nils-herrmann/clean_scirepeval huggingface dataset:
Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

Wetland vegetation plays a key role in the ecological functions of wetland environments. Remote sensing techniques offer timely, up-to-date, and relatively accurate information for sustainable and effective management of wetland vegetation. This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of wetland vegetation. Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed. The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation, discriminating wetland vegetation using broad- and narrow-bands, as well as estimating water content, biomass, and leaf area index. It can be concluded that the remote sensing of wetland vegetation has some particular challenges that require careful consideration in order to obtain successful results. These include an in-depth understanding of the factors affecting the interaction between electromagnetic radiation and wetland vegetation in a particular environment, selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation.
Ant Colony Optimization for Job Shop Scheduling Problem

This paper presents an application of the Ant colony optimization metaheuristic to the job shop scheduling problem. A pheromone alteration strategy which improves the basic ant system by utilizing the behaviour of artificial ants to perform local search is introduced. Experiments using well-known benchmark JSSP problems show that this approach improves on the performance obtained by the basic ant system and is competitive with another recently proposed extension of the ant system, the MAX-MIN algorithm.
New Position-Specific Movement Ability Test ( PoSMAT ) Protocol Suite and Norms for Talent Identification , Selection , and Personalized Training for Soccer Players

Jubjitt P, Tingsabhat J, Chaiwatcharaporn C. New PositionSpecific Movement Ability Test (PoSMAT) Protocol Suite and Norms for Talent Identification, Selection, and Personalized Training for Soccer Players. JEPonline 2017;20(1):59-82. The purpose of this study was to develop a soccer position-specific movement ability test (PoSMAT) Protocol Suite and establish their norms. Subjects consisted of six different position soccer players per team from six Thai Premier League 2013 teams. The first step was to identify position-specific high speed running/sprint speed with corresponding distances covered by TRAK PERFORMANCE software. The second step was to develop the PoSMAT Protocol Suite by incorporating position-specific movement patterns and speed-distance analyses from the first step into three test protocols for ATTK Attacker, CMCD Central Midfielder and Central Defender, and WMFB Wide Midfielder and Full Back with respect to the soccer players’ abilities in speed, agility, and cardiovascular endurance. The findings indicate that the PoSMAT Protocol Suite was statistically valid, objective, reliable, and discriminating. Also, PoSMAT norms from 360 Thai elite soccer players were established. Thus, the PoSMAT Protocol Suite and norms can be used for position-specific talent identification, selection for proper playing position placement, and individualized training to enhance the players’ soccer career.
Classification of low-grade and high-grade glioma using multi-modal image radiomics features

Gliomas are primary brain tumors arising from glial cells. Gliomas can be classified into different histopathologic grades according to World Health Oraganization (WHO) grading system which represents malignancy. In this paper, we present a method to predict the grades of Gliomas using Radiomics imaging features.  training data, its segmentation ground truth and the ground truth labels were used for this work. 45 radiomics features based on histogram, shape and gray-level co-occurrence matrix (GLCM) were extracted from each FLAIR, T1, T1-Contrast, T2 image to quantify the property of Gliomas. Significant features among 180 features were selected through L1-norm regularization (LASSO). Based on LASSO coefficient and selected feature values, we computed a LASSO score and gliomas were classified into low-grade glimoa (LGG) or high-grade glimoa (HGG) through logistic regression. Classification result was validated by a 10-fold cross validation. Our method achieved accuracy of 0.8981, sensitivity of 0.8889, specificity of 0.9074, and area under the curve (AUC) = 0.8870.
Gill remodelling and growth rate of striped catfish Pangasianodon hypophthalmus under impacts of hypoxia and temperature.

Gill morphometric and gill plasticity of the air-breathing striped catfish (Pangasianodon hypophthalmus) exposed to different temperatures (present day 27°C and future 33°C) and different air saturation levels (92% and 35%) during 6weeks were investigated using vertical sections to estimate the respiratory lamellae surface areas, harmonic mean barrier thicknesses, and gill component volumes. Gill respiratory surface area (SA) and harmonic mean water - blood barrier thicknesses (HM) of the fish were strongly affected by both environmental temperature and oxygen level. Thus initial values for 27°C normoxic fish (12.4±0.8g) were 211.8±21.6mm2g-1 and 1.67±0.12μm for SA and HM respectively. After 5weeks in same conditions or in the combinations of 33°C and/or PO2 of 55mmHg, this initial surface area scaled allometrically with size for the 33°C hypoxic group, whereas branchial SA was almost eliminated in the 27°C normoxic group, with other groups intermediate. In addition, elevated temperature had an astounding effect on growth with the 33°C group growing nearly 8-fold faster than the 27°C fish.
The constructive, destructive, and reconstructive power of social norms.

Despite a long tradition of effectiveness in laboratory tests, normative messages have had mixed success in changing behavior in field contexts, with some studies showing boomerang effects. To test a theoretical account of this inconsistency, we conducted a field experiment in which normative messages were used to promote household energy conservation. As predicted, a descriptive normative message detailing average neighborhood usage produced either desirable energy savings or the undesirable boomerang effect, depending on whether households were already consuming at a low or high rate. Also as predicted, adding an injunctive message (conveying social approval or disapproval) eliminated the boomerang effect. The results offer an explanation for the mixed success of persuasive appeals based on social norms and suggest how such appeals should be properly crafted.
Epigenetic mechanisms mediating the long-term effects of maternal care on development

The long-term consequences of early environmental experiences for development have been explored extensively in animal models to better understand the mechanisms mediating risk of psychopathology in individuals exposed to childhood adversity. One common feature of these models is disruption of the mother-infant relationship which is associated with impairments in stress responsivity and maternal behavior in adult offspring. These behavioral and physiological characteristics are associated with stable changes in gene expression which emerge in infancy and are sustained into adulthood. Recent evidence suggests that these long-term effects may be mediated by epigenetic modification to the promoter regions of steroid receptor genes. In particular, DNA methylation may be critical to maternal effects on gene expression and thus generate phenotypic differentiation of offspring and, through effects on maternal behavior of offspring, mediate the transmission of these effects across generations. In this review we explore evidence for the influence of mother-infant interactions on the epigenome and consider evidence for and the implications of such epigenetic effects for human mental health.
Analysis and implementation of BRO IDS using signature script

As the use of technology increases the malicious attacks also increases. Previously firewall is itself enough to detect the attack but now needs a strong and powerful tool to detect these attacks. So IDS is used. Here using the detecting tool i.e. BRO IDS which is becoming powerful tool for detecting and analyze the traffic. In this paper for detecting these attacks a signature script in bro is created. Signature is used as a pattern matching in bro.
An Integrative Contingency Model of Software Project Risk Management

Drawing both from the IS literature on software project risk management and the contingency research in Organization Theory literature, the present study develops an integrative contingency model of software project risk management. Adopting a profile deviation perspective of fit, the outcome of a software development project (Performance) is hypothesized to be influenced by the fit between the project's risk (Risk Exposure) and how project risk is managed (Risk Management Profile). The research model was tested with longitudinal data obtained from project leaders and key users of 75 software projects. The results support the contingency model proposed and suggest that in order to increase project performance a project's risk management profile needs to vary according to the project's risk exposure. Specifically, high-risk projects were found to call for high information processing capacity approaches in their management. However, the most appropriate management approach was found to depend on the perfonnance criterion used. When meeting project budgets was the performance criterion, successful high-risk projects had high levels of intemal integration, as well as high levels of formal planning. When system Journal of Manageintni tnformalioH Syslemi/Spring 2Q0l.Vo\. 17, No. 4, pp. 37-69. RIVARD.ANDTALBOT quality was the performance criterion, successful high-risk projects had high levels of user participation.
A Face Recognition Signature Combining Patch-based Features with Soft Facial Attributes

This paper focuses on improving face recognition performance with a new signature combining implicit facial features with explicit soft facial attributes. This signature has two components: the existing patch-based features and the soft facial attributes. A deep convolutional neural network adapted from state-of-the-art networks is used to learn the soft facial attributes. Then, a signature matcher is introduced that merges the contributions of both patch-based features and the facial attributes. In this matcher, the matching scores computed from patch-based features and the facial attributes are combined to obtain a final matching score. The matcher is also extended so that different weights are assigned to different facial attributes. The proposed signature and matcher have been evaluated with the UR2D system on the UHDB31 and IJB-A datasets. The experimental results indicate that the proposed signature achieve better performance than using only patch-based features. The Rank-1 accuracy is improved significantly by 4% and 0.37% on the two datasets when compared with the UR2D system.
Multimodal Generative Models for Scalable Weakly-Supervised Learning

Multiple modalities often co-occur when describing natural phenomena. Learning a joint representation of these modalities should yield deeper and more useful representations. Previous generative approaches to multi-modal input either do not learn a joint distribution or require additional computation to handle missing data. Here, we introduce a multimodal variational autoencoder (MVAE) that uses a product-of-experts inference network and a sub-sampled training paradigm to solve the multi-modal inference problem. Notably, our model shares parameters to efficiently learn under any combination of missing modalities. We apply the MVAE on four datasets and match state-of-the-art performance using many fewer parameters. In addition, we show that the MVAE is directly applicable to weaklysupervised learning, and is robust to incomplete supervision. We then consider two case studies, one of learning image transformations—edge detection, colorization, segmentation—as a set of modalities, followed by one of machine translation between two languages. We find appealing results across this range of tasks.
Improving Sentence Representations with Multi-view Frameworks

Multi-view learning can provide self-supervision when different views are avail1 able of the same data. Distributional hypothesis provides another form of useful 2 self-supervision from adjacent sentences which are plentiful in large unlabelled 3 corpora. Motivated by the asymmetry in the two hemispheres of the human brain 4 as well as the observation that different learning architectures tend to emphasise 5 different aspects of sentence meaning, we present two multi-view frameworks for 6 learning sentence representations in an unsupervised fashion. One framework uses 7 a generative objective and the other a discriminative one. In both frameworks, 8 the final representation is an ensemble of two views, in which, one view encodes 9 the input sentence with a Recurrent Neural Network (RNN), and the other view 10 encodes it with a simple linear model. We show that, after learning, the vectors 11 produced by our multi-view frameworks provide improved representations over 12 their single-view learned counterparts, and the combination of different views gives 13 representational improvement over each view and demonstrates solid transferability 14 on standard downstream tasks.
Deep Learning-Based Document Modeling for Personality Detection from Text

This article presents a deep learning based method for determining the author's personality type from text: given a text, the presence or absence of the Big Five traits is detected in the author's psychological profile. For each of the five traits, the authors train a separate binary classifier, with identical architecture, based on a novel document modeling technique. Namely, the classifier is implemented as a specially designed deep convolutional neural network, with injection of the document-level Mairesse features, extracted directly from the text, into an inner layer. The first layers of the network treat each sentence of the text separately; then the sentences are aggregated into the document vector. Filtering out emotionally neutral input sentences improved the performance. This method outperformed the state of the art for all five traits, and the implementation is freely available for research purposes.
A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities

Technologies to support the Internet of Things are becoming more important as the need to better understand our environments and make them smart increases. As a result it is predicted that intelligent devices and networks, such as WSNs, will not be isolated, but connected and integrated, composing computer networks. So far, the IP-based Internet is the largest network in the world; therefore, there are great strides to connect WSNs with the Internet. To this end, the IETF has developed a suite of protocols and open standards for accessing applications and services for wireless resource constrained networks. However, many open challenges remain, mostly due to the complex deployment characteristics of such systems and the stringent requirements imposed by various services wishing to make use of such complex systems. Thus, it becomes critically important to study how the current approaches to standardization in this area can be improved, and at the same time better understand the opportunities for the research community to contribute to the IoT field. To this end, this article presents an overview of current standards and research activities in both industry and academia.
Learning Region Features for Object Detection

While most steps in the modern object detection methods are learnable, the region feature extraction step remains largely handcrafted, featured by RoI pooling methods. This work proposes a general viewpoint that unifies existing region feature extraction methods and a novel method that is end-to-end learnable. The proposed method removes most heuristic choices and outperforms its RoI pooling counterparts. It moves further towards fully learnable object detection.
The Importance of Sleep for Athletic Performance

ENSURING THAT ATHLETES FUNCTION OPTIMALLY THROUGHOUT TRAINING IS AN IMPORTANT FOCUS FOR THE STRENGTH AND CONDITIONING COACH.  AN INFLUENTIAL FACTOR THAT AFFECTS THE QUALITY OF TRAINING, GIVEN ITS IMPLICATIONS ON THE RECOVERY PROCESS. INTENSE TRAINING MAY PREDISPOSE ATHLETES TO RISK FACTORS SURROUNDING DISTURBED SLEEP PATTERNS. THESE MAY BE DUE TO INHERENT PHYSICAL EXERTION, COMMITMENT TO EXTENSIVE TRAINING SCHEDULES, THE EFFECTS OF TRAVEL, DOMESTIC OR INTERNATIONAL, AND THE PRESSURES THAT COMPETITION EVOKES. EDUCATING ATHLETES ON THE IMPLICATIONS OF SLEEP SHOULD BE IMPLEMENTED BY STRENGTH AND CONDITIONING COACHES TO OPTIMIZE ATHLETE RECOVERY, PROMOTE CONSISTENT SLEEP ROUTINES, AND SLEEP LENGTH.
Autonomous robot navigation in outdoor cluttered pedestrian walkways

This paper describes an implementation of a mobile robot system for autonomous navigation in outdoor concurred walkways. The task was to navigate through non-modified pedestrian paths with people and bicycles passing by. The robot has multiple redundant sensors which include wheel encoders, an IMU, a DGPS and four laser scanner sensors. All the computation was done in a single laptop computer. A previously constructed map containing waypoints and landmarks for position correction is given to the robot. The robot system’s perception, road extraction and motion planning are detailed. The system was used and tested in a 1 km autonomous robot navigation challenge held in the City of Tsukuba, Japan named “Tsukuba Challenge 2007”. The proposed approach proved to be robust for outdoor navigation in cluttered and crowded walkways, first in campus paths and then running the challenge course multiple times between trials and the challenge final. The paper reports experimental results and overall performance of the system. Finally the lessons learned are discussed. The main contribution of this work is the report of a system integration approach for autonomous outdoor navigation and its evaluation.
Shear strength of R / C bridge cantilever slabs

This paper presents the results of six tests on R/C bridge cantilever slabs without shear reinforcement subjected to concentrated loading. The specimens represent actual deck slabs of box-girder bridges scaled 3/4. They were 10 m long with a clear cantilever equal to 2.78 m and with variable thickness (190 mm at the tip of the cantilever and 380 mm at the clamped edge). Reinforcement ratios for the specimens were equal to 0.78% and 0.60%. All tests failed in a brittle manner by development of a shear failure surface around the concentrated loads. The experimental results are investigated on the basis of linear elastic shear fields for the various tests. Taking advantage of the experimental and numerical results, practical recommendations for estimating the shear strength of R/C bridge cantilever slabs are proposed.
Detecting authorship deception: a supervised machine learning approach using author writeprints

We describe a new supervised machine learning approach for detecting authorship deception, a specific type of authorship attribution task particularly relevant for cybercrime forensic investigations, and demonstrate its validity on two case studies drawn from realistic online data sets. The core of our approach involves identifying uncharacteristic behavior for an author, based on a writeprint extracted from unstructured text samples of the author’s writing. The writeprints used here involve stylometric features and content features derived from topic models, an unsupervised approach for identifying relevant keywords that relate to the content areas of a document. One innovation of our approach is to transform the writeprint feature values into a representation that individually balances characteristic and uncharacteristic traits of an author, and we subsequently apply a Sparse Multinomial Logistic Regression classifier to this novel representation. Our method yields high accuracy for authorship deception detection on the two case studies, confirming its utility.
Real-Time Optimization and Adaptation of the Crosswind Flight of Tethered Wings for Airborne Wind Energy

Airborne wind energy systems aim to generate renewable energy by means of the aerodynamic lift produced using a wing tethered to the ground and controlled to fly crosswind paths. The problem of maximizing the average power developed by the generator, in the presence of limited information on wind speed and direction, is considered. At constant tether speed operation, the power is related to the traction force generated by the wing. First, a study of the traction force is presented for a general path parametrization. In particular, the sensitivity of the traction force on the path parameters is analyzed. Then, the results of this analysis are exploited to design an algorithm to maximize the force, hence the power, in real-time. The algorithm uses only the measured traction force on the tether and the wing's position, and it is able to adapt the system's operation to maximize the average force with uncertain and time-varying wind. The influence of inaccurate sensor readings and turbulent wind are also discussed. The presented algorithm is not dependent on a specific hardware setup and can act as an extension of existing control structures. Both numerical simulations and experimental results are presented to highlight the effectiveness of the approach.
Deep Reconstruction Models for Image Set Classification

Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it offers more promises and has therefore attracted significant research attention in recent years. Unlike many existing methods which assume images of a set to lie on a certain geometric surface, this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure. Specifically, a Template Deep Reconstruction Model (TDRM) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The initialized TDRM is then separately trained for images of each class and class-specific DRMs are learnt. Based on the minimum reconstruction errors from the learnt class-specific models, three different voting strategies are devised for classification. Extensive experiments are performed to demonstrate the efficacy of the proposed framework for the tasks of face and object recognition from image sets. Experimental results show that the proposed method consistently outperforms the existing state of the art methods.
A SVM-Based committee machine for prediction of Hong Kong horse racing

In Hong Kong and Macao, horse racing is the most famous gambling with a long history. This study proposes a novel approach to predict the horse racing results in Hong Kong. A three-years-long race records dataset obtained from Hong Kong Jockey Club was used for training a support-vector-machine-based committee machine. Bet suggestions could be made to gamblers by studying previous data though machine learning. In experiment, there are 2691 races and 33532 horse records obtained. Experiments focus on accuracy and return rate were conducted separately through constructing a committee machine. Experimental results showed that the accuracy and return rate achieve 70.86% and 800,000% respectively.