Reviewerly/exHarmony huggingface dataset:
Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions

Authors:
raw_author_name: Athanasios Tsadiras
raw_affiliation_string: School of Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Abstract: In today’s evolving global world, the pharmaceutical sector faces an emerging challenge, which is the rapid surge of the global population and the consequent growth in drug production demands. Recognizing this, our study explores the urgent need to strengthen pharmaceutical production capacities, ensuring drugs are allocated and stored strategically to meet diverse regional and demographic needs. Summarizing our key findings, our research focuses on the promising area of drug demand forecasting using artificial intelligence (AI) and machine learning (ML) techniques to enhance predictions in the pharmaceutical field. Supplied with a rich dataset from Kaggle spanning 600,000 sales records from a singular pharmacy, our study embarks on a thorough exploration of univariate time series analysis. Here, we pair conventional analytical tools such as ARIMA with advanced methodologies like LSTM neural networks, all with a singular vision: refining the precision of our sales. Venturing deeper, our data underwent categorisation and were segmented into eight clusters premised on the ATC Anatomical Therapeutic Chemical (ATC) Classification System framework. This segmentation unravels the evident influence of seasonality on drug sales. The analysis not only highlights the effectiveness of machine learning models but also illuminates the remarkable success of XGBoost. This algorithm outperformed traditional models, achieving the lowest MAPE values: 17.89% for M01AB (anti-inflammatory and antirheumatic products, non-steroids, acetic acid derivatives, and related substances), 16.92% for M01AE (anti-inflammatory and antirheumatic products, non-steroids, and propionic acid derivatives), 17.98% for N02BA (analgesics, antipyretics, and anilides), and 16.05% for N02BE (analgesics, antipyretics, pyrazolones, and anilides). XGBoost further demonstrated exceptional precision with the lowest MSE scores: 28.8 for M01AB, 1518.56 for N02BE, and 350.84 for N05C (hypnotics and sedatives). Additionally, the Seasonal Naïve model recorded an MSE of 49.19 for M01AE, while the Single Exponential Smoothing model showed an MSE of 7.19 for N05B. These findings underscore the strengths derived from employing a diverse range of approaches within the forecasting series. In summary, our research accentuates the significance of leveraging machine learning techniques to derive valuable insights for pharmaceutical companies. By applying the power of these methods, companies can optimize their production, storage, distribution, and marketing practices.
Unobtrusive Cognitive Assessment in Smart-Homes: Leveraging Visual Encoding and Synthetic Movement Traces Data Mining

Authors:
raw_author_name: Samaneh Zolfaghari
raw_affiliation_string: School of Innovation, Design and Engineering, Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden
raw_author_name: Annica Kristoffersson
raw_affiliation_string: School of Innovation, Design and Engineering, Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden
raw_author_name: Maria Lindén
raw_affiliation_string: School of Innovation, Design and Engineering, Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden
raw_author_name: Daniele Riboni
raw_affiliation_string: Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy

Abstract: The ubiquity of sensors in smart-homes facilitates the support of independent living for older adults and enables cognitive assessment. Notably, there has been a growing interest in utilizing movement traces for identifying signs of cognitive impairment in recent years. In this study, we introduce an innovative approach to identify abnormal indoor movement patterns that may signal cognitive decline. This is achieved through the non-intrusive integration of smart-home sensors, including passive infrared sensors and sensors embedded in everyday objects. The methodology involves visualizing user locomotion traces and discerning interactions with objects on a floor plan representation of the smart-home, and employing different image descriptor features designed for image analysis tasks and synthetic minority oversampling techniques to enhance the methodology. This approach distinguishes itself by its flexibility in effortlessly incorporating additional features through sensor data. A comprehensive analysis, conducted with a substantial dataset obtained from a real smart-home, involving 99 seniors, including those with cognitive diseases, reveals the effectiveness of the proposed functional prototype of the system architecture. The results validate the system’s efficacy in accurately discerning the cognitive status of seniors, achieving a macro-averaged F1-score of 72.22% for the two targeted categories: cognitively healthy and people with dementia. Furthermore, through experimental comparison, our system demonstrates superior performance compared with state-of-the-art methods.
Matrix product and quasi-twisted codes in one class

Authors:
raw_author_name: Ramy Taki Eldin

Abstract: 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.
Using fuzzy cognitive map in bow tie method for dynamic risk assessment of spherical storage tanks: A case study

Authors:
raw_author_name: Moslem Alimohammadlou
raw_affiliation_string: School of Economics, Management and Social Sciences, Department of Management, Shiraz University, Shiraz, Iran.

Abstract: 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

Authors:
raw_author_name: Mohamed Lotfy
raw_author_name: Ghada Soliman

Abstract: 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.
Recipe Generation from Unsegmented Cooking Videos

Authors:
raw_author_name: Atsushi Hashimoto
raw_affiliation_string: OMRON SINIC X Corporation, Bunkyo-ku, Japan
raw_author_name: Yoshitaka Ushiku
raw_affiliation_string: OMRON SINIC X Corporation, Bunkyo-ku, Japan
raw_author_name: Hirotaka Kameko
raw_affiliation_string: Academic Center for Computing and Media Studies, Kyoto University, Sakyo-ku, Japan
raw_author_name: Shinsuke Mori
raw_affiliation_string: Academic Center for Computing and Media Studies, Kyoto University, Sakyo-ku, Japan

Abstract: 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

Authors:
raw_author_name: Marco Siino
raw_affiliation_string: DEI Department University of Bologna  Bologna Italy
raw_author_name: Paolo Rosso
raw_affiliation_string: PRHLT Research Center Universitat Politècnica de València  Valencia Spain; ValgrAI–Valencian Graduate School and Research Network of Artificial Intelligence  Valencia Spain

Abstract: 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

Authors:
raw_author_name: Palanisamy Preethi
raw_affiliation_string: Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, Tamil Nadu, India

Abstract: 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

Authors:
raw_author_name: Sharon Lindberg
raw_affiliation_string: Dept. of Computer and Systems Sciences, Stockholm University, Kista, Sweden
raw_author_name: Chiara Rossitto
raw_affiliation_string: Dept. of Computer and Systems Sciences, Stockholm University, Kista, Sweden
raw_author_name: Ola Knutsson
raw_affiliation_string: Dept. of Computer and Systems Sciences, Stockholm University, Kista, Sweden
raw_author_name: Petter Karlström
raw_affiliation_string: Dept. of Computer and Systems Sciences, Stockholm University, Kista, Sweden
raw_author_name: Sirkku Männikkö Barbutiu
raw_affiliation_string: Dept. of Computer and Systems Sciences, Stockholm University, Kista, Sweden

Abstract: 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

Authors:
raw_author_name: Amit Chougule
raw_affiliation_string: Department of Electrical Electronics Engineering BITS‐Pilani, Pilani Campus  Pilani Rajasthan India
raw_author_name: Shreyas Kolte
raw_affiliation_string: Department of Electrical Electronics Engineering BITS‐Pilani, Pilani Campus  Pilani Rajasthan India
raw_author_name: Vinay Chamola
raw_affiliation_string: Department of Electrical and Electronics Engineering &amp; APPCAIR BITS‐Pilani, Pilani Campus  Pilani Rajasthan India
raw_author_name: Amir Hussain
raw_affiliation_string: School of Computing Edinburgh Napier University  Edinburgh UK

Abstract: 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 Geoeconomics of Traditional and Digital Government Money

Authors:
raw_author_name: Tim Marple
raw_affiliation_string: Political Science, University of California, Berkeley

Abstract: 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.
Hybrid collaborative filtering using matrix factorization and XGBoost for movie recommendation

Authors:
raw_author_name: Gopal Behera
raw_affiliation_string: Department of Computer Science and Engineering, Government College of Engineering, Kalahandi, 766002, Odisha, India
raw_author_name: Sanjaya Kumar Panda
raw_affiliation_string: Department of Computer Science and Engineering, National Institute of Technology, Warangal, 506004, Telangana, India
raw_author_name: Meng-Yen Hsieh
raw_affiliation_string: Department of Computer Science and Information Engineering, Providence University, Taichung, 43301, Taiwan
raw_author_name: Kuan-Ching Li
raw_affiliation_string: Department of Computer Science and Information Engineering, Providence University, Taichung, 43301, Taiwan

Abstract: 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

Authors:
raw_author_name: Yuan Han

Abstract: 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

Authors:
raw_author_name: Albert Ziegler
raw_affiliation_string: GitHub, Inc., San Francisco, CA, USA
raw_author_name: Eirini Kalliamvakou
raw_affiliation_string: GitHub, Inc., San Francisco, CA, USA
raw_author_name: X. Alice Li
raw_affiliation_string: GitHub, San Francisco, CA, USA
raw_author_name: Andrew Rice
raw_affiliation_string: GitHub, Inc., San Francisco, CA, USA
raw_author_name: Devon Rifkin
raw_affiliation_string: GitHub, Inc., San Francisco, CA, USA
raw_author_name: Shawn Simister
raw_affiliation_string: GitHub, Inc., San Francisco, CA, USA
raw_author_name: Ganesh Sittampalam
raw_affiliation_string: GitHub, Inc., San Francisco, CA, USA
raw_author_name: Edward Aftandilian
raw_affiliation_string: GitHub, Inc., San Francisco, CA, USA

Abstract: Case study asks Copilot users about its impact on their productivity, and seeks to find their perceptions mirrored in user data.