Brazil

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.

IWSSIP 2020

EvolveDTree: Analyzing Student Dropout in Universities

higher education

genetic algorithm

dropout

machine learning

clustering

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

technical paper

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

This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.

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

Smart cities should be built on harmonious relationships with the environment, with rational measures to use environmental resources in many ways. Resources essential to human life such as water and energy must have special attention. With the advance of technologies use, human being becomes one of the main promoters of information and solutions for smart cities. Stream machine learning is an approach where data becomes available sequentially, called data stream, which adapts the prediction model with fixed window-sized data stream. The objective of this work is to analyze the effectiveness of buildings energy consumption using stream learning. To achieve this goal, MOA tool was used to evaluate the performance of the selected stream learning algorithms on an an open database. Batch algorithms were also used as testbed, to analyze the performance of stream algorithms related to them

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.
Such devices, as traffic lights and cameras, have been providing data to assist decisions to improve the functionality of cities.
Verifying and certifying the systems used for critical decisions is one of the challenges that still require some effort.
Reo takes advantage of a formal logic-based framework which makes it possible to reason, guarantee requirements, absence of described failures and provide certified software (i.e.\ proved-to-be-correct regarding the specification).
This work is tailored to show how to use Reo to model Smart Cities software interaction compiling to an automated logic model able to guarantee the required constraints in a certified system.

Modelling and Certifying Smart Cities in Reo Circuits

Wi-Fi networks have attracted a lot of attention due to its large deployment. In this work, we evaluate the performance of Multi-label machine learning methods to build classification models capable of providing wireless network access points (AP) occupancy predictions throughout the day. The APs association dataset used in our experiment was built using data from SCIFI network of Fluminense Federal University for a period of 6 months.

An Experimental Analysis for Detecting Wi-Fi Network Associations Using Multi-label Learning

One of the most critical issues in Smart Cities (SC) is how to use the energy system properly. The capacity to provide more detailed consumption information and more accurate consumer feedback is an essential step towards energy sustainability. On the end-user side, a system that supports the measurement of several points of consumption and provides visual information can help consumers reduce energy waste. On the other side, electric utilities can use a real-time measurement system to infer the energy consumption behavior, to find metering inconsistencies, to optimize the system, or to maintain the reliability of the supply system based on data information. This work presents a demand-side management Framework for smart cities which aims to provide integration between consumers and energy distribution utilities. %with prototype and proof of concept,
This Framework can help the electricity demand control, presenting real-time consumption data and presenting relevant data on their consumption points to consumers. A prototype was developed as a proof of concept.

Demand-Side Management Framework for Smart Cities

The development of techniques to automatic set up parameters in image processing methods based on variational approaches is cumbersome because the model sensitivity to parameters values is not known in general. In this paper, we address this issue through a data-driven inverse problem. Specifically, our methodology receives pairs (input image(s), desired result(s)) and seeks for the near optimum parameter vector through an inverse problem based on minimization scheme. The methodology is not restricted to a particular functional and it does not require large annotated data sets as input. Besides, methods based on partial differential equations (PDEs) can be also accommodated in our approach. We validate the methodology for calibrating parameters using as test-bed a variation of Mumford-Shah method. The obtained solutions using the parameters found in this paper are compared with literature results in order to show the efficiency of our technique.


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