nils-herrmann/clean_scirepeval huggingface dataset:
Modeling Multibody Dynamic Systems With Uncertainties . Part I : Theoretical and Computational Aspects

This study explores the use of generalized polynomial chaos theory for modeling complex nonlinear multibody dynamic systems in the presence of parametric and external uncertainty. The polynomial chaos framework has been chosen because it offers an efficient computational approach for the large, nonlinear multibody models of engineering systems of interest, where the number of uncertain parameters is relatively small, while the magnitude of uncertainties can be very large (e.g., vehicle-soil interaction). The proposed methodology allows the quantification of uncertainty distributions in both time and frequency domains, and enables the simulations of multibody systems to produce results with “error bars”. The first part of this study presents the theoretical and computational aspects of the polynomial chaos methodology. Both unconstrained and constrained formulations of multibody dynamics are considered. Direct stochastic collocation is proposed as less expensive alternative to the traditional Galerkin approach. It is established that stochastic collocation is equivalent to a stochastic response surface approach. We show that multi-dimensional basis functions are constructed as tensor products of one-dimensional basis functions and discuss the treatment of polynomial and trigonometric nonlinearities. Parametric uncertainties are modeled by finite-support probability densities. Stochastic forcings are discretized using truncated Karhunen-Loeve expansions. The companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part II: Numerical Applications” illustrates the use of the proposed methodology on a selected set of test problems. The overall conclusion is that despite its limitations, polynomial chaos is a powerful approach for the simulation of multibody systems with uncertainties.
Effectiveness of a hospital-wide programme to improve compliance with hand hygiene

Hand hygiene prevents cross infection in hospitals, but compliance with recommended instructions is commonly poor. We attempted to promote hand hygiene by implementing a hospital-wide programme, with special emphasis on bedside, alcohol-based hand disinfection. We measured nosocomial infections in parallel.



We monitored the overall compliance with hand hygiene during routine patient care in a teaching hospital in Geneva, Switzerland, before and during implementation of a hand-hygiene campaign. Seven hospital-wide observational surveys were done twice yearly from December, 1994, to December, 1997. Secondary outcome measures were nosocomial infection rates, attack rates of methicillin-resistant Staphylococcus aureus (MRSA), and consumption of handrub disinfectant.



We observed more than 20,000 opportunities for hand hygiene. Compliance improved progressively from 48% in 1994, to 66% in 1997 (p<0.001). Although recourse to handwashing with soap and water remained stable, frequency of hand disinfection substantially increased during the study period (p<0.001). This result was unchanged after adjustment for known risk factors of poor adherence. Hand hygiene improved significantly among nurses and nursing assistants, but remained poor among doctors. During the same period, overall nosocomial infection decreased (prevalence of 16.9% in 1994 to 9.9% in 1998; p=0.04), MRSA transmission rates decreased (2.16 to 0.93 episodes per 10,000 patient-days; p<0.001), and the consumption of alcohol-based handrub solution increased from 3.5 to 15.4 L per 1000 patient-days between 1993 and 1998 (p<0.001).



The campaign produced a sustained improvement in compliance with hand hygiene, coinciding with a reduction of nosocomial infections and MRSA transmission. The promotion of bedside, antiseptic handrubs largely contributed to the increase in compliance.
Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging.

The relationship between brain structure and complex behavior is governed by large-scale neurocognitive networks. The availability of a noninvasive technique that can visualize the neuronal projections connecting the functional centers should therefore provide new keys to the understanding of brain function. By using high-resolution three-dimensional diffusion magnetic resonance imaging and a newly designed tracking approach, we show that neuronal pathways in the rat brain can be probed in situ. The results are validated through comparison with known anatomical locations of such fibers.
Continuous Auctions and Insider Trading

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Virtual Reality as an Innovative Setting for Simulations in Education

The increasingly widespread use of simulations in education underlines the fact that these teaching tools compared to other methods or media allow students to approach and experience new topics in a more realistic way. This realism enhances their learning and understanding of complex subjects. So far it has been difficult to interactively simulate three-dimensional dynamic learning content. In this field, the continuing development of Virtual Reality (VR) offers new opportunities for educators to convey a wide variety of subjects. It is the aim of this paper to characterize the nature of Virtual Reality as an educational setting for simulations, and to show that Studierstube, our multi-user collaborative Virtual Environment, comprises the necessary features for applying VR-techniques to educational purposes. We further discuss the general applicability of VR to various fields of education and demonstrate its specific application as a tool for teaching elementary three-dimensional geometry.
Emotion-sensitive Human-Computer Interaction ( HCI ) : State of the art-Seminar paper ∗

Emotion-sensitive Human-Computer Interaction (HCI) is a hot topic. HCI is the study, planning and design of the interaction between users an computer systems. Indeed today a lot of research is moving towards this direction. To attract users, more and more developers add the emotional side to their applications. To simplify the HCI, systems must be more natural, efficacious, persuasive and trustworthy. And to do that, system must be able to sense and response approriately to the user’s emotional states. This paper presents a short overview of the existing emotion-sensitive HCI applications. This paper focused on what is done today and brings out what are the most important features to take into account for emotion-sensitive HCI. The most important finding is that applications can help people to feel better with technologies used in the daily life.
RGB-D Camera-based Daily Living Activity Recognition

—In this paper, we propose a new activity analysis framework to facilitate the independence of older adults living in the community, reduce risks, and enhance the quality of life at home by recognizing activities of daily living (ADLs) by using RGB-D cameras. Comparing to the traditional RGB cameras, the depth information implicitly has advantages of handling illumination changes and protecting privacy. Our contributions include three aspects. First, to detect abnormal activities which are dangerous for elderly people, we recognize 5 activities related to fall including standing, fall from standing, fall from sitting, sit on chair, and sit on floor. Second, to recognize finer activities of daily living, we propose a discriminative representation of structure-motion features based on skeleton joints. Third, to continually track same person when there are multiple people appear in the same camera view, we further develop a binary classification based person identification method by combining appearance and depth information. The proposed framework is evaluated on a dataset we collected under different lighting conditions for fall detection and a benchmark dataset for daily living activity recognition. Experiment results demonstrate the effectiveness of the proposed framework and outperform the state-of-the-art method.
Emumaker86: a hardware simulator for teaching CPU design

This paper describes a new GUI-based tool to teach undergraduate students to design their own CPUs. The tool was purpose-built to teach a wide range of computer organization topics, including digital logic, instruction set encoding, datapath and control units, and pipelining. A datapath builder allows microarchitecture building blocks, such as registers, ALUs, and multiplexors, to be laid out, wired together, and simulated. A control builder allows students to develop control states and microinstructions for the datapath. Both units are integrated with a full PC emulator, allowing student-designed processors to use emulated devices, such as drives, video, and I/O ports. A tutorial teaches students to use the simulator to build a pipelined RISC processor.
Evaluation of the Minstrel rate adaptation algorithm in IEEE 802.11g WLANs

Rate adaptation varies the transmission rate of a wireless sender to match the wireless channel conditions, in order to achieve the best possible performance. It is a key component of IEEE 802.11 wireless networks. Minstrel is a popular rate adaptation algorithm due to its efficiency and availability in commonly used wireless drivers. However, despite its popularity, little work has been done on evaluating the performance of Minstrel or comparing it to the performance of fixed rates. In this paper, we conduct an experimental study that compares the performance of Minstrel against fixed rates in an IEEE 802.11g testbed. The experiment results show that whilst Minstrel performs reasonably well in static wireless channel conditions, in some cases the algorithm has difficulty selecting the optimal data rate in the presence of dynamic channel conditions. In addition, Minstrel performs well when the channel condition improves from bad quality to good quality. However, Minstrel has trouble selecting the optimal rate when the channel condition deteriorates from good quality to bad quality.
Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only

With the increasing availability of large databases of 3D CAD models, methods for depth-based recognition of localized objects can be trained on an uncountable number of synthetically rendered images. However, discrepancies with the real data acquired from various depth sensors still noticeably impede progress. Previous works adopted unsupervised approaches to generate more realistic depth data, but they all require real scans for training, even if unlabeled. This still represents a strong requirement, especially when considering real-life/industrial settings where real training images are hard or impossible to acquire, but texture-less 3D models are available. We thus propose a novel approach leveraging only CAD models to bridge the realism gap. Purely trained on synthetic data, playing against an extensive augmentation pipeline in an unsupervised manner, our generative adversarial network learns to effectively segment depth images and recover the clean synthetic-looking depth information even from partial occlusions. As our solution is not only fully decoupled from the real domains but also from the task-specific analytics, the pre-processed scans can be handed to any kind and number of recognition methods also trained on synthetic data. Through various experiments, we demonstrate how this simplifies their training and consistently enhances their performance, with results on par with the same methods trained on real data, and better than usual approaches doing the reverse mapping.
Data-driven enterprise architecture and the TOGAF ADM phases

This paper investigates how Data as a disruptive technology could be integrated into TOGAF. Given the recent attention of Big Data and Data Science as disruptors, this paper investigates what the impact on the enterprise could be and how Enterprise Architecture (EA) should accommodate data to enable data-driven EA. There is no model currently available that investigates how Big Data can be incorporated into data-driven EA solutions. This study specifically focuses on how the TOGAF ADM could support a data-driven enterprise. Through document analysis and a systematic literature review, a specific adaption of the TOGAF ADM is proposed that indicates the influence that Data and Big Data has on each phase within the ADM.
PC-01: Introduction to computational thinking: Educational technology in primary and secondary education

This article describes the design and implementation of the course Introduction to Computational Thinking (PC-01) for primary and secondary education. The course introduces the “concepts” and core “processes” of Computational Thinking using a visual programming environment. The design of the course PC-01 includes a set of multimedia content, learning tools and technologies that help teachers and students in the teaching-learning process. These tools allow the teacher the successful delivery of the course content as well as the individual/collective monitoring of students' progress in the course. Students have access to their progress throughout the course and participate in creative and collaborative tasks. This course also serves to introduce transversely educational technologies to many students of primary and secondary education. The technological environment integrates the online teaching resources and the methodological tools of the course. The course uses Scratch 2.0 as the programming environment and Moodle as the learning platform. At present the course is being implemented in public schools in the Dominican Republic.
Deep Neural Network Capacity

In recent years, deep neural network exhibits its powerful superiority on information discrimination in many computer vision applications. However, the capacity of deep neural network architecture is still a mystery to the researchers. Intuitively, larger capacity of neural network can always deposit more information to improve the discrimination ability of the model. But, the learnable parameter scale is not feasible to estimate the capacity of deep neural network. Due to the overfitting, directly increasing hidden nodes number and hidden layer number are already demonstrated not necessary to effectively increase the network discrimination ability. In this paper, we propose a novel measurement, named “total valid bits”, to evaluate the capacity of deep neural networks for exploring how to quantitatively understand the deep learning and the insights behind its super performance. Specifically, our scheme to retrieve the total valid bits incorporates the skilled techniques in both training phase and inference phase. In the network training, we design decimal weight regularization and 8-bit forward quantization to obtain the integer-oriented network representations. Moreover, we develop adaptive-bitwidth and non-uniform quantization strategy in the inference phase to find the neural network capacity, total valid bits. By allowing zero bitwidth, our adaptive-bitwidth quantization can execute the model reduction and valid bits finding simultaneously. In our extensive experiments, we first demonstrate that our total valid bits is a good indicator of neural network capacity. We also analyze the impact on network capacity from the network architecture and advanced training
Accuracy of deception judgments.

We analyze the accuracy of deception judgments, synthesizing research results from 206 documents and 24,483 judges. In relevant studies, people attempt to discriminate lies from truths in real time with no special aids or training. In these circumstances, people achieve an average of 54% correct lie-truth judgments, correctly classifying 47% of lies as deceptive and 61% of truths as nondeceptive. Relative to cross-judge differences in accuracy, mean lie-truth discrimination abilities are nontrivial, with a mean accuracy d of roughly .40. This produces an effect that is at roughly the 60th percentile in size, relative to others that have been meta-analyzed by social psychologists. Alternative indexes of lie-truth discrimination accuracy correlate highly with percentage correct, and rates of lie detection vary little from study to study. Our meta-analyses reveal that people are more accurate in judging audible than visible lies, that people appear deceptive when motivated to be believed, and that individuals regard their interaction partners as honest. We propose that people judge others' deceptions more harshly than their own and that this double standard in evaluating deceit can explain much of the accumulated literature.
Spelling Error Trends and Patterns in Sindhi

Statistical error Correction technique is the most accurate and widely used approach today, but for a language like Sindhi which is a low resourced language the trained corpora’s are not available, so the statistical techniques are not possible at all. Instead a useful alternative would be to exploit various spelling error trends in Sindhi by using a Rule based approach. For designing such technique an essential prerequisite would be to study the various error patterns in a language. This paper presents various studies of spelling error trends and their types in Sindhi Language. The research shows that the error trends common to all languages are also encountered in Sindhi but their do exist some error patters that are catered specifically to a Sindhi language.
Review: Comparison of QRS detection algorithms

An Electrocardiogram (ECG) is graphical representation of the electrical movement of human heart and used in diagnosis of various heart diseases. The primary function of ECG analysis is correct detection of QRS complex and other ECG characteristics . In this review paper, researchers provide a comparative study between four popular algorithms: namely Window pair algorithm, Dynamic Plosion Index algorithm, KNN algorithm, Slope vector waveform algorithm [2-5] against three assessment criteria 1) Cross Validation, 2) Threshold and 3) Robustness to noise in order to obtain a fast, robust and highly accurate QRS detection algorithm which helps in the development of a more robust clinical instrument by making the front end signal processing more effective [6].
Effects of an intervention to increase bed alarm use to prevent falls in hospitalized patients: a cluster randomized trial.

Bed alarm systems intended to prevent hospital falls have not been formally evaluated.



To investigate whether an intervention aimed at increasing bed alarm use decreases hospital falls and related events.



Pair-matched, cluster randomized trial over 18 months. Nursing units were allocated by computer-generated randomization on the basis of baseline fall rates. Patients and outcome assessors were blinded to unit assignment; outcome assessors may have become unblinded. 
27 672 inpatients in general medical, surgical, and specialty units.



Education, training, and technical support to promote use of a standard bed alarm system (intervention units); bed alarms available but not formally promoted or supported (control units).



Pre-post difference in change in falls per 1000 patient-days (primary end point); number of patients who fell, fall-related injuries, and number of patients restrained (secondary end points).



Prevalence of alarm use was 64.41 days per 1000 patient-days on intervention units and 1.79 days per 1000 patient-days on control units (P = 0.004). There was no difference in change in fall rates per 1000 patient-days (risk ratio, 1.09 [95% CI, 0.85 to 1.53]; difference, 0.41 [CI, -1.05 to 2.47], which corresponds to a greater difference in falls in control vs. intervention units) or in the number of patients who fell, injurious fall rates, or the number of patients physically restrained on intervention units compared with control units.



The study was conducted at a single site and was slightly underpowered compared with the initial design.



An intervention designed to increase bed alarm use in an urban hospital increased alarm use but had no statistically or clinically significant effect on fall-related events or physical restraint use.
Sky Detection in Hazy Image

Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.
Knowledge management and measurement: a critical review

Knowledge is the currency of the current economy, a vital organisational asset and a key to creating a sustainable competitive advantage. The consequent interest in Knowledge Management (KM) has spurred an exponential increase in publications covering a broad spectrum of diverse and overlapping research areas. The purpose of this paper is to provide a literature review and categorised analysis of the rapidly growing number of KM publications, and offer a comprehensive reference for newcomers embarking on research in the field with a particular focus on the area of Knowledge Measurement. A total of 350 articles published in peer-reviewed journals and conferences over the last decade are carefully reviewed, analysed and categorised according to their specific subject matter in the KM context. Social issues, and (5) Knowledge Measurement. Despite the accumulation of extensive publication efforts in some areas, a series of disagreements and a theory-practice gap are revealed as challenging issues that need to be addressed. The scope of this study does not cover KM research in its entirety due to the vast nature of the research field.  This paper presents a new birds-eye view of the KM landscape through a comprehensive taxonomy of KM research providing researchers with new insights for future applied research, and offers a critical review of major knowledge measurement frameworks.