With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT t...With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.展开更多
In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has b...In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.展开更多
For steam tubes used in thermal power plant,the inner and outer walls were operated in high-temperature steam and flue gas environments respectively.In this study,structure,microstructure and chemical composition of o...For steam tubes used in thermal power plant,the inner and outer walls were operated in high-temperature steam and flue gas environments respectively.In this study,structure,microstructure and chemical composition of oxide films on inner and outer walls of exservice low Cr ferritic steel G102 tube and exservice high Cr ferritic steel T91 tube were analyzed.The oxide film was composed of outer oxide layer,inner oxide layer and internal oxidation zone.The outer oxide layer on the original surface of tube had a porous structure containing Fe oxides formed by diffusion and oxidation of Fe.More specially,the outer oxide layer formed in flue gas environment would mix with coal combustion products during the growth process.The inner oxide layer below the original surface of tube was made of Fe–Cr spinel.The internal oxidation zone was believed to be the precursor stage of inner oxide layer.The formation of internal oxidation zone was due to O diffusing along grain boundaries to form oxide.There were Fe–Cr–Si oxides discontinuously distributed along grain boundaries in the internal oxidation zone of G102,while there were Fe–Cr oxides continuously distributed along grain boundaries in that of T91.展开更多
In the present study,the microstructure,fracture toughness,and fracture behavior of Inconel 617 B narrow gap tungsten inert gas(NG-TIG)welded joint were investigated systematically at the designed service temperature ...In the present study,the microstructure,fracture toughness,and fracture behavior of Inconel 617 B narrow gap tungsten inert gas(NG-TIG)welded joint were investigated systematically at the designed service temperature of 700℃.Fracture toughness(J0.2)of base metal(BM)and heat affected zone(HAZ)was higher than that of weld metal(WM).In HAZ and BM,strain mainly loc alised at grain boundaries with large misorientation and there were lots of coincidence site lattice(CSL)∑3 boundaries related to twins inside grains,which led to the much higher fracture toughness of BM and HAZ than WM.The high numbers of twins as well as the less serious strain localization at grain boundaries resulted in the most outstanding fracture toughness of BM.展开更多
We describe two novel approaches for the determination of glucosamine(GlcN).The first approach is based on the chemical derivatization of GlcN with the non-fluorophor 1,3-diphenyl-1,3-propanedione(DPPD),which results ...We describe two novel approaches for the determination of glucosamine(GlcN).The first approach is based on the chemical derivatization of GlcN with the non-fluorophor 1,3-diphenyl-1,3-propanedione(DPPD),which results in a condensation product with interesting fluorescent properties.The obtained compound was isolated by silica-gel chromatography and its structure elucidated by NMR and mass spectrometry.The second approach is based on a previously undescribed sensitivity of the enzyme glucosamine-6-phosphate deaminase(GPDA)towards GlcN,which resulted in the precipitation of the enzyme.Using a rational enzyme engineering approach and both liquid-based and plate-based screening methods,mutational GPDA variants with significantly improved precipitation properties were identified and characterized.These novel glucosamine detection methods may be a useful addition to the repertoire of currently available glucosamine detection sensors.展开更多
Cemented carbide tools are widely utilized in titanium alloy machining.However,severe tool wear usually occurs during machining;thus,the wear process has attracted widespread attention.Electromagnetic treatment was ap...Cemented carbide tools are widely utilized in titanium alloy machining.However,severe tool wear usually occurs during machining;thus,the wear process has attracted widespread attention.Electromagnetic treatment was applied in our previous study to significantly improve the tool life of cemented carbide tools in Ti6Al4V machining.To investigate the effect of electromagnetic treatment on wear performance,a multiscale analysis of the wear process of cemented carbide tools in the turning process,including microdefects and wear topography at various scales,was conducted in the present study.The distribution of dislocations in the tool material was measured through electron backscatter diffraction,and the surface topographies in the wear area during the Ti6Al4V cutting process were recorded via white light interferometry.Fractal analysis based on the scaling property of surface roughness was carried out to further quantify the wear performance of the tools.The results revealed that the wear mechanism of the cutting tools was mainly adhesion and diffusion,and the diffusion wear of the electromagnetically treated tools was less than that of the untreated tools.Based on the multiscale analysis of flank wear,the effect of electromagnetic treatment on the enhancement of the wear resistance of cemented carbide cutting tools was demonstrated.The multiscale analysis of the wear performance of cutting tools in this study effectively revealed the mechanism by which electromagnetic treatment enhances wear resistance,thus contributing to filling the research gap of traditional studies on tool wear that generally employ single scales.展开更多
Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.How...Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.However,these definitions could hardly reach universal acceptance.Rather than providing a formal definition of the Metaverse,we list four must-have characteristics of the Metaverse:socialization,immersive interaction,real world-building,and expandability.These characteristics not only carve the Metaverse into a novel and fantastic digital world,but also make it suffer from all security/privacy risks,such as personal information leakage,eavesdropping,unauthorized access,phishing,data injection,broken authentication,insecure design,and more.This paper first introduces the four characteristics,then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors.Based on the four characteristics and the findings of the current progress,the security and privacy issues in the Metaverse are investigated.We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics.Lastly,the paper also raises some other concerns regarding society and humanity.展开更多
The novel coronavirus,COVID-19,has caused a crisis that affects all segments of the population.As the knowledge and understanding of COVID-19 evolve,an appropriate response plan for this pandemic is considered one of ...The novel coronavirus,COVID-19,has caused a crisis that affects all segments of the population.As the knowledge and understanding of COVID-19 evolve,an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus.Recent studies indicate that a city Digital Twin(DT)is beneficial for tackling this health crisis,because it can construct a virtual replica to simulate factors,such as climate conditions,response policies,and people's trajectories,to help plan efficient and inclusive decisions.However,a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions,limiting its advantages when facing urgent crises,such as the COVID-19 pandemic.Federated Learning(FL),in which all clients can learn a shared model while retaining all training data locally,emerges as a promising solution for accumulating the insights from multiple data sources efficiently.Furthermore,the enhanced privacy protection settings removing the privacy barriers lie in this collaboration.In this work,we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly.In particular,an FL central server manages the local updates of multiple collaborators(city DTs),providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends.This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a"global view"of city crisis management.Meanwhile,it also helps improve each city's DT by consolidating other DT's data without violating privacy rules.In this paper,we use the COVID-19 pandemic as the use case of the proposed framework.The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.展开更多
The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph,which is amenable to be adopted in tra...The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph,which is amenable to be adopted in traditional machine learning algorithms in favor of vector representations.Graph embedding methods build an important bridge between social network analysis and data analytics,as social networks naturally generate an unprecedented volume of graph data continuously.Publishing social network data not only brings benefit for public health,disaster response,commercial promotion,and many other applications,but also gives birth to threats that jeopardize each individual’s privacy and security.Unfortunately,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social networks.To be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network embedding.In this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links,while persevering sufficient non-sensitive information,such as graph topology and node attributes in graph embedding.Extensive experiments are conducted to evaluate the proposed framework using ground truth social network datasets.展开更多
Social media has more than three billion users sharing events,comments,and feelings throughout the world.It serves as a critical information source with large volumes,high velocity,and a wide variety of data.The previ...Social media has more than three billion users sharing events,comments,and feelings throughout the world.It serves as a critical information source with large volumes,high velocity,and a wide variety of data.The previous studies on information spreading,relationship analyzing,and individual modeling,etc.,have been heavily conducted to explore the tremendous social and commercial values of social media data.This survey studies the previous literature and the existing applications from a practical perspective.We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques,such as topic analysis,time series analysis,sentiment analysis,and network analysis.After that,we present the impacts of such applications in three different areas,including disaster management,healthcare,and business.Finally,we list existing challenges and suggest promising future research directions in terms of data privacy,5 G wireless network,and multilingual support.展开更多
Internet of Things（IoT）is a new paradigm that the ubiquitous smart objects,such as devices,vehicles,buildings,etc.,interact and exchange data through emerging wireless technology with the intention of improving peo...Internet of Things（IoT）is a new paradigm that the ubiquitous smart objects,such as devices,vehicles,buildings,etc.,interact and exchange data through emerging wireless technology with the intention of improving people’s quality of lives in variety areas,such as transportation,manufacturing industry,health care industry,etc：Besides benefits,展开更多
Community search has been extensively studied in large networks,such as Protein-Protein Interaction(PPI)networks,citation graphs,and collaboration networks.However,in terms of widely existing multi-valued networks,whe...Community search has been extensively studied in large networks,such as Protein-Protein Interaction(PPI)networks,citation graphs,and collaboration networks.However,in terms of widely existing multi-valued networks,where each node has d(d 1)numerical attributes,almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute.To solve this problem,the concept of skyline community was presented,based on the concepts of k-core and skyline recently.The skyline community is defined as a maximal k-core that satisfies some influence constraints,which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks.However,the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute,and the computation complexity degrades exponentially as the number of attributes increases.In this work,we turn our attention to the general scenario where multiple nodes may have the same attribute value.Specifically,we first present an algorithm,called MICS,which can find all skyline communities in a multi-valued network.To improve computation efficiency,we then propose a dimension reduction based algorithm,called P-MICS,using the maximum entropy method.Our algorithm can significantly reduce the skyline community searching time,while is still able to find almost all cohesive skyline communities.Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.展开更多
Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology,blockchain has been broadly used in fintech,smart homes,public health,and intellige...Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology,blockchain has been broadly used in fintech,smart homes,public health,and intelligent transportation due to its properties of decentralization,collective maintenance,and immutability.Although the dynamism of blockchain abounds in various fields,concerns in terms of network communication interference and privacy leakage are gradually increasing.Because of the lack of reliable attack analysis systems,fully understanding some attacks on the blockchain,such as mining,network communication,smart contract,and privacy theft attacks,has remained challenging.Therefore,in this study,we examine the security and privacy of the blockchain and analyze possible solutions.We systematical classify the blockchain attack techniques into three categories,then discuss the corresponding attack and defense methods based on these categories.We focus on(1)the attack and defense methods of mining pool attacks for blockchain security issues,such as block withholding,51%,pool hopping,selfish mining,and fork after withholding attacks,in the attack type of consensus excitation;(2)the attack and defense methods of network communication and smart contracts for blockchain security issues,such as distributed denial-of-service,Sybil,eclipse,and reentrancy attacks,in the attack type of middle protocol;and(3)the attack and defense methods of privacy thefts for blockchain privacy issues,such as identity privacy and transaction information attacks,in the attack type of application service.Finally,we discuss future research directions for blockchain security.展开更多
IoT devices’storage and computation capacities are constantly increasing in recent years,which brings critical challenges in data privacy protection.Federated learning(FL)and blockchain technology are two popular tec...IoT devices’storage and computation capacities are constantly increasing in recent years,which brings critical challenges in data privacy protection.Federated learning(FL)and blockchain technology are two popular tech-niques used in IoT data aggregation,where FL enables data training with privacy protection,and blockchain provides a decentralized architecture for data storage and mining.However,very few the state-of-the-art works consider the applicability of the combination of FL and blockchain.In this paper,we adopt the federated aver-aging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance.We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy.Finally,we propose and implement a non-interactive Public Verifiable Secret Sharing(PVSS)algorithm with Distributed Hash Table(DHT)that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users.At last,we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.展开更多
Pulsed magnetic treatment(PMT)has been adopted as an effective strengthening method for engineering materials and components in recent years,and the development of its application depends on the comprehensive understa...Pulsed magnetic treatment(PMT)has been adopted as an effective strengthening method for engineering materials and components in recent years,and the development of its application depends on the comprehensive understanding of the nature of PMT.The deep mechanism was thought initially to be the magnetostrictive effect,while further investigation found that the magnetic field could lead to the change of the defect states in the crystal,which is called the magnetoplastic effect.Due to the complexity of the engineering materials,manifestations of the magnetoplastic effect become more diverse,and they were reviewed in the form of microstructure homogenization and interfacial stabilization.Further,the mechanism of the magnetoplastic effect was discussed,focusing on the changes in the spin states under the external magnetic field.Microstructure modifications could also alter material performances,especially the residual stress,plasticity,and fatigue properties.Therefore,PMT with specific parameters can be utilized to obtain an ideal combination of microstructure,residual stress,and mechanical properties for better service performance of different mechanical parts,and its applications on machining tools and bearings are perfect examples.This work reviews the effect of PMT on the microstructure and properties of different materials and the mechanism,and it also summarizes the fundamental applications of PMT on essential mechanical parts.展开更多
The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining.In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs,only very few inde...The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining.In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs,only very few indexes are proposed for the same in bipartite graphs.In this work,we present the index called˛.ˇ/-core number on vertices,which reflects the maximal cohesive and dense subgraph a vertex can be in,to help enumerate the(α,β)-cores,a commonly used dense structure in bipartite graphs.To address the problem of extremely high time and space cost for enumerating the(α,β)-cores,we first present a linear time and space algorithm for computing the˛.ˇ/-core numbers of vertices.We further propose core maintenance algorithms,to update the core numbers of vertices when a graph changes by avoiding recalculations.Experimental results on different real-world and synthetic datasets demonstrate the effectiveness and efficiency of our algorithms.展开更多
Deep learning based techniques are broadly used in various applications, which exhibit superior performance compared to traditional methods. One of the mainstream topics in computer vision is the image super-resolutio...Deep learning based techniques are broadly used in various applications, which exhibit superior performance compared to traditional methods. One of the mainstream topics in computer vision is the image super-resolution task. In recent deep learning neural networks, the number of parameters in each convolution layer has increased along with more layers and feature maps, resulting in better image super-resolution performance. In today’s era, numerous service providers offer super-resolution services to users, providing them with remarkable convenience. However, the availability of open-source super-resolution services exposes service providers to the risk of copyright infringement, as the complete model could be vulnerable to leakage. Therefore, safeguarding the copyright of the complete model is a non-trivial concern. To tackle this issue, this paper presents a lightweight model as a substitute for the original complete model in image super-resolution. This research has identified smaller networks that can deliver impressive performance, while protecting the original model’s copyright. Finally, comprehensive experiments are conducted on multiple datasets to demonstrate the superiority of the proposed approach in generating super-resolution images even using lightweight neural networks.展开更多
Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,a...Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,as some may not provide adequate protections for sensitive or personal information such as social network data.Additionally,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model used.Hence,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world scenarios.Few-shot graph classification,as one application of meta-learning in supervised graph learning,aims to classify unseen graph types by only using a small amount of labeled data.However,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets.Since structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information provided.Nevertheless,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels.Hence,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'attributes.With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research,we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information.Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.展开更多
Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs,which is crucial in detecting malicious activities,network analysis,social network analysis,and more.Despite its p...Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs,which is crucial in detecting malicious activities,network analysis,social network analysis,and more.Despite its paramount importance,conventional methods often grapple with inefficiencies and challenges tied to obtaining accurate query graph data.This paper introduces a neural-based inexact graph de-anonymization,which comprises an embedding phase,a comparison phase,and a matching procedure.The embedding phase uses a graph convolutional network to generate embedding vectors for both the query and anonymized graphs.The comparison phase uses a neural tensor network to ascertain node resemblances.The matching procedure employs a refined greedy algorithm to discern optimal node pairings.Additionally,we comprehensively evaluate its performance via well-conducted experiments on various real datasets.The results demonstrate the effectiveness of our proposed approach in enhancing the efficiency and performance of graph de-anonymization through the use of graph embedding vectors.展开更多
The realm of Artificial Intelligence(AI)has seen monumental shifts in recent years,particularly with the advent of large-scale models such as GPT-3,which have pushed the boundaries of natural language processing(NLP)a...The realm of Artificial Intelligence(AI)has seen monumental shifts in recent years,particularly with the advent of large-scale models such as GPT-3,which have pushed the boundaries of natural language processing(NLP)and other AI applications.These models,while offering unprecedented capabilities,also present significant challenges in terms of the immense computational resources and energy required for training and deployment.The sheer scale of these models—175 billion parameters and over 3 million GPU hours in the case of GPT-3—places their development and use beyond the reach of many organizations and individuals.展开更多
文摘With the development of Internet of Things(IoT),the delay caused by network transmission has led to low data processing efficiency.At the same time,the limited computing power and available energy consumption of IoT terminal devices are also the important bottlenecks that would restrict the application of blockchain,but edge computing could solve this problem.The emergence of edge computing can effectively reduce the delay of data transmission and improve data processing capacity.However,user data in edge computing is usually stored and processed in some honest-but-curious authorized entities,which leads to the leakage of users’privacy information.In order to solve these problems,this paper proposes a location data collection method that satisfies the local differential privacy to protect users’privacy.In this paper,a Voronoi diagram constructed by the Delaunay method is used to divide the road network space and determine the Voronoi grid region where the edge nodes are located.A random disturbance mechanism that satisfies the local differential privacy is utilized to disturb the original location data in each Voronoi grid.In addition,the effectiveness of the proposed privacy-preserving mechanism is verified through comparison experiments.Compared with the existing privacy-preserving methods,the proposed privacy-preserving mechanism can not only better meet users’privacy needs,but also have higher data availability.
基金supported in part by the National Natural Science Foundation of China under Grant 62072392,Grant 61822602,Grant 61772207,Grant 61802331,Grant 61602399,Grant 61702439,Grant 61773331,and Grant 62062034the China Postdoctoral Science Foundation under Grant 2019T120732 and Grant 2017M622691+2 种基金the Natural Science Foundation of Shandong Province under Grant ZR2016FM42the Major scientific and technological innovation projects of Shandong Province under Grant 2019JZZY020131the Key projects of Shandong Natural Science Foundation under Grant ZR2020KF019.
文摘In the era of the Internet of Things(IoT),the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world.As a part of the IoT ecosystem,task assignment has become an important goal of the research community.Existing task assignment algorithms can be categorized as offline(performs better with datasets but struggles to achieve good real-life results)or online(works well with real-life input but is difficult to optimize regarding in-depth assignments).This paper proposes a Cross-regional Online Task(CROT)assignment problem based on the online assignment model.Given the CROT problem,an Online Task Assignment across Regions based on Prediction(OTARP)algorithm is proposed.OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments.The first stage uses historical data to make offline predictions,with a graph-driven method for offline bipartite graph matching.The second stage uses a bipartite graph to complete the online task assignment process.This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies.To encourage crowd workers to complete crowd tasks across regions,an incentive strategy is designed to encourage crowd workers’movement.To avoid the idle problem in the process of crowd worker movement,a drop-by-rider problem is used to help crowd workers accept more crowd tasks,optimize the number of assignments,and increase utility.Finally,through comparison experiments on real datasets,the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.
基金supported by the National Natural Science Foundation of China (Nos.51901113 and 51775300)the State Key Laboratory of Tribology in Tsinghua University, and the State Key Lab of Advanced Welding and Joining in Harbin Institute of Technology (No.AWJ-21M03)。
文摘For steam tubes used in thermal power plant,the inner and outer walls were operated in high-temperature steam and flue gas environments respectively.In this study,structure,microstructure and chemical composition of oxide films on inner and outer walls of exservice low Cr ferritic steel G102 tube and exservice high Cr ferritic steel T91 tube were analyzed.The oxide film was composed of outer oxide layer,inner oxide layer and internal oxidation zone.The outer oxide layer on the original surface of tube had a porous structure containing Fe oxides formed by diffusion and oxidation of Fe.More specially,the outer oxide layer formed in flue gas environment would mix with coal combustion products during the growth process.The inner oxide layer below the original surface of tube was made of Fe–Cr spinel.The internal oxidation zone was believed to be the precursor stage of inner oxide layer.The formation of internal oxidation zone was due to O diffusing along grain boundaries to form oxide.There were Fe–Cr–Si oxides discontinuously distributed along grain boundaries in the internal oxidation zone of G102,while there were Fe–Cr oxides continuously distributed along grain boundaries in that of T91.
基金financially supported by the National Natural Science Foundation of China(Project No.51775300)Shanghai Turbine Company,Shanghai,Chinathe State Key Laboratory of Tribology,Beijing,China.
文摘In the present study,the microstructure,fracture toughness,and fracture behavior of Inconel 617 B narrow gap tungsten inert gas(NG-TIG)welded joint were investigated systematically at the designed service temperature of 700℃.Fracture toughness(J0.2)of base metal(BM)and heat affected zone(HAZ)was higher than that of weld metal(WM).In HAZ and BM,strain mainly loc alised at grain boundaries with large misorientation and there were lots of coincidence site lattice(CSL)∑3 boundaries related to twins inside grains,which led to the much higher fracture toughness of BM and HAZ than WM.The high numbers of twins as well as the less serious strain localization at grain boundaries resulted in the most outstanding fracture toughness of BM.
基金supported in parts by the National Natural Science Foundation of China (grant numbers 31471703,31671854,and 31871754)the 100 Foreign Talents Plan (grant number JSB2014012).
文摘We describe two novel approaches for the determination of glucosamine(GlcN).The first approach is based on the chemical derivatization of GlcN with the non-fluorophor 1,3-diphenyl-1,3-propanedione(DPPD),which results in a condensation product with interesting fluorescent properties.The obtained compound was isolated by silica-gel chromatography and its structure elucidated by NMR and mass spectrometry.The second approach is based on a previously undescribed sensitivity of the enzyme glucosamine-6-phosphate deaminase(GPDA)towards GlcN,which resulted in the precipitation of the enzyme.Using a rational enzyme engineering approach and both liquid-based and plate-based screening methods,mutational GPDA variants with significantly improved precipitation properties were identified and characterized.These novel glucosamine detection methods may be a useful addition to the repertoire of currently available glucosamine detection sensors.
基金supported by the National Natural Science Foundation of China(No.52275441)the Shenzhen Science and Technology Program(No.KJZD20230923114606013).
文摘Cemented carbide tools are widely utilized in titanium alloy machining.However,severe tool wear usually occurs during machining;thus,the wear process has attracted widespread attention.Electromagnetic treatment was applied in our previous study to significantly improve the tool life of cemented carbide tools in Ti6Al4V machining.To investigate the effect of electromagnetic treatment on wear performance,a multiscale analysis of the wear process of cemented carbide tools in the turning process,including microdefects and wear topography at various scales,was conducted in the present study.The distribution of dislocations in the tool material was measured through electron backscatter diffraction,and the surface topographies in the wear area during the Ti6Al4V cutting process were recorded via white light interferometry.Fractal analysis based on the scaling property of surface roughness was carried out to further quantify the wear performance of the tools.The results revealed that the wear mechanism of the cutting tools was mainly adhesion and diffusion,and the diffusion wear of the electromagnetically treated tools was less than that of the untreated tools.Based on the multiscale analysis of flank wear,the effect of electromagnetic treatment on the enhancement of the wear resistance of cemented carbide cutting tools was demonstrated.The multiscale analysis of the wear performance of cutting tools in this study effectively revealed the mechanism by which electromagnetic treatment enhances wear resistance,thus contributing to filling the research gap of traditional studies on tool wear that generally employ single scales.
基金supported by the National Natural Science Foundation of China(No.1912753).
文摘Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.However,these definitions could hardly reach universal acceptance.Rather than providing a formal definition of the Metaverse,we list four must-have characteristics of the Metaverse:socialization,immersive interaction,real world-building,and expandability.These characteristics not only carve the Metaverse into a novel and fantastic digital world,but also make it suffer from all security/privacy risks,such as personal information leakage,eavesdropping,unauthorized access,phishing,data injection,broken authentication,insecure design,and more.This paper first introduces the four characteristics,then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors.Based on the four characteristics and the findings of the current progress,the security and privacy issues in the Metaverse are investigated.We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics.Lastly,the paper also raises some other concerns regarding society and humanity.
基金supported in part by the National Key Research and Development program of China(Nos.2017YFA0604500 and 2018YFB2100303)in part by the National Natural Science Foundation of China(No.61701190)+1 种基金in part by the Key Technology Innovation Cooperation Project of the Government and University for the Entire Industry Demonstration(No.SXGJSF2017-4)the Program for Innovative Postdoctoral Talents in Shandong Province(No.40618030001)。
文摘The novel coronavirus,COVID-19,has caused a crisis that affects all segments of the population.As the knowledge and understanding of COVID-19 evolve,an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus.Recent studies indicate that a city Digital Twin(DT)is beneficial for tackling this health crisis,because it can construct a virtual replica to simulate factors,such as climate conditions,response policies,and people's trajectories,to help plan efficient and inclusive decisions.However,a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions,limiting its advantages when facing urgent crises,such as the COVID-19 pandemic.Federated Learning(FL),in which all clients can learn a shared model while retaining all training data locally,emerges as a promising solution for accumulating the insights from multiple data sources efficiently.Furthermore,the enhanced privacy protection settings removing the privacy barriers lie in this collaboration.In this work,we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly.In particular,an FL central server manages the local updates of multiple collaborators(city DTs),providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends.This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a"global view"of city crisis management.Meanwhile,it also helps improve each city's DT by consolidating other DT's data without violating privacy rules.In this paper,we use the COVID-19 pandemic as the use case of the proposed framework.The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.
基金supported by the National Science Foundation of USA(Nos.1829674,1912753,1704287,and 2011845)。
文摘The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph,which is amenable to be adopted in traditional machine learning algorithms in favor of vector representations.Graph embedding methods build an important bridge between social network analysis and data analytics,as social networks naturally generate an unprecedented volume of graph data continuously.Publishing social network data not only brings benefit for public health,disaster response,commercial promotion,and many other applications,but also gives birth to threats that jeopardize each individual’s privacy and security.Unfortunately,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social networks.To be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network embedding.In this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links,while persevering sufficient non-sensitive information,such as graph topology and node attributes in graph embedding.Extensive experiments are conducted to evaluate the proposed framework using ground truth social network datasets.
文摘Social media has more than three billion users sharing events,comments,and feelings throughout the world.It serves as a critical information source with large volumes,high velocity,and a wide variety of data.The previous studies on information spreading,relationship analyzing,and individual modeling,etc.,have been heavily conducted to explore the tremendous social and commercial values of social media data.This survey studies the previous literature and the existing applications from a practical perspective.We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques,such as topic analysis,time series analysis,sentiment analysis,and network analysis.After that,we present the impacts of such applications in three different areas,including disaster management,healthcare,and business.Finally,we list existing challenges and suggest promising future research directions in terms of data privacy,5 G wireless network,and multilingual support.
文摘Internet of Things（IoT）is a new paradigm that the ubiquitous smart objects,such as devices,vehicles,buildings,etc.,interact and exchange data through emerging wireless technology with the intention of improving people’s quality of lives in variety areas,such as transportation,manufacturing industry,health care industry,etc：Besides benefits,
基金partially supported by the National Key R&D Program of China(No.2019YFB2102600)the National Natural Science Foundation of China(Nos.61971269,61832012,616727321,and 61771289)
文摘Community search has been extensively studied in large networks,such as Protein-Protein Interaction(PPI)networks,citation graphs,and collaboration networks.However,in terms of widely existing multi-valued networks,where each node has d(d 1)numerical attributes,almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute.To solve this problem,the concept of skyline community was presented,based on the concepts of k-core and skyline recently.The skyline community is defined as a maximal k-core that satisfies some influence constraints,which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks.However,the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute,and the computation complexity degrades exponentially as the number of attributes increases.In this work,we turn our attention to the general scenario where multiple nodes may have the same attribute value.Specifically,we first present an algorithm,called MICS,which can find all skyline communities in a multi-valued network.To improve computation efficiency,we then propose a dimension reduction based algorithm,called P-MICS,using the maximum entropy method.Our algorithm can significantly reduce the skyline community searching time,while is still able to find almost all cohesive skyline communities.Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.
基金supported by the“Ling Yan”research and development project of Zhejiang Province of China under Grant No.2022C03122,Project Intelligentizationand Digitization for Airline Revolution#2018R02008Public Welfare Technology Application and Research Projects of Zhejiang Province of China under Grants No.LGF22F020006 and No.LGF21F010004.
文摘Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology,blockchain has been broadly used in fintech,smart homes,public health,and intelligent transportation due to its properties of decentralization,collective maintenance,and immutability.Although the dynamism of blockchain abounds in various fields,concerns in terms of network communication interference and privacy leakage are gradually increasing.Because of the lack of reliable attack analysis systems,fully understanding some attacks on the blockchain,such as mining,network communication,smart contract,and privacy theft attacks,has remained challenging.Therefore,in this study,we examine the security and privacy of the blockchain and analyze possible solutions.We systematical classify the blockchain attack techniques into three categories,then discuss the corresponding attack and defense methods based on these categories.We focus on(1)the attack and defense methods of mining pool attacks for blockchain security issues,such as block withholding,51%,pool hopping,selfish mining,and fork after withholding attacks,in the attack type of consensus excitation;(2)the attack and defense methods of network communication and smart contracts for blockchain security issues,such as distributed denial-of-service,Sybil,eclipse,and reentrancy attacks,in the attack type of middle protocol;and(3)the attack and defense methods of privacy thefts for blockchain privacy issues,such as identity privacy and transaction information attacks,in the attack type of application service.Finally,we discuss future research directions for blockchain security.
基金partly supported by the National Science Foundation of U.S.(1704287,1829674,1912753,and 2011845).
文摘IoT devices’storage and computation capacities are constantly increasing in recent years,which brings critical challenges in data privacy protection.Federated learning(FL)and blockchain technology are two popular tech-niques used in IoT data aggregation,where FL enables data training with privacy protection,and blockchain provides a decentralized architecture for data storage and mining.However,very few the state-of-the-art works consider the applicability of the combination of FL and blockchain.In this paper,we adopt the federated aver-aging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance.We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy.Finally,we propose and implement a non-interactive Public Verifiable Secret Sharing(PVSS)algorithm with Distributed Hash Table(DHT)that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users.At last,we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.
基金the financial supports from National Key R&D Program of China(No.2020YFA0714900)Joint Fund of the Ministry of Education(No.8091B012201)National Natural Science Foundation of China(No.52031003).
文摘Pulsed magnetic treatment(PMT)has been adopted as an effective strengthening method for engineering materials and components in recent years,and the development of its application depends on the comprehensive understanding of the nature of PMT.The deep mechanism was thought initially to be the magnetostrictive effect,while further investigation found that the magnetic field could lead to the change of the defect states in the crystal,which is called the magnetoplastic effect.Due to the complexity of the engineering materials,manifestations of the magnetoplastic effect become more diverse,and they were reviewed in the form of microstructure homogenization and interfacial stabilization.Further,the mechanism of the magnetoplastic effect was discussed,focusing on the changes in the spin states under the external magnetic field.Microstructure modifications could also alter material performances,especially the residual stress,plasticity,and fatigue properties.Therefore,PMT with specific parameters can be utilized to obtain an ideal combination of microstructure,residual stress,and mechanical properties for better service performance of different mechanical parts,and its applications on machining tools and bearings are perfect examples.This work reviews the effect of PMT on the microstructure and properties of different materials and the mechanism,and it also summarizes the fundamental applications of PMT on essential mechanical parts.
基金This work was supported by the National Key Research and Development Program of China(No.2019YFB2102600)the National Natural Science Foundation of China(Nos.62122042 and 61971269)the Blockchain Core Technology Strategic Research Program of Ministry of Education of China(No.2020KJ010301)fund。
文摘The prevalence of graph data has brought a lot of attention to cohesive and dense subgraph mining.In contrast with the large number of indexes proposed to help mine dense subgraphs in general graphs,only very few indexes are proposed for the same in bipartite graphs.In this work,we present the index called˛.ˇ/-core number on vertices,which reflects the maximal cohesive and dense subgraph a vertex can be in,to help enumerate the(α,β)-cores,a commonly used dense structure in bipartite graphs.To address the problem of extremely high time and space cost for enumerating the(α,β)-cores,we first present a linear time and space algorithm for computing the˛.ˇ/-core numbers of vertices.We further propose core maintenance algorithms,to update the core numbers of vertices when a graph changes by avoiding recalculations.Experimental results on different real-world and synthetic datasets demonstrate the effectiveness and efficiency of our algorithms.
基金supported by the SW Copyright Ecosystem R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports,and Tourism in 2023.Project Name:Development of Large-Scale Software License Verification Technology by Cloud Service Utilization and Construction Type(No.RS-2023-00224818).
文摘Deep learning based techniques are broadly used in various applications, which exhibit superior performance compared to traditional methods. One of the mainstream topics in computer vision is the image super-resolution task. In recent deep learning neural networks, the number of parameters in each convolution layer has increased along with more layers and feature maps, resulting in better image super-resolution performance. In today’s era, numerous service providers offer super-resolution services to users, providing them with remarkable convenience. However, the availability of open-source super-resolution services exposes service providers to the risk of copyright infringement, as the complete model could be vulnerable to leakage. Therefore, safeguarding the copyright of the complete model is a non-trivial concern. To tackle this issue, this paper presents a lightweight model as a substitute for the original complete model in image super-resolution. This research has identified smaller networks that can deliver impressive performance, while protecting the original model’s copyright. Finally, comprehensive experiments are conducted on multiple datasets to demonstrate the superiority of the proposed approach in generating super-resolution images even using lightweight neural networks.
基金supported by SW Copyright Ecosystem R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports,and Tourism in 2023(No.RS-2023-00224818).
文摘Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,as some may not provide adequate protections for sensitive or personal information such as social network data.Additionally,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model used.Hence,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world scenarios.Few-shot graph classification,as one application of meta-learning in supervised graph learning,aims to classify unseen graph types by only using a small amount of labeled data.However,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets.Since structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information provided.Nevertheless,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels.Hence,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'attributes.With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research,we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information.Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.
基金supported by the National Science Foundation of U.S.(2011845,2315596 and 2244219).
文摘Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs,which is crucial in detecting malicious activities,network analysis,social network analysis,and more.Despite its paramount importance,conventional methods often grapple with inefficiencies and challenges tied to obtaining accurate query graph data.This paper introduces a neural-based inexact graph de-anonymization,which comprises an embedding phase,a comparison phase,and a matching procedure.The embedding phase uses a graph convolutional network to generate embedding vectors for both the query and anonymized graphs.The comparison phase uses a neural tensor network to ascertain node resemblances.The matching procedure employs a refined greedy algorithm to discern optimal node pairings.Additionally,we comprehensively evaluate its performance via well-conducted experiments on various real datasets.The results demonstrate the effectiveness of our proposed approach in enhancing the efficiency and performance of graph de-anonymization through the use of graph embedding vectors.
文摘The realm of Artificial Intelligence(AI)has seen monumental shifts in recent years,particularly with the advent of large-scale models such as GPT-3,which have pushed the boundaries of natural language processing(NLP)and other AI applications.These models,while offering unprecedented capabilities,also present significant challenges in terms of the immense computational resources and energy required for training and deployment.The sheer scale of these models—175 billion parameters and over 3 million GPU hours in the case of GPT-3—places their development and use beyond the reach of many organizations and individuals.
