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AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity 1

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7th international conference on Intelligent Computing and Optimization, ICO 2023

作者： Sarker, Iqbal H. Janicke, Helge Mohammad, Nazeeruddin Watters, Paul Nepal, Surya Security Research Institute Edith Cowan University PerthWA6027 Australia Cyber Security Cooperative Research Centre JoondalupWA6027 Australia Cybersecurity Center Prince Mohammad Bin Fahd University Khobar Saudi Arabia Cyberstronomy Pty Ltd. MelbourneVIC Australia Data61 CSIRO SydneyNSW2122 Australia

ISBN: (数字)9783031508875

ISBN: (纸本)9783031508868

this position paper explores the broad landscape of AI potentiality in the context of cybersecurity, with a particular emphasis on its possible risk factors with awareness, which can be managed by incorporating human experts in the loop, i.e., "Human-AI" teaming. As artificial intelligence (AI) technologies advance, they will provide unparalleled opportunities for attack identification, incident response, and recovery. However, the successful deployment of AI into cybersecurity measures necessitates an in-depth understanding of its capabilities, challenges, and ethical and legal implications to handle associated risk factors in real-world application areas. Towards this, we emphasize the importance of a balanced approach that incorporates AI’s computational power with human expertise. AI systems may proactively discover vulnerabilities and detect anomalies through pattern recognition, and predictive modeling, significantly enhancing speed and accuracy. Human experts can explain AI-generated decisions to stakeholders, regulators, and end-users in critical situations, ensuring responsibility and accountability, which helps establish trust in AI-driven security solutions. therefore, in this position paper, we argue that human-AI teaming is worthwhile in cybersecurity, in which human expertise such as intuition, critical thinking, or contextual understanding is combined with AI’s computational power to improve overall cyber defenses. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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A Novel machine learning Based Conveyor Belt Health Monitoring Incorporating UHF RFID Backscattered Power 5

A Novel Machine Learning Based Conveyor Belt Health Monitori...

引用

5th IEEE international conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Zohra, Fatema - Tuz Salim, Omar Dey, Shuvashis Masoumi, Hossein Karmakar, Nemai Monash University Dept. Of ECSE Melbourne Australia North Dakota State University Dept. Of ECE FargoND United States Monash University Dept. Of CE Melbourne Australia

ISBN: (纸本)9781665401968

Ultra high frequency (UHF) radio frequency identification (RFID) based sensor is recently explored as a robust health monitoring technique for early crack detection of coal mining conveyor belts. However, a crack detection and predicting a crack pattern in terms of its width from the backscattered power of the sensors remain a challenging task. In this paper, an automatic machine learning (ML) based crack detection and crack width prediction is proposed. the representative crack features of the belt are obtained from the backscattered power of UHF RFID sensor based passive resonator. In order to enhance the crack profile, three different types of crack orientations and 6 different crack widths are investigated. A supervised multilayer perceptron neural network and a Naïve Bayes classifier are trained to classify a crack with width of 0.5 mm. Classification evaluation conducted on a data set of 180 features shows that the learned features with both the proposed ML frameworks provide superior performance in detecting a crack and also predicting its width. © 2021 IEEE.

关键词： Crack detection

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Offline Signature Verification Based on Feature Disentangling Aided Variational Autoencoder

Offline Signature Verification Based on Feature Disentanglin...

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machine learning and Computer Application (ICMLCA), international conference on

作者： Hansong Zhang Jiangjian Guo Kun Li Yang Zhang Yimei Zhao University of California San Diego La Jolla CA US University of Illinois Urbana-Champaign Champaign IL US Boston University Boston MA US

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

Offline handwritten signature verification systems are used to verify the identity of individuals, through recognizing their handwritten signature image as genuine signatures or forgeries. the main tasks of signature verification systems include extracting features from signature images and training a classifier for classification. the challenges of these tasks are twofold. First, genuine signatures and skilled forgeries are highly similar in their appearances, resulting in a small inter-class distance. Second, the instances of skilled forgeries are often unavailable, when signature verification models are being trained. To tackle these problems, this paper proposes a new signature verification method. It is the first model that employs a variational autoencoder (VAE) to extract features directly from signature images. To make the features more discriminative, it improves the traditional VAEs by introducing a new loss function for feature disentangling. In addition, it relies on SVM (Support Vector machine) for classification according to the extracted features. Extensive experiments are conducted on two public datasets: MCYT-75 and GPDS-synthetic where the proposed method significantly outperformed 13 representative offline signature verification methods. the achieved improvement in distinctive datasets indicates the robustness and great potential of the developed system in real application.

关键词： Support vector machines Training Image recognition machine learning Computer applications Feature extraction data augmentation Forgery Robustness Unsupervised learning

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Research on the Method of Constructing Knowledge Graph of Affective Disorder Based on Multi-Source data Fusion 22

Research on The Method of Constructing Knowledge Graph of Af...

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Proceedings of the 5th international conference on Information Technologies and Electrical Engineering

作者： Yang Wang Weihong Yang School of Artificial Intelligence and Big Data Luzhou Vocational and Technical College China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9781450396806

Affective disorder (Depression) has the characteristics of high incidence rate, low recognition rate, high disability and death rate, and has become a serious public health problem that needs to be solved urgently. It is particularly important to use machine learning and big data mining technology to build a complete knowledge graph of affective disorders. In this paper, multi-source data fusion is used to construct the original corpus of the knowledge graph of affective disorders. In the information extraction stage, we designed a machine learning algorithm based on Hidden Markov Model (HMM). the algorithm can extract entities and entity mapping relationships from corpus to form entity database and triplet relationship database. In the knowledge fusion stage, multi-source heterogeneous knowledge fusion algorithm is used to complete entity alignment and relationship deduction. Finally, the open-source graphic database Neo4j is used as the underlying storage structure, so as to realize the visualization of the knowledge graph of affective disorders.

关键词： Multi-source data Knowledge Graph Affective Disorder Knowledge Fusion Information Extraction

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Transfer learning Approaches for Improved thyroid Detection

Transfer Learning Approaches for Improved Thyroid Detection

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： D. M. Kalai Selvi Kireet Joshi Vani V Daxa Vekariya Natrayan L Harshal Patil Department of Computer Science and Engineering R.M.D Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Science and Engineering Bangalore Institute of Technology Bangalore Karnataka India Department of Computer Science & Engineering Parul institute of Engineering and Technology Parul University Post Limda Gujarat India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

thyroid disease is a broad term for any medical disorder that impairs the thyroid gland's ability to generate enough hormones. It is not limited to any particular age group; it can strike anyone at any time. Detecting thyroid diseases usually includes blood tests, which can be difficult to interpret due to the large amount of data required for accurate predictions. To find the best algorithm for predicting thyroid risk, this study examines different Transfer learning (TL) methods. the dataset utilized for this work was sourced from the machine learning repository at the University of California, Irvine. the distribution of thyroid class labels in this dataset is highly imbalanced. To address this, sampling techniques are used to balance the dataset. Transfer learning models like AlexNet, Inception, and MobileNet are employed. All models achieve an accuracy rate of 97%. Upon comparison, AlexNet demonstrates the highest accuracy among the three models. the research shows that proper preprocessing and data balancing can improve thyroid detection efficiency.

关键词： Surveys Accuracy Sensitivity machine learning algorithms Transfer learning Prediction algorithms data mining thyroid Diseases Biomedical imaging

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Implementation of Customer Segmentation Using machine learning

Implementation of Customer Segmentation Using Machine Learni...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Manish Kumar Amresh Kumar Munish Sabharwal School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

In recent years, every e-commerce enterprise focuses on Customer Relationship Management (CRM) to provide the better services to the customer as compared to their competitors. Building a better relationship with customer help the enterprises in increasing profit and customer’s retention and satisfaction. It is necessary for enterprises to identify the potential customers in the market by mining the customer data to gain profitable insight. One of the efficient ways to identify the different customer characteristics is by applying customer segmentation. In this Paper section 1 is defining the Introduction and then we have used a machine learning algorithm to find the results. Next is the proposed Methodology, methods shown to apply to the result and then the results, conclusion and future work

关键词： Automatic frequency control Solid modeling machine learning algorithms Computational modeling Buildings Customer relationship management Clustering algorithms

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Fragile Earth: Accelerating Progress towards Equitable Sustainability 21

<i>Fragile Earth</i>: Accelerating Progress towards Equitabl...

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27th ACM SIGKDD international conference on Knowledge Discovery and data mining (KDD)

作者： Abe, Naoki Buckingham, Kathleen Dilkina, Bistra Eftelioglu, Emre Ganguly, Auroop Hodson, James Kannan, Ramakrishnan IBM Res Yorktown Hts NY 10598 USA Tentree Washington DC USA Univ Southern Calif Los Angeles CA 90007 USA Amazon Seattle WA USA Northeastern Univ Boston MA 02115 USA AI Good Fdn Berkeley CA USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781450383325

Fragile Earth 2021, our annual workshop is taking place as part of the Earth Day events at ACM's KDD 2021 conference on research in machine learning and its applications. the 5th edition of Fragile Earth will bring together the research community, industry, and policy makers to develop radically new technological foundations for advancing and meeting the Sustainable Development Goals in a way that ensures equitable and inclusive progress.

关键词： agriculture climate change biodiversity sustainable development restoration earth systems theory guided data science artificial intelligence

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Skill Factor as a Key Indicator of Digital Dexterity in Emerging Countries: Cluster Analysis Approach

Skill Factor as a Key Indicator of Digital Dexterity in Emer...

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data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), international conference on

作者： Fahad Alhumaid Elsayed Elamir Management and Marketing University of Bahrain Sakhair Kingdom of Bahrain

ISBN: (数字)9798350368710

ISBN: (纸本)9798350368727

Cluster analysis is a data mining method that organizes similar objects based on their attributes. It is an unsupervised and quantitative form of classification that works on unlabeled data. One functional way to earn resilience and sustainability is through digital transformation which depends on digital skills. therefore, in this article, the cluster analysis is used to segment 39 emerging countries based on their skills factor, which is a key indicator of digital skill in fourth industrial revaluation (4IR) era. We have chosen cluster analysis because it has several advantages, such as identifying homogeneous groups of countries in terms of skills factor, facilitating the study of digital dexterity in complex system that include several dimensions, showing readiness of countries to the adoption of 4IR and enhancing cooperation opportunities among countries. We do not aim to explore the connection between skills factor and digital variable. We only focus on using skill factor as a key indicator of digital skills in emerging countries by clustering the countries into four groups according to their skills. this can assist the decision-makers to evaluate, improve and compare the digital skills, training policies and strategies with other countries.

关键词： Training Industries data analysis Digital transformation machine learning data mining Sustainable development Complex systems Resilience Business

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An Explainable Evaluation of Unsupervised Transfer learning for Parallel Sentences mining 5th

An Explainable Evaluation of Unsupervised Transfer Learning ...

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5th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) international Joint conference on Web and Big data

作者： Zhu, Shaolin Mi, Chenggang Shi, Xiayang Zhengzhou Univ Light Ind Zhengzhou 453000 Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9783030858964;9783030858957

the parallel sentences are known as very important resources for training cross-lingual natural language process applications, such as machine translation (MT) systems. However, these resources are not available for many low-resource language pairs. Existing methods mined parallel sentences using transfer learning. Although several attempts can get a good performance, they are not able to explain why transfer learning can help mining parallel sentences for low-resource language pairs. In this paper, we propose an explainable evaluation to quantity why transfer learning is useful for parallel sentence mining. Besides, we propose a novel unsupervised transfer learning that can maintain the robustness of transfer learning. Experiments show that our proposed method improves the performance of mined parallel sentences compared with previous methods in a standard evaluation set. In particular, we achieve good results at two real-world low-resource language pairs.

关键词： Natural language processing machine translation Transfer learning Parallel sentences Low-resource language pairs

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Face recognition with Disguise and Makeup Variations Using Image Processing and machine learning 5th

Face Recognition with Disguise and Makeup Variations Using I...

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5th international conference on Advances in Computing and data Sciences (ICACDS)

作者： Al-ghanim, Farah Jawad Al-juboori, Ali Mohsin Al Qadisiyah Univ Coll Comp Sci & Informat Technol Al Diwaniyah Iraq

ISBN: (纸本)9783030814625;9783030814618

Face recognition is a research challenge continuous and has seen colossal development during the last two decades. While coming algorithms keep on achieving improved the performance, a greater part of the face recognition systems are receptive to failure under disguise and makeup variations that is one of the common challenging covariates of facial recognition. In past researches, some algorithms show promising results on the existing disguise datasets, still, most of the disguise datasets include images with limited variations (oftentimes captured in controlled settings). this not simulate a real-world scenario, wherever both the intended/ unintended unconstrained disguises and makeup are encountered by a face recognition systems. In this paper, the disguised and makeup faces database (DMFD) is used. In order to handle this problem, One of simple, yet efficient ways for extracting face image features is (LBPH), Principal Component Analysis (PCA) that was majorly utilized in pattern recognition. Also, the technique of Linear Discriminant Analysis (LDA) employed for overcoming PCA limitations was efficiently used in face recognition. Further, classification is employed following the feature extraction. the Naive Bayes, KNN and Random forest RF algorithms are used. the results paper show the effectiveness and generalization of the proposed system on the Disguise and makeup face database (DMFD) and the features which are extracted by means of (LDA) with (RF) provided the better results of (F-measure, Recall, and Precision).

关键词： Face recognition Disguised and makeup faces machine learning Classification

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