The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading ***...
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The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading *** signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity ***,most certificateless signatures still suffer fromvarious security *** present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature *** ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and *** scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota *** addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing *** results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.
Index recommendation is essential for improving query performance in database management systems (DBMSs) through creating an optimal set of indexes under specific constraints. Traditional methods, such as heuristic an...
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Grid-based path planning is one of the classic problems in AI, and a popular topic in application areas such as computer games and robotics. Compressed Path databases (CPDs) are recognized as a state-of-the-art method...
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In this paper, we investigate and analyze the suboptimal adaptive control for finite impulse response(FIR) systems with binary-valued observations. As the parameters of FIR systems are unknown and the measurable obser...
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In this paper, we investigate and analyze the suboptimal adaptive control for finite impulse response(FIR) systems with binary-valued observations. As the parameters of FIR systems are unknown and the measurable observations can only provide limited information, we propose and analyze a two-segment design method of an adaptive control law. First, we divide the system running time axis into many sections;each of these sections is divided into two segments. During the short segment, we design the system inputs for estimating parameters. Thus, we employ the empirical-measure-based technique for designing the identification algorithm. Second, we introduce a tracking control law to track a given target based on the system parameter estimates obtained in the short segment. We achieve this using the certainty equivalent principle in the long segment. As the length of short segments tends to infinity, we observe that the parameter estimation algorithm is consistent. However, when the length of segments tends to infinity, we find that the adaptive tracking control law is asymptotically suboptimal. Finally, we demonstrate the efficiency of the two-segment design method using the simulation results.
In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge wi...
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In-context learning is a promising approach for offline reinforcement learning (RL) to handle online tasks, which can be achieved by providing task prompts. Recent works demonstrated that in-context RL could emerge with self-improvement in a trial-and-error manner when treating RL tasks as an across-episodic sequential prediction problem. Despite the self-improvement not requiring gradient updates, current works still suffer from high computational costs when the across-episodic sequence increases with task horizons. To this end, we propose an In-context Decision Transformer (IDT) to achieve self-improvement in a high-level trial-and-error manner. Specifically, IDT is inspired by the efficient hierarchical structure of human decision-making and thus reconstructs the sequence to consist of high-level decisions instead of low-level actions that interact with environments. As one high-level decision can guide multi-step low-level actions, IDT naturally avoids excessively long sequences and solves online tasks more efficiently. Experimental results show that IDT achieves state-of-the-art in long-horizon tasks over current in-context RL methods. In particular, the online evaluation time of our IDT is 36× times faster than baselines in the D4RL benchmark and 27× times faster in the Grid World benchmark. Copyright 2024 by the author(s)
Fine-grained change operations can help software developers fix software bugs more accurately and efficiently. However, the current fine-grained change operations are only used in specific fixing process, such as fixi...
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Fine-grained change operations can help software developers fix software bugs more accurately and efficiently. However, the current fine-grained change operations are only used in specific fixing process, such as fixing of If statement. In this paper, we conducted an empirical study to explore the fine-grained change operations for bug fixing. Based on the Mozilla bug data,we examined whether similar bugs are fixed with similar change operations. The results show that: First, for bug reports with similar descriptions or bug-fix commits with similar descriptions, their corresponding fine-grained change operations are not related; Second, in the case where the descriptions of both bug reports and bug-fix commits are similar, the fine-grained change operations in patch code are not related; Third, by classifying bug reports, we find that the change operations in the same bug report category are similar; Finally, by analyzing the fine-grained change operations for each bug, we present some combined patterns that are often used together.
Previous methods on knowledge base question generation (KBQG) primarily focus on refining the quality of a single generated question. However, considering the remarkable paraphrasing ability of humans, we believe that...
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Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature ext...
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Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.
Most existing Salient Object Detection (SOD) methods focus on achieving better performance, often resulting in models with a large number of parameters. However, there is limited research on lightweight models in this...
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Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus...
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