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6th International Conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Yang, Wangying Zhu, Guoping Chen, Qin Liu, Nianchao School of Computer Science Beijing University of Posts and Telecommunications Beijing China China Electronics Cloud Computing Technology Co. Ltd. Cloud Product R&d Department Wuhan China S&t Development China Electronics Corporation Shenzhen China

ISBN: (纸本)9798350353174

Users' requests for the computing power often exhibit suddenness and unpredictability, which creates challenges for workload scheduling of computing power platforms. Traditional scheduling methods struggle to adapt to such situations as they typically schedule based on real-time load conditions, ignoring long-term trends in load variations. To address these limitations, we propose a computing power scheduling method based on long-term workload prediction. First, we leverage a tree structure to aggregate temporal-spatial information from multi-variate operating time-series data, effectively describing the characteristics of workload on computing power platform. Building upon Informer, we employ residual mechanisms and dilated causal convolutions to extract deep features. Additionally, we integrate Wasserstein Generative Adversarial Network (WGAN) with Informer to accurately forecast future changes in workload for computing platforms, and introduce a novel training algorithm to enhance efficiency. We validate our method through experiments using real-world workload datasets, demonstrating the significant reduction in Mean Absolute Error (MAE) compared to LSTM. Furthermore, our model exhibits sensitivity to the length of encoder input sequences, indicating improved performance with longer series inputs. © 2024 IEEE.

关键词： computing power

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Improving Implicit Discourse Relation Recognition with Semantics Confrontation 30

Improving Implicit Discourse Relation Recognition with Seman...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cai, Mingyang Yang, Zhen Jian, Ping School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing China

ISBN: (纸本)9782493814104

Implicit Discourse Relation Recognition (IDRR), which infers discourse logical relations without explicit connectives, is one of the most challenging tasks in natural language processing (NLP). Recently, pre-trained language models (PLMs) have yielded impressive results across numerous NLP tasks, but their performance still remains unsatisfactory in IDRR. We argue that prior studies have not fully harnessed the potential of PLMs, thereby resulting in a mixture of logical semantics, which determine the logical relations between discourse arguments, and general semantics, which encapsulate the non-logical contextual aspects (detailed in Sec.1). Such a mixture would inevitably compromise the logic reasoning ability of PLMs. Therefore, we propose a novel method that trains the PLMs through two semantics enhancers to implicitly differentiate logical and general semantics, ultimately achieving logical semantics enhancement. Due to the characteristic of PLM in word representation learning, these two semantics enhancers will inherently confront with each other, facilitating an augmentation of logical semantics by disentangling them from general semantics. The experimental results on PDTB 2.0 dataset show that the confrontation approach exceeds our baseline by 3.81% F1 score, and the effectiveness of the semantics confrontation method is validated by comprehensive ablation experiments. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Web Service Recommendation Based on Knowledge Graph Enhanced

Web Service Recommendation Based on Knowledge Graph Enhanced

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2023 International Conference on cloud computing, Performance computing, and Deep Learning, CCPCDL 2023

作者： Huang, Xinyi Zhao, Gansen School of Computer Science South China Normal University Guang Zhou China Guangzhou Key Lab on Cloud Computing Security and Assessment Technology Guang Zhou China

ISBN: (纸本)9781510666474

Mashup is an information system development project based on Web services. It can build new business processes by mixing data, resources and APIs from different sites or applications, and meet new user needs. However, in recent years, the number of Web API is increasing quickly, and it is difficult for developers and users to find suitable Mashups, which influences the development and functional efficiency of information systems. Therefore, designing an accurate and efficient Web service recommendation method to improve the functional characteristics of information systems has become a research hotspot. At present, many algorithms, like matrix factorization and collaborative filtering, often have serious cold start and sparse problems. In order to alleviate the above problems and capture as much information as possible, we proposed a multi-modal knowledge graph enhancement recommendation method (MKE), which combines structured information and unstructured information of Mashup and Web API. Firstly, the feature information is mined from the text description of API and Mashup to assist the construction of the service recommendation system. Then, the knowledge graph is constructed by using the related attributes and relationships of Mashup and API, as well as the entity and relationship of Mashup and API, and the text features. The low-dimensional embedded Trans model provides Top-K recommendation results for the specified Mashup description. Finally, experiments are carried out on the latest ProgrammableWeb dataset, and the experimental results show that the recommendation performance of this method is better than that of RippleNet, NCF, CKE, KGCN. © 2023 SPIE.

关键词： Knowledge graph

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Differential and TriPhase Adaptive Learning-Based Privacy-Preserving Model for Medical Data in cloud Environment

IEEE Networking Letters

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IEEE Networking Letters 2022年第4期4卷 217-221页

作者： Gupta, Rishabh Saxena, Deepika Gupta, Ishu Singh, Ashutosh Kumar National Institute of Technology Kurukshetra Department of Computer Applications Kurukshetra136119 India National Sun Yat-sen University Cloud Computing Research Center Department of Computer Science and Engineering Kaohsiung804 Taiwan

This letter proposes a novel secure data protection model for privacy preservation in the cloud environment by partitioning, sanitizing, and analyzing the data effectively to improve the model's privacy. Several experiments and comparisons of the proposed model with existing works indicate that it protects data with high accuracy, precision, recall, and F1-score up to 87.03%, 84.87%, 87.03%, and 85.00%, for diverse datasets with a relative improvement up to 15.89%, 27.73%, 15.89%, and 21.44%, respectively. © 2019 IEEE.

关键词： Deep neural networks

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GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation

GraphQ IR: Unifying the Semantic Parsing of Graph Query Lang...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Nie, Lunyiu Cao, Shulin Shi, Jiaxin Sun, Jiuding Tian, Qi Hou, Lei Li, Juanzi Zhai, Jidong Department of Computer Science and Technology Tsinghua University China Huawei Cloud Computing Technologies Co. Ltd.

Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax. Recent works have proposed several forms of supplementary supervision but none is generalized across multiple formal languages. This paper proposes a unified intermediate representation (IR) for graph query languages, named GraphQ IR. It has a natural-language-like expression that bridges the semantic gap and formally defined syntax that maintains the graph structure. Therefore, a neural semantic parser can more precisely convert user queries into GraphQ IR, which can be later losslessly compiled into various downstream graph query languages. Extensive experiments on several benchmarks including KQA PRO, OVERNIGHT, GRAILQA and METAQACypher under standard i.i.d., out-of-distribution and low-resource settings validate GraphQ IR's superiority over the previous state-of-the-arts with a maximum 11% accuracy improvement. © 2022 Association for Computational Linguistics.

关键词： Formal languages

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Speech Translation Refinement using Large Language Models

arXiv

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arXiv 2025年

作者： Dou, Huaixia Tian, Xinyu Lyu, Xinglin Zhu, Jie Li, Junhui Guo, Lifan School of Computer Science and Technology Soochow University Suzhou China Alibaba Cloud Computing Hangzhou China

Recent advancements in large language models (LLMs) have demonstrated their remarkable capabilities across various language tasks. Inspired by the success of text-to-text translation refinement, this paper investigates how LLMs can improve the performance of speech translation by introducing a joint refinement process. Through the joint refinement of speech translation (ST) and automatic speech recognition (ASR) transcription via LLMs, the performance of the ST model is significantly improved in both training-free in-context learning and parameter-efficient fine-tuning scenarios. Additionally, we explore the effect of document-level context on refinement under the context-aware fine-tuning scenario. Experimental results on the MuST-C and CoVoST 2 datasets, which include seven translation tasks, demonstrate the effectiveness of the proposed approach using several popular LLMs including GPT-3.5-turbo, LLaMA3-8B, and Mistral-12B. Further analysis further suggests that jointly refining both transcription and translation yields better performance compared to refining translation alone. Meanwhile, incorporating document-level context significantly enhances refinement performance. We release our code and datasets on GitHub1 Copyright © 2025, The Authors. All rights reserved.

关键词： Speech enhancement

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Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning

arXiv

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arXiv 2024年

作者： Yue, Yuanhao Wang, Chengyu Huang, Jun Wang, Peng School of Computer Science Fudan University Shanghai China Alibaba Cloud Computing Hangzhou China

Instruction tuning aims to align large language models (LLMs) with open-domain instructions and human-preferred responses. While several studies have explored autonomous approaches to distilling and annotating instructions from powerful proprietary LLMs, such as ChatGPT, they often neglect the impact of the distributions and characteristics of tasks, together with the varying difficulty of instructions in training sets. This oversight can lead to imbalanced knowledge capabilities and poor generalization powers of student LLMs. To address these challenges, we introduce Task-Aware Curriculum Planning for Instruction Refinement (TAPIR), a multi-round distillation framework that utilizes an oracle LLM to select instructions that are difficult for a student LLM to follow. To balance the student’s capabilities, task distributions in training sets are adjusted with responses automatically refined according to their corresponding tasks. In addition, by incorporating curriculum planning, our approach systematically escalates the difficulty levels of tasks, progressively enhancing the student LLM’s capabilities. We rigorously evaluate TAPIR using several widely recognized benchmarks (such as AlpacaEval 2.0, MT-Bench, etc.) and multiple student LLMs. Empirical results demonstrate that student LLMs, trained with our method and less training data, outperform larger instruction-tuned models and strong distillation baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the cloud

arXiv

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arXiv 2024年

作者： Yue, Yuanhao Wang, Chengyu Huang, Jun Wang, Peng School of Computer Science Fudan University Shanghai China Alibaba Cloud Computing Hangzhou China

Specializing LLMs in various domain-specific tasks has emerged as a critical step towards achieving high performance. However, the construction and annotation of datasets in specific domains are always very costly. Apart from using superior and expensive closed-source LLM APIs to construct datasets, some open-source models have become strong enough to handle dataset construction in many scenarios. Thus, we present a family of data augmentation models designed to significantly improve the efficiency for model fine-tuning. These models, trained based on sufficiently small LLMs, support key functionalities with low inference costs: instruction expansion, instruction refinement, and instruction-response pair expansion. To fulfill this goal, we first construct an automatic data collection system with seed datasets generated from both public repositories and our in-house datasets. This system leverages powerful LLMs to expand, refine and re-write the instructions and responses, incorporating quality assessment techniques. Following this, we introduce the training process of our models, which effectively distills task-solving and text synthesis abilities from teacher LLMs. Finally, we demonstrate how we integrate these functionalities into a machine learning platform to support low-cost LLM fine-tuning from both dataset preparation and training perspectives for users. Experiments and an application study prove the effectiveness of our approach. Copyright © 2024, The Authors. All rights reserved.

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How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering 62

How Proficient Are Large Language Models in Formal Languages...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Liu, Jinxin Cao, Shulin Shi, Jiaxin Zhang, Tingjian Nie, Lunyiu Hu, Linmei Hou, Lei Li, Juanzi Department of Computer Science and Technology BNRist China THU - Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT Tsinghua University Beijing China Huawei Cloud Computing Technologies Co. Ltd. China School of Computer Science and Technology Beijing Institute of Technology China

ISBN: (纸本)9798891760998

Knowledge Base Question Answering (KBQA) aims to answer natural language questions based on facts in knowledge bases. A typical approach to KBQA is semantic parsing, which translates a question into an executable logical form in a formal language. Recent works leverage the capabilities of large language models (LLMs) for logical form generation to improve performance. However, although it is validated that LLMs are capable of solving some KBQA problems, there has been little discussion on the differences in LLMs' proficiency in formal languages used in semantic parsing. In this work, we propose to evaluate the understanding and generation ability of LLMs to deal with differently structured logical forms by examining the inter-conversion of natural and formal language through in-context learning of LLMs. Extensive experiments with models of different sizes show that state-of-the-art LLMs can understand formal languages as well as humans, but generating correct logical forms given a few examples remains a challenge. Most importantly, our results also indicate that LLMs exhibit considerable sensitivity. In general, the formal language with a lower formalization level, i.e., the more similar it is to natural language, is more friendly to LLMs. Code and data can be found at https://***/Matthewlliu/structure_probe. © 2024 Association for Computational Linguistics.

关键词： Formal languages

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Evaluation and Comparison of Novel Yolo V5 Algorithm in Identifying Land Cover Area of Geographical Regions over Single Shot Detector Algorithm Accuracy 2

Evaluation and Comparison of Novel Yolo V5 Algorithm in Iden...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Aravind, K. Kumar, S. Pradeep Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cloud Computing Tamilnadu Chennai India

ISBN: (纸本)9798350306415

The research aims to evaluate and compare the Land cover Image detection accuracy rate of Novel Yolo v5 algorithm to Single Shot Detection. The images of the land areas are sourced with a count of 5109 from the Kaggle website. Accuracy rate of the Novel Yolo v5 algorithm and Single Shot Detection is assessed in this study. The prediction of image detection is carried out with an iteration size of twenty, with ten coming from Group 1 and ten coming from Group 2. The goal is to get a higher rate of accuracy from the prediction. The Novel Yolo v5 approach has a success percentage of 96.81 percent, which is much higher than the Single shot detector (SSD) technique, which has a success rate of 91.62 percent. A statistically significant difference may be seen between these two methods, as shown by a p value of 0.00 (p © 2024 IEEE.

关键词： Image detection Land conservation Machine learning Novel Yolo v5 Algorithm Prediction Single Shot Detector

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