Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Social Network Analysis

How to ... be a reflective practitioner.

IEEE Transactions on Pattern Analysis and Machine Intelligence

Presentacio sobre l'Oficina de Proteccio de Dades Personals de la UAB i la politica Open Science. Va formar part de la conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona

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General data protection regulation

The emergence and evolution of Internet of Things (IoT) offers great advantages to improve substantially the management over electricity consumption and distribution to the benefit of consumers, suppliers and grid operators. However, introducing IoT related devices and technologies in smart grids might lead to new security and privacy challenges. Though necessary technological innovations to ensure secure communication are being developed, more work is still required towards more secure standards for communication between devices and Smart Grids. This paper provides an overview about the security and privacy challenges of IoT applications in smart grids. Furthermore, we highlight and analyze some solutions and practices being used to cope with security and privacy requirements for IoT on deployment and management of smart grid. We address three types of challenge domains: customer domain, information and communication domain, and the grid domain.

Security and Privacy Considerations for IoT Application on Smart Grids: Survey and Research Challenges

This paper presents a semantically rich document representation model for automatically classifying financial documents into predefined categories utilizing deep learning. The model architecture consists of two main modules including document representation and document classification. In the first module, a document is enriched with semantics using background knowledge provided by an ontology and through the acquisition of its relevant terminology. Acquisition of terminology integrated to the ontology extends the capabilities of semantically rich document representations with an in depth-coverage of concepts, thereby capturing the whole conceptualization involved in documents. Semantically rich representations obtained from the first module will serve as input to the document classification module which aims at finding the most appropriate category for that document through deep learning. Three different deep learning networks each belonging to a different category of machine learning techniques for ontological document classification using a real-life ontology are used. Multiple simulations are carried out with various deep neural networks configurations, and our findings reveal that a three hidden layer feedforward network with 1024 neurons obtain the highest document classification performance on the INFUSE dataset. The performance in terms of F1 score is further increased by almost five percentage points to 78.10% for the same network configuration when the relevant terminology integrated to the ontology is applied to enrich document representation. Furthermore, we conducted a comparative performance evaluation using various state-of-the-art document representation approaches and classification techniques including shallow and conventional machine learning classifiers.

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The impact of deep learning on document classification using semantically rich representations

Machine-learning techniques have received popularity in the intrusion-detection systems in recent years. Moreover, the quality of datasets plays a crucial role in the development of a proper machine-learning approach. Therefore, an appropriate feature-selection method could be considered to be an influential factor in improving the quality of datasets, which leads to high-performance intrusion-detection systems. In this paper, a hybrid multi-objective approach is proposed to detect attacks in a network efficiently. Initially, a multi-objective genetic method (NSGAII), as well as an artificial neural network (ANN), are run simultaneously to extract feature subsets. We modified the NSGAII approach maintaining the diversity control in this evolutionary algorithm. Next, a Random Forest approach, as an ensemble method, is used to evaluate the efficiency of the feature subsets. Results of the experiments show that using the proposed framework leads to better outcomes, which could be considered to be promising results compared to the solutions found in the literature.

https://www.mdpi.com/2079-9292/9/4/577/pdf

A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest

We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.

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Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction

In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted from the lecture transcript. The keywords undergo a series of refinement process to select few but meaningful set of tags. We use word similarity measure using visual ness and word sense disambiguation to select the tags from candidate keywords. These tags are finally associated with video segments in which they appear based on timestamp. Each video segment represents a key idea or a topic. We also evaluated the objective keyword selection criteria to subjective test with some interesting results.

Sule Yildirim Yayilgan

Papers

Security and Privacy Considerations for IoT Application on Smart Grids: Survey and Research Challenges

The impact of deep learning on document classification using semantically rich representations

A Novel Hybrid IDS Based on Modified NSGAII-ANN and Random Forest

Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction

Semantic Tags for Lecture Videos