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Attention-aligned mean-teacher learning for unsupervised domain adaptive person re-ID

Science China Technological Sciences 2025年

作者： You LYU Zhen ZHANG Guoliang KANG Wei WEI Institute of Artificial Intelligence Beihang University Key Laboratory of Mathematics Informatics Behavioral Semantics Ministry of Education Beihang University State Key Laboratory of Software Development Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing Beihang University School of Automation Science and Electrical Engineering Beihang University AVICAS Generic Technology Co. Ltd Zhongguancun Laboratory School of Mathematics Sciences Beihang University

In recent years, artificial intelligence has fueled the development of numerous applications [1, 2]. Person re-identification (re-ID) is a typical artificial intelligence system designed to automatically retrieve imag...

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CWT IoT Device for Detecting Rare Events of Orchid Disease

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IEEE Internet of Things Journal 2024年第12期11卷 22830-22842页

作者： Chen, Claire Y. T. Lin, Yi-Bing Chen, Wen-Liang Wu, Kuan-Chieh Lin, Yun-Wei Sun, Edward W. Liu, Chun-You Montpellier Business School Montpellier34090 France Department Of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan China Medical University Taichung 404 China Miin Wu School Of Computing National Cheng Kung University Tainan70101 Taiwan The Research Center For Information Technology Innovation Academia Sinica Taipei115 Taiwan College Of Biological Science And Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan College Of Artificial Intelligence National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Talence33405 France Department Of Biomedical Informatics China Medical University Taichung404 Taiwan

Existing non-image-based solutions for detecting Phalaenopsis orchid diseases rely on time-domain analysis of environmental conditions. These solutions are suitable for loosely controlled open farm fields. Conversely, the diseases are infrequent events in Internet of Things (IoT)-based smart orchid greenhouses, where environmental conditions are well-controlled. In tightly controlled settings, standard time domain methods fall short in detecting subtle changes in environmental conditions during rare orchid disease events. We observed that the influence of the time series of temperature and humidity on orchid fungus within the well-controlled greenhouse is not readily apparent, and the time-frequency coefficients of temperature and humidity must be used to detect orchid disease in this rare event case. In order to address this challenge, we propose OrchidTalk, an IoT-based deep learning platform, which employs a time-frequency domain analysis using the continuous wavelet transform (CWT) filter, for the purpose of detecting rare events of orchid diseases. Through a nontrivial process, we select the Gaussian wavelet with four derivatives to serve as the mother wavelet of the CWT in the data extraction phase, and invent the CWT IoT device to translate the time-domain IoT raw data to the time-frequency domain IoT data. The resulting CWT features are used as inputs to the Orchid-3D model (3-D ConvLSTM), which significantly outperforms the previously proposed Orchid-1D (CNN and LSTM) and Orchid-2D (2-D ConvLSTM) models. For the rare event detection of 0.2% of sick orchid plants, OrchidTalk achieves a recall of 0.902, precision of 0.92, accuracy of 0.99, and an F1-score of 0.911. To the best of our knowledge, this represents the highest prediction performance achieved for a well-controlled orchid greenhouse. © 2014 IEEE.

关键词： Time domain analysis

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Maximum Entropy Optimal Density Control of Discrete-time Linear Systems and Schrödinger Bridges

arXiv

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arXiv 2022年

作者： Ito, Kaito Kashima, Kenji The School of Computing Tokyo Institute of Technology Yokohama Japan The Graduate School of Informatics Kyoto University Kyoto Japan

We consider an entropy-regularized version of optimal density control of deterministic discrete-time linear systems. Entropy regularization, or a maximum entropy (MaxEnt) method for optimal control has attracted much attention especially in reinforcement learning due to its many advantages such as a natural exploration strategy. Despite the merits, high-entropy control policies induced by the regularization introduce probabilistic uncertainty into systems, which severely limits the applicability of MaxEnt optimal control to safety-critical systems. To remedy this situation, we impose a Gaussian density constraint at a specified time on the MaxEnt optimal control to directly control state uncertainty. Specifically, we derive the explicit form of the MaxEnt optimal density control. In addition, we also consider the case where density constraints are replaced by fixed point constraints. Then, we characterize the associated state process as a pinned process, which is a generalization of the Brownian bridge to linear systems. Finally, we reveal that the MaxEnt optimal density control gives the so-called Schrödinger bridge associated to a discrete-time linear system. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear systems

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Security in Post-Quantum Era: A Comprehensive Survey on Lattice-Based Algorithms

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IEEE Access 2025年 13卷 89003-89024页

作者： Nguyen, Hien Huda, Samsul Nogami, Yasuyuki Nguyen, Tuy Tan Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States Okayama University Interdisciplinary Education and Research Field Okayama700-8530 Japan Okayama University Graduate School of Environmental Natural Science and Technology Life Okayama700-8530 Japan

Lattice-based post-quantum cryptography (PQC) has attracted significant attention as a promising solution to the security challenges posed by quantum computing. Unlike traditional cryptographic algorithms, lattice-based schemes are expected to remain secure even in the face of quantum attacks, making them essential for future-proofing data security. Despite their strong theoretical foundations, lattice-based schemes face several practical challenges, particularly in optimizing performance and scalability for real-world applications. This survey provides a novel taxonomy that categorizes lattice-based PQC designs, emphasizing computational paradigms and security considerations. We systematically evaluated lattice-based PQC implementations on both software platforms, including central processing units and graphics processing units, as well as hardware platforms like field-programmable gate arrays and application-specific integrated circuits, highlighting their strengths and limitations. In addition, we explored the practical applications of lattice-based cryptography in fields such as secure communication, critical infrastructure, privacy-preserving data analytics, artificial intelligence, and trust and authentication systems. By offering a comprehensive overview of the current state of lattice-based PQC, this survey aims to provide valuable insights into the ongoing advancements and future research directions in the field as we transition to a post-quantum era. © 2013 IEEE.

关键词： Quantum cryptography

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Deep Learning Based Secure Transmissions for the UAV-RIS Assisted Networks: Trajectory and Phase Shift Optimization

Deep Learning Based Secure Transmissions for the UAV-RIS Ass...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Jiawei Wang, Dawei Zhang, Jiankang Alfarraj, Osama He, Yixin Al-Rubaye, Saba Yu, Keping Mumtaz, Shahid Northwestern Polytechnical University School of Electronics and Information Xi'an710072 China Bournemouth University Department of Computing and Informatics United Kingdom Jiaxing University College of Information Science and Engineering Jiaxing314001 China Cranfield University School of Aerospace Transport and Manufacturing United Kingdom King Saud University Community College Department of Computer Science Riyadh11437 Saudi Arabia Hosei University School of Science and Engineering Tokyo184-8584 Japan Silesian University of Technology Department of Applied Informatics Gliwice44-100 Poland Nottingham Trent University Departement of Computer Sciences United Kingdom

ISBN: (纸本)9798350351255

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Robust Pseudo-label Learning with Neighbor Relation for Unsupervised Visible-Infrared Person Re-Identification 24

Robust Pseudo-label Learning with Neighbor Relation for Unsu...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yin, Xiangbo Shi, Jiangming Zhang, Yachao Lu, Yang Zhang, Zhizhong Xie, Yuan Qu, Yanyun School of Informatics Xiamen University Xiamen China Institute of Artificial Intelligence Xiamen University Xiamen China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China School of Computer Science and Technology East China Normal University Shanghai China East China Normal University Chongqing Institute of East China Normal University Shanghai China

ISBN: (纸本)9798400706868

Unsupervised Visible-Infrared Person Re-identification (USVI-ReID) presents a formidable challenge, which aims to match pedestrian images across visible and infrared modalities without any annotations. Recently, clustered pseudo-label methods have become predominant in USVI-ReID, although the inherent noise in pseudo-labels presents a significant obstacle. Most existing works primarily focus on shielding the model from the harmful effects of noise, neglecting to calibrate noisy pseudo-labels usually associated with hard samples, which will compromise the robustness of the model. To address this issue, we design a Robust Pseudo-label Learning with Neighbor Relation (RPNR) framework for USVI-ReID. To be specific, we first introduce a straightforward yet potent Noisy Pseudo-label Calibration module to correct noisy pseudo-labels. Due to the high intra-class variations, noisy pseudo-labels are difficult to calibrate completely. Therefore, we introduce a Neighbor Relation Learning module to reduce high intra-class variations by modeling potential interactions between all samples. Subsequently, we devise an Optimal Transport Prototype Matching module to establish reliable cross-modality correspondences. On that basis, we design a Memory Hybrid Learning module to jointly learn modality-specific and modality-invariant information. Comprehensive experiments conducted on two widely recognized benchmarks, SYSU-MM01 and RegDB, demonstrate that RPNR outperforms the current state-of-the-art GUR with an average Rank-1 improvement of 10.3%. The code is available at https://***/XiangboYin/RPNR. © 2024 ACM.

关键词： Unsupervised learning

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FarExStance: Explainable Stance Detection for Farsi

arXiv

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arXiv 2024年

作者： Zarharan, Majid Hashemi, Maryam Behroozrazegh, Malika Eetemadi, Sauleh Pilehvar, Mohammad Taher Foster, Jennifer School of Computing Dublin City University Ireland Iran University of Science and Technology Iran Dadmatech Tehran Iran School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University United Kingdom

We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post towards that claim, and an extractive explanation which provides evidence for the stance label. We compare the performance of a fine-tuned multilingual RoBERTa model to several large language models in zero-shot, few-shot, and parameter-efficient fine-tuned settings on our new dataset. On stance detection, the most accurate models are the fine-tuned RoBERTa model, the LLM Aya-23-8B which has been fine-tuned using parameter-efficient fine-tuning, and few-shot Claude-3.5-Sonnet. Regarding the quality of the explanations, our automatic evaluation metrics indicate that few-shot GPT-4o generates the most coherent explanations, while our human evaluation reveals that the best Overall Explanation Score (OES) belongs to few-shot Claude-3.5-Sonnet. The fine-tuned Aya-32-8B model produced explanations most closely aligned with the reference explanations. © 2024, CC BY-NC-ND.

关键词： Modeling languages

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IoT Based Smart Fish-Feeder and Monitoring System

IoT Based Smart Fish-Feeder and Monitoring System

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2022 International Conference on Business Analytics for technology and Security, ICBATS 2022

作者： Abu-Khadrah, Ahmed Issa, Ghassan F. Aslam, Shaukat Shahzad, Muhammad Ateeq, Karamath Hussain, Munawar Saudi Electronic University College of Computing and Informatics Riyadh Saudi Arabia Skyline University College School of Information Technology Sharjah United Arab Emirates NCBAandE School of Computer Science Lahore Pakistan Riphah International University Department of Computing and Applied Sciences Lahore Pakistan Skyline University Bsit Faculty Sharjah United Arab Emirates

ISBN: (纸本)9781665409209

People have always enjoyed having pets around them, but in today's hectic world, it might be difficult to properly care for pets, particularly fish, which require more attention. When fish owners are out of town, their fish may be left with a feed shortfall or over-feed. Another consideration is the water quality (temperature, PH level, and so on). As a result, a solution is provided in this article to address the aforementioned issues. Two components comprise the proposed system. The first is for fish eating, while the second is for water feature and level monitoring. The first module also includes electrical, automatic, and communication parts that are connected to the Internet of Things (IoT) to allow fish to be fed on a schedule or remotely through a web interface. Microcontrollers with PH, Ammonia, Temperature and other sensors are included in the second module, which is used to monitor the aquarium's environment. © 2022 IEEE.

关键词： Internet of things

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PR-FCNN: a data-driven hybrid approach for predicting PM2.5 concentration

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Discover Artificial Intelligence 2024年第1期4卷 75页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Mazhar, Tehseen Ahmed, Noor Shahzad, Tariq Khan, Muhammad Amir Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Sciences Sindh Madressatul Islam University Karachi74000 Pakistan Department of Computer Science Bahria University Karachi Campus Sindh Karachi75000 Pakistan Department of Computer Science and information Technology School Education Department Government of Punjab Layyah 31200 Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg 2006 South Africa Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

The atmosphere’s fine articulate Matter (PM2.5) poses various health-related risks. Even though multiple efforts have been made to lower the emissions of these substances, the mortality rate is continuously increasing, requiring immediate inclination of the scientific community towards the design and development of advanced predictive models. Conventional statistical approaches have become dormant due to their limitations in capturing the innate relationships between the pollutants, particularly for predicting PM2.5 concentrations. In contrast, machine and deep learning techniques have shown great potential for forecasting air quality, providing more accuracy than its predecessor techniques. The present study investigates the utilization of hybrid approaches by integrating machine learning models with deep learning models to improve the prediction capabilities of PM2.5 concentration. It uses datasets from the World Air Quality Index (WAQI) and the State of Global Air (SOGA) to analyze the performance of the models on both the daily and annual data, respectively. This ensures the model’s effectiveness on a diversified dataset. The present study implements Random Forest (RF), Polynomial Regression (PR), XGBoost, and Extra Tree Regressor (ETR) coupled with Fully Connected Neural Network (FCNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) for obtaining optimized results. Finally, after a thorough investigation, the hybrid PR model coupled with FCNN (PR-FCNN) is found to be the best model with improved R-squared (R2) values, portraying its potential for predicting PM2.5 concentration accurately. Based on the experimentation, the preset study recommends implementing hybrid approaches, offering better predictive accuracy in forecasting air pollutants, especially PM2.5. © The Author(s) 2024.

关键词： Health risks

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Design of a Fogponics Farming System based on the Internet of Things and Fuzzy Logic

Design of a Fogponics Farming System based on the Internet o...

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Intelligent Cybernetics technology & Applications (ICICyTA), International Conference on

作者： Novian Anggis Suwastika Maulida Helmi M. Mirza Sholihul Aulia Aulia Arif Wardana Razak Faculty of Technology and Informatics and School of Computing Universiti Teknologi Malaysia and Telkom University Kuala Lumpur Malaysia Razak Faculty of Technology and Informatics and School of Computing Universiti Teknologi Malaysia and Telkom University Bandung Indonesia School of Computing Telkom University Bandung Indonesia Doctoral School of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9798350399141

Fogponics is a method of growing crops without using soil media. Fogponics is a type of aeroponic-based planting method. Fogponic has advantages in terms of efficiency in the use of nutrients. The obstacle faced in fogponics is maintaining environmental conditions that support plant development. This study applies the Internet of Things (IoT) to control environmental conditions. The system successfully retrieves temperature, humidity, and pH data and then processes it using the fuzzy algorithm. The calculated value from this fuzzy algorithm becomes an automatic regulator for actuator activation to maintain fogponics environmental conditions. The results of each data reading and calculation of the processed data using fuzzy logic are stored on the IoT platform, which can be accessed using devices connected to the internet. The system was successfully implemented at the laboratory level with a limited number of plants and a limited cultivation area.

关键词： Fuzzy logic Temperature distribution Actuators Regulators Humidity Soil Sensor systems

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