Reviewerly/exHarmony huggingface dataset:
WBC Subtype Detection using Deep Learning with Optimizing Hyperparameters by Genetic Algorithm

The human body's defense mechanism, the immune system, heavily relies on white blood cells (WBCs). These cells come in five subtypes: Eosinophil, Basophil, Neutrophils, Monocyte, and Lymphocyte. Each subtype plays a vital role in the diagnosis of hematological diseases, such as leukemia, as their presence or absence can indicate the presence of a disease. In the past, hematologists have manually examined blood smears under a microscope to detect the subtype of WBCs. However, this method is both time-consuming and prone to errors. This study proposes a new approach, which uses a deep learning-based convolutional neural network (CNN) architecture in combination with an evolution-based algorithm (genetic algorithm) to detect WBC subtypes with greater accuracy. A large dataset of 12,000 images was used for training and analysis. The proposed model, which utilizes the You Only Look Once version 5 (YOLOv5) for image localization and segmentation, was able to accurately localize WBCs with an IoU score of 94% for more than 80% of images and detect the subtypes with an accuracy of 98%. This study aims to overcome the limitations of previous research by providing a more efficient and accurate method for detecting WBC subtypes.
Live Code Digital Watermarking Technology Based on Chaotic Encryption

This paper proposes a digital image watermarking algorithm based on Lorenz chaotic function and live code. First, to balance the contradiction between imperceptibility and robustness, the host image is divided into 8×8 blocks, and the two-dimensional information entropy of each block is calculated to judge the embedding strength of the image block. Carry out DCT transformation, then use Lorenz chaotic model and Hash function to determine the position of watermark embedding to improve the security of the algorithm, and finally realize the embedding of live code watermark by adjusting the size of DCT domain coefficient value, and use live code to improve the flexibility and embedding capacity of watermarking algorithms significantly. Experimental results show that the algorithm can effectively resist conventional attacks and accurately decode the extracted live codes.
A Learning Resource Recommendation Algorithm Incorporating User Information and Rating Differences

Despite the growing popularity and development of large-scale open online learning platforms, they have been suffering from the problem of “information disorientation.” To increase students' learning efficiency, it is important to build recommendation algorithms based on students' basic information and historical rating data. In this research, we propose a new collaborative filtering recommendation algorithm that incorporates the user Information and the rating differences. The algorithm first uses the user information labels to calculate the user similarity, then introduces rating differences to enhance the conventional cosine similarity based on the characteristics of non-preferred rating data, and finally linearly combines the two similarities. The experimental results demonstrate that the algorithm enhances the recommendation effect of learning resources. The MAE and RMSE is employed to quantify the prediction accuracy of the recommendation algorithm.
BCL: A Branched CNN-LSTM Architecture for Human Activity Recognition Using Smartphone Sensors

Human activity recognition, or HAR for short, is a process of interpreting specific human motion based on sensor data. HAR has many human-centric applications, notably in eldercare and healthcare as an assistive service. However, due to the noisy sensor data, it requires domain analysis and signal processing to extract features from the raw data to fit into the machine learning models. The recent revolution of Deep Learning Models makes it possible to learn the features automatically instead of handcrafting features. This area extensively utilizes deep learning techniques like CNN and RNN. In this paper, we present a branch CNN and LSTM structure for recognizing human activity that yields cutting-edge outcomes. The experiment is conducted on the SHOAIB Al and UCI HAR datasets, which produce better results than the traditional approach.
xCRM: Blockchain Interoperable Crime Report Management System By Utilizing Hyperledger Cacti &amp; Private Data Collection (PDC)

The process of bringing criminals to justice can be complicated when reporter information and sensitive data related to the case are revealed and may involve international law enforcement cooperation, especially when a criminal flees to another country. To tackle this issue, an interoperable crime management system is necessary. This study proposes a blockchain-based interoperable crime management system that provides secure and decentralized communication between different blockchain-based platforms, ensuring anonymity, transparency, and immutability. We present a methodology for crime reporting, evidence management, forensic testing, a collaboration between investigation agencies, and resource sharing where anyone can report in two modes: anonymous mode, which is only for passing any information to the police, or generate mode, which will create an FIR (First Information Report) and subsequent procedures. To ensure the security of our data, we have implemented Hyperledger Fabric Private Data Collection (PDC) for each report and investigation. The PDC will consist of the team leader, investigation officer, reporter, and any other relevant users as members. All interactions will occur within the specific channel created for each report, and files and additional information will be shared through the PDC. This system uses Hyperledger Cacti to implement interoperability and allows investigation and collaboration with others, like courts, forensics, and special investigation agencies, even with foreign systems. This proposed system is effective and efficient, enhancing the performance of blockchain networks.
Balanced Overall and Local: Improving Image Captioning with Enhanced Transformer Model

This article proposes a Transformer-based image captioning model using computer vision and natural language processing techniques. The model is based on Multi-Featured Attention Module and Grid-Augmented Module and outperforms the original Transformer model on all evaluation metrics. Specifically, with a beam size of 7, the model achieves a BLEU-4 score of 0.409 and a CIDEr score of 1.008.
Android-Based Smart Wheelchair with Joystick, Gesture, and Voice Control: A Low-Cost Solution

People with motor disabilities are hoping to become more self-sufficient by using an electric wheelchair with standard joystick control. However, when these patients have upper-extremity disabilities, such as tetraplegia, a problem emerges. Voice control, chin control, eye blinking, and sip-and-puff have been used, but each has its own set of benefits and drawbacks. Furthermore, some methods are preferable to others in certain conditions. In this work, a mechanism for a low-cost wheelchair that integrates numerous control methods is proposed. The same wheelchair will have joystick, gesture, and voice control mechanisms, and the option to choose which one to use will be given to the user. As the controller for this entire system, a simple Android application will be employed. It will help the individual with a disability be more autonomous, as the method that suits them better can be decided at any particular time. Additionally, the addition of a distance sensor for the detection of objects on the back of the wheelchair and an LED light module around the wheelchair to illuminate the darkness and facilitate a safe ride for the user is also proposed.
Combinatorial neural network signal modulation recognition algorithm based on attention mechanism

Aiming at the problems of low recognition rate and confused signal classification of deep learning modulation recognition network, combined neural network of one-dimensional residual network and long short-term memory based on efficient channel attention (ECA-RLDNet) is proposed. The algorithm designs a one-dimensional efficient channel attention mechanism to connect two feature extraction network units, uses the one-dimensional residual network to extract signal time series features, the attention mechanism gives higher weight to the key information of signal features, and further uses the long short-term memory to extract time series association features to obtain comprehensive and effective feature information. By simulating the modulation signal dataset under non-ideal channel and experimenting with the algorithm, the experimental results indicate that the highest recognition accuracy of ECA-RLDNet reaches 92.32%, which reduces the probability of confusion of high-order digital modulated signals.
An Industrial-grade Solution for Convolutional Neural Network Optimization and Deployment

The deployment of deep learning models into practical production applications has become increasingly important with the rapid development of deep learning in theoretical research. Currently, the deployment of deep learning models faces numerous challenges due to the increasingly large scale and computational requirements of these models, along with the limited storage and computing resources of mobile devices. This paper proposes a high-performance and versatile solution to address the challenges of practical model deployment. This study significantly enhances model inference speed by employing techniques such as DepGraph model pruning, operator fusion, and the NCNN inference framework while reducing the model size and storage overhead.
Designing Data Permissions in the Enterprise Application Environment

With the continuous improvement of social informationization, the digitalization of corporate construction is also developing comprehensively. Data, as a crucial source for creating wealth and competitive advantage, is undoubtedly the most significant asset for all enterprises. However, many companies face the dilemma of insufficient data granularity and configuration flexibility, which undermines the security and availability of their data assets. Therefore, this paper proposes an Api-based data permission control model (ADPC) based on the theoretical foundation of RBAC in the background of data application in the enterprise environment. Firstly, the model maps data objects to different levels of environmental variables in the corporate department structure, then flexibly authorizes and controls permissions through API configuration, and finally verifies its security with SQL AST. The ADPC model was implemented using the Spring Boot, Spring Security and MyBatis framework and was applied in a real enterprise environment. Through this approach, the ADPC model effectively addresses the limitations of traditional data permission control mechanisms in the enterprise application environment.
Spatial-Temporal Local Augmentation Graph Convolutional Networks

Action recognition based on skeleton models has been widely focused in the field of computer vision in recent years. Most of the previous methods only focus on the change trajectory of the same joint point in the moving process, with less consideration of the correlation between joints in the moving process, and many of the current action recognition models lack sufficient consideration of local relationships, so this paper constructs a more generalized spatial-temporal skeleton graph considering the inter-frame dependence of neighboring skeletons, and introduces a local enhancement module, using the idea of local aggregation on each node for local aggregation, combining the node's own features with the aggregated features of neighboring nodes, so as to better capture the local relationships between nodes. The model can combine global and local information to provide a more comprehensive feature representation, thus improving the performance of the model. The introduction of local relationships can also increase the flexibility and sensitivity to the details of the model. Finally, we validate the proposed Spatial-Temporal Local Augmentation Graph Convolutional Networks (ST-LAGCN) model in two skeleton datasets, NTURGB+D and Kinetics, and compare it with several state-of-the-art graph neural network models for action recognition, both of which show improved performance.
Double encryption of digital grid edge cloud data in complex dynamic networks

The current conventional data encryption algorithms for digital grid edge cloud data mainly realize data encryption by generating dislocation matrix, which leads to poor encryption effect due to the weak access control performance of network data. In this regard, a double encryption algorithm for digital grid edge cloud data in complex dynamic networks is proposed. By constructing a complex dynamic network model, and constructing a privacy database according to the model characteristics. The binary tree algorithm is used to access control the digital grid edge cloud data, and the double encryption of the edge cloud data is realized by dynamic keys. In the experiments, the encryption performance of the designed encryption method is tested. The final results can prove that after the proposed method is used to encrypt the edge cloud data, the information integrity of the database is high and has a more excellent data encryption performance.
Convolutional Neural Networks with Modified Evaluation Functions for Image Semantic Segmentation

Image semantic segmentation (ISS) is a classic problem in the field of image technology, and artificial intelligence-based neural network segmentation methods are widely used in ISS. However, as the accuracy gradually converges to higher levels, the computational overhead from complex models becomes increasingly difficult to ignore. This paper focuses on this problem in practical application, and proposes the use of deep separable convolutions in restricted computational resource scenarios with a large number of model parameters, and the optimization of the original cross-entropy loss function for the loss function of small-sample segmentations. The experimental results show that model selection in deep learning tasks is dataset-oriented, and small-sample learning optimization is effective in improving a model's a bility to perceive details.
STM32F103-Based Intelligent Headrest Driving Safety Perception System Design

In the context of China's sustained and rapid economic development, the layout of intelligent road traffic facilities is increasingly improved, and the automotive industry and related hardware and equipment industries are showing rapid growth. Meanwhile, advanced digital technologies such as 5G communication, cloud computing, and big data are empowering the intelligence and networking of the automotive industry, heralding the development direction of the intelligence of automobiles and in-vehicle systems. The neck-based intelligent driving assistance system mainly monitors the driver's physical status and behavioral characteristics in real-time by means of neck sensors and surrounding environment sensors and thus provides accurate driving assistance and safety assurance. Based on this, we address the pain point of frequent traffic accidents, and the article designs an intelligent driving assistance system that integrates neck-based analysis of health indicators such as body temperature, heart rate, blood oxygen, and monitoring of driver behavior status to provide a complete and reliable solution for safe and healthy driving. This paper introduces the design method of the monitoring device, mainly including the design principle, structural composition, and program design.
A Mobile Terminal Authentication Method Based on Certificateless Signature

With the rapid development of information science and technology and the promotion of network applications, especially the development of mobile networks, intelligent mobile terminal, and mobile applications, mobile terminal should be applied in various aspects of life. However, due to the characteristics of mobile networks and terminal, various network attacks have caused increasingly serious information security issues. Therefore, researching secure access solutions for mobile terminal is also an urgent need. So, ensuring that confidential data is not compromised and achieving authentication of mobile access objects is also a top priority in secure access solutions. This article proposes a certificateless aggregation authentication scheme for mobile terminal, which can provide conditional privacy protection for mobile terminal and resist various attacks with strong security. The performance analysis of this scheme and similar schemes shows that this scheme has higher authentication efficiency.
Automated Writing Evaluation Using Sentence by Sentence Scoring Model

Automated writing evaluation is the use of computer programs to assess written work and offer feedback to the writer. This process is commonly used in educational settings [1] to grade essays and give students a better understanding of their writing abilities. Existing works score specific dimensions of an essay, grammar, word choice, coherence, and others to provide targeted feedback. Also, most systems focus on evaluating the essay as a whole rather than individual sentences. Sentence-level evaluation can provide more detailed and specific feedback to the writers, which may help to improve their writing skills over time. In this work, we propose a mechanism for evaluating student essays at the sentence level. A publicly available essay dataset was collected and then evaluated manually by experts to prepare a dataset with sentence-level scores. Two models were developed: the first model is based on pre-trained models (BigBird, Longformer, and DeBERTa), and the second model is a neural network architecture based on multi-head-attention mechanisms. Models calculate the holistic score for each sentence. The final score for each essay is obtained by summing up the marks for each sentence. Experimental results show that the proposed model can score essays with high accuracy and low average error.
Grid Transient Simulation Using Attention-Based Data Augmentation Technique with Supercomputing

The stability of power systems, central to the unimpeded flow of daily life and economic activities in our modern world, is a critical aspect requiring precise forecasting. Notwithstanding, predicting such stability becomes an arduous task, especially amidst situations fraught with high complexity. To mitigate this, our study presents an avant-garde approach for transient simulation of power systems, incorporating Transformer-based data augmentation techniques. We proceed to delineate the application of Transformer models for data augmentation in our methodology. The ensuing augmented data is then used for training models to predict both the stability result and stability index of power systems. Comparative analysis between predictions sourced from original and augmented data indicates that the utilisation of Transformer data augmentation significantly boosts the accuracy of our forecasts. Additionally, we undertake an exhaustive examination of the prediction outcomes, enabling the identification of key factors that impact the stability of power systems. This paper, therefore, offers a groundbreaking and highly effective predictive method for power system stability, yielding a significant advancement in our understanding of power system dynamics and offering preemptive measures to counter potential instability.
Identification and Analysis of Ancient Glass Based on Logistic Regression and Machine Learning Model

During the ancient Silk Road period, glass played a significant role in witnessing cultural integration. However, glass was highly susceptible to environmental and weathering effects. This article aims to explore the changes in elements that occur during the weathering process of glass and propose a method to identify and classify glass based on corresponding characteristics. To begin, an in-depth examination and classification of the components of ancient glass artifacts were conducted. Logistic regression models and ensemble learning techniques, specifically classification tree ensemble learning, a machine learning algorithm, were utilized to improve the understanding of the factors influencing glass properties. These methods enabled the training and optimization of two different types of ancient glass. Additionally, sensitivity analysis was carried out, revealing the significant impact of barium content on ancient glass. Finally, examples of the two glass types were analyzed, and the predicted results from the models were compared. This process led to the determination of an optimal classification model that exhibits excellent applicability, accuracy, and simplicity. The research presents innovative ideas for the identification and authentication of cultural relics such as glass.
Dual stream Dual branch Network with Cascaded Codec Framework for RGB-D Saliency Detection

Depth maps have been proven to complement the problem of RGB images not having obvious features in salient object detection (SOD). Most existing RGB-D SOD models lack the ability to capture depth features and RGB feature spatial information during cross-modal fusion, which may lead to inaccurate fusion of features from significant regions. And we found that compared to low-level features, higher-level features have a greater contribution to performance, and improving the accuracy of higher-level features can significantly improve saliency detection performance. Therefore, in this article, we propose a cascaded coder-decoder dual stream dual branch network, which uses the initial saliency prediction obtained by attention branching to refine the high-level features of the backbone network. We also designed a Cross-modal space fusion (CSF) module to utilize the spatial information in RGB features and depth features to promote cross-modal fusion. A large number of experiments have shown that our model outperforms other RGB D SOD models on four widely used datasets.
Improved Apriori Algorithm Using Hash Technique

Association rule mining techniques have significant drawbacks in the current data mining technology. This study addresses the issue that the Apriori algorithm's mining efficiency is low when it deals with huge databases, and introduces the hash technique as a means to enhance the Apriori algorithm. This paper explains the core idea and the theory of association rules, the fundamentals of Apriori algorithm in depth, and a proposed enhancement method based on a hash technique to address the shortcomings of the Apriori algorithm. This paper shows the reasons behind the inefficiency of Apriori algorithm. After introducing the explanation and workings of the Apriori algorithm in-depth, improvements to the coding scheme and support counting method that are appropriate are proposed. Finally, the hash technique and Apriori algorithm are combined to create association rule mining. The experimental findings demonstrate that the proposed method performs much better than the current methods in terms of mining efficiency and performance.