-line handwritten Chinese character classification task reaches 94.16%.
An East-West-Traffic Governance System Based on eBPF and Centralized Gateway

As the concept of cloud native continues to grow in popularity, Service Mesh has become a hot topic for developers. The traditional Service Mesh architecture(istio) uses the Sidecar-Per-Pod model, which has poor performance and resource utilization and high O&M costs. The paper adopts eBPF to redirect all East-West-Traffic to centralized gateway, thus enabling unified governance of East-West-Traffic. It is shown that the Centralized-Gateway mode improves the resource utilization and performance, and also reduces system O&M complexity compared to the sidecar per mode model.
Mask R-CNN based ski pose estimation

Skiing as a winter sport is showing an increasing trend of popularity worldwide. With the success of the Beijing Winter Olympics, more and more people are participating in this project. Human pose estimation as an emerging research area in computer vision is applied to more and more industries. In this paper, human pose estimation is applied to skiing sports scene based on Mask R-CNN to achieve the detection of overall pose of snowboarding, which can be applied to skiing pose rating, skiing pose classification and so on. Unlike the general pose detection, the 17 keypoints of pose are added to the head and tail of the ski, which can show the overall pose of the skiing process in a more comprehensive way and provide convenience for the subsequent skiing action recognition and skiing action generation, and help the model to understand the data more. It can accomplish classification, bbox regression, instance segmentation, and keypoints prediction at the same time by adding different branches to provide more comprehensive information for application scenarios. The model automatically generates candidate regions by neural network, which is better than SS algorithm (Selective Search), and filters some invalid candidate regions in the Proposal layer, which reduces redundant computation and improves the speed. In this paper, the traditional Mask R-CNN is lightened and improved, and we try to use MobileNetV2 as the backbone network to replace the original ResNet-101 network, introduce the Inverted Residuals block and Linear Bottlenecks , reduce the number of model parameters, and improve the detection efficiency, so as to realize the lightened Mask R-CNN algorithm. Based on the existing work, this paper contemplates the use of PoseCnov3D for further prediction to achieve the recognition of eight basic skiing actions for skiing pose classification.
Chinese Medical Record Entity Recognition Based on Lexicon and Self-attention

Named Entity Recognition (NER) is a pivotal challenge in the field of natural language processing. In the medical field, NER plays a crucial role in identifying medical entities, such as diagnoses and drugs, from electronic medical records. To address the issue of incomplete and inaccurate word recognition in Chinese electronic medical records, we propose a BERT-BiLSTM-CRF model that incorporates both a lexicon and self-attention mechanism.In our approach, we first utilize the BERT model for preprocessing to generate word vectors. These vectors capture important semantic information about the words. Next, we employ a BiLSTM network to further capture contextual information from the input sequence. This helps to enhance the understanding of the text's meaning. To reinforce the significance of the sequence information, we introduce a self-attention layer that learns to prioritize relevant contextual details. Subsequently,the extracted features are fed into a CRF model for final training. The CRF layer takes into account the dependencies among the labels and improves the overall sequence labeling accuracy. Given the specialized nature of the medical domain, we integrate a medical lexicon, incorporating external knowledge features into the model. This inclusion enables better recognition of medical terms and enhances performance on medical-related entities.Through extensive experiments on the CCKS2019 dataset, our proposed model demonstrates favorable performance. It get relatively excellent results in identifying medical entities accurately and effectively from Chinese electronic medical records.
Research and Application Based on Time Series Analysis with Least Squares Regression

In this paper, a simple model in the exponential smoothing model is developed based on the time series of the given data, which has a good fit with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.984 and passes the residual test. Finally, the number of outcomes reported on March 1, 2023 is expected to be within the 8% confidence interval of (7,478,30763). The specific number of reports was approximately 19,120, and a Spearman correlation analysis was subsequently performed to plot a heat map of the correlation coefficient. The analysis revealed no correlation between the two. Then, ten randomly selected words of data were retained for reliability calculations, and then seven expressions were obtained with six attributes as independent variables and correlation percentages (1, 2, 3, 4, 5, 6, X) of partial least squares regression analysis were performed daily as dependent variables. Then the eerii6 attributes were extracted for expressions and rounded to obtain 1: 1%, 2: 8%, 3: 23%, 4: 28%, 5: 25%, 6: 12 %, and X: 2%. Finally, the retained data are put into the expressions and their reliability is calculated. The relative error between the predicted data and the actual data was 7.53%, which means the reliability was 92.47%.
Research on the Application of ChatGPT Technology in the Semantic System of Text

This paper uses ChatGPT technology to construct a text semantic system. Through this project, we plan to build a lightweight natural language expression method based on multiple features. The extracted semantic features of words are fused with the semantic features of frame sequences, and then introduced into the natural language description of words to generate the natural language description of words. The experiment shows that this method has no restriction on the semantic type and the number of words. It can produce certain semantic characteristics in various cases. Good results are obtained in extracting and integrating the semantic features of Chinese characters. The development of this project will lay a foundation for the realization of lightweight natural language feature extraction.
Research on Circuit Optimization Design Based on Atomic Orbital Search Algorithm

Considering the complexity, multi-objective, and nonlinearity in the optimization design of electronic circuits, this paper proposes an optimization design based on atomic orbital search. First, the mathematical model of electronic circuit optimization design is established according to the structure and performance requirements of electronic circuits, and the objective function and constraint conditions of circuits are obtained. Second, an adaptive parameter adjustment strategy is proposed to improve the optimization efficiency and stability of the atomic orbital search algorithm. Then, according to the multi-objective nature of electronic circuits, we construct a fitness evaluation model that comprehensively considers Pareto optimality and diversity effects, making it more accurate in describing the objectives of electronic circuit optimization design. Finally, an electronic circuit optimization system based on an atomic orbital search algorithm is built. The effectiveness of the proposed method is proved by simulation and experiments.