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检索条件"任意字段=Conference on Data Mining and Knowledge Discovery - Theory, Tools, and Technology II"

共 841 条记录，以下是61-70 订阅

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Attackability Characterization of Adversarial Evasion Attack on Discrete data 20

Attackability Characterization of Adversarial Evasion Attack...

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26th ACM SIGKDD International conference on knowledge discovery and data mining (KDD)

作者： Wang, Yutong Han, Yufei Bao, Hongyan Shen, Yun Ma, Fenglong Li, Jin Zhang, Xiangliang King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Nortonlifelock Res Grp Sophia Antipolis France Nortonlifelock Res Grp Reading Berks England Penn State Univ State Coll PA USA Guangzhou Univ Guangzhou Peoples R China

ISBN: (纸本)9781450379984

Evasion attack on discrete data is a challenging, while practically interesting research topic. It is intrinsically an NP-hard combinatorial optimization problem. Characterizing the conditions guaranteeing the solvability of an evasion attack task thus becomes the key to understand the adversarial threat. Our study is inspired by the weak submodularity theory. We characterize the attackability of a targeted classifier on discrete data in evasion attack by bridging the attackability measurement and the regularity of the targeted classifier. Based on our attackability analysis, we propose a computationally efficient orthogonal matching pursuit-guided attack method for evasion attack on discrete data. It provides provably attack efficiency and performances. Substantial experimental results on real-world datasets validate the proposed attackability conditions and the effectiveness of the proposed attack method.

关键词： Attackability Evasion Attack Discrete data Deep Learning

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Construction of Radio Transmission Equipment Type Approval knowledge Graph and its Application

Construction of Radio Transmission Equipment Type Approval K...

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Big data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), International conference on

作者： Chun Xin Li Qian Nan Lu Ming Huang Jing Jing Yang Li Qin Chen School of Information Science and Engineering Yunnan University Kunming P.R. China Nujiang Prefecture Bureau of Industry and Information Technology Liuku Nujiang P.R. Chian

ISBN: (数字)9781665415408

ISBN: (纸本)9781665447065

Domain knowledge graph has become a research focus in the era of artificial intelligence. Radio transmission equipment type approval is the core function of radio equipment management. This paper introduces construction process of the radio transmission equipment type approval knowledge graph, and analyses algorithms of the knowledge graph by category, which is a great tool for knowledge mining. The question answering construction over the knowledge graph is also discussed as an application, which has laid the foundation for further research in radio regulatory knowledge graph.

关键词： Deep learning conferences tools knowledge discovery Graph theory Internet of Things Speech processing

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Secure Outsourced kNN data Classification over Encrypted data Using Secure Chain Distance Matrices 10th

Secure Outsourced kNN Data Classification over Encrypted Dat...

引用

10th International Joint conference on knowledge discovery, knowledge Engineering and knowledge Management, IC3K 2018

作者： Almutairi, Nawal Coenen, Frans Dures, Keith Department of Computer Science University of Liverpool Liverpool United Kingdom Information Technology Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (纸本)9783030495589

The paper introduces the Secure kNN (SkNN) approach to data classification and querying. The approach is founded on the concept of Secure Chain Distance Matrices (SCDMs) whereby the classification and querying is entirely delegated to a third party data miner without sharing either the original dataset or individual queries. Privacy is maintained using two property preserving encryption schemes, a homomorphic encryption scheme and bespoke order preserving encryption scheme. The proposed solution provides advantages of: (i) preserving the data privacy of the parties involved, (ii) preserving the confidentiality of the data owner encryption key, (iii) hiding the query resolution process and (iv) providing for scalability with respect to alternative data mining algorithms and alternative collaborative data mining scenarios. The results indicate that the proposed solution is both efficient and effective whilst at the same time being secure against potential attack. © 2020, Springer Nature Switzerland AG.

关键词： data mining

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An approach for the conceptual modeling of clustering mining in the kdd process

An approach for the conceptual modeling of clustering mining...

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2007 IADIS European conference on data mining, DM 2007, part of the 1st IADIS Multi conference on Computer Science and Information Systems, MCCSIS 2007

作者： Zubcoff, Jose Trujillo, Juan Departamento de Ciencias del Mar y Biología Aplicada Universidad de Alicante Spain Departamento de Lenguajes y Sistemas Informáticos Universidad de Alicante Spain

ISBN: (纸本)9789728924409

Clustering analysis is usually applied as an isolated method to not easily understandable flat files. This approach carries out three main drawbacks: (i) the pre-processing stage is not reusable between different data mining techniques, (ii) the analysts must focus on the low-level implementation details, and (iii) the analysts is missing the opportunity of learn previous knowledge about domain. data warehousing techniques are widely used to solve the first issue. However, there is still a lack of methods to accomplish the design of data mining in a suitable manner integrated into the knowledge discovery in databases (KDD) process. In this paper we propose a conceptual model for Clustering allowing designers to focus on the data mining domain abstracting from platform specific issues. Furthermore, it reduces developing time and cost, avoiding duplication of the time-consuming preprocessing steps. In addition, analysts can take advantage of the previous KDD stages. This is achieved by using an existing model of the data warehouse. In addition, this assures a proof understanding of data: the more information the user has about the data at hand, the more likely the user would be able to perform the data mining process. To show the feasibility of this proposal, we have implemented the conceptual modeling of Clustering in a case study using a commercial database system. © 2007 IADIS

关键词： data mining

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Introducing DASC-PM: A data Science Process Model 31

Introducing DASC-PM: A Data Science Process Model

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31st Australasian conference on Information Systems, ACIS 2019

作者： Schulz, Michael Neuhaus, Uwe Kaufmann, Jens Badura, Daniel Kuehnel, Stephan Badewitz, Wolfgang Dann, David Kloker, Simon Alekozai, Emal M. Lanquillon, Carsten NORDAKADEMIE University of Applied Sciences Elmshorn Germany Hochschule Niederrhein University of Applied Sciences Mönchengladbach Germany Valantic Business Analytics GmbH Hamburg Germany Germany Karlsruhe Institute of Technology Karlsruhe Germany Robert Bosch GmbH Stuttgart Germany Heilbronn University of Applied Sciences Heilbronn Germany

data-driven disciplines like data mining and knowledge management already provide process-based frameworks for data analysis projects, such as the well-known cross-industry standard process for data mining (CRISP-DM) or knowledge discovery in databases (KDD). Although the domain of data science addresses a much broader problem space, i.e., also considers economic, social, and ecological impacts of data-driven projects, a corresponding domain-specific process model is still missing. Consequently, based on a total of four identified meta requirements and 17 corresponding requirements that were collected from experts of theory and practice, this contribution proposes the empirically grounded data science process model (DASC-PM)—a framework that maps a data science project as a four-step process model and contextualizes it among scientific procedures, various areas of application, IT infrastructures, and impacts. To illustrate the phase-oriented specification capabilities of the DASC-PM, we exemplarily present competence and role profiles for the analysis phase of a data science project. © 2020 Schulz, Neuhaus, Kaufmann, Badura, Kuehnel, Badewitz, Dann, Kloker, Alekozai, Lanquillon.

关键词： knowledge management

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Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers 12th

Intrusion Detection in Wi-Fi Networks by Modular and Optimiz...

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12th International Joint conference on Computational Intelligence (IJCCI) / 12th International conference on Evolutionary Computation theory and Applications (ECTA)

作者： Granato, Giuseppe Martino, Alessio Baldini, Luca Rizzi, Antonello Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9789897584756

With the breakthrough of pervasive advanced networking infrastructures and paradigms such as 5G and IoT, cybersecurity became an active and crucial field in the last years. Furthermore, machine learning techniques are gaining more and more attention as prospective tools for mining of (possibly malicious) packet traces and automatic synthesis of network intrusion detection systems. In this work, we propose a modular ensemble of classifiers for spotting malicious attacks on Wi-Fi networks. Each classifier in the ensemble is tailored to characterize a given attack class and is individually optimized by means of a genetic algorithm wrapper with the dual goal of hyper-parameters tuning and retaining only relevant features for a specific attack class. Our approach also considers a novel false alarm management procedure thanks to a proper reliability measure formulation. The proposed system has been tested on the well-known AWID dataset, showing performances comparable with other state of the art works both in terms of accuracy and knowledge discovery capabilities. Our system is also characterized by a modular design of the classification model, allowing to include new possible attack classes in an efficient way.

关键词： Information Granulation data Clustering Supervised Learning Genetic Algorithms Malicious Traffic Detection Network Intrusion Detection Systems

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A Proposed data Preprocessing Method for an Industrial Prediction Process

A Proposed Data Preprocessing Method for an Industrial Predi...

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International conference on Information Science and technology (ICIST)

作者： Ilham Battas Ridouane Oulhiq Hicham Behja Laurent Deshayes Innovation Lab for Operations Mohammed VI Polytechnic University Benguerir Morocco Modeling System Architecture and modeling Team (EASM) Engineering research laboratory (LRI) National and high school of electricity and mechanic (ENSEM) Hassan II University Casablanca Morocco

ISBN: (数字)9781728166469

ISBN: (纸本)9781728166476

The studied mining production chain generally divided into three principal units: Destoning, screening and loading. The role of the screening unit is to screen the phosphate produced by the destoning unit before the loading to trains. Its efficiency depends on several parameters, which makes analysis and decision making for its improvement very complicated. The purpose of this paper is to propose a prediction system to evaluate and monitor in advance the efficiency of the screening unit. This system is based on knowledge discovery in databases that comprises generally three steps: data pre-processing, development of prediction models and finally validation and verification of the proposed models. The first consists in having in-depth information and knowledge about the application domain, in order to determine the set of parameters influencing the efficiency and to pre-process the data of the these parameters, in order to improve their quality before being used by the second step, which aims to develop predictive models that will be validated and verified with different evaluation criteria during the last step. This work focuses on the first level of development of the system in question, data pre-processing, which has been applied to the mine's screening unit facilities, and the results of this case study are also presented.

关键词： Information science Loading Production Predictive models knowledge discovery data models Monitoring

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Machine learning for discovery analytics to support criminal investigations 2

Machine learning for discovery analytics to support criminal...

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conference on Big data ii - Learning, Analytics, and Applications

作者： Muller, Wilmuth Pallmer, Dirk Muhlenberg, Dirk Loumiotis, Ioannis Remoundou, Konstantina Kosmides, Pavlos Demestichas, Konstantinos Fraunhofer Inst Optron Syst Technol & Image Expl Karlsruhe Germany Inst Commun & Comp Syst ICCS Athina Greece Natl Technol Univ Athens Athens Greece

ISBN: (纸本)9781510635685

Over the last decades, criminal activities have progressively expanded into the information technology (IT) world, adding to the "traditional" criminal activities, ignoring political boundaries and legal jurisdictions. Building upon the possibilities of technologies like Big data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support LEAs in their critical need to exploit all available resources and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions for information fusion and classification tools intended to support LEA's investigations. The Person Fusion Tool will be responsible for finding in an underlying knowledge graph different person instances that refer to the same person and fuse these instances. The general approach, the similarity metrics, the architecture of the tool and design choices as well as measures to improve the efficiency of the tool will be presented. The tool for classifying money transfer transactions uses decision trees. This is due to a requirement of easy explainability of the classification results, which is demanded from the ethical and legal perspective of the MAGNETO project. The design of the tool, the selected implementation and an evaluation based on anonymized financial data records will be presented.

关键词： Machine learning classification person fusion money transactions law enforcement agencies

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Open Science Graphs Must Interoperate! 24th

Open Science Graphs Must Interoperate!

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24th East-European conference on Advances in databases and Information Systems/24th International conference on theory and Practice of Digital Libraries/16th Workshop on Business Intelligence and Big data (ADBIS/TPDL/EDA)

作者： Aryani, Amir Fenner, Martin Manghi, Paolo Mannocci, Andrea Stocker, Markus Swinburne Univ Technol Hawthorn Vic Australia DataCite eV Hannover Germany CNR Inst Informat Sci & Technol Pisa Italy TIB Leibniz Informat Ctr Sci & Technol Hannover Germany MARUM Ctr Marine Environm Sci PANGAEA Data Publisher Earth & Environm Sci Bremen Germany

ISBN: (纸本)9783030558130;9783030558147

Open Science Graphs (OSGs) are Scientific knowledge Graphs whose intent is to improve the overall FAIRness of science, by enabling open access to graph representations of metadata about people, artefacts, institutions involved in the research lifecycle, as well as the relationships between these entities, in order to support stakeholder needs, such as discovery, reuse, reproducibility, statistics, trends, monitoring, impact, validation, and assessment. The represented information may span across entities such as research artefacts (e.g. publications, data, software, samples, instruments) and items of their content (e.g. statistical hypothesis tests reported in publications), research organisations, researchers, services, projects, and funders. OSGs include relationships between such entities and sometimes formalised (semantic) concepts characterising them, such as machine-readable concept descriptions for advanced discoverability, interoperability, and reuse. OSGs are generally valuable individually, but would greatly benefit from information exchange across their collections, thereby improving their efficacy to serve stakeholder needs. They could, therefore, reuse and exploit the data aggregation and added value that characterise each OSG, decentralising the effort and capitalising on synergies, as no one-size-fits-all solution exists. The RDA IG on Open Science Graphs for FAIR data is investigating the motivation and challenges underpinning the realisation of an Interoperability Framework for OSGs. This work describes the key motivations for i) the definition of a classification for OSGs to compare their features, identify commonalities and differences, and added value and for ii) the definition of an Interoperability Framework, specifically an information model and APIs that enable a seamless exchange of information across graphs.

关键词： Open science Research knowledge graph Interoperability

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On the Nature of data Science 21

On the Nature of Data Science

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Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining

作者： Jeffrey Ullman Stanford University Stanford CA USA

ISBN: (纸本)9781450383325

One can hear "data Science" defined as a synonym for machine learning or as a branch of Statistics. I shall argue that it is far more than that; it is the natural evolution of the technology of very large-scale data management to solve problems in scientific and commercial fields. To support my argument, I shall give a brief introduction to two algorithms that are important in data science but that are neither machine learning nor statistics: locality-sensitive hashing and counting distinct ***: Jeff Ullman is the Stanford W. Ascherman Professor of Engineering (Emeritus) in the Department of Computer Science at Stanford and CEO of Gradiance Corp. He received the B.S. degree from Columbia University in 1963 and the PhD from Princeton in 1966. Prior to his appointment at Stanford in 1979, he was a member of the technical staff of Bell Laboratories from 1966-1969, and on the faculty of Princeton University between 1969 and 1979. From 1990-1994, he was chair of the Stanford Computer Science Department. Ullman was elected to the National Academy of Engineering in 1989, the American Academy of Arts and Sciences in 2012, the National Academy of Sciences in 2020, and has held Guggenheim and Einstein Fellowships. He has received the Sigmod Contributions Award (1996), the ACM Karl V. Karlstrom Outstanding Educator Award (1998), the Knuth Prize (2000), the Sigmod E. F. Codd Innovations award (2006), the IEEE von Neumann medal (2010), the NEC C&C Foundation Prize (2017), and the ACM A.M. Turing Award (2020). He is the author of 16 books, including books on database systems, data mining, compilers, automata theory, and algorithms.

关键词： keynote data science statistics

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