Reviewerly/exHarmony huggingface dataset:
Quantum Attacks on Type‐1 Generalized Feistel Schemes

Abstract Generalized Feistel schemes (GFSs) are extremely important and extensively researched cryptographic schemes. In this paper, the security of Type‐1 GFS in quantum circumstances is investigated. On the one hand, in the qCCA setting, a new quantum polynomial‐time distinguisher on ‐round Type‐1 GFS with branches is given, which extends the previous results by rounds. This leads to a more efficient analysis of type‐1 GFS, that is, the complexity of some previous key‐recovery attacks is reduced by a factor of , where k is the key length of the internal round function. On the other hand, for CAST‐256, which is a certain block cipher based on Type‐1 GFS, a 17‐round quantum distinguisher in the qCPA setting is given. Based on this, an ‐round quantum key‐recovery attack with complexity is constructed.
Design and Control of Autonomous Rover for Foliage Navigation

Abstract Implementation of UGV in civilian applications is getting trendy and normalized due to its solid achievement in executing mission under harsh environments. The UGV foliage environment-oriented applications even attracted the people’s attention in correspond sectors to ease their tasks with guaranteed performance. Given this, the development of the autonomous rover from sensors and control aspects with the consideration of foliage environment interest people to enhance its functionality. To ensure the sensor is not easily affected by the environment factor, the radar and the camera are selected to provide the environment perception to the rover. The approach for a linear Proportional-Derivative (PD) controller with the dynamic modelling of the UGV is also discussed in this paper. The study shows preliminary results that are working.
Implementation of Integration of Advanced Optimized Models With Block Chain Models

There are several unanimity techniques for block chain technology, each of which has particular advantages and disadvantages. In real use, POA falls into a novel class of Byzantium Adaptable consensus mechanism as compared to conventional byzantine fault - tolerant methods. Several companies are switching to a more traditional POA coin with Byzantine Automatic Failover. It is exceedingly difficult to stop assaults inside nodes without a similarly strong consensus method. Our goal is to do research to determine the best way to develop contracts with enhanced techniques to lower the danger of cryptocurrency assaults. We construct blocks, add relating to an identified' voter profiles, modify them based on present in the study' voting habits, and gather chain rewards by using the POA and POV procedures. The bitcoin has been very popular over the past few years, and the underpinning blockchains have also received a lot of interest in the scholarly institution. In order to improve the functionality of Cryptocurrency and create an uniform and adaptable to operate on most systems, this project sought to examine various chain settlement techniques.A ground-breaking variation of blockchain technology is hybrid blockchain. In this manual, we will examine the fundamental components of a hybrid blockchain and discover its operation.The world is changing in a way that only the blockchain can. Corporations, government, and other organizations are able to oversee workflows more successfully and improve present system and better alternatives because to it. It is currently changing how we keep, retrieve, and utilize data in an effort to improve a never loop of growth in technology. Other aspects of it are also impacted, including how connections are accepted.Three different applications of blockchain exist: private, public, consortium, and hybrid. You may be familiar with how private and public blockchain operate if you've read about it before.
Variational quantum simulation of partial differential equations: applications in colloidal transport

Purpose This study aims to assess the use of variational quantum imaginary time evolution for solving partial differential equations using real-amplitude ansätze with full circular entangling layers. A graphical mapping technique for encoding impulse functions is also proposed. Design/methodology/approach The Smoluchowski equation, including the Derjaguin–Landau–Verwey–Overbeek potential energy, is solved to simulate colloidal deposition on a planar wall. The performance of different types of entangling layers and over-parameterization is evaluated. Findings Colloidal transport can be modelled adequately with variational quantum simulations. Full circular entangling layers with real-amplitude ansätze lead to higher-fidelity solutions. In most cases, the proposed graphical mapping technique requires only a single bit-flip with a parametric gate. Over-parameterization is necessary to satisfy certain physical boundary conditions, and higher-order time-stepping reduces norm errors. Practical implications Variational quantum simulation can solve partial differential equations using near-term quantum devices. The proposed graphical mapping technique could potentially aid quantum simulations for certain applications. Originality/value This study shows a concrete application of variational quantum simulation methods in solving practically relevant partial differential equations. It also provides insight into the performance of different types of entangling layers and over-parameterization. The proposed graphical mapping technique could be valuable for quantum simulation implementations. The findings contribute to the growing body of research on using variational quantum simulations for solving partial differential equations.
A semantic union model for open domain Chinese knowledge base question answering

In Open-domain Chinese Knowledge Base Question Answering (ODCKBQA), most common simple questions can be answered by a single relational fact in the knowledge base (KB). The abbreviations, aliases, and nesting of entities in Chinese question sentences, and the gap between them and the structured semantics in the knowledge base, make it difficult for the system to accurately return answers. This study proposes a semantic union model (SUM), which concatenates candidate entities and candidate relationships, using a contrastive learning algorithm to learn the semantic vector representation of question and candidate entity-relation pairs, and perform cosine similarity calculations to simultaneously complete entity disambiguation and relation matching tasks. It can provide information for entity disambiguation through the relationships between entities, avoid error propagation, and improve the system performance. The experimental results show that the system achieves a good average F1 of 85.94% on the dataset provided by the NLPCC-ICCPOL 2016 KBQA task.
Architectural Analysis of CKKS FHE Scheme

In Chap. 2 , we covered the basics of the CKKS FHE scheme and its operations to develop an intuition about the data intensiveness of FHE-based computing. Existing state-of-the-art FHE implementations for the CKKS scheme observe one or more orders of magnitude slow down when compared to implementations that operate on unencrypted data. For example, consider the addition/multiplication of a clear text message and a ciphertext message, both consisting of 32, 768 complex numbers.
Virtual Restoration System for 3D Digital Cultural Relics Based on a Fuzzy Logic Algorithm

This research proposes a virtual restoration system and method for 3D digital cultural relics based on a fuzzy logic algorithm, aiming to solve the problems of the low classification accuracy and poor splicing effect of Terra Cotta Warrior fragments. This method adopts a series of steps to improve the efficiency and accuracy of fragment splicing. Firstly, features such as curvature, torsion, and left and right chord lengths were extracted from the fracture surface contour lines of the cultural relic fragments to form feature vectors. Then, the feature vector was fused and compressed by using the multilayer perceptron. The multilayer perceptron is a neural network model that can process and learn input data via multiple levels of computation, resulting in more expressive feature representations. Next, we used the calculation results of the multilayer perceptron to perform the splicing operation on the fragments. This means that, based on the calculation results of the feature vectors, the system can automatically select appropriate splicing methods to accurately match and splice fragments. Finally, by adjusting the weight of the multilayer perceptron, the error rate of fragment splicing can be reduced, further improving the accuracy of repair. The experimental results show that the method proposed in this article is significantly better than traditional methods in terms of time consumption and can effectively improve the efficiency of fragment matching and stitching. Conclusion: The fragment-stitching algorithm based on multi-feature adaptive fusion improved the speed and effectiveness of stitching in fragment-stitching tasks. In summary, the fragment-stitching algorithm based on multi-feature adaptive fusion can improve the speed and effectiveness of stitching in fragment-stitching tasks. The application of this method is expected to play an important role in the field of cultural relic protection, such as the restoration of Terra Cotta Warrior fragments.
Assessment and Design of a PV Module’s Using Different Controllers

In comparison to stationary devices, solar tracker is a crucial instrument for increasing the power output of photovoltaic cells. This article suggests the development and testing of controller for a two-axis sunlight tracking system, that also retains the strength that is generated by photovoltaic panels at one‘s nominal voltage, in contrast to earlier techniques in which the goal is to preserve the suns radiation orthogonally to the exterior of the device as well as acquire a continuous power output. This one is done by creating and simulating computational equations of movements of the sunlight, the renewable radiation collected on the surface of the Earth, the two-axis tracking service in both its mechanical and electrical components, and the solar panel. In the solar monitoring system,, 2 controllers—one proportional-integral-derivative regulator as well as the a different by fuzzy logic. The examination of the models reveals that the fuzzy logical controllers performs better since it has a quicker stabilization period, a shock with a shorter magnitude, as well as a lesser fairly constant error rate. The basic idea behind how a solar tracking system works is to encourage photo - voltaic modules to be oriented in such a way that they can produce as much electricity as possible up until their nominal output. This is made possible by a gyroscope just on device’s exterior, which calculates its position in relation to the sun’s hour angular position and height; a data processing card is created to incorporate current and voltage detectors, which track the production strength the photovoltaic device generates over the course of the day. Every time of day and in any kind of climate. The outcomes of the implementations show that what a control using fuzzy logic for two-axis sun trackers keeps the photovoltaic device’s production energy at its nominal characteristics during in the periods of maximum sun.
Payroll Management Using Blockchain

Blockchain technology has the potential to minimize fraud and increase the efficiency of payroll systems. Every government, corporation, and other entity must have a payroll administration system in place to ensure correct and timely employee payment. Poor-country payroll systems face challenges such as a lack of decentralization, ghost work, cybercrime, and other human manipulations. This research looks at the problems with payroll systems in underdeveloped countries, and how blockchain technology might help to solve them. The rest of the article explains how blockchain technology might be used to solve problems with payroll systems. Through a review of the existing literature, we demonstrate the significance of blockchain technology in general and its application to payroll systems.
Open CV-Based Face Detection Security System

This article introduces a security system based on continuousfacerecognitionfor homes and businesses. It was built with the use of a Local Binary Pattern Histogram (LBPH) computation. The use of face identification effectively applied to nurture systems whose primary objective is worker security residences, just as these reconnaissance frameworks have monitored crimes using ID. Most face recognition calculations have been severely limited by differences humans’ facial features & stance, such as light; nevertheless, the LBPH computation compensates for both of these variances. This paper discusses the advantages of this computation over others, as well as the rationale for preferring OpenCV execution over MATLAB.
LSevoBN: a structure learning algorithm for large Bayesian networks

The issue of structure learning in large Bayesian networks has gained significant attention from the academic community in recent years due to its wide-ranging applications in various fields. Efficient algorithms for structure learning are crucial for enabling the practical use of Bayesian networks in real-world scenarios where the number of variables can be substantial. In previous approaches, addressing this problem has resulted in either prolonged convergence times or significant degradation of the problem. Although evolutionary algorithms have been effective in training Bayesian Networks (BNs), their application to large BNs has been impractical due to lengthy convergence times. In this paper, we introduce a modified evolutionary algorithm for training large BNs by dividing the network into smaller ones, coding and connecting them through evolutionary optimization. Our proposed approach significantly accelerates the training process, achieving a time acceleration of more than twenty times compared to existing solutions, while maintaining comparable or slightly degraded quality.
How to Make Taint Analysis Precise

During a lunch at the ceremony for his honorary degree from the University of Venice, Patrick Cousot asked us the following question: “Why everyone in security is using taint analysis, if it so imprecise?”. We answered that yes, taint analysis per se can be very imprecise, but it can be made extremely accurate when (i) it is refined to capture (the abstractions of) the execution flow and the data transformations; and (ii) it is based on the source language and not on some intermediate representation. We explained that at Meta more than $$50\%$$ of the security bugs are automatically detected using refined static taint analyses based on those principles. This short note expands and details the answer we gave Patrick.
Smart security system using Hybrid System with IoT and Blockchain: A security system Human sased Detection

By eliminating the need for information rehashing, blockchain technology enables the secure transmission of sensitive data without the need for duplication, reducing patient history errors and saving time. The information is made even more secure by the fact that it has a timestamp. Blockchain technology has the potential to improve financial transaction security and efficiency in a variety of healthcare applications. This prevents unauthorised individuals from seeing or altering the patient's medical information in addition to restricting access to it. In this study, it is suggested that using blockchain technology to connect patients and medical professionals to a mechanism for data retrieval and storage would aid in the development of a secure and effective healthcare system. Since 1970, there have been numerous token-based methods, including passports, bank cards, bank accounts, identification cards, and keys; however, none of them have been successful in developing a reliable and secure conduit for transactions. Personal Identification Recognition Numbers (PIRN), passwords, and other security measures are used in conventional knowledge-based systems. Since they are so easily lost, stolen, or misplaced, they often can't maintain confidentiality or establish the legitimacy of a claimant. Additionally, since usernames and PINs are so easily forgotten or guessed by a fraudster, personally identifiable information like this is particularly vulnerable to theft. Due to its inherent potential and advantages over other traditional techniques of identification and verification, biological identification and authentication, also known as biometrics, is currently garnering a lot of attention. Particularly in the context of information security systems, this is true. The device's distinctive biological properties, such as fingerprints, facepalms, gesture recognition (including iris), and the device's own iris, enable a wide range of applications. These consist of forensics, educational materials, security systems for airports, international ports of entry, and financial services kiosks. In addition, it is used in a variety of other fields, like as forensics and law enforcement. As a result, every layer of the system, including the data that is sensed, calculated, and processed, as well as the information that is kept and managed, may be impacted by the system's various vulnerabilities and risks.
User-centred Design and Development of a Graphical User Interface for Learning Classifier Systems

This study presents an application that offers an interactive representation of the learning cycle of a learning classifier system (LCS), a rule-based machine learning technique. The research approach utilized a combination of human-computer interaction requirements, design heuristics, and user-centred engineering to scaffold the development of a graphical user interface for an LCS. The evaluation of the application by LCS experts and a novice yielded encouraging results, demonstrating its ability to convey the LCS mechanics to the users. In addition, the experts validated the correctness of the learning cycle. The study's contribution is two-fold: it presents an innovative approach to making the user understand the underlying mechanics of LCS and validates the effectiveness of the developed application. Furthermore, this work sets the stage for future research towards design revision and to further develop the application to accommodate different LCS models and custom data.
Bridging the cyber skills gap

It has long been accepted that one of the greatest threats to your cyber security lies within your own organisation – your staff. Cyber security training is a challenge, as is keeping people aware of and alert to emerging threats. And this has been complicated further by new organisational structures and ways of working, in no small part imposed by the Covid pandemic. Solving this problem requires effort and investment.
Developing an Blockchain-Based System for E-Commerce Inventory Management

The era of directly saving data is over. We have decided to store and move our data online. The modern concept of data digitalization has advantages and disadvantages of its own. Nowadays, cyber security remains a major problem. The hazards of a breach and cyberattacks are unavoidable as more data is transferred online. The online conveyance of goods and payments that does not necessitate travel is the basis for all of ecommerce. Cyber-attacks can affect e-commerce transactions and the exchange of transactional data. The main goal of our study is to create a system that guards against certain anomalies, particularly while transferring transactional data, and to also put in place an automatic mechanism that makes sure these transactions go off without a hitch. We are utilizing cutting-edge technological advances like blockchain and smart contracts to put this into practice. Blockchain enables the safe storage and secure network-wide transit of data using a decentralized, unchangeable digital ledger. Blockchain technology enables quicker, safer, and more secure transaction transfers in ecommerce. The homologous transaction method and data encryption made possible by blockchain allow for the secure movement of transactional data. It is exchanged using blockchain. A smart contract is a unique software application that uses digital means to activate, verify, and execute a contract’s conditions. As long as the agreement is in force, it offers transactional security. Blockchain and smart contracts will transfer e-commerce in the coming years. We have coupled smart contracts with blockchain to preserve the transaction standard while also ensuring data security and user privacy. The outcomes of developing and putting into practice the suggested system—which offers a remedy for transactional data privacy—are described.
Initialization Matters for Asynchronous Steady-State Evolutionary Algorithms

Evaluating the fitness of individuals in the initial population of an evolutionary algorithm (EA) is usually straightforward and poses few theoretical problems. In asynchronous steady-state EAs (ASSEAs), however, the choice of initialization strategy can significantly alter the algorithm's long-term behavior. ASSEAs have long been recognized as an important alternative to parallel and distributed evolution, because unlike the more commonly used generational model, they avoid leaving processing resources idle while waiting for a generation boundary. In our work on ASSEAs, we have observed that colleagues' intuitions tend to differ about what a basic asynchronous initialization strategy should look like---but the implications of this choice have not been studied before. This paper analyzes the results of three competing initialization strategies that have appeared in prior literature---the immediate, until-finished, and extra strategies---and concludes that the immediate strategy in particular, which relies on maintaining a queue of jobs waiting to be evaluated, incurs a slow evolutionary feedback loop and should be used with caution. Queuing is a necessary part of scaling ASSEAs up to be resilient to node failures in large HPC environments, however---so this work suggests that further research is needed to understand how asynchronous initialization can be used most effectively for expensive optimization.
Evolutionary Algorithms for Segment Optimization in Vectorial GP

Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw vectorial data during training, not only enables Vec-GP to select appropriate aggregation functions itself, but also allows Vec-GP to extract segments from vectors prior to aggregation (like windows for time series data). This is a critical factor in many machine learning applications, as vectors can be very long and only small segments may be relevant. However, allowing aggregation over segments within GP models makes the training more complicated. We explore the use of common evolutionary algorithms to help GP identify appropriate segments, which we analyze using a simplified problem that focuses on optimizing aggregation segments on fixed data. Since the studied algorithms are to be used in GP for local optimization (e.g. as mutation operator), we evaluate not only the quality of the solutions, but also take into account the convergence speed and anytime performance. Among the evaluated algorithms, CMA-ES, PSO and ALPS show the most promising results, which would be prime candidates for evaluation within GP.