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The Open Source Threat Intelligence Relational Dataset and Its Optimal Implementation

The Open Source Threat Intelligence Relational Dataset and I...

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Yang, Yaru Li, Junyu Zhang, Ronghua Sah, Abhishek Pratap Yadav, Amit Khan, Asif Chengdu Neusoft University Department of Computer and Software Chengdu China University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China Charles Darwin University College of Engineering IT and Environment NT Australia Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350361537

Threat intelligence provides a platform for cybersecurity engineers for attack traceability, which provides substantial knowledge database logs to defend against future security threats. Threat intelligence relationship extraction based on deep learning solves the challenge of threat knowledge construction to a certain extent but still faces problems such as lack of open-source datasets and the inability of the model to accurately correlate threat entities with potential relationships. Therefore, for cybersecurity research work, this paper designs a threat ontology, constructs the threat relationship dataset TreatRE by remote supervision, and opens this dataset. The dataset contains 12000 utterances and 12 threat relations from 500 CTIs, and it performs well in multiple relation models trained on deep learning methods. Meanwhile, we propose a multisensory attention-based threat intelligence relationship extraction method MAtt, which combines location perception, self-attention perception, and neuronal memory perception to further improve the threat relationship extraction effect. Experimental results show that the trained model based on TreatRE can more accurately extract the knowledge objects and their relationships described in threat intelligence. An accuracy score of 95.4% can be obtained using the MAtt method, which is 3.48% more than the best baseline compared with the same type of relationship extraction model. © 2024 IEEE.

关键词： Cybersecurity

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Development of a Soft Hand and Wrist Rehabilitation Robot Utilizing Lattice Structure Actuators

Development of a Soft Hand and Wrist Rehabilitation Robot Ut...

引用

2024 International Convention on Rehabilitation engineering and Assistive Technology and World Rehabilitation Robot Convention, i-CREATE and WRRC 2024

作者： Lai, Jianwei Song, Aiguo Zhang, Wenbin Lu, Ye Wang, Jiajin Li, Ye School of Instrument Science and Engineering Southeast University State Key Laboratory of Digital Medical Engineering Jiangsu Key Laboratory of Remote Measurement and Control Nanjing210096 China College of Computer Science and Software Engineering Hohai University Nanjing210024 China

ISBN: (纸本)9798350355123

Soft robots offer promising solutions for rehabilitation due to their inherent attributes such as suppleness, lightweight construction, and safety features. However, prevalent soft actuators face limitations in output force and bending angle, impeding their effective application in rehabilitation. This paper proposes a novel design approach employing a two-layer lattice structure actuator, comprising flexion, guiding, and extension layers. This innovative design facilitates supple and efficient bi-directional rehabilitation training for fingers and wrists. Experimental results demonstrate the efficacy of the proposed dual-layer lattice structure actuator. At an air pressure of 150 kPa, it achieves an end force of 14.88 N, marking a remarkable 906% increase compared to its single-layer counterpart. Additionally, the dual-layer structure exhibits a minimum bending radius of 2.77 cm under 60 kPa, representing a 38.5% reduction relative to the single-layer actuator. Leveraging these actuators, a hand and wrist rehabilitation robot was developed and subjected to a clinical trial involving two healthy subjects. The trial outcomes reveal significant improvements in finger grasping ability and wrist range of motion with the assistance of the rehabilitation robot. These results underscore the practical usability and effectiveness of the soft hand and wrist rehabilitation robot, thereby holding promise for enhancing patient rehabilitation outcomes. © 2024 IEEE.

关键词： Patient rehabilitation

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Remote Sensing Based Advance Image Captioning Improved Feature Attention 7

Remote Sensing Based Advance Image Captioning Improved Featu...

引用

7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Sattar, Ammara Assam, Muhammad Alahmadi, Tahani Jaser Bhatti, Uzair Aslam Tang, Hao Aamir, Muhammad Harbin Engineering University Harbin China University of Science and Technology Department of Software Engineering KPK Bannu Pakistan College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia School of Information and Communication Engineering Hainan University Haikou China College of Computer Science Huanggang Normal University Huanggang China

ISBN: (纸本)9798350350890

In the domain of image description generation, the focal point mechanism is crucial for highlighting the most relevant features within an image, thereby influencing the quality of generated descriptions. To address this challenge, we introduce an innovative optimization module called the Enhanced Focal (EF) mechanism, which builds upon the traditional multi-head focal point mechanism. The EF module is designed to assess the correlation levels between objects within an image, thereby guiding the process of subtitle generation more effectively. The EF mechanism integrates the output of the multi-head focal point mechanism with the current context (i.e., the query) to generate two components: an 'information vector' and an 'attention gate.' The 'attention gate' is then applied to the 'information vector' to create a refined focal point mechanism. This refined mechanism undergoes element-wise multiplication with the 'information vector,' producing attention information that reveals a strong correlation between the target and the background in the image. This process leverages the Long Short-Term Memory (LSTM) mechanism to enhance the model's capability to capture intricate relationships. Upon training and evaluating our enhanced model on the MS COCO dataset, we observed significant improvements in key evaluation metrics. The BLEU-1 and METEOR scores reached 73.01% and 28.81%, respectively, showcasing the effectiveness of the proposed EF mechanism in improving image description generation. © 2024 IEEE.

关键词： Data description

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C2F-CCPE: Coarse-to-Fine Cross-View Camera Pose Estimation

C2F-CCPE: Coarse-to-Fine Cross-View Camera Pose Estimation

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yong Tang Qiang Huang Yingying Zhu College of Computer Science and Software Engineering Shenzhen University China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Image-based cross-view localization often yields imprecise camera pose estimations due to the limited sampling density in the satellite image database. Current cross-view camera pose estimation approaches often overlook the importance of multi-scale features. In this paper, we introduce a novel coarse-to-fine cross-view camera pose estimation method (C2F-CCPE) that leverages multi-scale feature fusion and a localization and orientation feature fusion module (LOFFM) to enhance performance in localization and directional prediction. The proposed method outperforms state-of-the-art approaches on two benchmark datasets, addressing limitations in existing methods that focus on single-scale feature representations. And C2F-CCPE captures global and local information simultaneously, improving robustness against occlusions and enhancing precision in complex scenes. LOFFM further aggregates directional and positional information, enabling the network to deeply comprehend location features. Quantitative and qualitative experiments on two datasets not only demonstrate that our proposed method outperforms state-of-the-art models but also verify the effectiveness of our proposed method.

关键词： Location awareness Accuracy Databases Aggregates Pose estimation Benchmark testing Cameras Robustness Satellite images

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Weakly Supervised Myeloma Cells Segmentation based on Point Annotation

Weakly Supervised Myeloma Cells Segmentation based on Point ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Zhao, Jia Tong, Guanjie Qiu, Xinyun Su, Huaqiang Lei, Baiying Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen University Medical School Shenzhen China

ISBN: (纸本)9798350337488

Multiple Myeloma (MM) is a growing global health concern, and early diagnosis is crucial for effective treatment. Efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Microscopic images have high resolution, where cells are enormous and dense. Therefore, the annotation process is time-consuming and complex for tasks such as segmentation due to pixel-level marking. In this paper, we design an end-to-end weakly supervised myeloma cell segmentation framework based on point annotation. It can achieve accurate cell segmentation comparable to fully supervised methods while reducing the need for manual annotation, greatly shortening annotation time. Experimental results demonstrate that our method achieves 98% of its fully-supervised performance with only 10 annotated random points per instance, and outperforms the fully-supervised Mask RCNN. © 2023 IEEE.

关键词： Cell Segmentation Point Annotation Weakly-supervised

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Smart Grid and Blockchain Technology for Community-Based P2P Renewable Energy Trading 2

Smart Grid and Blockchain Technology for Community-Based P2P...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Sarwar, Muhammad Imran Maghrabi, Louai A. The Superior University Lahore Department of Computer Science and It Lahore54500 Pakistan College of Engineering University of Business and Technology Department of Software Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350373363

Current electric grids were designed a century ago for simple and limited needs. An electric grid helps the power generation company (PGC) supply electricity to homes and industrial consumers. Traditional electric grids have one-way interactions that make it difficult to cater to ever-changing and increased demand. The Smart Grids (SGs) introduced the bidirectional flow of electricity and the exchange of information between consumers and PGCs. SG is a combination of networks of communication, controls, computers, and automations that allow its integration with emerging technologies. Blockchain (BC) is a digital distributed ledger (DDL) that ensures the security, reliability and immutability of data. The integration of BC and SGs is an emerging idea that could also provide a platform for community-based electricity trading. This study aims to review community-based energy trading with BC-based SGs and discusses the first ever community-based peer-to-peer (P2P) solar energy trading project, Quartierstrom, by the Swiss Federal Office of Energy (SFOE), Switzerland. The findings of this study contribute a valuable reference to the knowledge body and enhance the comprehension of P2P energy trading for both new and experienced researchers, enabling them to identify new opportunities and challenges. © 2023 IEEE.

关键词： Blockchain

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Improved Text-Driven Human Motion Generation via Out-of-Distribution Detection and Rectification 12th

Improved Text-Driven Human Motion Generation via Out-of-Dis...

引用

12th International Conference on Computational Visual Media, CVM 2024

作者： Fu, Yiyu Zhao, Baoquan Lv, Chenlei Yue, Guanghui Wang, Ruomei Zhou, Fan School of Artificial Intelligence Sun Yat-sen University Zhuhai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9789819720941

Text-driven human motion generation is gaining momentum lately thanks to its great potential in shaping the new pathway of interactive computer graphics in the era of AI. Despite the enormous efforts made so far, existing methods still struggle to ensure fluidity and body coordination when generating motions, which seriously hinders its application in a wide spectrum of areas such as gaming, animation, and the emerging metaverse. One of the many causes is, that learning directly from motion data is prone to interference from noise within the data, resulting in reduced quality of the generated motions. In this study, we for the first time propose to promote text-to-motion generation via out-of-distribution detection in the embedding space. Leveraging the Z-score-based outlier detection algorithm, we apply masking to motion data within the motion encoder and replace target data with means, ensuring the consistency of data distribution. To verify the effectiveness of the proposed method, we have conducted extensive experiments on the widely used KIT-ML dataset. Experimental results indicate that compared to previous frameworks, our solution significantly improves the quality of text-driven human motion generation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Interactive computer graphics

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Research on Human Resource Management and Prediction Model Based on Neural Network Optimized by Genetic Algorithm 2

Research on Human Resource Management and Prediction Model B...

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2nd IEEE International Conference on Image Processing and computer Applications, ICIPCA 2024

作者： Qi, Zexuan Yang, Sihan He, Xinjian Shi, Desheng Xinjiang University of Science & Technology Xinjiang Korla China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Guangzhou Huali College Guangzhou China Modern Agricultural College Honghe Vocational and Technical College Yunnan Mengzi China

ISBN: (纸本)9798350360240

This paper investigates the application of artificial neural networks optimized based on genetic algorithms in human resource management in hospital enterprises and constructs a human resource management and predictive analysis model. The model aims to support enterprises in achieving the optimal allocation of human resources by mining their internal human resource data and revealing the intrinsic correlation between personnel structure, business type and future development strategy. The study adopts multiple regression analysis method and combines the artificial neural network optimized by genetic algorithm to construct a human resource demand prediction model. Taking the operational data of Hospital X as an example, the model predicts the number of human resources in the hospital and analyzes in depth the demand for staff types in each department. The results show that the model has high accuracy and reliability in predicting the variables of hospital revenue, wage level, R&D investment and number of personnel, with a prediction error range between 0.09% and 1.27%. The research in this paper provides strong data support for enterprises, which helps them to realize the rational allocation of human resources and strategic transformation, and then maintain their competitive position in the market. © 2024 IEEE.

关键词： Prediction models

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RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models 32

RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion...

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32nd International Joint Conference on Artificial Intelligence, IJCAI 2023

作者： Zhou, Xingchen He, Ying Richard Yu, F. Li, Jianqiang Li, You College of Computer Science and Software Engineering Shenzhen University China China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (纸本)9781956792034

The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. Our work leverages existing diffusion models to guide changes in the designated 3D content. Specifically, we semantically select the target object and a pre-trained diffusion model will guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF. Experiment results show that our algorithm is effective for editing 3D objects in NeRF under different text prompts, including editing appearance, shape, and more. We validate our method on both real-world datasets and synthetic-world datasets for these editing tasks. Please visit https://*** for a better view of our results. © 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Semantics

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning 27

Video2Reward: Generating Reward Function from Videos for Leg...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781643685489

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. © 2024 The Authors.

关键词： Robots

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