检索结果-内蒙古大学图书馆

Demystifying Cyber terrorism: Causes, Costs & its impact on individuals' psyche and public confidence

Demystifying Cyber terrorism: Causes, Costs & its impact on ...

2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

作者： Zahra, Syed Rameem Saleem, Tausifa Jan Sonia Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India Indian Institute of Technology Delhi Department of Electrical Engineering Delhi India Shoolini University Yogananda School of Ai Computer and Data Science H.P Solan India

ISBN: (纸本)9781665493826

Just like the game for Information Technology (IT) security was turned topsy-turvy by the arrival of mobile devices a few decades ago, the Internet of Things (IoT) today is changing it again a big time. Data breaches, loss of productivity and profitability were quite a commonplace when mobile security became an issue. Today as IoT tries to connect everything to the internet, it intensifies the problem (more privacy violations, phishing/skimming attacks, security incidents) by creating a vast attack surface for the rogue players implying that important national (ports, airports, bridges, power-pants etc.) and cyber infrastructure could now be attacked. Moreover, if the network of your company has a weak point, it can easily be compromised to set off an attack. These Cyber terrorism attacks instigate identical responses from individuals as those for conventional terrorism attacks e.g. they heighten anxiety and stress, worsen feelings of vulnerability and harden the political attitudes of people. But this dimension is often overlooked by the policy makers. However, the increase in cyber terrorism cases has increased the research interest in the domain. Researchers are focusing on improving the security of internet and IoT devices. The field is still in its infancy. A lot needs to be done to mitigate these attacks. Motivated to demystify the repercussions of the cyber terrorism, this paper analyzes the distinctive characteristics of the most significant cyberattacks, investigates the expenditures incurred by the industries as a result of these cyber events, and then examines the impact of these attacks on the psychological health of the populace and the trust in public institutions. © 2023 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

air Quality Control using Machine Learning

Air Quality Control using Machine Learning

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： K. RamaKrishna Reddy D. Vidya Shree N. Vasu N. Muthukumaran K. Saketh Patel N. Vamshi Karthik Rao Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In recent times, the world has witnessed remarkable advancements driven by various technologies such as advanced computing, self-driving vehicles, and robot-assisted surgeries, among others. However, these technological strides have come with a significant environmental cost, contributing to the pollution of vital natural resources such as air and water. The contamination of air and water worsens health issues, particularly for individuals with respiratory ailments such as asthma and lung infections, posing risks to the overall population’s well-being. This research study has carried out a qualitative research and presents the challenges that demand careful navigation and interpretation in the near future.

关键词： Support vector machines Atmospheric modeling Data visualization Surgery Robot sensing systems Water pollution Decision trees

来源：评论

学校读者我要写书评

暂无评论

Efficient Cell Segmentation in Microscopy Images using Sub-Sampling Integrated SVM 2

Efficient Cell Segmentation in Microscopy Images using Sub-S...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ramprasath, J. Kumar, M. Arun Krishnaraj, N. Vignesh, C. Chandru Saranya, N. Ramprakash, P. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of AI&DS Coimbatore India

ISBN: (纸本)9798331534950

Support Vector Machine (SVM) integrated with subsampling methods provides the latest approach to cell segmentation of microscopic images. This approach aims to increase computing efficiency and also maintain segmentation accuracy. To begin with, the processing load associated with large-scale microscopic images is reduced by employing subsampling techniques. On applying Support Vector Machine (SVM), classification of the picture patches into cell and non-cell portions based on the gathered attributes. The produced method is highly valuable for high-throughput analysis in medical and biological diagnostics as it provides competitive segmentation performance while drastically cutting down time for its process. Through a detailed analysis from rigorous testing and comparison with existing methods, efficiency and efficacy are demonstrated in accurately differentiating cells from microscopic images and extending solutions for vast biomedical applications. © 2024 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

引用

computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

来源：评论

学校读者我要写书评

暂无评论

Slice-Level Label Attention with Global-Guided Attention Regularization for Multi-Label Classification in Knee MRI Sequences

Slice-Level Label Attention with Global-Guided Attention Reg...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Yang, Jingzhi Shen, Hua Liu, Ziyu Wu, Weilong Wu, Ji Ni, Ming Yuan, Huishu Fu, Xiangling Li, Miao National Pilot Software Engineering School Bupt School of Computer Science Beijing China Tsinghua University College of Ai Department of Electronic Engineering Beijing China Peking University Third Hospital Beijing China

ISBN: (纸本)9798350362480

Magnetic Resonance Imaging (MRI) is crucial for diagnosing various knee-related diseases, and developing automatic diagnostic models based on knee MRI data is highly valuable. However, this task presents significant challenges due to the need to manage MRI data with multiple sequences and numerous images, where different diseases are often associated with specific images within certain sequences. To address these challenges, we propose a multi-label classification framework designed to effectively process MRI data and handle a large-scale label space encompassing hundreds of disease categories. Our approach introduces a Slice-Level Label Attention mechanism, which enables the model to learn the alignment between labels and images within sequences, thereby enhancing both performance and interpretability. Additionally, we present a Global-Guided Attention Regularization mechanism that further improves the consistency and robustness of the Slice-Level Label Attention results. We validate our framework on a large-scale MRI dataset involving multi-label classification across hundreds of fine-grained disease categories. Experimental results demonstrate that our method not only achieves superior performance but also provides more robust and consistent interpretability. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Noise contrastive alignment of language models with explicit rewards 24

Noise contrastive alignment of language models with explicit...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Huayu Chen Guande He Lifan Yuan Ganqu Cui Hang Su Jun Zhu Department of Computer Science and Technology Tsinghua University and Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University Department of Computer Science and Technology Tsinghua University Department of Computer Science and Technology Tsinghua University and Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University and Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798331314385

User intentions are typically formalized as evaluation rewards to be maximized when fine-tuning language models (LMs). Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given. In this paper, we introduce a general framework for LM alignment, leveraging Noise Contrastive Estimation (NCE) to bridge the gap in handling reward datasets explicitly annotated with scalar evaluations. Our framework comprises two parallel algorithms, NCA and InfoNCA, both enabling the direct extraction of an LM policy from reward data as well as preference data. Notably, we show that the DPO loss is a special case of our proposed InfoNCA objective under pairwise preference settings, thereby integrating and extending current alignment theories. By comparing NCA and InfoNCA, we demonstrate that the well-observed decreasing-likelihood trend of DPO/InfoNCA is caused by their focus on adjusting relative likelihood across different responses. In contrast, NCA optimizes the absolute likelihood for each response, thereby effectively preventing the chosen likelihood from decreasing. We evaluate our methods in both reward and preference settings with Mistral-8 x7B and 7B models. Experiments suggest that InfoNCA/NCA surpasses various preference baselines when reward datasets are available. We also find NCA significantly outperforms DPO in complex reasoning tasks like math and coding. Code: https://***/thu-ml/Noise-Contrastive-Alignment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Dual-Prompting for Interpretable Mental Health Language Models 9

A Dual-Prompting for Interpretable Mental Health Language Mo...

引用

9th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2024

作者： Jeon, Hyolim Yoo, Dongje Lee, Daeun Son, Sejung Kim, Seungbae Han, Jinyoung Department of Applied Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Human-AI Interaction Sungkyunkwan University Seoul Korea Republic of Department of Computer Engineering Chung-Ang University Seoul Korea Republic of Computer Science & Engineering Department University of South Florida TampaFL United States

ISBN: (纸本)9798891760806

Despite the increasing demand for ai-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task1 aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content. We propose a dual-prompting approach: (i) Knowledge-aware evidence extraction by leveraging the expert identity and a suicide dictionary with a mental health-specific LLM;and (ii) Evidence summarization by employing an LLM-based consistency evaluator. Comprehensive experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements and the approach’s potential to aid clinicians in assessing mental state progression. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Supply Chain Regulatory Compliance with Blockchain and Lasso Regression Integration 2

Enhancing Supply Chain Regulatory Compliance with Blockchain...

引用

2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Aelgani, Vivekanand Jothi, E. Hussein, Ramy Riad Ramesh, Banoth Sharma, Himanshu Uganya, G. CMR College of Engineering & Technology Department of CSE Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of IT Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of AI&ML Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of ECE Chennai600062 India

ISBN: (纸本)9798350368109

Adopting lasso regression besides blockchain gives the best solution in improving the regulatory compliance in supply chains. For this integration, the feature of blockchain technology which applies for secure, transparency and unchangeable record keeping is used, For lasso regression the variable selection and predictive model are used. Thus, blockchain offers supply chain participants an opportunity to perform all transactions in a decentralized manner and maintain an unalterable, open record of all the transactions that occur in the supply chain. At the same time, the lasso regression models are used to predict compliance risks based on the data obtained and choosing the features that might be useful for this analysis from a vast set of possibilities. Smart contracts are the other strategies used in performing compliance checks in real-time and reporting instances that indicate non-compliance so that the necessary intervention is done. This system does not only administers and optimizes the press compliance procedure but also improves prediction rate and reduces the risk of encountering new compliance issues. Further, the changes are made continuously in the model and the feedbacks from the stakeholders add to the fact that the system is fit to adapt to the changes in the regulatory environment and supply chain industry. In doing so, it also enhances the visibility, relevancy, reliability, and effectiveness of the regulation compliance enhancing better trust and operation in the overall supply chain. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

DeepCNN:Spectro-temporal feature representation for speech emotion recognition

引用

CAai Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

来源：评论

学校读者我要写书评

暂无评论

News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

引用

3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (ai) and natural language processing (NLP) has significantly advanced stock market forecasting. ai's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where ai has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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：