nils-herrmann/clean_scirepeval huggingface dataset:
Fruit disease detection using color, texture analysis and ANN

Now-a-days as there is prohibitive demand for agricultural industry, effective growth and improved yield of fruit is necessary and important. For this purpose farmers need manual monitoring of fruits from harvest till its progress period. But manual monitoring will not give satisfactory result all the times and they always need satisfactory advice from expert. So it requires proposing an efficient smart farming technique which will help for better yield and growth with less human efforts. We introduce a technique which will diagnose and classify external disease within fruits. Traditional system uses thousands of words which lead to boundary of language. Whereas system that we have come up with, uses image processing techniques for implementation as image is easy way for conveying. In the proposed work, OpenCV library is applied for implementation. K-means clustering method is applied for image segmentation, the images are catalogue and mapped to their respective disease categories on basis of four feature vectors color, morphology, texture and structure of hole on the fruit. The system uses two image databases, one for implementation of query images and the other for training of already stored disease images. Artificial Neural Network (ANN) concept is used for pattern matching and classification of diseases.
Statistical Conclusion Validity: Some Common Threats and Simple Remedies

The ultimate goal of research is to produce dependable knowledge or to provide the evidence that may guide practical decisions. Statistical conclusion validity (SCV) holds when the conclusions of a research study are founded on an adequate analysis of the data, generally meaning that adequate statistical methods are used whose small-sample behavior is accurate, besides being logically capable of providing an answer to the research question. Compared to the three other traditional aspects of research validity (external validity, internal validity, and construct validity), interest in SCV has recently grown on evidence that inadequate data analyses are sometimes carried out which yield conclusions that a proper analysis of the data would not have supported. This paper discusses evidence of three common threats to SCV that arise from widespread recommendations or practices in data analysis, namely, the use of repeated testing and optional stopping without control of Type-I error rates, the recommendation to check the assumptions of statistical tests, and the use of regression whenever a bivariate relation or the equivalence between two variables is studied. For each of these threats, examples are presented and alternative practices that safeguard SCV are discussed. Educational and editorial changes that may improve the SCV of published research are also discussed.
Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v2

We review some of the most recent approaches to colorize gray-scale images using deep learning methods. Inspired by these, we propose a model which combines a deep Convolutional Neural Network trained from scratch with high-level features extracted from the InceptionResNet-v2 pre-trained model. Thanks to its fully convolutional architecture, our encoder-decoder model can process images of any size and aspect ratio. Other than presenting the training results, we assess the “public acceptance” of the generated images by means of a user study. Finally, we present a carousel of applications on different types of images, such as historical photographs.
Pareto Multi Objective Optimization

The goal of this chapter is to give fundamental knowledge on solving multi-objective optimization problems. The focus is on the intelligent metaheuristic approaches (evolutionary algorithms or swarm-based techniques). The focus is on techniques for efficient generation of the Pareto frontier. A general formulation of MO optimization is given in this chapter, the Pareto optimality concepts introduced, and solution approaches with examples of MO problems in the power systems field are given
Food Image Recognition by Using Convolutional Neural Networks (CNNs)

Food image recognition is one of the promising applications of visual object recognition in computer vision. In this study, a small-scale dataset consisting of 5822 images of ten categories and a five-layer CNN was constructed to recognize these images. The bag-of-features (BoF) model coupled with support vector machine was first tested as comparison, resulting in an overall accuracy of 56%; while the CNN performed much better with an overall accuracy of 74%. Data expansion techniques were applied to increase the size of training images, which achieved a significantly improved accuracy of more than 90% and prevent the overfitting issue that occurred to the CNN without using data expansion. Further improvement is within reach by collecting more images and optimizing the network architecture and relevant hyper-parameters.
Development of Mobile Game Using Multiplatform ( Unity 3 D ) Game Engine

In this study, the smart game based on Unity3D, a multiplatform game engine, was tried to be developed. Unity3D, as an integrated developed tool, provides itself the functions needed for game development, so in fact all games can be developed, and as required, the functions can be modified to meet the request of a user through Unity3D. In this study, for efficiency of the game development process, the multiplatform game engine based smart game was planned so that the game was developed in the form that the game could be played by using the touch function of a smart phone according to the voice.
An Examination of the Determinants of Mobile Shopping Continuance

While mobile shopping (m-shopping) is convenient in the age of mobile commerce, many people still discontinue their shopping using mobile devices even though they have the experience. Therefore, it is important for the m-shopping providers to understand the determinants of m-shopping continuance. This study extended Expectation-Confirmation Model (ECM) to examine the determinants of m-shopping continuance by incorporating trust. There were 244 effective questionnaires obtained for survey analysis. The results showed that trust can overcome the limitations of ECM on predicating m-shopping continuance and improve the explanatory power of initial ECM.
Text-based LSTM networks for Automatic Music Composition

In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn relationships within text documents that represent chord progressions and drum tracks in two case studies. In the experiments, word-RNNs (Recurrent Neural Networks) show good results for both cases, while character-based RNNs (char-RNNs) only succeed to learn chord progressions. The proposed system can be used for fully automatic composition or as semiautomatic systems that help humans to compose music by controlling a diversity parameter of the model.
Deep learning of the regulatory grammar of yeast 5' untranslated regions from 500,000 random sequences.

Our ability to predict protein expression from DNA sequence alone remains poor, reflecting our limited understanding of cis-regulatory grammar and hampering the design of engineered genes for synthetic biology applications. Here, we generate a model that predicts the protein expression of the 5' untranslated region (UTR) of mRNAs in the yeast Saccharomyces cerevisiae. We constructed a library of half a million 50-nucleotide-long random 5' UTRs and assayed their activity in a massively parallel growth selection experiment. The resulting data allow us to quantify the impact on protein expression of Kozak sequence composition, upstream open reading frames (uORFs), and secondary structure. We trained a convolutional neural network (CNN) on the random library and showed that it performs well at predicting the protein expression of both a held-out set of the random 5' UTRs as well as native S. cerevisiae 5' UTRs. The model additionally was used to computationally evolve highly active 5' UTRs. We confirmed experimentally that the great majority of the evolved sequences led to higher protein expression rates than the starting sequences, demonstrating the predictive power of this model.
What is "neuromarketing"? A discussion and agenda for future research.

Recent years have seen advances in neuroimaging to such an extent that neuroscientists are able to directly study the frequency, location, and timing of neuronal activity to an unprecedented degree. However, marketing science has remained largely unaware of such advances and their huge potential. In fact, the application of neuroimaging to market research--what has come to be called "neuromarketing"--has caused considerable controversy within neuroscience circles in recent times. This paper is an attempt to widen the scope of neuromarketing beyond commercial brand and consumer behaviour applications, to include a wider conceptualisation of marketing science. Drawing from general neuroscience and neuroeconomics, neuromarketing as a field of study is defined, and some future research directions are suggested.
A Compositional Textual Model for Recognition of Imperfect Word Images

Printed text recognition is an important problem for industrial OCR systems. Printed text is constructed in a standard procedural fashion in most settings. We develop a mathematical model for this process that can be applied to the backward inference problem of text recognition from an image. Through ablation experiments we show that this model is realistic and that a multi-task objective setting can help to stabilize estimation of its free parameters, enabling use of conventional deep learning methods. Furthermore, by directly modeling the geometric perturbations of text synthesis we show that our model can help recover missing characters from incomplete text regions, the bane of multicomponent OCR systems, enabling recognition even when the detection returns incomplete in-
Policy: Five priorities for the UN Sustainable Development Goals

Devise metrics. Scientists, social scientists and economists need to design a set of practical indices for tracking progress on each SDG. Ensuring access to sustainable and modern energy for all (goal 7), for example, will require indicators of improvements in energy efficiency and carbon savings from renewable-energy technologies (see go.nature.com/pkij7y). Parameters other than just economic growth must be included, such as income inequality, carbon emissions, population and lifespans 1 .
Distributed network anomaly detection on an event processing framework

Network Intrusion Detection Systems (NIDS) are an integral part of modern data centres to ensure high availability and compliance with Service Level Agreements (SLAs). Currently, NIDS are deployed on high-performance, high-cost middleboxes that are responsible for monitoring a limited section of the network. The fast increasing size and aggregate throughput of modern data centre networks have come to challenge the current approach to anomaly detection to satisfy the fast growing compute demand. In this paper, we propose a novel approach to distributed intrusion detection systems based on the architecture of recently proposed event processing frameworks. We have designed and implemented a prototype system using Apache Storm to show the benefits of the proposed approach as well as the architectural differences with traditional systems. Our system distributes modules across the available devices within the network fabric and uses a centralised controller for orchestration, management and correlation. Following the Software Defined Networking (SDN) paradigm, the controller maintains a complete view of the network but distributes the processing logic for quick event processing while performing complex event correlation centrally. We have evaluated the proposed system using publicly available data centre traces and demonstrated that the system can scale with the network topology while providing high performance and minimal impact on packet latency.
Can Intensive Exposure to Foreign Language Sounds Affect the Perception of Native Sounds?

A possible side-effect of exposure to non-native sounds is a change in the way we perceive native sounds. Previous studies have demonstrated that native speakers’ speech production can change as a result of learning a new language, but little work has been carried out to measure the perceptual consequences of exposure. The current study examined how intensive exposure to Spanish intervocalic consonants affected Chinese learners with no prior experience of Spanish. Before, during and after a training period, listeners undertook both an adaptive noise task, which measured the noise level at which listeners could identify native language consonants, and an assimilation task, in which listeners assigned Spanish consonants to Chinese consonant categories. Listeners exhibited a significantly reduced noise tolerance for the Chinese consonants /l/ and /w/ following exposure to Spanish. These two consonants also showed the largest reductions in Spanish to Chinese category assimilations. Taken together, these findings suggest that Chinese listeners modified their native language categories boundaries as a result of exposure to Spanish sounds in order to accommodate them, and that as a consequence their identification performance in noise reduced. Some differences between the two sounds in the time-course of recovery from perceptual adaptation were observed.
Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network

With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into “malignant” and “benign” cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.
Information security risk management for computerized health information systems in hospitals: a case study of Iran

In recent years, hospitals in Iran - similar to those in other countries - have experienced growing use of computerized health information systems (CHISs), which play a significant role in the operations of hospitals. But, the major challenge of CHIS use is information security. This study attempts to evaluate CHIS information security risk management at hospitals of Iran.



This applied study is a descriptive and cross-sectional research that has been conducted in 2015. The data were collected from 551 hospitals of Iran. Based on literature review, experts' opinion, and observations at five hospitals, our intensive questionnaire was designed to assess security risk management for CHISs at the concerned hospitals, which was then sent to all hospitals in Iran by the Ministry of Health.



Sixty-nine percent of the studied hospitals pursue information security policies and procedures in conformity with Iran Hospitals Accreditation Standards. At some hospitals, risk identification, risk evaluation, and risk estimation, as well as risk treatment, are unstructured without any specified approach or methodology. There is no significant structured approach to risk management at the studied hospitals.



Information security risk management is not followed by Iran's hospitals and their information security policies. This problem can cause a large number of challenges for their CHIS security in future. Therefore, Iran's Ministry of Health should develop practical policies to improve information security risk management in the hospitals of Iran.
Botulinum Toxin for the Treatment of Gummy Smile

An experimental in vivo study was conducted at a dermatology clinic in Riyadh in January 2016. The study included 23 female patients who ranged from 20 to 50 years and were treated with Botox injections due to excessive maxillary gingival display. The patients with short clinical crowns or long maxilla, those who were pregnant or breastfeeding, and patients with neuromuscular disorders were excluded. Patients received Botox type I, injected 3 mm lateral to the alar-fascial groove at the level of the nostril opening at the insertion of the levator labii superioris alaeque nasi muscle. Photos were taken of the patient’s smile before and after the treatment and were then uploaded to the SketchUp program to calculate improvements in gingival display. The distance from the lower margin of the upper lip to the gingival margin was calculated preand posttreatment. The amount of improvement was calculated as (pre-Botox treatment – post-Botox treatment/pre-Botox treatment × 100). The mean percentage of the total improvement was analyzed.
Psychiatric comorbidity in gender dysphoric adolescents.

This study examined psychiatric comorbidity in adolescents with a gender identity disorder (GID). We focused on its relation to gender, type of GID diagnosis and eligibility for medical interventions (puberty suppression and cross-sex hormones).



To ascertain DSM-IV diagnoses, the Diagnostic Interview Schedule for Children (DISC) was administered to parents of 105 gender dysphoric adolescents.



67.6% had no concurrent psychiatric disorder. Anxiety disorders occurred in 21%, mood disorders in 12.4% and disruptive disorders in 11.4% of the adolescents. Compared with natal females (n = 52), natal males (n = 53) suffered more often from two or more comorbid diagnoses (22.6% vs. 7.7%, p = .03), mood disorders (20.8% vs. 3.8%, p = .008) and social anxiety disorder (15.1% vs. 3.8%, p = .049). Adolescents with GID considered to be 'delayed eligible' for medical treatment were older [15.6 years (SD = 1.6) vs. 14.1 years (SD = 2.2), p = .001], their intelligence was lower [91.6 (SD = 12.4) vs. 99.1 (SD = 12.8), p = .011] and a lower percentage was living with both parents (23% vs. 64%, p < .001). Although the two groups did not differ in the prevalence of psychiatric comorbidity, the respective odds ratios ('delayed eligible' adolescents vs. 'immediately eligible' adolescents) were >1.0 for all psychiatric diagnoses except specific phobia.



Despite the suffering resulting from the incongruence between experienced and assigned gender at the start of puberty, the majority of gender dysphoric adolescents do not have co-occurring psychiatric problems. Delayed eligibility for medical interventions is associated with psychiatric comorbidity although other factors are of importance as well.
Serious Games Architectures and Engines

The term Serious Game includes a wide, heterogeneous field of digital games with varying purposes and objectives and for a multitude of different application areas. All in common is the underlying software. This chapter gives an overview on the technical aspects of serious games including their software architectures and engines. As the general topic is manifold and the technical aspects of serious game software are quite comprehensive, this chapter covers the basic principles of and requirements for serious game software. It depicts selected software architectures and provides examples for game engines including a description of selected components.
Analog Versus Digital: Extrapolating from Electronics to Neurobiology

We review the pros and cons of analog and digital computation. We propose that computation that is most efficient in its use of resources is neither analog computation nor digital computation but, rather, a mixture of the two forms. For maximum efficiency, the information and information-processing resources of the hybrid form must be distributed over many wires, with an optimal signal-to-noise ratio per wire. Our results suggest that it is likely that the brain computes in a hybrid fashion and that an underappreciated and important reason for the efficiency of the human brain, which consumes only 12 W, is the hybrid and distributed nature of its architecture.
Studying Effectiveness of Human Resource Management Actions and Organizational Agility: Resource Management Actions and Organizational Agility

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BlockNDN: A bitcoin blockchain decentralized system over named data networking

Blockchain provides a new approach for participants to maintain reliable databases in untrusted networks without centralized authorities. However, there are still many serious problems in real blockchain systems in IP network such as the lack of support for multicast and the hierarchies of status. In this paper, we design a bitcoin-like blockchain system named BlockNDN over Named Data Networking and we implement and deploy it on our cluster as well. The resulting design solves those problems in IP network. It provides completely decentralized systems and simplifies system architecture. It also improves the weak-connectivity phenomenon and decreases the broadcast overhead.
Quantitative anatomy of the lumbar musculature.

This paper describes the anatomy of the musculature crossing the lumbar spine in a standardized form to provide data generally suitable for static biomechanical analyses of muscle and spinal forces. The muscular anatomy from several sources was quantified and transformed to the mean bony anatomy of four young healthy adults measured from standing stereo-radiographs. The origins, insertions and physiological cross-sectional area (PCSA) of 180 muscle slips which act on the lumbar spine are given relative to the bony anatomy defined by the locations of 12 thoracic and five lumbar vertebrae, and the sacrum, and the shape and positions of the 24 ribs. The broad oblique abdominal muscles are each represented by six vectors and an appropriate proportion of the total PCSA was assigned to each to represent the muscle biomechanics.