ST-Map:an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

The Caching and Pricing Strategy for Information-Centric Networking with Advertisers’Participation

As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.

MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

Parameter calibration of the tensile-shear interactive damage constitutive model for sandstone failure

The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The basic parameters of sandstone are determined through a series of static and dynamic tests,including uniaxial compression,Brazilian disc,triaxial compression under varying confining pressures,hydrostatic compression,and dynamic compression and tensile tests with a split Hopkinson pressure bar.Based on the sandstone test results from this study and previous research,a step-by-step procedure for parameter calibration is outlined,which accounts for the categories of the strength surface,equation of state(EOS),strain rate effect,and damage.The calibrated parameters are verified through numerical tests that correspond to the experimental loading conditions.Consistency between numerical results and experimental data indicates the precision and reliability of the calibrated parameters.The methodology presented in this study is scientifically sound,straightforward,and essential for improving the TSID model.Furthermore,it has the potential to contribute to other rock constitutive models,particularly new user-defined models.

基于Interactive Physics的物理习题教学--以2023年高考全国乙卷25题和湖北卷15题为例

在传统高中物理教学中,多过程碰撞的物理过程很难在板书上展现出来,成为教学的一大难点。基于Interactive Physics仿真功能,以2023年高考全国乙卷25题和湖北卷15题为例,将多过程碰撞的物理过程可视化,直观展现物理模型,帮助学生理解题目的复杂情境,有效缩短学习时间,提高教学质量和教学效率,实现最优化的教学目标。

The Application of Formative Assessment in Interactive Comprehensive English Classroom Teaching

Teaching evaluation is an important part of the teaching process of college English courses,and effective formative assessment can reflect the entire teaching process from multiple perspectives and ensure the smooth achievement of teaching objectives.Formative assessment is an assessment that runs through the entire learning process,based on humanism and constructivism,with various forms of evaluation.

Exploration and Practice of“Guiding Interactive”Teaching in Advanced Geochemistry Courses from the Perspective of Constructivist Theory

The construction of geochemical disciplines has brought new vitality to the development of traditional geology.In the new round of“Double First-Class”discipline construction at Central South University,the course of Advanced Geochemistry has effectively stimulated students’interest in learning and further improved their scientific thinking and research innovation skills through the implementation of“Guiding Interactive”teaching reform measures,which has important theoretical significance and practical value.

Research on Visualization and Interactive Design of Plant Configuration Virtual Reality in the Context of Digitalization

This study aims to explore the application of digital technology in landscape design,focusing on the research of virtual reality visualization and interactive design in the process of plant configuration.Through an in-depth analysis of digital technology,the study outlines its important role in landscape design,especially in the application of plant configuration.The current application status of virtual reality technology in landscape design is discussed,as well as how interactive design can enhance user experience and participation.Furthermore,the achievements and challenges of digital technology in landscape design are summarized.Finally,it proposes future research directions and suggestions,aiming to provide new ideas and methods for practice and research in the field of landscape design and promote the further application and development of digital technology in landscape design.

Interactive Media Visual Interpretation of Traditional Chinese Graphics in Urban Public Spaces

This paper focuses on the application of interactive media technology in the visual interpretation of traditional graphic urban public spaces in China.Case studies and practical exploration show that interactive media technologies such as projection mapping,interactive devices,virtual reality technology,etc.,have realized the diversity of traditional graphics display forms in urban public space.The rich interactive experience design enhances the sense of participation and experience of urban citizens and tourists and promotes the visual culture transmission of traditional Chinese graphics.The future urban public space exhibition is destined to continue to deepen the integration of technology and graphics,promote the visual communication of traditional Chinese graphics visual interpretation in urban public space,and promote sustainable innovation in cultural output in urban public space exhibitions around the world.

Research and Application of AI-Based Interactive Exhibits in Wuhan Museum of Science and Technology

This article aims to explore the development and application of AI-based interactive exhibits in Wuhan Museum of Science and Technology.By utilizing computer vision,natural language processing,and machine learning technologies,an innovative exhibit development and application system is proposed.This system employs deep learning algorithms and data analysis methods to achieve real-time perception of visitor behavior and adaptive interaction.The development process involves designing user interfaces and interaction methods to effectively enhance visitor engagement and learning outcomes.Through evaluation and comparison in practical applications,the potential of this system in enhancing exhibit interaction,increasing visitor engagement,improving educational effectiveness,and expanding avenues for scientific knowledge dissemination are validated.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

A Machine Learning-Based Attack Detection and Prevention System in Vehicular Named Data Networking

Named Data Networking(NDN)is gaining a significant attention in Vehicular Ad-hoc Networks(VANET)due to its in-network content caching,name-based routing,and mobility-supporting characteristics.Nevertheless,existing NDN faces three significant challenges,including security,privacy,and routing.In particular,security attacks,such as Content Poisoning Attacks(CPA),can jeopardize legitimate vehicles with malicious content.For instance,attacker host vehicles can serve consumers with invalid information,which has dire consequences,including road accidents.In such a situation,trust in the content-providing vehicles brings a new challenge.On the other hand,ensuring privacy and preventing unauthorized access in vehicular(VNDN)is another challenge.Moreover,NDN’s pull-based content retrieval mechanism is inefficient for delivering emergency messages in VNDN.In this connection,our contribution is threefold.Unlike existing rule-based reputation evaluation,we propose a Machine Learning(ML)-based reputation evaluation mechanism that identifies CPA attackers and legitimate nodes.Based on ML evaluation results,vehicles accept or discard served content.Secondly,we exploit a decentralized blockchain system to ensure vehicles’privacy by maintaining their information in a secure digital ledger.Finally,we improve the default routing mechanism of VNDN from pull to a push-based content dissemination using Publish-Subscribe(Pub-Sub)approach.We implemented and evaluated our ML-based classification model on a publicly accessible BurST-Asutralian dataset for Misbehavior Detection(BurST-ADMA).We used five(05)hybrid ML classifiers,including Logistic Regression,Decision Tree,K-Nearest Neighbors,Random Forest,and Gaussian Naive Bayes.The qualitative results indicate that Random Forest has achieved the highest average accuracy rate of 100%.Our proposed research offers the most accurate solution to detect CPA in VNDN for safe,secure,and reliable vehicle communication.

Performance Evaluation of Topologies for Multi-Domain Software-Defined Networking

Software-defined networking(SDN)is widely used in multiple types of data center networks,and these distributed data center networks can be integrated into a multi-domain SDN by utilizing multiple controllers.However,the network topology of each control domain of SDN will affect the performance of the multidomain network,so performance evaluation is required before the deployment of the multi-domain SDN.Besides,there is a high cost to build real multi-domain SDN networks with different topologies,so it is necessary to use simulation testing methods to evaluate the topological performance of the multi-domain SDN network.As there is a lack of existing methods to construct a multi-domain SDN simulation network for the tool to evaluate the topological performance automatically,this paper proposes an automated multi-domain SDN topology performance evaluation framework,which supports multiple types of SDN network topologies in cooperating to construct a multi-domain SDN network.The framework integrates existing single-domain SDN simulation tools with network performance testing tools to realize automated performance evaluation of multidomain SDN network topologies.We designed and implemented a Mininet-based simulation tool that can connect multiple controllers and run user-specified topologies in multiple SDN control domains to build and test multi-domain SDN networks faster.Then,we used the tool to perform performance tests on various data center network topologies in single-domain and multi-domain SDN simulation environments.Test results show that Space Shuffle has the most stable performance in a single-domain environment,and Fat-tree has the best performance in a multi-domain environment.Also,this tool has the characteristics of simplicity and stability,which can meet the needs of multi-domain SDN topology performance evaluation.

Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking

Recent advancements in the Vehicular Ad-hoc Network(VANET)have tremendously addressed road-related challenges.Specifically,Named Data Networking(NDN)in VANET has emerged as a vital technology due to its outstanding features.However,the NDN communication framework fails to address two important issues.The current NDN employs a pull-based content retrieval network,which is inefficient in disseminating crucial content in Vehicular Named Data Networking(VNDN).Additionally,VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous vehicles.Although various solutions have been proposed for detecting vehicles’behavior,they inadequately addressed the challenges specific to VNDN.To deal with these two issues,we propose a novel push-based crucial content dissemination scheme that extends the scope of VNDN from pullbased content retrieval to a push-based content forwarding mechanism.In addition,we exploitMachine Learning(ML)techniques within VNDN to detect the behavior of vehicles and classify them as attackers or legitimate.We trained and tested our system on the publicly accessible dataset Vehicular Reference Misbehavior(VeReMi).We employed fiveML classification algorithms and constructed the bestmodel for illusion attack detection.Our results indicate that RandomForest(RF)achieved excellent accuracy in detecting all illusion attack types in VeReMi,with an accuracy rate of 100%for type 1 and type 2,96%for type 4 and type 16,and 95%for type 8.Thus,RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high accuracy.The ultimate goal of our research is to improve content exchange and secureVNDNfromattackers.Thus,ourML-based attack detection and preventionmechanismensures trustworthy content dissemination and prevents attacker vehicles from sharing misleading information in VNDN.

Recent Trends of In-Vehicle Time Sensitive Networking Technologies, Applications and Challenges

With the vigorous development of automobile industry,in-vehicle network is also constantly upgraded to meet data transmission requirements of emerging applications.The main transmission requirements are low latency and certainty especially for autonomous driving.Time sensitive networking(TSN)based on Ethernet gives a possible solution to these requirements.Previous surveys usually investigated TSN from a general perspective,which referred to TSN of various application fields.In this paper,we focus on the application of TSN to the in-vehicle networks.For in-vehicle networks,we discuss all related TSN standards specified by IEEE 802.1 work group up to now.We further overview and analyze recent literature on various aspects of TSN for automotive applications,including synchronization,resource reservation,scheduling,certainty,software and hardware.Application scenarios of TSN for in-vehicle networks are analyzed one by one.Since TSN of in-vehicle network is still at a very initial stage,this paper also gives insights on open issues,future research directions and possible solutions.

Interactive Restoration of Three-Dimensional Implicit Surface with Irregular Parts

Implicit surface generation based on the interpolation of surface points is one of the well-known modeling methods in the area of computer graphics.Several methods for the implicit surface reconstruction from surface points have been proposed on the basis of radial basis functions,a weighted sum of local functions,splines,wavelets,and combinations of them.However,if the surface points contain errors or are sparsely distributed,irregular components,such as curvature-shaped redundant bulges and unexpectedly generated high-frequency components,are commonly seen.This paper presents a framework for restoring irregular components generated on and around surfaces.Users are assumed to specify local masks that cover irregular components and parameters that determine the degree of restoration.The algorithm in this paper removes the defects based on the user-specific masks and parameters.Experiments have shown that the proposed methods can effectively remove redundant protrusions and jaggy noise.

An Overview of Interactive Immersive Services

Immersive services are the typical emerging services in current IMT-2020 network.With the development of network evolution,real-time interactive applications emerge one after another.This article provides an overview on immersive services which focus on real-time interaction.The scenarios,framework,requirements,key technologies,and issues of interactive immersive service are presented.

Implementation of Level-3 Autonomous Patient-Specific Quality Assurance with Automated Human Interactive Devices

Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.