Miracle-dz/UniFAR-FTUs huggingface dataset:
Scheduling Operations in a Flow Network with Flexible Preventive Maintenance: A (max, +)

The following work proposes a (max,+) optimization model for scheduling operations on an oil seaport considering flexible maintenance activities on valves. The work is based on previous results for the same case study, where fixed maintenance was studied in the framework of scheduling oil transfer operations through a pipeline network. The case study is a Venezuelan seaport for oil export and real operational constraints and goals are modeled. Results corroborate the drawbacks that arise when considering fixed maintenance in the system. Moreover, the adjustments made to obtain a model considering maintenance relaxation are straightforward and intuitive. Some linear representations of the problem are also explored through prioritization of certain tasks.
A Message-Passing Architecture without Public Ids Using Send-to-Behavior

We explore a novel model of computation based on nodes that have no public addresses (ids). We define nodes as concurrent, message-passing computational entities in an abstract communication medium, similar to the Actor model, but with all public node ids elided. Instead, drawing inspiration from biological systems, we postulate a send-to-behavior language construct to enable anonymous one-way communication. A behavior, defined as a function of input to actions, is also an intensional definition of the subset of nodes that express it. Sending to a behavior is defined to deliver the message to one or more nodes that implement that behavior.
Interweaving OAI-PMH data sources with the linked data cloud

The Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) has found widespread adoption for exchanging bibliographic metadata. In parallel, the W3C's Linking Open Data Initiative exposes and interlinks structured data from a variety of data sources on the web. Since many of these data sources contain valuable information for institutional repositories (e.g., shared concept definitions, thesauri, etc.), we believe that institutions that currently expose their data via OAI-PMH can benefit if they integrate their metadata with the data available in the Linked Data cloud. To achieve such an integration, we must bridge the OAI-PMH-specific protocol characteristics that currently prevent OAI-PMH metadata from being interoperable with the Linked Data approach of exposing data. As first contribution of this paper, we describe a possible solution for exposing OAI-PMH metadata on the web as part of the Linked Data cloud. As a second contribution, we present a rule-based mechanism for linking these metadata with other relevant data sources together with a case study that describes possible linking scenarios for three representative OAI-PMH data providers. Finally, we discuss certain quality criteria that OAI-PMH metadata must meet to benefit from data exposed by other Linked Data sources.
ISOMAP TRACKING WITH PARTICLE FILTER

The problem of tracking an object in an image sequence involv es challenges like translation, in-plane and out-of-plane rotations, sc aling, variations in ambient light and occlusions. A model of an object to be tracked is built off-line by making a training set with images of the object with differen t poses. A dimensionality reduction technique is used to capture the variations n the training images of the object. This gives a low-dimensional representation of high-dimensional data. Isometric feature mapping, also known as Isomap, is the unsu pervised nonlinear dimensionality reduction technique used to capture the tru degrees of freedom in high-dimensional data. Once the training data is reduced to low-dimensions it forms a part of the state-vector of the object to be tracked. Tracki ng is done in a stochastic recursive Bayesian framework. Particle filters, which are b ased on the recursive Bayesian framework, track the state of the object in presenc e of nonlinearity and non-Gaussianity. The focus of this thesis is the problem of t racking a person’s head and also estimating its pose in each frame using Isomap for di mensionality reduction and particle filter for tracking. ‘Isomap tracking with particle filter’ algorithm is capable of handling rapid translation and out-of-plane r otation of a person’s head with a relatively small amount of training data. The perform ance of the tracker is demonstrated on an image sequence with a person’s head under going translation and out-of-plane rotation.
Changes on EMG activation in healthy subjects and incomplete SCI patients following a robot-assisted locomotor training

The aim of this study was to understand and measure the lower limbs muscular activation patterns both in healthy and spinal cord injured (SCI) subjects during robot-assisted locomotor exercise. Electromyographic (EMG) activity of four leg's muscles (rectus and biceps femoris, tibialis anterioris and gastrocnemius) was recorded and analyzed at two different percentages of body weight support, three stepping velocities and three different modalities. SCI subjects were recorded also after four weeks training to evaluate the effectiveness of lower limb robot-assisted rehabilitative treatment. A multi-factor ANOVA on the integrated muscle activity (IEMG) parameters both in healthy and SCI subjects was performed. Higher muscular activities both in healthy subjects and SCI patients were found during the exercises using the "DGO active" modality and higher stepping velocities. A significant increased bilateral muscular activity was observed in each SCI subject after the rehabilitation treatment. The method proposed to analyze EMG data provides a quantitative description of the lower limb muscular recruitment and can contribute to identify the optimal rehabilitation treatment's conditions.
Efficient design of oversampled NPR GDFT filterbanks

We propose a flexible, efficient design technique for the prototype filter of an oversampled near perfect reconstruction (NPR) generalized discrete Fourier transform (GDFT) filterbank. Such filterbanks have several desirable properties for subband processing systems that are sensitive to aliasing, such as subband adaptive filters. The design criteria for the prototype filter are explicit bounds (derived herein) on the aliased components in the subbands and the output, the distortion induced by the filterbank, and the imaged subband errors in the output. It is shown that the design of an optimal prototype filter can be transformed into a convex optimization problem, which can be efficiently solved. The proposed design technique provides an efficient and effective tool for exploring many of the inherent tradeoffs in the design of the prototype filter, including the tradeoff between aliasing in the subbands and the distortion induced by the filterbank. We calculate several examples of these tradeoffs and demonstrate that the proposed method can generate filters with significantly better performance than filters obtained using current design methods.
Automated Tracking of Animal Posture and Movement during Exploration and Sensory Orientation Behaviors

Background The nervous functions of an organism are primarily reflected in the behavior it is capable of. Measuring behavior quantitatively, at high-resolution and in an automated fashion provides valuable information about the underlying neural circuit computation. Accordingly, computer-vision applications for animal tracking are becoming a key complementary toolkit to genetic, molecular and electrophysiological characterization in systems neuroscience. Methodology/Principal Findings We present Sensory Orientation Software (SOS) to measure behavior and infer sensory experience correlates. SOS is a simple and versatile system to track body posture and motion of single animals in two-dimensional environments. In the presence of a sensory landscape, tracking the trajectory of the animal's sensors and its postural evolution provides a quantitative framework to study sensorimotor integration. To illustrate the utility of SOS, we examine the orientation behavior of fruit fly larvae in response to odor, temperature and light gradients. We show that SOS is suitable to carry out high-resolution behavioral tracking for a wide range of organisms including flatworms, fishes and mice. Conclusions/Significance Our work contributes to the growing repertoire of behavioral analysis tools for collecting rich and fine-grained data to draw and test hypothesis about the functioning of the nervous system. By providing open-access to our code and documenting the software design, we aim to encourage the adaptation of SOS by a wide community of non-specialists to their particular model organism and questions of interest.
An Approach of Shadow Education in Greece: Sociological Perspectives

The purpose of this research was to investigate the phenomenon of shadow education in Greece during the second decade of the 21 st century, focusing on the differences between formal and shadow education, the characteristics (of shadow education) and the reasons of its existence. The results showed that the liberalization of education during the recent decades was accurately implemented in the institution of shadow education. Knowledge is becoming a commercialized product and the choice for private education is made through private-financial criteria. Restrictive and maladaptive educational policies and decisions about the educational needs of Greek students exacerbate the purposes of shadow education development.
The Solution of Weakly Nonlinear Oscillatory Problems with No Damping Using MAPLE

Weakly nonlinear oscillatory problems in engineering dynamics are frequently presented by nonlinear ordinary differential equations. Very few of such equations have exact solutions, thus the need for approximation techniques. In this study, the classical perturbation method, namely the Lindstedt-Poincare method (L-P method) is discussed. In most work, the L-P method is used to obtain uniformly valid approximate solutions to second order only, as perturbation procedure usually involves cumbersome algebraic manipulations and calculations which are time-consuming and prone to errors. Therefore, in this paper we manage to get the solution to fifth order by using our own built in Maple package based on Maple 14 and we include the graphical presentations of the results.
Count data models for demographic data.

"This paper deals with the estimation of single equation models in which the counts are regressed on a set of observed individual characteristics such as age, gender, or nationality. We propose a generalized event count model to simultaneously allow for a wide class of count data models and account for over- and underdispersion. This model is successfully applied to German data on fertility, divorces and mobility. " (SUMMARY IN FRE)
Augmented Multiple Instance Regression for Inferring Object Contours in Bounding Boxes

In this paper, we address the problem of the high annotation cost of acquiring training data for semantic segmentation. Most modern approaches to semantic segmentation are based upon graphical models, such as the conditional random fields, and rely on sufficient training data in form of object contours. To reduce the manual effort on pixel-wise annotating contours, we consider the setting in which the training data set for semantic segmentation is a mixture of a few object contours and an abundant set of bounding boxes of objects. Our idea is to borrow the knowledge derived from the object contours to infer the unknown object contours enclosed by the bounding boxes. The inferred contours can then serve as training data for semantic segmentation. To this end, we generate multiple contour hypotheses for each bounding box with the assumption that at least one hypothesis is close to the ground truth. This paper proposes an approach, called augmented multiple instance regression (AMIR), that formulates the task of hypothesis selection as the problem of multiple instance regression (MIR), and augments information derived from the object contours to guide and regularize the training process of MIR. In this way, a bounding box is treated as a bag with its contour hypotheses as instances, and the positive instances refer to the hypotheses close to the ground truth. The proposed approach has been evaluated on the Pascal VOC segmentation task. The promising results demonstrate that AMIR can precisely infer the object contours in the bounding boxes, and hence provide effective alternatives to manually labeled contours for semantic segmentation.
Efficient Reinforcement Learning for Motor Control

Artificial learners often require many more trials than humans or animals when learning motor control tasks in the absence of expert knowledge. We implement two key ingredients of biological learning systems, generalization and incorporation of uncertainty into the decision-making process, to speed up artificial learning. We present a coherent and fully Bayesian framework that allows for efficient artificial learning in the absence of expert knowledge. The success of our learning framework is demonstrated on challenging nonlinear control problems in simulation and in hardware.
Near-Circular Corner and Edge Detection Operators

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence corner detection is often used for this purpose. However, to obtain a higher level description of an image, such as identification of objects, additional information such as edges is required, and more recently detectors have been proposed that find both edges and corners. Recently, finite-element based methods have been used to develop gradient operators for edge detection that have improved angular accuracy over standard techniques. We extend this work to corner detection, enabling edge and corner detection to be integrated. In addition we present a combined operator, enabling edge and corner detection to be achieved concurrently, and we demonstrate that accuracy is comparable to well- known existing corner detectors and edge detectors, and, as standard post-smoothing of the corner map is not required, significantly reduced computation time can be achieved.
Exclusive electrodisintegration of 3He at high Q2.I. Generalized eikonal approximation

We develop a theoretical framework for calculation of high $Q^2$ exclusive electro-disintegration of A=3 systems. The main result of this work is the calculation of the final state interaction of the struck energetic nucleon with recoil nucleons within the generalized eikonal approximation (GEA) which allows us to account for the finite and relatively large momenta of the bound nucleons in the nucleus. The important advantage of this approach is the possibility to study in a self-consistent way the short range correlations in nuclei. This is due to the fact that GEA does not require a stationary condition for recoil nucleons as conventional Glauber approximation does. As a result GEA treats explicitly the Fermi motion of recoil nucleons in the nucleus.
Multi-vehicle path planning for non-line of sight communication

This paper presents the formulation and hardware results of a mixed-integer linear programming approach to online connectivity-constrained trajectory planning of autonomous helicopters through cluttered environments. A lead vehicle must execute a certain mission whereby wireless line of sight communication to its ground station is lost. Relay helicopters are therefore introduced that must position themselves in such a way that indirect line of sight connectivity between the leader and the ground station is always maintained. The corresponding coordinated multi-vehicle trajectory optimization is tackled using both centralized and distributed receding horizon planning strategies. Binary variables are used to capture connectivity, obstacle- and collision avoidance constraints, extending earlier formulations to model nonconvex obstacles more efficiently. Simulation, hardware in the loop, and flight test results are presented for a centralized two-helicopter mission. Simulation results for a distributed scenario are given as well
Minimizing Odometry Drift by Vanishing Direction References

Robotic systems that operate in environments without access to global references like GNSS normally estimate their position by odometry. The incremental nature of this measurement is subject to drift that accumulates to large errors in the position estimate. We minimize this drift by exploiting scene knowledge in form of vanishing directions. These provide a scene referenced orientation measurement which we apply in an orientation filter to correct the estimated attitude and in turn improve the position estimation. The experiments show that the resulting accuracy is comparable to a referenced inertial measurement unit and that our method can thus augment or replace such sensors.
Monitoring Intake Gestures using Sensor Fusion (Microsoft Kinect and Inertial Sensors) for Smart Hom

Smart home technologies help post-stroke patients complete activities of daily living (ADL) independently, while saving their time, money, and extra effort. The patients are otherwise required to visit rehabilitation clinics for formal care. Toward this goal, we present our approach to spot specific $'/¶s of eating and drinking in a home setting. We fuse inertial and Microsoft Kinect sensors WRPRQLWRUWKHSDWLHQWV� intake gestures including fine cutting, loading food, and maneuvering the food to the mouth. For both sides of the body, we measured (i) position of the wrist, elbow, and shoulder; (ii) angular displacements at the elbow and shoulder joints; and (iii) acceleration of the spoon/fork/cup which are held by the subject. The use of Kinect allows distinguishing between healthy and paralyzed body sides which is a common problem in tele-rehab. The system was tested successfully on healthy subjects; because stroke-patients show slower motion in a shorter range, the system would serve them at least equally well.
A Model-Based Approach for Discrete Data Clustering and Feature Weighting Using MAP and Stochastic Complexity

In this paper, we consider the problem of unsupervised discrete feature selection/weighting. Indeed, discrete data are an important component in many data mining, machine learning, image processing, and computer vision applications. However, much of the published work on unsupervised feature selection has concentrated on continuous data. We propose a probabilistic approach that assigns relevance weights to discrete features that are considered as random variables modeled by finite discrete mixtures. The choice of finite mixture models is justified by its flexibility which has led to its widespread application in different domains. For the learning of the model, we consider both Bayesian and information-theoretic approaches through stochastic complexity. Experimental results are presented to illustrate the feasibility and merits of our approach on a difficult problem which is clustering and recognizing visual concepts in different image data. The proposed approach is successfully applied also for text clustering.
Isms, ideologies and setting the agenda for public debate

ABSTRACT This article presents a long-term account of isms from antiquity to the present. It discusses the spread of isms into nearly all spheres of life and why isms are still perceived as ideological. Based on a combined analysis of word frequencies in large digital libraries and more detailed contextual discussions of patriotism, liberalism, nationalism and totalitarianism, the article studies the emergence of isms and their potential transformation from pejorative labels to concepts of group-identification and analytical categories. Isms have been highlighted as future-oriented concepts on which modern political ideologies were built from the early nineteenth century on. Isms have also been identified as being collectively responsible for the twentieth-century experience that has been conceptualized as totalitarianism. While elaborating the notion of isms as ideological future-oriented ‘concepts of movement’, the article points out their crucial role in forging historic roots for these modern ideologies as well as for religious and cultural phenomena. By emphasizing the rhetorical potential in using isms as words of abuse, forging traditions for isms and claiming universal applicability to isms, the article provides insight into the constant renegotiation of the relationship between ideologies and isms, a phenomenon that has gained increased topicality in recent decades.
The teacher scheme for educational dialogue analysis (T-SEDA): developing a research-based observation tool for supporting teacher inquiry into pupils’ participation in classroom dialogue

ABSTRACT In this paper we examine two systematic observation methods intended to be used by pre-service and in-service teachers to help increase their awareness of children's participation in productive classroom dialogue. We identify the affordances of these methods for supporting teachers’ reflective practice, focusing in this case on students’ equitable participation in science groupwork activities. This involves the use of Teacher-SEDA (T-SEDA), a sub-scheme of SEDA (Cam-UNAM Scheme for Educational Dialogue Analysis), which was empirically trialled by a mixed group of researchers and teachers, using video-recordings from primary science classrooms in the UK and Mexico. The T-SEDA trials reported in this paper compare a ‘simulated live’ approach based on time-sampling techniques, with a ‘follow-up analysis’ approach, which uses audio-recordings and transcripts. The findings suggest that using either technique regularly can aid teachers in noticing classroom events and adjusting teaching accordingly. The ‘live’ coding approach appears to be the more practical method that teachers could use to audit the development of classroom equitable participation. However, the follow-up analysis emerged as a more informative approach, shedding light on more ambiguous cases of relative participation.
SANATLA TERAPİ PROGRAMININ BİREYLERİN KAYGI, SOSYAL KAYGI VE SAĞLIK KAYGISI DÜZEYLERİ ÜZERİNE ETKİSİ

Bu calismanin amaci sanatla terapi programinin bireylerin kaygi, sosyal kaygi ve saglik kaygisi duzeyleri uzerindeki etkisini incelemektir. Bu etkinin degerlendirilmesi icin yari deneysel bir yonteme basvurulmus, kontrol grupsuz on-test son-test deneme modeli kullanilmistir. Program calisma grubuna haftada uc saatlik oturumlar seklinde, yedi hafta olarak uygulanmistir. Terapi programini degerlendirmek icin on test ve son testlerde Beck Anksiyete Olcegi (BAO), Liebowitz Sosyal Anksiyete Olcegi (LSAS) ve Saglik Anksiyetesi Olcegi (SAO) kullanilmistir. Arastirmada elde edilen verilerin analizinde Wilcoxon isaretli sira testi kullanilmistir. Yapilan analizlerde sanatla terapi programinin katilimcilarin sosyal kaygi ve saglik kaygisi duzeylerini azaltmada etkili oldugu, kaygi duzeyini azaltmada ise etkili olmadigi bulunmustur.
Low Bandwidth Vocoding using EM Sensor and Acoustic Signal Processing

Low-power EM radar-like sensors have made it possible to measure properties of the human speech production system in real-time, without acoustic interference [1]. By combining these data with the corresponding acoustic signal, we've demonstrated an almost 10-fold bandwidth reduction in speech compression, compared to a standard 2.4 kbps LPC10 protocol used in the STU-III (Secure Terminal Unit, third generation) telephone. This paper describes a potential EM sensor/acoustic based vocoder implementation.