Training and testing different models in the field of text classification mainly depend on the pre-classified text document datasets. Recently, seven datasets have emerged for Arabic text classification, including Sin...Training and testing different models in the field of text classification mainly depend on the pre-classified text document datasets. Recently, seven datasets have emerged for Arabic text classification, including Single-Label Arabic News Articles Dataset(SANAD), Khaleej, Arabiya, Akhbarona, KALIMAT, Waten2004, and Khaleej2004. This study investigates which of these datasets can provide significant training and fair evaluation for text classification(TC). In this investigation, well-known and accurate learning models are used, including naive Bayes(NB), random forest(RF), K-nearest neighbor(KNN), support vector machines(SVM), and logistic regression(LR) models. We present relevance and time measures of training the models with these datasets to enable Arabic language researchers to select the appropriate dataset to use based on a solid basis of comparison. The performances of the five learning models across the seven datasets are measured and compared with the performances of the same models trained on a well-known English language dataset. The analysis of the relevance and time scores shows that training the SVM model on Khaleej and Arabiya obtained the most significant results in the shortest amount of time,with the accuracy of 82%.展开更多
With the continuous advancement of communication and unmanned aerial vehicle(UAV)technologies,the collaborative operations of diverse platforms,including UAVs and ground vehicles,have been significantly promoted.Howev...With the continuous advancement of communication and unmanned aerial vehicle(UAV)technologies,the collaborative operations of diverse platforms,including UAVs and ground vehicles,have been significantly promoted.However,battlefield uncertainties,such as equipment failures and enemy attacks,can impact these collaborative operations'stability and communication efficiency.To this end,we design a highly destruction-resistant air-ground cooperative resilient networking platform that aims to enhance the robustness of network communications by integrating ground vehicle information for UAV network deployment.It then incorporates the concept of virtual guiding force,enabling the UAV swarm to adaptively configure its network layout based on ground vehicle information,thereby improving network destruction resistance.Simulation results demonstrate that the UAV swarm involved in the proposed platform exhibits balanced flight energy consumption and excellent performance in network destruction resistance.展开更多
Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction m...Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.展开更多
In this paper, we use the cycle basis from graph theory to reduce the size of the decision variable space of optimal network flow problems by eliminating the aggregated flow conservation constraint. We use a minimum c...In this paper, we use the cycle basis from graph theory to reduce the size of the decision variable space of optimal network flow problems by eliminating the aggregated flow conservation constraint. We use a minimum cost flow problem and an optimal power flow problem with generation and storage at the nodes to demonstrate our decision variable reduction method.The main advantage of the proposed technique is that it retains the natural sparse/decomposable structure of network flow problems. As such, the reformulated problems are still amenable to distributed solutions. We demonstrate this by proposing a distributed alternating direction method of multipliers(ADMM)solution for a minimum cost flow problem. We also show that the communication cost of the distributed ADMM algorithm for our proposed cycle-based formulation of the minimum cost flow problem is lower than that of a distributed ADMM algorithm for the original arc-based formulation.展开更多
Control of nonlinear systems is challenging in realtime.Decision making,performed many times per second,must ensure system safety.Designing input to perform a task often involves solving a nonlinear system of differen...Control of nonlinear systems is challenging in realtime.Decision making,performed many times per second,must ensure system safety.Designing input to perform a task often involves solving a nonlinear system of differential equations,which is a computationally intensive,if not intractable problem.This article proposes sampling-based task learning for controlaffine nonlinear systems through the combined learning of both state and action-value functions in a model-free approximate value iteration setting with continuous inputs.A quadratic negative definite state-value function implies the existence of a unique maximum of the action-value function at any state.This allows the replacement of the standard greedy policy with a computationally efficient policy approximation that guarantees progression to a goal state without knowledge of the system dynamics.The policy approximation is consistent,i.e.,it does not depend on the action samples used to calculate it.This method is appropriate for mechanical systems with high-dimensional input spaces and unknown dynamics performing Constraint-Balancing Tasks.We verify it both in simulation and experimentally for an Unmanned Aerial Vehicles(UAVs) carrying a suspended load,and in simulation,for the rendezvous of heterogeneous robots.展开更多
Our world becomes more and more dependent on communications infrastructure such as computer resources,network connections and devices for communications.However,current security techniques do not provide adequate prot...Our world becomes more and more dependent on communications infrastructure such as computer resources,network connections and devices for communications.However,current security techniques do not provide adequate protection against cyber attackers for these systems and devices.The increasing complexity展开更多
The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and propose...The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the consequents. Experiments show GRSET algorithm to be practicable and efficient.展开更多
Ireproving the quality and experience perceived by the user is fundamental when de- veloping multimedia technologies, products, and services. Quality of experience （QoE） involves subjective perception, user behavior...Ireproving the quality and experience perceived by the user is fundamental when de- veloping multimedia technologies, products, and services. Quality of experience （QoE） involves subjective perception, user behavior and needs, appropriateness, con- text, and usability of delivered content. Modeling QoE is critical for enhancing QoE in various nmhimedia applications. In this special issue,展开更多
As artificial intelligence algorithms improve,we will interact with programs that seem increasingly human.We may never know if these algorithms are sentient,yet this quality is crucial to ethical considerations regard...As artificial intelligence algorithms improve,we will interact with programs that seem increasingly human.We may never know if these algorithms are sentient,yet this quality is crucial to ethical considerations regarding their moral status.We will likely have to make important decisions without a full understanding of the relevant issues and facts.Given this ignorance,we ought to take seriously the prospect that some systems are sentient.It would be a moral catastrophe if we were to treat them as if they were not sentient,but,in reality they are.展开更多
Building on our previous work,we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic,by collecting and analyzing a large,novel,and longitudinal da...Building on our previous work,we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic,by collecting and analyzing a large,novel,and longitudinal dataset of migration-related tweets.To this end,we first annotate above 2000 tweets for(anti-)solidarity expressions towards immigrants,utilizing two annotation approaches(experts vs.crowds).On these annotations,we train a BERT model with multiple data augmentation strategies,which performs close to the human upper bound.We use this high-quality model to automatically label over 240000 tweets between September 2019 and June 2021.We then assess the automatically labeled data for how statements related to migrant(anti-)solidarity developed over time,before and during the COVID-19 crisis.Our findings show that migrant solidarity became increasingly salient and contested during the early stages of the pandemic but declined in importance since late 2020,with tweet numbers falling slightly below pre-pandemic levels in summer 2021.During the same period,the share of anti-solidarity tweets increased in a sub-sample of COVID-19-related tweets.These findings highlight the importance of long-term observation,pre-and post-crisis comparison,and sampling in research interested in crisis related effects.As one of our main contributions,we outline potential pitfalls of an analysis of social solidarity trends:for example,the ratio of solidarity and anti-solidarity statements depends on the sampling design,i.e.,tweet language,Twitter-user accounts’national identification(country known or unknown)and selection of relevant tweets.In our sample,the share of anti-solidarity tweets is higher in native(German)language tweets and among“anonymous”Twitter users writing in German compared to English-language tweets of users located in Germany.展开更多
Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings i...Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous （i.e., dirty） data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis （PCA） model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic （ROC） curves.展开更多
We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces(RISs)to maximize the dirty-paper-coding(DPC)sum rate of the RIS-assisted broadcast channel.Different from prior...We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces(RISs)to maximize the dirty-paper-coding(DPC)sum rate of the RIS-assisted broadcast channel.Different from prior works,which maximize the rate achievable by linear precoders,we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast channel.We propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold optimization.Our results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.展开更多
文摘Training and testing different models in the field of text classification mainly depend on the pre-classified text document datasets. Recently, seven datasets have emerged for Arabic text classification, including Single-Label Arabic News Articles Dataset(SANAD), Khaleej, Arabiya, Akhbarona, KALIMAT, Waten2004, and Khaleej2004. This study investigates which of these datasets can provide significant training and fair evaluation for text classification(TC). In this investigation, well-known and accurate learning models are used, including naive Bayes(NB), random forest(RF), K-nearest neighbor(KNN), support vector machines(SVM), and logistic regression(LR) models. We present relevance and time measures of training the models with these datasets to enable Arabic language researchers to select the appropriate dataset to use based on a solid basis of comparison. The performances of the five learning models across the seven datasets are measured and compared with the performances of the same models trained on a well-known English language dataset. The analysis of the relevance and time scores shows that training the SVM model on Khaleej and Arabiya obtained the most significant results in the shortest amount of time,with the accuracy of 82%.
基金supported by the Researchers Supporting Project of King Saud University,Riyadh,Saudi Arabia,under Project RSPD2025R681。
文摘With the continuous advancement of communication and unmanned aerial vehicle(UAV)technologies,the collaborative operations of diverse platforms,including UAVs and ground vehicles,have been significantly promoted.However,battlefield uncertainties,such as equipment failures and enemy attacks,can impact these collaborative operations'stability and communication efficiency.To this end,we design a highly destruction-resistant air-ground cooperative resilient networking platform that aims to enhance the robustness of network communications by integrating ground vehicle information for UAV network deployment.It then incorporates the concept of virtual guiding force,enabling the UAV swarm to adaptively configure its network layout based on ground vehicle information,thereby improving network destruction resistance.Simulation results demonstrate that the UAV swarm involved in the proposed platform exhibits balanced flight energy consumption and excellent performance in network destruction resistance.
基金supported in part by the National Natural Science Foundation of China(51975075)Chongqing Technology Innovation and Application Program(cstc2018jszx-cyzd X0183)。
文摘Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.
基金supported by National Science Foundation award ECCS-1653838
文摘In this paper, we use the cycle basis from graph theory to reduce the size of the decision variable space of optimal network flow problems by eliminating the aggregated flow conservation constraint. We use a minimum cost flow problem and an optimal power flow problem with generation and storage at the nodes to demonstrate our decision variable reduction method.The main advantage of the proposed technique is that it retains the natural sparse/decomposable structure of network flow problems. As such, the reformulated problems are still amenable to distributed solutions. We demonstrate this by proposing a distributed alternating direction method of multipliers(ADMM)solution for a minimum cost flow problem. We also show that the communication cost of the distributed ADMM algorithm for our proposed cycle-based formulation of the minimum cost flow problem is lower than that of a distributed ADMM algorithm for the original arc-based formulation.
文摘Control of nonlinear systems is challenging in realtime.Decision making,performed many times per second,must ensure system safety.Designing input to perform a task often involves solving a nonlinear system of differential equations,which is a computationally intensive,if not intractable problem.This article proposes sampling-based task learning for controlaffine nonlinear systems through the combined learning of both state and action-value functions in a model-free approximate value iteration setting with continuous inputs.A quadratic negative definite state-value function implies the existence of a unique maximum of the action-value function at any state.This allows the replacement of the standard greedy policy with a computationally efficient policy approximation that guarantees progression to a goal state without knowledge of the system dynamics.The policy approximation is consistent,i.e.,it does not depend on the action samples used to calculate it.This method is appropriate for mechanical systems with high-dimensional input spaces and unknown dynamics performing Constraint-Balancing Tasks.We verify it both in simulation and experimentally for an Unmanned Aerial Vehicles(UAVs) carrying a suspended load,and in simulation,for the rendezvous of heterogeneous robots.
文摘Our world becomes more and more dependent on communications infrastructure such as computer resources,network connections and devices for communications.However,current security techniques do not provide adequate protection against cyber attackers for these systems and devices.The increasing complexity
基金Supported by the National Natural Science Foundation of China (No.60474022) the Natural Science Foundation of Henan Province(No. G2002026,200510475028)
文摘The classical algorithm of finding association rules generated by a frequent itemset has to generate all non-empty subsets of the frequent itemset as candidate set of consequents. Xiongfei Li aimed at this and proposed an improved algorithm. The algorithm finds all consequents layer by layer, so it is breadth-first. In this paper, we propose a new algorithm Generate Rules by using Set-Enumeration Tree (GRSET) which uses the structure of Set-Enumeration Tree and depth-first method to find all consequents of the association rules one by one and get all association rules correspond to the consequents. Experiments show GRSET algorithm to be practicable and efficient.
文摘Ireproving the quality and experience perceived by the user is fundamental when de- veloping multimedia technologies, products, and services. Quality of experience （QoE） involves subjective perception, user behavior and needs, appropriateness, con- text, and usability of delivered content. Modeling QoE is critical for enhancing QoE in various nmhimedia applications. In this special issue,
文摘As artificial intelligence algorithms improve,we will interact with programs that seem increasingly human.We may never know if these algorithms are sentient,yet this quality is crucial to ethical considerations regarding their moral status.We will likely have to make important decisions without a full understanding of the relevant issues and facts.Given this ignorance,we ought to take seriously the prospect that some systems are sentient.It would be a moral catastrophe if we were to treat them as if they were not sentient,but,in reality they are.
文摘Building on our previous work,we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic,by collecting and analyzing a large,novel,and longitudinal dataset of migration-related tweets.To this end,we first annotate above 2000 tweets for(anti-)solidarity expressions towards immigrants,utilizing two annotation approaches(experts vs.crowds).On these annotations,we train a BERT model with multiple data augmentation strategies,which performs close to the human upper bound.We use this high-quality model to automatically label over 240000 tweets between September 2019 and June 2021.We then assess the automatically labeled data for how statements related to migrant(anti-)solidarity developed over time,before and during the COVID-19 crisis.Our findings show that migrant solidarity became increasingly salient and contested during the early stages of the pandemic but declined in importance since late 2020,with tweet numbers falling slightly below pre-pandemic levels in summer 2021.During the same period,the share of anti-solidarity tweets increased in a sub-sample of COVID-19-related tweets.These findings highlight the importance of long-term observation,pre-and post-crisis comparison,and sampling in research interested in crisis related effects.As one of our main contributions,we outline potential pitfalls of an analysis of social solidarity trends:for example,the ratio of solidarity and anti-solidarity statements depends on the sampling design,i.e.,tweet language,Twitter-user accounts’national identification(country known or unknown)and selection of relevant tweets.In our sample,the share of anti-solidarity tweets is higher in native(German)language tweets and among“anonymous”Twitter users writing in German compared to English-language tweets of users located in Germany.
基金sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001
文摘Sensor networks are deployed in many application areas nowadays ranging from environment monitoring, industrial monitoring, and agriculture monitoring to military battlefield sensing. The accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. Therefore, this work is motivated to propose approaches that can detect and repair erroneous （i.e., dirty） data caused by inevitable system problems involving various hardware and software components of sensor networks. As information about a single event of interest in a sensor network is usually reflected in multiple measurement points, the inconsistency among multiple sensor measurements serves as an indicator for data quality problem. The focus of this paper is thus to study methods that can effectively detect and identify erroneous data among inconsistent observations based on the inherent structure of various sensor measurement series from a group of sensors. Particularly, we present three models to characterize the inherent data structures among sensor measurement traces and then apply these models individually to guide the error detection of a sensor network. First, we propose a multivariate Gaussian model which explores the correlated data changes of a group of sensors. Second, we present a Principal Component Analysis （PCA） model which captures the sparse geometric relationship among sensors in a network. The PCA model is motivated by the fact that not all sensor networks have clustered sensor deployment and clear data correlation structure. Further, if the sensor data show non-linear characteristic, a traditional PCA model can not capture the data attributes properly. Therefore, we propose a third model which utilizes kernel functions to map the original data into a high dimensional feature space and then apply PCA model on the mapped linearized data. All these three models serve the purpose of capturing the underlying phenomenon of a sensor network from its global view, and then guide the error detection to discover any anomaly observations. We conducted simulations for each of the proposed models, and evaluated the performance by deriving the Receiver Operating Characteristic （ROC） curves.
基金supported by the National Science Foundation(Nos.CNS-2107216 and CNS-2128368).
文摘We consider a downlink multi-user scenario and investigate the use of reconfigurable intelligent surfaces(RISs)to maximize the dirty-paper-coding(DPC)sum rate of the RIS-assisted broadcast channel.Different from prior works,which maximize the rate achievable by linear precoders,we assume a capacity-achieving DPC scheme is employed at the transmitter and optimize the transmit covariances and RIS reflection coefficients to directly maximize the sum capacity of the broadcast channel.We propose an optimization algorithm that iteratively alternates between optimizing the transmit covariances using convex optimization and the RIS reflection coefficients using Riemannian manifold optimization.Our results show that the proposed technique can be used to effectively improve the sum capacity in a variety of scenarios compared to benchmark schemes.
