nils-herrmann/clean_scirepeval huggingface dataset:
Transcatheter Self Powered Ultrasensitive Endocardial Pressure Sensor
Year: 2019

Changes in endocardial pressure (EP) have important clinical significance for heart failure patients with impaired cardiac function. As a vital parameter for evaluating cardiac function, EP is commonly monitored by invasive and expensive cardiac catheterization, which is not feasible for long term and continuous data collection. In this work, a miniaturized, flexible, and selfpowered endocardial pressure sensor (SEPS) based on triboelectric nanogenerator (TENG) which is integrated with a surgical catheter for minimally invasive implantation, is reported. In a porcine model, SEPS is implanted into the left ventricle and the left atrium. The SEPS has a good response both in lowand high pressure environments. The SEPS achieves the ultrasensitivity, real time monitoring, and mechanical stability in vivo. An excellent linearity (R2 0.997) with a sensitivity of 1.195 mV mmHg 1 is obtained. Furthermore, cardiac arrhythmias such as ventricular fibrillation and ventricular premature contraction can also be detected by SEPS. The device may promote the development of miniature implantable medical sensors for monitoring and diagnosis of cardiovascular diseases.
Unifying Terrain Awareness for the Visually Impaired through Real Time Semantic Segmentation
Venue: Sensors
Year: 2018

Navigational assistance aims to help visually impaired people to ambulate the environment safely and independently. This topic becomes challenging as it requires detecting a wide variety of scenes to provide higher level assistive awareness. Vision based technologies with monocular detectors or depth sensors have sprung up within several years of research. These separate approaches have achieved remarkable results with relatively low processing time and have improved the mobility of impaired people to a large extent. However, running all detectors jointly increases the latency and burdens the computational resources. In this paper, we put forward seizing pixel wise semantic segmentation to cover navigation related perception needs in a unified way. This is critical not only for the terrain awareness regarding traversable areas, sidewalks, stairs and water hazards, but also for the avoidance of short range obstacles, fast approaching pedestrians and vehicles. The core of our unification proposal is a deep architecture, aimed at attaining efficient semantic understanding. We have integrated the approach in a wearable navigation system by incorporating robust depth segmentation. A comprehensive set of experiments prove the qualified accuracy over state of the art methods while maintaining real time speed. We also present a closed loop field test involving real visually impaired users, demonstrating the effectivity and versatility of the assistive framework.
Forecasting the Spread of COVID 19 in Kuwait Using Compartmental and Logistic Regression Models
Year: 2020

The state of Kuwait is facing a substantial challenge in responding to the spread of the novel coronavirus 2019 (COVID 19) The government's decision to repatriate stranded citizens back to Kuwait from various COVID 19 epicenters has generated a great concern. It has heightened the need for prediction models to estimate the epidemic size. Mathematical modeling plays a pivotal role in predicting the spread of infectious diseases to enable policymakers to implement various health and safety measures to contain the spread. This research presents a forecast of the COVID 19 epidemic size in Kuwait based on the confirmed data. Deterministic and stochastic modeling approaches were used to estimate the size of COVID 19 spread in Kuwait and determine its ending phase. In addition, various simulation scenarios were conducted to demonstrate the effectiveness of nonpharmaceutical intervention measures, particularly with time varying infection rates and individual contact numbers. Results indicate that, with data until 19 April 2020 and before the repatriation plan, the estimated reproduction number in Kuwait is 2.2. It also confirms the efficiency of the containment measures of the state of Kuwait to control the spread even after the repatriation plan. The results show that a high contact rate among the population implies that the epidemic peak value is yet to be reached and that more strict intervention measures must be incorporated.
PERANCANGAN SISTEM INFORMASI PENJUALAN KUE BERBASIS WEB PADA TOKO ANA CAKE CIKARANG
Year: 2016

In food science, birthday cake grouped into product, along with a bakery, donuts, biscuits, rolls, crackers and pie. In the group of cake, birthday cake is the most popular products to date. Just as in other countries, cultures eat birthday cake is also growing in Indonesia. then the birthday cake entrepreneurs market their business by using electronic commerce that sell online. With the web based system bake sale at Ana Cake Shop customers can access all the information they need bake sales and simplify the process of buying a cake because it can be done online. Intisari Di dalam ilmu pangan, Kue ulang tahun dikelompokkan dalam produk cake, bersama dengan bakery, donat, biskuit, roll, cracker dan pie. Di dalam kelompok cake, kue ulang tahun merupakan produk yang paling populer hingga saat ini. Sama halnya seperti di negara lain, budaya makan kue ulang tahun juga berkembang di Indonesia.maka para pengusaha kue ulang tahun memasarkan usahanya dengan cara menggunakan perdagangan elektronik yaitu berjualan secara online Dengan sistem penjualan kue berbasis web pada Toko Ana Cake pelanggan dapat mengakses seluruh informasi penjualan kue yang mereka butuhkan serta mempermudah proses pembelian kue karena dapat dilakukan secara online. Kata kunci Perancangan Sistem Informasi, Penjualan Kue Berbasis Web
Instrumentos eletroacusticos de tecla e seu uso na musica microtonal
Venue: OPUS
Year: 2019

Neste texto analisamos os instrumentos eletroacusticos de tecla que possibilitaram a execucao de estruturas microtonais e afinacoes alternativas. Alem de um levantamento historico sobre esses instrumentos, tambem nos interessa saber a relacao destes com a composicao microtonal. Investigamos tambem as razoes pelas quais esses instrumentos nao atingiram um nivel de difusao em massa. Concluimos que ha uma forte relacao entre os instrumentos microtonais com a cultura de percepcao sonora, e os problemas nao sao necessariamente de viabilidade de construcao de novos instrumentos. Para tanto, nos baseamos nos estudos de Joel Chadabe, Hugh Davies, Nicole Gagne, Franck Jedrzejewski, Jon Appleton, Ben Kettlewell, Thomas Patteson e Curtis Roads.
Lymphadenopathy associated with cat scratch disease in a child
Venue: Canadian Medical Association Journal
Year: 2021

CMAJ JUNE 21, 2021 VOLUME 193 ISSUE 25 (c) 2021 CMA Joule Inc. or its licensors A 10 year old boy was referred to the department of pediatrics with a 10 day history of increasing pain and swelling of the right elbow and intermittent low grade fever. He had no other constitutional symptoms, such as weight loss, night sweats or malaise, and his symptoms had not improved with oral antibiotic therapy using cefprozil (15 mg/kg/d, twice daily for 6 consecutive days) The child reported that a stray kitten had bitten his right index finger 4 weeks earlier, leaving a 0.5 cm residual lesion. His right supratrochlear and epitrochlear lymph nodes were enlarged and tender, and he had smaller tender nodes in the ipsilateral axilla. We did not observe any hepatosplenomegaly or other lymphadenopathy. Ultrasonograms of the right upper arm and axillae showed well defined, echogenic, hyperemic solid masses with prominent vascular pedicles, typical of enlarged lymph nodes  Serological test results for Epstein Barr virus, cytomegalovirus, adenovirus, mycoplasma, toxoplasmosis, syphilis and HIV were negative. Metagenomic nextgeneration sequencing analysis from a whole blood sample showed Bartonella henselae infection. The patient could not tolerate oral azithromycin, so we treated him with intravenous azithromycin (10 mg/kg/d, once daily) for 7 days. The patient's fever rapidly resolved and his lymph nodes progressively decreased in size. He remained well and reported no other symptoms at regular follow ups. Cat scratch disease, a worldwide zoonosis caused by the gram negative bacteria B. henselae, is characterized by a benign, self limiting regional lymphadenopathy that develops 1 8 weeks after a domestic feline scratch or bite.1,2 About 22 000 cases of catscratch disease are diagnosed annually in the United States, predominately in children.3,4 It is not a public health notifiable disease. Antibiotics are not suggested for mild presentations, but expert opinion supports the use of azithromycin for those with moderate disease and in immunocompromised patients.4,5 Physicians should consider cat scratch disease in their differential diagnosis of regional or unilateral lymphadenopathy.
Unsupervised Vanishing Point Detection and Camera Calibration from a Single Manhattan Image with Radial Distortion
Venue: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2017

The article concerns the automatic calibration of a camera with radial distortion from a single image. It is known that, under the mild assumption of square pixels and zero skew, lines in the scene project into circles in the image, and three lines suffice to calibrate the camera up to an ambiguity between focal length and radial distortion. The calibration results highly depend on accurate circle estimation, which is hard to accomplish because lines tend to project into short circular arcs. To overcome this problem, we show that, given a short circular arc edge, it is possible to robustly determine a line that goes through the center of the corresponding circle. These lines, henceforth called Lines of Circle Centres (LCCs) are used in a new method that detects sets of parallel lines and estimates the calibration parameters, including the center and amount of distortion, focal length, and camera orientation with respect to the Manhattan frame. Extensive experiments in both semi synthetic and real images show that our algorithm outperforms state of the art approaches in unsupervised calibration from a single image, while providing more information.
Leveraging auxiliary image descriptions for dense video captioning
Venue: Pattern Recognit. Lett.
Year: 2021

Collecting textual descriptions is an especially costly task for dense video captioning, since each event in the video needs to be annotated separately and a long descriptive paragraph needs to be provided. In this paper, we investigate a way to mitigate this heavy burden and propose to leverage captions of visually similar images as auxiliary context. Our model successfully fetches visually relevant images and combines noun and verb phrases from their captions to generating coherent descriptions. To this end, we use a generator and discriminator design, together with an attention based fusion technique, to incorporate image captions as context in the video caption generation process. The experiments on the challenging ActivityNet Captions dataset demonstrate that our proposed approach achieves more accurate and more diverse video descriptions compared to the strong baseline using METEOR, BLEU and CIDEr D metrics and qualitative evaluations.
Peroxynitrite activates NLRP3 inflammasome and contributes to hemorrhagic transformation and poor outcome in ischemic stroke with hyperglycemia.
Venue: Free radical biology medicine
Year: 2021

This study aims to test the hypothesis that peroxynitrite mediated inflammasome activation could be a crucial player in the blood brain barrier (BBB) disruption, hemorrhagic transformation (HT) and poor outcome in ischemic stroke with hyperglycemia. We used an experimental rat stroke model subjected to 90 min of middle cerebral artery occlusion plus 24 hours or 7 days of reperfusion with or without acute hyperglycemia. We detected the production of peroxynitrite, the expression of NADPH oxidase, iNOS, MMPs and NLRP3 inflammasome in the ischemic brains, and evaluated infarct volume, brain edema, HT, neurological deficit score and survival rates. Our results show that: (1) Hyperglycemia increased the expression of NADPH oxidase subunits p47phox and p67phox, and iNOS, and the production of peroxynitrite. (2) Hyperglycemia increased infarct volume, aggravated the BBB hyperpermeability, induced brain edema and HT, and worsened neurological outcomes. These brain damages and poor outcome were reversed by the treatments of FeTmPyP (a representative peroxynitrite decomposition catalyst, PDC) peroxynitrite scavenger uric acid, and iNOS inhibitor 1400W. Furthermore, the activations of MMPs and NLRP3 inflammasome including pro/active caspase 1 and IL 1b were inhibited both PDC and 1400W, indicating the roles of peroxynitrite in the inductions of MMPs and NLRP3 inflammasome in the ischemic brains under hyperglycemia. (3) NLRP3 inflammasome inhibitor MCC950, caspase 1 inhibitor VX 765 and IL 1b inhibitor diacerein attenuated brain edema, minimized hemorrhagic transformation and improved neurological outcome, demonstrating the roles of NLRP3 inflammasome in the hyperglycemia mediated HT and poor outcome in the ischemic stroke rats with acute hyperglycemia. In conclusion, peroxynitrite could mediate activations of MMPs and NLRP3 inflammasome, aggravate the BBB damage and HT, and induce poor outcome in ischemic stroke with hyperglycemia. Therefore, targeting peroxynitrite mediated NLRP3 inflammasome could be a promising strategy for ischemic stroke with hyperglycemia.
Towards a "Filipino" Video Game: Teaching Filipino Identity and Culture for Video Game Development
Year: 2019

This paper aims to outline the challenges of teaching Filipino identity and culture in the creation of Filipino video games. In mainstream games, Filipinoness is used to cater to an international audience while the dominance of foreign made games and the limitations of the Philippine market creates a difficult situation for Filipino developers to make Filipino video games for Filipinos. Using a method based on teaching literature and creative writing, four Filipino video games is discussed in the classroom to show students how these games use Filipino history, culture, and politics as source material for their narrative and design. The paper ends with a discussion of a student made game and how the academe can contribute in the critical understanding about Filipino video games and in defining Filipino identity and culture.
Improved Method for Enhancing Dark Images based on CLAHE and Morphological Reconstruction
Year: 2019

The image acquired are often flawed due to various defects from environmental scenarios such as indoor lighting, cloudy weather etc especially at night time. The dark image contains compressed dynamic range that can be enhanced for understanding the detailed information. In this investigative work, an improved method for enhancing extremely dark images is proposed utilizing the concept of illumination reflection model. This improved technique is based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and reconstruction done using morphological processing with Top hat transformation. The image is perceived in HSV color system and V component is estimated. This intensity (V) component is normalized and the inverse of the intensity component is computed. Then CLAHE algorithm is applied on the negative image. Then multiscale image enhancement is applied to the resultant image. Gamma enhancement is used to adaptively adjust the brightness component of an image. After gamma enhancement, the results of two gamma enhanced images with different gamma values are obtained. Principal Component Analysis extracts the significant information from these images that can be used for image fusion. During the PCA based image fusion, the weight value is adaptively calculated by using morphological Top hat transformation to improve the quality of the image and illuminate the inconsistent background pixels. The proposed method focuses detail enhancement of extremely dark images by preserving the edges and structures. Experimental validations and results show the better performance of the proposed method than the existing method in terms of qualitative and quantitative measures.
Do quality indicators prefer particular multi objective search algorithms in search based software engineering? (hot off the press track at GECCO 2021)
Venue: GECCO Companion
Year: 2021

In Search based Software Engineering (SBSE) researchers and practitioners (SBSE users) using multi objective search algorithms (MOSAs) often select commonly used MOSAs to solve their search problems. Such a selection is usually not justified, and the main selection criterion is the MOSA popularity. On the other hand, SBSE users are usually aware of the desired qualities of solutions of their search problem, captured by Quality Indicators (QIs) Consequently, to guide SBSE users in selecting MOSAs for their specific SBSE problems, we study preference relationships between QIs and MOSAs with an empirical evaluation. Given a QI or a quality aspect (e.g. convergence) we suggest a MOSA that is highly likely to produce solutions representing the QI or the quality aspect. Based on our experiments' results, we provide insights and suggestions for SBSE users to choose a MOSA based on experimental evidence.  S. Ali, P. Arcaini, and T. Yue, "Do Quality Indicators Prefer Particular Multi Objective Search Algorithms in Search Based Software Engineering?" 12th International Symposium on Search Based Software Engineering (SSBSE 2020)
ZnO Nanorod Based Ultraviolet Photodetector Prepared on Patterned Sapphire Substrates
Venue: IEEE Journal of Selected Topics in Quantum Electronics
Year: 2017

A patterned sapphire substrate with a submicron hole array structure was used to fabricate ZnO nanorod (NR) array by low temperature hydrothermal method. A ZnO seed layer was spin coated using sol gel process and then etched to form separated seed islands. Different concentrations of zinc acetate in the hydrothermal solution were performed for ZnO NR synthesis to investigate ultraviolet (UV) photo response properties. The ZnO NRs synthesized on the patterned substrates were oriented at various inclined angles. The diameter of the NRs increased and their surface to volume ratio decreased as the concentrations of zinc acetate increased from 0.01 to 0.04 M. X ray diffraction and photoluminescence analyses showed that the NRs synthesized at a higher zinc acetate concentration had better crystal quality and fewer defects. The current voltage characteristics of the ZnO NR based metal semiconductor metal photodetectors exhibited a linear relationship, suggesting an Ohmic contact between NRs and Al electrodes. The UV response, defined by the resistance ratio (Rdark/RUV) of the photodetectors increased with the decreasing zinc acetate concentration from 0.04 to 0.01 M. Compared to traditional vertically aligned NR array on plain substrates, the proposed ZnO NR based photodetector on the patterned substrates achieved improved UV responses, and the average increment for samples of 0.01 0.04 M was 145%
Deep Recurrent Neural Networks for Human Activity Recognition
Venue: Sensors
Year: 2017

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity recognition configurations and require fixed length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long range dependencies in variable length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k nearest neighbors (KNN) Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.
PifPaf: Composite Fields for Human Pose Estimation
Venue: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019

We propose a new bottom up method for multi person 2D human pose estimation that is particularly well suited for urban mobility such as self driving cars and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF) to localize body parts and a Part Association Field (PAF) to associate body parts with each other to form full human poses. Our method outperforms previous methods at low resolution and in crowded, cluttered and occluded scenes thanks to (i) our new composite field PAF encoding fine grained information and (ii) the choice of Laplace loss for regressions which incorporates a notion of uncertainty. Our architecture is based on a fully convolutional, single shot, box free design. We perform on par with the existing state of the art bottom up method on the standard COCO keypoint task and produce state of the art results on a modified COCO keypoint task for the transportation domain.
Internationally lost COVID 19 cases
Venue: Journal of Microbiology, Immunology and Infection
Year: 2020

With its epicenter in Wuhan, China, the COVID 19 outbreak was declared a pandemic by the World Health Organization (WHO) While many countries have implemented flight restrictions to China, an increasing number of cases with or without travel background to China are confirmed daily. These developments support concerns on possible unidentified and unreported international COVID 19 cases, which could lead to new local disease epicenters.  We have analyzed all available data on the development of international COVID 19 cases from January 20th, 2020 until February 18th, 2020. COVID 19 cases with and without travel history to China were divided into cohorts according to the Healthcare Access and Quality Index (HAQ Index) of each country. Chi square and Post hoc testing were performed.  While COVID 19 cases with travel history to China seem to peak for each HAQ cohort, the number of non travel related COVID 19 cases seem to continuously increase in the HAQ cohort of countries with higher medical standards. Further analyses demonstrate a significantly lower proportion of reported COVID 19 cases without travel history to China in countries with lower HAQ (HAQ I vs. HAQ II, posthoc p 0.01) Conclusions Our data indicate that countries with lower HAQ index may either underreport COVID 19 cases or are unable to adequately detect them. Although our data may be incomplete and must be interpreted with caution, inconsistencies in reporting COVID 19 cases is a serious problem which might sabotage efforts to contain the virus.