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Colombian Caribbean Conference (C3), IEEE

作者： Yair Rivera Julio Angel Pinto Mangones Juan Torres Tovio Milton De La Hoz Toscano Frank Ibarra Hernández Horderlin Robles Vega Mario Macea Anaya Ricardo Jose Ruiz Gaviria Dpt Computer Science Americana Corporación Universitaria Barranquilla Colombia Dpt Computer Science Universidad del Sinú Montería Colombia Dpt Industrial Engineering Americana Corporación Universitaria Barranquilla Colombia Dpt Electric Engineering Universidad del Sinú Montería Colombia Dpto Computer Science Universidad de Cordoba Montería Colombia

Within the innovative landscape of Industry 4.0, the seamless melding of digital systems has amplified the manufacturing arena's capacities. Notwithstanding these advancements, the latent vulnerabilities intrinsic to these novel systems necessitate rigorous analytical scrutiny. This research delineates an exhaustive vulnerability assessment rooted in the Octave Cybersecurity Framework's principles. Recognized for its meticulous approach, the Octave Cybersecurity Framework offers an intricate methodology tailored to unpack and counteract inherent risks in the Industry 4.0 environment. Harnessing the depth of this Framework, our investigation underscores the dual objectives of optimizing efficiency while bolstering cybersecurity resilience against nascent digital threats. Moreover, through empirical findings, we highlight the nuances of these vulnerabilities and advocate for preemptive strategies to enhance infrastructural robustness and operational continuity. The synthesis of our findings seeks to guide stakeholders in navigating the intricate cybersecurity landscape of Industry 4.0.

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Stock Price Manipulation Detection using Spiking Neural Networks

Stock Price Manipulation Detection using Spiking Neural Netw...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ammar Belatreche Obiye Ada-Ibrama Baqar Rizvi Dpt. of Computer and Information Sciences Fac. of Engineering and Environment Northumbria University Newcastle Upon Tyne United Kingdom Aquis Exchange PLC London United Kingdom

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Stock market is an open marketplace for the creation, acquisition, and exchange of stocks that trade over the counter or on a stock exchange, this market accommodates billions of transactions. Stock market manipulation occurs when dealers attempt to fraudulently raise or lower a stock price to personal benefit. Pump and dump is a common manipulation technique that is frequently used to artificially boost and collapse stock prices and manually determining such activity has proven to be cumbersome. Machine learning and deep learning models have been utilized to recognize a range of stock manipulation scenarios; however, they lack the ability to model dynamically in continuous real time and trend identification gets hindered due to significant noise-to-signal ratio. Hence, this work focused on the viability of Spiking Neural Networks (SNNs) to naturally adapt and manage non-linear and temporal based input data that classical neural networks struggle with. A feed-forward network of Leaky-Integrate and Fire (LIF) neurons is proposed for stock market manipulation detection. The data employed in this research was obtained from the LOBSTER project and the Bloomberg Newcastle Business School trading room. To find the ideal network design and encoding strategy for this task, extensive experiments are conducted and experimental results and their comparison against existing models revealed that the proposed method outperforms the chosen benchmark models and is more successful at identifying patterns of stock price manipulation.

关键词： Adaptation models Neurons Time series analysis Spiking neural networks Encoding Stability analysis Stock markets

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Adiabatic training for Variational Quantum Algorithms

arXiv

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arXiv 2024年

作者： Acosta, Ernesto Gutiérrez, Carlos Cano Botella, Guillermo Campos, Roberto Dpt. Computer Science and AI University of Granada Granada18071 Spain Computer Architecture and Automation Department Complutense University of Madrid Madrid28040 Spain Theoretical Physics Department Complutense University of Madrid Madrid28040 Spain

This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation;a Gate-based Quantum computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN);and an adiabatic Quantum computer where the optimization function is executed to find the best parameters for the VQA. As of the moment of this writing, the majority of QNNs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect [5]. Some gradient-free classical approaches such as Evolutionary Algorithms have also been proposed to overcome this effect [4]. However, to the knowledge of the authors, adiabatic quantum models have not been used to train VQAs. The paper compares the results of gradient-based classical algorithms against adiabatic optimizers showing the feasibility of integration for gate-based and adiabatic quantum computing models, opening the door to modern hybrid QML approaches for High Performance Computing. © 2024, CC BY-NC-SA.

关键词： Quantum computers

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Towards a model driven approach for integrating NWN models in CINCO

Towards a model driven approach for integrating NWN models i...

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International Conference on New Technologies of Information and Communication (NTIC)

作者： Amel Dembri Mohammed Redjimi Dpt. of Computer Science Université 20 Août 1955- LICUS Laboratory Skikda Algeria

In this paper, we propose a model driven approach to facilitate the integration of Nets within Nets (NWN) modeling language in CINCO modeling tool. Despite that, NWN has a sophisticated modeling-simulation tool called Renew, moving NWN models from a platform to another has been a challenge for programmers. The development of our tool is heavily benefits from being implemented with CINCO; a full generation of the application code is provided, a powerful tool is developed with a few efforts and facilities are provided to add semantic to the platform. Combine formal method with a sophisticated model driven tool simplifies the prototypical of domain specific systems, promotes the interoperability capabilities between different technologies and assists designer in the validation of the correctness of the modeled system.

关键词： Productivity Analytical models Codes Semantics Petri nets Task analysis Interoperability

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A PSO-based improved clustering algorithm for lifetime maximisation in wireless sensor networks

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International Journal of Information and Communication Technology 2021年第2期18卷 224-241页

作者： Singh, Santar Pal Sharma, S.C. Computer Science and Engineering Galgotias University Greater Noida Greater NoidaUttar Pradesh201310 India Electronics and Computer Discipline DPT Indian Institute of Technology Roorkee RoorkeeUttarakhand247667 India

A wireless sensor network (WSN) is comprised of miniature devices known as sensor nodes. Sensor nodes are highly energy restrained due to their battery dependent operations and harsh area deployment. It is unviable to change their batteries. For maximising the network lifetime, energy conservation is a crucial measure of performance of WSNs. So, energy consumption and network lifetime are crucial issues in WSNs. Large portion of energy consumed in communication process. Several routing algorithms have been proposed in WSNs. Based on network structure, routing algorithms are divided into flat based, cluster based, and location-based algorithms. Due to energy efficiency, cluster-based routing is the best choice. In this paper, we propose a swarm based clustering algorithms for WSNs. We perform the analysis of our new algorithms with LEACH, LEACH-C, and HEED on the basis of energy, number of alive nodes, delay, PDR, and throughput. Simulation confirms that proposed algorithm outperform the other ones. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Energy efficiency

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Improving Bidding and Playing Strategies in the Trick-Taking game Wizard using Deep Q-Networks

arXiv

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arXiv 2022年

作者： Schumacher, Jonas Pleines, Marco Nexocraft GmbH Bonn Germany Dpt. of Computer Science TU Dortmund University Dortmund Germany

In this work, the trick-taking game Wizard with a separate bidding and playing phase is modeled by two interleaved partially observable Markov decision processes (POMDP). Deep Q-Networks (DQN) are used to empower self-improving agents, which are capable of tackling the challenges of a highly non-stationary environment. To compare algorithms between each other, the accuracy between bid and trick count is monitored, which strongly correlates with the actual rewards and provides a well-defined upper and lower performance bound. The trained DQN agents achieve accuracies between 66% and 87% in self-play, leaving behind both a random baseline and a rule-based heuristic. The conducted analysis also reveals a strong information asymmetry concerning player positions during bidding. To overcome the missing Markov property of imperfect-information games, a long short-term memory (LSTM) network is implemented to integrate historic information into the decision-making process. Additionally, a forward-directed tree search is conducted by sampling a state of the environment and thereby turning the game into a perfect information setting. To our surprise, both approaches do not surpass the performance of the basic DQN agent. © 2022, CC BY.

关键词： Markov processes

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AI-Driven Anomaly and Intrusion Detection in Energy Systems: Current Trends and Future Direction

AI-Driven Anomaly and Intrusion Detection in Energy Systems:...

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Cyber Security and Resilience (CSR), IEEE International Conference on

作者： Georgios Andronikidis Charis Eleftheriadis Zisis Batzos Konstantinos Kyranou Nikolaos Maropoulos Gohar Sargsyan Panagiotis Radoglou Grammatikis Panagiotis Sarigiannidis Sidroco Holdings Ltd Nicosia Cyprus University of Western Macedonia Kozani Greece Celesta Advice Amsterdam Netherlands Dpt. of Electrical & Computer Engineering University of Western Macedonia Kozani Greece

ISBN: (数字)9798350375367

ISBN: (纸本)9798350375374

The growing digitalization and interconnection of energy infrastructures have improved operational efficiency but also heightened the risk of exposure to cyber threats. Traditional electrical power and energy systems encompass all infrastructure and processes for generating, transmitting, distributing, and consuming electricity. Conversely, the smart grid represents an advanced paradigm, integrating cyber-physical components to optimize efficiency, reliability, and sustainability. However, this paradigm shift renders the energy sector more susceptible to cyber threats and attacks, necessitating proactive identification and mitigation. This survey provides a comprehensive analysis of the current state of anomaly and intrusion detection systems specifically designed for the energy sector. We review recent advancements in detection methodologies, including machine learning, artificial intelligence, and hybrid techniques, highlighting their effectiveness in identifying potential threats.

关键词： Surveys Technological innovation Reviews Intrusion detection Market research Critical infrastructure Smart grids

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Determining the credibility of science communication

arXiv

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arXiv 2021年

作者： Augenstein, Isabelle Dpt. of Computer Science University of Copenhagen

Most work on scholarly document processing assumes that the information processed is trustworthy and factually correct. However, this is not always the case. There are two core challenges, which should be addressed: 1) ensuring that scientific publications are credible - e.g. that claims are not made without supporting evidence, and that all relevant supporting evidence is provided;and 2) that scientific findings are not misrepresented, distorted or outright misreported when communicated by journalists or the general public. I will present some first steps towards addressing these problems and outline remaining challenges. Copyright © 2021, The Authors. All rights reserved.

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An efficient heuristic for geometric analysis of cell deformations

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computers in Biology and Medicine 2025年 186卷 109709-109709页

作者： Soto, Yaima Paz Garcia, Silena Herold Gual-Arnau, Ximo Jaume-i-Capó, Antoni González-Hidalgo, Manuel Department of Informatics University of Guantánamo Guantánamo Cuba Department of Computer Science University of Oriente Santiago de Cuba Cuba Departament de Matemàtiques Institute of New Imaging Technologies Universitat Jaume I Castelló Spain UGiVIA Research Group University of the Balearic Islands Dpt. of Mathematics and Computer Science Crta. Valldemossa Km 7.5 PalmaE-07122 Spain SCOPIA Research Group University of the Balearic Islands Dpt. of Mathematics and Computer Science Crta. Valldemossa Km 7.5 PalmaE-07122 Spain PalmaE-07122 Spain PalmaE-07122 Spain PalmaE-07122 Spain

Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a high global prevalence and places a significant burden on healthcare systems, especially in resource-limited regions. Automated classification of sickle cells in blood images is crucial, allowing the specialist to reduce the effort required and avoid errors when quantifying the deformed cells and assessing the severity of a crisis. Recent studies have proposed various erythrocyte representation and classification methods (Jennifer et al., 2023 [1]). Since classification depends solely on cell shape, a suitable approach models erythrocytes as closed planar curves in shape space (Epifanio et al., 2020). This approach employs elastic distances between shapes, which are invariant under rotations, translations, scaling, and reparameterizations, ensuring consistent distance measurements regardless of the curves’ position, starting point, or traversal speed. While previous methods exploiting shape space distances had achieved high accuracy, we refined the model by considering the geometric characteristics of healthy and sickled erythrocytes. Our method proposes (1) to employ a fixed parameterization based on the major axis of each cell to compute distances and (2) to align each cell with two templates using this parameterization before computing distances. Aligning shapes to templates before distance computation, a concept successfully applied in areas such as molecular dynamics (Richmond et al., 2004 [2]), and using a fixed parameterization, instead of minimizing distances across all possible parameterizations, simplifies calculations. This strategy achieves 96.03% accuracy rate in both supervised classification and unsupervised clustering. Our method ensures efficient erythrocyte classification, maintaining or improving accuracy over shape space models while significantly reducing computational costs. © 2025 Elsevier Ltd

关键词： Template matching

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Conditionally Optimal Parallelization of Real-Time DAG Tasks for Global EDF 42

Conditionally Optimal Parallelization of Real-Time DAG Tasks...

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Cho, Youngeun Shin, Dongmin Park, Jaeseung Lee, Chang-Gun Dpt. of Computer Science Seoul National University Seoul Korea Republic of College of Liberal Studies Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665428026

Real-time applications with high computational demand, e.g., autonomous driving, are emerging and their complex nature conforms to a DAG(directed acyclic graph) structure. We propose a conditionally optimal parallelization for real-time DAG tasks for global EDF, ensuring complete execution of all tasks within the deadline. To achieve this, we formalize a monotonic increasing property of both tolerance and interference to the parallelization option. Using such properties, we develop a unidirectional search algorithm that can assign parallelization options in polynomial time, which we formally prove the optimality. We observe significant improvement of schedulability through simulation experiment, and then in the following implementation experiment, we demonstrate that the algorithm is practically applicable for real-world use-cases. ©2021 IEEE

关键词： Polynomial approximation

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