pinecone/core-2020-05-10-deduplication huggingface dataset:
Psychophysics, Gestalts and Games

Many psychophysical studies are dedicated to the evaluation of the human gestalt
detection on dot or Gabor patterns, and to model its dependence on the pattern and background
parameters. Nevertheless, even for these constrained percepts, psychophysics have
not yet reached the challenging prediction stage, where human detection would be quantitatively
predicted by a (generic) model. On the other hand, Computer Vision has attempted
at defining automatic detection thresholds. This chapter sketches a procedure to confront
these two methodologies inspired in gestaltism.
Using a computational quantitative version of the non-accidentalness principle, we raise the
possibility that the psychophysical and the (older) gestaltist setups, both applicable on dot
or Gabor patterns, find a useful complement in a Turing test. In our perceptual Turing test,
human performance is compared by the scientist to the detection result given by a computer.
This confrontation permits to revive the abandoned method of gestaltic games. We sketch
the elaboration of such a game, where the subjects of the experiment are confronted to an
alignment detection algorithm, and are invited to draw examples that will fool it. We show
that in that way a more precise definition of the alignment gestalt and of its computational
formulation seems to emerge.
Detection algorithms might also be relevant to more classic psychophysical setups, where
they can again play the role of a Turing test. To a visual experiment where subjects were
invited to detect alignments in Gabor patterns, we associated a single function measuring
the alignment detectability in the form of a number of false alarms (NFA). The first results
indicate that the values of the NFA, as a function of all simulation parameters, are highly
correlated to the human detection. This fact, that we intend to support by further experiments,
might end up confirming that human alignment detection is the result of a single
mechanism
Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications

Building intelligent tools for searching, indexing and retrieval applications is needed to congregate the rapidly increasing amount of visual data. This raised the need for building and maintaining ontologies and knowledgebases to support textual semantic representation of visual contents, which is an important block in these applications. This paper proposes a commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. This domain-independent knowledge is provided at different levels of semantics by a fully automated engine that analyses, fuses and integrates previous commonsense knowledgebases. This knowledgebase satisfies the levels of semantic by adding two new levels: temporal event scenarios and psycholinguistic understanding. Statistical properties and an experiment evaluation, show coherency and effectiveness of the proposed knowledgebase in providing the knowledge needed for wide-domain visual applications
Assessment of Financial Risk Prediction Models with Multi-criteria Decision Making Methods

A wide range of classification models have been explored for financial risk prediction, but conclusions on which technique behaves better may vary when different performance evaluation measures are employed. Accordingly, this paper proposes the use of multiple criteria decision making tools in order to give a ranking of algorithms. More specifically, the selection of the most appropriate credit risk prediction method is here modeled as a multi-criteria decision making problem that involves a number of performance measures (criteria) and classification techniques (alternatives). An empirical study is carried out to evaluate the performance of ten algorithms over six real-life credit risk data sets. The results reveal that the use of a unique performance measure may lead to unreliable conclusions, whereas this situation can be overcome by the application of multi-criteria decision making techniques
Improving Risk Predictions by Preprocessing Imbalanced Credit Data

Imbalanced credit data sets refer to databases in which the class of defaulters is heavily under-represented in comparison to the class of non-defaulters. This is a very common situation in real-life credit scoring applications, but it has still received little attention. This paper investigates whether data resampling can be used to improve the performance of learners built from imbalanced credit data sets, and whether the effectiveness of resampling is related to the type of classifier. Experimental results demonstrate that learning with the resampled sets consistently outperforms the use of the original imbalanced credit data, independently of the classifier used
Energy-efficient virtual machine placement in data centers by genetic algorithm

Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of data centers. Virtual machine placement is the key in the server consolidation. In the past few years, many approaches to the virtual machine placement have been proposed. However, existing virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center only, but do not consider the energy consumption in communication network in the data center. However, the energy consumption in the communication network in a data center is not trivial, and therefore should be considered in the virtual machine placement in order to make the data center more energy-efficient. In this paper, we propose a genetic algorithm for a new virtual machine placement problem that considers the energy consumption in both the servers and the communication network in the data center. Experimental results show that the genetic algorithm performs well when tackling test problems of different kinds, and scales up well when the problem size increases
Evaluating SPAN incremental learning for handwritten digit recognition

In a previous work [12, 11], the authors proposed SPAN: a learning algorithm based on temporal coding for Spiking Neural Network (SNN). The algorithm trains a neuron to associate target spike patterns to input spatio-temporal spike patterns. In this paper we present the details of experiment to evaluate the feasibility of SPAN learning on a real-world dataset: classifying images of handwritten digits. As spike encoding is an important issue in using SNN for practical applications, we discuss few methods for image conversion to spike patterns. The experiment yields encouraging results to consider the SPAN learning for practical temporal pattern recognition applications
Biomethanation potential of biological and other wastes

Anaerobic technology has been traditionally applied for the treatment of carbon rich wastewater and organic residues. Anaerobic processes can be fully integrated in the biobased economy concept for resource recovery. After a brief introduction about applications of anaerobic processes to industrial wastewater treatment, agriculture feedstock and organic fraction of municipal solid waste, the position of anaerobic processes in biorefinery concepts is presented. Integration of anaerobic digestion with these processes can help in the maximisation of the economic value of the biomass used, while reducing the waste streams produced and mitigating greenhouse gases emissions. Besides the integration of biogas in the existing full-scale bioethanol and biodiesel production processes, the potential applications of biogas in the second generation lignocellulosic, algae and syngas-based biorefinery platforms are discussed
At the eye of the cyclone: the Greek crisis in global media

Using discourse analysis, this paper offers an in-depth investigation of the discourse of key European and international newspapers on the Greek economic crisis. The aim is to analyse the way in which the issue of Greek economic crisis emerged in the public discourse of different countries and global regions, as well as to assess the impact that this process had on how Greece is viewed ‘from the outside’. The findings point to the generation and consolidation of very negative attitudes towards Greece. During the 14-month period of examination, Greece evolves from an ‘object of critique’ to a ‘negative reference point’. In some sense, Greece is (re)constructed in the international press as the (corrupted) other of the (rational) western society.
Parameterized domination in circle graphs.

A circle graph is the intersection graph of a set of chords in a circle. Keil [Discrete Applied Mathematics, 42(1):51-63, 1993] proved that Dominating Set, Connected Dominating Set, and Total Dominating Set are NP-complete in circle graphs. To the best of our knowledge, nothing was known about the parameterized complexity of these problems in circle graphs. In this paper we prove the following results, which contribute in this direction:\ud
Dominating Set, Independent Dominating Set, Connected Dominating Set, Total Dominating Set, and Acyclic Dominating Set are W[1]-hard in circle graphs, parameterized by the size of the solution.\ud
Whereas both Connected Dominating Set and Acyclic Dominating Set are W[1]-hard in circle graphs, it turns out that Connected Acyclic Dominating Set is polynomial-time solvable in circle graphs.\ud
If T is a given tree, deciding whether a circle graph has a dominating set isomorphic to T is NP-complete when T is in the input, and FPT when parameterized by |V(T)|. We prove that the FPT algorithm is subexponential
Measuring quality, reputation and trust in online communities

In the Internet era the information overload and the challenge to detect
quality content has raised the issue of how to rank both resources and users in
online communities. In this paper we develop a general ranking method that can
simultaneously evaluate users' reputation and objects' quality in an iterative
procedure, and that exploits the trust relationships and social acquaintances
of users as an additional source of information. We test our method on two real
online communities, the EconoPhysics forum and the Last.fm music catalogue, and
determine how different variants of the algorithm influence the resultant
ranking. We show the benefits of considering trust relationships, and define
the form of the algorithm better apt to common situations
Modeling a teacher in a tutorial-like system using Learning Automata

Published version of a chapter in the book: Transactions on Computational Collective Intelligence VIII. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-34645-3_2The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial- like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students. In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately. To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students
A genetic algorithm for berth allocation and quay crane assignment

13th Ibero-American Conference on Artificial Intelligence. IBERAMIA 2012, Cartagena de Indias, Colombia. 13-16 November 2012The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-34654-5_61Container terminals are facilities where cargo containers are transshipped between different transport vehicles, for onward transportation. They are open systems that carry out a large number of different combinatorial problems that can be solved by means of Artificial Intelligence techniques. In this work, we focus our attention on scheduling a number of incoming vessels by assigning to each a berthing position, a mooring time and a number of Quay Cranes. This problem is known as the Berthing Allocation and Quay Crane Assignment problem. To formulate the problem, we first propose a mixed integer linear programming model to minimize the total weighted service time of the incoming vessels. Then, a meta-heuristic algorithm (Genetic Algorithm (GA)) is presented for solving the proposed problem. Computational experiments are performed to evaluate the effectiveness and efficiency of the proposed metho
An innovative outlier detection method using localized thresholds

In this paper, we investigate the research problem of specifying the thresholds to effectively detect outliers and propose an innovative technique that leverages multiple localized thresholds for detecting outliers. Our technique is able to overcome a major limitation of the existing outlier detection approaches that use a single universal threshold to distinguish outliers from normal data. The experiments conducted demonstrate that our technique is able to achieve a better detection effectiveness than the traditional approaches.
A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI Decoding

International audienceFunctional Magnetic Resonance Imaging (fMRI) provides a unique opportunity to study brain functional architecture, while being minimally invasive. Reverse inference, a.k.a. decoding, is a recent statistical analysis approach that has been used with success for deciphering activity patterns that are thought to fit the neuroscientific concept of population coding. Decoding relies on the selection of brain regions in which the observed activity is predictive of certain cognitive tasks. The accuracy of such a procedure is quantified by the prediction of the behavioral variable of interest - the target. In this paper, we discuss the optimality of decoding methods in two different settings, namely intra- and inter-subject kind of decoding. While inter-subject prediction aims at finding predictive regions that are stable across subjects, it is plagued by the additional inter-subject variability (lack of voxel-to-voxel correspondence), so that the best suited prediction algorithms used in reverse inference may not be the same in both cases. We benchmark different prediction algorithms in both intra- and inter-subjects analysis, and we show that using spatial regularization improves reverse inference in the challenging context of inter-subject prediction. Moreover, we also study the different maps of weights, and show that methods with similar accuracy may yield maps with very different spatial layout of the predictive regions
Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify

International audienceThe prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accu- racy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected rele- vant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment
Statistical Learning for Resting-State fMRI: Successes and Challenges

International audienceIn the absence of external stimuli, fluctuations in cerebral activity can be used to reveal intrinsic structures. Well-conditioned probabilistic models of this so-called resting-state activity are needed to support neuroscientific hypotheses. Exploring two specific descriptions of resting-state fMRI, namely spatial analysis and connectivity graphs, we discuss the progress brought by statistical learning techniques, but also the neuroscientific picture that they paint, and possible modeling pitfalls
Small Work Space Algorithms for Some Basic Problems on Binary Images

International audienceThis paper presents space-efficient algorithms for some basic tasks (or problems) on a binary image of n pixels, assuming that an input binary image is stored in a read-only array with random-access. Although efficient algorithms are available for those tasks if O(n) work space (of O(n logn) bits) is available, we aim to propose efficient algorithms using only limited work space, i.e., O(1) or O(√n) space. Tasks to be considered are (1) CCC to count the number of connected components, (2) MERR to report the minimum enclosing rectangle of every connected component, and (3) LCCR to report a largest connected component. We show that we can solve each of CCC, MERR, and LCCR in O(n logn) time using only O(1) space. If we can use O(√n) work space, we can solve them in O(n), O(n), and O(n + m logm) time, respectively, where m is the number of pixels in the largest connected component
Towards In-Flight Measurements of Helicopter Blade Tip Vortices

In the framework of the AIM project the near field of the blade tip vortex of a full-scale helicopter in simulated hover flight was investigated by combining three-component Particle Image Velocimetry and Background Oriented Schlieren measurements. The velocity field measurements in the range of wake ages of  1° to 30° in azimuth provided a reference for a quantitative analysis of the Schlieren results yielding vortex core density estimates. Ongoing vortex roll-up was observed at  1° while considerable aperiodicity was persistent thereafter. The vortex parameters for vortices older than 1° were consistent with the Scully vortex model. However, the particular challenges of full-scale, outdoor testing, especially the limited spatial resolution and aperiodicity effects, yielded a higher level of measurement uncertainty as compared to sub-scale experiments
Enhancing the computation of distributed shortest paths on real dynamic networks

International audienceThe problem of finding and updating shortest paths in distributed networks is considered crucial in today's practical applications. In the recent past, there has been a renewed interest in devising new efficient distance-vector algorithms as an attractive alternative to link-state solutions for large-scale Ethernet networks, in which scalability and reliability are key issues or the nodes can have limited storage capabilities. In this paper we present Distributed Computation Pruning (DCP), a new technique, which can be combined with every distance-vector routing algorithm based on shortest paths, allowing to reduce the total number of messages sent by that algorithm and its space occupancy per node. To check its effectiveness, we combined DCP with DUAL (Diffuse Update ALgorithm), one of the most popular distance-vector algorithm in the literature, which is part of CISCO's widely used EIGRP protocol, and with the recently introduced LFR (Loop Free Routing) which has been shown to have good performances on real networks. We give experimental evidence that these combinations lead to a significant gain both in terms of number of messages sent and memory requirements per node
Graph Expansion Analysis for Communication Costs of Fast Rectangular
  Matrix Multiplication

Graph expansion analysis of computational DAGs is useful for obtaining
communication cost lower bounds where previous methods, such as geometric
embedding, are not applicable. This has recently been demonstrated for
Strassen's and Strassen-like fast square matrix multiplication algorithms. Here
we extend the expansion analysis approach to fast algorithms for rectangular
matrix multiplication, obtaining a new class of communication cost lower
bounds. These apply, for example to the algorithms of Bini et al. (1979) and
the algorithms of Hopcroft and Kerr (1971). Some of our bounds are proved to be
optimal
Sound environment analysis in smart home

International audienceThis study aims at providing audio-based interaction technology that lets the users have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. The paper presents the sound and speech analysis system evaluated thanks to a corpus of data acquired in a real smart home environment. The 4 steps of analysis are signal detection, speech/sound discrimination, sound classification and speech recognition. The results are presented for each step and globally. The very first experiments show promising results be it for the modules evaluated independently or for the whole system
The Complex Irradiation Facility at DLR-Bremen

The performance of solar sails is significantly affected by both the sail geometry and the physical properties of used materials which degrade under space conditions. Hence it is of utmost importance for a sail-mission to select such sail materials which degrade as little as possible in space. The Complex Irradiation Facility (CIF) is a new laboratory at DLR-Bremen to study the degradation behavior of materials under space conditions. The CIF allows the simultaneous irradiation with three light sources and with a dual beam irradiation system for the bombardment of materials with electrons and protons having energies up to 100 keV. It is eminently suitable to perform degradation investigations at solar sail foils
Gossamer roadmap technology reference study for a solar polar mission

A technology reference study for a solar polar mission is presented. The study uses novel analytical methods to quantify the mission design space including the required sail performance to achieve a given solar polar observation angle within a given timeframe and thus to derive mass allocations for the remaining spacecraft sub-systems, that is excluding the solar sail sub-system. A parametric, bottom-up, system mass budget analysis is then used to establish the required sail technology to deliver a range of science payloads, and to establish where such payloads can be delivered to within a given timeframe. It is found that a solar polar mission requires a solar sail of side-length 100 – 125 m to deliver a ‘sufficient value’ minimum science payload, and that a 2. 5μm sail film substrate is typically required, however the design is much less sensitive to the boom specific mass