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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Elsayaad, Farida Daker, Mahmoud Atia, Ayman Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Giza Egypt Faculty of Computer Science Giza Egypt

ISBN: (纸本)9798350362633

In this paper, we focus on tracking cattle's respiratory behavior to detect abnormalities. We use a non-intrusive method which depends on recorded videos of cattle. These videos are filmed from the side and then used to produce breathing patterns. These breathing patterns are then classified as either normal or abnormal. Our method involves using YOLOv8 trained on the Animals with Attributes (AwA) dataset to automatically detect a point of interest on the cow's body. The movement of this point acts as a representative of the breathing behavior of the cow. This point is tracked using optical flow throughout the video, resulting in a breathing pattern. We tested the efficiency of 6 pretrained models in the task of classifying the resulting breathing patterns. We then combined the 3 highest-performing models which are VGG19, InceptionV3, and ResNet50 using ensemble techniques. After using ensemble techniques, an accuracy of 95.31%, a precision of 97.8%, and a recall of 92.27% was achieved, surpassing all individual models. © 2024 IEEE.

关键词： Invertebrates

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Improving Land Cover Change Detection Using YOLO Segmentation and Comparative Polygon Analysis 2

Improving Land Cover Change Detection Using YOLO Segmentatio...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Abanoub, George Elkess Elmoushy, Seif Nagib, Abdelrahman Ezzeldin Atia, Ayman Faculty of Computer Science Cairo Egypt HCI-LAB Faculty of Computers and Artificial Intelligence Helwan University Giza Egypt

ISBN: (纸本)9798350362633

This research develops a methodology for analyzing changes in Earth's surface and atmosphere using remote sensing imagery from satellites, aircraft, and drones. The research shows a traditional labeling techniques with refined segmentation specifically tailored for the Land Use Dataset, thereby improving the accuracy of land-cover segmentation predictions. We assessed the performance of various YOLO models - YOLOv5, YOLOv7, and YOLOv8 - in our detection framework, with YOLOv8 proving to be the most effective. The YOLOv8 model achieved a mean Average Precision (mAP50) of 42.7% and a Boundary Box Precision of 55.9%. © 2024 IEEE.

关键词： Semantic Segmentation

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Comparative Analysis of Movement Segmentation Techniques in Untrimmed Videos Using Optical Flow and Frame Differencing Using the $1 Unistroke Recognizer 2

Comparative Analysis of Movement Segmentation Techniques in ...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Walid, Mazen Ameen, Mostafa Zaher, Moamen Atia, Ayman Faculty Of Computer Science Cairo Egypt Helwan University HCI-LAB Faculty of Computers and Artificial Intelligence Giza Egypt

ISBN: (纸本)9798350362633

Untrimmed video analysis presents a complex challenge due to diverse movements across different domains, crucial for applications like action recognition, surveillance, and human-computer interaction. This study aims to compare the effectiveness of two main segmenting methods for untrimmed video segmentation and classification, addressing the challenge of diverse movements in various domains. Leveraging optical flow, frame differencing, and the 1$ algorithm, the performance of these methods in enabling robust and adaptable analysis of untrimmed video sequences is examined, facilitating accurate movement recognition across different contexts. Through a comparative study, the effectiveness and versatility of these segmenting methods are evaluated, revealing their capabilities to accurately segment and classify movements across different domains. The findings provide insights into the strengths and limitations of each method, offering implications for various fields, including computer vision, machine learning, and multimedia processing, and opening avenues for enhanced applications in diverse domains. © 2024 IEEE.

关键词： Optical flows

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SG-NeRF:Sparse-Input Generalized Neural Radiance Fields for Novel View Synthesis

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Journal of computer science & Technology 2024年第4期39卷 785-797页

作者： Kuo Xu Jie Li Zhen-Qiang Li Yang-Jie Cao School of Cyber Science and Engineering Zhengzhou UniversityZhengzhou 450002China Intelligent Big Data System(iBDSys)Lab Shanghai Jiao Tong UniversityShanghai 200240China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China

Traditional neural radiance fields for rendering novel views require intensive input images and pre-scene optimization,which limits their practical *** propose a generalization method to infer scenes from input images and perform high-quality rendering without pre-scene optimization named SG-NeRF(Sparse-Input Generalized Neural Radiance Fields).Firstly,we construct an improved multi-view stereo structure based on the convolutional attention and multi-level fusion mechanism to obtain the geometric features and appearance features of the scene from the sparse input images,and then these features are aggregated by multi-head attention as the input of the neural radiance *** strategy of utilizing neural radiance fields to decode scene features instead of mapping positions and orientations enables our method to perform cross-scene training as well as inference,thus enabling neural radiance fields to generalize for novel view synthesis on unseen *** tested the generalization ability on DTU dataset,and our PSNR(peak signal-to-noise ratio)improved by 3.14 compared with the baseline method under the same input *** addition,if the scene has dense input views available,the average PSNR can be improved by 1.04 through further refinement training in a short time,and a higher quality rendering effect can be obtained.

关键词： Neural Radiance Fields(NeRF) multi-view stereo(MVS) new view synthesis(NVS)

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RL with Balanced Reward and Masking Mechanism for Multi-NUMA Virtual Machine Scheduling 19

RL with Balanced Reward and Masking Mechanism for Multi-NUMA...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Gao, Yucen Cheng, Yunlong Ping, Chan Tin Gao, Xiaofeng Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331507817

With the rapid development of data center and cloud computing, the importance of resource management is increasing in recent years. In this paper, we focus on the virtual machine scheduling problem in resource management, that is, how to allocate the limited virtual machine resources when a request arrives. Existing works usually model the virtual machine scheduling problem as a bin-packing problem, but they do not consider the multi-NUMA architecture servers that have been widely used in recent years. Compared with the traditional symmetric multiprocessing architecture, the multi-NUMA architecture has the advantages of faster access time and higher energy efficiency, which is more suitable for dealing with today’s scenarios with a large number of computing requirements. In this paper, we first formalize the multi-NUMA virtual machine scheduling problem as a combinatorial optimization problem and explain why it is suitable to be solved by reinforcement learning. A reinforcement learning model called BM-DQN is then proposed, which designs a balanced reward function suitable for the multi-NUMA architecture and adopts a masking mechanism to screen out the optional servers in each step of reinforcement learning, so as to improve the solution performance. Experimental results on the open-source real-world virtual machine scheduling dataset of Huawei Cloud show that BM-DQN is superior to the existing methods in terms of the number of completed requests. © 2025 IEEE.

关键词： Resource allocation

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Corruption Robust Dynamic Pricing in Liner Shipping under Capacity Constraint 40

Corruption Robust Dynamic Pricing in Liner Shipping under Ca...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Hu, Yongyi Lit, Xueyan Wei, Xikai Shi, Yangguang Gao, Xiaofeng Chen, Guihai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering China School of Computer Science and Technology Shandong University China

ISBN: (纸本)9798350317152

The shipping industry has irreplaceable importance in international trade and commerce. How to dynamically price different containers has long been a hot topic due to its direct connection to the final revenue. Two critical observations have been made after a comprehensive survey within a top liner company, China Ocean Shipping Company (COSCO). (1) Each type of container carried on a liner ship has its maximum capacity. (2) The sales volume is occasionally subject to huge fluctuations due to rare uncontrollable factors, such as COVID. Based on the above two points and the liner routine's periodic nature, we model the dynamic pricing problem as an episodic MDP model integrating with both capacity constraints and adversarial corruption, named C3-MDP. To maximize the cumulative revenue in the C3-MDP setting, we propose a programming framework, Bonus-Exploration based Episodic Programming (BEEP). This framework can directly accommodate the linear programming algorithm to form the algorithm BEEP-LP, which provides the episode-wise greedy optimal strategy. Furthermore, a detailed regret analysis is provided, showing that BEEP-LP has a regret that is sublinear in the number of episodes. Combining deep techniques, we also present an approximation algorithm BEEP-DQN in the case of large state-action space to strike a balance between the running time and the performance. Abundant experiments based on real container sales data exhibit the rationality of C3-MDP and the effectiveness of BEEP. © 2024 IEEE.

关键词： Linear programming

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Learning Divergence Fields for Shift-Robust Graph Representations 41

Learning Divergence Fields for Shift-Robust Graph Representa...

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41st International Conference on Machine Learning, ICML 2024

作者： Wu, Qitian Nie, Fan Yang, Chenxiao Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China

Real-world data generation often involves certain geometries (e.g., graphs) that induce instance-level interdependence. This characteristic makes the generalization of learning models more difficult due to the intricate interdependent patterns that impact data-generative distributions and can vary from training to testing. In this work, we propose a geometric diffusion model with learnable divergence fields for the challenging generalization problem with interdependent data. We generalize the diffusion equation with stochastic diffusivity at each time step, which aims to capture the multi-faceted information flows among interdependent data. Furthermore, we derive a new learning objective through causal inference, which can guide the model to learn generalizable patterns of interdependence that are insensitive across domains. Regarding practical implementation, we introduce three model instantiations that can be considered as the generalized versions of GCN, GAT, and Transformers, respectively, which possess advanced robustness against distribution shifts. We demonstrate their promising efficacy for out-of-distribution generalization on diverse real-world datasets. Source codes are available at https://***/fannie1208/GLIND. Copyright 2024 by the author(s)

关键词： Stochastic systems

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Quantum Algorithms and Lower Bounds for Finite-Sum Optimization 41

Quantum Algorithms and Lower Bounds for Finite-Sum Optimizat...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, *** this paper, we initiate the study of solving finite-sum optimization problems by quantum ***, let f1, ..., fn : d → be -smooth convex functions and ψ: d → be a µ-strongly convex proximal *** goal is to find an ϵ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x).We give a quantum algorithm with complexity Õ(Equation presented) 1 improving the classical tight bound (Equation presented).We also prove a quantum lower bound Ω˜(n + n3/4(/µ)1/4) when d is large *** our quantum upper and lower bounds can extend to the cases where ψ is not necessarily strongly convex, or each fi is Lipschitz but not necessarily *** addition, when F is nonconvex, our quantum algorithm can find_an ϵ-critial point using (Equation presented) queries. Copyright 2024 by the author(s)

关键词： Quantum computers

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SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification 41

SSL4Q: Semi-Supervised Learning of Quantum Data with Applica...

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41st International Conference on Machine Learning, ICML 2024

作者： Tang, Yehui Yang, Nianzu Long, Mabiao Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China

The accurate classification of quantum states is crucial for advancing quantum computing, as it allows for the effective analysis and correct functioning of quantum devices by analyzing the statistics of the data from quantum measurements. Traditional supervised methods, which rely on extensive labeled measurement outcomes, are used to categorize unknown quantum states with different properties. However, the labeling process demands computational and memory resources that increase exponentially with the number of qubits. We propose SSL4Q, manage to achieve (for the first time) semi-supervised learning specifically designed for quantum state classification. SSL4Q’s architecture is tailored to ensure permutation invariance for unordered quantum measurements and maintain robustness in the face of measurement uncertainties. Our empirical studies encompass simulations on two types of quantum systems: the Heisenberg Model and the Variational Quantum Circuit (VQC) Model, with system size reaching up to 50 qubits. The numerical results demonstrate SSL4Q’s superiority over traditional supervised models in scenarios with limited labels, highlighting its potential in efficiently classifying quantum states with reduced computational and resource overhead. Copyright 2024 by the author(s)

关键词： Qubits

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