In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interac...In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose estimation.Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained.Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized objects.The existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the pairs.Such estimation depends on appearance features and spatial information.Therefore,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI.Furthermore,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using YOLO.We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm.The interactions are linked with the human and object to predict the actions.The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.展开更多
With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application...With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application scenario,one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources.Although a variety of recommendation algorithms have been employed in this field,they ignore the massive text resources in the social internet of things,which can effectively improve the effect of recommendation.In this paper,a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed.The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the“thing-thing”relationship information in the internet of things to improve the effect of recommendation.Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.展开更多
The term Internet of Things refers to the networked interconnection of objects of diverse nature, such as electronic devices, sensors, but also physical objects and beings as well as virtual data and environments. Alt...The term Internet of Things refers to the networked interconnection of objects of diverse nature, such as electronic devices, sensors, but also physical objects and beings as well as virtual data and environments. Although the basic concept of the Internet of Things sounds simple, its application is difficult and, so far, the respective existing architectural models are rather monolithic and are dominated by several limitations. The paper introduces a generic Internet of Things architecture trying to resolve the existing restrictions of current architectural models by integrating both RFID and smart object-based infrastructures, while also exploring a third parameter, i.e. the social potentialities of the Internet of Things building blocks towards shaping the “Social Internet of Things”. The proposed architecture is based on a layered lightweight and open middle-ware solution following the paradigm of Service Oriented Architecture and the Semantic Model Driven Ap-proach, which is realized at both design-time and deployment–time covering the whole service lifecycle for the corresponding services and applications provided.展开更多
The object of criminal legal aid refers to the person in a criminal case who has the right or eligibility toapply for legal assistance and who receives it. According to jurispru- dence, the object （or aid recipient）...The object of criminal legal aid refers to the person in a criminal case who has the right or eligibility toapply for legal assistance and who receives it. According to jurispru- dence, the object （or aid recipient） is a party in a given legal case, who is granted legal aid. They are often among the disadvantaged group in criminal cases, since most of them are mentally challenged, lack free- dom or have health problems.＇ Both international and domestic laws have certain norms regarding objects of criminal legal aid. Our domestic law places more emphasis on ＂defen- dants＂ while downplaying ＂suspects＂ and ＂victims＂ in identifying objects.展开更多
This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion b...This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process.Since that human actions and interacted objects provide strong context information,i.e.some actions are usually related to some specific objects,the accuracy of recognition is significantly improved for both of them.Through the proposed method,both global and local temporal features from skeleton sequences are extracted to model human actions.In the meantime,kernel features are utilized to describe interacted objects.Finally,all possible solutions from actions and objects are optimized by modeling the context between them.The results of experiments demonstrate the effectiveness of our method.展开更多
From subject,object and target subsystems,we analyze the rural human resource development system.The subject system includes government,education and training organizations,society,and rural human resource itself.Diff...From subject,object and target subsystems,we analyze the rural human resource development system.The subject system includes government,education and training organizations,society,and rural human resource itself.Different development subject bears different responsibility.Object system includes farmers engaged in farming,farmer workers,rural unemployed people,rural students,rural left-behind people,and other people in rural areas.Different development object has different features.Development target system includes raising quality of rural human resource,keeping reasonable population size,optimizing structure of rural human resource,and improving vitality of rural human resource,etc.展开更多
In the Internet of Things(IoT),security and privacy issues of physical objects are crucial to the related applications.In order to clarify the complicated security and privacy issues,the life cycle of a physical objec...In the Internet of Things(IoT),security and privacy issues of physical objects are crucial to the related applications.In order to clarify the complicated security and privacy issues,the life cycle of a physical object is divided into three stages of pre-working,in-working,and post-working.On this basis,a physical object-based security architecture for the IoT is put forward.According to the security architecture,security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in detail.Considering the development of IoT technologies,potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized.At the same time,possible directions for dealing with these challenges are also pointed out.展开更多
The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integratio...The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things(IoT)technologies.The enhancement of its performance largely depends on breakthrough advancements in object detection technology.However,current object detection technology still faces numerous challenges,such as accuracy,robustness,and data privacy issues.These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions.This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS,aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems.To achieve this objective,we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)approach to search,screen,and assess the eligibility of relevant literature,ultimately including 88 studies.Through an analysis of these studies,we summarized the characteristics,advantages,and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage.Additionally,we examined its applications in ITS from three perspectives:vehicle detection,pedestrian detection,and traffic sign detection.We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues.This review offers researchers a comprehensive perspective,identifying potential improvement directions for object detection technology in ITS,including accuracy,robustness,real-time performance,data annotation cost,and data privacy.In doing so,it provides significant guidance for the further development of IoT-based intelligent transportation systems.展开更多
The Internet of Things(IoT)is a recent technology,which implies the union of objects,“things”,into a single worldwide network.This promising paradigm faces many design challenges associated with the dramatic increas...The Internet of Things(IoT)is a recent technology,which implies the union of objects,“things”,into a single worldwide network.This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices.Device identification is one of these challenges that becomes complicated with the increase of network devices.Despite this,there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications.In this regard,one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks.Many unique soft-ware and hardware solutions are used as a unique global identifier;however,such solutions have many limitations.This article proposes a novel solution,based on the Digital Object Architecture(DOA),that meets the requirements of identifying devices and applications of the IoT.This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks.We propose a model of an identification system based on the architecture of digital objects,which differs from the well-known ones.The proposed model ensures an acceptable quality of service(QoS)in the common architecture of the existing public communication networks.A novel interaction architecture is developed by introducing a Middle Handle Register(MHR)between the global register,i.e.,Global Handle Register(GHR),and local register,i.e.,Local Handle Register(LHR).The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented.The developed model is simulated for a wide-area network with allocated registers,and the results are introduced and discussed.展开更多
In Internet of Things （IoT） cial networks of physical objects , physical objects can build their own social networks. How do so- generate, and what characteristics do the social networks have. In order to solve thes...In Internet of Things （IoT） cial networks of physical objects , physical objects can build their own social networks. How do so- generate, and what characteristics do the social networks have. In order to solve these problems, according to the interaction of physical objects in IoT, this paper presents a growing social network model of physical objects and researches the attachment mecha- nism of the model that includes three modes, physical distance, social distance and preference. Through the simulation realizations of the model, the characteristics （e. g. degree distribution, com- munity structure） of social network are analyzed. The model can forecast the growth of social networks of physical object in IoT and simulate social networks of physical objects in the large scale IoT.展开更多
A new method of elastic articulated objects (human bodies) modeling was presented based on a new conic curve. The model includes 3D object deformable curves which can represent the deformation of human occluding conto...A new method of elastic articulated objects (human bodies) modeling was presented based on a new conic curve. The model includes 3D object deformable curves which can represent the deformation of human occluding contours. The deformation of human occluding contour can be represented by adjusting only four deformation parameters for each limb. Then, the 3D deformation parameters are determined by corresponding 2D contours from a sequence of stereo images. The algorithm presented in this paper includes deformable conic curve parameters determination and the plane, 3D conic curve lying on, parameter determination.展开更多
Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is ground...Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is grounded on a prepredicative,i. e. pre-linguistic and pre-communicative,level. I will endorse a Husserlian viewpoint on the issue,and I will try to develop some aspects of the Husserlian account of three-dimensional thing-perception by means of which I will showhowprepredicative experience can actually offer us a fundamental element of our common understanding of objectivity. In doing this,it will be necessary to acknowledge thing-perception as being primarily intertwined with indeterminacy. I will claim that only on the basis of such an intuitive and prepredicative access to the things as partially indeterminate,first,and as determinable,second,is it possible to have an understanding of the world as something (at least partially) independent from the intuition (s) all subjects can have of it. By means of the addition of a consciousness of the thing as accessible to other subjects,one achieves a vision of the thing as fully determinate in itself. This"vision",however,takes one to be aware of the determination of the thing as lying beyond any intuitive grasp of it. The result will,thus,be that the prepredicative constitution of our basic sense of objectivity leads us to intend the world as something which should be accounted for (also) by means of sources different from intuition.展开更多
Humans regularly interact with their surrounding objects.Such interactions often result in strongly correlated motions between humans and the interacting objects.We thus ask:"Is it possible to infer object proper...Humans regularly interact with their surrounding objects.Such interactions often result in strongly correlated motions between humans and the interacting objects.We thus ask:"Is it possible to infer object properties from skeletal motion alone,even without seeing the interacting object itself?"In this paper,we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone.This inference allows us to disentangle the motion from the object property and transfer object properties to a given motion.We collected a large number of videos and 3 D skeletal motions of performing actors using an inertial motion capture device.We analyzed similar actions and learned subtle differences between them to reveal latent properties of the interacting objects.In particular,we learned to identify the interacting object,by estimating its weight,or its spillability.Our results clearly demonstrate that motions and interacting objects are highly correlated and that related object latent properties can be inferred from 3 D skeleton sequences alone,leading to new synthesis possibilities for motions involving human interaction.Our dataset is available at http://vcc.szu.edu.cn/research/2020/IT.html.展开更多
Human Activity Recognition(HAR)represents a rapidly advancing research domain,propelled by continuous developments in sensor technologies and the Internet of Things(IoT).Deep learning has become the dominant paradigm ...Human Activity Recognition(HAR)represents a rapidly advancing research domain,propelled by continuous developments in sensor technologies and the Internet of Things(IoT).Deep learning has become the dominant paradigm in sensor-based HAR systems,offering significant advantages over traditional machine learning methods by eliminating manual feature extraction,enhancing recognition accuracy for complex activities,and enabling the exploitation of unlabeled data through generative models.This paper provides a comprehensive review of recent advancements and emerging trends in deep learning models developed for sensor-based human activity recognition(HAR)systems.We begin with an overview of fundamental HAR concepts in sensor-driven contexts,followed by a systematic categorization and summary of existing research.Our survey encompasses a wide range of deep learning approaches,including Multi-Layer Perceptrons(MLP),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory networks(LSTM),Gated Recurrent Units(GRU),Transformers,Deep Belief Networks(DBN),and hybrid architectures.A comparative evaluation of these models is provided,highlighting their performance,architectural complexity,and contributions to the field.Beyond Centralized deep learning models,we examine the role of Federated Learning(FL)in HAR,highlighting current applications and research directions.Finally,we discuss the growing importance of Explainable Artificial Intelligence(XAI)in sensor-based HAR,reviewing recent studies that integrate interpretability methods to enhance transparency and trustworthiness in deep learning-based HAR systems.展开更多
Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an image.Recent studies often exploit visual features and the spatial co...Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an image.Recent studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the pair.We argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level,but also at the part level at which a body part interacts with an object,and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration,to infer the action.We thus propose a multi-level pairwise feature network(PFNet)for detecting human–object interactions.The network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels;the three streams are finally fused to give the action prediction.Extensive experiments show that our proposed PFNet outperforms other state-of-the-art methods on the VCOCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.展开更多
Impressive advancements and novel techniques have been witnessed in AI-based Human Intelligent-Things Interaction(HITI)systems.Several technological breakthroughs have contributed to HITI,such as Internet of Things(Io...Impressive advancements and novel techniques have been witnessed in AI-based Human Intelligent-Things Interaction(HITI)systems.Several technological breakthroughs have contributed to HITI,such as Internet of Things(IoT),deep and edge learning for deducing intelligence,and 6G for ultra-fast and ultralow-latency communication between cyber-physical HITI systems.However,human-AI teaming presents several challenges that are yet to be addressed,despite the many advancements that have been made towards human-AI teaming.Allowing human stakeholders to understand AI’s decision-making process is a novel challenge.Artificial Intelligence(AI)needs to adopt diversified human understandable features,such as ethics,non-biases,trustworthiness,explainability,safety guarantee,data privacy,system security,and auditability.While adopting these features,high system performance should be maintained,and transparent processing involved in the‘human intelligent-things teaming’should be conveyed.To this end,we introduce the fusion of four key technologies,namely an ensemble of deep learning,6G,IoT,and corresponding security/privacy techniques to support HITI.This paper presents a framework that integrates the aforementioned four key technologies to support AI-based Human Intelligent-Things Interaction.Additionally,this paper demonstrates two security applications as proof of the concept,namely intelligent smart city surveillance and handling emergency services.The paper proposes to fuse four key technologies(deep learning,6G,IoT,and security and privacy techniques)to support Human Intelligent-Things interaction,applying the proposed framework to two security applications(surveillance and emergency handling).In this research paper,we will present a comprehensive review of the existing techniques of fusing security and privacy within future HITI applications.Moreover,we will showcase two security applications as proof of concept that use the fusion of the four key technologies to offer next-generation HITI services,namely intelligent smart city surveillance and handling emergency services.This proposed research outcome is envisioned to democratize the use of AI within smart city surveillance applications.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
Background Generally, it is difficult to obtain accurate pose and depth for a non-rigid moving object from a single RGB camera to create augmented reality (AR). In this study, we build an augmented reality system from...Background Generally, it is difficult to obtain accurate pose and depth for a non-rigid moving object from a single RGB camera to create augmented reality (AR). In this study, we build an augmented reality system from a single RGB camera for a non-rigid moving human by accurately computing pose and depth, for which two key tasks are segmentation and monocular Simultaneous Localization and Mapping (SLAM). Most existing monocular SLAM systems are designed for static scenes, while in this AR system, the human body is always moving and non-rigid. Methods In order to make the SLAM system suitable for a moving human, we first segment the rigid part of the human in each frame. A segmented moving body part can be regarded as a static object, and the relative motions between each moving body part and the camera can be considered the motion of the camera. Typical SLAM systems designed for static scenes can then be applied. In the segmentation step of this AR system, we first employ the proposed BowtieNet, which adds the atrous spatial pyramid pooling (ASPP) of DeepLab between the encoder and decoder of SegNet to segment the human in the original frame, and then we use color information to extract the face from the segmented human area. Results Based on the human segmentation results and a monocular SLAM, this system can change the video background and add a virtual object to humans. Conclusions The experiments on the human image segmentation datasets show that BowtieNet obtains state-of-the-art human image segmentation performance and enough speed for real-time segmentation. The experiments on videos show that the proposed AR system can robustly add a virtual object to humans and can accurately change the video background.展开更多
There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices...There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices.To maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a relay.Based on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot scheduling.As a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised.We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion.The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay.However,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance enhancement.Through simulation,the proposed approach is successfully shown.These improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.展开更多
Classification method is a formula, logical description generalizing characteristics of objects of related area. Nowadays, billions of smart objects are immersed in the environment, sensing, interacting, and cooperati...Classification method is a formula, logical description generalizing characteristics of objects of related area. Nowadays, billions of smart objects are immersed in the environment, sensing, interacting, and cooperating with each other to enable efficient services. When we think about IoT, classification is a major challenge particularly if our technology is international level applicable. So, this limitation needs clear and deep analysis of the existing classification matrixes and gives some future directions depending on the different researches in the area. The paper surveys the current state-of-art in the classification of IoT. First, we try to explain commonly existing classification matrixes;Second, cooperation of different methods defending on classification matrixes used. Then analyses challenges that IoT faced from classification angle and finally we give some direction for future IoT classification.展开更多
基金supported by Priority Research Centers Program through NRF funded by MEST(2018R1A6A1A03024003)the Grand Information Technology Research Center support program IITP-2020-2020-0-01612 supervised by the IITP by MSIT,Korea.
文摘In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose estimation.Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained.Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized objects.The existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the pairs.Such estimation depends on appearance features and spatial information.Therefore,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI.Furthermore,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using YOLO.We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm.The interactions are linked with the human and object to predict the actions.The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.
基金supported by National Key Research and Development Program of China (2019YFB2102500)China Postdoctoral Science Foundation (2021M700533)+1 种基金Natural Science Basic Research Program of Shaanxi Province of China (2021JQ-289,2020JQ-855)Social Science Fund of Shaanxi Province of China (2019S044).
文摘With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application scenario,one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources.Although a variety of recommendation algorithms have been employed in this field,they ignore the massive text resources in the social internet of things,which can effectively improve the effect of recommendation.In this paper,a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed.The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the“thing-thing”relationship information in the internet of things to improve the effect of recommendation.Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.
文摘The term Internet of Things refers to the networked interconnection of objects of diverse nature, such as electronic devices, sensors, but also physical objects and beings as well as virtual data and environments. Although the basic concept of the Internet of Things sounds simple, its application is difficult and, so far, the respective existing architectural models are rather monolithic and are dominated by several limitations. The paper introduces a generic Internet of Things architecture trying to resolve the existing restrictions of current architectural models by integrating both RFID and smart object-based infrastructures, while also exploring a third parameter, i.e. the social potentialities of the Internet of Things building blocks towards shaping the “Social Internet of Things”. The proposed architecture is based on a layered lightweight and open middle-ware solution following the paradigm of Service Oriented Architecture and the Semantic Model Driven Ap-proach, which is realized at both design-time and deployment–time covering the whole service lifecycle for the corresponding services and applications provided.
文摘The object of criminal legal aid refers to the person in a criminal case who has the right or eligibility toapply for legal assistance and who receives it. According to jurispru- dence, the object （or aid recipient） is a party in a given legal case, who is granted legal aid. They are often among the disadvantaged group in criminal cases, since most of them are mentally challenged, lack free- dom or have health problems.＇ Both international and domestic laws have certain norms regarding objects of criminal legal aid. Our domestic law places more emphasis on ＂defen- dants＂ while downplaying ＂suspects＂ and ＂victims＂ in identifying objects.
文摘This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process.Since that human actions and interacted objects provide strong context information,i.e.some actions are usually related to some specific objects,the accuracy of recognition is significantly improved for both of them.Through the proposed method,both global and local temporal features from skeleton sequences are extracted to model human actions.In the meantime,kernel features are utilized to describe interacted objects.Finally,all possible solutions from actions and objects are optimized by modeling the context between them.The results of experiments demonstrate the effectiveness of our method.
基金Supported by Project of National Social Science Foundation(09XMZ055)General Program of Scientific Research Project of Guangxi Provincial Department of Education (200911MS104)
文摘From subject,object and target subsystems,we analyze the rural human resource development system.The subject system includes government,education and training organizations,society,and rural human resource itself.Different development subject bears different responsibility.Object system includes farmers engaged in farming,farmer workers,rural unemployed people,rural students,rural left-behind people,and other people in rural areas.Different development object has different features.Development target system includes raising quality of rural human resource,keeping reasonable population size,optimizing structure of rural human resource,and improving vitality of rural human resource,etc.
基金This work was supported by the National Natural Science Foundation of China under Grant 61872038,61811530335in part by the UK Royal Society-Newton Mobility Grant(No.IEC∖NSFC∖170067).
文摘In the Internet of Things(IoT),security and privacy issues of physical objects are crucial to the related applications.In order to clarify the complicated security and privacy issues,the life cycle of a physical object is divided into three stages of pre-working,in-working,and post-working.On this basis,a physical object-based security architecture for the IoT is put forward.According to the security architecture,security and privacy requirements and related protecting technologies for physical objects in different working stages are analyzed in detail.Considering the development of IoT technologies,potential security and privacy challenges that IoT objects may face in the pervasive computing environment are summarized.At the same time,possible directions for dealing with these challenges are also pointed out.
文摘The Intelligent Transportation System(ITS),as a vital means to alleviate traffic congestion and reduce traffic accidents,demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things(IoT)technologies.The enhancement of its performance largely depends on breakthrough advancements in object detection technology.However,current object detection technology still faces numerous challenges,such as accuracy,robustness,and data privacy issues.These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions.This study provides a comprehensive review of the development of object detection technology and analyzes its specific applications in ITS,aiming to thoroughly explore the use and advancement of object detection technologies in IoT-based intelligent transportation systems.To achieve this objective,we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)approach to search,screen,and assess the eligibility of relevant literature,ultimately including 88 studies.Through an analysis of these studies,we summarized the characteristics,advantages,and limitations of object detection technology across the traditional methods stage and the deep learning-based methods stage.Additionally,we examined its applications in ITS from three perspectives:vehicle detection,pedestrian detection,and traffic sign detection.We also identified the major challenges currently faced by these technologies and proposed future directions for addressing these issues.This review offers researchers a comprehensive perspective,identifying potential improvement directions for object detection technology in ITS,including accuracy,robustness,real-time performance,data annotation cost,and data privacy.In doing so,it provides significant guidance for the further development of IoT-based intelligent transportation systems.
文摘The Internet of Things(IoT)is a recent technology,which implies the union of objects,“things”,into a single worldwide network.This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices.Device identification is one of these challenges that becomes complicated with the increase of network devices.Despite this,there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications.In this regard,one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks.Many unique soft-ware and hardware solutions are used as a unique global identifier;however,such solutions have many limitations.This article proposes a novel solution,based on the Digital Object Architecture(DOA),that meets the requirements of identifying devices and applications of the IoT.This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks.We propose a model of an identification system based on the architecture of digital objects,which differs from the well-known ones.The proposed model ensures an acceptable quality of service(QoS)in the common architecture of the existing public communication networks.A novel interaction architecture is developed by introducing a Middle Handle Register(MHR)between the global register,i.e.,Global Handle Register(GHR),and local register,i.e.,Local Handle Register(LHR).The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented.The developed model is simulated for a wide-area network with allocated registers,and the results are introduced and discussed.
基金Supported by the National Natural Science Foundation of China(No.61672178,61601458)
文摘In Internet of Things （IoT） cial networks of physical objects , physical objects can build their own social networks. How do so- generate, and what characteristics do the social networks have. In order to solve these problems, according to the interaction of physical objects in IoT, this paper presents a growing social network model of physical objects and researches the attachment mecha- nism of the model that includes three modes, physical distance, social distance and preference. Through the simulation realizations of the model, the characteristics （e. g. degree distribution, com- munity structure） of social network are analyzed. The model can forecast the growth of social networks of physical object in IoT and simulate social networks of physical objects in the large scale IoT.
基金the Postdoctoral Science Foundation of China(Grant No.20070421018)
文摘A new method of elastic articulated objects (human bodies) modeling was presented based on a new conic curve. The model includes 3D object deformable curves which can represent the deformation of human occluding contours. The deformation of human occluding contour can be represented by adjusting only four deformation parameters for each limb. Then, the 3D deformation parameters are determined by corresponding 2D contours from a sequence of stereo images. The algorithm presented in this paper includes deformable conic curve parameters determination and the plane, 3D conic curve lying on, parameter determination.
文摘Although objectivity is mainly accounted for in terms of linguistic thought and communication,in this article I will aim to showthat at least one condition of possibility for our understanding of objectivity is grounded on a prepredicative,i. e. pre-linguistic and pre-communicative,level. I will endorse a Husserlian viewpoint on the issue,and I will try to develop some aspects of the Husserlian account of three-dimensional thing-perception by means of which I will showhowprepredicative experience can actually offer us a fundamental element of our common understanding of objectivity. In doing this,it will be necessary to acknowledge thing-perception as being primarily intertwined with indeterminacy. I will claim that only on the basis of such an intuitive and prepredicative access to the things as partially indeterminate,first,and as determinable,second,is it possible to have an understanding of the world as something (at least partially) independent from the intuition (s) all subjects can have of it. By means of the addition of a consciousness of the thing as accessible to other subjects,one achieves a vision of the thing as fully determinate in itself. This"vision",however,takes one to be aware of the determination of the thing as lying beyond any intuitive grasp of it. The result will,thus,be that the prepredicative constitution of our basic sense of objectivity leads us to intend the world as something which should be accounted for (also) by means of sources different from intuition.
基金supported in part by Shenzhen Innovation Program(JCYJ20180305125709986)National Natural Science Foundation of China(61861130365,61761146002)+1 种基金GD Science and Technology Program(2020A0505100064,2015A030312015)DEGP Key Project(2018KZDXM058)。
文摘Humans regularly interact with their surrounding objects.Such interactions often result in strongly correlated motions between humans and the interacting objects.We thus ask:"Is it possible to infer object properties from skeletal motion alone,even without seeing the interacting object itself?"In this paper,we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone.This inference allows us to disentangle the motion from the object property and transfer object properties to a given motion.We collected a large number of videos and 3 D skeletal motions of performing actors using an inertial motion capture device.We analyzed similar actions and learned subtle differences between them to reveal latent properties of the interacting objects.In particular,we learned to identify the interacting object,by estimating its weight,or its spillability.Our results clearly demonstrate that motions and interacting objects are highly correlated and that related object latent properties can be inferred from 3 D skeleton sequences alone,leading to new synthesis possibilities for motions involving human interaction.Our dataset is available at http://vcc.szu.edu.cn/research/2020/IT.html.
文摘Human Activity Recognition(HAR)represents a rapidly advancing research domain,propelled by continuous developments in sensor technologies and the Internet of Things(IoT).Deep learning has become the dominant paradigm in sensor-based HAR systems,offering significant advantages over traditional machine learning methods by eliminating manual feature extraction,enhancing recognition accuracy for complex activities,and enabling the exploitation of unlabeled data through generative models.This paper provides a comprehensive review of recent advancements and emerging trends in deep learning models developed for sensor-based human activity recognition(HAR)systems.We begin with an overview of fundamental HAR concepts in sensor-driven contexts,followed by a systematic categorization and summary of existing research.Our survey encompasses a wide range of deep learning approaches,including Multi-Layer Perceptrons(MLP),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory networks(LSTM),Gated Recurrent Units(GRU),Transformers,Deep Belief Networks(DBN),and hybrid architectures.A comparative evaluation of these models is provided,highlighting their performance,architectural complexity,and contributions to the field.Beyond Centralized deep learning models,we examine the role of Federated Learning(FL)in HAR,highlighting current applications and research directions.Finally,we discuss the growing importance of Explainable Artificial Intelligence(XAI)in sensor-based HAR,reviewing recent studies that integrate interpretability methods to enhance transparency and trustworthiness in deep learning-based HAR systems.
基金supported by the National Natural Science Foundation of China(Project No.61902210),a Research Grant of Beijing Higher Institution Engineering Research Center,and the Tsinghua–Tencent Joint Laboratory for Internet Innovation Technology.
文摘Human–object interaction(HOI)detection is crucial for human-centric image understanding which aims to infer human,action,object triplets within an image.Recent studies often exploit visual features and the spatial configuration of a human–object pair in order to learn the action linking the human and object in the pair.We argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level,but also at the part level at which a body part interacts with an object,and at the semantic level by considering the semantic label of an object along with human appearance and human–object spatial configuration,to infer the action.We thus propose a multi-level pairwise feature network(PFNet)for detecting human–object interactions.The network consists of three parallel streams to characterize HOI utilizing pairwise features at the above three levels;the three streams are finally fused to give the action prediction.Extensive experiments show that our proposed PFNet outperforms other state-of-the-art methods on the VCOCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.
文摘Impressive advancements and novel techniques have been witnessed in AI-based Human Intelligent-Things Interaction(HITI)systems.Several technological breakthroughs have contributed to HITI,such as Internet of Things(IoT),deep and edge learning for deducing intelligence,and 6G for ultra-fast and ultralow-latency communication between cyber-physical HITI systems.However,human-AI teaming presents several challenges that are yet to be addressed,despite the many advancements that have been made towards human-AI teaming.Allowing human stakeholders to understand AI’s decision-making process is a novel challenge.Artificial Intelligence(AI)needs to adopt diversified human understandable features,such as ethics,non-biases,trustworthiness,explainability,safety guarantee,data privacy,system security,and auditability.While adopting these features,high system performance should be maintained,and transparent processing involved in the‘human intelligent-things teaming’should be conveyed.To this end,we introduce the fusion of four key technologies,namely an ensemble of deep learning,6G,IoT,and corresponding security/privacy techniques to support HITI.This paper presents a framework that integrates the aforementioned four key technologies to support AI-based Human Intelligent-Things Interaction.Additionally,this paper demonstrates two security applications as proof of the concept,namely intelligent smart city surveillance and handling emergency services.The paper proposes to fuse four key technologies(deep learning,6G,IoT,and security and privacy techniques)to support Human Intelligent-Things interaction,applying the proposed framework to two security applications(surveillance and emergency handling).In this research paper,we will present a comprehensive review of the existing techniques of fusing security and privacy within future HITI applications.Moreover,we will showcase two security applications as proof of concept that use the fusion of the four key technologies to offer next-generation HITI services,namely intelligent smart city surveillance and handling emergency services.This proposed research outcome is envisioned to democratize the use of AI within smart city surveillance applications.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
文摘Background Generally, it is difficult to obtain accurate pose and depth for a non-rigid moving object from a single RGB camera to create augmented reality (AR). In this study, we build an augmented reality system from a single RGB camera for a non-rigid moving human by accurately computing pose and depth, for which two key tasks are segmentation and monocular Simultaneous Localization and Mapping (SLAM). Most existing monocular SLAM systems are designed for static scenes, while in this AR system, the human body is always moving and non-rigid. Methods In order to make the SLAM system suitable for a moving human, we first segment the rigid part of the human in each frame. A segmented moving body part can be regarded as a static object, and the relative motions between each moving body part and the camera can be considered the motion of the camera. Typical SLAM systems designed for static scenes can then be applied. In the segmentation step of this AR system, we first employ the proposed BowtieNet, which adds the atrous spatial pyramid pooling (ASPP) of DeepLab between the encoder and decoder of SegNet to segment the human in the original frame, and then we use color information to extract the face from the segmented human area. Results Based on the human segmentation results and a monocular SLAM, this system can change the video background and add a virtual object to humans. Conclusions The experiments on the human image segmentation datasets show that BowtieNet obtains state-of-the-art human image segmentation performance and enough speed for real-time segmentation. The experiments on videos show that the proposed AR system can robustly add a virtual object to humans and can accurately change the video background.
文摘There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly.The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices.To maximise the data rate fairness of Narrow Band IoT devices,a multi‐dimensional indoor localisation model is devised,consisting of transmission power,data scheduling,and time slot scheduling,based on a network model that employs non‐orthogonal multiple access via a relay.Based on this network model,the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors,while taking into account the Narrow Band IoT network:The multidimensional indoor localisation optimisation model of equipment tends to minimize data rate,energy constraints and EH relay energy and data buffer constraints,data scheduling and time slot scheduling.As a result,each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised.We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion.The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay.However,the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference,which impacts NOMA's performance enhancement.Through simulation,the proposed approach is successfully shown.These improvements have boosted the network's energy efficiency by 44.1%,data rate proportional fairness by 11.9%,and spectrum efficiency by 55.4%.
文摘Classification method is a formula, logical description generalizing characteristics of objects of related area. Nowadays, billions of smart objects are immersed in the environment, sensing, interacting, and cooperating with each other to enable efficient services. When we think about IoT, classification is a major challenge particularly if our technology is international level applicable. So, this limitation needs clear and deep analysis of the existing classification matrixes and gives some future directions depending on the different researches in the area. The paper surveys the current state-of-art in the classification of IoT. First, we try to explain commonly existing classification matrixes;Second, cooperation of different methods defending on classification matrixes used. Then analyses challenges that IoT faced from classification angle and finally we give some direction for future IoT classification.
