Reviewerly/exHarmony huggingface dataset:
Cyclic and Constacyclic Codes for F&lt;sub&gt;2&lt;/sub&gt;[u,v]/&lt;u&lt;sup&gt;2&lt;/sup&gt;,v&lt;sup&gt;3&lt;/sup&gt; – v,uv,vu&gt;

In this paper, we study cyclic and β-constacyclic codes over the ?nite commutative ring R = F2[u,v]/&lt;u2,v3 ? v,uv,vu&gt; with ? = (1+u),(1+u+v+v2) and (1+v+v2). We establish a Gray map from R to F42 and prove that the Gray image of a cyclic code is a quasi-cyclic code of index 4. It is also shown that the Gray image of β-constacyclic code overRis either β-equivalent, β-equivalent or β-equivalent to a quasi-cyclic code of length 4n and index 4 over F2 when ? = (1 + u),(1 + u + v + v2) and (1 + v + v2), respectively.
Tight Analysis of Decryption Failure Probability of Kyber in Reality

Kyber is a candidate in the third round of the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) Standardization. However, because of the protocol’s independence assumption, the bound on the decapsulation failure probability resulting from the original analysis is not tight. In this work, we give a rigorous mathematical analysis of the actual failure probability calculation, and provides the Kyber security estimation in reality rather than only in a statistical sense. Our analysis does not make independency assumptions on errors, and is with respect to concrete public keys in reality. Through sample test and experiments, we also illustrate the difference between the actual failure probability and the result given in the proposal of Kyber. The experiments show that, for Kyber-512 and 768, the failure probability resulting from the original paper is relatively conservative, but for Kyber-1024, the failure probability of some public keys is worse than claimed. This failure probability calculation for concrete public keys can also guide the selection of public keys in the actual application scenarios. What’s more, we measure the gap between the upper bound of the failure probability and the actual failure probability, then give a tight estimate. Our work can also re-evaluate the traditional $$1-\delta $$ correctness in the literature, which will help re-evaluate some candidates’ security in NIST post-quantum cryptographic standardization.
Calibrating Learning Parity with Noise Authentication for Low-Resource Devices

Learning Parity with Noise (LPN) is an attractive post-quantum cryptosystem for low-resource devices due to its simplicity. Communicating parties only require the use of AND and XOR gates to generate or verify LPN cryptogram samples exchanged between the parties. However, the LPN setup is complicated by different parameter choices including key length, noise rate, sample size, and verification window which can determine the usability and security of the implementation. To address advances in LPN cryptanalysis, recommendations for ever increasing key lengths have made LPN no longer feasible for low-resource devices. In this paper, we use a series of experiments to simulate and cryptanalyze LPN authentication under different parameter values to arrive at recommended values suitable for low-resource devices. We also examine the impact of limiting the key lifespan of the LPN secret vector as a means to balance security while keeping key lengths relatively short.
Multitask Scheduling Method for Heterogeneous Computing Resources of Power Cryptographic Service

With the rapid development of the power cryptographic service, a single cryptographic computing resource, whether virtual cipher machine (VCM) or physical cipher machine (PCM), is hard to meet the security and efficiency requirements at the same time. Therefore, based on the PCM+VCM hybrid cryptographic computing platform, this brief proposes a novel multitask scheduling algorithm to satisfy the various security requirements and improve the computational efficiency of the power cryptographic service system (PCSS). Firstly, a scheduling architecture and a model for cryptographic service tasks considering security and efficiency are designed. Then, we propose a clustering mapping scheduling (CMS) algorithm based on weighted deep embedding clustering (WDEC) to implement scene partitioning and matching for cryptographic tasks. Moreover, we conduct case studies in a simulation platform that we build with S-9 PCMs and S-1 VCMs. Simulation results reveal that the trade-off between security and efficiency of task sequence is affected by the division of task execution scenarios.
Secure data sharing solution for mobile cloud storage

With the popularity of cloud storage technology, mobile devices can store or retrieve personal information more and more conveniently. However, the data in cloud storage faces more and more security risks, which limits the development speed of cloud storage technology. In order to promote the development of cloud storage technology, it is very important to solve the security problem. At present, there are many solutions for cloud storage security, but most of them do not work well in the mobile cloud storage environment. Therefore, this paper studies a secure sharing scheme of mobile cloud storage data based on attribute encryption. This scheme uses CP-ABE as the access control technology, which has been proved by experiments to ensure the security of user data in mobile cloud storage.
Named entity recognition of military requirements document based on BERT and global pointer

The Correct identification of entities in military requirements documents is an important basis for constructing military requirements graph and realizing information-based operations. Aiming at the problem that the existing named entity recognition methods cannot effectively solve the problem of inaccurate identification of complex named entity boundaries in requirements documents, a small data sample of military requirements documents is studied, and a named entity recognition model based on BERT and global pointers is proposed (BERT-GloP-Rule). The model first uses BERT to encode to obtain deep text semantic information at the word segmentation level and sentence level, and then uses the global pointer to decode, treats the beginning and end of the entity to be identified as a unified whole to discriminate, and finally combines rule matching to complete entity-level semantic information. Requirements document named entity recognition. The experimental results show that the precision of the model on the test set reaches 90%, the recall rate reaches 89%, and the F1 value reaches 91%, which verifies the recognition effect of the BERT-GloP-Rule model on complex entities in the field of military requirements. Significantly better than traditional machine learning models.
Intelligent import of standard address based on geocoding and TF-IDF

In this paper, aiming at the uniqueness check of standard address import in mobile resource management system, a standard address intelligent import method based on Geocoding and TF-IDF is proposed. Address names are standardized through Geocoding to uniform the naming rules of addresses at all levels and remove the interference of non-mandatory level names on results. The address names recorded in the system are used as the address database, and the similarity of address names is calculated through TF-IDF. The address names with high similarity are filtered out, and then the address names are judged manually. Experiments show that this method can effectively improve the accuracy of uniqueness verification and greatly reduce the manual workload.
Analysis and Application of Medical Images in the Field of Artificial Intelligence

In the development of digital technology, artificial intelligence has been widely used in various fields of innovation and has been the close attention of the whole society. Therefore, on the basis of understanding the current medical image analysis and the development of technology, this paper clarifies the importance of artificial intelligence algorithm and takes the medical image of mental illness as an example to analyze how to correctly apply artificial intelligence algorithm.
Theorizing Technologies for Learning as Mutable Mobiles

Everyday technologies like mobile phones and video games are routinely taken up by youth, educators, and researchers as technologies for learning.The unexpected ways in which these and other technologies are used by a variety of people, across diverse contexts, and for varied purposes indicates a need to better theorize the mutability of these technologies.In this theoretical paper, we interrogate how two technologies for learning -the game platform Minecraft and the annotation tool Hypothesis -may be understood as mutable mobiles (Law & Mol, 2001).By addressing why it is important to conceptualize some technologies for learning as mutable mobiles, we work to advance novel perspectives on the emergent relations among learning, technology, and mobility across settings. Total words: 1998"If you are reading [this text] then it has moved to another location… But then again, maybe, at the same time, it has become fluid.Some words have changed.It has been edited.While the circumstances in which it is read -in which you are reading it -also mean that it has been, however subtly, reconfigured in that reading.
Design of a cryptographic simulation system for classification privacy based on blockchain technology

At present, privacy issues have become one of the great obstacles to the further development of web technology. If it cannot ensure that privacy is protected, then all kinds of user data and related privacy can be stolen, which will seriously hinder the trust of individuals, enterprises and others in technology, and thus the progress of technology. Therefore, privacy protection technologies are of particular importance. Through the analysis of Internet security incidents, this paper explains the research background and significance of privacy protection, and analyses the development status of privacy protection and summarises the challenges faced by privacy protection; then by analysing blockchain technology, it makes a research analysis on the feasibility of its application to privacy protection, gives the research ideas and specific research contents of this thesis, and finally describes the organisational structure of this thesis. The study provides new ideas for subsequent research in this field.
Earth Science Week and Minerals Day: Two Exciting Opportunities for Public Outreach

"Earth Science Week and Minerals Day: Two Exciting Opportunities for Public Outreach." Rocks & Minerals, 97(5), pp. 410–411
Impact of Solar Thermal Energy on the Energy Matrix under Equatorial Andean Context

Low-temperature solar thermal energy is a viable and mature alternative to reduce the use of fossil fuels for domestic hot water heating. The impact of the inclusion of this technology in the energy matrix in the Andean city of Cuenca, Ecuador, is analyzed. In Ecuador, liquefied petroleum gas (LPG) is currently used to heat water due to the high state subsidy, with a cost of 0, 11 USD for an LPG kg compared to the international price of 1.18 USD a Kg of LPG in 2021. Sustainability indicators show that the urban energy matrix would be minimally affected by a high penetration of solar thermal systems. The indicators that are positively affected are related to energy self-sufficiency, emissions, and employment at the expense of an increase in the cost of energy. Moreover, the analysis of the impact on consumption exclusively in the residential sector shows that liquefied petroleum gas consumption would drop from 73 to 64%, mainly because the liquefied gas is used in cooking. However, the change in technology is limited by the subsidized cost of liquefied petroleum gas, which makes the adoption of this technology difficult in the current state
Voting-Based Extreme Learning Machine Approach for the Analysis of Sensor Data in Healthcare Analytics

There has been a huge surge in the production of sensor-based clinical devices for health monitoring systems over the last few years. This sudden spike has been due to many different factors such as development in sensor device technology and also the efforts to promote research that address the necessity for providing new ways for healthcare given the increasing challenges with an associated degree of an aging population. The processing and analysis of data is an important element of the research of such a system. The data generated from these healthcare devices are enormous and have the potential to ascertain well-being and to encourage effective management of health. In this work, a mechanism for the analysis of physiological sensor data from the healthcare devices, namely voting-based extreme learning machine, has been explored. The approach was also compared with the traditional extreme learning machine-based approach. Experimental results were very encouraging with respect to the performance accuracy as well as time taken by the voting-based extreme learning machine as compared to the traditional extreme learning machine to produce the output.
Peningkatan Keterampilan Menulis Teks Anekdot Melalui Genius Learning Siswa Kelas X Jurusan Bisnis dan Pemasaran SMK Negeri 1 Manado

This study aims to improve the skills of writing anecdotal texts for class X students of the Department of Business and Marketing at SMK Negeri 1 Manado in the 2020/2021 academic year by applying the genius learning strategy. Through genius learning learning strategies. The research model used is Classroom Action Research (CAR). The procedure for implementing the action and implementing the action at the research site is divided into two cycles. The results of the study show the following. First, the use of the genius learning strategy can improve the quality of the anecdote writing learning process. There is an increase in the process in aspects of the learning situation, student focus, and activity, making positive changes so that learning to write anecdotes is more interesting and fun. Second, learning to write anecdotal texts with a genius learning strategy can improve the results of anecdotal writing skills.
Technology Enabled Learning: Use of Smartphones in Higher Education

Because of their ever-expanding feature sets, smart phones are quickly becoming the computing device of choice for a growing number of users, particularly those enrolled in higher education. As a result of this, the utilisation of these gadgets as a learning aid must be considered as an unavoidable option open to higher education. The work discusses and review the use of smart phones in higher education, and it also lays the groundwork for future research that will investigate the digital gap between teachers and students regarding knowledge and use of smart phones in an existing university setting.
Morphological text analysis using neural networks

The article is devoted to applying neural networks technology to solve morphological text analysis. A marked-up text dataset was used for training, where each word was assigned its part of speech. The neural network approach was compared with dictionary methods.
The impact of a set of environmental observations in the problem of acquiring movement skills in three-dimensional space using reinforcement learning algorithms

In this paper, we study the influence of the composition of a set of environmental observations on the solution of the problem of acquiring movement skills by an agent in three-dimensional space. We found that some redundant data can slow down the learning process and interfere with problem solving. Experiments on the effects of rules are conducted in the Unity game engine's environment using the ML-Agents package. The algorithm under study, Soft Actor-Critic, was chosen because it is one of the best ways to learn how to move in three-dimensional space.
Video denoising with realistic noise generation

Along with noise reduction at images, video noise cancelling is an actual problem. But unlike images, it is difficult to get aligned frames to build clean samples for subsequent training of neural networks. In this paper, we propose using noise maps predictable by the transformer model as the FastDVDNet model input instead of Gaussian noise which is commonly used in modern noise suppression approaches.
Bayesian Tools for Reliable Multimedia Forensics

The goal of multimedia forensics is to determine origin and authenticity of images and video. The currently most successful approaches use machine learning, and demonstrate excellent performance in lab settings. However, these methods are still challenged to generalize to images from the internet with potentially complex and partially unknown processing. The current best counter-measure is extensive training data augmentation, but this is extremely costly considering the many possible processing variants that must be covered. In this work, we review and consolidate our recent efforts on a different approach to cope with the challenge of images from unknown provenance. We propose to concentrate the training to the forensic task at hand, and to additionally include a measure for uncertainty to detect when a classifier is not confident on a given input. We believe that uncertainty-aware tools can complement existing efforts when data augmentation fails, and additionally provide valuable feedback to forensic analysts.
An Evaluation of Security in Blockchain-based Sharing of Student Records in Higher Education

Blockchain has recently taken off as a disruptive technology, from its initial use in cryptocurrencies to wider applications in areas such as property registration and insurance due to its characteristic as a distributed ledger which can remove the need for a trusted third party to facilitate transactions. This spread of the technology to new application areas has been driven by the development of smart contracts – blockchain-based protocols which can automatically enforce a contract by executing code based on the logic expressed in the contract. One exciting area for blockchain is higher education. Students in higher education are ever more mobile, and in an ever more agile world, the friction and delays caused by multiple levels of administration in higher education can cause many anxieties and hardships for students as well as potential employers who need to examine and evaluate student credentials. Distance learning as a primary platform for higher education promises to open up higher education to a wider range of learners than ever before. Blockchain-based storage of academic credentials is being widely studied due to the advantages it can bring. As with any network-based system, blockchain comes with a number of security and privacy concerns. Blockchain needs to meet several security-related requirements to be widely accepted: decentralization; confidentiality; integrity; transparency; and immutability. Researchers have been busy devising schemes to ensure that such requirements can be met in blockchain-based systems. Several types of blockchain-specific attacks have been identified: 51% attacks; malicious contracts; spam attacks; mining pools; targeted DDoS attacks; and others. Real-world attacks on blockchain-based systems have been seen on cryptocurrency sites. In this paper, we will evaluate the specific privacy and security concerns for blockchain-based systems used for academic credentials as well as suggested solutions. We also examine the issues for academic credentials which are stored “off-chain” in such systems (as is often the case). In this case, a Distributed File System (DFS) implemented with a peer-to-peer (P2P) architecture is often the choice for the storage of the academic credentials since it matches the decentralized nature of blockchain. Blockchain then contributes much to the usefulness of such a DFS, making it in turn a good match for a P2P DFS such as IPFS.
Performance comparison of online and offline tracking algorithms

In this study, the performance of online and offline tracking algorithms, which are frequently used in the literature, were compared on the defined datasets. Therefore, six different datasets are prepared. The datasets consist of consecutive frames. In each dataset, target has different motion characteristics and the background types are different from each other. In addition, a total of six well known algorithms were used for comparison. These are KCF, MOSSE, CSRT, TLD, Go Turn and Siamese tracking algorithms. In conclusion, especially in cases where the target is small and the SNR value is low, the highest performance is obtained with the KCF algorithm. On the other hand, when the target is big and the SNR value is high, it is observed that the Siamese algorithm better handle changes in the target shape. In this context, considering the real scenarios, it may be possible to use the algorithms in a hybrid way to get the better performance.
UAV Path Planning using Genetic Algorithm with Parallel Implementation

The objective of path planning algorithms is to find the optimal path from a source position to a target position. This paper proposes a real-time path planner for UAVs based on the genetic algorithm. The proposed approach does not identify any specific points outside or between obstacles to solve the problems of the invisible path. In addition, this approach uses no additional steps in the genetic algorithm to handle the problems resulting from generating points inside the obstacles, or the intersection between path segments with obstacles. For these reasons, this paper introduces a simple evaluation method that takes into account the intersections between the path segments and obstacles to find a collision-free and near to optimal path. This evaluation method take into account overlapped and intersected obstacles. The sequential implementation for all of the genetic algorithm steps is detailed. This paper explores the Parallel Genetic Algorithms (PGA) models and introduces the parallel implementation of the proposed path planner on multi-core processors using OpenMP. The execution time of the proposed parallel implementation is reduced compared to sequential execution.
The experience of special education teachers in general elementary schools responding to COVID-19

연구목적: 본 연구는 코로나19에 대응하는 일반초등학교 특수교사의 경험을 연구하는 데 연구의 목적이 있다. 연구방법: 일반초등학교 특수학급에서 근무하고 있는 특수교사 총 8명을 대상으로 포커스 그룹을 구성하여 사전에 준비한 반구조화 된 질문지를 토대로 인터뷰를 실시하고 자료를 수집하였다. 자료 분석을 위해 초기코딩, 심층코딩, 주제 생성의 과정을 통해 관련 코드를 범주화하였다. 연구결과: 코로나19에 대응하는 일반초등학교 특수교사의 경험을 주제로 5개의 핵심범주와 17개의 하위범주가 도출되었다: “코로나19 초기 대응 혼선”, “특수학급에서 원격수업의 어려움”, “학부모의 수업영향도 증가”, ‘통합교육의 방향성 상실”, “포스트 코로나를 대비하는 특수교육 지원요구” 결론: 다가오는 포스트 코로나19 상황에서 내실 있는 교육과정을 운영하기 위해서는 특수교육과 특수교육 대상자의 개별적인 특성을 반영한 실제적인 지원책이 마련 되어야 한다. Purpose: The purpose of this study is to explore the experiences of special elementary school teachers responding to COVID-19. Method: A total of 8 special education teachers working in special classes in general elementary schools were interviewed and data were collected based on a semi-structured questionnaire prepared in advance for focus groups. For data analysis, related codes were categorized through the process of initial coding, in-depth coding, and topic generation. Result: As a result, 5 core categories and 17 subcategories were derived as the subject of the experiences of special education teachers in general elementary schools in COVID-19 were derived: “The confusion in the initial response to COVID-19;” “the difficulty of distance education in special classes;” “the increasing influence by parents on classes;” “the loss of direction of integrated education;” and “the need for supporting special education in preparation for post-coronavirus.” Conclusion: In order to operate a substantial curriculum in the upcoming post-COVID-19 situation, practical support measures should be prepared that reflect special education and the individual characteristics of special education subjects.