Practice training is very important for students learning Computer networks.But building a real laboratory is constrained and expensive.In this paper,we present an online experimental platform for computer networks co...Practice training is very important for students learning Computer networks.But building a real laboratory is constrained and expensive.In this paper,we present an online experimental platform for computer networks course based on Dynamips simulator.Instructors and students can access the platform by IE Browser to manage and take router experiments.On the basis of deployment and testing,the platform is effective and flexible.展开更多
Morally controversial content,such as offensive and hateful images over social media,is especially challenging to categorize,given widespread disagreement in how people interpret and evaluate this content.Numerous stu...Morally controversial content,such as offensive and hateful images over social media,is especially challenging to categorize,given widespread disagreement in how people interpret and evaluate this content.Numerous studies argue that a range of subjective biases,such as partisan differences in moral reasoning,lead people not only to diverge in their classifications of controversial content,but also to resist any attempts to change their classification judgments via social influence.Yet,recent large-scale analyses of classification patterns over social media suggest that separate populations,such as democrats and republicans,can reach surprising levels of agreement in the categorization of inflammatory content like fake news and hate speech,despite considerable differences in their moral reasoning and worldview.This poses a fundamental puzzle:how can populations of diverse individuals who disagree in the interpretation of controversial content nevertheless arrive at highly similar decisions for the classification and removal of such content?Here,we use an online platform to test the hypothesis that structural symmetries in information exchange networks can synchronize convergence on decisions regarding the classification and removal of controversial images across independent networks,leading them to independently reproduce consistent systems of classification.We find that isolated individuals diverge considerably in their classification of controversial content,whereas separate,structurally similar networks independently synchronize in their classifications and content removal decisions,reducing partisan biases across all networks.We also find that when participant experience is compared to subjects evaluating content individually in the control condition,participants within synchronizing networks reported having significantly more positive feelings about their task,and experience significantly less emotional stress when evaluating controversial content.展开更多
Digitalisation plays a pivotal role in enhancing energy efficiency;however,it also highlights significant gover-nance challenges and exacerbates various forms of energy injustice.This study explores how technological ...Digitalisation plays a pivotal role in enhancing energy efficiency;however,it also highlights significant gover-nance challenges and exacerbates various forms of energy injustice.This study explores how technological injustice exacerbates energy poverty,particularly via disparities in digital service access.The focus is on un-derstanding and addressing challenges faced by minority ethnic(ME)communities,who often encounter heightened barriers to essential online energy services.While previous research has noted barriers ME com-munities face in energy markets,this study broadens this literature to analyse these issues for access to digital energy services.The study integrates modelling,simulation,and AI to address these inequalities.The framework comprises three core modules:AI,Environment Configuration,and Agent-Based Modelling(ABM)and Simulation.Its primary aim is to identify effective strategies,policy changes,and adjustments that enhance online service ex-periences while addressing the unique challenges faced by these communities.The AI Module uses ensemble-based ML pipelines to develop region-specific models.It addresses issues such as high dimensionality and overfitting by incorporating methods like Principal Component Analysis,Recursive Feature Elimination,and hyperparameter optimization.The Environment Configuration Module supports tailored simulations by adapting datasets and regional characteristics,ensuring the accuracy and relevance of the simulations to the target communities.The ABM and Simulation Module facilitates in-depth analysis of policy impacts and service provider attributes.This framework offers valuable insights into improving online service delivery,promoting fairness,and addressing disparities in digital experiences.This work advances energy justice research by quantifying how socio-technical barriers disproportionately affect ME communities.展开更多
文摘Practice training is very important for students learning Computer networks.But building a real laboratory is constrained and expensive.In this paper,we present an online experimental platform for computer networks course based on Dynamips simulator.Instructors and students can access the platform by IE Browser to manage and take router experiments.On the basis of deployment and testing,the platform is effective and flexible.
基金from the Content Moderation Research Award granted by Facebook。
文摘Morally controversial content,such as offensive and hateful images over social media,is especially challenging to categorize,given widespread disagreement in how people interpret and evaluate this content.Numerous studies argue that a range of subjective biases,such as partisan differences in moral reasoning,lead people not only to diverge in their classifications of controversial content,but also to resist any attempts to change their classification judgments via social influence.Yet,recent large-scale analyses of classification patterns over social media suggest that separate populations,such as democrats and republicans,can reach surprising levels of agreement in the categorization of inflammatory content like fake news and hate speech,despite considerable differences in their moral reasoning and worldview.This poses a fundamental puzzle:how can populations of diverse individuals who disagree in the interpretation of controversial content nevertheless arrive at highly similar decisions for the classification and removal of such content?Here,we use an online platform to test the hypothesis that structural symmetries in information exchange networks can synchronize convergence on decisions regarding the classification and removal of controversial images across independent networks,leading them to independently reproduce consistent systems of classification.We find that isolated individuals diverge considerably in their classification of controversial content,whereas separate,structurally similar networks independently synchronize in their classifications and content removal decisions,reducing partisan biases across all networks.We also find that when participant experience is compared to subjects evaluating content individually in the control condition,participants within synchronizing networks reported having significantly more positive feelings about their task,and experience significantly less emotional stress when evaluating controversial content.
基金supported by the Engineering and Physical Sciences Research Council,part of the UK Research and Innovation(UKRI),under the grant number EP/W032082/1.
文摘Digitalisation plays a pivotal role in enhancing energy efficiency;however,it also highlights significant gover-nance challenges and exacerbates various forms of energy injustice.This study explores how technological injustice exacerbates energy poverty,particularly via disparities in digital service access.The focus is on un-derstanding and addressing challenges faced by minority ethnic(ME)communities,who often encounter heightened barriers to essential online energy services.While previous research has noted barriers ME com-munities face in energy markets,this study broadens this literature to analyse these issues for access to digital energy services.The study integrates modelling,simulation,and AI to address these inequalities.The framework comprises three core modules:AI,Environment Configuration,and Agent-Based Modelling(ABM)and Simulation.Its primary aim is to identify effective strategies,policy changes,and adjustments that enhance online service ex-periences while addressing the unique challenges faced by these communities.The AI Module uses ensemble-based ML pipelines to develop region-specific models.It addresses issues such as high dimensionality and overfitting by incorporating methods like Principal Component Analysis,Recursive Feature Elimination,and hyperparameter optimization.The Environment Configuration Module supports tailored simulations by adapting datasets and regional characteristics,ensuring the accuracy and relevance of the simulations to the target communities.The ABM and Simulation Module facilitates in-depth analysis of policy impacts and service provider attributes.This framework offers valuable insights into improving online service delivery,promoting fairness,and addressing disparities in digital experiences.This work advances energy justice research by quantifying how socio-technical barriers disproportionately affect ME communities.
