Reviewerly/exHarmony huggingface dataset:
Object Detection in Junk Image Based on SSD Model

This paper first introduces the background and significance of the direction in target detection, and introduces other target detection methods besides SSD. Secondly, this paper also introduces the principle and implementation process of SSD. It describes in detail the important steps to implement SSD and attaches relevant codes. Finally, the achieved results are compared with different methods, different data sets VOC 07 and networks using different methods, and conclusions are drawn.
Location of Scene Text Based on Yolov7

Text is everywhere in daily life, and it carries rich and accurate information. Natural scene text detection technology can be widely used in various fields of life. Chinese is an important tool to carry culture. Therefore, it is of great significance to study natural scene Chinese text detection. For text location in Chinese scene, we use target detection method to train based on YOLOv7 model, and obtain effective detection results.
A Side-Channel Analysis on the TALE

Lattice-based cryptography, as an active branch of post-quantum cryptography (PQC), has received extensive attention from side-channel analysis (SCA) researchers in recent years. The TALE is a candidate for China's post-quantum project, which aims to standardize cryptographic systems from attacks from quantum and classical computers. Although TALE relies on the theory of quantum-lattice resistance, practical implementations still have the vulnerability of side-channel analysis (SCA). In side-channel analysis (SCA), template attack is considered to be the most effective analysis method, and traditional template analysis are now gradually being replaced by machine learning-based template attack due to low computational efficiency. In this paper, the TALE is analyzed for vulnerability, feature extraction is performed for the leakage information of vulnerable points, and a template attack method based on multilayer perceptron (MLP) is used to attack vulnerable points.
Intellectual property protection for AI algorithms

In the 21st century, artificial intelligence technology is developing rapidly. Artificial intelligence technology combines multiple disciplines, involves multiple industries, and absorbs multiple talents, which is an extremely complex and huge technology. As the core technology of artificial intelligence, artificial intelligence algorithms have the ability to train and learn human-like self-training, and have become one of the most concerned fields. In recent years, there has also been an increasing discussion of artificial intelligence algorithms. How AI algorithms are protected has also become a matter of great concern for many companies and creators. In this context, this paper will take artificial intelligence algorithms as the research object to carry out the paper. This paper will introduce the legal nature of algorithms, reveal the introduction and ethical dilemma of the existing artificial intelligence algorithm rights ownership dilemma of artificial intelligence algorithm intellectual property protection, and put forward feasible suggestions for the construction of China's artificial intelligence algorithm intellectual property system. It should be made clear that China's existing patent protection path cannot fully respond to all the needs that artificial intelligence algorithms want to be protected, so some adjustments need to be made to the patent law. First of all, it should be clarified which artificial intelligence algorithms can and cannot become the object of patent authorization. As a new thing, artificial intelligence algorithms are different from patents that are obviously novel, practical and inventive in the traditional sense. In order to better protect artificial intelligence algorithms and promote the better development of artificial intelligence algorithm technology, in view of these special differences, certain adjustments have been made to the examination standards of China's patent substantive elements according to the characteristics of patentable artificial intelligence algorithm objects.
Design and Implementation of Online Education Platform Based on ThinkPHP Abstract

With the increasing progress of the times and the rapid development of the Internet, in order to solve the actual needs of students, this online education platform project has been launched in combination with the teaching characteristics of various disciplines, aiming to improve the flexibility of teachers' teaching, enrich the learning channels of students, so as to use and learn at any time. This platform is based on ThinkPHP framework and MySQL database implementation to achieve the front-end separation.
Communication Efficient Federated Personalized Recommendation

Recommendation systems that can correctly predict user preferences in the information age have become an important factor for business success. However, recommendation systems require users' personal information, and centralized collection and processing of user data may lead to serious privacy risks. Good progress has been made in recent years using federated learning techniques for privacy-preserving recommendations, but several key challenges remain to be addressed: most federated recommender systems ignore communication process optimization, inequities in aggregation of federated models, and lack of personalization to users. In this paper, we propose a communication efficient and fair personalized federated recommendation approach (CFFR) to address these challenges. CFFR uses adaptive client group selection to personalize models while accelerating the training process. A fair-aware model aggregation algorithm is proposed that adaptively captures the performance and data imbalance among different clients to address the unfairness problem. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method.
Trust Evaluation of Doctor Behavior Based on GCN Network in Medical Big-Data Access Control

The establishment of health care big data has brought great convenience to population health and medical research, but at the same time a series of privacy protection issues must be considered as a result. In this paper, we propose a graphical convolutional neural network to detect the access behavior of doctors in medical big data. In this paper, we propose a graphical convolutional neural network to model the access behavior of doctors in medical big data and perform trust evaluation, so as restrict such doctors or behaviors. In this paper, by taking the doctor behavior features and the doctor-doctor relationship network as input, the GCN network is used to supervise the learning of the department to which the doctor belongs, and the last layer is used as the characterization learning result. Finally, the similarity between doctor and department is used as the doctor behavior trust evaluation index. The experimental results show that the proposed model in this paper can well identify doctors' behaviors with malicious intent and assign a low trust value, laying the foundation for further research.
Improved Text Matching Model Based on BERT

Text matching is a basic and important task in natural language understanding, this paper proposes a new model BBMC for the problem of insufficient feature extraction ability of existing text matching models, which integrates BiLSTM and multi-scale CNN on the basis of BERT. First, the word embedding representation of the text is obtained by the BERT, and then the semantic features of the text are further extracted by the double-layer BiLSTM, followed by the multi-scale CNN model, the key local features are extracted, and finally the linear and SoftMax function are used to classify. Experimental results on the LCQMC dataset show that the BBMC has been improved to a certain extent compared with other methods, and the accuracy on the test set can be best achieved 88.01%.
Application of blockchain technology in the renovation management of old neighborhoods

In the old neighborhood renovation process in the past, because of the large amount of funds involved, cumbersome procedures and processes, many participating units and independent databases of different government departments, it directly leads to a series of problems such as the difficulty of data information integration. Blockchain has the characteristics of non-tampering, traceability and decentralization. In this context, applying blockchain technology to the management process of old neighborhood renovation has become a new idea.
Robot path planning based on improved genetic algorithm

In order to solve the problems of the basic genetic algorithm in robot path planning, such as the path is not smooth enough, the number of turns is too many, and it is easy to fall into the local optimal solution, an improved genetic algorithm is proposed. The method introduces the turning angle and turning times as the evaluation objectives, improving the practicability of the fitness function; The elitist retention strategy is added to improve the convergence speed of the algorithm; The adaptive change strategy of crossover probability and mutation probability is optimized, so that the algorithm can adapt to various scenarios and improve the optimization ability and convergence speed of the algorithm. The simulation results show that the improved genetic algorithm is more suitable for robot movement than the basic genetic algorithm.
Quantifying over information change with common knowledge

Abstract Public announcement logic (PAL) extends multi-agent epistemic logic with dynamic operators modelling the effects of public communication. Allowing quantification over public announcements lets us reason about the existence of an announcement that reaches a certain epistemic goal. Two notable examples of logics of quantified announcements are arbitrary public announcement logic (APAL) and group announcement logic (GAL). While the notion of common knowledge plays an important role in PAL, and in particular in characterisations of epistemic states that an agent or a group of agents might make come about by performing public announcements, extensions of APAL and GAL with common knowledge still haven’t been studied in detail. That is what we do in this paper. In particular, we consider both conservative extensions, where the semantics of the quantifiers is not changed, as well as extensions where the scope of quantification also includes common knowledge formulas. We compare the expressivity of these extensions relative to each other and other connected logics, and provide sound and complete axiomatisations. Finally, we show how the completeness results can be used for other logics with quantification over information change.
Blockchain in Supply Chain Management

Due to the expeditious growth in network and communication technologies, various industries such as healthcare, agriculture, supply chain and logistics, business, tourism, and hospitality as well as their various operations have undergone complete digitized transitions in industry 4.0. Supply Chain Management (SCM) and Demand Driven Supply Chain (DDSC) can be considered a core functionality of all these industries. Due to digitization in all the aforementioned industries and the related supply chain management, transparency, security, and privacy have become major aspects to be of concern. Innumerable open security solutions and research studies have been proposed in the existing literature and applied to enhance the security in variegated domains. However, the existing security mechanisms are either based on centralized architecture or they consume high computation power. To resolve these issues, Blockchain technology has been proven to be the best solution that depicts the new business aspects. Furthermore, Blockchain technology can address the additional requirements of supply chain management and demand-driven supply chain viz. scalability, robustness, data storage, network latency, auditability, immutability, and traceability. Moreover, the transparency and trust in the system can be achieved by Blockchain-based architecture without third-party intervention. Blockchain, as a distributed and peer-to-peer network technology facilitates immutable and transparent systems to satisfy the above-mentioned need of any industry including SCM. So, the discussion on the ecosystem of Blockchain-based supply chains as well as the demand-driven supply chain is emphasized in this chapter. In addition, the barriers and limitations to implementing Blockchain in supply chain management and demand-driven supply chain are explored along with its security aspects. Lastly, distinct case studies and schemes that discuss the success of Blockchain in supply chain management and demand-driven supply chain are presented.
Government Applications and Standards to Use Blockchain

This chapter examines the public sector applications of blockchain technology in government. Blockchain has found quick adoption in the public sector with versatile uses across several domains. We survey these extant and potential uses of blockchain in this chapter. Governments mediate or undertake a plethora of transactions which require transparency, security, and integrity in the long term. Blockchain technology has a strong potential to fulfill these requirements for governmental operations. We highlight the technology’s application in transforming various such government functions. Exemplary governmental uses of blockchain include those related to real estate, digital identity, infrastructure management, safety, and emergency management, and smart contracts. These cases demonstrate how the blockchain technology’s applications transcend those of the traditional use in cryptocurrency. We posit that the blockchain technology can potentially enhance efficiencies in the delivery of public services securely across several domains, but the realization of these uses requires more institutional learning and acceptance of the technology.
Rapid elemental prediction of heterogeneous tropical soils from pXRF data: a comparison of models via linear regressions and machine learning algorithms

Context USEPA 3051a is a standard analytical methodology for the extraction of inorganic substances in soils. However, these analyses are expensive, time-consuming and produce chemical residues. Conversely, proximal sensors such as portable X-ray fluorescence (pXRF) spectrometry reduce analysis time, costs and consequently offer a valuable alternative to laboratory analyses. Aim We aimed to investigate the feasibility to predict the results of the USEPA 3051a method for 28 chemical elements from pXRF data. Methods Samples (n = 179) representing a large area from Brazil were analysed for elemental composition using the USEPA 3051a method and pXRF scanning (Al, Ca, Cr, Cu, Fe, K, Mn, Ni, P, Pb, Sr, Ti, V, Zn and Zr). Linear regressions (simple linear regression – SLR and stepwise multiple linear regressions – SMLR) and machine learning algorithms (support vector machine – SVM and random forest – RF) were tested and compared. Modelling was developed with 70% of the data, while the remaining 30% were used for validation. Key results Results demonstrated that SVM and RF performed better than SLR and SMLR for the prediction of Al, Ba, Bi, Ca, Cd, Ce, Co, Cr, Cu, Fe, Mg, Mn, Mo, P, Pb, Sn, Sr, Ti, Tl, V, Zn and Zr; R2 and RPD values ranged from 0.52 to 0.94 and 1.43 to 3.62, respectively, as well as the lowest values of RMSE and NRMSE values (0.28 to 0.70 mg kg−1). Conclusions and implications Most USEPA 3051a results can be accurately predicted from pXRF data saving cost, time, and ensuring large-scale routine geochemical characterisation of tropical soils in an environmentally friendly way.
Novel Smart Homecare IoT System with Edge-AI and Blockchain

Healthcare data is sensitive information that demands security and confidentiality. Currently, remote monitoring is widely applied as it provides many advantages, including improving the quality of healthcare services. However, data must be transmitted and exchanged over a network or Internet where authorized parties can leverage invulnerability in remote health monitoring systems. Although many previous studies mention possible solutions for an intelligent healthcare system, healthcare challenges are still open, mainly in the centralized system. Thus, attention to a decentralized environment has recently risen in healthcare system construction. However, a decentralized formation requires a specific technology to store and reliably communicate among network participants immutably. As the candidate for this consideration, blockchain technology is a prominent solution to forming decentralized systems. Further, due to the demand for swift reaction, alerts require the minimization of latency in communication, which brings to the examination of edge computing. This work introduces a novel intelligent homecare system based on blockchain technology placed at the network's edge. The proposed system overcomes the existing system's security limitations and provides advanced services that help in improving the quality of healthcare service.
Optimized CNN Model with Deep Convolutional GAN for Brain Tumor Detection

Brain tumors are viewed as a deadly form of cancer. Early and precise detection of brain tumors is critical to tumor cure. Magnetic resonance imaging and computed tomography are two widely used methods of examining brain tissue that is abnormal in size, location, or shape, which can aid in the early detection of tumors in the brain. More data is required in the field of artificial intelligence, particularly in medical imaging; thus, in order to find a faster way to diagnose a specific disease using AI, we first require a large set of image-accurate datasets to feed into a neural network. Convolutional Neural Networks achieve superior computational diagnostics by using well-annotated training data. Most medical image datasets, on the other hand, are fragmented and small. In this case, Generative Adversarial Networks can generate realistic or a variety of additional brain tumor data for training to fill in the gaps in the actual image distribution by following its respective objective function. Deep Convolutional Generative Adversarial Network was used in this paper to improve the accuracy of the original Convolutional Neural Network model. The Deep Convolutional Generative Adversarial Networks are used to generate new data with reference to the existing training data distribution. Using this method to generate the images, we increased the dataset size by 700 images, increasing the model's accuracy from 97.26 to 98.85%.
Future Commercial Prospects of Unmanned Aerial Vehicles (UAVs)

This correspondence addresses the rising demand of unmanned aerial vehicles (UAVs), also referred as drones, for civilian as well as military applications. With the recent advancement in the drone industry, drones are now being manufactured/developed not only as toys but also cater to the unexplored applications with the use of some sophisticated machinery. In view of the increased utilization of drones in diverse professional sectors, the commercial market of UAVs or drones is set to rise exponentially in the coming years. These flying devices can be utilized in impending or pre-existing commercial markets for the fulfillment of underlying objectives (spanning social, economic, and environmental facets) of the businesses. In the present work, we discussed multiple applications of drones in line with the current relevant business landscape, future prospects and opportunities, and also the markets that can be addressed with drone usage in the near future.
Blockchain in Big Data for Agriculture Supply Chain

Agricultural food supply chain is a complex system starting from the production of food on a farm to the table of the consumer involving multiple stakeholders and a variety of processes. In recent years, the food supply chain has grown rapidly across nations, with customers demanding fresh, exotic foods all year round. The global shutdown due to the COVID pandemic has further complicated the food supply chain which has become prone to various contaminations and adulterations. Adulterated food is highly toxic to human health leading to several health issues, nutritional deficiencies, kidney disorders, and failure of vital organs. The existing systems used in the food supply chain do not provide enough transparency, traceability, food safety, or consumer trust. With today’s Big Data integrated supply chains, such technologies are highly ineffective. In order to ensure food safety and consumer satisfaction, this chapter proposes using blockchain as an efficient technology to provide transparency, traceability, and trust in food supply chains. The chapter discusses the significance of smart agriculture and how blockchain might help agricultural supply chains that have Big Data incorporated overcome their difficulties. A thorough description of exclusive applications of blockchain in Big Data integrated food supply chains is provided. The chapter also describes how blockchain is integrated into each stage of the food supply chain management process and explores the challenges in implementing blockchain in Big Data integrated food supply chain systems.
Improving K-means by an Agglomerative Method and Density Peaks

K-means is one of the most used clustering algorithms in many application domains including image segmentation, text mining, bioinformatics, machine learning and artificial intelligence. Its strength derives from its simplicity and efficiency. K-means clustering quality, though, usually is low due to its “modus operandi” and local semantics, that is, its main ability to fine-tune a solution which ultimately depends on the adopted centroids’ initialization method. This paper proposes a novel approach and supporting tool named ADKM which improves K-means behavior through a new centroid initialization algorithm which exploits the concepts of agglomerative clustering and density peaks. ADKM is currently implemented in Java on top of parallel streams, which can boost the execution efficiency on a multi-core machine with shared memory. The paper demonstrates by practical experiments on a collection of benchmark datasets that ADKM outperforms, by time efficiency and reliable clustering, the standard K-means algorithm, although iterated a large number of times, and its behavior is comparable to that of more sophisticated clustering algorithms. Finally, conclusions are presented together with an indication of further work.
Optimized GrabCut Algorithm in Medical Image Analyses

In this contribution, we study discrete and graph methods of image segmentation. We mainly focus on an image segmentation method called “Grabcut,” which we also implement in the C++ programming language. We do and illustrate medical, biological, and even technical applications. One of the applications is analyses of medical scans of brain tumors called “glioblastomas” and other medical and engineering applications. The goal of this paper is to get acquainted results with the use of image segmentation for medical, biological, and possibly engineering image processing and analysis. The aim was to create a new software and improve this algorithm for a specific type of medical, biological, and technical images with a focus on the sharp boundaries of objects. In addition to this, we are looking into cluster analysis and graph theory, on which the GrabCut method is based. The result is a new software with an improved algorithm and its optimization as well as many medical applications. We improved segmentation of the brain, especially focused on getting a better boundary of the brain tumor. The results are segmented brain tumors with better and sharper boundaries. There is still space to extend this optimized method to other medical applications, e.g., breast tumors.