Reviewerly/exHarmony huggingface dataset:
Goodput Improvement with Low–Latency in Data Center Network

Data Center is used for large data storage in various industries and Organizations. The data traversing from source to destination is a process of Data Center Network (DCN). In the recent years, many modified TCP protocols were used because TCP is not capable for DCN. One of the modified TCP protocol snamed MPTCP (Multipath Transmission control Protocol) is efficient for DCN. This protocol has multiple paths from data transferring. MPTCP is highly suitable for a large volume of data packets and not efficient for transferring a small volume of data packets. The transmission of the small and large volume of data packets takes equal time, so there is a possibility of latency in time. These MPTCP issues are eliminated by the proposed Enhanced Multipath Transmission Control Protocol (EMPTCP). This protocol further adds a Packet sprinkling method to resolve the issues like low data transmission, timeout, and latency of MPTCP. Packet Sprinkling is a method which focuses on sending a small volume of data packets effectively. MPTCP is also efficient for transmission of the large volume of data packets. The implementation is done in NS2 for evaluating the proposed protocol and the results show that EMTCP is better than MPTCP.
An Approach for DC Motor Speed Control with Off-Policy Reinforcement Learning Method

In the literature, interest in automatic control systems that do not require human intervention and perform at the desired level increases day by day. In this study, a Twin Delay Deep Deterministic Policy Gradient (TD3), a reinforcement learning algorithm, automatically controls a DC motor system. A reinforcement learning method is an approach that learns what should be done to reach the goal and observes the results that come out with the interaction of both itself and the environment. The proposed method aims to adjust the voltage value applied to the input of the DC motor in order to reach output with single input and single output structure to the desired speed.
Differences in learning retention and experience of augmented reality notes compared to traditional paper notes in a chiropractic technique course: A randomized trial

ABSTRACT Objective To investigate if a written guide or augmented reality (AR) guide improves free recall of diversified chiropractic adjusting technique and to capture participants' impressions of the study in a poststudy questionnaire. Methods Thirty-eight chiropractic students were evaluated for diversified listing (a nomenclature denoting vertebral malposition and correction) recall, pre-AR and post-AR, or written guide review. The vertebral segments used were C7 and T6. Two randomized groups reviewed an original course written guide (n = 18) or a new AR guide (n = 20). A Wilcoxon-Mann-Whitney (C7) and t test (T6) compared group differences in reevaluation scores. A poststudy questionnaire was given to capture participants' impressions of the study. Results Both groups showed no significant differences in free recall scores after reviewing the guides for C7 or T6. The poststudy questionnaire suggested a number of strategies could be used to improve current teaching material such as more detail in the written guides and organizing content into smaller blocks. Conclusion Use of an AR or written guide does not seem to change participants' free recall ability when used to review diversified technique listings. The poststudy questionnaire was useful to identify strategies to improve currently used teaching material.
Practice and Exploration of the Construction of New Engineering Skills Studios under the "Three navigation and Five Integration" Model from the Perspective of Industrial Demands

According to the talent demand statistics of the IoT industry by the China Academy of Information and Communication Technology, the total talent demand gap in the intelligent hardware industry in the next few years will exceed 16 million people. It is urgent for vocational colleges to cultivate a large number of technologies integrated technical and skilled talents in the form of artificial intelligence professional groups. Starting from the needs of the positions, various mentors deeply participate in the development of studio training courses, "1+X" certificate training, order project guidance, innovation and entrepreneurship guidance, and other routine work. Using the studio management system as a driving force, clarify the division of labor for each mentor, specific requirements for mentoring and passing on skills, and performance evaluation indicators, to ensure that mentors are not "absent" and provide protection for students' growth. We precisely meet the needs of talent training for new positions in the information technology industry. Driven by the application research and development task, guided by students' interests, driven by enterprise product research and development, and driven by the research and development of intelligent hardware and consumer electronics, we complete the whole process from professional training to precision employment, and improve employment competitiveness. Students participate in real projects or enterprise real orders, master the latest industry development, technical information, mainstream work methods, and achieve precise education.
Design and Implementation of Data Service Middleware in Earthquake Precursor Instrument Platform

With the gradual development of the earthquake signal early warning system, more and more earthquake precursor signal observation instruments and equipment have been added to the earthquake signal early warning system, which has also caused various compatibility issues. This article designs a data service middleware solution based on B/S architecture for the interaction between earthquake precursor signal observation equipment and remote access terminals on the earthquake precursor instrument platform. Combining embedded technology, it achieves compatible and universal deployment of heterogeneous terminals, and achieves the ideal function of data service middleware. At the same time, in the data service middleware system, real-time data access, presentation, and log upload and download of earthquake precursor instruments are implemented, as well as remote editing of basic information such as instrument IP addresses and station positions, as well as instrument status monitoring and other functions.
Review on Privacy Protection of Electronic Medical Data in Cross-domain Sharing

With the rapid development of information technology, the medical industry is rapidly moving in the direction of smart medical care, and more and more users tend to put their medical data in cloud servers, so as to share them safely among Internet hospitals. Electronic medical data stores all patients' medical information, and its high value is obvious to all, which also leads to the attack of illegal elements, which leads to the leakage of patients' private data. In addition, the cloud server used to store data is semi-trusted, so it is necessary for the intelligent development of the medical industry to ensure that the private data is not leaked when the electronic medical data is shared among internet hospitals. This paper deeply discusses different technical schemes to protect medical data, and then realizes the privacy protection of electronic medical data.
Survival Hierarchical Agglomerative Clustering: A Semi-Supervised Clustering Method Incorporating Survival Data

Heterogeneity in patient populations presents a significant challenge for healthcare professionals, as different sub-populations may require individualized therapeutic approaches. To address this issue, clustering algorithms are often employed that identify patient groups with homogeneous characteristics. Clustering algorithms are mainly unsupervised, resulting in clusters that are biologically meaningful, but not necessarily correlated with a clinical or therapeutical outcome of interest. In this study we introduce survival hierarchical agglomerative clustering (S-HAC), a novel semi-supervised clustering method that extends the popular hierarchical agglomerative clustering algorithm. Our approach makes use of both patients' descriptive variables and survival times to form clusters that are homogeneous in the descriptive space and include cohesive survival times. In a benchmark study, S-HAC outperformed several existing semi-supervised clustering algorithms. The algorithm was also evaluated on a critical care database of atrial fibrillation management, where it identified clusters that could readily be mapped to existing knowledge of treatment effects such as contraindications and adverse events following drug exposures. These results demonstrate the effectiveness of the algorithm in identifying clinically relevant sub-populations within heterogeneous patient cohorts. S-HAC represents an attractive clustering method for the biomedical domain due to its interpretability and computational simplicity. Its application to different patient cohorts may enable healthcare professionals to tailor treatments and more effectively meet the needs of individual patients. We believe that this approach has the potential to greatly improve patient outcomes and enhance the efficiency of healthcare delivery.
The Impact of Bias on Drift Detection in AI Health Software

Despite the potential of AI in healthcare decision-making, there are also risks to the public for different reasons. Bias is one risk: any data unfairness present in the training set, such as the under-representation of certain minority groups, will be reflected by the model resulting in inaccurate predictions. Data drift is another concern: models trained on obsolete data will perform poorly on newly available data. Approaches to analysing bias and data drift independently are already available in the literature, allowing researchers to develop inclusive models or models that are up-to-date. However, the two issues can interact with each other. For instance, drifts within under-represented subgroups might be masked when assessing a model on the whole population. To ensure the deployment of a trustworthy model, we propose that it is crucial to evaluate its performance both on the overall population and across under-represented cohorts. In this paper, we explore a methodology to investigate the presence of drift that may only be evident in sub-populations in two protected attributes, i.e., ethnicity and gender. We use the BayesBoost technique to capture under-represented individuals and to boost these cases by inferring cases from a Bayesian network. Lastly, we evaluate the capability of this technique to handle some cases of drift detection across different sub-populations.
Spatial Knowledge Transfer with Deep Adaptation Network for Predicting Hospital Readmission

A hospital readmission risk prediction model based on electronic health record (EHR) data can be an important tool for identifying high-risk patients in need of additional support. Performant readmission models based on deep learning approaches require large, high-quality training datasets to perform optimally. Utilizing EHR data from a source hospital system to enhance prediction on a target hospital using traditional approaches might bias the dataset if distributions of the source and target data are different. There is a lack of an end-to-end readmission model that can capture cross-domain knowledge. Herein, we propose an early readmission risk temporal deep adaptation network, ERR-TDAN, for cross-domain spatial knowledge transfer. ERR-TDAN transforms source and target data to a common embedding space while capturing temporal dependencies of the sequential EHR data. Domain adaptation is then applied on a domain-specific fully connected linear layer. The model is optimized by a loss function that combines distribution discrepancy loss to match the mean embeddings of the two distributions and the task loss to optimize predicting readmission at the target hospital. In a use case of patients with diabetes, a model developed using target data of 37,091 patients from an urban academic hospital was enhanced by transferring knowledge from high-quality source data of 20,471 patients from a rural academic hospital. The proposed method yielded a 5% increase in F1-score compared to baselines. ERR-TDAN may be an effective way to increase a readmission risk model's performance when data from multiple sites are available.
Explainable AI for Medical Event Prediction for Heart Failure Patients

The past decade has witnessed significant progress in deploying AI in the medical field. However, most AI models are considered black-boxes, making predictions neither understandable nor interpretable by humans. This limitation is especially significant when they contradict clinicians' expectations based on medical knowledge. This can lead to a lack of trust in the model. In this work, we propose a pipeline to explain AI models. We used a previously devised Neural Network model to present our approach. It predicts the daily risk for patients with heart failure and is a part of a Decision Support System. In our pipeline, we deployed DeepSHAP algorithm to receive global and local explanations. With a global explanation, we defined the most important features in the model and their influence on the prediction. With local explanation, we analyzed individual observations and explained why a specific prediction was made. To validate the clinical relevance of our results, we consulted them with medical experts and made a literature review. Moreover, we described how the proposed pipeline can be integrated into Decision Support Systems. With the above tools, medical personnel can analyze the root of decisions and have insights into how medical parameters should be changed to improve the patient's health state.
Patient Event Sequences for Predicting Hospitalization Length of Stay

Predicting patients' hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. This paper proposes a novel transformer-based model, termed Medic-BERT (M-BERT), for predicting LOS by modeling patient information as sequences of events. We performed empirical experiments on a cohort of 48k emergency care patients from a large Danish hospital. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional non-sequence-based machine learning approaches.
Federated Learning to Improve Counterfactual Explanations for Sepsis Treatment Prediction

In recent years, we have witnessed both artificial intelligence obtaining remarkable results in clinical decision support systems (CDSSs) and explainable artificial intelligence (XAI) improving the interpretability of these models. In turn, this fosters the adoption by medical personnel and improves trustworthiness of CDSSs. Among others, counterfactual explanations prove to be one such XAI technique particularly suitable for the healthcare domain due to its ease of interpretation, even for less technically proficient staff. However, the generation of high-quality counterfactuals relies on generative models for guidance. Unfortunately, training such models requires a huge amount of data that is beyond the means of ordinary hospitals. In this paper, we therefore propose to use federated learning to allow multiple hospitals to jointly train such generative models while maintaining full data privacy. We demonstrate the superiority of our approach compared to locally generated counterfactuals on a CDSS for sepsis treatment prescription using various metrics. Moreover, we prove that generative models for counterfactual generation that are trained using federated learning in a suitable environment perform only marginally worse compared to centrally trained ones while offering the benefit of data privacy preservation.
A Robust BKSVD Method for Blind Color Deconvolution and Blood Detection on H &amp;E Histological Images

Hematoxylin and Eosin (H &E) color variation between histological images from different laboratories degrades the performance of Computer-Aided Diagnosis systems. Histology-specific models to solve color variation are designed taking into account the staining procedure, where most color variations are introduced. In particular, Blind Color Deconvolution (BCD) methods aim to identify the real underlying colors in the image and to separate the tissue structure from the color information. A commonly used assumption is that images are stained with and only with the pure staining colors (e.g., blue and pink for H &E). However, this assumption does not hold true in the presence of common artifacts such as blood, where the blood cells need a third color component to be represented. Blood usually hampers the ability of color standardization algorithms to correctly identify the stains in the image, producing unexpected outputs. In this work, we propose a robust Bayesian K-Singular Value Decomposition (BKSVD) model to simultaneously detect blood and separate color from structure in histological images. Our method was tested on synthetic and real images containing different amounts of blood pixels.
Gender policy at the institution of higher education: current problems and activities

Background: Loneliness as a problem of personality existed at all times, but nowadays it`s becoming more important like a social problem. It is hard to find yourself and relize your identity because of steep conditions in all spheres of life of society. A state of loneliness, according to sociologists, is related with social factors and is appreciated as a comparative quality of person that is influenced by social reality. Purpose: to outline the main factors of social loneliness in modern society. Methods: Method of analysis, method of thoughts comparison, method of theoretical and empirical exploring of blighty and foreign sociologists were used. Results: According to sociologists, an integral features of loneliness are: the low level of social identity and interpersonal trust, faint adaptation of person to conditions in society, lack of pleasure in personal life, faith of individual that his behavior is determined by himself. Social loneliness can be related with imperfection of society, people’s insensibility, loss of close person or insulation. Conclusion: At the present historical stage social loneliness is growing up in geometric progression. Feeling of abandonment, needlessness and alienation actualize the problem of loneliness. Understanding of the loneliness phenomenon doesn’t have unique source of definition, but a common feature of all these theories is the fact, that this problem is formed by the influence of social factors, personal identity with other people and self-identification. Keywords: solitude, social loneliness, identification, self-identification, social exclusion.
An Upper Ontology for Modern Science Branches and Related Entities

Recent developments in the context of semantic technologies have given rise to ontologies for modelling scientific information in various fields of science. Over the past years, we have been engaged in the development of the Science Knowledge Graph Ontologies (SKGO), a set of ontologies for modelling research findings in various fields of science. This paper introduces the Modern Science Ontology (ModSci), an upper ontology for modelling relationships between modern science branches and related entities, including scientific discoveries, phenomena, prominent scientists, instruments, etc. ModSci provides a unifying framework for the various domain ontologies that make up the Science Knowledge Graph Ontology suite. Well-known ontology development guidelines and principles have been followed in the development and publication of the resource. We present several use cases and motivational scenarios to express the motivation behind developing the ontology and, therefore, its potential uses. We deem that within the next few years, a science knowledge graph is likely to become a crucial component for organizing and exploring scientific work.
Two-View Graph Neural Networks for Knowledge Graph Completion

We present an effective graph neural network (GNN)-based knowledge graph embedding model, which we name WGE, to capture entity- and relation-focused graph structures. Given a knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a GNN-based architecture to better learn vector representations of entities and relations from these two single entity- and relation-focused graphs. WGE feeds the learned entity and relation representations into a weighted score function to return the triple scores for knowledge graph completion. Experimental results show that WGE outperforms strong baselines on seven benchmark datasets for knowledge graph completion.
Enabling Live SPARQL Queries over ConceptNet Using Triple Pattern Fragments

We describe how we used a Triple Pattern Fragments (TPF) interface and the Comunica knowledge graph querying framework to enable live SPARQL queries over ConceptNet, one of largest knowledge graphs for commonsense reasoning publicly available on the Web. Despite being a Linked Data resource, the official ConceptNet is not published in RDF and does not support SPARQL. Instead, it provides a RESTful API for live queries, which are restricted to simple triple patterns. This limited API makes it hard for users to search for non-trivial patterns in the graph and hinders the possibility of federated queries offered by SPARQL. There have been attempts to convert ConceptNet to RDF but such proposals tend to quickly become obsolete. In this paper, we take a different route. We use TPF to expose a low-level RDF query interface to ConceptNet. This low-level interface is built on top of the ConceptNet API and can be used by TPF-compatible SPARQL engines such as Comunica. Using this approach, we were able evaluate non-trivial SPARQL queries, including federated queries, over ConceptNet on-the-fly. Our experiments showed that overhead incurred is small and can be further reduced by optimizing ConceptNet’s internal edge representation. We argue that such overhead is justified by the gains in expressivity and flexibility. Moreover, the overall approach is general and can be extended to other non-RDF knowledge graphs.