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arXiv 2023年

作者： Stumme, Gerd Dürrschnabel, Dominik Hanika, Tom Knowledge & Data Engineering Group Research Center for Information System Design Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Order is one of the main instruments to measure data. In this paper, we will therefore discuss difthe relationship between objects in (empirical) data. ferent means for measuring and ‘calculating’ with However, compared to methods that use numerical ordinal structures — a specific class of directed properties of objects, the amount of ordinal meth- graphs — and show how to infer knowledge from ods developed is rather small. One reason for this is them. Our aim is to establish Ordinal data Science the limited availability of computational resources as a fundamentally new research agenda. Besides in the last century that would have been required cross-fertilization with other cornerstone machine for ordinal computations. Another reason — partic- learning and knowledge representation methods, a ularly important for this line of research — is that broad range of disciplines will benefit from this en-order-based methods are often seen as too mathe- deavor, including, psychology, sociology, economics, matically rigorous for applying them to real-world web science, knowledge engineering, *** Codes 03G10, 06A15, 68T27, 68T30 Copyright © 2023, The Authors. All rights reserved.

关键词： data Science

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Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and data Visualization 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Alfredo Vellido Karina Gibert Cecilio Angulo José David Martín Guerrero

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On the robustness of EEG tensor completion methods

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Science China(Technological Sciences) 2021年第9期64卷 1828-1842页

作者： DUAN Feng JIA Hao ZHANG ZhiWen FENG Fan TAN Ying DAI YangYang CICHOCKI Andrzej YANG ZhengLu CAIAFA Cesar F. SUN Zhe SOLE-CASALS Jordi College of Artificial Intelligence Nankai UniversityTianjin 300350China Skolkowo Institute of Science and Technology Moscow 121205Russia College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China Department of Informatics Nicolaus Copernicus UniversityTorun 87-100Poland Systems Research Institute Polish Academy of SciencesWarsaw 01-447Poland College of Computer Science Nankai UniversityTianjin 300350China Instituto Argentino de Radioastronom´ıa IAR-CCT La Plata CONICET/CIC-PBA/UNLPVilla Elisa 1894Argentina Computational Engineering Applications Unit Head Office for Information Systems and CybersecurityRIKENWako-Shi 351-0106Japan Department of Psychiatry University of CambridgeCambridge CB28AHUK Data and Signal Processing Research Group University of Vic-Central University of CataloniaCatalonia 08500Spain

During the acquisition of electroencephalographic(EEG) signals, data may be missing or corrupted by noise and artifacts. To reconstruct the incomplete data, EEG signals are firstly converted into a three-order tensor(multi-dimensional data) of shape time × channel × trial. Then, the missing data can be efficiently recovered by applying a tensor completion method(TCM).However, there is not a unique way to organize channels and trials in a tensor, and different numbers of channels are available depending on the EEG setting used, which may affect the quality of the tensor completion results. The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials, the number of channels, and the amount of missing data. In this work, the results of completing missing data by several TCMs were compared. To emulate different scenarios of missing data, three different patterns of missing data were ***, the amount of missing data on completion effects was analyzed, including the time lengths of missing data and the number of channels or trials affected by missing data. Secondly, the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor. Finally, the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels. Among all the applied TCMs, the simultaneous tensor decomposition and completion(STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed. In other words, STDC proves to be an excellent choice of TCM, since permutations of trials or channels have almost no influence on the complete results. The STDC method can efficiently complete the missing EEG signals. The designed simulations can be regarded as a procedure to validate whether or

关键词： electroencephalogram corrupted data missing data tensor completion tensor decomposition

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On the Problem of Flying Robots Deployment to Improve Cellular User Experience

On the Problem of Flying Robots Deployment to Improve Cellul...

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Chinese Control Conference (CCC)

作者： Hailong Huang Andrey V. Savkin Ming Ding Mohamed Ali Kaafar Chao Huang Networks Research Group Data61 CSIRO Eveleigh Sydney Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia

In this paper, we consider the problem of using flying robots to provide internet service to mobile users. Two important aspects are accounted in this context: the robot deployment problem and the relocation problem. Where to deploy the flying robots directly influences the user coverage and the quality of service, and the relocation problem looks for an efficient scheme to relocate the flying robots such that the total moving distance is minimized, with the aim of saving the previous energy resource preloaded on the flying robots. We formulate these two problems and show that the drone deployment problem is a NP-hard problem. Then, we propose a heuristic algorithm to address it. For the relocation problem, we formulate it as the conventional assignment problem and use a liner programming algorithm to solve it. We conduct extensive computer simulations to demonstrate the effectiveness of the proposed approaches.

关键词： Robots Drones Electronic mail Australia Quality of service Wireless communication Signal to noise ratio

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Resource management of enterprise cloud systems using layered queuing and historical performance models

Resource management of enterprise cloud systems using layere...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： David A. Bacigalupo Jano van Hemert Asif Usmani Donna N. Dillenberger Gary B. Wills Stephen A. Jarvis School of Electronics and Computer Science University of Southampton UK Data-Intensive Research Group School of Informatics University of Edinburgh UK BRE Centre of Fire Safety Engineering School of Engineering University of Edinburgh UK IBM Thomas J. Watson Research Center Yorktown Heights NY USA High Performance Systems Group Department of Computer Science University of Warwick UK

ISBN: (纸本)9781424465330

The automatic allocation of enterprise workload to resources can be enhanced by being able to make `what-if' response time predictions, whilst different allocations are being considered. It is important to quantitatively compare the effectiveness of different prediction techniques for use in cloud infrastructures. To help make the comparison of relevance to a wide range of possible cloud environments it is useful to consider the following. 1.) urgent cloud customers such as the emergency services that can demand cloud resources at short notice (e.g. for our FireGrid emergency response software). 2.) dynamic enterprise systems, that must rapidly adapt to frequent changes in workload, system configuration and/or available cloud servers. 3.) The use of the predictions in a coordinated manner by both the cloud infrastructure and cloud customer management systems. 4.) A broad range of criteria for evaluating each technique. However, there have been no previous comparisons meeting these requirements. This paper, meeting the above requirements, quantitatively compares the layered queuing and (¿HYDRA¿) historical techniques - including our initial thoughts on how they could be combined. Supporting results and experiments include the following: i.) defining, investigating and hence providing guidelines on the use of a historical and layered queuing model; ii.) using these guidelines showing that both techniques can make low overhead and typically over 70% accurate predictions, for new server architectures for which only a small number of benchmarks have been run; and iii.) defining and investigating tuning a prediction-based cloud workload and resource management algorithm.

关键词： Resource management Clouds Delay Predictive models computer science data engineering Emergency services Disaster management Guidelines Quality of service

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Admission Control with Latency Considerations for 5G Mobile Edge Computing

Admission Control with Latency Considerations for 5G Mobile ...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Ye Zhang Wuyungerile Li Winston K.G. Seah Inner Mongolia Key Lab. of Wireless Networking & Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education School of Computer Science Inner Mongolia University Hohhot China Wireless Networks Research Group School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

The fifth generation (5G) mobile network is a new generation of broadband mobile communication technology with the potential to address the increasing demands of new user services and applications that have stringent low latency and high bandwidth requirements. Besides the enhanced mobile broadband (eMBB) which is an evolution of broadband services from previous generations, the ultra reliable low latency communication (URLLC) service comes with stringent delay requirements that are needed to support new applications like autonomous vehicles, augmented/virtual reality, etc. Mobile or multiple access edge computing (MEC) emerged to provide services and computing resources for users at the network edge to provide faster access speeds and lower end-to-end delays. To better meet user needs and maximize resource utilization, network resources need to be allocated and managed efficiently. Admission control for user requests is one of the methods used that can effectively prevent network congestion, thereby improving the overall performance of the system. In this paper, we propose a RED-based Admission Control with Latency Considerations (REDAL) algorithm for user admission control that aims to increase throughput, meet users’ delay requirements and reduce packet discard rate. By explicitly accounting for user traffic delay constraints and bandwidth requirements, we are able to meet the strict delay constraints of URLLC traffic while meeting the bandwidth requirements of eMBB traffic. We validate our approach in an MEC scenario to demonstrate high resource utilization and also keeping the request discard rate below 20%.

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EABC: Energy-aware Centrality-based Caching for Named data Networking in the IoT

EABC: Energy-aware Centrality-based Caching for Named Data N...

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IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Xingyun He Hu Liu Wuyungerile Li Alvin Valera Winston K.G. Seah Inner Mongolia Key Lab. of Wireless Networking & Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education School of Computer Science Inner Mongolia University Hohhot China Wireless Networks Research Group School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798350394665

ISBN: (纸本)9798350394672

Named data Networking (NDN) is an information-centric internet architecture that delivers packets based on the name of the content in the packet. A key component of NDN is the caching strategy designed to reduce total network latency and load on content producers. To improve the speed and reliability of web content delivery, existing caching strategies typically cache content on a large number of intermediate nodes, which incur significant energy consumption and memory overhead. However, in Internet of Things (IoT) scenarios, memory and energy of nodes are scarce resources. Therefore, in NDN-based IoT applications, traditional caching strategies can cause node failures due to energy depletion, which can significantly reduce the network operational lifetime as well as create problems that caching is supposed to solve. In this paper, a caching strategy based on node centrality and energy availability, called Energy-aware Approximate Betweenness Centrality (EABC) is proposed for NDN-based IoT. EABC uses a topology-based heuristic to cache data content on nodes with high centrality and makes caching decisions based on the remaining energy of the nodes. We evaluate EABC using simulations based on ndnSIM in different topologies and compare it with several existing NDN caching strategies. The results show that EABC performs better in different types of network topologies, reduces the average transmission delay of data and balances the energy consumption of highly central nodes, thus extending the network lifetime.

关键词： Wireless communication Energy consumption Network topology Memory management Topology Delays Internet of Things

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Sherlock in OSS: A Novel Approach of Content-Based Searching in Object Storage System

arXiv

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arXiv 2023年

作者： Noor, Jannatun Ratul, Rizwanul Haque Uday, Mir Rownak Ali Mondal, Joyanta Jyoti Sakif, Md. Sadiqul Islam Islam, A.B.M. Alim Al The Department of CSE Bangladesh University of Engineering and Technology Dhaka Bangladesh Research Group School of Data and Sciences BRAC University Dhaka Bangladesh C2SG Research Group Optimizely Dhaka Bangladesh C2SG Research Group Bangladesh The Department of Computer Science University of Alabama Birmingham United States mPower-Social Enterprise Dhaka Bangladesh Next-generation Computing Research Group Department of CSE Bangladesh University of Engineering and Technology Dhaka Bangladesh

Object Storage Systems (OSS) inside a cloud promise scalability, durability, availability, and concurrency. However, open-source OSS does not have a specific approach to letting users and administrators search based on the data, which is contained inside the object storage, without involving the entire cloud infrastructure. Therefore, in this paper, we propose Sherlock, a novel Content-Based Searching (CoBS) architecture to extract additional information from images and documents. Here, we store the additional information in an Elasticsearch-enabled database, which helps us to search for our desired data based on its contents. This approach works in two sequential stages. First, the data will be uploaded to a classifier that will determine the data type and send it to the specific model for the data. Here, the images that are being uploaded are sent to our trained model for object detection, and the documents are sent for keyword extraction. Next, the extracted information is sent to Elasticsearch, which enables searching based on the contents. Because the precision of the models is so fundamental to the search’s correctness, we train our models with comprehensive datasets (Microsoft COCO dataset for multimedia data and SemEval2017 dataset for document data). Furthermore, we put our designed architecture to the test with a real-world implementation of an open-source OSS called OpenStack Swift. We upload images into the dataset of our implementation in various segments to find out the efficacy of our proposed model in real-life Swift object storage. © 2023, CC BY-NC-SA.

关键词： Object detection

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Ripple Sparse Self-Attention for Monaural Speech Enhancement

Ripple Sparse Self-Attention for Monaural Speech Enhancement

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qiquan Zhang Hongxu Zhu Qi Song Xinyuan Qian Zhaoheng Ni Haizhou Li School of Electrical Engineering and Telecommunications University of New South Wales Australia Department of Electrical and Computer Engineering National University of Singapore Singapore Alibaba Group China Meta United States Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

The use of Transformer represents a recent success in speech enhancement. However, as its core component, self-attention suffers from quadratic complexity, which is computationally prohibited for long speech recordings. Moreover, it allows each time frame to attend to all time frames, neglecting the strong local correlations of speech signals. This study presents a simple yet effective sparse self-attention for speech enhancement, called ripple attention, which simultaneously performs fine- and coarse-grained modeling for local and global dependencies, respectively. Specifically, we employ local band attention to enable each frame to attend to its closest neighbor frames in a window at fine granularity, while employing dilated attention outside the window to model the global dependencies at a coarse granularity. We evaluate the efficacy of our ripple attention for speech enhancement on two commonly used training objectives. Extensive experimental results consistently confirm the superior performance of the ripple attention design over standard full self-attention, blockwise attention, and dual-path attention (Sep-Former) in terms of speech quality and intelligibility.

关键词： Training Correlation Computational modeling Speech enhancement Transformer cores Transformers Acoustics

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Realizability of Rectangular Euler Diagrams

arXiv

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arXiv 2024年

作者： Dürrschnabel, Dominik Priss, Uta Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design Kassel Germany Faculty of Computer Science Ostfalia University of Applied Sciences Wolfenbüttel Germany

Euler diagrams are a tool for the graphical representation of set relations. Due to their simple way of visualizing elements in the sets by geometric containment, they are easily readable by an inexperienced reader. Euler diagrams where the sets are visualized as aligned rectangles are of special interest. In this work, we link the existence of such rectangular Euler diagrams to the order dimension of an associated order relation. For this, we consider Euler diagrams in one and two dimensions. In the one-dimensional case, this correspondence provides us with a polynomial-time algorithm to compute the Euler diagrams, while the two-dimensional case is linked to an NP-complete problem which we approach with an exponential-time *** Codes 06A07, 68R05 Copyright © 2024, The Authors. All rights reserved.

关键词： Formal concept analysis

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