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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain intelligence

IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Sparse regularization based feature selection: A survey 10

Sparse regularization based feature selection: A survey

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10th International Conference on Information technology and Quantitative Management, ITQM 2023

作者： Zhao, Jingjing Liu, Yashu Cheng, Li Zhang, Maoqing Jiang, Mengwan Miao, Jianyu College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China

Feature selection, as an essential preprocessing tool, aims to identify a subset of crucial features by eliminating redundant and noisy features according to a predefined criterion. In recent years, sparse learning has received considerable attention. As an important component of sparse learning, sparse regularization has been widely used in feature selection to enforce irrelevant feature coefficients to be small or exactly zero, which is especially suitable for selecting the discriminative and relevant features. In this paper, we attempt to provide a survey on various sparse regularization based methods. We group the existing sparse regularization based methods into two categories, i.e., convex sparse regularization based feature selection and non-convex sparse regularization based feature selection. This survey not only compares the common and different characters of sparse regularizations based feature selection methods, but also offers useful guidance for practitioners of feature selection. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information technology and Quantitative Management.

关键词： Feature Selection

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Generalized Orthogonal Chirp Division Multiplexing in Doubly Selective Channels

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IEEE Internet of Things Journal 2024年第12期12卷 18495-18507页

作者： Liu, Yun Zhao, Hao Yao, Huazhen Hu, Zeng Cui, Yinming Wan, Dehuan Guangdong University of Finance School of Internet Finance and Information Engineering Guangzhou510521 China Guangzhou Maritime University Department of Electronic Engineering Guangzhou510725 China Zhongkai University of Agriculture and Engineering School of Information Science and Technology Guangzhou510225 China South China University of Technology School of Electronics and Information Engineering Guangzhou510640 China Guangdong University of Finance School of Data Science and Artificial Intelligence Guangzhou510521 China

In recent years, orthogonal chirp division multiplexing (OCDM) has gained attention as a robust communication waveform due to its strong resistance to both time-domain and frequency-domain interference. However, similar to orthogonal frequency division multiplexing (OFDM), OCDM suffers from a high peak-to-average power ratio (PAPR), resulting in increased hardware costs and reduced energy efficiency of the transmitter's power amplifiers. In this work, we introduce a novel unitary transform called the Generalized Discrete Fresnel Transform (GDFnT) and propose a new waveform based on this transform, named Generalized Orthogonal Chirp Division Multiplexing (GOCDM). In GOCDM, data symbols from the constellation diagram are independently placed in the Generalized Fresnel (GF) domain. We derive the system's GF-domain channel matrix under a class of time-frequency doubly selective channels. These channels are characterized by multiple lags and multiple Doppler shifts (MLMDS), making them suitable for application scenarios such as vehicular mobile communication and narrowband underwater acoustic communication. We leverage the sparsity of the GF-domain channel matrix to design an iterative receiver based on the message-passing algorithm. Simulation results demonstrate that GOCDM achieves better PAPR performance than OCDM without compromising bit error rate (BER) performance. © 2014 IEEE.

关键词： Bit error rate

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intelligence Non-Invasive Blood Group Prediction using Thermography

Intelligence Non-Invasive Blood Group Prediction using Therm...

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2023 International Conference on data science, Agents and artificial intelligence, ICDSAAI 2023

作者： Babu, M.C. Muthusundar, S.K. Kirubashankar, K. Chennai Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350348910

Blood bunch forecast is pivotal in bonding medication, as it guarantees protected and compelling blood bondings. Predicting a person's blood group has traditionally been done through serological methods, which look at whether or not particular antigens are present on the surface of red blood cells. Be that as it may, these techniques are tedious and require specific hardware and prepared faculty. Thermography is a method that uses infrared imaging to detect temperature differences [1] on the surface of an object or organism. As a result, there is a need for methods that are both non-invasive and quick to predict blood groups. This method has been utilized in different clinical applications, including bosom disease recognition and skin temperature checking. On account of blood bunch expectation, thermography can be utilized to recognize temperature contrasts brought about by the presence or nonattendance of explicit antigens on the outer layer of red platelets [2]. To use thermography for blood group prediction, a thermograph image of a person it can be taken using a thermal camera. The image can then be analyzed to detect temperature differences on the surface of the it. These temperature differences can be compared to a database of known blood group patterns to predict the person's blood group [3]. © 2023 IEEE.

关键词： Antigens

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Bilevel Optimization-Based Decomposition for Solving Single and Multiobjective Optimization Problems 13th

Bilevel Optimization-Based Decomposition for Solving Single...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Sinha, Ankur Pujara, Dhaval Singh, Hemant Kumar Brij Disa Centre for Data Science and Artificial Intelligence Indian Institute of Management Gujarat Ahmedabad380015 India School of Engineering and Technology University of New South Wales CanberraACT2610 Australia

ISBN: (纸本)9789819635054

Real world optimization problems contain multiple complexities that are often not tractable if one completely relies on mathematical programming or metaheuristic approaches, as each approach has unique strengths and limitations in dealing with the complexities in optimization problems. For instance, metaheuristic methods have an edge in tackling non-regularities like discontinuity, non-convexity, and discreteness, given that problem has few decision variables (low dimensionality). Contrary to that, mathematical programming based classical methods can efficiently solve high dimensional problems if the problem encompasses regular functions as constraints and objective function. Thus, non-regular and high-dimensional optimization problems pose challenges to both approaches for getting solved using the method explicitly from any one approach. To excel in this situation, this study proposes a difficulties separation approach that enables to convert a single level optimization problem into bilevel optimization problem and solve the problem synergistically by applying methods from both complementary approaches. We demonstrate the benefits of proposed bilevel based decomposition approach on a wide range of single and multiobjective test problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Don’t Ignore the Drive of Curiosity: Rethinking Subtleties Between Universality of Commonsense Knowledge and Excellence of Large Language Models

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SN Computer science 2024年第6期5卷 1-14页

作者： Wang, Chao Chen, Tao Liu, Jingping School of Future Technology Shanghai University Shanghai China Institute of Artificial Intelligence Shanghai University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

Commonsense reasoning is one of the abilities necessary for artificial intelligence to be as intelligent as humans. However, how to make AI understand commonsense has been a problem that has plagued artificial intelligence for more than 60 years. Existing efforts focus more on the means of knowledge acquisition and strive to enrich the capacity of commonsense knowledge (CSK) bases and dimensions of CSK through advanced methods. Unfortunately, this exuberance has obscured a general consideration of CSK, such as how to follow human habits to obtain the most representative knowledge we need to understand the world. In this paper, this representative knowledge is referred to as core CSK. The influence of core CSK is extensive, and it constitutes almost the fundamental element of human life and the most fundamental cognition of the world. Harnessing human curiosity to find solutions to the above problems is an effective and straightforward route. Specifically, we focus on a special corpus to mine core CSK, namely, why-questions. For example, we can harvest “the sky is blue” from “why is the sky blue?”. To this end, we propose a novel method to extract CSK from why-questions, which mainly consist of two modules. The first is a question classification module used to determine whether a question contains CSK. In this module, we propose a classifier based on a one-sided bootstrapping method and design several informative features for the classifier. The second is a crowdsourcing module used to improve the quality of the extracted commonsense. We conduct extensive experiments, and the experimental results show that our method effectively mines CSK from question corpora. Furthermore, statistical analysis demonstrates the feasibility of this curiosity-driven approach, implying that we provide a basic idea for collecting core CSK. Remarkably, today’s outstanding large language models do not have such simple knowledge summarization capabilities, demonstrating the barrier between

关键词： Classification Core Commonsense knowledge Crowdsourcing Curiosity Psychologically

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Design and Performance Comparison of Latency Efficient Scheduling Algorithms for Smart Environment 4

Design and Performance Comparison of Latency Efficient Sched...

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4th International Conference on Communication, Computing and Industry 6.0, C216 2023

作者： Boomiga, S.S. Venkatesan, V. Prasanna Department of Artificial Intelligence and Data Science Sri Manakula Vinayagar Engineering College Puducherry India Department of Banking Technology Pondicherry University Puducherry India

ISBN: (纸本)9798350327328

Smart Environment aims to improve humans' quality of life. IoT is an emerging paradigm that makes the environment smart by using sensing technologies in IoT devices and cloud computing technology. Various surveys portray that IoT devices are growing rapidly which results in more data which leads to more delay in response time for the services provided in the smart environment since the Cloud server imposes more transmission latency. This transmission latency is reduced by the emergence of Edge computing in which the services can be provided by using Edge as a computing device. However, the Edge is a resource-constrained device, so the proposed work aims to identify the process that can be processed at the Edge based on context and priority based scheduling. Therefore, an algorithm is designed to execute the process that needs immediate attention by applying context and priority-based scheduling. In a comparative analysis of algorithms, the experimental results showcase that the proposed algorithms notably reduce latency in IoT processes. © 2023 IEEE.

关键词： Scheduling algorithms

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Classification Analysis of Motor Imagery EEG Signals Using Bidirectional LSTM Model

Classification Analysis of Motor Imagery EEG Signals Using B...

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International Conference on Advances and Applications of artificial intelligence and Machine Learning, ICAAAIML 2023

作者： Helen, R. Thenmozhi, T. Mythili, S. Raghavan, N. Griharan, S. Department of Biomedical Engineering Saveetha Engineering College Chennai India Department of Artificial Intelligence and Data Science Velammal College of Engineering and Technology Madurai India Department of Biomedical Engineering PSNA College of Engineering and Technology Dindigul India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai India

ISBN: (纸本)9789819795062

In the field of Brain–Computer Interface (BCI), identification of Motor Imagery (MI) EEG signals is a challenging task. People with neuromuscular impairments can use the MI-BCI system to perform their day-to-day activities on their own. In this work, MI-EEG signals are obtained from BCI dataset Iva, and spatial features (CSP) are helpful to identify the distinct characteristics of MI signal. The motor imagery actions are further classified by Bidirectional LSTM (BiLSTM) Neural Network with maximum mean accuracy of 98.36%. An enhanced LSTM model called the BiLSTM’s ability to handle the complex temporal dynamics of EEG signals showcases the effectiveness of this robust approach. This BiLSTM with spatial features is compared with Random Forest, XGBoost, and LSTM models and outperforms by 4.7%. This approach mainly focuses on minimizing the validation loss and contributes to a reduced misclassification rate. This research highlights the potential of deep learning algorithms by means of improved accuracy and reliability of BCI systems. These implications made a significant impact on the advancement of future BCI systems and could potentially lead to the development of more sophisticated assistive devices for motor impaired patients. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Brain computer interface

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Clustering-Based Negative Sampling Approaches for Protein-Protein Interaction Prediction 19th

Clustering-Based Negative Sampling Approaches for Protein-P...

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19th International Meeting on Computational intelligence Methods for Bioinformatics and Biostatistics, CIBB 2024

作者： Kesemen, Zehra Karadeniz, İlknur Aydoğan, Reyhan Computer Science Özyeğin University Istanbul Turkey Artificial Intelligence and Data Engineering Özyeğin University Istanbul Turkey Interactive Intelligence Group Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031897030

The lack of confirmed negative interactions poses a major challenge to the prediction of protein-protein interactions. The reliable selection of these negative samples within a dataset is crucial for a better understanding of the underlying patterns and dynamics. The random sampling method is the most widely used negative sampling method, where negative pairs are randomly selected from unlabelled samples (i.e., samples not experimentally confirmed as positive interactions). However, they tend to introduce inaccurately labelled negative samples, resulting in less reliable predictions, which may affect the efficiency of the learning process. Our study aims to assess the reliability of clustering-based negative sampling methods and highlight their fundamental differences from the widely used random sampling method. To achieve this goal, we propose a hierarchical clustering-based algorithm that uses different mechanisms to select negative instances from unlabelled instances. We investigated the effectiveness of our proposed approach compared to existing clustering-based negative sampling methods and random sampling on four different datasets. The results indicate that clustering-based methods surpass the commonly used random sampling method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hierarchical clustering

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