pinecone/core-2020-05-10-deduplication huggingface dataset:
Attack on the clones: managing player perceptions of visual variety and believability in video game crowds

Crowds of non-player characters are increasingly common in contemporary video games. It is often the case that individual models are re-used, lowering visual variety in the crowd and potentially affecting realism and believability. This paper explores a number of approaches to increase visual diversity in large game crowds, and discusses a procedural solution for generating diverse non-player character models. This is evaluated using mixed methods, including a “clone spotting” activity and measurement of impact on computational overheads, in order to present a multi-faceted and adjustable solution to increase believability and variety in video game crowds
BOLLOCKS!! Designing pervasive games that play with the social rules of built environments

We propose that pervasive games designed with mechanics that are specifically in\ud
opposition with, or disruptive of, social rules of the environment in which they are played, have\ud
unique potential to provide interesting, provocative experiences for players. We explore this\ud
concept through the design and evaluation of an experimental game prototype, Shhh!, inspired\ud
by the juvenile game Bollocks, and implemented on Android mobile devices, which challenges\ud
players to make loud noises in libraries. Six participants played the game before engaging in\ud
semi-structured interviews, explored through inductive thematic analysis. Results suggest that\ud
the game provoked in players a heightened awareness of social rules, as well as a complex\ud
social dilemma of whether or not to act. We conclude by presenting a model for designing\ud
games that play with the social, as well as physical, rules of the environments in which they are\ud
set
Development challenges in web apps for public displays

Digital public displays can have a key role in urban ubiquitous computing
infrastructures, but they have not yet managed to fill this role. A key step
in that direction would be the emergence of an application model for open display
networks that would enable anyone to create applications for display infrastructures.
In this work, we study the development of web-based applications
for public displays. We report on our experience of application development for
real world public deployment and also on an experiment with external web developers
to assess their ability to create such applications using our own development
tools. The results show that the web-based app model can effectively be
used in the context of public displays and that web developers are able to leverage
upon their expertise to create this type of applications
Fault Estimation and Control for a Quad-Rotor MAV Using a Polynomial Observer. Part I: Fault Detection

International audienceThis work addresses the problem of fault detection and diagnosis (FDD) for a quad-rotor mini aerial vehicle (MAV). Actuator faults are considered on this paper. The basic idea behind the proposed method is to estimate the faults signals using the extended state observers theory. To estimate the faults, a polynomial observer is presented by using the available measurements and know inputs of the system. In order to investigate the observability and diagnosability properties of the system, a differential algebra approach is proposed. Furthermore, an evaluation function depending on the system states is developed, in order to be used in a controller, which will compensate the failures. The effectiveness of the methodology is illustrated by means of numerical simulations and some experimental tests
Human-robot collaborative scene mapping from relational descriptions

In this article we propose a method for cooperatively building a scene map between a human and a robot by using a spatial relational model employed by the robot to interpret human descriptions of the scene. The description will consist in a set of spatial relations between the objects in the scene. The scene map will contain the position of these objects. For this end we propose a model based on the generation of scalar fields of applicability for each of the available relations. The method can be summarized as follows. In first place a person will come into the room and describe the scene to the robot, including in the description semantic information about the objects which the robot can't get from its sensors. From the description the robot will form the scene mental map. In second place the robot will sense the scene with a 2D range laser building the scene sensed map. The objects positions in the mental map will be used to guide the sensing process. In a third step the robot will fuse the two maps, linking the semantic information about the described objects to the corresponding sensed ones. The resulting map is called the scene enriched map.Peer ReviewedPostprint (author’s final draft
Event-based visual servoing with features’ prediction

Event-based visual servoing is a recently presented approach that performs the positioning of a robot using visual information only when it is required. From the basis of the classical image-based visual servoing control law, the scheme proposed in this paper can reduce the processing time at each loop iteration in some specific conditions. The proposed control method enters in action when an event deactivates the classical image-based controller (i.e. when there is no image available to perform the tracking of the visual features). A virtual camera is then moved through a straight line path towards the desired position. The virtual path used to guide the robot improves the behavior of the previous event-based visual servoing proposal.The research leading to these results has received funding from the Spanish Ministry of Education and Science and European FEDER funds, the Valencia Regional Government and the Research and Innovation Vice-president Office of the University of Alicante, through the research projects DPI2012-32390, GV2012/102 and PROMETEO/2013/085, GRE12-17, respectively
Real-time vision-based pedestrian detection in a truck’s blind spot zone using a warping window approach

Van Beeck K., Goedemé G., Tuytelaars T., ''Real-time vision-based pedestrian detection in a truck’s blind spot zone using a warping window approach'', Informatics in control, automation and robotics - lecture notes in electrical engineering, vol. 283, pp. 251-264, Ferrier J.-L., Bernard A., Gusikhin O. and Madani K., eds., 2014.In this chapter we present a vision-based pedestrian tracking system targeting a specific application: avoiding accidents in the blind spot zone of trucks. Existing blind spot safety systems do not offer a complete solution to this problem. Therefore we propose an active alarm system, which automatically detects vulnerable road users in blind spot camera images, and warns the truck driver about their presence. The demanding time constraint, the need for a high accuracy and the large distortion that a blind spot camera introduces makes this a challenging task. To achieve this we propose a warping window multi-pedestrian tracking algorithm. Our algorithm achieves real-time performance while maintaining high accuracy. To evaluate our algorithm we recorded several pedestrian datasets with a real blind spot camera mounted on a real truck, consisting of realistic simulated dangerous blind spot situations. Furthermore we recorded and performed preliminary experiments with datasets including bicyclists.status: publishe
A Multi-signal Variant for the GPU-based Parallelization of Growing
  Self-Organizing Networks

Among the many possible approaches for the parallelization of self-organizing
networks, and in particular of growing self-organizing networks, perhaps the
most common one is producing an optimized, parallel implementation of the
standard sequential algorithms reported in the literature. In this paper we
explore an alternative approach, based on a new algorithm variant specifically
designed to match the features of the large-scale, fine-grained parallelism of
GPUs, in which multiple input signals are processed at once. Comparative tests
have been performed, using both parallel and sequential implementations of the
new algorithm variant, in particular for a growing self-organizing network that
reconstructs surfaces from point clouds. The experimental results show that
this approach allows harnessing in a more effective way the intrinsic
parallelism that the self-organizing networks algorithms seem intuitively to
suggest, obtaining better performances even with networks of smaller size.Comment: 17 page
Automated reasoning in metabolic networks with inhibition

The use of artificial intelligence to represent and reason about metabolic networks has been widely investigated due to the complexity of their imbrication. Its main goal is to determine the catalytic role of genomes and their interference in the process. This paper presents a logical model for metabolic pathways capable of describing both positive and negative reactions (activations and inhibitions) based on a fragment of first order logic. We also present a translation procedure that aims to transform first order formulas into quantifier free formulas, creating an efficient automated deduction method allowing us to predict results by deduction and infer reactions and proteins states by abductive reasoning
A hybrid neuro--wavelet predictor for QoS control and stability

For distributed systems to properly react to peaks of requests, their
adaptation activities would benefit from the estimation of the amount of
requests. This paper proposes a solution to produce a short-term forecast based
on data characterising user behaviour of online services. We use \emph{wavelet
analysis}, providing compression and denoising on the observed time series of
the amount of past user requests; and a \emph{recurrent neural network} trained
with observed data and designed so as to provide well-timed estimations of
future requests. The said ensemble has the ability to predict the amount of
future user requests with a root mean squared error below 0.06\%. Thanks to
prediction, advance resource provision can be performed for the duration of a
request peak and for just the right amount of resources, hence avoiding
over-provisioning and associated costs. Moreover, reliable provision lets users
enjoy a level of availability of services unaffected by load variations
Generalized Quantitative Analysis of Metric Transition Systems

International audienceThe formalism of metric transition systems, as introduced by de Alfaro, Faella and Stoelinga, is convenient for modeling systems and properties with quantitative information, such as probabilities or time. For a number of applications however, one needs other distances than the point-wise (and possibly discounted) linear and branching distances introduced by de Alfaro et.al. for analyzing quantitative behavior. In this paper, we show a vast generalization of the setting of de Alfaro et.al., to a framework where any of a large number of other useful distances can be applied. Concrete instantiations of our framework hence give e.g. limit-average, discounted-sum, or maximum-lead linear and branching distances; in each instantiation, properties similar to the ones of de Alfaro et.al. hold. In the end, we achieve a framework which is not only suitable for modeling different kinds of quantitative systems and properties, but also for analyzing these by using different application-determined ways of mea-suring quantitative behavior
On Bar Recursion and Choice in a Classical Setting

International audienceWe show how Modiﬁed Bar-Recursion, a variant of Spector's Bar-Recursion due to Berger and Oliva can be used to realize the Axiom of Choice in Parigot's Lambda-Mu-calculus, a direct-style language for the representation and evaluation of classical proofs. We rely on Hyland-Ong innocent games. They provide a model to perform the usual inﬁnitary reasoning on Bar-Recursion needed for the realization of classical choice, and where, moreover, the standard datatype of natural numbers is in the image of a CPS-translation
Machine Assisted Proof of ARMv7 Instruction Level Isolation Properties

In this paper, we formally verify security properties of the ARMv7 Instruction Set Architecture (ISA) for user mode executions.\ud
To obtain guarantees that arbitrary (and unknown) user processes are able to run isolated from privileged software and other user processes, instruction level noninterference and integrity properties are provided, along with proofs that transitions to privileged modes can only occur in a controlled manner.\ud
This work establishes a main requirement for operating system and hypervisor verification, as demonstrated for the PROSPER separation kernel. The proof is performed in the HOL4 theorem prover, taking the Cambridge model of ARM as basis.\ud
To this end, a proof tool has been developed, which assists the verification of relational state predicates semi-automatically
Maintaining Balanced Trees For Structured Distributed Streaming Systems

International audienceIn this paper, we propose and analyze a simple localized algorithm to balance a tree. The motivation comes from live distributed streaming systems in which a source di uses a content to peers via a tree, a node forwarding the data to its children. Such systems are subject to a high churn, peers frequently joining and leaving the system. It is thus crucial to be able to repair the di usion tree to allow an e cient data distribution. In particular, due to bandwidth limitations, an e cient di usion tree must ensure that node degrees are bounded. Moreover, to minimize the delay of the streaming, the depth of the di usion tree must also be controlled. We propose here a simple distributed repair algorithm in which each node carries out local operations based on its degree and on the subtree sizes of its children. In a synchronous setting, we rst prove that starting from any n-node tree our process converges to a balanced tree in O(n^2) turns. We then describe a more restrictive model, adding a small extra information to each node, for which the convergence is reached in O(n log n) turns and this bound is tight. We then exhibit by simulation that the convergence is much faster (logarithmic number of turns in average) for a random tree
Social media as online information grounds : a preliminary conceptual framework

Researchers are increasingly grappling with ways of theorizing social media and its use. This review essay proposes that the theory of Information Grounds (IG) may provide a valuable lens for understanding how social media fosters collaboration and social engagement among information professionals. The paper presents literature that helps us understand how social media can be seen as IG, and maps the characteristics of social media to the seven propositions of IG theory. This work is part of a wider study investigating the ways in which Information Technology (IT) professionals experience social media
A Comparative Analysis of Empirical Studies

Test-driven development is a software development practice where small sections of test code are used to direct the development of program units. Writing test code prior to the production code promises several positive effects on the development process itself and on associated products and processes as well. However, there are few comparative studies on the effects of test-driven development. Thus, it is difficult to assess the potential process and product effects when applying test-driven development. In order to get an overview of the observed effects of test-driven development, an in-depth review of existing empirical studies was carried out. The results for ten different internal and external quality attributes indicate that test-driven development can reduce the amount of introduced defects and lead to more maintainable code. Parts of the implemented code may also be somewhat smaller in size and complexity. While maintenance of test-driven code can take less time, initial development may last longer. Besides the comparative analysis, this article sketches related work and gives an outlook on future research
Improvement in accuracy for three-dimensional sensor (Faro
Photon 120 scanner)

The ability to provide actual information and attractive presentation, three-dimensional (3D) information has been
widely used for many purposes especially for documentation,
management and analysis. As a non-contact 3D sensor, terrestrial laser scanners (TLSs) have the capability to provide dense of 3D data (point clouds) with speed and accuracy. However, similar to other optical and electronic sensors, data obtained from TLSs can be impaired by errors coming from different sources. In order to ensure the high quality of the data, a calibration routine is crucial for TLSs to make it suitable for accurate 3D applications (e.g. industrial measurement, reverse engineering and monitoring). There are two calibration approaches available: 1) component, and 2)system calibration. Due to the requirement of special laboratories and tools to perform component calibration, the task cannot be carried out by most TLSs users. In contrast, system calibration only requires a room with appropriate targets.
Through self-calibration, this study involved a system calibration for Faro Photon 120 scanner in a laboratory with dimensions of 15.5m x 9m x 3m and 138 well-distributed planar targets. Four calibration parameters were derived from well-known error sources of geodetic instruments. Data obtained using seven scan stations were processed, and statistical analysis (e.g. t-test) shows that all error models, the constant error (8.9mm), the collimation axis error (-4.3), the trunnion axis error (-11.6) and the vertical circle index error (8.0) were significant for the calibrated 3D sensor.
Towards exploiting the advantages of colour in scan matching

© Springer International Publishing Switzerland 2014. Colour plays an important role in the perception systems of the human beings. In robotics, the development of new sensors has made it possible to obtain colour information together with depth information about the environment. The exploitation of this type of information has become more and more important in numerous tasks. In our recent work, we have developed an evolutionary-based scan matching method. The aim of this work is to modify this method by the introduction of colour properties, taking the first steps in studying how to use colour to improve the scan matching. In particular, we have applied a colour transition detection method based on the delta E divergence between neighbours in a scan. Our algorithm has been tested in a real environment and significant conclusions have been reached
Analysis of methods for playing human robot hide-and-seek in a simple real world urban environment

The hide-and-seek game has many interesting aspects for studying cognitive functions in robots and the interactions between mobile robots and humans. Some MOMDP (Mixed Observable Markovian Decision Processes) models and a heuristic-based method are proposed and evaluated as an automated seeker. MOMDPs are used because the hider's position is not always known (partially observable), and the seeker's position is fully observable. The MOMDP model is used in an o-line method for which two reward functions are tried. Because the time complexity of this model grows exponentially with the number of (partially observable) states, an on-line hierarchical MOMDP model was proposed to handle bigger maps. To reduce the states in the on-line method a robot centered segmentation is used. In addition to extensive simulations, games with a human hider and a real mobile robot as a seeker have been done in a simple urban environment.Peer ReviewedPostprint (author’s final draft
Diversify Intensification Phases in Local Search  for SAT with a New Probability Distribution

A key challenge in developing efficient local search solvers is  to intelligently balance diversification and intensification. This study pro-  poses a heuristic that integrates a new dynamic scoring function and two  different diversification criteria: variable weights and stagnation weights.  Our new dynamic scoring function is formulated to enhance the diversifi-  cation capability in intensification phases using a user-defined diversifica-  tion parameter. The formulation of the new scoring function is based on  a probability distribution to adjust the selecting priorities of the selection  between greediness on scores and diversification on variable properties.  The probability distribution of variables on greediness is constructed to  guarantee the synchronization between the probability distribution func-  tions and score values. Additionally, the new dynamic scoring function  is integrated with the two diversification criteria. The experiments show  that the new heuristic is efficient on verification benchmark, crafted and  random instances.Griffith Sciences, Griffith School of EngineeringFull Tex
Propositionalisation of multi-instance data using random forests

Multi-instance learning is a generalisation of attribute-value learning where examples for learning consist of labeled bags (i.e. multi-sets) of instances. This learning setting is more computationally challenging than attribute-value learning and a natural fit for important application areas of machine learning such as classification of molecules and image classification. One approach to solve multi-instance learning problems is to apply propositionalisation, where bags of data are converted into vectors of attribute-value pairs so that a standard propositional (i.e. attribute-value) learning algorithm can be applied. This approach is attractive because of the large number of propositional learning algorithms that have been developed and can thus be applied to the propositionalised data. In this paper, we empirically investigate a variant of an existing propositionalisation method called TLC. TLC uses a single decision tree to obtain propositionalised data. Our variant applies a random forest instead and is motivated by the potential increase in robustness that this may yield. We present results on synthetic and real-world data from the above two application domains showing that it indeed yields increased classification accuracy when applying boosting and support vector machines to classify the propositionalised data
Protein Fold Recognition Using an Overlapping  Segmentation Approach and a Mixture of  Feature Extraction Models

Protein Fold Recognition (PFR) is considered as a critical step towards the protein structure prediction problem. PFR has also a profound impact on protein function determination and drug design. Despite all the enhancements achieved by using pattern recognition-based approaches in the protein fold recognition, it still remains unsolved and its prediction accuracy remains limited. In this study, we propose a new model based on the concept of mixture of physicochemical and evolutionary features. We then design and develop two novel overlapping segmented-based feature extraction methods. Our proposed methods capture more local and global discriminatory information than previously proposed approaches for this task. We investigate the impact of our novel approaches using the most promising attributes selected from a wide range of physicochemical-based attributes (117 attributes) which is also explored experimentally in this study. By using Support Vector Machine (SVM) our experimental results demonstrate a significant improvement (up to 5.7%) in the protein fold prediction accuracy compared to previously reported results found in the literature.Griffith Sciences, Griffith School of EngineeringFull Tex