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A Variational Quantum Soft Actor-Critic Algorithm for Continuous Control Tasks 4th

A Variational Quantum Soft Actor-Critic Algorithm for Conti...

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4th international conference on Numerical Computations: theory and Algorithms, NUMTA 2023

作者： Policicchio, Antonio Acuto, Alberto Barillà, Paola Bozzolo, Ludovico Conterno, Matteo NTT DATA Italia S.p.A. Via Ernesto Calindri 4 Milan20143 Italy

ISBN: (纸本)9783031812460

Quantum Computing promises the availability of computational resources and generalization capabilities well beyond the possibilities of classical computers. An interesting approach for leveraging the near-term, Noisy Intermediate-Scale Quantum Computers, is the hybrid training of Parameterized Quantum Circuits (PQCs), i.e. the optimization of a parameterized quantum algorithms as a function approximation with classical optimization techniques. When PQCs are used in Machine Learning models, they may offer some advantages over classical models in terms of memory consumption and sample complexity for classical data analysis. In this work we explore and assess the advantages of the application of Parametric Quantum Circuits to one of the state-of-art Reinforcement Learning algorithm for continuous control - namely Soft Actor-Critic. We investigate its performance on the control of a virtual robotic arm by means of digital simulations of quantum circuits. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further developments and studies. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further developments and studies. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum computers

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Lightweight object detection algorithm for road scenes based on YOLOV8 4

Lightweight object detection algorithm for road scenes based...

引用

4th international conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Huang, Chengqin Yang, Degang Zhang, Xin College of Computer and Information Science Chongqing Normal University Chongqing401331 China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing Normal University Chongqing401331 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Object detection in road scenes is a crucial component of autonomous driving. Due to the significant variations in target scales, it is prone to false positives, false negatives, and some underperforming devices cannot deploy the state-of-the-art detectors. To address these issues, we propose a lightweight algorithm based on an improved YOLOv8. We simplify the model by using the FasterNet Block from FasterNet to replace the BottleNeck module in YOLOv8's backbone network C2f, reducing parameters and floating-point computations. We also enhance feature extraction by substituting RFCAConv for the downsampling standard convolution in C2f. Additionally, we introduce Wise-IoU to replace the original activation function, directing the network's focus towards anchor boxes of average *** promote effective fusion of original, shallow, and deep features, we introduce the BiFPN structure to replace YOLOv8's PAN structure. Furthermore, a small object detection layer is added to the head to handle the drastic scale variations in road scenes. Experimental results on the SODA10M dataset demonstrate that the improved YOLOv8 model achieves a 55.8% mAP@0.5 and a 35.5% mAP@0.5:0.95. the model's parameter count, size, and floating-point computations decrease by 58.3%, 57.0%, and 21.1%, respectively. Analysis of the experimental results confirms that the proposed model is effective and superior, striking a balance between detection accuracy and model lightweightness. © 2024 SPIE.

关键词： Object detection

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Deep Learning Model to Detect HTTP-Based Attack on Internet of things 4th

Deep Learning Model to Detect HTTP-Based Attack on Internet ...

引用

4th international conference on Data, engineering, and Applications, IDEA 2022

作者： Dhillon, Sumeet Mishra, Nishchol Shakya, Devendra Kumar Department of Computer Science and Engineering SATI Vidisha India School of Information Technology RGPV Bhopal India Department of Electronics Engineering SATI Vidisha India

ISBN: (纸本)9789819700363

the information exchange between devices and remote servers occurs through the Application programming Interface (API), which was created based on Internet technology (commonly referred to as web technology), in most of edge computing applications, including Internet of things (IoT) devices. the majority of users cannot use the edge device APIs directly;hence, they experience relatively fewer threats than users of typical web applications. However, as IoT devices have gained popularity, attacks targeting APIs have started to acquire attraction. In order to identify online traffic received by IoT services and mine dangerous traffic, this paper provides a web attack vector identification approach that will deliver security information to the Security Operation Center (SOC). this approach is based on the feature extraction, targeting the mostly constant message format of the API request, in combination with the Bidirectional Long Short-Term Memory (BLSTM) attack vector identification for online traffic. the experimental findings demonstrate that, in comparison to standard machine learning approaches and rule-based web application firewalls (WAF), the suggested approach is more successful at identifying attacks against IoT service APIs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： HTTP

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RAG Beyond Text: Enhancing Image Retrieval in RAG Systems 4

RAG Beyond Text: Enhancing Image Retrieval in RAG Systems

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Bag, Sukanya Gupta, Ayushman Kaushik, Rajat Jain, Chirag Data Science and Insights Genpact India Private Limited Bengaluru India

ISBN: (纸本)9798350395914

this paper presents a novel methodology for the extraction and retrieval of images in RAG (Retrieval Augmented Generation) powered Question Answering Conversational Systems that circumvents the limitations of Optical Character Recognition and Large Language Model (OCR-LLM) powered traditional image retrieval approaches. We are leveraging the positional information of images in a vast array of multi-modal (text/image) documents for ingesting image information alongside text, followed by advanced retrieval and prompt engineering techniques to develop an RAG system that maintains the integrity of textual and visual data correlation in responses to queries pertaining to both text and images in QnA solutions and is adept at retrieving both OCR-compatible and OCR-incompatible images. We have successfully incorporated this approach over a variety of multimodal documents ranging from research papers, application documentations, surveys to guides and manuals containing text, images and even tables with images and managed to achieve SoTA (State of the art) performance over simple to complex queries asked on the mentioned documents. Furthermore, our approach performed explicitly better in cases where Vision Models like GPT-4 Vision fails to accurately retrieve images which are OCR incompatible and pertains to highly customized scientific devices or diagrams and in cases where the image's visual representation is not semantically aligned with textual information but is important to be retrieved for completeness in the response. © 2024 IEEE.

关键词： Semantics

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B⁴: Towards Optimal Assessment of Plausible Code Solutions with Plausible Tests 24

B<SUP>4</SUP>: Towards Optimal Assessment of Plausible Code ...

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39th ACM/IEEE international conference on Automated Software engineering (ASE)

作者： Chen, Mouxiang Liu, Zhongxin Tao, He Hong, Yusu Lo, David Xia, Xin Sun, Jianling Zhejiang Univ State Key Lab Blockchain & Data Secur Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Singapore Management Univ Singapore Singapore Hangzhou High Tech Zone Binjiang Inst Blockchain Hangzhou Peoples R China

ISBN: (纸本)9798400712487

Selecting the best code solution from multiple generated ones is an essential task in code generation, which can be achieved by using some reliable validators (e.g., developer-written test cases) for assistance. Since reliable test cases are not always available and can be expensive to build in practice, researchers propose to automatically generate test cases to assess code solutions. However, when both code solutions and test cases are plausible and not reliable, selecting the best solution becomes challenging. Although some heuristic strategies have been proposed to tackle this problem, they lack a strong theoretical guarantee and it is still an open question whether an optimal selection strategy exists. Our work contributes in two ways. First, we show that within a Bayesian framework, the optimal selection strategy can be defined based on the posterior probability of the observed passing states between solutions and tests. the problem of identifying the best solution is then framed as an integer programming problem. Second, we propose an efficient approach for approximating this optimal (yet uncomputable) strategy, where the approximation error is bounded by the correctness of prior knowledge. We then incorporate effective prior knowledge to tailor code generation tasks. Both theoretical and empirical studies confirm that existing heuristics are limited in selecting the best solutions with plausible test cases. Our proposed approximated optimal strategy B-4 significantly surpasses existing heuristics in selecting code solutions generated by large language models (LLMs) with LLM-generated tests, achieving a relative performance improvement by up to 50% over the strongest heuristic and 246% over the random selection in the most challenging scenarios. Our code is publicly available at https://***/ZJU-CTAG/B4.

关键词： Code Generation Software engineering Large Language Models

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KNN-augmented attention transformer for hybrid deep-learning-based meteorological forecasting 4

KNN-augmented attention transformer for hybrid deep-learning...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Lu, Yuhao Guo, Haoran Department of Computer Science Wenzhou Kean University Zhejiang Wenzhou325060 China

ISBN: (纸本)9781510686106

Recent advances in deep learning have significantly impacted weather forecasting, where accurate predictions are crucial for effective decision-making across various sectors. this paper presents a novel approach to weather forecasting by introducing an advanced transformer-based model, which harnesses the capabilities of the Focused Transformer (FOT) framework with significant adaptations for meteorological applications. Central to our method is the substitution of the exact k-nearest-neighbor (k-NN) lookup with an approximate k-NN method, enhancing the model’s efficiency and scalability. this modification enables the transformer to dynamically integrate expansive temporal and spatial data sequences more effectively, crucial for accurate weather predictions. We adopt the contrastive training technique to refine the context scaling, which is critical for processing the complex dynamics of weather patterns. Performance evaluations on several weather datasets demonstrate that our model achieves superior forecasting accuracy, indicated by improvements in root mean squared error and mean absolute error metrics, compared to existing state-of-the-art models. Our findings suggest that applying transformer models with approximate k-NN lookups offers a promising direction for developing more robust and efficient weather forecasting systems. © 2024 SPIE.

关键词： Mean square error

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DROM: Read Only Memory Storage in DNA Computing 4

DROM: Read Only Memory Storage in DNA Computing

引用

4th international conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Tabassum, Tamanna Akter, Fatema Uddin, Mohammed Nasir Jagannath University Dept of Computer Science and Engineering Bangladesh Green University Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781665490450

Biomolecular programming performs computing utilizing many sorts of reactions and stores the information in biological macromolecules (proteins and nucleic acids). DNA (Deoxyribose Nucleic Acid) exhibits highly predictable molecular behavior, which assists in the construction of logical versions of operating systems and molecular computers. Read Only Memory (ROM), is a form of electronic storage that is built into a device during the manufacturing process. It might be a programmable general-purpose logic device, and a client can customize the storage device as a basic logic gate operation to complex mechanisms on a chip. In this proposed work, a ROM is designed using DNA-based gates. the attributes of DNA-based computing enable DNA-based ROM to execute billions of operations consecutively as well as provide immense storage in a compact space. © 2022 IEEE.

关键词： DNA

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Identification and Classification of Types of Malarial Parasites Using Customized CNN Model 4

Identification and Classification of Types of Malarial Paras...

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4th international conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Pattanaik, Priyadarshini Ibeh, Lawrence Cuong, Nguyen Manh Faculty of Computer Science and Informatics Alte Post Karl-Marx-Straße 97-99 Berlin12043 Germany Alte Post Karl-Marx-Straße 97-99 Berlin12043 Germany

ISBN: (纸本)9798350391183

Over the past decade, medical imaging has achieved many remarkable milestones in medical science. However, the incidence of infectious diseases like malaria continues to rise rapidly. Malaria a serious infection caused by the Plasmodium parasite, is a life-threatening paradox in global health. Even though conventional methods could identify the malaria parasite, they still failed to present accurate results and are time-consuming manual processes. Early detection and analysis of the complex morphological structure of blood cells are challenging tasks and prevalent using morphological detection tools. In our work, our primary goal is to develop a customized CNN model to pick malaria species and classify the different variants of malaria species in microscopic thin blood smear images. this specialized CNN model leverages deep learning abilities and fine-Tunes the model capacity employing dropout techniques. the model can successfully validate quantitative analyses of different variants i.e. Plasmodium falciparum (or P. falciparum), Plasmodium malariae (or P. malariae), Plasmodium vivax (or P. vivax), Plasmodium ovale (or P. ovale) improving patient outcomes and presents overall classification results with an accuracy of 93.30 %, specificity of 97.6%, F measure of 96.5%, and AUC of 95.21 %. through careful hyperparameter tuning, the customized CNN model has achieved superior performance results as compared to other state-of-the-art methods. © 2024 IEEE.

关键词： Diseases

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Conception and Evaluation of a Permanent Magnet Direct Current Motor for Electric Vehicles 4

Conception and Evaluation of a Permanent Magnet Direct Curre...

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4th international conference on Emerging Research in Electronics, Computer science and Technology, ICERECT 2022

作者： Manikandan, V. Mishra, Shashank Jain University Department of Computer Science and Engineering Bangalore India Teerthanker Mahaveer University Department of Electrical Engineering Moradabad India

ISBN: (纸本)9781665456357

In this study, a multistage Permanent Magnet Direct Current (PMDC) engine for electric vehicles is planned and examined. In this work, centre point wheels with appraised values of 110Nm, 900rpm have been designed for the proposed EV. Without using the transmission, differential, or other mechanical parts that have incredibly high failure rates due to the mechanical part, this EV can produce 440 Nm. Infolytica/Engine Address programming has designed the engines to be used in the EV as 3-, 5-, and 7-stage to examine their exhibitions at comparable load conditions. For the engines, a similar rotor math has been applied. Despite this, taking into account the engine stage number does not mean that stator elements and space numbers are inflexible. For these engines under similar operating conditions, execution bends of stage flows, yield powers, forces, efficiencies, and power factors have been introduced. © 2022 IEEE.

关键词： Engines

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FedQP: Towards Accurate Federated Learning using Quadratic programming Guided Mutation 36

FedQP: Towards Accurate Federated Learning using Quadratic P...

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36th international conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Weng, Jiawen Xia, Zeke Li, Ran Hu, Ming Chen, Mingsong Software/Hardware Co-Design Engineering Research Center Ministry of Education East China Normal University Shanghai China School of Software Nanjing University of Information Science and Technology Nanjing China Research Lab for Intelligent Software Engineering Singapore Management University Singapore

ISBN: (纸本)1891706594

Due to the advantages of privacy-preserving, Federated Learning (FL) is widely used in distributed machine learning systems. However, existing FL methods suffer from low-inference performance caused by data heterogeneity. Specifically, due to heterogeneous data, the optimization directions of different local models vary greatly, making it difficult for the traditional FL method to get a generalized global model that performs well on all clients. As one of the state-of-the-art FL methods, the mutation-based FL method attempts to adopt a stochastic mutation strategy to guide the model training towards a well-generalized area (i.e., flat area in the loss landscape). Specifically, mutation allows the model to shift within the solution space, providing an opportunity to escape areas with poor generalization (i.e., sharp area). However, the stochastic mutation strategy easily results in diverse optimal directions of mutated models, which limits the performance of the existing mutation-based FL method. To achieve higher performance, this paper proposes a novel mutation-based FL approach named FedQP, utilizing a quadratic programming strategy to regulate the mutation directions wisely. By biasing the model mutation towards the direction of gradient update rather than traditional random mutation, FedQP can effectively guide the model to optimize towards a well-generalized area (i.e., flat area). Experiments on multiple well-known datasets show that our quadratic programming-guided mutation strategy effectively improves the inference accuracy of the global model in various heterogeneous data scenarios. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Federated learning

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