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Computing (ICOCO), IEEE International Conference on

作者： Nik Aqil Firdaus Afifi Faiz Zaki Nor Badrul Anuar Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu Malaysia

ISBN: (纸本)9781665489973

The Internet of Things (IoT) has gained attention for its rapid growth in the past few years. IoT devices such as temperature and humidity sensors and voice controllers are implemented widely, from household appliances to industrial machines. However, with the rapid growth and benefits IoT offers, we are exposed to various security vulnerabilities, such as data breaches and IoT-specific malware. Researchers are using IoT device identification as a solution for IoT security issues. IoT device identification helps network administrators identify network traffic into its originating devices. However, researchers often overlook an important issue in IoT device identification, which is accuracy degradation over time. Thus, this paper explores the severity of accuracy degradation in IoT device identification on different traffic representation approaches, which are flow, sub-flow, and packet. This paper utilizes a private, and the UNSW IoT Traffic Traces public dataset. Based on the experimental findings, the sub-flow-based approach recorded the best overall performance, with only 8% degradation in the private dataset and 1% degradation in the public dataset. Meanwhile, even though the packet-based approach only degraded 5% on the private dataset, it recorded up to an 11% accuracy decrease in the public dataset.

关键词： Degradation Temperature sensors Performance evaluation Training data Feature extraction Temperature control Internet of Things

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Self-attention transfer networks for speech emotion recognition

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Virtual Reality & Intelligent Hardware 2021年第1期3卷 43-54页

作者： Ziping ZHAO Keru Wang Zhongtian BAO Zixing ZHANG Nicholas CUMMINS Shihuang SUN Haishuai WANG Jianhua TAO Björn WSCHULLER College of Computer and Information Engineering Tianjin Normal UniversityTianjin 300387China GLAM-Group on Language Audio&MusicImperial College LondonSW72AZUK Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg86159Germany Department of Biostatistics and Health Informatics IoPPNKing's College LondonLondonSE58AFUK Department of Computer Science and Engineering Fairfield University 06824USA National Laboratory of Pattern Recognition CASIABeijing 100190China

Background A crucial element of human-machine interaction,the automatic detection of emotional states from human speech has long been regarded as a challenging task for machine learning *** vital challenge in speech emotion recognition(SER)is learning robust and discriminative representations from *** machine learning methods have been widely applied in SER research,the inadequate amount of available annotated data has become a bottleneck impeding the extended application of such techniques(e.g.,deep neural networks).To address this issue,we present a deep learning method that combines knowledge transfer and self-attention for SER ***,we apply the log-Mel spectrogram with deltas and delta-deltas as ***,given that emotions are time dependent,we apply temporal convolutional neural networks to model the variations in *** further introduce an attention transfer mechanism,which is based on a self-attention algorithm to learn long-term *** self-attention transfer network(SATN)in our proposed approach takes advantage of attention transfer to learn attention from speech recognition,followed by transferring this knowledge into *** evaluation built on Interactive Emotional Dyadic Motion Capture(IEMOCAP)dataset demonstrates the effectiveness of the proposed model.

关键词： Speech emotion recognition Attention transfer Self-attention Temporal convolutional neural networks(TCNs)

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High isolation and gain metamaterial based MIMO antenna for 5G millimiter wave applications

High isolation and gain metamaterial based MIMO antenna for ...

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Circuit, Systems and Communication (ICCSC), International Conference on

作者： Ouafae Elalaouy Bilal Aghoutane Mohammed El Ghzaoui Sudipta Das Jaouad Foshi Hanan El Faylali Faculty of Sciences and techniques Errachidia University of Moulay Ismail Errachidia Morocco Department of Computer Science Informatics Systems and Optimization Laboratory Faculty of Sciences IbnTofail University Kenitra Morroco Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fes Morocco Department of Electronics and Communication Engineering IMPS College of Engineering and Technology Malda W.B India

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

In the contemporary era, harnessing millimeter-wave frequencies emerges as a compelling strategy to fulfill the swift data transfer requirements for $\mathbf{5 G}$ communications. Consequently, this study involves designing a metamaterial-based multiple-input multiple-output (MIMO) antenna with a focus on achieving high isolation and gain within the millimeter-wave spectrum. The suggested MIMO is printed on a Rogers RT5800 measuring $18 \times 38 \times 0.8 \mathrm{~mm}^{3}$. The single antenna element demonstrates a broad frequency coverage from 27.2 to $\mathbf{2 8. 7 2} \mathbf{G H z}$. To elevate the antenna’s effectiveness, a two-port MIMO configuration is implemented by introducing a second antenna element parallel to benchmark antenna. However, the isolation between the two elements is deemed satisfactory. To address this, a $4 \times 1$ metamaterial unit cell is introduced, significantly enhancing isolation from 31 dB to 39 dB at 28 GHz. Furthermore, it improves the gain by 0.5 dBi at 28 GHz, elevating it from 8.5 to 9 dBi. The collective features of the suggested two ports MIMO antenna, encompassing its compactness and simplicity, position it as an excellent option for upcoming portable and shrink devices in Internet of Things uses.

关键词： Performance evaluation Mutual coupling Millimeter wave devices Millimeter wave measurements Metamaterials Millimeter wave integrated circuits Broadband antennas

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Pregnancy monitoring mobile application user experience assessment 2

Pregnancy monitoring mobile application user experience asse...

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2nd International Conference on Information and Communications Technology, ICOIACT 2019

作者： Wicahyono, Gunawan Setyanto, Arief Raharjo, Suwanto Munandar, Arief Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia Department of Informatics Engineering IST AKPRIND Yogyakarta Indonesia

ISBN: (纸本)9781728116556

Pregnancy or fetal monitoring is an important medical examination of health issue. Hospital in a certain area is responsible for monitoring pregnant women to ensure their condition during pregnancy. Regular assessment in health facilities by a medical doctor has to be performed to measure some parameters, such as blood pressure, weight, baby movement, and other criteria. Nowadays, as mobile phone develops rapidly, it embeds many sensors. Those sensors help the medical practitioner to observe some of the parameters automatically. This research, therefore, proposes a mobile application to automatically record pregnant women mobility and the facility of image processing to read the weight of the scale. This paper aims to discuss the user experience in the implementation of the mobile smart birth monitoring application. The user's experience is measured by user experience questionnaire (UEQ) from 30 pregnant women in one of the districts in Central Java. According to the user responses, user experience shows that 5 out of 6 dimension of UEQ consider to be above average. Novelty and efficiency of the smart birth application need major improvement. © 2019 IEEE

关键词： mHealth

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Training Artificial Neural Networks by Coordinate Search Algorithm

Training Artificial Neural Networks by Coordinate Search Alg...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Ehsan Rokhsatyazdi Shahryar Rahnamayan Sevil Zanjani Miyandoab Azam Asilian Bidgoli H.R. Tizhoosh Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada Department of Artificial Intelligence and Informatics Mayo Clinic Rhazes Lab Rochester MN USA

Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously; for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such

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Boundary Surface Visualization of MVL Memory: From 2D to 3D 20

Boundary Surface Visualization of MVL Memory: From 2D to 3D

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20th IEEE International Conference on Modern Electrical and Energy Systems, MEES 2021

作者： Guzan, Milan Vince, Tibor Jacko, Patrik Kiresova, Simona Sobota, Branislav Slavko, Olena FEI TU of Košice Department of Theoretical and Industrial Electrical Engineering Košice Slovakia FEI TU of Košice Department of Computers and Informatics Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Computer and Information Systems Department Kremenchuk Ukraine

ISBN: (纸本)9781665423663

The article presents for the first time a gallery of 3D boundary surfaces of a multi-value memory cell. In the analysis, we chose such sizes of parasitic elements on the chip, in which there are no stable limit cycles present in the memory, assuming that the absence of stable limit cycles will greatly simplify the transcription of information in the memory. However, this fact was not confirmed and the 3D gallery of boundary surfaces points to the need to know the boundary surface for each case of changing the size of parasitic capacitances on the chip. This will make it possible to explain the memory failure associated with, for example, a change in technology in the memory production. At the same time, visualization of boundary surfaces, attractors or chaotic attractors using virtual reality technologies is offered for another technical activities. © 2021 IEEE.

关键词： Capacitance

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Shun-Cheng Chang Pochuang Wang Weichung Wang Tung-Hung Su Jia-Horng Kao Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Computer Science and Information Engineering NTU Taipei Taiwan NTU Institute of Applied Mathematical Sciences Taipei Taiwan Department of Internal Medicine Division of Gastroenterology and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan NTU Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages.

关键词： Liver cancer Accuracy Computed tomography Biological system modeling Transformers Planning Biomedical imaging

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

arXiv

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arXiv 2022年

作者： Zhang, Chengming Geng, Tong Guo, Anqi Tian, Jiannan Herbordt, Martin Li, Ang Tao, Dingwen School of Electrical Engineering and Computer Science Washington State University PullmanWA United States Mathematics and Computer Science Division Pacific Northwest National Laboratory RichlandWA United States Department of Electrical and Computer Engineering Boston University BostonMA United States Department of Electrical and Computer Engineering University of Rochester RochesterNY United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability to extend Machine Learning (ML) approaches to applications broadly-defined as having unstructured data, especially graphs. Compared with other Machine Learning (ML) modalities, the acceleration of Graph Neural Networks (GNNs) is more challenging due to the irregularity and heterogeneity derived from graph typologies. Existing efforts, however, have focused mainly on handling graphs’ irregularity and have not studied their heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) and AIE (AI Engine) based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal Adaptive Compute Acceleration Platforms (ACAPs) to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity, and processes them using PL and AIE, respectively. To further improve performance, we explore the sparsity support of AIE and develop an efficient density-aware method to automatically map tiles of sparse matrix-matrix multiplication (SpMM) onto the systolic tensor array. Compared with state-of-the-art GCN accelerators, H-GCN achieves, on average, speedups of 1.1∼2.3×. © 2022, CC BY.

关键词： Graph neural networks

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Graphical interface adaption for children to explain astronomy proportions and distances 30

Graphical interface adaption for children to explain astrono...

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30th International Conference in Central Europe on computer Graphics, Visualization and computer Vision, WSCG 2022

作者： Martinez, Kim Lebiedź, Jacek Bustillo, Andres Department of History Geography and Communication University of Burgos PºComendadores s/n Burgos09001 Spain Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology ul. G. Narutowicza 11/12 Gdańsk80-233 Poland Department of Computer Engineering University of Burgos Avda. de Cantabria s/n Burgos09006 Spain

ISBN: (纸本)9788086943336

Mobile Science Center is a Polish project that seeks to bring astronomy knowledge to wider social groups through various applications. In its development it is necessary to design a graphical interface that explains a concept that is difficult to assimilate such as spatial proportions and distances. This paper develops a framework to create graphical representations that explain this learning to the target audience of children. Important aspects of this interface are the inclusion of storytelling to guide the educational content, along with feedback and difficulty and accessibility adaptations. Regarding spatial representation, previous works highlight the use of shapes and geometric objects, cartographic tools, reference points, and comparison with known velocities and spaces. The graphical interface proposed is based on a decimal system scale that compares traveling at the speed of light with a person walking. There are 4 proposals that represent the units of this scale with different geometric shapes and interrelated structures, in addition to assigned colors and positions. Future development of this project will apply these ideas to identify the optimal graphical interface so children can learn spatial proportions and distances. © 2022 University of West Bohemia. All rights reserved.

关键词： Astronomy

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