Reviewerly/exHarmony huggingface dataset:
Secure Feature Selection for Vertical Federated Learning in eHealth Systems

Privacy-preserving vertical federated learning (VFL) has been widely applied in electronic health (eHealth) systems. However, existing VFL schemes rarely consider the data pre-processing step including feature selection, which will lead to poor convergence rate and even damaging the model utility. In this paper, we propose an efficient and privacy-preserving feature selection scheme for VFL. Specifically, we first propose a general Gini-impurity based feature selection framework, which is compatible with most existing machine learning models in VFL. With the framework, we present two concrete protocols (dubbed π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SS−FS</inf> and π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H−FS</inf> , respectively) customized for different eHealth scenarios. π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SS−FS</inf> exploits a lightweight additive secret sharing technique, such that it can be executed in comparable time as the evaluation of the plaintext scheme. π <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H−FS</inf> is a hybrid feature selection protocol that additionally utilizes a linear homomorphic encryption technique, to reduce the communication overhead at the cost of a moderate runtime. Moreover, extensive evaluations conducted on real-world medical datasets demonstrate that our scheme realizes up to 27% accuracy gains.
A Random Access based Approach to Communication-Efficient Distributed SGD

In this paper, we study communication-efficient distributed stochastic gradient descent (SGD) with data sets of users distributed over a certain area and communicating through wireless channels. Since the time for one iteration in the proposed approach is independent of the number of users, it is well-suited to distributed SGD with a large number of users or devices. Furthermore, since the proposed approach is based on random access in machine-type communication (MTC), it can be easily employed for training models with a large number of devices in various Internet-of-Things (IoT) applications where MTC is used for their connectivity. For fading channel, we show that noncoherent combining can be used. As a result, no channel state information (CSI) estimation is required.
Topology-Embedded Temporal Attention for Fine-Grained Skeleton-Based Action Recognition

In recent years, graph convolutional networks (GCNs) have been extensively applied in numerous fields, demonstrating strong performances. Although existing GCN-based models have extraordinary feature representation capabilities in spatial modeling and perform exceptionally well in skeleton-based action recognition, they work poorly for fine-grained recognition. The key issue involves tiny distinctions between multiple classes. To address this issue, we propose a novel module named the topology-embedded temporal attention module (TE-TAM). Through embedding the temporal-different topology modeled with local area skeleton points in spatial and temporal dimensions, the TE-TAM achieves dynamical attention learning for the temporal dimensions of distinct data samples, to capture minor differences among intra-frames and inter-frames, making the characteristics more discriminating, and increasing the distances between various classes. To verify the validity of the proposed module, we inserted the module into the GCN-based models and tested them on FSD-30. Experimental results show that the GCN-based models with TE-TAMs outperformed the property of pred GCN-based models.
Detection of Forged Currency Notes using Machine learning algorithms

Currency is a medium of exchange either to purchase or sell the goods and services. In today’s world, With enhanced technology, counterfeit currency has become a concerning threat. Counterfeit currency leads to harm the country’s economy. Severe implications are such as artificial inflation, terror funding. These concerning causes destabilize the real value of money and inflation surges over its sky. Moreover, due to terror-related activities the social-harmony gets disturbed, this can be termed as "Economic Terrorism". The proposed methodology in this paper uses Random Forest & SVM Machine Learning Algorithms to classify forged currency notes The algorithms are evaluated using metrics Accuracy, Confusion Matrix and Classification Report. The results show that SVM has outperformed Random Forest by achieving an accuracy of 99.63%.
Twitter Sentiment Analysis on Approval of Covaxin for Children in India

Covid-19 and its different variants are still a big issue the whole world is facing right now. At present different SARS-CoV-2 vaccines are playing vital role to combat the coronavirus. The objective of this paper is to perform sentiment analysis on approval of Bharat Biotech covaxin for emergency use for children. The presented paper emphasizes on the sentiment analysis of tweets of the microblogging site Twitter. Python programming language with Natural Language processing toolkit (NLTK), TextBlob library and tweepy twitter API are used for the process. Machine learning algorithms are used for the classification of tweeets. Graphical representation has been used for the representation of the data after sentiment analysis based on hashtags.
An Analysis of Vulnerability Scanners in Web Applications for VAPT

Though the development in cybersecurity for protecting the websites and applications are growing rapidly, the attacks on these crucial websites are still happening. Irrespective of the development of society, third-party attacks would always increase correspondingly. To protect these websites and web applications, the developers would always use defensive mechanisms to survive third party attacks. But the reliability of the website's strength against third party attacks depends on several factors. Intruders are now using automated structures in form of BOTS, AI, ML, to perform these attacks.Need of security arises from the initial stages of planning and development. As part of SEP (Security Engineering Process) defined by individual vendors and companies for securing the system and performing risk analysis, Vulnerability Analysis are essential as part of standard procedures. Apart from SEP, VAPT (Vulnerability Analysis and Penetration Testing) now forms the standard operating measures for prevention of intrusion activities. We hereby focus on various vulnerability analysis tools forming basis for vulnerability analysis and Penetration testing (VAPT) for short and try to analyze the various impact with respect to stages of penetration testing.
Optical Text Translator from Images using Machine Learning

In the actual world, when people go to a new location, they may have difficulties with language, signboards, or other kinds of communication. People in the digital age utilize Google Translate, Pocket Dictionary, and other similar applications. A signboard or other kind of notice may communicate important information or even act as a warning. Important information may be lost if the message is unavailable to individuals of different linguistic backgrounds; furthermore, visitors may struggle to carry out their duties in a new place if they do not comprehend the language spoken there. A portable dictionary, on the other hand, may be ineffective if users wish to translate a language in which words are not organized alphabetically. In another research, it is also understood in the same manner that users are unable to type the text of what they see. Because they are unable to comprehend the language despite the pocket dictionary and online translation service offered, people from various linguistic backgrounds may experience a breakdown in communication. The purpose of this article is to create an app that will translate information shown on a signboard into the language of the user's choice. When the user points their phone's camera at the signboard, the App takes a picture of it and then translates the text on the signboard.
A Fundamental Example of Bibliographic Perfectionism

This review summarises the contents of the consolidated catalogue by A. I. Razdorsky which contains printed all-subject reports of governors-general, governors, and city managers of the Russian Empire between 1845 and 1916 (St Petersburg, 2020). The author-compiler of this handbook published by the St Petersburg publisher Dmitry Bulanin describes 2,309 reports, which have had limited circulation and are preserved mainly in the Russian State Historical Archive. The book characterises their content and substantiates their scholarly value as sources of information on the state of various subjects of the Russian Empire. The reports (handwritten, typed, and printed) were official documents that reflected the views of the higher Russian bureaucracy on the processes taking place in the political, economic, and cultural life of the country in the nineteenth and early twentieth centuries. The consolidated catalogue is intended as a guide amongst a vast, poorly studied, and highly informative conglomerate of printed reports. The information from these publications needs to be thoroughly analysed and researched at the current stage.
Research on Management Method of System of Records in Power Industry

Data is one of the core production factors and coreassets of the power industry. The effective management of system of records is the key to efficient data sharing and effective value development. We considered the limitations of the existing data management methods in the power industry, and analyzed the new demand of the power industry for system of records management in digital transformation. On the basis of “a data has only one data source, a data is managed by a subject of liability, a data can be used many times”, we proposed a new management system of system of records that enables the system of records management institutionalized and standardized. Combined with typical application scenarios, the specific application process of the method is given in this paper.
Bi-level Planning Method for Internet Data Center and Distribution Network Considering Demand Response

This paper presents a bi-level optimal planning framework for Internet data centers (IDCs) and the distribution network considering demand response (DR). Firstly, according to the characteristics of IDC, the energy consumption characteristic model of IDC is established, and the demand response model is established from the perspective of three types of data loads: time-transferable load, reducible load and rigid load. On this basis, the information transmission and interactive response relationship between IDC and distribution network are analyzed, and a bi-level optimization planning model is established, in which distribution network operators (DNO) is the upper leaders to determine the node price to maximize its interests, and data center operator (DCO) is the lower follower to determine the location and capacity configuration to minimize its costs. The rationality and validity of the model are proved by the results of numerical examples.
Electricity Meter Detection and Recognition Algorithm Based on Mixed Feature Perception Neural Network and Classification Fitting Pointer

The meter detection and recognition algorithm based on neural network and cluster analysis is often used in power vision, but the recognition accuracy and speed should still be improved. Therefore, this paper proposes a new pointer reading strategy for feature extraction and classification fitting of power meters, in order to improve the speed and accuracy of pointer reading method and simplify the complexity of algorithm. Firstly, the median filter of noise adaptive fuzzy switching and homomorphic enhancement are used to preprocess the image. Secondly, AEMYOLOV5-EOSHHO model is proposed to remove irrelevant background factors and obtain regions of interest, the attention enhancement module is added to the YOLOV5 overall structure, and the Elite opposition-learning strategy based Harris hawks optimization (EOSHHO) method is used for hyperparameter optimization. Thirdly, the binary table cluster analysis method is used to binarize the region of interest, and the disk part is retained according to the clustering situation. Finally, the method of rotating straight line fitting meter reading is proposed to fit the pointer, so as to obtain the pointer reading. The experimental results show that the algorithm can accurately find the region of interest, and read the pointer reading of the meter.
BA-CPABE : An auditable Ciphertext-Policy Attribute Based Encryption Based on Blockchain

At present, the ciphertext-policy attribute based encryption (CP-ABE) has been widely used in different fields of data sharing such as cross-border paperless trade, digital government and etc. However, there still exist some challenges including single point of failure, key abuse and key unaccountable issues in CP-ABE. To address these problems. We propose an accountable CP-ABE mechanism based on block chain system. First, we establish two authorization agencies MskCA and AttrVN(Attribute verify Network),where the MskCA can realize master key escrow, and the AttrVN manages and validates users' attributes. In this way, our system can avoid the single point of failure and improve the privacy of user attributes and security of keys. Moreover, in order to realize auditability of CP-ABE key parameter transfer, we introduce the did and record parameter transfer process on the block chain. Finally, we theoretically prove the security of our CP-ABE. Through comprehensive comparison, the superiority of CP-ABE is verified. At the same time, our proposed schemes have some properties such as fast decryption and so on.
IAM-BDSS: A Secure Ciphertext-Policy and Identity- Attribute Management Data Sharing Scheme based on Blockchain

CP-ABE (Ciphertext-policy attribute based encryption) is considered as a secure access control for data sharing. However, the SK(secret key) in most CP-ABE scheme is generated by Centralized authority(CA). It could lead to the high cost of building trust and single point of failure. Because of the characters of blockchain, some schemes based on blockchain have been proposed to prevent the disclosure and protect privacy of users' attribute. Thus, a new CP-ABE identity-attribute management(IAM) data sharing scheme is proposed based on blockchain, i.e. IAM-BDSS, to guarantee privacy through the hidden policy and attribute. Meanwhile, we define a transaction structure to ensure the auditability of parameter transmission on blockchain system. The experimental results and security analysis show that our IAM-BDSS is effective and feasible.
Research on Intellectual Property Protection of Artificial Intelligence Creation in China Based on SVM Kernel Methods

Artificial intelligence creation comes into fashion and has brought unprecedented challenges to intellectual property law. In order to study the viewpoints of AI creation copyright ownership from professionals in different institutions, taking the papers of AI creation on CNKI from 2016 to 2021, we applied orthogonal design and analysis of variance method to construct the dataset. A kernel-SVM classifier with different kernel methods in addition to some shallow machine learning classifiers are selected in analyzing and predicting the copyright ownership of AI creation. Support vector machine (svm) is widely used in statistics and the performance of SVM method is closely related to the choice of the kernel function. SVM with RBF kernel surpasses the other seven kernel-SVM classifiers and five shallow classifier, although the accuracy provided by all of them was not satisfactory. Various performance metrics such as accuracy, F1-score are used to evaluate the performance of KSVM and other classifiers. The purpose of this study is to explore the overall viewpoints of AI creation copyright ownership, investigate the influence of different features on the final copyright ownership and predict the most likely viewpoint in the future. And it will encourage investors, researchers and promote intellectual property protection in China.
The Secure Data Sharing and Interchange Model Based on Blockchain for Single Window in Trade Facilitation

Nowadays, ȜBlockchain + Single Window(SW)”, as a potential solution to one-stop electronic platform, has been considered to open new opportunities to improve user experience quality of data, risk management and the operation, in SW. However, despite these opportunities hopefully enhancing the efficiency, transparency and reduce the cost in trade single window, there still exits challenges addressed including technical issue such as on-chain privacy-preserving, storage limitation and trade document provenances. In this paper, we put forward a model of data interchange based on blockchain to build trusted and coordinated architecture while guaranteeing robust blockchain for cross-border data sharing and verification in trade single window. First, a decentralized digital identity compliant W3C DID method is introduced to create self-sovereign identity layer for interoperability between all trade participants. Secondly, cipher-text policy attribute-based encryption(CP-ABE) scheme is integrated with the blockchain system to realize fine-grained access control and privacy protection for trade documents. Finally, we design an on-chain verification and off-chain data interchange protocol on inter-agency collaboration without revealing data. the theoretically analysis and computing simulation demonstrate the advantages of our proposed scheme.
A Logistics Privacy Protection Scheme Based on Ciphertext Policy Attribute-Based Key Encapsulation

With the rapid development of the e-commerce, the issue of information security in the transportation process has gradually received attention. Users' privacy is extremely possible to be compromised when they get convenient service from the logistic industry. This paper proposes a logistics privacy protection system based on ciphertext policy attribute based key encapsulation (CP-ABKE) to address the information security problems. To achieve the design goals about decentralization, plaintext hiding and character verification of user privacy in logistics process, we use attribute encryption, cryptographic courier order with QR code, digital signature and other technologies. This system realizes the whole process protection of user privacy in logistics system, especially to prevent the internal personnel of logistics companies from leaking customer privacy.
Transforming Points of Single Contact Data into Linked Data

Open data portals contain valuable information for citizens and business. However, searching for information can prove to be tiresome even in portals tackling domains similar information. A typical case is the information residing in the European Commission’s portals supported by Member States aiming to facilitate service provision activities for EU citizens and businesses. The current work followed the FAIR principles (Findability, Accessibility, Interoperability, and Reuse of digital assets) as well as the GO-FAIR principles and tried to transform raw data into fair data. The innovative part of this work is the mapping of information residing in various governmental portals (Points of Single Contacts) by transforming information appearing in them in RDF format (i.e., as Linked data), in order to make them easily accessible, exchangeable, interoperable and publishable as linked open data. Mapping was performed using the semantic model of a single portal, i.e., the enriched Greek e-GIF ontology and by retrieving and analyzing raw, i.e., non-FAIR data, by defining the semantic model and by making data linkable. The Data mapping process proved to require a significant manual effort and revealed that data value remains unexplored due to poor data representation. It also highlighted the need for appropriately designing and implementing horizontal actions addressing an important number of recipients in an interoperable way.
Specificities Introduced by Mobile Phones in the Relationship Between Children and Commercial Content

This article explores the specificities of the relationship between children and the advertising to which they are exposed through their cell phones, and reports on a mixed methodology study conducted in the metropolitan area of Santiago de Chile. The study aims to learn about children’s predisposition, interaction, and perceptions with regards to mobile advertising. Most significant is the finding that this young generation (10-14 years old) identify commercial messages with which they claim low levels of interaction. Regarding interaction, the ads which most likely attract minors’ attention are placed on social networks, are presented in formats which provide some value (in terms of information or entertainment) and are for on products of interest to the audience studied. Finally, for many minors advertising eminently belongs to the digital world and their perceptions may not be transferable to traditional media.
The Analysis of Factors Affecting Bitcoin Price

As a popular cryptocurrency, Bitcoin has been an important investment tool in recent years. This study aims to analyze the factors that affect the Bitcoin price to help investors make better investment choices. Applying the simple linear regression model and Granger causality test to the data from January 2015 to December 2021, the research first examines the stationary of the data and then studies the relationships between Bitcoin price and other factors including Dow Jones Industrial Index, U.S. currency in circulation, U.S. disposable income. According to the result, all three factors have a positive effect on the price of Bitcoin and the Bitcoin price will in turn influence the Dow Jones Industrial Index and U.S. disposable income. This finding helps explain how certain economic indicators and Bitcoin prices interact. Since investment is always risky, investors must consider certain factors like the trend of DOW, M2, or PCI in advance to make a reasonable investment decision.
Image two-classification algorithm based on continuous-time quantum walk

To further improve the performance of image retrieval, we propose an image two-classification algorithm based on continuous-time quantum walk (CTQW) model. First, a weighted undirected complete graph is constructed based on a new weighted edge method, that is, the images in the image set are used to represent the nodes on the weighted complete graph, and every two images are connected to represent the edge on the weighted complete image, the weighted value of which represents the similarity between adjacent images. Then, the CTQW is executed to realize the image two-classification based on the quantum walk model of the weighted complete graph, that is, the CTQW is performed from two types of unrelated images, and the two-classification of images is realized that the relevant and unrelated labels are assigned to each image according to the obtained probability distribution two-classification. Finally, the experimental results show that the proposed two-classification algorithm based on CTQW has a good classification effect from the visual effect, and the evaluation indexes of image classification, such as the average precision rate, the recall rate, and the accuracy rate, are all higher than 0.85. In addition, the proposed image two-classification algorithm based on CTQW can be extended to image multiclassification if the quantum walk is performed using multiple irrelevant pictures as the starting point. Similarly, the extended image multiclassification method also has a better classification effect through the experiments and the evaluation indexes of image classification.