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Proceedings of the Winter Simulation Conference

作者： Luzalen Marcos Kristiina Valter Mai Abdolreza Abhari Resilience Engineering Lab Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Distributed Systems and Multimedia Processing (DSMP) Lab Department of Computer Science Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9798350369663

Good quality healthcare services require effective communication between the patient and the healthcare provider. This work will help improve the areas of healthcare systems automation and optimization by applying Speech Emotion Recognition (SER) in health consultations to prevent miscommunication between patients and healthcare providers. Crowd-Sourced Emotional Multimodal Actors Dataset (CREMA-D) was used to compare the performances of different machine learning models in classifying emotions. Before feeding the raw dataset to the models, exploratory data analysis was done to determine features that should be considered for future analysis. Our results showed that depending on the emotion, there are some syllables in the text that were emphasized or took time to be pronounced by the speaker. After data analysis, the dataset was fed into different models and determined that the Support Vector Machine (SVM) is a machine-learning model for SER.

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Fourth Workshop on Big (and Small) Data in science and Humanities (BigDS 2023)

Fourth Workshop on Big (and Small) Data in Science and Human...

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2023 Datenbanksysteme fur Business, Technologie und Web, BTW 2023 - 2023 Database systems for Business, Technology and Web, BTW 2023

作者： Henrich, Andreas Karam, Naouel König-Ries, Birgitta Seeger, Bernhard University of Bamberg Media Informatics Bamberg96047 Germany Fraunhofer FOKUS & InfAI e.V. Berlin Germany University of Jena Heinz Nixdorf Chair for Distributed Information Systems Jena07743 Germany University of Marburg Department of Mathematics and Computer Science Marburg35032 Germany

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GPU-Based Efficient Parallel Heuristic Algorithm for High-Utility Itemset Mining in Large Transaction Datasets (Extended Abstract)

GPU-Based Efficient Parallel Heuristic Algorithm for High-Ut...

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International Conference on Data Engineering

作者： Wei Fang Haipeng Jiang Hengyang Lu Jun Sun Xiaojun Wu Jerry Chun-Wei Lin Department of Computer Science and Technology Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China Department of Distributed Systems and IT Devices Silesian University of Technology Poland

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Heuristic algorithms have been developed to find approximate solutions for high-utility itemset mining (HUIM) problems that compensate for the performance bottlenecks of exact algorithms. However, heuristic algorithms still face the problem of long runtime and insufficient mining quality, especially for large transaction datasets with thousands to tens of thousands of items and up to millions of transactions. To solve these problems, a novel GPU-based efficient parallel heuristic algorithm for HUIM (PHA-HUIM) is proposed in this paper. The iterative process of PHA-HUIM consists of three main steps: the search strategy, fitness evaluation, and ring topology communication. The search strategy and ring topology communication are designed to run in constant time on GPU. The parallelism of fitness evolution helps to substantially accelerate the algorithm. To improve the mining quality, a multi-start strategy with an unbalanced allocation strategy is employed in the search process. Ring topology communication is adopted to maintain population diversity. A load balancing strategy is introduced to reduce the thread divergence to improve the parallel efficiency. The experimental results on nine large datasets show that PHA-HUIM outperforms state-of-the-art HUIM algorithms in terms of speedup performance, runtime, and mining quality.

关键词： Runtime Itemsets Heuristic algorithms Sociology Approximation algorithms Search problems Topology

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A Communication-Induced Checkpointing Algorithm for Consistent-Transaction in distributed Database systems 1

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8th International Symposium on Security in Computing and Communications, SSCC 2020

作者： Mansouri, Houssem Pathan, Al-Sakib Khan Laboratory of Networks and Distributed Systems Computer Science Department Faculty of Sciences Ferhat Abbas Setif University 1 Setif Algeria Department of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (数字)9789811604225

ISBN: (纸本)9789811604218

For better protection of distributed systems, two well-known techniques are: checkpointing and rollback recovery. While failure protection is often considered a separate issue, it is crucial for establishing better secure distributed systems. This article is dedicated to the proposal of a new checkpointing algorithm for saving a consistent-transaction state in distributed databases ensuring that database management systems are able, after a failure, to recover the state of the database. The proposed communication-induced algorithm does not hamper the normal transaction processing and saves a global consistent-transaction state that records only the fully completed transactions. Analysis and experimental results of our proposal show that the proposed scheme saves a minimum number of forced checkpoints and has some performance gains compared to the alternative approaches. © 2021, Springer Nature Singapore Pte Ltd.

关键词： distributed database systems

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Securing ML-based Android Malware Detectors: A Defensive Feature Selection Approach against Backdoor Attacks

Securing ML-based Android Malware Detectors: A Defensive Fea...

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Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE/ACM International Symposium on

作者： Bartłomiej Marek Kacper Pieniążek Filip Ratajczak Wojciech Adamczyk Bartosz Bok Mateusz Krzysztoń Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wrocław Poland Department of Distributed Systems Research and Academic Computer Network (NASK-PIB) Warsaw Poland

ISBN: (数字)9798350377514

ISBN: (纸本)9798350377521

This work investigates the vulnerability of ML-based Android malware detectors to backdoor attacks, and proposes a novel feature selection method to reduce system vulnerability. We present a realistic attack scenario and enhance the state-of-the-art genetic algorithm for trigger generation. The algorithm is used to design a feature selection method that prioritises attributes less prone to exploitation by attackers. The solution was evaluated using a dataset from the Koodous platform. The results show that the proposed improvements to the trigger selection algorithm were effective, resulting in a 10 percentage point increase. Furthermore, the proposed feature selection method significantly reduced the vulnerability of the ML system by 30%, without affecting the quality of the malicious application detection task execution.

关键词： Training Measurement Operating systems Computational modeling Neural networks Machine learning Predictive models Feature extraction Malware Genetic algorithms

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A New Approach for the Analysis of Resistance to Change in the Digital Transformation Context 11th

A New Approach for the Analysis of Resistance to Change in t...

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10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： El Karam, Bachira Abou Marghoubi, Rabia Khanboubi, Fadoua Allaki, Driss InnovatiVE REsearch on Software Systems and data INPT Rabat Morocco LIM Lab. IOS team Department of Computer Science FSTM Casablanca Morocco Smart Embedded Enterprise and Distributed Systems INPT Rabat Morocco

ISBN: (纸本)9783031473654

In a context marked by the rapid evolution of technologies and the omnipresence of IT at the heart of all activities, the digital transformation of companies is no longer a luxury but an absolute necessity. This technological transformation can lead to resistance from the employees affected by the change. In order to properly conduct the change management process, it is necessary to ensure the involvement of employees from the beginning of this process, which is composed of three phases: the change management strategy, the diagnosis, and the actions implementation. This paper focuses on the diagnostic phase and more specifically on the evaluation stage. This evaluation measures the employees’ hesitation to express their degree of resistance toward the digital initiatives launched in the company. However, this assessment is usually done subjectively, and employees express their hesitation in non-uniform manners. In this work, we propose a weighted generalized hesitating fuzzy approach based on the fusion of theoretical concepts of generalized hesitant fuzzy sets and formal concept analysis (FCA), to formalize the measurement of degree hesitation in change resistance. This approach is flexible and allows the top management of each organizational unit to correctly measure the resistance to change of their teams and then evaluate that measure at the global organizational level. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Exploiting Open Data for Co2 Estimation Via Artificial Intelligence and Explainable Ai

SSRN

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SSRN 2024年

作者： Bilotta, Stefano Palesi, Luciano Alessandro Ipsaro Nesi, Paolo University of Florence Department of Mathematics and Computer Science "U. Dini" Firenze50134 Italy University of Florence Distributed Systems and Internet Technology DISIT Lab Firenze50139 Italy

In present paper, a general CO2 emission estimation approach is developed by means of a large range of open data as predictions. Such open data are related to human activity regarding services and (socio-economic) conditions arising in a given (urban) area. The proposed model focuses on fine-scale prediction to better understand the dynamics of emissions via machine (or deep) learning approaches and taking into account an innovative study based on open data sources including city services. The goal is to understand how specific predictors can contribute to or mitigate CO2 emissions in the observed area. To this end, the impact of several features has been analyzed in order to identify the key factors influencing emissions. Thus, a formal study has been conducted to perform feature relevance analysis by using eXplainable AI (XAI) approach. The proposed model is useful to define targeted policies reducing the pollutant impact of cities, promote a more ecologically sustainable urban lifestyle and improve sustainable urban planning. Both solution and model have been improved to be more flexible by using transfer learning techniques. This study and its related results have been produced and validated by exploiting the Snap4City framework for smart city, mobility and transport and data analytics. In particular, such results deal with the metropolitan cities of Florence and Bologna. © 2024, The Authors. All rights reserved.

关键词： Open Data

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Unsupervised Spatio-Temporal Anomalous Thermal Behavior Monitoring of Inside-Built Environments

Unsupervised Spatio-Temporal Anomalous Thermal Behavior Moni...

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International Conference on distributed Computing in Sensor systems (DCOSS)

作者： Naima Khan Md Abdullah Al Hafiz Khan Nirmalya Roy Department of Information Systems University of Maryland Baltimore County (UMBC) Center for Real-time Distributed Sensing and Autonomy UMBC Department of Computer Science Kennesaw State University

Continual wavering of outside weather degrades the efficiency of inside building envelope over time and leads to additional energy consumption, various structural damages, etc. Frequent monitoring of the indoor built environment with thermal images can assist in identifying the energy-leaking and potentially damage-prone areas. Although in recent years different researches performed deep learning and computer vision based thermal anomaly detection in built environment, several issues related to conducting strategic non-intrusive indoor thermal inspection using temporal thermal images, are still unresolved in uncontrolled environment of residential buildings. In this work, we propose a scalable thermal image-based monitoring approach for building envelopes combining the visual knowledge of structural joint information among different building components and their corresponding temporal thermal status. We collected longitudinal thermal images from indoor scenes of different building components (e.g., door, window, wall) and employed a high-level spatio-temporal graph (st-graph) to represent the structural connection among different building components and their temporal self-changes. Our proposed novel unsupervised spatio-temporal clustering framework assigns the cluster label to nodes in st-graph, combining its structural (the self and neighboring component) and temporal features which achieves better performance in identifying thermal variation compared to other clustering based approaches. We demonstrate thermal variation across the spots which indicates the potential energy leakage areas inside the built environment. The cluster patterns obtained from our proposed model assist in understanding the thermal characteristics of various surfaces at certain conditions, such as sun reflection and airflow in the inside built environment.

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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Procedia computer science 2023年 220卷 470-477页

作者： Rima Kaafarani Leila Ismail Oussama Zahwe ICCS-Lab Computer Science Department American University of Culture and Education 1507 Beirut Lebanon Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

关键词： Blockchain Decision-Map Health Insurance Frauds Detection Hyperledger Fabric Neo Smart Contract Smart Healthcare

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A Simulative Approach to AMR Fleet Sizing in Decentralized Multi-Robot Task Allocation

A Simulative Approach to AMR Fleet Sizing in Decentralized M...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Jana Gödeke Peter Detzner Department for AI and Autonomous Systems Fraunhofer Institute for Material Flow and Logistics Dortmund Germany Ruhr University Bochum Faculty of Computer Science Distributed and Networked Systems Group Bochum Germany

As the Industry 4.0 shifts towards the adoption of autonomous mobile robots (AMRs) in warehouses, decentralized decision-making has become a key design principle. Multi-robot task allocation (MRTA) is a problem that involves assigning tasks to AMRs while optimizing the performance of the system. However, modeling decentralized MRTA applications for optimization without a central instance poses significant challenges due to the autonomy and flexibility of AMRs. In this paper, we propose a simulative approach to address the fleet sizing problem combined with decentralized MRTA applications for AMRs. Based on simulation data, models have been developed that predict key performance indicators (KPIs) for different warehouse layouts and requirements, using techniques from machine learning and mathematical optimization. The model represents KPIs such as constraint satisfaction and utilization rates in a decentralized MRTA scenario including a self-organizing material flow application. Based on this model, we introduce a fleet size selection mechanism. This research contributes to the field of Industry 4.0 by providing a generalizable simulative approach that is adaptable to flexible warehouse environments, allowing for the application of any MRTA algorithm. Moreover, this approach allows the integration of different KPIs, facilitating the adaptation of requirements.

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