nils-herrmann/clean_scirepeval huggingface dataset:
Boosting Trees for Anti-Spam Email Filtering

This paper describes a set of comparative exper iments for the problem of automatically ltering unwanted electronic mail messages Several vari ants of the AdaBoost algorithm with con dence rated predictions Schapire Singer have been applied which di er in the complexity of the base learners considered Two main conclu sions can be drawn from our experiments a The boosting based methods clearly outperform the baseline learning algorithms Naive Bayes and Induction of Decision Trees on the PU corpus achieving very high levels of the F measure b Increasing the complexity of the base learners al lows to obtain better high precision classi ers which is a very important issue when misclassi cation costs are considered
Trust in Automation A Literature Review

Increasingly complex autonomous systems require the human operator to appropriately calibrate their trust in the automation in order to achieve performance and safety goals. Although humanautomation trust has been shown to be similar in many respects to human-human trust, precisely defining and measuring trust in automation has proved to be one of the greatest challenges facing research in this area. This report reviews literature pertaining to trust in automated systems to provide an integrated summary of the major theoretical and empirical work in the field to date.
Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation

We present a simple method to improve neural translation of a low-resource language pair using parallel data from a related, also low-resource, language pair. The method is based on the transfer method of Zoph et al., but whereas their method ignores any source vocabulary overlap, ours exploits it. First, we split words using Byte Pair Encoding (BPE) to increase vocabulary overlap. Then, we train a model on the first language pair and transfer its parameters, including its source word embeddings, to another model and continue training on the second language pair. Our experiments show that transfer learning helps word-based translation only slightly, but when used on top of a much stronger BPE baseline, it yields larger improvements of up to 4.3 BLEU.
Diffusion, Seeding, and the Value of Network Information

Identifying the optimal set of individuals to first receive information (`seeds') in a social network is a widely-studied question in many settings, such as the diffusion of information, microfinance programs, and new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes, randomly seeding S + x individuals can prompt a larger cascade than optimally targeting the best S individuals, for a small x. We prove our results for large classes of random networks, but also show that they hold in simulations over several real-world networks. This suggests that the returns to collecting and analyzing network information to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.
The effects of violent video game habits on adolescent hostility, aggressive behaviors, and school performance.

Video games have become one of the favorite activities of American children. A growing body of research is linking violent video game play to aggressive cognitions, attitudes, and behaviors. The first goal of this study was to document the video games habits of adolescents and the level of parental monitoring of adolescent video game use. The second goal was to examine associations among violent video game exposure, hostility, arguments with teachers, school grades, and physical fights. In addition, path analyses were conducted to test mediational pathways from video game habits to outcomes. Six hundred and seven 8th- and 9th-grade students from four schools participated. Adolescents who expose themselves to greater amounts of video game violence were more hostile, reported getting into arguments with teachers more frequently, were more likely to be involved in physical fights, and performed more poorly in school. Mediational pathways were found such that hostility mediated the relationship between violent video game exposure and outcomes. Results are interpreted within and support the framework of the General Aggression Model.
Joint Training of Generic CNN-CRF Models with Stochastic Optimization

We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e. it applies to arbitrary CNN and CRF architectures and potential functions; (ii) scalable, i.e. it has a low memory footprint and straightforwardly parallelizes on GPUs; (iii) easy in implementation. Additionally, the unified CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.
Humans are still the best lossy image compressors

Lossy image compression has been studied extensively in the context of typical loss functions such as RMSE, MS-SSIM, etc. However, it is not well understood what loss function might be most appropriate for human perception. Furthermore, the availability of massive public image datasets appears to have hardly been exploited in image compression. In this work, we perform compression experiments in which one human describes images to another, using publicly available images and text instructions. These image reconstructions are rated by human scorers on the Amazon Mechanical Turk platform and compared to reconstructions obtained by existing image compressors. In our experiments, the humans outperform the state of the art compressor WebP in the MTurk survey on most images, which shows that there is significant room for improvement in image compression for human perception.  The images, results and additional data is available at https://compression. stanford.edu/human-compression.
Turbulent mixing in a stratified fluid

The strength of diapycnal mixing by small-scale motions in a stratified fluid is investigated through changes to the mean buoyancy profile. We study the mixing in laboratory experiments in which an initially linearly stratified fluid is stirred with a rake of vertical bars. The flow evolution Ž . depends on the Richardson number Ri , defined as the ratio of buoyancy forces to inertial forces. At low Ri, the buoyancy flux is a function of the local buoyancy gradient only, and may be modelled as gradient diffusion with a Ri-dependent eddy diffusivity. At high Ri, vertical vorticity shed in the wakes of the bars interacts with the stratification and produces well-mixed layers separated by interfaces. This process leads to layers with a thickness proportional to the ratio of Ž . grid velocity to buoyancy frequency for a wide range of Reynolds numbers Re and grid solidities. In this regime, the buoyancy flux is not a function of the local gradient alone, but also depends on the local structure of the buoyancy profile. Consequently, the layers are not formed by the PhillipsrPosmentier mechanism, and we show that they result from vortical mixing previously thought to occur only at low Re. The initial mixing efficiency shows a maximum at a critical Ri which separates the two classes of behaviour. The mixing efficiency falls as the fluid mixes and as the layered structure intensifies and, therefore, the mixing efficiency depends not only on the overall Ri, but also on the dynamics of the structure in the buoyancy field. We discuss some implications of these results to the atmosphere and oceans. q 1999 Elsevier Science B.V. All rights reserved.
Mechanisms of cancer drug resistance.

The design of cancer chemotherapy has become increasingly sophisticated, yet there is no cancer treatment that is 100% effective against disseminated cancer. Resistance to treatment with anticancer drugs results from a variety of factors including individual variations in patients and somatic cell genetic differences in tumors, even those from the same tissue of origin. Frequently resistance is intrinsic to the cancer, but as therapy becomes more and more effective, acquired resistance has also become common. The most common reason for acquisition of resistance to a broad range of anticancer drugs is expression of one or more energy-dependent transporters that detect and eject anticancer drugs from cells, but other mechanisms of resistance including insensitivity to drug-induced apoptosis and induction of drug-detoxifying mechanisms probably play an important role in acquired anticancer drug resistance. Studies on mechanisms of cancer drug resistance have yielded important information about how to circumvent this resistance to improve cancer chemotherapy and have implications for pharmacokinetics of many commonly used drugs.
Internet of Things

Recently, wireless sensor networks draw much attention as the sensor nodes equipped with various monitoring capabilities have additive advantages over the traditional communication technologies. In this paper, we propose a wireless sensing method for monitoring temperature and humidity of a smart greenhouse in a real time process. The propagation characteristics of communications in a greenhouse have been investigated first in terms of transmission and reception signal strength and signal reception rate. Those properties are further used to characterize the propagation property that can properly represent the communication environment of a greenhouse. The proposed propagation characterization not only well represents the pass loss in the greenhouse but also provides valuable insights and basic guidelines when designing a sensor network for a smart greenhouse.
The Shape of Digital Transformation: A Systematic Literature Review

Digital market has never been so unstable due to more and more demanding users and new disruptive competitors. CEOs from most of industries investigate digitalization opportunities. Through a Systematic Literature Review, we found that digital transformation is more than just a technological shift. According to this study, these transformations have had an impact on the business models, the operational processes and the end-users experience. Considering the richness of this topic, we had proposed a research agenda of digital transformation in a managerial perspective.
Violent Death Rates: The US Compared with Other High-income OECD Countries, 2010.

Violent death is a serious problem in the United States. Previous research showing US rates of violent death compared with other high-income countries used data that are more than a decade old.



We examined 2010 mortality data obtained from the World Health Organization for populous, high-income countries (n = 23). Death rates per 100,000 population were calculated for each country and for the aggregation of all non-US countries overall and by age and sex. Tests of significance were performed using Poisson and negative binomial regressions.



US homicide rates were 7.0 times higher than in other high-income countries, driven by a gun homicide rate that was 25.2 times higher. For 15- to 24-year-olds, the gun homicide rate in the United States was 49.0 times higher. Firearm-related suicide rates were 8.0 times higher in the United States, but the overall suicide rates were average. Unintentional firearm deaths were 6.2 times higher in the United States. The overall firearm death rate in the United States from all causes was 10.0 times higher. Ninety percent of women, 91% of children aged 0 to 14 years, 92% of youth aged 15 to 24 years, and 82% of all people killed by firearms were from the United States.



The United States has an enormous firearm problem compared with other high-income countries, with higher rates of homicide and firearm-related suicide. Compared with 2003 estimates, the US firearm death rate remains unchanged while firearm death rates in other countries decreased. Thus, the already high relative rates of firearm homicide, firearm suicide, and unintentional firearm death in the United States compared with other high-income countries increased between 2003 and 2010.
Fragmentation or cohesion? Visualizing the process and consequences of information system diversity, 1993-2012

27 October 2014 Revised: 29 December 2015  12 January 2016  In information systems (IS) literature, there is ongoing debate as to whether the field has become fragmented and lost its identity in response to the rapid changes of the field. The paper contributes to this discussion by providing quantitative measurement of the fragmentation or cohesiveness level of the field. A co-word analysis approach aiding in visualization of the intellectual map of IS is applied through application of clustering analysis, network maps, strategic diagram techniques, and graph theory for a collection of 47,467 keywords from 9551 articles, published in 10 major IS journals and the proceedings of two leading IS conferences over a span of 20 years, 1993 through 2012. The study identified the popular, core, and bridging topics of IS research for the periods 1993–2002 and 2003–2012. Its results show that research topics and subfields underwent substantial change between those two periods and the field became more concrete and cohesive, increasing in density. Findings from this study suggest that the evolution of the research topics and themes in the IS field should be seen as part of the natural metabolism of the field, rather than a process of fragmentation or disintegration.
Sim-to-Real Transfer of Accurate Grasping with Eye-In-Hand Observations and Continuous Control

In the context of deep learning for robotics, we show effective method of training a real robot to grasp a tiny sphere (1.37cm of diameter), with an original combination of system design choices. We decompose the end-to-end system into a vision module and a closed-loop controller module. The two modules use target object segmentation as their common interface. The vision module extracts information from the robot end-effector camera, in the form of a binary segmentation mask of the target. We train it to achieve effective domain transfer by composing real background images with simulated images of the target. The controller module takes as input the binary segmentation mask, and thus is agnostic to visual discrepancies between simulated and real environments. We train our closed-loop controller in simulation using imitation learning and show it is robust with respect to discrepancies between the dynamic model of the simulated and real robot: when combined with eye-in-hand observations, we achieve a 90% success rate in grasping a tiny sphere with a real robot. The controller can generalize to unseen scenarios where the target is moving and even learns to recover from failures.
Gated Bi-directional CNN for Object Detection

The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. How to effectively integrate local and contextual visual cues from these regions has become a fundamental problem in object detection. Most existing works simply concatenated features or scores obtained from support regions. In this paper, we proposal a novel gated bi-directional CNN (GBD-Net) to pass messages between features from different support regions during both feature learning and feature extraction. Such message passing can be implemented through convolution in two directions and can be conducted in various layers. Therefore, local and contextual visual patterns can validate the existence of each other by learning their nonlinear relationships and their close iterations are modeled in a much more complex way. It is also shown that message passing is not always helpful depending on individual samples. Gated functions are further introduced to control message transmission and their on-and-off is controlled by extra visual evidence from the input sample. GBD-Net is implemented under the Fast RCNN detection framework. Its effectiveness is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO.
Review articles.

Epidemiological studies have revealed the important role that foodstuffs of vegetable origin have to play in the prevention of numerous illnesses. The natural antioxidants present in such foodstuffs, among which the fl avonoids are widely present, may be responsible for such an activity. Flavonoids are compounds that are low in molecular weight and widely distributed throughout the vegetable kingdom. They may be of great utility in states of accute or chronic diarrhoea through the inhibition of intestinal secretion and motility, and may also be benefi cial in the reduction of chronic infl ammatory damage in the intestine, by affording protection against oxidative stress and by preserving mucosal function. For this reason, the use of these agents is recommended in the treatment of infl ammatory bowel disease, in which various factors are involved in extreme immunological reactions, which lead to chronic intestinal infl ammation.
Deep API Programmer: Learning to Program with APIs

We present DAPIP, a Programming-By-Example system that learns to program with APIs to perform data transformation tasks. We design a domainspecific language (DSL) that allows for arbitrary concatenations of API outputs and constant strings. The DSL consists of three family of APIs: regular expression-based APIs, lookup APIs, and transformation APIs. We then present a novel neural synthesis algorithm to search for programs in the DSL that are consistent with a given set of examples. The search algorithm uses recently introduced neural architectures to encode input-output examples and to model the program search in the DSL. We show that synthesis algorithm outperforms baseline methods for synthesizing programs on both synthetic and real-world benchmarks.
Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation

Recent work in computer vision has yielded impressive results in automatically describing images with natural language. Most of these systems generate captions in a single language, requiring multiple language-specific models to build a multilingual captioning system. We propose a very simple technique to build a single unified model across languages, using artificial tokens to control the language, making the captioning system more compact. We evaluate our approach on generating English and Japanese captions, and show that a typical neural captioning architecture is capable of learning a single model that can switch between two different languages.
Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet.

Trials comparing the effectiveness and safety of weight-loss diets are frequently limited by short follow-up times and high dropout rates.



In this 2-year trial, we randomly assigned 322 moderately obese subjects (mean age, 52 years; mean body-mass index [the weight in kilograms divided by the square of the height in meters], 31; male sex, 86%) to one of three diets: low-fat, restricted-calorie; Mediterranean, restricted-calorie; or low-carbohydrate, non-restricted-calorie.



The rate of adherence to a study diet was 95.4% at 1 year and 84.6% at 2 years. The Mediterranean-diet group consumed the largest amounts of dietary fiber and had the highest ratio of monounsaturated to saturated fat (P<0.05 for all comparisons among treatment groups). The low-carbohydrate group consumed the smallest amount of carbohydrates and the largest amounts of fat, protein, and cholesterol and had the highest percentage of participants with detectable urinary ketones (P<0.05 for all comparisons among treatment groups). The mean weight loss was 2.9 kg for the low-fat group, 4.4 kg for the Mediterranean-diet group, and 4.7 kg for the low-carbohydrate group (P<0.001 for the interaction between diet group and time); among the 272 participants who completed the intervention, the mean weight losses were 3.3 kg, 4.6 kg, and 5.5 kg, respectively. The relative reduction in the ratio of total cholesterol to high-density lipoprotein cholesterol was 20% in the low-carbohydrate group and 12% in the low-fat group (P=0.01). Among the 36 subjects with diabetes, changes in fasting plasma glucose and insulin levels were more favorable among those assigned to the Mediterranean diet than among those assigned to the low-fat diet (P<0.001 for the interaction among diabetes and Mediterranean diet and time with respect to fasting glucose levels).



Mediterranean and low-carbohydrate diets may be effective alternatives to low-fat diets. The more favorable effects on lipids (with the low-carbohydrate diet) and on glycemic control (with the Mediterranean diet) suggest that personal preferences and metabolic considerations might inform individualized tailoring of dietary interventions.
A Review of School-Based Interventions for Children and Adolescents who suffer from Depressive Symptoms

A Review of School-Based Interventions for Children and Adolescents who suffer from Depressive Symptoms by Sherry L. Hlavaty, Master of Science Utah State University, 2005 Major Professor: Dr. Gretchen A. Gimpel Department: Psychology This paper provides a brief review of childhood and adolescent depressive disorders and the importance of treating such symptoms within a school setting. The purpose of this paper was to examine published studies that were conducted in schools and were designed to treat symptoms of depression in children and adolescents. Active treatments utilized in this review were shown to be effective in reducing symptoms of depression. Most active treatment conditions involved intervention components that are commonly used in conjunction with typical cognitive-behavioral therapies . Further research is needed to determine the effectiveness of specific programs and program components. Implications of the present research is presented for use by school-based mental health practitioners.