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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

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Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction

Disconnected Emerging Knowledge Graph Oriented Inductive Lin...

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International Conference on data engineering

作者： Yufeng Zhang Weiqing Wang Hongzhi Yin Pengpeng Zhao Wei Chen Lei Zhao School of Computer Science and Technology Soochow University Department of Data Science and AI Monash University School of Information Technology and Electrical Engineering The University of Queensland

Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is that emerging KGs consist of only unseen entities without any edge connected to original KGs, called as disconnected emerging KGs (DEKGs). Existing studies for DEKGs only focus on predicting enclosing links, i.e., predicting links inside the emerging KG. The bridging links, which carry the evolutionary information from the original KG to DEKG, have not been investigated by previous work so far. To fill in the gap, we propose a novel model entitled DEKG-ILP (Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction) that consists of the following two components. (1) The module CLRM (Contrastive Learning-based Relation-specific Feature Modeling) is developed to extract global relation-based semantic features that are shared between original KGs and DEKGs with a novel sampling strategy. (2) The module GSM (GNN-based Subgraph Modeling) is proposed to extract the local subgraph topological information around each link in KGs. The extensive experiments conducted on several benchmark datasets demonstrate that DEKG-ILP has obvious performance improvements compared with state-of-the-art methods for both enclosing and bridging link prediction.

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Efficient Segment-Level Waveform Anomaly Detection for Memory Devices

Efficient Segment-Level Waveform Anomaly Detection for Memor...

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IEEE International Conference on Communications (ICC)

作者： Ching-Lun Tai Peter Harrington School of Electrical and Computer Engineering Georgia Institute of Technology Technology and Products Group - Data Science Micron Technology

With the emergence of big data and advanced technologies, memory devices play an important role. However, there is a lack of efficient approaches for waveform anomaly detection to validate the reliability of memory devices under various operations. In this paper, we propose an efficient segment-level waveform anomaly detection method for memory devices that includes the stages of waveform segmentation and anomaly analysis. Being compatible with general waveform sequences, the proposed method features lightweight computational complexity. During the waveform segmentation stage, we execute downsampled segment collection and downsampled segment pair matching to obtain downsampled segments that are associated with individual operations. During the anomaly analysis stage, we execute ensemble denoising, entrywise alignment, anomaly presence examination through excess kurtosis, and anomaly detection with density-based spatial clustering of applications with noise (DBSCAN) to predict anomaly locations. Simulation results confirm the outstanding performance of the proposed method in terms of matching accuracy, excess kurtosis measurements, and segment-level anomaly detection.

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Why AI still doesn't have consciousness?

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 175-179页

作者： Deyi Li Wen He Yike Guo Department of Computer Science and Technology Tsinghua UniversityBeijingChina Instltute of Systems Engineering Academy of Military SciencesPLABeijingChina Data Science Institute Imperial College LondonUK Hongkong Baptist University Hong KongChina

Consciousness is one of the unique features of creatures,and is also the root of biological *** to now,all machines and robots havenJt had ***,will the artificial intelligence(AI)be conscious?Will robots have real intelligence without consciousness?The most primitive consciousness is the perception and expression of *** order to perceive the existence of the concept of‘Ij,a creature must first have a perceivable boundary such as skin to separate‘I’from‘non-1’.For robots,to have the self-awareness,they also need to be wrapped by a similar sensory ***,as intelligent tools,AI systems should also be regarded as the external extension of human *** tools are *** development of AI shows that intelligence can exist without *** human beings enter into the era of life intelligence from AI,it is not the AI became conscious,but that conscious lives will have strong ***,it becomes more necessary to be careful on applying AI to living creatures,even to those lower-level animals with only *** subversive revolution of such application may produce more careful thinking.

关键词： intelligence consciousness conscious

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Semantic Level Image Retrieval in Domain Adaptive Semantic Segmentation 11

Semantic Level Image Retrieval in Domain Adaptive Semantic S...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Zhu, Siyu Zhou, Fenfen Li, Kai Wen, Haonan Liu, Minghao Computer School Beijing Information Science and Technology University 102206 No.55 Taihang RoadChangping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China College of Robotics Beijing Union University Beijing100027 China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing100190 China Yuquan Road Shijingshan District Beijing100049 China

The traditional domain adaptation task is not sufficient in mining inter domain image context. We propose a solution from the perspective of image semantic level retrieval. Image semantic level retrieval is based on the high-level semantics of images, retrieving the similarity or correspondence between images in terms of content and scene between domains. Our work focuses on the meaning, structure, and contextual information expressed by the target image, and retrieves the most relevant image from the source domain to improve the accuracy of domain adaptation. Furthermore, in response to the negative transfer brought about by image retrieval, we will introduce a category level weight adjustment module to further improve the accuracy of semantic level retrieval and weaken the negative impact caused by low precision categories. Finally, we demonstrated the effectiveness of semantic level image retrieval through experiments. © 2024 The Authors.

关键词： Semantic Segmentation

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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Design of Medicine Recommendation System (MRS) for Biomedical Application Using Machine Learning Techniques

Design of Medicine Recommendation System (MRS) for Biomedica...

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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Aditi Sarode Sarang Dineshrao Pojage Kapil Jajulwar Snehlata Dongre Priya Maidamwar Data Science (Centre of Excellence) G H Raisoni College of Engineering Nagpur India Department of Electronics & Telecommunication Engineering G H Raisoni College of Engineering Nagpur India Department of Computer Science & Engineering G H Raisoni College of Engineering Nagpur India

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Machine learning has been more prevalent recently in the medical industry. These systems can both assist individuals in locating the most appropriate drug for their conditions as well as assist physicians in making better treatment choices. To create pharmaceutical recommendation systems, a variety of different machine learning techniques may be applied. Collaborative filtering is a popular technique that suggest medicines to patients based on those that people with comparable diseases have used. A different strategy like Content-based filtering, Random forest approach, and Decision tree makes prescription recommendations by considering different healthcare parameters of patients like symptoms, duration of symptoms and temperature. Medical recommendations systems based on machine learning have the potential to raise the standard of patient care. These technologies can aid in ensuring that patients receive the care they require by making personalized suggestions to them. Additionally, these technologies can contribute to enhancing the security and effectiveness of drug therapy by assisting physicians in making more knowledgeable treatment choices. Basically, this technique analyses the symptoms, duration of symptom, Body temperature and predicts the best suitable medicine. It involve multiple comparisons and multiple decisions to predicted the medicine. The results are represented graphically that gives the relation between the features as symptoms, Duration, Temperature and Target as medicines. It gives the whole account of recommending the medicines based on all the above parameters. The research will aid in the healthcare sector's transition from the conventional scenario to a more personalize model in a tele-health environment.

关键词： Drugs Technological innovation Accuracy Biological system modeling Predictive models Decision trees Recommender systems

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MLOps: A Review

arXiv

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arXiv 2023年

作者： Wazir, Samar Kashyap, Gautam Siddharth Saxena, Parag Department of Computer Science and Engineering SEST Jamia Hamdard New Delhi India School of Data Science University of North Carolina Charlotte United States

Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The significance of the Machine Learning Operations (MLOps) methods, which can provide acceptable answers for such problems, is examined in this study. To assist in the creation of software that is simple to use, the authors research MLOps methods. To choose the best tool structure for certain projects, the authors also assess the features and operability of various MLOps methods. A total of 22 papers were assessed that attempted to apply the MLOps idea. Finally, the authors admit the scarcity of fully effective MLOps methods based on which advancements can self-regulate by limiting human engagement. © 2023, CC BY.

关键词： Deep learning

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Mentored learning: improving generalization and convergence of student learner via teaching feedback

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 15891-15935页

作者： Xiaofeng Cao Yaming Guo Heng Tao Shen Ivor W. Tsang James T. Kwok School of Artificial Intelligence Jilin University Changchun China and Australian Artifcial Intelligence Institute (AAII) University of Technology Sydney (UTS) NSW Australia School of Artificial Intelligence Jilin University Changchun China School of Computer Science and Technology Tongji University Shanghai China and School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Centre for Frontier AI Research and Institute of High Performance Computing Agency for Science Technology and Research(A*STAR) Singapore Singapore and College of Computing and Data Science Nanyang Technological University Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

Student learners typically engage in an iterative process of actively updating its hypotheses, like active learning. While this behavior can be advantageous, there is an inherent risk of introducing mistakes through incremental updates including weak initialization, inaccurate or insignificant history states, resulting in expensive convergence cost. In this work, rather than solely monitoring the update of the learner's status, we propose monitoring the disagreement w.r.t. $\mathcal{F}^\mathcal{T}(\cdot)$ between the learner and teacher, and call this new paradigm "Mentored Learning", which consists of 'how to teach' and 'how to learn'. By actively incorporating feedback that deviates from the learner's current hypotheses, convergence will be much easier to analyze without strict assumptions on learner's historical status, then deriving tighter generalization bounds on error and label complexity. Formally, we introduce an approximately optimal teaching hypothesis, hT, incorporating a tighter slack term $\left(1+\mathcal{F}^{\mathcal{T}}(\widehat{h}_t)\right)\Delta_t$ to replace the typical 2Δt used in hypothesis pruning. Theoretically, we demonstrate that, guided by this teaching hypothesis, the learner can converge to tighter generalization bounds on error and label complexity compared to non-educated learners who lack guidance from a teacher: 1) the generalization error upper bound can be reduced from R(h*)+4ΔT-1 to approximately R(hT )+2ΔT-1, and 2) the label complexity upper bound can be decreased from 4θ(TR(h*) + 2O(√T)) to approximately 2θ(2TR(hT) + 3O(√T)). To adhere strictly to our assumption, self-improvement of teaching is proposed when hT loosely approximates h*. In the context of learning, we further consider two teaching scenarios: instructing a white-box and black-box learner. Experiments validate this teaching concept and demonstrate superior generalization performance compared to fundamental active learning strategies, such as IWAL (Beygelzimer et al

关键词： machine teaching hypothesis pruning active learning error disagreement convergence generalization error label complexity

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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： K Sangeetha Vs Balaji M Manju S Renuka Devi P Velayutham Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline; this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis.

关键词： Emotion recognition Accuracy Face recognition Pipelines Real-time systems Multilingual Interviews Random forests Standards Testing

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