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The Impact of data Valuation on Feature Importance in Classification Models 3rd

The Impact of Data Valuation on Feature Importance in Classi...

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3rd international conference on computing and Communication Networks, ICCCN 2023

作者： Ebiele, Malick Bendechache, Malika Ward, Marie Geary, Una Byrne, Declan Creagh, Donnacha Brennan, Rob University College Dublin Dublin Ireland University of Galway Galway Ireland Saint James’s Hospital Dublin Ireland

ISBN: (纸本)9789819708918

This paper investigates the impact of data valuation metrics (variability and coefficient of variation) on the feature importance in classification models. data valuation is an emerging topic in the fields of data science, accounting, data quality, and information economics concerned with methods to calculate the value of data. Feature importance or ranking is important in explaining how black-box machine learning models make predictions as well as selecting the most predictive features while training these models. Existing feature importance algorithms are either computationally expensive (e.g. SHAP values) or biased (e.g. Gini importance in Tree-based models). No previous investigation of the impact of data valuation metrics on feature importance has been conducted. Five popular machine learning models (eXtreme Gradient Boosting (XGB), Random Forest (RF), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and Naive Bayes (NB)) have been used as well as six widely implemented feature ranking techniques (Information Gain, Gini importance, Frequency Importance, Cover Importance, Permutation Importance, and SHAP values) to investigate the relationship between feature importance and data valuation metrics for a clinical use case. XGB outperforms the other models with a weighted F1-score of 79.72%. The findings suggest that features with variability greater than 0.4 or a coefficient of variation greater than 23.4 have little to no value;therefore, these features can be filtered out during feature selection. This result, if generalisable, could simplify feature selection and data preparation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

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Risk-Free Interest Rates in Decentralized Finance

Risk-Free Interest Rates in Decentralized Finance

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Blockchain computing and Applications (BCCA), international conference on

作者： Ben Charoenwong Robert M. Kirby Jonathan Reiter Business School National University of Singapore Singapore Singapore School of Computing University of Utah Salt Lake City USA Data Finnovation Singapore Singapore

Decentralized Finance (DeFi) aims to use advancements in both computation and cryptography to tackle economic problems. Therefore, it must operate within the intersection of constraints from both the computer science and economic domains. We explore a foundational question at the junction of those fields: Can we synthesize variable market-clearing risk-free yield for native tokens via smart contracts? We use a stylized model representing a large class of decentralized consensus algorithms to show this is impossible. This undecidability result places bounds on what decentralized financial products can be built and constrains the shape of future developments in DeFi. Among other limitations, our results reveal that markets in DeFi are incomplete.

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data Locality Aware Computation Offloading in Near Memory processing Architecture for Big data Applications

Data Locality Aware Computation Offloading in Near Memory Pr...

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international conference on High Performance computing

作者： Satanu Maity Mayank Goel Manojit Ghose Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

The data-intensive applications of today's big data era often produce a large memory footprint. As a result, a significant volume of data needs to travel from memory to the CPU under the traditional Von-Neumann computing paradigm. Near-memory processing (NMP) or processing-in-memory (PIM) is a potential alternate computation framework where a computation unit is placed near the memory (or inside the memory) and a portion of an application is executed on it (termed computation offloading) aiming to reduce the amount of data movement and its consequences. Although a few computation offloading strategies have been proposed in recent times, the existing approaches do not consider the data locality offered by the last level cache and the overall execution time of the application while designing their policies. In this paper, we propose a data locality-aware computation offloading strategy for a hybrid computing system comprising the host processor and NMP-enabled 3D memory. After the application code is instrumented using the LLVM compiler framework, the strategy offloads a portion of an application to NMP if its estimated overall execution time is less. An extensive simulation performed on a set of standard simulators for a bunch of large graph-based application benchmarks reports the effectiveness of the proposed strategy by achieving a maximum speedup of 40% and 11.8% as compared to the host-only configuration and the state-of-art policy, respectively. The proposed strategy also reduces the off-chip data transfer and energy consumption by a significant margin as compared to the host-only configuration (avg 27%) and the state-of-art policy (avg 28%). Further, the proposed policy reduces the LLC miss rate by 57% as compared to the state-of-art policy.

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Human Activity Tracking Using Mobile Sensor data and an Optimised LSTM 3rd

Human Activity Tracking Using Mobile Sensor Data and an Opti...

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3rd international conference on Smart computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Anandarao, Sarvani Mastani, Shaik Geeta, Tata Vamsi, Bolleddu Reddy, Puritipati Sai Santhosh Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819705726

Since many years smartphones are utilised for human activity recognition (HAR), important healthcare recommendations and telemedicine. Deep learning (DL) and machine learning techniques are commonly employed in studies of statistical models of human behaviour. However, the performance of present HAR platforms is constrained by complex physical activities. In this study, we developed the Ada-HAR human activity identification and real-time monitoring system, which is able to recognise more human motions in erratic situations. The chosen hierarchical clustering and classification methods are able automatically identify and categorise 12 activities (five dynamics, six statics, and a series of transitions). Finally, actual tests were run to verify the effectiveness and reliability of the suggested methods. The results demonstrate that the DL-based classifier achieves a higher identification rate (95.15% for waist and 92.20% for pocket) in comparison to the techniques discussed in the literature. Finally, the Ada-HAR system can track human behaviour in real-time regardless of how the smartphone is pointed. Here sensor activity dataset is considered which consists of user's activity log. By applying algorithms named LSTM, Adamax, Adagrad, SGD, indentifiesExi user’s activity based on the movement of the mobile. Existing algorithms named LSTM, K-NN, DT, ANN, SVM, NB applied on different dataset produced better result and accuracy. RNN is an updated version of LSTM. RNN stores only the current activities and fails to store previous conclusions. Fortunately, LSTM stores existing and completed work in any dynamic situations. The LSTM works with random weights which led to local optimum and high time complexity. This paper has addressed the above issue by the usage of optimization algorithms in weight updation of LSTM. This increase the accuracy of the model. Here Adamax, Adagram, Stochastic gradient descent are used for weight updation. © The Author(s), under exclusive license

关键词： Smartphones

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Semi-supervised Learning with Nearest-Neighbor Label and Consistency Regularization 1

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4th international conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zheng, Guolin Li, Zuoyong Hu, Wenkai Fan, Haoyi Ching, Fum Yew Yu, Zhaochai Chen, Kaizhi College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Computer Sciences Universiti Sains Malaysia Penang11800 Malaysia Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

Semi-supervised learning, a system dedicated to making networks less dependent on labeled data, has become a popular paradigm due to its strong performance. A common approach is to use pseudo-labels with unlabeled data for training, however, pseudo-labels cannot correct their own errors. In this paper, we propose a semi-supervised method that uses nearest neighbor samples to obtain pseudo-labels and combines consistency regularization for image classification. Our method obtains pseudo-labels by computing the similarity of the data distribution between the weakly-augmented version of the unlabeled data and the labeled data stored in the support set and combines the consistency of the strongly-augmented version and the weakly-augmented version of the unlabeled data. We compared with several standard semi-supervised learning benchmarks and achieved a competitive performance. For example, we achieved an accuracy of 94.02 % on CIFAR-10 with 250 labels and 97.50 % on SVNH with 250 labels. It even achieved 91.59 % accuracy with only 40 labels data in the CIFAR-10. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Benchmarking

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Experimental Studies of the Complex Method of Increasing the Load processing Efficiency in Distributed data Centers

Experimental Studies of the Complex Method of Increasing the...

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international Black Sea conference on Communications and Networking (BlackSeaCom)

作者： Larysa Globa Andrii Raichuk Nataliia Gvozdetska Igor Sikorsky Kyiv Politechnic Institute” Institute of telecomunication system Kyiv Ukraine

In this paper, the experimental studies for increasing the load processing efficiency in distributed data centers of communication operators are presented. The communication operators widely use the up-to-day technologies function virtualization such as NFV, SDN, Network Slicing, Edge computing, and bDDN, which require a significant amount of calculations, and accordingly consume significant amounts of energy during load processing in data centers. Existing approaches do not provide simultaneous improvements in energy efficiency and performance while meeting SLA requirements. To ensure energy-efficient data processing and improve its processing performance, while maintaining the quality of SLA requirements, an approach was proposed, the essence of which is to take into account the daily load and allocate data processing resources in such a way as to minimize energy consumption and not lose performance during processing. When comparing the proposed approach with Backfill load planning, it was possible to determine an increase in energy efficiency indicators up to 9.953%, and up to 26.382% in comparison with Round Robin.

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Experimental Validation of a Photovoltaic/Electrolysis System Dedicated to Supplying an Alternating Load and Producing Hydrogen 3rd

Experimental Validation of a Photovoltaic/Electrolysis Syste...

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3rd international conference on Electronic Engineering and Renewable Energy, ICEERE 2022

作者： Yahyaoui, S. Aziz, A. Messaoudi, A. Blaacha, J. Dahbi, S. Messaoudi, I. Electrical Engineering and Maintenance Laboratory Higher School of Technology Mohamed First University BP 473 Oujda60000 Morocco Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University BP 473 Oujda60000 Morocco

ISBN: (纸本)9789811962226

In this paper, we present an experimental validation of a photovoltaic/electrolysis system dedicated to supplying an alternating load and producing hydrogen. The system uses new way to produce hydrogen by adapting the electrolysis to a renewable source of energy such as photovoltaic (PV) by electrolysis of water and powering an AC load. Complete modeling and the control of this system are based on the use of several control techniques, and the main components of the proposed system are two converters, one DC/DC buck controlled by an MPPT control, the other DC/DC boost ensures the voltage control. The conversion from AC to DC is done by a modified single-phase full bridge. All these controls are digital and implemented in a microcontroller that allows us to minimize wiring and get accurate results. The experimental results show that the proposed system PV-electrolysis equipped with the different control algorithms can supply the energy required by the AC load and of storing the excess energy in the form of hydrogen production. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrolysis

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VDSL Single Ended Line Test baselining with Uncalibrated Echo Response data

VDSL Single Ended Line Test baselining with Uncalibrated Ech...

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international conference on Signal processing and Communication Systems, ICSPCS

作者： Feiyu Fang Lykourgos Kekempanos Applied Research BT plc Ipswich United Kingdom

Single Ended Line Testing can provide diagnostic data on telecommunications access networks such as VDSL without an electrical cable connection to the local exchange building. This information includes Uncalibrated Echo Response data which may be used to perform baselining and fingerprinting on a tested line. We present novel algorithms to detect changes in the electrical state of a line, terminations within a DSL cabinet, modem power state changes, and in-premises loop length changes to produce a highly specific model of long term physical line conditions and provide business benefit in improved knowledge for engineer dispatch and in providing a frequency-domain alternative to existing time-domain methods without requiring additional equipment.

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Deep learning based land cover assessment using high resolution satellite data

Deep learning based land cover assessment using high resolut...

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8th international conference on computing in Engineering and Technology (ICCET 2023)

作者： S. Srivastava Y. Sharma A. Prakash G. Gupta Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Satellite imagery analysis is crucial for various applications such as environmental monitoring, land-use management, and disaster management. However, traditional image processing methods often struggle with accurately classifying different land use types due to variations in texture, shape, and pattern. This research work proposes a deep learning-based approach for land use classification in satellite imagery data. In this work, a Convolutional Neural Network (CNN) model has been trained on a large dataset of satellite images and corresponding land use labels, achieving high performance in land use classification as measured by metrics such as Jaccard index and accuracy from the dice function and focal function. The proposed approach offers several advantages as compared to the tradition ones. This trained model can recognize subtle differences in land use types that might be challenging to discern using traditional methods. The model is highly efficient, capable of processing large volumes of data in a relatively short time. It can be easily adapted to different satellite imagery datasets and land use classification tasks, making it a flexible and versatile approach. With the proposed methodology on analysing images against 6 classes we get a land cover classification accuracy of 81.47%.

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Research on the Application of Computer Big data Technology in Information Security Management

Research on the Application of Computer Big Data Technology ...

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Electrical Engineering, Big data and algorithms (EEBDA), IEEE international conference on

作者： Yong Zhang School of Foreign Studies Chengdu Neusoft University Chengdu China

This paper innovatively proposes a database access information security management method under the big data platform. This paper first designs and implements a network information security management system based on B/S architecture. The system consists of a safety management platform management center, a WEB console, an agency center and a processing center. At the same time, the system covers a total of five sub-modules including security monitoring management, security policy management, event analysis and processing, security incident response and system management. Secondly, this paper constructs an attribute-based multi-authorization encryption system, which reduces the number of matching operations and improves the efficiency of password utilization. Then, this paper builds a user hierarchical role key tree in the big data platform user role authentication center, and describes the relationship between the big data platform user role and the encryption key of extremely sensitive information. Finally, simulation experiments are carried out in this paper. The results show that the establishment of the encryption system enhances the security of web page access information, and realizes the security management of information platform access information under the big data platform.

关键词： databases Security management Information security Web pages Safety management Passwords Big data

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