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arXiv 2024年

作者： Li, Boquan Zhang, Mengdi Zhang, Peixin Sun, Jun Wang, Xingmei Fu, Zirui College of Computer Science and Technology Harbin Engineering University China School of Computing and Information Systems Singapore Management University Singapore

With the development of large language models, multiple AIs have become available for code generation (such as ChatGPT and StarCoder) and are adopted widely. It is often desirable to know whether a piece of code is generated by AI, and furthermore, which AI is the author. For instance, if a certain version of AI is known to generate vulnerable codes, it is particularly important to know the creator. Watermarking is broadly considered a promising solution and is successfully applied for identifying AI-generated text. However, existing efforts on watermarking AI-generated codes are far from ideal, and pose more challenges than watermarking general text due to limited flexibility and encoding space. In this work, we propose ACW (AI Code Watermarking), a novel method for watermarking AI-generated codes. The key idea of ACW is to selectively apply a set of carefully-designed semantic-preserving, idempotent code transformations, whose presence (or absence) allows us to determine the existence of watermarks. It is efficient as it requires no training or fine-tuning and works in a black-box manner. Our experimental results show that ACW is effective (i.e., achieving high accuracy on detecting AI-generated codes and extracting watermarks) as well as resilient, significantly outperforming existing approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Valorisation of macroalgae for biofuels in Indonesia: an integrated biorefinery approach

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Environmental technology Reviews 2024年第1期13卷 269-304页

作者： Suhartini, Sri Pangestuti, Martasari Beti Elviliana Rohma, Novita Ainur Junaidi, Mochammad Agus Paul, Roshni Nurika, Irnia Rahmah, Nur Lailatul Melville, Lynsey Department of Agro-industrial Technology Faculty of Agricultural Technology Universitas Brawijaya Malang Indonesia Research Group Universitas Brawijaya Malang Indonesia Centre of Excellence in Bioenergy and Biorefinery Faculty of Agricultural Technology Universitas Brawijaya Malang Indonesia INRAE OPAALE Rennes France Department of Agricultural Economic Faculty of Agriculture Universitas Brawijaya Malang Indonesia SPJAIN Global School of Management Dubai United Arab Emirates Bioresource and Bioeconomy Research Group Faculty of Computing Engineering and Built Environment Birmingham City University Birmingham United Kingdom

Globally, bioenergy development depends on the efficacy and affordability of conversion technologies and the availability of renewable biomass sources. As a tropical country, Indonesia has a wide range of biomass sources, either from land or marine biomass (i.e. macroalgae). The current estimation of macroalgae potential in Indonesia is estimated at approximately 9.96 million tonnes or about 26.86% share of world production in 2021. Specifically, marine macroalgae (wild or cultivated) have received attention for their potential as renewable resources for the sustainable bioenergy production, supporting a move towards a circular economy. However, as a developing country, Indonesia still needs to evolve and further advances its technology and skill capacity to address research, development, and innovation challenges in this area. Thus, this paper examines the potential biorefinery approach for application and commercialisation in Indonesia. It discusses cultivation practices and the future direction of the most sustainable and feasible routes for bioenergy production from macroalgae, exploring recent developments, opportunities, and challenges towards circular processes. The study proposed that the biorefining of macroalgae into bioethanol, biogas, compost, and solid fuels, either as mono–or co-production, are potential. Therefore, this paper may offer to narrowing the literature’s gap and adding a new perspective on the adoption of macroalgae-based bioenergy with integrated biorefinery and closed-loop systems approaches. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Supply chains

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Machine Learning Model for Predicting Community Integrity Towards Solid Waste Management and Environmental Care 5

Machine Learning Model for Predicting Community Integrity To...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Zulkipli, Faridah Jamian, Nurul Husna Masrom, Suraya Khalid, Khairul Irwan Nopiah, Zulkifli Mohd Kerdprasop, Nittaya College of Computing Informatics and Mathematics Universiti Teknologi Mara Perak Branch Tapah CampusTapah Road Malaysia Block J MKN Embassy Techzone Jalan Teknokrat 2 Cyberjaya Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Selangor Malaysia School of Computer Engineering Suranaree University of Technology Thailand

ISBN: (纸本)9798331528553

Solid waste management and environmental conservation are global concerns that play a crucial role in both developed and developing countries. This worldwide issue makes a substantial contribution to the achievement of sustainable development goals and, as a result, helps to create a brighter future for a sustainable environment. The primary objective of this project is to create a machine learning model that can accurately assess and monitor the level of community integrity in relation to the management of solid waste and environmental conservation. Community integrity was used as a dependent variable (predictor variable). The independent factors considered in this study include gender, cumulative grade point average (CGPA), residence status, awareness, practice, attitude, perception, and knowledge. Five machine learning algorithms were utilized to determine the optimal prediction model for assessing community integrity in solid waste management and environmental care. The models used were Generalized Linear Model, Random Forest, Decision Tree, Gradient Boosted Tree, and Support Vector Model. The Generalized Linear Model machine learning algorithm was identified as the most appropriate model, based on its ability to predict community integrity in solid waste management and environmental care, as indicated by its lowest Root Mean Square Error (RMSE). © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Disaster Response: A Dual-Drone Approach for Efficient Area Scanning and Life Detection 7th

Enhancing Disaster Response: A Dual-Drone Approach for Effic...

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7th International Conference on Soft computing and Signal Processing, ICSCSP 2024

作者： Mummoorthy, A. Balamurugan, P. Patibandla, Anitha Department of Information Technology Malla Reddy College of Engineering and Technology Hyderabad India School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of ECE Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819799251

The paper proposes a new methodology for utilizing drones and UAVs during natural disasters such as floods and earthquakes. Natural disasters represent some of the most devastating events imaginable, underscoring the critical importance of preparedness to swiftly aid affected individuals. While natural disasters occur with varying frequency across the globe, their occurrence can severely impede human communication and mobility within affected regions. Providing assistance and procuring necessary equipment become significantly challenging, compounded by the constrained time available for response efforts. The initial 72 h following a disaster is pivotal for rescue operations. Developing an optimal rescue plan from ground-level assessments proves impractical. This is where drones and Unmanned Aerial Vehicles (UAVs) come into play. With advancements in drone technology, it becomes imperative to devise intelligent strategies for their deployment during natural disasters. This paper endeavors to propose an intuitive methodology aimed at analyzing affected areas, providing optimized routes, and predicting safe zones through modern machine learning algorithms. The proposed methodology seeks to expedite the analysis of current conditions and enhance the efficiency and safety of rescue operations. Central to this project is the establishment of reliable communication between the two drones and the server, along with the development of efficient task allocation strategies for each drone. Additionally, the methodology includes an interactive user interface housing all the information communicated and computed by the system. In conclusion, the proposed methodology aims to streamline rescue operations during natural disasters by harnessing the capabilities of drones and UAVs. Through improved efficiency and informed decision-making, this approach endeavors to enhance the effectiveness of disaster response efforts, ultimately contributing to saving lives and mitigating the impact

关键词： Drones

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Collaborative Satellite computing through Adaptive DNN Task Splitting and Offloading

arXiv

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arXiv 2024年

作者： Peng, Shifeng Hou, Xuefeng Shen, Zhishu Zheng, Qiushi Jin, Jiong Tagami, Atsushi Yuan, Jingling School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Science Computing and Engineering Technologies Swinburne University of Technology Australia KDDI Research Inc. Japan

Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates the widespread implementation of artificial intelligence (AI)-based applications, especially for image processing tasks involving deep neural network (DNN). With the limited computing resources of an individual satellite, independently handling DNN tasks generated by diverse user equipments (UEs) becomes a significant challenge. One viable solution is dividing a DNN task into multiple subtasks and subsequently distributing them across multiple satellites for collaborative computing. However, it is challenging to partition DNN appropriately and allocate subtasks into suitable satellites while ensuring load balancing. To this end, we propose a collaborative satellite computing system designed to improve task processing efficiency in satellite networks. Based on this system, a workload-balanced adaptive task splitting scheme is developed to equitably distribute the workload of DNN slices for collaborative inference, consequently enhancing the utilization of satellite computing resources. Additionally, a self-adaptive task offloading scheme based on a genetic algorithm (GA) is introduced to determine optimal offloading decisions within dynamic network environments. The numerical results illustrate that our proposal can outperform comparable methods in terms of task completion rate, delay, and resource utilization. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Enhancing Ensemble Model Accuracy and Interpretability: A Framework Integrating Rough Set Theory and Recursive Feature Elimination for Feature Selection and Interpretability with Association Rule Analysis in Ensemble Models

SSRN

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SSRN 2024年

作者： Mwangi, Isaac Kega Nderu, Lawrence Mwangi, Ronald Waweru Njagi, Dennis Gitari School of ICT Media and Engineering Zetech University Nairobi2768 – 00200 Kenya School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi62000-00200 Kenya

In machine learning, feature selection is of utmost importance for augmenting the predictive capabilities of ensemble models. This paper presents an innovative hybrid framework for selecting features in ensemble models, which combines Rough Set Theory (RST) with Recursive Feature Elimination (RFE), complemented by Association Rule Mining, to enhance interpretability. The suggested method considerably improves ensemble models' prognostic accuracy and comprehensibility, particularly Random Forests and Gradient Boosting Machines. The framework starts with the RFE process, meticulously eliminating less influential features, and then applies RST to refine the feature set further by eliminating redundancies. This two-phase approach results in a feature set that is optimally reduced yet highly influential. By implementing this hybrid method on ensemble models, significant improvements in predictive accuracy are demonstrated across three diverse datasets: cancer, Pima Indians Diabetes, and a weather dataset from Underground. The accomplished accuracies for these datasets were 0.9663, 0.8793, and 0.8427, respectively, highlighting the proposed approach's effectiveness. This article also proposes the incorporation of association rule mining to analyze the outcomes of the models. This technique improves the understandability of the models, offering more profound insights into the connections and patterns, thus tackling the difficulty of interpretability in intricate ensemble models. Our empirical analysis confirms the effectiveness of the proposed hybrid feature selection model, representing a significant advancement in the field. The integration of RFE and RST optimizes the feature selection process and bridges the gap in interpretability, offering robust solutions for applications where accuracy and understanding of model decisions are crucial. © 2024, The Authors. All rights reserved.

关键词： Rough set theory

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Humanoid Robot Collaborative Lifting Integrating Executable Judgment

Humanoid Robot Collaborative Lifting Integrating Executable ...

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2022 IEEE International Symposium on Product Compliance engineering - Asia, ISPCE-ASIA 2022

作者： Chang, Hua Yi, Pengfei Liu, Rui Dong, Jing Hou, Yaqing Zhou, Dongsheng The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Anthropomorphic robots

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Quarks: A Secure and Decentralized Blockchain-Based Messaging Network 10

Quarks: A Secure and Decentralized Blockchain-Based Messagin...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Shuhan, Mirza K. B. Islam, Tariqul Shuvo, Enam A. Bappy, Faisal H. Hasan, Kamrul Caicedo, Carlos Software Research and Engineering BKash Limited Dhaka Bangladesh SyracuseNY United States Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia Tennessee State University NashvilleTN United States

ISBN: (纸本)9798350312461

Over the past two decades, the popularity of messaging systems has increased both in enterprise and consumer level. Many of these systems used secure protocols like end-to-end encryption to ensure strong security features such as 'future secrecy' for one-to-one communication. However, the majority of them rely on centralized servers owned by big IT companies, which allows them to use their users' personal data. Also it allows the government to track and regulate their citizens' activities, which poses significant threats to 'digital freedom'. Also, these systems have failed to achieve security attributes like confidentiality, integrity, privacy, and future secrecy for group communications. In this paper, we present a novel blockchain-based secure messaging system named Quarks that overcomes the security pitfalls of the existing systems and eliminates the centralized control. We have analyzed our design of the system with security models and definitions from existing literature to demonstrate the system's reliability and usability. We have developed a Proof of Concept (PoC) of the Quarks system leveraging Distributed Ledger technology (DLT), and conducted load testing on that. We noticed that our PoC system achieves all the desired attributes that are prevalent in a traditional centralized messaging scheme despite the limited capacity of the development and testing environment. Therefore, this assures us the applicability of such systems in near future if scaled up properly. © 2023 IEEE.

关键词： Blockchain

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Symmetrical Voltages Dips Analysis in a Wind Turbine Based on DFIG for High Power Conversion 6th

Symmetrical Voltages Dips Analysis in a Wind Turbine Based o...

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6th International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2022

作者： Bouregba, H. Hachemi, M. Mekhilef, S. Ratni, A. Laboratoire Energétique Mécanique & Ingénierie Faculté de Technologie Université M’Hamed Bougara Boumerdes Algeria School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia

ISBN: (纸本)9783031212154

The most common source of renewable energy generation worldwide is wind turbines (WT) based on doubly-fed induction generators (DFIGs). To keep such sources connected to the grid during outages, low voltage ride-through (LVRT) capacity must be increased. Due to its vulnerability to grid disruptions, it is essential to follow stringent methods to keep DFIG connected to the grid. As a result, this study employs the crowbar resistance protection approach on a practical 2MW to improve WT’s LVRT. Under steady-state and symmetrical grid disturbances, the effectiveness of the DFIG-based WT with crowbar resistance is investigated and compared. The dynamic performance of the proposed scheme is analyzed to that of the existing capacitor bank system using a variety of metrics in order to demonstrate its efficacity. The modeling and simulation tools MATLAB and Simulink are used in this study. The findings reveal that the suggested LVRT capability outperforms the competition in practice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wind turbines

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Selective State Space Model for Monaural Speech Enhancement

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Moran Zhang, Qiquan Wang, Mingjiang Zhang, Xiangyu Liu, Hexin Ambikairaiah, Eliathamby Chen, Deying Harbin Institute of Technology School of Electronic and Information Engineering Shenzhen518055 China The University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia Nanyang Technological University College of Computing and Data Science Singapore Singapore

Voice user interfaces (VUIs) have facilitated the efficient interactions between humans and machines through spoken commands. Since real-word acoustic scenes are complex, speech enhancement plays a critical role for robust VUI. Transformer and its variants, such as Conformer, have demonstrated cutting-edge results in speech enhancement. However, both of them suffers from the quadratic computational complexity with respect to the sequence length, which hampers their ability to handle long sequences. Recently a novel State Space Model called Mamba, which shows strong capability to handle long sequences with linear complexity, offers a solution to address this challenge. In this paper, we propose a novel hybrid convolution-Mamba backbone, denoted as MambaDC, for speech enhancement. Our MambaDC marries the benefits of convolutional networks to model the local interactions and Mamba's ability for modeling long-range global dependencies. We conduct comprehensive experiments within both basic and state-of-the-art (SoTA) speech enhancement frameworks, on two commonly used training targets. The results demonstrate that MambaDC outperforms Transformer, Conformer, and the standard Mamba across all training targets. Built upon the current advanced framework, the use of MambaDC backbone showcases superior results compared to existing SoTA systems. This sets the stage for efficient long-range global modeling in speech enhancement. © 2025 IEEE.

关键词： State space methods

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