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data Analytics for Health and Connected Care: Ontology, Knowledge Graph and Applications 16th

Data Analytics for Health and Connected Care: Ontology, Know...

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16th international conference on Pervasive computing Technologies for Healthcare

作者： Steenwinckel, Bram De Brouwer, Mathias Stojchevska, Marija Van der Donckt, Jeroen Nelis, Jelle Ruyssinck, Joeri van der Herten, Joachim Casier, Koen Van Ooteghem, Jan Crombez, Pieter De Turck, Filip Van Hoecke, Sofie Ongenae, Femke Univ Ghent IDLab IMEC Technol Pk 126 B-9050 Ghent Belgium ML2Grow Reigerstr 8 B-9000 Ghent Belgium Amaron Kapellestr 13 B-8755 Ruiselede Belgium Telev Healthcare Leo Bekaertlaan 1 B-8870 Izegem Belgium

ISBN: (纸本)9783031345852;9783031345869

Connected care applications are increasingly used to achieve a more continuous and pervasive healthcare follow-up of chronic diseases. Within these applications, objective insights are collected by using Artificial Intelligence (AI) models on Internet of Things (IoT) devices in patient's homes and by using wearable devices to capture biomedical parameters. However, to enable easy re-use of AI applications trained and designed on top of sensor data, it is important to uniformly describe the collected data and how this links to the health condition of the patient. In this paper, we propose the DAHCC (data Analytics For Health and Connected Care) ontology, dataset and Knowledge Graph (KG). The ontology allows capturing the metadata about the sensors, the different designed AI algorithms and the health insights and their correlation to the medical condition of the patients. To showcase the use of the ontology, a large dataset of 42 participants performing daily life activities in a smart home was collected and annotated with the DAHCC ontology into a KG. Three applications using this KG are provided as inspiration on how other connected care applications can utilize DAHCC. The ontology, KG and the applications are made publicly available at https://***. DAHCC's goal is to integrate care systems such that their outcomes can be visualised, interpreted and acted upon without increasing the burden of healthcare professionals who rely on such systems.

关键词： Connected Care Open health data Ontology

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Geometric Reconstruction of a Biological Structure by Explicit Modal Methods. A Case-Study in Human Corneal Surface 31st

Geometric Reconstruction of a Biological Structure by Explic...

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31st international conference on The Digital Transformation in the Graphic Engineering, INGEGRAF 2022

作者： Sáez-Gutiérrez, Francisco L. Velázquez, José S. Mira, Jorge Gómez, Carmelo Alió, Jorge Cavas, Francisco Department of Structures Construction and Graphical Expression Technical University of Cartagena Cartagena30202 Spain International School of Doctorate Technical University of Cartagena Cartagena30202 Spain Keratoconus Unit of Vissum Corporation Alicante C/Cabañal nº1 Alicante03016 Spain

ISBN: (纸本)9783031203244

Geometric reconstruction of biological structures is currently a scientific challenge. Nowadays, computer software is a tool often used for modelling biological structures drawn from external acquired data. This article compares explicit modal rebuilding methods of the human eye’s corneal surface, evaluating the root mean square (RMS) and the geometric reconstruction parameters. Different approaches for the fitting function were used to obtain the surface and to compare fitting methods, respectively. Then, a tailored script was used to obtain the rebuilt surface from the acquired data. The dataset used consisted in corneal surface points obtained by Scheimpflug tomography techniques from 33 patients. The corneal tomographer model used was the Sirius (CSO, Florence, Italy). The geometric reconstruction was made using an adjustment to a biconical modal model. Convergence was reached with different mathematical computer algorithms. The analysis of the results suggests what would be the best algorithm and the computing parameters for the geometric reconstruction of the corneal surface of the human eye. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Geometry

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Autonomous Vehicle Image Classification using Deep Learning

Autonomous Vehicle Image Classification using Deep Learning

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Sustainable computing and data Communication Systems (ICSCDS), international conference on

作者： P Ramakrishnan K Dhanavel K Deepak R Dhinakaran Dept. of Electronics and Communication M. Kumarasamy College of Engineering Karur India

High-performance computer systems, Deep learning algorithms, and sensors work together to enable autonomous driving. First, information about the surroundings around the vehicle is gathered via sensors like lidar, radar, and cameras. Then, deep learning algorithms evaluate this data to determine how the car should react, such as with steering or braking. These computations are done in real-time using high-performance computer devices There is ongoing research towards the creation of autonomous vehicle image categorization systems, however the systems confront several difficulties. In recent years, several approaches—including the application of deep learning algorithms and the incorporation of sensor fusion technology have been suggested to solve these problems. The precise and effective categorization of photographs taken in actual surroundings continues to present difficulties. The requirement to analyse massive volumes of data in real-time, which necessitates powerful computation and effective algorithms, is one of the key obstacles. In this paper we include the idea of detecting vehicles and things on the road, such as vehicles and lane detection, which can be used for autonomous vehicles. The main objective to achieve this our proposed system includes a popular algorithm YOLO (You Only Look Once) YOLOV3. By including the concept of detecting objects using YOLOV3, better results can be obtained by increasing the grid size to $26*26$. This extended grid size helps to detect the smaller objects.

关键词： Deep learning Training Navigation Sensor fusion Real-time systems data models Sensors

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Modeling a Hybrid Stemmer for Kokborok

Modeling a Hybrid Stemmer for Kokborok

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international Symposium on Advanced computing and Communication (ISACC)

作者： Pankaj Debbarma Pusparwng Hrangkhawl Swapan Debbarma Department of Computer Science and Engineering Tripura Institute of Technology Narsingarh India Department of Information Technology Ambedkar College Fatikroy India Department of Computer Science and Engineering National Institute of Technology Agartala India

Kokborok is a resource scarce and vulnerable language and is spoken only by around a million people in the north-east Indian state of Tripura. Lots of unstructured textual data in Kokborok is now available however efficient data mining for such a resource is not applicable. Reasons are the absence of an efficient pre-processing step that can help in solving the information retrieval or natural language processing. In this work, we present a model of hybrid stemming algorithm for the vulnerable and resource-scarce Kokborok language and assess its performance using various methods. Evaluation of the hybrid stemmer was performed on a corpus of 52,482 words that resulted in achieving an accuracy of 89.28%. Recall and Precision of 89.28% and 89.61 %, respectively, with F1-measure of 89.44 has been found for the stemmer.

关键词： Computational modeling Information retrieval Natural language processing data mining Intelligent systems

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Terpsichora: A Tool to Generate Synthetic MP-Declare Process Models

Terpsichora: A Tool to Generate Synthetic MP-Declare Proce...

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international Workshops which were held in conjunction with the 6th international conference on Process Mining, ICPM 2024

作者： da Silva Santos, Wesley Coutinho, Juliana Rezende Baião, Fernanda Spyrides, Georges Miranda Lopes, Hélio Côrtes Vieira Department of Informatics PUC-Rio Rio de Janeiro Brazil Department of Industrial Engineering PUC-Rio Rio de Janeiro Brazil

ISBN: (纸本)9783031822247

Process models play a fundamental role in the Business Process Management lifecycle and are crucial for assessing the robustness of proposed algorithms and conducting benchmarks among different tools. However, public models are limited as they expose strategic knowledge. While some researchers developed public repositories of imperative models, there remains a lack of diverse, publicly available multi-perspective declarative models. Our work aims to bridge this gap by providing a tool for generating synthetic MP-Declare process models. We leverage Large Language Models to generate these models, ensuring coverage of diverse aspects and enhancing the resource pool for the BPM community. © The Author(s) 2025.

关键词： declarative process management large language models mp-declare process models synthetically generated data

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Multicriteria data processing Management in the Geographically Distributed computing Systems with QoS Time Requirements

Multicriteria Data Processing Management in the Geographical...

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international conference on Management of Large-Scale System Development (MLSD)

作者： Anna Klimenko Institute of IT and Security Technologies Russian State University for Humanities Moscow Russia

This paper is devoted to the problem of data processing management in fog- and edge-computing environments. The main issue of task planning is that the environments of the fog and edge network tiers are dynamic and heterogeneous in terms of optimization criteria. Previously published papers do not consider the heterogeneity of the criteria in the tasks of data processing, although the heterogeneity of the network generates a wide range of criteria and constraints for the workload distribution. The main contribution of this paper is as follows: the general model of a multicriteria workload distribution problem, which differs from previously proposed ones by the heterogeneous criteria usage, and by the integration of the reconfiguration process into the problem description, taking into account the main QoS time constraint. The basic method of the problem solution is proposed, too, with paying attention to the data transit extra-costs. Some selected simulation examples are described as well, which present the possibility to achieve the solution of the problem formulated.

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Temporal Global Re-detection Based on Interaction-Fusion Attention in Long-Term Visual Tracking 12th

Temporal Global Re-detection Based on Interaction-Fusion At...

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12th international conference on Image and Graphics, ICIG 2023

作者： Ma, Jingyuan Hou, Zhiqiang Han, Ruoxue Ma, Sugang School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing of Shaanxi Province Xi’an710121 China

ISBN: (纸本)9783031463075

In long-term visual tracking, target occlusion and out-of-view are common problems that lead to target drift, adding re-detection to short-term tracking algorithms is a general solution. To better handle the problem of target disappearing and reappearing in long-term visual tracking, this paper proposes a temporal global re-detection method based on interaction-fusion attention. Firstly, ResNet50 is used as the feature extraction network to obtain the depth features of the template and the search region. Then, a new interaction-fusion attention is added to extract the connection of different dimensionality of features. Finally, Temporal ROI Align is introduced to select candidate boxes, increasing the use of historical information by re-detection method, and improving the accuracy of target localization. STMTrack algorithm is selected as the short-term tracking algorithm, which works with the proposed re-detection method to construct a long-term tracking algorithm, and experiments are conducted on the UAV123, LaSOT, UAV20L, and VOT2018-LT datasets, and the effectiveness of this re-detection method can be seen from the experimental results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Chemical detection

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Scalable algorithms for Densest Subgraph Discovery

Scalable Algorithms for Densest Subgraph Discovery

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international conference on data Engineering

作者： Wensheng Luo Zhuo Tang Yixiang Fang Chenhao Ma Xu Zhou College of Computer Science and Electronic Engineering Hunan University China School of Data Science The Chinese University of Hong Kong Shenzhen China

As a fundamental problem in graph data mining, Densest Subgraph Discovery (DSD) aims to find the subgraph with the highest density from a graph. It has been studied for several decades and found a large number of real-world applications, such as network community detection, regulatory motif discovery in DNA, graph index construction, and fake follower detection. Although there are many existing DSD algorithms, they are often not scalable or efficient to process large-scale graphs, since most of them are serial algorithms and can only leverage the computing resource of a single CPU core. To tackle these issues, in this paper we propose efficient parallel algorithms for solving the DSD problems on both undirected and directed graphs at scale. Our main idea is to use the k-cores (a kind of dense subgraph) to approximate the densest subgraph in the undirected graphs, and then propose efficient parallel algorithms for computing the cores by optimizing the iterative process and also reducing the number of iterations. We further extend this idea for directed graphs by introducing a novel concept, named w-induced subgraph, to avoid unnecessary enumerations of x or y when searching [x,y]-cores (a kind of directed dense subgraph to approximate the densest). To verify the scalability and efficiency of the proposed algorithms, we have conducted extensive experiments on 12 large real-world graphs, and four of them are billion-scale. The experimental results show that our proposed algorithms outperform the state-of-the-art algorithms on both undirected and directed graphs, in terms of scalability and efficiency.

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Blockchain Security Encryption to Preserve data Privacy and Integrity in Cloud Environment

Blockchain Security Encryption to Preserve Data Privacy and ...

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international conference on Future Internet of Things and Cloud (FiCloud)

作者： Abel Yeboah-Ofori Sayed Kashif Sadat Iman Darvishi School of Computing and Eng University of West London United Kingdom

Blockchain security issues in relation to encryption for data privacy and integrity in cloud computing have become challenging due to the decentralized and peer-to-peer systems for securing data storage and transfer in smart contracts. Further, Blockchain technology continues revolutionizing how we handle data, from improving transparency to enhancing security. However, various instances of data breaches, piracy, and hacking attacks have compromised the safety measures employed by these providers. The paper aims to explore Blockchain technology and how encryption algorithms are used to leverage security properties to uphold data privacy and integrity in a cloud environment to enhance security. The novelty contribution of the paper is threefold. First, we explore existing blockchain attacks, vulnerabilities, and their impact on the cloud computing environment supported by numerous cloud services that enable clients to store and share data online. Secondly, we used an encryption approach to detect data security by combining AES encryption, cloud storage, and Ethereum smart contracts in cloud AWS S3. Finally, we recommend control mechanisms to improve blockchain security in the cloud environment. The paper results show that AES algorithms can be used in blockchain smart contracts to enhance security, privacy, and integrity to ensure secure data in transit and at rest.

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Comparative analysis of multi-loss functions for enhanced multi-modal speech emotion recognition

Comparative analysis of multi-loss functions for enhanced mu...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Phuong-Nam Tran Thuy-Duong Thi Vu Nhat Truong Pham Hanh Dang-Ngoc Duc Ngoc Minh Dang Dept. of Computing Fundamental FPT University Ho Chi Minh City Vietnam Dept. of Integrative Biotechnology Sungkyunkwan University Suwon Republic of Korea School of Electrical and Data Engineering University of Technology Sydney Sydney NSW Australia

In recent years, multi-modal analysis has gained significant prominence across domains such as audio/speech processing, natural language processing, and affective computing, with a particular focus on speech emotion recognition (SER). The integration of data from diverse sources, encompassing text, audio, and images, in conjunction with classifier algorithms has led to the realization of enhanced performance in SER tasks. Traditionally, the cross-entropy loss function has been employed for the classification problem. However, it is challenging to discriminate the feature representations among classes for multi-modal classification tasks. In this study, we focus on the impact of the loss functions on multi-modal SER rather than designing the model architecture. Mainly, we evaluate the performance of multi-modal SER with different loss functions, such as cross-entropy loss, center loss, contrastive-center loss, and their combinations. Based on extensive comparative analysis, it is proven that the combination of cross-entropy loss and contrastive-center loss achieves the best performance for multi-modal SER. This combination reaches the highest accuracy of 80.27% and the highest balanced accuracy of 81.44% on the IEMOCAP dataset.

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