Reviewerly/exHarmony huggingface dataset:
Generalization of PhaseNet in Shandong and its application to the Changqing M4.1 earthquake sequence

Waveforms of seismic events, extracted from January 2019 to December 2021 were used to construct a test dataset to investigate the generalizability of PhaseNet in the Shandong region. The results show that errors in the picking of seismic phases (P- and S-waves) had a broadly normal distribution, mainly concentrated in the ranges of −0.4–0.3 s and −0.4–0.8 s, respectively. These results were compared with those published in the original PhaseNet article and were found to be approximately 0.2–0.4 s larger. PhaseNet had a strong generalizability for P- and S-wave picking for epicentral distances of less than 120 km and 110 km, respectively. However, the phase recall rate decreased rapidly when these distances were exceeded. Furthermore, the generalizability of PhaseNet was essentially unaffected by magnitude. The M4.1 earthquake sequence in Changqing, Shandong province, China, that occurred on February 18, 2020, was adopted as a case study. PhaseNet detected more than twice the number of earthquakes in the manually obtained catalog. This further verified that PhaseNet has strong generalizability in the Shandong region, and a high-precision earthquake catalog was constructed. According to these precise positioning results, two earthquake sequences occurred in the study area, and the southern cluster may have been triggered by the northern cluster. The focal mechanism solution, regional stress field, and the location results of the northern earthquake sequence indicated that the seismic force of the earthquake was consistent with the regional stress field.
An Overview of Modern Fully Homomorphic Encryption Schemes

In this paper, research is carried out to ensure the security of cloud storages. The work is devoted to homomorphic encryption. Even though the very concept of homomorphic encryption appeared in the late 70s, a fully homomorphic encryption scheme was obtained only in 2009. Since that time, a variety of fully homomorphic encryption schemes have been developed. In this paper, we analyze the most popular encryption schemes, as well as their software implementations. As a result of the study, it was found that the most promising scheme is CKKS. This scheme can perform homomorphic arithmetic on rational numbers, and already has several software implementations distributed under a free license.
Study on the Selection Method of Federated Learning Clients for Smart Manufacturing

Artificial intelligence technology in the context of smart manufacturing uses manufacturing data to enable the automatic detection, classification, and identification of products in the production process, reducing production costs and human consumption, thereby improving production efficiency and product quality. Federated learning enables the distributed implementation of AI technologies, keeping data local to avoid privacy leaks. However, data heterogeneity factors have an impact on federated learning in a manufacturing context, and this paper proposes a customer degree selection method based on model parameter variation. The method relies on transmitting the local model changes in the participants to reflect the data characteristics, calculates the model similarity of the participants using graph theory and similarity, and uses the Top-K mechanism to filter the original participant set through the similarity scores of graph nodes to reduce the influence of heterogeneity factors in the participant set and maximize the training effect and accuracy of federated learning. The effectiveness of this method was verified by using the Dirichlet distribution to perform non-IID data partitioning on the power system attack dataset and the hard disk fault detection dataset.
Analysis of Mixed English Teaching Model based on Rasch Model and Construction of DE-FAHP-based Comprehensive Weight Quantization Model

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Kemampuan literasi remaja wanita di Desa Tawangrejo Kabupaten Blitar dalam menghadapi budaya fashion pada era teknologi informasi

Introduction. The more information a person needs, the more active the person is in searching for information. Adolescents tend to follow fashion updates because of, their curiosity. This study aims to identify the triggers for youth information needs for fashion.&#x0D; Data Collection Methods. The paper used a qualitative approach with descriptive methods.&#x0D; Data Analysis. Data analysis was carried out first by reducing the data obtained through observations, interviews, and literature study.&#x0D; Results and Discussion. There are five factors that encourage the process of information seeking behavior in adolescents, psychological conditions, demographics, interpersonal relationships, environment, and characteristics of information. Information seeking behavior by youth accidentally tend to create further interests. Several obstacles appeared in search fing information, these include difficulties to find relevant information, lack of satisfaction in model of clothes, and hard to find information . However, these obstacles can be overcome by digging information through other sources and conducting searches immediately after the needs.&#x0D; Conclusion. The trigger for the information needs of adolescents related to fashion is the need to support their appearance. Many adolescents see fashion a value system in the society.
Efficient Feature Extraction for Recognition of Human Emotions through Facial Expressions Using Image Processing Algorithms

Face emotion recognition is a challenging problem in computer vision that has been extensively studied in recent years. The project investigates the performance of Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), K- Nearest Neighbour (KNN), and Support Vector Machine (SVM) for face emotion recognition. The aim of this study is to evaluate the performance of different combinations of these techniques and to identify the most effective approach for this task. To achieve this, we first collected a dataset of facial expressions that includes seven basic emotions such as happy, sad, angry, surprise, Neutral, fear, and disgust. We then extract LBP and HOG features from the facial images and then KNN and SVM classifiers to classify the emotions. We experimented with various combinations of LBP, HOG, KNN, and SVM and evaluated the performance of each approach using metrics such as accuracy, precision, recall, and F1 score. This study demonstrates the effectiveness of combining LBP and HOG features with KNN and SVM for face emotion recognition. Our results suggest that SVM is the most effective model for this task, when it is combined with HOG features and can further improve the system39;s performance. The model can be implemented using MATLAB and GUI Interface. These findings have important implications for the development of accurate and reliable face emotion recognition systems for various applications, including human-computer interaction, gaming, and healthcare.
Ethical Hacking: A Solution for Most Dangerous Threat

An ethical hacker is the network specialist and computer, who dive into some security systems seeking responsibility that could be exploited by a malicious hacker. The purpose of ethical hacking can be to deal with the breaches of cyber security with the knowledge of laws and also can be to build an effective cyber security wall against your organization. An ethical hacker can help the people who are suffered by the malicious hacking. Ethical hacker can analyse the vulnerabilities before the attacker may strike. It can help people to recovery the lost information and to perform penetration testing to strengthen computer and the network security. This paper describes about ethical hacking and aspects of ethical hacking
Spectral complexity-scaled generalisation bound of complex-valued neural networks

Complex-valued neural networks (CVNNs) have been widely applied in various fields, primarily in signal processing and image recognition. Few studies have focused on the generalisation of CVNNs, although it is vital to ensure the performance of CVNNs on unseen data. This study is the first to prove a generalisation bound for complex-valued neural networks. The bounds increase as the spectral complexity increases, with the dominant factor being the product of the spectral norms of the weight matrices. Furthermore, this work provides a generalisation bound for CVNNs trained on sequential data, which is also affected by the spectral complexity. Theoretically, these bounds are derived using the Maurey Sparsification Lemma and Dudley entropy integral. We conducted empirical experiments on various datasets including MNIST, ashionMNIST, CIFAR-10, CIFAR-100, Tiny ImageNet, and IMDB by training complex-valued convolutional neural networks. The Spearman rank-order correlation coefficient and the corresponding p-values on these datasets provide strong proof of the statistically significant correlation between the spectral complexity of a network and its generalisation ability, as measured by the spectral norm product of the weight matrices. The code is available at https://github.com/LeavesLei/cvnn_generalization.
A Research of the Influence of Quantum Annealing Parameters on the Quality of the Solution of the Number Factorization Problem

Introduction. Modern information security systems use methods of asymmetric cryptography to transfer encryption keys, which are based on the high computational complexity of factorization of large numbers. Quantum computers (QCs) theoretically make it possible to accelerate the solution of the problem of factorization of numbers in comparison with classical computers and pose a potential threat to information security systems. However, real QCs have a limited number of connections between them and problems with preserving a stable low temperature, which reduces the probability of detecting a global minimum. The joint use of QCs with classical computers based on hybrid cloud services is advisable when the search for the optimal solution by direct methods is a complex problem both in the theoretical sense and in the sense of the required amount of calculations for tasks with specific data. The article proposes a method for improving the accuracy of solving the factorization problem based on multiple minimum search by the method of hardware reverse quantum annealing with a variation of its parameters. The results of numerical experiments for two different QC processors and a hybrid quantum-classical computer by D-Wave are presented, it is shown that the maximum number that can be factorized exclusively by direct annealing is 143, and with a combination of direct and reverse annealing 255. The purpose. Examination of the influence of the parameters of quantum annealing and the corresponding solutions for the adiabatic CC, developed by D-Wave, on the quality of the solution of the factorization problem. To give recommendations for improving the accuracy of solving the factorization problem and increasing the statistical frequency of the appearance of correct pairs of multipliers. Results. Numerical experiments have shown that for the problem of factorization of numbers, the successive application of direct and reverse annealing makes it possible to improve the probability of obtaining the correct pair of multipliers and to more than double the statistical frequency of its occurrence. Quantum annealing modes: pause and quenching reduce the probability of obtaining the correct solution and worsen the statistical frequency of the appearance of correct pairs of multipliers. Conclusions. The use of direct and reverse annealing makes it possible to increase the probability of obtaining the correct solution of the factorization problem for the adiabatic QC of D-Wave. Increasing the calculation time of the problem is justified, since it allows increasing the probability of a correct solution. The use of hybrid quantum-classical computing and cloud services allows factorization for numbers with a bit depth of up to twenty-two bits. Keywords: quantum annealing, factorization of natural numbers, asymmetric shifts, hardening, reverse annealing, combinatorial optimization.
Investigation into Perceptual-Aware Optimization for Single-Image Super-Resolution in Embedded Systems

Deep learning has been introduced to single-image super-resolution (SISR) in the last decade. These techniques have taken over the benchmarks of SISR tasks. Nevertheless, most architectural designs necessitate substantial computational resources, leading to a prolonged inference time on embedded systems or rendering them infeasible for deployment. This paper presents a comprehensive survey of plausible solutions and optimization methods to address this problem. Then, we propose a pipeline that aggregates the latter in order to enhance the inference time without significantly compromising the perceptual quality. We investigate the effectiveness of the proposed method on a lightweight Generative Adversarial Network (GAN)-based perceptual-oriented model as a case study. The experimental results show that our proposed method leads to significant improvement in the inference time on both Desktop and Jetson Xavier NX, especially for higher resolution input sizes on the latter, thereby making it deployable in practice.
Pengembangan koleksi Perpustakaan Samudra Pustaka Desa Plosorejo Kecamatan Kademangan Kabupaten Blitar

Introduction. This study aims to describe the collection development of the Samudra Pustaka Library.. Samudra Pustaka Library is a village library that became the best village library in 2021 nationally. The completeness of the collection is one of the indicators of the success of managing the village library management.&#x0D; Data Collection Methods. The method used was a qualitative descriptive with several data collection techniques including interviews, observations, and documentation studies.&#x0D; Data Analysis. Data analysis was conducted by following stages of collection development developed by Evans and Saponaro &#x0D; Results and Discussion. Samudra Pustaka Library currently does not have their own collection development policy Societal analysis was conducted based on user suggestions and identified community livelihoods.. Selection criteria focus on purchases, donations, and collaboration. Weeding activities are carried out by withdrawing collections deemed unreadable. Evaluation activities are carried out by taking into account user suggestions related to collections. Several identified constraints were found on the budget, collections, and human resources.&#x0D; Conclusion. All collection development processes are on the right track. A collection development policy should be created immediately.
Quantum Control of a Nonlinear Time-Dependent Interaction of a Damped Three-Level Atom

We investigate some new aspects of the nonlinear interaction between a three-level Ξ-type atom and bimodal field. The photon-assisted atomic phase damping, detuning parameter, Kerr nonlinearity and the time-dependent coupling have been considered. The general solution has been obtained by using the Schrődinger equation when the atom and the field are initially prepared in the excited state and coherent state, respectively. The atomic population inversion and concurrence are discussed. It is shown that the time-dependent coupling parameter and the detuning parameter can be considered as quantum controller parameters of the atomic population inversion and quantum entanglement in the considered model.
Semantic approach for solving the decision problem in natural language

Abstract Every language, whether formal or natural, has a corresponding decision problem, the problem of whether a statement is well-formed and a valid sentence of the language. While compilers usually solve the decision problem for a computer language during compilation, there is no mathematical method to solve the decision problem in natural language. Although a human can use natural language grammar to reason whether a sentence is correct, computers have problems with this because natural language grammar is not a closed theory. We show that the decision problem for the German language can not be solved entirely by phrase structure grammar, dependency parsing or grammar checkers and develop a parser for the German language, which solves the decision problem for that language using a new semantic approach.
Optimization of a Multi-Type PMSM Based on Pyramid Neural Network

In this paper, a novel bat algorithm based on the quantum computing concept and pyramid neural network (PNN) is presented and applied to the electromagnetic motor optimization problem. Due to the problems of high loss, high temperature rise and threatening motor safety, it is necessary to optimize the design of high-speed permanent magnet synchronous motor (HPMSM) structure. In order to use less training data and avoid the problem of large computational costs due to repeated finite element simulation in the electromagnetic structure design, this paper adopted a performance-driven method to establish the PMSM model. This model could effectively reduce the dimensions of the parameter space and establish an effective high-quality model within a wide range of parameters. For the purpose of obtaining a reliable proxy model with less training data, this paper adopted a pyramid-shaped neural network, which could reduce the risk of overtraining and improve the utilization of specific problem knowledge embedded in the training data set. The quantum bat algorithm (QBA) was used to optimize the structure of the PMSM. Compared with the classical GA and PSO algorithms, the QBA has the characteristics of a rapid convergence speed, simple structure, strong searching ability and stronger local jumping mechanism. The correctness and effectiveness of the proposed PNN-based QBA method were verified using simulation analysis and a prototype test.
Hardware Design and Implementation of a Lightweight Saber Algorithm Based on DRC Method

With the development of quantum computers, the security of classical cryptosystems is seriously threatened, and the Saber algorithm has become one of the potential candidates for post-quantum cryptosystems (PQCs). To address the problems of long delay and the high power consumption of Saber algorithm hardware implementation, a lightweight Saber algorithm hardware design scheme based on the joint optimization of data readout and clock (DRC) was proposed. Firstly, an analysis was carried out on the hardware architecture, timing overhead and power consumption distribution of the Saber algorithm, and the key circuits that limit the performance of the algorithm were identified; secondly, a dual-port SRAM parallel reading method was adopted to improve the data reading efficiency and reduce the timing overhead of double data reading in the multiplier module. Then, a clock gating technology was used to reduce the dynamic flipping probability of internal registers and reduce the hardware power consumption of the Saber algorithm; finally, data reading and clock gating were jointly optimized to design a high-speed and low-power Saber algorithm hardware IP core. Lightweight IP cores were integrated into RISC-V SoC systems via APB bus in a TSMC 65 nm process to complete the digital back-end design. The experimental results show an IP core area of 0.99 mm2 and power consumption of 8.49 mW, which is 33% lower than that reported in the related literature. Under 72 MHz &amp; 1 V operating conditions, the number of clock cycles for the Saber algorithm’s key generation, encryption and decryption are 3315, 9204 and 1420, respectively.
Image Recognition of Group Point Objects under Interference Conditions

The process of forming a vector-field model of flat images of group point objects, with various field-forming functions, is considered in this paper. Algorithms for recognizing group point objects in the presence of false and missing point objects are proposed. The quality of the recognition of group point objects by the proposed algorithms is also evaluated.
Blockchain based Transparent Donating System

Blockchain is a promising technology that is quickly gaining traction in the realm of security that is regulated by both governmental and commercial organizations. Donors are unable to know whether their donations are being used effectively due to a complete lack of transparency in donation-related transactions, which has prompted many to stop believing in charities. The immutability, traceability, and reliability properties of blockchain technology make it a viable solution for enhancing efficiency and transparency for charity. This research work is based on the Ethereum Blockchain, the decentralized donation tracking system that will permit transparent accountability, openness, and direct communication with the intended targets. The blockchain network would be made up of well-known, reliable, and esteemed companies.
Dual-Layer Index for Efficient Traceability Query of Food Supply Chain Based on Blockchain

Blockchain techniques have been introduced to achieve decentralized and transparent traceability systems, which are critical components of food supply chains. Academia and industry have tried to enhance the efficiency of blockchain-based food supply chain traceability queries. However, the cost of traceability queries remains high. In this paper, we propose a dual-layer index structure for optimizing traceability queries in blockchain, which consists of an external and an internal index. The dual-layer index structure accelerates the external block jump and internal transaction search while preserving the original characteristics of the blockchain. We establish an experimental environment by modeling the blockchain storage module for extensive simulation experiments. The results show that although the dual-layer index structure introduces a little extra storage and construction time, it significantly improves the efficiency of traceability queries. Specifically, the dual-layer index improves the traceability query rate by seven to eight times compared with that of the original blockchain.
A Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection

Social media has revolutionized the way individuals connect and share information globally. However, the rise of these platforms has led to the proliferation of cyber-hate, which is a significant concern that has garnered attention from researchers. To combat this issue, various solutions have been proposed, utilizing Machine learning and Deep learning techniques such as Naive Bayes, Logistic Regression, Convolutional Neural Networks, and Recurrent Neural Networks. These methods rely on a mathematical approach to distinguish one class from another. However, when dealing with sentiment-oriented data, a more “critical thinking” perspective is needed for accurate classification, as it provides a more realistic representation of how people interpret online messages. Based on a literature review conducted to explore efficient classification techniques, this study applied two machine learning classifiers, Multinomial Naive Bayes and Logistic Regression, to four online hate datasets. The results of the classifiers were optimized using bio-inspired optimization techniques such as Particle Swarm Optimization and Genetic Algorithms, in conjunction with Fuzzy Logic, to gain a deeper understanding of the text in the datasets.