nils-herrmann/clean_scirepeval huggingface dataset:
An Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting.

Recently, back propagation neural network (BPNN) has been applied successfully in many areas with excellent generalization results, for example, rule extraction, classification and evaluation. In this paper the Levenberg-Marquardt back-propagation algorithm is used for training the network and reconstructs the image. It is found that Marquardt algorithm is significantly more proficient. A practical problem with MLPs is to select the correct complexity for the model, i.e., the right number of hidden units or correct regularization parameters. In this paper, a study is made to determine the issue of number of neurons in every hidden layer and the quantity of hidden layers needed for getting the high accuracy. We performed regression R analysis to measure the correlation between outputs and targets.
Image Quality Estimation : Software for Objective Evaluation

Liu, He M , Purdue University, August 2016. Image Quality Estimation: Soft-ware for  Digital images are widely used in our daily lives and the quality of images is important to the viewing experience. Low quality images may be blurry or contain noise or compression artifacts. Humans can easily estimate image quality, but it is not practical to use human subjects to measure image quality in real applications. Image Quality Estimators (QE) are algorithms that evaluate image qualities automatically. These QEs compute scores of any input images to represent their qualities. This thesis mainly focuses on evaluating the performance of QEs. Two approaches used in this work are objective software analysis and the subjective database design. For the first, we create a software consisting of functional modules to test QE performances. These modules can load images from subjective databases or generate distortion images from any input images. Their QE scores are computed and analyzed by the statistical method module so that they can be easily interpreted and reported. Some modules in this software are combined and formed into a published software package: Stress Testing Image Quality Estimators (STIQE). In addition to the QE analysis software, a new subjective database is designed and implemented using both online and in-lab subjective tests. The database is designed using the pairwise comparison method and the subjective quality scores are computed using the Bradley-Terry model and Maximum Likelihood Estimation (MLE). While four testing phases are designed for this databases, only phase 1 is reported in this
Directions on the use of stevia leaves (Stevia Rebauidana) as an additive in food products.

Due to the high intake of sugars, especially sucrose, global trends in food processing have encouraged producers to use sweeteners, particularly synthetic ones, to a wide extent. For several years, increasing attention has been paid in the literature to the stevia (Stevia rebauidana), containing glycosidic diterpenes, for which sweetening properties have been identified. Chemical composition, nutritional value and application of stevia leaves are briefl y summarized and presented.
Chemical screening identifies ATM as a target for alleviating senescence.

Senescence, defined as irreversible cell-cycle arrest, is the main driving force of aging and age-related diseases. Here, we performed high-throughput screening to identify compounds that alleviate senescence and identified the ataxia telangiectasia mutated (ATM) inhibitor KU-60019 as an effective agent. To elucidate the mechanism underlying ATM's role in senescence, we performed a yeast two-hybrid screen and found that ATM interacted with the vacuolar ATPase V1 subunits ATP6V1E1 and ATP6V1G1. Specifically, ATM decreased E-G dimerization through direct phosphorylation of ATP6V1G1. Attenuation of ATM activity restored the dimerization, thus consequently facilitating assembly of the V1 and V0 domains with concomitant reacidification of the lysosome. In turn, this reacidification induced the functional recovery of the lysosome/autophagy system and was coupled with mitochondrial functional recovery and metabolic reprogramming. Together, our data reveal a new mechanism through which senescence is controlled by the lysosomal-mitochondrial axis, whose function is modulated by the fine-tuning of ATM activity.
Measurement of individual differences: lessons from memory assessment in research and clinical practice.

An examination of test manuals and published research indicates that widely used memory tests (e.g., Verbal Paired Associates and Word List tests of the Wechsler Memory Scale, Rey Auditory Verbal Learning Test, and California Verbal Learning Test) are afflicted by severe ceiling effects. In the present study, the true extent of memory ability in healthy young adults was tested by giving 208 college undergraduates verbal paired-associate and verbal learning tests of various lengths; the findings demonstrate that healthy adults can remember much more than is suggested by the normative data for the memory tests just mentioned. The findings highlight the adverse effects of low ceilings in memory assessment and underscore the severe consequences of ceiling effects on score distributions, means, standard deviations, and all variability-dependent indices, such as reliability, validity, and correlations with other tests. The article discusses the optimal test lengths for verbal paired-associate and verbal list-learning tests, shows how to identify ceiling-afflicted data in published research, and explains how proper attention to this phenomenon can improve future research and clinical practice.
Anonymity and Language Usage: A Natural Experiment of Social Network Integration

The Internet creates an anonymous and non-authoritarian environment where people do not have social inhibitions and can express opinions freely. However, such disinhibition at times leads to abusive use of language and uncivil behavior in the online environment. This paper leverages data from a natural experiment on an online review platform that integrated social network platform personalization features, which exposes users in an anonymous environment to a social environment. Interestingly, our preliminary findings show that after the social network platform integration, users express more emotions (specifically, they become more positive but less negative), are less likely to use inappropriate language that include sexually explicit words or words that shows rage. Further, users are less egocentric and more social in their language use. We discuss the implication of this study for creating a civil online environment.
Framework for learning agents in quantum environments

Vedran Dunjko, 2, ∗ Jacob M. Taylor, 4, † and Hans J. Briegel ‡ Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 25, A-6020 Innsbruck, Austria Division of Molecular Biology, Rud̄er Bošković Institute, Bijenička cesta 54, 10002 Zagreb,  University of Maryland, College Park, MD 20742 USA (Dated: November 6, 2018)
Soybean Plant Disease Identification Using Convolutional Neural Network

Plants have become an important source of energy, and are a fundamental piece in the puzzle to solve the problem of global warming. However, plant diseases are threatening the livelihood of this important source. Convolutional neural networks (CNN) have demonstrated great performance (beating that of humans) in object recognition and image classification problems. This paper describes the feasibility of CNN for plant disease classification for leaf images taken under the natural environment. The model is designed based on the LeNet architecture to perform the soybean plant disease classification. 12,673 samples containing leaf images of four classes, including the healthy leaf images, were obtained from the PlantVillage database. The images were taken under uncontrolled environment. The implemented model achieves 99.32% classification accuracy which show clearly that CNN can extract important features and classify plant diseases from images taken in the natural environment.
Excessive Computer Game Playing: Evidence for Addiction and Aggression?

Computer games have become an ever-increasing part of many adolescents' day-to-day lives. Coupled with this phenomenon, reports of excessive gaming (computer game playing) denominated as "computer/video game addiction" have been discussed in the popular press as well as in recent scientific research. The aim of the present study was the investigation of the addictive potential of gaming as well as the relationship between excessive gaming and aggressive attitudes and behavior. A sample comprising of 7069 gamers answered two questionnaires online. Data revealed that 11.9% of participants (840 gamers) fulfilled diagnostic criteria of addiction concerning their gaming behavior, while there is only weak evidence for the assumption that aggressive behavior is interrelated with excessive gaming in general. Results of this study contribute to the assumption that also playing games without monetary reward meets criteria of addiction. Hence, an addictive potential of gaming should be taken into consideration regarding prevention and intervention.
The relationships of customer satisfaction , customer loyalty , and profitability : an empirical study

This paper’s purpose is to illustrate the relationship of profitability to intermediate, customer-related outcomes that managers can influence directly. It is predominantly a general management discussion, consistent with the Nordic School’s view that services are highly interdisciplinary, requiring a “service management” approach (see Grönroos, 1984, 1991). Its findings support the theory that customer satisfaction is related to customer loyalty, which in turn is related to profitability (Heskett et al., 1994, and discussed in Storbacka et al., 1994). While this theory has been advocated for service firms as a class, this paper presents an empirical analysis of one retail bank, limiting the findings’ generalizability. The service profit chain (Heskett et al., 1994) hypothesizes that:
Enhancing the security of user data using the keyword encryption and hybrid cryptographic algorithm in cloud

Cloud computing is a popular model for accessing the computer resources. The data owner outsources their data on cloud server that can be accessed by an authorized user. In Cloud Computing public key encryption with equality test (PKEET) provides an alternative to public key encryption by simplify the public key and credential administration at Public Key Infrastructure (PKI). However it still faces the security risk in outsourced computation on encrypted data. Therefore this paper proposed a novel identity based hybrid encryption (RSA with ECC) to enhance the security of outsourced data. In this approach sender encrypts the sensitive data using hybrid algorithm. Then the proxy re encryption is used to encrypt the keyword and identity in standardize toward enrichment security of data.
Learning Dota 2 Team Compositions

Dota 2 is a multiplayer online game in which two teams of five players control “heroes” and compete to earn gold and destroy enemy structures. Teamwork is essential and heroes are chosen to create a balanced team that will counter the opponents’ selections. We studied how the win rate depends on hero selection by performing logistic regression with models that incorporate interactions between heroes. Our models did not match the naive model without interactions which had a 62% win prediction rate, suggesting cleaner data or better models are needed.
Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations

Sarcasm can radically alter or invert a phrase’s meaning. Sarcasm detection can therefore help improve natural language processing (NLP) tasks. The majority of prior research has modeled sarcasm detection as classification, with two important limitations: 1. Balanced datasets, when sarcasm is actually rather rare. 2. Using Twitter users’ self-declarations in the form of hashtags to label data, when sarcasm can take many forms. To address these issues, we create an unbalanced corpus of manually annotated Twitter conversations. We compare human and machine ability to recognize sarcasm on this data under varying amounts of context. Our results indicate that both class imbalance and labelling method affect performance, and should both be considered when designing automatic sarcasm detection systems. We conclude that for progress to be made in real-world sarcasm detection, we will require a new class labelling scheme that is able to access the ‘common ground’ held between conversational parties.
Early Goal-Directed Therapy in the Treatment of Severe Sepsis and Septic Shock

A BSTRACT Background Goal-directed therapy has been used for severe sepsis and septic shock in the intensive care unit. This approach involves adjustments of cardiac preload, afterload, and contractility to balance oxygen delivery with oxygen demand. The purpose of this study was to evaluate the efficacy of early goal-directed therapy before admission to the intensive care unit.  We randomly assigned patients who arrived at an urban emergency department with severe sepsis or septic shock to receive either six hours of early goal-directed therapy or standard therapy (as a control) before admission to the intensive care unit. Clinicians who subsequently assumed the care of the patients were blinded to the treatment assignment. In-hospital mortality (the primary efficacy outcome), end points with respect to resuscitation, and Acute Physiology and Chronic Health Evaluation (APACHE II) scores were obtained serially for 72 hours and compared between the study groups.  Of the 263 enrolled patients, 130 were randomly assigned to early goal-directed therapy and 133 to standard therapy; there were no significant differences between the groups with respect to base-line characteristics. In-hospital mortality was 30.5 percent in the group assigned to early goal-directed therapy, as compared with 46.5 percent in the group assigned to standard therapy (P=0.009). During the interval from 7 to 72 hours, the patients assigned to early goaldirected therapy had a significantly higher mean (±SD) central venous oxygen saturation (70.4±10.7 percent vs. 65.3±11.4 percent), a lower lactate concentration (3.0±4.4 vs. 3.9±4.4 mmol per liter), a lower base deficit (2.0±6.6 vs. 5.1±6.7 mmol per liter), and a higher pH (7.40±0.12 vs. 7.36±0.12) than the patients assigned to standard therapy (P«0.02 for all comparisons). During the same period, mean APACHE II scores were significantly lower, indicating less severe organ dysfunction, in the patients assigned to early goal-directed therapy than in those assigned to standard therapy (13.0±6.3 vs. 15.9±6.4, P<0.001).  Early goal-directed therapy provides significant benefits with respect to outcome in patients with severe sepsis and septic shock. ( 2001;345:1368-77.
A review of family and social determinants of children's eating patterns and diet quality.

With the growing problem of childhood obesity, recent research has begun to focus on family and social influences on children's eating patterns. Research has demonstrated that children's eating patterns are strongly influenced by characteristics of both the physical and social environment. With regard to the physical environment, children are more likely to eat foods that are available and easily accessible, and they tend to eat greater quantities when larger portions are provided. Additionally, characteristics of the social environment, including various socioeconomic and sociocultural factors such as parents' education, time constraints, and ethnicity influence the types of foods children eat. Mealtime structure is also an important factor related to children's eating patterns. Mealtime structure includes social and physical characteristics of mealtimes including whether families eat together, TV-viewing during meals, and the source of foods (e.g., restaurants, schools). Parents also play a direct role in children's eating patterns through their behaviors, attitudes, and feeding styles. Interventions aimed at improving children's nutrition need to address the variety of social and physical factors that influence children's eating patterns.
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an economical alternative, with which training phase could hardly generate satisfactory performance unfortunately. In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale. Extensive experiments on the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural networks trained with the enriched annotations from our framework yield a significant improvement over that trained with the original coarse labels. It is highly competitive to the performance obtained by using human annotated dense annotations. The proposed method also outperforms among other state-of-the-art weakly-supervised segmentation methods.
New standard of care for appendiceal epithelial neoplasms and pseudomyxoma peritonei syndrome?

Appendiceal mucinous neoplasms sometimes present with peritoneal dissemination, which was previously a lethal condition with a median survival of about 3 years. Traditionally, surgical treatment consisted of debulking that was repeated until no further benefit could be achieved; systemic chemotherapy was sometimes used as a palliative option. Now, visible disease tends to be removed through visceral resections and peritonectomy. To avoid entrapment of tumour cells at operative sites and to destroy small residual mucinous tumour nodules, cytoreductive surgery is combined with intraperitoneal chemotherapy with mitomycin at 42 degrees C. Fluorouracil is then given postoperatively for 5 days. If the mucinous neoplasm is minimally invasive and cytoreduction complete, these treatments result in a 20-year survival of 70%. In the absence of a phase III study, this new combined treatment should be regarded as the standard of care for epithelial appendiceal neoplasms and pseudomyxoma peritonei syndrome.
A modular software architecture for UAVs

There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module.
Facebook in context(s): Measuring emotional responses across time and space

This article advances a contextual approach to understanding the emotional and social outcomes of Facebook use. In doing so, we address the ambiguity of previously reported relationships between Facebook use and well-being. We test temporal (shorter vs longer time spans) and spatial (at home vs away from home) dimensions of Facebook activity using an innovative approach. By triggering smartphone surveys in response to users’ naturalistic Facebook posting, we captured the immediate context of both mobile and desktop activities during daily life. Findings indicated positive—yet fleeting—emotional experiences up to 10 minutes after active posting and higher arousal for 30 minutes following posting at home. Nonetheless, Facebook activities predicted no changes in aggregate mood over 2 weeks, despite showing positive relationships to bridging social capital during the same period. Our results call attention to fleeting experiences (vs Corresponding author: Joseph Bayer, School of Communication, The Ohio State University, 154 N. Oval Mall, Columbus, OH MI 43210, USA.  Bayer.66@osu.edu 681522 NMS0010.1177/1461444816681522new media & societyBayer et al. research-
ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment

This paper presents our approach to semantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two questions: (1) Can we solve these two subtasks together? (2) Are features proposed for textual entailment task still effective for semantic relatedness task? To address them, we extracted seven types of features including text difference measures proposed in entailment judgement subtask, as well as common text similarity measures used in both subtasks. Then we exploited the same feature set to solve the both subtasks by considering them as a regression and a classification task respectively and performed a study of influence of different features. We achieved the first and the second rank for relatedness and entailment task respectively.
Zero-shot Neural Transfer for Cross-lingual Entity Linking

Cross-lingual entity linking maps an entity mention in a source language to its corresponding entry in a structured knowledge base that is in a different (target) language. While previous work relies heavily on bilingual lexical resources to bridge the gap between the source and the target languages, these resources are scarce or unavailable for many low-resource languages. To address this problem, we investigate zero-shot cross-lingual entity linking, in which we assume no bilingual lexical resources are available in the source low-resource language. Specifically, we propose pivot-based entity linking, which leverages information from a highresource “pivot” language to train character-level neural entity linking models that are transferred to the source lowresource language in a zero-shot manner. With experiments on 9 low-resource languages and transfer through a total of 54 languages, we show that our proposed pivot-based framework improves entity linking accuracy 17% (absolute) on average over the baseline systems, for the zero-shot scenario. Further, we also investigate the use of language-universal phonological representations which improves average accuracy (absolute) by 36% when transferring between languages that use different scripts.
X-ray detectors for digital radiography.

Digital radiography offers the potential of improved image quality as well as providing opportunities for advances in medical image management, computer-aided diagnosis and teleradiology. Image quality is intimately linked to the precise and accurate acquisition of information from the x-ray beam transmitted by the patient, i.e. to the performance of the x-ray detector. Detectors for digital radiography must meet the needs of the specific radiological procedure where they will be used. Key parameters are spatial resolution, uniformity of response, contrast sensitivity, dynamic range, acquisition speed and frame rate. The underlying physical considerations defining the performance of x-ray detectors for radiography will be reviewed. Some of the more promising existing and experimental detector technologies which may be suitable for digital radiography will be considered. Devices that can be employed in full-area detectors and also those more appropriate for scanning x-ray systems will be discussed. These include various approaches based on phosphor x-ray converters, where light quanta are produced as an intermediate stage, as well as direct x-ray-to-charge conversion materials such as zinc cadmium telluride, amorphous selenium and crystalline silicon.