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IEEE Congress on Evolutionary Computation, CEC 2015

作者： Chen, Zong-Gan Du, Ke-Jing Zhan, Zhi-Hui Zhang, Jun Department of Computer Science Sun Yat-Sen University Guangzhou510275 China School of Advanced Computing Sun Yat-Sen University Guangzhou510275 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software Ministry of Education China Key Laboratory of Software Technology Department of Guangdong Province China

ISBN: (纸本)9781479974924

Cloud computing resources scheduling is significant for executing the workflows in cloud platform because it relates to both the execution time and execution cost. In order to take both the time and cost into consideration, Rodriguez and Buyya have proposed a cost-minimization and deadline-constrained workflow scheduling model on cloud computing. Their model has great applicability but the solution of their particle swarm optimization (PSO) approach is not good enough and cannot meet a tight deadline condition. In this paper, we propose a genetic algorithm (GA) approach to solve this model. In order to tackle with the tight deadline condition, a dynamic objective strategy is further proposed to let GA focus on optimize the execution time objective to meet the deadline constraint when the feasible solution hasn't been obtained. After obtaining a feasible solution, the GA focuses on optimizing the execution cost within the deadline constraint. Therefore, the proposed dynamic objective GA (DOGA) has adaptive ability to the search environment to different objectives. We have conduct extensive experiments based on workflows with different scales and different cloud resources. Experimental results show that DOGA can find better solution with smaller cost than PSO does on different scheduling scales and different deadline conditions. DOGA approach is more applicable to be used in commercial activities. © 2015 IEEE.

关键词： Genetic algorithms

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Easy and Effective! Data Augmentation for Knowledge-Aware Dialogue Generation Via Multi-Perspective Sentences Interaction

SSRN

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SSRN 2024年

作者： Peng, Sisi Qu, Dan Zhang, Wenlin Zhang, Hao Li, Shunhang Xu, Minchen Information Engineering University Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China

In recent years, knowledge-based dialogue generation has become a popular research topic due to its ability to generate informative and coherent responses by incorporating external knowledge into models. However, acquiring high-quality knowledge that corresponds to the dialogue content can be difficult and requires significant time and resources. To address the issue of limited dialogue data, most research efforts focus on data augmentation to obtain more training data. Unfortunately, these approaches do not consider knowledge augmentation, resulting in limited diversity of input data and only achieving improvements on a single metric. Real-life conversations are diverse, with repetitions, reversals, and interruptions, which present a higher demand for data diversity. In this paper, we propose a simple and effective data augmentation method called Multi-perspective Sentence Interaction to enhance the connections between sentences from different perspectives. By analyzing the target responses from multiple dialogue perspectives, we gain a better understanding of the relationships between dialogue sentences, thereby achieving an expanded knowledge-based dialogue data. We conduct experiments on multiple knowledge-based dialogue datasets and different models, and the results demonstrate that our method significantly improves the quality of model generation. Specifically, we achieved a 3.5% increase in reply accuracy and a 0.1506 improvement in diversity (DIST-2). Furthermore, the accuracy of knowledge selection improved by 19.04%, and the model perplexity decreased by 31.48%. © 2024, The Authors. All rights reserved.

关键词： Knowledge based systems

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Using Knowledge Induction Strategies: Llms Can Do Better in Knowledge-Driven Dialogue Tasks

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SSRN 2023年

作者： Peng, Sisi Qu, Dan Zhang, Hao Zhang, Wenlin Li, Shunhang Information Engineering University Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China

Acquiring and utilizing knowledge is essential for dialogue systems to generate high-quality responses. In recent years, Large Language Models (LLMs) have garnered significant attention in research due to their ability to learn general knowledge from vast amounts of training data and complex textual semantics using deep neural network structures. However, LLMs face certain limitations in knowledge-driven dialogue tasks, including a lack of factual knowledge and poor utilization of commonsense knowledge. Many efforts alleviate these issues by providing an external knowledge base, which is time-consuming and labor-intensive. To address these shortcomings, this paper introduces two Knowledge Induction (KI) strategies: Explicit Knowledge Induction (EKI) and Implicit Knowledge Induction (IKI), to mine LLMs’ internel knowledge and stimulate their own reasoning ability. EKI involves an iterative automatic prompt generation process that guides LLMs in generating explicit knowledge, while IKI utilizes Chain-Of-Thought (CoT) prompts to construct dialogue thinking chains. These strategies empower LLMs to accurately and comprehensively leverage internal knowledge, thereby enabling them to tackle knowledge-driven dialogue tasks without relying on external knowledge bases. Furthermore, the detailed conditional constraints embedded in the prompts facilitate the control of knowledge generation formats, streamlining the knowledge post-processing process. To validate the efficacy of the KI strategies, a series of experiments were performed across multiple models and public datasets. The experimental results demonstrate that the KI strategies effectively enhance knowledge utilization and improve the quality of dialogue generation. Specifically, ChatGLM achieved significant improvements in BLEU (+3.86/+0.83), ROUGE (+7.55/+7.94), and MAUVE (+0.1/+0.08) metrics. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Exploiting Multiple Features for Hash Codes Learning with Semantic-Alignment-Promoting Variational Auto-encoder 12th

Exploiting Multiple Features for Hash Codes Learning with Se...

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12th National CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Chen, Jiayang Su, Qinliang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China

ISBN: (纸本)9783031446924

Semantic hashing is an effective technique to empower information retrieval. Currently, considerable efforts have been dedicated to generating high-quality hash codes by modeling document features using generative models and other approaches. However, most of these methods rely solely on a single type of feature, such as TFIDF features, BERT embeddings, etc. As different types of features have distinct but complementary information of documents, e.g. TFIDF mainly contains the keywords information and BERT focuses on the semantics, hash codes generated solely from either may not capture the full essence of the documents. To overcome this challenge, we propose a semantic-alignment-promoting variational auto-encoder to generate hash codes from multiple document features. Specifically, a VAE-based generative model is first developed to model the multiple features. Then, we propose a semantic-alignment-promoting inference network to estimate the parameters of the variational posterior from multiple features. Additionally, the quality of hash codes is further improved by promoting the semantic alignment between the hash codes of connected documents in a constructed connection graph. The results of extensive experiments on three public datasets demonstrate that our proposed model significantly outperforms current state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Meta-Adaptable-Adapter: Efficient Adaptation of Self-Supervised Models for Low-Resource Speech Recognition

SSRN

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SSRN 2023年

作者： Chen, Yaqi Zhang, Hao Yang, Xukui Zhang, Wenlin Qu, Dan Information Engineering University Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China

Self-supervised models have demonstrated remarkable performance in speech processing by learning latent representations from large amounts of unlabeled data. Adapting these models to low-resource languages yields promising results, but the computational cost of fine-tuning all model parameters is prohibitively high. Adapters offer a solution by introducing lightweight bottleneck structures into pre-trained models for downstream tasks, enabling efficient parameter adaptation. However, randomly initialized adapters often underperform in extremely low-resource scenarios. To address this issue, we explore the Meta-Adapter for self-supervised models and analyzed some limitations of Meta-Adapter including poor learning in language-specific knowledge and meta-overfitting problems. To relieve these problems, we propose the Meta-Adaptable-Adapter (MAA), a new meta leaning algorithm that adapts to low-resource languages quickly and effectively. MAA learns task-specific adapters for feature extraction, and task-independent adapters for feature combination. The experiments on the Common Voice and FLEURS datasets show superior performance on sixteen low-resource languages across four different language families compared to other adapters, showing better generalization and extensibility. © 2023, The Authors. All rights reserved.

关键词： Speech recognition

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A pso approach for software project planning 14

A pso approach for software project planning

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Jia, Ya-Hui Chen, Wei-Neng Hu, Xiao-Min Department of Computer Science Sun Yat-sen University China School of Advanced Computing Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software Ministry of Education China School of Public Health Sun Yat-sen University China

ISBN: (纸本)9781450328814

Search-based software project management is a hot research point in software engineering. Based on the event-based scheduler (EBS) we have proposed in previous work [1], this paper intends to further propose a two-phase particle swarm optimization approach which uses a set-based representation for task scheduling and an integer representation for workload assignment scheduling to improve planning performance. Experimental results on 83 instances demonstrate the effectiveness of the proposed approach.

关键词： Scheduling

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Preparation and Simulation of Quantum States of Matrices 6

Preparation and Simulation of Quantum States of Matrices

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6th International Conference on Electronic Information Technology and Computer engineering, EITCE 2022

作者： Liu, Zhengyu Liu, Xiaonan Xie, Haoshan Zhao, Chenyan School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450000 China National Supercomputing Center in Zhengzhou Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450000 China

ISBN: (纸本)9781450397148

A basic problem of quantum computing is how to effectively express classical data in quantum systems. This problem is called state preparation problem, and the process of preparing quantum states is called coding. In this paper, the classical data of matrix is transformed into quantum states in the form of amplitude coding and simulated. This paper first introduces the detailed process of matrix transformation into the linear combination of quantum ground state, then designs a quantum circuit model to realize amplitude coding according to the idea of binary tree model and quantum random walk algorithm, and finally simulates the three matrices on IBM quantum cloud platform, and analyzes and introduces many situations. The experimental results verify the feasibility of converting the matrix into quantum states by amplitude coding, analyze the causes of errors in the experimental process, and put forward the corresponding solutions. This paper introduces in detail the whole process of matrix conversion to quantum states and the construction of quantum circuits for simulation. Compared with the existing experiments, the quantum circuits constructed in this paper are shorter in length, less in number of quantum gates, and more versatile. © 2022 Association for computing Machinery.

关键词： Matrix algebra

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Parameter-efficient fine-tuning of Whisper for low-resource speech recognition

Parameter-efficient fine-tuning of Whisper for low-resource ...

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Artificial intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yunpeng Liu Dan Qu Department of Artificial Intelligence Laboratory for Advanced Computing and Intelligence Engineering School of Information Systems Engineering University of Information Engineering Henan China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Limited data availability remains a significant challenge for Whisper's low-resource speech recognition performance, falling short of practical application requirements. While previous studies have successfully reduced the recognition error rates of target language speech through fine-tuning, a comprehensive exploration and analysis of Whisper's parameter-efficient fine-tuning capabilities, the advantages and disadvantages are still lacking. This paper aims to fill this gap by conducting comprehensive experimental exploration for Whisper's low-resource speech recognition performance using two parameter-efficient fine-tuning strategies with limited supervised data from multiple low-resource languages. The results and analysis demonstrate that all fine-tuning strategies explored in this paper significantly enhance Whisper's performance. However, different strategies vary in their suitability and practical effectiveness, highlighting the need for careful selection based on specific use cases and resources available.

关键词： Seminars Costs Target recognition Error analysis Speech recognition Data models Task analysis

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Mitigating Barren Plateaus of Variational Quantum Eigensolvers

IEEE Transactions on Quantum Engineering

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IEEE Transactions on Quantum engineering 2024年第1期5卷 1-19页

作者： Liu, Xia Liu, Geng Zhang, Hao-Kai Huang, Jiaxin Wang, Xin Thrust of Artificial Intelligence Information Hub Guangzhou510530 China Institute of Information Engineering Chinese Academy of Sciences Key Laboratory of Cyberspace Security Defense Beijing100085 China Baidu Research Institute for Quantum Computing Beijing100193 China Tsinghua University Institute for Advanced Study Beijing100084 China

Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the expressibility of the ansatzes and is seriously limited by optimization issues, such as barren plateaus (i.e., vanishing gradients). This article proposes the state-efficient ansatz (SEA) for accurate ground state preparation with improved trainability. We show that the SEA can generate an arbitrary pure state with much fewer parameters than a universal ansatz, making it efficient for tasks like ground state estimation. Then, we prove that barren plateaus can be efficiently mitigated by the SEA and the trainability can be further improved most quadratically by flexibly adjusting the entangling capability of the SEA. Finally, we investigate a plethora of examples in ground state estimation where we obtain significant improvements in the magnitude of the cost gradient and the convergence speed. © 2020 IEEE.

关键词： Timing circuits

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Salient Foreground-Aware Network for Person Search 16th

Salient Foreground-Aware Network for Person Search

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Chen, Hongxu Zhang, Quan Lai, Jianhuang The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangzhou510006 China

ISBN: (纸本)9783031202322

Person search aims to simultaneously localize and identify a query person from realistic and uncropped images, which consists of person detection and re-identification. In existing methods, the extracted features come from the low-quality proposals generated by the structure like Region Proposal Network (RPN), and convolution is used to learn local features in the process of extracting features while the receptive field cannot grasp the global structural information. We propose an end-to-end network embedded with our Salient Foreground-Aware Module (SFAM). Self-attention mechanism in SFAM allows for better capture of global information. Our network incorporates our embedding method for person detection and person re-identification, which can effectively optimize the process of extracting features and improve feature expression capabilities. We merge the above modules into our Salient Foreground-Aware Network (SFAN). Extensive experiments have shown that our SFAN significantly improves the performance of end-to-end models with acceptable time-consuming and achieves state-of-the-art results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Embeddings

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