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
Robot World Cup Initiative (RoboCup) is a worldwide competition proposed to advance research in robotics and artificial intelligence. It has a league called RoboCup soccer devoted for soccer robots, which is a challen...Robot World Cup Initiative (RoboCup) is a worldwide competition proposed to advance research in robotics and artificial intelligence. It has a league called RoboCup soccer devoted for soccer robots, which is a challenge because robots are mobile, fully autonomous, multi-agents, and they play on a dynamic environment. Moreover, robots must recognize the game entities, which is a crucial task during a game. A camera is usually used as an input system to recognize ball, opponents, soccer field, and so on. These elements may be recognized applying some tools of computational intelligence, for example an artificial neural network. This paper describes the application of an artificial neural network on middle size robotic football league, where a multilayer perceptron neural network is trained with the backpropagation algorithm, to classify elements on the image. Each output neuron represents an entity and its output value depends on the current entity that is present on the image. The results show that an artificial neural network successfully classified the entities. They were recognized even when similar color entities were present on the image.展开更多
A resilient Occupational Safety and Health(OSH)management system is crucial for effectively addressing potential future public emergencies,ensuring the continuous protection of workers'safety and health.Therefore,...A resilient Occupational Safety and Health(OSH)management system is crucial for effectively addressing potential future public emergencies,ensuring the continuous protection of workers'safety and health.Therefore,it is essential for organizations,particularly hospitals,to assess their resilient performance and employ tools that are appropriate and tailored to their specific context.This study aims to enhance the understanding of resilience potentials in OSH management within hospital settings.To this end,an assessment tool was developed based on the Resilience Assessment Grid(RAG).A Delphi study involving subject matter experts was conducted to refine the tailored RAG tool.Following this,a pilot test was administered to 404 healthcare professionals across three public hospitals,with subsequent psychometric analysis.Exploratory Factor Analysis(EFA)identified a four-dimensional structure.Goodness-of-fit indices demonstrated acceptable values,confirming the adequacy of the measurement model.Reliability testing indicated that the 29 item assessment tool is both valid and reliable.The tailored RAG tool was successfully validated,enabling the identification of strengths and weaknesses in OSH management.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
Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group...Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specifically designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms, and agents' modeling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problem by considering and defining strategies to deal with important points such as the type of attributes in the multicriterion problems, agents' reasoning, and intelligent dialogues.展开更多
Multipath interference poses substantial challenges to global navigation satellite system(GNSS)receivers,leading to inaccuracies in the time of arrival(TOA)measurement of the line of sight(LOS)signal.Therefore,to miti...Multipath interference poses substantial challenges to global navigation satellite system(GNSS)receivers,leading to inaccuracies in the time of arrival(TOA)measurement of the line of sight(LOS)signal.Therefore,to mitigate the impact of multipath on receivers,the problem has been approached at several system development stages—signal design,reception,and processing.While efforts and advancements have been achieved over the years at each stage seeking navigation robustness,this article focuses on the signal processing stage by presenting a review of advanced multipath mitigation techniques using adaptive channel parameter estimation at the correlation level.The multipath mitigation literature often resorts to optimistic assumptions—high signal-to-noise ratio(SNR),static multipath channel,single fading channel distribution,and so on—while in real-world scenarios noise is prominent,the number of paths and their states vary at different rates,channels are nonstationary,along with other nonideal conditions.Moreover,it is important to directly compare different techniques to characterize their applicability and limitations.An analysis of adaptive algorithms is conducted for multipath mitigation applications.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.
文摘Robot World Cup Initiative (RoboCup) is a worldwide competition proposed to advance research in robotics and artificial intelligence. It has a league called RoboCup soccer devoted for soccer robots, which is a challenge because robots are mobile, fully autonomous, multi-agents, and they play on a dynamic environment. Moreover, robots must recognize the game entities, which is a crucial task during a game. A camera is usually used as an input system to recognize ball, opponents, soccer field, and so on. These elements may be recognized applying some tools of computational intelligence, for example an artificial neural network. This paper describes the application of an artificial neural network on middle size robotic football league, where a multilayer perceptron neural network is trained with the backpropagation algorithm, to classify elements on the image. Each output neuron represents an entity and its output value depends on the current entity that is present on the image. The results show that an artificial neural network successfully classified the entities. They were recognized even when similar color entities were present on the image.
基金supported by the FCT – Fundação para a Ciência e Tecnologia within the PhD research scholarship: 2020.06905.BD.
文摘A resilient Occupational Safety and Health(OSH)management system is crucial for effectively addressing potential future public emergencies,ensuring the continuous protection of workers'safety and health.Therefore,it is essential for organizations,particularly hospitals,to assess their resilient performance and employ tools that are appropriate and tailored to their specific context.This study aims to enhance the understanding of resilience potentials in OSH management within hospital settings.To this end,an assessment tool was developed based on the Resilience Assessment Grid(RAG).A Delphi study involving subject matter experts was conducted to refine the tailored RAG tool.Following this,a pilot test was administered to 404 healthcare professionals across three public hospitals,with subsequent psychometric analysis.Exploratory Factor Analysis(EFA)identified a four-dimensional structure.Goodness-of-fit indices demonstrated acceptable values,confirming the adequacy of the measurement model.Reliability testing indicated that the 29 item assessment tool is both valid and reliable.The tailored RAG tool was successfully validated,enabling the identification of strengths and weaknesses in OSH management.
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.
基金supported by the COMPETE Programme(No.POCI-01-0145-FEDER-007043)the Portuguese Foundation for Science and Technology(Nos.UID/CEC/00319/2013,UID/EEA/00760/2013,and SFRH/BD/89697/2012)the ECSEL JU(No.662189)
文摘Supporting group decision-making in ubiquitous contexts is a complex task that must deal with a large amount of factors to succeed. Here we propose an approach for an intelligent negotiation model to support the group decision-making process specifically designed for ubiquitous contexts. Our approach can be used by researchers that intend to include arguments, complex algorithms, and agents' modeling in a negotiation model. It uses a social networking logic due to the type of communication employed by the agents and it intends to support the ubiquitous group decision-making process in a similar way to the real process, which simultaneously preserves the amount and quality of intelligence generated in face-to-face meetings. We propose a new look into this problem by considering and defining strategies to deal with important points such as the type of attributes in the multicriterion problems, agents' reasoning, and intelligent dialogues.
基金supported by national funds,through the Operational Competitiveness and Internationalization Programme(COMPETE 2020)(project no.179491,funding reference:SIFN-01-9999-FN-179491).
文摘Multipath interference poses substantial challenges to global navigation satellite system(GNSS)receivers,leading to inaccuracies in the time of arrival(TOA)measurement of the line of sight(LOS)signal.Therefore,to mitigate the impact of multipath on receivers,the problem has been approached at several system development stages—signal design,reception,and processing.While efforts and advancements have been achieved over the years at each stage seeking navigation robustness,this article focuses on the signal processing stage by presenting a review of advanced multipath mitigation techniques using adaptive channel parameter estimation at the correlation level.The multipath mitigation literature often resorts to optimistic assumptions—high signal-to-noise ratio(SNR),static multipath channel,single fading channel distribution,and so on—while in real-world scenarios noise is prominent,the number of paths and their states vary at different rates,channels are nonstationary,along with other nonideal conditions.Moreover,it is important to directly compare different techniques to characterize their applicability and limitations.An analysis of adaptive algorithms is conducted for multipath mitigation applications.
