nils-herrmann/clean_scirepeval huggingface dataset:
Pneumatic Artificial Muscles : actuators for robotics and automation

This article is intended as an introduction to and an overview of Pneumatic Artificial Muscles (PAMs). These are pneumatic actuators made mainly of a flexible and inflatable membrane. First, their concept and way of operation are explained. Next, the properties of these actuators are given, the most important of which are the compliant behavior and extremely low weight. A classification and review is following this section. Typical applications are dealt with in the last but one section and, finally, some concluding remarks are made.
Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies

The telecommunication industry faces fierce competition to retain customers, and therefore requires an efficient churn prediction model to monitor the customer’s churn. Enormous size, high dimensionality and imbalanced nature of telecommunication datasets are main hurdles in attaining the desired performance for churn prediction. In this study, we investigate the significance of a Particle Swarm Optimization (PSO) based undersampling method to handle the imbalance data distribution in collaboration with different feature reduction techniques such as Principle Component Analysis (PCA), Fisher’s ratio, Fscore and Minimum Redundancy and Maximum Relevance (mRMR). Whereas Random Forest (RF) and K Nearest Neighbour (KNN) classifiers are employed to evaluate the performance on optimally sampled and reduced features dataset. Prediction performance is evaluated using sensitivity, specificity and Area under the curve (AUC) based measures. Finally, it is observed through simulations that our proposed approach based on PSO, mRMR, and RF termed as Chr-PmRF, performs quite well for predicting churners and therefore can be beneficial for highly competitive telecommunication industry.
A Flexible Technique for Accurate Omnidirectional Camera Calibration and Structure from Motion

In this paper, we present a flexible new technique for single viewpoint omnidirectional camera calibration. The proposed method only requires the camera to observe a planar pattern shown at a few different orientations. Either the camera or the planar pattern can be freely moved. No a priori knowledge of the motion is required, nor a specific model of the omnidirectional sensor. The only assumption is that the image projection function can be described by a Taylor series expansion whose coefficients are estimated by solving a two-step least-squares linear minimization problem. To test the proposed technique, we calibrated a panoramic camera having a field of view greater than 200 in the vertical direction, and we obtained very good results. To investigate the accuracy of the calibration, we also used the estimated omni-camera model in a structure from motion experiment. We obtained a 3D metric reconstruction of a scene from two highly distorted omnidirectional images by using image correspondences only. Compared with classical techniques, which rely on a specific parametric model of the omnidirectional camera, the proposed procedure is independent of the sensor, easy to use, and flexible.
Deep Learning Architecture for Univariate Time Series Forecasting

This paper studies the problem of applying machine learning with deep architecture to time series forecasting. While these techniques have shown promise for modeling static data, applying them to sequential data is gaining increasing attention. This paper overviews the particular challenges present in applying Conditional Restricted Boltzmann Machines (CRBM) to univariate time-series forecasting and provides a comparison to common algorithms used for time-series prediction. 1Department of Computer Science Stanford University davenger@stanford.com
ESTIMATION METHODS FOR STOCHASTIC VOLATILITY MODELS : A SURVEY

Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been intro duced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.
Explaining contradictory relations between risk perception and risk taking.

Different studies have documented opposite relations between perceived risk and behavior. The present study tested a theoretical explanation that reconciles these conflicting results. Adolescents (N= 596) completed alternative measures of risk perception that differed in cue specificity and response format. As predicted by fuzzy-trace theory, measures that emphasized verbatim retrieval and quantitative processing produced positive correlations between perceived risk and risky behavior; risk perceptions reflected the extent to which adolescents engaged in risky behavior. In contrast, measures that assessed global, gist-based judgments of risk produced negative correlations; higher risk perceptions were associated with less risk taking, a protective rather than reflective relation. Endorsement of simple values and principles provided the greatest protection against risk taking. Results support a dual-processes interpretation of the relation between risk perception and risk taking according to which observed relations depend on whether the cues in questions trigger verbatim or gist processing.
Big omics data experience

As personalized medicine becomes more integrated into healthcare, the rate at which human genomes are being sequenced is rising quickly together with a concomitant acceleration in compute and storage requirements. To achieve the most effective solution for genomic workloads without re-architecting the industry-standard software, we performed a rigorous analysis of usage statistics, benchmarks and available technologies to design a system for maximum throughput. We share our experiences designing a system optimized for the "Genome Analysis ToolKit (GATK) Best Practices" whole genome DNA and RNA pipeline based on an evaluation of compute, workload and I/O characteristics. The characteristics of genomic-based workloads are vastly different from those of traditional HPC workloads, requiring different configurations of the scheduler and the I/O subsystem to achieve reliability, performance and scalability. By understanding how our researchers and clinicians work, we were able to employ techniques not only to speed up their workflow yielding improved and repeatable performance, but also to make more efficient use of storage and compute resources.
The Light Field Video Camera

We present the Light Field Video Camera, an array of CMOS image sensors for video image based rendering applications. The device is designed to record a synchronized video dataset from over one hundred cameras to a hard disk array using as few as one PC per fifty image sensors. It is intended to be flexible, modular and scalable, with much visibUity and control over the cameras. The Light Field Video Camera is a modular embedded design based on the IEEE1394 High Speed Serial Bus, with an image sensor and MPEG2 compression at each node. We show both the flexibility and scalability of the design with a six camera prototype.
ON RANDOM DEEP AUTOENCODERS: EXACT ASYMP-

We study the behavior of weight-tied multilayer vanilla autoencoders under the assumption of random weights. Via an exact characterization in the limit of large dimensions, our analysis reveals interesting phase transition phenomena when the depth becomes large. This, in particular, provides quantitative answers and insights to three questions that were yet fully understood in the literature. Firstly, we provide a precise answer on how the random deep weight-tied autoencoder model performs “approximate inference” as posed by Scellier et al. (2018), and its connection to reversibility considered by several theoretical studies. Secondly, we show that deep autoencoders display a higher degree of sensitivity to perturbations in the parameters, distinct from the shallow counterparts. Thirdly, we obtain insights on pitfalls in training initialization practice, and demonstrate experimentally that it is possible to train a deep autoencoder, even with the tanh activation and a depth as large as 200 layers, without resorting to techniques such as layer-wise pre-training or batch normalization. Our analysis is not specific to any depths or any Lipschitz activations, and our analytical techniques may have broader applicability.
Teaching Life-Saving Manoeuvres in Primary School

In the event of sudden cardiac arrest (SCA) early intervention provided by a layperson can be life-saving. Teaching first aid in primary school may increase the lifelong ability and motivation of young people to take action in an emergency.  The aim of this article is to report a training experience on BLSD (Basic Life Support and Defibrillation) designed for a group of pupils in an Italian primary school, with assessment of its effectiveness at a distance.  The assessment was carried out using a multiple choice questionnaire on a sample of 130 pupils aged 11-12, 62 trained in BLSD and 68 as a control group. The trained group also performed an emergency simulation to assess their learning of practical skills.  Using the t test, significant differences emerged in the questionnaire scores between the case-control group. The results of the skill test were positive, even for the most difficult manoeuvres such as opening airways, assessing breathing, or using an AED (Automated External Defibrillator).  Although there are still some open questions regarding the ability to retain these skills in the medium/long term, the study shows that life-saving manoeuvres can be effectively taught to primary school pupils.
Conservative surgical management of subungual (matrix derived) melanoma: report of seven cases and literature review.

Subungual melanoma (SUM) is a rare entity, comprising approximately 0·7-3·5% of all melanoma subtypes. SUM histopathologically belongs to the acral lentiginous pathological subtype of malignant melanoma. Its diagnosis is helped by dermoscopy but pathological examination of doubtful cases is required. Classical management of SUM is based on radical surgery, namely distal phalanx amputation. Conservative treatment with nonamputative wide excision of the nail unit followed by a skin graft has been insufficiently reported in the medical literature even though it is performed in many centres.



To report a series of patients with in situ or minimally invasive SUM treated by conservative surgery, to investigate the postoperative evolution and to evaluate the outcome with a review of the literature.



We performed a retrospective extraction study from our melanoma register of all patients with in situ and minimally invasive SUM treated with conservative surgery in the University Hospital Department of Dermatology, Lyon, France from 2004 to 2009. The patient demographics, disease presentation, delay to diagnosis, histopathology and postoperative evolution were reviewed.



Seven cases of SUM treated as such were identified in our melanoma database. All cases had a clinical presentation of melanonychia striata. The mean delay to diagnosis was 2years. Surgical excision of the entire nail unit with a 5-10mm safety margin without bone resection followed by full-thickness skin graft taken from the arm was performed in all cases. No recurrence was observed with a mean follow-up of 45months. Functional results were found satisfactory by all patients and their referring physicians. Sixty-two other cases have been found in the literature and are also discussed.



Conservative surgical management in patients with in situ or minimally invasive SUM is a procedure with good cosmetic and functional outcome and, in our cases as well as in the literature, the prognosis is not changed.
An accelerated gradient method for trace norm minimization

We consider the minimization of a smooth loss function regularized by the trace norm of the matrix variable. Such formulation finds applications in many machine learning tasks including multi-task learning, matrix classification, and matrix completion. The standard semidefinite programming formulation for this problem is computationally expensive. In addition, due to the non-smooth nature of the trace norm, the optimal first-order black-box method for solving such class of problems converges as O(1/√k), where k is the iteration counter. In this paper, we exploit the special structure of the trace norm, based on which we propose an extended gradient algorithm that converges as O(1/k). We further propose an accelerated gradient algorithm, which achieves the optimal convergence rate of O(1/k2) for smooth problems. Experiments on multi-task learning problems demonstrate the efficiency of the proposed algorithms.
File-level vs. module-level regression test selection for .NET

Regression testing is used to check the correctness of evolving software. With the adoption of Agile development methodology, the number of tests and software revisions has dramatically increased, and hence has the cost of regression testing. Researchers proposed regression test selection (RTS) techniques that optimize regression testing by skipping tests that are not impacted by recent program changes. Ekstazi is one such state-of-the art technique; Ekstazi is implemented for the Java programming language and has been adopted by several companies and open-source projects. 
 We report on our experience implementing and evaluating Ekstazi#, an Ekstazi-like tool for .NET. We describe the key challenges of bringing the Ekstazi idea to the .NET platform. We evaluate Ekstazi# on 11 open-source projects, as well as an internal Microsoft project substantially larger than each of the open-source projects. Finally, we compare Ekstazi# to an incremental build system (also developed at Microsoft), which, out of the box, provides module-level dependency tracking and skipping tasks (including test execution) whenever dependencies of a task do not change between the current and the last successful build. Ekstazi# on average reduced regression testing time by 43.70% for the open-source projects and by 65.26% for the Microsoft project (the latter is in addition to the savings provided by incremental builds).
Approximation Schemes for Euclidean k-Medians and Related Problems

In the k-median problem we are given a set S of n points in a metric space and a positive integer k. We desire to locate k medians in space, such that the sum of the distances from each of the points of S to the nearest median is minimized. This paper gives an approximation scheme for the plane that for any c > 0 produces a solution of cost at most 1 + 1/c times the optimum and runs in time O(n). The approximation scheme also generalizes to some problems related to k-median. Our methodology is to extend Arora’s [1, 2] techniques for the TSP, which hitherto seemed inapplicable to problems such as the k-median problem.
Tamper Resistance — a Cautionary Note

An increasing number of systems, from pay-TV to electronic purses, rely on the tamper resistance of smartcards and other security processors. We describe a number of attacks on such systems — some old, some new and some that are simply little known outside the chip testing community. We conclude that trusting tamper resistance is problematic; smartcards are broken routinely, and even a device that was described by a government signals agency as ‘the most secure processor generally available’ turns out to be vulnerable. Designers of secure systems should consider the consequences with care. 1 Tamperproofing of cryptographic
Disseminating cardiopulmonary resuscitation training by distributing 35,000 personal manikins among school children.

Because most cardiac arrests occur at home, widespread training is needed to increase the incidence of cardiopulmonary resuscitation (CPR) by lay persons. The aim of this study was to evaluate the effect of mass distribution of CPR instructional materials among schoolchildren.



We distributed 35,002 resuscitation manikins to pupils (12 to 14 years of age) at 806 primary schools. Using the enclosed 24-minute instructional DVD, they trained in CPR and subsequently used the kit to train family and friends (second tier). They completed a questionnaire on who had trained in CPR using the kit. Teachers also were asked to evaluate the project. The incidence of bystander CPR in out-of-hospital cardiac arrest in the months following the project was compared with the previous year. In total, 6947 questionnaires (19.8%) were returned. The 6947 kits had been used to train 17,140 from the second tier (mean, 2.5 persons per pupil; 95% confidence interval, 2.4 to 2.5). The teachers had used a mean of 64 minutes (95% confidence interval, 60 to 68) for preparation and a mean of 13 minutes (95% confidence interval, 11 to 15) to tidy up. Incidence of bystander CPR in the months after the project did not increase significantly compared with the previous year (25.0% versus 27.9%; P=0.16).



CPR training can be disseminated in a population by distributing personal resuscitation manikins among children in primary schools. The teachers felt able to easily facilitate CPR training. The incidence of bystander CPR did not increase significantly in the months following the project.
A trace-based framework for analyzing and synthesizing educational progressions

A key challenge in teaching a procedural skill is finding an effective progression of example problems that the learner can solve in order to internalize the procedure. In many learning domains, generation of such problems is typically done by hand and there are few tools to help automate this process. We reduce this effort by borrowing ideas from test input generation in software engineering. We show how we can use execution traces as a framework for abstracting the characteristics of a given procedure and defining a partial ordering that reflects the relative difficulty of two traces. We also show how we can use this framework to analyze the completeness of expert-designed progressions and fill in holes. Furthermore, we demonstrate how our framework can automatically synthesize new problems by generating large sets of problems for elementary and middle school mathematics and synthesizing hundreds of levels for a popular algebra-learning game. We present the results of a user study with this game confirming that our partial ordering can predict user evaluation of procedural difficulty better than baseline methods.
Deadline Guaranteed Service for Multi-Tenant Cloud Storage

It is imperative for cloud storage systems to be able to provide deadline guaranteed services according to service level agreements (SLAs) for online services. In spite of many previous works on deadline aware solutions, most of them focus on scheduling work flows or resource reservation in datacenter networks but neglect the server overload problem in cloud storage systems that prevents providing the deadline guaranteed services. In this paper, we introduce a new form of SLAs, which enables each tenant to specify a percentage of its requests it wishes to serve within a specified deadline. We first identify the multiple objectives (i.e., traffic and latency minimization, resource utilization maximization) in developing schemes to satisfy the SLAs. To satisfy the SLAs while achieving the multi-objectives, we propose a Parallel Deadline Guaranteed (PDG) scheme, which schedules data reallocation (through load re-assignment and data replication) using a tree-based bottom-up parallel process. The observation from our model also motivates our deadline strictness clustered data allocation algorithm that maps tenants with the similar SLA strictness into the same server to enhance SLA guarantees. We further enhance PDG in supplying SLA guaranteed services through two algorithms: i) a prioritized data reallocation algorithm that deals with request arrival rate variation, and ii) an adaptive request retransmission algorithm that deals with SLA requirement variation. Our trace-driven experiments on a simulator and Amazon EC2 show the effectiveness of our schemes for guaranteeing the SLAs while achieving the multi-objectives.
Learning Kernels with Random Features

Randomized features provide a computationally efficient way to approximate kernel machines in machine learning tasks. However, such methods require a user-defined kernel as input. We extend the randomized-feature approach to the task of learning a kernel (via its associated random features). Specifically, we present an efficient optimization problem that learns a kernel in a supervised manner. We prove the consistency of the estimated kernel as well as generalization bounds for the class of estimators induced by the optimized kernel, and we experimentally evaluate our technique on several datasets. Our approach is efficient and highly scalable, and we attain competitive results with a fraction of the training cost of other techniques.
GPS Based Autonomous Agricultural Robot

Agriculture is chosen as the primary occupation over majority population in the world. In the recent times, development of autonomous vehicles like robots has increased in agricultural field. The existing agricultural robots performs basic elementary functions like harvesting, planting and watering. This proposed system aims at designing multi-purpose autonomous agricultural robotic vehicle which is controlled by GPS and MAGNETOMETER for ploughing, seeding, leveling and message indication to start irrigation. This robot can especially be used in the areas which are harmful for humans. These robots are used to reduce human work, ensuring efficient and proper utilization of resources. These robots are mainly used for fertilization based on soil condition and several testing.
Towards 3D object maps for autonomous household robots

This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and ingredients. The models enable the recognition of the objects in cluttered scenes and the classification of newly encountered objects. Key technical contributions include (1) a robust, accurate, and efficient algorithm for constructing complete object models from 3D point clouds constituting partial object views, (2) feature-based recognition procedures for cabinets, tables, and other task-relevant furniture objects, and (3) automatic inference of object instance and class signatures for objects of daily use that enable robots to reliably recognize the objects in cluttered and real task contexts. We present results from the sensor-based mapping of a real kitchen.