nils-herrmann/clean_scirepeval huggingface dataset:
Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.

Type 2 diabetes affects approximately 8 percent of adults in the United States. Some risk factors--elevated plasma glucose concentrations in the fasting state and after an oral glucose load, overweight, and a sedentary lifestyle--are potentially reversible. We hypothesized that modifying these factors with a lifestyle-intervention program or the administration of metformin would prevent or delay the development of diabetes.



We randomly assigned 3234 nondiabetic persons with elevated fasting and post-load plasma glucose concentrations to placebo, metformin (850 mg twice daily), or a lifestyle-modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The mean age of the participants was 51 years, and the mean body-mass index (the weight in kilograms divided by the square of the height in meters) was 34.0; 68 percent were women, and 45 percent were members of minority groups.



The average follow-up was 2.8 years. The incidence of diabetes was 11.0, 7.8, and 4.8 cases per 100 person-years in the placebo, metformin, and lifestyle groups, respectively. The lifestyle intervention reduced the incidence by 58 percent (95 percent confidence interval, 48 to 66 percent) and metformin by 31 percent (95 percent confidence interval, 17 to 43 percent), as compared with placebo; the lifestyle intervention was significantly more effective than metformin. To prevent one case of diabetes during a period of three years, 6.9 persons would have to participate in the lifestyle-intervention program, and 13.9 would have to receive metformin.



Lifestyle changes and treatment with metformin both reduced the incidence of diabetes in persons at high risk. The lifestyle intervention was more effective than metformin.
ON THE SOCIAL PSYCHOLOGY OF THE PSYCHOLOGICAL EXPERIMENT : WITH PARTICULAR REFERENCE TO DEMAND CHARACTERISTICS AND THEIR IMPLICATIONS 1

It is to the highest degree probable that the subject['s] ... general attitude of mind is that of ready complacency and cheerful willingness to assist the investigator in every possible way by reporting to him those very things which he is most eager to find, and that the very questions of the experimenter ... suggest the shade of reply expected . . . . Indeed . it seems too often as if the subject were now regarded as a stupid automaton .... A. H. PIERCE, 1908 3
Forensic analysis of wireless networking evidence of Android smartphones

This paper introduces a method for acquiring forensic-grade evidence from Android smartphones using open source tools. We investigate in particular cases where the suspect has made use of the smartphone's Wi-Fi or Bluetooth interfaces. We discuss the forensic analysis of four case studies, which revealed traces that were left in the inner structure of three mobile Android devices and also indicated security vulnerabilities. Subsequently, we propose a detailed plan for forensic examiners to follow when dealing with investigations of potential crimes committed using the wireless facilities of a suspect Android smartphone. This method can be followed to perform physical acquisition of data without using commercial tools and then to examine them safely in order to discover any activity associated with wireless communications. We evaluate our method using the Association of Chief Police Officers' (ACPO) guidelines of good practice for computer-based, electronic evidence and demonstrate that it is made up of an acceptable host of procedures for mobile forensic analysis, focused specifically on device Bluetooth and Wi-Fi facilities.
Phishing for user security awareness

User security education and training is one of the most important aspects of an organizations security posture. Using security exercises to reinforce this aspect is frequently done by education and industry alike; however these exercises usually enlist willing participants. We have taken the concept of using an exercise and modified it in application to evaluate a users propensity to respond to email phishing attacks in an unannounced test. This paper describes the considerations in establishing and the process used to create and implement an evaluation of one aspect of our user information assurance education program. The evaluation takes the form of a exercise, where we send out a phishing styled email record the responses.
Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems

Automatic recording of user behavior within a system (instrumentation) to develop and test theories has a rich history in psychology and system design. Often, researchers analyze instrumented behavior in isolation from other data. The problem with collecting instrumented behaviors without attitudinal, demographic, and contextual data is that researchers have no way to answer the 'why' behind the 'what'. We have combined the collection and analysis of behavioral instrumentation with other HCI methods to develop a system for Tracking Real-Time User Experience (TRUE). Using two case studies as examples, we demonstrate how we have evolved instrumentation methodology and analysis to extensively improve the design of video games. It is our hope that TRUE is adopted and adapted by the broader HCI community, becoming a useful tool for gaining deep insights into user behavior and improvement of design for other complex systems.
Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models

In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object’s orientation and on a sensor’s flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic la∗Corresponding  Diego Marcos, diego.marcos@wur.nl ISPRS J. Photo. Remote Sens. Preprint compiled on March 19, 2018 ar X iv :1 80 3. 06 25 3v 1 [ cs .C V ] 1 6 M ar 2 01 8 ISPRS Journal of Photogrammetry and Remote Sensing DOI: 10.1016/j.isprsjprs.2018.01.021 beling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters.
Minimum entropy, k-means, spectral clustering

This paper addresses an information-theoretic aspect of k-means and spectral clustering. First, we revisit the k-means clustering and show that its objective function is approximately derived from the minimum entropy principle when the Renyi's quadratic entropy is used. Then we present a maximum within-clustering association that is derived using a quadratic distance measure in the framework of minimum entropy principle, which is very similar to a class of spectral clustering algorithms that is based on the eigen-decomposition method.
Training RNNs as Fast as CNNs

Recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. For instance, the forward pass computation of ht is blocked until the entire computation of ht−1 finishes, which is a major bottleneck for parallel computing. In this work, we propose an alternative RNN implementation by deliberately simplifying the state computation and exposing more parallelism. The proposed recurrent unit operates as fast as a convolutional layer and 5-10x faster than cuDNN-optimized LSTM. We demonstrate the unit’s effectiveness across a wide range of applications including classification, question answering, language modeling, translation and speech recognition. We open source our implementation in PyTorch and CNTK1.
Values as lived experience: evolving value sensitive design in support of value discovery

The Value Sensitive Design (VSD) methodology provides a comprehensive framework for advancing a value-centered research and design agenda. Although VSD provides helpful ways of thinking about and designing value-centered computational systems, we argue that the specific mechanics of VSD create thorny tensions with respect to value sensitivity. In particular, we examine limitations due to value classifications, inadequate guidance on empirical tools for design, and the ways in which the design process is ordered. In this paper, we propose ways of maturing the VSD methodology to overcome these limitations and present three empirical case studies that illustrate a family of methods to effectively engage local expressions of values. The findings from our case studies provide evidence of how we can mature the VSD methodology to mitigate the pitfalls of classification and engender a commitment to reflect on and respond to local contexts of design.
Temporal frequency probing for 5D transient analysis of global light transport

We analyze light propagation in an unknown scene using projectors and cameras that operate at transient timescales. In this new photography regime, the projector emits a spatio-temporal 3D signal and the camera receives a transformed version of it, determined by the set of all light transport paths through the scene and the time delays they induce. The underlying 3D-to-3D transformation encodes scene geometry and global transport in great detail, but individual transport components (e.g., direct reflections, inter-reflections, caustics, etc.) are coupled nontrivially in both space and time.
 To overcome this complexity, we observe that transient light transport is always separable in the temporal frequency domain. This makes it possible to analyze transient transport one temporal frequency at a time by trivially adapting techniques from conventional projector-to-camera transport. We use this idea in a prototype that offers three never-seen-before abilities: (1) acquiring time-of-flight depth images that are robust to general indirect transport, such as interreflections and caustics; (2) distinguishing between direct views of objects and their mirror reflection; and (3) using a photonic mixer device to capture sharp, evolving wavefronts of "light-in-flight".
Neuromorphic Silicon Neurons and Large-Scale Neural Networks: Challenges and Opportunities

Neuromorphic silicoN NeuroNs: state of the art Complementary metal-oxide-semiconductor (CMOS) transistors are commonly used in very-large-scale-integration (VLSI) digital circuits as a basic binary switch that turns on or off as the transistor gate voltage crosses some threshold. Carver Mead first noted that CMOS transistor circuits operating below this threshold in current mode have strikingly similar sigmoidal current– voltage relationships as do neuronal ion channels and consume little power; hence they are ideal analogs of neuronal function (Mead, 1989). This unique device physics led to the advent of “neuromorphic” silicon neurons (SiNs) which allow neuronal spiking dynamics to be directly emulated on analog VLSI chips without the need for digital software simulation (Mahowald and Douglas, 1991). In the inaugural issue of this Journal, Indiveri et al. (2011) review the current state of the art in CMOS-based neuromorphic neuron circuit designs that have evolved over the past two decades. The comprehensive appraisal delineates and compares the latest SiN design techniques as applied to varying types of spiking neuron models ranging from realistic conductancebased Hodgkin–Huxley models to simple yet versatile integrate-and-fire models. The timely and much needed compendium is a tour de force that will certainly provide a valuable guidepost for future SiN designs and applications.
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.
Novel Flux-Weakening Control of an IPMSM for Quasi-Six-Step Operation

This paper proposes a novel flux-weakening control algorithm of an interior permanent-magnet synchronous motor for ldquoquasirdquo six-step operation. The proposed method is composed of feedforward and feedback paths. The feedforward path consists of 1-D lookup table, and the feedback is based on the difference between the reference voltage updated by current regulator and the output voltage limited by the overmodulation. Using this method, the flux-weakening and the antiwindup controls can be achieved simultaneously. In addition, the quasi-six-step operation can be obtained. That is, the available maximum output torque in the flux-weakening region is close to that in the six-step operation while the ability of the current control is maintained. The effectiveness of this method is proved by the experimental results.
Fashionista: A Fashion-aware Graphical System for Exploring Visually Similar Items

To build a fashion recommendation system, we need to help users retrieve fashionable items that are visually similar to a particular query, for reasons ranging from searching alternatives (i.e., substitutes), to generating stylish outfits that are visually consistent, among other applications. In domains like clothing and accessories, such considerations are particularly paramount as the visual appearance of items is a critical feature that guides users’ decisions. However, existing systems like Amazon and eBay still rely mainly on keyword search and recommending loosely consistent items (e.g. based on co-purchasing or browsing data), without an interface that makes use of visual information to serve the above needs. In this paper, we attempt to fill this gap by designing and implementing an image-based query system, called Fashionista, which provides a graphical interface to help users efficiently explore those items that are not only visually similar to a given query, but which are also fashionable, as determined by visually-aware recommendation approaches. Methodologically, Fashionista learns a low-dimensional visual space as well as the evolution of fashion trends from large corpora of binary feedback data such as purchase histories of Women’s Clothing & Accessories from Amazon, which we use for this demonstration.
Analysis of universal adversarial perturbations

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we propose a quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exists shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.
Character Level based Detection of DGA Domain Names

Recently several different deep learning architectures have been proposed that take a string of characters as the raw input signal and automatically derive features for text classification. Few studies are available that compare the effectiveness of these approaches for character based text classification with each other. In this paper we perform such an empirical comparison for the important cybersecurity problem of DGA detection: classifying domain names as either benign vs. produced by malware (i.e., by a Domain Generation Algorithm). Training and evaluating on a dataset with 2M domain names shows that there is surprisingly little difference between various convolutional neural network (CNN) and recurrent neural network (RNN) based architectures in terms of accuracy, prompting a preference for the simpler architectures, since they are faster to train and to score, and less prone to overfitting.
A Mutually Beneficial Integration of Data Mining and Information Extraction

Text miningconcerns applying data mining techniques to unstructured text.Information extraction(IE) is a form of shallow text understanding that locates specific pieces of data in natural language documents, transforming unstructured text into a structured database. This paper describes a system called DISCOTEX, that combines IE and data mining methodologies to perform text mining as well as improve the performance of the underlying extraction system. Rules mined from a database extracted from a corpus of texts are used to predict additional information to extract from future documents, thereby improving the recall of IE. Encouraging results are presented on applying these techniques to a corpus of computer job announcement postings from an Internet newsgroup.
A comparative review of camera calibrating methods with accuracy evaluation

Camera calibrating is a crucial problem for further metric scene measurement. Many techniques and some studies concerning calibration have been presented in the last few years. However, it is still di1cult to go into details of a determined calibrating technique and compare its accuracy with respect to other methods. Principally, this problem emerges from the lack of a standardized notation and the existence of various methods of accuracy evaluation to choose from. This article presents a detailed review of some of the most used calibrating techniques in which the principal idea has been to present them all with the same notation. Furthermore, the techniques surveyed have been tested and their accuracy evaluated. Comparative results are shown and discussed in the article. Moreover, code and results are available in internet. ? 2002 Pattern Recognition Society.
Anomaly detection in monitoring sensor data for preventive maintenance

Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and the exact localization of a detected anomaly.
Large Margin Multi-Task Metric Learning

Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations. One of the most prominent multi-task learning algorithms is an extension to support vector machines (svm) by Evgeniou et al. . Although very elegant, multi-task svm is inherently restricted by the fact that support vector machines require each class to be addressed explicitly with its own weight vector which, in a multi-task setting, requires the different learning tasks to share the same set of classes. This paper proposes an alternative formulation for multi-task learning by extending the recently published large margin nearest neighbor (lmnn) algorithm to the MTL paradigm. Instead of relying on separating hyperplanes, its decision function is based on the nearest neighbor rule which inherently extends to many classes and becomes a natural fit for multi-task learning. We evaluate the resulting multi-task lmnn on real-world insurance data and speech classification problems and show that it consistently outperforms single-task kNN under several metrics and state-of-the-art MTL classifiers.
Multi-step Off-policy Learning Without Importance Sampling Ratios

To estimate the value functions of policies from exploratory data, most model-free offpolicy algorithms rely on importance sampling, where the use of importance sampling ratios often leads to estimates with severe variance. It is thus desirable to learn off-policy without using the ratios. However, such an algorithm does not exist for multi-step learning with function approximation. In this paper, we introduce the first such algorithm based on temporal-difference (TD) learning updates. We show that an explicit use of importance sampling ratios can be eliminated by varying the amount of bootstrapping in TD updates in an action-dependent manner. Our new algorithm achieves stability using a two-timescale gradient-based TD update. A prior algorithm based on lookup table representation called Tree Backup can also be retrieved using action-dependent bootstrapping, becoming a special case of our algorithm. In two challenging off-policy tasks, we demonstrate that our algorithm is stable, effectively avoids the large variance issue, and can perform substantially better than its state-of-the-art counterpart.