The proceedings contain 13 papers. The special focus in this conference is on Personal Analytics and Privacy. The topics include: Churn prediction using dynamic RFM-augmented Node2vec;multi-scale community detection i...
ISBN:
(纸本)9783319719696
The proceedings contain 13 papers. The special focus in this conference is on Personal Analytics and Privacy. The topics include: Churn prediction using dynamic RFM-augmented Node2vec;multi-scale community detection in temporal networks using spectral graph wavelets;influence maximization-based event organization on social networks;from self-data to self-preferences: Towards preference elicitation in personal information management systems;assessing privacy risk in retail data;differential privacy and neural networks: A preliminary analysis;co-clustering for differentially private synthetic data generation;evaluating the impact of friends in predicting user’s availability in online social networks;movement behaviour recognition for water activities;automatic recognition of public transport trips from mobile device sensor data and transport infrastructure information;Guess the movie - Linking facebook pages to IMDb movies.
learning meaningful representations from network data is critical to ease the adoption of AI as a cornerstone to process network logs. Since a large portion of such data is textual, Natural Language Processing (NLP) a...
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ISBN:
(纸本)9781450398879
learning meaningful representations from network data is critical to ease the adoption of AI as a cornerstone to process network logs. Since a large portion of such data is textual, Natural Language Processing (NLP) appears as an obvious candidate to learn their representations. Indeed, the literature proposes impressive applications of NLP applied to textual network data. However, in the absence of labels, objectively evaluating the goodness of the learned representations is still an open problem. We call for a systematic adoption of domain-specific pretext tasks to select the best representation from network data. Relying on such tasks enables us to evaluate different representations on side machinelearning problems and, ultimately, unveiling the best candidate representations for the more interesting downstream tasks for which labels are scarce or unavailable. We apply pretext tasks in the analysis of logs collected from SSH honeypots. Here, a cumbersome downstream task is to cluster events that exhibit a similar attack pattern. We propose the following pipeline: first, we represent the input data using a classic NLP-based approach. Then, we design pretext tasks to objectively evaluate the representation goodness and to select the best one. Finally, we use the best representation to solve the unsupervised task, which uncovers interesting behaviours and attack patterns. All in all, our proposal can be generalized to other text-based network logs beyond honeypots.
We investigate a multi-task approach to similarity discriminant analysis, where we propose treating the estimation of the different class-conditional distributions of the pairwise similarities as multiple tasks. We sh...
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ISBN:
(纸本)9783642244704
We investigate a multi-task approach to similarity discriminant analysis, where we propose treating the estimation of the different class-conditional distributions of the pairwise similarities as multiple tasks. We show that regularizing these estimates together using a least-squares regularization weighted by a task-relatedness matrix can reduce the resulting maximum a posteriori classification errors. Results are given for benchmark data sets spanning a range of applications. In addition, we present a new application of similarity-based learning to analyzing the rhetoric of multiple insurgent groups in Iraq. We show how to produce the necessary task relatedness information from standard given training data, as well as how to derive task-relatedness information if given side information about the class relatedness.
To gain insight into regulatory mechanisms underlying the transcription process of gene expressions, we need to understand the co-expressed gene sets under common regulatory mechanisms. Though computational methods ha...
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ISBN:
(纸本)9781457709661
To gain insight into regulatory mechanisms underlying the transcription process of gene expressions, we need to understand the co-expressed gene sets under common regulatory mechanisms. Though computational methods have been developing to identify expression module, challenges still remain for cancer related gene expression profiling. In this paper, we have developed a method of data preprocessing and two different association rule mining approaches for discovering breast cancer regulatory mechanisms of gene module. Our data preprocessing task involved with two independent data sources: (a) a single breast cancer patient profile data file, (b) a candidate enhancer information data file. Using the integrated data, we also conducted four experiments of the association rule mining.
In this paper, we delve into the intricate relationship between technology, music, and success. Our research focuses on leveraging the capabilities of machinelearning algorithms to forecast the success and popularity...
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Knowledge Engineering is key to enable knowledge extraction, representation and reasoning, leading to better business insights and decisions. Current advances in machinelearning and new trends in AI are bringing a pl...
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ISBN:
(纸本)9781538692097
Knowledge Engineering is key to enable knowledge extraction, representation and reasoning, leading to better business insights and decisions. Current advances in machinelearning and new trends in AI are bringing a plethora of algorithms capable of performing advanced patternrecognition and data classification. The ability to link, to organize and to query the outputs of these algorithms as well as the ability to handle huge amounts of data and its multiple sources is crucial to maximize the potential of such advances, specially over large datasets. This paper presents challenges in the context of digital agriculture and our position in moving forward with these capabilities whilst using knowledge engineering techniques.
data clustering is one of the important datamining tasks. It is the process of grouping objects into clusters such that objects in the same clusters are more similar to each other than the objects in different cluste...
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Aerodynamic parameter estimation entails modelling force and moment coefficients as well as computing stability and control derivatives from flight data. This topic has been thoroughly researched utilizing traditional...
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The proceedings contain 12 papers. The special focus in this conference is on Explainable Artificial Intelligence in Healthcare. The topics include: Interpreting machinelearning Models for Survival Analysis: A S...
ISBN:
(纸本)9783031543029
The proceedings contain 12 papers. The special focus in this conference is on Explainable Artificial Intelligence in Healthcare. The topics include: Interpreting machinelearning Models for Survival Analysis: A study of Cutaneous Melanoma Using the SEER database;probExplainer: A Library for Unified Explainability of Probabilistic Models and an Application in Interneuron Classification;An Explainable AI Framework for Treatment Failure Model for Oncology Patients;explanations of Symbolic Reasoning to Effect Patient Persuasion and Education;explainable Artificial Intelligence in Response to the Failures of Musculoskeletal Disorder Rehabilitation;phenotypes vs Processes: Understanding the Progression of Complications in Type 2 Diabetes. A Case study;A data-Driven Framework for Improving Clinical Managements of Severe Paralytic Ileus in ICU: From Path Discovery, Model Generation to Validation;preface;understanding Prostate Cancer Care Process Using Process mining: A Case study;From Script to Application. A bupaR Integration into PMApp for Interactive Process mining Research.
Recently, support vector machine (SVM) has become a popular tool in patternrecognition. In developing a successful SVM classifier, the firststep is feature extraction. This paper proposes the application of independ...
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ISBN:
(纸本)0780393953
Recently, support vector machine (SVM) has become a popular tool in patternrecognition. In developing a successful SVM classifier, the firststep is feature extraction. This paper proposes the application of independent component analysis (ICA) to SVM for feature extraction. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the statlog heart disease data and satimage data, the experimental shows that SVM by feature extraction using ICA can perform better than that without feature extraction.
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