Reviewerly/exHarmony huggingface dataset:
XorshiftH128+: A hybrid random number generator for lightweight IoT

The main purpose of this paper is to develop a Hybrid Random Number Generator for resource-constrained slim nodes operating within a lightweight Internet of Things network. Our algorithm extends the lightweight Xorshift128+ random number generator by developing a randomized seed generation algorithm that leverages the network’s random features. Simulation of the network was conducted using Omnet++ software followed by the validation process of cryptographic security of our proposal using NIST SP 800-22, Diehard, and TestU01 statistical tests.
A Machine Learning Algorithm With Adaptive Truncated Concentrated Differential Privacy

The rapid advancement of artificial intelligence technology, particularly driven by machine learning, has substantially impacted different industries and daily life. Regrettably, as the machine learning parameters are obtained from the mathematical expressions of datasets, Attackers can use these parameters to deduce sensitive information that the data owner with no intention to disclose reveal. Therefore, this paper proposes a novel approach that utilizes differential privacy technology to safeguard model parameters. It introduces an adaptive algorithm that provides differential privacy protection to machine learning. More specifically, this paper utilizes truncated concentrated differential privacy (tCDP) to perturb gradients in the local model training process of participants. Additionally, the algorithm incorporates adaptive step sizes and dynamic privacy budget adjustment techniques to enable the flexible distribution of privacy budgets. Based on the experimental and privacy analyses, the algorithm presented in this paper outperforms DP-AGD, thereby ensuring model effectiveness while preventing privacy leakage in the local models of the participating parties.
Early Detection and Classification of Melanoma Based on Android MobileNet V2 Convolutional Neural Network

Melanoma is a malignant form of cancer that affects the skin and has a particularly high mortality rate, so it requires early detection to increase the level of safety for users. Diagnosis and detection of skin cancer are usually done through manual screening and visual inspection. This process requires a long time, has high complexity, is subjective, and is prone to errors. CNN is one of the algorithms with advantages in accurate classification. In this research, early detection and classification of melanoma cancer were carried out based on two classes, namely benign and malignant using the Convolutional Neural Network method. Our proposed method yields an accuracy of 81.11% for the validation data. The accuracy results obtained can be improved by using more datasets and increasing the number of layers used. This study uses the CNN method using MobileNet V2 architecture to detect melanoma skin cancer. The class used is benign and malignant.
Food Allergen Database for Japanese Restaurants and Its Application to Menu Recommendation System to Foreign Travelers

The number of people with food allergies has been on the rise in recent years. Recent data estimate that between 1.1% and 10.8% of the global population suffer from food allergies. Confirming whether the corresponding allergic substances are present in the food is very important for people with allergic reactions. Through the investigation of allergens, we know that for people with allergies, the distribution of allergen information is scattered, it is relatively difficult to query, and there is also a lack of allergen information when eating out. In addition, with the gradual recovery of the tourism industry, for people with food allergies if they travel abroad, catering will become a problem for them. In order to face different situations and solve these problems, this article takes Japan as a tourist destination and collects allergen information scattered on various restaurant websites. Creates tables in MySQL, a database designed to unify the data, and the recommendation system accesses the database—a dataset of restaurant allergens with 340 menus in 13 restaurants. In the system, we search the database for foods and restaurants that meet the requirements according to the user's allergen information and preferences and recommend this information to the user as a reference. In order to test the function of the software and the effect of data collection, we used the software to conduct experiments on 30 Chinese people. The experiments assumed allergens according to the selection of allergens, regions, and dishes. From the results of the experimental data, 28 out of 30 people could find a suitable menu. Fundamental problems for users traveling, such as information confusion, language barriers, and lack of information when dining out, have been solved.
A Global Path Planning Method for USV Based on Improved A_Star Algorithm

USVs(Unmanned Surface Vehicles) have developed rapidly and are widely used in various fields recently. However, the control system of USVs is underactuated, and conventional global path planning algorithms are not suitable for USVs. The existing A* algorithm has the following shortcomings in global path planning for USVs: the search neighborhood is not adjustable or the steering direction is unconstrained, and the path smoothness is poor and the search time for high-resolution grid map is too long. This paper proposes a global path planning method for USVs based on an improved A* algorithm. This method adds unmanned boat steering costs to the objective function, and dynamically adjusts the search step size during search, thereby achieving improved path smoothness while reducing path planning time.
Research on Text Classification Based on Convolutional Neural Networks

Nowadays, with the development of media technology, people receive more and more information. Among them, the most important is the text classification technology. This paper aims to improve text classification methods using Convolutional Neural Networks(CNNs) and natural language processing techniques. The technical requirements and application scenarios of text classification are combined with Machine Learning to optimize classification, and for the application of Natural Language Processing in text classification, and a text classification algorithm is proposed, and the accuracy rate of TPM algorithm in different data sets is above 95%.
Self-Cure facial expression recognition based on Coordinate attention and Lsland loss

Facial expression recognition (FER) is a challenging task. The following two significant problems often exist in real-life FER tasks: first, facial expression images in the wild are uncertain, i.e., occlusion or blurred images; and second, there is little intra-class similarity and high inter-class similarity. In this paper, we propose a Self-Curing network based on Coordinate attention and Island loss(CoIsland-SCN) to address the above two problems. Firstly, the coordinate attention module aggregates the input features in vertical and horizontal directions into two independent direction-aware feature mappings, and the encoded attention contains channel, directional and positional information at the same time, which well solves the facial expression uncertainty problem; secondly, the Self-Cure module performs ranking regularization and relabeling according to the image importance and calculates the cross-entropy loss and the ranking regularization loss; and finally, combines the island loss to adjust the model to solve the problem of poor intra-class tightness and poor inter-class separability. The method proposed in this paper achieves an accuracy of 86.41% on the public dataset RAF-DB, which is very superior compared with recent experiments and proves the effectiveness of the proposed method.
An Attentional Interactive Deep Learning Model for Multi-source Electrical Consistency Checking in Power Grid Measurement Center

With the rapid development of the Internet and big data, artificial intelligence technology represented by deep neural network, DNN, has ushered in a golden period of development. The application of deep learning method in the field of power grid data checking has also attracted more and more scholars' attention. A deep learning model based on attentional interaction module is proposed for multi-source electrical consistency checking. Firstly, the circuit switch and the grid data are mapped into a fixed length vector, and the corresponding embedding matrix of the switch and data is obtained. Then, the embedded matrix is sent into the attentional interaction module to obtain an embedded matrix that comprehensively considers all elements of the switch and data, and is sent to the deep neural network after adding the previous embedded matrix to extract the intrinsic characteristics of the switch and data. Finally, the vector representation of switch and data is obtained and the matching score is calculated. Experimental results show that the proposed model is effective for multi-source electrical consistency checking.
Research on SQL Injection Detection Based on Deep Learning

With the rapid development of modern information technology, web applications have gradually become important service providers in people's daily work and life. However, while web applications bring convenience, they also face various challenges. Among them, SQL injection attacks, characterized by their diversity, rapid changes and strong concealment, have always been one of the main threats to web applications. Currently, most mainstream SQL injection detection tools on the market are based on established rules and cannot cope with the constantly changing challenges. Therefore, this paper proposes a deep learning-based approach using pretraining and fine-tuning with BERT for SQL statement pretraining. An improved TextCNN algorithm is used as the classification algorithm. Experimental results show that the proposed algorithm has significant improvements compared to other text vectorization algorithms in terms of detection performance and has certain application prospects.
Improved Cervical Cell Segmentation Algorithm Based on Cascade Mask RCNN

Cervical cell early detection can timely discover abnormal cells and take treatment to avoid cervical cancer. With the development of deep learning technology, cervical cell smear detection technology has made rapid progress. However, current deep learning segmentation algorithms still have problems with inaccurate segmentation and low segmentation accuracy when dealing with high overlap of cervical cells. Therefore, we propose the cell overlap degree to measure the degree of cell overlap, in order to compare the segmentation algorithm more targetedly. At the same time, to address the misclassification problem of the traditional Cascade Mask RCNN algorithm, we propose an improved algorithm based on Cascade Mask RCNN. This algorithm uses a more advanced backbone network to improve the segmentation accuracy of the algorithm, introduces a cross-scale feature fusion structure to improve the network’s ability to extract cell cytoplasm edge feature information, and improves the ROI pooling method to further enhance the algorithm’s feature extraction ability. The improved algorithm is compared in experiments on the dataset, and the results show that our algorithm has certain effectiveness and progressiveness.
A violation behavior recognition method for substation operators based on three-dimensional data and object detection

Substation inspection or construction still relies on manual operation. Safety accidents caused by violation behaviors or insufficient knowledge and skills are inevitable. The existing methods for substation operation safety management use two-dimensional map and image to recognize violation behavior including fast moving, cross-border, trespass, high-altitude operation without safety tether, ladder operation without support. However, the accuracy of distance calculation based on two-dimensional map is limited to meter level, which belongs to extensive safety management. Furthermore, it’s difficult to capture accurate spatial position information in two-dimensional images, which pose challenges to violation behavior recognition of substation operators. To tackle the above limitations, this paper presents a violation behavior recognition method for substation operators based on three-dimensional data and object detection. Based on three-dimensional space model and personnel positioning spatial-temporal information, it improves the accuracy of safety distance calculation among personnel, equipment and authorized work areas to the decimeter level. The experiments demonstrate that the proposed method can improve the accuracy of fast moving, cross-border, trespass, high-altitude operation without safety tether, ladder operation without support recognition.
Research on the Complexity Characteristics of Convolutional Neural Networks

In order to quickly realize the convolutional neural network that meets the given complexity characteristics, a generation algorithm from complex network topology to convolutional neural networks is given. By establishing a series of convolutional neural networks with different topology characteristics, CIFAR 10 and CIFAR10 dataset analysis uses analysis The effects of topological properties such as average aggregation coefficient, average path length, diagram density, and modularity affect the effectiveness of the validity of convolutional neural network recognition. Experiments show that when the number of parameters of the neural network is basically equal, the average cluster coefficient will affect the performance of the convolutional neural network. In the end, in the statistically significant conclusion, the network structure with a small concentration coefficient will have better performance, which provides a theoretical basis for further designing a better convolutional neural network.
Research and application of zero trust based secure access to the power Internet of Things

The massive power grid IoT terminals are characterized by intelligence, networking, and interactivity, and are often deployed in uncontrolled environments. These massive IoT terminals are widely interconnected and interact, making it difficult to achieve low-cost terminal access authentication and efficient secure interaction technology. In order to comprehensively improve the comprehensive defense capability of the power Internet of Things security and address the deficiencies and deficiencies of current power Internet of Things terminal security protection, terminal authentication mechanism, and dynamic access control, this article first studies the power Internet of Things and zero trust security mechanism, and then conducts in-depth research on key technologies such as power Internet of Things terminal trustworthiness, power Internet of Things terminal layer device access authentication, and power Internet of Things dynamic access control, Propose a zero trust based security protection framework for the power Internet of Things, and combine it with the zero trust security protection system. In the actual environment of the power Internet of Things, study the security protection architecture of the power Internet of Things, and achieve the security protection requirements of the perception layer, network layer, and application layer of the power Internet of Things. In the scenario of secure access to distributed power sources, method validation is carried out. In response to common flooding attacks, packet attacks, and malicious code attacks, the zero trust architecture based secure access method to the power Internet of Things can effectively detect and timely block it, avoiding attackers from controlling the power Internet of Things terminals to carry out network attacks on the smart grid and triggering power grid security incidents.
UWDepth:Underwater image Depth estimation

In this paper, we propose a robust and efficient end-to-end model named UWDepth for fast monocular depth estimation that incorporates underwater domain knowledge into it as a supervised learning pipeline. We employ a new input space RMI for underwater domain-aware learning, including the red channel (R), the maximum of the green and blue channels (M) and the grayscale intensity (I). Since the attenuation constraint of the underwater light pillar (M) depends on the wavelength (M), the relative difference between the R and M channels embeds useful scene depth information in the I channel to preserve the structural content of the image. More importantly, visual results of experimental data show that its coarse pre-representation is quite accurate and can be used for 3D perception of low-cost underwater robots.
DigiSign: A Mobile Application for Comparing Digitized Signature Using Feature Detection and Matching Algorithms

A signature is a formal declaration of a person’s legal identity but can be copied and forged by others. Fake signatures in online transactions, including shipping, purchasing, and banking, can result in financial losses. This study examines how digitized signatures comparing systems work on touchscreen-based mobile devices. DigiSign is a mobile application designed to detect and match digitized signatures to determine whether they are similar or not. The application employs an image processing algorithm to transform digitized signatures into images, enabling the detection of keypoints through feature detection and matching to assess their similarities. By utilizing feature detection and feature matching to recognize keypoints, DigiSign ensures secure data storage in image memory or RAM, avoiding the need for storing sensitive information in databases. DigiSign utilized the BRISK algorithm and the hamming distance algorithm, resulting in a 38% acceptance rate for similarity. The application’s performance was evaluated according to ISO/IEC 25010 standards, achieving a mean score of 4.26 out of 5.0. The study confirms its effectiveness in accurately interpreting the resemblance of digital signatures, as indicated by the strongly agreed responses.
Smoking Detection Algorithm Based On Improved YOLOv5

Aiming at the problem of low detection accuracy of small and medium-sized targets in complex scenes, a smoking detection method in public places based on improved YOLOv5 was proposed.Firstly, Mosaic-9 was used to replace the traditional Mosaic data enhancement algorithm to increase the amount of image mixing and improve the learning ability of the algorithm model for small targets. Secondly, the dual channel attention mechanism (CBAM) is introduced to pay more attention to the location of the target in the image and improve the recognition ability of the algorithm model. Then, the upsampling method is changed to transpose convolution, so that the model can better recognize the semantic information in the image and improve the recognition ability of the target region. Then, the SPD-Conv module is introduced to improve the recognition ability of low resolution image and small target image.
Introduction

The increasing affordances of new technologies provide both new opportunities and challenges for teaching in higher education. Equipping instructors with the necessary teaching competencies to overcome these challenges in the digital era is imperative. This handbook serves as a reference for higher education stakeholders. It aims to cultivate high-quality instructors, improve higher education quality, and drive digital education transformation. It covers the context, theoretical foundations, and standards for teaching competency development. The book provides a practical framework for fostering teaching competencies across various levels and includes real-world examples of current practices.
Out-of-distribution Detection in Time-series Domain: A Novel Seasonal Ratio Scoring Approach

Safe deployment of time-series classifiers for real-world applications relies on the ability to detect the data that is not generated from the same distribution as training data. This task is referred to as out-of-distribution (OOD) detection. We consider the novel problem of OOD detection for the time-series domain. We discuss the unique challenges posed by time-series data and explain why prior methods from the image domain will perform poorly. Motivated by these challenges, this article proposes a novel Seasonal Ratio Scoring (SRS) approach. SRS consists of three key algorithmic steps. First, each input is decomposed into class-wise semantic component and remainder. Second, this decomposition is employed to estimate the class-wise conditional likelihoods of the input and remainder using deep generative models. The seasonal ratio score is computed from these estimates. Third, a threshold interval is identified from the in-distribution data to detect OOD examples. Experiments on diverse real-world benchmarks demonstrate that the SRS method is well-suited for time-series OOD detection when compared to baseline methods.
Application of SCADA System in an Electrical Substation and Remote Terminal Unit Parametrization

In order to efficiently and practically solve the problem of SCADA (Supervisory Control and Data Acquisition) system performance analysis of the power grid, this paper proposes to change a solution with PLC and software for protocol conversion installed on the local HMI to a remote terminal unit (A8000) specialized for data storage and protocol conversion in power substations. The paper discusses the main Programmable Logic Controller (PLC) tasks, their classificationfrom a structural point of view and their operation mode. Also, the structure of an RTU including CPU, process interface, communication interface, user interface and their role in the electrical substation is presented. In the last part of the paper, the parameterization of the RTU A8000 in an electrical substation in Romania is presented and highlighted with screenshots.
Towards Robust Consensus for Intelligent Decision-making in IoT Blockchain Networks

Distributed consensus is the core aspect of blockchain protocol security design. Recent protocols like IOTA have improved concurrency and scalability over Proof-of-work (PoW) with Bitcoin, but have core design decisions that are inefficient for limited devices and do not take advantage of previous network experience to reduce calculations. This work proposes the first blockchain consensus protocol based on active machine-learning decisions, called Proof-of-history (PoH). PoH is setup as a distributed reinforcement-learning task for monitoring classification and training of blockchain transactions with an inner deep classifier. Early theoretical analysis and simulations show that PoH is robust to uncoordinated byzantine attacks through the use of a opinion-consolidation scheme for deep reinforcement-learning and weighted threshold voting scheme for decision-making. Future work will expand on these results to prove robustness and improve auditability with the deep networks.
A authentication method of near field Ad hoc network for electrical mobile safety inspection

At present, the application of wireless Ad hoc network in the field of mobile security inspection is in its infancy, and the network security protection means for the power industry are still insufficient, which is highlighted by the lack of efficient security authentication means for Ad hoc network, and it is difficult to completely eliminate security risks such as illegal terminal intrusion, data counterfeiting and tampering. A decentralized security authentication scheme suitable for Ad hoc network is designed, which can solve the security trust transfer problem on the variable network topology. Under any network route, the security trust is transferred to the proxy node step by step through multiple peer authentication, and the authentication chain is eUEblished between the digital intelligence edge proxy device, the proxy node and the node to be accessed. On the one hand, it can effectively solve the counterfeit problem of A-nodes and proxy nodes; on the other hand, it can greatly reduce the problem of reduced security authentication efficiency caused by deepening network hierarchy.