Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Generation of Constraints from Use Case Specifications to Support System Testing

System testing plays a crucial role in safety-critical domains, e.g., automotive, where system test cases are used to demonstrate the compliance of software with its functional and safety requirements. Unfortunately, since requirements are typically written in natural language, significant engineering effort is required to derive test cases from requirements. In such a context, automated support for generating system test cases from requirements specifications written in natural language would be highly beneficial. Unfortunately, existing approaches have limited applicability. For example, some of them require that software engineers provide formal specifications that capture some of the software behavior described using natural language. The effort needed to define such specifications is usually a significant deterrent for software developers. This paper proposes an approach, OCLgen, which largely automates the generation of the additional formal specifications required by an existing test generation approach named UMTG. More specifically, OCLgen relies on semantic analysis techniques to automatically derive the pre- and post-conditions of the activities described in use case specifications. The generated conditions are used by UMTG to identify the test inputs that cover all the use case scenarios described in use case specifications. In practice, the proposed approach enables the automated generation of test cases from use case specifications while avoiding most of the additional modeling effort required by UMTG. Results from an industrial case study show that the approach can automatically and correctly generate more than 75% of the pre- and post-conditions characterizing the activities described in use case specifications.
Beyond the Waterbed Effect: Development of Fractional Order CRONE Control with Non-Linear Reset

In this paper a novel reset control synthesis method is proposed: CRONE reset control, combining a robust fractional CRONE controller with non-linear reset control to overcome waterbed effect. In CRONE control, robustness is achieved by creation of constant phase behaviour around bandwidth with the use of fractional operators, also allowing more freedom in shaping the open-loop frequency response. However, being a linear controller it suffers from the inevitable trade-off between robustness and performance as a result of the waterbed effect. Here reset control is introduced in the CRONE design to overcome the fundamental limitations. In the new controller design, reset phase advantage is approximated using describing function analysis and used to achieve better open-loop shape. Sufficient quadratic stability conditions are shown for the designed CRONE reset controllers and the control design is validated on a Lorentz-actuated nanometre precision stage. It is shown that for similar phase margin, better performance in terms of reference-tracking and noise attenuation can be achieved.
Face Hallucination Using Cascaded Super-Resolution and Identity Priors

In this paper we address the problem of hallucinating high-resolution facial images from low-resolution inputs at high magnification factors. We approach this task with convolutional neural networks (CNNs) and propose a novel (deep) face hallucination model that incorporates identity priors into the learning procedure. The model consists of two main parts: i) a cascaded super-resolution network that upscales the low-resolution facial images, and ii) an ensemble of face recognition models that act as identity priors for the super-resolution network during training. Different from most competing super-resolution techniques that rely on a single model for upscaling (even with large magnification factors), our network uses a cascade of multiple SR models that progressively upscale the low-resolution images using steps of $2\times $ . This characteristic allows us to apply supervision signals (target appearances) at different resolutions and incorporate identity constraints at multiple-scales. The proposed C-SRIP model (Cascaded Super Resolution with Identity Priors) is able to upscale (tiny) low-resolution images captured in unconstrained conditions and produce visually convincing results for diverse low-resolution inputs. We rigorously evaluate the proposed model on the Labeled Faces in the Wild (LFW), Helen and CelebA datasets and report superior performance compared to the existing state-of-the-art.
Coordination is Hard: Electronic Market Mechanisms For Increased Eﬃciency in Transportation Logistics

The lack of coordination among carriers leads to substantial ineﬃciencies in logistics. Such coordination problems constitute fundamental problems in supply chain management for their computational and strategic complexity. We consider the problem of slot booking by independent carriers at an operator of several warehouses, and investigate recent developments in the design of electronic market mechanisms promising to address both types of complexity. Relax-and-round mechanisms describe a class of approximation mechanisms that is truthful in expectation and runs in polynomial time. While the solution quality of these mechanisms is low, we introduce a variant able to solve real-world problem sizes with high solution quality while still being incentive-compatible. We compare these mechanisms to core-selecting auctions which are not incentive-compatible, but provide stable outcomes with respect to the bids. In addition to a theoretical analysis we report results from extensive numerical experiments based on ﬁeld data. The experimental results yield a clear ranking of the mechanisms in terms of waiting time reductions and computation times.
Knowledge-based extraction of cerebral vasculature from anatomical MRI

A vessel extraction approach is presented that permits visualization of the cerebral vasculature in 3D from anatomical proton density (PD) weighted magnetic resonance imaging (MRI) volumes. The approach presented utilizes general knowledge about the shape and size of the cerebral vasculature and is divided into multi-scale vessel enhancement filtering, centre-line extraction, and surface modeling. To improve the discrimination between blood vessels and other tissue a multi-scale filtering method that enhances tubular structures is used as a pre-processing step. Centre-line extraction is applied to roughly estimate the centre-line of the vasculature involving both segmentation and skeletonization. The centre-line is used to initialize an active contour modeling process where cylinders are used to model the 3D surface of the blood vessels. The accuracy and robustness of the vessel extraction approach have been demonstrated on both simulated and real data (1mm3 voxels). On simulated data, the mean error of the estimated radii was found to be less than 0.4mm. On real data, the vasculature was successfully extracted from 20 MRI data sets using the same input parameters. An expert found the extracted vessel surfaces to coincide with the vessel walls in the data. Results from CTA data indicate that the approach will work successfully with other imaging modalities as well.
Monetary Policy and Dutch Disease: The Case of Price and Wage Rigidity

We study a model of a small open economy that specializes in the production of com- modities and that exhibits frictions in the setting of both prices and wages. We study the optimal response of monetary and exchange rate policy following a positive (negative) shock to the price of the exportable that generates an appreciation (depreciation) of the local currency. According to the calibrated version of the model, deviations from full price stability can generate welfare gains that are equivalent to almost 0.5% of lifetime consump- tion, as long as there is a signi? cant degree of rigidity in nominal wages. On the other hand, if the rigidity is concentrated in prices, the welfare gains can be at most 0.1% of lifetime consumption. We also show that a rule - formally de? ned in the paper - that resembles a "dirty ? oating" regime can approximate the optimal policy remarkably well.
Transductive Label Augmentation for Improved Deep Network Learning

A major impediment to the application of deep learning to real-world problems is the scarcity of labeled data. Small training sets are in fact of no use to deep networks as, due to the large number of trainable parameters, they will very likely be subject to overfitting phenomena. On the other hand, the increment of the training set size through further manual or semi-automatic labellings can be costly, if not possible at times. Thus, the standard techniques to address this issue are transfer learning and data augmentation, which consists of applying some sort of “transformation” to existing labeled instances to let the training set grow in size. Although this approach works well in applications such as image classification, where it is relatively simple to design suitable transformation operators, it is not obvious how to apply it in more structured scenarios. Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a label augmentation approach. We start from a small, curated labeled dataset and let the labels propagate through a larger set of unlabeled data using graph transduction techniques. This allows us to naturally use (second-order) similarity information which resides in the data, a source of information which is typically neglected by standard augmentation techniques. In particular, we show that by using known game theoretic transductive processes we can create larger and accurate enough labeled datasets which use results in better trained neural networks. Preliminary experiments are reported which demonstrate a consistent improvement over standard image classification datasets.
Deep fMRI: AN end-to-end deep network for classification of fMRI data

With recent advancements in machine learning, the research community has made tremendous advances towards the classification of neurological disorders from time-series functional MRI signals. However, existing classification techniques rely on hand-crafted features and classical machine learning models. In this paper, we propose an end-to-end model that utilizes the representation learning capability of deep learning to classify a neurological disorder from fMRI data. The proposed DeepFMRI model is comprised of three networks, namely (1) a feature extractor, (2) a similarity network, and (3) a classification network. The model takes fMRI raw time-series signals as input and outputs the predicted labels; and is trained end-to-end using back-propagation. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative model outperforms previous state-of-the-art.
Values of Estonian students, teachers and parents

R. Inglehart (1990, 2005) considers values to be one's reactions to changes in the environment. According to his approach values develop in the socialisation process. Values can be divided into traditional, modernist and postmodernist. According to Rokeach (1973), values are an element of culture, an image of the desirable that might not be directly expressed in human behaviour. Kalmus and Vihalemm (2004) found, based on Rokeach's and Schwartz's (1992) questionnaires, that Estonians consider most important values to be health, strong family ties, peace in the world, clean environment, happiness and state security. Also, the results of the study "infants' and toddlers' intelligence and the impact of the growth environment" financed by Estonian Science Foundation, allow to conclude that parents consider most important that children are healthy, happy and smart (Veisson, 2001). In the framework of the state financed project of Tallinn University "school as developmental environment and students' coping" (2003-2007) questionnaires were administered to 3838 students, 2048 parents, 620 teachers and 120 school directors. According to the mean value a hierarchy of 14 values was formed. It appeared that students and parents think that the most important is academic success, whereas teachers place academic success on the 3rd-4th place and school directors even on the 8th place. Teachers and school directors consider the most important is their school students' security and the second is honesty. Also students and parents think that honesty is the 3rd most important value at school. Students consider politeness and parents discipline worth giving the second place among school values. Students' health is relatively highly valued by teachers and school directors (in case of both groups the 3rd place). Unfortunately, children themselves and their parents think that in their school health is not very highly valued (10th place). Joy of school came on the last place in the values hierarchy.
Aponeurosis behaviour during muscular contraction: A narrative review

Abstract There is an abundance of evidence that suggests elastic tendons can enhance both animal and human muscle performance. However, in many terrestrial animals, including humans, a large proportion of the elastic tissue within the muscle-tendon unit is located within the muscle. This continuous elastic sheet, which provides muscle fibre attachment, is known as the aponeurosis. The aponeurosis has a much more complicated shape than the free tendon and it undergoes a more complicated loading regime during contraction, due to its relationship with the bulging muscle fibres, which remain isovolumetric during force production. Muscle contraction may dynamically modulate the stiffness of the aponeurosis at the same active versus passive force, by increasing the intramuscular pressure and transverse forces within the muscle, which may stretch the aponeurosis in width and subsequently reduce its longitudinal strain. Some evidence also suggests that the aponeurosis mechanical properties may be affected by muscle length, which appears to reduce the fascicle strains for a given muscle force at longer muscle lengths. This narrative review outlines the animal and human studies that have investigated aponeurosis behaviour during contraction and discusses how an elastic sheet with a variable stiffness under activation might be beneficial for muscle performance. While it is clear that our understanding of the role of aponeurosis is lacking, it is hoped that further work will attempt to determine how this tissue contributes to power amplification and elastic energy savings during locomotion and potentially uncover how aponeurosis behaviour contributes to injury risk.
Circular RNAs: a new class of biomarkers as a rising interest in laboratory medicine

Abstract Circular RNAs (circRNAs) are a distinct family of RNAs derived from the non-regular process of alternative splicing. CircRNAs have recently gained interest in transcriptome research due to their potential regulatory functions during gene expression. CircRNAs can act as microRNA sponges and affect transcription through their complex involvement in regular transcriptional processes. Some early studies also suggested significant roles for circRNAs in human diseases, especially cancer, as biomarkers and potential clinical targets. Therefore, there is a great need for laboratory scientists to translate these findings into clinical tools to advance testing for human diseases. To facilitate a better understanding of the promise of circRNAs, we focus this review on selected basic aspects of circRNA research, specifically biogenesis, function, analytical issues regarding identification and validation and examples of expression data in relation to human diseases. We further emphasize the unique challenges facing laboratory medicine with regard to circRNA research, particularly in the development of robust assays for circRNA detection in different body fluids and the need to collaborate with clinicians in the design of clinical studies.
Bike-Sharing Dynamic Scheduling Model Based on Spatio-Temporal Graph

With the fast development of bicycle sharing, an excellent distribution system is a necessary factor for the development of the industry, unsuitable system will lead to the resource waste. We proposed a novel dynamic scheduling model to build road network using Spatio-Temporal Graph (STG) to calculate the number of bicycles in each area. Our method has higher accuracy when use bike trip data in New York City. We also get mobility patterns and rules which can help us to improve the urban planning.
Ensemble of Rotation Trees for Imbalanced Medical Datasets

Medical datasets are often predominately composed of “normal” examples with only a small percentage of “abnormal” ones and how to correctly recognize the abnormal examples is very meaningful. However, conventional classification learning methods try to pursue high accuracy by assuming that the number of any class examples is similar to each other, which lead to the fact that the abnormal class examples are usually ignored and misclassified to normal ones. In this paper, we propose a simple but effective ensemble method called ensemble of rotation trees (ERT) to handle this problem in imbalanced medical datasets. ERT learns an ensemble through the following four stages: (1) undersampling subsets from normal class, (2) obtaining new balanced training sets through combining each subset and abnormal class, (3) inducing a rotation matrix on randomly sampling subset of each new balanced set, and in each rotation matrix space, (4) learning a decision tree on each balanced training data. Here, the rotation matrix is mainly to improve the diversity between ensemble members, and undersampling technique aims to improve the performance of learned models on abnormal class. Experimental results show that, compared with other state-of-the-art methods, ERT shows significantly better performance for imbalanced medical datasets.
Determining the Most Harmful Roads in Search for System Optimal Routing

Recent advances in Intelligent Transportation Systems (ITS), navigation tools and personal smart devices can be used to relieve congestion and thus improve traffic performance. Transportation networks, however, are seen as complex networks and therefore present a control challenge. In this paper we demonstrate that using an information dissemination technique and providing minimal but the right context to the population can steer the system into a more efficient operational state. As every commuter chooses the most optimal route from his/her own perspective, traffic distribution on the road network becomes heterogeneous, resulting in a small number of roads, which are largely overpopulated, while others remain underutilized [1]. In order to achieve a more homogeneous road utilization and thus reduce congestion levels, we proposea simple routing control strategy of informing drivers to avoid certain roads, which are chosen based on simulated outcomes of their closing. We demonstrate that the full removal of certain road segments from the network can redistribute traffic in a socially beneficial way, leading to an increase in transit performance on a city level. By considering a real road network and realistic traffic patterns we are able to validate our approach on a city scale. We identify the most harmful roads and quantify their negative e ffect on the system. Furthermore, since completely removing roads can be considered a rather extreme measure, we introduce the concept of soft closing. Instead of informing the whole population to avoid a certain road, we inform only a portion of the drivers, further improving the network utilization. We use the city of Singapore as a case study for our traffic assignment model which we calibrate and validate using both survey and GPS tracking devices data. By identifying and soft closing one road segment from the entire Singaporean road network (240,000 segments) we can reduce the average travel time of all 300,000 daily commuters by 6%, equating to 8,000 saved man hours.
On Perfect Nash Equilibria of Polymatrix Games

When confronted with multiple Nash equilibria, decision makers have to refine their choices. Among all known Nash equilibrium refinements, the perfectness concept is probably the most famous one. It is known that weakly dominated strategies of two-player games cannot be part of a perfect equilibrium. In general, this undominance property however does not extend to -player games (E. E. C. van Damme, 1983). In this paper we show that polymatrix games, which form a particular class of -player games, verify the undominance property. Consequently, we prove that every perfect equilibrium of a polymatrix game is undominated and that every undominated equilibrium of a polymatrix game is perfect. This result is used to set a new characterization of perfect Nash equilibria for polymatrix games. We also prove that the set of perfect Nash equilibria of a polymatrix game is a finite union of convex polytopes. In addition, we introduce a linear programming formulation to identify perfect equilibria for polymatrix games. These results are illustrated on two small game applications. Computational experiments on randomly generated polymatrix games with different size and density are provided.
A Posteriori Hyperspectral Anomaly Detection for Unlabeled Classification

Anomaly detection (AD) generally finds targets that are spectrally distinct from their surrounding neighborhoods but cannot discriminate its detected targets one from another. It cannot even perform classification because there is no prior knowledge about the data. This paper presents a new approach to AD, to be called a posteriori AD for unlabeled anomaly classification where a posteriori indicates that information obtained directly from processing data is used as new information for subsequent data processing. In particular, a posteriori AD uses a Gaussian filter to capture spatial correlation of detected anomalies as a posteriori information which is included as new information for further AD. In doing so, a posteriori AD develops an iterative version of AD, referred to as iterative anomaly detection (IAD), which implements AD by feeding back Gaussian-filtered AD maps in an iterative manner. It then uses an unsupervised target detection algorithm to identify spectrally distinct anomalies that can be used to specify particular anomaly classes. To terminate IAD, an automatic stopping rule is also derived. Finally, it uses identified distinct anomalies as desired target signatures to implement constrained energy minimization (CEM) to classify all detected anomalies into unlabeled classes. The experimental results show that a posteriori AD is indeed very effective in unlabeled anomaly classification.