Miracle-dz/UniFAR-FTUs huggingface dataset:
Music Genre Classification using the multivariate AR feature integration model

Music genre classification systems are normally build as a feature extraction module followed by a classifier. The features are often short-time features with time frames of 10-30ms, although several characteristics of music require larger time scales. Thus, larger time frames are needed to take informative decisions about musical genre. For the MIREX music genre contest several authors derive long time features based either on statistical moments and/or temporal structure in the short time features. In our contribution we model a segment (1.2 s) of short time features (texture) using a multivariate autoregressive model. Othe r authors have applied simpler statistical models such as the mean-variance model, which also has been included in several of this years MIREX submissions, see e.g. Tzanetakis (2005); Burred (2005); Bergstra et al. (2005); Lidy and Rauber (2005).
An Overview of Eddies and STAIRs

In databases where different resources have much changing characteristics, static query plans is often not a good solution. The problem is that, while a particular query plan might be a good alternative with some initial characteristics of the database, it may be a bad alternative when the characteristics changes. Consider, for example, joining the tables R, S and T , and assume first that R and S contain 1000 rows and T contains 10 rows. Clearly, it is best to not use the join ordering (R on S) on T but to choose any other ordering such as (T on R) on S. However, if the size of T drastically increases to 1, 000, 000 rows, this is not a good choice anymore. In this overview we present the ideas of [1, 2] which suggests two adaptive query operators called Eddies and STAIRs. Using these operators instead of traditional ones, the query plans need not be fixed, but can be changed on a tuple-by-tuple basis. In the following, Section 2 starts by presenting the Eddy operator and discuss some of its shortcomings. After that, Section 3 presents the STAIR operator and how it can be used to overcome these shortcomings. Finally, Section 4 concludes the overview. For in-depth information about Eddies and STAIRs see [1, 2].
Placing a robot manipulator amid obstacles for optimized execution

This paper presents an efficient algorithm for optimizing the base location of a robot manipulator in an environment cluttered with obstacles, in order to execute specified tasks as fast as possible. The algorithm uses randomized motion planning techniques and exploits geometric "coherence" in configuration space to achieve fast computation. The performance of the algorithm is demonstrated on both synthetic examples and real-life CAD data from the automotive industry. The computation time ranges from under a minute for simple problems to a few minutes for more complex ones.
Corticotropin-Releasing Hormone Receptor 2 Gene Variants in Irritable Bowel Syndrome

Background Corticotropin-releasing hormone (CRH) plays an important role in the pathophysiology of irritable bowel syndrome (IBS) and regulates the stress response through two CRH receptors (R1 and R2). Previously, we reported that a CRHR1 gene polymorphism (rs110402, rs242924, and rs7209436) and haplotypes were associated with IBS. However, the association between the CRHR2 gene and IBS was not investigated. We tested the hypothesis that genetic polymorphisms and haplotypes of CRHR2 are associated with IBS pathophysiology and negative emotion in IBS patients. Methods A total of 142 IBS patients and 142 healthy controls participated in this study. Seven single nucleotide polymorphisms (SNPs) of the CRHR2 gene (rs4722999, rs3779250, rs2240403, rs2267710, rs2190242, rs2284217, and rs2284220) were genotyped. Subjects' psychological states were evaluated using the Perceived-Stress Scale, the State-Trait Anxiety Inventory, and the Self-Rating Depression Scale. Results We found that rs4722999 and rs3779250, located in intronic region, were associated with IBS in terms of genotype frequency (rs4722999: P = 0.037; rs3779250: P = 0.017) and that the distribution of the major allele was significantly different between patients and controls. There was a significant group effect (controls vs. IBS), and a CRHR2 genotype effect was observed for three psychological scores, but the interaction was not significant. We found a haplotype of four SNPs (rs4722999, rs3779250, rs2240403, and rs2267710) and two SNPs (rs2284217 and rs2284220) in strong linkage disequilibrium (D′ > 0.90). We also found that haplotypes of the CRHR2 gene were significantly different between IBS patients and controls and that they were associated with negative emotion. Conclusion Our findings support the hypothesis that genetic polymorphisms and haplotypes of CRHR2 are related to IBS. In addition, we found associations between CRHR2 genotypes and haplotypes and negative emotion in IBS patients and controls. Further studies on IBS and the CRH system are warranted.
Intrinsic Camera Calibration Based on Radical Center Estimation

Camera calibration is an important step in obtaining 3D information from 2D images. Vanishing points of parallel lines have proven to be useful features for selfcalibration task. Most tasks using vanishing points estimate parameters using three orthogonal vanishing points (OVPs). However, in a real scene it is hard to find views that capture a scene including three OVPs. Fortunately, in many such cases the vertical and horizontal vanishing points can still be known. Accordingly, the current paper proposes a simple, geometrically intuitive method to calibrate a camera using two orthogonal vanishing points from image streams without an assumption of the principal point is known. The Thales theorem [16] is devised for geometric constraints and the candidate space of principal point and focal length is derived from the relation of multiple hemispheres. Through a set of experiments we demonstrate that the optimally estimated calibration can be possible.
Towards knowledge federation in biomedical applications

Knowledge federation is a particular matter of concern in the life sciences domain. The magnitude of data generated by biomedical software and hardware since the initial discovery of the human genome is tremendous in quantity and diversity. Consequently, state-of-the-art software solutions have always lagged behind researchers' demands, even more so with recent developments in high-throughput sequencing technology and open access to digital disease records. The maturity of Semantic Web technologies brought with it new strategies to tackle life sciences challenges. In spite of the increased awareness regarding these technologies' advantages, its adoption has been slow paced. With a steep learning curve and lacking turnkey solutions, bioinformatics developers are faced with many roadblocks, resulting in a low number of purely semantic solutions. Our research springs new life to knowledge federation in the life sciences with a new application deployment framework, which includes a native federation layer connecting and federating deployed instances by default. This feature is integrated in a complete software stack for creating new applications, easing the troubled development cycle associated with Semantic Web tools. As a result, the door is opened for harnessing more insightful knowledge from the aggregated network of relationships amongst biological data.
Decision Rules Used in Academic Program Closure

The "economic fundamentals" for many American colleges and universities have changed, creating a new playing field with a different set of fiscal rules (Breneman, 1997; Massey, 1994). States are allocating less money, federal research dollars are drying up, and the tuition-paying public is adverse to large tuition increases, all while higher education costs are increasing (Breneman, 1993; Hovey, 1999; O'Keefe, 1987). At the same time, those paying public higher education's billstaxpayers, state legislatures, and tuition-paying students and their families-are demanding high quality and more from their investments, and they are increasing their scrutiny (Harvey & Immerwahr, 1995; Haworth & Conrad, 1997). The demands of the increasingly influential market to do new things and to set different priorities are difficult to meet without the extra cushion of uncommitted resources that was available in the past (Levine, 1997). Many suggest that higher education's programs are too costly and out of date and that institutions cannot continue to offer such expansive ranges of programs. As intentional strategies, more and more institutions are adjusting academic priorities and, in many cases, closing academic programs. Recent high profile closures occurred at the University of Chicago, St. Olaf College, and Auburn University. Program discontinuance is a "necessary adaptive mechanism" (Dougherty, 1979, p. 1) in organizations that are constrained in the range of ways they can increase or reallocate their
Sunrise: towards location based clustering for assisted photo management

We present an end to end system 'Sunrise' to assist users in using location based data to tag, cluster, and find digital photos. We suggest that location tagging of photos is a common desire of users but existing manual solutions are too time consuming and complex. Our 3-tier system offers a solution that integrates automated location tagging with existing manual approaches. The mobile component of our system captures GPS based data at user defined intervals to create trail logs of the user's movements. The desktop component automates location tagging of the photos and makes use of the GPS location data to provide assisted photo keywording through a process of reverse geo-coding. The server component uses automated location clustering techniques to produce hierarchical location representations, to simplify photo navigation and generate temporal/spatial data combinations enabling assisted photo management.
Airline Schedule Recovery in Flow Management: An Application for Departure Re-Routing

This paper presents a flexible modeling methodology that is designed to assess a range of operational concepts for collaborative flow management. The particular focus is on the problem of airline schedule recovery in conditions where airspace sectors are capacity limited due to convective weather events. This model is embedded in a dynamic simulation environment, the Boeing national flow model (NFM), representing the US National Airspace System (NAS). The airline schedule recovery model is based on an optimization formulation that allows a representation of adaptive airline behavior in current and future operations. The schedule recovery options considered include ground delay, flight cancellation, and pre-departure re-routing. The paper presents simulation results based upon a convective weather scenario in the Houston TRACON area. The primary objective of this study was to illustrate, in a simple scenario, how sophisticated planning algorithms can re-plan traffic flows around a network of traffic constraints, and to quantify the potential benefits of automation-supported planning. Comparisons are made concerning the effectiveness of differing levels of automation in re-planning departure schedules using ground delays, flight cancellations, and pre-departure re-routing using published coded departure routes (CDR). An idealized convective weather disruption was designed to generate significant airspace capacity outages over a period of several hours to illustrate the difference between current and more advanced planning techniques. Although the results presented in this paper are specialized to the case involving only departures from a single airport, the methodology is sufficiently general to be applied to NAS-wide airline schedules. Additionally, the method can address both airport and airspace capacities and can account for delay associated with tail-routed schedules. The paper shows that significant delay reduction benefits can be achieved if more automation support is available to support traffic flow management during convective weather events
DECONTAMINATION METHODS USED FOR THE DECONTAMINATION IN JASLOVSKE BOHUNICE

Many decontamination methods have been developed for the decommissioning of the NPP A-1. In the last few years, the activities have been directed mainly at trials and applications of the developed methods in dismantling tasks. Decontaminated are steel for free release, also tanks and circuits containing sludge and deposits with high contents of radionuclides. The combination of decontamination methods, such as chemical and electrochemical bath decontamination, ultrasound, solid particles blasting, and electrochemical decontamination in situ, are often used. Soil decontamination by phytoextraction is under development.
Experimental realization of dynamic walking for a human-riding biped robot, HUBO FX-1

This paper describes a control strategy of the stable walking for the human-riding biped robot, HUBO FX-1. HUBO FX-1 largely consists of two legs with 12 d.o. f. , a pelvis and a cockpit. A normal adult can easily ride on HUBO FX-1 by means of a foothold, and can change the walking direction and speed continuously through the use of a joystick. Principally, this kind of robot must be able to carry a payload of at least 100 kg in order to carry a person easily. A sufficient payload can be accomplished by two ways. The first is through the choice of a highly efficient actuator. The second is through weight reduction of the robot body frames. As an efficient actuator, a high-power AC servo motor and a backlash-free harmonic drive reduction gear were utilized. Furthermore, the thickness and the size of the aluminum body frames were sufficiently reduced so that the weight of HUBO FX-1 is light enough. The disadvantage of the weight reduction is that HUBO FX-1 was not able to walk stably due to structural vibrations, as the body structures become more flexible due to this procedure. This problem was solved through the use of a simple theoretical model and a vibration reduction controller based on sensory feedback. In order to endow the robot with a stable biped walking capability, a standard walking pattern and online controllers based on the real-time sensory feedback were designed. Finally, the performance of the real-time balance control strategy was experimentally verified and stable dynamic walking of the human-riding biped robot, HUBO FX-1, carrying one passenger was realized.
Towards a high performance neural branch predictor

The main aim of this short paper is to propose a new branch prediction approach called by us "neural branch prediction". We developed a first neural predictor model based on a simple neural learning algorithm, known as learning vector quantization algorithm. Based on a trace driven simulation method we investigated the influences of the learning step, training processes, etc. Also we compared the neural predictor with a powerful classical predictor and we establish that they result in close performances. Therefore, we conclude that in the near future it might be necessary to model and simulate other more powerful neural adaptive predictors, based on more efficient neural networks architectures, in order to obtain better prediction accuracies compared with the previous known schemes.
A robust local descriptor method for registering Māori artefacts using colour images

Local descriptors are a relatively new type of image registration methods and there exist numerous studies in the area over the past few years. The methods can be divided into three stages: region detection, descriptor formation and descriptor matching. This paper looked at the performance of current state-of-the-art local descriptor methods for registering images of Māori artefacts under various image transformations such as rotation, scale, tilt and viewpoint changes. In particular, the focus was on the second stage which is the formation of local descriptors. The problems encountered in a previous experimental work conducted were discussed and based on the results, two new local descriptor methods were proposed which aimed to take advantage of the additional information provided by using colour images instead of greyscale images. Experimental work conducted indicate that the proposed methods out-perform existing methods when registering images of Māori artefacts with gains in matching accuracy of up to 10% depending on the image transformation involved.
Multiobjective Optimization of Finite Queueing Networks

We aim at studying a multi-objective algorithm to simultaneously optimize the total number of buffers, the overall service rate, and the throughput of a general-service finite queueing network. These conflicting objectives are optimized by means of a multi-objective genetic algorithm, designed to produce solutions for more than one objective. Computational experiments are shown, in order to determine the efficacy and efficiency of the approach. Instigating news insights are given.
α-SNAP regulates dynamic, on-site assembly and calcium selectivity of Orai1 channels

Ion channel subunits typically assemble in the endoplasmic reticulum. α-SNAP orchestrates a unique assembly and calcium selectivity of Orai1 subunits into functional multimers. Dynamic assembly of Orai1 and its dependence on α-SNAP could enable localization of calcium signals and regulation of rate and amount of calcium influx.
Extracting Product Comparisons from Discussion Boards

In recent years, product discussion forums have become a rich environment in which consumers and potential adopters exchange views and information. Researchers and practitioners are starting to extract user sentiment about products from user product reviews. Users often compare different products, stating which they like better and why. Extracting information about product comparisons offers a number of challenges; recognizing and normalizing entities (products) in the informal language of blogs and discussion groups require different techniques than those used for entity extraction in the more formal text of newspapers and scientific articles. We present a case study in extracting information about comparisons between running shoes and between cars, describe an effective methodology, and show how it produces insight into how consumers view the running shoe and car markets.
Image encryption with dynamic chaotic Look-Up Table

In this paper we propose a novel image encryption scheme. The proposed method is based on the chaos theory. Our cryptosystem uses the chaos theory to define a dynamic chaotic Look-Up Table (LUT) to compute the new value of the current pixel to cipher. Applying this process on each pixel of the plain image, we generate the encrypted image. The results of different experimental tests, such as Key space analysis, Information Entropy and Histogram analysis, show that the proposed encryption image scheme seems to be protected against various attacks. A comparison between the plain and encrypted image, in terms of correlation coefficient, proves that the plain image is very different from the encrypted one.
Power, Competence, and Professionalism in Medical Education

Introduction: This paper explores how competence of medical learners is socially constructed within power structures embedded in medical education. This social dynamic is contrasted with professionalism and its context dependent definition in medical education compared to more common conceptualizations for the medical profession in general. Methods: Anthropological, ethnographic methods were used to collect data, primarily participant observation and ethnographic interviewing. The fieldwork site was a teaching hospital where the same “team” of attending physicians and residents were observed on both inpatient and outpatient settings. The theoretical frameworks included Paolo Friere, Pierre Bourdieu / Jean-Claude Passeron. Results: 1. Competence is a result of acquiring a diagnostic “gaze” and once achieved, results in autonomy and authority within the medical hierarchy. 2. Professionalism in the context of medical education modulates the entire hierarchical structure and protects vulnerable individuals from harm due to potential misuse of power. Discussion: The relationship between power, competence, and professionalism needs to be considered by educators because learners will hide important information unless their need for safety is addressed.
Stable real-time 3D tracking using online and offline information

We propose an efficient real-time solution for tracking rigid objects in 3D using a single camera that can handle large camera displacements, drastic aspect changes, and partial occlusions. While commercial products are already available for offline camera registration, robust online tracking remains an open issue because many real-time algorithms described in the literature still lack robustness and are prone to drift and jitter. To address these problems, we have formulated the tracking problem in terms of local bundle adjustment and have developed a method for establishing image correspondences that can equally well handle short and wide-baseline matching. We then can merge the information from preceding frames with that provided by a very limited number of keyframes created during a training stage, which results in a real-time tracker that does not jitter or drift and can deal with significant aspect changes.
Rethinking democratic evaluation for a polarised and mediatised society

This article discusses how democratic evaluation can manage threats to democracy, democratic renewal, and the mediatisation of public policy and governance. It considers the readiness of five democratic evaluation orientations to deal with current threats and discusses how to develop them. It demonstrates that democratic evaluation is poorly prepared to manage current threats to democracy or the mediatisation of public policy. Progressive evaluation is the only approach offering some new keys to addressing certain current threats and challenges. The other orientations have some capacity to manage threats to democracy and support democratic renewal, but need further development. The article suggests that democratic evaluation could be a constructive tool for maintaining and developing democracy in an increasingly polarised and mediatised society if evaluators gain knowledge of threats to democracy, democratic transition, and democratic renewal and, informed by mediatisation and democracy research, develop the necessary awareness and competence to deal with these challenges.
Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train

This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via TT (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via TT (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods.