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检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是161-170 订阅

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ACM KDD AI4Cyber: The 1st workshop on Artificial Intelligence-enabled Cybersecurity Analytics 21

ACM KDD AI4Cyber: The 1st Workshop on Artificial Intelligenc...

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27th ACM SIGKDD international Conference on Knowledge Discovery and data mining (KDD)

作者： Samtani, Sagar Yang, Shanchieh Chen, Hsinchun Indiana Univ Dept Operat & Decis Technol Bloomington IN 47405 USA Rochester Inst Technol Dept Comp Engn Henrietta NY USA Univ Arizona Dept Management Informat Syst Tucson AZ 85721 USA

ISBN: (纸本)9781450383325

Despite significant contributions to various aspects of cybersecurity, cyber-attacks remain on the unfortunate rise. Increasingly, internationally recognized entities such as the National Science Foundation and National Science & Technology Council have noted Artificial Intelligence can help analyze billions of log files, Dark Web data, malware, and other data sources to help execute fundamental cybersecurity tasks. Our objective for the 1st workshop on Artificial Intelligence-enabled Cybersecurity Analytics (half-day;co-located with ACM KDD) was to gather academic and practitioners to contribute recent work pertaining to AI-enabled cybersecurity analytics. We composed an outstanding, inter-disciplinary Program Committee with significant expertise in various aspects of AI-enabled Cybersecurity Analytics to evaluate the submitted work. Significant contributions to the half-day workshop were made in the areas of CTI, vulnerability assessment, and malware analysis.

关键词： Cybersecurity artificial intelligence analytics machine learning

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The Vending Shopper Science Lab: Deep learning for Consumer Research 2nd

The Vending Shopper Science Lab: Deep Learning for Consumer ...

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2nd international workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international workshop eHealth in the Big data and Deep learning Era, e-BADLE 2019, international workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international Conference on Image Analysis and Processing, ICIAP 2019

作者： Allegrino, Fioravante Gabellini, Patrizia Di Bello, Luigi Contigiani, Marco Placidi, Valerio Sogeda S.R.L. Via Volturno 10 San Giovanni TeatinoChieti66020 Italy Grottini Lab S.R.L. Via Santa Maria in Potenza Porto Recanati62017 Italy

ISBN: (纸本)9783030307530

To understand human behavior, a fundamental aspect is the analysis of the face and movement. This aspect is particularly important in the context of sales, where to know the shopper also means to guide purchases. A major challenge for vending environment is to predict the shopper behavior, with the aim to influence and increase purchases. In this ambit, vending machine industry is actually an interesting and growing data-driven marketing area of research. In this context, the aim of this paper is to propose an innovative architecture that is able to integrate face and movement understanding in a common strategy for real time consumer modeling. The vending machine and the decision support system process multimedia data to smartly respond with dynamic pricing and product proposal to the particular shopper which is in front of a vending machine. The aim is to build an intelligent vending machine which in real time is able to suitably propose products to a labelled shopper. The results come from real environments vending lab with 30 locations and about 1 million consumers in Italy, and have the aim to demonstrate the good performances and high efficiency of our solution in recognizing the age and the gender of consumer and different interactions with the vending machine. © 2019, Springer Nature Switzerland AG.

关键词： Deep learning

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Proceedings of 1st Int. workshop on Big data, streams and Heterogeneous Source mining: Algorithms, Systems, Programming Models and Applications, BigMine-12 - Held in Conjunction with SIGKDD Conference

Proceedings of 1st Int. Workshop on Big Data, Streams and He...

引用

1st international workshop on Big data, streams and Heterogeneous Source mining: Algorithms, Systems, Programming Models and Applications, BigMine-12 - Held in Conjunction with SIGKDD Conference

ISBN: (纸本)9781450315470

The proceedings contain 16 papers. The topics discussed include: CluChunk: clustering large scale user-generated content incorporating chunklet information;parallel rough set based knowledge acquisition using mapreduce from big data;delta-SimRank computing on MapReduce;incrementally optimized decision tree for noisy big data;a density-based clustering structure mining algorithm for data streams;subscriber classification within telecom networks utilizing big data technologies and machine learning;accelerating minor allele frequency computation with graphics processors;online feature selection for mining big data;accelerating bayesian network parameter learning using hadoop and MapReduce;stream-dashboard: a framework for mining, tracking and validating clusters in a data stream;and a kernel fused perceptron for the online classification of large-scale data.

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An anti-compression watermarking scheme for vector map based on improved Douglas-Peucker algorithm

An anti-compression watermarking scheme for vector map based...

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1st international workshop on Education Technology and Computer Science, ETCS 2009

作者： Zhang, Zuoli Wang, Yaming Sun, Shusen Research Institute of Computer Vision and Pattern Recognition ZheJiang Sci.-Tech. University Hangzhou China School of Information and Electronics ZheJiang Sci.-Tech. University Hangzhou China

ISBN: (纸本)9780769535579

An anti-compression watermarking scheme is proposed. The scheme uses improved Dougleas-Peucker algorithm to compress redundant vertex data, which is important character of vector map, and embeds the watermark into compressed vertex data. Experimental results show that the anticompression performance of the watermarking algorithm is satisfied. © 2009 IEEE.

关键词： pattern matching

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CAM-stITCH: Trajectory Cavity stitching Method for stereo Vision Cameras in a Public Building 1

CAM-STITCH: Trajectory Cavity Stitching Method for Stereo Vi...

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1st international workshop on Challenges in Artificial Intelligence and machine learning for Internet of Things (AIChallengeIoT)

作者： Das, Anooshmita Raun, Emil stubbe Kolvig Kjaergaard, Mikkel Baun Univ Southern Denmark Odense Denmark

ISBN: (纸本)9781450370134

Transforming the sensor data from building systems into meaningful information could be used to build data-driven reference models for occupant behavior and actions using machine learning (ML) and Deep learning (DL) techniques. These models can also be implemented for different notable applications such as - controlling heating, ventilation and air conditioning (HVAC) parameters, automated lighting, safety and security, and efficient space utilization. In this paper, we deployed 3D stereo Vision Cameras from Xovis in a public building to capture trajectory data for multiple occupants. If there are inconsistencies/a gap in the field of view (FoV) of the cameras, continuous tracking of multiple occupants becomes quite challenging. If the inconsistencies persist, a single occupant is assigned multiple occupant-ID's, which is misleading and inaccurate for both occupancy count and tracking measurements within the monitored area. To mitigate and overcome the inconsistencies in the FoV of the deployed cameras, we propose the CAM-stITCH algorithm, which would enable multi-sensor stitching of the occupant trajectories using a variant of Recurrent Neural Network known as Long Short-Term Memory (LstM) model. The CAM-stITCH algorithm is evaluated by calculating the Root Mean Squared Error (RMSE) between the measured and predicted position coordinates (x, y), which resulted in an average RMSE of 9.53 centimeters on trajectory 1 and an average RMSE of 12.72 centimeters on trajectory 2 for multiple occupants. CAM-stITCH is also designed to overcome dynamic occlusions posed by the 3D stereo Vision Cameras. The proposed algorithm CAM-stITCH can ensure that the gathered data from the building is reliable for accurate trajectory measurements and can further assist in performing intelligent building operations. CCS CONCEPTS Human-centered computing -> Scenario-based design;Computing methodologies -> Activity recognition and understanding;machine learning;Computer systems organization -

关键词： Occupancy Presence Sensing Modalities pattern recognition Trajectory Cavity stitching Deep learning LstM

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CEUR workshop Proceedings

CEUR Workshop Proceedings

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1st international workshop on Advanced Analytics and learning on Temporal data, AALTD 2015 - workshop co-located with The European Conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 2015

The proceedings contain 17 papers. The topics discussed include: capturing time-structures in earth observation data with Gaussian processes;monitoring short term changes of malaria incidence in Uganda with Gaussian processes;bag-of-temporal-sift-words for time series classification;an exploratory analysis of multiple multivariate time series;symbolic representation of time series: a hierarchical coclustering formalization;temporal and frequential metric learning for time series;estimating dynamic graphical models from multivariate time-series data;sequential pattern mining on multimedia data;time series classification in dissimilarity spaces;temporal density extrapolation;fuzzy clustering of series using quantile autocovariances;and causality on longitudinal data: stable specification search in constrained structural equation modeling.

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Applying machine learning Technology for Weather Forecasting: A Case study of the Logistic Regression Model 24

Applying Machine Learning Technology for Weather Forecasting...

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1st international Conference on Image Processing machine learning and pattern recognition

作者： Shen, Kaiwei Beijing Inst Petrochem Technol China Big Data Management & Applicat Beijing Peoples R China

ISBN: (纸本)9798400707032

This study is focused on improving the dependability and precision of weather forecasting by employing the capabilities of Artificial Intelligence. Specifically, this study utilizes Logistic Regression and machine learning techniques to forecast weather, demonstrating the potential in optimizing weather-related activities and disaster management strategies. The study relies on comprehensive weather data observed over several years, sourced from Kaggle, and handles missing data and outliers during its pre-processing stages. The primary machine learning tool applied is Logistic Regression, followed by a stepwise feature selection to identify influential features for accurate weather prediction. The workflow also involves data collection, pre-processing, model building, training, and testing, with provisions for handling both numeric and categorical features along with imputations. The accuracy, precision, and recall of the prediction module are tested using appropriate statistical tools. The Logistic Regression model, upon implementation, demonstrated considerable accuracy, with an ability to predict rainy days and non-rainy days efficiently. An analytical approach was used to examine the model's sensitivity towards the removal of each feature, thereby ascertaining the relative importance of each. Critical predictors like 'Rainfall', 'Pressure9am', and 'WindGustSpeed' exhibited significant effects on the probability of rain. Overall, the use of Logistic Regression and machine learning techniques notably improved rain prediction, offering potential for further advancements in the field of weather forecasting.

关键词： Artificial intelligence data preprocessing Logistic regression model Weather forecasting

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DDDSM 2017 - 1st international workshop on Digital Disease Detection using Social Media, Proceedings of the workshop

DDDSM 2017 - 1st International Workshop on Digital Disease D...

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1st international workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th international Joint Conference on Natural Language Processing, IJCNLP 2017

ISBN: (纸本)9781948087070

The proceedings contain 9 papers. The topics discussed include: automatic detection of stance towards vaccination in online discussion forums;analyzing the causes of depressed mood from depression vulnerable individuals;multivariate linear regression of symptoms-related tweets for infectious gastroenteritis scale estimation;incorporating dependency trees improve identification of pregnant women on social media platforms;using a recurrent neural network model for classification of tweets conveyed influenza-related information;ZikaHack 2016: a digital disease detection competition;a method to generate a machine-labeled data for biomedical named entity recognition with various sub-domains;enhancing drug-drug interaction classification with corpus-level feature and classifier ensemble;and chemical-induced disease detection using invariance-based pattern learning model.

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1st international workshop on machine learning, Optimization, and Big data, MOD 2015

1st International Workshop on Machine Learning, Optimization...

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1st international workshop on machine learning, Optimization, and Big data, MOD 2015

ISBN: (纸本)9783319279251

The proceedings contain 32 papers. The special focus in this conference is on machine learning, Optimization, and Big data. The topics include: Automatic tuning of algorithms through sensitivity minimization;step down and step up statistical procedures for stock selection with sharp ratio;differentiating the multipoint expected improvement for optimal batch design;dynamic detection of transportation modes using keypoint prediction;effect of the dynamic topology on the performance of pso-2s algorithm for continuous optimization;heuristic for site-dependent truck and trailer routing problem with soft and hard time windows and split deliveries;cross-domain matrix factorization for multiple implicit-feedback domains;advanced metamodeling techniques applied to multidimensional applications with piecewise responses;alternating direction method of multipliers for regularized multiclass support vector machines;a single-facility manifold location routing problem with an application to supply chain management and robotics;an efficient many-core implementation for semi-supervised support vector machines;intent recognition in a simulated maritime multi-agent domain;an adaptive classification framework for unsupervised model updating in nonstationary environments;global optimization with sparse and local Gaussian process models;condense mixed convexity and optimization with an application in data service optimization;soc-based pattern recognition systems for non destructive testing;an efficient numerical approximation for the Monge-Kantorovich mass transfer problem;outlier detection in COX proportional hazards models based on the concordance c-index and data clustering by particle swarm optimization with the focal particles.

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The Epistle to Cangrande Through the Lens of Computational Authorship Verification 2nd

The Epistle to Cangrande Through the Lens of Computational A...

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作者： Corbara, Silvia Moreo, Alejandro Sebastiani, Fabrizio Tavoni, Mirko Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Pisa56124 Italy Dipartimento di Filologia Letteratura e Linguistica Università di Pisa Pisa56126 Italy

ISBN: (纸本)9783030307530

The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open. © 2019, Springer Nature Switzerland AG.

关键词： machine learning

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