Reviewerly/exHarmony huggingface dataset:
Blind quantum computation with a client performing different single-qubit gates

Abstract In the field of single-server blind quantum computation(BQC), a major focus is to make the client as classical as possible. To achieve this goal, we propose two single-server BQC protocols, which can achieve verifiable universal quantum computation. In these two protocols, the client only needs to perform either the gate T (in the first protocol) or the gates H and X (in the second protocol). With assistance from a single-server, the client can utilize his quantum capabilities to generate some single-qubit states while keeping the actual state of these qubits confidential from others. By using these single-qubit states, the verifiable universal quantum computation can be achieved.
Research on medical data storage and sharing model based on blockchain

With the process of medical informatization, medical diagnosis results are recorded and shared in the form of electronic data in the computer. However, the security of medical data storage can not be effectively protected and the unsafe sharing of medical data among different institutions is still a hidden danger that can not be underestimated. To solve the above problems, a secure storage and sharing model of private data based on blockchain technology and homomorphic encryption is constructed. Based on the idea of blockchain decentralization, the model maintains a reliable medical alliance chain system to ensure the safe transmission of data between different institutions; A privacy data encryption and computing protocol based on homomorphic encryption is constructed to ensure the safe transmission of medical data; Using its complete anonymity to ensure the Blockchain of medical data and patient identity privacy; A strict transaction control management mechanism of medical data based on Intelligent contract automatic execution of preset instructions is proposed.After security verification, compared with the traditional medical big data storage and sharing mode, the model has better security and sharing.
Review of using machine learning in secure IoT healthcare

The healthcare industry is experiencing a digital transformation through telehealth. As a result, users’ information is at risk of being compromised by intruders. Machine learning can provide the sector with reliable protection against potential threats to address security and privacy concerns. In this chapter, we explore possible machine-learning solutions to two security challenges in the telehealth system: continuous authentication and detecting insider attacks. Authentication is the process of confirming the identity of a device or a person before connecting to the system. One of the most effective authentication techniques in telehealth is to verify IoT devices constantly to enhance cybersecurity protection on an ongoing basis. Continuous authentication uses machine learning algorithms to monitor all telehealth network activities, from users and devices, and utilizes classification techniques to detect unauthorized activities. This method relies on the verification of the identity of registered users to avoid unauthorized access as a proactive approach to mitigate the security risks. The main advantage of using machine learning over traditional authentication methods is that it does not need the user's attention while being authenticated continuously over time. Another advantage is higher reliability and accuracy. The second security challenge is insider attacks. Insider attacks occur when a user is authenticated legitimately to the system and aims to perform malicious activities such as stealing confidential information. Machine learning-based outlier detection methods are often used to identify users’ abnormal behaviors and prevent further access to confidential information.
An Analysis of the Privacy and Security Issues Affecting the Usage of Social Media

Social media have become extremely popular in recent years, and their widespread adoption has led to the presence of huge volumes of users’ personal information on the Internet. The ever-increasing number of social media users on one hand and the massive amount of information being shared daily on the other hand have encouraged attackers to develop and use different techniques to collect and analyze such information for a number of malicious purposes, including spamming, attacking through viruses, phishing, spear-phishing attacks and identity theft among others. Clearly, this trend represents a significant challenge affecting both users and administrators, and likewise create a privacy and security issues for social media users. In this paper, common security and privacy issues were examined along with recommendations to social media users to protect themselves from these issues whenever they use social media.
“Believing in God’s Presence” as a Religious Factor to Succeed in Pre-Sea Training: A Three-Round Delphi Approach to Develop Questionnaire Items

Despite all the hurdles during pre-sea training, faith in the presence of Allah, the Exalted, should help the marine students to become stronger and gain more experience when they return from training at sea.Therefore, this study was conducted with the aim of identifying elements of "Believing in God's Presence"as a religious factor that helps marine students during pre-sea training.A three-round Delphi procedure was used, starting with conceptualisation and operationalisation to form the items in the first round.Ten experts had agreed to participate in the two subsequent rounds.All items were analysed using SPSS version 23.It was found that the consensus among the experts on the items in the first and second rounds was high (IQ≤1.00)and there were no significant changes in the scores from Round 2 to Round 3. Items such as tawakkal, believing in the All-Seeing Power, learning from the creation of other living beings and seeking help through prayer are discussed.Perhaps they can help the marine students to feel the presence of Allah during their pre-sea training and make them strong.This study also illustrates the implementation of the three-round Delphi process using conceptualisation and oprationalisation for future research.
Accountable Clouds Through Blockchain

We present a solution for accountability in Cloud infrastructures based on blockchain. We show that, through smart contracts, it is possible to create an unforgeable log that can be used for auditing and automatic Service Level Agreement (SLA) verification. As a practical case study, we consider Cloud storage services and define interaction protocols for registering the outcome of each file operation in the blockchain. We developed a prototype implementation that runs on the GoQuorum, Hyperledger Besu, and Polygon blockchains, using different consensus protocols. Using a dedicated testbed, we discuss the performance of our implementation in terms of latencies, error rates and gas usage. Results demonstrate the viability of our approach over permissioned blockchains, with better performance for the Polygon and GoQuorum Raft decentralized systems. Our implementation enables interoperability, given that it is supported by the Ethereum Virtual Machine which currently is underlying several blockchain platforms.
Strategic Decision-Making for Pedagogical Course Planning Using NLP in Social Media Data

A higher education system needs to ensure its teaching process and materials are aligned with the graduate employment market needs and expectations. If a university has built trust in the industry, it would be more likely to have its graduates to find suitable relevant jobs in the market. As a result, this will have positive feedback to absorb better students to select the desired university as their first choice to study; consequently, the university’s reputation will improve. In this research, a novel prototype has been suggested to support strategic decision-making in academic curriculum planning based on the university’s ‘business-focus’ vision. To achieve the aim, the CRISP-DM model was used, and different Python libraries were applied for scraping data, creating a dataset, data preparation, and weighting keyword technologies depending on the most demanded skills. A job dataset was created by scraping job descriptions from social media. In this research, the skills, gathered from Stack Overflow’s survey, were counted in job descriptions and the most popular skills were identified. Finally, the job dataset was categorised based on the top-ranked skills.
Deep-Reuse-Enabled Common Satellite Network via Collaborative Blockchains: Architecture, Challenges, and Solutions

Satellite networks, as a recent deployment for technical and economic progress, have shown great advantages to provide diversiform Internet services. However, the networks are grappling with severe congestion caused by a surge in simultaneous requests, which has become more prevalent due to the widespread adoption of satellite constellations. The reuse of satellite components or orbital computing is a practicable method to detach thi <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> hindrance, but current approaches have only achieved customized services, and limited access, as well as one-way profits with satellites serving users. Additionally, the unrecorded distribution and centralized management of satellite networks may result in contentious interaction and exposed infrastructure. In this article, we first propose an architecture of Deep-reuse-enabled Common Satellite Network via Collaborative Blockchains (named DCSN-CB), where the notion of deep reuse is described from resources and information. Then, we highlight potential security and efficiency issues faced by DCSN-CB from the viewpoint of authentication, command control, and data management. As a countermeasure, we outline the prospective blockchain-based technologies, namely, the cross-domain authentication, digital-twins-enabled edge computing, and data auto-audit. The further estimations of these solutions indeed demonstrate their feasibility. Finally, we point out several interesting directions for future efforts in this emerging field.
Logical reasoning over knowledge graphs in vector space

Due to the incompleteness of knowledge map, the existing knowledge map is usually missing, and the task of knowledge reasoning is to predict the missing facts according to a large number of information such as entities and relationships. In order to satisfy the interpretability and accuracy, this paper proposes a multi-hop reasoning method combining first-order logic and graph embedded vector, which has the mathematical rigor of logical reasoning and the high accuracy of embedded vector. For the specified query, it is transformed into a first-order logical operation representation, and the query dependency graph is instantiated, and the reverse sampling from the root node to the anchor point in the instantiation process ensures that the query must have an answer. According to the query calculation diagram combined with logical operators, reasoning is based on embedded method, which avoids explicitly traversing KG and obtains the query answer.
Research on anomalous behavior detection of federated deep learning network intrusion based on FATE-CNN

In recent years, with the rapid development of China's network scale, the frequency of power Internet has also increased sharply. But at the same time, in the process of power grid intelligent terminal access, all kinds of abnormal network attacks are increasingly frequent. As a key link in the power network security protection, the detection of network abnormal intrusion behavior has been paid more and more attention by researchers in recent years. In view of this, this paper proposes a network anomalous intrusion behavior detection method based on FATE-CNN. It corresponds several local intrusion detection datasets to federated learning devices one by one, and uses a dynamic local iteration method to gradually obtain the best global model. Through the comparative experiment with four intrusion detection models of LIBSVM, CNN, DNN and DBN-EGWO-K-KELM, it is verified that the model algorithm can effectively improve the value of abnormal network intrusion behavior of NSL-KDD, UNSW-NB 15 and CICIDS2017, and improve the network security of the intelligent terminal of the power grid.
Metaverse and Healthcare: An Empirical Investigation

A virtual setting that provides experiences in real-time is introduced through a new field of research called metaverse. Combining the metaverse with healthcare results in a unique real-space experience that enables both patients and physicians to evaluate the health of their respective patients and engage in in-person interactions. The metaverse is a collection of technologies that includes elements of artificial intelligence, virtual reality, augmented reality, the Internet of Things, and more. The integration of healthcare and the metaverse has made it possible to focus on the patient's health in a more accurate, effective, and precise manner. Using the many imaging technologies in the metaverse, researchers are working to speed up the usage of the metaverse for patient diagnosis in addition to clinical diagnostics. It is no longer necessary for a doctor to be there in person while making a diagnosis. In order to diagnose patients in a highly effective and exact manner, there is a lot of research being done to improve the metaverse environment. This study's main focus will be on how the metaverse has streamlined the healthcare industry and the various healthcare amenities it offers. This article offers a thorough examination of clinical and diagnostic developments achieved in the metaverse in an effort to enhance the field of healthcare in the metaverse. Also, the focus is on the cutting-edge developments that will transform the metaverse healthcare industry.
Handling Imbalanced Data in Predictive Maintenance: A Resampling-Based Approach

Imbalanced data is a common problem in many areas, and it can have significant impacts on the performance and generalizability of machine learning models. This is because the models fail to create a good representation of the examples in the minority class. This study aims at improving the classification success for the predictive maintenance tasks in which the data is generally imbalanced. To this end, we use resampling methods that target creating balanced data. We present various oversampling and undersampling techniques and apply them to both synthetic and real-world datasets. We then perform classification experiments with imbalanced and balanced datasets by using different classifiers. The performances of different classifiers have been compared. More importantly, we evaluate the effectiveness of resampling techniques to provide insights into their usefulness in handling class imbalance. Our study contributes to the growing body of literature on addressing the class imbalance in classification tasks and provides practical guidance for selecting appropriate sampling methods based on the characteristics of the dataset.
Towards an Evaluation Process for Regime Recognition Approaches: Addressing Variability in Labeling Training Data

Regime recognition is an important tool for monitoring aircraft usage. Algorithms for this task are normally trained and tested on flight load survey data. In many instances, significant portions of the flight data are not used because of labeling uncertainties. Flight test data is expensive to generate, but machine learning-based solutions rely on copious amounts of training data, so the idea of discarding data is unappealing. This paper presents a process to consistently and systematically label flight data with common helicopter regimes that would reduce the amount of unlabeled flight test data. The approach makes use of regime descriptions and parameter time histories to assign labels, which are then verified using flight path and flight test card information. Although the implementation of the approach is challenging, the initial results from Bell 206 test flights demonstrate that this approach can significantly reduce the amount of unlabeled flight data, enabling much more usable data for training algorithms.
Application of Neural Network Keyword Extraction Methods for Student’s CV Compilation from Discipline Work Programs

The paper considers application of natural language processing methods (NLP) to solve the task of identifying the core skills acquired by students in the process of education. An ensemble solution, consisting of models WikiNEuRAL, KBIR-Inspec, ruGTP3Large, word2vec, and other NLP methods, is used to extract and process Russian language keywords. The described method was developed to automate the compilation of a student’s CV in the information system called “Partner’s Account” of the LETI” university.
On the Discussion of the Semantic Communication with the Transmission of MNIST Dataset

Inspired by the success of artificial intelligence (AI) in various fields, recently, AI-based semantic communication has been studied extensively and achieved great success. However, the existing works may not provide a comprehensive understanding of how channels affect semantic communication from phenomena. In this paper, based on the observations on the MNIST dataset, the following questions are discussed: (1) Why is semantic communication robust? We believe and observe in the experiments that the trade-off learning between the characteristic and common information of data is one of the key properties of semantic communication. The “channel robustness” and “hard generalization” phenomena are explained in this perspective. (2) How does noise affect semantic communication in the AWGN channel? Because of noise, there is a smallest distinguishable unit, called as semantic constellation cluster (SCC), where the features in the SCC are confused by noise and can not be distinguishable. The feature criterion (FC) is analyzed and observed in the experiments, which means the minimum distance of features that the features can just be distinguishable and is also the radius of SCC in the AWGN channel. (3) How does the semantic decoder distinguish noised features under the large noise in the AWGN channel? It is found that the outer edge of the expending space brought by noise is easy to be distinguish for the decoder, thus maintaining fairly good performance under the large noise.
Anomaly Detection Method for Time Series Data Based on Transformer Reconstruction

Multiple temporal anomaly detection algorithms have important research significance in many application fields, such as system state estimation, fault prediction and diagnosis, network behavior anomaly detection and so on. Aiming at the problems of abnormal noise, high dimensionality, lack of labeling, and difficulty in learning abnormal features of various temporal data, an anomaly detection model TRAD based on Transformer reconstruction was proposed, which used self-conditioning to extract robust multi-modal features to obtain the stability of training. At the same time, the adversarial training process is used to amplify the reconstruction error. Experiments on three public datasets show that the proposed model not only has excellent detection performance, but also has strong applicability and generalization ability for unknown heterogeneous time series data.
Comparative analysis of simulated dual axis solar tracker with its real time model

In field of Generation, Dual axis solar tracker plays a crucial role in electricity generation.Nowadays, world going towards the renewable sources like wind plant, solar plant etc. because we can't rely on non renewable sources, they will be deplete with time that's why need to step up towards the renewable sources.Renewable sources are less pollutant than non renewable sources.Solar trackers lie under the category of renewable sources.Single axis and dual axis comes under the category of solar tracker.DAST is way potential than single axis solar tracker because it rotates in both the axis Primary and Secondary (east-west and north-south) and we are using manual cleaning too for this project so that we don't need to hire someone to clean the solar panels.So in this paper, we will discuss about dual axis solar tracker.
Secure image transmission through an OFDM and MIMO OFDM systems with different cryptographic algorithms (2020)

Data security and privacy concerns are now hindering the development of wireless communication networks.The need for a secure and dependable wireless network to transmit digital data (including text, images, and video) between origin and destination is growing.There are two main tiers at which security may be implemented: the data layer and the network layer.Encryption methods are used to ensure the safety of the data itself.They immediately operate on the data to alter its proper format, making interpretation more difficult.However, network-level security entails implementing certain network-related security processes to protect data from unwanted access through the network.Secure data transfer (in the form of pictures) through an air interface is the focus of this work, which focuses on the former type of security in wireless communication systems.
Implementation Of Affine Cipher Combination And Merkle Hellman On The Process Digital Image Security

The use of image media information has several weaknesses, one of which is the ease with which it can be manipulated by irresponsible people. Efforts made in increasing the security of this image is cryptography, namely the science and art of maintaining the security and confidentiality of images. Cryptography is used so that the confidentiality of the image can be maintained, so that it is not known by others. In general, there are 2 (two) techniques for carrying out cryptographic processes, namely encryption and decryption. In this study, the encryption process was first carried out using the Affine Cipher algorithm and then continued with the Merkle Hellman algorithm, while the decryption was carried out first by Merkle Hellman and then decrypted again with the Affine Cipher. The results of this study indicate that applying the Affine Cipher and Merkle Hellman algorithms can secure images. At the end of this system is an image that is in the form of blur so that it is not known and not understood by others. That way people who are not entitled cannot understand from the image.
Synthetic Image Generation for Robot Simulation: Quantifying the Impact of Model Modifications on Perception

Modeling cameras for the simulation of autonomous robotics is critical for generating synthetic images with appropriate realism to effectively evaluate a perception algorithm in simulation. In many cases though, simulated images are produced by traditional rendering techniques that exclude or superficially handle processing steps and aspects encountered in the actual camera pipeline. The purpose of this contribution is to quantify the effect that modifying the camera model has on the perception algorithm evaluated in simulation. We investigate what happens if one ignores aspects tied to processes from within the physical camera, e.g., lens distortion, noise, and signal processing; scene effects, e.g., lighting and reflection; and rendering quality. The results quantifiably indicate that, for the evaluated task, modeling modifications that result in large-scale changes to color, scene, and location had far greater impact on perception than model aspects concerned with local, feature-level artifacts.