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Exact satisfiability and phase transition analysis of the regular(k,d)-CNF formula

Frontiers of computer Science 2024年第1期18卷 263-265页

作者： Guoxia NIE Daoyun XU Xi WANG Zaijun ZHANG College of Computer Science and Technology Guizhou UniversityGuiyang 550025China School of Mathematics and Statistics Qiannan Normal University for NationalitiesDuyun 558000China Key Laboratory of Industrial Automation and Machine Vision of Qiannan Duyun 558000China

1Introduction The satisfiability(SAT)problem has been considered the"seed"of other NP-complete *** regular partial exact(k,d)-SAT problem is an important extension of the SAT *** any(k,d)-CNF formula with a variable set V,V'is a proper subset of V,the problem involves determining whether a truth assignment set on V'exists such that only a literal in each clause is *** V'=V,it is a regular exact(k,d)-SAT ***,both experimental verifications and theoretical analyses of k-SAT problem have shown that the ratioα(clause constraint density)of the number of clauses m to the number of variables n is an important parameter affecting the satisfiability of the formula[1].However,the regular(k,d)-SAT problem has the same clause constraint density d/k.

关键词： formula constraint exact

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QAVA: Query-Agnostic Visual Attack to Large vision-Language Models

arXiv

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arXiv 2025年

作者： Zhang, Yudong Xie, Ruobing Sun, Xingwu Chen, Jiansheng Kang, Zhanhui Wang, Yu Department of Electronic Engineering Tsinghua University China Machine Learning Platform Department Tencent China School of Computer and Communication Engineering University of Science andTechnology Beijing China Faculty of Science and Technology University of Macau China State Key Laboratory of Space Network and Communications Tsinghua University China

In typical multimodal tasks, such as Visual Question Answering (VQA), adversarial attacks targeting a specific image and question can lead large vision-language models (LVLMs) to provide incorrect answers. However, it is common for a single image to be associated with multiple questions, and LVLMs may still answer other questions correctly even for an adversarial image attacked by a specific question. To address this, we introduce the query-agnostic visual attack (QAVA), which aims to create robust adversarial examples that generate incorrect responses to unspecified and unknown questions. Compared to traditional adversarial attacks focused on specific images and questions, QAVA significantly enhances the effectiveness and efficiency of attacks on images when the question is unknown, achieving performance comparable to attacks on known target questions. Our research broadens the scope of visual adversarial attacks on LVLMs in practical settings, uncovering previously overlooked vulnerabilities, particularly in the context of visual adversarial threats. The code is available at https://***/btzyd/qava. © 2025, CC BY.

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A mathematical formula retrieval method using structure sub-tree

A mathematical formula retrieval method using structure sub-...

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3rd International Conference on Materials Science and Information Technology, MSIT 2013

作者： Guan, Ming Jie Tian, Xue Dong Yang, Fang Yang, Song Qiang College of Mathematics and Computer Science Hebei University Baoding China Hebei key laboratory of Machine Learning and Computational Intelligence Hebei University China

ISBN: (纸本)9783037857700

It is quite inadequate in providing formula retrieval function by traditional retrieval techniques used in full-text information retrieval system. The main reason is that there are many difficulties to extract the keywords of the mathematical formulas. In this paper, a detailed analysis of the structural characteristics of mathematical formulas and existing index mechanism of mathematical formula searching engine is fulfilled. Then a full-text index (named SLIndex) of mathematical formulas with B+ tree structure is designed and implemented which extracts the structured logic sub-tree feature as keywords of formulas and employs inverted index. Finally, a formula search engine model based on SLIndex is implemented in Apache 2.0 web server. © (2013) Trans Tech Publications, Switzerland.

关键词： Search engines

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Wi-Fi Signal Analysis for Heartbeat and Metal Detection: A Comparative Study of Reliable Contactless Systems 6

Wi-Fi Signal Analysis for Heartbeat and Metal Detection: A C...

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6th International Conference on Dependable Systems and Their Applications, DSA 2019

作者： Khan, Faiza Alam Bhuiyan, Md Zakirul Islam, Md Monirul Wang, Tian Zaman, Aliuz Tao, Hai Fordham University Department of Computer and Information Sciences New York United States Huaqiao University College of Computer Science and Technology Key Laboratory of Computer Vision and Machine Learning Huaqiao361021 China School of Computer Science Baoji University of Arts and Sciences Baoji Shaanxi721007 China

ISBN: (纸本)9781728160573

In this survey paper, we present various proposals of Wi-Fi applications which can benefit society, namely through detection of vital signs and detection of suspicious objects. Based on the previous research conducted in this field, we analyze how heart rate detection using Wi-Fi dependent technologies compare to older, sensor-based methods and the necessity of modern implementations especially in the medical field. People can have heart problems such as tachycardia and bradycardia at any instance, thus systems which do not require contact are most practical in detection of vital sign irregularities. Sensorbased systems may depend on Wi-Fi technologies only for the communication between different components in the system, such as in the Message Queuing Telemetry Transport System. However, the presence of a sensor in these systems can be replaced by Wi-Fi technologies to detect the presence of people and monitor their corresponding heart rates in a contactless manner. Additionally, Wi-Fi dependent systems such as Vital Radio and Wi-Sleep can monitor the heart rates of multiple people at once. Therefore contactless, Wi-Fi based systems are most efficient and practical for daily use. Moreover, although contactless systems demonstrate the benefits of technological advancements, these systems can still be further improved since they tend to produce higher accuracy rates from shorter distances, as demonstrated by the UWB radar system and the Fresnel model. Contactless systems can be enhanced to detect multiple users at once at extended distances. After providing details of contact requiring and contactless systems for heart rate detection, we also provide other useful applications of Wi-Fi technology, such as in the field of metal detection, human fall detection, etc. © 2020 IEEE.

关键词： Heart

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Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering

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Tsinghua Science and Technology 2022年第5期27卷 855-868页

作者： Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province the College of Mathematics and Information ScienceHebei UniversityBaoding 071002China School of Applied Mathematics Beijing Normal University ZhuhaiZhuhai 519087China Department of Computer Teaching Hebei UniversityBaoding 071002China

The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation *** a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among *** these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering ***,the diversity and amount of input information are enhanced to generate highly useful representations for downstream ***,the topological graph is constructed to express the spatial relationship of *** a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively *** external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering *** results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service *** GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech ***,DBI indicates the dispersion of cluster distribution and compactness within the cluster.

关键词： self-supervised clustering graph convolutional network feature correspondence semantic feature guidance confusion matrix evaluation indicator

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Unsupervised ensemble learning based on graph embedding for image clustering 25th

Unsupervised ensemble learning based on graph embedding for ...

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25th International Conference on Neural Information Processing, ICONIP 2018

作者： Luo, Xiaohui Zhang, Li Li, Fanzhang Hu, Chengxiang School of Computer Science and Technology and Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (纸本)9783030041816

Manifold learning has attracted more and more attention in machine learning for past decades. Unsupervised Large Graph Embedding (ULGE), which performs well on the large-scale data, has been proposed for manifold learning. To improve the clustering performance, a novel Unsupervised Ensemble learning based on Graph Embedding (UEL-GE) is explored, which takes ULGE to get low-dimensional embeddings of the given data and uses the K-means method to obtain the clustering results. Furthermore, the multiple clusterings are corrected by using the bestMap method. Finally, the corrected clusterings are combined to generate the final clustering. Extensive experiments on several data sets are conducted to show the efficiency and effectiveness of the proposed ensemble learning method. © Springer Nature Switzerland AG 2018.

关键词： K-means clustering

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Exploring the usefulness of light field super-resolution for object detection

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The Journal of China Universities of Posts and Telecommunications 2021年第5期28卷 68-81页

作者： Zhang Wenzhe Shi Fan Zhao Meng Chen Shengyong Engineering Research Center of Learning-Based Intelligent System of Ministry of Education Tianjin University of TechnologyTianjin 300384China The Key Laboratory of Computer Vision and System of Ministry of Education Tianjin University of TechnologyTianjin 300384China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China The MOE Key Laboratory of Weak-Light Nonlinear Photonics Nankai UniversityTianjin 300457China

In order to solve the impact of image degradation on object detection, an object detection method based on light field super-resolution(LFSR) is proposed. This method takes LFSR as an image enhancement step to provide high-quality images for object detection without using expensive imaging equipment. To evaluate this method, three types of objects: person, bicycle, and car, are chosen and the results are compared from 5 parts: detected object quantity, mean confidence score, detection results in different scenes, error detection, and detection results from different images sizes and detection speed. Experimental results based on the common object in context(COCO) dataset show that the method incorporated LFSR improves performance of object detection models.

关键词： light-field(LF) super-resolution(SR) object detection RetinaNet YOLOv3 TinyYOLOv3

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An improved particle swarm optimization algorithm for task scheduling in cloud computing

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4313-4327页

作者： Pirozmand, Poria Jalalinejad, Hoda Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Li, Yingqiu Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding071002 China Department of Mathematics and Computer Science Bandar Abbas Branch Islamic Azad University Bandar Abbas Iran Department of Computer Science University of Regina Regina Canada Tehran Iran

In the context of cloud computing, the task scheduling issue has an immediate effect on service quality. Task scheduling is the process of assigning work to available resources based on requirements. The objective of this NP-hard problem is to identify the ideal timetable for resource allocation so that more tasks can be done in less time. Several algorithms have been presented thus far to solve the problem of work scheduling. In this paper proposes an Improved Particle Swarm Optimization (IPSO) algorithm to address the aforementioned issue. In order to shorten the execution time of the original Particle Swarm Optimization (PSO) algorithm for task scheduling in the cloud computing environment, a multi-adaptive learning strategy is employed. In its initial population phase, the proposed Multi Adaptive learning for Particle Swarm Optimization (MALPSO) defines two sorts of particles: ordinary particles and locally best particles. During this phase, the population's variety is reduced and the likelihood of reaching the local optimum rises. This study compares the proposed approach to various algorithms based on four criteria: makespan, load balancing, stability, and efficiency. Additionally, we examine the proposed technique using the CEC 2017 benchmark. Compared to what is currently known, the suggested method can solve the problem in less time and get the best answer for most of the criteria. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cloud computing

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UNDERSTANDING DOMAIN RANDOMIZATION FOR SIM-TO-REAL TRANSFER 10

UNDERSTANDING DOMAIN RANDOMIZATION FOR SIM-TO-REAL TRANSFER

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10th International Conference on learning Representations, ICLR 2022

作者： Chen, Xiaoyu Hu, Jiachen Jin, Chi Li, Lihong Wang, Liwei Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University China Department of Electrical and Computer Engineering Princeton University United States Amazon Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University International Center for Machine Learning Research Peking University China

Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization-one of the most popular algorithms for sim-to-real transfer-has been demonstrated to be effective in various tasks in robotics and autonomous driving. Despite its empirical successes, theoretical understanding on why this simple algorithm works is limited. In this paper, we propose a theoretical framework for sim-to-real transfers, in which the simulator is modeled as a set of MDPs with tunable parameters (corresponding to unknown physical parameters such as friction). We provide sharp bounds on the sim-to-real gap-the difference between the value of policy returned by domain randomization and the value of an optimal policy for the real world. We prove that sim-to-real transfer can succeed under mild conditions without any real-world training samples. Our theory also highlights the importance of using memory (i.e., history-dependent policies) in domain randomization. Our proof is based on novel techniques that reduce the problem of bounding the sim-to-real gap to the problem of designing efficient learning algorithms for infinite-horizon MDPs, which we believe are of independent interest. © 2022 ICLR 2022 - 10th International Conference on learning Representationss. All rights reserved.

关键词： learning algorithms

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Task-agnostic meta-learning for few-shot learning

arXiv

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arXiv 2018年

作者： Jamal, Muhammad Abdullah Qi, Guo-Jun Shah, Mubarak Laboratory for MAchine Perception and LEarning Center for Research in Computer Vision University of Central Florida

Meta-learning approaches have been proposed to tackle the few-shot learning problem. Typically, a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks. However, the generalizability on new tasks of a meta-learner could be fragile when it is over-trained on existing tasks during meta-training phase. In other words, the initial model of a meta-learner could be too biased towards existing tasks to adapt to new tasks, especially when only very few examples are available to update the model. To avoid a biased meta-learner and improve its generalizability, we propose a novel paradigm of Task-Agnostic Meta-learning (TAML) algorithms. Specifically, we present an entropy-based approach that meta-learns an unbiased initial model with the largest uncertainty over the output labels by preventing it from over-performing in classification tasks. Alternatively, a more general inequality-minimization TAML is presented for more ubiquitous scenarios by directly minimizing the inequality of initial losses beyond the classification tasks wherever a suitable loss can be defined. Experiments on benchmarked datasets demonstrate that the proposed approaches outperform compared meta-learning algorithms in both few-shot classification and reinforcement learning tasks. Copyright © 2018, The Authors. All rights reserved.

关键词： learning algorithms

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