nils-herrmann/clean_scirepeval huggingface dataset:
The Synapse Project: Engagement in mentally challenging activities enhances neural efficiency
Venue: Restorative neurology and neuroscience
Year: 2015

Correlational and limited experimental evidence suggests that an engaged lifestyle is associated with the maintenance of cognitive vitality in old age. However, the mechanisms underlying these engagement effects are poorly understood. We hypothesized that mental effort underlies engagement effects and used fMRI to examine the impact of high challenge activities (digital photography and quilting) compared with low challenge activities (socializing or performing low challenge cognitive tasks) on neural function at pretest, posttest, and one year after the engagement program.  In the scanner, participants performed a semantic classification task with two levels of difficulty to assess the modulation of brain activity in response to task demands.  The High Challenge group, but not the Low Challenge group, showed increased modulation of brain activity in medial frontal, lateral temporal, and parietal cortex regions associated with attention and semantic processing some of which were maintained a year later. This increased modulation stemmed from decreases in brain activity during the easy condition for the High Challenge group and was associated with time committed to the program, age, and cognition.  Sustained engagement in cognitively demanding activities facilitated cognition by increasing neural efficiency. Mentally challenging activities may be neuroprotective and an important element to maintaining a healthy brain into late adulthood.
Investigating the english speaking problems faced by students at SMU Panjura Malang
Year: 2019

The study is aimed to find out the problems causing the students fail to learn English speaking in EFL classrooms at high school level in SMA Panjura Malang. The researcher used three data gathering instruments namely questionnaire, interview and classroom observation. 33 eleventh Grader students answered a 12 item questionnaire regarding factors that cause students fail in learning English speaking in the classroom. In addition to this, 1 EFL teacher was interviewed. Data gathered through these tools were analyzed using mixed approach data analysis method. The research findings indicated that a considerable number of students were reluctant and failed to respond to the teacher and remained silent in oral English language classrooms due to many causes as follows: (1)pyschological factors such as shy and afraid influenced them to hesitate to speak, (2)limited vocabularies complicated them to deliver their ideas, (3)a little comprehension to grammar make them difficult for them to speak English in correct way, (4)a little knowledge of topics given make them burdened to speak English, (5)the time given for practicing speaking holds them up to develop their ability to speak. Another problems is pronunciation, because of the rarity of having listening lesson. Based on the findings also, the classroom activities, the use of media, and supplementary program somehow helped the learners to get inlvoved actively in EFL classroom. In addition, teacher has an important role to facilitate, prepare, design good activities to improve students capability to speak. Conclusions and recommendations were drawn and made in relation with the aim of enhancing the learning of spoken English in foreign language contexts.  speaking problem in English, learning speaking.
Income distribution, structural competitiveness and financial fragility of the Greek economy
Venue: International Review of Applied Economics
Year: 2019

The aim of this paper is to examine the growth prospects of the Greek economy, after the implementation of the Economic Adjustment Programs (EAPs) The main focus is on the impact of investment, and the structural competitiveness of the productive sector, on effective demand and the financial fragility of corporations. For this purpose, a Post Keynesian SVAR model is developed which connects functional income distribution and the regime of accumulation, with the structural competitiveness and the non performing loans of corporations. The findings indicate that the distribution of income and fiscal consolidation as established after the implementation of the EAPs have destabilized the economy both in macroeconomic and financial terms. The structural competitiveness of the Greek economy is particularly weak, the regime of accumulation is not profit led, while the non performing loans are in a negative relation with the capacity utilization ratio. In this respect, policies that aim to increase the capacity utilization ratio will determine the sustainability of the macro financial system of Greece.
A Novel HOSFS Algorithm for Online Streaming Feature Selection
Venue: 2020 International Conference on System, Computation, Automation and Networking (ICSCAN)
Year: 2020

In recent days, Data stream mining is important for many of the real time and IOT based applications. Online feature selection is the one big topic of data stream mining which attracted researchers with intensive interest. This technique reduces the dimensionality of the streaming features by excluding inappropriate and redundant features. The researchers have proposed many online feature selection algorithm for streaming features like Grafting, Alpha investing, OSFS, OGFS and SAOLA. Based on above studies the exiting algorithm has limitation over prediction accuracy and the large number of selected features. To overcome the limitations of above mentioned approaches, we propose an online feature selection algorithm for streaming features called Heuristic Online Streaming Feature Selection (HOSFS) which has advantages on choosing features from streaming features and omits the irrelevant and redundant features in real time by using self adaption sliding window protocol, and Heuristic function. The HOSFS algorithm assigns heuristic value to the features using the trained heuristic function and selects features with higher heuristic value where other features are considered as irrelevant features. This proposed technique results reduced number of strongly related features and obtains greater prediction accuracy with optimal features. HOSFS algorithm efficiency was tested with three different Health care datasets using MOA tools. Through the experimental outcomes, HOSFS has greater prediction accuracy and reduced number of selected features than alpha investing, OSFS, and SAOLA.
Strongly Enhanced Sensitivity in Planar Microwave Sensors Based on Metamaterial Coupling
Venue: IEEE Transactions on Microwave Theory and Techniques
Year: 2018

Limited sensitivity and sensing range are arguably the greatest challenges in microwave sensor design. Recent attempts to improve these properties have relied on metamaterial (MTM) inspired open loop resonators coupled to transmission lines (TLs) Although the strongly resonant properties of the resonator sensitively reflect small changes in the environment through a shift in its resonance frequency, the resulting sensitivities remain ultimately limited by the level of coupling between the resonator and the TL. This paper introduces a novel solution to this problem that employs negative refractive index TL MTMs to substantially improve this coupling so as to fully exploit its resonant properties. A MTM infused planar microwave sensor is designed for operation at 2.5 GHz, and is shown to exhibit a significant improvement in sensitivity and linearity. A rigorous signal flow analysis of the sensor is proposed and shown to provide a fully analytical description of all salient features of both the conventional and MTM infused sensors. Full wave simulations confirm the analytical predictions, and all data demonstrate excellent agreement with measurements of a fabricated prototype. The proposed device is shown to be especially useful in the characterization of commonly available high permittivity liquids as well as in sensitively distinguishing concentrations of ethanol/methanol in water.
Prospects for a safe COVID 19 vaccine
Venue: Science Translational Medicine
Year: 2020

Prior vaccine development experiences do not raise concerns about vaccine associated enhanced disease for COVID 19 vaccine candidates. Rapid development of an efficacious vaccine against the viral pathogen severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) the cause of the coronavirus disease 2019 (COVID 19) pandemic, is essential, but rigorous studies are required to determine the safety of candidate vaccines. Here, on behalf of the Accelerating COVID 19 Therapeutic Interventions and Vaccines (ACTIV) Working Group, we evaluate research on the potential risk of immune enhancement of disease by vaccines and viral infections, including coronavirus infections, together with emerging data about COVID 19 disease. Vaccine associated enhanced disease has been rarely encountered with existing vaccines or viral infections. Although animal models of SARS CoV 2 infection may elucidate mechanisms of immune protection, we need observations of enhanced disease in people receiving candidate COVID 19 vaccines to understand the risk of immune enhancement of disease. Neither principles of immunity nor preclinical studies provide a basis for prioritizing among the COVID 19 vaccine candidates with respect to safety at this time. Rigorous clinical trial design and postlicensure surveillance should provide a reliable strategy to identify adverse events, including the potential for enhanced severity of COVID 19 disease, after vaccination.
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
Venue: IJCNLP
Year: 2017

We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential short text classification (i.e. classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.
Automatic Calibration of the Step Length Model of a Pocket INS by Means of a Foot Inertial Sensor
Venue: Sensors
Year: 2020

All non foot mounted inertial localization systems have a common challenge: the need for calibrating the parameters of the step length model. The calibration of the parameters of a step length model is key for an accurate estimation of the pedestrian's step length, and therefore, for the accuracy of the position estimation. In a previous work, we provided a proof of concept on how to calibrate step length models with a foot inertial navigation system (INS) i.e. an INS based on an inertial measurement unit (IMU) mounted on the upper front part of the foot. The reason is that the foot INS does not require calibration thanks to the implementation of the strapdown algorithm. The goal of this article is to automatically calibrate the parameters of a step length model of the pocket INS by means of the foot INS. The step length model of the pocket INS has two parameters: the slope and offset of a first order linear regression that relates the amplitude of the thigh pitch with the user's step length. Firstly, we show that it is necessary to estimate the two parameters of the step length model. Secondly, we propose a method to automatically estimate these parameters by means of a foot INS. Finally, we propose a practical implementation of the proposed method in the pocket INS. We evaluate the pocket INS with the proposed calibration method and we compare the results to the state of the art implementations of the pocket INS. The results show that the proposed automatic calibration method outperforms the previous work, which proves the need for calibrating all the parameters of the step length model of the pocket INS. In this work, we conclude that it is possible to use a foot INS to automatically calibrate all parameters of the step length model of the pocket INS. Since the calibration of the step length model is always needed, our proposed automatic calibration method is a key enabler for using the pocket INS.
A Universal and Adaptive Fabric Defect Detection Algorithm Based on Sparse Dictionary Learning
Venue: IEEE Access
Year: 2020

Due to the complex diversity of both fabric texture and defect, fabric defect detection is a challenge topic. However, most of existing defect detection methods can still detect only one type of fabric defects. In order to solve this problem, we propose a universal and adaptive defect detection algorithm based on dictionary learning for detecting various defects of different fabric texture. Firstly, in order to make the image more balanced and improve the detection accuracy, according to the complexity of fabric texture and the degree of brightness and darkness of the background, we segment defect free image according to a certain size to obtain the image joint matrix. In order to make the algorithm more universal, next we form a random dictionary instead of over complete dictionary and fixed dictionary by randomly select feature columns from the image joint matrix, which can extract the most important texture and background information of fabric. Then, we use the Orthogonal Matching Pursuit algorithm to obtain the sparse representation of defect free image. On this basis, we use the K Singular Value Decomposition algorithm to update the dictionary iteratively to get the final dictionary. Afterward, the defective image is reconstructed with the learned dictionary to obtain the reconstruction error. Finally, the correction coefficient is obtained through reconstruction error analysis, then the corresponding adaptive threshold is used to detect and mark the location of the defects. The performance of the algorithm is evaluated on the fabric database of raw fabric, yarn dyed fabric and patterned fabric. The experimental results show that the proposed algorithm has good universality, adaptability and a high detection success rate for different types of fabrics. Particularly, the average detection success rate of dark red fabric and dot patterned fabric can reach 100% and the Precision and the Recall of defect detection evaluation index can also reach 100%
Exploring the role of context on the existing evidence for reconsolidation of episodic memory
Venue: Memory
Year: 2019

Recent research has provided evidence for memory modifications when a post reactivation treatment (e.g. drugs, new learning) interferes with the memory re stabilisation (reconsolidation) process. This finding contradicts the long standing consolidation theory and has high practical and theoretical implications. With an object learning paradigm, it was shown that episodic memory is highly susceptible to interfering material presented after its reactivation [Hupbach, A. Gomez, R. Hardt, O. Nadel, L. (2007) Reconsolidation of episodic memories: A subtle reminder triggers integration of new information. Learning Memory, 14, 47 53.  The reactivation of a learned list (List 1) before a second learned list (List 2) led to intrusion errors from List 2 when trying to recall List 1, but not vice versa. Their work has been widely cited and their findings have been explained according to reconsolidation theory. For the first time, we systematically explored the role of retrieval context as an alternative explanation for Hupbach's results. Our results showed that the intrusion effect occurs independently of the retrieval context (Experiment 1) Additionally, even when the intrusion rate probability is increased (i.e. List 1 memory test is performed in the List 2 learning context) the groups that did not reactivate the original list did not commit intrusion errors (Experiment 2) In sum, we found that the intrusion effect critically depends on the presence of reactivation, discarding alternative interpretations of the results.
Effect of topical tranexamic acid on post traumatic elbow stiffness in patients treated with open arthrolysis: a prospective comparative study.
Venue: Journal of shoulder and elbow surgery
Year: 2020

Elbow joint open arthrolysis is an effective method to release contracted tissue and debride heterotopic ossification in cases of post traumatic elbow stiffness. Recurrence remains one of the most common concerns for surgeons. Soft tissue contracture may result from intra and/or extra articular bleeding, edema, effusion, and granulation. The increasing incidence of intraoperative and postoperative bleeding has caused uncertainty about surgical outcomes. Tranexamic acid (TXA) is effective for reducing surgery related bleeding and effusions in total hip or knee arthroplasty.  To investigate whether topical TXA can decrease blood loss and effusions in patients treated with elbow joint open arthrolysis and whether it affects final function.  A prospective comparative study was conducted. Sixty one patients with joint stiffness were enrolled and randomly divided into 2 groups: one consisting of 31 patients treated with topical TXA intraoperatively after open arthrolysis (experimental group) and the other consisting of 30 patients who received saline administration (control group) The operation time, tourniquet time, and intraoperative blood loss were recorded. Drainage volume, elbow rotation, elbow motion arc, Mayo Elbow Performance Score, and operation related complications were followed up and recorded, whereas hematoma volume remaining in the joint space after drainage tube removal was assessed on ultrasonography.  Tourniquet time, intraoperative blood loss, and postoperative drainage were significantly lower in the TXA group than in the control group. However, no significant intergroup differences were found in the incidence of related complications and final function evaluated at the final follow up.  Topical TXA improves surgical quality by controlling intraoperative bleeding, decreases the amount of blood loss soon after surgery, and could become a routine procedure in elbow joint open arthrolysis.
Adaptive U net: Conditional Neural Network for Monaural Speech Enhancement using Memory Mechanism
Venue: 2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP)
Year: 2020

While the attention mechanisms have been widely used for speech enhancement, usage of memory mechanisms is less common. In this paper, we investigate the task of monaural speech enhancement in a memory based Adaptive system, namely Adaptive U net (Ada U net) Specifically, this system consists of auxiliary classifier and backbone U net, where the classifier is used to provide the category sensitive high level feature, the U net fetches affine parameters adapted to category attribute through memory mechanism. Moreover, we explore the impact of adaptive targets on noise reduction performance of the proposed system. The experimental results show that the denoise ability achieves substantial improvement when there is no artificial design target is set. To further improve the denoise capability of the system, the waveform angle ratio loss is designed. Compared with typical monaural speech enhancement models, the Ada U net has better performance in multiple speech quality metrics.
Efektivitas Penggunaan Media Pembelajaran Berbasis Teknologi Informasi dan Komunikasi (TIK) Analysis Toolpak pada Materi Matriks di Kelas XI MAN 4 Balangan Tahun Pelajaran 2019/2020
Year: 2020

Pendidikan menghendaki pembelajaran yang meningkatkan kualitas pembelajaran dan mampu meningkatkan hasil belajar siswa dengan menggunakan media yang efektif dan efisien. Salah satu medianya adalah media pembelajaran berbasis Teknologi Informasi dan Komunikasi (TIK) Analysis Toolpak. Penelitian ini bertujuan untuk mengetahui hasil efektivitas penggunaan media pembelajaran berbasis Teknologi Informasi dan Komunikasi (TIK) Analysis Toolpak pada materi matriks di kelas XI MIA MAN 4 Balangan tahun pelajaran 2019/2020 berupa hasil belajar siswa pada materi matriks. Jenis penelitian ini adalah penelitian lapangan (field research) dengan pendekatan kuantitatif. Penelitian ini menggunakan metode eksperimen preexperimental. Desain penelitian yang digunakan adalah One Shot Case Study. Populasi dalam penelitian ini adalah seluruh siswa kelas XI MIA MAN 4 Balangan berjumlah 22 siswa dan pengambilan sampel menggunakan teknik sampling jenuh. Teknik pengumpulan data dalam penelitian ini adalah tes, dokumentasi, observasi dan wawancara. Dalam penelitian ini menggunakan desain pengukuran. Hasil penelitian menunjukkan bahwa media pembelajaran berbasis Teknologi Informasi dan Komunikasi (TIK) Analysis Toolpak efektif digunakan pada materi matriks di kelas XI MIA MAN 4 Balangan tahun pelajaran 2019/2020.
Resena historica del COVID 19: ?Como y por que llegamos a esta pandemia?
Year: 2020

espanolA finales del 2019 en el mes de diciembre, Wuhan, China se convirtio en el epicentro de un brote de neumonia de etiologia desconocida que no cedia ante tratamientos actualmente utilizados. En pocos dias los contagios aumentaron exponencialmente, no solo en China Continental sino tambien en diferentes paises. El agente causal fue identificado, un nuevo coronavirus (2019 nCoV) posteriormente clasificado como SARS CoV2 causante de la enfermedad COVID 19. El 11 de marzo del 2020 la Organizacion Mundial de Salud declara a esta enfermedad como una pandemia. La presente revision tiene como finalidad exponer las causas y el origen de esta pandemia, asi como las posibles medidas para contenerla. EnglishAt the end of 2019 in December, Wuhan, China became the epicenter of an outbreak of pneumonia of unknown etiology that did not respond to currently used treatments. In a few days the contagious disease increased exponentially not only in Mainland China. Also in different countries. The causative agent was identified, a new coronavirus (2019 nCoV) Later classified as SARS CoV2 causing COVID 19 disease. On March 11, 2020 the World Health Organization declared this disease a pandemic. The purpose of this review is to present in a brief way, the causes and origin of this pandemic. As well a small outline of possible measures to try to contain it.
Mercury health risk assessment among petrochemical workers in Rayong Province, Thailand
Year: 2019

Mercury occurs naturally in environment; thus, retention of fossil fuels used as feedstock in petrochemical plants is commonly found. The purpose of this study was to assess mercury health risks among petrochemical workers.  In all, 188 operators and 30 office workers were recruited from 3 petrochemical plants. A total of 83 and 56 air samples were collected during normal working days and turnaround (TA) periods, respectively. Three main meals over 5 consecutive days, drinking water and spot urine samples were collected. Demographics and lifestyle data were collected using questionnaires. USEPA guidelines for mercury health risks were applied.  The inhalation exposure during normal working days of the two groups was lower than 5% of the Threshold Limit Value (TLV) but during TA some operators' exposure exceeded the TLV. The average urinary mercury concentrations of the two groups did not significantly differ. The mercury concentration in the water samples was undetected and did not differ in the food samples of the two groups. Sixty six operators presented a hazard quotient, HQinh greater than 0.2, but none of office staff, and 98 of 218 participants had hazard index, HI >1.  Unacceptable mercury health risk among the petrochemical worker mostly cause by mercury in cooked food.