nils-herrmann/clean_scirepeval huggingface dataset:
Potentiality of Bacterial Cellulose as the Scaffold of Tissue Engineering of Cornea

The bacterial cellulose (BC) secreted by Gluconacetobacter xylinus was explored as a novel scaffold material due to its unusual biocompatibility, light transmittance and material properties. The specific surface area of the frozendried BC sheet based on BET isotherm was 22.886 m/g, and the porosity was around 90%. It is known by SEM graphs that significant difference in porosity and pore size exists in the two sides of air-dried BC sheets. The width of cellulose ribbons was 10 nm to 100 nm known by AFM image. The examination of the growth of human corneal stromal cells on BC demonstrated that the material supported the growth and proliferation of human corneal stromal cells. The ingrowth of corneal stromal cells into the scaffold was verified by Laser Scanning Confocal Microscope. The results suggest the potentiality for this biomaterial as a scaffold for tissue engineering of artificial cornea. KeywordsBacterial cellulose; Cornea; Tissue engineering; Scaffold; Corneal stromal cells
Towards Model-based Acceptance Testing for Scrum

In agile processes like Scrum, strong customer involvement demands for techniques to facilitate the requirements analysis and acceptance testing. Additionally, test automation is crucial, as incremental development and continuous integration require high efforts for testing. To cope with these challenges, we propose a modelbased technique for documenting customer’s requirements in forms of test models. These can be used by the developers as requirements specification and by the testers for acceptance testing. The modeling languages we use are light-weight and easy-to-learn. From the test models, we generate test scripts for FitNesse or Selenium which are well-established test automation tools in agile community.
Compact Quad-Band Bandpass Filter for DCS/WLAN/WiMAX/5G Wi-Fi Application

This letter presents a compact quad-band bandpass filter for DCS/WLAN/WiMAX/5G Wi-Fi applications. It utilizes two resonators. One is a multi-stub-loaded resonator operating at the first, second and fourth passband frequencies. The other one is a short-end stub-loaded resonator used to yield the third passband. Among the four passbands, the highest one is designed to cover a wide bandwidth (5.15-5.85 GHz) for 5G Wi-Fi applications. The design procedures are discussed in detail and the simulated and measured results are presented.
Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion

Deep Neural Networks (DNNs) provide state-of-the-art solutions in several difficult machine perceptual tasks. However, their performance relies on the availability of a large set of labeled training data, which limits the breadth of their applicability. Hence, there is a need for new semisupervised learning methods for DNNs that can leverage both (a small amount of) labeled and unlabeled training data. In this paper, we develop a general loss function enabling DNNs of any topology to be trained in a semi-supervised manner without extra hyper-parameters. As opposed to current semi-supervised techniques based on topology-specific or unstable approaches, ours is both robust and general. We demonstrate that our approach reaches state-of-the-art performance on the SVHN (9.82% test error, with 500 labels and wide Resnet) and CIFAR10 (16.38% test error, with 8000 labels and sigmoid convolutional neural network) data sets.
Unit commitment in power generation - a basic model and some extensions

Optimization problems in the power industry have attracted researchers from engineering, operations research and mathematics for many years. The complex nature of generation, transmission, and distribution of electric power implies ample opportunity of improvement towards the optimal. Mathematical models have proven indispensable in deepening the understanding of these optimization problems. The progress in algorithms and implementations has an essential share in widening the abilities to solve these optimization problems on hardware that is permanently improving. In the present paper we address unit commitment in power operation planning. This problem concerns the scheduling of start-up/shut-down decisions and operation levels for power generation units such that the fuel costs over some time horizon are minimal. The diversity of power systems regarding technological design and economic environment leads to a variety of issues potentially occurring in mathematical models of unit commitment. The ongoing liberalization of electricity markets will add to this by shifting the objective in power planning from fuel cost minimization to revenue maximization. For an introduction into basic aspects of unit commitment the reader is referred to the book by Wood and Wollenberg [35]. A literature synopsis on various traditional methodological approaches has been compiled by Sheble and Fahd [29]. In our paper, we present some of the more recent issues in modeling and algorithms for unit commitment. The present paper grew out of a collaboration with the German utility VEAG Vereinigte Energiewerke AG Berlin whose generation system comprises conventional coal and gas fired thermal units as well as pumped-storage plants.
Rule-based system for automated classification of non-functional requirements from requirement specifications

Unmasking the non-functional requirements (NFRs) such as quality attributes, interface requirements and design constraints of software is crucial in finding the architectural alternatives for software starting from early design opinions. For developing quality software product, extraction of NFRs from requirement documents is needed to be carried out and it's beneficiary if this process becomes automated, reducing the human efforts, time and mental fatigue involved in identifying specific requirements from a large number of requirements in a document. The proposal presented in this paper combines automated identification and classification of requirement sentences into NFR sub-classes with the help of rule-based classification technique using thematic roles and identifying the priority of extracted NFR sentences within the document according to their occurrence in multiple NFR classes. F1-measure of 97% is obtained on PROMISE corpus and 94% F1-Measure on Concordia RE corpus. The results established validates the claim that proposal provides specific and higher results than the previous state of art approaches.
Development of machine learning models for diagnosis of glaucoma

The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF). We also developed synthesized features from original features. We then selected the best features proper for classification (diagnosis) through feature evaluation. We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset. To develop the glaucoma prediction model, we considered four machine learning algorithms: C5.0, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). We repeatedly composed a learning model using the training dataset and evaluated it by using the validation dataset. Finally, we got the best learning model that produces the highest validation accuracy. We analyzed quality of the models using several measures. The random forest model shows best performance and C5.0, SVM, and KNN models show similar accuracy. In the random forest model, the classification accuracy is 0.98, sensitivity is 0.983, specificity is 0.975, and AUC is 0.979. The developed prediction models show high accuracy, sensitivity, specificity, and AUC in classifying among glaucoma and healthy eyes. It will be used for predicting glaucoma against unknown examination records. Clinicians may reference the prediction results and be able to make better decisions. We may combine multiple learning models to increase prediction accuracy. The C5.0 model includes decision rules for prediction. It can be used to explain the reasons for specific predictions.
Airport Traffic Simulation Using Petri Nets

Airport traffic consists of aircraft performing landing, takeoff and taxi procedures. It is controlled by air traffic controller (ATC). To safely perform this task he/she uses traffic surveillance equipment and voice communication systems to issue control clearances. One of the most important indicators of this process efficiency is practical airport capacity, which refers to the number of aircraft handled and delays which occurred at the same time. This paper presents the concept of airport traffic modelling using coloured, timed, stochastic Petri nets. By the example of the airport with one runway and simultaneous takeoff and landing operations, the applicability of such models in analysis of air traffic processes is shown. Simulation experiments, in which CPN Tools package was used, showed the impact of the initial formation of landing aircraft stream on airside capacity of the airport. They also showed the possibility of its increase by changes in the organisation of takeoff and landing processes.
Virtual reality therapy for refractory auditory verbal hallucinations in schizophrenia: A pilot clinical trial

Schizophrenia is a chronic and severe mental illness that poses significant challenges. While many pharmacological and psychosocial interventions are available, many treatment-resistant schizophrenia patients continue to suffer from persistent psychotic symptoms, notably auditory verbal hallucinations (AVH), which are highly disabling. This unmet clinical need requires new innovative treatment options. Recently, a psychological therapy using computerized technology has shown large therapeutic effects on AVH severity by enabling patients to engage in a dialogue with a computerized representation of their voices. These very promising results have been extended by our team using immersive virtual reality (VR). Our study was a 7-week phase-II, randomized, partial cross-over trial. Nineteen schizophrenia patients with refractory AVH were recruited and randomly allocated to either VR-assisted therapy (VRT) or treatment-as-usual (TAU). The group allocated to TAU consisted of antipsychotic treatment and usual meetings with clinicians. The TAU group then received a delayed 7weeks of VRT. A follow-up was ensured 3months after the last VRT therapy session. Changes in psychiatric symptoms, before and after TAU or VRT, were assessed using a linear mixed-effects model. Our findings showed that VRT produced significant improvements in AVH severity, depressive symptoms and quality of life that lasted at the 3-month follow-up period. Consistent with previous research, our results suggest that VRT might be efficacious in reducing AVH related distress. The therapeutic effects of VRT on the distress associated with the voices were particularly prominent (d=1.2). VRT is a highly novel and promising intervention for refractory AVH in schizophrenia.
Rationale of subdermal superficial liposuction related to the anatomy of subcutaneous fat and the superficial fascial system

The liposuction technique has changed greatly over the years. In 1989, the authors presented subdermal superficial liposuction which treats the superficial fat layer and yields better skin retraction. With this technique the surgeon can treat thin adipose layers to obtain better results in more cases than the traditional liposuction technique. The technique can be used in cases with difficult skin adjustment and in secondary cases when “deep only” liposuction has been performed and there were residual adiposities. Subdermal superficial liposuction evolved so that one could obtain good skin retraction by performing massive liposuction of all the fat layers. The authors named this technique MALL (Massive All Layer Liposuction). The technique is applied in body areas where the fat layer is very thick and stretches the skin because of its volume and weight such as in the abdomen, posterior arms, and internal surface of the upper third of the thighs. MALL liposuction drastically reduces the indications for abdominoplasty and inner thigh and arm dermolipectomies. Knowledge of the anatomy of the subcutaneous fat and the superficial fascial system allows one to explain the subdermal superficial liposuction from an anatomical point of view, to perform a more rational and effetive procedure, and to differentiate the technique depending on the area of the body.
Fooling Vision and Language Models Despite Localization and Attention Mechanism

Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question answering (VQA) models. Our evaluation shows that we can generate adversarial examples with a high success rate (i.e., > 90%) for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses.
CMOS Digital Image Stabilization

In this paper, we present a CMOS digital image stabilization algorithm based on the characteristics of a rolling shutter camera. Due to the rolling shuttering mechanism of a CMOS sensor, a CMOS video frame shows CMOS distortions which are not observed in a CCD video frame, and previous video stabilization techniques cannot handle these distortions properly even though they can make a visually stable CMOS video sequence. In our proposed algorithm, we first suggest a CMOS distortion model. This model is based on a rolling shutter mechanism which provides a solution to solve the CMOS distortion problem. Next, we estimate the global image motion and the CMOS distortion transformation directly from the homography between CMOS frames. Using the two transformations, we remove CMOS distortions as well as jittering motions in a CMOS video. In the experimental results, we demonstrate that our proposed algorithm can handle the CMOS distortion problem more effectively as well as the jittering problem in a CMOS video compared to previous CCD-based digital image stabilization techniques.
Children's engagement during digital game-based learning of reading: The effects of time, rewards, and challenge

This study investigated the effects of two game features (the level of challenge and the reward system) on first and second graders' engagement during digital game-based learning of reading. We were particularly interested in determining how well these features managed to maintain children's engagement over the 8-week training period. The children (N ¼ 138) used GraphoGame, a web-based game training letter–sound connections, at home under the supervision of parents. Data regarding the children's gaming and engagement were stored on the GraphoGame online server. A 2 Â 2 factorial design was used to investigate the effects of the level of challenge (high challenge vs. high success) and the presence of the reward system (present vs. absent). Children's engagement was measured by session frequency and duration and through an in-game self-report survey that was presented at the end of the each session. According to the results, the children enjoyed GraphoGame but used it less frequently than expected. The reward system seemed to encourage the children to play longer sessions at the beginning of the training period, but this effect vanished after a few sessions. The level of challenge had no significant effect on children's engagement. The results suggest a need to investigate further the effectiveness of various game features in maintaining learner's engagement until the goals set for learning are achieved. Playing computer games is a popular leisure-time activity among young children. In Finland, 84 percent of first graders play computer games at least sometimes, 31 percent every day (Hirvonen, 2012). Computer games can hold children's attention for hours a day, so it is not surprising that many parents and teachers are interested in their potential as educational and motivational tools. Several studies suggest that children enjoy computer-based learning tasks more than traditional learning tasks Despite the apparent motivational appeal of digital learning, little experimental research concerning the long-term development of engagement during digital game-based learning has been conducted. When computer-based learning activities or games are introduced to young learners, they typically trigger curiosity and interest (Mitchell, 1993; Seymour et al., 1987). However, this type of interest is situational, triggered by environmental stimuli and it may or may not last over time (Hidi & Renninger, 2006). Some earlier studies imply that interest triggered by educational software may be short-, but also relatively long-lasting interest has been observed (Rosas et al., 2003). If the situational interest triggered by a novel learning tool can …
Evaluating the effect of centralization on routing convergence on a hybrid BGP-SDN emulation framework

A lot of applications depend on reliable and stable Internet connectivity. These characteristics are crucial for missioncritical services such as telemedical applications. An important factor that can affect connection availability is the convergence time of BGP, the de-facto inter-domain routing (IDR) protocol in the Internet. After a routing change, it may take several minutes until the network converges and BGP routing becomes stable again [13]. Kotronis et al.  propose a novel Internet routing approach based on SDN principles that combines several Autonomous Systems (AS) into groups, called clusters, and introduces a logically centralized routing decision process for the cluster participants. One of the goals of this concept is to stabilize the IDR system and bring down its convergence time. However, testing whether such approaches can improve on BGP problems requires hybrid SDN and BGP experimentation tools that can emulate multiple ASes. Presently, there is a lack of an easy to use public tool for this purpose. This work fills this gap by building a suitable emulation framework and evaluating the effect that a proof-of-concept IDR controller has on IDR convergence time.
A fully planar antenna for millimeter-wave and 5G communications based on a new CSRR-enhanced substrate-integrated waveguide

We present a fully planar, easy to fabricate antenna for millimeter-wave communications, based on a new substrate-integrated waveguide (SIW). The SIW itself is entirely planar, since it is designed using series of side-by-side complementary split-ring resonators (CSRR), instead of vias, with the CSRR being etched on top and bottom metal ground surfaces that cover the dielectric substrate. This metamaterial-inspired structure provides a single-negative effective material parameter behavior that blocks wave propagation to the perpendicular direction. Hence, two parallel structures of this kind can be used to design an SIW, with propagation losses comparable to the conventional one. The antenna structure is consequently designed, by the use of proper cavities within the substrate and radiating slots on the ground structure and provides an easy to fabricate alternative for millimeter-wave and 5G communications.
An Empirical and Analytic Study of Stack vs . Heap Cost for Languages with Closures

It has been proposed that allocating procedure activation records on a garbage collected heap is more e cient than stack allocation. However, previous comparisons of heap vs. stack allocation have been over-simplistic, neglecting (for example) frame pointers, or the better locality of reference of stacks. We present a comprehensive analysis of all the components of creation, access, and disposal of heap-allocated and stack-allocated activation records. Among our results are: Although stack frames are known to have a better cache read-miss rate than heap frames, our simple analytical model (backed up by simulation results) shows that the di erence is too trivial to matter. The cache write-miss rate of heap frames is very high; we show that a variety of miss-handling strategies (exempli ed by speci c modern machines) can give good performance, but not all can. The write-miss policy of the primary cache is much more important than the write-miss policy of the secondary cache. Stacks restrict the exibility of closure representations (for higher-order functions) in important (and costly) ways. The extra load placed on the garbage collector by heap-allocated frames is very small. The demands of modern programming languages make stacks quite complicated to implement e ciently and correctly. Overall, the execution cost of stack-allocated and heap-allocated frames is very similar; but heap frames are simpler to implement and allow very e cient rst-class continuations (call/cc). 1 Garbage-collected frames In a programming language implementation that uses garbage collection, all procedure activation records (frames) can be allocated on the heap. This is quite convenient for higher-order languages (Scheme, ML, etc.) whose \closures" can have inde nite extent, and it is even more convenient for languages with rst-class continuations. One might think that it would be expensive to allocate, at every procedure call, heap storage that becomes garbage on return. But not necessarily : modern generational garbage-collection algorithms[31] can reclaim dead frames extremely e ciently, even cheaper than the one-instruction cost to pop the stack. But there are many other costs involved in creating, accessing, and destroying activation records| whether on a heap or a stack. These costs are summarized in Figure 1, and explained and analyzed in the remainder of the paper. These numbers depend on many assumptions. The most critical assumptions are these: The runtime system in question has static scope, higher order functions, and garbage collection. The only question being investigated is whether there is an activation-record stack in addition to the garbage collection of other objects. The compiler and garbage collector are required to be \safe for space complexity;" that is, statically dead pointers (in the data ow sense) do not keep objects live. There are few side e ects in compiled programs, so that generational garbage collection will be e cient. These assumptions, and many others, will be explained in the rest of the paper.
Superpixel-based spatial amplitude and phase modulation using a digital micromirror device.

We present a superpixel method for full spatial phase and amplitude control of a light beam using a digital micromirror device (DMD) combined with a spatial filter. We combine square regions of nearby micromirrors into superpixels by low pass filtering in a Fourier plane of the DMD. At each superpixel we are able to independently modulate the phase and the amplitude of light, while retaining a high resolution and the very high speed of a DMD. The method achieves a measured fidelity F = 0.98 for a target field with fully independent phase and amplitude at a resolution of 8 × 8 pixels per diffraction limited spot. For the LG10 orbital angular momentum mode the calculated fidelity is F = 0.99993, using 768 × 768 DMD pixels. The superpixel method reduces the errors when compared to the state of the art Lee holography method for these test fields by 50% and 18%, with a comparable light efficiency of around 5%. Our control software is publicly available.