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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Urban Vertical Farming with IoT and Precision Monitoring for a Sustainable Future

Urban Vertical Farming with IoT and Precision Monitoring for...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Unni Krishnn A Amit Dutt Jay Singh Munugapati Bhavana Swathi Baswaraju Baydaa Fathil Mohammed Akila Venkatraman Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Lovely Professional University Phagwara India Dept. of EEE GL Bajaj Institute of Technology and Management Gr. Noida U.P. India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Computer Science-Data Science New Horizon College of Engineering Bangalore India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Information Technology GRIET Bachupally Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The fast expansion of population worldwide and urbanisation had been estimated to have 80 % of the population in urban areas in the upcoming 50 years leads to an exponential rise in demand of food. These needs provide an urgent call for developing novel approaches to achieve global food demands amid sustainability problems. Urban Vertical Farming (UVF) offers an attractive solution for growing crops in the vertical dimension of structures, the urban skyscrapers, where land use is limited. However, it is fairly limited research which applies the current state of art in cutting edge technologies such as the Internet of Things, to try to automate and monitor farm processing. Thus, in this review paper, gaps in existing research, the potential for IoT enabled vertical farming, as well as challenges such as energy use, economic feasibility and urban policy integration are examined. Vertical farming can be enhanced through resource efficiency, scalability and level of sustainability through the use of IoT systems and AI driven data analytics which will also help promote urban food security. The research gives a summary of the current state, technological capabilities, and potential way forward in the process of successful vertical farming implementation in urban sets.

关键词： Temperature sensors Food manufacturing Urban areas Urban policy Food security Sustainable development Artificial intelligence Monitoring Water resources Farming

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MORIX: Machine learning-aided framework for lethality detection and MORtality inference with eXplainable artificial intelligence in MAFLD subjects

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Computer Methods and Programs in Biomedicine Update 2025年 7卷

作者： Lofù, Domenico Sorino, Paolo Colafiglio, Tommaso Bonfiglio, Caterina Donghia, Rossella Giannelli, Gianluigi Lombardi, Angela Di Noia, Tommaso Di Sciascio, Eugenio Narducci, Fedelucio Dept. of Electrical and Information Engineering (DEI) Politecnico di Bari BA Bari 70125 Italy Dept. of Computer Automatic and Management Engineering (DIAG) Sapienza Università di Roma RM Roma 00185 Italy Unit of Data Science National Institute of Gastroenterology ”Saverio de Bellis” IRCCS Hospital BA Castellana Grotte 70013 Italy Scientific Direction National Institute of Gastroenterology ”Saverio de Bellis” IRCCS Hospital BA Castellana Grotte 70013 Italy

Metabolic dysfunction-associated fatty liver disease (MAFLD) introduces new diagnostic criteria for fatty liver disease that are independent of alcohol consumption and viral hepatitis infection. Therefore, investigating how biochemical and anthropometric factors influence mortality in MAFLD subjects is of significant interest. In this work, we propose MORIX, an Artificial Intelligence-based framework capable of predicting fatal mortality outcomes in subjects with MAFLD. MORIX utilizes data from epidemiological datasets containing carefully selected anthropometric and biochemical information. This selection is achieved through Recursive Feature Elimination (RFE) using a Random Forest (RF) to train Machine Learning (ML) algorithms and provide a mortality risk (Yes/No) output. To provide physicians with a valuable tool, MORIX was trained and tested on a dataset of MAFLD subjects, comparing five different models: Random Forest (RF), eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Light Gradient Boosting Model (LGBM) in a 5-fold cross-validation training strategy. Experimental results identified the RF as the best model, achieving a high accuracy for both mortality risks predicted. Additionally, an eXplainable Artificial Intelligence (XAI) analysis was conducted to clarify the diagnostic logic of the RF model and to assess the impact of each feature to the prediction. Moreover, a web application was developed to predict mortality risk and provide explanations of how the input features influenced the final prediction. In conclusion, the MORIX framework is easy to apply, and the required parameters are readily available in healthcare datasets, making it a practical tool for medical professionals. © 2025 The Authors

关键词： Epidemiology Interpretability Machine learning techniques MAFLD Mortality

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Spatial and Temporal Analysis of Flood Risk in Massachusetts Environmental Justice Communities

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Journal of Water Resources Planning and Management 2025年第7期151卷

作者： Sharkus, Cielo A. Givens, Jennifer E. Saia, Sheila M. Knighton, James Vogel, Eve Salap-Ayça, Seda Hatch, Christine E. Guzman, Christian D. Dept. of Civil and Environmental Engineering Univ. of Massachusetts Amherst 130 Natural Resources Rd AmherstMA01003 United States Dept. of Sociology Anthropology and Criminal Justice Utah State Univ 0730 Old Main Hill LoganUT84322-0730 United States Environmental Data Scientist Ecological Analytics and Modeling Group Tetra Tech 4000 Sancar Wy #200 Research Triangle Park DurhamNC27709 United States Dept. of Natural Resources and the Environment Univ. of Connecticut 1376 Storrs Rd. Storrs MansfieldCT06269-4087 United States Dept. of Earth Geographic Climate Sciences Univ. of Massachusetts Amherst 233 Morrill Science Center II 627 North Pleasant St AmherstMA01003 United States Dept. of Earth Environmental and Planetary Sciences Brown Univ 324 Brook St ProvidenceRI02912 United States Dept. of Earth Environmental and Planetary Sciences Institute at Brown for Environment and Society Brown Univ 324 Brook St ProvidenceRI02912 United States Dept. of Civil and Environmental Engineering Univ. of Massachusetts Amherst 21 Marston Hall 130 Natural Resources Rd AmherstMA01003 United States

During the past decade, changing population dynamics in Massachusetts raised concerns about inequitable exposure to floods in historically underserved communities. To examine the interplay between socioeconomic and demographic characteristics and flood risk, we constructed spatially explicit geospatial models to assess the distribution of flood risk from 2010 to 2020 across the 351 municipalities and 4,985 census block groups using the Environmental Justice Index (EJI) developed by the Massachusetts Executive Office of Energy and Environmental Affairs. EJI uses individual categorical variables to describe eight different combinations of socioeconomic and demographic characteristics, allowing for assessments through different types of models We found an increasing presence by 69.9% of EJ communities residing in flood zones during the past decade. Specific combinations of socioeconomic indicators, such as minority status linked with limited English proficiency and low-income status, exhibit a statistically significant likelihood of residing in flood zones from 2010 to 2020, relative to non-EJ. Of these socioeconomic indicators, we note a 124.4% increase by area in minority status with limited English proficiency living in flood-prone block groups (3.8 km2 in 2020), a 79.6% increase by area for those with only minority status (92 km2 in 2020), and a 522.6% increase in low-income with limited English proficiency zones by area occurring in the flood-prone block group (0.07 km2 in 2020). Our findings demonstrate that racial and ethnic composition in addition to income inequality are correlated to flood exposure in Massachusetts at both the census block group and at the municipality level. Finer scale analysis revealed additional hotspots of flooding that are obscured at the municipality level. These results not only underscore the potential for harm with increasing intensity and magnitude of flooding in EJ communities but also demonstrate the need for disaster risk reduct

关键词： Risk assessment

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Prediction of Small Molecules Binding Site with RNA as a Target 15

Prediction of Small Molecules Binding Site with RNA as a Tar...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Majumder, Subhasree Pal, Debnath Indian Institute of Science Bangalore Dept. of Computational and Data Science Bangalore India

ISBN: (纸本)9798350370249

RNA are essential biomolecules and are potent drug targets, as many small RNA molecules are known to play significant roles in many disease pathways. Predicting drug binding sites in RNA molecules is thus an essential step towards RNA-based drug discovery. Several previous works have focussed on sequence-based and structure-based features to predict binding sites in proteins and RNA. The low availability of RNA-small structures is a bottleneck for predictive model performance. We show that using an atomic-level feature set instead of residue-level feature set, increases the dataset size and can help build a promising model using a dataset of smaller size, which is the primary concern in RNA-Small molecule complexes. We focus on a rich set of physio-chemical, structural information, and dynamic features at the atomic level. Lastly we use a voting scheme to predict the small molecule binding sites for RNA. K-Nearest Neighbour (KNN), Multilayer Perceptron (MLP), Decision Tree and Random Forest models performed well with MCC values above 0.5. Among them, Random Forest performed best with an MCC of 0.78, and an accuracy of 0.89. © 2024 IEEE.

关键词： Binding sites

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On limiters for minimal vorticity dissipation

On limiters for minimal vorticity dissipation

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作者： Löhner, Rainald Dept. of Computational and Data Science College of Science George Mason University United States

ISBN: (纸本)9781563479694

Limiters that minimize the numerical dissipation of vorticity are considered. It is shown that for a typical vortex the velocity in the direction normal to the vorticity and the gradient of the absolute value of the vorticity reaches a maximum, switching on whatever limiter is being employed, and dissipating the vortex. Based on this insight a local change of coordinates is performed in order to limit differently the velocities in the characteristic directions of the vortical flow. This reduces the dissipation markedly. An example is included that compares the new limiter to existing ones, as well as to results using the usual vorticity confinement options. Copyright © 2009 by The author.

关键词： Vorticity

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Security of hash-then-CBC key wrapping revisited

Security of hash-then-CBC key wrapping revisited

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13th IMA International Conference on Cryptography and Coding, IMACC 2011

作者： Osaki, Yasushi Iwata, Tetsu Dept. of Computational Science and Engineering Nagoya University Japan

ISBN: (纸本)9783642255151

Key wrapping schemes are used to encrypt data of high entropy, such as cryptographic keys. There are two known security definitions for key wrapping schemes. One captures the security against chosen plaintext attacks (called DAE-security), and the other captures known plaintext attacks (called AKW-security). In this paper, we revisit the security of Hash-then-CBC key wrapping schemes. At SKEW 2011, Osaki and Iwata showed that the U CC -then-CBC key wrapping scheme, a key wrapping scheme that uses the U CC hash function and the CBC mode, has provable AKW-security. In this paper, we show that the scheme achieves the stronger notion of DAE-security. We also show our proof in the variable input length setting, where the adversary is allowed making queries of varying lengths. To handle such a setting, we generalize the previous definition of the U CC hash function to the variable input length setting, and show an efficient construction that meets the definition. We next consider linear-then-CBC, 2nd-preimage-resistant-then-CBC, and universal-then-CBC schemes. At SAC 2009, Gennaro and Halevi noted that these schemes do not achieve DAE-security. However, details were not presented, and we show concrete and efficient chosen plaintext attacks on these schemes, and confirm that they do not achieve DAE-security. © 2011 Springer-Verlag.

关键词： Hash functions

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Constraint-Guided Learning of data-driven Health Indicator Models: An Application on the Pronostia Bearing dataset

arXiv

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arXiv 2025年

作者： Tefera, Yonas Van Baelen, Quinten Meire, Maarten Luca, Stijn Karsmakers, Peter KU Leuven Dept. of Computer Science ADVISE-DTAI Kleinhoefstraat 4 GeelB-2440 Belgium Leuven.AI - KU Leuven Institute for AI Belgium Flanders Make @ KU Leuven Belgium Addis Ababa University School of Electrical and Computer Engineering Addis Ababa Ethiopia Ghent University Department of Data Analysis and Mathematical Modelling Coupure Links 653 Gent9000 Belgium

This paper presents a constraint-guided deep learning framework to develop physically consistent health indicators in bearing prognostics and health management. Conventional data-driven approaches often lack physical plausibility, while physics-based models are limited by incomplete knowledge of complex systems. To address this, we integrate domain knowledge into deep learning models via constraints, ensuring monotonicity, bounding output ranges between 1 and 0 (representing healthy to failed states respectively), and maintaining consistency between signal energy trends and health indicator estimates. This eliminates the need for complex loss term balancing to incorporate domain knowledge. We implement a constraint-guided gradient descent optimization within an autoencoder architecture, creating a constrained autoencoder. However, the framework is flexible and can be applied to other architectures as well. Using time-frequency representations of accelerometer signals from the Pronostia dataset, the constrained model generates more accurate and reliable representations of bearing health compared to conventional methods. It produces smoother degradation profiles that align with the expected physical behavior. Model performance is assessed using three metrics: trendability, robustness, and consistency. When compared to a conventional baseline model, the constrained model shows significant improvement in all three metrics. Another baseline incorporated the monotonicity behavior directly into the loss function using a soft-ranking approach. While this approach outperforms the constrained model in trendability, due to its explicit monotonicity enforcement, the constrained model performed better in robustness and consistency, providing stable and interpretable health indicator estimates over time. The results of the ablation study confirm that the monotonicity constraint enhances trendability, the boundary constraint ensures consistency, and the energy-health indicator con

关键词： Electronic health record

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An Intelligent LLM-Powered Personalized Assistant for Digital Banking Using LangGraph and Chain of Thoughts 22

An Intelligent LLM-Powered Personalized Assistant for Digita...

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22nd IEEE International Symposium on Intelligent Systems and Informatics, SISY 2024

作者： Easin, Arafat Md Sourav, Saha Tamas, Orosz Data Science and Engineering Dept. Budapest Hungary Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385601

Text-based applications and chatbots are increasingly popular for delivering banking services and educational tools, offering convenient and efficient solutions for users. Whereas, personalized assistants have transformed user engagement in the digital banking space by utilizing Large Language Models (LLMs) in conjunction with autonomous agents. This study proposes the development of an intelligent personalized assistant for digital banking, utilizing a multi-agent framework based on the LangGraph and Chain of Thoughts (COT) prompting. While COT guarantees context-aware replies, the LangGraph design maps characteristics to nodes to improve user interactions. The objectives of this system are to enhance task efficiency and elevate the capabilities of digital banking assistants. We present a customizable digital banking system powered by LLM-based models, designed to deliver an interactive and personalized banking experience. The system supports a range of services, including adding money, transferring funds, paying bills, accessing telco services like mobile recharge, managing savings interest rates, DPS schemes, fixed deposits, and answering FAQs related to banking information. Therefore, integrating COT for logical reasoning enhances the effectiveness of multi-agent systems, as each single agent benefits from the structured reasoning process. In addition, LangGraph is employed for structured data management, enabling the assistant to support and accelerate various digital banking processes efficiently. The code implementation of this work is available for public access at: https://***/srv-sh/digital-agent. © 2024 IEEE.

关键词： Information management

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Crack Catcher AI — Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI — Enabling Smart Fracture Mechanics Approa...

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Annual International Symposium on Reliability Physics

作者： Gabriel Preston Greer Gonzolez Andrea S. Karnyoto Fitya S. Mozar Matthew M. Henry Mahmud Isnan Kuncahyo S. Nugroho Henry Candra Muhamad Doris Dianing N. N. Putri Tyas K Sari Bens Pardamean Endang Djuana Derrric Speaks Arief S. Budiman Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls OR USA Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls OR USA Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta Indonesia Computer Sci. Dept. BINUS Grad. Program - Master of Computer Sci. Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas TRISAKTI Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas TRISAKTI Jakarta Indonesia Industrial Eng. Dept. BINUS Grad. Program – Master of Industrial Eng. Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331504779

ISBN: (纸本)9798331504786

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability.

关键词： Semiconductor device measurement Three-dimensional displays Accuracy Semiconductor device reliability Silicon Artificial intelligence Bonding Stress Three-dimensional integrated circuits Stress measurement

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