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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yuan, Xiaosong Shen, Chen Yan, Shaotian Zhang, Xiaofeng Xie, Liang Wang, Wenxiao Guan, Renchu Wang, Ying Ye, Jieping College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering MoE Jilin University China Alibaba Cloud Computing China Shanghai Jiao Tong University China College of Computer Science and Technology Zhejiang University of Technology China College of Software Zhejiang University China

Zero-shot Chain-of-Thought (CoT) prompting emerges as a simple and effective strategy for enhancing the performance of large language models (LLMs) in real-world reasoning tasks. Nonetheless, the efficacy of a singular, task-level prompt uniformly applied across the whole of instances is inherently limited since one prompt cannot be a good partner for all, a more appropriate approach should consider the interaction between the prompt and each instance meticulously. This work introduces an instance-adaptive prompting algorithm as an alternative zero-shot CoT reasoning scheme by adaptively differentiating good and bad prompts. Concretely, we first employ analysis on LLMs through the lens of information flow to detect the mechanism under zero-shot CoT reasoning, in which we discover that information flows from question to prompt and question to rationale jointly influence the reasoning results most. We notice that a better zero-shot CoT reasoning needs the prompt to obtain semantic information from the question, and then the rationale aggregates sufficient information from the question directly and via the prompt indirectly. On the contrary, lacking any of those would probably lead to a bad one. Stem from that, we further propose an instance-adaptive prompting strategy (IAP) for zero-shot CoT reasoning. Experiments conducted with LLaMA-2, LLaMA-3, and Qwen on math, logic, and commonsense reasoning tasks (e.g., GSM8k, MMLU, Causal Judgement) obtain consistent improvement, demonstrating that the instance-adaptive zero-shot CoT prompting performs better than other task-level methods with some curated prompts or sophisticated procedures, showing the significance of our findings in the zero-shot CoT reasoning mechanism. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing the Identification of Fake Indian Currency Using Ensemble Decision Tree in Comparison with Ridge Linear Classification 4

Enhancing the Identification of Fake Indian Currency Using E...

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4th IEEE International Conference on Data Engineering and Communication Systems, ICDECS 2024

作者： Kishore Kumar, R. Nelson Kennedy Babu, C. Saveetha Institute of Medical and Technical Science Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Cloud Computing Tamilnadu Chennai India

ISBN: (纸本)9798350393354

The objective of this work is Identification of Fake Indian Currency using Ensemble Decision Tree in Comparison with Ridge Linear Classification for better accuracy. Materials and Methods: For predicting the fake currency, an Ensemble Decision Tree is used in comparison to a Ridge Linear Classification with different training and testing splits. The EDT classifier model, which is suggested as the recommended choice, is trained using 80% of the dataset, while the remaining 20% is reserved for testing purposes. For SPSS analysis, the outcome of two classifiers is categorized as two groups and each group consists of 10 outcome values under different functional operations;finally, it counts 20. During SPSS analysis, the parameters CI of 95% and G power of 0.85 are used. The dataset contains 1,050 unique values related to the currencies and the factors considered are size, color, special attributes. Result: The SPSS analysis performed with the outcome of group 1 and group 2 and achieved mean accuracy of 88.47% and 85.759% respectively. It shows that there is no statistical significance difference between the Novel EDT algorithm and Ridge Linear algorithm with p=0.203 (p>0.05). Conclusion: The suggested approach - Ensemble Decision Tree successfully attained better accuracy in predicting the fake currency in comparison with Ridge Linear Classification. © 2024 IEEE.

关键词： Decision trees

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A Novel Approach for Service Selection and Ranking in Federated cloud 1

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5th International Conference on Information Systems and Management science, ISMS 2022

作者： Saxena, Rajat Dubey, Shatendra Kumar, Upanshu Cloud Computing Lab Department of Computer Science and Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Department of Information Technology NRI Institute of Information Science and Technology Bhopal India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

In Federated cloud environment, cloud service ranking and selection is a very tedious work because of complexion involved Service Level Agreement (SLA) and Service specifications. Quality of Service (QoS) expected from cloud users may be conflicting based on their applications. cloud Brokers needs to be deal with cloud users and Federated cloud like a heterogeneous interface. A cloud Brokers also fulfils many suitable services from available resources. cloud Broker also choose the federated cloud services based on their rank for available service. Noncommercial and conflicting demands of users evolves cloud Service Selection a very competitive multi-criteria based service ranking and selection problem. cloud User requests from different federated clouds based on some preference order of QoS parameters. This preference order converts into individual QoS parameters by assigning suitable weight. The weighted demands are estimated the available QoS values for each cloud service. In the next step, modified VIKOR method is applied for finding the rank of the services. This rank of service is based on preference of QoS parameters. The service with highest rank is selected and provided to the user. We used cloudSim for the testing of this method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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Calibrated Disambiguation for Partial Multi-label Learning 39

Calibrated Disambiguation for Partial Multi-label Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Zhuoming Jia, Yuheng Yu, Mi Miao, Zicong School of Computer Science and Engineering Southeast University China China Telecom Cloud Computing Corporation Beijing 100088 China

ISBN: (纸本)157735897X

Partial multi-label learning (PML) aims to train a classifier on dataset whose instances are over-annotated with not only relevant labels but also irrelevant labels, which is common when datasets are collected from crowd-sourcing platform. Existing works primarily approach it from a curriculum learning perspective, leveraging the memorization effect to disambiguate noisy labels and produce robust predictions. However, these methods are based on non-adaptive weighting functions and lack theoretical guidance for optimal weighting. To overcome these issues, a calibrated disambiguation model named PML-CD is proposed. We firstly formulate the optimal weighting function for curriculum-based disambiguation, which is equivalent to the calibration of the model's predicted confidences, thus provide a guidance for curriculum designing. To obtain the optimal weighting function from PML dataset during the training, a transferable calibrator is designed, which takes the histogram of positive samples' confidences as input, and outputs the optimal curriculum weighting for training. Prototype alignment regularization is also proposed to promote the model's performance. Experiments conducted on Pascal VOC, MS-COCO, NUS-WIDE and CUB have verified that our method outperforms existing state-of-the-art PML methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Curricula

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Sentiment classification of MOOC courses by merging local context focus and Bi-directional gated recurrent unit 4

Sentiment classification of MOOC courses by merging local co...

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4th International Conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Jin, Jiayong Dong, Yongquan He, Bugui Zhou, Nan Yan, Linke School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou221116 China Xuzhou Cloud Computing Engineering Technology Research Center Jiangsu Xuzhou221116 China Jiangsu Engineering Technology Research Center of ICT in Education Jiangsu Xuzhou221116 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Existing MOOC review sentiment classification methods do not fully utilize the local context information associated with aspect, and they ignore the connection between local and global contexts, while resulting in modeled features that lack the information connection between aspect and contexts. In this paper, we propose a model that incorporates Local Context Focus (LCF) and Bi-Directional Gated Recurrent Unit (Bi-GRU). First, the BERT model is used to dynamically encode course reviews. Then, global semantic features are extracted using the Bi-GRU model to strengthen the connection between the preceding and following texts. Then, the LCF model based on multi-head self-attention is used to obtain local contextual features and splice them with global semantic features. Finally, the Softmax function is utilized to output the classification results. The experimental accuracies of the proposed model on the three MOOC course review datasets reach 97.96%, 96.76%, and 94.16%, respectively, which are improved by 0.70%, 0.42%, and 0.03% over the suboptimal baseline model. The proposed model significantly improves the effectiveness of MOOC course review sentiment classification, and provides a useful reference for the optimization and improvement of MOOC courses. © 2025 SPIE.

关键词： Semantics

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Performance characterization of communication protocols in microservice applications

Performance characterization of communication protocols in m...

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2021 International Conference on Smart Applications, Communications and Networking, SmartNets 2021

作者： Kumar, Prajwal Kiran Agarwal, Radhika Shivaprasad, Rahul Sitaram, Dinkar Kalambur, Subramaniam PES University Computer Science and Engineering Cloud Computing Innovation Council of India

ISBN: (纸本)9781665435451

Industrial applications are moving towards deployment using the microservice architecture. These microservices heavily rely on the networking stack for communication which, along with the container abstraction layer causes a significant overhead. With increasing core count on modern processors, these microservices are likely to be scheduled on the same host. In this paper, we attempt to analyze and optimize inter-microservice communication on the same host. We characterize three popular communication protocols - REST, gRPC, and Thrift, in terms of their network, memory, CPU utilization, and response time. Our studies showed that Thrift and gRPC are relatively faster than REST and hence, suitable for inter-microservice interactions owing to their speeds and compression. We leverage the shared memory between the microservices on the same host, using Unix Domain Sockets along with appropriate NUMA tuning that achieved an 86.48% reduction in response time. © 2021 IEEE.

关键词： Abstracting

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Plan-on-graph: self-correcting adaptive planning of large language model on knowledge graphs 24

Plan-on-graph: self-correcting adaptive planning of large la...

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

作者： Liyi Chen Panrong Tong Zhongming Jin Ying Sun Jieping Ye Hui Xiong University of Science and Technology of China and Alibaba Cloud Computing Alibaba Cloud Computing Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guangzhou) Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (Guangzhou) and Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (纸本)9798331314385

Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and editable knowledge for LLMs to alleviate these issues. Existing paradigm of KG-augmented LLM manually predefines the breadth of exploration space and requires flawless navigation in KGs. However, this paradigm cannot adaptively explore reasoning paths in KGs based on the question semantics and self-correct erroneous reasoning paths, resulting in a bottleneck in efficiency and effect. To address these limitations, we propose a novel self-correcting adaptive planning paradigm for KG-augmented LLM named Plan-on-Graph (PoG), which first decomposes the question into several sub-objectives and then repeats the process of adaptively exploring reasoning paths, updating memory, and reflecting on the need to self-correct erroneous reasoning paths until arriving at the answer. Specifically, three important mechanisms of Guidance, Memory, and Reflection are designed to work together, to guarantee the adaptive breadth of self-correcting planning for graph reasoning. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of PoG.

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ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

ReAssigner: A Plug-and-Play Virtual Machine Scheduling Inten...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Cui, Haochuan Sheng, Junjie Jin, Bo Hu, Yiqiu Su, Li Zhu, Lei Zhou, Wenli Wang, Xiangfeng East China Normal University School of Computer Science and Technology Shanghai200062 China Huawei Cloud Computing Technologies Co. Algorithm Innovation Lab. Xi'an710049 China Huawei Cloud Computing Technologies Co. Computing Service Product Dept. Xi'an710049 China

ISBN: (纸本)9781665480451

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods. © 2022 IEEE.

关键词： cloud computing

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Combined Effect of Concept Drift and Class Imbalance on Model Performance During Stream Classification

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computers, Materials & Continua 2023年第4期75卷 1827-1845页

作者： Abdul Sattar Palli Jafreezal Jaafar Manzoor Ahmed Hashmani Heitor Murilo Gomes Aeshah Alsughayyir Abdul Rehman Gilal Department of Computer and Information Sciences Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610Malaysia Centre for Research in Data Science UTPPerak32610Malaysia High Performance Cloud Computing Centre(HPC3) UTPPerak32610Malaysia School of Engineering and Computer Science Victoria University of WellingtonWellington6012New Zealand AI Institute University of Waikato WellingtonHamilton3240New Zealand Anti-Narcotics Force Ministry of Narcotics ControlIslamabad46000Pakistan College of Computer Science and Engineering Taibah UniversityMadinah42353Saudi Arabia

Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more *** existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s *** analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream ***, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority *** study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.

关键词： Classification data streams class imbalance concept drift class imbalance ratio

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DON’T TAKE THINGS OUT OF CONTEXT: ATTENTION INTERVENTION FOR ENHANCING CHAIN-OF-THOUGHT REASONING IN LARGE LANGUAGE MODELS

arXiv

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arXiv 2025年

作者： Yan, Shaotian Shen, Chen Wang, Wenxiao Xie, Liang Liu, Junjie Ye, Jieping Alibaba Cloud Computing China College of Software Zhejiang University China Zhejiang University of Technology College of Computer Science and Technology China

Few-shot Chain-of-Thought (CoT) significantly enhances the reasoning capabilities of large language models (LLMs), functioning as a whole to guide these models in generating reasoning steps toward final answers. However, we observe that isolated segments, words, or tokens within CoT demonstrations can unexpectedly disrupt the generation process of LLMs. The model may overly concentrate on certain local information present in the demonstration, introducing irrelevant noise into the reasoning process and potentially leading to incorrect answers. In this paper, we investigate the underlying mechanism of CoT through dynamically tracing and manipulating the inner workings of LLMs at each output step, which demonstrates that tokens exhibiting specific attention characteristics are more likely to induce the model to take things out of context;these tokens directly attend to the hidden states tied with prediction, without substantial integration of non-local information. Building upon these insights, we propose a Few-shot Attention Intervention method (FAI) that dynamically analyzes the attention patterns of demonstrations to accurately identify these tokens and subsequently make targeted adjustments to the attention weights to effectively suppress their distracting effect on LLMs. Comprehensive experiments across multiple benchmarks demonstrate consistent improvements over baseline methods, with a remarkable 5.91% improvement on the AQuA dataset, further highlighting the effectiveness of FAI. © 2025, CC BY.

关键词： Chains

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