nils-herrmann/clean_scirepeval huggingface dataset:
Beyond Mechanical Turk : An Analysis of Paid Crowd Work Platforms

Amazon’s Mechanical Turk (AMT) has revolutionized data processing and collection in both research and industry and remains one of the most prominent paid crowd work platforms today (Kittur et al., 2013). Unfortunately, it also remains in beta nine years after its launch with many of the same limitations as when it was launched: lack of worker profi indicating skills or experience, inability to post worker or employer ratings and reviews, minimal infrastructure for eff ely managing workers or collecting analytics, etc. Difficulty accomplishing quality, complex work with AMT continues to drive active research. Fortunately, many other alternative platforms now exist and off a wide range of features and workflow models for accomplishing quality work (crowdsortium.org). Despite this, research on crowd work has continued to focus on AMT near-exclusively. By analogy, if one had only ever programmed in Basic, how might this limit one’s conception of programming? What if the only search engine we knew was AltaVista? Adar (2011) opined that prior research has often been envisioned too narrowly for AMT, “...writing the user’s manual for MTurk ... struggl[ing] against the limits of the platform...”. Such narrow focus risks AMT’s particular vagaries and limitations unduly shape research questions, methodology, and imagination. To assess the extent of AMT’s infl upon research questions and use, we review its impact on prior work, assess what functionality and workflows other platforms off and consider what light other platforms’ diverse capabilities may shed on current research practices and future directions. To this end, we present a qualitative content analysis (Mayring, 2000) of ClickWorker, CloudFactory, CrowdComputing Systems, CrowdFlower, CrowdSource, MobileWorks, and oDesk. To characterize and diff entiate crowd work platforms, we identify several key categories for analysis. Our qualitative content analysis assesses each platform by drawing upon a variety of information sources: Webpages, blogs, news articles, white papers, and research papers. We also shared our analyses with platform representatives and incorporated their feedback.  Our content analysis of crowd work platforms represents the first such study we know of by researchers for researchers, with categories of analysis chosen based on research relevance. Contributions include our review of how AMT assumptions and limitations have influenced prior research, the detailed criteria we developed for characterizing crowd work platforms, and our analysis.
Securing Advanced Metering Infrastructure Using Intrusion Detection System with Data Stream Mining

Advanced metering infrastructure (AMI) is an imperative component of the smart grid, as it is responsible for collecting, measuring, analyzing energy usage data, and transmitting these data to the data concentrator and then to a central system in the utility side. Therefore, the security of AMI is one of the most demanding issues in the smart grid implementation. In this paper, we propose an intrusion detection system (IDS) architecture for AMI which will act as a complimentary with other security measures. This IDS architecture consists of three local IDSs placed in smart meters, data concentrators, and central system (AMI headend). For detecting anomaly, we use data stream mining approach on the public KDD CUP 1999 data set for analysis the requirement of the three components in AMI. From our result and analysis, it shows stream data mining technique shows promising potential for solving security issues in AMI.
Validation of analytical methods used in cleaning validation

15 The information used to establish a positive cleaning validation is based on the result of validated analytical measurements. There must be a high degree of confidence in these results, as human safety depends on the lack of residues remaining on equipment. This article will describe various aspects regarding the validation of analytical methods used in cleaning validations. The validation elements are explored from both a theoretical point of view and through examples. References are provided to guide the reader to more in-depth information. A n analytical method is one of the deciding factors in establishing the cleanliness of pharmaceutical manufacturing equipment. It is, therefore, important that there be a high level of confidence in the results obtained using the method. This high level of confidence is established by testing and defining the usefulness of the analytical method. A properly developed cleaning validation strategy includes the analytical method validation, which defines the method parameters necessary in providing a high level of confidence in the cleaning results. The analytical method validation study demonstrates to scientific staffs, manufacturing personnel, and regulatory agencies, that the method performs as required, and that the results are reliable. There are many articles available that address analytical method validation within and outside of the pharmaceutical industry, both domestically and worldwide. Personnel other than analytical chemists may not understand the need for analytical method validation, let alone the extent to which these methods need to be evaluated. They may not understand that analytical method validation, as well as cleaning validation, has an important impact on everyday pharmaceutical manufacturing. 9 As is the case with cleaning validation, analytical methods may need to be revalidated.  This revalidation may arise from changes in in-strumentation, analytes or manufacturing methods, or cleaning processes that affect the ability of the analytical method to determine the correct analyte level. Measuring cleanliness is a difficult task. Essentially, trace residues on surfaces are the target analytes. The residue must first be extracted from a surface, recovered from the extraction medium, and then suitably quantitated. Residue analysis is quite different from analyzing bulk or formulated drug ac-tives, as obtainable precisions and accuracies may be larger than the analyst is accustomed. Sensitivity levels of the techniques employed need to be considered for linearity, precision , and accuracy. The first decision to be made is the decision as to which residue will be measured. This residue could be the …
Extrinsic calibration of camera and 3D laser sensor system

Robots are typically equipped with multiple complementary sensors such as cameras and laser range finders. Camera generally provides dense 2D information while range sensors give sparse and accurate depth information in the form of a set of 3D points. In order to represent the different data sources in a common coordinate system, extrinsic calibration is needed. This paper presents a pipeline for extrinsic calibration a zed setero camera with Velodyne LiDAR puck using a novel self-made 3D marker whose edges can be robustly detected in the image and 3d point cloud. Our approach first estimate the large sensor displacement using just a single frame. then we optimize the coarse results by finding the best align of edges in order to obtain a more accurate calibration. Finally, the ratio of the 3D points correctly projected onto proper image segments is used to evaluate the accuracy of calibration.
PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques

Stochastic Gradient TreeBoost is often found in many winning solutions in public data science challenges. Unfortunately, the best performance requires extensive parameter tuning and can be prone to overfitting. We propose PaloBoost, a Stochastic Gradient TreeBoost model that uses novel regularization techniques to guard against overfitting and is robust to parameter settings. PaloBoost uses the under-utilized out-of-bag samples to perform gradient-aware pruning and estimate adaptive learning rates. Unlike other Stochastic Gradient TreeBoost models that use the out-of-bag samples to estimate test errors, PaloBoost treats the samples as a second batch of training samples to prune the trees and adjust the learning rates. As a result, PaloBoost can dynamically adjust tree depths and learning rates to achieve faster learning at the start and slower learning as the algorithm converges. We illustrate how these regularization techniques can be efficiently implemented and propose a new formula for calculating feature importance to reflect the node coverages and learning rates. Extensive experimental results on seven datasets demonstrate that PaloBoost is robust to overfitting, is less sensitivity to the parameters, and can also effectively identify meaningful features.
"FRCEF: The new friction reduced and coupling enhanced finger for the Awiwi hand"

The combination of tendon driven robotic fingers and variable impedance actuation in the DLR hand arm system brings benefits in robustness and dynamics by enabling energy storage. Since the force measurement and motors are in the forearm the tendon path should have low friction for accurate movements and precise finger control. In this paper an enhanced generation of the Awiwi hand finger design is presented. It reduces the friction in the actuation system about 20 percent and increases the maximum fingertip force about 33 percent. A test finger was designed to evaluate different tendon couplings and to test a magnetic sensor to measure the joint position. In a next step a new finger design for DLR hand arm system has been developed. Finally, the low friction and the robustness are proven using several experiments.
Sentinel Node Biopsy Using a Magnetic Tracer Versus Standard Technique: The SentiMAG Multicentre Trial

The SentiMAG Multicentre Trial evaluated a new magnetic technique for sentinel lymph node biopsy (SLNB) against the standard (radioisotope and blue dye or radioisotope alone). The magnetic technique does not use radiation and provides both a color change (brown dye) and a handheld probe for node localization. The primary end point of this trial was defined as the proportion of sentinel nodes detected with each technique (identification rate). A total of 160 women with breast cancer scheduled for SLNB, who were clinically and radiologically node negative, were recruited from seven centers in the United Kingdom and The Netherlands. SLNB was undertaken after administration of both the magnetic and standard tracers (radioisotope with or without blue dye). A total of 170 SLNB procedures were undertaken on 161 patients, and 1 patient was excluded, leaving 160 patients for further analysis. The identification rate was 95.0 % (152 of 160) with the standard technique and 94.4 % (151 of 160) with the magnetic technique (0.6 % difference; 95 % upper confidence limit 4.4 %; 6.9 % discordance). Of the 22 % (35 of 160) of patients with lymph node involvement, 16 % (25 of 160) had at least 1 macrometastasis, and 6 % (10 of 160) had at least a micrometastasis. Another 2.5 % (4 of 160) had isolated tumor cells. Of 404 lymph nodes removed, 297 (74 %) were true sentinel nodes. The lymph node retrieval rate was 2.5 nodes per patient overall, 1.9 nodes per patient with the standard technique, and 2.0 nodes per patient with the magnetic technique. The magnetic technique is a feasible technique for SLNB, with an identification rate that is not inferior to the standard technique.
Ion sensitive field effect transistor for applications in bioelectronic sensors: A research review

During recent decades increasing interest has been shown in the development of bioelectronic sensors based on ion sensitive field effect transistors (ISFETs). Many ISFET-based pH sensors have been commercialized and attempts have also been made to commercialize ISFET based bioelectronic sensors for applications in the fields of medical, environmental, food safety, military and biotechnology areas. The growing interest for development of these sensors is due to the fact that they are manufactured by means of semiconductor technology which has already entered into multifunctional “More than Moore” regime that is increasingly multidisciplinary in nature. Technology involved has, therefore, innovative potential that may result in the appearance of new sensor and device technologies in future. The basic theoretical principles of ISFET usage in bioanalytical practice, the operation principle of ISFET, its modeling and a brief introduction of ISFET technology are considered in this review.
A 32.75-Gb/s voltage mode transmitter with 3-tap FFE in 16nm CMOS

This paper describes the design of a highly flexible voltage-mode transmitter with 3-tap Feed Forward Equalization fabricated in a 16nm CMOS technology. The transmitter uses a dual regulator architecture to allow for independent control of swing, common-mode and equalization. The transmitter employs a hybrid impedance control scheme with both slice-based and analog impedance control loops. A FIR compensation circuit is used to improve deterministic jitter. The transmitter consumes 120.8mW, provides 0.25–0.9Vpp output swing, and achieves TJ of 6.49ps-pp and RJ of 220fs-rms at 32.75Gb/s.
Ensemble Deep Learning for Biomedical Time Series Classification

Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.
A Comparative study of Transportation Problem under Probabilistic and Fuzzy Uncertainties

Transportation Problem is an important aspect which has been widely studied in Operations Research domain. It has been studied to simulate different real life problems. In particular, application of this Problem in NPHard Problems has a remarkable significance. In this Paper, we present a comparative study of Transportation Problem through Probabilistic and Fuzzy Uncertainties. Fuzzy Logic is a computational paradigm that generalizes classical two-valued logic for reasoning under uncertainty. In order to achieve this, the notation of membership in a set needs to become a matter of degree. By doing this we accomplish two things viz., (i) ease of describing human knowledge involving vague concepts and (ii) enhanced ability to develop cost-effective solution to real-world problem. The multi-valued nature of Fuzzy Sets allows handling uncertain and vague information. It is a model-less approach and a clever disguise of Probability Theory. We give comparative simulation results of both approaches and discuss the Computational Complexity. To the best of our knowledge, this is the first work on comparative study of Transportation Problem using Probabilistic and Fuzzy Uncertainties.
Session search modeling by partially observable Markov decision process

Session search, the task of document retrieval for a series of queries in a session, has been receiving increasing attention from the information retrieval research community. Session search exhibits the properties of rich user-system interactions and temporal dependency. These properties lead to our proposal of using partially observable Markov decision process to model session search. On the basis of a design choice schema for states, actions and rewards, we evaluate different combinations of these choices over the TREC 2012 and 2013 session track datasets. According to the experimental results, practical design recommendations for using PODMP in session search are discussed.
Natural language question-answering systems: 1969

Recent experiments in programming natural language question-answering systems are reviewed to summarize the methods that have been developed for syntactic, semantic, and logical analysis of English strings. It is concluded that at least minimally effective techniques have been devised for answering questions from natural language subsets in small scale experimental systems and that a useful paradigm has evolved to guide research efforts in the field. Current approaches to semantic analysis and logical inference are seen to be effective beginnings but of questionable generality with respect either to subtle aspects of meaning or to applications over large subsets of English. Generalizing from current small-scale experiments to language-processing systems based on dictionaries with thousands of entries—with correspondingly large grammars and semantic systems—may entail a new order of complexity and require the invention and development of entirely different approaches to semantic analysis and question answering.
A tubular linear machine with dual Halbach array

Force output is extremely important for electromagnetic linear machines. The purpose of this study is to explore new permanent magnet (PM) array and winding patterns to increase the magnetic flux density and thus to improve the force output of electromagnetic tubular linear machines.  Based on investigations on various PM patterns, a novel dual Halbach PM array is proposed in this paper to increase the radial component of flux density in three-dimensional machine space, which in turn can increase the force output of tubular linear machine significantly. The force outputs and force ripples for different winding patterns are formulated and analyzed, to select optimized structure parameters.  The proposed dual Halbach array can increase the radial component of flux density and force output of tubular linear machines effectively. It also helps to decrease the axial component of flux density and thus to reduce the deformation and vibration of machines. By using analytical force models, the influence of winding patterns and structure parameters on the machine force output and force ripples can be analyzed. As a result, one set of optimized structure parameters are selected for the design of electromagnetic tubular linear machines.  The proposed dual Halbach array and winding patterns are effective ways to improve the linear machine performance. It can also be implemented into rotary machines. The analyzing and design methods could be extended into the development of other electromagnetic machines.
VHF/UHF synthetic aperture radar - principles and motivation

At frequencies below 1 GHz, vegetation is becoming transparent, the more so the lower the frequency. Tree clutter on the other hand tends to be as strong as in the microwave regime as at frequencies above 200 MHz. Below 100 MHz, i.e. in the VHF band, tree clutter levels are significantly smaller. Foliage penetration SAR is feasible at both UHF and VHF but has to overcome significant challenges. For one, resolution must be high, viz. of meter order at VHF and submeter order for UHF. In both cases resolution of wavelength order is thus called for, requiring special processing methods which will be discussed here. Secondly, the signal-to-noise budget is critical due to the severe radio frequency interference below 1 GHz. In fact SAR operation at these frequencies is not feasible unless there are some means to identify and remove the RF1. Thirdly, for SAR surveillance the target detection method is crucial. VHF resolution is too low to make any target recognition scheme effective as a means to reduce clutter false alarms. At UHF, even though resolution can be made high, intense forest clutter level creates a very difficult environment for target discrimination. These concerns and their remedies are discussed in the paper.
Image autocoregistration and InSAR interferogram estimation using joint subspace projection

In this paper, we propose a new method to estimate synthetic aperture radar interferometry (InSAR) interferometric phase in the presence of large coregistration errors. The method takes advantage of the coherence information of neighboring pixel pairs to automatically coregister the SAR images and employs the projection of the joint signal subspace onto the corresponding joint noise subspace to estimate the terrain interferometric phase. The method can automatically coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the terrain interferometric phase (interferogram) as the coregistration error reaches one pixel. The effectiveness of the method is also verified with the real data from the Spaceborne Imaging Radar-C/X Band SAR and the European Remote Sensing 1 and 2 satellites.
Vocational Rehabilitation of Individuals with Autism Spectrum Disorder with Virtual Reality

In this article, a virtual reality system for vocational rehabilitation of individuals with disabilities (VR4VR) is presented. VR4VR uses immersive virtual environments to assess and train individuals with cognitive and physical disabilities. This article focuses on the system modules that were designed and developed for the Autism Spectrum Disorder (ASD) population. The system offers training on six vocational skills that were identified as transferrable to and useful in many common jobs. These six transferable skills are cleaning, loading the back of a truck, money management, shelving, environmental awareness, and social skills. This article presents the VR4VR system, the design considerations for the ASD population, and the findings with a cohort of nine neurotypical individuals (control group) and nine high-functioning individuals with ASD (experiment group) who used the system. Good design practices gathered throughout the study are also shared for future virtual reality applications targeting individuals with ASD. Research questions focused on the effectiveness of the virtual reality system on vocational training of high-functioning individuals with ASD and the effect of distracters on task performance of high-functioning individuals with ASD. Follow-up survey results indicated that for individuals with ASD, there was improvement in all of the trained skills. No negative effects of the distracters were observed on the score of individuals with ASD. The proposed VR4VR system was found by professional job trainers to provide effective vocational training for individuals with ASD. The system turned out to be promising in terms of providing an alternative practical training tool for individuals with ASD.
Adaptive Game AI and Video Game Enjoyability

In modern times nearly every person in the country is aware of video games and the majority of them have played one at least once. Because video games represent controlled environments that can be created to mimic real world environments, they are ideal testing spaces for ideas that may be difficult or dangerous when tested in reality. It is for this reason that video games are widely used to test different types of Artificial Intelligence (AI) such as Recurrence Learning Algorithms [Liu et al., 2011], Q-learning a reinforcement learning technique [Patel et al., 2011], Ambient intelligence systems [Sadri, 2011], and what is known as intelligent agent processing [Wickramasinghe and Alahakoon, 2004]. There are an almost innumerable number of different intelligence systems out there, but most have the same basic building blocks. Using a simple model laid out by Pieter Spronck, author of the 2005 book, Adaptive Game AI, I plan to test the effectiveness of an adaptive AI as compared to a non-adaptive AI.[Spronck, 2005] I wish to test how effective a behaviorally adaptive video game AI is at improving video game enjoyability according to user survey response in a simple and controlled video game environment.