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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling Computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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Evaluating the Effectiveness of Deep Convolutional Neural Networks for Picture Quality Prediction

Evaluating the Effectiveness of Deep Convolutional Neural Ne...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Sushant Jhingran Rekha Chaturvedi Nidhi Bansal Suraj Malik Department of Computer Science & Engineering Sharda University Greater Noida India Department of Data Science and Engineering School of Information Security and Data Science Manipal University Jaipur Jaipur Rajasthan Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering IIMT University Meerut Uttar Pradesh India

ISBN: (数字)9798350392319

ISBN: (纸本)9798350356137

Since the advent of smartphones, capturing images has become deeply embedded in human behavior, evolving into a fundamental part of daily life. Research into human perception of image quality is crucial as people frequently acquire and interpret images. Automation in image processing systems is vital for quantifying image quality, addressing issues like blurriness, noise, and compression that can significantly degrade the visual experience. While image processing professionals can quickly identify such distortions, integrating human visual perception into automated system design remains challenging. Consequently, machine-based evaluation of image quality is a critical research area. Image quality assessment algorithms aim to approximate human visual judgments. Although deep learning has shown remarkable success in various real-world tasks, such as object detection and image classification, its application in image quality assessment is still evolving. The primary challenge is assessing image quality without explicitly considering human perception, making it a dynamic area of ongoing research. A significant hurdle is the lack of images with quality scores, particularly differential average feedback scores provided by image quality experts on a 1-100 scale. This limitation hinders the development of robust models for image quality assessment, highlighting the need for innovative solutions in this evolving research domain.

关键词： Image quality Wireless communication Visualization Image coding Noise Object detection Multimedia communication System analysis and design Smart phones Visual perception

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Doubly stochastic continuous time random walk

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Physical Review Research 2024年第1期6卷 L012033-L012033页

作者： Maxence Arutkin Shlomi Reuveni School of Chemistry Center for the Physics & Chemistry of Living Systems Tel Aviv University 6997801 Tel Aviv Israel Service d'Epidémiologie-Data-Biostatistiques Délégation à la Recherche Clinique et à l'Innovation Hôpital Foch 92150 Suresnes France Sackler Center for Computational Molecular & Materials Science Ratner Institute for Single Molecule Chemistry Tel Aviv University 6997801 Tel Aviv Israel

Since its introduction some 60 years ago, the Montroll-Weiss continuous time random walk has found numerous applications due its ease of use and ability to describe both regular and anomalous diffusion. Yet, despite its broad applicability and generality, the model cannot account for effects coming from random diffusivity fluctuations, which have been observed in the motion of asset prices and molecules. To bridge this gap, we introduce a doubly stochastic version of the model in which waiting times between jumps are replaced with a fluctuating jump rate. We show that this newly added layer of randomness gives rise to a rich phenomenology while keeping the model fully tractable, allowing us to explore general properties and illustrate them with examples. In particular, we show that the model presented herein provides an alternative pathway to Brownian yet non-Gaussian diffusion, which has been observed and explained via diffusing diffusivity approaches.

关键词： Anomalous diffusion Continuous-time random walk Escape problems Random walks Stochastic processes

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Text-Driven Video Style Transfer with State-Space Models: Extending StyleMamba for Temporal Coherence

arXiv

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arXiv 2025年

作者： Li, Chao Park, Minsu Rossi, Cristina Li, Zhuang Dept. of Computer Science Eastern Asia Institute of Technology Beijing China AI Research Center Seoul National Technical Labs Seoul Korea Republic of School of Computing University of South Europe Rome Italy Department of Data Science Western Frontier University CA United States

StyleMamba has recently demonstrated efficient text-driven image style transfer by leveraging state-space models (SSMs) and masked directional losses. In this paper, we extend the StyleMamba framework to handle video sequences. We propose new temporal modules, including a Video State-Space Fusion Module to model inter-frame dependencies and a novel Temporal Masked Directional Loss that ensures style consistency while addressing scene changes and partial occlusions. Additionally, we introduce a Temporal Second-Order Loss to suppress abrupt style variations across consecutive frames. Our experiments on DAVIS and UCF101 show that the proposed approach outperforms competing methods in terms of style consistency, smoothness, and computational efficiency. We believe our new framework paves the way for real-time text-driven video stylization with state-of-the-art perceptual results. Copyright © 2025, The Authors. All rights reserved.

关键词： Video recording

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An Exploratory data Analysis and Visualizations of Underprivileged Communities Diabetes dataset for Public Good

An Exploratory Data Analysis and Visualizations of Underpriv...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Majdi Owda Amani Yousef Owda Maria Fasli Faculty of Data Science UNESCO Chair in Data Science for Sustainable Development Arab American University Ramallah West Bank Palestine Deprtment of Natural Engineering and Technology Sciences Faculty of Graduate Studies Arab American University Ramallah West Bank Palestine University of Essex School of Computer Science and Electronic Engineering Institute for Analytics and Data Science Colchester UK

Exploratory data analysis and visualization are essential tools for discovering patterns, highlighting anomalies, and discovering insights from datasets. In addition, visualizations tend to provide information in a way that is more accessible for the audience to understand and comprehend. Underprivileged communities will need an easier way to comprehend issues, challenges, and threats such as avoiding diseases. The use of exploratory data analysis and visualizations of diabetes datasets can be used as a means to make this topic easier to understand for underprivileged communities. A methodology has been developed to explore a dataset collection of diabetes patients. The dataset was obtained from the Palestinian community medical center (first-hand dataset), then the dataset was prepared and cleaned, and all rows with missing values or wrong values were dropped and removed, then an exploratory data analysis with a large number of data visualizations was produced, and last, a sample of the produced visualizations have been used to conduct a social acceptance experiment to educate an underprivileged population in the Palestinian community about the risks of diabetes. The result of the experiment was excellent and very promising in which participants understood the contents in a much easier way and provided very positive feedback on utilizing visualizations of diabetes datasets for public awareness and good.

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Prediction of AIDS/HIV Infection using Custom Artificial Neural Networks Model

Prediction of AIDS/HIV Infection using Custom Artificial Neu...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Sneha BJ Shriyashreshta Ashwini Kodipalli Roshan Joy Martis Ushasree. A Shridhar B. Devamane Department of AI & Data Science Global Academy of Technology Bangalore India Department of Electronics & Communication Engineering Manipal Institute of Technology Bangalore India Department of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India Department of CS School of Computing Amrita Vishwavidyapeetam Mysuru India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

HIV is a serious disease that impairs immunity. Without treatment, the infection may progress through three stages, which might drastically shorten a person's life. Artificial neural networks (ANNs) are utilized to detect the HIV infection and even to find treatments for it. The AIDS viral infection is predicted in this review. The aim of ANN is to precisely classify patients according to whether or not they have HIV/AIDS. We have formed a custom ANN model and applied it to the Kaggle dataset that is accessible to the public, compared which optimizers offer the best accuracy and whether employing dropouts yields improved accuracy. With Gradient Descent, we achieved the maximum accuracy of 86.92% it is also one of the most commonly used optimizers due to its simplicity and effectiveness, followed by SGD with 86.45% it is a variation of gradient descent in which a random subset of data as opposed to the complete dataset is used to update the parameter and RMSprop with 86.21% uses the average of recent gradient magnitudes to modify the learning rate for each parameter. This aids with training stabilization, particularly when dealing with sparse or noisy data. The accuracy increased to 87.62% after we introduced dropouts to the hidden layer with optimizer as gradient descent, which produced the best results. Loss vs. Accuracy graphs and ROC curve have been plotted. With dropouts, the custom ANN model performed even better on the Gradient Descent optimizer.

关键词： Training Adaptation models Accuracy Reviews Computational modeling Artificial neural networks Predictive models data models Noise measurement Viruses (medical)

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BADROBOT: JAILBREAKING EMBODIED LLMS IN THE PHYSICAL WORLD

arXiv

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arXiv 2024年

作者： Zhang, Hangtao Zhu, Chenyu Wang, Xianlong Zhou, Ziqi Yin, Changgan Li, Minghui Xue, Lulu Wang, Yichen Hu, Shengshan Liu, Aishan Guo, Peijin Zhang, Leo Yu National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Beihang University China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Embodied AI represents systems where AI is integrated into physical entities. Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue remains overlooked: could these embodied LLMs perpetrate harmful behaviors? In response, we introduce BADROBOT, a novel attack paradigm aiming to make embodied LLMs violate safety and ethical constraints through typical voice-based user-system interactions. Specifically, three vulnerabilities are exploited to achieve this type of attack: (i) manipulation of LLMs within robotic systems, (ii) misalignment between linguistic outputs and physical actions, and (iii) unintentional hazardous behaviors caused by world knowledge’s flaws. Furthermore, we construct a benchmark of various malicious physical action queries to evaluate BADROBOT’s attack performance. Based on this benchmark, extensive experiments against existing prominent embodied LLM frameworks (e.g., Voxposer, Code as Policies, and ProgPrompt) demonstrate the effectiveness of our BADROBOT. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Distributed adaptive online learning for convex optimization with weight decay

Distributed adaptive online learning for convex optimization...

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作者： Shen, Xiuyu Li, Dequan Fang, Runyue Zhou, Yuejin Wu, Xiongjun School of Mathematics and Big Data Anhui University of Science and Technology Huainan China National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering The 802 Institute of Shanghai Academy of Space Flight Technology The Eighth Academy of China Aerospace Science and Technology Corporation Shanghai China

This paper investigates an adaptive gradient-based online convex optimization problem over decentralized networks. The nodes of a network aim to track the minimizer of a global time-varying convex function, and the communication pattern among nodes is captured as a connected undirected graph. To tackle such optimization problems in a collaborative and distributed manner, a weight decay distributed adaptive online gradient algorithm, called WDDAOG, is firstly proposed, which incorporates distributed optimization methods with adaptive strategies. Then, our theoretical analysis clearly illustrates the difference between weight decay and L2 regularization for distributed adaptive gradient algorithms. The dynamic regret bound for the proposed algorithm is further analyzed. It is shown that the dynamic regret bound for convex functions grows with order of (Formula presented.), where T and n represent the time horizon and the number of nodes associated with the network, respectively. Numerical experiments demonstrate that WDDAOG works well in practice and compares favorably to existing distributed online optimization schemes. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd.

关键词： Convex optimization

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How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?

arXiv

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arXiv 2024年

作者： Liu, Tianchi Zhang, Lin Das, Rohan Kumar Ma, Yi Tao, Ruijie Li, Haizhou Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore National Institute of Informatics Tokyo Japan Fortemedia Singapore Singapore School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Partially manipulating a sentence can greatly change its meaning. Recent work shows that countermeasures (CMs) trained on partially spoofed audio can effectively detect such spoofing. However, the current understanding of the decision-making process of CMs is limited. We utilize Grad-CAM and introduce a quantitative analysis metric to interpret CMs’ decisions. We find that CMs prioritize the artifacts of transition regions created when concatenating bona fide and spoofed audio. This focus differs from that of CMs trained on fully spoofed audio, which concentrate on the pattern differences between bona fide and spoofed parts. Our further investigation explains the varying nature of CMs’ focus while making correct or incorrect predictions. These insights provide a basis for the design of CM models and the creation of datasets. Moreover, this work lays a foundation of interpretability in the field of partial spoofed audio detection that has not been well explored previously. © 2024, CC BY-NC-ND.

关键词： Decision making

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Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clustering

arXiv

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arXiv 2024年

作者： Li, Bo Xu, Zhiwei Yun, Jing Wang, Jiatai College of Data Science and Application Inner Mongolia University of Technology Huhhot China Haihe Lab of ITAI Tianjin China Institute of Computing Technology Chinese Academy of Sciences Beijing China

This paper study one challenging issue in incomplete multiview clustering, where valuable complementary information from other views is always ignored. To be specific, we propose a framework that effectively balances Complementarity and Consistency information in Incomplete Multi-view Clustering (CoCo-IMC). Specifically, we design a dual network of delayed activation, which achieves a balance of complementarity and consistency among different views. The delayed activation could enriches the complementarity information that was ignored during consistency learning. Then, we recover the incomplete information and enhance the consistency learning by minimizing the conditional entropy and maximizing the mutual information across different views. This could be the first theoretical attempt to incorporate delayed activation into incomplete data recovery and the balance of complementarity and consistency. We have proved the effectiveness of CoCo-IMC in extensive comparative experiments with 12 state-of-the-art baselines on four publicly available datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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