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检索条件"任意字段=2023 International Conference on Algorithms, Computing and Data Processing, ACDP 2023"

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Dictionary Learning on Graph data with Weisfieler-Lehman Sub-Tree Kernel and Ksvd

Dictionary Learning on Graph Data with Weisfieler-Lehman Sub...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Kaveen Liyanage Reese Pearsall Clemente Izurieta Bradley M. Whitaker Electrical and Computer Engineering Gianforte School of Computing Montana State University Bozeman MT USA

Graph representation has gained wide popularity as a data representation method in many applications. Graph embedding methods convert graphs to a vector representation and are an important part of a data processing pipeline. In this paper, we utilize sparse dictionary learning techniques as a graph embedding solution. Sparse representation has notable applications in signal image processing. Inspired by the Graph2Vec algorithm, we aim to modify the Doc2Vec model training portion of the Graph2Vec by incorporating unsupervised dictionary learning. We investigate the viability of using the sparse dictionary learning technique KSVD for graph data. We train the dictionary on Weisfeiler-Lehman graph sub-tree kernel features. Furthermore, we use graph-based labeled data sets to compare classification results with several existing graph embedding methods. Findings show that using the learned sparse coefficients as features for a supervised machine learning algorithm provides on-par classification results when compared to other graph embedding methods.

关键词： Training Vocabulary Dictionaries Pipelines Machine learning Signal processing Feature extraction

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Ultimate Negative Sampling for Contrastive Learning

Ultimate Negative Sampling for Contrastive Learning

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Huijie Guo Lei Shi Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Unsupervised learning has received more attention due to the superior performance of contrastive learning methods. Most contrastive methods use data augmentation techniques to construct positive and negative pairs. The augmented view of the same sample is regarded as a positive sample, while the rest are negative samples. This negative sampling strategy has strong randomness and ignores samples that are semantically similar to anchors, namely sampling bias. This problem has been addressed by weighting the similarity of negative samples. In this paper, we propose a novel ultimate negative sampling for contrastive learning. Unlike random sampling, we set a more extreme negative sample selection mechanism based on the ideal representation of the sample. Furthermore, we constrain the consistency between samples across the space. Experiment results demonstrate the proposed method’s superiority on multiple benchmark datasets.

关键词： Benchmark testing Signal processing Acoustics Speech processing Unsupervised learning

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Minimization of data centers in survivable dynamic SDM-EONs

Minimization of data centers in survivable dynamic SDM-EONs

引用

international conference on computing and Networking Technology (ICCNT)

作者： Sourabh Chandra Khokan Mondal Dept. of Computer Science & Engineering Calcutta Institute of Technology Howrah India Dept. of Computer Science & Engineering Techno India University Kolkata India

Energy consumption due to massive use of data center (DC) is increasing rapidly. The need to improve the energy efficiency of such DC aims to develop an effective DC-placement algorithm. Again, a sudden disaster in the network can affect both fiber path as well as the DC nodes resulting in fiber cuts and huge data loss. Space division multiplexing-based elastic optical network (SDM-EON), being more efficient than the traditional elastic optical network (EON) due to the inclusion of multiple cores in a single fiber link, tries to maximize the utilization of resources by reducing blocking probability. This paper presents a novel DC-placement algorithm in dynamic DC-based SDM-EONs which focuses on enhancing energy efficiency in the network, thereby ensuring the survivability of the network against any sudden disaster. Rigorous simulation analysis proves the efficacy of the proposed algorithm over other existing algorithms.

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Efficient data Loading with Quantum Autoencoder

Efficient Data Loading with Quantum Autoencoder

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Siang-Ruei Wu Chun-Tse Li Hao-Chung Cheng Department of Electrical Engineering and Graduate Institute of Communication Engineering National Taiwan University Physics Division National Center for Theoretical Sciences Taipei Taiwan Hon Hai (Foxconn) Quantum Computing Center Center for Quantum Science and Engineering National Taiwan University

Faithfully loading classical data into a quantum system is a core problem in quantum machine learning and various quantum information processing tasks. In this work, we propose an efficient quantum autoencoder architecture that can construct a quantum state approximating the unknown classical distribution with high precision and with only linear circuit depth. Simulation experiments show that our proposed method substantially outperforms state-of-the-art methods on a wide range of datasets by evaluating divergences between the loaded distributions and the target distribution, and it also enjoys a faster convergence rate and stability. Moreover, the proposed scheme can be efficiently implemented on near-term hybrid classical-quantum systems with very shallow circuit depths.

关键词： Training Loading Machine learning Signal processing Stability analysis Complexity theory Task analysis

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ANN-Based Scalable Video Encoding Method for Crime Surveillance-Intelligence of Things Applications

ANN-Based Scalable Video Encoding Method for Crime Surveilla...

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data Science and Network Security (ICDSNS), international conference on

作者： Rupanshi Agarwal Paresh Pathak Rupesh Kumar Tipu Digvijay Singh Aarti Kalnawat Dharmesh Dhabliya Department of Computer Science & Engineering Invertis University Bareilly India School of Computer Science and Applications IIMT University Meerut India Department of Civil Engineering K. R. Mangalam University Gurugram India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of IT VIIT Pune India

The proliferation of high-quality, networked CCTV cameras in public areas has raised the volume of constantly generated video data significantly in recent years. The need for scalable, distributed video processing infrastructure is growing as a result. In order to make unstructured videos supplied to the platform searchable and comprehensible by both humans and machines, an intelligent video analytics platform runs them through a series of trans-disciplinary algorithms. The use of video analytics extends well beyond just surveillance to include things like video asset management. Many different commercial and academic approaches exist here. Most current systems for face and object recognition, however, have a time-honored client-server architecture that leaves out support for increasingly intricate use cases. Furthermore, distributed computing is seldom used to manage such frameworks at a scale. Furthermore, no current works provide any kind of assistance with low-level networked video processing APIs. Not only did they ignore the expanding needs of customers, academics, and developers, but they also neglected to address a comprehensive service-oriented environment. In this work, we offer an Artificial Neural Network based Scalable Video Coding (ANN-SVC) framework for intelligent video surveillance as a means of resolving these problems. The suggested system can handle both live video feeds and video analytics in batches. Similarly, the batch processing data for each realtime stream is available. This work therefore has a connection to the idea of symmetry. We also provide Spark's first distributed video processing library. To guarantee scalability, efficacy, and fault-tolerance, SIAT takes use of cutting-edge distributed computing technology. Finally, in an effort to back up our assertions, we implement and test our suggested framework. The simulation shows better results in throughput, gooodput and PSNR.

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EEG-based Emotion Recognition in Immersive Virtual Reality: Meeting the Requirement of Accuracy and Computational Efficiency 23

EEG-based Emotion Recognition in Immersive Virtual Reality: ...

引用

7th international conference on Digital Signal processing, ICDSP 2023

作者： Pei, Guanxiong Fan, Cunhang Li, Taihao Jin, Jia Wang, Rui Research Institute of Artificial Intelligence Zhejiang Lab China School of Computer Science and Technology Anhui University China Laboratory of Applied Brain and Cognitive Sciences Shanghai International Studies University China School of Business and Management Shanghai International Studies University China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China

ISBN: (纸本)9781450398626

Virtual reality technology provides a strong sense of immersion and interactivity. It is widely used in the fields of anxiety relief, fear therapy, and depression regulation. However, objectively evaluating the emotional intervention effect of virtual reality technology is a difficult problem. The main purpose of this paper is to explore the use of EEG signals to identify individual emotional states in virtual reality scenarios and to improve the computational efficiency and recognition accuracy of emotional valence. To induce the target emotional state of the participants, we established a relatively standard emotion-induced virtual reality video library. The EEG data of the participants were collected synchronously as they watched the virtual reality video. The results show that the emotion recognition performance of multiple features (energy spectrum, differential entropy, differential asymmetry, and rational asymmetry) is better than that of a single feature. The radial basis function neural network (RBFNN) performed better than the deep belief network (DBN). RBFNN achieves the highest average classification accuracy of 91.1%. By combining the feature selection (F-test) method with the RBFNN, an ideal classification performance can be maintained with computational efficiency improvements. Furthermore, it is demonstrated that the features extracted from the theta band outperform features extracted from other bands in emotional valence decoding. These results may contribute to the application of EEG-based affective computing technology in the field of psychological rehabilitation and assessment. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtual reality

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An Energy-Efficient Metaheuristic for Offloading Security-Critical Tasks in Mobile Edge computing with Integrated DPUs

An Energy-Efficient Metaheuristic for Offloading Security-Cr...

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Intelligent Technology and Embedded Systems (ICITES), international conference on

作者： Shaoying Hong Jiayin Zhu Yiqi Yuan Jin Sun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China International Joint Audit Institute Nanjing Audit Technology Nanjing China

In mobile edge computing (MEC) paradigm, for the security-critical computation tasks offloaded from the mobile device, the MEC server needs to decrypt the encrypted task data before task execution, leading to heavy computation load on the CPUs of the MEC server. The integration of data processing unit (DPU) into the MEC server can release CPUs from data decrpytion for the purpose of reducing computation and energy overheads. This paper studies a task offloading problem in a MEC system with integrated DPUs, which aims at reducing the total energy consumption under the deadline constraint on task completion time. We formulate the studied problem as an combinatorial optimization model and propose a group mapping-based cuckoo search (GMCS) metaheuristic algorithm to explore high-quality energy-efficient solutions to the formulated model. The proposed GMCS introduces a group mapping operator and a greedy-based task offloading strategy for converting cuckoo individuals into offloading solutions and evaluating the objective values of the converted solutions, respectively. We provide a theoretical analysis to justify that the mapping operator can improve the diversity of the converted solutions and in turn the metaheuristic’s searching capability. We create various testing instances for a MEC system equipped with DPUs to demonstrate the proposed GMCS produces high-quality solutions to the studied offloading problem, with reduced energy consumption compared with baseline algorithms.

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Design of restaurant intelligent seat-seeking system based on ESP32

Design of restaurant intelligent seat-seeking system based o...

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Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Tianqi Ying Dingkun Zhu Yang Zou Yingjie Huang Peisen Zhao Hohai Unversity Nanjing China Nanjing Forestry University Nanjing China

In view of the recent increase in the number of colleges and universities, the university campus is more open than before, which makes it difficult for students to find seats in public places. This paper puts forward a restaurant intelligent seat finding system based on ESP32 to solve this kind of phenomenon. Through the combination of software and hardware of the system, using edge AI technology, cloud platform, Internet of Things platform and WeChat applet, the function of real-time monitoring the seating situation of restaurant seats and feeding the dynamic data of real-time restaurant seating information back to the user’s WeChat applet through the cloud platform is realized by assisting data visualization. Through the delivery of real-time information, students can arrange meals more efficiently, and solve the problem of finding seats during meals. This work can also be applied to library, study room and other application scenes.

关键词： Cloud computing Social networking (online) data visualization Message services Real-time systems Libraries Internet of Things

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Harvesting Insights: Unveiling the Future of Mango Yield Using Real-Time Bangladesh Climate data by Supervised Machine Learning Approach

Harvesting Insights: Unveiling the Future of Mango Yield Usi...

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international conference on computing and Networking Technology (ICCNT)

作者： Md Mahfujur Rahman Sarker Md. Sazzadur Ahamed Lamia Binte Iqbal Shahriar Mamun Juliya Yasmin Dept. Of CSE Daffodil International University Dhaka Bangladesh Dept. of CSE National University Dhaka Bangladesh

Mango, a unique fruit of the Indian subcontinent, was cultivated under the supervision of Mughal emperor Akbar [1]. Its exceptional taste impressed Alexander the Great [2]. Considering the potentiality of this unique fruit this study focuses on predicting mango yield in Bangladesh using machine learning algorithms. Bangladesh's economy heavily relies on the export of Ready-made Garments (RMG) and Knitwear, and there is a need to explore alternative revenue sources due to the increasing trade deficit. Mango production has the potential to contribute significantly to export earnings and GDP growth. However, predicting mango yield has become challenging due to climate change and natural calamities. To address this, a comprehensive dataset covering 54 years of mango production and weather conditions in all 64 districts of Bangladesh was collected. Regression algorithms were employed to predict mango yield, with the Random Forest Regression (RFR) model exhibiting the lowest error rates. The contributions and practical impact of this study include the creation of a real-time dataset, demonstrating the accuracy of the proposed algorithms, and facilitating improved health outcomes and economic benefits through effective production planning.

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Estimation of High-Dimensional Differential Graphs from Multi-Attribute data

Estimation of High-Dimensional Differential Graphs from Mult...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jitendra K. Tugnait Department of Electrical & Computer Engineering Auburn University Auburn AL USA

We consider the problem of estimating differences in two Gaussian graphical models (GGMs) which are known to have similar structure. The GGM structure is encoded in its precision (inverse covariance) matrix. In many applications one is interested in estimating the difference in two precision matrices to characterize underlying changes in conditional dependencies of two sets of data. Existing methods for differential graph estimation are based on single-attribute models where one associates a scalar random variable with each node. In multi-attribute graphical models, each node represents a random vector. In this paper, we analyze a group lasso penalized D-trace loss function approach for differential graph learning from multi-attribute data. An alternating direction method of multipliers (ADMM) algorithm is presented to optimize the objective function. Theoretical analysis establishing consistency in support recovery and estimation in high-dimensional settings is provided. We illustrate our approach using a numerical example where the multi-attribute approach is shown to outperform a single-attribute approach.

关键词： Measurement Graphical models Estimation Signal processing algorithms Signal processing Linear programming Convex functions

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