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International Conference on Data engineering

作者： Laizhong Cui Sudipta Acharya Sumit Mishra Yi Pan Joshua Zhexue Huang College of Computer Science and Software Engineering Shenzhen University Shenzhen P.R. China School of Computing Queen’s University Ontario Canada Department of CSE Indian Institute of Information Technology Guwahati Assam India Shenzhen Institute of Advanced Technology Chinese Academy of Sciences P.R. China

Dimensionality reduction through feature selection becomes inevitable to overcome the problem of the Curse of dimensionality. In this article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a Multi-objective optimization-based Multi-view Co-Clustering algorithm (named MMCo-Clus). A thorough comparative analysis with existing feature selection algorithms using external/internal evaluation metrics supports our proposed method’s potency.

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Applications of Machine Learning in Diabetic Foot Ulcer Diagnosis using Multimodal Images: A Review

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IAENG International Journal of Applied Mathematics 2023年第3期53卷 75页

作者： Mayya, Veena Tummala, Venkat Reddy, Chaduvula Upendra Mishra, Pranjal Boddu, Rajasekhar Olivia, Diana Kamath, S Sowmya Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Software Engineering College of Computing and Informatics Haramaya University Dire Dawa Ethiopia Department of Information & Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Department of Information Technology National Institute of Technology Karnataka SurathkalMangalore575025 India

Diabetes related complications such as Diabetic Foot Ulcers (DFU) may necessitate recurrent hospitalisations and expensive treatments. Uncontrolled diabetes can result in severe DFUs, resulting in amputation of lower limbs or feet, prolonged debilitation and diminished quality of life. Early diagnosis and proactive management are reported to significantly enhance the prognosis and reduce the onset of further complications. In this study, research works on developing clinical decision support systems (CDSS) for the identification and segmentation of DFU are systematically reviewed. The techniques employed range from traditional image processing techniques to approaches based on deep learning (DL). A taxonomy of DFU CDSSs is presented, categorised into two groups: RGB-based techniques and thermal imaging-based approaches. To the best of our knowledge, this is the first attempt at a comprehensive study of CDSSs for DFU related investigative tasks, based on different imaging modalities. We also delve into the difficulties experienced in the process of creating efficient, reliable, and accurate models for the early detection of DFU, and highlight the vast potential for further research in this emerging domain. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： Diseases

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Symmetric Pruning in Quantum Neural Networks

arXiv

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arXiv 2022年

作者： Wang, Xinbiao Liu, Junyu Liu, Tongliang Luo, Yong Du, Yuxuan Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University China JD Explore Academy China Pritzker School of Molecular Engineering The University of Chicago United States Chicago Quantum Exchange United States Kadanoff Center for Theoretical Physics United States qBraid Co. United States The University of Sydney Australia National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory Wuhan China

Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance, ground states preparation (GSP) is classically intractable for most large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a leading protocol to conquer this issue. As such, the performance enhancement of QNNs becomes the core in GSP. Empirical evidence showed that QNNs with handcraft symmetric ansätze generally experience better trainability than those with asymmetric ansätze, while theoretical explanations remain vague. To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima. We uncover that the advance of symmetric ansätze attributes to their large EQNTK value with low effective dimension, which requests few parameters and quantum circuit depth to reach the over-parameterization regime permitting a benign loss landscape and fast convergence. Guided by EQNTK, we further devise a symmetric pruning (SP) scheme to automatically tailor a symmetric ansatz from an over-parameterized and asymmetric one to greatly improve the performance of QNNs when the explicit symmetry information of Hamiltonian is unavailable. Extensive numerical simulations are conducted to validate the analytical results of EQNTK and the effectiveness of SP. © 2022, CC BY.

关键词： Hamiltonians

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Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

Research Square

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Research Square 2021年

作者： Ryan, Conor Kshirsagar, Meghana Vaidya, Gauri Cunningham, Andrew Sivaraman, R. Biocomputing and Developmental Systems Group Lero The Science Foundation Ireland Research Centre for Software Computer Science and Information System Department University of Limerick LimerickV94 T9PX Ireland Intel Research and Development Ireland Limited LeixlipW23 CX68 Ireland Department of Electronics Communication Engineering School of Electrical and Electronics Engineering Shanmugha Arts Science Technology and Research Academy Deemed to be University Thanjavur613401 India

This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG. The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve. © 2021, CC BY.

关键词： Monte Carlo methods

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Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach

arXiv

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arXiv 2024年

作者： Jiang, Feihu Qin, Chuan Zhang, Jingshuai Yao, Kaichun Chen, Xi Shen, Dazhong Zhu, Chen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University China Institute of Software Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China The Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute China

In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured information from resume documents automatically. Compared to the traditional rule-based approaches, the utilization of recently proposed pre-trained document understanding models can greatly enhance the effectiveness of resume understanding. The present approaches have, however, disregarded the hierarchical relations within the structured information presented in resumes, and have difficulty parsing resumes in an efficient manner. To this end, in this paper, we propose a novel model, namely ERU, to achieve efficient resume understanding. Specifically, we first introduce a layout-aware multi-modal fusion transformer for encoding the segments in the resume with integrated textual, visual, and layout information. Then, we design three self-supervised tasks to pre-train this module via a large number of unlabeled resumes. Next, we fine-tune the model with a multi-granularity sequence labeling task to extract structured information from resumes. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of ERU. © 2024, CC BY.

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Influence of Lung Cancer Model Characteristics on Nanodrugs Tumor Targeting Behavior Based on Convolutional Kernel Extreme Learning Machine Model

Influence of Lung Cancer Model Characteristics on Nanodrugs ...

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Advancement in Technology (ICONAT), International Conference for

作者： Manthan S. Manavadaria Srikanth Chintakindi Rajasree Rajamohanan S Krishna Prasad P. Janardhan Saikumar Neerav Nishant EC Department CSPIT CHARUSAT Changa Petlad Gujarat India Associate Project Manager Cloud Software Group Department of Computer Science (Artificial intelligence and Data Science) Karpagam Academy of Higher Education Coimbatore India Department of Mechanical Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte Udupi India Department of ECE Audisankara College of Engineering and Technology Gudur India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

One of the main killers on a worldwide basis is cancer. In terms of global persistence, lung cancer stands out among the most frequent cancers. For lung cancer, the standard medical approach includes chemotherapy, surgical removal, and radiation therapy. When compared to other methods, this one isn’t extremely targeted and may even damage surrounding healthy cells. There has been recent recognition of nanotechnology as a possible tool for the treatment and management of lung cancer. Preprocessing, feature selection, segmentation, and model training must be executed in this precise sequence to ensure accuracy. As part of the preprocessing, the proposed approach employ the kernel correlation approach. Splitting the input image into smaller pieces with the goal of reducing the overall representation of the image is called image segmentation. To find features with high predictive power and remove redundant ones, feature selection was done in the training cohort. The model was trained using a Convolutional-KELM. As compared to CNN and ELM, the suggested approach performs better. Following implementation of the technique, the accuracy increased by 94.25%.

关键词： Training Image segmentation Accuracy Lungs Lung cancer Feature extraction Convolutional neural networks Kernel Medical diagnostic imaging Tumors

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大规模科学计算性能评估的FFT标准

大规模科学计算性能评估的FFT标准

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第8届全国并行计算大会

作者：侯晓吻唐渊 Department of Computer Science and Engineering Beijing University of Posts and TelecommunicationsBe Lab.of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing 100080China

在大规模科学计算中，对整个系统性能评估的正确与否已经越来越成为成功掏建系统不可小视的一环，现代超级计算机(千亿次，万亿次)的性能测试与评估最常用的一个测试基准程序就是所谓的LINPACK。当今超级计算界的TOP500排名也主要是基于... 详细信息

在大规模科学计算中，对整个系统性能评估的正确与否已经越来越成为成功掏建系统不可小视的一环，现代超级计算机(千亿次，万亿次)的性能测试与评估最常用的一个测试基准程序就是所谓的LINPACK。当今超级计算界的TOP500排名也主要是基于LINPACK峰值的，但我们在实际应用中发现，LINPACK峰值高并不等于超级计算机/并行机的利用率/并行效率一定也高。本文指出了LINPACK测试程序包自身的局限性，进而提出一个大规模科学计算性能测试与评估的FFT标准,并对影响并行FFT算法性能的一些主要因素进行分析，结合一套完善的软件包“FFTH”以满足此类需求。

关键词：科学计算并行计算基准程序程序测试

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A Decentralized Collaborative Approach to Online Edge User Allocation in Edge Computing Environments

A Decentralized Collaborative Approach to Online Edge User A...

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IEEE International Conference on Web Services (ICWS)

作者： Qinglan Peng Yunni Xia Yan Wang Chunrong Wu Wanbo Zheng Xin Luo Shanchen Panz Yong Ma Chunxu Jiang Software Theory and Technology Chongqing Key Lab Chongqing University Chongqing China Macquarie University Sydney NSW Australia Data Science Research Center Kunming University of Science and Technology Kunming China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China School of Computer and Communication Engineering China University of Petroleum Qingdao China School of Computer and Information Engineering Jiangxi Normal University Nanchang China Chongqing Key Laboratory of Smart Electronics Reliability Technology Chongqing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Edge computing is a promising paradigm that can boost the performance of novel mobile applications and energize the real-time governance of Internet-of-Things (IoT) big data. In edge computing, mobile application vendors are allowed to employ edge resources to speed up end-users' applications in an elastic and on-demand manner. However, due to the complex geographical distribution of edge servers and users, how to decide the most appropriate destination edge server to hire and how to decide the corresponding user-server allocation plan with as-low-as-possible monetary cost are the key problems for application vendors. Instead of assuming a simultaneous-batch-arrival pattern of incoming users and considering static optimization of the Edge User Allocation (EUA) problem by most existing studies, in this paper, we consider an online EUA problem where users' arrival and departure follow a general pattern. We take the long-term edge user allocation rate and edge server leasing cost as scheduling targets and propose a decentralized collaborative and fuzzy-control-based approach to yielding real-time user-edge-server allocation schedules. In this approach, edge users are allowed to independently make their own allocation decision only based on local information (i.e., the status of nearby edge servers). Experiments on real-world edge datasets demonstrate our approach outperforms state-of-the-art approaches in terms of long-term allocation rate and system cost.

关键词： Web services Collaboration Real-time systems Mobile applications Resource management Servers Edge computing

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Cache Optimization Method to Reduce Network Traffic in Communication Systems

Cache Optimization Method to Reduce Network Traffic in Commu...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Ruey-Shun Chen Yu-Xi Hu Xiaopeng Fan Yeh-Cheng Chen Shi-Jinn Horng T.-C. Wang Naixue Xiong Department of computer engineering Dongguan Polytechnic Dongguan P.R. China Department of computer science University of California Davis CA USA The School of Software and Microelectronic Northwestern Polytechnical University Xian P.R. China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen P.R. China Institute of Information Management National Chiao Tung University School of Computer Science and Technology Tianjin University Tianjin P. R. China

ISBN: (纸本)9781538694046;9781538694039

Using the cloud technology with Smart Protect Network (SPN) to do file virus scanning. The overwhelming increase of population will lead to an increase network traffic usage. In most products, local cache mechanism is implemented for the purpose of reducing the network traffics. However, the database designed to store cache is limited, lot of times, cache will get purged when there are lots of queries sent and replaced by other newest queries. It is not an efficient by using traditional methodology to build or forming cache. In order to reduce the network traffic usage, the paper propose a solution, which utilize data mining technique with clustering concept, by gathering the current feedback data we have from our SPN, we are able to form these data in groups with similarity, and by deploying these data to client side, to achieve the reduction of traffic usage. In prototyping, this design for military communications can really reduce network traffic more than 20%. And the speed of file scanning time can faster more than 12%.

关键词： Parallel architectures Programming Data mining Optimization Telecommunication traffic

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Model-enhanced Vector Index

arXiv

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arXiv 2023年

作者： Zhang, Hailin Wang, Yujing Chen, Qi Chang, Ruiheng Zhang, Ting Miao, Ziming Hou, Yingyan Ding, Yang Miao, Xupeng Wang, Haonan Pang, Bochen Zhan, Yuefeng Sun, Hao Deng, Weiwei Zhang, Qi Yang, Fan Xie, Xing Yang, Mao Cui, Bin School of Computer Science Key Lab of High Confidence Software Technologies Peking University China Microsoft United States Aerospace Information Research Institute Key Laboratory of Target Cognition and Application Technology Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China Carnegie Mellon University United States National University of Singapore Singapore National Engineering Laboratory for Big Data Analysis and Applications Peking University China

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with endto- end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Modelenhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embeddingbased models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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