检索结果-内蒙古大学图书馆

International Conference on Control Communication & computing India (ICCC)

作者： Varsha Varghese Mahalakshmi S Senthilkumar Kb Cyber Forensics and Information Security Department of Computer Science & Engineering ER&DCI Institute of Technology Thiruvananthapuram Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram Scientict_E Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of Cyber Threat Intelligence which is the information about the techniques, tactics, or motives of an emerging threat. The life cycle of a Darknet-based Cyber Threat Intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of Data from the Darknet. The Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. The Analysis part is integral to the Threat Intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. The production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making Cyber Threat Intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. The collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. The experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. This could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Robust Domain Generalization for Multi-modal Object Recognition

arXiv

引用

arXiv 2024年

作者： Qiao, Yuxin Li, Keqin Lin, Junhong Wei, Rong Jiang, Chufeng Luo, Yang Yang, Haoyu Department of Computer Information Technology Northern Arizona University FlagstaffAZ United States Department of Computer Science AMA University Quezon Philippines Electrical Engineering & Computer Science Department Massachusetts Institute of Technology CambridgeMA United States Academy for Advanced Interdisciplinary Studies Peking University Beijing China Department of Computer Science The University of Texas at Austin AustinTX United States Department of Computer Science University of Southern California Los AngelesCA United States College of Computing Georgia Institute of Technology AtlantaGA United States

In multi-label classification, machine learning encounters the challenge of domain generalization when handling tasks with distributions differing from the training data. Existing approaches primarily focus on vision object recognition and neglect the integration of natural language. Recent advancements in vision-language pre-training leverages supervision from extensive visual-language pairs. This allows learning across diverse domains and enhances recognition in multi-modal scenarios, showcasing superior transfer learning capabilities in methods like CLIPood. However, CLIPood has several limitations: differences in the utilized loss, loss of generality in evaluating only a single backbone, and neglect of class-aware visual fusion. To address these, we propose this paper that infers the actual loss based on the implementation, broadens evaluations to larger vision-language backbones, and introduces Mixup-CLIPood with a novel mix-up loss for enhanced class-aware visual fusion. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Efficient Quantum Simulation Algorithms in the Path Integral Formulation

arXiv

引用

arXiv 2024年

作者： Shum, Serene Wiebe, Nathan University of Toronto Department of Physics Toronto Canada University of Toronto Department of Computer Science Toronto Canada Pacific Northwest National Laboratory High Performance Computing Division Richland United States Canadian Institute for Advanced Study Toronto Canada

We provide a new paradigm for quantum simulation that is based on path integration that allows quantum speedups to be observed for problems that are more naturally expressed using the path integral formalism rather than the conventional sparse Hamiltonian formalism. We provide a quantum algorithm for Lagrangians of the form ∑γ mγ/2 x 2γ − V (x), as well as two novel quantum algorithms based on Hamiltonian versions of the path integral formulation. The Lagrangian path integral algorithm is based on a new rigorous derivation of a discrete version of the Lagrangian path integral. We show that for the time evolution of a system with η particles in D + 1 dimensions for time T, our Lagrangian simulation algorithm requires a number of queries to an oracle that computes the discrete Lagrangian that scales in the continuum limit as Oe(ηDT2/ϵ), if V (x) is bounded and finite and the wave function obeys appropriate position and momentum cutoffs. This shows that Lagrangian dynamics can be efficiently simulated on quantum computers and opens up the possibility for quantum field theories for which the Hamiltonian is unknown to be efficiently simulated on quantum computers. Our first Hamiltonian path integral method breaks up the paths into short time steps. It is efficient under appropriate sparsity assumptions and requires a number of queries to oracles that give the eigenvalues and overlaps between the eigenvectors of the Hamiltonian terms that scales as to(1)/ϵo(1) for simulation time t and error ϵ. The second approach uses long-time path integrals for near-adiabatic systems and has query complexity that scales as O(1/√ϵ) if the energy eigenvalue gaps and simulation time is sufficiently long. © 2024, CC BY.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Efficiently Verifiable Quantum Advantage on Near-Term Analog Quantum Simulators

引用

PRX Quantum 2025年第1期6卷 010341-010341页

作者： Zhenning Liu Dhruv Devulapalli Dominik Hangleiter Yi-Kai Liu Alicia J. Kollár Alexey V. Gorshkov Andrew M. Childs Joint Center for Quantum Information and Computer Science NIST/University of Maryland College Park Maryland 20742 USA Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Department of Computer Science University of Maryland College Park Maryland 20742 USA Simons Institute for the Theory of Computing University of California at Berkeley California 94720 USA Applied and Computational Mathematics Division National Institute of Standards and Technology (NIST) Gaithersburg Maryland 20899 USA Department of Physics University of Maryland College Park Maryland 20742 USA Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA

Existing schemes for demonstrating quantum computational advantage are subject to various practical restrictions, including the hardness of verification and challenges in experimental implementation. Meanwhile, analog quantum simulators have been realized in many experiments to study novel physics. In this work, we propose a quantum advantage protocol based on single-step Feynman-Kitaev verification of an analog quantum simulation, in which the verifier need only run an O(λ2)-time classical computation, and the prover need only prepare O(1) samples of a history state and perform O(λ2) single-qubit measurements, for a security parameter λ. We also propose a near-term feasible strategy for honest provers and discuss potential experimental realizations.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Maestro: A Robust Multi-Head Attention Enhanced CNN Architecture for Heat-Induced Stress Recognition Using EEG Signals

Maestro: A Robust Multi-Head Attention Enhanced CNN Architec...

引用

International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Sagnik De Sreenija Pavuluri Amaan Sayyad Anil Kumar Gupta Institute of Radio Physics and Electronics University of Calcutta Kolkata India Department of Computer Science Engineering GVPCE Visakhapatnam AP India Department of Computer Engineering MESWCOE Pune Maharashtra India HPC I&E Group Centre for Development of Advanced Computing Pune Maharashtra India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Heat-induced stress impacts various physiological parameters in the body. Elevated temperature can cause tachycardia (an increase in heart rate), as the body attempts to dissipate heat through vasodilation, leading to dehydration and electrolyte imbalances. In addition, the hypothalamus triggers sweating in order to regulate body temperature; that can culminate in fluid and electrolyte loss, which could impact metabolic processes and blood pressure. A prolonged exposure to high temperatures can cause heat stroke, heat exhaustion, and other ailments including organ damage and systemic dysfunction. Existing electroencephalography (EEG)-based heat-induced stress detection often considers the entire EEG frequency range (delta to gamma), concealing redundant and lossy information and increasing the likelihood of false detection rates. To address the limitations of conventional handcrafted feature engineering approaches in heat stress detection, this paper introduces MAESTRO, a novel model comprising two blocks: Convolutional and Multi-head Attention. The Convolutional block extracts precise information from individual EEG frequency bands, while the Multi-head Attention block enhances feature representation through attention mechanism. Finally, two dense layers are employed to classify heat stress into three classes: Acute, Chronic, and Control. The proposed framework undergoes validation using EEG data obtained from 40 rodents in a simulated laboratory environment. The outcomes illustrate the viability of the method in classifying heat-induced stress, yielding remarkable results for overall accuracy, precision, recall, and F1 score of 98.88 %, 98.54 %, 98. 67 %, and 98.60 %, respectively.

关键词： Heating systems Accuracy Convolution Rodents Feature extraction Brain modeling Electrolytes Electroencephalography Data models Stress

来源：评论

学校读者我要写书评

暂无评论

Cloud-based Decentralized Smart Healthcare for Patient Monitoring on Deep Learning 2

Cloud-based Decentralized Smart Healthcare for Patient Monit...

引用

2nd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2023

作者： Arunachalam, Sripriya Shanthi, H.J. Sivagurunathan, G. Das, Shyamali Anand, D. Raj M, Thanga School of Computing Sciences Vels Institute of Science Technology & Advanced Studies - Vistas Computer Applications- Pg India Amet University Ecr Kanathur India St. Joseph's College of Engineering Omr Department of Electronics and Communication Engineering Chennai600119 India Cmr Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Persistent Systems Limited Pune India

ISBN: (纸本)9781665456302

Over the past few years, there has been a meteoric surge in the quantity of digital information available online for instantaneous sharing, persistent archiving, and inquiring. It has expanded the possibilities for using digital data that is both decentralised and ad hoc in order to make decisions quickly. At present, e-Healthcare is among the most sought-after sectors for EHR and telemedicine communication. Securing electronic health records (EHR) has become a topic of intense interest in recent years, with previous works employing a wide range of methods to better ensure the confidentiality and security of EHR at a reasonable price. There are a number of serious problems with the current research, including computational complexity, increased process time, information leakage, vulnerability to various assaults, scalability difficulty, etc. Clinical data analysis presents several difficulties, but disease prediction is one of the most significant ones. The suggested study aims to apply deep learning (DL) classification algorithms for disease prediction. A technique that utilises cloud computing, fog computing, and IoMT more recently has been presented for diagnosing illness. Fast DL classification analysis is performed in the fog layer. When compared to the alternative proposed model Bi-CNN, the healthcare model's efficiency in the Bi-LSTM simulation yields significantly better results: 97.31% of accuracy, 97.58% of recall, 96.90% of precision, 94.90% of F1-measure, 97.25% of specificity, and 94.80% of G-mean. © 2023 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning

arXiv

引用

arXiv 2024年

作者： Huan, Ouwen Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Air Quality Index Prediction Using Support Vector Regression Based on African Buffalo Optimization 9th

Air Quality Index Prediction Using Support Vector Regression...

引用

9th International Conference on computing and Informatics, ICOCI 2023

作者： Yusof, Yuhanis Maijama’a, Inusa Sani School of Computing Institute for Advanced and Smart Digital Opportunities Universiti Utara Malaysia UUM Sintok Kedah Bukit Kayu Hitam06010 Malaysia Department of Computer Science Hussaini Adamu Federal Polytechnic Kazaure Kazaure Nigeria

ISBN: (纸本)9789819995912

Support Vector Regression (SVR) is one of the machine learning models widely used in regression analysis. As an alternative for fitting a line to the data points like typical linear regression algorithms, it finds a hyperplane that is effective in fitting data points in a continuous space. The kernel type and hyperparameters significantly influence the performance and effectiveness of SVR. Determination of the optimal values is crucial in ensuring the success of prediction, regardless of the application domain. This study adapts the African Buffalo Optimization (ABO) algorithm to determine SVR’s regularization and kernel parameters. The ABO algorithm mirrors African buffaloes’ hunting and defensive behavior, offering ability to track the best position and extensive memory capacity to discover the best solution for problems under analysis. Evaluation is then performed on the air quality index benchmark dataset, and the prediction results of SVR-ABO are compared against other optimized SVR prediction models. The results show that SVR-ABO is a better algorithm because it produces smaller errors and best fits the data. Such an outcome indicates that the proposed SVR optimized by ABO is a competitive prediction model in data analytics. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Risk Assessment for Nonlinear Cyber-Physical Systems under Stealth Attacks

arXiv

引用

arXiv 2024年

作者： Chen, Guang Sun, Zhicong Ding, Yulong Yang, Shuang-Hua Department of Mechanical and Energy Engineering Southern University of Science and Technology China Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet SUSTech Shenzhen518055 China Department of Computer Science and Engineering SUSTech Shenzhen518055 China Department of Computer Science University of Reading United Kingdom

Stealth attacks pose potential risks to cyber-physical systems because they are difficult to detect. Assessing the risk of systems under stealth attacks remains an open challenge, especially in nonlinear systems. To comprehensively quantify these risks, we propose a framework that considers both the reachability of a system and the risk distribution of a scenario. We propose an algorithm to approximate the reachability of a nonlinear system under stealth attacks with a union of standard sets. Meanwhile, we present a method to construct a risk field to formally describe the risk distribution in a given scenario. The intersection relationships of system reachability and risk regions in the risk field indicate that attackers can cause corresponding risks without being detected. Based on this, we introduce a metric to dynamically quantify the risk. Compared to traditional methods, our framework predicts the risk value in an explainable way and provides early warnings for safety control. We demonstrate the effectiveness of our framework through a case study of an automated warehouse. Copyright © 2024, The Authors. All rights reserved.

关键词： Cyber Physical System

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：