nils-herrmann/clean_scirepeval huggingface dataset:
Reliability , Availability and Maintenance aspects of large-scale offshore wind farms , a concepts study

The DOWEC projects aims at implementation of large wind turbines in large scale wind farms. part of the DOWEC project a concepts study was performed regarding the achievable reliability and availability levels. A reduction with a factor of 2 with regard to the present state of the art seems fairly easy achievable. This is however not sufficient for application at more exposed sites. Availability levels are lower than targeted, but moreover the O&M cost turn out to be substantially higher than initially anticipated. The main cause for the high O&M costs is the rather frequent need for an expensive external crane vessel. A second design round is necessary to reconsider the reliability levels adopted for almost all concepts. Furthermore a more "farm like design approach" is needed to reduce major maintenance cost and increase availability.
Insufficient effort responding: examining an insidious confound in survey data.

Insufficient effort responding (IER; Huang, Curran, Keeney, Poposki, & DeShon, 2012) to surveys has largely been assumed to be a source of random measurement error that attenuates associations between substantive measures. The current article, however, illustrates how and when the presence of IER can produce a systematic bias that inflates observed correlations between substantive measures. Noting that inattentive responses as a whole generally congregate around the midpoint of a Likert scale, we propose that Mattentive, defined as the mean score of attentive respondents on a substantive measure, will be negatively related to IER's confounding effect on substantive measures (i.e., correlations between IER and a given substantive measure will become less positive [or more negative] as Mattentive increases). Results from a personality questionnaire (Study 1) and a simulation (Study 2) consistently support the hypothesized confounding influence of IER. Using an employee sample (Study 3), we demonstrated how IER can confound bivariate relationships between substantive measures. Together, these studies indicate that IER can inflate the strength of observed relationships when scale means depart from the scale midpoints, resulting in an inflated Type I error rate. This challenges the traditional view that IER attenuates observed bivariate correlations. These findings highlight situations where IER may be a methodological nuisance, while underscoring the need for survey administrators and researchers to deter and detect IER in surveys. The current article serves as a wake-up call for researchers and practitioners to more closely examine IER in their data.
Design and operation of Wi-Fi agribot integrated system

Robotics in agriculture is not a new concept; in controlled environments (green houses), it has a history of over 20 years. Research has been performed to develop harvesters for cherry tomatoes, cucumbers, mushrooms, and other fruits. In horticulture, robots have been introduced to harvest citrus and apples. In this paper autonomous robot for agriculture (AgriBot) is a prototype and implemented for performing various agricultural activities like seeding, weeding, spraying of fertilizers, insecticides. AgriBot is controlled with a Arduino Mega board having At mega 2560 microcontroller. The powerful Raspberry Pi a mini computer is used to control and monitor the working of the robot. The Arduino Mega is mounted on a robot allowing for access to all of the pins for rapid prototyping. Its hexapod body can autonomously walk in any direction, avoiding objects with its ultrasonic proximity sensor. Its walking algorithms allow it to instantly change direction and walk in any new direction without turning its body. An underbody sensory array allows the robot to know if a seed has been planted in the area at the optimal spacing and depth. AgriBot can then dig a hole, plant a seed in the hole, cover the seed with soil, and apply any pre-emergence fertilizers and/or herbicides along with the marking agent. AgriBot can then signal to other robots in the immediate proximity that it needs help planting in that area or that this area has been planted and to move on by communicating through Wi-Fi.
Geospatial Databases and Augmented Reality Visualization for Improving Safety in Urban Excavation Operations

The U.S. has more than 14 million miles of buried pipelines and utilities, many of which are in congested urban environments where several lines share the underground space. Errors in locating excavations for new installation or for repair/rehabilitation of existing utilities can result in significant costs, delays, loss of life, and damage to property (Sterling 2000). There is thus a clear need for new solutions to accurately locate buried infrastructure and improve excavation safety. This paper presents ongoing research being collaboratively conducted by the University of Michigan and DTE Energy (Michigan’s largest electric and gas utility company) that is investigating the use of Real-Time Kinematic GPS, combined with Geospatial Databases of subsurface utilities to design a new visual excavator-utility collision avoidance technology. 3D models of buried utilities are created from available geospatial data, and then superimposed over an excavator’s work space using geo-referenced Augmented Reality (AR) to provide the operator and the spotter(s) with visual information on the location and type of utilities that exist in the excavator’s vicinity. This paper describes the overall methodology and the first results of the research.
A Secure Color Image Steganography In Transform Domain

Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.
The Theoretical Foundations of Knowledge Management

15 April 2005  31 March 2006  Knowledge management has emerged as an important field for practice and research in information systems. This field is building on theoretical foundations from information economics, strategic management, organizational culture, organizational behavior, organizational structure, artificial intelligence, quality management, and organizational performance measurement. These theories are being used as foundations for new concepts that provide a rationale for managing knowledge, define the process of managing knowledge, and enable us to evaluate the results of this process. Based on articles published between 1995 and 2005, new concepts are emerging, including knowledge economy, knowledge alliance, knowledge culture, knowledge organization, knowledge infrastructure, and knowledge equity. An analysis of the theoretical foundations of knowledge management reveals a healthy arena with a strong foundation and clear directions for future work. Knowledge Management Research & Practice (2006) 4, 83–105. doi:10.1057/palgrave.kmrp.8500090
Robot-aided neurorehabilitation.

Our goal is to apply robotics and automation technology to assist, enhance, quantify, and document neurorehabilitation. This paper reviews a clinical trial involving 20 stroke patients with a prototype robot-aided rehabilitation facility developed at the Massachusetts Institute of Technology, Cambridge, (MIT) and tested at Burke Rehabilitation Hospital, White Plains, NY. It also presents our approach to analyze kinematic data collected in the robot-aided assessment procedure. In particular, we present evidence 1) that robot-aided therapy does not have adverse effects, 2) that patients tolerate the procedure, and 3) that peripheral manipulation of the impaired limb may influence brain recovery. These results are based on standard clinical assessment procedures. We also present one approach using kinematic data in a robot-aided assessment procedure.
PuDianNao: A Polyvalent Machine Learning Accelerator

Machine Learning (ML) techniques are pervasive tools in various emerging commercial applications, but have to be accommodated by powerful computer systems to process very large data. Although general-purpose CPUs and GPUs have provided straightforward solutions, their energy-efficiencies are limited due to their excessive supports for flexibility. Hardware accelerators may achieve better energy-efficiencies, but each accelerator often accommodates only a single ML technique (family). According to the famous No-Free-Lunch theorem in the ML domain, however, an ML technique performs well on a dataset may perform poorly on another dataset, which implies that such accelerator may sometimes lead to poor learning accuracy. Even if regardless of the learning accuracy, such accelerator can still become inapplicable simply because the concrete ML task is altered, or the user chooses another ML technique.
 In this study, we present an ML accelerator called PuDianNao, which accommodates seven representative ML techniques, including k-means, k-nearest neighbors, naive bayes, support vector machine, linear regression, classification tree, and deep neural network. Benefited from our thorough analysis on computational primitives and locality properties of different ML techniques, PuDianNao can perform up to 1056 GOP/s (e.g., additions and multiplications) in an area of 3.51 mm^2, and consumes 596 mW only. Compared with the NVIDIA K20M GPU (28nm process), PuDianNao (65nm process) is 1.20x faster, and can reduce the energy by 128.41x.
Blending liquids

We present a method for smoothly blending between existing liquid animations. We introduce a semi-automatic method for matching two existing liquid animations, which we use to create new fluid motion that plausibly interpolates the input. Our contributions include a new space-time non-rigid iterative closest point algorithm that incorporates user guidance, a subsampling technique for efficient registration of meshes with millions of vertices, and a fast surface extraction algorithm that produces 3D triangle meshes from a 4D space-time surface. Our technique can be used to instantly create hundreds of new simulations, or to interactively explore complex parameter spaces. Our method is guaranteed to produce output that does not deviate from the input animations, and it generalizes to multiple dimensions. Because our method runs at interactive rates after the initial precomputation step, it has potential applications in games and training simulations.
Designing Wide-band Transformers for HF and VHF Power Amplifiers

In the design of RF power amplifiers, wide-band transformers play an important role in the quality of the amplifier as they are fundamental in determining the input and output impedances, gain flatness, linearity, power efficiency and other performance characteristics. The three forms of transformers that are encountered, unbalanced-to-unbalanced (unun), balanced-to-balanced (balbal), and balanced-to-unbalanced (balun), are used in various combinations to accomplish the desired goals. Careful consideration needs to be given when making choices of the magnetic materials (if any is to be used), the conductors, and the method of construction, as the choices made weigh significantly in the overall performance of the transformer. The type and length of the conductors and the permeability of the magnetic material are the primary factors that determine the coupling, which in turn determines the transmission loss and the low frequency cutoff. The type and length of conductor used and the loss characteristics of the magnetic material also affects the coupling, and further influences the parasitic reactances that affect the high frequency performance.
Binarized-BLSTM-RNN based Human Activity Recognition

High computational complexity hinders the widespread usage of neural networks, especially in mobile devices, which are often the basis of fine-grained localization technology for ubiquitous health monitoring, context awareness, and indoor location tracking. In this paper, we present a binarized recurrent neural network whose weight parameters, input, and intermediate hidden layer output signals, are all binary-valued, and require only basic bit logic for the evaluation and training process. The proposed Binarized Long Short-Term Memory Network (B-BLSTM-RNN) is especially suitable for resource-constrained environments since it replaces either floating or fixed-point arithmetic with significantly more efficient bitwise operations. The model is based on a bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN). Designed to take contextual information into account, the network can process data gathered from different positions, resulting in a system, that's invariant to transformations and distortions of the input patterns. During the forward pass, the B-BLSTM drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. The binarized network is simple, accurate, efficient, and works on challenging gesture recognition tasks using raw MEM data. To validate the effectiveness of the network we conduct three sets of experiments. We achieved a classification accuracy with a the proposed network of about 90% which only 2% less than the full-precision network. We also compare our method with recent methods and outperform these methods by large margins on the conducted datasets.
On the Robustness of Information-Theoretic Privacy Measures and Mechanisms

Consider a data publishing setting for a dataset composed of non-private features correlated with a set of private features not necessarily present in the dataset. The objective of the publisher is to maximize the amount of information about the non-private features in a revealed dataset (utility), while keeping the information leaked about the private attributes bounded (privacy). Here, both privacy and utility are measured using information leakage measures that arise in adversarial settings. We consider a practical setting wherein the publisher uses an estimate of the joint distribution of the features to design the privacy mechanism. For any privacy mechanism, we provide probabilistic upper bounds for the discrepancy between the privacy guarantees for the empirical and true distributions, and similarly for utility. These bounds follow from our main technical results regarding the Lipschitz continuity of the considered information leakage measures. We also establish the statistical consistency of the notion of optimal privacy mechanism. Finally, we introduce and study the family of uniform privacy mechanisms, mechanisms designed upon an estimate of the joint distribution which are capable of providing privacy to a whole neighborhood of the estimated distribution, and thereby, guaranteeing privacy for the true distribution with high probability.
Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation

This paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a ``seed'' artist and the listener's user ID. Playlist generation may be informed by a number of information sources including: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary depending on the specific combination of user and seed~artist.
 The paper presents a method based on a variational Bayes solution for learning the parameters of a model containing a four-level hierarchy of global preferences, genres, sub-genres and artists. The proposed model further incorporates a personalization component for user-specific preferences. Empirical evaluations on both proprietary and public datasets demonstrate the effectiveness of the algorithm and showcase the contribution of each of its components.
Automatic weed detection system and smart herbicide sprayer robot for corn fields

The goal of this paper is to develop a new weed detection and classification method that can be applied for autonomous weed control robots. In order to achieve this goal plants must be classified into crops and weeds according to their properties which is done by a machine vision algorithm. Plants growing between rows are considered as weed, while inside a row, where crops are mixed with weeds, a classification method is required. Accordingly in the initial step, plants pixels were segmented from background with an adaptive method which is robust against variable light conditions as well as plant species. After that, crops and weeds were classified according to features extracted from wavelet analysis of the image. Finally, based on positions of weeds, herbicide sprayers are told to spray right on desired spots. The whole algorithm is implemented in LabVIEW software which is appropriate for real-time in-field purposes. In order to evaluate the performance of the algorithm 73 corn field images have been taken and selected, overall classification accuracy of 95.89% was achieved.
News via Voldemort: Parody accounts in topical discussions on Twitter

Parody accounts are prevalent on Twitter, offering irreverent interpretations of public figures, fictional characters, and more. These accounts post comments framed within the context of their fictional universes or stereotypes of their subjects, responding in-character to topical events. This article positions parody accounts as a ritualised social media practice, an extension of fan practices and irreverent internet culture. By providing a typology of parody accounts and analysing the topicality of selected parody accounts’ tweets, the research examines how these accounts contribute to topical discussions. In-character framing of topical comments allows parody accounts to offer original interpretations of breaking news
Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of people’s interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.
Aggregating User Input in Ecology Citizen Science Projects

Camera traps (remote, automatic cameras) are revolutionizing large-scale studies in ecology. The Serengeti Lion Project has used camera traps to produce over 1.5 million pictures of animals in the Serengeti. To analyze these pictures, the Project created Snapshot Serengeti, a citizen science website where volunteers can help classify animals. To increase accuracy, each photo is shown to multiple users and a critical step is aggregating individual classifications. In this paper, we present a new aggregation algorithm which achieves an accuracy of 98.6%, better than many human experts. Our algorithm also requires fewer users per photo than existing methods. The algorithm is intuitive and designed so that nonexperts can understand the end results. Ecology seeks to understand the interrelationships of species with one another and with their environment. Monitoring many species of animals simultaneously has traditionally been very difficult. Camera traps (remote, automatic cameras) are revolutionizing ecological research by providing a non-invasive, cost-effective approach for large-scale monitoring. Ecologists are currently using these traps in the Serengeti National Park, one of the world’s last large intact natural areas, to understand the dynamics of its dozens of large mammals (Swanson et al. 2014). As of November 2013, the ecologists have spent 3 years using more than 200 cameras spread over 1,125 square kilometers to take more than 1.5 million photos. In order to process so many images, the ecologists, along with Zooniverse (a citizen science platform), created Snapshot Serengeti, a web site where over 35,000 volunteers helped classify the species in the photos (Zooniverse 2014a). Since volunteers can make mistakes, each photo is shown to multiple users. A critical step is to combine these classifications into one aggregate classification: e.g., if 4 out of 5 users classify a photo as containing a zebra, we might ⇤These authors are part of the Zooniverse project, funded in part by MICO.  Classification aggregation is an active area in machine learning; however, we show that much of the existing literature is based on assumptions which do not apply to Snapshot Serengeti, and must therefore develop a novel approach. 1 In addition, current machine learning work on classification aggregation often draws on ideas such as expectation maximization and Bayesian reasoning. While powerful, these methods obscure the connection between input and results, making it hard for non-machine learning experts to understand the end results. Thus, our algorithm must be both accurate and intuitive. Our paper proceeds as follows. We begin by discussing Snapshot Serengeti and previous machine learning literature on classifier aggregation. We then discuss why much of this existing work is not applicable to Snapshot Serengeti. We next introduce a new classifier aggregation algorithm for Snapshot Serengeti and compare it against the current algorithm. Finally, we conclude and discuss possible future work.
A dynamic graph visualization perspective on eye movement data

During eye tracking studies, vast amounts of spatio-temporal data in the form of eye gaze trajectories are recorded. Finding insights into these time-varying data sets is a challenging task. Visualization techniques such as heat maps or gaze plots help find patterns in the data but highly aggregate the data (heat maps) or are difficult to read due to overplotting (gaze plots). In this paper, we propose transforming eye movement data into a dynamic graph data structure to explore the visualization problem from a new perspective. By aggregating gaze trajectories of participants over time periods or Areas of Interest (AOIs), a fair trade-off between aggregation and details is achieved. We show that existing dynamic graph visualizations can be used to display the transformed data and illustrate the approach by applying it to eye tracking data recorded for investigating the readability of tree diagrams.