This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contra...This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contrary to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy budget.Besides,when collecting user statistics longitudinally,privacy progressively degrades.Indeed,the“multiple”settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under LDP.To tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response–GRR,Optimized Unary Encoding–OUE,and Symmetric Unary Encoding–SUE)for both longitudinal and multidimensional data collections.While the known literature uses OUE and SUE for two rounds of sanitization(a.k.a.memoization),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).Also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary encoding.Last,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates(ALLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or L-OSUE.As shown in the results,ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.展开更多
通过研究一个具有代表性的UML/MARTE(unified modeling language/modeling and analysis of real time and embedded systems)模型向FIACRE(intermediate format for the architectures of embedded distributed components)形式模型的...通过研究一个具有代表性的UML/MARTE(unified modeling language/modeling and analysis of real time and embedded systems)模型向FIACRE(intermediate format for the architectures of embedded distributed components)形式模型的转换实例,探讨了异构模型之间在语义和语法层的相互转换问题.在语义层,通过模型转换技术构造语义映射规则,实现元语言之间的转换;在语法层,通过构造元模型的具体语法,反映元语言的语法规则,从而产生目标模型的程序实体.基于此实例研究,探讨了通用转换途径的相关框架和关键技术,并讨论了转换工作的优缺点和实用性.展开更多
The paper introduces an electroencephalography(EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential(ErrP) to stop the movement of the individual...The paper introduces an electroencephalography(EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential(ErrP) to stop the movement of the individual links, following a fixed(pre-defined) order of link selection. The right(left)hand motor imagery is used to turn a link clockwise(counterclockwise) and foot imagery is used to move a link forward. The occurrence of ErrP here indicates that the link under motion crosses the visually fixed target position, which usually is a plane/line/point depending on the desired transition of the link across 3D planes/around 2D lines/along 2D lines respectively. The imagined task about individual link's movement is decoded by a classifier into three possible class labels: clockwise, counterclockwise and no movement in case of rotational movements and forward, backward and no movement in case of translational movements. One additional classifier is required to detect the occurrence of the ErrP signal, elicited due to visually inspired positional link error with reference to a geometrically selected target position. Wavelet coefficients and adaptive autoregressive parameters are extracted as features for motor imagery and ErrP signals respectively. Support vector machine classifiers are used to decode motor imagination and ErrP with high classification accuracy above 80%. The average time taken by the proposed scheme to decode and execute control intentions for the complete movement of three links of a robot is approximately33 seconds. The steady-state error and peak overshoot of the proposed controller are experimentally obtained as 1.1% and4.6% respectively.展开更多
The research work presented in this paper is based on the concrete background ofthe Cooperative Graphics Editor (CGE), allowing two or more persons to remotely edit a graphicdocument simultaneously. A new concurrency ...The research work presented in this paper is based on the concrete background ofthe Cooperative Graphics Editor (CGE), allowing two or more persons to remotely edit a graphicdocument simultaneously. A new concurrency control algorithm based on partial order set ispresented, which has fast response and less undo-redo operations as there are no lock mechanisms.It is used to solve inconsistency caused by operation on intersecting graphics concurrently. CGEalso possesses a mask strategy to solve inconsistency caused by operation on the same graphicconcurrently.展开更多
This review deals with restricted Boltzmann machine(RBM) under the light of statistical physics.The RBM is a classical family of machine learning(ML) models which played a central role in the development of deep learn...This review deals with restricted Boltzmann machine(RBM) under the light of statistical physics.The RBM is a classical family of machine learning(ML) models which played a central role in the development of deep learning.Viewing it as a spin glass model and exhibiting various links with other models of statistical physics,we gather recent results dealing with mean-field theory in this context.First the functioning of the RBM can be analyzed via the phase diagrams obtained for various statistical ensembles of RBM,leading in particular to identify a compositional phase where a small number of features or modes are combined to form complex patterns.Then we discuss recent works either able to devise mean-field based learning algorithms;either able to reproduce generic aspects of the learning process from some ensemble dynamics equations or/and from linear stability arguments.展开更多
The paper presents a finite volume numerical method universally applicable for solving both linear and nonlinear aeroacoustics problems on arbitrary unstructured meshes. It is based on the vertexcentered multi-paramet...The paper presents a finite volume numerical method universally applicable for solving both linear and nonlinear aeroacoustics problems on arbitrary unstructured meshes. It is based on the vertexcentered multi-parameter scheme offering up to the 6th accuracy order achieved on the Cartesian meshes. An adaptive dissipation is added for the numerical treatment of possible discontinuities. The scheme properties are studied on a series of test cases, its efficiency is demonstrated at simulating the noise suppression in resonance-type liners.展开更多
The emerging technologies of Internet of Things （IoT）, soft-ware defined networking （SDN）, and network function virtualization （NFV） have great potential for the information service innovation in the cloud and b...The emerging technologies of Internet of Things （IoT）, soft-ware defined networking （SDN）, and network function virtualization （NFV） have great potential for the information service innovation in the cloud and big data era. The architecture models of IoT, SDN with NFV implementation are studied in this paper. A general SDN-based loT framework with NFV implantation is presented. This framework takes advantages of SDN and NFV and improves IoT architecture.展开更多
基金supported by the Agence Nationale de la Recherche(ANR)(contract“ANR-17-EURE-0002”)by the Region of Bourgogne Franche-ComtéCADRAN Projectsupported by the European Research Council(ERC)project HYPATIA under the European Union's Horizon 2020 research and innovation programme.Grant agreement n.835294。
文摘This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)guarantees.Contrary to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy budget.Besides,when collecting user statistics longitudinally,privacy progressively degrades.Indeed,the“multiple”settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under LDP.To tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response–GRR,Optimized Unary Encoding–OUE,and Symmetric Unary Encoding–SUE)for both longitudinal and multidimensional data collections.While the known literature uses OUE and SUE for two rounds of sanitization(a.k.a.memoization),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).Also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary encoding.Last,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates(ALLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or L-OSUE.As shown in the results,ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.
文摘通过研究一个具有代表性的UML/MARTE(unified modeling language/modeling and analysis of real time and embedded systems)模型向FIACRE(intermediate format for the architectures of embedded distributed components)形式模型的转换实例,探讨了异构模型之间在语义和语法层的相互转换问题.在语义层,通过模型转换技术构造语义映射规则,实现元语言之间的转换;在语法层,通过构造元模型的具体语法,反映元语言的语法规则,从而产生目标模型的程序实体.基于此实例研究,探讨了通用转换途径的相关框架和关键技术,并讨论了转换工作的优缺点和实用性.
基金supported by UGC Sponsored UPE-ⅡProject in Cognitive Science of Jadavpur University,Kolkata
文摘The paper introduces an electroencephalography(EEG) driven online position control scheme for a robot arm by utilizing motor imagery to activate and error related potential(ErrP) to stop the movement of the individual links, following a fixed(pre-defined) order of link selection. The right(left)hand motor imagery is used to turn a link clockwise(counterclockwise) and foot imagery is used to move a link forward. The occurrence of ErrP here indicates that the link under motion crosses the visually fixed target position, which usually is a plane/line/point depending on the desired transition of the link across 3D planes/around 2D lines/along 2D lines respectively. The imagined task about individual link's movement is decoded by a classifier into three possible class labels: clockwise, counterclockwise and no movement in case of rotational movements and forward, backward and no movement in case of translational movements. One additional classifier is required to detect the occurrence of the ErrP signal, elicited due to visually inspired positional link error with reference to a geometrically selected target position. Wavelet coefficients and adaptive autoregressive parameters are extracted as features for motor imagery and ErrP signals respectively. Support vector machine classifiers are used to decode motor imagination and ErrP with high classification accuracy above 80%. The average time taken by the proposed scheme to decode and execute control intentions for the complete movement of three links of a robot is approximately33 seconds. The steady-state error and peak overshoot of the proposed controller are experimentally obtained as 1.1% and4.6% respectively.
文摘The research work presented in this paper is based on the concrete background ofthe Cooperative Graphics Editor (CGE), allowing two or more persons to remotely edit a graphicdocument simultaneously. A new concurrency control algorithm based on partial order set ispresented, which has fast response and less undo-redo operations as there are no lock mechanisms.It is used to solve inconsistency caused by operation on intersecting graphics concurrently. CGEalso possesses a mask strategy to solve inconsistency caused by operation on the same graphicconcurrently.
基金supported by the Comunidad de Madrid and the Complutense University of Madrid (Spain) through the Atracción de Talento program (Ref. 2019-T1/TIC-13298)
文摘This review deals with restricted Boltzmann machine(RBM) under the light of statistical physics.The RBM is a classical family of machine learning(ML) models which played a central role in the development of deep learning.Viewing it as a spin glass model and exhibiting various links with other models of statistical physics,we gather recent results dealing with mean-field theory in this context.First the functioning of the RBM can be analyzed via the phase diagrams obtained for various statistical ensembles of RBM,leading in particular to identify a compositional phase where a small number of features or modes are combined to form complex patterns.Then we discuss recent works either able to devise mean-field based learning algorithms;either able to reproduce generic aspects of the learning process from some ensemble dynamics equations or/and from linear stability arguments.
基金Russian Foundation of Basic Research(No. 04-01-08034, 06-01-00293-a)
文摘The paper presents a finite volume numerical method universally applicable for solving both linear and nonlinear aeroacoustics problems on arbitrary unstructured meshes. It is based on the vertexcentered multi-parameter scheme offering up to the 6th accuracy order achieved on the Cartesian meshes. An adaptive dissipation is added for the numerical treatment of possible discontinuities. The scheme properties are studied on a series of test cases, its efficiency is demonstrated at simulating the noise suppression in resonance-type liners.
文摘The emerging technologies of Internet of Things （IoT）, soft-ware defined networking （SDN）, and network function virtualization （NFV） have great potential for the information service innovation in the cloud and big data era. The architecture models of IoT, SDN with NFV implementation are studied in this paper. A general SDN-based loT framework with NFV implantation is presented. This framework takes advantages of SDN and NFV and improves IoT architecture.
