Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the be...Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots:a higher number of connected components weakens their collaborative capabilities,thereby reducing their proportion within the overall network.However,current social bot detection methods still make limited use of topological features.Furthermore,both graph neural network(GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations,and existing studies lack an effective fusion of multi-scale information.To address these issues,this paper proposes a topology-aware multi-scale social bot detection method,which jointly learns local and global representations through a co-training mechanism.At the local level,topological features are effectively embedded into node representations,enhancing expressiveness while alleviating the over-smoothing problem in GNNs.At the global level,a clustering attention mechanism is introduced to learn global node representations,mitigating the over-globalization problem.Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches.Our code is publicly available at https://anonymous.4open.science/r/TopoMSG-2C41/(accessed on 27 October 2025).展开更多
In modern computer games, ＂bots＂ - intelligent realistic agents play a prominent role in the popularity of a game in the market. Typically, bots are modeled using finite-state machine and then programmed via simple ...In modern computer games, ＂bots＂ - intelligent realistic agents play a prominent role in the popularity of a game in the market. Typically, bots are modeled using finite-state machine and then programmed via simple conditional statements which are hard-coded in bots logic. Since these bots have become quite predictable to an experienced games＇ player, a player might lose interest in the game. We propose the use of a game theoretic based learning rule called fictitious play for improving behavior of these computer game bots which will make them less predictable and hence, more a enjoyable game.展开更多
Intelligent blockchain is an emerging field that integrates Artificial Intelligence(AI)techniques with blockchain networks,with a particular emphasis on improving the performance of blockchain,especially in cryptocurr...Intelligent blockchain is an emerging field that integrates Artificial Intelligence(AI)techniques with blockchain networks,with a particular emphasis on improving the performance of blockchain,especially in cryptocurrencies exchanges.Meanwhile,arbitrage bots are widely deployed and increasing in intelligent blockchain.These bots exploit the characteristics of cryptocurrencies exchanges to engage in frontrunning,generating substantial profits at the expense of ordinary users.In this paper,we address this issue by proposing a more efficient asynchronous Byzantine ordered consensus protocol,which can be used to prevent arbitrage bots from changing the order of the transactions for profits in intelligent blockchain-based cryptocurrencies.Specifically,we present two signal asynchronous common subset protocols,the more optimal one with only constant time complexity.We implement both our protocol and the optimal existing solution Chronos with Go language in the same environment.The experiment results indicate that our protocols achieve a threefold improvement over Chronos in consensus latency and nearly a tenfold increase in throughput.展开更多
基金supported by“the Fundamental Research Funds for the Central Universities”(Grant No.CUCAI2511).
文摘Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots:a higher number of connected components weakens their collaborative capabilities,thereby reducing their proportion within the overall network.However,current social bot detection methods still make limited use of topological features.Furthermore,both graph neural network(GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations,and existing studies lack an effective fusion of multi-scale information.To address these issues,this paper proposes a topology-aware multi-scale social bot detection method,which jointly learns local and global representations through a co-training mechanism.At the local level,topological features are effectively embedded into node representations,enhancing expressiveness while alleviating the over-smoothing problem in GNNs.At the global level,a clustering attention mechanism is introduced to learn global node representations,mitigating the over-globalization problem.Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches.Our code is publicly available at https://anonymous.4open.science/r/TopoMSG-2C41/(accessed on 27 October 2025).
文摘In modern computer games, ＂bots＂ - intelligent realistic agents play a prominent role in the popularity of a game in the market. Typically, bots are modeled using finite-state machine and then programmed via simple conditional statements which are hard-coded in bots logic. Since these bots have become quite predictable to an experienced games＇ player, a player might lose interest in the game. We propose the use of a game theoretic based learning rule called fictitious play for improving behavior of these computer game bots which will make them less predictable and hence, more a enjoyable game.
基金supported by the National Key R&D Program of China under Grant(2022YFB2702702)in part by the National Natural Science Foundation of China under Grants(62372020,72031001)+1 种基金in part by the Beijing Natural Science Foundation under Grants(L222050)in part by the Fundamental Research Funds for the Central Universities under Grant(YWF-23-L-1032).
文摘Intelligent blockchain is an emerging field that integrates Artificial Intelligence(AI)techniques with blockchain networks,with a particular emphasis on improving the performance of blockchain,especially in cryptocurrencies exchanges.Meanwhile,arbitrage bots are widely deployed and increasing in intelligent blockchain.These bots exploit the characteristics of cryptocurrencies exchanges to engage in frontrunning,generating substantial profits at the expense of ordinary users.In this paper,we address this issue by proposing a more efficient asynchronous Byzantine ordered consensus protocol,which can be used to prevent arbitrage bots from changing the order of the transactions for profits in intelligent blockchain-based cryptocurrencies.Specifically,we present two signal asynchronous common subset protocols,the more optimal one with only constant time complexity.We implement both our protocol and the optimal existing solution Chronos with Go language in the same environment.The experiment results indicate that our protocols achieve a threefold improvement over Chronos in consensus latency and nearly a tenfold increase in throughput.
