Reviewerly/exHarmony huggingface dataset:
Matrix product and quasi-twisted codes in one class

Abstract Many classical constructions, such as Plotkin’s and Turyn’s, were generalized by matrix product (MP) codes. Quasi-twisted (QT) codes, on the other hand, form an algebraically rich structure class that contains many codes with best-known parameters. We significantly extend the definition of MP codes to establish a broader class of generalized matrix product (GMP) codes that contains QT codes as well. We propose a generator matrix formula for any linear GMP code and provide a condition for determining the code size. We prove that any QT code has a GMP structure. Then we show how to build a generator polynomial matrix for a QT code from its GMP structure, and vice versa. Even though the class of QT codes contains many codes with best-known parameters, we present different examples of GMP codes with best-known parameters that are neither MP nor QT. Two different lower bounds on the minimum distance of GMP codes are presented; they generalize their counterparts in the MP codes literature. The second proposed lower bound replaces the non-singular by columns matrix with a less restrictive condition. Some examples are provided for comparing the two proposed bounds, as well as showing that these bounds are tight.
Uno <i>straniero</i> in <i>Terra matrigna</i> La traduzione italiana del romanzo <i>Yaban</i> di Yakup Kadri Karaosmanoğlu

Between 1941 and 1942, the novel Yaban by Yakup Kadri (Karaosmanoğlu), published in Turkey in 1932, appeared in the ‘Medusa’ series of Mondadori editions. It was the first time that a twentieth‑century Turkish novel was translated into Italian and it was not done in a specialist field, i.e. by scholars, orientalists or even more precisely Turkish scholars, but within a series of a prestigious and innovative publishing house that had been set up to promote great international literature. The novel, which in Italian is entitled Terra matrigna , is however not a translation from the original but from German. Only a couple of years earlier, in fact, the same novel had been translated from Turkish into German, raising a certain clamour, especially in Turkey. This essay proposes a reconstruction of the editorial process that accompanied the Italian publication of Karaosmanoğlu’s novel, in an attempt to situate this first diffusion of a modern Turkish literary work in the Italian and European context, in order to understand the cultural relations and the framework of the understanding and reception of early twentieth‑century Turkish literature in Europe.
Using fuzzy cognitive map in bow tie method for dynamic risk assessment of spherical storage tanks: A case study

The present study aimed to perform a dynamic risk assessment of liquefied petroleum gas (LPG) in spherical storage tanks using the Hazard and Operability (HAZOP) method. The "LPG release from spherical storage tank" scenario was determined to be a top event based on the risk level. The causes and consequences of this scenario were then specified using the Bow-Tie method. Additionally, fuzzy cognitive mapping (FCM) was utilized to identify the most critical threats, consequences, and effective barriers in a dynamic approach. Results showed that fracture/rupture in pipelines/a storage tank due to fire had the highest output degree and was identified as the most influential threat in the occurrence of the LPG release. Fatality/injury due to fire and explosion had the highest input degree and was more influenced by the release of LPG as the main consequence in the selected scenario. The findings of this study enabled us to make a logical decision about effective barriers for avoiding and minimizing the release of LPG and its threats and consequences based on the fuzzy cognitive map. Moreover, the results of this study showed that the FCM method could determine the most critical nodes with a higher degree and represent their relationships.
CNN-optimized text recognition with binary embeddings for Arabic expiry date recognition

Abstract Recognizing Arabic dot-matrix digits is a challenging problem due to the unique characteristics of dot-matrix fonts, such as irregular dot spacing and varying dot sizes. This paper presents an approach for recognizing Arabic digits printed in dot matrix format. The proposed model is based on convolutional neural networks (CNN) that take the dot matrix as input and generate embeddings that are rounded to generate binary representations of the digits. The binary embeddings are then used to perform Optical Character Recognition (OCR) on the date images. To overcome the challenge of the limited availability of dotted Arabic expiration date images, we developed a True Type Font (TTF) for generating synthetic images of Arabic dot-matrix characters. The model was trained on a synthetic dataset of 3287 images and 658 synthetic images for testing, representing realistic expiration dates from 2019 to 2027 in the format of yyyy/mm/dd and yy/mm/dd . Our model achieved an accuracy of 98 . 94% on the expiry date recognition with Arabic dot matrix format using fewer parameters and less computational resources than traditional CNN-based models. By investigating and presenting our findings comprehensively, we aim to contribute substantially to the field of OCR and pave the way for advancements in Arabic dot-matrix character recognition. Our proposed approach is not limited to Arabic dot matrix digit recognition but can be also extended to text recognition tasks, such as text classification and sentiment analysis.
Quality Control Analysis Using the Failure Mode and Effect Analysis Method

This study aimed to investigate critical factors contributing to defective Epodion® rh-Erythropoietin Alfa syringes, specifically focusing on reducing cracks during manufacturing. A Failure Mode and Effect Analysis (FMEA) approach was employed to identify the primary causes of failure, revealing cracking as the predominant issue affecting 43% of defective units. To mitigate this concern, we calculated a Risk Priority Number (RPN) of 162, highlighting the significance of addressing material-related issues. By recommending enhanced quality control measures for raw materials prior to their utilization within the production process, our findings suggest potential improvements in material strength, thereby minimizing product defects&#x0D; Highlight :&#x0D; &#x0D; The study aimed to investigate factors contributing to defective Epodion® rh-Erythropoietin Alfa syringes, with a focus on reducing cracks during manufacturing.&#x0D; A Failure Mode and Effect Analysis (FMEA) approach was used to identify the primary causes of failure, revealing cracking as the predominant issue affecting 43% of defective units.&#x0D; Enhanced quality control measures for raw materials prior to their utilization within the production process were recommended to minimize product defects.&#x0D; &#x0D; Keywords: Epodion, rh-Erythropoietin Alfa, syringes, FMEA, quality control.
Recipe Generation from Unsegmented Cooking Videos

This paper tackles recipe generation from unsegmented cooking videos, a task that requires agents to (1) extract key events in completing the dish and (2) generate sentences for the extracted events. Our task is similar to dense video captioning (DVC), which aims at detecting events thoroughly and generating sentences for them. However, unlike DVC, in recipe generation, recipe story awareness is crucial, and a model should extract an appropriate number of events in the correct order and generate accurate sentences based on them. We analyze the output of the DVC model and confirm that although (1) several events are adoptable as a recipe story, (2) the generated sentences for such events are not grounded in the visual content. Based on this, we set our goal to obtain correct recipes by selecting oracle events from the output events and re-generating sentences for them. To achieve this, we propose a transformer-based multimodal recurrent approach of training an event selector and sentence generator for selecting oracle events from the DVC’s events and generating sentences for them. In addition, we extend the model by including ingredients to generate more accurate recipes. The experimental results show that the proposed method outperforms state-of-the-art DVC models. We also confirm that, by modeling the recipe in a story-aware manner, the proposed model outputs the appropriate number of events in the correct order.
Backtranslate what you are saying and I will tell who you are

Abstract With this work, we hypothesize that semantically enriching a user's text corpus using backtranslation and expansion modules can improve performance for author profiling tasks. To perform this textual enrichment, we translate an author's representative text. Translations are made from one language—the source language—into another—the target language—and then back to the original one. Finally, we expand an author's text by integrating the original version with the back‐translated one. Our framework includes these backtranslation and expansion modules followed by a SOTA classifier successfully employed for text classification. The framework is tested on three author profiling datasets from the last three years’ shared tasks on fake news, hate speech, irony and stereotypes detection hosted at the CLEF conference for the PAN Lab. This work is an extension of our previous one where we just presented our main idea. Here we improve our framework, and we also investigate more languages and more datasets. Finally, a qualitative analysis is provided. The results confirm that the backtranslation and expansion add‐on modules improve model performance on all three datasets evaluated.
From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications

Artificial intelligence (AI), a branch of computer science, involves developing intelligent computer programs to mimic human intelligence and automate various processes [...]
Doing Good Business? Design Leaders' Perspectives on Ethics in Design

Design is not neutral but involves ethical aspects, from addressing power dynamics to enabling broader accessibility and inclusion. In industry settings, design leaders can play an essential role in promoting ethical design by, for instance, setting visions, developing and advocating values, and providing guidance for design. Through co-design workshops with ten design leaders working in Sweden, we investigated how these professionals envision the cultivation-that is, the development, promotion, and dissemination-of ethically responsible design. Our study outlines what the participants regard as the main problems that stifle ethical design and designing, and how to practically address them. The findings highlight that design leaders consider the lack of awareness about the ethical dimensions of design to be one of the main problems. Additionally, they point to various strategies for addressing the problems, such as communicating compelling reasons for prioritizing ethics, creating inclusive and welcoming spaces for collaborative problem-solving and learning about ethics, and folding ethics into current practices. This could involve, for instance, adding the dimension of ethics to 'design critique' sessions, or re-evaluating portfolio reviews to prioritize diversity in recruitment. We conclude by discussing design leaders' potential for furthering ethically responsible design, and provide a practical tool, a design brief, to support this endeavor.
A novel generative adversarial network‐based super‐resolution approach for face recognition

Abstract Face recognition is an essential feature required for a range of computer vision applications such as security, attendance systems, emotion detection, airport check‐in, and many others. The super‐resolution of subject images is an important and challenging element in numerous scenarios. At times the images are low resolution and need to be processed through super‐resolution techniques to gain more accurate results. For the problem of image super‐resolution, deep learning‐based face recognition systems have been explored in recent years; however, low‐resolution face recognition remains an arduous task. Generative adversarial network (GAN) based models are a promising approach to address this challenge. However, conventional GAN‐based models may generate images that differ significantly from an original high‐resolution image in the test set to the point that the identity of the target face may be changed. To address this shortcoming, we propose a novel U‐Net style generator architecture, where skip‐connections between the encoder and decoder layer can help in preserving the facial characteristics of the input image in the generated image, thus curbing the generator's ability to generate an entirely new image and training it to generate an image more similar in characteristics to the original image. In addition to statistical metrics like structural similarity index measure and Fréchet inception distance, we compute the pixel‐wise distance between the original and model‐generated images to ascertain that our model generates as close to the original images as possible. While we train the model for 4× super‐resolution (64 × 64 images to 256 × 256), our architecture can also be trained for an arbitrary resizing scale. Finally, the number of faces detected over high‐resolution images generated by our model is shown to be higher than state‐of‐the‐art high‐resolution image creation models for face recognition tasks.
The Influential People and Memories in the Formation of My Approach to Contextual Functional Interpretation

It will be noted that in all my scholarly writings and studies I have used an approach I call “contextual functional interpretation”, a problem-solving method. Every word has a meaning specific to its cultural context and carries a text on its shoulders. If this text is not correctly understood, the accuracy of the sentence cannot reach a sufficient and necessary level of reliability. By ensuring the correct understanding of each word that makes up the sentence, a reliable accuracy of explanation is achieved. In the genesis of this idea, scientists and mentors who have had an impact on the formation of my mind, identity and embodiment have played a significant role. In this article, I would like to talk to you about the people who influenced me in the birth of the contextual-functional approach to interpretation, and share some memories of them.
Freeform Metasurface-Assisted Waveguide Coupler for Guided Wave Polarization Manipulation and Spin–Orbit Angular Momentum Conversion

Metasurface integrated waveguide couplers provide a promising pathway toward advanced manipulation of free-space light for guided wave coupling, such as polarization routing, mode-selective coupling, and wavelength (de)multiplexing. However, it is a great challenge to manipulate the guided wave polarization state to be completely decoupled from the polarization of incident light. Here, an adjoint-based topology optimization method is employed to inversely design a freeform metasurface for unidirectionally coupling free-space light into customized waveguide mode, with the capabilities of polarization manipulation and guided mode selection. An arbitrary transverse magnetic or transverse electric eigenmode can be unidirectionally excited for any incident polarization state conditions, with the maximum polarization-rotated mode purity and unidirectional coupling efficiency approaching 90.97 and 43%, respectively. Furthermore, by using multiobjective topology optimization, a meta-waveguide coupler with dual functions of generating polarization-controllable hybrid mode and realizing reconfigurable spin–orbit angular momentum conversion is inversely designed, leading to the increase of a mode (de)multiplexer channel number. Our work provides an effective strategy for multifunctional integrated on-chip devices and may have potential applications in optical communication, integrated optics, and quantum optics.
The Geoeconomics of Traditional and Digital Government Money

Abstract Government money is an instrument of state power. While extensive research has examined the strategic benefits states can extract from geoeconomic applications of their traditional fiat currencies, this work has largely examined the geoeconomics of fiat money from a perspective of unchanging monetary technology. As such, there currently exists no scholarship on the topic of geoeconomics in digital government money, despite the implications of this technological transition for strategic interaction among countries in traditional geoeconomic frameworks. This chapter organizes existing scholarship on the issue of geoeconomics in traditional government money and subsequently presents novel statistical results from new, large datasets of central bank digital currency (CBDC) projects to demonstrate the already salient issues around CBDCs in geoeconomics. The chapter concludes with policy implications for decision makers, not least including strong advocacy for proactive and informed design in central bank digital currencies, and also proposes lines of future research on strategic interaction in digital government money.
Coregistration of EEG and eye-tracking in infants and developing populations

Abstract Infants cannot be instructed where to look; therefore, infant researchers rely on observation of their participant’s gaze to make inferences about their cognitive processes. They therefore started studying infant attention in the real world from early on. Developmental researchers were early adopters of methods combining observations of gaze and behaviour with electroencephalography (EEG) to study attention and other cognitive functions. However, the direct combination of eye-tracking methods and EEG to test infants is still rare, as it includes specific challenges. The current article reviews the development of co-registration research in infancy. It points out specific challenges of co-registration in infant research and suggests ways to overcome them. It ends with recommendations for implementing the co-registration of EEG and eye-tracking in infant research to maximise the benefits of the two measures and their combination and to orient on Open Science principles while doing so. In summary, this work shows that the co-registration of EEG and eye-tracking in infant research can be beneficial to studying natural and real-world behaviour despite its challenges.
Algorithmic Text Analysis

Abstract Algorithmic analysis of “text as data” has proliferated throughout academic research over the past decade, offering the opportunity to examine ever-larger and more diverse collections of documents, address more complex issues, and incorporate underrepresented voices into social science inquiry. Drawing from a new survey of twelve top political science journals, this chapter discusses how current methods address a multitude of questions—pointing to what their structural assumptions and fundamental characteristics imply about their insight into latent constructs and meanings. Particular consideration is paid to the mismatch between the objective orientation of computational methods and subjective nature of social scientists’ contextual, situated inquiry. Finally, the chapter looks into areas of potential expansion that would allow these algorithmic methods to enhance concept and discourse analysis, further advancing an interpretive and inclusive understanding of political communication.
Hybrid collaborative filtering using matrix factorization and XGBoost for movie recommendation

Nowadays, e-commerce platforms, such as Amazon, Flipkart, Netflix and YouTube, extensively use recommender systems (RS) techniques. Collaborative filtering (CF) is used widely among all RS techniques. A CF analyzes the user’s preference from past data, like ratings, and then suggests actual items to the intended user. The existing techniques compute the similarity between users/items and predict the ratings. However, most of them indicate the user’s preference for the items using a single technique, which may produce poor results. This paper proposes a hybrid CF technique to enhance the movie recommendation (HCFMR). The HCFMR consists of two modules. The first module finds the prediction score with the help of matrix factorization (MF) and passes the prediction score as input to the prediction algorithm, i.e., extreme gradient boosting (XGBoost). The second module generates handcrafted features, such as similar users and movies, along with the user, item and global average. Finally, these features are supplied to the XGBoost to predict the rating score of the movie and recommend the topmost movie to the user. We conduct various simulations on real-world datasets to verify the effectiveness of the proposed technique against the baseline techniques. The exploratory outcomes signify that the HCFMR technique outperforms the baselines and provides a better prediction on the benchmark datasets.
Image Identification and Land Use Management of Anomalous Minerals from Geophysical Information and Deep Convolutional Networks

Next article No AccessImage Identification and Land Use Management of Anomalous Minerals from Geophysical Information and Deep Convolutional NetworksYuan Han, Xu Zhang, and Yongxing ZhangYuan Han, Xu Zhang, and Yongxing ZhangPDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by The Journal of Geology Just Accepted Article DOIhttps://doi.org/10.1086/730159 HistoryAccepted February 20, 2024 © 2024 The University of Chicago. All Rights reserved.PDF download Crossref reports no articles citing this article.
Measuring GitHub Copilot's Impact on Productivity

Case study asks Copilot users about its impact on their productivity, and seeks to find their perceptions mirrored in user data.
Metadata application proﬁle as a mechanism for semantic interoperability in FAIR and open data publishing

Application profiles, also known as metadata application profiles, are customised collections of vocabularies adapted from various namespaces and tailored for specific local applications. These profiles act as constrainers and explainers for the (meta)data. Semantic interoperability is the ability of computer systems to exchange data in a mutually understandable manner, facilitating data sharing across diverse platforms and applications without compromising its meaning. As a critical component of semantic interoperability, application profiles enforce semantics to (meta)data, enhancing its openness, interoperability, and reusability. This study assesses the feasibility of representing a comprehensive application profile in a format aligned with the semantic web, ensuring interoperability between profiles and datasets. Dublin Core Description Set Profiles (DSP) is adapted as the modeling framework for metadata application profiles, steering the associated datasets toward RDF compliance. The research outcomes include "Yet Another Metadata Application Profiles" (YAMA) as a preprocessor grounded in the DSP framework for developing and managing metadata application profiles. YAMA facilitates the generation of various standard formats of application profiles, ensuring they are represented in human-readable documentation, machine-actionable forms, and even data validation languages. A data mapping extension to YAMA is proposed to ensure the semantic interoperability of open data, bridging non-RDF data structures to RDF, thus enabling the publication of 5-star open data. This ensures smooth dataset integration and the creation of linkable, semantically rich open datasets. The work emphasizes the pivotal role of application profiles in fortifying the semantic interoperability of (meta)data, thereby elevating dataset openness.