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Real-Time Diagnosis of Configuration Errors for Software of AI Server Infrastructure

IEEE Transactions on Dependable and Secure computing 2023年 1-12页

作者： Xu, Guangquan Ding, Xinru Xu, Sihan Jia, Yan Liu, Shaoying Feng, Shicheng Zheng, Xi Big Data School Qingdao Huanghai University Qingdao China College of Intelligence and Computing Tianjin University Tianjin China College of Cyber Science Nankai University Tianjin China Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima Japan Department of Computing Macquarie University Sydney NSW Australia

Artificial intelligence (AI) server infrastructure has been built to support AI applications and handle data-intensive workloads. AI server infrastructure is the essential building blocks, and errors in AI server infrastructure may lead to fatal consequences to any AI applications built upon it. Compared to traditional software, software for AI server infrastructure is more configurable, and thus more likely to have configuration errors that might prevent correct software behaviors. Previous work on misconfiguration diagnosis requires sufficient execution history or manual intervention, and can hardly diagnose potential misconfigurations which are not triggered at launching. In this paper, we propose a real-time method to address these issues. Specifically, we combine program analysis and real-time log parsing to diagnose configuration errors. It maps each configuration option to the log code by applying program slicing only once, and parses real-time logs during the operation of the AI server without manual intervention. We evaluate the effectiveness of our approach on the core components of Hadoop, an exemplar AI Server Infrastructure Software. The results show that our method mapped more than 80% of the configuration options to log outputs, identified 90% of the configuration read sites as the slicing seeds, and successfully diagnosed about 10% configuration errors that can not be addressed by previous studies. IEEE

关键词： Real time systems

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Comparison of Various Computational Models for Accurate Stroke Prediction and Interpretation Using LIME and SHAP Kernels 1

Comparison of Various Computational Models for Accurate Stro...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Arun, Chalana B Anusha, M. Kodipalli, Ashwini Gargi, N. Harish, L. Devamane, Shridhar B Global Academy of Technology Dept. of Artificial Intelligence and Data Science Bangalore India School of Computing Amrita Vishwavidhyapeetam Department of Computer Science Mysuru India

ISBN: (纸本)9798331518943

Stroke is one dangerous illness that kills people. Stroke is so called because of the way it strikes people down. Across the globe, it is among the main causes of mortality and disability. It requires early identification and precise measures to be taken in order to prevent stroke and further damages to the brain and other parts of body. Our study investigates the use of machine learning techniques that includes Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Free Classifier, Random Forest Classifier, Naive Bares and multiple Boosting Classifiers. The 'Healthcare dataset Stroke data' is the dataset used, which is preprocessed and then trained upon by Ml. models. For Random Forest Classifier, the maximum accuracy observed was 94.91%, and upon Hyper parameter Tuning- there was rise in accuracy to 95.64%, Evaluation criteria like F1-score, precision, and recall provides additional evidence for the effectiveness of these models: Further in order to explain how each feature contributes differently to each prediction we have made use of two popular techniques for XAT, LIME and SHAP. LIVE and SHIP provides local, interpretable explanations and global explanations respectively. © 2024 IEEE.

关键词： Random forests

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Adaptive strategy automation with large language models in paradoxical games

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Physical Review Research 2025年第2期7卷 L022012-L022012页

作者： Kang Hao Cheong Jie Zhao Tao Wen Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University Singapore S637371 Singapore College of Computing and Data Science (CCDS) Nanyang Technological University Singapore S639798 Singapore

Finding an ideal sequence in games is crucial for maximizing gains in various scenarios. This process requires extensive investigation, which is both time consuming and demands significant domain expertise. The emergence of large language models (LLMs) represents a significant turning point in addressing this issue, attributable to their potent analytical capabilities. In this context, LLMs can serve to substantially alleviate the human labor needed to manage these complexities. Through comprehensive simulations of coin-tossing games, we have demonstrated that the adaptive switching strategies formulated by LLMs surpass predefined sequences in profitability when applied to certain paradoxical games. Furthermore, our experimental findings indicate that the proposed method not only automates the identification of effective strategies but also provides adaptability to various forms of these paradoxical games.

关键词： Computer simulation languages

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Intelligent Chatbot for Hospital Recommendation System 2

Intelligent Chatbot for Hospital Recommendation System

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rajkumar, K. Ragupathi, T. Karthikeyan, S. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Department of Computer Science and Engineering Bangalore562112 India

ISBN: (纸本)9798350371055

The main objective of this project is to create a user-friendly intelligent chatbot for Medical guidance and support. This application has been classified into two different modes based on user preference, those are Online and Offline modes of interaction. In the Online mode of interaction, there is no need for any automation, there will be a real person at the other end answering user queries. Users can even ask for untrained data and will receive instant replies. This application is similar to a normal chat application, but the main disadvantage is that a real person is needed at the other end at all times. In the Offline mode of conversation which is entirely automated the user will be performing an interaction with the chatbot itself. The chatbot must consist of data sets and trained data collection, in this process there is no need for any real person, the chatbot itself will reply instantly based on the trained data. The chatbot will be more accurate when a large number of data is trained. This application is compatible with working in various browsers and varied networks. The Admin of the application plays a significant role. Admin can access all the given functions and is responsible for data training. data training plays a vital role in this application, the trained data will be used by the chatbot while answering user queries i.e. Offline mode. This chatbot application can be accessed by several users at the same time, where IP address plays the main role. The main function of this application is that user can post their corresponding queries and the application will be providing relevant information based on queries. The Latest Modern chatbots are used in applications where simple interaction is needed, for example, service and marketing technology to provide answers such as products, services, and consultancy. If the customer's queries don't match with the trained data sets, the customer will be escalated to manual interaction i.e. Online mode. © 2024 IE

关键词： Automated Intelligent Chat IP Address Offline mode Online mode Trained data

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Optimised Design of a Current Mirror in 150 nm GaAs Technology 5

Optimised Design of a Current Mirror in 150 nm GaAs Technolo...

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5th International Conference on computing, Networking, Telecommunications and Engineering sciences Applications, CoNTESA 2024

作者： Qian, Lua Ying Kang, Chia Chao Ding, Tan Jian Bhuiyan, Mohammad Arif Sobhan Minhad, Khairun Nisa Miraz, Mahdi H. Electrical and Electronics Engineering Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia

ISBN: (纸本)9798331534530

The Current Mirror (CM) is a basic building block commonly used in analogue and mixed-signal integrated circuits. Its significance lies in its ability to replicate and precisely regulate the current, making it crucial in various applications such as amplifiers, filters and data converters. Recently, there has been a growing need for smaller and more energy-efficient Radio Frequency (RF) devices due to the advancements in wireless communication, the Internet of Things (IoT) and portable electronics. This research aims to propose an improved and optimised CM design focusing on compactness and energy-efficient operation. Through a comprehensive methodology involving transistor sizing, biasing techniques, load resistance selection, frequency response stabilisation and noise analysis, the proposed high swing CM design achieves a gain of at least 6.005 dB, a reduced power consumption of 91.17 mW, a wide bandwidth of 22.60 kHz and improved linearity as well as accuracy through precise current matching and minimised mismatch. This optimised CM design will further boost the realisation of compact and lower power RF devices, contributing to the advancement of analogue circuit design techniques and enhancing system performance, accuracy and reliability. © 2024 IEEE.

关键词： Gallium arsenide

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Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

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Nuclear science and Techniques 2024年第5期35卷 173-185页

作者： He-Lin Gong Han Li Dunhui Xiao Sibo Cheng Paris Elite Institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China School of Mathematical Sciences Tongji UniversityShanghai 200092China Department of Computing Data Science InstituteImperial College LondonLondon SW72AZUK

The aging of operational reactors leads to increased mechanical vibrations in the reactor *** vibration of the incore sensors near their nominal locations is a new problem for neutronic field *** field-reconstruction methods fail to handle spatially moving *** this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this *** from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the *** convolutional neural networks were used to learn maps from observations to the global *** proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.

关键词： Voronoi tessellation Field reconstruction Nuclear reactors Reactor physics On-line monitoring

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua science and Technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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SpeedLoader: An I/O efficient scheme for heterogeneous and distributed LLM operation 38

SpeedLoader: An I/O efficient scheme for heterogeneous and d...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Yiqi You, Yang Institute of Data Science National University of Singapore Singapore119077 Singapore School of Computing National University of Singapore Singapore119077 Singapore

With the surging growth of model parameters, foundation models pose unprecedented challenges to traditional computational infrastructures. These large models inherently require substantial accelerator memory to accommodate massive tensors during pre-training, fine-tuning, and even inference stages, making it even more challenging to deploy a model with restricted computational resources. Given this challenge, distribution and offloading the model states are two major solutions. Partitioning the required states to participating workers, and storing them in lower speed media, such as host DRAM and block devices, largely alleviate the accelerator memory pressure. However, the prohibitive costs of tensor communication render it a theoretically plausible yet practically inefficient solution. Previous efforts to improve efficiency include maximizing rematerialization and employing chunk-based tensor management to reduce host-device communication. Despite these efforts, the reported training throughput only achieves 36.54% of model FLOPs utilization (MFUs), still not comparable to full on-device training. In this work, we redesign the data flow of heterogeneous hardware and sharded model training to minimize the excessive communication overhead. Our proposed scheme significantly enhances training and inference throughput of large language models under restrictive computational resources. We confirmed a large leap in effective compute time by looking into the kernel-level runtime behavior of our trials, where the MFUs can achieve up to 51%. Compared to the state-of-the-art approach, our framework robustly achieves remarkable speedups from 3x to 30x in multiple distributed heterogeneous training setups and inference speedups of 1.5x to 2.35x without compromising arithmetic precision. © 2024 Neural information processing systems foundation. All rights reserved.

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Smartphone Fundus Photography Enhancement for Retinopathy of Prematurity Disease Diagnosis Using Deep Learning 6

Smartphone Fundus Photography Enhancement for Retinopathy of...

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6th International Conference on Computer and Informatics Engineering, IC2IE 2023

作者： Mutua, Elizabeth Ndunge Kasamani, Bernard Shwabwabo Reich, Christoph School of Computing & Engineering Sciences Strathmore University Nairobi Kenya Institute for Data Science Cloud Computing & It Society Furtwangen University Furtwangen Germany

ISBN: (纸本)9798350345162

Retinopathy of Prematurity (ROP) is the highest cause of childhood blindness globally with babies born preterm having a higher probability of contracting the disease. The disease diagnosis remains an economic burden to many countries, lack of enough ophthalmologists for the disease diagnosis coupled with non-existent national screening guidelines still remains a challenge. To diagnose the disease, a fundus photography is conducted, printout images are analyzed to determine the presence or absence of the disease. With the increase in the development of smartphones having advanced image capturing and processing features, the utilization of smartphones to capture retina image for disease diagnosis is becoming a common trend. For regions where ophthalmologists are few and/or for low resource regions with few or no retina capturing equipment, the use of smartphones to capture retina images for retina diseases is an effective method. This, however, is challenged: different smartphones produce images of different resolutions;some images are darker others lighter and with different resolution. A smartphone retina image capturing has a smaller field of view ranging between 450-900 which is a major limitation. A lens to support a bigger view can be combined with this approach to provide a wide view of 1300. This enlargement however distorts the image quality and may result in losing some image features. To overcome these challenges, this work develops an improved U-Net model to preprocess images captured using smartphones for ROP disease diagnosis. Our focus is to determine the presence or absence of the disease from smartphone captured images. Because the images are captured under a smaller field of view (FOV), we develop an improved U-Net model by adding patches to enhance image circumference and extract all features from the image and use the extracted features to train a U-Net model for the disease diagnosis. The model results outperformed similar recent developments with

关键词： convolutional neural network fundus photography retina vessel enhancement smartphone photography

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PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud computing

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IEEE Transactions on Cloud computing 2025年第2期13卷 680-693页

作者： Chen, Junsong Zeng, Shengke Han, Song Yin, Jin Chen, Peng Xihua University School of Computer and Software Engineering Chengdu610039 China Hangzhou City University School of Computer and Computing Science Hangzhou310015 China Sichuan University West China Biomedical Big Data Center West China Hospital Chengdu610041 China

Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore. © 2013 IEEE.

关键词： Ciphertext

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