nils-herrmann/clean_scirepeval huggingface dataset:
Citation content analysis (cca): A framework for syntactic and semantic analysis of citation content

This study proposes a new framework for citation content analysis (CCA), for syntactic and semantic analysis of citation content that can be used to better analyze the rich sociocultural context of research behavior. This framework could be considered the next generation of citation analysis. The authors briefly review the history and features of content analysis in traditional social sciences and its previous application in library and information science (LIS). Based on critical discussion of the theoretical necessity of a new method as well as the limits of citation analysis, the nature and purposes of CCA are discussed, and potential procedures to conduct CCA, including principles to identify the reference scope, a two-dimensional (citing and cited) and two-module (syntactic and semantic) codebook, are provided and described. Future work and implications are also suggested.
Gluteal muscle activation during common therapeutic exercises.

Experimental laboratory study.



To quantify and compare electromyographic signal amplitude of the gluteus maximus and gluteus medius muscles during exercises of varying difficulty to determine which exercise most effectively recruits these muscles.



Gluteal muscle weakness has been proposed to be associated with lower extremity injury. Exercises to strengthen the gluteal muscles are frequently used in rehabilitation and injury prevention programs without scientific evidence regarding their ability to activate the targeted muscles.



Surface electromyography was used to quantify the activity level of the gluteal muscles in 21 healthy, physically active subjects while performing 12 exercises. Repeated-measures analyses of variance were used to compare normalized mean signal amplitude levels, expressed as a percent of a maximum voluntary isometric contraction (MVIC), across exercises.



Significant differences in signal amplitude among exercises were noted for the gluteus medius (F5,90 = 7.9, P<.0001) and gluteus maximus (F5,95 = 8.1, P<.0001). Gluteus medius activity was significantly greater during side-lying hip abduction (mean +/- SD, 81% +/- 42% MVIC) compared to the 2 types of hip clam (40% +/- 38% MVIC, 38% +/- 29% MVIC), lunges (48% +/- 21% MVIC), and hop (48% +/- 25% MVIC) exercises. The single-limb squat and single-limb deadlift activated the gluteus medius (single-limb squat, 64% +/- 25% MVIC; single-limb deadlift, 59% +/- 25% MVIC) and maximus (single-limb squat, 59% +/- 27% MVIC; single-limb deadlift, 59% +/- 28% MVIC) similarly. The gluteus maximus activation during the single-limb squat and single-limb deadlift was significantly greater than during the lateral band walk (27% +/- 16% MVIC), hip clam (34% +/- 27% MVIC), and hop (forward, 35% +/- 22% MVIC; transverse, 35% +/- 16% MVIC) exercises.



The best exercise for the gluteus medius was side-lying hip abduction, while the single-limb squat and single-limb deadlift exercises led to the greatest activation of the gluteus maximus. These results provide information to the clinician about relative activation of the gluteal muscles during specific therapeutic exercises that can influence exercise progression and prescription.  2009;39(7):532-540, Epub 24 February 2009.
Applying ISO 45001 as a Model By

Walter G. Rostykus, B.S., M.S.P.H., CSP, CPE, CIH, is principal consultant at Humantech Inc., Ann Arbor, MI. He has more than 30 years’ experience delivering and managing ergonomic, occupational safety, industrial hygiene and environmental programs. He holds a B.S. from Washington State University and an M.S. in Public Health, with a focus on Industrial Hygiene and Safety/Environmental Health and Sanitation, from University of Washington. Rostykus is an affiliate instructor in the Department of Environmental and Occupational Health Science, School of Public Health, University of Washington. He is a member of AIHA, a professional member of ASSE’s New Mexico Chapter and Administrator of the Society’s Ergonomics Practice Specialty.
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors

Motivations like domain adaptation, transfer learning, and feature learning have fueled interest in inducing embeddings for rare or unseen words, n-grams, synsets, and other textual features. This paper introduces à la carte embedding, a simple and general alternative to the usual word2vec-based approaches for building such representations that is based upon recent theoretical results for GloVe-like embeddings. Our method relies mainly on a linear transformation that is efficiently learnable using pretrained word vectors and linear regression. This transform is applicable “on the fly” in the future when a new text feature or rare word is encountered, even if only a single usage example is available. We introduce a new dataset showing how the à la carte method requires fewer examples of words in context to learn high-quality embeddings and we obtain state-of-the-art results on a nonce task and some unsupervised document classification tasks.
Recurrent Spatial-Temporal Attention Network for Action Recognition in Videos

Recent years have witnessed the popularity of using recurrent neural network (RNN) for action recognition in videos. However, videos are of high dimensionality and contain rich human dynamics with various motion scales, which makes the traditional RNNs difficult to capture complex action information. In this paper, we propose a novel recurrent spatial-temporal attention network (RSTAN) to address this challenge, where we introduce a spatial-temporal attention mechanism to adaptively identify key features from the global video context for every time-step prediction of RNN. More specifically, we make three main contributions from the following aspects. First, we reinforce the classical long short-term memory (LSTM) with a novel spatial-temporal attention module. At each time step, our module can automatically learn a spatial-temporal action representation from all sampled video frames, which is compact and highly relevant to the prediction at the current step. Second, we design an attention-driven appearance-motion fusion strategy to integrate appearance and motion LSTMs into a unified framework, where LSTMs with their spatial-temporal attention modules in two streams can be jointly trained in an end-to-end fashion. Third, we develop actor-attention regularization for RSTAN, which can guide our attention mechanism to focus on the important action regions around actors. We evaluate the proposed RSTAN on the benchmark UCF101, HMDB51 and JHMDB data sets. The experimental results show that, our RSTAN outperforms other recent RNN-based approaches on UCF101 and HMDB51 as well as achieves the state-of-the-art on JHMDB.
Applying Asynchronous Deep Classification Networks and Gaming Reinforcement Learning-Based Motion Planners to Mobile Robots

In this paper, we propose a new methodology to embed deep learning-based algorithms in both visual recognition and motion planning for general mobile robotic platforms. A framework for an asynchronous deep classification network is introduced to integrate heavy deep classification networks into a mobile robot with no loss of system bandwidth. Moreover, a gaming reinforcement learning-based motion planner, a novel and convenient embodiment of reinforcement learning, is introduced for simple implementation and high applicability. The proposed approaches are implemented and evaluated on a developed robot, TT2-bot. The evaluation was based on a mission devised for a qualitative evaluation of the general purposes and performances of a mobile robotic platform. The robot was required to recognize targets with a deep classifier and plan the path effectively using a deep motion planner. As a result, the robot verified that the proposed approaches successfully integrate deep learning technologies on the stand-alone mobile robot. The embedded neural networks for recognition and path planning were critical components for the robot.
Biopython: freely available Python tools for computational molecular biology and bioinformatics

The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST,  and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning.


AVAILABILITY
Biopython is freely available, with documentation and source code at (www.biopython.org) under the Biopython license.
Anchor-free backscatter positioning for RFID tags with high accuracy

RFID technology has been widely adopted in a variety of applications from logistics to access control. Many applications gain benefits from knowing the exact position of an RFID-tagged object. Existing localization algorithms in wireless network, however, can hardly be directly employed due to tag's limited capabilities in terms of energy and memory. For example, the RSS based methods are vulnerable to both distance and tag orientation, while AOA based methods put a strict constraint on the antennas' spacing that reader's directional antennas are too large to meet. In this paper, we propose BackPos, a fine-grained backscatter positioning technique using the COTS RFID products with detected phase. Our study shows that the phase is indeed a stable indicator highly related to tag's position and preserved over frequency or tag orientation, but challenged by its periodicity and tag's diversity. We attempt to infer the distance differences from phases detected by antennas under triangle constraint. Further, hyperbolic positioning using the distance differences is employed to shrink the tag's candidate positions until filtering out the real one. In combination with interrogation zone, we finally relax the triangle constraint and allow arbitrary deployment of antennas by sacrificing the feasible region. We implement a prototype of BackPos with COTS RFID products and evaluate this design in various scenarios. The results show that BackPos achieves the mean accuracy of 12.8cm with variance of 3.8cm.
A Distributed Anomaly Detection System for In-Vehicle Network Using HTM

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall.
Accelerating Compute-Intensive Applications with GPUs and FPGAs

Accelerators are special purpose processors designed to speed up compute-intensive sections of applications. Two extreme endpoints in the spectrum of possible accelerators are FPGAs and GPUs, which can often achieve better performance than CPUs on certain workloads. FPGAs are highly customizable, while GPUs provide massive parallel execution resources and high memory bandwidth. Applications typically exhibit vastly different performance characteristics depending on the accelerator. This is an inherent problem attributable to architectural design, middleware support and programming style of the target platform. For the best application-to-accelerator mapping, factors such as programmability, performance, programming cost and sources of overhead in the design flows must be all taken into consideration. In general, FPGAs provide the best expectation of performance, flexibility and low overhead, while GPUs tend to be easier to program and require less hardware resources. We present a performance study of three diverse applications - Gaussian elimination, data encryption standard (DES), and Needleman-Wunsch - on an FPGA, a GPU and a multicore CPU system. We perform a comparative study of application behavior on accelerators considering performance and code complexity. Based on our results, we present an application characteristic to accelerator platform mapping, which can aid developers in selecting an appropriate target architecture for their chosen application.
Learning Longer-term Dependencies in RNNs with Auxiliary Losses

Despite recent advances in training recurrent neural networks (RNNs), capturing long-term dependencies in sequences remains a fundamental challenge. Most approaches use backpropagation through time (BPTT), which is difficult to scale to very long sequences. This paper proposes a simple method that improves the ability to capture long term dependencies in RNNs by adding an unsupervised auxiliary loss to the original objective. This auxiliary loss forces RNNs to either reconstruct previous events or predict next events in a sequence, making truncated backpropagation feasible for long sequences and also improving full BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel image classification with sequence lengths up to 16 000, and a real document classification benchmark. Our results highlight good performance and resource efficiency of this approach over competitive baselines, including other recurrent models and a comparable sized Transformer. Further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization, as well as extreme cases where there is little to no backpropagation.
Regular or stochastic dynamics in real analytic families of unimodal maps

In this paper we prove that in any non-trivial real analytic family of quasiquadratic maps, almost any map is either regular (i.e., it has an attracting cycle) or stochastic (i.e., it has an absolutely continuous invariant measure). To this end we show that the space of analytic maps is foliated by codimension-one analytic submanifolds, “hybrid classes”. This allows us to transfer the regular or stochastic property of the quadratic family to any non-trivial real analytic family.
VAIN: Attentional Multi-agent Predictive Modeling

Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of modeling multi-agent physical systems. One of the drawbacks of INs is scaling with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce VAIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that VAIN is effective for multiagent predictive modeling. Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches.
Inductive logic programming - techniques and applications

Spend your few moment to read a book even only few pages. Reading book is not obligation and force for everybody. When you don't want to read, you can get punishment from the publisher. Read a book becomes a choice of your different characteristics. Many people with reading habit will always be enjoyable to read, or on the contrary. For some reasons, this inductive logic programming techniques and applications tends to be the representative book in this website.
Coronary morphologic and clinical determinants of procedural outcome with angioplasty for multivessel coronary disease. Implications for patient selection. Multivessel Angioplasty Prognosis Study Group.

To assess the likelihood of procedural success in patients with multivessel coronary disease undergoing percutaneous coronary angioplasty, 350 consecutive patients (1,100 stenoses) from four clinical sites were evaluated. Eighteen variables characterizing the severity and morphology of each stenosis and 18 patient-related variables were assessed at a core angiographic laboratory and at the clinical sites. Most patients had Canadian Cardiovascular Society class III or IV angina (72%) and two-vessel coronary disease (78%). Left ventricular function was generally well preserved (mean ejection fraction, 58 +/- 12%; range, 18-85%) and 1.9 +/- 1.0 stenoses per patient had attempted percutaneous coronary angioplasty. Procedural success (less than or equal to 50% final diameter stenosis in one or more stenoses and no major ischemic complications) was achieved in 290 patients (82.8%), and an additional nine patients (2.6%) had a reduction in diameter stenosis by 20% or more with a final diameter stenosis 51-60% and were without major complications. Major ischemic complications (death, myocardial infarction, or emergency bypass surgery) occurred in 30 patients (8.6%). In-hospital mortality was 1.1%. Stepwise regression analysis determined that a modified American College of Cardiology/American Heart Association Task Force (ACC/AHA) classification of the primary target stenosis (with type B prospectively divided into type B1 [one type B characteristic] and type B2 [greater than or equal to two type B characteristics]) and the presence of diabetes mellitus were the only variables independently predictive of procedural outcome (target stenosis modified ACC/AHA score; p less than 0.001 for both success and complications; diabetes mellitus: p = 0.003 for success and p = 0.016 for complications). Analysis of success and complications on a per stenosis dilated basis showed, for type A stenoses, a 92% success and a 2% complication rate; for type B1 stenoses, an 84% success and a 4% complication rate; for type B2 stenoses, a 76% success and a 10% complication rate; and for type C stenoses, a 61% success and a 21% complication rate. The subdivision into types B1 and B2 provided significantly more information in this clinically important intermediate risk group than did the standard ACC/AHA scheme. The stenosis characteristics of chronic total occlusion, high grade (80-99% diameter) stenosis, stenosis bend of more than 60 degrees, and excessive tortuosity were particularly predictive of adverse procedural outcome. This improved scheme may improve clinical decision making and provide a framework on which to base meaningful subgroup analysis in randomized trials assessing the efficacy of percutaneous coronary angioplasty.
Defining, Assessing, and Promoting E‐Learning Success: An Information Systems Perspective*

This research advances the understanding of how to define, evaluate, and promote e-learning success from an information systems perspective. It introduces the E-Learning Success Model, which posits that the overall success of an e-learning initiative depends on the attainment of success at each of the three stages of e-learning systems development: system design, system delivery, and system outcome. To study this model, an online version of an undergraduate quantitative methods core course for business students is developed using a prototyping strategy. Four cycles of development are traced, each comprised analysis, design, implementation, testing, and enhancement. Findings from the study confirm the validity of using the proposed success model for e-learning success assessment. In addition, an action research methodology is also found to be a valuable impetus for promoting e-learning success through an iterative process of diagnosing, action planning, action taking, evaluating, and learning.  Action Research, E-Learning Success Model, and Online Courses.
Non-linear Attitude Control and Guidance of a Quadrotor UAV

In this work, inspired by two different approaches available in the literature, we present two ways of nonlinear control for the attitude of a quadrotor unmanned aerial vehicle (UAV) : the first one is based on backstepping and the second one is developed directly on the special orthogonal group, SO(3), using the Lyapunov stability theory. In order to prove the advantages of these nonlinear controllers, they will be compared with a proporcional derivative (PD) and a linear quadratic regulator (LQR) controllers, which are the typical solutions for controlling the quadrotor attitude. About the attitude estimation, a set of sensors composed by a 3-axis accelerometer, a 3-axis gyroscope and a 3-axis magnetometer will be used and several estimators based on the Kalman Filter will be studied. Once the full model is developed (made up of the quadrotor motion, actuators and sensors models) and a simulator is built, two levels of control will be implemented in a cascade control configuration: a low level control, for stabilizing or tracking attitude and altitude, and a high level control (by means of an horizontal guidance controller) for tracking a desired path in an horizontal plane. Our simulation shows that the PD controller is not very reliable working with estimators, and that the nonlinear controllers present the best performace, although the LQR controller has also a quite acceptable behaviour.
Digital Forensics and Cyber Crime Datamining

Digital forensics is the science of identifying, extracting, analyzing and presenting the digital evidence that has been stored in the digital devices. Various digital tools and techniques are being used to achieve this. Our paper explains forensic analysis steps in the storage media, hidden data analysis in the file system, network forensic methods and cyber crime data mining. This paper proposes a new tool which is the combination of digital forensic investigation and crime data mining. The proposed system is designed for finding motive, pattern of cyber attacks and counts of attacks types happened during a period. Hence the proposed tool enables the system administrators to minimize the system vulnerability.
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main contribution is a novel convolution operator based on B-splines, that makes the computation time independent from the kernel size due to the local support property of the B-spline basis functions. As a result, we obtain a generalization of the traditional CNN convolution operator by using continuous kernel functions parametrized by a fixed number of trainable weights. In contrast to related approaches that filter in the spectral domain, the proposed method aggregates features purely in the spatial domain. In addition, SplineCNN allows entire end-to-end training of deep architectures, using only the geometric structure as input, instead of handcrafted feature descriptors. For validation, we apply our method on tasks from the fields of image graph classification, shape correspondence and graph node classification, and show that it outperforms or pars state-of-the-art approaches while being significantly faster and having favorable properties like domain-independence. Our source code is available on GitHub1.