Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently r...Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Greening Internet is an important issue now, which studies the way to reduce the increas- ing energy expenditure. Our work focuses on the network infrastructure and considers its energy awareness in traffic routing. W...Greening Internet is an important issue now, which studies the way to reduce the increas- ing energy expenditure. Our work focuses on the network infrastructure and considers its energy awareness in traffic routing. We formulate the model by traffic engineering to achieve link rate a- daption, and also predict traffic matrices to pre- serve network stability. However, we realize that there is a tradeoff between network performance and energy efficiency, which is an obvious issue as Internet grows larger and larger. An essential cause is the huge traffic, and thus we try to fred its so- lution from a novel architecture called Named Data Networking （NDN） which tent in edge routers and can flexibly cache con- decrease the backbone traffic. We combine our methods with NDN, and finally improve both the network performance and the energy efficiency. Our work shows that it is effective, necessary and feasible to consider green- ing idea in the design of future Internet.展开更多
Named Data Networking(NDN)is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by.It greatly reduces network traffic and improves the speed of...Named Data Networking(NDN)is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by.It greatly reduces network traffic and improves the speed of content distribution and retrieval.In order to make full use of the limited caching space in routers,it is an urgent challenge to make an efficient cache replacement policy.However,the existing cache replacement policies only consider very few factors that affect the cache performance.In this paper,we present a cache replacement policy based on multi-factors for NDN(CRPM),in which the content with the least cache value is evicted from the caching space.CRPM fully analyzes multi-factors that affect the caching performance,puts forward the corresponding calculation methods,and utilize the multi-factors to measure the cache value of contents.Furthermore,a new cache value function is constructed,which makes the content with high value be stored in the router as long as possible,so as to ensure the efficient use of cache resources.The simulation results show that CPRM can effectively improve cache hit ratio,enhance cache resource utilization,reduce energy consumption and decrease hit distance of content acquisition.展开更多
Named Data Networking(NDN)improves the data delivery efficiency by caching contents in routers. To prevent corrupted and faked contents be spread in the network,NDN routers should verify the digital signature of each ...Named Data Networking(NDN)improves the data delivery efficiency by caching contents in routers. To prevent corrupted and faked contents be spread in the network,NDN routers should verify the digital signature of each published content. Since the verification scheme in NDN applies the asymmetric encryption algorithm to sign contents,the content verification overhead is too high to satisfy wire-speed packet forwarding. In this paper, we propose two schemes to improve the verification performance of NDN routers to prevent content poisoning. The first content verification scheme, called "user-assisted",leads to the best performance, but can be bypassed if the clients and the content producer collude. A second scheme, named ``RouterCooperation ‘', prevents the aforementioned collusion attack by making edge routers verify the contents independently without the assistance of users and the core routers no longer verify the contents. The Router-Cooperation verification scheme reduces the computing complexity of cryptographic operation by replacing the asymmetric encryption algorithm with symmetric encryption algorithm.The simulation results demonstrate that this Router-Cooperation scheme can speed up18.85 times of the original content verification scheme with merely extra 80 Bytes transmission overhead.展开更多
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 ND...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.展开更多
Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offer...Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.展开更多
With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so ...With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.展开更多
This paper proposes a new approach for ranking efficiency units in data envelopment analysis as a modification of the super-efficiency models developed by Tone [1]. The new approach based on slacks-based measure of ef...This paper proposes a new approach for ranking efficiency units in data envelopment analysis as a modification of the super-efficiency models developed by Tone [1]. The new approach based on slacks-based measure of efficiency (SBM) for dealing with objective function used to classify all of the decision-making units allows the ranking of all inefficient DMUs and overcomes the disadvantages of infeasibility. This method also is applied to rank super-efficient scores for the sample of 145 agricultural bank branches in Viet Nam during 2007-2010. We then compare the estimated results from the new SCI model and the exsisting SBM model by using some statistical tests.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data communications.This paper focuses on secure vehicular data communications in the Named Data N...Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data communications.This paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure rates.This paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure rates.SCD constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse paths.Only the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are achieved.SCD is evaluated,and the data results demonstrate that SCD achieves the above objectives.展开更多
Data augmentation methods are often used to address data scarcity in natural language processing(NLP).However,token-label misalignment,which refers to situations where tokens are matched with incorrect entity labels i...Data augmentation methods are often used to address data scarcity in natural language processing(NLP).However,token-label misalignment,which refers to situations where tokens are matched with incorrect entity labels in the augmented sentences,hinders the data augmentation methods from achieving high scores in token-level tasks like named entity recognition(NER).In this paper,we propose embedded prompt tuning(EPT)as a novel data augmentation approach to low-resource NER.To address the problem of token-label misalignment,we implicitly embed NER labels as prompt into the hidden layer of pre-trained language model,and therefore entity tokens masked can be predicted by the finetuned EPT.Hence,EPT can generate high-quality and high-diverse data with various entities,which improves performance of NER.As datasets of cross-domain NER are available,we also explore NER domain adaption with EPT.The experimental results show that EPT achieves substantial improvement over the baseline methods on low-resource NER tasks.展开更多
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 fe...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.展开更多
Geographical studies of outdoor activities have increased in recent years with the rise in popularity of these activities worldwide,including in Japan.Volunteered geographic information(VGI)is a key tool for organizin...Geographical studies of outdoor activities have increased in recent years with the rise in popularity of these activities worldwide,including in Japan.Volunteered geographic information(VGI)is a key tool for organizing outdoor activities as it offers a means to determine the locational information and names of places.To evaluate the quality of VGI,geospatial data generated by land survey agencies and other VGI are often utilized as reference data.However,since these reference data may not be available,other methods are necessary to assure the quality of VGI.In this study,we examined five trust indicators based on the inherent characteristics of VGI through an empirical case study.We used mountain names extracted from OpenStreetMap in Japan as data because there were almost no other VGI in the vicinity.As a result,we isolated three trust indicators,namely versions,users,and tag corrections,to examine the thematic accuracy of VGI because these were the only statistically significant indicators.However,we found that the prediction rate of thematic accuracy was very low.To improve thematic accuracy,this study recommends using the most accurate versions,applying correctly given tags,and considering the motivations and characteristics of the VGI contributors.展开更多
Vehicular Social Networks(VSNs)is the bridge of social networks and Vehicular Ad-Hoc Networks(VANETs).VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicl...Vehicular Social Networks(VSNs)is the bridge of social networks and Vehicular Ad-Hoc Networks(VANETs).VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication protocols.Vehicular Named Data Networking(VNDN)is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations.However,content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’high mobility.Our aim with this paper is to improve content delivery and cache hit ratio,as well as decrease the transmission delay between end-users.In this regard,we propose a novel hybrid VNDN-VSN forwarding technique based on social communities,which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area.Furthermore,we introduce an effective caching mechanism by dividing the content store into two parts,one for community private contents and the second one for public contents.Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN,in terms of data delivery ratio,average data delivery delay,and cache hit ratio.展开更多
This work is dedicated to formation of data warehouse for processing of a large volume of registration data of domain names. Data cleaning is applied in order to increase the effectiveness of decision making support. ...This work is dedicated to formation of data warehouse for processing of a large volume of registration data of domain names. Data cleaning is applied in order to increase the effectiveness of decision making support. Data cleaning is ap- plied in warehouses for detection and deletion of errors, discrepancy in data in order to improve their quality. For this purpose, fuzzy record comparison algorithms are for clearing of registration data of domain names reviewed in this work. Also, identification method of domain names registration data for data warehouse formation is proposed. Deci- sion making algorithms for identification of registration data are implemented in DRRacket and Python.展开更多
In order to extract the boundary of rural habitation, based on geographic name data and basic geographic information data, an extraction method that use polygon aggregation is raised, it can extract the boundary of th...In order to extract the boundary of rural habitation, based on geographic name data and basic geographic information data, an extraction method that use polygon aggregation is raised, it can extract the boundary of three levels of rural habitation consists of town, administrative village and nature village. The method first extracts the boundary of nature village by aggregating the resident polygon, then extracts the boundary of administrative village by aggregating the boundary of nature village, and last extracts the boundary of town by aggregating the boundary of administrative village. The related methods of extracting the boundary of those three levels rural habitation has been given in detail during the experiment with basic geographic information data and geographic name data. Experimental results show the method can be a reference for boundary extraction of rural habitation.展开更多
Named-data Networking(NDN) is a promising future Internet architecture, which introduces some evolutionary elements into layer-3, e.g., consumer-driven communication, soft state on data forwarding plane and hop-byhop ...Named-data Networking(NDN) is a promising future Internet architecture, which introduces some evolutionary elements into layer-3, e.g., consumer-driven communication, soft state on data forwarding plane and hop-byhop traffic control. And those elements ensure data holders to solely return the requested data within the lifetime of the request, instead of pushing data whenever needed and whatever it is. Despite the dispute on the advantages and their prices, this pattern requires data consumers to keep sending requests at the right moments for continuous data transmission, resulting in significant forwarding cost and sophisticated application design. In this paper, we propose Interest Set(IS) mechanism, which compresses a set of similar Interests into one request, and maintains a relative long-term data returning path with soft state and continuous feedback from upstream. In this way, IS relaxes the above requirement, and scales NDN data forwarding by reducing forwarded requests and soft states that are needed to retrieve a given set of data.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 61972424 and 62372479in part by the High Value Intellectual Property Cultivation Project of Hubei Province,China,under grant D2021002094+1 种基金in part by JSPS KAKENHI under Grants JP16K00117 and JP19K20250in part by the Leading Initiative for Excellent Young Researchers(LEADER),MEXT,Japan,and KDDI Foundation.
文摘Named data networking(NDNs)is an idealized deployment of information-centric networking(ICN)that has attracted attention from scientists and scholars worldwide.A distributed in-network caching scheme can efficiently realize load balancing.However,such a ubiquitous caching approach may cause problems including duplicate caching and low data diversity,thus reducing the caching efficiency of NDN routers.To mitigate these caching problems and improve the NDN caching efficiency,in this paper,a hierarchical-based sequential caching(HSC)scheme is proposed.In this scheme,the NDN routers in the data transmission path are divided into various levels and data with different request frequencies are cached in distinct router levels.The aim is to cache data with high request frequencies in the router that is closest to the content requester to increase the response probability of the nearby data,improve the data caching efficiency of named data networks,shorten the response time,and reduce cache redundancy.Simulation results show that this scheme can effectively improve the cache hit rate(CHR)and reduce the average request delay(ARD)and average route hop(ARH).
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
基金This work was supported by the National Key Basic Re- search Program of China under Grant No. 2011 CB302702 the National Natural Science Foundation of China under Grants No. 61132001, No. 61120106008, No. 61070187, No. 60970133, No. 61003225 the Beijing Nova Program.
文摘Greening Internet is an important issue now, which studies the way to reduce the increas- ing energy expenditure. Our work focuses on the network infrastructure and considers its energy awareness in traffic routing. We formulate the model by traffic engineering to achieve link rate a- daption, and also predict traffic matrices to pre- serve network stability. However, we realize that there is a tradeoff between network performance and energy efficiency, which is an obvious issue as Internet grows larger and larger. An essential cause is the huge traffic, and thus we try to fred its so- lution from a novel architecture called Named Data Networking （NDN） which tent in edge routers and can flexibly cache con- decrease the backbone traffic. We combine our methods with NDN, and finally improve both the network performance and the energy efficiency. Our work shows that it is effective, necessary and feasible to consider green- ing idea in the design of future Internet.
基金This research was funded by the National Natural Science Foundation of China(No.61862046)the Inner Mongolia Natural Science Foundation of China under Grant No.2018MS06024+2 种基金the Research Project of Higher Education School of Inner Mongolia Autonomous Region under Grant NJZY18010the Inner Mongolia Autonomous Region Science and Technology Achievements Transformation Project(No.CGZH2018124)the CERNET Innovation Project under Grant No.NGII20180626.
文摘Named Data Networking(NDN)is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by.It greatly reduces network traffic and improves the speed of content distribution and retrieval.In order to make full use of the limited caching space in routers,it is an urgent challenge to make an efficient cache replacement policy.However,the existing cache replacement policies only consider very few factors that affect the cache performance.In this paper,we present a cache replacement policy based on multi-factors for NDN(CRPM),in which the content with the least cache value is evicted from the caching space.CRPM fully analyzes multi-factors that affect the caching performance,puts forward the corresponding calculation methods,and utilize the multi-factors to measure the cache value of contents.Furthermore,a new cache value function is constructed,which makes the content with high value be stored in the router as long as possible,so as to ensure the efficient use of cache resources.The simulation results show that CPRM can effectively improve cache hit ratio,enhance cache resource utilization,reduce energy consumption and decrease hit distance of content acquisition.
基金financially supported by Shenzhen Key Fundamental Research Projects(Grant No.:JCYJ20170306091556329).
文摘Named Data Networking(NDN)improves the data delivery efficiency by caching contents in routers. To prevent corrupted and faked contents be spread in the network,NDN routers should verify the digital signature of each published content. Since the verification scheme in NDN applies the asymmetric encryption algorithm to sign contents,the content verification overhead is too high to satisfy wire-speed packet forwarding. In this paper, we propose two schemes to improve the verification performance of NDN routers to prevent content poisoning. The first content verification scheme, called "user-assisted",leads to the best performance, but can be bypassed if the clients and the content producer collude. A second scheme, named ``RouterCooperation ‘', prevents the aforementioned collusion attack by making edge routers verify the contents independently without the assistance of users and the core routers no longer verify the contents. The Router-Cooperation verification scheme reduces the computing complexity of cryptographic operation by replacing the asymmetric encryption algorithm with symmetric encryption algorithm.The simulation results demonstrate that this Router-Cooperation scheme can speed up18.85 times of the original content verification scheme with merely extra 80 Bytes transmission overhead.
基金Supporting Project Number(RSPD2023R553),King Saud University,Riyadh,Saudi Arabia.
文摘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.
文摘Over the last few years, the Internet of Things (IoT) has become an omnipresent term. The IoT expands the existing common concepts, anytime and anyplace to the connectivity for anything. The proliferation in IoT offers opportunities but may also bear risks. A hitherto neglected aspect is the possible increase in power consumption as smart devices in IoT applications are expected to be reachable by other devices at all times. This implies that the device is consuming electrical energy even when it is not in use for its primary function. Many researchers’ communities have started addressing storage ability like cache memory of smart devices using the concept called—Named Data Networking (NDN) to achieve better energy efficient communication model. In NDN, memory or buffer overflow is the common challenge especially when internal memory of node exceeds its limit and data with highest degree of freshness may not be accommodated and entire scenarios behaves like a traditional network. In such case, Data Caching is not performed by intermediate nodes to guarantee highest degree of freshness. On the periodical updates sent from data producers, it is exceedingly demanded that data consumers must get up to date information at cost of lease energy. Consequently, there is challenge in maintaining tradeoff between freshness energy consumption during Publisher-Subscriber interaction. In our work, we proposed the architecture to overcome cache strategy issue by Smart Caching Algorithm for improvement in memory management and data freshness. The smart caching strategy updates the data at precise interval by keeping garbage data into consideration. It is also observed from experiment that data redundancy can be easily obtained by ignoring/dropping data packets for the information which is not of interest by other participating nodes in network, ultimately leading to optimizing tradeoff between freshness and energy required.
基金This research was supported by the National Natural Science Foundation of China under Grant(No.42050102)the Postgraduate Education Reform Project of Jiangsu Province under Grant(No.SJCX22_0343)Also,this research was supported by Dou Wanchun Expert Workstation of Yunnan Province(No.202205AF150013).
文摘With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.
文摘This paper proposes a new approach for ranking efficiency units in data envelopment analysis as a modification of the super-efficiency models developed by Tone [1]. The new approach based on slacks-based measure of efficiency (SBM) for dealing with objective function used to classify all of the decision-making units allows the ranking of all inefficient DMUs and overcomes the disadvantages of infeasibility. This method also is applied to rank super-efficient scores for the sample of 145 agricultural bank branches in Viet Nam during 2007-2010. We then compare the estimated results from the new SCI model and the exsisting SBM model by using some statistical tests.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the National Natural Science Foundation of China under Grant No.62032013the LiaoNing Revitalization Talents Program under Grant No.XLYC1902010.
文摘Vehicular data misuse may lead to traffic accidents and even loss of life,so it is crucial to achieve secure vehicular data communications.This paper focuses on secure vehicular data communications in the Named Data Networking(NDN).In NDN,names,provider IDs and data are transmitted in plaintext,which exposes vehicular data to security threats and leads to considerable data communication costs and failure rates.This paper proposes a Secure vehicular Data Communication(SDC)approach in NDN to supress data communication costs and failure rates.SCD constructs a vehicular backbone to reduce the number of authenticated nodes involved in reverse paths.Only the ciphtertext of the name and data is included in the signed Interest and Data and transmitted along the backbone,so the secure data communications are achieved.SCD is evaluated,and the data results demonstrate that SCD achieves the above objectives.
文摘Data augmentation methods are often used to address data scarcity in natural language processing(NLP).However,token-label misalignment,which refers to situations where tokens are matched with incorrect entity labels in the augmented sentences,hinders the data augmentation methods from achieving high scores in token-level tasks like named entity recognition(NER).In this paper,we propose embedded prompt tuning(EPT)as a novel data augmentation approach to low-resource NER.To address the problem of token-label misalignment,we implicitly embed NER labels as prompt into the hidden layer of pre-trained language model,and therefore entity tokens masked can be predicted by the finetuned EPT.Hence,EPT can generate high-quality and high-diverse data with various entities,which improves performance of NER.As datasets of cross-domain NER are available,we also explore NER domain adaption with EPT.The experimental results show that EPT achieves substantial improvement over the baseline methods on low-resource NER tasks.
基金supported by the Researchers Supporting Project Number(RSP2023R34)King Saud University,Riyadh,Saudi Arabia。
文摘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.
文摘Geographical studies of outdoor activities have increased in recent years with the rise in popularity of these activities worldwide,including in Japan.Volunteered geographic information(VGI)is a key tool for organizing outdoor activities as it offers a means to determine the locational information and names of places.To evaluate the quality of VGI,geospatial data generated by land survey agencies and other VGI are often utilized as reference data.However,since these reference data may not be available,other methods are necessary to assure the quality of VGI.In this study,we examined five trust indicators based on the inherent characteristics of VGI through an empirical case study.We used mountain names extracted from OpenStreetMap in Japan as data because there were almost no other VGI in the vicinity.As a result,we isolated three trust indicators,namely versions,users,and tag corrections,to examine the thematic accuracy of VGI because these were the only statistically significant indicators.However,we found that the prediction rate of thematic accuracy was very low.To improve thematic accuracy,this study recommends using the most accurate versions,applying correctly given tags,and considering the motivations and characteristics of the VGI contributors.
文摘Vehicular Social Networks(VSNs)is the bridge of social networks and Vehicular Ad-Hoc Networks(VANETs).VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle(V2V)and Vehicle-to-Infrastructure(V2I)communication protocols.Vehicular Named Data Networking(VNDN)is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations.However,content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’high mobility.Our aim with this paper is to improve content delivery and cache hit ratio,as well as decrease the transmission delay between end-users.In this regard,we propose a novel hybrid VNDN-VSN forwarding technique based on social communities,which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area.Furthermore,we introduce an effective caching mechanism by dividing the content store into two parts,one for community private contents and the second one for public contents.Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN,in terms of data delivery ratio,average data delivery delay,and cache hit ratio.
文摘This work is dedicated to formation of data warehouse for processing of a large volume of registration data of domain names. Data cleaning is applied in order to increase the effectiveness of decision making support. Data cleaning is ap- plied in warehouses for detection and deletion of errors, discrepancy in data in order to improve their quality. For this purpose, fuzzy record comparison algorithms are for clearing of registration data of domain names reviewed in this work. Also, identification method of domain names registration data for data warehouse formation is proposed. Deci- sion making algorithms for identification of registration data are implemented in DRRacket and Python.
文摘In order to extract the boundary of rural habitation, based on geographic name data and basic geographic information data, an extraction method that use polygon aggregation is raised, it can extract the boundary of three levels of rural habitation consists of town, administrative village and nature village. The method first extracts the boundary of nature village by aggregating the resident polygon, then extracts the boundary of administrative village by aggregating the boundary of nature village, and last extracts the boundary of town by aggregating the boundary of administrative village. The related methods of extracting the boundary of those three levels rural habitation has been given in detail during the experiment with basic geographic information data and geographic name data. Experimental results show the method can be a reference for boundary extraction of rural habitation.
基金supported by the National Hightech R&D Program ("863" Program) of China (No.2013AA013505)the National Science Foundation of China (No.61472213)
文摘Named-data Networking(NDN) is a promising future Internet architecture, which introduces some evolutionary elements into layer-3, e.g., consumer-driven communication, soft state on data forwarding plane and hop-byhop traffic control. And those elements ensure data holders to solely return the requested data within the lifetime of the request, instead of pushing data whenever needed and whatever it is. Despite the dispute on the advantages and their prices, this pattern requires data consumers to keep sending requests at the right moments for continuous data transmission, resulting in significant forwarding cost and sophisticated application design. In this paper, we propose Interest Set(IS) mechanism, which compresses a set of similar Interests into one request, and maintains a relative long-term data returning path with soft state and continuous feedback from upstream. In this way, IS relaxes the above requirement, and scales NDN data forwarding by reducing forwarded requests and soft states that are needed to retrieve a given set of data.
