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Mitigating Human Errors and Cognitive Bias for Human-AI Synergy in Cybersecurity 1

Mitigating Human Errors and Cognitive Bias for Human-AI Syne...

1st International Workshop on Detection and Mitigation of Cyber Attacks that Exploit Human VuLnerabilitiES, DAMOCLES 2024

作者： Cau, Federico Maria Spano, Lucio Davide University of Cagliari Dept. of Mathematics and Computer Science Via Ospedale 72 Cagliari09124 Italy

Cybersecurity advancements necessitate effective measures to combat rising and sophisticated threats. Artificial Intelligence (AI) and eXplainable AI (XAI) solutions have demonstrated significant capabilities in predicting and responding to cyber threats. Moreover, integrating AI components with Intelligent User Interfaces (IUI) has been explored as a promising approach, emphasizing user experience and interaction policies. Despite these advancements, the primary challenge remains addressing human errors, particularly those induced by cognitive biases. This paper provides an overview of possible recommendations on AI integration with cybersecurity systems and human cognitive bias mitigation solutions. © 2024 Copyright for this paper by its authors.

关键词： Artificial intelligence

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Enhancing Software Quality Through Defect Prediction 3

Enhancing Software Quality Through Defect Prediction

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Gita Gayatri, Nyoman Ayu Soeparno, Haryono Gaol, Ford Lumban Arifin, Yulyani Bina Nusantara University Binus Graduate Program Computer Science Dept. of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350367492

Prediction of software defects is an important aspect of software quality assurance, aiming to identify defective modules to improve reliability and reduce costs in the maintenance process. A systematic literature review (SLR) on software defect prediction is presented in this study, focusing on identifying commonly used datasets, methods, and frameworks in this domain between 2014 and 2023. The review follows established SLR guidelines to synthesize data from primary studies, providing insight into research trends as well as methodological advances. The results show a significant reliance on public datasets such as the NASA Metrics Data Program (MDP) and the PROMISE repository. Various machine-learning techniques have been used, with Neural networks, support vector machines, and random forests becoming the most popular techniques. This review aims to guide future research by highlighting key trends and gaps in the existing literature. © 2024 IEEE.

关键词： Software quality

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Detecting Feet's Bending Patterns With Smart Insoles 5

Detecting Feet's Bending Patterns With Smart Insoles

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Zhong, Yuchen Ying, Qijun Cai, Xiaohui dept. of Computer Science and Technology Hefei China dept. of Data Science Hefei China

ISBN: (纸本)9798350355925

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. The smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring. © 2024 IEEE.

关键词： Wearable technology

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Data Complexity and Stability Induced Predictive Distributed Learning Approach 19th

Data Complexity and Stability Induced Predictive Distributed...

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19th International Middle Eastern Simulation and Modelling Conference, MESM 2024

作者： Igodan, E.C. Ukaoha, K.C. Al-Akaidi, M. Dept. of Computer Science University of Benin Benin City Nigeria Dept. of Computer Science Wigwe University Isiokpo Nigeria College of Science & Computing Wigwe University Isiokpo Nigeria

ISBN: (纸本)9789492859341

The performance of most classifiers degrades because the majority of feature selection algorithms do not scale well, primarily due to the inherent characteristics in high-dimensional microarray datasets, making them eventually inapplicable. This research study proposed a novel predictive distributed FS model by sample partitioning while maintaining class distribution using data complexity and stability measures. For implementation, brain tumor microarray datasets were adopted. Six feature selection algorithms and classifiers, along with their ensemble learning methodology, were implemented. The dataset was preprocessed and the SMOTE technique applied, so as to balance the class distribution and the feature/sample size ratio. The study showed significant improvement in the proposed distributed model with an accuracy of 99.67% and demonstrated a sharp reduction both in the runtime complexity and the number of selected features respectively. © 2025 EUROSIS-ETI.

关键词： Contrastive Learning

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Asynchronous Telegate and Teledata Protocols for Distributed Quantum Computing 10

Asynchronous Telegate and Teledata Protocols for Distributed...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Peckham, Jacob Makaroff, Dwight Rayan, Steven University of Saskatchewan Dept.of Computer Science Saskatoon Canada Dept. of Mathematics and Statistics Saskatoon Canada

ISBN: (纸本)9798350373011

The cost of distributed quantum operations like the telegate and teledata protocols is high due to latencies from distributing entangled photons and classical information. This paper proposes an extension to the telegate and teledata protocols to allow for asynchronous classical communication which hides the cost of distributed quantum operations. We then discuss the benefits and limitations of these asynchronous protocols and propose a potential way to improve these asynchronous protocols using nonunitary operators. Finally, a quantum network card is described as an example of how asynchronous quantum operations could be used. © 2024 IEEE.

关键词： Quantum communication

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Land Cover Extraction from Satellite Imagery using U-Net Model 18

Land Cover Extraction from Satellite Imagery using U-Net Mod...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, K. Pushpa Sreedevi, Pogula Dastagiriah, C. Rao, K. Srinivas Subbaraju, G.V. Padmavathi, Jasti Mlr Institute of Technology Dept. of Computer Science and Engineering Hyderabad India G. Pulla Reddy Engineering College Dept. of Computer Science and Engineering Kurnool India Anurag University Dept. of Computer Science and Engineering Hyderabad India Scetw Dept. of Computer Science and Engineering Hyderabad India Hyderabad India

ISBN: (纸本)9789380544519

Satellite imagery offers extensive information that can be used for a variety of societal applications, from the number of buildings in a metropolis to the land cover types of a specific area. However, extracting such information is a difficult task. In the past, images were scanned manually by domain experts to extract features like buildings, infrastructure, and vegetation, which would take weeks or even months depending on the image size. To address this challenge, we trained our model using U-Net architecture, a deep learning model, because of its advantages in multiscale feature learning and faster processing. Data augmentation is done on the dataset in order to enhance the amount of data and prevent overfitting artificially. The trained model achieved an F1 score of 92% and an impressive precision score of 95%. This automated method can be scaled to large sets of data, reducing the need for human labeling. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Data augmentation Deep learning Machine learning Multiscale feature learning U-Net architecture

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A Theoretical Framework for Temporal Graph Warehousing with Applications

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International Journal of Advanced computer science and Applications 2024年第6期15卷 260-270页

作者： Chou, Annie Y.H. Tseng, Frank S.C. Dept. Computer and Information Science ROC Military Academy ROC Kaohsiung Taiwan Dept. Information Management National Kaohsiung Univ. of Science and Technology ROC Kaohsiung Taiwan

The evolution of data management systems has witnessed a paradigm shift towards dynamic and temporal representations of relationships. Graph databases, positioned as key players in managing highly-connected data with a fundamental requirement for relationship analysis, have recognized the need for incorporating temporal features. These features are crucial for capturing the temporal dynamics inherent in various applications, offering a more comprehensive understanding of relationships over time. This theoretical exploration emphasizes the importance of incorporating temporal dimensions into graph data warehousing for contemporary applications. Temporal features introduce a dynamic dimension to graph data, enabling a more nuanced understanding of relationships and patterns over time. The integration of temporal features in graph data management and analysis not only addresses the dynamic nature of contemporary applications but also contributes to enhanced modeling and analytical capabilities. © (2024), (science and Information Organization). All Rights Reserved.

关键词： Graph Databases

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Deep Learning content-based search in media files 25

Deep Learning content-based search in media files

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Benarous, Leila Bendjazia, Ihssene Khadidja University of Amar Telidji Lim Laboratory Faculty of Science Dept. Computer Science Laghouat Algeria University of Amar Telidji Faculty of Science Dept. Computer Science Laghouat Algeria

ISBN: (纸本)9798331540012

The rapid growth of digital multimedia content has created a new demand for technologies that not only process but also sort and locate images and videos amidst the vast amounts of content. Traditional text-based search engines often struggle to retrieve specific visual content based on visual features alone, which has spurred the development of computer vision technologies for content-based image and video retrieval. This work presents the design, implementation, and evaluation of a user-friendly application, "Search by visual content,"that leverages artificial intelligence (AI) and, more particularly, the deep learning model of YOLOv8. Our application allows users to search multimedia sources such as images and videos not by their names, types, and metadata, but by using similar images or videos as input. It also offers the feature of searching multimedia content containing a certain object inputted in the search box. Our easy-to-use application delivers prompt and accurate search results, and it can function across multiple operating systems as it was made using Python language. © 2024 IEEE.

关键词： Multimedia systems

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Compromising insecure crypto implementations: A deep-learning based cryptosystem-agnostic testing framework

Compromising insecure crypto implementations: A deep-learnin...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Hill, Leonard Schimanke, Florian Schiffner, Stefan Mertens, Robert Hsw University of Applied Sciences Dept. of Computer Science Hameln Germany Berufliche Hochschule Hamburg Dept. of Computer Science Hamburg Germany

ISBN: (纸本)9798350394528

The use of insecure implementations of cryptographic systems makes encrypted communications vulnerable to practical attacks. Today, attacking, i.e. testing implementations requires human labour and an understanding of the cryptographic system. Automated systematic testing can reduce the insight needed to discover faulty implementations. The approach presented in this paper employs neural networks as the core of a universal framework for cryptographic attacks on arbitrary black-box encryption schemes. The framework trains a neuronal network to automatically perform decryption of ciphertext without knowing the corresponding decryption key. The network approximates the decryption function by encrypting randomly generated plaintext using an arbitrary encryption function and attempting to learn the relationship between plain-and ciphertext. If the decryption function for a certain key is successfully approximated by the framework, the plaintext of any message encrypted with this key can be restored. © 2024 IEEE.

关键词： Cryptography

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ElHa Net: A Pattern Recognition Neural Network 8

ElHa Net: A Pattern Recognition Neural Network

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Babiker, Elsadig Ahmed Mohamed Adlan, Hanan Hassan Ali Prince Sattam Bin Abdulaziz University College of Computer Engineering and Science Dept. of Computer Science Al-Kharj Saudi Arabia Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Dept. of Computer Science Riyadh Saudi Arabia Dept. of Computer Science Faculty of Mathematical Sciences United Kingdom

ISBN: (纸本)9798331540104

Neural Networks are the state-of-the-art models that derive intelligent systems. Generalization of the neural network model is a judge of how well an architecture mimic human intelligence. Self-extraction neural networks become the dominant neural networks, especially in autonomous systems such as intelligent robotics, autonomous vehicles, ṫ etc. This paper presents a neural network based on distance measure, ElHaNet Neural Network. ElHaNet network is a self-extraction neural network and is capable of pattern recognition. ElHaNet architecture composed of an extraction phase and a classification phase. Neurons in the extraction network capture patterns in the presented input. The classification network performs on the input patterns to be classified according to the problem in hand. The developed architecture is bench marked against well-known architectures for hand written digit recognition. The performance of the network is found to compete favorably with state-of-the-art models in the literature. © 2024 IEEE.

关键词： Neural network models

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