Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Smart trains and railways are gaining increasing significance in major global cities as they offer solutions to issues like traffic congestion and environmental pollution.Technological advancements have facilitated th...Smart trains and railways are gaining increasing significance in major global cities as they offer solutions to issues like traffic congestion and environmental pollution.Technological advancements have facilitated the transition from conventional systems to more advanced,highly efficient,and personalized railway systems.However,the complexity of these systems presents challenges,especially in terms of reliability,interoperability security,and privacy.With the potential vulnerability of railway systems to cyberattacks,it becomes crucial for these emerging smart systems to establish stringent privacy and security requirements.Cybersecurity is a key requirement to enable railways to deploy and take advantage of the full extent of a connected,digital environment.This research explores the cybersecurity landscape within Smart Railways aiming to identify potential threats and associated risks on these systems,focusing on analyzing the current literature related to Smart Railways and cybersecurity aspects,then listing key technologies used by smart systems,and finally proposing an illustration of use cases application to call attention to the impact of attacks,providing then as a set of good practices that must be followed to reduce risks and to the safeguard the operability for Rail Transportation.The research findings suggest that over the last few years,there has been a significant increase in research activity in this area,indicating a growing recognition of the importance of cybersecurity in the railway industry.The results also pointed out several gaps related to this topic,namely the lack of standardization in cybersecurity practices and limited consideration of human factors that can impact cybersecurity.展开更多
In the era of Big Data,many NoSQL databases emerged for the storage and later processing of vast volumes of data,using data structures that can follow columnar,key-value,document or graph formats.For analytical contex...In the era of Big Data,many NoSQL databases emerged for the storage and later processing of vast volumes of data,using data structures that can follow columnar,key-value,document or graph formats.For analytical contexts,requiring a Big Data Warehouse,Hive is used as the driving force,allowing the analysis of vast amounts of data.Data models in Hive are usually defined taking into consideration the queries that need to be answered.In this work,a set of rules is presented for the transformation of multidimensional data models into Hive tables,making available data at different levels of detail.These several levels are suited for answering different queries,depending on the analytical needs.After the identification of the Hive tables,this paper summarizes a demonstration case in which the implementation of a specific Big Data architecture shows how the evolution from a traditional Data Warehouse to a Big Data Warehouse is possible.展开更多
Spare parts management is a function of maintenance management that aims to support maintenance activities,giving real-time information on the available quantities of each spare part and adopting the inventory policie...Spare parts management is a function of maintenance management that aims to support maintenance activities,giving real-time information on the available quantities of each spare part and adopting the inventory policies that ensure their availability when required,minimizing costs.The classification of spare parts is crucial to control the vast number of parts that have different characteristics and specificities.Spare parts management involves mainly two areas,maintenance and logistics.Therefore,the integration of both input information is recommended to make decisions.This paper presents a multicriteria classification methodology combining maintenance and logistics perspectives that intends to differentiate and group spare parts to,subsequently,define the most appropriate stock management policy for each group.The methodology was developed based on a case study carried out in a multinational manufacturing company and is intended to be included in its computerized maintenance management system to support decision-making.展开更多
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金supported by national funds through FCT-Fundaçao para a Ciencia e Tecnologia,Portugal through project UIDB/04728/2020。
文摘Smart trains and railways are gaining increasing significance in major global cities as they offer solutions to issues like traffic congestion and environmental pollution.Technological advancements have facilitated the transition from conventional systems to more advanced,highly efficient,and personalized railway systems.However,the complexity of these systems presents challenges,especially in terms of reliability,interoperability security,and privacy.With the potential vulnerability of railway systems to cyberattacks,it becomes crucial for these emerging smart systems to establish stringent privacy and security requirements.Cybersecurity is a key requirement to enable railways to deploy and take advantage of the full extent of a connected,digital environment.This research explores the cybersecurity landscape within Smart Railways aiming to identify potential threats and associated risks on these systems,focusing on analyzing the current literature related to Smart Railways and cybersecurity aspects,then listing key technologies used by smart systems,and finally proposing an illustration of use cases application to call attention to the impact of attacks,providing then as a set of good practices that must be followed to reduce risks and to the safeguard the operability for Rail Transportation.The research findings suggest that over the last few years,there has been a significant increase in research activity in this area,indicating a growing recognition of the importance of cybersecurity in the railway industry.The results also pointed out several gaps related to this topic,namely the lack of standardization in cybersecurity practices and limited consideration of human factors that can impact cybersecurity.
基金This work has been supported by COMPETE:POCI-01-0145-FEDER-007043 and FCT(Fundação para a Ciência e Tecnologia)within the Project Scope:UID/CEC/00319/2013This work has been funded by the SusCity project(MITP-TB/CS/0026/2013)by Portugal Incentive System for Research and Technological Development,Project in co-promotion no 002814/2015(iFACTORY 2015-2018).
文摘In the era of Big Data,many NoSQL databases emerged for the storage and later processing of vast volumes of data,using data structures that can follow columnar,key-value,document or graph formats.For analytical contexts,requiring a Big Data Warehouse,Hive is used as the driving force,allowing the analysis of vast amounts of data.Data models in Hive are usually defined taking into consideration the queries that need to be answered.In this work,a set of rules is presented for the transformation of multidimensional data models into Hive tables,making available data at different levels of detail.These several levels are suited for answering different queries,depending on the analytical needs.After the identification of the Hive tables,this paper summarizes a demonstration case in which the implementation of a specific Big Data architecture shows how the evolution from a traditional Data Warehouse to a Big Data Warehouse is possible.
基金This work was supported by European Structural and Investment Funds in the FEDER component,through the Operational Competitiveness and Internationalization Programme(COMPETE 2020)[Project n°002814Funding Reference:POCI-01-0247-FEDER-002814]。
文摘Spare parts management is a function of maintenance management that aims to support maintenance activities,giving real-time information on the available quantities of each spare part and adopting the inventory policies that ensure their availability when required,minimizing costs.The classification of spare parts is crucial to control the vast number of parts that have different characteristics and specificities.Spare parts management involves mainly two areas,maintenance and logistics.Therefore,the integration of both input information is recommended to make decisions.This paper presents a multicriteria classification methodology combining maintenance and logistics perspectives that intends to differentiate and group spare parts to,subsequently,define the most appropriate stock management policy for each group.The methodology was developed based on a case study carried out in a multinational manufacturing company and is intended to be included in its computerized maintenance management system to support decision-making.
