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8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023

作者： Micluța-Câmpeanu, Marius Dinu, Liviu P. Faculty of Mathematics and Computer Science University of Bucharest Romania Human Language Technologies Research Center University of Bucharest Romania

In this paper, we present our approach for performing relation extraction on clinical texts in the context of the CLinkaRT task at EVALITA 2023. Our system ranked first in this task with an F1-score of 62.99, outperforming most other submissions by a significant margin, with an increase of 6.5% over the second best score of 59.16, while also improving over the mBERT baseline of 62.83. We pursue a simple yet unexplored method to determine sentence level relations in text by relying on Named Entity Recognition models to identify the components of a relation. We apply this method to link laboratory results to their appropriate events in medical reports. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Extraction

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Evaluation of CNNs for Wildcats Classification in Real World Scenario 1

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International Conference on Advanced Engineering, Technology and Applications, ICAETA 2024

作者： Fargetta, Georgia Ortis, Alessandro Anile, Stefano Battiato, Sebastiano Department of Mathematics and Computer Science University of Catania Catania Italy Department of Biological Chemical and Pharmaceutical Sciences and Technologies University of Palermo Palermo Italy

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

Biodiversity is threatened by habitat destruction and fragmentation;in this context, the knowledge and monitoring of patterns of occupancy of species is crucial for management of areas and planning proper actions. This study focuses on the monitoring of the presence of the European Wildcat (Felis silvestris Schreber) which conservation is threatened by several factors, including hybridization with domestic cats. This paper presents the evaluation state-of-the-art Convolutional Neural Networks (CNNs) for classification purpose. The employed dataset includes images of three main classes of cat species (i.e., Domestic, Wild, Hybrid) with three sub-classes each (i.e., ALIVE, DEAD, NECRO) captured in real world scenario using trap cameras or smartphones. The challenges are mostly related to the quality of the dataset, which is strongly biased and imbalanced, due to the nature of this task. Moreover, the images span within a wide range of qualities and acquisition settings including low lighting, specific poses, and backgrounds. The experimental results have highlighted that it is a highly complex task, and the data exhibit high specificity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Classification Convolutional Neural Network Dataset Felis Silvestris

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Detection of Pneumonia Using Deep Learning Algorithms: Yolo V5 and CNN on X-Ray Images

Detection of Pneumonia Using Deep Learning Algorithms: Yolo ...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2024

作者： Abiodun, Moses Kazeem Farhaoui, Yousef Adeniyi, Abidemi Emmanuel Imoize, Agbotiname Lucky Ekomobong, Edeme Awotunde, Joseph Bamidele Department of Computer Science Landmark University Kwara State Omu-Aran Nigeria T-IDMS Department of Computer Science Faculty of Sciences and Techniques Errachidia Moulay Ismail University of Meknes Meknes Morocco Department of Computer Science Bowen University Iwo Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria

ISBN: (纸本)9783031883033

Pneumonia is one of the acute respiratory illnesses that affects the lungs, namely the alveoli, it is a dangerous respiratory infection that can result in major health issues. Deep learning models have recently demonstrated encouraging outcomes in medical image analysis applications, such as the diagnosis of pneumonia. The usefulness of two well-known deep learning models, You Only Look Once (YOLOv5) and Convolutional Neural Network (CNN), in identifying pneumonia from chest X-rays are investigated in this study. Experimental results on a chest X-ray image data set show that the YOLOv5 and CNN models can effectively detect cases of pneumonia. The YOLOv5 model achieved a significantly higher precision of 0.97, surpassing the CNN model's precision of 0.91. The YOLOv5 model demonstrated superior performance in accurately classifying positive cases and a high percentage of predicted positive results were validated. Further research and validation in larger and more diverse datasets is recommended to strengthen the generalizability and reliability of these models in clinical practice. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： CNN Detection Pneumonia YoloV5

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Blockchain-based Internet of Musical Things

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Blockchain(Research and Applications) 2022年第3期3卷 60-69页

作者： Luca Turchet Chan Nam Ngo Department of Information Engineering and Computer Science University of TrentoTrento38122Italy Faculty of Mathematics Informaticsand Mechanics of the University of WarsawWarsaw00-927Poland

Blockchain technology is impacting several industries,including the creative industries and those operating in the Internet of Things(IoT).Lately,researchers'attention has been devoted to the application of blockchain in the recorded music ***,thus far,no research has investigated the use of such technology in the Internet of Musical Things(IoMusT).The IoMusT is a new area emerging in industry and academy as an extension of the IoT to the musical *** IoMusT itself,as the IoT,is a distributed network of musical things,which are objects augmented with information and communication technologies serving a musical *** IoMusT vision requires,above all,IoT features such as decentralization,seamless authentication,transparency,data integrity and privacy,and self-maintenance,as well as the musical domain features such as efficient handling of copyrights and speed of royalties *** features can be brought by *** this paper,we investigate the integration of blockchain technology with the IoMusT,and we name such synthesis“Blockchain-based IoMusT”.We present a vision for this new paradigm in terms of the novel opportunities that are enabled,and we propose a set of application scenarios enabled by technological ***,we outline the open research directions in this promising area.

关键词： Internet of Musical Things Blockchain Music industry Privacy Security Internet of Things

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Keep That Card in Mind: Card Guessing with Limited Memory 13

Keep That Card in Mind: Card Guessing with Limited Memory

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13th Innovations in Theoretical computer science Conference, ITCS 2022

作者： Menuhin, Boaz Naor, Moni Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)9783959772174

A card guessing game is played between two players, Guesser and Dealer. At the beginning of the game, the Dealer holds a deck of n cards (labeled 1,..., n). For n turns, the Dealer draws a card from the deck, the Guesser guesses which card was drawn, and then the card is discarded from the deck. The Guesser receives a point for each correctly guessed card. With perfect memory, a Guesser can keep track of all cards that were played so far and pick at random a card that has not appeared so far, yielding in expectation ln n correct guesses, regardless of how the Dealer arranges the deck. With no memory, the best a Guesser can do will result in a single guess in expectation. We consider the case of a memory bounded Guesser that has m 2 n) memory bits that scores a near optimal result against any static Dealer. 2. We show that no Guesser with m bits of memory can score better than O(√m) correct guesses against a random Dealer, thus, no Guesser can score better than min{√m, ln n}, i.e., the above Guesser is optimal. 3. We show an efficient adaptive Dealer against which no Guesser with m memory bits can make more than ln m + 2 ln log n + O(1) correct guesses in expectation. These results are (almost) tight, and we prove them using compression arguments that harness the guessing strategy for encoding. © Boaz Menuhin and Moni Naor;licensed under Creative Commons License CC-BY 4.0

关键词： Information theory

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Straightforward Guess and Determine Analysis Based on Genetic Algorithm

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Journal of Systems science & Complexity 2022年第5期35卷 1988-2003页

作者： CAO Chunping CEN Zhe FENG Xiutao WANG Zhangyi ZHU Yamin Department of Computer Science and Technology University of Shanghai for Science and TechnologyShang-hai 200093China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China School of Science Xi'an Technological UniversityXi'an 710021China

With the development of artificial intelligence,the genetic algorithm has been widely used in many *** cryptography,the authors find it is natural to code an individual and design its fitness in a genetic algorithm for a straightforward guess and determine analysis(SGDA,in short).Based on this observation,the authors propose an SGDA based on genetic *** it with the other three SGDAs based on exhaustive search,MILP method and CPP method respectively,the authors illustrate its effectiveness by three stream ciphers:Small scale SNOW 2.0,medium scale Enocoro-128v2 and large scale *** results show our method is significantly superior to them,especially for Trivium,the method can find a solution of 165 variables in less than one hour,while the other three methods are not applicable due to its enormous search space of size 2^(619.37).As far as we know,it is a best solution in an SGDA for Trivium so far.

关键词： Enocoro-128v2 genetic algorithm guess and determine analysis SNOW 2.0 trivium

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Artificial Intelligence in Cybersecurity: Enhancing Automated Defense Mechanisms to Combat Sophisticated Cyber Threats and Guarantee Digital Resilience 5

Artificial Intelligence in Cybersecurity: Enhancing Automate...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prakash, V.S. Murugesan, P. Poongothai, P. Shivakumar, B.N. Vijayakumar, P. Shriidhar, P.J. Department of Computer Science Bengaluru India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Cmr Institute of Technology Department of Mathematics Bengaluru India Kongu Engineering College Department of Computer Applications Perundurai Erode India Department of Cse Salem India

ISBN: (纸本)9798350376685

The rapid evolution of cyber threats has necessitated a paradigm shift in cybersecurity approaches, with a growing emphasis on leveraging artificial intelligence (AI) to bolster automated defense mechanisms. This research delves into the integration of AI technologies within cybersecurity frameworks, exploring their potential to enhance digital resilience against sophisticated cyber threats. By examining the current landscape of cyber attacks and the limitations of traditional defense strategies, the study underscores the need for intelligent, adaptive systems capable of learning from vast datasets and responding in real-time to emerging threats. The research focuses on the development and evaluation of AI-driven defense mechanisms, including machine learning algorithms for anomaly detection, predictive analytics for threat forecasting, and natural language processing for cyber threat intelligence. Through a series of controlled experiments and case studies, the effectiveness of these AI-enhanced systems is assessed in terms of their accuracy, speed, and adaptability to new attack vectors. Findings indicate that AI-augmented cybersecurity solutions can significantly improve the detection and mitigation of advanced persistent threats, zero-day exploits, and other complex cyber attacks. Moreover, the research highlights the importance of ethical considerations and the responsible use of AI in cybersecurity to prevent the misuse of powerful AI tools and ensure privacy and fairness. The study concludes with recommendations for the strategic implementation of AI in cybersecurity, emphasizing the need for a holistic approach that combines human expertise with AI capabilities to achieve robust digital resilience. By fostering a collaborative ecosystem between cybersecurity professionals, AI developers, and policymakers, this research aims to contribute to the ongoing effort to safeguard digital infrastructure against the ever-evolving landscape of cyber threats. © 2024 IEE

关键词： Adversarial machine learning

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The Impact of Augmentation and SMOTE Implementation on the Classification Models Performance: A Case Study on Student Academic Performance Dataset 9th

The Impact of Augmentation and SMOTE Implementation on the C...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Sano, Albert Verasius Dian Bhatti, Faqir M. Miranda, Eka Aryuni, Mediana Zakiyyah, Alfi Yusrotis Bernando, Charles Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Riphah Institute of Computing and Applied Sciences Riphah Intternational University Raiwind Lahore Pakistan Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of computer Science Bina Nusantara University Jakarta11480 Indonesia

This study aims to find out the impact of data augmentation and Synthetic Minority Over-sampling Techniques (SMOTE) implementation on the classification models performance using small and imbalanced dataset of student academic performance. The design of this study involved a comprehensive experiment by comparing four scenarios: 1) comparing classification models without both data augmentation and SMOTE, 2) models with data augmentation, 3) models with SMOTE, and 4) models with both data augmentation and SMOTE. The model's performances were each measured based on standard evaluation metrices such as accuracy, precision, recall, F1-score. To test the results validity, there were three classification algorithms implemented and evaluated for each scenario, that is, Random Forest, XGBoost, and AdaBoost. The finding of this study highlights the significant impact of data augmentation and SMOTE to the increase of classification models performance, particularly over the small and imbalanced dataset. Results showed that the implementation of both techniques simultaneously brought about the most significant increase in the evaluation metrices compared to the implementation of both techniques separately. The originality of this study lies in its comprehensive approach in comparing the effectiveness of data augmentation and SMOTE, as well as the use of student academic performance dataset, which is, a real case in the context of artificial intelligence. This finding gives a valuable insight to the researchers and practitioners in choosing appropriate techniques to handle small and imbalanced class datasets. This study is expected to make an important contribution to the more effective development of classification methodology in various domains. © 2024 The Authors.

关键词： Data accuracy

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Efficiency and Effectiveness in Utilizing Blended Learning in Context Aware Ubiquitous Learning Systems 8

Efficiency and Effectiveness in Utilizing Blended Learning i...

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8th International Conference on Contemporary Information Technology and mathematics, ICCITM 2022

作者： Alkhayat, Rayan Yousif Yacob Alobaydi, Esraa Khalid Ahmed Abdullah, Omar Muayad Mohd Arshad, Muhammad Rafie Hj. University of Mosul College of Computer Science and Mathematics Computer Science Department Mosul41002 Iraq University of Mosul College of Computer Science and Mathematics Software Department Mosul41002 Iraq Universiti Sains Malaysia School of Computer Science Pinang Pulau11800 Malaysia

ISBN: (纸本)9798350334869

Blended learning is widely spreading and uses many technologies as well as many serves learning objects. Various methods and models implemented and developed in this field. Blended learning definition, components, tools, and aspects are modified according to the rapid evolution of learning subjects, materials, and resources. Meantime the presence of context aware ubiquitous learning opens the way for enabling learners to learn about various subjects in learning systems that are free from the restrictions of time and place. Additionally, efficiency and effectiveness requirements of any learning framework have high consideration when applying any learning theory in particular subject. This paper presents a proposed framework with a summarization of the factors, criteria, and possible ways to achieve efficiency and effectiveness of utilizing blended learning models in context aware ubiquitous learning systems. © 2022 IEEE.

关键词： Efficiency

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Modeling Hot Rolling Process Forces Using Bio-Inspired Metaheuristic Search Algorithm 2

Modeling Hot Rolling Process Forces Using Bio-Inspired Metah...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Sheta, Alaa Braik, Malik Oznergiz, Ertan Elashmawi, Walaa H. Rausch, Peter Othman, Emad S. Southern Connecticut State University Department of Computer Science New HavenCT United States Al-Balqa Applied University Computer Science Department Salt Jordan Istanbul Technical University Mechanical Engineering Faculty Istanbul Turkey Misr International University Dept of Computer Science Cairo Egypt George Simon Ohm Univ. of Applied Sciences Computer Science Department Nuremberg Germany AL-Shorouk Academy Management Information System Dept. Cairo Egypt

ISBN: (纸本)9798350362633

This research introduces the application of an innovative bio-inspired metaheuristic technique, termed the Crow Search Algorithm (CSA), to model a crucial industrial process - hot rolling manufacturing. Inspired by the foraging patterns of crows, the CSA algorithm has demonstrated its prowess in solving diverse optimization challenges. In the context of this study, the CSA algorithm is harnessed to fine-tune the parameters of a simulation model focused on predicting the force exerted during a hot rolling procedure. The proposed model takes into consideration a range of influential factors, including the initial temperature (Ti), width (Ws), carbon equivalent (Ce), gauge (hi), draft (i), and roll diameter (R). The findings underscore the CSA's capability to deliver an exceptional modeling performance characterized by swift convergence and high solution quality. By getting along very well with the proposed model with the CSA algorithm, a robust and efficient avenue to optimize the hot rolling process emerges, with the potential for expansion into other manufacturing domains. The computational and simulation results demonstrated that the proposed approach-based CSA outperformed different meta-heuristic search algorithms, such as the Salp Swarm Algorithm (SSA), Dandelion Optimizer (DO), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), and Moth-Flame Optimization (MFO), in all test cases. The CSA has achieved the highest coefficient of determination (R2), equal to 0.97244, and the lowest mean squared error (MSE), equal to 1904.97, compared to its opponent algorithms. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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