Conference Papers

Fast object detection by regression in Robot Soccer

Brandao S., Veloso M., Costeira J.P.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2012

Abstract:

Visual object detection in robot soccer is fundamental so the robots can act to accomplish their tasks. Current techniques rely on manually highly polished definitions of object models, that lead to accurate detection, but are quite often computationally inefficient. In this work, we contribute an efficient object detection through regression (ODR) method based on offline training. We build upon the observation that objects in robot soccer are of a well defined color and investigate an offline learning approach to model such objects. ODR consists of two main phases: (i) offline training, where the objects are automatically labeled offline by existing techniques, and (ii) online detection, where a given image is efficiently processed in real-time with the learned models. For each image, ODR determines whether the object is present and provides its position if so. We show comparing results with current techniques for precision and computational load.

FastStep: Scalable boolean matrix decomposition

Araujo M., Ribeiro P., Faloutsos C.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2016

Abstract:

Matrix Decomposition methods are applied to a wide range of tasks, such as data denoising, dimensionality reduction, co-clustering and community detection. However, in the presence of boolean inputs, common methods either do not scale or do not provide a boolean reconstruction, which results in high reconstruction error and low interpretability of the decomposition. We propose a novel step decomposition of boolean matrices in non-negative factors with boolean reconstruction. By formulating the problem using threshold operators and through suitable relaxation of this problem, we provide a scalable algorithm that can be applied to boolean matrices with millions of non-zero entries. We show that our method achieves significantly lower reconstruction error when compared to standard state of the art algorithms. We also show that the decomposition keeps its interpretability by analyzing communities in a flights dataset (where the matrix is interpreted as a graph in which nodes are airports) and in a movie-ratings dataset with 10 million non-zeros.

Fault Injection to Generate Failure Data for Failure Prediction: A Case Study

Campos J.; Costa E.

2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)

2020

Abstract:

Due to the complexity of modern software, identifying every fault before deployment is extremely difficult or even not possible. Such residual faults can ultimately lead to failures, often incurring considerable risks or costs. Online Failure Prediction (OFP) is a fault-tolerance technique that attempts to predict the occurrence of failures in the near future and thus prevent/mitigate their consequences. Combined with recent technological developments, Machine Learning (ML) has been successfully used to create predictive models for OFP. However, as failures are rare events, failure data are often not available for building accurate models. Although fault injection has been accepted as a viable solution to generate realistic failure data, fault injectors are difficult to implement/update and thus research on Operating System (OS)-level OFP has become stale, with most works using data from outdated OSs. In this paper, we conduct a comprehensive fault injection campaign on an up-to-date Linux kernel and thoroughly study its behavior in the presence of faults. We then transform the data to explore and assess the predictive performance of various ML techniques for OFP. Finally, we study the influence of different OFP parameters (i.e., lead-time, prediction-window) and compare the results with existing related work. Results suggest that the various failures observed can be grouped into categories that can then be accurately predicted and distinguished by diverse ML models.

Feature and Region Selection for Visual Learning

Zhao J., Wang L., Cabral R., De La Torre F.

IEEE Transactions on Image Processing

2016

Abstract:

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ 2 and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Feature extraction in densely sensed environments

Vahabi M., Gupta V., Albano M., Tovar E.

Proceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2014

2014

Abstract:

With the reduction in size and cost of sensor nodes, dense sensor networks are becoming more popular in a wide-range of applications. Many such applications with dense deployments are geared towards finding various patterns or features such as peaks, boundaries and shapes in the spread of sensed physical quantities over an area. However, collecting all the data from individual sensor nodes can be impractical both in terms of timing requirements and the overall resource consumption. Hence, it is imperative to devise distributed information processing techniques that can help in identifying such features with a high accuracy and within certain time constraints. In this paper, we exploit the prioritized channel-access mechanism of dominance-based Medium Access Control (MAC) protocols to efficiently obtain exterma of the sensed quantities. We show how by the use of simple transforms that sensor nodes employ on local data it is also possible to efficiently extract certain features such as local extrema and boundaries of events. Using these transformations, we show through extensive evaluations that our proposed technique is fast and efficient at retrieving only sensor data point with the most constructive information, independent of the number of sensor nodes in the network.

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

Vahabi M., Gupta V., Albano M., Rangarajan R., Tovar E.

International Journal of Distributed Sensor Networks

2015

Abstract:

The vision of the Internet of Things (IoT) includes large and dense deployment of interconnected smart sensing and monitoring devices. This vast deployment necessitates collection and processing of large volume of measurement data. However, collecting all the measured data from individual devices on such a scale may be impractical and time-consuming. Moreover, processing these measurements requires complex algorithms to extract useful information. Thus, it becomes imperative to devise distributed information processing mechanisms that identify application-specific features in a timely manner and with low overhead. In this paper, we present a feature extraction mechanism for dense networks that takes advantage of dominance-based medium access control (MAC) protocols to (i) efficiently obtain global extrema of the sensed quantities, (ii) extract local extrema, and (iii) detect the boundaries of events, by using simple transforms that nodes employ on their local data. We extend our results for a large dense network with multiple broadcast domains (MBD). We discuss and compare two approaches for addressing the challenges with MBD and we show through extensive evaluations that our proposed distributed MBD approach is fast and efficient at retrieving the most valuable measurements, independent of the number sensor nodes in the network.

Filter design with secrecy constraints: The multiple-input multiple-output Gaussian wiretap channel with zero forcing receive filters

Reboredo H., Prabhu V., Rodrigues M.R.D., Xavier J.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

2011

Abstract:

This paper considers the problem of filter design with secrecy constraints, where two legitimate parties (Alice and Bob) communicate in the presence of an eavesdropper (Eve), over a Gaussian multiple-input multiple-output (MIMO) wiretap channel. This problem involves the design of transmit and receive filters which minimize the mean-square error (MSE) between the legitimate parties, whilst assuring that the eavesdropper MSE remains above a certain level. We characterize the form of the optimal transmit filter when both the legitimate receiver and the eavesdropper employ Zero-Forcing (ZF) filters. By capitalizing on the dual problem, we also show that the original matrix optimization problem can be reduced to a simple scalar optimization problem, whose solution can be readily computed by employing a simple bisection method. Numerical results illustrate the main conclusions.

Finding function in form: Compositional character models for open vocabulary word representation

Ling W., Luis T., Marujo L., Astudillo R.F., Amir S., Dyer C., Black A.W., Trancoso I.

Conference Proceedings - EMNLP 2015: Conference on Empirical Methods in Natural Language Processing

2015

Abstract:

We introduce a model for constructing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word representation models that have independent vectors for each word type, our model requires only a single vector per character type and a fixed set of parameters for the compositional model. Despite the compactness of this model and, more importantly, the arbitrary nature of the form–function relationship in language, our “composed” word representations yield state-of-the-art results in language modeling and part-of-speech tagging. Benefits over traditional baselines are particularly pronounced in morphologically rich languages (e.g., Turkish).

Findings of the WMT 2020 Shared Task on Chat Translation

Farajian M., Lopes A., Martins A., Maruf S., Haffari G.

Proceedings of the Fifth Conference on Machine Translation

2020

Abstract:

We report the results of the first edition of the WMT shared task on chat translation. The task consisted of translating bilingual conversational text, in particular customer support chats for the English-German language pair (English agent, German customer). This task varies from the other translation shared tasks, i.e. news and biomedical, mainly due to the fact that the conversations are bilingual, less planned, more informal, and often ungrammatical. Furthermore, such conversations are usually characterized by shorter and simpler sentences and contain more pronouns. We received 14 submissions from 6 participating teams, all of them covering both directions, i.e. En->De for agent utterances and De->En for customer messages. We used automatic metrics (BLEU and TER) for evaluating the translations of both agent and customer messages and human document-level direct assessments (DDA) to evaluate the agent translations.