nils-herrmann/clean_scirepeval huggingface dataset:
Improved gait recognition by gait dynamics normalization

Potential sources for gait biometrics can be seen to derive from two aspects: gait shape and gait dynamics. We show that improved gait recognition can be achieved after normalization of dynamics and focusing on the shape information. We normalize for gait dynamics using a generic walking model, as captured by a population hidden Markov model (pHMM) defined for a set of individuals. The states of this pHMM represent gait stances over one gait cycle and the observations are the silhouettes of the corresponding gait stances. For each sequence, we first use Viterbi decoding of the gait dynamics to arrive at one dynamics-normalized, averaged, gait cycle of fixed length. The distance between two sequences is the distance between the two corresponding dynamics-normalized gait cycles, which we quantify by the sum of the distances between the corresponding gait stances. Distances between two silhouettes from the same generic gait stance are computed in the linear discriminant analysis space so as to maximize the discrimination between persons, while minimizing the variations of the same subject under different conditions. The distance computation is constructed so that it is invariant to dilations and erosions of the silhouettes. This helps us handle variations in silhouette shape that can occur with changing imaging conditions. We present results on three different, publicly available, data sets. First, we consider the HumanID gait challenge data set, which is the largest gait benchmarking data set that is available (122 subjects), exercising five different factors, i.e., viewpoint, shoe, surface, carrying condition, and time. We significantly improve the performance across the hard experiments involving surface change and briefcase carrying conditions. Second, we also show improved performance on the UMD gait data set that exercises time variations for 55 subjects. Third, on the CMU Mobo data set, we show results for matching across different walking speeds. It is worth noting that there was no separate training for the UMD and CMU data sets.
Equidistant Fish-Eye Calibration and Rectification by Vanishing Point Extraction

In this paper, we describe a method to photogrammetrically estimate the intrinsic and extrinsic parameters of fish-eye cameras using the properties of equidistance perspective, particularly vanishing point estimation, with the aim of providing a rectified image for scene viewing applications. The estimated intrinsic parameters are the optical center and the fish-eye lensing parameter, and the extrinsic parameters are the rotations about the world axes relative to the checkerboard calibration diagram.
Robot manipulator modeling in Matlab-SimMechanics with PD control and online gravity compensation

The objective of this article is to present a method to model the mechanics of robot manipulators. A complete description of the procedure to model and control a two-link planar robot arm is detailed and simulated using Matlab/Simulink from the generation of a mechanical model in SimMechanics Toolbox. One of the simplest position controllers for robot manipulators is the PD control with desired gravity compensation. The implementation of this method was verified on this model. The results of simulation show that SimMechanics can be used to model the mechanics of robot and the better performance can be obtained with the online gravity compensation method as compared to the case of constant gravity compensation method.
Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011

Implementing instructional interventions to accommodate learner differences has received considerable attention. Among these individual difference variables, the empirical evidence regarding the pedagogical value of learning styles has been questioned, but the research on the issue continues. Recent developments in Web-based implementations have led scholars to reconsider the learning style research in adaptive systems. The current study involved a content analysis of recent studies on adaptive educational hypermedia (AEH) which addressed learning styles. After an extensive search on electronic databases, seventy studies were selected and exposed to a document analysis. Study features were classified under several themes such as the research purposes, methodology, features of adaptive interventions and student modeling, and findings. The analysis revealed that the majority of studies proposed a framework or model for adaptivity whereas few studies addressed the effectiveness of learning style-based AEH. Scales were used for learning style identification more than automatic student modeling. One third of the studies provided a framework without empirical evaluation with students. Findings on concrete learning outcomes were not strong enough; however, several studies revealed that suggested models influenced student satisfaction and success. Current trends, potential research gaps and implications
Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition consists of 12,600 radiological images. Each radiograph in this dataset is an image of a left hand labeled with bone age and sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and prediction. This approach allows us to measure the importance of specific hand bones for automated bone age analysis. We further evaluate the performance of the suggested method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.
Yet another network simulator

We report on the design objectives and initial design of a new discrete-event network simulator for the research community. Creating Yet Another Network Simulator (yans, http://yans.inria.fr/yans) is not the sort of prospect network researchers are happy to contemplate, but this effort may be timely given that ns-2 is considering a major revision and is evaluating new simulator cores. We describe why we did not choose to build on existing tools such as ns-2, GTNetS, and OPNET, outline our functional requirements, provide a high-level view of the architecture and core components, and describe a new IEEE 802.11 model provided with yans.
Self-calibrating multi-sensor fusion with probabilistic measurement validation for seamless sensor switching on a UAV

Fusing data from multiple sensors on-board a mobile platform can significantly augment its state estimation abilities and enable autonomous traversals of different domains by adapting to changing signal availabilities. However, due to the need for accurate calibration and initialization of the sensor ensemble as well as coping with erroneous measurements that are acquired at different rates with various delays, multi-sensor fusion still remains a challenge. In this paper, we introduce a novel multi-sensor fusion approach for agile aerial vehicles that allows for measurement validation and seamless switching between sensors based on statistical signal quality analysis. Moreover, it is capable of self-initialization of its extrinsic sensor states. These initialized states are maintained in the framework such that the system can continuously self-calibrate. We implement this framework on-board a small aerial vehicle and demonstrate the effectiveness of the above capabilities on real data. As an example, we fuse GPS data, ultra-wideband (UWB) range measurements, visual pose estimates, and IMU data. Our experiments demonstrate that our system is able to seamlessly filter and switch between different sensors modalities during run time.
Minimum volume bounding box decomposition for shape approximation in robot grasping

Thinking about intelligent robots involves consideration of how such systems can be enabled to perceive, interpret and act in arbitrary and dynamic environments. While sensor perception and model interpretation focus on the robot's internal representation of the world rather passively, robot grasping capabilities are needed to actively execute tasks, modify scenarios and thereby reach versatile goals. These capabilities should also include the generation of stable grasps to safely handle even objects unknown to the robot. We believe that the key to this ability is not to select a good grasp depending on the identification of an object (e.g. as a cup), but on its shape (e.g. as a composition of shape primitives). In this paper, we envelop given 3D data points into primitive box shapes by a fit-and-split algorithm that is based on an efficient Minimum Volume Bounding Box implementation. Though box shapes are not able to approximate arbitrary data in a precise manner, they give efficient clues for planning grasps on arbitrary objects. We present the algorithm and experiments using the 3D grasping simulator Grasplt!.
A Comprehensive UAV Indoor Navigation System Based on Vision Optical Flow and Laser FastSLAM

This paper presents a comprehensive control, navigation, localization and mapping solution for an indoor quadrotor unmanned aerial vehicle (UAV) system. Three main sensors are used onboard the quadrotor platform, namely an inertial measurement unit, a downward-looking camera and a scanning laser range finder. With this setup, the UAV is able to estimate its own velocity and position robustly, while flying along the internal walls of a room without collisions. After one complete flight, with the collected data the historic UAV path and the indoor environment can be well estimated. The autonomous navigation part of the system does not require any remote sensory information or off-line computational power, while the mapping is done off-line. Complete flight tests have been carried out to verify fidelity and performance the navigation solution.
An efficient stream cipher using Genetic Algorithm

Cryptography is essential to protect and secure data using a key. Different types of cryptographic techniques are found for data security. Genetic Algorithm is essentially used for obtaining optimal solution. Also, it can be efficiently used for random number generation which are very important in cryptography. This paper discusses the application of genetic algorithms for stream ciphers. Key generation is the most important factor in stream ciphers. In this paper Genetic Algorithm is used in the key generation process where key selection depends upon the fitness function. Here genetic algorithm is repeated for key selection. In each iteration, the key having highest fitness value is selected which further be compared with the threshold value. Selected key was unique and non-repeating. Therefore encryption with selected key are highly encrypted because of more randomness of key. This paper shows that the generated keys using GA are unique and more secure for encryption of data.
A Modified FCM Algorithm for MRI Brain Image Segmentation

Image segmentation is often required as a preliminary and indispensable stage in the computer aided medical image process, particularly during the clinical analysis of magnetic resonance (MR) brain image. Fuzzy c-means (FCM) clustering algorithm has been widely used in many medical image segmentations. However, the conventionally standard FCM algorithm is sensitive to noise because of not taking into account the spatial information. To overcome the above problem, a modified FCM algorithm (called mFCM later) for MRI brain image segmentation is presented in this paper. The algorithm is realized by incorporating the spatial neighborhood information into the standard FCM algorithm and modifying the membership weighting of each cluster. The proposed algorithm is applied to both artificial synthesized image and real image. Segmentation results not only on synthesized image but also MRI brain image which degraded by Gaussian noise and salt-pepper noise demonstrates that the presented algorithm performs more robust to noise than the standard FCM algorithm.
Grip Force Vectors for Varying Handle Diameters and Hand Sizes

Grip force was measured along two orthogonal axes and vector summed. Sixty-one participants recruited from a manufacturing facility (29 men and 32 women) grasped instrumented cylinders (2.54, 3.81, 5.08, 6.35, and 7.62 cm diameter) using a maximal voluntary power grip. Two orthogonal force measurements relative to the third metacarpal were resolved into a magnitude and corresponding angle. On average, magnitude increased 34.8 N as handle diameter increased from 2.54 cm to 3.81 cm, and then monotonically declined 103.8 N as the handle diameter increased to 7.62 cm. The average direction monotonically decreased from 59.2 degrees to 37.7 degrees as handle diameter decreased from the largest to the smallest. When the diameter was smallest, the greatest force component, Fx (168.6 N), was in the direction where the fingertips opposed the palm. Conversely, when the diameter was largest, the smallest component, Fx (77.7 N), was in the same direction. These values are averaged for the left and right hand. The angle for the largest diameter increased with increasing hand size. These relationships should be useful for the design of handles that require gripping in specific directions, such as for hand tools and controls. Actual or potential applications of this research include the design of handles that require gripping in specific directions, such as for hand tools and controls, that reduce effort, and that prevent fatigue and overexertion.
Discovering discrete subword units with binarized autoencoders and hidden-Markov-model encoders

In this paper we address the problem of unsupervised learning of discrete subword units. Our approach is based on Deep Autoencoders (AEs), whose encoding node values are thresholded to subsequently generate a symbolic, i.e., 1-of-K (with K = No. of subwords), representation of each speech frame. We experiment with two variants of the standard AE which we have named Binarized Autoencoder and Hidden-Markov-Model Encoder. The first forces the binary encoding nodes to have a Ushaped distribution (with peaks at 0 and 1) while minimizing the reconstruction error. The latter jointly learns the symbolic encoding representation (i.e., subwords) and the prior and transition distribution probabilities of the learned subwords. The ABX evaluation of the Zero Resource Challenge Track 1 shows that a deep AE with only 6 encoding nodes, which assigns to each frame a 1-of-K binary vector with K = 2, can outperform real-valued MFCC representations in the acrossspeaker setting. Binarized AEs can outperform standard AEs when using a larger number of encoding nodes, while HMM Encoders may allow more compact subword transcriptions without worsening the ABX performance.
Recent Progress on Programming Methods for Industrial Robots

Although an automated flexible production cell is an intriguing prospect for small to median enterprises (SMEs) in current global market conditions, the complexity of programming remains one of the major hurdles preventing automation using industrial robots for SMEs. This paper provides a comprehensive review of the recent research progresses on the programming methods for industrial robots, including online programming, offline programming (OLP), and programming using Augmented Reality (AR). With the development of more powerful 3D CAD/PLM software, computer vision, sensor technology, etc. new programming methods suitable for SMEs are expected to grow in years to come.
Dual mode bandpass filter using trefoil torus knot resonator

In this paper we present compact dual mode band pass filter based on trefoil torus knot resonator. The resonator is realized using additive manufacturing technology. Size of the resonator is 0.16 λ at the center frequency of operation. Fabricated resonator prototype is about half the size of ring resonator. Simulated and measured results are presented which are in good agreement.
Efficient VLSI Implementation of Neural Networks With Hyperbolic Tangent Activation Function

Nonlinear activation function is one of the main building blocks of artificial neural networks. Hyperbolic tangent and sigmoid are the most used nonlinear activation functions. Accurate implementation of these transfer functions in digital networks faces certain challenges. In this paper, an efficient approximation scheme for hyperbolic tangent function is proposed. The approximation is based on a mathematical analysis considering the maximum allowable error as design parameter. Hardware implementation of the proposed approximation scheme is presented, which shows that the proposed structure compares favorably with previous architectures in terms of area and delay. The proposed structure requires less output bits for the same maximum allowable error when compared to the state-of-the-art. The number of output bits of the activation function determines the bit width of multipliers and adders in the network. Therefore, the proposed activation function results in reduction in area, delay, and power in VLSI implementation of artificial neural networks with hyperbolic tangent activation function.
Measuring Meaningful Work : The Work and Meaning Inventory ( WAMI )

Many people desire work that is meaningful. However, research in this area has attracted diverse ideas about meaningful work (MW), accompanied by an equally disparate collection of ways of assessing MW. To further advance study in this area, the authors propose a multidimensional model of work as a subjectively meaningful experience consisting of experiencing positive meaning in work, sensing that work is a key avenue for making meaning, and perceiving one’s work to benefit some greater good. The development of a scale to measure these dimensions is described, an initial appraisal of the reliability and construct validity of the instrument’s scores is reported using a sample of university employees (N 1⁄4 370) representing diverse occupations. MW scores correlated in predicted ways with work-related and general well-being indices, and accounted for unique variance beyond common predictors of job satisfaction, days reported absent from work, and life satisfaction. The authors discuss ways in which this conceptual model provides advantages to scholars, counselors, and organizations interested in fostering MW.
How to Recommend?: User Trust Factors in Movie Recommender Systems

How much trust a user places in a recommender is crucial to the uptake of the recommendations. Although prior work established various factors that build and sustain user trust, their comparative impact has not been studied in depth. This paper presents the results of a crowdsourced study examining the impact of various recommendation interfaces and content selection strategies on user trust. It evaluates the subjective ranking of nine key factors of trust grouped into three dimensions and examines the differences observed with respect to users' personality traits.
The Social Connectedness of Older Adults: A National Profile*

For decades, scholars have wrestled with the notion that old age is characterized by social isolation. However, there has been no systematic, nationally representative evaluation of this possibility in terms of social network connectedness. In this paper, the authors develop a profile of older adults' social integration with respect to nine dimensions of connectedness to interpersonal networks and voluntary associations. The authors use new data from the National Social Life, Health, and Aging Project (NSHAP), a population-based study of non-institutionalized older Americans aged 57-85 conducted in 2005-2006. Findings suggest that among older adults, age is negatively related to network size, closeness to network members, and number of non-primary-group ties. On the other hand, age is positively related to frequency of socializing with neighbors, religious participation, and volunteering. In addition, it has a U-shaped relationship with volume of contact with network members. These findings are inconsistent with the notion that old age has a universal negative influence on social connectedness. Instead, life course factors have divergent consequences for different forms of social connectedness. Some later life transitions, like retirement and bereavement, may prompt greater connectedness. The authors close by urging increased dialogue between social gerontological and social network research.
The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones

The use of smartphones for human activity recognition has become popular due to the wide adoption of smartphones and their rich sensing features. This article introduces a benchmark dataset, the MobiAct dataset, for smartphone-based human activity recognition. It comprises data recorded from the accelerometer, gyroscope and orientation sensors of a smartphone for fifty subjects performing nine different types of Activities of Daily Living (ADLs) and fifty-four subjects simulating four different types of falls. This dataset is used to elaborate an optimized feature selection and classification scheme for the recognition of ADLs, using the accelerometer recordings. Special emphasis was placed on the selection of the most effective features from feature sets already validated in previously published studies. An important qualitative part of this investigation is the implementation of a comparative study for evaluating the proposed optimal feature set using both the MobiAct dataset and another popular dataset in the domain. The results obtained show a higher classification accuracy than previous reported studies, which exceeds 99% for the involved ADLs.
Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention

Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing. For such a complex text processing task, different from previous studies, we argue that it is necessary to repeatedly read the arguments and dynamically exploit the efficient features useful for recognizing discourse relations. To mimic the repeated reading strategy, we propose the neural networks with multi-level attention (NNMA), combining the attention mechanism and external memories to gradually fix the attention on some specific words helpful to judging the discourse relations. Experiments on the PDTB dataset show that our proposed method achieves the state-ofart results. The visualization of the attention weights also illustrates the progress that our model observes the arguments on each level and progressively locates the important words.
A Comparison Between Five Models Of Software Engineering

This research deals with a vital and important issue in computer world. It is concerned with the software management processes that examine the area of software development through the development models, which are known as software development life cycle. It represents five of the development models namely, waterfall, Iteration, V-shaped, spiral and Extreme programming. These models have advantages and disadvantages as well. Therefore, the main objective of this research is to represent different models of software development and make a comparison between them to show the features and defects of each model.