Brazil

In this presentation, the comparison between deep learning methods and feature extraction algorithms is presented. The principle of Grey-Level Co-occurrence Matrix (GLCM) and its modifications are used for our research. The main idea was to design a method for the description of combined features and textures. The texture classification process is carried out with the robust support vector machine classifier (SVM). We compare these feature extraction methods with proposed Convolutional Neural Networks (CNN). This proposed network contains 25 layers. Finally, the all evaluation and comparison of color texture retrieval results for all used methods are presented. The all feature extraction algorithms and proposed CNN have been tested on two different color texture datasets (Outex and Vistex datasets).

IWSSIP 2020

The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods

imageitextureianalysis

glcm

co-occurrence matrix

featurexextraction

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

technical paper

The 27th International Conference on Systems, Signals and Image Processing.

Efficient management of cloud traffic leads to resource sharing among clients. To meet clients’ performance restrictions, however, cloud providers adopt strict resource allocation that implies idle resources as clients’ bandwidth thresholds are over-provisioned. We propose a dynamic mechanism to provide resources on-demand in a multitenant data center. The mechanism relies on Q-learning multi-agent for managing each client access to the cloud resources. The proposed mechanism either measure the throughput continuously or map the CPU usage into bandwidth usage. We assess our mechanism in an emulated environment, in which a network controller provisions bandwidth. Results show that the mechanism reduces cloud idleness, allowing low-priority clients to use bandwidth while there is idle capacity. Our mechanism incurs low processing overhead, even when the number of states and action space grows, while the memory cost per agent increases, peaking at 300 kB for 200 states and actions. When mapping CPU usage into bandwidth usage, the mechanism achieves fair bandwidth sharing at the cost of a small increase on the convergence time to an optimal policy.

A Lightweight Reinforcement-Learning-based Mechanism for Bandwidth Provisioning on Multitenant Data Center

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Wind Turbine Fault Detection: A Semi-Supervised Learning Approach With Automatic Evolutionary Feature Selection

In order to minimize evasion losses, socioeconomic policies and programs were created to assist and support actions seeking to maximize the number of students that graduate in suitable program time. This work presents a methodology that aims to predict evasion by using machine learning. Our approach was able to classify student abandonment with an average f-score and accuracy results above 95%. Our approach combines a decision tree alongside a genetic algorithm and cluster stratified sampling.

EvolveDTree: Analyzing Student Dropout in Universities

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Off-the-Shelf Based System for Urban Environment Video Analytics

Dynamic smart cities models should provide enhanced descriptions on the corresponding systems, processes and services to facilitate their continuous improvement, learning and innovation, oriented to satisfy the increasing necessities of the involved educated citizens. This paper proposes a cognitive enterprise architecture framework (EAF) for smart cities, supported by a hybrid model based on a geographic information system (GIS) and associated graph-database (GDB). The corresponding framework allows managing and integrating: spatial and nonspatial data, dynamic and static data, numeric and semantic data, physical and conceptual data, information and knowledge, and associated relations of the corresponding city systems, environment and context. The augmented space model considered is based on augmented reality (AR) and augmented space (AS) principles, which includes augmented systems, channels, images, models and maps. This cognitive space can improve the governance, observability and controllability, interactions, visibility, transparency, monitoring, and review of the corresponding city services. The model supports the selected enterprise architecture framework for continuously aligning the diverse city stakeholder goals, technologies, ecosystems and roadmaps. Several application factors, at different levels, are analyzed to demonstrate the capabilities and possibilities of the proposed model.

The Augmented Space of a Smart City

Smart parking systems can manage real-time city resources, and geo-locally provides them to drivers. It involves on– and off–street parking management; that is why smart parking has been considered promising inception and a leading paradigm of a smart city. The design of a smart city ecosystem, especially on urban mobility, has a tremendous impact on resource status and city capacities. Although its high capacity to work for quality of experience and Business Intelligence, works about smart parking in the literature do not take advantage of this in its proposes. This work proposes the BEM framework that intends to take advantage of the car park Management and improve Quality Experience and Business Intelligence being an essential vector for the advancement of smart cities. A proof of concept was implemented, proving the solution viability.

BEM: A Framework based on Business Intelligence, Quality of Experience, and Car Park Management

Evaluation of Output Representations in Neural Network-based Trajectory Predictions Systems

Intelligent transport support systems have had a major impact on people’s urban mobility. In large urban centers, transportation services still need ways to optimize vehicle supply in certain areas, according to the demand in each of them. This work proposes a stream learning methodology for taxi demand prediction, that tackles concept drift detection on a real taxi data stream.

A Methodology for Taxi Demand Prediction Using Stream Learning

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Energy Consumption Prediction using Data Stream Learning for Commercial Buildings

Monitoring and using city devices to improve the life-quality of citizens is currently a challenge.
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The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods

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The Comparison of Color Texture Features Extraction based on 1D GLCM with Deep Learning Methods

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A Lightweight Reinforcement-Learning-based Mechanism for Bandwidth Provisioning on Multitenant Data Center

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Convolutional humor

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