nils-herrmann/clean_scirepeval huggingface dataset:
NOS2 and CCL27: clinical implications for psoriasis and eczema diagnosis and management
Venue: Expert review of clinical immunology
Year: 2015

Chronic inflammatory skin diseases such as psoriasis and eczema are a major medical challenge. Development of highly specific therapies for both conditions is opposed by the lack of translation of basic knowledge into biomarkers for clinical use. Furthermore, to distinguish psoriasis from eczema might be difficult occasionally, but specific and costly therapies would not be efficient in misdiagnosed patients. In the era of high throughput 'omics' technologies, comparing the molecular signature of psoriasis and eczema is a promising approach to gain insight into their complex pathogeneses and develop new diagnostic and therapeutic strategies. Investigating patients affected by both psoriasis and eczema simultaneously, we recently constructed a disease classifier consisting of only two genes (NOS2 and CCL27) that reliably predicts the correct diagnosis even in clinically unclear cases. When such easy to handle approaches are combined with individual therapeutic response, we might reach the ultimate goal of personalized medicine in inflammatory skin diseases in near future.
CHEMICAL COMPOSITION AND ANTIBACTERIAL ACTIVITY OF SOME MEDICINAL PLANTS FROM LAMIACEAE FAMILY.
Venue: Acta poloniae pharmaceutica
Year: 2015

Chemical composition and antibacterial activity of aqueous (ethanolic and methanolic) extracts from herbs often used in Polish cuisine and traditional herbal medicine including thyme (Thymus vulgaris L. rosemary (Rosmarinus officinalis L. oregano (Origanum vulgare L. peppermint (Mentha piperita L. and sage (Salvia officinalis L. were compared. The aqueous ethanolic extracts contained slightly higher levels of phenolics compared to the aqueous methanolic extracts. In turn, GC MS analysis showed that the aqueous methanolic extracts of thyme, rosemary and sage contained several additional compounds such as eugenol or ledol. The present studies also indicated that the bacterial species applied in the experiment exhibited different sensitivities towards tested extracts. Staphylococcus aureus strains were found to be the most sensitive bacteria to aqueous (ethanolic and methanolic) rosemary and sage extracts and aqueous methanolic thyme extract. Klebsiella pneumoniae ATCC 13883 and Proteus vulgaris NCTC 4635 were more susceptible to the aqueous methanolic thyme extract. However, Listeria monocytogenes 1043S was the most sensitive to the aqueous ethanolic rosemary extract. Gram positive bacteria were generally more sensitive to the tested extracts than Gram negative ones.
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration
Venue: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020

Filter pruning has been widely applied to neural network compression and acceleration. Existing methods usually utilize pre defined pruning criteria, such as Lp norm, to prune unimportant filters. There are two major limitations to these methods. First, existing methods fail to consider the variety of filter distribution across layers. To extract features of the coarse level to the fine level, the filters of different layers have various distributions. Therefore, it is not suitable to utilize the same pruning criteria to different functional layers. Second, prevailing layer by layer pruning methods process each layer independently and sequentially, failing to consider that all the layers in the network collaboratively make the final prediction. In this paper, we propose Learning Filter Pruning Criteria (LFPC) to solve the above problems. Specifically, we develop a differentiable pruning criteria sampler. This sampler is learnable and optimized by the validation loss of the pruned network obtained from the sampled criteria. In this way, we could adaptively select the appropriate pruning criteria for different functional layers. Besides, when evaluating the sampled criteria, LFPC comprehensively consider the contribution of all the layers at the same time. Experiments validate our approach on three image classification benchmarks. Notably, on ILSVRC 2012, our LFPC reduces more than 60% FLOPs on ResNet 50 with only 0.83% top 5 accuracy loss.
A framework of conversational recommender system based on user functional requirements
Venue: 2014 2nd International Conference on Information and Communication Technology (ICoICT)
Year: 2014

Specifying requirements based on technical features of the product are frequently difficult for most customers in purchasing a multi function and multi feature products. A more natural way to identify customers' needs is by asking what they really want to use with the product they are looking for. To address this issue, we propose a framework of conversational recommender system that is based on user functional requirement. This framework covers ontology structure and interaction mechanism that can explore user's preference from their functional requirements. The ontology is composed of three different interacting hierarchical classes: functionality requirement class, component class and product class. Interaction in this system is performed through question answering dialogue, product recommendation and explanation, similar to the interaction between customer and professional sales support.
QUICK RESPONSE BASED STRATEGIC INTERVENTION MATERIAL (QRSIM) EFFECTS ON THE ACHIEVEMENT LEVEL OF 11TH GRADE LEARNERS IN GENERAL MATHEMATICS
Year: 2019

This study focused on the effect of a QR embedded Strategic Intervention Material (QRSIM) on the achievement level of 11th Grade Students in General Mathematics via Solomon four group research design, focusing on the least mastered competencies in solving exponential equations. A total of 120 students participated in the experiment at Dolores National High School during the school year 2019 2020. Findings revealed that; the students were unable to reach the expected level of achievement based on the pretest; the experimental and control groups with pretest performed better in the posttest assessment; no significant difference was observed between the pretest achievement level of the students; the pretest and posttest achievement levels of the experimental and control groups shows a significant achievement; and that the achievement levels among the four groups significantly improved in the posttest. Findings of this study highlights the use of an innovative SIM based instruction and its counterpart in teaching frequently least mastered competencies in the subject, General Mathematics that paves way in developing mathematical competencies among 11th grade learners. Furthermore, the researcher recommends that; assessing the learners' level of achievement prior to instruction will aid teachers in developing instructional tools and approaches suited to their scholastic needs; Mathematics teachers should be geared with appropriate knowledge on the use of 21st century teaching and learning resources such as Strategic Intervention Material to help student who find mathematics concepts difficult to comprehend; and that a similar study be conducted to cover other least learned competencies in the Eastern Samar division to further probe and enrich the findings of this study.
The Complexity of Theorem Proving in Autoepistemic Logic
Venue: SAT
Year: 2013

Autoepistemic logic is one of the most successful formalisms for nonmonotonic reasoning. In this paper we provide a proof theoretic analysis of sequent calculi for credulous and sceptical reasoning in propositional autoepistemic logic, introduced by Bonatti and Olivetti  We show that the calculus for credulous reasoning obeys almost the same bounds on the proof size as Gentzen's system LK. Hence proving lower bounds for credulous reasoning will be as hard as proving lower bounds for LK. This contrasts with the situation in sceptical autoepistemic reasoning where we obtain an exponential lower bound to the proof length in Bonatti and Olivetti's calculus.
Detecting Redundancy in Electronic Medical Records Using Clinical BERT
Year: 2020

Semantic Textual Similarity (STS) computes the degree of semantic equivalence between text snippets. STS is used in various natural language processing tasks, including detecting redundant information in texts. Although STS tasks have been widely studied in the general English domain, there exists very few resources for STS tasks in the clinical domain. The methods used for general domain STS tasks might not work well in the clinical domain because of variability of natural language expressions, and clinical domain expressions are different from general domain expressions. We present a Clinical BERT based model for redundancy detection in clinical texts. Our experiments show that domain specific BERT improves performance of the model.
Context Aware Model of Abstractive Text Summarization for Research Articles
Year: 2021

Research article comprises of different sections each holds its own characteristic domain information. Summarization of entire article from multiple documents of multiple sections in precise form with special focus to contextual information is tedious. We proposed context aware model to extract contextual texts from research article by utilizing multi document directed graph for contextual matching phrases We customized extractive summarization for abstractive text summarization with lucid information as prime criterion. Decision matrix with elitism identification further fine tunes the abstractive text summary and outperforms at sentence level Rouge L measures 9.32 and summary level measures 89.65. Keywords1 Glowworm Swarm Optimization, Analytical Hierarchical Processing (AHP) Skip Gram Model, Word2vectors, Maximum Marginal Relevance, Log Likelihood Rate,N Gram.
Human disturbance has contrasting effects on niche partitioning within carnivore communities
Venue: Biological reviews of the Cambridge Philosophical Society
Year: 2020

Among species, coexistence is driven partly by the partitioning of available resources. The mechanisms of coexistence and competition among species have been a central topic within community ecology, with particular focus on mammalian carnivore community research. However, despite growing concern regarding the impact of humans on the behaviour of species, very little is known about the effect of humans on species interactions. The aim of this review is to establish a comprehensive framework for the impacts of human disturbance on three dimensions (spatial, temporal and trophic) of niche partitioning within carnivore communities and subsequent effects on both intraguild competition and community structure. We conducted a systematic review of the literature on carnivore niche partitioning (246 studies) and extracted 46 reported effects of human disturbance. We found evidence that human disturbance impacts resource partitioning, either positively or negatively, in all three niche dimensions. The repercussions of such variations are highly heterogeneous and differ according to both the type of human disturbance and how the landscape and/or availability of resources are affected. We propose a theoretical framework of the three main outcomes for the impacts of human disturbance on intraguild competition and carnivore community structure: (i) human disturbance impedes niche partitioning, increasing intraguild competition and reducing the richness and diversity of the community; (ii) human disturbance unbalances niche partitioning and intraguild competition, affecting community stability; and (iii) human disturbance facilitates niche partitioning, decreasing intraguild competition and enriching the community. We call for better integration of the impact of humans on carnivore communities in future research on interspecific competition.
Development and evaluation of an open online course in sport psychology using self determination theory principles
Year: 2017

The provision of free, open, and online content is a relatively new phenomenon through which education providers are exploring innovative new business models to bring their content to a global audience. Learning materials that are openly licensed and readily accessible have the potential to transform educational delivery and present learning and psychological growth opportunities for all. However, there is little consensus as to how best design and deliver open online courses, where barriers to access are low and dropout rates are high. With its strong focus on autonomy and high quality motivation, self determination theory (Deci Ryan, 2000) provides an ideal frame of reference to approach the design and development of open online courses. The present research describes the design and evaluation of an open online course titled Elite Sport Performance: Psychological Perspectives, being the first of its type in the world. Three related studies were conducted, each building on the next: Study 1 provides an evaluation of a first iteration of the course run in 2013, which was deemed unsuccessful. The course was evaluated mainly using web analytic criteria and provided useful insights to support a completely new iteration of the course. Study 2 articulates a redesign of Elite Sport Performance: Psychological Perspectives that addressed supporting the three basic psychological needs of self determination theory: autonomy, competence and relatedness, with a view to fostering intrinsic motivation to engage learners and optimise learning outcomes. A step by step approach using best practice techniques is provided. Study 3 presents an empirical evaluation of Elite Sport Performance: Psychological Perspectives that was released to a global audience in late 2015 with 1007 registrations from 73 countries. The final study includes engagement data derived from web analytics, self determination theory metrics (e.g. satisfaction of basic psychological needs) and qualitative feedback from participants. Additionally, data are presented around continuance intention and reasons for non engagement to better contextualise motivational dynamics in an open online course environment. In general terms, when taken together, the second iteration of the course was successful and supported the design approach taken. The implications of the findings of the present research suggest that designing for open online courses is a unique context in which the needs of the learner need to be carefully considered. A set of recommendations to enhance the design of open online courses such as Elite Sport Performance: Psychological Perspectives are provided. Suggestions for further research, particularly surrounding the potential benefits to well being and eudaimonic flourishing offered by open online courses are speculated.
Deep Convolutional Neural Network for Inverse Problems in Imaging
Venue: IEEE Transactions on Image Processing
Year: 2017

In this paper, we propose a novel deep convolutional neural network (CNN) based algorithm for solving ill posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyperparameter selection. The starting point of this paper is the observation that unrolled iterative methods have the form of a CNN (filtering followed by pointwise non linearity) when the normal operator <inline formula> <tex math notation="LaTeX"$H^}H$ /tex math>/inline formula> where <inline formula> <tex math notation="LaTeX"$H^ /tex math>/inline formula> is the adjoint of the forward imaging operator, <inline formula> <tex math notation="LaTeX"$H$ /tex math>/inline formula> of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse view reconstruction (down to 50 views) on parallel beam X ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a <inline formula> <tex math notation="LaTeX"$512\times 512$ /tex math>/inline formula> image on the GPU.
"e Hang Hai "Shi Dai Hai Shi Dong Tai Jian Guan Xi Tong Dian Zi Xun Hang
Year: 2014

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Evaluating a national science and technology program using the human capital and relational asset perspectives.
Venue: Evaluation and program planning
Year: 2010

The purpose of this research is to evaluate the performance of the National Science and Technology Program (NSTP) by targeting the Taiwan National Telecommunication Program (NTP) initiated in 1998. The Taiwan telecommunications industry has prospered, currently occupying key positions in global markets even though NTP seldom contributes positively to patent citation performance. Hence, the authors of this study investigate the qualitative perspective of intellectual capital rather than quantitative technological indices. The current study focuses on both human capital and relational assets through surveys of 53 principal investigators of NTP projects and 63 industrial R&D managers of telecommunications corporations in the Taiwan market. Results show that NSTP member quality and the flow of employment are good indicators of human capital and that both perform better than the middle value in the case of Taiwan NTP. In addition, we find that industrial participants are more likely to share R&D resources than other academic researchers with higher intention of co publishing, co funding, and sharing equipment and facilities. The industrial NTP participants also have higher expectations regarding achieving advanced technology breakthroughs in contrast to non NTP industrial interviewees. Moreover, industrial participants with greater industry university cooperation intensity indeed obtain a particular advantage, that is, greater knowledge acquisition from other fields related to the effect of knowledge spillovers through the particular NSTP linkage. Accordingly, from the perspectives of human capital and relational assets, the authors conclude by articulating the importance of absorptive capacity resulting from good human capital and knowledge spillover contributed by relational assets within governmental technology policy and NSTP programming.
Factors affecting employee performance: an empirical approach
Year: 2019

Nowadays, the phenomenon of increased competition between firms and their need to respond effectively to rapidly changing operational conditions, as well as to personnel requirements, has escalated the necessity to identify those factors that affect employee performance (EP) The purpose of this paper is to examine the interrelations between firm/environment related factors (training culture, management support, environmental dynamism and organizational climate) job related factors (job environment, job autonomy, job communication) and employee related factors (intrinsic motivation, skill flexibility, skill level, proactivity, adaptability, commitment) and their impact on EP.,A new research model that examines the relationships between these factors and EP is proposed utilizing the structural equation modeling approach.,The results indicate that job environment and management support have the strongest impacts (direct and indirect) on job performance, while adaptability and intrinsic motivation directly affect job performance.,A potential limitation of this research is that it is not focused only on one business sector (i.e. the sample is  this study, firm/environmental related factors, job related factors, employee related factors and EP are incorporated in a single model using data from small and medium sized enterprises. Overall, the final model can explain 27 percent of EP variance (first level analysis) and 42 percent of EP variance (second level analysis)
DAGN: A Real Time UAV Remote Sensing Image Vehicle Detection Framework
Venue: IEEE Geoscience and Remote Sensing Letters
Year: 2020

Real time small object detection from the remote sensing images taken by unmanned aerial vehicles (UAVs) is a challenging but fundamental problem for many UAV applications because of the complex scales, densities, and shapes of objects that are the result of the shooting angle of the UAV. In this letter, we focus on real time small vehicle detection for UAV remote sensing images and propose a depthwise separable attention guided network (DAGN) based on YOLOv3. First, we combine the feature concatenation and attention block to provide the model with the excellent ability to distinguish important and inconsequential features. Then, we improve the loss function and candidate merging algorithm in YOLOv3. Through these strategies, the performance of vehicle detection is improved, while some detection speed is sacrificed. To accelerate our model, we replace some standard convolutions with depthwise separable convolutions. Compared to YOLOv3 and other two stage state of the art models that are applied to Vehicle Detection in Aerial Imagery (VEDAI) data sets, DAGN has a detection accuracy of 0.671, which is 5.5% better than that of YOLOv3, and it achieves the same results as two stage methods. In addition, DAGN achieves real time detection using GeForce GTX 1080Ti.
Fast identification of inorganic and organic gunshot residues by LIBS and electrochemical methods
Year: 2018

In this study, electrochemical and laser based spectroscopic methods (LIBS) are proposed as screening methods that are quicker, more selective and more effective than any current field testing technique. These methods offer superior information by simultaneous detection of organic and inorganic gunshot residues, including a substantial number of elements used in modern ammunition. Moreover, the selected analytical scheme permits subsequent confirmatory analysis (SEM EDS) on the same sample. The rapid scanning of the laser beam favors the identification of up to 5 different emission lines per target element in less than one minute, with repeatability better than 11% RSD and limits of detection for the species of interest in the range of 0.2 200 ng. Optimization of the electrochemical sensors demonstrated the feasibility of Square Wave Anodic Stripping Voltammetry (SWASV) for the rapid detection of inorganic and organic target compounds using carbon printed disposable electrodes (repeatability less than 8% RSD, detection limits 0.1 1 ng/uL, linearity 0.991) The separation and detection of mixtures of Pb, Sb, Cu, DNT, and NG was possible after LIBS analysis, in approximately 3 min per sample. A set of 112 samples, 20 samples from non shooters and 92 samples from 28 shooters, were collected as part of the validation study. Pistol (9 mm and .22) and revolver .357 Magnum) were fired at indoor and outdoor shooting ranges. Metrics of performance, such as error rates (false positives and false negatives) specificity, sensitivity, and accuracy, indicate the combination of LIBS and electrochemical methods are a reliable and promising approach to advance current practice.