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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Zhou, Pengfei Pan, Yijie Zhang, Xun Shou, Jibin Guo, Jiayang Xu, Meiyan Gu, Peipei College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo China Zhejiang Moorgen Group Co. Ltd. Zhejiang310000 China Department of Hematology School of Medicine Xiamen University Xiamen361000 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361000 China College of Computer Science Minnan Normal University Zhangzhou363000 China

ISBN: (纸本)9789819619061

Epilepsy, characterized by abnormal neuronal discharges, is a widespread neurological disorder. Traditional detection methods primarily focus on extracting time-frequency features from raw electroencephalogram (EEG) signals, often overlooking the complex interactions between brain regions. To address this limitation, we propose an automated approach for epileptic seizure detection. We first construct a fused brain network (TFPP) by integrating the Pearson Correlation Coefficient (PCC) and Phase Lag Index (PLV) across both temporal and frequency domains, enhancing EEG signals by capturing synchronized activities across different brain regions. Next, we introduce the CNN_sk_BiLSTM model, which leverages Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to sequentially extract spatial and temporal features. The SKAttention mechanism further refines feature extraction across multiple scales. Using the CHB-MIT scalp EEG dataset, the proposed method achieved an average sensitivity of 90.86%, specificity of 98.79%, and accuracy of 97.88%. These results confirm that fused brain functional networks effectively integrate the intra-channel characteristics of EEG signals, demonstrating the superior performance of the proposed approach in epileptic seizure detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

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Annual IEEE Symposium on Foundations of computer science

作者： Debarati Das Jacob Gilbert MohammadTaghi Hajiaghayi Tomasz Kociumaka Barna Saha Hamed Saleh Dept. of Computer Science & Engineering Pennsylvania State University State College PA United States Dept. of Computer Science University of Maryland College Park MD United States Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Dept. of Computer Science & Engineering Halıcıoğlu Data Science Institute University of California San Diego San Diego CA United States

ISBN: (数字)9781665455190

ISBN: (纸本)9781665455206

Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann; TALG’10); however, Mao (FOCS’21) broke the cubic barrier for the tree edit distance problem using fast matrix *** a parameter k as an upper bound on the distance, an $\mathcal{O}(n+k^{2})$-time algorithm for edit distance has been known since the 1980s due to works of Myers (Algorithmica’86) and Landau and Vishkin (JCSS’88). The existence of an $\tilde{\mathcal{O}}(n+poly(k))$-time algorithm for tree edit distance has been posed as open question, e.g., by Akmal and Jin (ICALP’21), who give a stateof-the-art $O(nk^{2})$-time algorithm. In this paper, we answer this question positively.

关键词： computer science Upper bound Costs databases Task analysis Optimization

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Finding Simplex Items in data Streams

Finding Simplex Items in Data Streams

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International Conference on data engineering

作者： Zhuochen Fan Jiarui Guo Xiaodong Li Tong Yang Yikai Zhao Yuhan Wu Bin Cui Yanwei Xu Steve Uhlig Gong Zhang School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Theory Lab Central Research Institute 2012 Labs Huawei Technologies Co. Ltd. Hong Kong SAR China School of Electronic Engineering and Computer Science Queen Mary University of London London UK

In this paper, we propose a new type of item in data streams, called simplex items. Simplex items have frequencies in consecutive p windows that can be approximated by a polynomial of degree at most k, where k = 0, 1, 2. These low-order representable simplex items have a wide range of potential applications. For example, when k = 1, we can leverage these items whose frequency has obvious linear increase or decrease to speed up the running time of a class of machine learning models and detect network attacks such as distributed denial-of-service (DDoS), etc. To find k-degree simplex items in real time, we propose a novel sketch, namely X-Sketch, to accurately record simplex items in a compact space. The key idea of X-Sketch is to effectively filter out non-simplex items with less memory overhead, and then monitor the remaining potential simplex items and keep those items with more consecutive windows. We conduct extensive experiments, and the experimental results show that the F1 Score of X-Sketch is on average 68.6%, 57.9%, and 42.2% higher than the baseline solution for k = 0, 1, 2, respectively. Finally, we also provide a case study that applies X-Sketch to "accelerate" the two machine learning models through end-to-end experiments. We have released our source code at GitHub.

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The science of Startups: The Impact of Founder Personalities on Company Success

arXiv

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arXiv 2023年

作者： McCarthy, Paul X. Gong, Xian Stephany, Fabian Braesemann, Fabian Rizoiu, Marian-Andrei Kern, Margaret L. The Data Science Institute University of Technology SydneyNSW Australia School of Computer Science and Engineering UNSW Sydney NSW Australia Faculty of Engineering and Information Technology University of Technology Sydney Australia Oxford Internet Institute University of Oxford Oxford United Kingdom DWG Datenwissenschaftliche Gesellschaft Berlin Germany Melbourne Graduate School of Education The University of Melbourne ParkvilleVIC Australia

Startup companies solve many of today’s most complex and challenging scientific, technical and social problems, such as the decarbonisation of the economy[1], air pollution[2], and the development of novel life-saving vaccines[3]. Startups are a vital source of social, scientific and economic innovation, yet the most innovative are also the least likely to survive[4]. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day[5]. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures[6], their centrality in a global network of other founders and investors[7] as well as the team’s size[8]. The effects of founders’ personalities on the success of new ventures are mainly unknown. Here we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n=26,781). We found that the Big 5 personality traits of startup founders across 30 dimensions significantly differed from that of the © 2023, CC BY.

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A Comprehensive Survey on RPL Routing-based Attacks, Defences and Future Directions in Internet of Things

arXiv

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arXiv 2025年

作者： Prajapati, Anil Kumar Pilli, Emmanuel S. Battula, Ramesh Babu Varadharajan, Vijay Verma, Abhishek Joshi, R.C. Malaviya National Institute of Technology Jaipur JLN Marg Malviya Nagar Jaipur India School of Information and Physical Sciences The University of Newcastle CallaghanNSW Australia Department of Information Technology Babasaheb Bhimrao Ambedkar University Lucknow India Department of Electronics & Computer Engineering Indian Institute of Technology Roorkee Roorkee India Department of Data Science and Engineering Manipal University Jaipur India

The Internet of Things (IoT) is a network of digital devices like sensors, processors, embedded and communication devices that can connect to and exchange data with other devices and systems over the internet. IoT devices have limitations on power, memory, and computational resources. Researchers have developed the IPv6 Over Low-power Wireless Personal Area Network (6LoWPAN) protocols to provide wireless connectivity among these devices while overcoming the constraints on resources. 6LoWPAN has been approved subsequently by the Internet engineering Task Force (IETF). The IETF Routing Over Low-power and Lossy Networks (ROLL) standardized the Routing Protocol for LLNs known as RPL (IETF RFC 6550), which is part of the 6LoWPAN stack. However, IoT devices are vulnerable to various attacks on RPL-based routing. This survey provides an in depth study of existing RPL-based attacks and defense published from year 2011 to 2024 from highly reputed journals and conferences. By thematic analysis of existing routing attacks on RPL, we developed a novel attack taxonomy which focuses on the nature of routing attacks and classifies them into 12 major categories. Subsequently, the impact of each attack on the network is analyzed and discussed real life scenarios of these attacks. Another contribution of this survey proposed a novel taxonomy for classification of defense mechanisms into 8 major categories against routing attacks based on type of defense strategy. The detailed analysis of each defense mechanism with real life applicability is explained. Furthermore, evaluation tools such as testbeds and simulators for RPL-based attack and defense are discussed and critically analyzed in terms of real world applicability. Finally, open research challenges are presented on the basis of research gaps of existing literature along with research directions for practitioners and researchers. We believe our study will give actionable insights and solid foundation for researchers to expand eff

关键词： Internet of things

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AI-Driven Test and Measurement Automation in Electronics Manufacturing

AI-Driven Test and Measurement Automation in Electronics Man...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Anurag Vijay Agrawal Kommisetti Murthy Raju Aparna P Gade Sravya A Chandrashekhar J. Ramya Department of Electronics and Communication Engineering J. P. Institute of Engineering and Technology Meerut Uttar Pradesh India Department of Electronics and Communication Engineering Shri Vishnu Engineering College For Women Bhimavaram Andha Pradesh India Department of Computer Science Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Electronics and Communication Engineering Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh India Department of Mechanical Engineering Faculty of Science and Technology ICFAI Foundation for Higher Education Hyderabad Telangana India Department of Artificial Intelligence and Data Science OMR St. Joseph's College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

Innovative strategies to increase output while decreasing expenses and enhancing product quality are essential in the dynamic electronics manufacturing industry. A robust AI-based system for automating test and measurement tasks in electronics manufacture is detailed in this research. Manual testing and measurement is a part of traditional production procedures; it's labor-intensive, time-consuming, and prone to mistakes. Smarter, more adaptable, and more efficient processes are achieved through the automation of tests and measurements facilitated by AI. The proposed approach makes use of machine learning techniques to sift through mountains of industrial data. In order to identify patterns, outliers, and problems, the AI system examines both past and present data. Reducing the likelihood of defective items reaching the final stage of production, this proactive technique helps identify and rectify issues fast. Product quality, cost, scrap rate, and production yield are all enhanced by AI-driven test and measurement automation in electronics manufacturing. In keeping with Industry 4.0, this ground-breaking method improves the manufacturing processes of electronic devices. Industry intelligence, sustainability, and efficiency are all enhanced by it.

关键词： Manufacturing processes Production Metrology Product design Mathematics Quality assessment Task analysis

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Feel Old Yet? Updating Mode of Transportation Distributions from Travel Surveys using data Fusion with Mobile Phone data

arXiv

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arXiv 2022年

作者： Graells-Garrido, Eduardo Opitz, Daniela Rowe, Francisco Arriagada, Jacqueline Department of Computer Science University of Chile Chile Chile Data Science Institute Faculty of Engineering Universidad del Desarrollo Chile Department of Geography and Planning University of Liverpool United Kingdom University of Leeds United Kingdom

Up-to-date information on different modes of travel to monitor transport traffic and evaluate rapid urban transport planning interventions is often lacking. Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost. To address this issue, we propose a method that leverages mobile phone data as a cost-effective and rich source of geospatial information to capture current human mobility patterns at unprecedented spatiotemporal resolution. Our approach employs mobile phone application usage traces to infer modes of transportation that are challenging to identify (bikes and ride-hailing/taxi services) based on mobile phone location data. Using data fusion and matrix factorization techniques, we integrate official data sources (household surveys and census data) with mobile phone application usage data. This integration enables us to reconstruct the official data and create an updated dataset that incorporates insights from digital footprint data from application usage. We illustrate our method using a case study focused on Santiago, Chile successfully inferring four modes of transportation: mass-transit (all public transportation), motorised (cars and similar vehicles), active (pedestrian and cycle trips), and taxi (traditional taxi and ride-hailing services). Our analysis revealed significant changes in transportation patterns between 2012 and 2020. We quantify a reduction in mass-transit usage across municipalities in Santiago, except where metro/rail lines have been more recently introduced, highlighting added resilience to the public transport network of these infrastructure enhancements. Additionally, we evidence an overall increase in motorised transport throughout Santiago, revealing persistent challenges in promoting urban sustainable transportation. Findings also point to a rise in the share of taxi usage, and a drop in active mobility, suggesting a mod

关键词： data fusion

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Channel Estimation for 29GHz mm Wave MIMO Systems in Tropical Region

Channel Estimation for 29GHz mm Wave MIMO Systems in Tropica...

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IEEE International Symposium on Telecommunication Technologies (ISTT)

作者： Li Lu Aduwati Sali School of Microelectronics and Data Science Anhui University of Technology Maanshan China Department of Computer and Communication Systems Engineering Wireless and Photonics Networks Research Centre of Excellence (WiPNET) Faculty of Engineering Universiti Putra Malaysia Serdang Selangor Malaysia Institute for Mathematical Research (INSPEM) Universiti Putra Malaysia Serdang Selangor Malaysia

ISBN: (数字)9798350392074

ISBN: (纸本)9798350392081

This paper focuses on the 29GHz mmWave MIMO communication system in tropical environments, considering the impact of rain attenuation and providing a detailed analysis of channel estimation and system performance. Experimental results indicate that as the signal-to-noise ratio (SNR) increases, the mean square error (MSE) of channel estimation decreases. Under the same SNR conditions, MIMO systems with different antenna configurations exhibit distinct channel estimation error characteristics. Notably, in the 128x128 antenna configuration, the channel estimation error is most pronounced. Furthermore, increasing the number of training signals significantly reduces channel estimation errors, especially in the 128x128 antenna configuration. This study offers a performance evaluation of the 29GHz mmWave MIMO communication system in tropical regions, providing valuable insights for further research and optimization in this field.

关键词： Training Rain System performance Channel estimation Mean square error methods Attenuation Millimeter wave communication Antennas MIMO communication Signal to noise ratio

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The Role of Generative Artificial Intelligence in Internet of Electric Vehicles

arXiv

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arXiv 2024年

作者： Zhang, Hanwen Niyato, Dusit Zhang, Wei Zhao, Changyuan Du, Hongyang Jamalipour, Abbas Sun, Sumei Pei, Yiyang College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Information and Communications Technology Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Electrical and Computer Engineering The University of Sydney Australia

With the advancements of generative artificial intelligence (GenAI) models, their capabilities are expanding significantly beyond content generation and the models are increasingly being used across diverse applications. Particularly, GenAI shows great potential in addressing challenges in the electric vehicle (EV) ecosystem ranging from charging management to cyber-attack prevention. In this paper, we specifically consider Internet of electric vehicles (IoEV) and we categorize GenAI for IoEV into four different layers namely, EV’s battery layer, individual EV layer, smart grid layer, and security layer. We introduce various GenAI techniques used in each layer of IoEV applications. Subsequently, public datasets available for training the GenAI models are summarized. Finally, we provide recommendations for future directions. This survey not only categorizes the applications of GenAI in IoEV across different layers but also serves as a valuable resource for researchers and practitioners by highlighting the design and implementation challenges within each layer. Furthermore, it provides a roadmap for future research directions, enabling the development of more robust and efficient IoEV systems through the integration of advanced GenAI techniques. © 2024, CC BY.

关键词： Generative adversarial networks

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Monocular Depth Estimation for UAV Navigation Using Segmental Fully Convolutional Prairie Dog Neural Networks

Monocular Depth Estimation for UAV Navigation Using Segmenta...

引用

Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Indradeep Kumar A. Elumalai G. Santha Meena Jagath Narayana Kamineni Joshuva Arockia Dhanraj M. Revathi Amity Institute of Technology Amity University Noida Uttar Pradesh India Department of Mathematics R.M.K. Engineering College Chennai Tamil Nadu India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Mechanical Engineering Sri Venkateshwara College of Engineering Tirupati Andhra Pradesh India Department of Computer Science and Engineering (AI&ML) School of Engineering Dayananda Sagar University Bengaluru Karnataka India Department of Artificial Intelligence and Data Science St.Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Unmanned Aerial Vehicles (UAVs) play an important role in very precise navigation for its usage in applications like disaster management, agricultural surveillance, and as delivery drones. Precise depth estimation is crucial for UAV to safely avoid obstacles and thus obtain proper paths for flying. Monocular Depth Estimation (MDE), is another method which is becoming popular due to its easy implementation as compared to stereo vision and LiDAR systems in which single camera depth information is inferred. However, found that there are still some problems in the current a few deep learning methods of MDE, including high computational cost, low prediction accuracy in dynamic conditions, and unsuitability for real-time or online applications. In order to solve these problems, the current research work introduces a Segmental Fully Convolutional Neural Network (SFCNN) tended by the Prairie Dog Optimization (PDO) for UAV flight. The block structure improves adaptability as well as computational complexity; PDO seeks to minimize network's parameters to increase the result precision of the depth forecast and lessen the computation time. tarting from this background, the proposed approach is intended to support lightweight real-time and reliable means for MDE to enhance UAV navigation in complex environments.

关键词： Grasslands Navigation Depth measurement Dogs Autonomous aerial vehicles Real-time systems Stereo vision Convolutional neural networks Vehicle dynamics Optimization

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