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Point Biserial Correlation Coefficient on Climate Variables and Dengue Cases Using R Programming 1

4th International Conference on Mathematics, Statistics, and computing Technology, ICMSCT 2023

作者： Zukarnain, Zuriani Ahmad Muhamad Krishnan, Nor Farisha Jamaludin, Marhainis Rahman, Noorihan Abdul Ahmad, Azlin College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Kelantan Kelantan Machang Malaysia Centre for Research in Data Science Universiti Teknologi PETRONAS Perak Seri Iskandar Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam Malaysia

ISBN: (数字)9789819734504

ISBN: (纸本)9789819734498

The number of people infected with dengue fever is on the increase across the world. Dengue fever is present in urban and semi-urban settings, and rural areas are also affected in certain nations. Dengue fever is affected by rain, relative humidity, temperature, and unplanned fast urbanization. This study focuses on Kota Bharu, aiming to identify whether the climate characteristics, including average temperature, mean relative humidity, and total rainfall, affect dengue cases. This study employed a point biserial correlation coefficient to see if the features correspond to the output. R programming was applied to check whether there was a correlation between dengue cases (yes/no) and climate parameters (average temperature, mean relative humidity, and rainfall). Point biserial correlation was used as the target variable for dichotomous variables. The methodology involved several steps, including data pre-processing, cleaning, and analysis. According to the findings, only mean relative humidity correlates with dengue cases in Kota Bharu. Since there is a negative correlation, dengue fever rises with low humidity. However, different regions might give different results of the correlation. Understanding the factors that lead to a rise in dengue cases and education initiatives can assist in enhancing a region’s early warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rain

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Efficient Resource Allocation in computing Power Networks Considering Similar Task Merging: A Lyapunov Optimization-Based DRL Approach

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IEEE Internet of Things Journal 2025年第13期12卷 23118-23138页

作者： Jia, Zhonghai Xue, Junxiao Shi, Lei Li, Jie He, Mengyang Zhejiang Lab Research Center for Space Computing System Hangzhou310000 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450002 China SongShan Laboratory Zhengzhou450040 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3 Shanghai China

The cloud-edge-terminal architecture relies on hierarchy for resource allocation but lacks global optimization. The computing power network (CPN) introduces a new distributed computing paradigm, integrating cross-domain, heterogeneous resources for global scheduling. However, most CPN research focuses on task optimization during resource allocation, while neglecting the similarity of random tasks before the allocation stage. Additionally, fragmented CPN resources and complex task demands pose challenges to global load balancing. This paper proposes a deep reinforcement learning framework with task merging and congestion avoidance for on-demand resource allocation. Specifically, a low-complexity similar task merging algorithm reduces redundant resource consumption during task preprocessing. In task offloading, the principal neighborhood aggregated graph neural network captures CPN's intricate features. Lyapunov optimization, integrated into a multi-threaded training framework, minimizes resource backlog congestion. A carefully designed reward function balances multiple objectives, enhancing computing resource utilization efficiency and ensuring system stability. Theoretical analysis shows that with control parameter V, the trade-off between resource utilization efficiency and system stability follows the relationship O(1/V), O(V). Extensive experiments demonstrate a 33.5% improvement in resource utilization efficiency and a 62.7% increase in task offloading success rates with respect to those in state-of-the-art algorithms. The proposed algorithm exhibits robustness and effectiveness, particularly in high-load and real network topologies. © 2014 IEEE.

关键词： computing power

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GAM-GCN: Decomposing Graph Convolutional Networks via Generalized Additive Models

GAM-GCN: Decomposing Graph Convolutional Networks via Genera...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Chen Liu Lequan Yu Kai Han Guosheng Yin School of Computing and Data Science The University of Hong Kong Hong Kong China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

The vast adoption of graph neural networks (GNNs) in broad applications calls for versatile interpretability tools so that a better understanding of the GNNs’ intrinsic structures can be gained. We propose an interpretable GNN framework to decompose the prediction into the additive combination of node features’ main effects and the contributions of edges. The key component of our framework is the generalized additive model with the graph convolutional network (GAM-GCN) that allows for global node feature interpretations. The inherent interpretability of GAM and the expressive power of GCN are preserved and naturally connected. Further, the effects of neighboring edges are measured by edge perturbation and surrogate linear modeling, and the most important subgraph can be selected. We evaluate the proposed approach using extensive experiments and show that it is a useful tool for interpreting GNNs in the attempt to unlock the black box.

关键词： Computer vision Additives Graph convolutional networks Image edge detection Perturbation methods Semantics Knowledge graphs Computer architecture Feature extraction IEEE Regions

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Scheduling Multiple Mobile Agents with Abilities of Sensing and Executing Simultaneously

Scheduling Multiple Mobile Agents with Abilities of Sensing ...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Guo, Zhipeng Yu, Zhiyong Huang, Fangwan Tu, Chunyu Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350312270

How to schedule public resources to maximize the coverage of crowds has always been a hot topic. There are mainly two types of research. One is the scheduling of static resources, whose coverage efficiency is lower because the resources cannot be moved;the other is the dynamic scheduling of movable public resources, but the existing methods require additional devices to collect crowd volume data. To address this issue, this article introduces a new category of mobile agents that can sense and execute simultaneously. This means that an agent can simultaneously perform crowd volume data collection tasks and crowds coverage tasks in its area. Taking these multi-ability agents as public resources, this article proposes a scheduling method called GE-STMC (Greedy Execute - Spatial and Temporal Monte-Carlo dropout). The method considers the spatiotemporal correlation between areas and evaluates the sensing value and the executing value of areas comprehensively to allocate the agents accordingly. Through the experimental on the real data of Beijing Happy Valley, this method can get 16% larger crowds volume coverage than other baseline methods with the same number of agents. © 2023 IEEE.

关键词： Mobile agents

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POINE2: Improving Poincaré Embeddings for Hierarchy-Aware Complex Query Reasoning over Knowledge Graphs 26

POINE2: Improving Poincaré Embeddings for Hierarchy-Aware C...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liu, Junnan Mao, Qianren Li, Jianxin Fu, Xingcheng Wang, Zheng School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China Zhongguancun Laboratory Beijing China The School of Computing University of Leeds United Kingdom

ISBN: (纸本)9781643684369

Reasoning complex logical queries on incomplete and massive knowledge graphs (KGs) remains a significant challenge. The prevailing method for this problem is query embedding, which embeds KG units (i.e., entities and relations) and complex queries into low-dimensional space. Recent developments in the field show that embedding queries as geometric shapes is a viable means for modeling entity set and logical relationships between them. Despite being promising, current geometric-based methods face challenges in capturing hierarchical structures of complex queries, which leaves considerable room for improvement. This paper presents POINE2, a geometric-based query embedding framework based on hyperbolic geometry to handle complex queries on knowledge graphs. POINE2 maps entities and queries as geometric shapes on a Cartesian product space of Poincaré ball spaces. To capture the hierarchical structures of complex queries, we use the Poincaré radius to represent the different levels of the hierarchy, and we use the aperture of the shape to indicate semantic differences at the same level of the hierarchy. Additionally, POINE2 offers a flexible and expressive definition of logical operations. Experimental results show that POINE2 outperforms existing salient geometric-based embedding methods and significantly improves these methods on evaluation datasets. © 2023 The Authors.

关键词： Knowledge graph

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A Comprehensive Approach to Mitigate Return-Oriented Programming Attacks: Combining Operating System Protection Mechanisms and Hardware-Assisted Techniques

A Comprehensive Approach to Mitigate Return-Oriented Program...

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International Conference on Software Engineering and Computer Systems (ICSECS)

作者： Xingnan Zhang Jingjia Huang Yue Feng Computing & Data Science Xiamen University Sepang Malaysia

This paper proposes a comprehensive approach to mitigate ROP (Return-Oriented Programming) attacks by combining internal operating system protection mechanisms and hardware-assisted techniques. Through extensive literature review, we identify the effectiveness of ASLR (Address Space Layout Randomization) and LBR (Last Branch Record) in preventing ROP attacks. We present a process involving buffer overflow detection, hardware-assisted ROP attack detection, and the use of Turing detection technology to monitor control flow behavior. We envision a specialized tool that views and analyzes the last branch record, compares control flow with a baseline, and outputs differences in natural language. This tool offers a graphical interface, facilitating the prevention and detection of ROP attacks. The proposed approach and tool provide practical solutions for enhancing software security.

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai 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 School of Computer Science and Technology Huazhong University of Science and Technology 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

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models

arXiv

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arXiv 2025年

作者： Chen, Michael K. Zhang, Xikun Tao, Dacheng Raffles Institution Singapore College of Computing & Data Science Nanyang Technological University Singapore

Logical reasoning is a critical component of Large Language Models (LLMs), and substantial research efforts in recent years have aimed to enhance their deductive reasoning capabilities. However, existing deductive reasoning benchmarks, which are crucial for evaluating and advancing LLMs, are inadequate due to their lack of task complexity, presence of prior knowledge as a confounder, and superficial error analysis. To address these deficiencies, we introduce JustLogic, a synthetically generated deductive reasoning benchmark designed for rigorous evaluation of LLMs. JustLogic is (i) highly complex, capable of generating a diverse range of linguistic patterns, vocabulary, and argument structures;(ii) prior knowledge independent, eliminating the advantage of models possessing prior knowledge and ensuring that only deductive reasoning is used to answer questions;and (iii) capable of in-depth error analysis on the heterogeneous effects of reasoning depth and argument form on model accuracy. Our experimental results on JustLogic reveal that most state-of-the-art (SOTA) LLMs perform significantly worse than the human average, demonstrating substantial room for model improvement. All code and data are available at https://***/michaelchen-lab/JustLogic © 2025, CC BY.

关键词： Linguistics

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RETHINKING LIGHT DECODER-BASED SOLVERS FOR VEHICLE ROUTING PROBLEMS

arXiv

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arXiv 2025年

作者： Huang, Ziwei Zhou, Jianan Cao, Zhiguang Xu, Yixin School of Computing and Information Systems Singapore Management University Singapore College of Computing and Data Science Nanyang Technological University Singapore

Light decoder-based solvers have gained popularity for solving vehicle routing problems (VRPs) due to their efficiency and ease of integration with reinforcement learning algorithms. However, they often struggle with generalization to larger problem instances or different VRP variants. This paper revisits light decoder-based approaches, analyzing the implications of their reliance on static embeddings and the inherent challenges that arise. Specifically, we demonstrate that in the light decoder paradigm, the encoder is implicitly tasked with capturing information for all potential decision scenarios during solution construction within a single set of embeddings, resulting in high information density. Furthermore, our empirical analysis reveals that the overly simplistic decoder struggles to effectively utilize this dense information, particularly as task complexity increases, which limits generalization to out-of-distribution (OOD) settings. Building on these insights, we show that enhancing the decoder capacity, with a simple addition of identity mapping and a feed-forward layer, can considerably alleviate the generalization issue. Experimentally, our method significantly enhances the OOD generalization of light decoder-based approaches on large-scale instances and complex VRP variants, narrowing the gap with the heavy decoder paradigm. Our code is available at: https://***/ziweileonhuang/reld-nco. Copyright © 2025, The Authors. All rights reserved.

关键词： Vehicle routing

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The Study of Power Planning for Cyclists based on Genetic Algorithm 3

The Study of Power Planning for Cyclists based on Genetic Al...

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3rd International Conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Wu, Sixuan Tan, Qiancheng Xu, Dewei Mao, Rui School of Mathematics and Computing Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

This paper studies the power curve of two types riders (the Time Trial Specialist and the Sprinter) and set up a multi-objective optimization model by taking the total energy consumed and the total time used by a rider during the race as objective functions. By using the genetic algorithm to solve the multi-objective optimization model, the total energy and total time as far as possible to get the optimal solution under the condition of small number. The number of the optimal solutions corresponding to the rider in the process of competition has the opportunity to go beyond the opponent's corner number. The number of the optimal solutions can determine the relationship between the rider’s position on the course and the power the rider applies. Combining 2021 Tokyo Olympic Games, the model is applied to the individual time trials of two types of riders. © 2023 SPIE.

关键词： Genetic algorithms

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