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Parametric Evaluation Of Collaborative Filtering Over Apache Spark

Parametric Evaluation Of Collaborative Filtering Over Apache...

South Eastern European Design Automation, computer engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Athanasios Alexopoulos Georgios Drakopoulos Andreas Kanavos Spyros Sioutas Gerasimos Vonitsanos Computer Engineering and Informatics Department (CEID) University of Patras Department of Informatics Ionian University

ISBN: (数字)9781728164458

ISBN: (纸本)9781728164465

Recommender systems are mechanisms that filter information in order to predict the preference of a user for an item drawn from a finite collection. Prime examples include recommendation platforms for movies, games, travel destinations, and books. Such systems rely on identifying users with similar preferences, as indicated by an appropriately selected metric, to yield reliable preference estimations. Recently, distributed implementations of recommender systems have improved scalability and can be applied by Internet-based companies to obtain insight for their respective products through latent patterns, typically of higher order nature, with having considerable impact on their profits. One of the potent personalization technologies currently powering the adaptive Web is collaborative filtering. The latter brings together the opinions of large interconnected communities on the Web, supporting filtering of substantial quantities of data. An implementation over Apache Spark of a typical recommender system is presented here. The evaluation of its prediction correctness, based on the Web movies dataset from Amazon as a benchmark, is in terms of the mean absolute error, the mean square error, and the root mean square error. Based on the results, suggestions for improved ratings are given, mainly by increasing user engagement and immersion.

关键词： Recommender Systems Apache Spark Collaborative Filtering Apache MLlib Big Data 10V Data DataFrame PySpark MAE MSE RMSE Recommender systems collaborative filtering Mean square error big data MAE ROOT-MEAN-SQUARE ERRORS ISOCHRONOUS CYCLOTRONS Preferences

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MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach

MADP-IIME: malware attack detection protocol in IoT-enabled ...

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作者： Pundir, Sumit Obaidat, Mohammad S. Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Rodrigues, Joel J. P. C. Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248 002 India College of Computing and Informatics University of Sharjah UAE with King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan University of Science and Technology Beijing Beijing 100083 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Federal University of Piauí (UFPI) PI Teresina 64049-550 Brazil Instituto de Telecomunicações Covilhã 6201-001 Portugal

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such

关键词： Internet of things

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Cramér-Rao Lower Bounds of Model-Based Electrocardiogram Parameter Estimation

TechRxiv

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TechRxiv 2022年

作者： Fattahi, Davood Sameni, Reza The School of Electrical and Computer Engineering Shiraz University Mollasadra Avenue Shiraz71348–51154 Iran The Department of Biomedical Informatics School of Medicine Emory University 101 Woodruff Circle AtlantaGA30322 United States

Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameter estimation by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, all estimation algorithms used in this context have performance bounds in terms of the achievable mean squared error, which are not exceedable. These bounds depend on the adopted data-model, the estimation scheme (least-squares error, maximum likelihood, or Bayesian), and prior assumptions on the model parameters and noise distributions. In this research, we develop a comprehensive theoretical framework for ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs) for the most popular signal models used in the ECG modeling literature, namely functional expansions (including polynomials) and sum of Gaussian functions. The developed framework is evaluated over real and synthetic data for three popular applications: T-to-R wave ratio estimation, ST-segment analysis and QT-interval estimation, using the state-of-the-art estimators in each context. The proposed framework and the derived CRLBs provide practical guidelines for the selection of data-models, sampling frequency (beyond the Nyquist rate), modeling segment length, number of beats required for ECG beat averaging, and other factors that influence the accuracy of ECG-based clinical parameter estimation. © 2022, CC BY.

关键词： Electrocardiograms

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Converging for Security: Blockchain, Internet of Things, Artificial Intelligence - Why Not Together?

Converging for Security: Blockchain, Internet of Things, Art...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Ahmad Anwar Zainuddin Dini Handayani Isyraq Haziq Mohd Ridza Siti Husna Abdul Rahman Saidatul Izyanie Kamarudin Khairul Zakwan Ahmad Mirza Darwisy Mahazir Muazzam Hazmi Sukhaimi Krishnan Subramaniam Mohamad Irfan Firdaus Basri Nurul Hanis Mohd Dhuzuki Dept. of Computer Science KICT International Islamic University Malaysia Gombak Malaysia Dept. of Computer Science KICT International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Computing Informatics Multimedia University Cyberjaya Selangor Malaysia Kolej Pengajian Pengkomputeran Informatik dan Matematik (KPPIM) Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mechanical Engineering Mila University Nilai Negeri Sembilan Malaysia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

Blockchain, renowned for its decentralized and secure nature, and Artificial Intelligence, the pinnacle of machine intelligence, stand as pillars of innovation. However, despite the recognition of their few successful collaborations, the interrelation among these triumvirates - Internet-of-Things (IoT), Blockchain, and Artificial Intelligence (AI) - still remains relatively unexplored terrain. Issues arise regarding their synergy: the challenges of integration, the potential improvements in existing technologies, and the careful consideration required before their unification. While evidence suggests their individual prowess, understanding their convergence and the implications thereof is crucial for shaping a technologically robust future. This paper delves into the complex interplay between IoT, Blockchain, and AI, navigating their relationship and exploring opportunities for enhancement. In addition, this paper will also look into several challenges and complexities hindering seamless integration.

关键词： Ethics Technological innovation Navigation Collaboration Blockchains Complexity theory Stakeholders

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Design of a Controllable Antenna Based on Embedded Differential PSK Modulation

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Progress In Electromagnetics Research B 2021年 90卷 43-62页

作者： Alnaiemy, Yahiea Nagy, Lajos Department of Broadband Infocommunications and Electromagnetic Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Computer Science College of Science University of Diyala Iraq

Direct Antenna Modulation (DAM) is explored recently in many wireless communication systems. In this paper, we explore the modulation process of electromagnetic signals in the antenna circuit design directly. The proposed antenna consists of two non-concentric elliptical patches for broadband applications to suit the spread spectrum applications. To perform a Differential Phase Shift Keying (DPSK) modulation, two identical antennas are fed by a two-branch microstrip line with a phase shift. Utilizing computer Simulation Technology of Microwave Studio (CSTMWS) based on Finite Integral Technique (FIT), an optimization based-on numerical analysis is adopted for designing the transmission line configuration at the desired frequency bands. The other significant aspect that has been achieved in this research is reducing the patch size to be suitable for wearable devices. Therefore, a cylindrical substrate is utilized for bending the proposed antenna structure. The proposed antenna design shows a gain of 4.73 dBi and 2.5 dBi for the planar and folded antenna profile, respectively. Two high-speed Positive Intrinsic Negative (PIN) diodes as switching elements of the RF signal are inserted between the identical antenna elements through a transmission line. Switch 1 (SW1) and switch 2 (SW2) are used to control the phase shift between the antenna elements by changing the switching state from (OFF-ON) and vice versa. The designed antenna is further investigated to realize the effects of radiation leakage from the antenna elements on the human body in the context of wearable applications. This study is conducted to the antenna performance when it is bent on a cylinder and compared to the flat case on four human body regions: arm, head, thigh, and chest. The proposed antenna based on PIN diodes is fabricated, measured, and tested. Using a 3D axis field strength meter, the proposed antenna system field strength is measured for different conditions at various locations of the human

关键词： Electric lines

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Rando Noor Akhmad Setiawan Adhistya Erna Permanasari Riries Rulaningtyas Andriyan Bayu Suksmono Imas Sukaesih Sitanggang Dept. of Electrical and Information Engineering Universitas Gadjah Mada Yogyakarta Indonesia Dept. of Physics Universitas Airlangga Biomedical Engineering Program Surabaya Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department of Computer Science IPB University Bogor Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%.

关键词： Satellites Extreme learning machines Medical services Feature extraction Generative adversarial networks Entropy Lesions

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A class of Generic Approximate Sparse Pseudoinverse Matrix Techniques based on incomplete QR factorization

A class of Generic Approximate Sparse Pseudoinverse Matrix T...

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2020 International Conference on Computational Science and Computational Intelligence, CSCI 2020

作者： Lipitakis, A.D. Filelis-Papadopoulos, C.K. Gravvanis, G.A. Anagnostopoulos, D. Harokopio University of Athens Department of Informatics and Telematics Tavros17778 Greece University College Cork Cork Ireland School of Engineering Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece

ISBN: (纸本)9781728176246

A class of Generic Approximate Sparse Pseudoin-verse matrices based on modified incomplete QR factorizations with Givens rotations is presented. The filtering criteria for the Incomplete QR factorization have been modified to improve the quality of the incomplete upper triangular factor R, by removing the restriction on the number of nonzero elements per row. Moreover, the Givens rotation matrices are dropped during the incomplete factorization process in order to reduce memory requirements. The proposed approximate pseudoinverse preconditioning scheme is computed, using a restricted solution process, based on approximate pseudoinverse sparsity patterns, derived from powers of the sparsified incomplete upper triangular matrix R. The proposed preconditioning scheme is used in conjunction with the Explicit Preconditioned Conjugate Gradient Least Squares method. Numerical results indicating the applicability and efficiency of the proposed algorithmic schemes for solving various model problems are given. © 2020 IEEE.

关键词： Matrix algebra

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A multimodal framework for extraction and fusion of satellite images and public health data

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Scientific Data 2024年第1期11卷 634页

作者： Moukheiber, Dana Restrepo, David Cajas, Sebastián Andrés Montoya, María Patricia Arbeláez Celi, Leo Anthony Kuo, Kuan-Ting López, Diego M. Moukheiber, Lama Moukheiber, Mira Moukheiber, Sulaiman Osorio-Valencia, Juan Sebastian Purkayastha, Saptarshi Paddo, Atika Rahman Wu, Chenwei Kuo, Po-Chih Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States Departamento de Telemática Universidad del Cauca Cauca Popayán Colombia John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA United States School of Computer Science University College Dublin Dublin Ireland Grupo de Epidemiología Facultad Nacional de Salud Pública Universidad de Antioquia Medellín Colombia Department of Biostatistics Harvard TH Chan School of Public Health Boston MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science Worcester Polytechnic Institute Worcester MA United States ScienteLab Cundinamarca Bogota Colombia Department of BioHealth Informatics Indiana University Luddy School of Informatics Computing and Engineering Indianapolis IN United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health. © The Author(s) 2024.

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User-experience oriented POI recommendation with pseudo rating

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Multimedia Tools and Applications 2024年 1-21页

作者： Yi, Kun Yamagishi, Ryu Li, Jiyi Li, Taishan Bai, Zhengyang Ma, Qiang Kyoto Institute of Economic Research Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Graduate School of Informatics Kyoto University Yoshida-honmachi Sakyo-ku Kyoto Kyoto606-8501 Japan Department of Computer Science and Engineering University of Yamanashi Takeda 4-3-11 Kofu 400- 8511 Yamanashi Japan Research Institute of Non-Timber Forestry Chinese Academy of Forestry Key Laboratory of Non-Timber Forest Germplasm Enhancement & ampUtilization of State Administration of Forestry and Grassland No. 3 Weiwu Road Jinshui District Zhengzhou450003 China RIKEN Center for Computational Science RIKEN Nihonbashi 1-chome Mitsubishi Bld. 15th floor 1-4-1 Nihonbashi Chuo-ku Tokyo103-0027 Japan Department of Information Science Kyoto Institute of Technology Matsugasaki Sakyo-ku Kyoto Kyoto606-8585 Japan

Point-of-interest (POI) recommendation faces challenges in managing data sparsity and cold-start problems, particularly in terms of personalized recommendation. Firstly, most existing users only visit a small number of POIs, which results in severe data sparsity and makes most conventional methods unsuitable because they require adequate historical data from each user to learn the model. In addition, new users have little or no historical record. Existing ask-to-rate methods for cold start user scenarios treat user answers as historical data, which is inappropriate and causes difficulty in capturing user preferences. Therefore, in this paper, to tackle the data sparsity problem, we propose a user-experience model (UEM) to uncover user behaviors from several aspects. The proposed UEM involves four factors: Who (does it), What (the user does), Where (the user does it), and When (the user does it). In contrast to other methods that model user behavior from individual records, our model introduces a latent variable of user experience to extract similarity among users. This feature makes our model suitable for mitigating the severe sparse-data issue in POI recommendations. Furthermore, to handle the cold-start problem for new users, we propose a pseudo-rating mechanism (PRM) to capture new users’ preferences and recommend POIs. For new users, photos are provided for rating, and these ratings are then used to capture their preferences and estimate the probability of their visits. Extensive experimental results demonstrate that the proposed method achieves better recommendation performance and fairness than the state-of-the-art methods. Moreover, user study results validates that the users’ preferences captured via PRM are satisfactory, and UEM provides satisfactory POI recommendations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Behavioral research

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Dynamic analysis of high-response storage systems to minimize the generation ramping requirements 12

Dynamic analysis of high-response storage systems to minimiz...

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12th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2020

作者： Nikolaidis, Pavlos Fotiou, Stelios Kasparis, Takis Poullikkas, Andreas Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Cyprus Energy Regulatory Authority P.O. Box 24936 Nicosia1305 Cyprus

ISBN: (纸本)9781839535635

The expansion of intermittent renewable energy sources for electricity production transformed power grids into complex systems. At high penetration levels, the conventional generating units occur inadequate for load following calling for more flexible resources. In this work, we provide the outcomes derived by a Genetic algorithm-driven priority list approach which effectively quantifies the benefits of electricity storage. Once optimally sized, different battery storage systems are subjected into life-cycle cost analysis based on realistic modelling and updated data found in the literature. According to the results, zinc-air offers the best feasible solution due to its low capital cost, followed by sodium-sulphur and lead-acid batteries which constitute viable solutions for minimizing the generation ramping requirements and maximizing renewable penetration rates. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Genetic algorithms

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