nils-herrmann/clean_scirepeval huggingface dataset:
Laptop initiative: Impact on instructional technology integration and student learning

The purpose of this study was to examine how 1:1 laptop initiative affected student learning at a selected rural Midwestern high school. A total of 105 high school students enrolled in 10th–12th grades during the 2008–2009 school year participated in the study. A survey instrument created by the Mitchell Institute was modified and used to collect data on student perceptions and faculty perceptions of the impact of 1:1 laptop computing on student learning and instructional integration of technology in education. Study findings suggest that integration of 1:1 laptop computing positively impacts student academic engagement and student learning. Therefore, there is need for teachers to implement appropriate computing practices to enhance student learning. Additionally, teachers need to collaborate with their students to learn and understand various instructional technology applications beyond basic Internet browsing and word processing.
Descriptive statistics: the specification of statistical measures and their presentation in tables and graphs. Part 7 of a series on evaluation of scientific publications.

Descriptive statistics are an essential part of biometric analysis and a prerequisite for the understanding of further statistical evaluations, including the drawing of inferences. When data are well presented, it is usually obvious whether the author has collected and evaluated them correctly and in keeping with accepted practice in the field.



Statistical variables in medicine may be of either the metric (continuous, quantitative) or categorical (nominal, ordinal) type. Easily understandable examples are given. Basic techniques for the statistical description of collected data are presented and illustrated with examples.



The goal of a scientific study must always be clearly defined. The definition of the target value or clinical endpoint determines the level of measurement of the variables in question. Nearly all variables, whatever their level of measurement, can be usefully presented graphically and numerically. The level of measurement determines what types of diagrams and statistical values are appropriate. There are also different ways of presenting combinations of two independent variables graphically and numerically.



The description of collected data is indispensable. If the data are of good quality, valid and important conclusions can already be drawn when they are properly described. Furthermore, data description provides a basis for inferential statistics.
Deep Structured Energy-Based Image Inpainting

In this paper, we propose a structured image inpainting method employing an energy based model. In order to learn structural relationship between patterns observed in images and missing regions of the images, we employ an energy-based structured prediction method. The structural relationship is learned by minimizing an energy function which is defined by a simple convolutional neural network. The experimental results on various benchmark datasets show that our proposed method significantly outperforms the state-of-the-art methods which use Generative Adversarial Networks (GANs). We obtained 497.35 mean squared error (MSE) on the Olivetti face dataset compared to 833.0 MSE provided by the state-of-the-art method. Moreover, we obtained 28.4 dB peak signal to noise ratio (PSNR) on the SVHN dataset and 23.53 dB on the CelebA dataset, compared to 22.3 dB and 21.3 dB, provided by the state-of-the-art methods, respectively. The code is publicly available.11https:llgithub.com/cvlab-tohoku/DSEBImageInpainting.
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents

The goal of sentence and document modeling is to accurately represent the meaning of sentences and documents for various Natural Language Processing tasks. In this work, we present Dependency Sensitive Convolutional Neural Networks (DSCNN) as a generalpurpose classification system for both sentences and documents. DSCNN hierarchically builds textual representations by processing pretrained word embeddings via Long ShortTerm Memory networks and subsequently extracting features with convolution operators. Compared with existing recursive neural models with tree structures, DSCNN does not rely on parsers and expensive phrase labeling, and thus is not restricted to sentencelevel tasks. Moreover, unlike other CNNbased models that analyze sentences locally by sliding windows, our system captures both the dependency information within each sentence and relationships across sentences in the same document. Experiment results demonstrate that our approach is achieving state-ofthe-art performance on several tasks, including sentiment analysis, question type classification, and subjectivity classification.
Certified Defenses for Data Poisoning Attacks

Machine learning systems trained on user-provided data are susceptible to data poisoning attacks, whereby malicious users inject false training data with the aim of corrupting the learned model. While recent work has proposed a number of attacks and defenses, little is understood about the worst-case loss of a defense in the face of a determined attacker. We address this by constructing approximate upper bounds on the loss across a broad family of attacks, for defenders that first perform outlier removal followed by empirical risk minimization. Our approximation relies on two assumptions: (1) that the dataset is large enough for statistical concentration between train and test error to hold, and (2) that outliers within the clean (nonpoisoned) data do not have a strong effect on the model. Our bound comes paired with a candidate attack that often nearly matches the upper bound, giving us a powerful tool for quickly assessing defenses on a given dataset. Empirically, we find that even under a simple defense, the MNIST-1-7 and Dogfish datasets are resilient to attack, while in contrast the IMDB sentiment dataset can be driven from 12% to 23% test error by adding only 3% poisoned data.
Horizontal gene transfer between bacteria.

Horizontal gene transfer (HGT) refers to the acquisition of foreign genes by organisms. The occurrence of HGT among bacteria in the environment is assumed to have implications in the risk assessment of genetically modified bacteria which are released into the environment. First, introduced genetic sequences from a genetically modified bacterium could be transferred to indigenous micro-organisms and alter their genome and subsequently their ecological niche. Second, the genetically modified bacterium released into the environment might capture mobile genetic elements (MGE) from indigenous micro-organisms which could extend its ecological potential. Thus, for a risk assessment it is important to understand the extent of HGT and genome plasticity of bacteria in the environment. This review summarizes the present state of knowledge on HGT between bacteria as a crucial mechanism contributing to bacterial adaptability and diversity. In view of the use of GM crops and microbes in agricultural settings, in this mini-review we focus particularly on the presence and role of MGE in soil and plant-associated bacteria and the factors affecting gene transfer.
Consumer Attitude Metrics for Guiding Marketing Mix Decisions

Marketing managers often use consumer attitude metrics such as awareness, consideration, and preference as performance indicators because they represent their brand’s health and are readily connected to marketing activity. However, this does not mean that financially focused executives know how such metrics translate into sales performance, which would allow them to make beneficial marketing mix decisions. We propose four criteria – potential, responsiveness, stickiness and sales conversion – that determine the connection between marketing actions, attitudinal metrics, and sales outcomes. We test our approach with a rich dataset of four-weekly marketing actions, attitude metrics, and sales for several consumer brands in four categories over a sevenyear period. The results quantify how marketing actions affect sales performance through their differential impact on attitudinal metrics, as captured by our proposed criteria. We find that marketing-attitude and attitude-sales relationships are predominantly stable over time, but differ substantially across brands and across product categories with different levels of involvement. We also establish that combining marketing and attitudinal metrics improves the prediction of brand sales performance, often substantially so. Based on these insights, we provide specific recommendations on improving the marketing mix for different brands, and we validate them in a hold-out sample. For managers and researchers alike, our criteria offer a verifiable explanation for differences in marketing elasticities and an actionable connection between marketing and financial performance metrics.
Health Informatics via Machine Learning for the Clinical Management of Patients.

To review how health informatics systems based on machine learning methods have impacted the clinical management of patients, by affecting clinical practice.



We reviewed literature from 2010-2015 from databases such as Pubmed, IEEE xplore, and INSPEC, in which methods based on machine learning are likely to be reported. We bring together a broad body of literature, aiming to identify those leading examples of health informatics that have advanced the methodology of machine learning. While individual methods may have further examples that might be added, we have chosen some of the most representative, informative exemplars in each case.



Our survey highlights that, while much research is taking place in this high-profile field, examples of those that affect the clinical management of patients are seldom found. We show that substantial progress is being made in terms of methodology, often by data scientists working in close collaboration with clinical groups.



Health informatics systems based on machine learning are in their infancy and the translation of such systems into clinical management has yet to be performed at scale.
Behavioral and Hematological Response of Clarias gariepinus juvenile exposed to Acute concentrations of Aqueous Extract of Siam Weed (Chloromolaena odorata) leaf as Anesthetic agent

*Iheanacho Stanley Chidi and Nworu Shedrack Department of Fisheries and Aquaculture, Federal University Ndufu Alike Ikwo, Ebonyi State, Nigeria 2 Department of Fisheries and Aquaculture, Ebonyi State University Abakaliki, Ebonyi State, Nigeria Corresponding author: stanley.iheanacho@funai.edu.ng, +2348063279905 Abstract This experiment was carried out to investigate the effect of Siam Weed (chloromelena odorata) on the heamatology of Clarias gariepinus juvenile. A total of one hundred and fifty (150) juvenile of Clarias gariepinus were randomly assigned to different concentrations of C. odorata leave aqueous extract in a completely randomize design (CRD). The concentrations were 50mg/l, 100mg/l, 150mg/l, 200mg/l. Distilled water (0.00 mg/l) was used as the control. The fish exhibited stressful behavior which was higher as the concentration of Chromolaena odorata leave extract increased. There was a gradual decrease with time until a state of calmness, which was subsequently followed by death. The effect on 96hr exposed period was recorded and blood samples collected at 24hr and 96hr interval. Result on hematological parameters revealed significant difference (P<0.05) among treatments with increase in exposure time for all the blood parameters. C. odorata at increased concentrations affected the behavior and hematology of C. gariepinus.
A Miniaturized CPW-Fed Antipodal Vivaldi Antenna With Enhanced Radiation Performance for Wideband Applications

A miniaturized coplanar waveguide (CPW)-fed antipodal Vivaldi antenna (AVA) is presented in this letter. Elliptically shaped strip conductors are used to lower the antenna operating frequency. With two pairs of tapered slots and circularly shaped loads, the radiation performance in lower operating band can be greatly enhanced. The fabricated prototype of this proposed antenna with a size of 90 ×93.5 ×0.8 mm3 was measured to confirm the simulated results. The wide measured impedance band from 1.32 GHz to more than 17 GHz for can be obtained. Meanwhile, the good measured radiation patterns with gain 3.5- 9.3 dBi in end-fire direction achieve in the whole measured working band.
A Graph-Based Approach to Skill Extraction from Text

This paper presents a system that performs skill extraction from text documents. It outputs a list of professional skills that are relevant to a given input text. We argue that the system can be practical for hiring and management of personnel in an organization. We make use of the texts and the hyperlink graph of Wikipedia, as well as a list of professional skills obtained from the LinkedIn social network. The system is based on first computing similarities between an input document and the texts of Wikipedia pages and then using a biased, hub-avoiding version of the Spreading Activation algorithm on the Wikipedia graph in order to associate the input document with skills.
Weakly-supervised Transfer for 3D Human Pose Estimation in the Wild

Appendix A. Detail of geometry constraint loss 0.0.1 Forward We consider four groups of bones, defined by G = {arm, leg, shoulder, hip}. Rarm = {left lower arm, left upper arm, right lower arm, right upper arm}, Rleg = {left lower leg, left upper leg, right lower leg, right upper leg}, Rshoulder = { left shoulder bone, left shoulder bone}, Rhip = {left hip bone, left hip bone}. A bone (e.g. left lower arm) is represented by the index of its two end-points, e = (jL, jR), i.e., left lower arm = (left wrist, left elbow). Let Y (j) 2D = (u , v), we have le = ||Y (jL) 3D , Y (jR) 3D ||
On Accurate Evaluation of GANs for Language Generation

Generative Adversarial Networks (GANs) are a promising approach to language generation. The latest works introducing novel GAN models for language generation use n-gram based metrics for evaluation and only report single scores of the best run. In this paper, we argue that this often misrepresents the true picture and does not tell the full story, as GAN models can be extremely sensitive to the random initialization and small deviations from the best hyperparameter choice. In particular, we demonstrate that the previously used BLEU score is not sensitive to semantic deterioration of generated texts and propose alternative metrics that better capture the quality and diversity of the generated samples. We also conduct a set of experiments comparing a number of GAN models for text with a conventional Language Model (LM) and find that neither of the considered models performs convincingly better than the LM.
Perfectionism, procrastination, and psychological distress.

Using a cross-panel design and data from 2 successive cohorts of college students (N = 357), we examined the stability of maladaptive perfectionism, procrastination, and psychological distress across 3 time points within a college semester. Each construct was substantially stable over time, with procrastination being especially stable. We also tested, but failed to support, a mediational model with Time 2 (mid-semester) procrastination as a hypothesized mechanism through which Time 1 (early-semester) perfectionism would affect Time 3 (end-semester) psychological distress. An alternative model with Time 2 perfectionism as a mediator of the procrastination-distress association also was not supported. Within-time analyses revealed generally consistent strength of effects in the correlations between the 3 constructs over the course of the semester. A significant interaction effect also emerged. Time 1 procrastination had no effect on otherwise high levels of psychological distress at the end of the semester for highly perfectionistic students, but at low levels of Time 1 perfectionism, the most distressed students by the end of the term were those who were more likely to have procrastinated earlier in the semester. Implications of the stability of the constructs and their association over time, as well as the moderating effects of procrastination, are discussed in the context of maladaptive perfectionism and problematic procrastination.
Reconfigurable Computing Architecture for Accurate Disparity Map Calculation in Real-Time Stereo Vision

This paper presents a novel hardware architecture using FPGA-based reconfigurable computing (RC) for accurate calculation of dense disparity maps in real-time, stereo-vision systems. Recent stereo-vision hardware solutions have proposed local-area approaches. Although parallelism can be easily exploited using local methods by replicating the window-based image elaborations, accuracy is limited because the disparity result is optimized by locally searching for the minimum value of a cost function. Global methods improve the quality of the stereo-vision disparity maps at the expense of increasing computational complexity, thus making real-time application not viable for conventional computing. This problem becomes even more evident when stereo vision is a single step integrated into a more complete image elaboration flow, where the depth maps are used for further detection, recognition, stereo reconstruction, or 3D enhancement processing. Our approach exploits a parallel and fully pipelined architecture to implement a global method for the calculation of dense disparity maps based on the dynamic programming optimization of the Hamming distance of the Census-transform cost function. The resulting stereovision core produces results that are significantly more accurate than existing hardware solutions using FPGAs that are based upon local approaches. The design was implemented and evaluated on an Altera Stratix-III E260 FPGA in a GiDEL PROCStar-III board. Tests were performed on 640×480 stereo images, with a Census transform window size = 3, correlation window size = 5, and disparity ranges of 30 and 50. Our hardware architecture achieved a speedup of about 319 and 512 respectively for the two disparity ranges, when compared to an optimized C++ implementation executed on a 2.26 GHz Xeon E5520 core. High accuracy in the output disparity map, together with high performance in terms of frames per second, make the proposed architecture an ideal solution for 3D robot-assisted medical systems, tracking, and autonomous navigation systems, where accuracy and speed constraints are very stringent.
Adversary Resistant Deep Neural Networks with an Application to Malware Detection

Outside the highly publicized victories in the game of Go, there have been numerous successful applications of deep learning in the fields of information retrieval, computer vision, and speech recognition. In cybersecurity, an increasing number of companies have begun exploring the use of deep learning (DL) in a variety of security tasks with malware detection among the more popular. These companies claim that deep neural networks (DNNs) could help turn the tide in the war against malware infection. However, DNNs are vulnerable to adversarial samples, a shortcoming that plagues most, if not all, statistical and machine learning models. Recent research has demonstrated that those with malicious intent can easily circumvent deep learning-powered malware detection by exploiting this weakness.
 To address this problem, previous work developed defense mechanisms that are based on augmenting training data or enhancing model complexity. However, after analyzing DNN susceptibility to adversarial samples, we discover that the current defense mechanisms are limited and, more importantly, cannot provide theoretical guarantees of robustness against adversarial sampled-based attacks. As such, we propose a new adversary resistant technique that obstructs attackers from constructing impactful adversarial samples by randomly nullifying features within data vectors. Our proposed technique is evaluated on a real world dataset with 14,679 malware variants and 17,399 benign programs. We theoretically validate the robustness of our technique, and empirically show that our technique significantly boosts DNN robustness to adversarial samples while maintaining high accuracy in classification. To demonstrate the general applicability of our proposed method, we also conduct experiments using the MNIST and CIFAR-10 datasets, widely used in image recognition research.
The Effectiveness of Data Augmentation in Image Classification using Deep Learning

In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification. Previous work has demonstrated the effectiveness of data augmentation through simple techniques, such as cropping, rotating, and flipping input images. We artificially constrain our access to data to a small subset of the ImageNet dataset, and compare each data augmentation technique in turn. One of the more successful data augmentations strategies is the traditional transformations mentioned above. We also experiment with GANs to generate images of different styles. Finally, we propose a method to allow a neural net to learn augmentations that best improve the classifier, which we call neural augmentation. We discuss the successes and shortcomings of this method on various datasets.
Pre-training Long Short-term Memory Neural Networks for Efficient Regression in Artificial Speech Postfiltering

Several attempts to enhance statistical parametric speech synthesis have contemplated deep-learning-based postfil-ters, which learn to perform a mapping of the synthetic speech parameters to the natural ones, reducing the gap between them. In this paper, we introduce a new pre-training approach for neural networks, applied in LSTM-based postfilters for speech synthesis, with the objective of enhancing the quality of the synthesized speech in a more efficient manner. Our approach begins with an auto-regressive training of one LSTM network, whose is used as an initialization for postfilters based on a denoising autoencoder architecture. We show the advantages of this initialization on a set of multi-stream postfilters, which encompass a collection of denoising autoencoders for the set of MFCC and fundamental frequency parameters of the artificial voice. Results show that the initialization succeeds in lowering the training time of the LSTM networks and achieves better results in enhancing the statistical parametric speech in most cases, when compared to the common random-initialized approach of the networks.
Accurate localization of RFID tags using phase difference

Due to their light weight, low power, and practically unlimited identification capacity, radio frequency identification (RFID) tags and associated devices offer distinctive advantages and are widely recognized for their promising potential in context-aware computing; by tagging objects with RFID tags, the environment can be sensed in a cost- and energy-efficient means. However, a prerequisite to fully realizing the potential is accurate localization of RFID tags, which will enable and enhance a wide range of applications. In this paper we show how to exploit the phase difference between two or more receiving antennas to compute accurate localization. Phase difference based localization has better accuracy, robustness and sensitivity when integrated with other measurements compared to the currently popular technique of localization using received signal strength. Using a software-defined radio setup, we show experimental results that support accurate localization of RFID tags and activity recognition based on phase difference.