Computational approaches can speed up the drug discovery process by predicting drug-target affinity, otherwise it is time-consuming. In this study, we developed a convolutional neural network (CNN)-based model named S...
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Offline reinforcement learning(RL) has gathered increasing attention in recent years, which seeks to learn policies from static datasets without active online exploration. However, the existing offline RL approaches o...
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Offline reinforcement learning(RL) has gathered increasing attention in recent years, which seeks to learn policies from static datasets without active online exploration. However, the existing offline RL approaches often require a large amount of pre-collected data and hence are hardly implemented by a single agent in practice. Inspired by the advancement of federated learning(FL), this paper studies federated offline reinforcement learning(FORL),whereby multiple agents collaboratively carry out offline policy learning with no need to share their raw ***, a straightforward solution is to simply retrofit the off-the-shelf offline RL methods for FL, whereas such an approach easily overfits individual datasets during local updating, leading to instability and subpar performance. To overcome this challenge, we propose a new FORL algorithm, named model-free(MF)-FORL, that exploits novel“proximal local policy evaluation” to judiciously push up action values beyond local data support, enabling agents to capture the individual information without forgetting the aggregated knowledge. Further, we introduce a model-based variant, MB-FORL, capable of improving the generalization ability and computational efficiency via utilizing a learned dynamics model. We evaluate the proposed algorithms on a suite of complex and high-dimensional offline RL benchmarks, and the results demonstrate significant performance gains over the baselines.
Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational trip...
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Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.
Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving ...
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Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.
If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must...
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This study proposes a contactless and real-time hand gesture recognition system suitable for smartwatches. The proposed system adopts inductive proximity sensing to collect Mechanomyography (MMG) signals induced by fi...
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Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojournin...
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This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mis...
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This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.
The article addresses the output-feedback control issue for a class of multi-input multi-output(MIMO)uncertain nonlinear systems with multiple event-triggered mechanisms(ETM).Compared to previous event-triggering stud...
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The article addresses the output-feedback control issue for a class of multi-input multi-output(MIMO)uncertain nonlinear systems with multiple event-triggered mechanisms(ETM).Compared to previous event-triggering studies,this paper aims to trigger both the output and filtered *** nonlinear dynamics are approximated using fuzzy logic systems(FLSs).Then,a novel kind of state observer has been designed to deal with unmeasurable state problems using the triggered output *** sampled estimated state,the triggered output signal,and the filtered signal are utilized to propose an event-triggering mechanism that consists of sensor-to-observer(SO)and observer-to-controller(OC).An event-triggered output feedback control approach is given inside backstepping control,whereby the filter may be employed to circumvent the issue of the virtual control function not being differentiable at the trigger *** is testified that,according to the Lyapunov stability analysis scheme,all closed-loop signals and the system output are ultimately uniformly constrained by our control ***,the simulation examples are performed to confirm the theoretical findings.
To enhance the capability of classifying and localizing defects on the surface of hot-rolled strips, this paper proposed an algorithm based on YOLOv7 to improve defect detection. The BI-SPPFCSPC structure was incorpor...
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