Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the ...Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and sta- tion management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips （i.e., origin-destination station pairs） is much larger than the number of stations, we define the trip infer- ence as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data from Washington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.展开更多
This paper presents a survey of technologies for personal data self-management interfacing with administrative and territorial public service providers.It classifies a selection of scientific technologies into four ca...This paper presents a survey of technologies for personal data self-management interfacing with administrative and territorial public service providers.It classifies a selection of scientific technologies into four categories of solutions:Personal Data Store(PDS),Identity Manager(IdM),Anonymous Certificate System and Access Control Delegation Architecture.Each category,along with its technological approach,is analyzed thanks to 18 identified functional criteria that encompass architectural and communication aspects,as well as user data lifecycle considerations.The originality of the survey is multifold.First,as far as we know,there is no such thorough survey covering such a panel of a dozen of existing solutions.Second,it is the first survey addressing Personally Identifiable Information(PII)management for both administrative and private service providers.Third,this paper achieves a functional comparison of solutions of very different technical natures.The outcome of this paper is the clear identification of functional gaps of each solution.As a result,this paper establishes the research directions to follow in order to fill these functional gaps.展开更多
DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning thataims to identify human activities in smart homes from distributed, heterogeneous and dynamicsensor data. In this model, dis...DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning thataims to identify human activities in smart homes from distributed, heterogeneous and dynamicsensor data. In this model, distributed learning agents with diverse classifiers, detect sensorstream data, make local predictions, communicate and collaborate to identify current activities.Then, they learn from their collaborations to improve their own performance in activity recognition.Conflict resolution strategies are applied to generate one final predicted activity when thelocal predicted activity of an agent is different from received predicted activities of other agents.In this paper, two conflict resolution strategies using online learning, w-max-trust and w-maxfreq,are proposed. We experimentally test these strategies by performing an evaluation studyon the Aruba dataset. The obtained results indicate an enhancement in terms of accuracy and Fmeasuremetrics compared to the offline strategies max-trust and max-freq and also to the onlineexisting one max-wPerf .展开更多
文摘Bike sharing systems are booming globally as a green and flexible transportation mode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and sta- tion management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips （i.e., origin-destination station pairs） is much larger than the number of stations, we define the trip infer- ence as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data from Washington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.
文摘This paper presents a survey of technologies for personal data self-management interfacing with administrative and territorial public service providers.It classifies a selection of scientific technologies into four categories of solutions:Personal Data Store(PDS),Identity Manager(IdM),Anonymous Certificate System and Access Control Delegation Architecture.Each category,along with its technological approach,is analyzed thanks to 18 identified functional criteria that encompass architectural and communication aspects,as well as user data lifecycle considerations.The originality of the survey is multifold.First,as far as we know,there is no such thorough survey covering such a panel of a dozen of existing solutions.Second,it is the first survey addressing Personally Identifiable Information(PII)management for both administrative and private service providers.Third,this paper achieves a functional comparison of solutions of very different technical natures.The outcome of this paper is the clear identification of functional gaps of each solution.As a result,this paper establishes the research directions to follow in order to fill these functional gaps.
文摘DCR-OL is a Distributed Collaborative Reasoning multi-agent model with an Online Learning thataims to identify human activities in smart homes from distributed, heterogeneous and dynamicsensor data. In this model, distributed learning agents with diverse classifiers, detect sensorstream data, make local predictions, communicate and collaborate to identify current activities.Then, they learn from their collaborations to improve their own performance in activity recognition.Conflict resolution strategies are applied to generate one final predicted activity when thelocal predicted activity of an agent is different from received predicted activities of other agents.In this paper, two conflict resolution strategies using online learning, w-max-trust and w-maxfreq,are proposed. We experimentally test these strategies by performing an evaluation studyon the Aruba dataset. The obtained results indicate an enhancement in terms of accuracy and Fmeasuremetrics compared to the offline strategies max-trust and max-freq and also to the onlineexisting one max-wPerf .
