Concurrent Network Research Articles

Abstract 4117479: INTERPRETABLE PREDICTION OF HOSPITALIZATION COSTS FOR PATIENTS WITH ISCHEMIC HEART DISEASE USING NETWORK ANALYTICS AND MACHINE LEARNING

Background: Ischemic heart disease (IHD) represents a significant global health challenge, imposing a considerable social and economic burden on individuals and healthcare systems worldwide. To optimize medical resource allocation and better serve populations, accurately predicting healthcare costs is paramount. Methods: This study proposes an interpretable feature extraction method based on network analytics to improve the prediction of IHD hospitalization costs. A diagnosis-procedure concurrence network is constructed, and a graph kernel-based approach is utilized to capture and encode information on diseases and treatments. The study introduces a hospitalization cost prediction model that utilizes a diagnosis-procedure concurrence network to extract features that strike a balance between predictability and interpretability. Results: Our model demonstrates superior performance, considering that the average cost for all patients in the HQMS dataset is approximately 20,000 CNY, whereas the average cost for our IHD patient dataset is 37,853 CNY. This indicates the effectiveness of our approach in predicting hospitalization costs for a specific patient population with complex treatment options such as IHD. Conclusion: This study aims to develop an interpretable and precise model for forecasting hospitalization costs of Ischemic Heart Disease (IHD) patients in China. It achieves this goal by integrating machine learning with network analytics and incorporating cost ratio features.

MRD‐GAN: Multi‐representation discrimination GAN for enhancing the diversity of the generated data

AbstractThe generative adversarial network (GAN) is a highly effective member of the generative models category and is extensively employed for generating realistic samples across various domains. The fundamental concept behind GAN involves two networks, a generator and a discriminator, competing against each other. During the training process, generator and discriminator networks encounter several issues that can potentially affect the quality and diversity of the generated samples. One such critical issue is mode collapse, where the generator fails to create varied samples. To tackle this issue, this article introduces a GAN approach called the multi‐representation discrimination GAN (MRD‐GAN). In this approach, the discriminator supports concurrent network discrimination flows to manage different representations of the data through various transformation functions, such as dimension rescaling, brightness adjustment, and gamma correction applied to the input data of the discriminator. We use a fusion function to aggregate the output of all flows and return a consolidated loss value to update the generator's weights. Hence, the discriminator conveys diverse feedback to the generator. The proposed approach has been evaluated on four distinct benchmarks, namely CelebA, Cifar‐10, Fashion‐Mnist, and Mnist. The experimental results demonstrate that the proposed approach surpasses the existing state‐of‐the‐art GAN models in terms of FID metric that measures the diversity of the generated samples. Significantly, the proposed approach demonstrates remarkable FID scores of 14.02, 30.19, 9.42, and 3.14 on the CelebA, Cifar‐10, Fashion‐Mnist, and Mnist datasets, respectively.

Cost prediction for ischemic heart disease hospitalization: Interpretable feature extraction using network analysis

Objectives: Ischemic heart disease (IHD) is a significant contributor to global mortality and disability, imposing a substantial social and economic burden on individuals and healthcare systems. To enhance the efficient allocation of medical resources and ultimately benefit a larger population, accurate prediction of healthcare costs is crucial. Methods:We developed an interpretable IHD hospitalization cost prediction model that integrates network analysis with machine learning. Specifically, our network-enhanced model extracts explainable features by leveraging a diagnosis-procedure concurrence network and advanced graph kernel techniques, facilitating the capture of intricate relationships between medical codes. Results:The proposed model achieved an R2 of 0.804 ± 0.008 and a root mean square error (RMSE) of 17,076 ± 420 CNY on the temporal validation dataset, demonstrating comparable performance to the model employing less interpretable code embedding features (R2: 0.800 ± 0.008; RMSE: 17,279 ± 437 CNY) and the hybrid graph isomorphism network (R2: 0.802 ± 0.007; RMSE: 17,249 ± 387 CNY). The interpretation of the network-enhanced model assisted in pinpointing specific diagnoses and procedures associated with higher hospitalization costs, including acute kidney injury, permanent atrial fibrillation, intra-aortic balloon bump, and temporary pacemaker placement, among others. Conclusion:Our analysis results demonstrate that the proposed model strikes a balance between predictive accuracy and interpretability. It aids in identifying specific diagnoses and procedures associated with higher hospitalization costs, underscoring its potential to support intelligent management of IHD.

MoMENt: Marked Point Processes with Memory-Enhanced Neural Networks for User Activity Modeling

Marked temporal point process models (MTPPs) aim to model event sequences and event markers (associated features) in continuous time. These models have been applied to various application domains where capturing event dynamics in continuous time is beneficial, such as education systems, social networks, and recommender systems. However, current MTPPs suffer from two major limitations, i.e., inefficient representation of event dynamic’s influence on marker distribution and losing fine-grained representation of historical marker distributions in the modeling. Motivated by these limitations, we propose a novel model called M arked P o int Processes with M emory- E nhanced N eural Ne t works (MoMENt) that can capture the bidirectional interrelations between markers and event dynamics while providing fine-grained marker representations. Specifically, MoMENt is constructed of two concurrent networks: Recurrent Activity Updater (RAU) to capture model event dynamics and Memory-Enhanced Marker Updater (MEMU) to represent markers. Both RAU and MEMU components are designed to update each other at every step to model the bidirectional influence of markers and event dynamics. To obtain a fine-grained representation of maker distributions, MEMU is devised with external memories that model detailed marker-level features with latent component vectors. Our extensive experiments on six real-world user interaction datasets demonstrate that MoMENt can accurately represent users’ activity dynamics, boosting time, type, and marker predictions, as well as recommendation performance up to 76.5%, 65.6%, 77.2%, and 57.7%, respectively, compared to baseline approaches. Furthermore, our case studies show the effectiveness of MoMENt in providing meaningful and fine-grained interpretations of user-system relations over time, e.g., how user choices influence their future preferences in the recommendation domain.

VIRTUAL THREADS IN JAVA 19 AND SPRING BOOT 3: ENHANCING PERFORMANCE AND EFFICIENCY OF APPLICATIONS

This article explores virtual threads, a new feature introduced in Java version 19. Virtual threads are an abstraction that allows the creation of numerous threads without requiring dedicated operating system threads for each. This significantly reduces the overhead costs of multithreading, leading to improved performance and efficiency of applications. The article discusses the working principles of virtual threads, their advantages and disadvantages. It also examines the support for virtual threads in the Spring Boot 3 framework. Research indicates that virtual threads can significantly enhance the performance of applications handling concurrent network requests. For instance, a Spring Boot application executing 1000 concurrent network requests demonstrated a 20% performance improvement when using virtual threads compared to traditional threads.

Chronic neck and low back pain from personal experiences: a written narrative approach.

Background: Chronic neck and low back pain are very common and have detrimental effects for people and society. In this study, we explore the experiences of individuals with neck and/or back pain using a written narrative methodology. Materials & methods: A total of 92 individuals explained their pain experience using written narratives. Narratives were analyzed through thematic analysis and text data mining. Results: Participants wrote about their experience in terms of pain characteristics, diagnosis process, pain consequences, coping strategies, pain triggers, well-beingand future expectations. Text data mining allowed us to identify concurrent networks that were basically related with pain characteristics, management and triggers. Conclusion: Written narratives are useful to understand individuals' experiences from their point of view.

Highly Concurrent TCP Session Connection Management System on FPGA Chip

Transmission Control Protocol (TCP) is a connection-oriented data transmission protocol, and it is also the main communication protocol used for end-to-end data transmission in the current Internet. At present, the mainstream TCP protocol processing service is implemented by software running on the Central Processing Unit (CPU). However, with the rapid growth of transmission bandwidth and the number of connections, the software-based processing method is not ideal in terms of delay and throughput, and also affects the processing performance of the CPU in other applications such as virtualization services. Moreover, other hardware solutions can only support a limited number of TCP session connections. In order to improve the processing efficiency of the TCP protocol and achieve highly concurrent network services, this paper proposes a TCP offload engine (TOE) prototype system based on field programmable gate array (FPGA) chips. It not only provides hardware-based data path processing, but also realizes hardware management of large-scale TCP session connection status through a multi-level cache management mechanism. Studies have shown that this solution can support 100 Gbps high-performance throughput characteristics, and allow concurrent processing of hundreds to 250,000 TCP connection state hardware maintenance on a single network node, improving the overall performance of the network system.

Secure and Reliable Network Updates

Software-defined wide area networking (SD-WAN) enables dynamic network policy control over a large distributed network via network updates . To be practical, network updates must be consistent (i.e., free of transient errors caused by updates to multiple switches), secure (i.e., only be executed when sent from valid controllers), and reliable (i.e., function despite the presence of faulty or malicious members in the control plane), while imposing only minimal overhead on controllers and switches. We present SERENE: a protocol for se cure and re liable ne twork updates for SD-WAN environments. In short: Consistency is provided through the combination of an update scheduler and a distributed transactional protocol. Security is preserved by authenticating network events and updates, the latter with an adaptive threshold cryptographic scheme. Reliability is provided by replicating the control plane and making it resilient to a dynamic adversary by using a distributed ledger as a controller failure detector. We ensure practicality by providing a mechanism for scalability through the definition of independent network domains and exploiting the parallelism of network updates both within and across domains. We formally define SERENE’s protocol and prove its safety with regards to event-linearizability. Extensive experiments show that SERENE imposes minimal switch burden and scales to large networks running multiple network applications all requiring concurrent network updates, imposing at worst a 16% overhead on short-lived flow completion and negligible overhead on anticipated normal workloads.

CKRIPT: A NEW SCRIPTING LANGUAGE FOR WEB APPLICATIONS

The project aimed to develop and implement an efficient web server in the C++ programming language. A highly concurrent network server was achieved using system calls such as polls and a limited number of threads. The server has built-in support for a new scripting language called Ckript. It is an original project that exposes most of the server’s functionality and is the primary way of developing back-end web applications. Ckript is an interpreted language with a strong typing system, garbage collection, semi-manual memory management, first-class functions, explicit variable references, support for certain object-oriented patterns and many others. In the article the syntax of the language but also the environment architecture has been explained. Finally, the testing procedure has been described with the results’ presentation and discussion at each step.

Feature-Based Convolutional Neural Network for Very-high-resolution Urban Imagery Classification

In the process of manual image interpretation, the use of a combination of spectral and spatial features can aid in more accurately classifying urban land coverage. In this study, to simulate this procedure, we use two concurrent convolutional neural networks (CNNs) with two scales of input to represent fields of view corresponding to object detail and the related information among objects. In our approach, the results derived from every convolution process are retained and stacked together at the end of the convolution process. Thus, not only are the spectral and spatial features combined, but all the scales of spatial features are also considered. When applied to very-high-resolution remote sensing images, our proposed model with its feature-based CNN achieves a noticeable improvement over other state-of-the-art methods, which helps to assess the urban environment to some extent. In addition, we show that the digital surface model features, either in image form or in numerical characteristic form, can improve the overall accuracy rate of current structures.

Research on the optimum synchronous network search data extraction based on swarm intelligence algorithm

Existing synchronous network search data extraction techniques suffer from load unbalance in the face of increasing number of concurrent users. For this reason, this paper presents the research on the optimum synchronous network search data extraction based on swarm intelligence algorithm. A traffic balancing model is constructed to determine the node balance state according to the traffic variation and influence factors of network nodes. Under the condition that the node state is known, the swarm intelligence algorithm is used to cluster the data to be synchronized and adjust the node state so that it is kept stable throughout the synchronization process. The clustered data act as the target to connect the user with the server side to achieve the optimum network search data extraction and synchronization. The experimental results show that when the number of concurrent network users is increasing, the designed technique features stable load balancing, and achieves optimum data extraction performance and low execution cost when the task completion time is less than 0.5 s.

Traditional weight loss and dukan diets as to nutritional and laboratory results

Traditional weight loss and dukan diets as to nutritional and laboratory results

HCMonitor: An accurate measurement system for high concurrent network services

AbstractThis article aims to enhance the monitoring accuracy of high concurrent network services. As modern network services grow rapidly in data centers, tail latency has become one of the most crucial deciding factors on user experience. Latency measurement and anomaly detection are essential in evaluating service performance. Existing monitoring tools can be divided into two categories according to estimation methods. First, approaches based on sample traffic sample network packets to unburden the measurement. Second, approaches based on full traffic like wrk, analyze all of the packets from the kernel network stack and load the client‐side overhead into response delay. Therefore, we propose a high‐performance monitor system named HCMonitor, which computes the server‐side response latency and the round‐trip time of per‐request. It can afford full traffic monitoring on the basis of userspace, “zero copy” and pipeline. By switch mirroring, the measured latency eliminates the kernel network stack overhead and the queuing delay of the client‐side. Such measurement results in improved accuracy, online analysis, anomaly detection, real‐time display and transparent to network services. Our evaluations show HCMonitor obtains a higher throughput compared with tcpdump by over 200 times. Compared with wrk, the tail latency accuracy shows an increase by up to 72%–76% in high concurrent networks.

Teaching Management and Monitoring Abnormal Network Behaviors Under COVID-19

Due to the epidemic of COVID-19, more social activities have been moved to the internet, such as online education and online learning. The education management to avoid burst events is a basic requirement of online education, especially when a huge number of persons are visiting at the same time. In order to monitor the abnormal and burst access in online education systems, this paper proposes an anomaly detection method by using data flow to mining high frequency events among massive network traffic data during online education. First, the data flow in traffic network is described as a special structure which is used to establish an efficient high frequent event detection algorithm. Second, the network traffic flow is reduced to make it possible to monitor large-scale concurrent network visiting. The effectiveness of the abnormal network behavior detection method is verified through the experiment on a real network environment for online education.

Study of the Constrained Resources to Projects Based on the Random Networks

In this paper, a number of concurrent network projects with random activities are studied, and the proposed model enables people to control various resources such as rare and expensive resources transported from outside in a short time. For each project, the schedule date and the admissible confidence probability of timely completion of the project were defined. The initial instants of realization of all projects, the required total power of each of the reproducible resources, the delivery schedule of rare reproducible resources that are not at the disposal of the project management system, and the schedule of beginning all activities were determined. The minimal nonoperational costs, such as penalty provisions for failure to execute the project in time, fines for idling of the rare external resources, costs of resource lease were used as the target function. The developed model can be used to plan and supervise one or more random PERT-like network projects. It can significantly reduce the cost of project implementation.

Practical generation of common random strings for secure multiparty computations over the Internet

It is known that most of the interesting multiparty cryptographic tasks cannot be implemented securely without trusted setup in a general concurrent network environment like the Internet. We need an appropriate trusted third party to solve this problem. An important trusted setup is a public random string shared by the parties. We present a practical n-bit coin toss protocol for provably secure implementation of such setup. Our idea is inviting external peers into the execution of the protocol to establish an honest majority among the parties. We guarantee security in the presence of an unconditional, static, malicious adversary. Additionally, we present an original practical idea of using live public radio broadcast channels for the generation of common physical random source.

Minimizing Wireless Delay with a High-Throughput Side Channel

Performance of modern cognitive and interactive mobile applications highly depends on the transmission delay in the wireless link that is vital to supporting real-time wireless traffic. To eliminate wireless network congestion caused by large amounts of concurrent network traffic and minimize such transmission delay, traditional schemes adopt various flow control and QoS-aware traffic scheduling techniques, but fail when the amount of network traffic further increases. In this paper, we present a novel design of high-throughput wireless side channel, which operates concurrently with the existing wireless network channel over the same spectrum but dedicates to real-time traffic. Our key idea of realizing such a side channel is to exploit the excessive SNR margin in the wireless network to encode data as patterned interference. We design such patterned interference in the form of energy erasure over specific subcarriers in an OFDM-based wireless network, and achieve a data rate of 1.25 Mbps in the side channel without affecting the existing wireless network links. Experimental results over both software-defined radios and custom wireless hardware demonstrate the effectiveness of our side channel design in reducing the latency of real-time wireless traffic, while providing a sufficient data throughput for such traffic.

Emergency OPM Recreation and Telemetry for Disaster Recovery in Optical Networks

Optical performance monitoring (OPM) and the corresponding telemetry systems play an important role in modern optical transport networks based on software-defined networking (SDN). There have been extensive studies and standardization activities to build high-speed and high-accuracy OPM/telemetry systems that can ensure sufficient monitoring data for effective network control and management. However, current solutions for OPM/telemetry assume that control and management planes (C/M-plane) always provide sufficient bandwidth (BW) to deliver telemetry data. Unfortunately, in the event of several concurrent network failures (e.g., following a large-scale disaster), C/M-plane networks can become heavily degraded and/or unstable, and even experience isolation of some of their parts. Under such circumstances, the existing OPM systems would hardly function. To enhance resiliency and to ensure the quick recovery of OPM/telemetry in case of disaster, we propose an approach for quick recreation of OPM and for achieving robust telemetry based on OpenConfig YANG. Our proposal addresses three key problems: (1) how to quickly recreate the lost OPM capability, (2) how to address the mismatch between the high data rate of OPM and the low BW in the C/M-plane network, and (3) how to flexibly reconfigure the telemetry system to be adaptive to sudden BW changes in the C/M-plane network. We implement a testbed and experimentally demonstrate that our proposal can tolerate low post-disaster bandwidth and can adapt the telemetry system following the changing conditions of the C/M-plane network.

Fast key-frame image retrieval of intelligent city security video based on deep feature coding in high concurrent network environment

Aiming at the problems faced by the connection management module in single packet processing in intelligent city security video retrieval, we firstly propose the traffic locality quantization index based on the traffic characteristics of the backbone link to quantitatively analyze the traffic locality characteristics in the backbone link. And then, a key-frame abstraction and retrieval of videos based on deep learning is proposed to improve the efficiency and accuracy of video retrieval, where an adaptive key-frame selection algorithm is designed and the existing convolutional neural network framework is used to extract the features of key-frames, and unsupervised, semi-supervised and supervised retraining models are designed to improve the effectiveness of the feature extraction of the convolutional neural network and the accuracy of the video retrieval. Experimental results based on the public video datasets show that our proposed key-frame image retrieval model realizes a good precision for key-frame representation, and achieves high accuracy and efficiency for video retrieval.

The impact of the simultaneous networking training classroom teachers to instill in the initial application efficiencies of continuous assessment

The aim of the study was to identify the effectiveness of simultaneous network training in providing the teachers of the primary grades some of the competencies of the application of continuous evaluation. The researcher used the "Semi-Experimental Method". The study population is one of the primary school teachers in the schools of the Education Bureau in Qalwa Governorate. The study population is 150 teachers. The sample of the study is 30 teachers. The researcher used the observation as a tool for this study, A tribal observation, and a remote observation. Statistical processing was done with spss, repetitive tables, percentages and arithmetic mean. The results of the study are the most important of which are the effectiveness of simultaneous network training in the development of activating the teachers of the first grades of the continuous evaluation of their multiple variables and their effective use within the classrooms. We conclude that the effect of the independent variable "concurrent network training" on the dependent variable " May range from (0.89,0,97). The most important recommendations of the study are the design of training programs similar to the teachers in all disciplines and at all educational levels, simultaneous networking in the provision of continuous guidance and guidance and overcoming difficulties in applying the system of continuous evaluation according to its correct mechanisms and in light of specific criteria that can be referred to continuously. Contributes to overcoming traditional training problems.