Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitat...
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Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple *** these achieve-ments,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific *** address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model ***,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge ***,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge ***,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual *** illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various *** conclude by outlining future pathways for further advancement and applications.
Person Re-Identification falls within the scope of computer vision, acting a technique to ascertain the presence of a specified pedestrian within a video or image library. The related research is of great significance...
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Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on...
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Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these *** by the theory of software cybernetics,some strategies have been proposed to dynamically se-lect partitions based on the feedback information gained during *** basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly ac-cording to the previous test *** a feedback-driven mechanism can be considered as a learning process—it makes decisions based on the observations acquired in the test ***,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition *** this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing *** algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed *** studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 *** experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing *** study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.
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.
Building Automation Systems(BASs)are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control,HVAC systems,entry systems,and lighting *** BASs in use...
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Building Automation Systems(BASs)are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control,HVAC systems,entry systems,and lighting *** BASs in use are outdated and suffer from numerous vulnerabilities that stem from the design of the underlying BAS *** this paper,we provide a comprehensive,up-to-date survey on BASs and attacks against seven BAS protocols including BACnet,EnOcean,KNX,LonWorks,Modbus,ZigBee,and *** studies of secure BAS protocols are also presented,covering BACnet Secure Connect,KNX Data Secure,KNX/IP Secure,ModBus/TCP Security,EnOcean High Security and Z-Wave *** and ZigBee do not have security *** point out how these security protocols improve the security of the BAS and what issues remain.A case study is provided which describes a real-world BAS and showcases its vulnerabilities as well as recommendations for improving the security of *** seek to raise awareness to those in academia and industry as well as highlight open problems within BAS security.
Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a...
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Wireless sensor networks (WSNs) play a vital role in modern research and applications due to their potential to gather data from various environments. Because sensor nodes (SNs) within WSNs have limited battery life, ...
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In Currently, research in the field of infrared road object detection is primarily focused on enhancing model performance and robustness to address the challenges posed by complex real-world driving scenarios. In resp...
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The Deep Learning model benefits from the information extracted from images, which enhances its understanding of human vision with comparable accuracy and speed. However, the publicly available dataset lacks the neces...
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In recent years, object detection (OD) has become essential in computer vision for identifying and localizing objects in digital images, prompting various sectors to adopt this technology. However, increased reliance ...
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In recent years, object detection (OD) has become essential in computer vision for identifying and localizing objects in digital images, prompting various sectors to adopt this technology. However, increased reliance on OD has also revealed vulnerabilities to attacks, highlighting the need for effective detection methods to mitigate potential risks. Therefore, the present paper primarily surveys existing studies on OD in the context of security and surveillance, highlighting its significance in these critical areas. The discussion includes an examination of conventional techniques such as HOG, DPM, and the Viola‒Jones detector. While these traditional methods have laid the groundwork for object detection, they are often considered inadequate because of their time-consuming and labor-intensive nature. Consequently, the focus shifts to DL (deep learning)-based OD models such as YOLO (you only look once), single shot detector (SSD), and Fast R-CNN. Among these, the present survey paper emphasizes YOLO models for their speed and efficiency, as they utilize a unified architecture for both region proposal and classification, making them particularly suitable for real-time applications. However, the distinguishing feature of the proposed survey lies in its comprehensive coverage, which not only encompasses YOLO models but also integrates an analysis of generative AI (GenAI) models and metaheuristic approaches. This multifaceted exploration allows for a richer understanding of the current landscape in computer vision and AI, highlighting the synergies and potential applications that arise from combining these diverse methodologies. Furthermore, the paper explores a wide range of applications for OD in real-time security and surveillance settings, illustrating its effectiveness in addressing contemporary security challenges. This highlights how advanced OD techniques can enhance situational awareness and response capabilities in various scenarios. By focusing on these aspect
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