|
|
Client-side execution of PHP applications compiled to .NET
Husák, Tomáš ; Zavoral, Filip (advisor) ; Peška, Ladislav (referee)
Peachpie is a modern compiler enabling the compilation of PHP scripts into .NET. Blazor is a new part of the ASP.NET platform offering the usage of C# on a client side due to a new web standard, WebAssembly. This thesis explores a new approach of execution based on the integration between Peachpie and Blazor. PHP scripts can be referenced from Blazor and evaluated, but there are many steps to make it work. We analyzed these steps and designed a solution for inserting these scripts to Razor pages, navigating, and evaluating them. It results in a library containing helper classes to enable PHP programmers to move the PHP execution to a client side with the advantages of the Blazor environment. However, the difference of used technologies limits usage possibilities, which are shown by two benchmarks. 1
|
|
|
Link Prediction in Inferred Social Networks
Měkota, Ondřej ; Holubová, Irena (advisor) ; Peška, Ladislav (referee)
Social networks can be helpful for the analysis of behaviour of people. An existing social network is rarely available, and its nodes and edges have to be inferred from not necessarily graph data. Link prediction can be used to either correct inaccuracies or to forecast links about to appear in the future. In this work, we study the prediction of miss- ing links in a social network inferred from real-world bank data. We review and compare both verified and modern approaches to link prediction. Following the advancements of deep learning in recent years, we primarily focus on graph neural networks, and their ability to scale to large networks. We propose an adjustment to an existing graph neural network method and show that its performance is either comparable with or outperform- ing the original method. The comparison is performed on two social networks inferred from the same data. We show that it is relatively hard to outperform the verified link prediction methods with graph neural networks. 1
|
|
|
Generating synthetic data for an assembly of police lineups
Dokoupil, Patrik ; Peška, Ladislav (advisor) ; Škoda, Petr (referee)
Eyewitness identification plays an important role during criminal proceedings and may lead to prosecution and conviction of a suspect. One of the methods of eyewitness identification is a police photo lineup when a collection of photographs is presented to the witness in order to identify the perpetrator of the crime. In the lineup, there is typically at most one photograph (typically exactly one) of the suspect and the remaining photographs are the so-called fillers, i.e. photographs of innocent people. Positive identification of the suspect by the witness may result in charge or conviction of the suspect. Assembly of the lineup is a challenging and tedious problem, because the wrong selection of the fillers may end up in a biased lineup, where the suspect will stand out from the fillers and would be easily identifiable even by a highly uncertain witness. The reason why it is tedious is due to the fact that this process is still done manually or only semi-automatically. This thesis tries to solve both issues by proposing a model that will be capable of generating synthetic data, together with an application that will allow users to obtain the fillers for a given suspect's photograph. 1
|
|
|
Nutrition assistant
Maďar, Matúš ; Kopecký, Michal (advisor) ; Peška, Ladislav (referee)
Title: Nutrition assistant Author: Matúš Maďar Department: Department of Software Engineering Supervisor: RNDr. Michal Kopecký, Ph.D. Abstract: This thesis explores and implements the options for adaptive generation of meal plans. We implement our approach as a mobile application for the Android operating system. The application aims to help the user to achieve the desired weight loss or weight gain results by custom made recommendations and, at the same time, allowing small deviations from the meal plan. In case of this deviation from the proposed meal plan, the application adapts to such cases with respect to user preference. The additional feature of the application is built-in support for exploring the meal options in nearby restaurants. Our application design is modular. The implementation of the user interface is separate from the logic behind the meal plan generation. The module for meal plan generation can also be easily extended to support new meal plan generation approaches. Also, the data we currently use can be easily replaced by any other data source for food information. Keywords: Android, Nutrition, Heuristic
|
|
|
Predicting the Behaviour of Streaming Services Users
Stachura, Šimon ; Pilát, Martin (advisor) ; Peška, Ladislav (referee)
Title: Predicting the Behaviour of Streaming Services Users Autor: Bc. Šimon Stachura Department: Katedra teoretické informatiky a matematické logiky Supervisor: Mgr. Martin Pilát, Ph. D. Abstract: Streaming services are one of the phenomena of the last decade, allowing online legal access to media for a large number of users. The media is usually provided to the users as an automatically generated sequence, created by some form of a recommender system. The preferences of individual users are usually estimated based on historical data from their previous usage of the service. Skipping behaviour on individual elements of the generated sequence (songs, for instance) is one of the basic signals expressing these preferences. Goal of this work is to predict users' behaviour based on their previous experience with the service. We chose a large dataset consisting of real data from usage of the Spotify service, and considered options for preprocessing and representing them. We decided to use recurrent neural networks with the Encoder-Decoder architecture for modelling the behaviour of the users. These models encode the information about users' historical behaviour into a compact inner representation of the session, and based on that representation they generate expected behaviour in the next time steps. We created...
|
|
|
Optimizing the deployment of cloud applications for multiple QoS parameters
Khalyeyev, Danylo ; Hnětynka, Petr (advisor) ; Peška, Ladislav (referee)
Guaranteeing Quality of Service (QoS) in an (edge-)cloud environment is one of the biggest open problems in the field of cloud computing. Currently, deploy- ment of cloud applications is managed by cloud orchestration systems, such as Kubernetes. These systems make deployment of applications in cloud easier than ever, offering their users the benefits of availability, scalability and resilience. However, at the moment they are not capable of optimizing the deployment of cloud applications with respect to performance QoS metrics, such as response time and throughput. The thesis proposes an approach that provides probabilistic guarantees on the performance QoS metrics in an (edge-)cloud environment. The approach is based on assessing the performance of cloud applications and subsequently controlling their deployment in a way that the applications are deployed only in the environ- ments in which their performance does not violate their QoS requirements. The thesis also presents a proof-of-concept implementation of that approach. The im- plementation verifies the effectiveness of the approach and will serve for further research.
|
|
|
Deep Learning For Implicit Feedback-based Recommender Systems
Yöş, Kaan ; Peška, Ladislav (advisor) ; Balcar, Štěpán (referee)
The research aims to focus on Recurrent Neural Networks (RNN) and its application to the session-aware recommendations empowered by implicit user feedback and content-based metadata. To investigate the promising architecture of RNN, we implement seven different models utilizing various types of implicit feedback and content information. Our results showed that using RNN with complex implicit feedback increases the next-item prediction comparing the baseline models like Cosine Similarity, Doc2Vec, and Item2Vec.
|
|
|
Detekce střihů a vyhledávání známých scén ve videu s pomocí metod hlubokého učení
Souček, Tomáš ; Lokoč, Jakub (advisor) ; Peška, Ladislav (referee)
Video retrieval represents a challenging problem with many caveats and sub-problems. This thesis focuses on two of these sub-problems, namely shot transition detection and text-based search. In the case of shot detection, many solutions have been proposed over the last decades. Recently, deep learning-based approaches improved the accuracy of shot transition detection using 3D convolutional architectures and artificially created training data, but one hundred percent accuracy is still an unreachable ideal. In this thesis we present a deep network for shot transition detection TransNet V2 that reaches state-of- the-art performance on respected benchmarks. In the second case of text-based search, deep learning models projecting textual query and video frames into a joint space proved to be effective for text-based video retrieval. We investigate these query representation learning models in a setting of known-item search and propose improvements for the text encoding part of the model. 1
|
|
|
User Identity Verification Based on Behavioral Characteristics
Kuchyňová, Karolína ; Skopal, Tomáš (advisor) ; Peška, Ladislav (referee)
Verifying the identity of a user logged into a secure system is an important task in the field of information security. In addition to a password, it may be appropriate to include behavioral biometrics in the authentication process. The biometrics-based system monitors the user's behavior, compares it with his usual actions, and can thus point out suspicious inconsistencies. The goal of this thesis is to explore the possibility of creating a user identity verification model based on his behavior (usage of mouse and keyboard) in a web application. The work includes creation of a new keystroke and mouse dynamics dataset. The main part of the thesis provides the analysis of features (user characteristics) which can be extracted from the obtained data. Subsequently, we report the authentication accuracy rates achieved by basic machine learning models using the selected set of features. 1
|
|
|
Recommender systems for fashion outfits
Nepožitek, David ; Peška, Ladislav (advisor) ; Skopal, Tomáš (referee)
Outfit recommendation is a task of suggesting fashion products that complement a given set of garments. Traditional recommender systems rely primarily on similarities between items or users; however, that is not sufficient for a recommendation of comple- mentary products. Thus, outfit recommendation systems use machine learning techniques to learn more subtle relations between items. In this thesis, we explore the possibility of employing recent natural language processing approaches in outfit recommendation. We propose a novel architecture based on the Transformer, and we evaluate the model on standard datasets. We show that our approach is capable of learning some relations between items. However, its performance does not exceed the state-of-the-art models. 1
|
guest ::
