Reviewerly/exHarmony huggingface dataset:
DFL: High-Performance Blockchain-Based Federated Learning

Many researchers have proposed replacing the aggregation server in federated learning with a blockchain system to improve privacy, robustness, and scalability. In this approach, clients would upload their updated models to the blockchain ledger and use a smart contract to perform model averaging. However, the significant delay and limited computational capabilities of blockchain systems make it inefficient to support machine learning applications on the blockchain. In this article, we propose a new public blockchain architecture called DFL, which is specially optimized for distributed federated machine learning. Our architecture inherits the merits of traditional blockchain systems while achieving low latency and low resource consumption by waiving global consensus. To evaluate the performance and robustness of our architecture, we implemented a prototype and tested it on a physical four-node network, and also developed a simulator to simulate larger networks and more complex situations. Our experiments show that the DFL architecture can reach over 90% accuracy for non-I.I.D. datasets, even in the presence of model poisoning attacks, while ensuring that the blockchain part consumes less than 5% of hardware resources.
Virtual Sound Image Reconstruction Method for Multi-objective Optimization of Folk Music Based on Evolutionary Algorithm

At present, the exchanges in various fields such as culture, economy and politics are becoming more and more close in the world. From the perspective of the relationship between national music and cultural development, the development of national music has also received more and more attention, and it has become an inevitable trend in the development of today's era. The purpose of this paper was to perform virtual reconstruction of the sound image of folk music through multiple objective optimization, and to model the virtual sound image of folk music as a multi-objective optimization problem. According to the research on sound image positioning, the relevant noise factors were removed, so as to achieve the playback of ethnic music that enhanced the surround effect and visual enjoyment. The evolutionary algorithm in this paper was mainly based on the multi-objective optimized sound image localization technology. For the music virtual sound image, an improved FCM algorithm and an evolutionary multi-objective optimization algorithm combining local and non-local information of the sound image were proposed respectively. Through the analysis of the traditional sound image algorithm method, the accuracy of the sound image localization on the horizontal plane could be effectively improved. After the conversion, the audience could feel the better stereo image and surround feeling of the folk music. Through experimental analysis, it can be seen that the system can not only perform virtual conversion of audio-visual signals of different frequencies, but also provide data for different audio playback systems. The feature point registration error is low, and the reconstruction effect is good, especially the random delay processing in the range of 0∼20m, and the performance is better than the traditional method. Finally, virtual left and right surround sound image signals were obtained, which effectively improved the three-dimensional surround feeling of folk music.
How to Improve English Teaching in High School under the Background of NECS

There are some problems in high school English teaching, notably serious imbalance in teaching content, mechanical teaching methods and fixed teaching evaluation. In the final analysis, these problems are caused by teachers' failure to deeply understand and give a farewell to the requirements of ordinary high school English curriculum standards. Teachers should make efforts to solve these problems. Firstly, the teaching content should cover the six elements required by NECS to promote the all-round development of students; Secondly, teachers should change the mechanical teaching method to realize efficient English learning; Lastly, it’s necessary to carefully design teaching evaluation and make comprehensive investigation of students' quality.
Translation norms and bilingual dictionaries

Abstract This article applies translational norm theory to bilingual lexicography, arguing that the bilingual lexicographer serves as a “norm authority,” and the bilingual dictionary functions as a “norm statement” that prescribes the scope of what is considered legitimate interlingual equivalence within a given society. To demonstrate how the content of a bilingual dictionary can be used to promote specific translation norms, the headwords, equivalents, directives, and examples found in the North Korean bilingual dictionary, Jo-Yeong Sajeon ( JYS ) [Korean–English Dictionary] (1987/1991) , were analyzed as a case study. The Korean–English lexical pairings presented in the JYS are matched with exemplary Korean–English translations listed in two North Korean translation textbooks, Jo-Yeong Beonyeokbeop [Korean–English Translation Method] ( Min 2012 ) and Yeong-Jo Beonyeokbeop [English–Korean Translation Method] ( Min 2014 ), affirming that the translation norms featured in the JYS hold normative force over the decisions made by professional translators.
LabVIEW-Based Temperature Control Using Fuzzy Logic Controller

This paperwork aims design, implementation of fuzzy logic controller for controlling of water in the tank using LabVIEW software. This system mainly contains the single tank, resistance temperature detector sensor, and myrio. The signal from the temperature sensor is transmitted to LabVIEW software via myrio interface, connected to the system. Fuzzy logic controller (FLC) algorithm is designed using knowledge-based inference engine, which can be used to replace the conventional controllers tuning parameters using analytical equations. The performing of fuzzy logic controller for controlling the temperature has been explored in detail. It provides an improved response and quickly tracks the setpoint. FLC functions well with the system involved uncertainties, noise at sensory signal. This developed FLC could also be used to measure several variables like temperature at industrial applications.
Enhancing Recommender Systems Using Sentiment and Emotion Analysis of Reviews

With the tremendous growth in the Internet technologies and e-commerce platforms, many people are showing interest in procuring products and services online. In this scenario, recommender systems are a great way to promote new business and get more customers for the relevant products and services and thereby increase the business. However, it is crucial to be able to predict what a customer would like and need based on the products he has purchased or he is interested in. In order to achieve this, the machine learning model would have to accomplish various tasks such as word segmentation, stop-words, extraction of features, and finding similar products other users have purchased, etc. In this project, we take the example of movie recommendation system, and we tried to categorize the movie reviews as positive or negative using sentiment analysis and have built a recommender system using an improved item based collaborative filtering based on the sentiment of users which can suggest movies that a user may like based on the list of movies he has already watched.
Statistics in the public interest: In memory of Stephen E. FienbergAlicia L.Carriquiry, Judith M.Tanur, William F.Eddy, Margaret L.Smykla (Eds.), New York City: Springer. 2022.

BiometricsVolume 79, Issue 2 p. 1586-1587 BOOK REVIEWS Statistics in the public interest: In memory of Stephen E. Fienberg Alicia L. Carriquiry, Judith M. Tanur, William F. Eddy, Margaret L. Smykla (Eds.), New York City: Springer. 2022. David Banks, Corresponding Author David Banks [email protected] orcid.org/0000-0002-2376-0710 Department of Statistical Science, Duke University, Durham, North Carolina, USA Correspondence David Banks, Department of Statistical Science, Duke University, Durham, North Carolina, USA. Email: [email protected]Search for more papers by this author David Banks, Corresponding Author David Banks [email protected] orcid.org/0000-0002-2376-0710 Department of Statistical Science, Duke University, Durham, North Carolina, USA Correspondence David Banks, Department of Statistical Science, Duke University, Durham, North Carolina, USA. Email: [email protected]Search for more papers by this author First published: 20 June 2023 https://doi.org/10.1111/biom.13869Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume79, Issue2June 2023Pages 1586-1587 RelatedInformation
Japan Association of Mineralogical Sciences

Asteroids are thought to be remnants of planet formation that can provide clues to the origin and evolution of the Solar System.The Hayabusa2 spacecraft explored the carbonaceous asteroid 162173 Ryugu and brought back surface samples to Earth in late 2020.A mission goal was to investigate the formation and evolution of carbonaceous asteroids and their relationships with carbonaceous chondrite meteorites.The initial analysis of the Ryugu samples was performed with numerous cutting-edge techniques over a one-year period starting in June 2021.Many researchers affiliated with the Japan Association of Mineral Sciences and societies participating with Elements magazine have joined in the petrological and mineralogical characterization of the Ryugu samples.The main results of the analysis have recently been published in Science (Nakamura et al. 2022) and Nature Astronomy (Noguchi et al. 2022).Here, we briefly introduce these two papers discussing the formation and evolution of Ryugu and its surface evolution.
A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters’ Diacritics and Image Layers

Steganography is a widely used technique for concealing confidential data within images, videos, and audio. However, using text for steganography has not been sufficiently explored. Text-based steganography has the advantage of a low bandwidth overhead, making it a promising alternative for protecting sensitive information. Among languages, Arabic is known for its linguistic richness, making it ideal for text-based steganography. This paper proposes a robust, dynamic, and multi-layered steganography approach that uses text, encryption algorithms, and images. This approach utilizes Arabic diacritic features to hide limited-size and highly classified information. The algorithm uses several scenarios and is extensively tested to ensure the required level of security and user performance. The experimental results on actual data demonstrate the robustness of the proposed algorithm, with no noticeable impact on the carrier message (original text). Furthermore, no known potential attack can break the proposed algorithm, making it a promising solution for text-based steganography.
NSNet: An N-Shaped Convolutional Neural Network with Multi-Scale Information for Image Denoising

Deep learning models with convolutional operators have received widespread attention for their good image denoising performance. However, since the convolutional operation prefers to extract local features, the extracted features may lose some global information, such as texture, structure, and color characteristics, when the object in the image is large. To address this issue, this paper proposes an N-shaped convolutional neural network with the ability to extract multi-scale features to capture more useful information and alleviate the problem of global information loss. The proposed network has two main parts: a multi-scale input layer and a multi-scale feature extraction layer. The former uses a two-dimensional Haar wavelet to create an image pyramid, which contains the corrupted image’s high- and low-frequency components at different scales. The latter uses a U-shaped convolutional network to extract features at different scales from this image pyramid. The method sets the mean-squared error as the loss function and uses the residual learning strategy to learn the image noise directly. Compared with some existing image denoising methods, the proposed method shows good performance in gray and color image denoising, especially in textures and contours.
Image Retrieval Through Free-Form Query using Intelligent Text Processing

Image Retrieval is the process of retrieving images from the image/multimedia databases. Retrieval of images are carried out with various types of queries, free-form query is a text-query that consists of single or multiple keywords and/or concepts or descriptions of images with or without the inclusion of wild-card characters and/or punctuations. This work aims to handle image retrieval based on free-form text queries. Simple &amp; complex queries of conceptual descriptions of images are explored and an intelligent processing system with free-form queries based on the Bag-of-Words model is modified and built for natural scene images and on Diverse Social Images using the Damerau-Levenshtein edit distance measure. The efficacy of the proposed system is evaluated by testing 1500 free-form text queries and has resulted in a recall accuracy of 91.3% on natural scene images (of Wang/Corel database) and 100% on Diverse Social Images (of DIV400 dataset). These results show that the system proposed has produced satisfactory performance compared to published results such as the harmonic mean of precision and recall (i.e. F1-Score) of 76.70% &amp; 63.32% at retrieval of 20 images etc in reported works.
Information support of scientific research and development in medicine: The approaches and solutions

The authors discuss intellectualization of information analytics; in particular, they analyze the experience of medicine information support through the established system of databases and services of the Coсkrane Collaboration (Cockrane academic network). The authors reconstruct and explore the Cockrane method (model) of information analysis within the subject field of bibliography and informatics based on one of the authors’ personal experience of building systematic reviews of clinical trial results. The goal of the study is to reveal the potential and quality of the information analysis tools within the field of medicine, as well as to explore the feasibility and possibility of their adaptation and limitations in other fields. The study is based on the subject approach in bibliography and information analytics, informological approach, and theoretical tools of applied informatics. The structural functional and iterative methods were applied to study Cockrane through the prism of information analytics, professional user orientation, and knowledge management. The factors of user quality and performance of meta-analytical products for the purposes of evidence medicine are revealed; among them, source selection methods, data processing, procedural and organizational models, critical reviewing, and user differentiation in presenting information search findings. The analysis of meta-analytical information product building in evidence medicine enables to identify the range of methodological problems and promising vectors in the information studies.
Review of studies on scientific literacy promotion in foreign libraries

Science plays an important role in ensuring the society’s well-being and performs a number of functions, of which the most significant are to build new knowledge, improve education, and increase the quality of life. The new stage of science-society communications is characterized by the expansion of civil participation in planning, conducting and financing of scientific research. One of the main tasks of science communication is to increase scientific literacy (SL) of nonscientists. The documents analysis demonstrates that the developed countries pay special attention to scientific thinking and SL promotion, and all social institutions, including libraries, are engaged in appropriate activities. In recent decades, the study of the SL formation features has become a popular research topic worldwide. However, Russian library studies pay insufficient attention to the foreign experience in the field. The goal of this paper is to review and analyze English-language publications on “scientific literacy in libraries”. The author characterizes in detail such vectors of activities as development of popular science collections, participation of libraries in STEM education, and other practices of foreign libraries in teaching SL.
Bibliographic practice as an object of bibliographic studies

Review of the joint monograph “Essays on bibliographic activity of the Russian State Library: A monograph” / Russian State Library ; ed. by G. L. Levin. Moscow : Pashkov dom, 2022. 734 p. The author reviews the joint monograph by the leading specialists of the Russian State Library (RSL). The article is publishedon the occasion of the 100th anniversary of the reference and information office as the division of the library of State Rumyantsev Museum in December 1918. The office rendered all types of bibliographic user services (e. g. address, subject, specifying bibliographic references). The reviewed monograph covers all the main fields of RSL’s bibliographic activity: development of reference library collection, reference and information services, building of all the main types of bibliographic directories, bibliographical information service in culture and arts, making of bibliographical indices of Russian emigration literature, research in theory of bibliography, etc. The choice of RSL bibliographic activity as the main object of study makes an appropriate contribution to the development of Russian theory of bibliography. The reviewer regards intensification of research activities of larger libraries (e. g. national) as an attribute of the modern theory of bibliography. The joint monograph is one of few large-scale research works covering the full range of the national library's bibliographic activities of the last century; therefore, it is of high value.
Media analytics in library research

The paper goal is to define the role of media analytics in library research. The author characterizes the main types and methods of applied library research (sociological, library statistics, bibliometrics). As the media tools used in applied library studies are limited, the author discusses the potentialities of media analytics as a new and promising research vector. Monitoring of library media presence enables to assess the library’s communication PR policy, its ability to attract audience attention, and to demonstrate its potential and social role. The functionality of Medialogia computerized mass and social media monitoring system is discussed; the data formats, tools and library opportunities are examined. The media analytics methods, i. e. monitoring, statistical and infographical methods, as well as content analysis, are described. The author analyzes the results of search of RNPLS&amp;T media hits in the period from May 30, 2021, to May 30, 2022. She concludes that the Library has been intensifying its media activity, and its PRperformance has been increasing. The author also offers the recommendations on upgrading this vector of activities and on how to enhance the role of media analytics in the applied library research.
Through the Window: Exploitation and Countermeasures of the ESP32 Register Window Overflow

With the increasing popularity of IoT (Internet-of-Things) devices, their security becomes an increasingly important issue. Buffer overflow vulnerabilities have been known for decades, but are still relevant, especially for embedded devices where certain security measures cannot be implemented due to hardware restrictions or simply due to their impact on performance. Therefore, many buffer overflow detection mechanisms check for overflows only before critical data are used. All data that an attacker could use for his own purposes can be considered critical. It is, therefore, essential that all critical data are checked between writing a buffer and its usage. This paper presents a vulnerability of the ESP32 microcontroller, used in millions of IoT devices, that is based on a pointer that is not protected by classic buffer overflow detection mechanisms such as Stack Canaries or Shadow Stacks. This paper discusses the implications of vulnerability and presents mitigation techniques, including a patch, that fixes the vulnerability. The overhead of the patch is evaluated using simulation as well as an ESP32-WROVER-E development board. We showed that, in the simulation with 32 general-purpose registers, the overhead for the CoreMark benchmark ranges between 0.1% and 0.4%. On the ESP32, which uses an Xtensa LX6 core with 64 general-purpose registers, the overhead went down to below 0.01%. A worst-case scenario, modeled by a synthetic benchmark, showed overheads up to 9.68%.
<scp>Learning‐based</scp> encoded target detection on iteratively orthorectified images for accurate fisheye calibration

Abstract Fisheye camera calibration is an essential task in photogrammetry. However, previous calibration patterns and the robustness of the adjoint processing methods are limited due to the fisheye distortion and various lighting. This problem leads to additional manual intervention in the data collection. Moreover, it is arduous to accurately detect the board target under fisheye's distortion. To increase the robustness in this task, we present a novel encoded board “Meta‐Board” and a learning‐based target detection method. Additionally, an automatic image orthorectification is integrated to alleviate the distortion effect on the target iteratively until convergence. A low‐cost control field with the proposed boards is built for the experiment. Results on both virtual and real camera lenses and multi‐camera rigs show that our method can be robustly used in calibrating the fisheye camera and reaches state‐of‐the‐art accuracy.
Understanding algorithm bias in artificial intelligence-enabled ERP software customization

Purpose Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered. Design/methodology/approach As guided by the previous research (Akter et al. , 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm. Findings This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice. Originality/value To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).