Exploration of latent space of LOD2 GML dataset to identify similar buildings

Explainable numerical representations of otherwise complex datasets are vital as they extract relevant information, which is more convenient to analyze and study. These latent representations help identify clusters and outliers and assess the similarity between data points. The 3-D model of buildings is one dataset that possesses inherent complexity given the variety in footprint shape, distinct roof types, walls, height, and volume. Traditionally, comparing building shapes requires matching their known properties and shape metrics with each other. However, this requires obtaining a plethora of such properties to calculate similarity. In contrast, this study utilizes an autoencoder-based method to compute the shape information in a fixed-size vector form that can be compared and grouped with the help of distance metrics. This study uses "FoldingNet," a 3D autoencoder, to generate the latent representation of each building from the obtained LOD2 GML dataset of German cities and villages. The Cosine distance is calculated for each latent vector to determine the locations of similar buildings in the city. Further, a set of geospatial tools is utilized to iteratively find the geographical clusters of buildings with similar forms. The state of Brandenburg in Germany is taken as an example to test the methodology. The study introduces a novel approach to finding similar buildings and their geographical location, which can define the neighborhood's character, history, and social setting. Further, the process can be scaled to include multiple settlements where more regional insights can be made.Comment: 10 pages, 6 figure

A Hybrid Travel Recommender Model Based on Deep Level Autoencoder And Machine Learning Algorithms

This research investigates the application of autoencoders in processing travelogues written in the Malayalam language on Facebook. The main objective is to harness the capabilities of autoencoders to learn a compressed representation of the input data and employ it to train various machine learning models for enhanced accuracy and efficiency. The major challenge of unavailability of a benchmark dataset in the Malayalam language for the travel domain was overcome by employing NLP techniques on the unstructured, lengthy, imbalanced travelogues, applying some additional filtering methods, and the creation of an exclusive Part of Travel Tagger (POT Tagger) along with lookup dictionaries. As this pioneering work focuses on Malayalam travel reviews posted on social media, the model presents a valuable opportunity for extension to other low-resourced Indian languages. The study follows a two-step approach. Initially, an autoencoder neural network architecture is utilized to encode the travelogues into a lower-dimensional latent space representation. The encoder network adeptly captures crucial features and patterns within the data. The compressed representation obtained from the encoder is then fed into the decoder, which reconstructs the original travelogues. Subsequently, the encoded model is employed to train diverse machine learning models, including logistic regression, decision tree classifier, support vector machine (SVM), random forest classifier (RFC), K-nearest neighbours (KNN), stochastic gradient descent (SGD), and multilayer perceptron (MLP). By utilizing the encoded features as inputs, these models effectively learn from the concise representation of the Malayalam travelogues. Experimental results reveal that the trained machine learning models, using the encoded features, achieve higher accuracy rates compared to conventional approaches. This improvement demonstrates the effectiveness of autoencoders in capturing and representing vital characteristics of the Malayalam travelogues on Facebook. By leveraging capabilities of autoencoder model, we successfully learned a compressed representation of the input data, attaining an impressive validation accuracy of 95.84%. This finding highlights the potential of autoencoders to enhance the overall accuracy and efficiency of travel recommendation systems for Malayalam users on social media platforms. &nbsp

Sheaf Neural Networks for Graph-based Recommender Systems

Recent progress in Graph Neural Networks has resulted in wide adoption by many applications, including recommendation systems. The reason for Graph Neural Networks' superiority over other approaches is that many problems in recommendation systems can be naturally modeled as graphs, where nodes can be either users or items and edges represent preference relationships. In current Graph Neural Network approaches, nodes are represented with a static vector learned at training time. This static vector might only be suitable to capture some of the nuances of users or items they define. To overcome this limitation, we propose using a recently proposed model inspired by category theory: Sheaf Neural Networks. Sheaf Neural Networks, and its connected Laplacian, can address the previous problem by associating every node (and edge) with a vector space instead than a single vector. The vector space representation is richer and allows picking the proper representation at inference time. This approach can be generalized for different related tasks on graphs and achieves state-of-the-art performance in terms of F1-Score@N in collaborative filtering and Hits@20 in link prediction. For collaborative filtering, the approach is evaluated on the MovieLens 100K with a 5.1% improvement, on MovieLens 1M with a 5.4% improvement and on Book-Crossing with a 2.8% improvement, while for link prediction on the ogbl-ddi dataset with a 1.6% refinement with respect to the respective baselines.Comment: 9 pages, 7 figure

Recommendation system using autoencoders

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.This research has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D UnitsProject Scope: UIDB/00319/202

Dietary Behavior Based Food Recommender System Using Deep Learning and Clustering Techniques

Deep learning algorithms have been highly successful in various domains, including the development of collaborative filtering recommender systems. However, one of the challenges associated with deep learning-based collaborative filtering methods is that they require the involvement of all users to construct the latent representation of the input data, which is then utilized to predict the missing ratings of each user. This can be problematic as some users may have different preferences or interests, which may affect the accuracy of the prediction generation process. The research proposed a food recommender system, which tries to find users with similar dietary behavior and involve them in the recommendations generation process by combining clustering technique with denoising autoencoder to generate a rate prediction model. It is applied to “Food.com Recipes and Interactions” dataset. RMSE score was used to evaluate the performance of the proposed model which is 0.1927. It outperformed the other models that used autoencoder and denoising autoencoder without clustering where the RMSE values are 0. 4358 and 0.4354 consequently

Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems

With the exponentially increasing volume of online data, searching and finding required information have become an extensive and time-consuming task. Recommender Systems as a subclass of information retrieval and decision support systems by providing personalized suggestions helping users access what they need more efficiently. Among the different techniques for building a recommender system, Collaborative Filtering (CF) is the most popular and widespread approach. However, cold start and data sparsity are the fundamental challenges ahead of implementing an effective CF-based recommender. Recent successful developments in enhancing and implementing deep learning architectures motivated many studies to propose deep learning-based solutions for solving the recommenders' weak points. In this research, unlike the past similar works about using deep learning architectures in recommender systems that covered different techniques generally, we specifically provide a comprehensive review of deep learning-based collaborative filtering recommender systems. This in-depth filtering gives a clear overview of the level of popularity, gaps, and ignored areas on leveraging deep learning techniques to build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure

A Deep Learning-based Approach to Identifying and Mitigating Network Attacks Within SDN Environments Using Non-standard Data Sources

Modern society is increasingly dependent on computer networks, which are essential to delivering an increasing number of key services. With this increasing dependence, comes a corresponding increase in global traffic and users. One of the tools administrators are using to deal with this growth is Software Defined Networking (SDN). SDN changes the traditional distributed networking design to a more programmable centralised solution, based around the SDN controller. This allows administrators to respond more quickly to changing network conditions. However, this change in paradigm, along with the growing use of encryption can cause other issues. For many years, security administrators have used techniques such as deep packet inspection and signature analysis to detect malicious activity. These methods are becoming less common as artificial intelligence (AI) and deep learning technologies mature. AI and deep learning have advantages in being able to cope with 0-day attacks and being able to detect malicious activity despite the use of encryption and obfuscation techniques. However, SDN reduces the volume of data that is available for analysis with these machine learning techniques. Rather than packet information, SDN relies on flows, which are abstract representations of network activity. Security researchers have been slow to move to this new method of networking, in part because of this reduction in data, however doing so could have advantages in responding quickly to malicious activity. This research project seeks to provide a way to reconcile the contradiction apparent, by building a deep learning model that can achieve comparable results to other state-of-the-art models, while using 70% fewer features. This is achieved through the creation of new data from logs, as well as creation of a new risk-based sampling method to prioritise suspect flows for analysis, which can successfully prioritise over 90% of malicious flows from leading datasets. Additionally, provided is a mitigation method that can work with a SDN solution to automatically mitigate attacks after they are found, showcasing the advantages of closer integration with SDN