Reviewerly/exHarmony huggingface dataset:
A deep learning model for detection of leukocytes under various interference factors

The accurate detection of leukocytes is the basis for the diagnosis of blood system diseases. However, diagnosing leukocyte disorders by doctors is time-consuming and requires extensive experience. Automated detection methods with high accuracy can improve detection efficiency and provide recommendations to inexperienced doctors. Current methods and instruments either fail to automate the identification process fully or have low performance and need suitable leukocyte data sets for further study. To improve the current status, we need to develop more intelligent strategies. This paper investigates fulfilling high-performance automatic detection for leukocytes using a deep learning-based method. We established a new dataset more suitable for leukocyte detection, containing 6273 images (8595 leukocytes) and considering nine common clinical interference factors. Based on the dataset, the performance evaluation of six mainstream detection models is carried out, and a more robust ensemble model is proposed. The mean of average precision (mAP) @IoU = 0.50:0.95 and mean of average recall (mAR)@IoU = 0.50:0.95 of the ensemble model on the test set are 0.853 and 0.922, respectively. The detection performance of poor-quality images is robust. For the first time, it is found that the ensemble model yields an accuracy of 98.84% for detecting incomplete leukocytes. In addition, we also compared the test results of different models and found multiple identical false detections of the models, then provided correct suggestions for the clinic.
Analysis of Student’s Academic Performance based on their Time Spent on Extra-Curricular Activities using Machine Learning Techniques

The foundational tenet of any nation's prosperity, character, and progress is education.Thus, a lot of emphasis is laid on quality of education and education delivery system in India with current financial year (2022-23) education budget outlay of Rs. 1,04,277.72crores.This research contributes in analyzing how students perform in academics depending upon the time spent on their extracurricular activities with the help of three Machine Learning prediction algorithms namely Decision Tree, Random Forest and KNN.Additionally, in order to comprehend the underlying causes of the shortcomings in each machine learning technique, comparisons of the prediction outcomes obtained by these various techniques are made.On our dataset, the Decision Tree outscored all other algorithms, achieving F1 84 and an accuracy of 85%.The research, which is at an introductory level, is meant to open the door for more complexes, specialised, and in-depth studies in the area of predicting the performance in academics.
Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo

Proximal Markov chain Monte Carlo is a novel construct that lies at the intersection of Bayesian computation and convex optimization, which helped popularize the use of nondifferentiable priors in Bayesian statistics. Existing formulations of proximal MCMC, however, require hyperparameters and regularization parameters to be prespecified. In this article, we extend the paradigm of proximal MCMC through introducing a novel new class of nondifferentiable priors called epigraph priors. As a proof of concept, we place trend filtering, which was originally a nonparametric regression problem, in a parametric setting to provide a posterior median fit along with credible intervals as measures of uncertainty. The key idea is to replace the nonsmooth term in the posterior density with its Moreau-Yosida envelope, which enables the application of the gradient-based MCMC sampler Hamiltonian Monte Carlo. The proposed method identifies the appropriate amount of smoothing in a data-driven way, thereby automating regularization parameter selection. Compared with conventional proximal MCMC methods, our method is mostly tuning free, achieving simultaneous calibration of the mean, scale and regularization parameters in a fully Bayesian framework. Supplementary materials for this article are available online.
Necessity and solutions for ethical education among teachers in the framework of industrial revolution 4.0

At present, the education and training of countries in the world, especially developing countries, strive to globalization from the region to the world. Vietnam is not out of the development of the country, so now Vietnam has developed comprehensive strategies in all areas, especially education - training is of particular concern. Vietnam has been implementing comprehensive educational Reform. In response to that development, the teacher needs a lot of changes in research and teaching, personal development capacity etc. to meet the globalization requirement of education training. Currently, the contingent of teachers in Vietnam has been constantly developing in quantity and quality, continuing to be standardized to better meet social requirements. However, there are some limitations. On the basis of studying the state’s regulatory documents, and the evaluation of educational institutions, in this article, the author has discussed the rationale for building the ethics model of the teacher. From that solutions to build and perfect teachers’ personalities in the context of globalization and international integration.
Cloud Computing in Blockchain

Blockchain is a decentralised ledger that captures and stores data including but not limited to all transactional details. These documents are dispersed to all of the connected devices. There is no way to alter the data after it has been recorded, and all internal transactions are approved by consensus mechanisms. Blockchain technology is a critical enabler for the broad adoption of digital currencies like Bitcoin. The phrase “cloud computing” is used to describe the practice of avoiding the use of a single computer or server in favour of a distributed network of remotely located ones for the purposes of data storage, management, and processing. “Compliance, data management, data security, and reliability are just some of the many problems it still faces. In this article, we’ll talk about some of the cloud’s biggest problems right now and some possible solutions, such as combining the cloud with blockchain. To demonstrate blockchains’ dominance, we choose to conduct a brief analysis of prior efforts that focussed on integrating blockchains with cloud computing. As part of this study, we also developed an architecture to highlight the interactions between blockchain and cloud computing.
Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning

It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today’s hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher risk of mortality, morbidity, longer length of stay, and increased cost. The methodology proposed to predict ICU readmission could improve the patients’ care. The objective of this work is to explore and evaluate the potential improvement of existing models for predicting early ICU patient readmission by using optimized artificial intelligence algorithms and explainability techniques. In this work, XGBoost is used as a predictor model, combined with Bayesian techniques to optimize it. The results obtained predicted early ICU readmission (AUROC of 0.92 ± 0.03) improves state-of-the-art consulted works (whose AUROC oscillate between 0.66 and 0.78). Moreover, we explain the internal functioning of the model by using Shapley Additive Explanation-based techniques, allowing us to understand the model internal performance and to obtain useful information, as patient-specific information, the thresholds from which a feature begins to be critical for a certain group of patients, and the feature importance ranking.
A Lightweight Encryption Algorithm to Enhance Wireless Sensor Network Security on the Internet of Things

The Internet of Things (IoT) is an anticipated future technology that promises to connect a massive number of devices over the internet. Wireless Sensor Networks (WSNs) are regarded as one of the most essential subnetworks of the IoT. Sensor networks are being utilized by IoT to gather, monitor, and send sensitive data across wireless networks. Because the information transferred through WSNs is easily exposed to cyber-attacks, data security is critical. In WSNs, the attacker's adversary aims are to deteriorate and halt the network's effective use, as well as to interrupt network services, rendering them unreachable to the users or providing a user with false feedback. Since the users don’t have control over their data transmitted on the wireless medium or stored in the middleware, anyone with internet access can access it. This puts data confidentiality, authenticity, and integrity at risk since users with unauthorized access can easily access, alter, and manipulate data in transit. The proposed Lightweight Security Algorithm (LSA) is a hybrid algorithm created by combining the Security Protocol for Sensor Networks (SPINS) with the Secure IoT (SIT) encryption technique to improve WSN’s data security while lowering the threshold of attacks and minimize power consumption in WSNs without impacting network performance. Furthermore, the proposed LSA reduces the key generation time by 102mS thus improving the security by 99%. During data transmission, the power consumption is reduced by an average of 411.2uJ and the Packet Drop Ratio (PDR) is between 90 and 99% when comparing it with SPN and Feistel techniques.
ICT applications and the COVID-19 pandemic: Impacts on the individual's digital data, digital privacy, and data protection

A prominent move amid the COVID-19 pandemic is related to the wide use of ICT applications for various reasons. Such services are context-specific and sector-specific, but we see transformative directions leading to digital data collection, monitory, and management platforms. For now, these have been beneficial to evaluate trends and issues related to the pandemic. Some aggregated data could also help decision-making processes, which are helpful to contain such disruptive events. However, the main concern is the use of the individual's data and information, which means we may shift to micro-management and eventual controlling tools that could harm data protection processes. Undoubtedly, the use of ICTs during the COVID-19 pandemic has been primarily positive at multiple scales, but we have to evaluate the pros and cons before accepting all data collection processes. Such ICT-mediated platforms and/or applications must remain beneficial to all and avoid breaching the individual's data protection. This short communication paper first introduces ICT applications during the COVID-19 before providing an overview and further analysis of the situation of the ICT applications. Afterward, it discusses issues of data privacy, data collection, and data use, which are the backbone of ICT applications. The discussions highlight that future research in this area could look into associated issues related to digital privacy, data-driven approaches, and data protection regulations.
Personalized Latent Structure Learning for Recommendation

In recommender systems, users' behavior data are driven by the interactions of user-item latent factors. To improve recommendation effectiveness and robustness, recent advances focus on latent factor disentanglement via variational inference. Despite significant progress, uncovering the underlying interactions, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , dependencies of latent factors, remains largely neglected by the literature. To bridge the gap, we investigate the joint disentanglement of user-item latent factors and the dependencies between them, namely latent structure learning. We propose to analyze the problem from the causal perspective, where a latent structure should ideally reproduce observational interaction data, and satisfy the structure acyclicity and dependency constraints, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , causal prerequisites. We further identify the recommendation-specific challenges for latent structure learning, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the subjective nature of users' minds and the inaccessibility of private/sensitive user factors causing universally learned latent structure to be suboptimal for individuals. To address these challenges, we propose the personalized latent structure learning framework for recommendation, namely PlanRec, which incorporates 1) differentiable Reconstruction, Dependency, and Acyclicity regularizations to satisfy the causal prerequisites; 2) Personalized Structure Learning (PSL) which personalizes the universally learned dependencies through probabilistic modeling; and 3) uncertainty estimation which explicitly measures the uncertainty of structure personalization, and adaptively balances personalization and shared knowledge for different users. We conduct extensive experiments on two public benchmark datasets from MovieLens and Amazon, and a large-scale industrial dataset from Alipay. Empirical studies validate that PlanRec discovers effective shared/personalized structures, and successfully balances shared knowledge and personalization via rational uncertainty estimation.
Cycle optimization metric learning for few-shot classification

Metric learning methods are widely used in few-shot learning due to their simplicity and effectiveness. Most existing methods directly predict query labels by comparing the similarity between support and query samples. In this paper, we design a cycle optimization metric network for few-shot classification task that optimizes model performance based on loop-prediction of the labels of query samples and support samples. Specifically, we construct a forward network and reverse network based on a geometric algebra Graph Neural Network (GA-GNN). These two networks form the loop prediction from support samples to query samples and then back to support samples, guided by a cycle-consistency loss. We also introduce an optimization module that is able to correct the predicted results of query samples to further improve the network performance. Our extensive experimental results demonstrate that the proposed cycle optimization metric network outperforms existing state-of-the-art few-shot learning methods on classification tasks.
CoT-UNet++: A medical image segmentation method based on contextual transformer and dense connection

&lt;abstract&gt; &lt;p&gt;Accurate depiction of individual teeth from CBCT images is a critical step in the diagnosis of oral diseases, and the traditional methods are very tedious and laborious, so automatic segmentation of individual teeth in CBCT images is important to assist physicians in diagnosis and treatment. TransUNet has achieved success in medical image segmentation tasks, which combines the advantages of Transformer and CNN. However, the skip connection taken by TransUNet leads to unnecessary restrictive fusion and also ignores the rich context between adjacent keys. To solve these problems, this paper proposes a context-transformed TransUNet++ (CoT-UNet++) architecture, which consists of a hybrid encoder, a dense connection, and a decoder. To be specific, a hybrid encoder is first used to obtain the contextual information between adjacent keys by CoTNet and the global context encoded by Transformer. Then the decoder upsamples the encoded features by cascading upsamplers to recover the original resolution. Finally, the multi-scale fusion between the encoded and decoded features at different levels is performed by dense concatenation to obtain more accurate location information. In addition, we employ a weighted loss function consisting of focal, dice, and cross-entropy to reduce the training error and achieve pixel-level optimization. Experimental results demonstrate that the proposed CoT-UNet++ method outperforms the baseline models and can obtain better performance in tooth segmentation.&lt;/p&gt; &lt;/abstract&gt;
Open Data and the 2023 NIH Data Management and Sharing Policy

As the largest public funder of biomedical research in the world, the National Institutes of Health’s (NIH) new Data Management and Sharing (DMS) Policy is a large step toward shifting the culture of medical research toward a broader sharing of scientific data. Librarians in the health sciences support researchers by assisting with data management plans, research dissemination, abiding by publisher/grant requirements on data sharing, and suggesting recommended repositories for data preservation. This article will serve as a primer on open data, data sharing, the NIH’s DMS Policy and its implications, and how librarians can support researchers in this landscape.
Information Systems Effect on Enabling Knowledge Management

&#x0D; &#x0D; &#x0D; &#x0D; Purpose: This research aims to neutralize the impact of information systems on enabling knowledge management in the banking sector in Syria.&#x0D; &#x0D; Theoretical framework: IT resources positively affect knowledge management capability. As studies (Watson 2007; O'Brien &amp; Marakas 2011; Mao et al., 2016) and. We document a relationship between information systems enabling knowledge management in Syrian banks&#x0D; &#x0D; Design/methodology/approach: This study of measuring this effect was based on the design of a questionnaire distributed to 369 individuals working in Syrian government banks in 2022.&#x0D; &#x0D; Findings: This study concluded that there is a direct impact of information systems on enabling knowledge management, and the technological requirements of information systems have a greater impact than the organizational requirements. The study also showed that there were differences between the answers of the sample members in all the research axes, as it showed the lack of information systems or enabling knowledge management in banks for most workers from the second and third job categories, as opposed to workers from the first job category with great job experience, whose answers were positive towards the two research variables.&#x0D; &#x0D; Research, Practical &amp; Social implications: This article offers empirical support for theory-based claims and helps create a empirical framework for the success of knowledge management activities.&#x0D; &#x0D; Originality/value: Very few research on management level employees' opinions of knowledge sharing in banks have been done in Syria.&#x0D; &#x0D; &#x0D; &#x0D;
Evaluating the use of virtual microscopy in cytology education

Whole slide imaging is a promising tool for cytology. In the present study, we assessed the performance of and user experience with virtual microscopy (VM) to determine its feasibility and usage in an educational setting.From January 1 through August 31, 2022, 46 Papanicolaou slides, of which 22 (48%) were abnormal, 23 (50%) were negative, and 1 (2%) was unsatisfactory, were reviewed by the students using both VM and light microscopy (LM) platforms. In addition to assessing VM's overall performance, the SurePath imaged slides' accuracy was reviewed as a potential alternative to ThinPrep because of its cloud storage advantage. Finally, the students' weekly feedback logs were analyzed to gain insights for improving the digital screening experience.The overall diagnostic concordance difference was significant between the 2 screening platforms (Z = 5.38; P < 0.001), favoring LM (86% correct diagnosis) over VM (70% correct diagnosis). The overall sensitivity of VM and LM was 54.0% and 89.6%, respectively. VM also had an overall higher specificity (91.8%) compared with LM (81.3%). LM performed better than whole slide imaging for the correct identification of an organism when one was present, with 77.6% sensitivity compared with 58.9% for the digital platform. The rate of agreement for the SurePath imaged slides with the reference diagnosis was 74.3% compared with 65.7% for the ThinPrep slides. Finally, 4 themes were discerned from reviewing the user logs, with issues about image quality and the lack of fine focus functionality most frequently mentioned, followed by themes associated with a higher learning curve and novelty associated with the digital screening.Although the VM results were poorer than the LM results in our validation, its use in an educational setting is promising considering the continued technological improvements and the renewed focus on improving the digital user experience.
Identification of soil parent materials in naturally high background areas based on machine learning

Recently, farmlands with high geological background of Cd derived from carbonate rock (CA) and black shale areas (BA) have received wide attention. However, although both CA and BA belong to high geological background areas, the mobility of soil Cd differs significantly between them. In addition to the difficulty in reaching the parent material in deep soil, it is challenging to perform land use planning in high geological background areas. This study attempts to determine the key soil geochemical parameters related to the spatial patterns of lithology and the main factors influencing the geochemical behavior of soil Cd, and ultimately uses them and machine-learning methods to identify CA and BA. In total, 10,814 and 4323 surface soil samples were collected from CA and BA, respectively. Hot spot analysis revealed that soil properties and soil Cd were significantly correlated with the underlying bedrock, except for TOC and S. Further research confirmed that the concentration and mobility of Cd in high geological background areas were mainly affected by pH and Mn. The soil parent materials were then predicted using artificial neural network (ANN), random forest (RF) and support vector machine (SVM) models. The ANN and RF models showed higher Kappa coefficients and overall accuracies than those of the SVM model, suggesting that ANNs and RF have the potential to predict soil parent materials from soil data, which might help in ensuring safe land use and coordinating activities in high geological background areas.
Hexapartite steering based on a four-wave-mixing process with a spatially structured pump

Multipartite Einstein-Podolsky-Rosen (EPR) steering has been widely studied, for realizing safer quantum communication. The steering properties of six spatially separated beams from the four-wave-mixing process with a spatially structured pump are investigated. Behaviors of all (1+i)/(i+1)-mode (i=1,2,3) steerings are understandable, if the role of the corresponding relative interaction strengths are taken into account. Moreover, stronger collective multipartite steerings including five modes can be obtained in our scheme, which has potential applications in ultra-secure multiuser quantum networks when the issue of trust is critical. By further discussing about all monogamy relations, it is noticed that the type-IV monogamy relations, which are naturally included in our model, are conditionally satisfied. Matrix representation is used to express the steerings for the first time, which is very useful to understand the monogamy relations intuitively. Different steering properties obtained in this compact phase-insensitive scheme have potential applications for different kinds of quantum communication tasks.