nils-herrmann/clean_scirepeval huggingface dataset:
Robust Interpolation of Correspondences for Large Displacement Optical Flow

The interpolation of correspondences (EpicFlow) was widely used for optical flow estimation in most-recent works. It has the advantage of edge-preserving and efficiency. However, it is vulnerable to input matching noise, which is inevitable in modern matching techniques. In this paper, we present a Robust Interpolation method of Correspondences (called RicFlow) to overcome the weakness. First, the scene is over-segmented into superpixels to revitalize an early idea of piecewise flow model. Then, each model is estimated robustly from its support neighbors based on a graph constructed on superpixels. We propose a propagation mechanism among the pieces in the estimation of models. The propagation of models is significantly more efficient than the independent estimation of each model, yet retains the accuracy. Extensive experiments on three public datasets demonstrate that RicFlow is more robust than EpicFlow, and it outperforms state-of-the-art methods.
Development of Implantable Wireless Sensor Nodes for Animal Husbandry and MedTech Innovation

In this paper, we report the development, evaluation, and application of ultra-small low-power wireless sensor nodes for advancing animal husbandry, as well as for innovation of medical technologies. A radio frequency identification (RFID) chip with hybrid interface and neglectable power consumption was introduced to enable switching of ON/OFF and measurement mode after implantation. A wireless power transmission system with a maximum efficiency of 70% and an access distance of up to 5 cm was developed to allow the sensor node to survive for a duration of several weeks from a few minutes' remote charge. The results of field tests using laboratory mice and a cow indicated the high accuracy of the collected biological data and bio-compatibility of the package. As a result of extensive application of the above technologies, a fully solid wireless pH sensor and a surgical navigation system using artificial magnetic field and a 3D MEMS magnetic sensor are introduced in this paper, and the preliminary experimental results are presented and discussed.
Takagi-Sugeno Fuzzy Control of Induction Motor

This paper deals with the synthesis of fuzzy state feedback controller of induction motor with optimal performance. First, the Takagi-Sugeno (T-S) fuzzy model is employed to approximate a non linear system in the synchronous d-q frame rotating with electromagnetic field-oriented. Next, a fuzzy controller is designed to stabilise the induction motor and guaranteed a minimum disturbance attenuation level for the closed-loop system. The gains of fuzzy control are obtained by solving a set of Linear Matrix Inequality (LMI). Finally, simulation results are given to demonstrate the controller’s effectiveness. Rejection disturbance, fuzzy modelling, open-loop control, Fuzzy feedback controller, fuzzy observer, Linear Matrix Inequality (LMI)
D Mapping with an RGB-D Camera

In this article we present a novel mapping system that robustly generates highly accurate 3D maps using an RGB-D camera. Our approach does not require any further sensors or odometry. With the availability of low-cost and light-weight RGB-D sensors such as the Microsoft Kinect, our approach applies to small domestic robots such as vacuum cleaners as well as flying robots such as quadrocopters. Furthermore, our system can also be used for free-hand reconstruction of detailed 3D models. In addition to the system itself, we present a thorough experimental evaluation on a publicly available benchmark dataset. We analyze and discuss the influence of several parameters such as the choice of the feature descriptor, the number of visual features, and validation methods. The results of the experiments demonstrate that our system can robustly deal with challenging scenarios such as fast cameras motions and feature-poor environments while being fast enough for online operation. Our system is fully available as open-source and has already been widely adopted by the robotics community.
InterChain : A Framework to Support Blockchain Interoperability

Blockchains have supported applications in a large set of areas, and the demand for a framework by which blockchains interact with each other has emerged. We propose a new blockchain interoperability framework, called InterChain, which supports scalable and secure inter-operabilities between any pair of blockchains. We present the architecture of the InterChain, and give a threehandshaking method to achieve the asset transfer between separated blockchains. The framework can be easily extended to other applications of cross-chain transactions besides asset transfers.
An Automated Bengali Sign Language Recognition System Based on Fingertip Finder Algorithm

This paper presents a new algorithm to identify Bengali Sign Language (BdSL) for recognizing 46 hand gestures, including 9 gestures for 11 vowels, 28 gestures for 39 consonants and 9 gestures for 9 numerals according to the similarity of pronunciation. The image was first re-sized and then converted to binary format to crop the region of interest by using only top-most, left-most and right-most white pixels. The positions of the finger-tips were found by applying a fingertip finder algorithm. Eleven features were extracted from each image to train a multilayered feedforward neural network with a back-propagation training algorithm. Distance between the centroid of the hand region and each finger tip was calculated along with the angles between each fingertip and horizontal x axis that crossed the centroid. A database of 2300 images of Bengali signs was constructed to evaluate the effectiveness of the proposed system, where 70%, 15% and 15% images were used for training, testing, and validating, respectively. Experimental result showed an average of 88.69% accuracy in recognizing BdSL which is very much promising compare to other existing methods.
Generation of locomotion trajectories for series elastic and viscoelastic bipedal robots

Series-elastic and viscoelastic robots can provide performance gains in applications with high dynamics. Harnessing these, requires an understanding of the dynamics of the system, which can be gained using optimization-based methods. The result are motions which make optimal use of the intrinsic behavior, possibly exceeding the performance of an equivalent rigid-body robot. We present a collocation framework which enables both automatic computation of contact-switching patterns and allows the full utilization of the dynamics of the compliant system. The formulation also addresses the problem of redundant torque generation in viscoelastic actuators. The effectiveness of this method was demonstrated in simulations as well as experiments with a compliant bipedal robot. The approach is capable of providing gait primitives, longer gait sequences containing multiple steps as well as generating extremely dynamic motions, e.g. somersaults.
Machine Learning for the Detection of Oil Spills in Satellite Radar Images

During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community. We use our particular case study to illustrate such issues as problem formulation, selection of evaluation measures, and data preparation. We relate these issues to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications. Our solutions to these issues are implemented in the Canadian Environmental Hazards Detection System (CEHDS), which is about to undergo field testing.
Self-Presentation and Verbal Deception : Do Self-Presenters Lie More ?

This study examined the effects of self-presentation goals on the amount and type of verbal deception used by participants in same-gender and mixed-gender dyads. Participants were asked to engage in a conversation that was secretly videotaped. Self-presentational goal was manipulated, where one member of the dyad (the self-presenter) was told to either appear (a) likable, (b) competent, or (c) was told to simply get to know his or her partner (control condition). After the conversation, self-presenters were asked to review a video recording of the interaction and identify the instances in which they had deceived the other person. Overall, participants told more lies when they had a goal to appear likable or competent compared to participants in the control condition, and the content of the lies varied according to self-presentation goal. In addition, lies told by men and women differed in content, although not in quantity.
Domain Modeling and Application Development of an Archetype- and XML-based EHRS. Practical Experiences and Lessons Learnt.

Access to patient data within the hospital or between hospitals is still problematic since a variety of information systems is in use applying different vendor specific terminologies and underlying knowledge models. Beyond, the development of electronic health record systems (EHRSs) is time and resource consuming. Thus, there is a substantial need for a development strategy of standardized EHRSs. We are applying a reuse-oriented process model and demonstrate its feasibility and realization on a practical medical use case, which is an EHRS holding all relevant data arising in the context of treatment of tumors of the sella region. In this paper, we describe the development process and our practical experiences.



Requirements towards the development of the EHRS were collected by interviews with a neurosurgeon and patient data analysis. For modelling of patient data, we selected openEHR as standard and exploited the software tools provided by the openEHR foundation. The patient information model forms the core of the development process, which comprises the EHR generation and the implementation of an EHRS architecture. Moreover, a reuse-oriented process model from the business domain was adapted to the development of the EHRS.



The reuse-oriented process model is a model for a suitable abstraction of both, modeling and development of an EHR centralized EHRS. The information modeling process resulted in 18 archetypes that were aggregated in a template and built the boilerplate of the model driven development. The EHRs and the EHRS were developed by openEHR and W3C standards, tightly supported by well-established XML techniques. The GUI of the final EHRS integrates and visualizes information from various examinations, medical reports, findings and laboratory test results.



We conclude that the development of a standardized overarching EHR and an EHRS is feasible using openEHR and W3C standards, enabling a high degree of semantic interoperability. The standardized representation visualizes data and can in this way support the decision process of clinicians.
Ocular manifestations of scleroderma.

Systemic sclerosis is a chronic multi-system disorder predominantly affecting the skin, musculoskeletal, gastrointestinal, pulmonary, and renal systems. Although the exact etiology is unknown, recent evidence suggests that immune activation play a pivotal role in the pathogenesis. Ocular involvement in systemic sclerosis has been documented; however, due to the rare nature of the disease, most papers have been single case reports or small case series. This review paper aims to consolidate the findings of previous papers with a view to providing a comprehensive review of the ocular manifestations of systemic sclerosis.
Chapter 6using environmental niche modeling to study the late devonian biodiversity crisis

Geographic ranges are estimated for brachiopod and bivalve species during the late Middle (mid-Givetian) to the middle Late (terminal Frasnian) Devonian to investigate range changes during the time leading up to and including the Late Devonian biodiversity crisis. Species ranges were predicted using GARP (Genetic Algorithm using Rule-set Prediction), a modeling program developed to predict fundamental niches of modern species. This method was applied to fossil species to examine changing ranges during a critical period of Earth’s history. Comparisons of GARP species distribution predictions with historical understanding of species occurrences indicate that GARP models predict accurately the presence of common species in some depositional settings. In addition, comparison of GARP distribution predictions with species-range reconstructions from geographic information systems (GIS) analysis suggests that GARP modeling has the potential to predict species ranges more completely and tailor ranges more specifically to environmental parameters than GIS methods alone. Thus, GARP modeling is a potentially useful tool for predicting fossil species ranges and can be used to address a wide array of palaeontological problems. The use of GARP models allows a statistical examination of the relationship of geographic range size with species survival during the Late Devonian. Large geographic range was statistically associated with species survivorship across the crisis interval for species examined in the linguiformis Zone but not for species modeled in the preceding Lower varcus or punctata zones. The enhanced survival benefit of having a large geographic range, therefore, appears to be restricted to the biodiversity crisis interval.
A prospective study on the clinical features of chronic canine atopic dermatitis and its diagnosis.

Canine atopic dermatitis (CAD) is a multifaceted disease associated with exposure to various offending agents such as environmental and food allergens. The diagnosis of this condition is difficult because none of the typical signs are pathognomonic. Sets of criteria have been proposed but are mainly used to include dogs in clinical studies. The goals of the present study were to characterize the clinical features and signs of a large population of dogs with CAD, to identify which of these characteristics could be different in food-induced atopic dermatitis (FIAD) and non-food-induced atopic dermatitis (NFIAD) and to develop criteria for the diagnosis of this condition. Using simulated annealing, selected criteria were tested on a large and geographically widespread population of pruritic dogs. The study first described the signalment, history and clinical features of a large population of CAD dogs, compared FIAD and NFIAD dogs and confirmed that both conditions are clinically indistinguishable. Correlations of numerous clinical features with the diagnosis of CAD are subsequently calculated, and two sets of criteria associated with sensitivity and specificity ranging from 80% to 85% and from 79% to 85%, respectively, are proposed. It is finally demonstrated that these new sets of criteria provide better sensitivity and specificity, when compared to Willemse and Prélaud criteria. These criteria can be applied to both FIAD and NFIAD dogs.
Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study

It is well known that clothing fashion is a distinctive and often habitual trend in the style in which a person dresses. Clothing fashions are usually expressed with visual stimuli such as style, color, and texture. However, it is not clear which visual stimulus places higher/lower influence on the updating of clothing fashion. In this study, computer vision and machine learning techniques are employed to analyze the influence of different visual stimuli on clothing-fashion updates. Specifically, a classification-based model is proposed to quantify the influence of different visual stimuli, in which each visual stimulus’s influence is quantified by its corresponding accuracy in fashion classification. Experimental results demonstrate that, on clothing-fashion updates, the style holds a higher influence than the color, and the color holds a higher influence than the texture.
CPS approach to checking norm operation of a brake-by-wire system

For better controllability and energy-efficiency, more vehicle functions are being implemented via electronic control systems in place of traditional mechanical control systems. However, such transitions are creating new, unprecedented risks such as software bugs or hardware glitches, all of which can lead to serious safety risks. Recent real-world examples and research literature have been covering them under the name of vehicle misbehavior. In this paper, we present a new way of checking norm operations, called BAD (Brake Anomaly Detection), which detects any vehicle misbehavior in the Brake-by-Wire system. We focus on the braking system since it is a prototypical safety-critical and cyber-physical system. We first propose a new method for constructing norm models of braking and then show how anomalies are detected by BAD using the constructed models. Finally, we discuss how to verify the results, especially in the context of false positives. Our evaluation results show that BAD can effectively detect various types of anomaly in the braking system.
Applying Particle Swarm Optimization to Pairwise Testing

Combinatorial testing (also called interaction testing) is an effective specification-based test input generation technique. By now most of research work in combinatorial testing aims to propose novel approaches trying to generate test suites with minimum size that still cover all the pairwise, triple, or n-way combinations of factors. Since the difficulty of solving this problem is demonstrated to be NP-hard, existing approaches have been designed to generate optimal or near optimal combinatorial test suites in polynomial time. In this paper, we try to apply particle swarm optimization (PSO), a kind of meta-heuristic search technique, to pairwise testing (i.e. a special case of combinatorial testing aiming to cover all the pairwise combinations). To systematically build pairwise test suites, we propose two different PSO based algorithms. One algorithm is based on one-test-at-a-time strategy and the other is based on IPO-like strategy. In these two different algorithms, we use PSO to complete the construction of a single test. To successfully apply PSO to cover more uncovered pairwise combinations in this construction process, we provide a detailed description on how to formulate the search space, define the fitness function and set some heuristic settings. To verify the effectiveness of our approach, we implement these algorithms and choose some typical inputs. In our empirical study, we analyze the impact factors of our approach and compare our approach to other well-known approaches. Final empirical results show the effectiveness and efficiency of our approach.
Virtual-Machine Abstraction and Optimization Techniques

Many users and companies alike feel uncomfortable with execution performance of interpreters, often also dismissing their use for specific projects. Specifically virtual machines whose abstraction level is higher than that of the native machine they run on, have performance issues. Several common existing optimization techniques fail to deliver their full potential on such machines. This paper presents an explanation for this situation and provides hints on possible alternative optimization techniques, which could very well provide substantially higher speedups.
Calibrating Time-variant, Device-specific Phase Noise for COTS WiFi Devices

Current COTS WiFi based work on wireless motion sensing extracts human movements such as keystroking and hand motion mainly from amplitude training to classify different types of motions, as obtaining meaningful phase values is very challenging due to time-varying phase noises occurred with the movement. However, the methods based only on amplitude training are not very practical since their accuracy is not environment and location independent. This paper proposes an effective phase noise calibration technique which can be broadly applicable to COTS WiFi based motion sensing. We leverage the fact that multi-path for indoor environment contains certain static paths, such as reflections from wall or static furniture, as well as dynamic paths due to human hand and arm movements. When a hand moves, the phase value of the signal from the hand rotates as the path length changes and causes the superposition of signals over static and dynamic paths in antenna and frequency domain. To evaluate the effectiveness of the proposed technique, we experiment with a prototype system that can track hand gestures in a non-intrusive manner, i.e. users are not equipped with any device, using COTS WiFi devices. Our evaluation shows that calibrated phase values provide much rich, yet robust information on motion tracking -- 80th percentile angle estimation error up to 14 degrees, 80th percentile tracking error up to 15 cm, and its robustness to the environment and the speed of movement.
Diagnostic Performance of (68)Ga-DOTATATE PET/CT, (18)F-FDG PET/CT and (131)I-MIBG Scintigraphy in Mapping Metastatic Pheochromocytoma and Paraganglioma.

To evaluate the diagnostic performance of (68)Ga-DOTATATE (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET)/computed tomography (CT), (18)F-FDG PET/CT and (131)I-MIBG scintigraphy in the mapping of metastatic pheochromocytoma and paraganglioma.



Seventeen patients (male = 8, female = 9; age range, 13-68 years) with clinically proven or suspicious metastatic pheochromocytoma or paraganglioma were included in this prospective study. Twelve patients underwent all three modalities, whereas five patients underwent (68)Ga-DOTATATE and (131)I-MIBG without (18)F-FDG. A composite reference standard derived from anatomical and functional imaging findings, along with histopathological information, was used to validate the findings. Results were analysed on a per-patient and on per-lesion basis. Sensitivity and accuracy were assessed using McNemar's test.



On a per-patient basis, 14/17 patients were detected in (68)Ga-DOTATATE, 7/17 patients in (131)I-MIBG, and 10/12 patients in (18)F-FDG. The sensitivity and accuracy of (68)Ga-DOTATATE, (131)I-MIBG and (18)F-FDG were (93.3 %, 94.1 %), (46.7 %, 52.9 %) and (90.9 %, 91.7 %) respectively. On a per-lesion basis, an overall of 472 positive lesions were detected; of which 432/472 were identified by (68)Ga-DOTATATE, 74/472 by (131)I-MIBG, and 154/300 (patient, n = 12) by (18)F-FDG. The sensitivity and accuracy of (68)Ga-DOTATATE, (131)I-MIBG and (18)F-FDG were (91.5 %, 92.6 % p < 0.0001), (15.7 %, 26.0 % p < 0.0001) and (51.3 %, 57.8 % p < 0.0001) respectively. Discordant lesions were demonstrated on (68)Ga-DOTATATE, (131)I-MIBG and (18)F-FDG.



Ga-DOTATATE PET/CT shows high diagnostic accuracy than (131)I-MIBG scintigraphy and (18)F-FDG PET/ CT in mapping metastatic pheochromocytoma and paraganglioma.
Statistical process control charts for attribute data involving very large sample sizes: a review of problems and solutions.

The use of statistical process control (SPC) charts in healthcare is increasing. The primary purpose of SPC is to distinguish between common-cause variation which is attributable to the underlying process, and special-cause variation which is extrinsic to the underlying process. This is important because improvement under common-cause variation requires action on the process, whereas special-cause variation merits an investigation to first find the cause. Nonetheless, when dealing with attribute or count data (eg, number of emergency admissions) involving very large sample sizes, traditional SPC charts often produce tight control limits with most of the data points appearing outside the control limits. This can give a false impression of common and special-cause variation, and potentially misguide the user into taking the wrong actions. Given the growing availability of large datasets from routinely collected databases in healthcare, there is a need to present a review of this problem (which arises because traditional attribute charts only consider within-subgroup variation) and its solutions (which consider within and between-subgroup variation), which involve the use of the well-established measurements chart and the more recently developed attribute charts based on Laney's innovative approach. We close by making some suggestions for practice.