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machine learning Independent of Population Distributions for Measurement 4

Machine Learning Independent of Population Distributions for...

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4th IEEE / ACM / ASA international conference on Data Science and Advanced Analytics (DSAA)

作者： Washio, Takashi Imamura, Gaku Yoshikawa, Genki Osaka Univ Inst Sci & Ind Res Osaka Ibaraki 5670047 Japan Natl Inst Mat Sci Int Ctr Mat Nanoarchitecton Tsukuba Ibaraki 3050044 Japan

ISBN: (纸本)9781509050048

Many of recent advanced measurement techniques acquire highly complex information through elaborate measurement processes, and estimate the measurement objects from their corresponding patterns reflected in the outcome of these processes. the introduction of advanced statistical and machine learning methods to measurement techniques is now inevitable to solve these complicated inverse problems. However, the state of the art remains the straightforward application of problem settings and their solutions studied in statistics and machine learning which assume a steady population distribution providing the objective data, whereas every measurement is performed under a distinct distribution of disturbances and noises. As claimed and demonstrated in this paper, this fact largely degrades the accuracy and robustness of the measurements. To effectively overcome this issue, we present a framework of robust and accurate machine learning against deviations of population distributions between calibration data for the training and a new single observation in the measurement. this is achieved by properly reflecting generic measurement processes to their estimations. the significant advantages of the presented framework are demonstrated through a real-world application to olfactory sensing.

关键词： Sociology Statistics Semiconductor device measurement Extraterrestrial measurements Estimation Time measurement Training data

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EEG Based Emotion Monitoring Using Wavelet and learning Vector Quantization 4

EEG Based Emotion Monitoring Using Wavelet and Learning Vect...

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4th international conference on Electrical Engineering, computer Science and Informatics (EECSI)

作者： Djamal, Esmeralda C. Lodaya, Poppi Univ Jenderal Achmad Yani Dept Informat Cimahi Indonesia

ISBN: (纸本)9781538605493

Emotional identification is necessary for example in Brain computer Interface (BCI) application and when emotional therapy and medical rehabilitation take place. Some emotional states can be characterized in the frequency of EEG signal, such excited, relax and sad. the signal extracted in certain frequency useful to distinguish the three emotional state. the classification of the EEG signal in real time depends on extraction methods to increase class distinction, and identification methods with fast computing. this paper proposed human emotion monitoring in real time using Wavelet and learning Vector Quantization (LVQ). the process was done before the machine learning using training data from the 10 subjects, 10 trial, 3 classes and 16 segments (equal to 480 sets of data). Each data set processed in 10 seconds and extracted into Alpha, Beta, and theta waves using Wavelet. then they become input for the identification system using LVQ three emotional state that is excited, relax, and sad. the results showed that by using wavelet we can improve the accuracy of 72% to 87% and number of training data variation increased the accuracy. the system was integrated with wireless EEG to monitor emotion state in real time with change each 10 seconds. It takes 0.44 second, was not significant toward 10 seconds.

关键词： emotion monitoring EEG signal Wavelet LVQ Brain computer Interface

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machine learning for Anomaly Detection and Categorization in Multi-Cloud Environments 4

Machine Learning for Anomaly Detection and Categorization in...

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4th IEEE international conference on Cyber Security and Cloud Computing, CSCloud 2017 and 3rd IEEE international conference of Scalable and Smart Cloud, SSC 2017

作者： Salman, Tara Bhamare, Deval Erbad, Aiman Jain, Raj Samaka, Mohammed Washington University in St. Louis St. LouisWA United States Qatar University Doha Qatar

ISBN: (纸本)9781509066438

Cloud computing has been widely adopted by application service providers (ASPs) and enterprises to reduce both capital expenditures (CAPEX) and operational expenditures (OPEX). applications and services previously running on private data centers are now being migrated to private or public clouds. Since most of the ASPs and enterprises have globally distributed user bases, their services need to be distributed across multiple clouds, spread across the globe which can achieve better performance in terms of latency, scalability and load balancing. the shift has eventually led the research community to study multi-cloud environments. However, the widespread acceptance of such environments has been hampered by major security concerns. Firewalls and traditional rule-based security protection techniques are not sufficient to protect user-data in multi-cloud scenarios. Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. this is, in fact, necessary for appropriate countermeasures and defense against attacks. In this paper, we investigate both detecting and categorizing anomalies rather than just detecting, which is a common trend in the contemporary research works. We have used a popular publicly available dataset to build and test learning models for both detection and categorization of different attacks. To be precise, we have used two supervised machine learning techniques, namely linear regression (LR) and random forest (RF). We show that even if detection is perfect, categorization can be less accurate due to similarities between attacks. Our results demonstrate more than 99% detection accuracy and categorization accuracy of 93.6%, with the inability to categorize some attacks. Further, we argue that such categorization can be applied t

关键词： Intrusion detection

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4th international conference on HCI in Business, Government and Organizations, HCIBGO 2017, held as part of the 19th international conference on Human-computer Interaction , HCI 2017

4th International Conference on HCI in Business, Government ...

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4th international conference on learning and Collaboration Technologies, LCT 2017, held as part of the 19th international conference on Human-computer Interaction, HCI 2017

ISBN: (纸本)9783319585147

the proceedings contain 75 papers. the special focus in this conference is on HCI in Business, Government and Organizations. the topics include: Using augmented reality interactive system to support digital electronics learning;an AI system for coaching novice programmers;affective walkthroughs and heuristics;a creative engineering experience;manipulation of mathematical expressions in collaborative environments;designing tools that allows children in the early childhood to program robots;decision making for interactive systems;preschool learning with a fingertip;augmentative and alternative communication in the literacy teaching for deaf children;a model for collaboration in virtual worlds bringing together cultures in conflict;challenges of integrating non-traditional students in higher education and how electronic learning can support inclusion;training socially responsible engineers by developing accessible video games;the use of a new visual language as a supporting resource for people with intellectual disabilities;dashboard for actionable feedback on learning skills;learning analytics and spelling acquisition in German;data analysis of coaching and advising in undergraduate students;learning analytics and its paternalistic influences;development of a dashboard for learning analytics in higher education;mixing and matching learning design and learning analytics;a guidance and evaluation approach for mhealth education applications;collaborative hybrid agent provision of learner needs using ontology based semantic technology and designing a peer feedback mobile application as a professional development tool.

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

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8th Balkan conference in Informatics, BCI 2017

ISBN: (纸本)9781450352857

the proceedings contain 29 papers. the topics discussed include: machine learning-based approach to automatic POS tagging of Macedonian language;a novel model for measuring component-based system agility;information retrieval with reinforced word classes;towards dynamic cross-platform component-driven applications with girders elements framework;a time-critical mobile application based on ECG medical monitoring;reasons for and benefits of teaching internet of things basics in the eve of the 4th industrial revolution;a systematic mapping study of computer vision approaches based on deep learning and neural network;extended Tuple constraint type in relational and xml data model - definition and enforcement;structured component and connector communication;Albanian dynamic dactyls recognition using Kinect technology and DTW;using lexical resources for irony and sarcasm classification;extracting text keywords using WordNet;the invalidity of validating emotional multi-agent systems simulations;the new era of AI will revolutionize our wellness;proposed architecture to manage critical states predictions in IoT applications;a survey on access control mechanisms in e-commerce environments;and stimulating intellectual activity with adaptive environment (SMILE).

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learning through Utility Optimization in Regression Tasks 4

Learning Through Utility Optimization in Regression Tasks

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4th IEEE / ACM / ASA international conference on Data Science and Advanced Analytics (DSAA)

作者： Branco, Paula Torgo, Luis Ribeiro, Rita P. Frank, Eibe Pfahringer, Bernhard Rau, Markus Michael Univ Porto Fac Ciencias DCC LIAAD INESC TEC Porto Portugal Univ Waikato Dept Comp Sci Hamilton New Zealand Univ Auckland Dept Comp Sci Auckland New Zealand Ludwig Maximilians Univ Munchen Max Planck Inst Extraterr Phys Munich Germany

ISBN: (纸本)9781509050048

Accounting for misclassification costs is important in many practical applications of machine learning, and cost sensitive techniques for classification have been studied extensively. Utility-based learning provides a generalization of purely cost-based approaches that considers both costs and benefits, enabling application to domains with complex cost-benefit settings. However, there is little work on utility- or cost-based learning for regression. In this paper, we formally define the problem of utility-based regression and propose a strategy for maximizing the utility of regression models. We verify our findings in a large set of experiments that show the advantage of our proposal in a diverse set of domains, learning algorithms and cost/benefit settings.

关键词： Predictive models Electronic mail Measurement Surface treatment Optimization computer science

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Who Changed You? Obfuscator Identification for Android 4

Who Changed You? Obfuscator Identification for Android

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4th IEEE/ACM international conference on Mobile Software Engineering and Systems, MOBILESoft 2017

作者： Wang, Yan Rountev, Atanas Ohio State University ColumbusOH United States

ISBN: (纸本)9781538626696

Android developers commonly use app obfuscation to secure their apps and intellectual property. Although obfuscation provides protection, it presents an obstacle for a number of legitimate program analyses such as detection of app cloning and repackaging, malware detection, identification of third-party libraries, provenance analysis for digital forensics, and reverse engineering for test generation and performance analysis. If the obfuscator used to create an app can be identified, and if some details of the obfuscation process can be inferred, subsequent analyses can exploit this knowledge. thus, it is desirable to be able to automatically analyze a given app and determine (1) whether it was obfuscated, (2) which obfuscator was used, and (3) how the obfuscator was configured. We have developed novel techniques to identify the obfuscator of an Android app for several widely-used obfuscation tools and for a number of their configuration options. We define the obfuscator identification problem and propose a solution based on machine learning. To the best of our knowledge, this is the first work to formulate and solve this problem. We identify a feature vector that represents the characteristics of the obfuscated code. We then implement a tool that extracts this feature vector from Dalvik bytecode and uses it to identify the obfuscator provenance information. We evaluate the proposed approach on real-world Android apps obfuscated with different obfuscators, under several configurations. Our experiments indicate that the approach identifies the obfuscator with about 97% accuracy and recognizes the configuration with more than 90% accuracy. © 2017 IEEE.

关键词： application programs

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Combinatorial Transformation of learning Outcomes in the Affective & Cognitive Domain for its Impact on Students learning, Performance, Relationships and Behavior 8

Combinatorial Transformation of Learning Outcomes in the Aff...

引用

8th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2017

作者： Lakshmi, S. Nawas, G.M. Kadhar Department of Master of Computer Application Sona College of Technology Salem India

ISBN: (纸本)9781509010622

this exploratory research is an attempt to unveil the impact of the learning outcomes of a particular course on the student's attitude, behavior and performance skills. the objective and outcome of a curriculum or a course is always student centric, which is meant to escalate or map his skill set and technological knowhow onto a new dimension. the affirmative part of this work is that it is an attempt to discuss the various ways of defining the learning outcomes in such way to reflect their transformative dimensions on the adult's performance and the behavioral aspects i.e. in precise a combinatorial transformation both on the cognitive and affective domain. Initially the various techniques on defining the learning outcome by different institutions is discussed then a combinatorial transformation of these techniques for an effective and efficient implementation is discussed. © 2016 IEEE.

关键词： Students

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4th international Workshop on computer Assisted and Robotic Endoscopy, CARE 2017 and 6th international Workshop on Clinical Image-Based Procedures, CLIP 2017 held in Conjunction with 20th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2017

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4th international Workshop on computer Assisted and Robotic Endoscopy, CARE 2017 and 6th international Workshop on Clinical Image-Based Procedures, CLIP 2017 held in Conjunction with 20th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2017

ISBN: (纸本)9783319675428

the proceedings contain 17 papers. the special focus in this conference is on computer Assisted and Robotic Endoscopy. the topics include: Shape-Based pose estimation of robotic surgical instruments;3D endoscope system using asynchronously blinking grid pattern projection for HDR image synthesis;towards real-time polyp detection in colonoscopy videos;progressive hand-eye calibration for laparoscopic surgery navigation;learning camera pose from optical colonoscopy frames through deep convolutional neural network (CNN);motion vector for outlier elimination in feature matching and its application in SLAM based laparoscopic tracking;image-based smoke detection in laparoscopic videos;fully automatic detection of distal radius fractures from posteroanterior and lateral radiographs;automated characterization of pyelocalyceal anatomy using CT urograms to aid in management of kidney stones;local phase-based learning for needle detection and localization in 3D ultrasound;intracranial volume quantification from 3D photography;automatic near real-time evaluation of 3D ultrasound scan adequacy for developmental dysplasia of the hip;automatic sentinel lymph node localization in head and neck cancer using a coupled shape model algorithm;towards an automated segmentation of the ventro-intermediate thalamic nucleus;classification of confocal endomicroscopy patterns for diagnosis of lung cancer;automated classification for breast cancer histopathology images and hybrid tracking for improved registration of laparoscopic ultrasound and laparoscopic video for augmented reality.

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proceedings - 2017 4th international conference on Mathematics and computers in Sciences and in Industry, MCSI 2017

Proceedings - 2017 4th International Conference on Mathemati...

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4th international conference on Mathematics and computers in Sciences and in Industry, MCSI 2017

ISBN: (纸本)9781538628201

the proceedings contain 64 papers. the topics discussed include: potential of epidemiological imaging for image analysis and visualization applications: a brief review;expression recognition using sparse selection of log-Gabor facial features;DeepMBS: prediction of protein metal binding-site using deep learning networks;competition of motor controllers using a simplified robot leg: PID vs fuzzy logic;generator model extension for higher accuracy simulation of power system transients in OpenModelica;complex software system for data management and analysis of power distribution grids;optimization of neural network parameters using Taguchi robust design: application in plasma arc cutting process;experiments and modelling of explosive mixture formation in a closed space as a result of flammable gas leak;efficient location-aware scheduling of maintenance tasks in shop floors;assurance case for green IT applications: proof of compliance with power consumption claims;determining traffic levels in cities using Google Maps;from game to guidance: the innovative evaluation approach of the P4G simulation business game;the CICT IOU reference framework for stronger AMS system simulation in science and industry;efficiency investigation of BoF, SVT and pyramid match algorithms in practical recognition applications;local fractional operator on quadratic Riccati differential equation with variable coefficients;acceleration of grammatical evolution with multiple chromosome by using stochastic schemata exploiter;dual decomposition methods for nonlinear resource allocation problems in telecommunication networks;and adaptive sampling technique for computer network traffic parameters using a combination of fuzzy system and regression model.

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