Reviewerly/exHarmony huggingface dataset:
Breast Cancer Localization and Classification in Mammograms Using YoloV5

Mammography screening is the main examination for breast cancer early detection and has shown important benefits in reducing advanced and fatal disease rates. In this paper, a YoloV5 model for simultaneous breast cancer localization and classification in mammograms was proposed. Two public datasets were used for training and testing. The CBIS-DDSM dataset, composed of scanned film mammograms, was used as a source dataset to implement the transfer learning technique on the target INbreast dataset, composed of Full-Field Digital mammograms. The Small YoloV5 model combined with a large data-augmentation strategy was the best-developed solution. An improvement of 0.103 mAP was observed with the implementation of the transfer learning techinique on the INbreast dataset. The performance was encouraging, resulting in an mAP of 0.838 ± 0.042, a recall of 0.722 ± 0.096, and a precision of 0.917 ± 0.077, calculated using the five-fold CV. The recognition rate achieved with the transfer learning on full-field digital mammograms, encouraging future analysis on a proprietary dataset.
Metaverse Based Emergency Nursing Educational Program Using V-story

This study aims to evaluate the effect of a metaverse-based emergency nursing education program (MEEP) using V-story on nursing students. The MEEP included a five-hour orientation session, team-based case analysis, and role-play scenario writing and demonstration. In September and October 2021, the MEEP was used on 72 third-year nursing students from one college who had been simply divided into a control group (n = 38) or an intervention group (n = 34). Results indicated no significant difference in clinical competency between the two groups (t = 1.82, p = .073), but communication and nursing processes, which are sub-factors of clinical performance, significantly improved in the intervention group (t = 2.93, p = .005; t = 2.25, p = .027). No significant difference in problem-solving efficacy was found (t = −0.60, p = .549), but the intervention group reported significantly higher learning satisfaction (t = 2.48, p = .016). The study suggests that nursing education programs utilizing the metaverse should be developed on various topics.
Asynchronous Parallel Fuzzy Stochastic Gradient Descent for High-Dimensional Incomplete Data Representation

A latent feature analysis (LFA) model is very efficient in representation learning to high-dimensional incomplete (HDI) data emerging from various big data-based applications. The stochastic gradient descent (SGD) is frequently adopted as the learning algorithm by an LFA model owing to its ease of implementation. However, the standard SGD-based LFA model's training process is serial, which greatly decreases its scalability on massive HDI data. On the other hand, existing parallel SGD-based LFA models all suffer from low speedup due to their frequent synchronizations during the training process. Motivated by this discovery, this paper proposes an Asynchronous Parallel Fuzzy Stochastic Gradient Descent (APF-SGD) algorithm to establish an efficiently parallelized LFA model on shared-memory with three fold-ideas: a) decoupling the update inter-dependences among heterogeneous latent features alternatively, thereby achieving two parallelizable subtasks without learning information loss; b) proposing a novel parallelized learning scheme that eliminates the synchronizations from both the subtask and thread perspectives, i.e., sub-tasks and their affiliated threads are taken simultaneously and continuously without any synchronization; c) applying the fuzzy optimization to its hyper-parameters, thus enabling its adaptation in training. Theoretical convergence proof illustrates that the newly-proposed APF-SGD algorithm guarantees the convergence of a resultant LFA model. Experimental results on four real HDI datasets illustrate that an APF-SGD-based LFA model outperforms several state-of-the-art parallel SGD-based LFA models in both missing data estimation accuracy and parallelization speedup.
Parameter-free shifted laplacian reconstruction for multiple kernel clustering

Dear Editor, This letter proposes a parameter-free multiple kernel clustering (MKC) method by using shifted Laplacian reconstruction. Traditional MKC can effectively cluster nonlinear data, but it faces two main challenges: 1) As an unsupervised method, it is up against parameter problems which makes the parameters intractable to tune and is unfeasible in real-life applications; 2) Only considers the clustering information, but ignores the interference of noise within Laplacian. To solve these problems, this letter proposes a parameter-free shifted Laplacian reconstruction (PF-SLR) method for MKC, relying on shifted Laplacian rather than traditional Laplacian. Specifically, we treat each base kernel as an affinity graph, and construct its corresponding shifted Laplacian with only low-frequency components. Then, by convex combination reconstructing a high-quality shifted Laplacian, PF-SLR can preserve the energy information and clustering information on the largest eigenvalues simultaneously. After that, the cluster assignments can be obtained by performing k-means on the co-product, without any parameters involved throughout the whole process. Compared with eight state-of-the-art methods, the effectiveness and feasibility of our method are verified.
Acquiring 360° Light Field by a Moving Dual-Fisheye Camera

In this paper, we propose an efficient deep learning pipeline for light field acquisition using a back-to-back dual-fisheye camera. The proposed pipeline generates a light field from a sequence of 360° raw images captured by the dual-fisheye camera. It has three main components: a convolutional network (CNN) that enforces a spatiotemporal consistency constraint on the subviews of the 360° light field, an equirectangular matching cost that aims at increasing the accuracy of disparity estimation, and a light field resampling subnet that produces the 360° light field based on the disparity information. Ablation tests are conducted to analyze the performance of the proposed pipeline using the HCI light field datasets with five objective assessment metrics (MSE, MAE, PSNR, SSIM, and GMSD). We also use real data obtained from a commercially available dual-fisheye camera to quantitatively and qualitatively test the effectiveness, robustness, and quality of the proposed pipeline. Our contributions include: 1) a novel spatiotemporal consistency loss that enforces the subviews of the 360° light field to be consistent, 2) an equirectangular matching cost that combats severe projection distortion of fisheye images, and 3) a light field resampling subnet that retains the geometric structure of spherical subviews while enhancing the angular resolution of the light field.
GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection

Facial forgery by deepfakes has raised severe societal concerns. Several solutions have been proposed by the vision community to effectively combat the misinformation on the internet via automated deepfake detection systems. Recent studies have demonstrated that facial analysis-based deep learning models can discriminate based on protected attributes. For the commercial adoption and massive roll-out of the deepfake detection technology, it is vital to evaluate and understand the fairness (the absence of any prejudice or favoritism) of deepfake detectors across demographic variations such as gender and race. As the performance differential of deepfake detectors between demographic sub-groups would impact millions of people of the deprived sub-group. This paper aims to evaluate the fairness of the deepfake detectors across males and females. However, existing deepfake datasets are not annotated with demographic labels to facilitate fairness analysis. To this aim, we manually annotated existing popular deepfake datasets with gender labels and evaluated the performance differential of current deepfake detectors across gender. Our analysis on the gender-labeled version of the datasets suggests (a) current deepfake datasets have skewed distribution across gender, and (b) commonly adopted deepfake detectors obtain unequal performance across gender with mostly males outperforming females. Finally, we contributed a gender-balanced and annotated deepfake dataset, GBDF, to mitigate the performance differential and to promote research and development towards fairness-aware deep fake detectors. The GBDF dataset is publicly available at: https://github.com/aakash4305/GBDF
Postdigital Validity: Peer Reviews on the Edges of Modernity

This chapter explores how to engage with today’s muddy, lumpy, racist, scrappy, classist, glitchy, noisy sociotechnical practices to generate insights which are accepted as ‘valid’ by other scholars. Drawing on the concept of the postdigital as both a diagnosis of contemporary life and as a critical perspective, the chapter reflects on modernist understandings of validity and revisionings of validity ‘after the posts’. It shares episodes from the writing process in which peer reviewers questioned the validity of the arguments. Reflecting on these reviews and how we responded to them, the chapter suggests that a ‘postdigital validity’ draws on the rich tradition of rethinking validity, but also highlights the specific blurring of boundaries (e.g., digital/material binaries or epistemic demarcations) that is key to much postdigital research.
Թվայնացված գրականությունից մինչեվ լեզվական շտեմարաններ. դրանց նշանակությունը, հեռանկարներն ու մարտահրավերները

The Republic of Armenia has traditionally had a solid culture of preserving written heritage. However, in the last few decades, due to various geopolitical circumstances, we have lagged behind the global advancement of digitization, information, and communication technologies in terms of natural language processing for the Armenian language, access to the expanding opportunities of systematising and utilising the collated enormous amount of literature. This paper presents the current global state of natural language processing and the related methodological problems of the field in our country. The process of dataset development and consumption, from the primary stage of text digitising collected in physical databases, libraries, and archives, to the very use of the knowledge and information stored in these texts for various scientific, educational and practical purposes, requires a clear scientific-methodological program introducing not only the importance and perspectives of the field but will also ensure the conditions for its sustainable development, including the processing of written (and spoken) digitized language data and the retrieval and “industrialization” of the information and knowledge contained in the language corpora.
Determinants of blockchain adoption and organizational performance in the healthcare sector in India

Purpose Blockchain technology (BCT) has multiple benefits across industries in varied contexts, but limited organizations have adopted such disruptive innovative technologies in the healthcare industry in India. The research on advancing the understanding of blockchain adoption (BCA) determinants in India's healthcare industry is limited. Thus, the study aims to identify the BCA determinants in the healthcare sector in India. Further, the impact of BCA was examined on organizational performance (OP). Design/methodology/approach The study utilizes Technology, Organization, and Environment (TOE) framework to investigate the determinants of BCA in the healthcare sector in India. The data were gathered using a seven-point Likert seven-point ranging from “strongly agree” to “strongly disagree” from 272 respondents working in the healthcare industry in India. The relationship within the framework was investigated using structural equation modeling. Findings The results demonstrate the positive impact of top management support, organizational size, organizational readiness, competitive pressure and government support on BCA in the healthcare sector. On the other hand, compatibility, security and privacy issues do not affect BCA. The results emphasize and validate blockchain’s importance in improving OP in the healthcare sector. Further, the results indicate that non-technological factors are paramount to improving BCA within the healthcare sector. Organizations should invest in employee training and development to ensure their staff have the necessary knowledge and skills to effectively manage BCT. Research limitations/implications The model was developed for BCA in the healthcare sector in the Indian context; however, the model applies to other countries with the same business environment. Hence, the model can be further examined in diverse countries to generalize the findings. Practical implications The study offers valuable insights into the factors that influence BCA and OP in the healthcare sector. The results of this research can be used to inform policy decisions and guide practitioners toward promoting and facilitating the use of BCT in healthcare organizations. Originality/value To the best of the author’s knowledge, the present study is the first of its kind to examine the TOE framework in BCA within the healthcare sector and its implications on OP.
Technologies of blockchain interoperability: a survey

Blockchain technology is increasingly popular and has been widely applied in many industrial fields, due to its unique properties of decentralization, immutability, and traceability. Blockchain systems in different fields vary, with different block structures, consensus mechanisms and access permission models. These differences make it hard for different blockchain systems to interoperate with each other, which makes them isolated. Cross-chain technologies have been developed to solve this isolation problem in order to improve the interoperability of blockchains. Although some surveys on cross-chain technologies can be found, they are unable to keep up with the latest research progress due to their extremely fast pace of development. Moreover, the literature misses general criteria to evaluate the quality of cross-chain technologies. In this paper, a comprehensive literature review of cross-chain technologies is conducted by employing a comprehensive set of evaluation criteria. The preliminaries on blockchain interoperability are first presented. Then, a set of evaluation criteria is proposed in terms of security, privacy, performance, and functionality. The latest cutting-edge works are reviewed based on the proposed taxonomy of cross-chain technologies and their performance is evaluated against our proposed criteria. Finally, some open issues and future directions of cross-chain research are pointed out.
From Co-location to Remote Work: Remedies for Agile Project Management during COVID-19 Pandemic

The spread of COVID-19 in the beginning of 2019 has raised many challenges affecting agile team members. One of the major characteristics of Agile Project Management (APM) teams is co-location. Co-location enhances collaboration among team members who are self-organized and self-managed. Due to the pandemic, agile team members were obliged to start working remotely facing many challenges that compromised team productivity and efficiency. This paper proposes remedies that could help agile teams improve their collaboration and overcome the challenges imposed by the pandemic when working remotely. The information is based mainly on insights, suggestions and recommendations from agile management practitioners and subject matter experts.
IoT security: On-chip secure deletion scheme using ECC modulation in IoT appliances

NAND flash memory-based IoT devices inherently suffer from data retention issues. In IoT security, these retention issues are significant and require a robust solution for secure deletion. Secure deletion methods can be categorized into off-chip and on-chip schemes. Off-chip secure deletion schemes, based on block-level erasure operations, are unable to perform real-time trim operations. Consequently, they are vulnerable to hacking threats. On the other hand, on-chip secure deletion schemes enable real-time trim operations by performing deletion on a page-by-page basis. However, the on-chip scheme introduces a challenge of program disturbance for neighboring page data. The proposed on-chip deletion scheme tackles this problem by utilizing ECC code modulation through a partial program operation. This approach significantly reduces the program disturbance issue associated with neighboring page data. Moreover, the proposed code modulation secure deletion scheme allows for real-time verification of the deletion of original data.
Gender Classification from Offline Handwriting Images in Urdu Script: LeNet-5 and Alex-Net

Everyone has a distinct handwriting style, even identical twins who share the same genes. This distinctiveness has allowed researchers to develop systems that can classify the writer's traits, including gender, age, handedness, and others. A gender classification system, for instance, classifies handwriting patterns to predict the writer's gender. With the advancement of deep learning, it is now possible to automatically extract features from the document and classify them. In this paper, two gender classification models based on Alex-Net and LeNet-5 CNN were proposed, trained, and tested on a private Urdu handwriting dataset containing 284,000 pre-segmented characters from 200 males and 200 females. The proposed models achieved state-of- the-art performance compared to existing deep learning models for gender classification. The Alex-Net model achieved an overall accuracy of 99.14%, while the LeNet-5 model achieved an overall accuracy of 98.55%.
DWT-CompCNN: deep image classification network for high throughput JPEG 2000 compressed documents

For any digital application with document images such as retrieval, the classification of document images becomes an essential stage. Conventionally for the purpose, the full versions of the documents, that is the uncompressed document images make the input dataset, which poses a threat due to the big volume required to accommodate the full versions of the documents. Therefore, it would be novel, if the same classification task could be accomplished directly (with some partial decompression) with the compressed representation of documents in order to make the whole process computationally more efficient. In this research work, a novel deep learning model—DWT-CompCNN—is proposed for classification of documents that are compressed using High Throughput JPEG 2000 (HTJ2K) algorithm. The proposed DWT-CompCNN comprises of five convolutional layers with filter sizes of 16, 32, 64, 128, and 256 consecutively for each increasing layer to improve learning from the wavelet coefficients extracted from the compressed images. Experiments are performed on two benchmark datasets, Tobacco-3482 and RVL-CDIP, which demonstrate that the proposed model is time and space efficient, and also achieves a better classification accuracy in compressed domain.
Reactjs and Expressjs Implementation In PMK ITB STIKOM Bali Activity Management

In the Christian Student Brotherhood Student Activity Unit (PMK) ITB Stikom Bali the management of data, information and documentation is still done conventionally, namely hardcopy printing. These documents are vulnerable to being lost, damaged or difficult to find at a later date. Therefore we need a system to be able to manage data, documentation and other information regarding activities in PMK. In developing this activity management application using ReactJS and ExpressJS. This activity management application is expected to be a means of connecting information on activities carried out and making it easier for administrators to be able to store information from activities carried out. There are 5 stages in this research, namely data collection by direct observation of the object under study, literature study and stakeholder interviews. The second stage is an analysis from the user side, the data needed and the existing processes. The third stage is system design using conceptual database design, context diagrams and web architecture. The fourth stage is system implementation using the Javascript programming language with ReactJS on the frontend and ExpressJS on the backend and MongoDB as the database. The last stage is testing the system using the Blackbox testing method which focuses on testing the functionality of the system. The results of this study resulted in a website-based activity management system with predetermined user access as well as a liaison for activity information in the form of activity galleries and activity articles in UKM PMK using ReactJS and ExpressJS.
PMU-Spill: A New Side Channel for Transient Execution Attacks

Performance Monitor Unit (PMU) is an important hardware module in mainstream processors, which counts various architectural and microarchitectural events during the run-time of the processor. Theoretically, if an instruction is executed but doesn’t successfully retire (this is called transient execution), the events it triggers needn’t be recorded by PMU. However, in this study, we discover that current PMU implementations are capable of recording some events that are triggered in transient executions, which is a hardware vulnerability. Based on this vulnerability, we propose the attack, a new kind of side channel attack that enables attackers to maliciously leak secret data in transient executions. We perform a thorough study of PMU counters on five Intel processors and find that they all have vulnerable PMU counters that will measure transient execution events (there are 162 vulnerable PMU counters among all the 383 PMU counters). We demonstrate on real hardware that 112 vulnerable PMU counters can be utilized in attack to leak the secret data protected by Intel Software Guard Extensions (SGX). Besides, our experiments suggest that the throughput of attack is up to 291.2 bytes per second (Bps) with an error rate of 2.45% on average. This discovery and the corresponding mitigation methods can be helpful for microarchitecture designers to reevaluate the security risks induced by the PMU module.
Mathematics education in the age of artificial intelligence: How artificial intelligence can serve mathematical human learning

Click to increase image sizeClick to decrease image size AcknowledgmentsThe authors would like to thank LPDP (Lembaga Pengelola Dana Pendidikan), which is part of the Ministry of Finance of the Republic of Indonesia, and PUSLAPDIK (Pusat Layanan Pembiayaan Pendidikan), which is part of the Ministry of Education and Culture of the Republic of Indonesia, for assisting the authors in financing the master’s program and writing this article to be published.Disclosure statementNo potential conflict of interest was reported by the author(s).