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45th International Symposium on Mathematical Foundations of computer science, MFCS 2020

作者： Dey, Palash Radhakrishnan, Jaikumar Velusamy, Santhoshini Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India School of Technology and Computer Science Tata Institute of Fundamental Research Mumbai India School of Engineering and Applied Sciences Harvard University CambridgeMA United States

ISBN: (纸本)9783959771597

We consider the bit-probe complexity of the set membership problem: represent an n-element subset S of an m-element universe as a succinct bit vector so that membership queries of the form "Is x ∈ S" can be answered using at most t probes into the bit vector. Let s(m, n, t) (resp. sN(m, n, t)) denote the minimum number of bits of storage needed when the probes are adaptive (resp. non-adaptive). Lewenstein, Munro, Nicholson, and Raman (ESA 2014) obtain fully-explicit schemes that show that s(m, n, t) = O((2t − 1)m1/(t−min{2blog nc,n−3/2})) for n ≥ 2, t ≥ blog nc + 1 . In this work, we improve this bound when the probes are allowed to be superlinear in n, i.e., when t ≥ Ω(n log n), n ≥ 2, we design fully-explicit schemes that show that s(m, n, t) = O((2t − 1)m1/(t- n-1/2t/(2(n−1)))), asymptotically (in the exponent of m) close to the non-explicit upper bound on s(m, n, t) derived by Radhakrishan, Shah, and Shannigrahi (ESA 2010), for constant n. In the non-adaptive setting, it was shown by Garg and Radhakrishnan (STACS 2017) that for a large constant n0, for n ≥ n0, sN(m, n, 3) ≥ √mn. We improve this result by showing that the same lower bound holds even for storing sets of size 2, i.e., sN(m, 2, 3) ≥ Ω(√m). © Nathalie Bertrand;licensed under Creative Commons License CC-BY 45th International Symposium on Mathematical Foundations of computer science (MFCS 2020).

关键词： Data structures

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Unveiling the Post-Covid Economic Impact Using NLP Techniques 15

Unveiling the Post-Covid Economic Impact Using NLP Technique...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Barhanpurkar, Kanishk Mandlik, Nikita Rajawat, Anand Singh Goyal, S.B. Mihaltan, Traian Candin Verma, Chaman Raboaca, Maria Simona Thomas J. Watson College of Engineering and Applied Science Binghamton University United States School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest1053 Hungary Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Ramnicu Valcea Romania

ISBN: (纸本)9798350321388

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies. © 2023 IEEE.

关键词： Economic and social effects

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Poster: Selection of Optimal Neural Model using Spiking Neural Network for Edge Computing*

Poster: Selection of Optimal Neural Model using Spiking Neur...

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International Conference on Distributed Computing Systems

作者： Sanaullah Kaushik Roy Ulrich Ruckert Thorsten Jungeblut Dep. of Eng. and Math. Industrial IOT University of Applied Sciences and Arts Bielefeld Germany Dep. of Computer Science Cyber Defense and AI Lab Lab North Carolina A&T State University NC USA Dep. of Faculty of Technology Cognitive Interaction Technology Universitat Bielefeld Bielefeld Germany

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Spiking Neural Networks (SNNs), are inspired by the biological brain's complicated signaling mechanisms and possess unique characteristics that set them apart from traditional artificial neural networks. This research study explores the challenging domain of image classification, specifically utilizing the well-known MNIST dataset through the development and thorough evaluation of different neural models for edge computing. However, the primary contribution is the autonomous selection of the best-performing SNN model through various early stopping approaches and validation functions, allowing the models to autonomously adapt during training. In addition, this article presents the standalone AutoML-SNN model, which is the introduction of dynamic elements into selected SNN domains, enhancing their adaptability to complex patterns within the dataset. Furthermore, the early stopping methodologies are used to reduce overfitting hazards, and using the 3000-neuron set, the LIF appeared as the most proficient neural model.

关键词： Training Adaptation models Computational modeling Biological system modeling Neurons Spiking neural networks Hazards

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Design of a Fuzzy Type-2 Controller for 1-DOF Pitch Axis Twin Rotor System

Design of a Fuzzy Type-2 Controller for 1-DOF Pitch Axis Twi...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Abdelrahman Sayed Sayed Walid K. Shaker Hossam Hassan Ammar Ahmad Taher Azar Chakib Ben Njima Department of Marine Technology Norwegian University of Science and Technology (NTNU) Trondheim Norway Université de Toulon Toulon France School of Engineering and Applied Science Nile University Giza Egypt Hosted by Global Academic Foundation new administrative capital School of Engineering and Computer Sciences University of Hertfordshire Egypt College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial intelligence Benha University Benha Egypt University of Sousse Tunisia

This paper presents the design, manufacturing, modeling, and control of a novel 1-DOF pitch axis Twin Rotor system. An IMU sensor is used to precisely access the desired pitch angle of the system. A type-2 fuzzy controller is designed and implemented to achieve fast rise time and low overshoot, which are the desired response specifications for the system. In terms of overshoot and settling time, the performance of the type-2 fuzzy-PID controller is compared to that of the type-l fuzzy-PID controller and the conventional PID controller. The type-2 fuzzy-PID controller's performance is promising when compared to that of the other controllers, proving that it is useful for taming the Twin Rotor system's non-linearities.

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Cooperative Localization Using Expected Minimum Segment for Irregular Multi-Hop Networks

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IEEE/ACM Transactions on Networking 2024年

作者： Yan, Xiaoyong Cao, Jiannong Zhang, Shigeng Ding, Chuntao Wu, Chenhuang Liu, Alex X. Song, Aiguo Nanjing University of Posts and Telecommunications School of Modern Posts Nanjing210003 China Putian University Key Laboratory of Applied Mathematics Fujian Putian351200 China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Central South University School of Computer Science and Engineering Changsha410083 China Changsha410003 China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China Midea Group Guangdong Foshan528311 China Southeast University School of Instrument Science and Engineering Nanjing211102 China

For the creation of wireless network applications, node locations are frequently necessary. However, communication effectiveness, measurement accuracy, and localization stability will be low in irregular multi-hop networks when locating nodes using conventional algorithms. To this end, a novel cooperative localization algorithm using expected minimum segments (LEMS, for short) is proposed in this paper. LEMS begins by measuring the distance between paired nodes, which is completed along with network initialization. Then, each unlocated node constructs its own sub-network, including it, based on the error characteristics among anchor nodes. Finally, each unlocated node searches for its estimated location in its sub-region based on the objective function generated by the chaotic mapping. Simulation results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art regarding efficiency, accuracy, and stability for various irregular networks. Specifically, our proposed algorithm achieves a median improvement in localization accuracy of 0.62 to 29.57 times and a reduction in the range of localization errors of 0.06 to 16.8 times. © 1993-2012 IEEE.

关键词： chaotic mapping Cooperative localization error analysis expected minimum segment

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Weather of the dorm Wi-Fi ecosystem at the University of Colorado Boulder: Fall semester 2019 to spring semester 2020 - A case study of Wi-Fi including a campus response to the COVID-19 perturbation

arXiv

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arXiv 2021年

作者： McGrath, Jake Davis, Armen Curry, James Gartner, Orrie Rodrigues, Glenn Spielman, Seth Massey, Daniel Aeropace Engineering Applied Mathematics Office of Information Technology Office of Data Analytics Department of Computer Science

"Growing use of network-enabled technology in Institutions of Higher Education (IHEs) among students, staff, and faculty means that there has been increasing demand to adapt technology platforms and tools that transform student learning strategies, faculty teaching, research modalities, as well as general operations. In fact, many of the new modalities are a necessity for doing IHE business. In August 2019, our research team, at the University of Colorado Boulder, began collecting and analyzing data from the campus Wi-Fi network. A goal of the research was to answer the question of "what passive sensing" of the IHE's Wi-Fi might be able to tell you about the gross dynamics of the "Wi-Fi weather" in the IHE ecosystem? Or more generally, what does anonymized data tell us about the dynamics in a IHE's ecosystem. Anonymized data were made available by the University of Colorado Boulder (CU) Office of Information technology (OIT). Our goal was to understand the campus' dynamical ecosystem as a reflection of its collected Wi-Fi data. Those data could then be used to develop forecast models and an understanding of the dynamics of the university ecosystem where the dynamics of Wi-Fi connected device count could be used as a proxy for the ebb and flow of large scale, and small scale, behavior in the ecosystem. The analogy with weather prediction seemed appropriate and a viable strategy. Starting Fall 2019, data were collected in the 'observational phase' (data collection is ongoing). In the 'analysis phase,' briefly touched on here, we applied Singular Spectrum Analysis (SSA) "eigen decomposition," to deconstruct Wi-Fi data from dorms, the central campus dining cafeteria, the recreation center, and other buildings on campus. That analysis led to the identification of clusters of buildings that behaved similarly. Several campus buildings are dual use and they are placed in different clusters. Just as in the case of models of the weather, a final component of this research was

关键词： Weather forecasting

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Improved Detection of Four Attacks Based on Particle Method

Improved Detection of Four Attacks Based on Particle Method

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International Conference on Computational Intelligence and Security

作者： Tao Yu Zhen Liu Graduate School of Engineering Nagasaki Institute of Applied Science Japan College of Computer Science and Technology Jilin University China

ISBN: (纸本)9781665494908

Network security has attracted more and more attention. In order to improve the defense ability against network attacks, researchers have put forward many solutions, and an intrusion detection system is one of them. In this paper, the NSL-KDD data set contains four types of attacks (U2R, R2L, Probe, DoS) and non-attacks. By adding the particle method, the density of each data is calculated and combined with the characteristic quantity of each data, and the data is divided into five categories. In order to improve the computational efficiency and remove the features of low correlation and high interference, we reduce the dimension of data features. The attack detection rates of U2R, R2L, Probe, and DoS, are calculated by machine learning methods such as DT, NN, SVM, K-NN, and NB. Experiments show that the particle method improves the detection rate of four types of attacks.

关键词： Support vector machines Correlation Intrusion detection Artificial neural networks Machine learning Interference Network security

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Sakorn Mekruksavanich Ponnipa Jantawong Datchakorn Tancharoen Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Faculty of Engineering and Technology Panyapiwat Institute of Management Nonthaburi Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance.

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IoT Wearable Breast Temperature Assessment System

IoT Wearable Breast Temperature Assessment System

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： B. Ashreetha Dankan Gowda V Harishchander Anandaram Nithya B A Neeraj Gupta Basant Kumar Verma Department of Electronics & Communication Engineering College Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamilnadu Department of Computer Science School of Applied Sciences REVA UNIVERSITY Bengaluru Karnataka Department of Computer Science and Engineering SGT University Gurugram Haryana

Cancer is an undesirable cell with odd characteristic varies from normal cell of the breast tissue. This will develop swiftly and infiltrate surrounding tissue and forms as tumor which happens in both men and women. After lung cancer, breast cancer has become the largest cause of malignancies in women which raise the mortality rate. In this article, Sensor enabled wearable device framework to detect breast temperature abnormalities is suggested. Early work on wearable sensors supported to monitor breast temperature is presented. The concept of a wearable garment with sensor-enabled patches is also considered. Detailed architecture of the proposed framework is explained, and implementation specifics are examined. Later the statistical highlights help to process the sensor-generated breast temperature data are explained. The performance of the proposed framework is examined by taking abnormal and normal patient temperature dataset and the findings are described using attractive figure representations.

关键词： Temperature sensors Performance evaluation Temperature measurement Temperature distribution Wearable computers Microprocessors computer architecture

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Rlsnake: A hybrid reinforcement learning approach for road detection 24

Rlsnake: A hybrid reinforcement learning approach for road d...

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2021 24th ISPRS Congress Commission III: Imaging Today, Foreseeing Tomorrow

作者： Botteghi, N. Sirmacek, B. Ünsalan, C. Robotics and Mechatronics Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands Smart Cities School of Creative Technology Saxion University of Applied Sciences Netherlands Electrical and Electronics Engineering Faculty of Engineering Marmara University Turkey

Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques. © 2021 International Archives of the Photogrammetry, Remote Sensing and Spatial Information sciences - ISPRS Archives. All rights reserved.

关键词： Reinforcement learning

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