nils-herrmann/clean_scirepeval huggingface dataset:
Accurate realtime full-body motion capture using a single depth camera

We present a fast, automatic method for accurately capturing full-body motion data using a single depth camera. At the core of our system lies a realtime registration process that accurately reconstructs 3D human poses from single monocular depth images, even in the case of significant occlusions. The idea is to formulate the registration problem in a Maximum A Posteriori (MAP) framework and iteratively register a 3D articulated human body model with monocular depth cues via linear system solvers. We integrate depth data, silhouette information, full-body geometry, temporal pose priors, and occlusion reasoning into a unified MAP estimation framework. Our 3D tracking process, however, requires manual initialization and recovery from failures. We address this challenge by combining 3D tracking with 3D pose detection. This combination not only automates the whole process but also significantly improves the robustness and accuracy of the system. Our whole algorithm is highly parallel and is therefore easily implemented on a GPU. We demonstrate the power of our approach by capturing a wide range of human movements in real time and achieve state-of-the-art accuracy in our comparison against alternative systems such as Kinect [2012].
Media, society, world: Social theory and digital media practice

Reading is a hobby to open the knowledge windows. Besides, it can provide the inspiration and spirit to face this life. By this way, concomitant with the technology development, many companies serve the e-book or book in soft file. The system of this book of course will be much easier. No worry to forget bringing the media society world social theory and digital media practice book. You can open the device and get the book by on-line.
Cross-modality correlation propagation for cross-media retrieval

We consider the problem of cross-media retrieval, where the query and the retrieved results can be of different modalities. In this paper, we propose a novel cross-modality correlation propagation approach to simultaneously deal with positive correlation and negative correlation between media objects of different modalities, while existing works focus solely on the positive correlation. Negative correlation is very important because it provides the effective exclusive information. The correlation is modeled as must-link constraints and cannot-link constraints respectively. Furthermore, our approach is able to propagate the correlation between heterogeneous modalities. Experiments on the wikipedia dataset show the effectiveness of our cross-modality correlation propagation approach, compared with state-of-the-art methods.
Movement control of two wheels balancing robot using cascaded PID controller

Balancing robot which is proposed in this paper is a robot that relies on two wheels in the process of movement. Unlike the other mobile robot which is mechanically stable in its standing position, balancing robot need a balancing control which requires an angle value to be used as tilt feedback. The balancing control will control the robot, so it can maintain its standing position. Beside the balancing control itself, the movement of balancing robot needs its own control in order to control the movement while keeping the robot balanced. Both controllers will be combined since will both of them control the same wheel as the actuator. In this paper we proposed a cascaded PID control algorithm to combine the balancing and movement or distance controller. The movement of the robot is controlled using a distance controller that use rotary encoder sensor to measure its traveled distance. The experiment shows that the robot is able to climb up on 30 degree sloping board. By cascading the distance control to the balancing control, the robot is able to move forward, turning, and reach the desired position by calculating the body's tilt angle.
A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression.

Depression is a common mental health condition for which many mobile apps aim to provide support. This review aims to identify self-help apps available exclusively for people with depression and evaluate those that offer cognitive behavioural therapy (CBT) or behavioural activation (BA). One hundred and seventeen apps have been identified after searching both the scientific literature and the commercial market. 10.26% (n = 12) of these apps identified through our search offer support that seems to be consistent with evidence-based principles of CBT or BA. Taking into account the non existence of effectiveness/efficacy studies, and the low level of adherence to the core ingredients of the CBT/BA models, the utility of these CBT/BA apps are questionable. The usability of reviewed apps is highly variable and they rarely are accompanied by explicit privacy or safety policies. Despite the growing public demand, there is a concerning lack of appropiate CBT or BA apps, especially from a clinical and legal point of view. The application of superior scientific, technological, and legal knowledge is needed to improve the development, testing, and accessibility of apps for people with depression.
Substructure counting graph kernels for machine learning from RDF data

In this paper we introduce a framework for learning from RDF data using graph kernels that count substructures in RDF graphs, which systematically covers most of the existing kernels previously defined and provides a number of new variants. Our definitions include fast kernel variants that are computed directly on the RDF graph. To improve the performance of these kernels we detail two strategies. The first strategy involves ignoring the vertex labels that have a low frequency among the instances. Our second strategy is to remove hubs to simplify the RDF graphs. We test our kernels in a number of classification experiments with real-world RDF datasets. Overall the kernels that count subtrees show the best performance. However, they are closely followed by simple bag of labels baseline kernels. The direct kernels substantially decrease computation time, while keeping performance the same. For the walks counting kernel the decrease in computation time of the approximation is so large that it thereby becomes a computationally viable kernel to use. Ignoring low frequency labels improves the performance for all datasets. The hub removal algorithm increases performance on two out of three of our smaller datasets, but has little impact when used on our larger datasets.
A BRIEF STUDY AND COMPARISON OF , OPEN SOURCE INTRUSION DETECTION SYSTEM TOOLS

As the world becomes more connected to the cyber world, attackers and hackers are becoming increasingly sophisticated to penetrate computer systems and networks. Intrusion Detection System (IDS) plays a vital role in defending a network against intrusion. Many commercial IDSs are available in marketplace but with high cost. At the same time open source IDSs are also available with continuous support and upgradation from large user community. Each of these IDSs adopts a different approaches thus may target different applications. This paper provides a quick review of six Open Source IDS tools so that one can choose the appropriate Open Source IDS tool as per their organization requirements.
Blind separation of sources: A nonlinear neural algorithm

In many signal processing applications, the signals provided by the sensors are mixtures of many sources. The problem of separation of sources is to extract the original signals from these mixtures. A new algorithm, based on ideas of backpropagation learning, is proposed for source separation. No a priori information on the sources themselves is required, and the algorithm can deal even with non-linear mixtures. After a short overview of previous works in that eld, we will describe the proposed algorithm. Then, some experimental results will be discussed.
Hough transform and thermo-vision for monitoring pantograph – catenary system

The pantograph-overhead contact wire system is investigated by using an infrared camera. As the pantograph has a vertical motion because of the non-uniform elasticity of the catenary, in order to detect the temperature along the strip from a sequence of infrared images, a segment-tracking algorithm, based on the Hough transformation, has been employed. An analysis of the stored images could help maintenance operations revealing, for example, overheating of the pantograph strip, bursts of arcing, or an irregular positioning of the contact line. Obtained results are relevant for monitoring the status of the quality transmission of the current and for a predictive maintenance of the pantograph and of the catenary system. Examples of analysis from experimental data are reported in the paper.
The Cray-1 Computer System

This paper describes the CRAY-1, discusses the evolution of its architecture, and gives an account of some of the problems that were overcome during its manufacture.
The CRAY-1 is the only computer to have been built to date that satisfies ERDA's Class VI requirement (a computer capable of processing from 20 to 60 million floating point operations per second) .
The CRAY-1's Fortran compiler (CFT) is designed to give the scientific user immediate access to the benefits of the CRAY-1's vector processing architecture. An optimizing compiler, CFT, “vectorizes” innermost DO loops. Compatible with the ANSI 1966 Fortran Standard and with many commonly supported Fortran extensions, CFT does not require any source program modifications or the use of additional nonstandard Fortran statements to achieve vectorization. Thus the user's investment of hundreds of man months of effort to develop Fortran programs for other contemporary computers is protected.
Experimental evaluation of model predictive control of ball and beam systems

In this paper, we discuss the control of a ball and beam system subject to an input constraint. Model predictive control (MPC) approaches are employed to derive a nonlinear control law satisfying the constraint. The control law is given by solving the optimization problem at each sample time, where the primal-dual interior point algorithm is implemented and used as the optimization solver. An experimental comparison of three control methods, two different MPCs and saturated LQR, has been presented for the control of the ball and beam system.
Mapping and localization from planar markers

Squared planar markers are a popular tool for fast, accurate and robust camera localization, but its use is frequently limited to a single marker, or at most, to a small set of them for which their relative pose is known beforehand. Mapping and localization from a large set of planar markers is yet a scarcely treated problem in favour of keypoint-based approaches. However, while keypoint detectors are not robust to rapid motion, large changes in viewpoint, or significant changes in appearance, fiducial markers can be robustly detected under a wider range of conditions. This paper proposes a novel method to simultaneously solve the problems of mapping and localization from a set of squared planar markers. First, a quiver of pairwise relative marker poses is created, from which an initial pose graph is obtained. The pose graph may contain small pairwise pose errors, that when propagated, leads to large errors. Thus, we distribute the rotational and translational error along the basis cycles of the graph so as to obtain a corrected pose graph. Finally, we perform a global pose optimization by minimizing the reprojection errors of the planar markers in all observed frames. The experiments conducted show that our method performs better than Structure from Motion and visual SLAM techniques.
Transient potential distribution on stress grading system of rotating machines under repetitive impulse voltages

For advanced designing of end-winding insulation system of rotating machines, the experimental information on the potential distribution on such stress grading system under the application of PWM voltage is essential. In this paper, the potential distributions on the stress grading system of a model coil under various shape of voltages such as 50Hz sinusoidal ac voltage, bipolar and unipolar 1kHz-square pulse voltages, repetitive impulses of 250ns in their rise-time, were measured by a Pockels sensor which can respond up to several GHz. The surface temperature on the model coil was also observed by an infrared camera. When the repetitive impulses were applied to the model coil, the potential gradient became high on the corona armor tape (CAT) closer to the stator core during the wave-front of the impulse, and on the stress grading tape (SGT) during the wave-tail of the impulse. This phenomenon can be explained by a simplified RC-ladder equivalent circuit, which has low pass filtering characteristics of around 1 MHz in cut off frequency. In addition, the measured temperature distribution on model coil was compared with the computed distribution of the power dissipation. The influence of dc component in applied voltage on the potential and temperature distributions was experimentally confirmed to be little.
Algorithms and system for segmentation and structure analysis in soccer video

In this paper, we present a novel system and effective algorithms for soccer video segmentation. The output, about whether the ball is in play, reveals high-level structure of the content. The first step is to classify each sample frame into 3 kinds of view using a unique domain-specific feature, grass-area-ratio. Here the grass value and classification rules are learned and automatically adjusted to each new clip. Then heuristic rules are used in processing the view label sequence, and obtain play/break status of the game. The results provide good basis for detailed content analysis in next step. We also show that lowlevel features and mid-level view classes can be combined to extract more information about the game, via the example of detecting grass orientation in the field. The results are evaluated under different metrics intended for different applications; the best result in segmentation is 86.5%.
Real-time deformable models for surgery simulation: a survey

Simulating the behaviour of elastic objects in real time is one of the current objectives of computer graphics. One of its fields of application lies in virtual reality, mainly in surgery simulation systems. In computer graphics, the models used for the construction of objects with deformable behaviour are known as deformable models. These have two conflicting characteristics: interactivity and motion realism. The different deformable models developed to date have promoted only one of these (usually interactivity) to the detriment of the other (biomechanical realism). In this paper, we present a classification of the different deformable models that have been developed. We present the advantages and disadvantages of each one. Finally, we make a comparison of deformable models and perform an evaluation of the state of the art and the future of deformable models.
Dopamine in drug abuse and addiction: results from imaging studies and treatment implications

The involvement of dopamine in drug reinforcement is well recognized but its role in drug addiction is much less clear. Imaging studies have shown that the reinforcing effects of drugs of abuse in humans are contingent upon large and fast increases in dopamine that mimic but exceed in the intensity and duration those induced by dopamine cell firing to environmental events. In addition, imaging studies have also documented a role of dopamine in motivation, which appears to be encoded both by fast as well as smooth DA increases. Since dopamine cells fire in response to salient stimuli, the supraphysiological activation by drugs is likely to be experienced as highly salient (driving attention, arousal conditioned learning and motivation) and may also reset the thresholds required for environmental events to activate dopamine cells. Indeed, imaging studies have shown that in drug-addicted subjects, dopamine function is markedly disrupted (decreases in dopamine release and in dopamine D2 receptors in striatum) and this is associated with reduced activity of the orbitofrontal cortex (neuroanatomical region involved with salience attribution and motivation and implicated in compulsive behaviors) and the cingulate gyrus (neuroanatomical region involved with inhibitory control and attention and implicated in impulsivity). However, when addicted subjects are exposed to drug-related stimuli, these hypoactive regions become hyperactive in proportion to the expressed desire for the drug. We postulate that decreased dopamine function in addicted subjects results in decreased sensitivity to nondrug-related stimuli (including natural reinforcers) and disrupts frontal inhibition, both of which contribute to compulsive drug intake and impaired inhibitory control. These findings suggest new strategies for pharmacological and behavioral treatments, which focus on enhancing DA function and restoring brain circuits disrupted by chronic drug use to help motivate the addicted subject in activities that provide alternative sources of reinforcement, counteract conditioned responses, enhance their ability to control their drive to take drugs and interfere with their compulsive administration.
Active contours with selective local or global segmentation: A new formulation and level set method

A novel region-based active contour model (ACM) is proposed in this paper. It is implemented with a special processing named Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method, which first selectively penalizes the level set function to be binary, and then uses a Gaussian smoothing kernel to regularize it. The advantages of our method are as follows. First, a new region-based signed pressure force (SPF) function is proposed, which can efficiently stop the contours at weak or blurred edges. Second, the exterior and interior boundaries can be automatically detected with the initial contour being anywhere in the image. Third, the proposed ACM with SBGFRLS has the property of selective local or global segmentation. It can segment not only the desired object but also the other objects. Fourth, the level set function can be easily initialized with a binary function, which is more efficient to construct than the widely used signed distance function (SDF). The computational cost for traditional re-initialization can also be reduced. Finally, the proposed algorithm can be efficiently implemented by the simple finite difference scheme. Experiments on synthetic and real images demonstrate the advantages of the proposed method over geodesic active contours (GAC) and Chan–Vese (C–V) active contours in terms of both efficiency and accuracy.
The Power of Personality: The Comparative Validity of Personality Traits, Socioeconomic Status, and Cognitive Ability for Predicting Important Life Outcomes.

The ability of personality traits to predict important life outcomes has traditionally been questioned because of the putative small effects of personality. In this article, we compare the predictive validity of personality traits with that of socioeconomic status (SES) and cognitive ability to test the relative contribution of personality traits to predictions of three critical outcomes: mortality, divorce, and occupational attainment. Only evidence from prospective longitudinal studies was considered. In addition, an attempt was made to limit the review to studies that controlled for important background factors. Results showed that the magnitude of the effects of personality traits on mortality, divorce, and occupational attainment was indistinguishable from the effects of SES and cognitive ability on these outcomes. These results demonstrate the influence of personality traits on important life outcomes, highlight the need to more routinely incorporate measures of personality into quality of life surveys, and encourage further research about the developmental origins of personality traits and the processes by which these traits influence diverse life outcomes.
Hybrid prediction model for Type-2 diabetic patients

A wide range of computational methods and tools for data analysis are available. In this study we took advantage of those available technological advancements to develop prediction models for the prediction of a Type-2 Diabetic Patient. We aim to investigate how the diabetes incidents are affected by patients’ characteristics and measurements. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes Hybrid Prediction Model (HPM) which uses Simple K-means clustering algorithm aimed at validating chosen class label of given data (incorrectly classified instances are removed, i.e. pattern extracted from original data) and subsequently applying the classification algorithm to the result set. C4.5 algorithm is used to build the final classifier model by using the k-fold cross-validation method. The Pima Indians diabetes data was obtained from the University of California at Irvine (UCI) machine learning repository datasets. A wide range of different classification methods have been applied previously by various researchers in order to find the best performing algorithm on this dataset. The accuracies achieved have been in the range of 59.4–84.05%. However the proposed HPM obtained a classification accuracy of 92.38%. In order to evaluate the performance of the proposed method, sensitivity and specificity performance measures that are used commonly in medical classification studies were used.
Sick but yet at work. An empirical study of sickness presenteeism.

The study is an empirical investigation of sickness presenteeism in relation to occupation, irreplaceability, ill health, sickness absenteeism, personal income, and slimmed down organisation.



Cross sectional design.



Swedish workforce.



The study group comprised a stratified subsample of 3801 employed persons working at the time of the survey, interviewed by telephone in conjunction with Statistics Sweden's labour market surveys of August and September 1997. The response rate was 87 per cent.



A third of the persons in the total material reported that they had gone to work two or more times during the preceding year despite the feeling that, in the light of their perceived state of health, they should have taken sick leave. The highest presenteeism is largely to be found in the care and welfare and education sectors (nursing and midwifery professionals, registered nurses, nursing home aides, compulsory school teachers and preschool/primary educationalists. All these groups work in sectors that have faced personnel cutbacks during the 1990s). The risk ratio (odds ratio (OR)) for sickness presenteeism in the group that has to re-do work remaining after a period of absence through sickness is 2.29 (95% CI 1.79, 2.93). High proportions of persons with upper back/neck pain and fatigue/slightly depressed are among those with high presenteeism (p< 0.001). Occupational groups with high sickness presenteeism show high sickness absenteeism (rho = 0. 38; p<.01) and the hypothesis on level of pay and sickness presenteeism is also supported (rho = -0.22; p<0.01).



Members of occupational groups whose everyday tasks are to provide care or welfare services, or teach or instruct, have a substantially increased risk of being at work when sick. The link between difficulties in replacement or finding a stand in and sickness presenteeism is confirmed by study results. The categories with high sickness presenteeism experience symptoms more often than those without presenteeism. The most common combination is low monthly income, high sickness absenteeism and high sickness presenteeism.