An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version

A Computer Vision System to Localize and Classify Wastes on the Streets

Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available, we built our acquisition system mounted on a vehicle. Collected images containing different types of wastes. These images are then annotated for training and benchmarking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds

Стійка технологія переробки відходів електричного та електронного обладнання

Об’єкт досліджень: технологічні основи «переробки відходів електричного та електронного обладнання». Предмет досліджень: механізм піролізу, отримання рідкого палива, подрібнення друкованих плат після піролізу, вібраційний млин та його сили, які впливають на подрібнення. Вихідні дані для проведення роботи: характеристики друкованих плат та їх переробка у світі. Наукова новизна: відокремлення металевої фракції від наповнювачів, за рахунок ковкості металевої фракції при подрібнені та подальшому розділення при грохочені. Практична цінність: поліпшення екологічної складової за рахунок втілення нових технологій в сектор управління відходів та рециклінгу вже добутих мінералів. Дипломна робота написана англійською мовою та надалі буде захищена в ТУ "Фрайберзька гірнича академія"

The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism

The INTERSPEECH 2013 Computational Paralinguistics Challenge provides for the first time a unified test-bed for Social Signals such as laughter in speech. It further introduces conflict in group discussions as new tasks and picks up on autism and its manifestations in speech. Finally, emotion is revisited as task, albeit with a broader ranger of overall twelve emotional states. In this paper, we describe these four Sub-Challenges, Challenge conditions, baselines, and a new feature set by the openSMILE toolkit, provided to the participants. \em Bj\"orn Schuller$^1$, Stefan Steidl$^2$, Anton Batliner$^1$, Alessandro Vinciarelli$^{3,4}$, Klaus Scherer$^5$}\\ {\em Fabien Ringeval$^6$, Mohamed Chetouani$^7$, Felix Weninger$^1$, Florian Eyben$^1$, Erik Marchi$^1$, }\\ {\em Hugues Salamin$^3$, Anna Polychroniou$^3$, Fabio Valente$^4$, Samuel Kim$^4$

Reforming Buffalo\u27s Tax Foreclosure Process

The City of Buffalo holds an annual foreclosure auction to collect on delinquent taxes and fees owed by its residents. This is a way for the City to raise revenue that would otherwise go unpaid and for Buffalo citizens to buy buildings and lots at bargain prices. But the foreclosure process is imposing a high cost upon some of Buffalo’s most vulnerable citizens, creating an unnecessary burden on people trying to stay in their homes, and adding to the already existing epidemic of housing abandonment and blight

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, June 201

A Multi-Core Solver for Parity Games

We describe a parallel algorithm for solving parity games,\ud with applications in, e.g., modal mu-calculus model\ud checking with arbitrary alternations, and (branching) bisimulation\ud checking. The algorithm is based on Jurdzinski's Small Progress\ud Measures. Actually, this is a class of algorithms, depending on\ud a selection heuristics.\ud \ud Our algorithm operates lock-free, and mostly wait-free (except for\ud infrequent termination detection), and thus allows maximum\ud parallelism. Additionally, we conserve memory by avoiding storage\ud of predecessor edges for the parity graph through strictly\ud forward-looking heuristics.\ud \ud We evaluate our multi-core implementation's behaviour on parity games\ud obtained from mu-calculus model checking problems for a set of\ud communication protocols, randomly generated problem instances, and\ud parametric problem instances from the literature.\ud \u