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A Review of Methods Used in machine learning and Data Analysis 19

A Review of Methods Used in Machine Learning and Data Analys...

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11th international conference on machine learning and computing (icmlc)

作者： Wu, Qingyang St Marys Sch Cambridge England

This report provides an overview of machine learning and data analysis with explanation of the advantages and disadvantages of different methods. I also demonstrate a practical implementation of the described methods ... 详细信息

ISBN: (纸本)9781450366007

关键词： Data exploration principal component analysis machine learning

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The Contestation Caf A Manifesto for Contestation: Prototyping an agonistic place 12

The Contestation Caf A Manifesto for Contestation: Prototypi...

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12th Nordic conference on Human-Computer Interaction: Participative computing for Sustainable Futures, NordiCHI 2022

作者： Collins, Robert Redström, Johan Umeå Institute of Design Umeå University Sweden

ISBN: (纸本)9781450396998

The Contestation Manifesto and its associated paraphernalia are artefacts from a speculative, near-future community action known as the Contestation Café. Being one in a series of research through design projects on contestation, the Contestation Café is a critical, yet also practical, design intervention rooted in how the act of repair has moved through the physical into the digital, and the shared values therein. Tracing the history of electronic repair - from the early valve radios, the invention of the transistor, microchips, programmable devices and through to IoT, connected devices and the "fluid assemblages"that emerge - we can see the shared values of the physical act of repair with the more intangible act of contestation in the digital world of algorithmic systems. Overlaying the values and tactics of the Right to Repair movement with the emerging concerns around data-driven systems we find ourselves examining the Repair Café as a potential model for community contestation and the construction of publics. The Repair Café started in Amsterdam in 2009 and has since spread to 35 countries with over 1700 instances of these cafés. The Repair Café is not intended to be a place where you bring your broken appliances for someone else to repair - rather, it is a place where you learn how to repair and recycle your own products and devices, and - more importantly - a place where you simply learn that things can be fixed rather than thrown out. In a similar spirit, the Contestation Café would be a place for those who feel mistreated by automated decision systems, AIs and algorithms, to bring their broken interactions and their unfair decisions to learn how to contest, push back and reclaim their agency and autonomy. Rather than Repairers, the Contestation Café has a panel of Fixers - people who inhabit the space between designers and users, with a particular knowledge of these systems and how to map and navigate them - who are there to share their experience and knowl

关键词： machine learning

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Role of machine learning for Solving Satisfiability Problems and its Applications in Cryptanalysis

Role of Machine Learning for Solving Satisfiability Problems...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Manish Gupta Satish Kumar Kalhotra Harish Reddy Gantla Gaurav Kumar Arora S. Vaishnodevi Algubelly Yashwanth Reddy Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad India Department of Education Rajiv Gandhi University Rono Hills Doimukh Arunachal Pradesh India Department of Computer Science and Engineering Vignan Institute of Technology and Science Telangana India IBM India Pvt. Ltd. Greater Noida Uttar Pradesh India Department of Biomedical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem India Vice Principal & HoD -CSE Sree Dattha Group of Institutions Hyderabad Telangana India

The efficiency of "conflict-based Clause-learning Boolean Fulfilment (CDCLSAT)" solvers on engineering problems from several fields has been seeing notable modifications during the last 20 years. Various analyses have proposed SAT-based tactics for cryptanalysis, like finding conflicts in hash capabilities and disassembling symmetrical encryption schemes, as just a result of the availability of efficient broad sense look solvers like SAT Solver. The majority of the currently suggested SAT-based cryptanalysis techniques are Black Box inventions, in which the cryptanalysis problem is encrypted as SAT and illuminated using CDCLSAT Solvers. The method has the issue that now the encoding created in this method may be too extensive for any slashing arrangements to decode successfully. This approach's solution is not manner set up to handle a specific or provided example, which is a further notable drawback. The disadvantage of this technique is that decoding created in this manner may be too extensive for any sophisticated organization to correctly decode. One notable drawback of this strategy that the solution does not way set up to deal with a specific or given scenario. In order to understand these problems, SAT Solutions have provided solutions to two related existing gaps (exhibited in Fast Programme Encrypted 2009) about the presence or absence of these minute secrets. SAT nameservers have provided an effective solution, even though that such problems might appear difficult to address. SAT nameservers have provided a useful plan of action, despite the fact that such problems it might seem difficult to fix and also discuss the experiment that was conducted to see how well an intermediate SAT Solution performed when doing encrypted cryptanalytic tasks that proved to be relatively challenging. Allocation and or before assaults here on MD4 computation are far less similar.

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A Survey of Feature Selection for Vulnerability Prediction Using Feature-based machine learning 19

A Survey of Feature Selection for Vulnerability Prediction U...

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11th international conference on machine learning and computing (icmlc)

作者： Li, ZhanJun Shao, Yan Univ Sci & Technol China Sch Comp Sci & Technol 96 Jinzhai Rd Hefei Anhui Peoples R China

ISBN: (纸本)9781450366007

This paper summarized the basic process of software vulnerability prediction using feature-based machine learning for the first time. In addition to sorting out the related types and basis of vulnerability features definition, the advantages and disadvantages of different methods are compared. Finally, this paper analyzed the difficulties and challenges in this research field, and put forward some suggestions for future work.

关键词： Software vulnerability prediction machine learning feature

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Deep learning Model for Detection of Attacks in the Internet of Things Based Smart Home Environment

Deep Learning Model for Detection of Attacks in the Internet...

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international conference on Recent Trends in machine learning, IOT, Smart Cities and Applications, ICMISC 2020

作者： Bokka, Raveendranadh Sadasivam, Tamilselvan Department of Electronics and Communication Engineering Pondicherry Engineering College Puducherry India

ISBN: (纸本)9789811572333

The number of devices getting connected to the internet is growing exponentially day by day with the advent of the Internet of Things (IoT), commensurately in every domain of IoT attacks and threats are also growing in alarming rate because of the lack of proper security measures in the devices and network. Proper methods are required in the IoT environment for detecting attacks thus by providing effective defence and security. In this paper, we developed Deep learning (DL) based Deep Neural Network (DNN) to detect attacks like Denial of Service (DoS), spying, malicious control in IoT based smart homes. The model was evaluated using DS2OS (Distributed Smart Space Orchestration System) dataset for detection of DoS, Data Type Probing, Scan, Spying, Malicious Control, Wrong setup and Malicious Operation attacks which have given best accuracy as 99.42% and performance metrics like precision, F1-score and recall are evaluated. The proposed model was compared to the DL methods proposed by other authors. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Distributed machine learning over Directed Network with Fixed Communication Delays 19

Distributed Machine Learning over Directed Network with Fixe...

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11th international conference on machine learning and computing (icmlc)

作者： Guo Zhenning Beijing Univ Technol Beijing Dublin Int Coll Dublin Beijing Peoples R China

ISBN: (纸本)9781450366007

In this paper, we present a distributed machine learning algorithm over a network with fixed-delay tolerance. The network is directed and strongly connected. The training dataset is distributed to all agents in the network. We combine the distributed convex optimization (which utilizes double linear iterations) and corresponding machine learning algorithm. Each agent can only access its own local dataset. Suppose the delay between any pair of agents is time-invariant. The simulation shows that our algorithm is able to work under delayed transmission, in the sense that over time at each agent i the ratio of the estimate value x(i)(t) and scaling variable y(i)(t) can converge to the optimal point of the global cost function corresponding to the machine learning problem.

关键词： Distributed system machine learning network convex optimization

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PREDICTING GLOBAL computing POWER OF BLOCKCHAIN USING CRYPTOCURRENCY PRICES 18

PREDICTING GLOBAL COMPUTING POWER OF BLOCKCHAIN USING CRYPTO...

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international conference on machine learning and Cybernetics (IMLC)

作者： Li, Guangcheng Zhao, Qinglin Song, Mengfei Du, Daidong Yuan, Jianwen Chen, Xuanhui Liang, Hong Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China Lingnan Big Data Inst Zhuhai Guangdong Peoples R China

ISBN: (纸本)9781728128160

Blockchain is a disruptive technology that enables disparate users to share their information in blocks trustworthily without a centralized entity. One fundamental problem is how to stable the block interval. To address this problem, our method is: 1. predict the computing power (i.e., hashrate) of a blockchain system by the cryptocurrency price;2. stable the interval according to the predicted power. This paper focuses on the prediction of the global computing power. In our prediction, we adopt a LSTM-based regression algorithm to handle the hysteresis of computing power changes in response to the price changes. Taking the Bitcoin system as an example, we run extensive experiments that verify that our prediction algorithm is very accurate.

关键词： Blockchain computing power prediction Cryptocurrency prices LSTM

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An Over-sampling Method Based on Margin Theory 19

An Over-sampling Method Based on Margin Theory

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11th international conference on machine learning and computing (icmlc)

作者： Zhang, Zongtang Chen, Zhe Dai, Weiguo Cheng, Yusheng Navy Submarine Acad Qingdao Shandong Peoples R China

ISBN: (纸本)9781450366007

Imbalanced data widely exists in real life, while the traditional classification method usually takes accuracy as the classification criterion, which is not suitable for the classification of imbalanced data. Resampling is an important method to deal with imbalanced data classification. In this paper, a margin based random over sampling (MR0) method is proposed, and then MROBoost algorithm is proposed by combining the AdaBoost algorithm. Experimental results on the UCI dataset show that the MROBoost algorithm is superior to AdaBoost for imbalanced data classification problem.

关键词： machine learning AdaBoost imbalanced data over-sampling

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Explainable deep reinforcement learning for portfolio management: an empirical approach 21

Explainable deep reinforcement learning for portfolio manage...

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Proceedings of the Second ACM international conference on AI in Finance

作者： Mao Guan Xiao-Yang Liu Columbia University

ISBN: (纸本)9781450391481

Deep reinforcement learning (DRL) has been widely studied in the portfolio management task. However, it is challenging to understand a DRL-based trading strategy because of the black-box nature of deep neural networks. In this paper, we propose an empirical approach to explain the strategies of DRL agents for the portfolio management task. First, we use a linear model in hindsight as the reference model, which finds the best portfolio weights by assuming knowing actual stock returns in foresight. In particular, we use the coefficients of a linear model in hindsight as the reference feature weights. Secondly, for DRL agents, we use integrated gradients to define the feature weights, which are the coefficients between reward and features under a linear regression model. Thirdly, we study the prediction power in two cases, single-step prediction and multi-step prediction. In particular, we quantify the prediction power by calculating the linear correlations between the feature weights of a DRL agent and the reference feature weights, and similarly for machine learning methods. Finally, we evaluate a portfolio management task on Dow Jones 30 constituent stocks during 01/01/2009 to 09/01/2021. Our approach empirically reveals that a DRL agent exhibits a stronger multi-step prediction power than machine learning methods.

关键词： explainable deep reinforcement learning

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HARDWARE-SOFTWARE CODESIGN OF HISTOGRAM OF ORIENTED GRADIENTS ON HETEROGENEOUS computing PLATFORM 18

HARDWARE-SOFTWARE CODESIGN OF HISTOGRAM OF ORIENTED GRADIENT...

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international conference on machine learning and Cybernetics (IMLC)

作者： Wang, Yuan-Kai Chen, Hung-Yu Chen, Kuan-Yu Huang, Shih-Yu Fu Jen Catholic Univ Dept Elect Engn New Taipei City Taiwan Fu Jen Catholic Univ Grad Inst Appl Sci & Engn New Taipei City Taiwan Ming Chuan Univ Dept Informat & Commun Engn Taipei Taiwan

ISBN: (纸本)9781728128160

Histogram of oriented gradients (HOG) is a highly important feature representation in computer vision for many applications such as objection detection. The HOG computes local histograms of oriented gradients of pixel luminance on a dense grid of uniformly spaced cells and normalized to be a feature vector. Its computational complexity is high, and its implementation on edge computing and embedded devices is challenging. This paper proposes a hardware software codesign strategy to redesign the HOG algorithm. Pipelining and hardware acceleration by FPGA are applied in the design to the performance improvement of HOG. The design is implemented on a heterogeneous computing platform and with high level synthesis techniques exploiting C-code to accelerate the design of hardware circuits. Our results of full HD images achieve 500 times speed-up compared with software implementation.

关键词： Histogram of oriented gradients Heterogeneous computing Hardware acceleration Zynq FPGA

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