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A cost-efficient protocol for open blockchains 5

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A cost-efficient protocol for open blockchains

5th International Conference on Cyber Security and Protection of Digital Services, Cyber Security 2019

作者： Li, Chunlei Rong, Chunming Jaatun, Martin Gilje Department of Informatics University of Bergen Bergen Norway Dept. of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway

Current proof-of-work blockchains are not sustainable in terms of energy needed to run them. In this paper we propose a new scheme that avoids wasted proof-of-work by a dynamic probabilistic method, where the consensu... 详细信息

ISBN: (纸本)9781728102290

关键词： Blockchain

Multilingual chart-based constituency parse extraction from pre-trained language models

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arXiv 2020年

作者： Kim, Taeuk Li, Bowen Lee, Sang-Goo Dept. of Computer Science Hanyang University Dept. of Artificial Intelligence Hanyang University ILCC School of Informatics University of Edinburgh Dept. of Computer Science and Engineering Seoul National University

As it has been unveiled that pre-trained language models (PLMs) are to some extent capable of recognizing syntactic concepts in natural language, much effort has been made to develop a method for extracting complete (binary) parses from PLMs without training separate parsers. We improve upon this paradigm by proposing a novel chart-based method and an effective top-K ensemble technique. Moreover, we demonstrate that we can broaden the scope of application of the approach into multilingual settings. Specifically, we show that by applying our method on multilingual PLMs, it becomes possible to induce non-trivial parses for sentences from nine languages in an integrated and language-agnostic manner, attaining performance superior or comparable to that of unsupervised PCFGs. We also verify that our approach is robust to cross-lingual transfer. Finally, we provide analyses on the inner workings of our method. For instance, we discover universal attention heads which are consistently sensitive to syntactic information irrespective of the input language. Copyright © 2020, The Authors. All rights reserved.

关键词： Syntactics

Generative Adversarial Networks Based Cognitive Feedback Analytics System for Integrated Cyber-Physical System and Industrial Iot Networks

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Research Square

Research Square 2021年

作者： Upreti, Kamal Syed, Mohammad Haider Alam, Mohammad Shabbir Alhudhaif, Adi Shuaib, Mohammed Sharma, Arvind K. Department of Information & Technology Inderprastha Engineering College Ghaziabad India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer Science College of computer science and Information Technology Jazan University Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 al-Kharj11942 Saudi Arabia College of Computer Science & IT Jazan University Saudi Arabia Dept. of CSI University of Kota Rajasthan India

In the modern era of technologies, the internet grows in the advancement of our day-to-day life like automation devices. The devices to set up industries with integrated cyber-physical systems and industrial IoT applications. Generative adversarial networks (GAN) can generate Cognitive feedback analysis with various data for both generator and discriminator in a supervised model. Neural networks are used for artificial intelligence algorithms, but in adversarial networks, feedback analytics is analyzed with the significance of data. The modern age of intelligent manufacturing will indeed be ushered in by Cyber-Physical Production Systems (CPPS). However, because of the connections between the virtual and physical worlds, CPPS would be subject to cross-domain assaults. Against Denial-of-Service (DoS) threats, this paper concentrates on complex performance feedback management of Cyber-Physical Systems (CPS). To begin, a swapping system modelling approach for the complex response feedback CPS is provided by analyzing the distinct effects of DoS assaults on the sensor-controller (SC) and controller-to-actuator (C-A) channels, accordingly. Given the difference in bandwidth between the dual channels and the accused's energy cap, it is reasonable to conclude that an offender can only jam a single communication stream at a point and also that the possible number of successive DoS attacks is limited. Second, using a packet-based transfer scheme, a nested switching paradigm is built on the foundation of the switching mechanism, considering both the spatial heterogeneity and the temporal durability of DoS attacks. The probability of discriminator gets analyzed feedback data to check whether actual data or fake data is sampled, and it is generated. Cognitive feedback supports genetic algorithms to sample the feedback data in a system for advanced technologies. © 2021, CC BY.

关键词： Cyber Physical System

Local Competition and Uncertainty for Adversarial Robustness in Deep Learning

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arXiv 2020年

作者： Alexos, Antonios Panousis, Konstantinos P. Chatzis, Sotirios Department of Electrical and Computer Engineering University of Thessaly Volos Greece Dept. of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Dept. of Electrical Eng. Computer Eng. Informatics Cyprus University of Technology Limassol Cyprus

This work attempts to address adversarial robustness of deep networks by means of novel learning arguments. Specifically, inspired from results in neuroscience, we propose a local competition principle as a means of adversarially-robust deep learning. We argue that novel local winner-takes-all (LWTA) nonlinearities, combined with posterior sampling schemes, can greatly improve the adversarial robustness of traditional deep networks against difficult adversarial attack schemes. We combine these LWTA arguments with tools from the field of Bayesian non-parametrics, specifically the stick-breaking construction of the Indian Buffet Process, to flexibly account for the inherent uncertainty in data-driven modeling. As we experimentally show, the new proposed model achieves high robustness to adversarial perturbations on MNIST and CIFAR10 datasets. Our model achieves state-of-the-art results in powerful white-box attacks, while at the same time retaining its benign accuracy to a high degree. Equally importantly, our approach achieves this result while requiring far less trainable model parameters than the existing state-of-the-art. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

Energy-Aware Multi-Goal Motion Planning Guided by Monte Carlo Search

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Energy-Aware Multi-Goal Motion Planning Guided by Monte Carl...

IEEE International Conference on Automation Science and engineering (CASE)

作者： Yazz Warsame Stefan Edelkamp Erion Plaku Dept. of Informatics King’s College London London Dept. of Informatics University of Koblenz-Landau Mainz Germany Dept. of Electrical Engineering and Computer Science Catholic University of America Washington DC USA

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Autonomous robots need a reliable way to preserve their energy level while performing a persistent task such as inspection or surveillance. Toward this objective, this paper considers the multi-goal motion-planning problem with multiple recharging stations where a robot operating in a complex environment has to reach each goal while reducing the travel distance and the number of times it recharges. This paper develops an integrated approach that couples sampling-based motion planning with Monte-Carlo Tree Search (MCTS). The proposed MCTS searches over a discrete abstraction, which is obtained via a probabilistic roadmap, and uses a reward function to calculate when, where, and whether it is beneficial to recharge. This results in short tours that also reduce the number of recharges. Such tours are used to guide sampling-based motion planning as it expands a tree of collision-free and dynamically-feasible motions. Experiments with nonlinear dynamical robot models operating in obstacle-rich environments demonstrate the efficiency of the approach.

关键词： Robots Collision avoidance Planning Energy states Charging stations Mathematical model Monte Carlo methods

Cost optimization in CNN for big data of share price

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AIP Conference Proceedings 2023年第1期2587卷

作者： R. Saravanakumar Gokul Talele Ali Hazim Alyamoor Kaushal Kishor Bhatt Prashant Vats Malik Bader Alazzam Dept. of Computer Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore India Dept. of Data Science IIIT Banglore India Dept. of Accounting Economic and Administration College University of Mosul Ninawa Iraq Dept. of Computer Science and Engineering Birla Institute of Applied Science Bhimtal Nainital Uttarakhand India Dept. of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Faculty of Computer Science and Informatics Amman Arab University Mubis-Amman Jordan

Optimization of share price in order to evaluate whether to invest in specific share or not is subject of study paper. Optimized value has been utilized to enhance the performance and accuracy when dataset is trained using CNN. Review has been given on big cap, mid cap & small cap fund which are accessible in large, medium and small amount for the purpose of investing. It has been found that share in big cap are more dependable but offers limited return. However share in case of mid cap are considered comparatively more hazardous than big cap yet return may be substantial. The suggested study has given mechanism that will make use of optimization mechanism to filter the data set considering PSO and MVO mechanism. The suggested CNN based training model is claimed high performance system with improved accuracy.

关键词：

HPTMT: Operator-based architecture for scalable high-performance data-intensive frameworks

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arXiv 2021年

作者： Kamburugamuve, Supun Widanage, Chathura Perera, Niranda Abeykoon, Vibhatha Uyar, Ahmet Kanewala, Thejaka Amila von Laszewski, Gregor Fox, Geoffrey Luddy School of Informatics Computing and Engineering BloomingtonIN47408 United States Digital Science Center BloomingtonIN47408 United States Indiana University Alumni IN47408 United States Biocomplexity Institute Initiative and Computer Science Dept. University of Virginia

Data-intensive applications impact many domains, and their steadily increasing size and complexity demands high-performance, highly usable environments. We integrate a set of ideas developed in various data science and data engineering frameworks. They employ a set of operators on specific data abstractions that include vectors, matrices, tensors, graphs, and tables. Our key concepts are inspired from systems like MPI, HPF (High-Performance Fortran), NumPy, Pandas, Spark, Modin, PyTorch, TensorFlow, RAPIDS(NVIDIA), and OneAPI (Intel). Further, it is crucial to support different languages in everyday use in the Big Data arena, including Python, R, C++, and Java. We note the importance of Apache Arrow and Parquet for enabling language agnostic high performance and interoperability. In this paper, we propose High-Performance Tensors, Matrices and Tables (HPTMT), an operator-based architecture for data-intensive applications, and identify the fundamental principles needed for performance and usability success. We illustrate these principles by a discussion of examples using our software environments, Cylon and Twister2 that embody HPTMT. © 2021, CC BY.

关键词： Matrix algebra

Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

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arXiv 2022年

作者： Shoeibi, Afshin Ghassemi, Navid Khodatars, Marjane Moridian, Parisa Khosravi, Abbas Zare, Assef Gorriz, Juan M. Chale-Chale, Amir Hossein Khadem, Ali Rajendra Acharya, U. Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Deakin University Geelong Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy. © 2022, CC BY-NC-ND.

关键词： Support vector machines

Attention based CNN-LSTM Network for Pulmonary Embolism Prediction on Chest Computed Tomography Pulmonary Angiograms

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arXiv 2021年

作者： Suman, Sudhir Singh, Gagandeep Sakla, Nicole Gattu, Rishabh Green, Jeremy Phatak, Tej Samaras, Dimitris Prasanna, Prateek Dept. of Electrical Engineering Indian Institute of Technology Bombay India Dept. of Radiology Newark Beth Israel Medical Center NJ United States Dept. of Computer Science Stony Brook University NY United States Dept. of Biomedical Informatics Stony Brook University NY United States

With more than 60,000 deaths annually in the United States, Pulmonary Embolism (PE) is among the most fatal cardiovascular diseases. It is caused by an artery blockage in the lung;confirming its presence is time-consuming and is prone to over-diagnosis. The utilization of automated PE detection systems is critical for diagnostic accuracy and efficiency. In this study we propose a two-stage attention-based CNN-LSTM network for predicting PE, its associated type (chronic, acute) and corresponding location (leftsided, rightsided or central) on computed tomography (CT) examinations. We trained our model on the largest available public Computed Tomography Pulmonary Angiogram PE dataset (RSNA-STR Pulmonary Embolism CT (RSPECT) Dataset, N=7279 CT studies) and tested it on an in-house curated dataset of N=106 studies. Our framework mirrors the radiologic diagnostic process via a multi-slice approach so that the accuracy and pathologic sequela of true pulmonary emboli may be meticulously assessed, enabling physicians to better appraise the morbidity of a PE when present. Our proposed method outperformed a baseline CNN classifier and a single-stage CNN-LSTM network, achieving an AUC of 0.95 on the test set for detecting the presence of PE in the study. Copyright © 2021, The Authors. All rights reserved.

关键词： computerized tomography