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arXiv 2025年

作者： Tang, Yining Luo, Qihang Han, Runchao Niu, Jianyu Feng, Chen Zhang, Yinqian Research Institute of Trustworthy Autonomous Systems The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Babylon Labs United States Blockchain@UBC The School of Engineering The University of British Columbia Okanagan Campus KelownaBC Canada

With the advancement of blockchain technology, chained Byzantine Fault Tolerant (BFT) protocols have been increasingly adopted in practical systems, making their performance a crucial aspect of the study. In this paper, we introduce a unified framework utilizing Markov Decision Processes (MDP) to model and assess the performance of three prominent chained BFT protocols. Our framework effectively captures complex adversarial behaviors, focusing on two key performance metrics: chain growth and commitment rate. We implement the optimal attack strategies obtained from MDP analysis on an existing evaluation platform for chained BFT protocols and conduct extensive experiments under various settings to validate our theoretical results. Through rigorous theoretical analysis and thorough practical experiments, we provide an in-depth evaluation of chained BFT protocols under diverse attack scenarios, uncovering optimal attack strategies. Contrary to conventional belief, our findings reveal that while responsiveness can enhance performance, it is not universally beneficial across all scenarios. This work not only deepens our understanding of chained BFT protocols, but also offers valuable insights and analytical tools that can inform the design of more robust and efficient protocols. Copyright © 2025, The Authors. All rights reserved.

关键词： Delay tolerant networks

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mmMC: A Contactless Wood Moisture Content Measurement System based on COTS FMCW mmWave Radar

mmMC: A Contactless Wood Moisture Content Measurement System...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Xueyang Hu Tian Liu Minarul Islam Tao Shu Department of Broadband Communication Peng Cheng Laboratory China Institute of Agricultural Equipment Zhejiang Academy of Agricultural Sciences Hangzhou China Department of Computer Science and Software Engineering Auburn University USA

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Estimation of wood moisture content (MC) is a fundamental aspect of woodworking, construction, and various other industries that rely on the versatile properties of wood. In this paper, we present mmMC, a novel system that estimates the MC of wood by a single commercial off-the-shelf (COTS) mmWave radar, which provides accurate end-to-end real-time wood moisture measurement results while being non-invasive, portable, and flexible in deployment. The proposed system uses a novel target reflection feature (TRF) to determine the reflectivity of the wood, thereby correlating it with specific MC levels. To robustly and accurately estimate the TRF of the wood from the mmWave signal, a signal processing pipeline is proposed. Specifically, an object detection and signal extraction module is designed to resolve the range of the object and eliminate multipath effects of the original signal. Then, a multiple chirps/antennas signals processing module is proposed to obtain a stable TRF from the extracted signals by using signals from different antennas and chirps. The TRF is associated with the MC of wood by a regression to enable real-time MC estimation. Through extensive real-world experiments, we have demonstrated that the proposed system has high accuracy and reliability.

关键词： Reflectivity Accuracy Radar measurements Moisture measurement Pipelines Moisture Reflection Real-time systems Sensors Millimeter wave communication

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An Interpretable Mechanical Fault Diagnosis Approach Based on Dynamics Embedding Neural Network and Contrastive Learning for Label-Free Vibration Signals

SSRN

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SSRN 2023年

作者： Sun, Shilong Peng, Tengyi Zhou, Yu Zhang, Xiao School of Mechanical Engineering and Automation Harbin Institute of Technology Guangdong Shenzhen518055 China College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Computer Science South-Central Minzu University Hubei Wuhan430074 China

The industrial vibration signals usually have no labels, and the intelligent fault diagnosis has low interpretability for the results. The supervised learning algorithm, for example, deep neural networks, cannot be directly applied to distinguish different vibration faults. This study proposes an automatic fault diagnosis method based on the Bootstrap Your Own Latent contrastive learning and dynamical systems model discovery algorithm (BYOLDIS), which can deal with the unlabeled signals and explain the diagnosis results. Firstly, deriving the differential equations matrix to develop the faulty bearing dynamical systems. Secondly, investigating the contrastive learning network with a time-delay embedding matrix to reconstruct the coordinates of the fault-dynamical system. Finally, constructing a library of fault machine dynamic polynomial equations can offer physical model-based prior constraints to reduce the dependence of neural networks on data. Therefore, the above three steps can make BYOLDIS achieve effective label-free and explainable mechanical fault automatic diagnosis. The simulated and experimental studies are conducted to verify the validity and robustness of the proposed method. The case studies demonstrate that BYOLDIS can achieve an accurate bearing fault diagnosis and provide a dynamic explanation for the diagnostic results. Therefore, the proposed BYOLDIS is a promising diagnostic method for processing unlabeled vibrational data. © 2023, The Authors. All rights reserved.

关键词： Failure analysis

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A Transformation Model for Different Granularity Linguistic Concept Formal Context 2nd

A Transformation Model for Different Granularity Linguistic ...

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2nd International Conference on Artificial Intelligence Logic and Applications, AILA 2022

作者： Kang, Ning Pang, Kuo Zou, Li Sun, Meiqiao Software Engineering Institute East China Normal University Shanghai200062 China Information Science and Technology College Dalian Maritime University Dalian116026 China School of Computer Science and Technology Shandong Jianzhu University Jinan250102 China School of Computer and Information Technology Liaoning Normal University Dalian116081 China

ISBN: (纸本)9789811975097

Due to certain differences in people's knowledge level and personal preferences, for the description of the same thing phenomenon, different people usually choose different granularity of linguistic term sets to make judgments, so it is necessary to propose a transformation method for linguistic concept formal context with different granularity. In this paper, we firstly define the normalized distance between multi-granularity linguistic formal contexts and linguistic terms for different granularity linguistic concept formal context. Then, the normalized distance from linguistic terms to intermediate linguistic terms is kept constant to realize the transformation for different granularity linguistic concept formal context. Finally, under different threshold constraints, we achieve dynamic transformation of formal contexts of linguistic values by adjusting the thresholds. The transformation method process is reversible and it can avoid information loss. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linguistics

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What is the Expected Transient Behavior of Opinion Evolution for Two Communities?

arXiv

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arXiv 2023年

作者： Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden

We study the transient behavior of a gossip model, in which agents randomly interact pairwise over a weighted graph with two communities. Edges within each community have identical weights, different from the weights between communities. It is shown that, at the early stage of the opinion evolution, the expected agent states in the same community have identical sign, despite influence of stubborn agents. Moreover, it is shown that the expected states of the agents in the same community concentrate around the initial average opinion of that community, if the weights within communities are larger than between. In contrast, if the edge weights between communities are larger, then the expected states of all agents concentrate around everyone’s initial average opinion. Different from the traditional asymptotic analysis in the opinion dynamics literature, these results focus on the initial phase of opinion evolution and establish a correspondence between community structure and transient behavior of the gossip model. The results are illustrated by numerical examples. © 2023, CC BY.

关键词： Asymptotic analysis

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Metrics in a Dynamic Gaze Environment

Metrics in a Dynamic Gaze Environment

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IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Aidan Lochbihler Bruce Wallace Kathleen Van Benthem Chris Herdman Will Sloan Kirsten Brightman Rafik Goubran Frank Knoefel Shawn Marshall Systems and Computer Engineering Carleton University Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute SAM Innovation Hub Ottawa Canada Department of Cognitive Science Carleton University Ottawa Canada SAM Innovation Hub Ottawa Canada Bruyère Research Institute Elisabeth-Bruyère Hospital Systems and Computer Engineering Carleton University SAM 3 Innovation Hub Ottawa Canada Ottawa Hospital Carleton University

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

The development of algorithms for Areas of Interest (AOIs) segmentation represents a significant leap in dynamic gaze data analysis. These algorithms enable the transformation of two-dimensional raw data into meaningful one-dimensional metrics to unlock insights into human attention. Unlike traditional methods that focus on static gaze points in controlled environments, this research introduces novel algorithms for interpreting eye-tracking data in dynamic settings. The proposed techniques specifically address the challenge of processing gaze information in scenarios with changing frames of reference, such as driving, where understanding visual attention is critical for safety and to improve performance. This paper presents two innovative metrics designed to quantify the focus areas, duration, and scanning patterns of users in dynamic driving conditions. By employing advanced transfer learning for improved multiclass image segmentation methods, a percentage time AOI metric can be created. This metric gives insight into the amount of time a driver spends fixating on a given area. An enhancement in gaze pattern determination, achieved through class segmentation, provides a clearer depiction of drivers' true gaze patterns. This nuanced approach suggests that comprehensive attention assessment cannot rely solely on AOI metrics but requires the integration of gaze pattern derived metrics for a more accurate determination of attention-related scores. The introduction of these innovative gaze analysis methods marks a pivotal advancement in cognitive research and has practical applications. These metrics promise significant contribution to driver safety and attentiveness assessment methodologies.

关键词： Measurement Image segmentation Visualization Data analysis Heuristic algorithms Transfer learning Gaze tracking

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Better Language Models of Code through Self-Improvement

arXiv

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arXiv 2023年

作者： To, Hung Quoc Bui, Nghi D.Q. Guo, Jin Nguyen, Tien N. FPT Software AI Center Department of Computer Science Fulbright University Viet Nam Viet Nam School of Computer Science McGill University Canada Mila - Quebec AI Institute Canada School of Engineering and Computer Science The University of Texas Dallas United States

Pre-trained language models for code (PLMCs) have gained attention in recent research. These models are pre-trained on large-scale datasets using multi-modal objectives. However, fine-tuning them requires extensive supervision and is limited by the size of the dataset provided. We aim to improve this issue by proposing a data augmentation framework using knowledge distillation. Our framework utilizes knowledge gained during the pre-training and fine-tuning stage to generate pseudo data, which is then used as training data for the next step. We incorporate this framework into the state-of-the-art language models, such as CodeT5, CodeBERT, and UnixCoder. The results show that our framework significantly improves PLMCs’ performance in sequence-generation tasks, such as code summarization and code generation in the CodeXGLUE benchmark. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Multiple Time-Periods POI Recommendation Model Based on Distributed ELM

Multiple Time-Periods POI Recommendation Model Based on Dist...

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Zhen Zhang Yaqi Gong Xiangguo Zhao Jun Chen Jianyu Ren Jintao Ouyang School of Electrical Engineering Yingkou Institute of Technology Yingkou China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China School of Software Northeastern University Shenyang China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

To solve the problems of low efficiency of algorithm execution and not fully considering the POI needs of users in different time-periods in point-of-interest (POI) recommendation. In this paper, propose a Multiple time-periods POI recommendation model MP-DELM based on distributed extreme learning machine (DELM). According to the user's different periods and different types of POI access requirements, the data set was processed in different periods; extract POI preference features: distance preference feature, personalized preference feature, popularity preference feature, and friend preference feature, in which for the friend preference feature, social friend feature and the trajectory feature are integrated for the first time, and a new trajectory friend similarity calculation method is proposed; in order to improve the efficiency of POI recommendation, improve DELM classifier. And the online sequential extreme learning machine (OS-ELM) is used to update training data in real time. Finally, we prove that the MP-DELM recommendation model can effectively improve the accuracy and efficiency of POI recommendation in the real datasets.

关键词： Extreme learning machines Social networking (online) Computational modeling Training data Feature extraction Real-time systems Vectors

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Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video

arXiv

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arXiv 2024年

作者： Fan, Junkai Wang, Kun Yan, Zhiqiang Chen, Xiang Gao, Shangbing Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Huaiyin Institute of Technology China

In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enhanced depth estimation improves dehazing via the atmospheric scattering model (ASM), while superior dehazing contributes to more accurate depth estimation through the brightness consistency constraint (BCC). To tackle these intertwined tasks, we propose a novel depth-centric learning framework that integrates the ASM model with the BCC constraint. Our key idea is that both ASM and BCC rely on a shared depth estimation network. This network simultaneously exploits adjacent dehazed frames to enhance depth estimation via BCC and uses the refined depth cues to more effectively remove haze through ASM. Additionally, we leverage a non-aligned clear video and its estimated depth to independently regularize the dehazing and depth estimation networks. This is achieved by designing two discriminator networks: DMFIR enhances high-frequency details in dehazed videos, and DMDR reduces the occurrence of black holes in low-texture regions. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art techniques in both video dehazing and depth estimation tasks, especially in real-world hazy scenes. Project page: https://***/projectpage/DCL/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Demulsification

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Smart System to Support Hearing Impaired Students in Tamil

Smart System to Support Hearing Impaired Students in Tamil

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2023 International Research Conference on Smart Computing and systems engineering, SCSE 2023

作者： Thahseen, Ahamed Tissera, Wishalya Vidhanaarachchi, Samitha Aaron, N. Rajapaksha, D.C.N. Fernando, P.V. Dias, Thisuru Fernando, Thatamathy Sri Lanka Institute of Information Technology New Kandy Road Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology New Kandy Road Department of Computer Science & Software Engineering Malabe Sri Lanka YMCA Young Men's Christian Association 26/4A Boundary Road South Batticaloa Sri Lanka

ISBN: (纸本)9798350341454

This research paper introduces a groundbreaking smart system designed to assist hearing-impaired students in comprehending spoken Tamil, the second language in Sri Lanka. The system addresses the challenges faced by these students by incorporating cutting-edge deep learning techniques, including Convolutional Neural Networks, Hidden Markov Models, and Recurrent Neural Networks, for efficient feature extraction and sequence modeling within an Automatic Speech Recognition framework. Additionally, the paper proposes a novel method for automatically recognizing Tamil Sign Language gestures using advanced hand gesture recognition algorithms and a comprehensive dataset of Tamil Sign Language. The system encompasses four primary classification approaches, enabling the conversion of Tamil Sign Language to Text. Text to Tamil Sign Language, lip reading to Tamil Sign Language, Normal voice to Tamil Sign Language -Sign Language to Normal Voice and physical object identification to both text and Tamil Sign Language. Notably, the system achieves remarkable results, boasting an impressive accuracy rate of 0.99% surpassing existing Automatic Speech Recognition and Text-to-Speech systems. This significant breakthrough holds immense potential in enhancing the learning experience of hearing-impaired students in Tamil-speaking regions. Furthermore, the system's adaptability allows for future expansion to support additional languages, making it highly versatile for diverse educational and communication settings. © 2023 IEEE.

关键词： Students

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