检索结果-内蒙古大学图书馆

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Manière, Quentin Przybyłko, Marcin Department of Computer Science Leipzig University Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Leipzig Germany Institute of Informatics University of Warsaw Poland

ISBN: (纸本)157735897X

Recent works have explored the use of counting queries coupled with Description Logic ontologies. The answer to such a query in a model of a knowledge base is either an integer or ∞, and its spectrum is the set of its answers over all models. While it is unclear how to compute and manipulate such a set in general, we identify a class of counting queries whose spectra can be effectively represented. Focusing on atomic counting queries, we pinpoint the possible shapes of a spectrum over ALCIF ontologies: they are essentially the subsets of N ∪ {∞} closed under addition. For most sublogics of ALCIF, we show that possible spectra enjoy simpler shapes, being Jm, ∞K or variations thereof. To obtain our results, we refine constructions used for finite model reasoning and notably rely on a cycle-reversion technique for the Horn fragment of ALCIF. We also study the data complexity of computing the proposed effective representation and establish the FP^NP[log]-completeness of this task under several settings. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Expert systems

来源：评论

学校读者我要写书评

暂无评论

Multi-task Learning with Pre-trained Language Models for Mental Illness Screening

Multi-task Learning with Pre-trained Language Models for Men...

引用

IEEE International Conference on Big data

作者： Avantika Shrestha ML Tlachac Ricardo Flores Kevin Hickey Elke A. Rundensteiner Data Science Department Worcester Polytechnic Institute Worcester MA USA Department of Information Systems and Analytics Center for Health and Behavioral Sciences Bryant University Smithfield RI USA Department of Computer Science University of Concepción Concepción Chile Data Science Department Computer Science Department Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are mental disorders that reduce quality of life. As they are challenging to detect in a timely manner, recent studies explore the mental illness screening potential of language models on modalities such as transcripts. Such datasets suffer from a limited number of participants. To overcome these challenges, we take a two-pronged approach: (1) we leverage multi-task learning to model multiple illnesses concurrently namely MDD and PTSD screening, and (2) we plug in pre-trained language models as a backbone, namely, Bidirectional Encoder Representations from Transformers (BERT) variants – which are capable of learning the linguistic content of clinical interview transcripts. In particular, we experiment with three multi-task weighting strategies and five BERT variants, applying them to 15 transcript sets extracted from the Distress Analysis Interview Corpus. Our results indicate that leveraging multi-task learning, especially with meta-weighting, increases the screening performance compared to single-task learning. Our multi-task learning model strategies improved the F1 scores for all 15 datasets for both MDD and PTSD screening. Notably, multitask learning improved MDD screening ability by 20 percent with transcripts regarding ‘regret’ and achieved F1 of 0.89 and 0.82 respectively for MDD and PTSD screening with transcripts regarding ‘medical history’. Our findings may help researchers develop more effective mental illness screening models.

关键词： Mental disorders Bidirectional control Human factors Linguistics Multitasking Transformers Depression Encoding Interviews Plugs

来源：评论

学校读者我要写书评

暂无评论

Parallel Implementation of Random Forest using MPI

Parallel Implementation of Random Forest using MPI

引用

data analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Zyad Atef Allam Fatehan Tael Shajera Reem Jamal Majed Abdulla Alqaddoumi MSc. in Big Data Science and Analytics University of Bahrain Sakhir Bahrain Department of Computer Science University of Bahrain Sakhir Bahrain

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

computer vision is a quickly rising field in machine learning. It has many use cases and implementations across various industries. Powerful classifiers can be trained to recognize visual patterns and detect people and objects. However, since these models use images as inputs, they often end up being trained with very complex architectures, with a high number of epochs and very large data sizes. This often results in very long training times, especially for weaker machines. This study investigated the use of parallel processing to speed up machine learning model training. Message-passing Interface (MPI) is used to parallelize the training of Random Forest, achieving speed-ups of up to 5 and an efficiency of up to 1.1. The study concludes that when dealing with big and complex data and building complex models, parallel processing can significantly reduce the training time of the model with minimal loss in accuracy.

关键词： Training Industries Radio frequency Visualization Parallel processing data models Hardware

来源：评论

学校读者我要写书评

暂无评论

Multi-agent Scheduling for Shared Manufacturing Systems

引用

Procedia computer science 2025年 253卷 727-736页

作者： Ege Duran Cemalettin Ozturk Barry O’Sullivan SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments.

关键词： Multiagent systems Scheduling planning parallel machine

来源：评论

学校读者我要写书评

暂无评论

Exploring the Intersection of Blockchain and Distributed Quantum Computing: Synergies, Challenges, and Future Directions

Exploring the Intersection of Blockchain and Distributed Qua...

引用

Microwave, Antenna and Communication (MAC), International Conference on

作者： Madhusudan Singh Bharat Rawal Department of Data Analytics Long Island University Brooklyn New York USA Dept. of Computer Science Granbling State University Louisiana USA

ISBN: (数字)9798350350104

ISBN: (纸本)9798350350111

Distributed quantum computing has become a potential way to solve hard computing problems by combining the power of many quantum devices that are connected to each other. But it's hard to make sure that distributed quantum computer nodes can trust each other, stay safe, and work together well. The decentralized and open nature of blockchain technology holds significant potential in addressing these challenges and revolutionizing the domain of distributed quantum computing. This scholarly essay examines the potential of blockchain technology in facilitating the coordination, resource sharing, and data management within distributed quantum computing networks, with a focus on ensuring safety and efficiency. We provide an overview of the primary advantages, challenges, and potential applications of integrating blockchain technology into distributed quantum computing frameworks. This has the potential to result in a quantum computing environment that is both more stable and scalable. The objective of this scholarly essay is to elucidate the potential impact of integrating blockchain technology into distributed quantum computing. Distributed quantum computing networks may effectively coordinate, share resources, and manage data by using the decentralized, transparent, and secure nature of blockchain technology.

关键词： Microwave antennas Quantum computing Scalability Collaboration Termination of employment Blockchains Computational efficiency Resource management Research and development Standards

来源：评论

学校读者我要写书评

暂无评论

Multi-Modal data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

引用

2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Kandpal, Jyoti Jonitha, A. Sivakumar, Janaki Ray, Rejon Kumar School of Computing Graphic Era Hill University Uttarakhand Dehradun India Universiti Malaysia Faculty of Data Science & Computing Kelantan Malaysia Global College of Engineering and Technology Computer Science and Creative Technologies Muscat Oman Gannon University Department of Business Analytics PA Erie United States

ISBN: (纸本)9798350329773

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. 'Modality' is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the 'why' and the 'how' of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of 'diversity' as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis. © 2023 IEEE.

关键词： Modal analysis

来源：评论

学校读者我要写书评

暂无评论

Received Signal Strength Prediction Using Generative Adversarial Networks for Indoor Localization

Received Signal Strength Prediction Using Generative Adversa...

引用

2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Wu, Haochang Qin, Hao Ma, Siteng Lang, Hans-Dieter Zhang, Xingqi School of Electrical and Electronic Engineering University College Dublin Dublin Ireland School of Computer Science University College Dublin Insight Centre for Data Analytics Dublin Ireland Eastern Switzerland University of Applied Sciences OST SG Rapperswil Switzerland University of Alberta Department of Electrical and Computer Engineering Edmonton Canada

ISBN: (纸本)9798350312843

The popularity of WiFi networks has led to the adoption of fingerprint-based WiFi localization as one primary method for indoor location tracking. However, this technique requires a significant amount of time and effort to collect data at numerous reference points (RPs) to ensure accuracy. To reduce the cost and improve efficiency, generative models can be used to generate received signal strength (RSS) fingerprints. This paper proposes a Deep Convolutional Generative Adversarial Network (DCGAN) based model for building RSS fingerprint maps that can generate a comprehensive fingerprint database using only the location of a wireless access point. The proposed approach is expected to lower the cost and effort involved in the collection of RSS fingerprints while maintaining a high level of accuracy. © 2023 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

AgriLLM:Harnessing Transformers for Framer Queries

arXiv

引用

arXiv 2024年

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering India Department of Data Science & Computer Applications India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. Copyright © 2024, The Authors. All rights reserved.

关键词： Agriculture

来源：评论

学校读者我要写书评

暂无评论

Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Recognition Framework 1

Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Reco...

引用

1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Bhaskar, Seelam Ch Vijaya Kalyani, Gedela Al-Fatlawy, Ramy Alsalamy, Fatima Kurdi, Waleed Hadi Madhloom Al-Aboudy, H.M. Sujatha, G. MVSR Engineering College Department of IT Telangana Hyderabad India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Altoosi University College Department of Computer Science Iraq Mazaya University College Department of Computer Techniques Engineering DhiQar Iraq Arunai Engineering College Department of ECE Tamil Nadu Tiruvannamalai India

ISBN: (纸本)9798350367003

Applications such as industrial automation, healthcare, and environmental monitoring need the use of wireless sensor networks (WSNs). However, due to their dispersed organizational makeup, they have become vulnerable to security risks, particularly clone assaults. To protect confidentiality, availability, and confidentiality, several attacks must be recognized and prevented. This project aims to offer an effective method for identifying and averting clone assaults. To identify cloned both nationally and internationally use a low-cost verification process. In this study, we offer a new adaptive sea-horse optimized light gradient boosting machine (ASHO-LGBM) technique for protecting the network against node identity duplicates. The ASHO approach is used in the ASHO-LGBM framework to improve the recognition accuracy of the light gradient boosting machine (LGBM) characteristics. The replications with the nodes intrusion detection (ID) are used to choose a most trustworthy communication mode. The procedure is intended to be implemented and used for gathering data through an internet component. Using a Python tool, the suggested technique is simulated and its delay, packet delivery ratio, packet drop, and energy are evaluated. When compared to other approaches, the study's results show that the ASHO-LGBM strategy's performance analysis achieves the highest accuracy rate. © 2024 IEEE.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems

arXiv

引用

arXiv 2024年

作者： Xu, Shengzhe Thomas, Christo Kurisummoottil Hashash, Omar Muralidhar, Nikhil Saad, Walid Ramakrishnan, Naren Sanghani Center for Artificial Intelligence and Data Analytics Computer Science Department Virginia Tech China Bradley Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA United States Department of Computer Science Stevens Institute of Technology NJ United States

Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. However, recent efforts on LLMs for wireless networks are limited to a direct application of existing language models that were designed for natural language processing (NLP) applications. To address this challenge and create wireless-centric foundation models, this paper presents a comprehensive vision on how to design universal foundation models that are tailored towards the unique needs of next-generation wireless systems, thereby paving the way towards the deployment of artificial intelligence (AI)-native networks. Diverging from NLP-based foundation models, the proposed framework promotes the design of large multi-modal models (LMMs) fostered by three key capabilities: 1) processing of multi-modal sensing data, 2) grounding of physical symbol representations in real-world wireless systems using causal reasoning and retrieval-augmented generation (RAG), and 3) enabling instructibility from the wireless environment feedback to facilitate dynamic network adaptation thanks to logical and mathematical reasoning facilitated by neuro-symbolic AI. In essence, these properties enable the proposed LMM framework to build universal capabilities that cater to various cross-layer networking tasks and alignment of intents across different domains. Preliminary results from experimental evaluation demonstrate the efficacy of grounding using RAG in LMMs, and showcase the alignment of LMMs with wireless system designs. Furthermore, the enhanced rationale exhibited in the responses to mathematical questions by LMMs, compared to vanilla LLMs, demonstrates the logical and mathematical reasoning capabilities inherent in LMMs. Building on those results, we present a sequel of open questions and challenges for LMMs. We then conclude with a set of recommendations that ignite the path towards LMM-empowered AI-native systems. © 2024, CC BY.

关键词： Natural language processing systems

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：