Reviewerly/exHarmony huggingface dataset:
Breast cancer detection accuracy of AI in an entire screening population: a retrospective, multicentre study

Abstract Background Artificial intelligence (AI) systems are proposed as a replacement of the first reader in double reading within mammography screening. We aimed to assess cancer detection accuracy of an AI system in a Danish screening population. Methods We retrieved a consecutive screening cohort from the Region of Southern Denmark including all participating women between Aug 4, 2014, and August 15, 2018. Screening mammograms were processed by a commercial AI system and detection accuracy was evaluated in two scenarios, Standalone AI and AI-integrated screening replacing first reader, with first reader and double reading with arbitration (combined reading) as comparators, respectively. Two AI-score cut-off points were applied by matching at mean first reader sensitivity (AI sens ) and specificity (AI spec ). Reference standard was histopathology-proven breast cancer or cancer-free follow-up within 24 months. Coprimary endpoints were sensitivity and specificity, and secondary endpoints were positive predictive value (PPV), negative predictive value (NPV), recall rate, and arbitration rate. Accuracy estimates were calculated using McNemar’s test or exact binomial test. Results Out of 272,008 screening mammograms from 158,732 women, 257,671 (94.7%) with adequate image data were included in the final analyses. Sensitivity and specificity were 63.7% (95% CI 61.6%-65.8%) and 97.8% (97.7-97.8%) for first reader, and 73.9% (72.0-75.8%) and 97.9% (97.9-98.0%) for combined reading, respectively. Standalone AI sens showed a lower specificity (-1.3%) and PPV (-6.1%), and a higher recall rate (+ 1.3%) compared to first reader ( p &lt; 0.0001 for all), while Standalone AI spec had a lower sensitivity (-5.1%; p &lt; 0.0001), PPV (-1.3%; p = 0.01) and NPV (-0.04%; p = 0.0002). Compared to combined reading, Integrated AI sens achieved higher sensitivity (+ 2.3%; p = 0.0004), but lower specificity (-0.6%) and PPV (-3.9%) as well as higher recall rate (+ 0.6%) and arbitration rate (+ 2.2%; p &lt; 0.0001 for all). Integrated AI spec showed no significant difference in any outcome measures apart from a slightly higher arbitration rate ( p &lt; 0.0001). Subgroup analyses showed higher detection of interval cancers by Standalone AI and Integrated AI at both thresholds ( p &lt; 0.0001 for all) with a varying composition of detected cancers across multiple subgroups of tumour characteristics. Conclusions Replacing first reader in double reading with an AI could be feasible but choosing an appropriate AI threshold is crucial to maintaining cancer detection accuracy and workload.
External Financing and Financial Development in Developing Countries: Aggregate and Disaggregate Empirical Evidence from West Africa

External financing as one of the drivers of financial development remains a subject of ongoing debate. While some studies assert a positive influence, others dispute this claim. This study contributes to the existing studies on the subject matter, with the primary objective of examining the overall or aggregate effects of external financing on financial development. Specifically, the study examines the individual effects of external financing factors (FDI, ODA, and remittances) on financial development, both in the short and long run, by employing annual panel data spanning from 1980 to 2021 for the selected West African countries. To achieve this, the study employs the panel autoregressive distributed lag (PARDL) variants (mean group - MG, dynamic fixed effect - DFE, and pooled mean group - PMG) techniques. The results from the PMG and DFE estimations reveal that external financing as a whole has an impact on financial development in West African countries both in the long and short run. Remittance, economic growth, and inflation rate also have a significant influence on financial development both in the long and short run. While ODA is detrimental to financial development in the long run, FDI does not influence financial development. It is therefore imperative for stakeholders within the sub-region to encourage and facilitate the use of remittances, economic growth, and inflation rate as means of promoting financial development in West Africa, while simultaneously ensuring that FDI and ODA are used to promote self-sufficiency and financial stability and align with the region’s developmental goals.
IMPROVING STUDENTS’ READING COMPREHENSION OF NARRATIVE TEXT BY ACTIVATING GROUP WORKS IN SECONDARY SCHOOL

The aim of writing this paper is to find out the importance of learning English at school. Writing the paper uses the literature review method (library research). From the discussion it can be concluded that learning English at school is very important. The learning method that is very often used is CLT because it is effectively used as a method in the English language teaching and learning process. This is based on the communication skills obtained by students, not just mere knowledge, students will be motivated and feel that something they have learned can be useful and used in language.
INVESTIGATING LEARNERS’ PERCEPTION OF ENGLISH ONLINE LEARNING IN BANYUWANGI SECONDARY SCHOOL

This research aims to discover and describe some of the learners' perceptions of English online learning. This research’s approach or design is qualitative, carried out in two stages. All the eleventh grade high school participants were given an online questionnaire in the first stage. Then in the second stage, several participants were interviewed to clarify the research result. Based on the research result, students had several perceptions about English online learning. The first perception, students stated that the learning environment between English online learning and face to face learning is so different. They also added that the interaction in English online learning did not go well. The third perception, students said that the assignments given during English online learning were so many that they were less than optimal in doing assignments. Lastly, students revealed that when English online learning, they prefer to deliver material using videos and tend not to read the material. In brief, this research discover several students’ perception during their online learning, they are the differentiation of the learning environment, the interaction in English online learning didn’t go well, too many assignments given in online learning makes students less optimal, and students prefer to deliver material using videos.
All the world’s a virtual mash-up: Adapting Shakespeare for animated virtual reality

Shakespeare’s global reach is undeniable; one only has to look to Shakespeare’s role in the western literary and cultural canon for proof. With the variety of people staging and watching Shakespeare all over the world, it is to be expected that Shakespeare’s plays are also the target of many adaptations that take advantage of evolving technologies to reach new audiences in new ways. This article briefly addresses common modes of adaptation and its impact on audience engagement before moving onto considering newer technologies – specifically virtual reality (VR) – that have yet to become as well utilized. Investigating the advantages and disadvantages of VR animation allows us to contextualize our own work: ShakesVR , a mash-up of three Shakespeare plays into an animated VR experience. This article also discusses the importance we place on the viewer’s interactions with Shakespeare’s world, our own goals for audience engagement and how these goals influenced our design decisions in ShakesVR .
Performance management process changes on the work identity of employees during COVID-19

Orientation: Performance management is a vital process across multiple businesses and is proven to be an integral element in navigating employees through the coronavirus disease 2019 (COVID-19) pandemic while determining possible influences on the work identity of employees during this period.Research purpose: The purpose of this study was to understand the influence of performance management process changes on the work identity of employees during the COVID-19 pandemic.Motivation for the study: There is little research available that discusses performance management and work identity. As such, this research works towards closing this gap by understanding the influence of performance management process changes on the identity of employees.Research approach/design and method: A qualitative approach was followed through an interpretivist research paradigm. A purposive sampling technique was used to select 15 participants. The participants took part in semi-structured interviews. Grounded theory was followed, and the constant comparative method was used.Main findings: The performance management process changes had no influence on the work identity and work role of participants. Instead, the pandemic itself and the way in which the organisation supported its employees while they navigated the pandemic had an influence on the work identity and work roles of employees.Practical/managerial implications: Organisations can adopt the research findings to enhance employee work identity and performance throughout future changes that affect the organisation.Contribution/value-add: This article offers insights into the lack of literature available that discusses performance management and work identity collectively.
Combining deep learning and physical models: a benchmark study on all‐sky imager‐based solar nowcasting systems

Intermittent solar irradiance due to passing clouds poses challenges for integrating solar energy into existing infrastructure. By making use of intra‐hour nowcasts (very short‐term forecasts), changing conditions of solar irradiance can be anticipated. All‐sky imagers, capturing sky conditions at high spatial and temporal resolution, can be the basis of such nowcasting systems. In this work, a deep learning (DL) model for solar irradiance nowcasts based on the transformer architecture is presented. The model is trained end‐to‐end using sequences of sky images and irradiance measurements as input to generate point‐forecasts up to 20 min ahead. Further, the effect of integrating this model into a hybrid system, consisting of a physics‐based model and smart persistence, is examined. A comparison between the DL and two hybrid models (with and without the DL model) is conducted on a benchmark dataset. Forecast accuracy for deterministic point‐forecasts is analyzed under different conditions using standard error metrics like root‐mean‐square error (RMSE) and forecast skill (FS). Furthermore, spatial and temporal aggregation effects are investigated. In addition, probabilistic nowcasts for each model are computed via a quantile approach. Overall, the DL model outperforms both hybrid models under the majority of conditions and aggregation effects. This article is protected by copyright. All rights reserved.
Perancangan Sistem Informasi Akuntansi Persediaan Barang Dagang Pada Toko Rianzi Menggunakan PHP Dan MySQL

the aim of this research is to determine the merchandise inventory accounting information system used by Toko Rianzi. This research method uses descriptive research by looking for theoretical research related to the merchandise inventory accounting information system. The data collection used was observation and interviews. The type of data used is quantitative data. The results of this research are expected to provide information regarding the merchandise inventory accounting information system. The design of this merchandise inventory accounting information system uses the system development method Dynamic System Development Method with 3 main stages, namely, before the project, project life cycle and after the project. As well as 5 sub stages (feasibility study, business study, model iteration, iteration design, manufacture and implementation). Discussion of design researchThis merchandise inventory accounting information system is expected to be able to makes it easier for Toko Rianzi to make inventory reports and financial reports.
Federated Multi-View Synthesizing for Metaverse

The metaverse is expected to provide immersive entertainment, education, and business applications. However, virtual reality (VR) transmission over wireless networks is data- and computation-intensive, making it critical to introduce novel solutions that meet stringent quality-of-service requirements. With recent advances in edge intelligence and deep learning, we have developed a novel multi-view synthesizing framework that can efficiently provide computation, storage, and communication resources for wireless content delivery in the metaverse. We propose a three-dimensional (3D)-aware generative model that uses collections of single-view images. These single-view images are transmitted to a group of users with overlapping fields of view, which avoids massive content transmission compared to transmitting tiles or whole 3D models. We then present a federated learning approach to guarantee an efficient learning process. The training performance can be improved by characterizing the vertical and horizontal data samples with a large latent feature space, while low-latency communication can be achieved with a reduced number of transmitted parameters during federated learning. We also propose a federated transfer learning framework to enable fast domain adaptation to different target domains. Simulation results have demonstrated the effectiveness of our proposed federated multi-view synthesizing framework for VR content delivery.
Scientometric Analysis for Access Control in the Social Robots Era

Social robots are cyber-physical systems consisting of cyber and physical robotic components that connect to Cloud services to improve the ease and productivity of activities through networking, multi-media, and sensory technologies. However, social robots introduce new security threats beyond what a regular computing system may tackle. The International Organization for Standardization (ISO) specifies requirements and guidelines for the inherently safe design and protective measures for personal care robots. However, ISO only covers human-robot types of physical contact applications, not from the data security and privacy perspective. However, while social robots attract new research, security and privacy issues are still thoroughly investigated. This paper provides a scientometric approach to propose research gaps and future trends related to access control and social robot’s research.
A Comparative Study of Multi-Class Sentiment Classification Models for Hotel Customer Reviews

This research intends to provide a comparative analysis of multi-class sentiment classification models for hotel customer reviews. This study utilized a dataset of hotel reviews downloaded from the TripAdvisor website. These hotel reviews were composed in English and were based on a five-star rating scale. The rating of each hotel reflected its class. There was a comparison between transformer-based sentiment classification algorithms (such as BERT) and traditional sentiment classification algorithms. The traditional algorithms for sentiment classification in this study can be machine learning (e.g. Multinomial Naïve Bayes, Random Forest, and Support Vector Machines with linear kernel function) and deep learning (e.g. Convolutional Neural Networks). After evaluation via recall, precision, F1, accuracy, and AUC, the BERT model outperformed other models such as Multinomial Nave Bayes, Random Forest, and Support Vector Machines, and Convolutional Neural Networks.
Research on Edge Cloud Storage Identity Authentication Mechanism Based on Multi-Layer Integration

Edge cloud storage is a novel data storage technology that utilizes the resources at the network edge to provide efficient and secure data services. However, there are multiple layers of identity authentication in edge cloud storage, which pose security risks. To address this problem, this paper proposes an edge cloud storage identity authentication mechanism based on multi-layer Integration that integrates the authentication of the terminal layer, transport layer, and application layer. The mechanism achieves the session binding of multi-layer authentication and improves the security and flexibility of authentication. It uses the edge cloud storage gateway as a proxy and establishes the mapping of authentication for each layer through the user authentication information directory. This paper analyzes and experiments with the proposed mechanism to verify its effectiveness.
Nucleus Detection Using Deep Learning Approach on Pap Smear Images

Cervical cancer is caused by the abnormal growth of female cervix cells. It is one of the most familiar factors for women”s death worldwide. Therefore., early detection of cervical cancer leads to a reduced mortality rate and increased chance of being alive. The Papanicolaou is a common method for screening and identifying the cancerous cells in a woman”s cervix. The resultant pap smear images may help the physician diagnose the cervix cells. The crucial part of the cell is the nucleus. Therefore., auto-detection of the nucleus is the core point in this paper. A deep learning algorithm is employed to segment the nucleus in pap smear images. Two network structures., known as ResNet18 and ResNet50., are exploited to detect the nucleus part in the cell. The results are compared with ground truth and between the two structures. Both networks., ResNet18 and ResNet50., perform almost the same., with test accuracy reaching 92%. This work distinguishes it from other work in simplicity., fast., and accuracy. Therefore., it can be recommended to be used in clinical units and rural countries which suffer from the lack of specialist physician.
Counting Non-Overlapping Abnormal Cervical Cells in Whole Slide Images

Cervical cancer is one of the most common cancer among women globally. The Pap smear test has been widely used to detect cervical cancers according to the morphological characteristics of the cell nuclei on the micrograph. The aim of this paper is to count the non-overlapping abnormal cervical cells in whole slide images automatically by employing various image techniques. The proposed approach consists of four main steps; image enhancement, transform the extended minima, remove small pixels, and count the number of abnormal cells in the image. The proposed system used 250 cervical pap smear images where the overlap between cells is minimal. The performance of the proposed system is evaluated based on comparing the manual counting and automating counting over whole images. Therefore, the accuracy is evaluated mainly on the difference between manual and automated, and it is 92.5%. The proposed method can be used in laboratory to decrease the false positive rates in counting abnormal cells.
Fruit Fly Optimization with Hybrid Deep Learning Based Sentiment Analysis During COVID-19 Pandemic

A new COVID-19 or coronavirus disease becomes a current pandemic with huge global attention. But, increasing false news on social media platforms such as Twitter has generated redundant anxiety nearby this disease. During the COVID-19 pandemic, decision-makers worldwide formed a count of crucial decisions concentrated on public view for combat coronavirus. This paper proposes a Fruit fly Optimization Algorithm with a Hybrid Deep Learning Based Sentiment Analysis (FFOA-HDLSA) during the COVID-19 Pandemic. The presented FFOA-HDLSA technique makes use of Twitter data for the identification and classification of sentiments. To accomplish this, the presented FFOA-HDLSA method follows different pre-process stages of transforming the actual input data into pre-processed data. This study exploits the HDL model (LSTM, GRU, BiLSTM, CNN, and BiGRU) for sentiment analysis and classification. To effectively modify the hyperparameters related to the HDL technique, the FFOA was employed in this paper. For exhibiting the improved performance of the FFOA-HDLSA model, a wide range set of simulations are carried out on the emotion dataset with 2000 samples. The comparison study reported the improved performance of the FFOA-HDLSA model over the existing techniques with a maximum average accuracy of 94.04% and minimum computation time of 4.36s.
A Lightweight Encryption Model Tailored to Medical Information Security

As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important. However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed. Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them. First, a deep learning classifier is used to classify records according to the degree of concealment they require. Medical images that aren't needed can be saved by using this method, which cuts down on security costs. Encryption is one of the most effective methods for protecting medical images after this step. Confusion and dispersion are two fundamental encryption processes. A new encryption algorithm for very sensitive data is developed in this study. Picture splitting with image blocks is now developed by using Zigzag patterns, rotation of the image blocks, and random permutation for scrambling the blocks. After that, this research suggests a Region of Interest (ROI) technique based on selective picture encryption. For the first step, we use an active contour picture segmentation to separate the ROI from the Region of Background (ROB). Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map. Once all of the blocks have been encrypted, they are combined to create encrypted images. The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.
EOG Based Eye Movements and Blinks Classification Using Irisgram and CNN-SVM Classifier

The classification of eye movements and blinks is an important task in various fields, including ophthalmology, psychology, and human-computer interaction. In recent years, the use of EOG signals and convolutional neural networks (CNNs) has shown promising results in accurately classifying different types of eye movements and blinks. The Irisgram, which is a two-dimensional representation of the short-time Fourier transform in the shape of a human iris, has been used as a feature for distinguishing between different types of eye movements and blinks. Additionally, CNNs have been utilized to learn the features automatically from Irisgrams and classify the eye movements and blinks based on these learned features. In this paper, we provide a methodology to classify blinks and four eye movements by employing Irisgram as input to the CNN-SVM classifier which achieved test accuracy of 96.2% in the testing dataset.