nils-herrmann/clean_scirepeval huggingface dataset:
The Rutgers Master II — New Design Force-Feedback Glove

The Rutgers Master II-ND glove is a haptic interface designed for dextrous interactions with virtual environments. The glove provides force feedback up to 16 N each to the thumb, index, middle, and ring fingertips. It uses custom pneumatic actuators arranged in a direct-drive configuration in the palm. Unlike commercial haptic gloves, the direct-drive actuators make unnecessary cables and pulleys, resulting in a more compact and lighter structure. The force-feedback structure also serves as position measuring exoskeleton, by integrating noncontact Hall-effect and infrared sensors. The glove is connected to a haptic-control interface that reads its sensors and servos its actuators. The interface has pneumatic servovalves, signal conditioning electronics, A/D/A boards, power supply and an imbedded Pentium PC. This distributed computing assures much faster control bandwidth than would otherwise be possible. Communication with the host PC is done over an RS232 line. Comparative data with the CyberGrasp commercial haptic glove is presented.
IoT based classification of vital signs data for chronic disease monitoring

Nowadays chronic diseases are the leading cause of deaths in India. These diseases which include various ailments in the form of diabetes, stroke, cardiovascular diseases, mental health illness, cancers, and chronic lung diseases. Chronic diseases are the biggest challenge for India and these diseases are the main cause of hospitalization for elder people. People who have suffered from chronic diseases are needed to repeatedly monitor the vital signs periodically. The number of nurses in hospital is relative low compared to the number of patients in hospital, there may be a chance to miss to monitor any patient vital signs which may affect patient health. In this paper, real time monitoring vital signs of a patient is developed using wearable sensors. Without nurse help, patient know the vital signs from the sensors and the system stored the sensor value in the form of text document. By using data mining approaches, the system is trained for vital sign data. Patients give their text document to the system which in turn they know their health status without any nurse help. This system enables high risk patients to be timely checked and enhance the quality of a life of patients.
Brief review of invariant texture analysis methods

This paper considers invariant texture analysis. Texture analysis approaches whose performances are not a,ected by translation, rotation, a.ne, and perspective transform are addressed. Existing invariant texture analysis algorithms are carefully studied and classi0ed into three categories: statistical methods, model based methods, and structural methods. The importance of invariant texture analysis is presented 0rst. Each approach is reviewed according to its classi0cation, and its merits and drawbacks are outlined. The focus of possible future work is also suggested. ? 2001 Pattern Recognition Society.
An analysis of open information extraction based on semantic role labeling

Open Information Extraction extracts relations from text without requiring a pre-specified domain or vocabulary. While existing techniques have used only shallow syntactic features, we investigate the use of semantic role labeling techniques for the task of Open IE. Semantic role labeling (SRL) and Open IE, although developed mostly in isolation, are quite related. We compare SRL-based open extractors, which perform computationally expensive, deep syntactic analysis, with TextRunner, an open extractor, which uses shallow syntactic analysis but is able to analyze many more sentences in a fixed amount of time and thus exploit corpus-level statistics. Our evaluation answers questions regarding these systems, including, can SRL extractors, which are trained on PropBank, cope with heterogeneous text found on the Web? Which extractor attains better precision, recall, f-measure, or running time? How does extractor performance vary for binary, n-ary and nested relations? How much do we gain by running multiple extractors? How do we select the optimal extractor given amount of data, available time, types of extractions desired?
Gaussian Process Latent Variable Models for Human Pose Estimation

We describe a method for recovering 3D human body pose from silhouettes. Our model is based on learning a latent space using the Gaussian Process Latent Variable Model (GP-LVM)  encapsulating both pose and silhouette features Our method is generative, this allows us to model the ambiguities of a silhouette representation in a principled way. We learn a dynamical model over the latent space which allows us to disambiguate between ambiguous silhouettes by temporal consistency. The model has only two free parameters and has several advantages over both regression approaches and other generative methods. In addition to the application shown in this paper the suggested model is easily extended to multiple observation spaces without constraints on type.
Tamper-proofing of electronic and printed text documents via robust hashing and data-hiding

In this paper, we deal with the problem of authentication and tamper-proofing of text documents that can be distributed in electronic or printed forms. We advocate the combination of robust text hashing and text data-hiding technologies as an efficient solution to this problem. First, we consider the problem of text data-hiding in the scope of the Gel'fand-Pinsker data-hiding framework. For illustration, two modern text data-hiding methods, namely color index modulation (CIM) and location index modulation (LIM), are explained. Second, we study two approaches to robust text hashing that are well suited for the considered problem. In particular, both approaches are compatible with CIM and LIM. The first approach makes use of optical character recognition (OCR) and a classical cryptographic message authentication code (MAC). The second approach is new and can be used in some scenarios where OCR does not produce consistent results. The experimental work compares both approaches and shows their robustness against typical intentional/unintentional document distortions including electronic format conversion, printing, scanning, [...] VILLAN SEBASTIAN, Renato Fisher, et al. Tamper-proofing of Electronic and Printed Text Documents via Robust Hashing and Data-Hiding.
Moral psychology is relationship regulation: moral motives for unity, hierarchy, equality, and proportionality.

Genuine moral disagreement exists and is widespread. To understand such disagreement, we must examine the basic kinds of social relationships people construct across cultures and the distinct moral obligations and prohibitions these relationships entail. We extend relational models theory (Fiske, 1991) to identify 4 fundamental and distinct moral motives. Unity is the motive to care for and support the integrity of in-groups by avoiding or eliminating threats of contamination and providing aid and protection based on need or empathic compassion. Hierarchy is the motive to respect rank in social groups where superiors are entitled to deference and respect but must also lead, guide, direct, and protect subordinates. Equality is the motive for balanced, in-kind reciprocity, equal treatment, equal say, and equal opportunity. Proportionality is the motive for rewards and punishments to be proportionate to merit, benefits to be calibrated to contributions, and judgments to be based on a utilitarian calculus of costs and benefits. The 4 moral motives are universal, but cultures, ideologies, and individuals differ in where they activate these motives and how they implement them. Unlike existing theories (Haidt, 2007; Hauser, 2006; Turiel, 1983), relationship regulation theory predicts that any action, including violence, unequal treatment, and "impure" acts, may be perceived as morally correct depending on the moral motive employed and how the relevant social relationship is construed. This approach facilitates clearer understanding of moral perspectives we disagree with and provides a template for how to influence moral motives and practices in the world.
Developing early warning systems to predict students' online learning performance

An early warning system can help to identify at-risk students, or predict student learning performance by analyzing learning portfolios recorded in a learning management system (LMS). Although previous studies have shown the applicability of determining learner behaviors from an LMS, most investigated datasets are not assembled from online learning courses or from whole learning activities undertaken on courses that can be analyzed to evaluate students’ academic achievement. Previous studies generally focus on the construction of predictors for learner performance evaluation after a course has ended, and neglect the practical value of an ‘‘early warning’’ system to predict at-risk students while a course is in progress. We collected the complete learning activities of an online undergraduate course and applied data-mining techniques to develop an early warning system. Our results showed that, timedependent variables extracted from LMS are critical factors for online learning. After students have used an LMS for a period of time, our early warning system effectively characterizes their current learning performance. Data-mining techniques are useful in the construction of early warning systems; based on our experimental results, classification and regression tree (CART), supplemented by AdaBoost is the best classifier for the evaluation of learning performance investigated by this study.
Focused ultrasound modulates region-specific brain activity

We demonstrated the in vivo feasibility of using focused ultrasound (FUS) to transiently modulate (through either stimulation or suppression) the function of regional brain tissue in rabbits. FUS was delivered in a train of pulses at low acoustic energy, far below the cavitation threshold, to the animal's somatomotor and visual areas, as guided by anatomical and functional information from magnetic resonance imaging (MRI). The temporary alterations in the brain function affected by the sonication were characterized by both electrophysiological recordings and functional brain mapping achieved through the use of functional MRI (fMRI). The modulatory effects were bimodal, whereby the brain activity could either be stimulated or selectively suppressed. Histological analysis of the excised brain tissue after the sonication demonstrated that the FUS did not elicit any tissue damages. Unlike transcranial magnetic stimulation, FUS can be applied to deep structures in the brain with greater spatial precision. Transient modulation of brain function using image-guided and anatomically-targeted FUS would enable the investigation of functional connectivity between brain regions and will eventually lead to a better understanding of localized brain functions. It is anticipated that the use of this technology will have an impact on brain research and may offer novel therapeutic interventions in various neurological conditions and psychiatric disorders.
Recent approaches to global optimization problems through Particle Swarm Optimization

This paper presents an overview of our most recent results concerning the Particle Swarm Optimization (PSO) method. Techniques for the alleviation of local minima, and for detecting multiple minimizers are described. Moreover, results on the ability of the PSO in tackling Multiobjective, Minimax, Integer Programming and ℓ1 errors-in-variables problems, as well as problems in noisy and continuously changing environments, are reported. Finally, a Composite PSO, in which the heuristic parameters of PSO are controlled by a Differential Evolution algorithm during the optimization, is described, and results for many well-known and widely used test functions are given.
SYSTRAN's Pure Neural Machine Translation Systems

Since the first online demonstration of Neural Machine Translation (NMT) by LISA (Bahdanau et al., 2014), NMT development has recently moved from laboratory to production systems as demonstrated by several entities announcing rollout of NMT engines to replace their existing technologies. NMT systems have a large number of training configurations and the training process of such systems is usually very long, often a few weeks, so role of experimentation is critical and important to share. In this work, we present our approach to production-ready systems simultaneously with release of online demonstrators covering a large variety of languages ( 12 languages, for32 language pairs). We explore different practical choices: an efficient and evolutive open-source framework; data preparation; network architecture; additional implemented features; tuning for production; etc. We discuss about evaluation methodology, present our first findings and we finally outline further work. Our ultimate goal is to share our expertise to build competitive production systems for ”generic” translation. We aim at contributing to set up a collaborative framework to speed-up adoption of the technology, foster further research efforts and enable the delivery and adoption to/by industry of use-case specific engines integrated in real production workflows. Mastering of the technology would allow us to build translation engines suited for particular needs, outperforming current simplest/uniform systems.
A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split

We give a theoretical and experimental analysis of the generalization error of cross validation using two natural measures of the problem under consideration. The approximation rate measures the accuracy to which the target function can be ideally approximated as a function of the number of parameters, and thus captures the complexity of the target function with respect to the hypothesis model. The estimation rate measures the deviation between the training and generalization errors as a function of the number of parameters, and thus captures the extent to which the hypothesis model suffers from overfitting. Using these two measures, we give a rigorous and general bound on the error of the simplest form of cross validation. The bound clearly shows the dangers of making the fraction of data saved for testingtoo large or too small. By optimizing the bound with respect to , we then argue that the following qualitative properties of cross-validation behavior should be quite robust to significant changes in the underlying model selection problem: When the target function complexity is small compared to the sample size, the performance of cross validation is relatively insensitive to the choice of . The importance of choosing optimally increases, and the optimal value for decreases, as the target function becomes more complex relative to the sample size. There is nevertheless a single fixed value for that works nearly optimally for a wide range of target function complexity.
Invariant Scattering Convolution Networks

A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. The first network layer outputs SIFT-type descriptors, whereas the next layers provide complementary invariant information that improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State-of-the-art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.
Application of structured light imaging for high resolution mapping of underwater archaeological sites

This paper presents results from recent work using structured light laser profile imaging to create high resolution bathymetric maps of underwater archaeological sites. Documenting the texture and structure of submerged sites is a difficult task and many applicable acoustic and photographic mapping techniques have recently emerged. This effort was completed to evaluate laser profile imaging in comparison to stereo imaging and high frequency multibeam mapping. A ROV mounted camera and inclined 532 nm sheet laser were used to create profiles of the bottom that were then merged into maps using platform navigation data. These initial results show very promising resolution in comparison to multibeam and stereo reconstructions, particularly in low contrast scenes. At the test sites shown here there were no significant complications related to scattering or attenuation of the laser sheet by the water. The resulting terrain was gridded at 0.25 cm and shows overall centimeter level definition. The largest source of error was related to the calibration of the laser and camera geometry. Results from three small areas show the highest resolution 3D models of a submerged archaeological site to date and demonstrate that laser imaging will be a viable method for accurate three dimensional site mapping and documentation.
An energy efficient pedestrian aware Smart Street Lighting system

Conventional street lighting systems in areas with a low frequency of passersby are online most of the night without purpose. The consequence is that a large amount of power is wasted meaninglessly. With the broad availability of flexible-lighting technology like light-emitting diode lamps and everywhere available wireless internet connection, fast reacting, reliably operating, and power-conserving street lighting systems become reality. The purpose of this work is to describe the Smart Street Lighting (SSL) system, a first approach to accomplish the demand for flexible public lighting systems.  This work presents the SSL system, a framework developed for a dynamic switching of street lamps based on pedestrians’ locations and desired safety (or “fear”) zones. In the developed system prototype, each pedestrian is localized via his/her smartphone, periodically sending location and configuration information to the SSL server. For street lamp control, each and every lamppost is equipped with a ZigBee-based radio device, receiving control information from the SSL server via multi-hop routing.  This research paper confirms that the application of the proposed SSL system has great potential to revolutionize street lighting, particularly in suburban areas with low-pedestrian frequency. More important, the broad utilization of SSL can easily help to overcome the regulatory requirement for CO2 emission reduction by switching off lampposts whenever they are not required.  The paper discusses in detail the implementation of SSL, and presents results of its application on a small scale. Experiments have shown that objects like trees can interrupt wireless communication between lampposts and that inaccuracy of global positioning system position detection can lead to unexpected lighting effects.  This paper introduces the novel SSL framework, a system for fast, reliable, and energy efficient street lamp switching based on a pedestrian’s location and personal desires of safety. Both safety zone definition and position estimation in this novel approach is accomplished using standard smartphone capabilities. Suggestions for overcoming these issues are discussed in the last part of the paper.
Cohabiting family members share microbiota with one another and with their dogs

Human-associated microbial communities vary across individuals: possible contributing factors include (genetic) relatedness, diet, and age. However, our surroundings, including individuals with whom we interact, also likely shape our microbial communities. To quantify this microbial exchange, we surveyed fecal, oral, and skin microbiota from 60 families (spousal units with children, dogs, both, or neither). Household members, particularly couples, shared more of their microbiota than individuals from different households, with stronger effects of co-habitation on skin than oral or fecal microbiota. Dog ownership significantly increased the shared skin microbiota in cohabiting adults, and dog-owning adults shared more 'skin' microbiota with their own dogs than with other dogs. Although the degree to which these shared microbes have a true niche on the human body, vs transient detection after direct contact, is unknown, these results suggest that direct and frequent contact with our cohabitants may significantly shape the composition of our microbial communities.
Virtualizing Deep Neural Networks for Memory-Efficient Neural Network Design

The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher’s flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU memory can simultaneously be utilized for training larger DNNs. Our virtualized DNN (vDNN) reduces the average memory usage of AlexNet by 61% and OverFeat by 83%, a significant reduction in memory requirements of DNNs. Similar experiments on VGG-16, one of the deepest and memory hungry DNNs to date, demonstrate the memory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256 (requiring 28 GB of memory) to be trained on a single NVIDIA K40 GPU card containing 12 GB of memory, with 22% performance loss compared to a hypothetical GPU with enough memory to hold the entire DNN.
Efficient and Flexible Sampling with Blue Noise Properties of Triangular Meshes

This paper deals with the problem of taking random samples over the surface of a 3D mesh describing and evaluating efficient algorithms for generating different distributions. We discuss first the problem of generating a Monte Carlo distribution in an efficient and practical way avoiding common pitfalls. Then, we propose Constrained Poisson-disk sampling, a new Poisson-disk sampling scheme for polygonal meshes which can be easily tweaked in order to generate customized set of points such as importance sampling or distributions with generic geometric constraints. In particular, two algorithms based on this approach are presented. An in-depth analysis of the frequency characterization and performance of the proposed algorithms are also presented and discussed.