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4th International Conference on artificial intelligence and computer Engineering, ICAICE 2023

作者： Jiang, Deyue Chen, Liming Lin, Ziqiong Zhang, Wenjie Zheng, Yifeng Key Lab of Data Science and Intelligence Application School of Computer Sciences Minnan Normal University Zhangzhou China Zhangzhou Affiliated Hospital of Fujian Medical University Zhangzhou China

ISBN: (纸本)9798400708831

To address the bottleneck of energy performance, this paper considers an energy harvesting (EH)-enabled Mobile Edge Computing (MEC) system with contract users (CUs) and random users (RUs). The resource demands of CUs are known in advance, while RUs are mobile and randomly arriving. We propose a resource reservation framework based on contract theory with the goal of maximizing the utility of agent node (AN) under information asymmetry and demand uncertainty. First, for symmetric information scenario, the relationship between the optimal resource reservation and the demands of CUs is obtained. Then, for asymmetric information scenario, a contract-based resource reservation scheme is proposed to motivate wireless access point (WAP) to share the demand information of CUs and take part of over-reservation risk cost. Based on this, the theoretical expression for the optimal reserved resource is obtained by exploring the features of the feasible contracts under individual rationality and incentive compatibility constraints. The effectiveness of proposed contract-based resource reservation scheme is validated through simulation results. © 2023 ACM.

关键词： Energy harvesting

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Random Forest based Coral Reef Health Classification Using Satellite Imagery and Geographical Information System

Random Forest based Coral Reef Health Classification Using S...

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2024 International Conference on Intelligent Algorithms for Computational intelligence Systems, IACIS 2024

作者： Jagadish, Sripelli Pradeep Kumar, S. Riad Al-Fatlawy, Ramy Ameer, S. Abdul Kiran, J. Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Ahl Al Bayt University Karbala Iraq Reva University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350360660

Understanding the coral reefs' health in varied climate changes is challenging with the traditional methods. To address this, the proposed research developed a Machine Learning (ML) framework for effective classification using Geographical Information System (GIS). The data in this research, is collected from Great Barrier Reef (GBR) in Australia which includes the satellite Landsat 8 images for monitoring coral reefs condition. The data is pre-processed using normalization and scatter plots to analyse various coral reef classes using the training samples. The images features are extracted using Gray-Level Co-occurrence Matrix (GLCM) and analysed the spectral indices to capture specific type of information about the coral reefs. The coral reef images are then segmented using Pixel Based (PB) and Object Based (OB) segmentation to achieve effective Coral Reef Health Classification (CRHC). To analyse the large amount of data with the non-linear relationships between the variables, the Random Forest (RF) classifier is used. The experimental results showed that the proposed RF-CRHC achieved better accuracy with 99.59% and precision with 97.79% when compared to the existing methods Multi-Local Perception Network (ML-Net) and Clustering based RF. © 2024 IEEE.

关键词： Landsat

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Predicting Pedestrian Movement in Unsignalized Crossings: A Contextual Cue-Based Approach

Predicting Pedestrian Movement in Unsignalized Crossings: A ...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Kaliprasana Muduli Vikas Sahu Indrajit Ghosh Civil Engineering Department Indian Institute of Technology Roorkee Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Civil Engineering Department & Joint Faculty Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India

To ensure safe and secure coexistence between pedestrians and autonomous vehicles (AVs), AVs must be able to anticipate pedestrian behavior and respond to it. This research gathers video data from real traffic scenes to predict pedestrian crossing intentions at unsignalized crossings. computer vision techniques such as YOLOv4, Deep SORT, and perspective transformation are employed for road user detection, tracking, and mapping image coordinates to world coordinates to prepare trajectory datasets. Using trajectory data, several features influencing pedestrian intention like walking speed, location in the road environment, count and direction of approaching traffic, speed and type of closest approaching vehicle upstream, etc., are extracted. The dataset for this study was obtained by analyzing 1,411 pedestrians, resulting in 223,136 samples. To predict pedestrian crossing intentions, LSTM and Bi-LSTM with an attention mechanism model were built and trained to anticipate the pedestrian crossing intention at unsignalized crossing. The proposed model successfully combined the characteristics and surrounding dynamics of pedestrians to produce accurate predictions, Bi-LSTM with an attention mechanism outperformed LSTM, achieving an AUC of 95.3%, 91.1%, 89.2%, 87.5%, and 84% on the testing dataset at unsignalized crossing on the 0.6 sec, 1.2 s, 1.8 s, 2.4 s, and 3 s time horizons. These outcomes can be used to improve Connected and Autonomous Vehicle (CAV) technologies, infrastructure-to-vehicle (I2V) connectivity, and driver assistance systems to enhance pedestrian safety while navigating through pedestrian crosswalks.

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Corruption Robust Dynamic Pricing in Liner Shipping under Capacity Constraint

Corruption Robust Dynamic Pricing in Liner Shipping under Ca...

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International Conference on data Engineering

作者： Yongyi Hu Xueyan Lit Xikai Wei Yangguang Shi Xiaofeng Gao Guihai Chen Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University China School of Computer Science and Technology Shandong University China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The shipping industry has irreplaceable importance in international trade and commerce. How to dynamically price different containers has long been a hot topic due to its direct connection to the final revenue. Two critical observations have been made after a comprehensive survey within a top liner company, China Ocean Shipping Company (COSCO). (1) Each type of container carried on a liner ship has its maximum capacity. (2) The sales volume is occasionally subject to huge fluctuations due to rare uncontrollable factors, such as COVID. Based on the above two points and the liner routine's periodic nature, we model the dynamic pricing problem as an episodic MDP model integrating with both capacity constraints and adversarial corruption, named C3-MDP. To maximize the cumulative revenue in the C3-MDP setting, we propose a programming framework, Bonus-Exploration based Episodic Programming (BEEP). This framework can directly accommodate the linear programming algorithm to form the algorithm BEEP-LP, which provides the episode-wise greedy optimal strategy. Furthermore, a detailed regret analysis is provided, showing that BEEP-LP has a regret that is sublinear in the number of episodes. Combining deep techniques, we also present an approximation algorithm BEEP-DQN in the case of large state-action space to strike a balance between the running time and the performance. Abundant experiments based on real container sales data exhibit the rationality of C3-MDP and the effectiveness of BEEP.

关键词： Industries Surveys Pandemics Heuristic algorithms Oceans Pricing Companies

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Investigating the 3D cardiomyocyte arrangement in human interventricular septum sample using X-ray phase-contrast microtomography

Investigating the 3D cardiomyocyte arrangement in human inte...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Wenfeng Li Zhaorui Li Yongbin Du Shunli Wang Institute of Big Data and Artificial Intelligence Zhengzhou University of Science and Technology Zhengzhou China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China School of Electrical and Control Engineering Heilongjiang University of Science and Technology Harbin China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

A good knowledge of the three-dimensional cardiomyocyte arrangement in interventricular septum is crucial for understanding both ventricle’s functions. In this preliminary study, we investigate the local myocardial architecture in a human interventricular septum sample imaged by X-ray phase-contrast micro-tomography. This imaging modality allows us to visualize the complex structure of myocardial tissue at high resolution. We first cut the reconstruction data into sub-volume of 32×32×32 voxels (i.e. 112×112×112 μm 3 ) in size, and compute the elevation angle of the local cardiomyocyte using Fast Fourier Transform based method. Then, we describe the distribution of cardiomyocyte elevation angle in the sample globally and locally. We note that, in part of regions, the running of cardiomyocyte aggregates is winding. Though the function of this specific cardiomyocyte arrangement manner is unclear, its existence is inspiring and merits further investigation.

关键词： Three-dimensional displays Image resolution Fast Fourier transforms Windings Imaging data visualization Myocardium Signal processing X-ray imaging Image reconstruction

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A Hybrid Feature Integration Network for Robust Person Re-Identification 5

A Hybrid Feature Integration Network for Robust Person Re-Id...

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5th International Conference on computer, Big data and artificial intelligence, ICCBD+AI 2024

作者： Zhou, Mengting Ouyang, Xinyu Lian, Guoyun Du, Jingyu Song, Qiqi Yang, Jinfeng Shenzhen Polytechnic University Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China University of Science and Technology Liaoning School of Electronic and Information Engineering Anshan China

ISBN: (纸本)9798331533991

In the task of person re-identification (ReID), cross-view and cross-scale feature representation is crucial for model accuracy. To address the limitations of traditional deep learning methods in feature representation and input robustness, this paper proposes a novel Hybrid Feature Integration Network (HFI-Net), which combines Convolutional Neural Networks and Vision Transformers. It introduces a Unified Integration Module (UIM) and an Enhanced Fusion Attention Module (EFAM). UIM implements a weighted mechanism for multi-view feature fusion, while EFAM, based on channel and spatial attention mechanisms, integrates convolution and feature mixing operations to enhance feature discrimination. By jointly optimizing multiple loss functions, HFI-Net efficiently integrates multi-view and multi-scale information. Experiments on mainstream datasets such as Market-1501 and DukeMTMC-ReID demonstrate significant improvements in Rank-1 and mAP, validating the model's effectiveness and robustness. © 2024 IEEE.

关键词： Convolutional neural networks

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Can Language Models Serve as Temporal Knowledge Bases?

Can Language Models Serve as Temporal Knowledge Bases?

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Ruilin Zhao, Feng Xu, Guandong Zhang, Sixiao Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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BEAVERTAILS: towards improved safety alignment of llm via a human-preference dataset 23

BEAVERTAILS: towards improved safety alignment of llm via a ...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jiaming Ji Mickel Liu Juntao Dai Xuehai Pan Chi Zhang Ce Bian Boyuan Chen Ruiyang Sun Yizhou Wang Yaodong Yang Institute for Artificial Intelligence Peking University CFCS School of Computer Science Peking University Institute for Artificial Intelligence Peking University and CFCS School of Computer Science Peking University

In this paper, we introduce the BEAVERTAILS dataset, aimed at fostering research on safety alignment in large language models (LLMs). This dataset uniquely separates annotations of helpfulness and harmlessness for question-answering pairs, thus offering distinct perspectives on these crucial attributes. In total, we have gathered safety meta-labels for 333,963 question-answer (QA) pairs and 361,903 pairs of expert comparison data for both the helpfulness and harmlessness metrics. We further showcase applications of BeaverTails in content moderation and reinforcement learning with human feedback (RLHF), emphasizing its potential for practical safety measures in LLMs. We believe this dataset provides vital resources for the community, contributing towards the safe development and deployment of LLMs. Our project page is available at the following URL: https://***/view/pku-beavertails.

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Smart Personal Protective Equipment in Ambulance Services with IoT Integration for Safety 2

Smart Personal Protective Equipment in Ambulance Services wi...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Senkamalavalli, R. Merlin Livingston, L.M. Guruva Reddy, Elangovan Basavaraddi, Chethan Chandra S. Latha, N. Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur Karnataka Tumkur India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Internet of Things (IoT) technology has improved ambulance safety and efficiency. In ambulance services, Smart PPE (Personal Protective Equipment) improves patient and healthcare worker safety via IoT connectivity. In crises, ambulances are vital. However, healthcare professionals risk contagious infections and physical damage. Though vital, traditional PPE lacks real-time monitoring and communication, leaving healthcare workers exposed. These issues are addressed with Smart PPE using IoT. This novel invention adds sensors and communication devices to paramedic and EMT PPE. These sensors measure air quality, temperature, and the wearer's heart and body temperature. A centralized system can remotely monitor healthcare personnel's safety and well-being using real-time data. Smart PPE has two-way communicators and emergency alert systems. Emergency coordination is improved by ambulance crews and dispatch centers communicating seamlessly. The Smart PPE can automatically inform the team and dispatch in urgent situations, increasing response times and patient outcomes. IoT enables predictive maintenance and data analytics. Trends and dangers may be identified from gathered data, allowing proactive risk mitigation. Smart PPE can measure use and remind users to maintain and replace equipment, assuring dependability when required most. By continually monitoring conditions, allowing real-time communication, and enabling data-driven decision-making, this unique technology improves ambulance services and reduces dangers to devoted personnel. © 2023 IEEE.

关键词： Ambulance Services Efficiency Improvement Emergency Healthcare Real-time Alerts Safety Enhancement

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Around2center: Self-Supervised Denoising from the Around to the Center Using Masks

SSRN

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SSRN 2024年

作者： Zhang, Yaru Liu, Jiantao Xu, Guoqing Han, Yuxi School of Artificial Intelligence Anhui Unversity of Science and Technology Anhui Huainan232001 China

In image denoising, particularly for applications like coal mine imaging, acquiring paired clean-noisy datasets is often prohibitively expensive. While self-supervised learning promises to mitigate reliance on manual labels, generating meaningful supervisory signals remains a challenge. Existing masking strategies, while augmenting training data diversity, can alter noise structure and lead to edge effects. To address these challenges, we proposed an framework named Around2center for denoising using only a single noisy image. Firstly, to enhance local feature extraction and accentuate noise-signal boundaries, we partitioned the input image into multiple scales. Then, we employed a novel edge-corner masker to strategically mask the image, enabling targeted processing of specific regions while mitigating identity mapping and preserving relevant information. A mask integrator innovatively fused the denoised image and masked plates, organically combining local and global information to effectively address denoising tasks in diverse scenarios, including challenging coal underground mine images. Notably, the generated integrated image served as a robust self-supervised label, eliminating the need for paired clean-noisy datasets. Finally, we provided a theoretical analysis of the loss function’s convergence properties, ensuring the framework’s soundness and efficacy. Extensive quantitative and qualitative evaluations demonstrated the superior denoising performance of our proposed method on both natural and coal underground mine image datasets. © 2024, The Authors. All rights reserved.

关键词： Coal

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