nils-herrmann/clean_scirepeval huggingface dataset:
ACQUISITION BASED ON OPENCV FOR CLOSE-RANGE PHOTOGRAMMETRY APPLICATIONS

Development of the technology in the area of the cameras, computers and algorithms for 3D the reconstruction of the objects from the images resulted in the increased popularity of the photogrammetry. Algorithms for the 3D model reconstruction are so advanced that almost anyone can make a 3D model of photographed object. The main goal of this paper is to examine the possibility of obtaining 3D data for the purposes of the close-range photogrammetry applications, based on the open source technologies. All steps of obtaining 3D point cloud are covered in this paper. Special attention is given to the camera calibration, for which two-step process of calibration is used. Both, presented algorithm and accuracy of the point cloud are tested by calculating the spatial difference between referent and produced point clouds. During algorithm testing, robustness and swiftness of obtaining 3D data is noted, and certainly usage of this and similar algorithms has a lot of potential in the real-time application. That is the reason why this research can find its application in the architecture, spatial planning, protection of cultural heritage, forensic, mechanical engineering, traffic management, medicine and other sciences.  Corresponding author
Genetic diversity and domestication origin of tea plant Camellia taliensis (Theaceae) as revealed by microsatellite markers

Tea is one of the most popular beverages in the world. Many species in the Thea section of the Camellia genus can be processed for drinking and have been domesticated. However, few investigations have focused on the genetic consequence of domestication and geographic origin of landraces on tea plants using credible wild and planted populations of a single species. Here, C. taliensis provides us with a unique opportunity to explore these issues. Fourteen nuclear microsatellite loci were employed to determine the genetic diversity and domestication origin of C. taliensis, which were represented by 587 individuals from 25 wild, planted and recently domesticated populations. C. taliensis showed a moderate high level of overall genetic diversity. The greater reduction of genetic diversity and stronger genetic drift were detected in the wild group than in the recently domesticated group, indicating the loss of genetic diversity of wild populations due to overexploitation and habitat fragmentation. Instead of the endangered wild trees, recently domesticated individuals were used to compare with the planted trees for detecting the genetic consequence of domestication. A little and non-significant reduction in genetic diversity was found during domestication. The long life cycle, selection for leaf traits and gene flow between populations will delay the emergence of bottleneck in planted trees. Both phylogenetic and assignment analyses suggested that planted trees may have been domesticated from the adjacent central forest of western Yunnan and dispersed artificially to distant places. This study contributes to the knowledge about levels and distribution of genetic diversity of C. taliensis and provides new insights into genetic consequence of domestication and geographic origin of planted trees of this species. As an endemic tea source plant, wild, planted and recently domesticated C. taliensis trees should all be protected for their unique genetic characteristics, which are valuable for tea breeding.
Simultaneous Optical Flow and Intensity Estimation from an Event Camera

Event cameras are bio-inspired vision sensors which mimic retinas to measure per-pixel intensity change rather than outputting an actual intensity image. This proposed paradigm shift away from traditional frame cameras offers significant potential advantages: namely avoiding high data rates, dynamic range limitations and motion blur. Unfortunately, however, established computer vision algorithms may not at all be applied directly to event cameras. Methods proposed so far to reconstruct images, estimate optical flow, track a camera and reconstruct a scene come with severe restrictions on the environment or on the motion of the camera, e.g. allowing only rotation. Here, we propose, to the best of our knowledge, the first algorithm to simultaneously recover the motion field and brightness image, while the camera undergoes a generic motion through any scene. Our approach employs minimisation of a cost function that contains the asynchronous event data as well as spatial and temporal regularisation within a sliding window time interval. Our implementation relies on GPU optimisation and runs in near real-time. In a series of examples, we demonstrate the successful operation of our framework, including in situations where conventional cameras suffer from dynamic range limitations and motion blur.
Evolutionary optimization in uncertain environments-a survey

Evolutionary algorithms often have to solve optimization problems in the presence of a wide range of uncertainties. Generally, uncertainties in evolutionary computation can be divided into the following four categories. First, the fitness function is noisy. Second, the design variables and/or the environmental parameters may change after optimization, and the quality of the obtained optimal solution should be robust against environmental changes or deviations from the optimal point. Third, the fitness function is approximated, which means that the fitness function suffers from approximation errors. Fourth, the optimum of the problem to be solved changes over time and, thus, the optimizer should be able to track the optimum continuously. In all these cases, additional measures must be taken so that evolutionary algorithms are still able to work satisfactorily. This paper attempts to provide a comprehensive overview of the related work within a unified framework, which has been scattered in a variety of research areas. Existing approaches to addressing different uncertainties are presented and discussed, and the relationship between the different categories of uncertainties are investigated. Finally, topics for future research are suggested.
Fractional CO2 lasers for the treatment of atrophic acne scars: a review of the literature.

This review examines the efficacy and safety of fractional CO2 lasers for the treatment of atrophic scarring secondary to acne vulgaris. We reviewed 20 papers published between 2008 and 2013 that conducted clinical studies using fractional CO2 lasers to treat atrophic scarring. We discuss the prevalence and pathogenesis of acne scarring, as well as the laser mechanism. The histologic findings are included to highlight the ability of these lasers to induce the collagen reorganization and formation that improves scar appearance. We considered the number of treatments and different laser settings to determine which methods achieve optimal outcomes. We noted unique treatment regimens that yielded superior results. An overview of adverse effects is included to identify the most common ones. We concluded that more studies need to be done using uniform treatment parameters and reporting in order to establish which fractional CO2 laser treatment approaches allow for the greatest scar improvement.
Becoming a Gamer: Cognitive Effects of Real-Time Strategy Gaming

Video games are rich, dynamic, and fast-paced virtual environments that tap a number of cognitive processes. Prior work reveals that experienced video game players (and novices who become experienced) demonstrate enhanced cognitive and perceptual performance in a range of tasks. It is not clear why video gaming leads to these enhancements. To address this, we use real-time strategy games which require dynamic attention and multitasking skills. We employ a variety of novel methods to capture which game features are associated with various cognitive enhancements. Specifically, we use model-based analysis of empirical feature selection priorities, tests of generation via multiple game maps, and a map manipulation of centered versus bimodal attentional load. Novice participants engaged in 40 hours of game-training, with cognitive testing occurring before, during, and after training. We consider the plasticity of cognitive abilities over training in light of these well controlled factors.
Active deep learning method for semi-supervised sentiment classification

In natural language processing community, sentiment classification based on insufficient labeled data is a well-known challenging problem. In this paper, a novel semi-supervised learning algorithm called active deep network (ADN) is proposed to address this problem. First, we propose the semi-supervised learning framework of ADN. ADN is constructed by restricted Boltzmann machines (RBM) with unsupervised fine-tuned by gradient-descent based supervised learning with an exponential loss function. Second, in the semi-supervised learning framework, we apply active learning to identify reviews that should be labeled as training data, then using the selected labeled reviews and all unlabeled reviews to train ADN architecture. Moreover, we combine the information density with ADN, and propose information ADN (IADN) method, which can apply the information density of all unlabeled reviews in choosing the manual labeled reviews. Experiments on five sentiment classification datasets show that ADN and IADN outperform classical semi-supervised learning algorithms, and deep learning techniques applied for sentiment classification. & 2013 Elsevier B.V. All rights reserved.
Motivation Interventions in Education : A Meta-Analytic Review

This meta-analysis provides an extensive and organized summary of intervention studies in education that are grounded in motivation theory. We identified 74 published and unpublished papers that experimentally manipulated an independent variable and measured an authentic educational outcome within an ecologically valid educational context. Our analyses included 92 independent effect sizes with 38,377 participants. Our results indicated that interventions were generally effective, with an average mean effect size of d = 0.49 (95% confidence interval = [0.43, 0.56]). Although there were descriptive differences in the effect sizes across several moderator variables considered in our analyses, the only significant difference found was for the type of experimental design, with randomized designs having smaller effect sizes than quasi-experimental designs. This work illustrates the extent to which interventions and accompanying theories have been tested via experimental methods and provides information about appropriate next steps in developing and testing effective motivation interventions in education.
HKU-Face : A Large Scale Dataset for Deep Face Recognition Final Report

Current face recognition usually faces problems with the training dataset due to the insufficient size and potential manual labelling errors. The project introduces a dataset construction and filtering process to deal the problem with less cost. FaceNet[35] and Sphereface[29] are harnessed for the purpose of filtering the dataset scratched from Google. Results show the impressive effectiveness of automatic filtering and purity enhancement after filtering with considerable attention on labeling errors in the view of web search. Except exclusively self-constructed dataset, filtered and merged dataset from CASIA-WebFace[54] and VGG Face [32] were also tested and analyzed. Subsequent research and experiment can target at the further improvement of filtering process with lower false negative rate as well as getting rid of labeling errors due to web search. And those further improvements are expected to contribute more to the unsupervised learning in the general fine-grained object recognition.
Inhibitory Effects of Echinacea Angustifolia Essential Oils on the Growth of Five Pathogenic Organisms : Coliform Spp , Pseudomonas Spp , Saccharomyces Cerevisiae , Zygosaccharomyces Bailii and Lactobacillus Plantarum

Echinacea commonly called the Purple coneflowers, is a genus of nine species of herbaceous plants in the Family Asteraceae. Three of them are important in commerce, with the majority of wild harvest being E. angustifolia. It has been used for a variety of ailments, including toothache, coughs, colds, sore throats, snakebite, and as a painkiller. In the current study, in vitro inhibitory activity of Echinacea angustifolia essential oils were screened against Coliform spp, Pseudomonas spp, Saccharomyces cerevisiae (EC1118), Zygosaccharomyces bailii (DSM 70492) and Lactobacillus plantarum (DSM2601). Agar well diffusion assay was adopted for the study. E. angustifolia oils showed very weak antimicrobial activity against the microorganisms tested with diameter of inhibition zone not exceeding 3 mm. The highest activities were observed for Z. baillii and S. cereviceae at a concentration of 10 and 100 ppm respectively, while for the rest of the strains the diameter of inhibition zone were ranged 1 and 2.5 mm, except Coliform spp which was not affected by the presence of the essential oil at a concentration of 50 ppm. The low bacteriostatic effect of this plant essential oil against some of the most important causes of infections provides an exciting potential for the future, especially in the light of the shift away from commonly used antibiotics and the move towards more natural alternatives.
Mapping with an Autonomous Car

In this paper, we present an approach towards mapping and safe navigation in real, large-scale environme nts with an autonomous car. The goal is to enable the car to autonomously navigate on roads while avoiding obstacles and w hile simultaneously learning an accurate three-dimensional mo del of the environment. To achieve these goals, we apply probabili stic state estimation techniques, network-based pose optimiza tion, and a sensor-based traversability analysis approach. In or der to achieve fast map learning, our system compresses the sens or data using multi-level surface maps. The overall systems ru ns on a modified Smart car equipped with different types of sensors. We present several results obtained from extensive experim ents which illustrate the capabilities of our vehicle.
Enamel Deproteinization after Acid Etching - Is it Worth the Effort?

This study was undertaken to observe the topographical features of enamel surface deproteinized with 5.25% sodium hypochlorite (NaOCl) after phosphoric acid (H3PO4) etching using Scanning Electron Microscope (SEM) analysis and also the effect of enamel deproteinization after acid etching on the shear bond strength (SBS) of AdperTM Single Bond 2 adhesive and FiltekTM Z350 XT composite resin.  10 enamel blocks of 1 mm2 from 10 human sound permanent molar teeth were obtained and treated with 37% H3PO4 gel for 15 seconds followed by treatment with 5.25% NaOCl for 60 seconds. All the 10 samples were subjected to SEM analysis and 5 microphotographs of each sample were obtained at 500X magnification and evaluated for the occurrence of Type I – II etching pattern in percentage (%) using Auto – CAD 2007 software.  A 5×4 mm window of the enamel surface was etched with 37% H3PO4 gel for 15 seconds, washed with distilled water and air dried. The etched enamel surface was then treated with 5.25% NaOCl for 60 seconds, washed with distilled water and air dried. A single coat of AdperTM Single Bond 2 adhesive was applied and photo polymerized for 20 seconds and FiltekTM Z350 XT composite resin block of length 5mm, width 4 mm and height 5 mm respectively was built and photo polymerized in increments for 20 seconds each. The shear bond strength of all the 20 test samples (permanent molar teeth) were measured (in MPa) on Instron Mechanical Testing Machine.  The mean value of Type I – II etching pattern of all the test samples was observed to be 40.68 + 26.38% and the mean SBS value for all the test samples was observed to be 17.35 + 7.25 MPa.  No significant enhancive effect of enamel deproteinization after acid etching with respect to the occurrence of Type I-II etching patterns as well as on the SBS of adhesive resin and composite resin complex to the enamel surface was observed in this study. The use of 37% phosphoric acid alone for 15 seconds still remains the best method for pretreatment of the enamel. Corresponding author: Dr. Ramakrishna Yeluri. M.D.S, F.P.F.A, Professor, Department of Pedodontics and Preventive Dentistry, K.D. Dental College and Hospital, Mathura – Delhi N.H #2, Mathura – 281001, Uttar Pradesh, India, Tel: +919997951558; 2530764; E-mail: drramakrishnay@indiatimes.com, kittypedo@yahoo.com Received December 17, 2013;  2014;   Ramakrishna Y, Bhoomika A, Harleen N, Munshi AK (2014) Enamel Deproteinization after Acid Etching Is it Worth the Effort? Dentistry 4: 200. doi:10.4172/2161-1122.1000200
A method of CAD/CAE data integration based on XML

In spite of the widespread use of CAD systems for design and CAE systems for analysis, the two processes are not well integrated because CAD and CAE models inherently use different types of geometric models and there currently exists no generic, unified model that allows both design and analysis information to be specified and shared. XML has become the de-facto standard for data representation and exchange on the World-Wide Web. This paper proposes a data integration method based on the XML technique in order to resolve the problem of data transmission for CAD and CAE. Designers parametrically model the bridges through 3D CAD platform. CAE analysts conduct explicit dynamic FEA (Finite Element Analysis) on the designed bridge structure. CAD and CAE functions are accomplished through C/S architecture. An XML and Web Service based DAC (Design-Analysis Connection) is developed to maintain a consistence between CAD model and FEA model. The design is then displayed to the customers through B/S mechanism, which provides a convenient method for the customers to participate the design process. Since all the operations are conducted through internet/intranet, customers, designers and analysts are able to participate the design process at different geographical locations. According to the interface procedure of the model transformation compiled in this paper, the finite element model was successfully transformed from CAD system to CAE system.
Heuristic Feature Selection for Clickbait Detection

We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017 . The challenge’s task is to score the “clickbaitiness” of a given Twitter tweet on a scale from 0 (no clickbait) to 1 (strong clickbait). Unlike most other approaches submitted to the challenge, the baseline approach is based on manual feature engineering and does not compete out of the box with many of the deep learning-based approaches. We show that scaling up feature selection efforts to heuristically identify better-performing feature subsets catapults the performance of the baseline classifier to second rank overall, beating 12 other competing approaches and improving over the baseline performance by 20%. This demonstrates that traditional classification approaches can still keep up with deep learning on this task.
ABA-Mediated ROS in Mitochondria Regulate Root Meristem Activity by Controlling PLETHORA Expression in Arabidopsis


Although research has determined that reactive oxygen species (ROS) function as signaling molecules in plant development, the molecular mechanism by which ROS regulate plant growth is not well known. An aba overly sensitive mutant, abo8-1, which is defective in a pentatricopeptide repeat (PPR) protein responsible for the splicing of NAD4 intron 3 in mitochondrial complex I, accumulates more ROS in root tips than the wild type, and the ROS accumulation is further enhanced by ABA treatment. The ABO8 mutation reduces root meristem activity, which can be enhanced by ABA treatment and reversibly recovered by addition of certain concentrations of the reducing agent GSH. As indicated by low ProDR5:GUS expression, auxin accumulation/signaling was reduced in abo8-1. We also found that ABA inhibits the expression of PLETHORA1 (PLT1) and PLT2, and that root growth is more sensitive to ABA in the plt1 and plt2 mutants than in the wild type. The expression of PLT1 and PLT2 is significantly reduced in the abo8-1 mutant. Overexpression of PLT2 in an inducible system can largely rescue root apical meristem (RAM)-defective phenotype of abo8-1 with and without ABA treatment. These results suggest that ABA-promoted ROS in the mitochondria of root tips are important retrograde signals that regulate root meristem activity by controlling auxin accumulation/signaling and PLT expression in Arabidopsis.
DSOD: Learning Deeply Supervised Object Detectors from Scratch

We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely heavily on the off the-shelf networks pre-trained on large-scale classification datasets like Image Net, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks. Model fine-tuning for the detection task could alleviate this bias to some extent but not fundamentally. Besides, transferring pre-trained models from classification to detection between discrepant domains is even more difficult (e.g. RGB to depth images). A better solution to tackle these two critical problems is to train object detectors from scratch, which motivates our proposed DSOD. Previous efforts in this direction mostly failed due to much more complicated loss functions and limited training data in object detection. In DSOD, we contribute a set of design principles for training object detectors from scratch. One of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector. Combining with several other principles, we develop DSOD following the single-shot detection (SSD) framework. Experiments on PASCAL VOC 2007, 2012 and MS COCO datasets demonstrate that DSOD can achieve better results than the state-of-the-art solutions with much more compact models. For instance, DSOD outperforms SSD on all three benchmarks with real-time detection speed, while requires only 1/2 parameters to SSD and 1/10 parameters to Faster RCNN.
Dementia Wandering Detection and Activity Recognition Algorithm Using Tri-Axial Accelerometer Sensors

Better life of human is a central goal of information technology. To make a useful technology, in sensor network area, activity recognition (AR) is becoming a key feature. Using the AR technology it is now possible to know peoples behaviors like what they do, how they do and when they do etc. In recent years, there have been frequent accidental reports of aged dementia patients, and social cost has been increasing to take care of them. AR can be utilized to take care of these patients. In this paper, we present an efficient method that converts sensor’s raw data to readable patterns in order to classify their current activities and then compare these patterns with previously stored patterns to detect several abnormal patterns like wandering which is one of the early symptoms of dementia and so on. In this way, we digitalize human activities and can detect wandering and so can infer dementia through activity pattern matching. Here, we present a novel algorithm about activity digitalization using acceleration sensors as well as a wandering estimation algorithm in order to overcome limitations of existing models to detect/infer dementia.