Reviewerly/exHarmony huggingface dataset:
SSCDS: a secure supply chain data sharing scheme based on blockchain and IPFS

The supply chain is a system that comprises multiple entities such as suppliers, manufacturers, carriers, customers, etc. Data sharing safely between these entities is vital for the success of a supply chain. This paper proposes a secure supply chain data sharing scheme based on blockchain and IPFS, including decentralized architecture, data management of capability-based access control mechanism and message acquisition mechanism based on the publish-subscribe model. Further more, a BIZi storage method is proposed to enhance the reliability of the above solution and streamline the backup of data by combining smart contract, the Bitswap protocol of IPFS and the Zigzag code. Finally, a prototype of a supply chain platform is implemented based on the Ethereum. The performance test shows that BIZi can save more storage resources at the same fault tolerant rate.
Research on Internet of Things information security based on blockchain

In recent years, the Internet of Things industry has developed rapidly, and various intelligent devices have been connected to the Internet of Things, gradually improving people's daily lives. With the continuous increase in the number of Internet of Things devices, the resulting user privacy data is also exploding, ensuring information security is very important. Decentralized blockchain technology involves disciplines such as computer programming and cryptography, and is a distributed database. Compared to traditional information technology, blockchain has significant technical advantages in information disclosure and transparency. This article builds a blockchain based IoT management platform based on the Hyperledger Fabric framework. The platform uses blockchain as the underlying technology, cooperates with storing source data to the IPFS network, uploads the returned hash value to the blockchain network, and applies blockchain technology to the field of information security. Combining the application characteristics of the technology, it provides a new solution for solving the information security issues of the Internet of Things.
Research and design of teaching method on blind signature and certificateless signature in network security and cryptography

Certificateless signature (CLess) scheme and paritial blind signature scheme with certificateless (CLess) are two important concepts in network security and cryptography. In a CLess scheme, the key escrow problem can be solved by the signature key being generated by the signer and PKG (Private Key Generator). In a CParBSS scheme, the abusing of the signature for illegal message can be solved by inserting a piece of common information negotiated by the signer and the requestor in advance. In this paper, we use a scheme as the CParBSS case to analyze its security by presenting the detail process of replacement attack of the public key and forgeability attack respectively. We also use a scheme as the CLess case to analyze its security by presenting the forgeability attack from anyone user. Based on the security analysis, we present an efficiency comparison and analysis between different schemes, and a design of the teaching method on CLess and CParBSS.
Algorithms and Politics

chapter Share on Algorithms and Politics Author: Domenico Talia University of Calabria University of CalabriaView Profile Authors Info & Claims From Algorithms to Thinking Machines: The New Digital PowerOctober 2023https://doi.org/10.1145/3603178.3603187Published:20 October 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Algorithms and Data—The Pillars of Our Future

chapter Share on Algorithms and Data—The Pillars of Our Future Author: Domenico Talia University of Calabria University of CalabriaView Profile Authors Info & Claims From Algorithms to Thinking Machines: The New Digital PowerOctober 2023https://doi.org/10.1145/3603178.3603182Published:20 October 2023Publication History 0citation1DownloadsMetricsTotal Citations0Total Downloads1Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Transparency and Accountability of AI Algorithms

chapter Share on Transparency and Accountability of AI Algorithms Author: Domenico Talia University of Calabria University of CalabriaView Profile Authors Info & Claims From Algorithms to Thinking Machines: The New Digital PowerOctober 2023https://doi.org/10.1145/3603178.3603189Published:20 October 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Technology for Assessing the Level of Achievement of Educational Results of Future Teachers Based on a Case Assignment

The article is devoted to the problem of assessing the educational results of future teachers. The technology for assessing the level of achievement of educational results based on a case assignment is revealed. As a methodological basis for constructing an assessment tool, the provisions of the systemic, activity-based, competency-based and criterion-based approaches are used, which make it possible to determine the observable and measurable actions of students that are part of the future professional activity of the teacher. The main stages of the technology for assessing educational results in the field of methodological training of future teachers are considered using the example of a case assignment. The content and organizational and methodological foundations of the identified stages are revealed. The results of testing case tasks to assess educational results and the level of their achievement in the field of methodological training of future teachers are presented.
Implementation of the Requirements of the Federal State Education Standard of the Basic General Education in Teaching Mathematics

The article discusses the requirements of the Federal State Education Standard of the Basic General Education in terms of the formation of functional literacy of students. The concept of mathematical literacy as a component of functional literacy of students is presented. Special attention is paid to the problem of constructing mathematical literacy problems. A case of tasks for the formation of mathematical literacy for 9th grade students based on the results of the PISA study is proposed. The article is addressed to teachers and students of pedagogical universities, methodologists, teachers of mathematics.
Implementation of the Requirements of the Updated Standard of Secondary General Education When Teaching the Topics of the Section �Polyhedra�

The article is based on the main key ideas of the formation of the planned subject and meta-subject results. The article discusses the methodology for implementing the requirements of the updated standard, taking into account the goals of teaching mathematics, under which conditions are created that ensure the possibility of forming students planned results in accordance with the requirements of the standard. An approach is proposed to the formation of individual skills of cognitive universal educational actions of students when teaching the topics of the polyhedra section, using educational tasks and training tasks that develop the ability to solve a wide range of problems.
MoS2-based ultra-scaled photodetectors overcoming the diffraction limit

An ultra-scaled MoS2-based phototransistor with a 20-nm channel length, leveraging the photogating mechanism, is shown to overcome the diffraction limit in a new article by Saptarshi Das and co-workers in this issue of Device. The resulting device exhibits an outstanding photosensitivity with a greatly enhanced response time. The authors demonstrate spectral uniformity using the gate bias as a control knob.
Project Activity: a Conscious Need

The article examines the project activity presented as a key modern educational technology. Aspects explaining the theory and practice of project activity in an educational institution are proposed for consideration. The variants of solutions to specific problematic issues aimed at assisting the pedagogical worker in the processing of didactic materials used in the course of lessons are given.
Image inpainting with gated convolution and interactive multi-scale feature fusion

Image inpainting aims to fill damaged regions with non-damaged regions and semantic reasonableness while ensuring consistency of an image. The result of inpainting often suffers from smooth edges and blurred details when faced with larger and more complicated damaged regions. In this paper, an end-to-end dual stream network that fuses the texture and structure features, aiming to restore intricate details in filled regions is proposed. For details enhancement, gated convolutions are introduced to pick valid pixels, reducing blur in damaged regions; For more comprehensive features representation, multi-scale parallel dilated convolutions are used to fuse features from different receptive fields and positions in the image. Extensive experimental results on three common datasets demonstrate the superiority of the proposed network in terms of quantitative and qualitative evaluation.
Research on radar emitter signal classification technology based on SqueezeNet lightweight network

With the rapid advancement of information technology, the modern battlefield is characterized by a highly complex electromagnetic environment. Radar radiation sources exhibit wide-ranging parameter variations and strong random characteristics, presenting formidable challenges to the signal selection of radar radiation sources in missile-borne countermeasure systems. This paper addresses the issue of reliable identification and selection of radar source signals by on-board countermeasures systems. Through the analysis of source signal characteristics, the Smooth Pseudo Wigner-Ville Distribution (SPWVD) method is employed for time-frequency analysis to extract the time-frequency features of the source signals. Furthermore, a lightweight network based on SqueezeNet is implemented to achieve high-precision source signal selection. The results demonstrate that, when the SNR of the source signals is greater than 0dB, the network model achieves a recognition accuracy above 94.59%. The selection accuracy is comparable to that of the Convolutional Neural Network (CNN), thereby meeting the requirements of on-board countermeasure systems for reliable selection of radar source signals. The analysis confirms that under low signal-to-noise ratio conditions, noise significantly affects the network's selection accuracy by impacting the time-frequency clarity of the modulation signals.
A dodging algorithm for large-scale spaceborne SAR images

Achieving high-quality output if large-scale SAR image stitching demands color balancing of the images to be stitched. However, adjacent SAR images may exhibit large differences in radiation intensity, particularly in the context of different-track SAR images. Furthermore, the diverse sizes and uneven spatial distribution of SAR images from different sources pose a challenge for color balancing in image stitching. To alleviate these issues, we propose a large-scale multi-track spaceborne SAR image dodging stitching scheme based on the working characteristics of spaceborne SAR. We leverage different algorithms for along-track and across-track SAR image dodging, based on the statistical characteristics of the overlap region and block processing, to ensure uniform luminance and contrast in the color-balanced stitched images. We introduce an evaluation metric to assess the extent of contrast preservation between the images before and after dodging. In our experiments, we utilize 116 Gaofen-3 and Sentinel-1 SAR images for dodging stitching and compare our proposed method to traditional dodging methods. The proposed method outperforms traditional methods both in terms of visual effects and evaluation metrics.
Design and implementation of personal online disk system based on DBN image recognition algorithm

With the growth of the Internet and the age of information explosion, more and more people need network drives to store and share the resources they find online. While downloading files to a local hard drive can ensure data security, this also brings with it many inconveniences, such as the need to share with others using third-party websites or communication software, or the need to transfer files using a removable hard drive. As a result, personal network drives have emerged on the market to make data sharing easier by transferring files to a server. This project will retain the basic functions of a traditional network drive system, such as file uploading, downloading, and deletion, while taking into account the security of the data and using deep learning-based DBN algorithms for image recognition, using artificial intelligence and image recognition techniques to detect pirated and illegal image material. Through the combined application of the above technologies, an efficient personal web-disk system is designed and implemented.
Underwater image enhancement network with spatial and frequency domain dual path fusion

Underwater images play a crucial role in underwater exploration tasks. However, due to the unique physical and chemical properties of the underwater environment, underwater images often suffer from issues such as low contrast, color cast, and blurriness. To address these challenges, this paper proposes a dual-path fusion model (UW-DSFNet) for underwater image enhancement. The model aims to extract both color and texture features from underwater images comprehensively, utilizing spatial and frequency domains. In the spatial domain path, a low-complexity NAFNet network is employed along with gate residual and GELU activation functions to extract color features from the images. In the frequency domain path, an MLP framework is utilized, and Fourier transform is applied to obtain frequency domain texture feature maps. Finally, the features extracted from the spatial and frequency domains are fused, followed by a detail enhancement process. Experimental results demonstrate that the proposed model effectively enhances underwater images, producing clear and visually appealing results with rich colors.
The numbers’ sign language detection based on deep learning

This paper builds an end-to-end custom object detection model that could translate Chinese numbers’ sign language in real time based on deep leaning. The main work of this paper is as follows, collect images for deep learning using OpenCV, label images for sign language detection using LabelImg, setup Tensorflow Object Detection pipeline configuration, and use transfer learning to train a deep learning model. At last, detect sign language in real time using OpenCV.
A new traffic signal detection system with high robustness and real-time requirements

Automatic traffic signal detection has great significance to the development of autonomous driving technology and vehicle warning of dangerous driving behavior. Detecting traffic lights by analyzing images obtained from mobile device cameras is a feasible solution. In this paper, a new traffic signal detection system with high robustness and real-time requirements. Firstly, the HSV color segmentation method is used to segment the original map. Then, the aspect ratio information and area information of traffic lights are utilized for secondary filtering. The morphological processing method is used to filter the background in the image. Finally, intercept the crucial area to obtain the final significance segmentation image of preprocessing. Combining the salient feature preprocessing based on traditional image processing methods with the target detection model based on deep learning, the algorithm of traffic signal detection and recognition is improved.
Image processing algorithm based on soft computing and mathematical morphology

With the continuous progress of science and technology, image processing technology is increasingly used in real life. Due to a series of advantages such as large amount of information, fast transmission speed, and long operating distance, image processing technology has become the main source of human information acquisition and an important means of utilizing information. In recent years, morphological image processing has become an important direction in image processing. Early image processing was mainly aimed at improving the quality of images, taking people as research objects, mainly to improve people's visual perception. In the process of image processing, various methods such as image enhancement, restoration, encoding, and compression are commonly used to process images. Many scholars at home and abroad are committed to the research of computer image processing, including image segmentation, image compression, image restoration, and other aspects. This paper introduces an image preprocessing method based on wavelet transform. In conventional preprocessing, we use smooth and sharp methods. For example, for Gaussian noise, we usually use linear filtering; When dealing with salt and pepper noise, median filtering is usually used, and better filtering results can be obtained. Then analyze the advantages of algorithms from the perspective of algorithm data, and have the courage to innovate to find algorithms to alleviate and eliminate imbalances. The research results obtained are expected to serve as a reference and supplement for the current research on image processing algorithms in China.
Cryptanalysis of RSA with composed decryption exponent with few most significant bits of one of the primes

RSA is well known public-key cryptosystem in modern-day cryptography. Since the inception of the RSA, several attacks have been proposed on RSA. The Boneh–Durfee attack is the most prominent and they showed that if the secrete exponent is less than 0.292, RSA is completely vulnerable. In this paper, we further investigate the vulnerability of RSA whenever a secret exponent is large and the composite form with a few most significant bits of one of the primes exposed. Having a large secret exponent can avoid the Boneh–Durfee attack, but in this attack, we show that even though the secret exponent is large and has some specialized structure then RSA is still vulnerable. We follow the Jochemsz and May strategy for constructing the lattice, and the LLL algorithm is used for lattice reduction. Our attack outperforms most of the previous attacks.
A Simplified Convex Optimization Model for Image Restoration with Multiplicative Noise

In this paper, we propose a novel convex variational model for image restoration with multiplicative noise. To preserve the edges in the restored image, our model incorporates a total variation regularizer. Additionally, we impose an equality constraint on the data fidelity term, which simplifies the model selection process and promotes sparsity in the solution. We adopt the alternating direction method of multipliers (ADMM) method to solve the model efficiently. To validate the effectiveness of our model, we conduct numerical experiments on both real and synthetic noise images, and compare its performance with existing methods. The experimental results demonstrate the superiority of our model in terms of PSNR and visual quality.
A Geometric Feature-Based Algorithm for the Virtual Reading of Closed Historical Manuscripts

X-ray Computed Tomography (CT), a commonly used technique in a wide variety of research fields, nowadays represents a unique and powerful procedure to discover, reveal and preserve a fundamental part of our patrimony: ancient handwritten documents. For modern and well-preserved ones, traditional document scanning systems are suitable for their correct digitization, and, consequently, for their preservation; however, the digitization of ancient, fragile and damaged manuscripts is still a formidable challenge for conservators. The X-ray tomographic approach has already proven its effectiveness in data acquisition, but the algorithmic steps from tomographic images to real page-by-page extraction and reading are still a difficult undertaking. In this work, we propose a new procedure for the segmentation of single pages from the 3D tomographic data of closed historical manuscripts, based on geometric features and flood fill methods. The achieved results prove the capability of the methodology in segmenting the different pages recorded starting from the whole CT acquired volume.
Secure Healthcare Website using Blockchain

Blockchain technology is consistently being an interested topic of research for researchers, and multiple of businesses as organizations are highly benefitted from its features. Blockchain technology has many benefits for the healthcare sector, including security, privacy, secrecy, and decentralisation. Privacy and protection of medical data are some of the important issues to looked after while availing any medical services. Secure storage is something which people look up for. Efficient Healthcare System is which allows the users to communicate securely without any worries about their data. This study is about how the blockchain technology can be applied to change the EHR systems and potentially provide a fix for these problems. A framework integrating EHR and blockchain to enable a secure medical data transfer between patients and hospitals is the objective of this study.
Manipulation of Population Levels through Zeno-Type Measurements

We present a scheme, based on Bloch equations and Zeno-type measurements, that allows the control of the probability density evolution of the eigenstates of a V-type system. The equations are solved numerically and we present how the population in each level can be controlled using different sequences of “pulse measurements”. The entropy between the “measurement device” and the field used to perform the measurement process is evaluated for different strengths of such field, these calculations show that the entropy is maximized when we are in the Zeno regime. The results shown here unveil different possible strategies for controlling the population levels of a V-type system and could be implemented, for example, in trapped ions or RMN qubits.