Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithm Steering for Mixed-Initiative Robot Teams

While much research has been devoted to human-robot interaction (HRI) with individually controlled robots or round-robin control for independently operating ones, the problems of controlling autonomously coordinating robot teams remain largely unexplored. Although MAS researchers have devoted significant effort to understand human interaction with teamwork algorithms other significant classes of coordination algorithms have not received comparable attention. In particular, human interaction with biologically inspired and optimizing control algorithms has been long neglected. These algorithms which are ideal for tightly coordinated tasks such as formation flying or simultaneous rendezvous require highly coordinated mutual adjustments yet have goals that can be simply specified. The problem arises when the operator wants the system to do anything else. Because there is little or no connection between the parameters available to the operator and the behavior that results we call such algorithms opaque. We conjecture that in many cases this problem may be relatively easy to solve and propose a taxonomy-in-progress to help identify classes of algorithms to be considered.
Why laptops should be screened separately when conventional x-ray screening is used

Security screening at airports is a highly relevant topic for more than a decade. Large amounts of money are still being invested to continuously improve the efficiency and effectiveness of passenger and baggage screening processes. State-of-the-art x-ray screening machines provide good quality images with high resolutions. Yet, the detection of prohibited items remains a challenging task for screening officers (screeners) and becomes even more difficult when dense electronic devices, such as laptops, are contained in baggage. Due to their compact construction, laptops can easily conceal other parts of luggage or could be used to hide threat items. International and national regulations specify that laptops need to be taken out of passenger bags at security checkpoints for x-ray screening, sometimes causing lower passenger throughput and even annoyance among passengers. The aim of this study was to investigate how leaving laptops inside passenger bags actually affects detection performance of x-ray screening officers, when state-of-the-art technology providing single-view images is applied. The experiment was conducted with 40 certified screening officers from an international European airport. Participants were divided into two experimental groups. Both groups conducted an image interpretation test containing bags and laptops. For the first group, images of bags and laptops were displayed separately, while for the second group, laptops were left inside the passenger bags and displayed as one image. All participants had to judge whether the displayed bags and laptops contained a threat item or not. Threat items of four different categories were used: guns, knives, improvised explosive devices (IEDs) and others (e.g. electronic shock devices). Detection performance (A') and reaction times were measured. A highly significant difference in detection performance between both groups was observed. Throughout all analyses, detection performance scores were much higher when bags and laptops were x-rayed separately. Largest differences were found for the category IEDs. Altogether, the results imply that when no automatic threat detection and only single view images are available, the detection of prohibited items is still far more reliable when bags and laptops are screened separately.
Short cycle distribution in random regular graphs recursively generated by pegging

We introduce a new method of generating random d‐regular graphs by repeatedly applying an operation called pegging. The pegging operation is abstracted from a type of basic operation applied in a type of peer‐to‐peer network called the SWAN network. We prove that for the resulting graphs, the limiting joint distribution of the numbers of short cycles is independent Poisson. We also use coupling to bound the rate at which the distribution approaches its limit. The coupling involves two different, though quite similar, Markov chains that are not time‐homogeneous. © 2008 Wiley Periodicals, Inc. Random Struct. Alg. , 2009
Ieee Workshop on Machine Learning for Signal Processing Part-of-speech Enhanced Context Recognition

Language independent 'bag-of-words' representations are surprisingly effective for text classification. In t h i s communication our a i m is t o elucidate the synergy between language independent features a n d simple language model features. We consider term tag features estimated by a so-called part-of-speech tagger. T h e feature sets are combined i n a n early binding design w i t h an optimized binding coefficient t h a t allows weighting of t h e relative variance contributions of the participating feature sets. W i t h the combined features d o c u m e n t s are classified using a latent semantic indexing representation and a probabilistic neural network classi-fier. Three medium size data-sets are analyzed a n d w e find consistent synergy between the term and n a t u r a l language features i n all three sets for a range of training set sizes. The most significant enhancement is found for small text databases where high recognition rates are possible.
Experience in Discovering , Modeling , and Reenacting Open Source Software Development Processes

Process discovery has been shown to be a challenging problem offering limited results. This paper describes a new approach to process discovery that examines the Internet information spaces of open source software development projects. In particular, we examine challenges, strengths, weaknesses and findings when seeking to discover, model, and reenact processes associated with large, global OSSD projects like NetBeans.org. The longer-term goal of this approach is to determine the requirements and design of more fully integrated process discovery and modeling mechanisms that can be applied to Web-based, open source software development projects.
Gradient Field Correlation for Keypoint Correspondence

This paper presents an alternative approach to existing and widely used correlation metrics through the use of orientation information. The gradient field correlation method presented here utilises derivative of Gaussian (DoG) operators for estimating directional derivatives of an image for two matching applications: classical planar object detection and point correspondence matching. The experimental results confirm that a suitably normalised gradient vector field, which emphasises gradient direction information in an image, leads to better selectivity when applied to classical template matching problems. For the case of establishing point correspondences, combinations of gradient vector field metrics yield higher in lying match percentages (by RANSAC) relative to normalised cross-correlation with little extra computational cost, particularly at smaller patch sizes. It is also shown that pixel-wise field component normalisation is critical to the success of this approach.
Using Virtual Clusters to Decouple Computation and Data Management in High Throughput Analysis Applications

The rapid growth in the throughput to cost ratio of experimental data production technologies is generating vast amounts of scientific data, often organized into "large" objects (genomes, bio-images) exhibiting complex internal structures. Frequently, datasets must be shared between multiple research groups interested not only in the final results, but also in how they are produced. The practical difficulties of moving terabytes or more of data across the network, as well as the need to maintain a clear separation between software stack and storage infrastructure, are thus raising interest in the use of virtual clusters for HPC and data intensive applications. In this paper we employ a MapReduce implementation of an image analysis pipeline used by deep sequencing platforms to analyse different virtual cluster scenarios and their impact on system performance.
Long-term survival and quality of life in dogs with clinical signs associated with a congenital portosystemic shunt after surgical or medical treatment.

OBJECTIVE To compare long-term survival and quality of life data in dogs with clinical signs associated with a congenital portosystemic shunt (CPSS) that underwent medical or surgical treatment. DESIGN Prospective cohort study. ANIMALS 124 client-owned dogs with CPSS. PROCEDURES Dogs received medical or surgical treatment without regard to signalment, clinical signs, or clinicopathologic results. Survival data were analyzed with a Cox regression model. Quality of life information, obtained from owner questionnaires, included frequency of CPSS-associated clinical signs (from which a clinical score was derived), whether owners considered their dog normal, and (for surgically treated dogs) any ongoing medical treatment for CPSS. A Mann-Whitney U test was used to compare mean clinical score data between surgically and medically managed dogs during predetermined follow-up intervals. RESULTS 97 dogs underwent surgical treatment; 27 were managed medically. Median follow-up time for all dogs was 1,936 days. Forty-five dogs (24 medically managed and 21 surgically managed) died or were euthanized during the follow-up period. Survival rate was significantly improved in dogs that underwent surgical treatment (hazard ratio, 8.11; 95% CI, 4.20 to 15.66) than in those treated medically for CPSS. Neither age at diagnosis nor shunt type affected survival rate. Frequency of clinical signs was lower in surgically versus medically managed dogs for all follow-up intervals, with a significant difference between groups at 4 to 7 years after study entry. CONCLUSIONS AND CLINICAL RELEVANCE Surgical treatment of CPSS in dogs resulted in significantly improved survival rate and lower frequency of ongoing clinical signs, compared with medical management. Age at diagnosis did not affect survival rate and should not influence treatment choice.
Vitality assessment of boar sperm using NCSR texture descriptor in digital images

Two new textural descriptor, named N Concentric Squares Resized (NCSR) and N Concentric Squares Resized (NCSH), have been proposed. These descriptors were used to classify 472 images of alive spermatozoa heads and 376 images of dead spermatozoa heads. The results obtained with these two novel descriptors have been compared with a number of classical descriptors such as Haralick, Pattern Spectrum, WSF, Zernike, Flusser and Hu. The feature vectors computed have been classified using kNN and a backpropagation Neural Network. The error rate obtained for NCSR with N = 11 was of 23.20% outperforms the rest of descriptors. Also, the area under the ROC curve (AUC) and the values observed in the ROC curve indicates the performance of the proposed descriptor is better than the others texture description methods.
Disease-Specific Regions Outperform Whole-Brain Approaches in Identifying Progressive Supranuclear Palsy: A Multicentric MRI Study

To identify progressive supranuclear palsy (PSP), we combined voxel-based morphometry (VBM) and support vector machine (SVM) classification using disease-specific features in multicentric magnetic resonance imaging (MRI) data. Structural brain differences were investigated at four centers between 20 patients with PSP and 20 age-matched healthy controls with T1-weighted MRI at 3T. To pave the way for future application in personalized medicine, we applied SVM classification to identify PSP on an individual level besides group analyses based on VBM. We found a major decline in gray matter density in the brainstem, insula, and striatum, and also in frontomedian regions, which is in line with current literature. Moreover, SVM classification yielded high accuracy rates above 80% for disease identification in imaging data. Focusing analyses on disease-specific regions-of-interest (ROI) led to higher accuracy rates compared to a whole-brain approach. Using a polynomial kernel (instead of a linear kernel) led to an increased sensitivity and a higher specificity of disease detection. Our study supports the application of MRI for individual diagnosis of PSP, if combined with SVM approaches. We demonstrate that SVM classification provides high accuracy rates in multicentric data—a prerequisite for potential application in diagnostic routine.
Transparency and International Portfolio Investment in Asia , Latin America and Elsewhere

Does country transparency affect international portfolio investment? We examine this and related questions using some new measures of transparency and a unique micro dataset on international portfolio holdings. We distinguish between government and corporate transparency. There is clear evidence that international funds invest systematically less in less transparent countries. On the other hand, herding among funds tends to be more prevalent in less transparent countries. There is also some evidence that during crises, funds flee non-transparent countries by a greater amount. The authors wish to thank Philippe Bacchetta, Andrew Berg, Patrick Bolton, Tito Cordella, Douglas Gale, Graciela Kaminsky, Philippe Martin, Paolo Mauro, Alessandro Prati, David Robinson, Nouriel Roubini, Antonio Spilimbergo, Jiang Wang, and seminar participants at the IMF and at the LABEA conference at the Brookings for helpful discussions, Torbjörn Becker, Przemek Gajdeczka, Hunter Monroe, and Anthony Richards for detailed comments on earlier drafts, and Peter Allum for sharing data. Nese Erbil and Chi Nguyen provided excellent research assistance. The views in the paper are the authors’ own and may not reflect those of the IMF or any other organization that they are or have been associated with.
Examining Validity-Related Correlations and Their Differences Under Assumption Violations

A procedure for evaluation of validity related coefficients and their differences is discussed, which is applicable when one or more frequently used assumptions in empirical educational, behavioral and social research are violated. The method is developed within the framework of the latent variable modeling methodology and accomplishes point and interval estimation of convergent and discriminant correlations as well as differences between them in cases of incomplete data sets with data not missing at random, nonnormality, and clustering effects. The procedure uses the full information maximum likelihood approach to model fitting and parameter estimation, does not assume availability of multiple indicators for underlying latent constructs, includes auxiliary variables, and accounts for within-group correlations on main response variables resulting from nesting effects involving studied respondents. The outlined procedure is illustrated on empirical data from a study using tertiary education entrance examination measures.
2 D to 3 D Conversion for Outdoor Scenes

DSP/IC LAB, Graduate Institute of Electronic Engineering, National Taiwan University {mpemial, ztdjbdy, kevin, penal, lgchen}@video.ee. ntu.edu. tw Do not type page numbers; Full papers are limited to 3 (minimum) to 5 (maximum) pages. Abstract This paper provides 2D to 3D conversion system based on dark channel prior. This method has low computation and high performance on outdoor scene. However traditional dark channel results suffer from noise that generated from wrong assumption to monocular cues. We use scene estimation and cubicspline interpolation to reduce these artifacts from finding the global depth map. By applying HHT object-based filtering, the algorithm can smooth the depth map and increase quality of viewing experience.
Proofs, tests and continuation passing style

The concept of syntactical duality is central in logic. In particular, the duality defined by classical negation, or more syntactically by left and right in sequents, has been widely used to relate logic and computations. We study the proof/test duality proposed by Girard in his 1999 paper on the meaning of logical rules. In detail, starting from the notion of “test” proposed by Girard, we develop a notion of test for intuitionistic logic and we give a complete deductive system whose computational interpretation is the target language of the call-by-value and call-by-name continuation passing style translations.
Job Scheduling on the Grid: Towards SLA-Based Scheduling

This paper argues for the need to provide more flexibility in the level of service offered by Grid-enable high-performance, parallel, supercomputing resources. It is envisaged that such need could be satisfied by making separate Service Level Agreements (SLAs) between the resource owner and the user who wants to submit and run a job on these resources. A number of issues related to the materialization of this vision are highlighted in the paper.
Developmental Human-Robot Imitation Learning with Phased Structuring in Neuro Dynamical System

This paper mainly deals with influences of teaching style and developmental processes in learning model to the acquired representations (primitives). We investigate these influences by introducing a hierarchical recurrent neural network for robot model, and a form of motionese (a caregiver’s use of simpler and more exaggerated motions when showing a task to an infants). We modified a Multiple Timescales Recurrent Neural Network (MTRNN) for robot’s self-model. The number of layers in the MTRNN increases according to learn complex events. We investigate our approach with a humanoid robot “Actroid” through conducting an imitation experiment in which a human caregiver gives the robot a task of pushing two buttons. Experiment results and analysis confirm that learning with phased teaching and structuring enables to acquire the clear motion primitives as the activities in the fast context layer of MTRNN and to the robot to handle unknown motions.
Large Java arrays and their applications

All current implementations of Java Virtual Machines allow the creation of one-dimensional arrays of length smaller than 231 elements. In addition, since Java lacks true multidimensional arrays, most of numerical libraries use one-dimensional arrays to store multidimensional data. With the current limitation, it is not possible to store volumes of size larger than 12903. On the other hand, the data from scientific simulations or medical scanners continuously grow in size and it is not uncommon to go beyond that limit. This work addresses the problem of maximal size of one-dimensional Java arrays. JLargeArrays is a Java library of one-dimensional arrays that can store up to 263 elements. Performance comparison with native Java arrays and Fastutil library shows that JLargeArrays is the fastest solution overall. Possible applications in Java collections as well as numerical and visualization frameworks are also discussed.
The Framework of Infrared Video Mining Based on Topic Model

We proposed a framework of infrared video mining based on topic model. It aims to learn motion patterns for a crowded and complicated infrared scene. After video preprocessing, motion features are extracted from each pair of consecutive frames at first, and quantized into visual words. Motion pattern are modeled as distributions over visual words in topic model. Experiments about BOVW demonstrate the feasibility of the framework.
Large Scale Real-time Ridesharing with Service Guarantee on Road Networks

Urban traffic gridlock is a familiar scene. At the same time, the mean occupancy rate of personal vehicle trips in the United States is only 1.6 persons per vehicle mile. Ridesharing has the potential to solve many environmental, congestion, pollution, and energy problems. In this paper, we introduce the problem of large scale real-time ridesharing with service guarantee on road networks. Trip requests are dynamically matched to vehicles while trip waiting and service time constraints are satisfied. We first propose two scheduling algorithms: a branch-and-bound algorithm and an integer programing algorithm. However, these algorithms do not adapt well to the dynamic nature of the ridesharing problem. Thus, we propose kinetic tree algorithms which are better suited to efficient scheduling of dynamic requests and adjust routes on-the-fly. We perform experiments on a large Shanghai taxi dataset. Results show that the kinetic tree algorithms outperform other algorithms significantly.
Midair User Interfaces Employing Particle Screens

Recent developments with low-cost optical sensors and ultralight, mobile fogscreens enable new applications such as midair volumetric screens and augmented reality. The major advantages of the fogscreen are its walk-through and immaterial nature, translucency, good image quality, possibility for direct interaction, and the intriguing appearance of the screen. Fogscreens can create a collaborative visualization space or even personal views for each viewer. Dual-sided face-to-face interaction mimics most real-world competitive games and sports and enables body language and free switching of sides. This article reports the authors' experiments with these emerging technologies and their suitability for novel midair user interfaces.
On Load Balancing in

The dynamic resource allocation problem arises in a variety of applications. The generic resource allocation setting involves a number of locations containing resources. The dynamic aspect of the problem is the arrivals of consumers, each of which requires a certain amount of service from the resources, and the control variable of the problem is the \allocation policy", which speciies at which location each consumer is to be served. Oftentimes in applications, locations contain nitely many resources, hence the main objective of the allocation policy is to guarantee low b l o c king probability. On the other hand, in some applications such as spread spectrum mobile radio networks, there are no sharp capacity constraints, and the goal becomes to dynamicallybalance the load. In either case, one wants the allocation policy to have l o w complexity, require little information about the system state, and be robust to changes in the traac parameters. An instance of resource allocation arises in the wireless network pictured in Figure 1.1. The network consists of a number of base stations and users. The users require communication channels that are available at the base stations, whereas each station may s e r v e the users within its geographical range. The resource allocation problem in this setting concerns the question of station selection. Load balancing is a possible guiding principle for resource allocation, whereby the load is allocated across locations as evenly as possible. It is well known that load balancing can be an eeective allocation strategy, when the cost is convex (or the reward concave) as a function of the allocated loads. For example, x 2 +y 2 +z 2 U(1) U(2) U(3) V(1) V(2) Fig. 1.1. A t ypical wireless network with overlapping neighborhoods.
Hermite Interpolation of Implicit Surfaces with Radial Basis Functions

We present the Hermite radial basis function (HRBF) implicits method to compute a global implicit function which interpolates scattered multivariate Hermite data (unstructured points and their corresponding normals). Differently from previous radial basis functions (RBF) approaches, HRBF implicits do not depend on offset points to ensure existence and uniqueness of its interpolant. Intrinsic properties of this method allow the computation of implicit surfaces rich in details, with irregularly spaced points even in the presence of close sheets. Comparisons to previous works show the effectiveness of our approach. Further, the theoretical background of HRBF implicits relies on results from generalized interpolation theory with RBFs, making possible powerful new variants of this method and establishing connections with previous efforts based on statistical learning theory.