Reviewerly/exHarmony huggingface dataset:
A Breast Ultrasound Tumor Detection Framework Using Convolutional Neural Networks

Accurate and efficient breast cancer screening is of great significance to women's health. In order to solve the severe challenges in mass breast screening, such as poor ultrasound image quality, differences in the age and geographical distribution of the population, we proposed a detection framework based on convolution neural networks for tumor detection and tracking in ultrasound video. Firstly, some data pre-processing and tricks are adjust to improve YOLOv4 for making it more suitable for tumor detection task. Secondly, Kernelized Correlation Filters (KCF) tracking algorithm as post-processing is used to track and fuse all the detection bounding boxes. In this way, all the detection results can be aggregated to form a smaller number of tumor sequences, and some false positives can also be filtered out. The proposed method was evaluated on 251 cases with tumors. It obtains a promising result with sensitivity 97.62% and 12.3 false positives per case. Experimental results demonstrate that our method has better performance on tumor detection for ultrasound videos from mass breast screening.
The Application of Artificial Intelligence in Football Risk Prediction

Football is the most popular sports in the World, with an estimated global following of 4.0 billion fans worldwide. Football draws attention from people of various age groups. The result of the game only decides the performance of the team and individual players. The player has to train smarter to avoid a career-ending injury. Sports have also entered into the new era of artificial intelligence as any industry. Artificial intelligence (AI) in football acts like a teammate to the players and also plays the role of an assistant coach. The coach uses artificial intelligence and incorporates it into the traditional way of training. The Football Associations have already implemented sensors to collect data in the form of technologies such as Video Assistant Referee and Goal Line Technology. Additionally, the quality of the players and the coaches is improved with smart technological implementation. This technology itself incorporates the utilization of smart technologies for data acquisition using sensor networks and an intelligent data analysis. The proposed algorithm is compared with the fuzzy logic model (FLM) and found that it is 7.2% of higher risk predication by the proposed model than the existing.
Analyzing the effects of COVID-19 pandemic on supplier performance evaluation factors via fuzzy cognitive map with a hybrid learning system

Purpose The COVID-19 pandemic has proven that how supply chain management (SCM) can become a crucial process for sustainability of the world's production/service. The global supply chain crisis during pandemic has affected most of the sectors. Home and personal care products manufacturers are among them. In this study (1) the problems at SCM of personal and home care products manufacturers during pandemic are discussed with the help of medium-size manufacturer and (2) the factors affecting suppliers' performance for the relevant sector during COVID-19 are analyzed comprehensively. Design/methodology/approach The importance of the factors is evaluated using fuzzy cognitive maps that can help to reveal hidden casual relationships with the help of expert knowledge. In order to eliminate subjectivity due to usage of expert knowledge, the maps are trained with a hybrid learning approach that consists of Non-linear Learning and Extended Great Deluge Algorithms to increase robustness of the analysis. Findings The findings of the study indicate that the factors such as general quality level of products/services, compliance to delivery time, communication skills and total production capacity of suppliers have been crucial factors during pandemic. Originality/value While the implementation of the hybrid learning approach on supply chain can fill the gap in the relevant literature, the promising results of the study can prove the convenience of the methodology to model the of complex systems like supply chain processes.
Quantum Cryptography

In today’s world, security in data communication has become a top-most priority in every organization. In general, cryptography is defined as the methodology to ensure data is secure by employing suitable encryption and decryption techniques. On the transmitter side, the original message is encrypted using a key. Then the encrypted data is decrypted with the duplicate key at the receiver side as per the accepted protocol between the two parties. In conventional cryptographic techniques, eavesdropping is prevented from accessing the contents of encrypted message by employing certain mathematical function and techniques, but this security methodology does not guarantee any key security. In many scenarios, a secure communication link is established by using a key, but a third party with a bad intent may steal the session keys, giving rise to data security, getting compromised. So in this regard, quantum cryptography is a better alternative when it comes to data security integrity. In this research, a detail about quantum cryptography is elaborated on, and also discussed are various concepts like single photon protocol, loss of data in quantum computing, how quantum is affecting computer communication systems. In conventional cryptographic methodology of data transmission and reception, there is always a probability of eavesdropping leading to the compromising of data integrity and security. The solution to this problem can be obtained by integrating the quantum cryptographic technique with data at the sender as well as the receiver, to detect and minimize various errors by employing suitable bit error scheme. This involves the usage of programming language specific to quantum computing like qcl and quipper to achieve efficient encryption and decryption with faster processing capability.
Digital currencies and great power rivalry: China as a disseminator in the digital age

The United States reign as global hegemon has been empowered by massive structural power over key international economic, technological and ideological networks. As chief disseminator during the era of emerging computer technology and communications, the U.S. became a rules-maker, empowered to shape international governance in its favour. However, a new era of digitalized assets, transactions and networks will provide an opportunity for Central Bank Digital Currencies (‘CBDCs’) to constitute the technological-financial-business bones of the developing digitalized global economy. CBDCs will exert massive influence on the international economic, technological and ideological orders. Moreover, all of these impacts are inextricably connected to the hegemonic rivalry between the United States and China. Astutely, China has been developing the digital Yuan and is the first major economy to distribute a CBDC. If China's CBDC successfully migrates to external usage, and depending upon the degree of crucial Chinese domestic reforms, the digital Yuan may offer an opportunity for China to be a disseminator in the digital age. Chinese influence over global networks and governance frameworks would vest China with advantageous structural power in the economic, technological and ideological spheres – the fulcrums of the U.S.-China hegemonic rivalry.
Development of digital competence of school teachers

The article is devoted to the problem of solving the problems of forming and improving the digital competence of teachers. In determining the essence of the concepts of competence, quantitative competence of the teacher, the conclusions of scientists-researchers are given. Upon writing this article, the authors used the sociological method and conducted a survey among teachers. The survey involved 2.840 teachers from districts and cities of the Turkestan region of the Republic of Kazakhstan. The results of a survey of secondary school teachers on the implementation of methods based on digital technologies in education and the development of multimedia content for educational purposes are presented. It was concluded that in the Turkestan region there is an urgent need to consider operational measures to form digital competencies of teachers.&#x0D; &#x0D; Keywords: digitalization, educational content, knowledge, educational process, information.
Excitation of semiconductor nanosystems by a few-photon frequency-multimode optical field

Abstract The excitation of a semiconductor quantum nanosystem induced by a frequency-multimode nonclassical electromagnetic field is analytically investigated. The possibility to control the features and dynamics of different excitation channels by varying the weights and initial energy distribution in the considered field spectral modes is demonstrated. Strong coupling between different frequency modes is found to arise due to their interaction with electronic subsystem and is shown to influence dramatically on the excitation. The peculiarities of the excitation process induced by a multi-frequency Schmidt mode, which characterizes spectral distribution of squeezed vacuum light, are revealed. The optimal spectral profile of the Schmidt mode and initial energy distribution of different field spectral components providing the most efficient excitation of a nanosystem are found.
Hyaku: A qualitative negotiation-through-interaction interface to support runners in achieving balanced training sessions

Wearable systems and apps for recreational running provide awareness on one's performance and activity and aim for increased engagement.Yet, these systems focus on sensor-generated data and do little to integrate subjective and contextual factors as meaningful insights.Their interaction and feedback mechanisms mostly rely on numbers and do not always match users' real-life needs.To address these gaps, we explore human-computer negotiation as an interactive mechanism to enable recreational runners to adjust the recommendations delivered by a system.We do so by designing tangible qualitative interfaces labelled "Tradeables" (whose label is inspired by the idea of "trading" with a system).By reaching a trade-off between objective sensor evaluation and subjective feelings, we gather insights into the design of negotiation inter-faces for sport and well-being.We present Hyaku, a research artefact providing insights on the design considerations, challenges, and opportunities of tangible and qualitative interfaces for negotiation.
Small Sample Estimation of Classification Metrics

Estimation of classification evaluation metrics for small sample classification data is one of the most challenging task in Machine Learning model building process. The value of an evaluation metric based on the test data is considered to be the generalized performance measure of a classification model. Measuring the performance of a model based only on the test data works well when we have a large data set and both train and test data follow the same population distribution. However, this assumption does not always work for small data set. Therefore, measuring performance of a model based on test data set may mislead us for small sample classification problems. To deal with such situations, in this paper, we propose a modified method to estimate classification evaluation metrics. The new method provides an improved and consistent estimation of classification evaluation metrics.
Troubleshooting Immunohistochemistry

Immunohistochemistry assays can be technically difficult. There are countless opportunities for errors. Some faults can be fixed, and this chapter contains a precise appraisal of common problems encountered along with suggested solutions. Incorporating both pre-analytic conundrums and analytic challenges from a staining perspective, it is written by a medical scientist able to think outside the square and offer resolutions ranging from the practical to the unconventional. All prescribed advice has been tested and proven successful. Suggesting corrective actions is an exercise in giving the worker options and workarounds for certain situations. Doing more with less and less testing material and lifting sections from slides to be used again when the paraffin block is exhausted are two examples of what can be achieved. Ultimately, it is about tuning protocols to make the most of the precious test tissue given whilst eliminating unnecessary wastage.
Can PCI DSS 4.0 reverse the decline in compliance?

The Payment Card Industry Security Standards Council (PCI SSC) finally released the long-awaited version 4.0 of the PCI Data Security Standard (PCI DSS) at the end of March after three long years of consultation. It's been described as the most substantial update to the standard for 17 years and the Council received unprecedented levels of feedback, with more than 6,000 items from over 200 members of the global payments industry. But what are its effects likely to be?
Digital teaching competence development in higher education: key elements for an institutional strategic approach

Digital teaching competence is a crucial cornerstone of the development of educational institutions when the ecosystem of people as learners is mainly digital. One of the main challenges of higher educational institutions is becoming systems that strategically support this competence and not just entities that demand it from their teaching staff. This chapter systematically explores some of the most relevant and updated literature on the previous statements and models of digital teaching competence and institutional implementation of ICT (information and communication technologies) and some of the most current plans of digital transformation in higher educational institutions. The aim is to identify, through a grounded theory approach, the key elements to be taken into account when designing strategic plans to promote digital teaching competence in higher educational institutions. The work proposes a general open framework that can be used by managers and members of an educational community as a compass to design these strategic plans.
Transfer learning-based approach for automated kidney segmentation on multiparametric MRI sequences

Purpose: Multiparametric magnetic resonance imaging (mp-MRI) is being investigated for kidney cancer because of better soft tissue contrast ability. The necessity of manual labels makes the development of supervised kidney segmentation algorithms challenging for each mp-MRI protocol. Here, we developed a transfer learning-based approach to improve kidney segmentation on a small dataset of five other mp-MRI sequences. Approach: We proposed a fully automated two-dimensional (2D) attention U-Net model for kidney segmentation on T1 weighted-nephrographic phase contrast enhanced (CE)-MRI (T1W-NG) dataset (N = 108). The pretrained weights of T1W-NG kidney segmentation model transferred to five other distinct mp-MRI sequences model (T2W, T1W-in-phase (T1W-IP), T1W-out-of-phase (T1W-OP), T1W precontrast (T1W-PRE), and T1W-corticomedullary-CE (T1W-CM), N = 50) and fine-tuned by unfreezing the layers. The individual model performances were evaluated with and without transfer-learning fivefold cross-validation on average Dice similarity coefficient (DSC), absolute volume difference, Hausdorff distance (HD), and center-of-mass distance (CD) between algorithm generated and manually segmented kidneys. Results: The developed 2D attention U-Net model for T1W-NG produced kidney segmentation DSC of 89.34 ± 5.31 % . Compared with randomly initialized weight models, the transfer learning-based models of five mp-MRI sequences showed average increase of 2.96% in DSC of kidney segmentation (p = 0.001 to 0.006). Specifically, the transfer-learning approach increased average DSC on T2W from 87.19% to 89.90%, T1W-IP from 83.64% to 85.42%, T1W-OP from 79.35% to 83.66%, T1W-PRE from 82.05% to 85.94%, and T1W-CM from 85.65% to 87.64%. Conclusions: We demonstrate that a pretrained model for automated kidney segmentation of one mp-MRI sequence improved automated kidney segmentation on five other additional sequences.
The Role of Religious Poetry in Enhancing Children’s Spiritual Intelligence (SI)

The nature and quality of presenting religious literature to children have engaged the minds of many scholars of children’s literature, sometimes posing remarkable challenges. Today’s knowledge declares that children brim with a variety of intelligences and modes of interpretation. A great portion of their competence, dubbed “spiritual intelligence,” may be created and reinforced by dint of religious literature. Mastering the working of the elements of spiritual intelligence and applying them to poetry may contribute to the enhancement of the quality of children’s religious poetry. To this end, the authors analyzed 300 pieces of poetry written for children with a religious theme, using descriptive and analytic methods in restoring and appraising the elements of spiritual intelligence. The findings suggest that self-awareness and posing questions are among the most efficacious elements of spiritual intelligence in children’s religious poetry whereas some aspects are neglected.
Identifying Objects and Remembering Images: Insights From Deep Neural Networks

People have a remarkable ability to identify the objects that they are looking at, as well as remember the images that they have seen. Researchers know that high-level visual cortex contributes in important ways to supporting both of these functions, but developing models that describe how processing in high-level visual cortex supports these behaviors has been challenging. Recent breakthroughs in this modeling effort have arrived by way of the illustration that deep artificial neural networks trained to categorize objects, developed for computer vision purposes, reflect brainlike patterns of activity. Here we summarize how deep artificial neural networks have been used to gain important insights into the contributions of high-level visual cortex to object identification, as well as one characteristic of visual memory behavior: image memorability, the systematic variation with which some images are remembered better than others.
Analysis and optimization of shielding in measuring helical gear tooth flank by laser interferometry

Abstract Phase shifting laser interferometry is an effective method to measure the form error of a helical gear tooth flank. However, during measurement, part of the measurement light is inevitably shielded by an adjacent tooth or its own structure, and thus cannot be captured by the detector, resulting in the inability to collect a complete set of tooth surface shape information. This problem restrains the practicality of measuring a gear tooth flank by laser interferometry. We propose an analysis method and optimal search algorithm for shielding, to collect a complete set of tooth surface information from a measured gear. First, we propose an analysis method for shielding, based on ray-tracing, which allows the measurable region of helical gear tooth flanks with common parameters to be statistically analyzed. The variation law of the main parameters and a measurable region of the common helical gear tooth surface is expounded. Second, in order to obtain the complete interferogram, we propose an optimal search algorithm for shielding, by changing the relative position between the measured gear and the measurement optical system. Finally, relevant simulation and experiments prove that the proposed analysis and optimization methods are feasible. Furthermore, when the oblique incidence method is used to measure the shielded precision complex surface, the proposed methods of analyzing and optimizing shielding can be used for reference, to realize the complete measurement of the measured surface.
Blockchain in the Banking Sector

Financial institutions opting for blockchain technology raise controversy about how this new technology can disrupt the traditional banking sector. Many issues like a decentralized system with no middlemen and untrusted parties pose serious threats to the banking system. Hence, there is a serious need to do research on the impact of blockchain on financial institutions, especially in the banking sector, to understand the influence of blockchain as a disruptor and a technology that is revolutionizing the banking sector. The chapter will explain the concepts and role of blockchain in the banking domain. Secondly, the ongoing criticism of the blockchain as a disruptor in the banking domain is answered in this chapter. Third, the role of blockchain in the transformation of the banking sector is discussed by explaining the blockchain process in the key banking services.
Text-guided visual representation learning for medical image retrieval systems

Radiologists are now confronted with the difficulty of interpreting cross-sectional studies composed of thousands of images. In hospitals, all acquired imaging data are stored in a picture archiving and communication system (PACS). To take advantage of these masses of previously interpreted images, with the ultimate goal to facilitate the diagnosis of new cases, a promising approach would be to integrate a Content-Based Image Retrieval (CBIR) system into PACS. CBIR system performances are inherently limited by the features considered to represent the images. The current state of the art for the extraction of visual features relies on deep learning which requires a sufficient amount of annotated data to learn a generalizable model, such annotated data being rare and difficult to use in medical imaging. At the same time, PACS contain additional information such as radiological reports which supplement visual information carried by the images. We study here how such semantic information, hidden in these reports, can be used to supervise the learning of neuronal models to build a better visual representation of images. In this context our contribution is threefold. We first adapted a contrastive learning approach, which is usually used to learn representation from pairs of positive images in an unsupervised manner, to deal with in-domain medical data. Second, to train such a model to be robust and generalizable with a sufficient amount of data, we propose to re-employ the "dormant" medical imaging literature. Finally, the visual features and the deep models learned in this way, can be considered in CBIR systems as coarse-grained information which can then be fine-tuned in PACS, with more specific images depending on the applications. The obtained experimental result with state of the art contrastive learning methods highlight the interest of this approach.
The Cover Source Mismatch Problem in Deep-Learning Steganalysis

This paper studies the problem of Cover Source Mismatch (CSM) in steganalysis, i.e. the impact of a testing set which does not originate from the same source than the training set. In this study, the trained steganalyzer uses state of the art deep-learning architecture prone to better generalization than feature-based steganalysis. Different sources such as the sensor model, the ISO sensitivity, the processing pipeline and the content, are investigated. Our conclusions are that, on one hand, deep learning steganalysis is still very sensitive to the CSM, on the other hand, the holistic strategy leverages the good generalization properties of deep learning to reduce the CSM with a relatively small number of training samples.