nils-herrmann/clean_scirepeval huggingface dataset:
Potential self-regulatory mechanisms of yoga for psychological health

Research suggesting the beneficial effects of yoga on myriad aspects of psychological health has proliferated in recent years, yet there is currently no overarching framework by which to understand yoga's potential beneficial effects. Here we provide a theoretical framework and systems-based network model of yoga that focuses on integration of top-down and bottom-up forms of self-regulation. We begin by contextualizing yoga in historical and contemporary settings, and then detail how specific components of yoga practice may affect cognitive, emotional, behavioral, and autonomic output under stress through an emphasis on interoception and bottom-up input, resulting in physical and psychological health. The model describes yoga practice as a comprehensive skillset of synergistic process tools that facilitate bidirectional feedback and integration between high- and low-level brain networks, and afferent and re-afferent input from interoceptive processes (somatosensory, viscerosensory, chemosensory). From a predictive coding perspective we propose a shift to perceptual inference for stress modulation and optimal self-regulation. We describe how the processes that sub-serve self-regulation become more automatized and efficient over time and practice, requiring less effort to initiate when necessary and terminate more rapidly when no longer needed. To support our proposed model, we present the available evidence for yoga affecting self-regulatory pathways, integrating existing constructs from behavior theory and cognitive neuroscience with emerging yoga and meditation research.
Effect of action video games on the spatial distribution of visuospatial attention.

The authors investigated the effect of action gaming on the spatial distribution of attention. The authors used the flanker compatibility effect to separately assess center and peripheral attentional resources in gamers versus nongamers. Gamers exhibited an enhancement in attentional resources compared with nongamers, not only in the periphery but also in central vision. The authors then used a target localization task to unambiguously establish that gaming enhances the spatial distribution of visual attention over a wide field of view. Gamers were more accurate than nongamers at all eccentricities tested, and the advantage held even when a concurrent center task was added, ruling out a trade-off between central and peripheral attention. By establishing the causal role of gaming through training studies, the authors demonstrate that action gaming enhances visuospatial attention throughout the visual field.
I find your behavior disturbing: Static and dynamic app behavioral analysis for detection of Android malware

Malicious Android applications are currently the biggest threat in the scope of mobile security. To cope with their exponential growth and with their deceptive and hideous behaviors, static analysis signature based approaches are not enough to timely detect and tackle brand new threats such as polymorphic and composition malware. This work presents BRIDEMAID, a novel framework for analysis of Android apps' behavior, which exploits both a static and dynamic approach to detect malicious apps directly on mobile devices. The static analysis is based on n-grams matching to statically recognize malicious app execution patterns. The dynamic analysis is instead based on multi-level monitoring of device, app and user behavior to detect and prevent at runtime malicious behaviors. The framework has been tested against 2794 malicious apps reporting a detection accuracy of 99,7% and a negligible false positive rate, tested on a set of 10k genuine apps.
Nonlinear Modelling and Identification of Torsional Behaviour in Harmonic Drives

The demand for accurate and reliable positioning in industrial applications, especially in robotics and high-precision machines, has led to the increased use of Harmonic Drives. The unique performance features of harmonic drives, such as high reduction ratio and high torque capacity in a compact geometry, justify their widespread application. However, nonlinear torsional compliance and friction are the most fundamental problems in these components and accurate modelling of the dynamic behaviour is expected to improve the performance of the system. This paper offers a model for torsional compliance of harmonic drives. A statistical measure of variation is defined, by which the reliability of the estimated parameters for different operating conditions, as well as the accuracy and integrity of the proposed model, are quantified. The model performance is assessed by simulation to verify the experimental results. Two test setups have been developed and built, which are employed to evaluate experimentally the behaviour of the system. Each setup comprises a different type of harmonic drive, namely the high load torque and the low load torque harmonic drive. The results show an accurate match between the simulation torque obtained from the identified model and the measured torque from the experiment, which indicates the reliability of the proposed model.
Unsupervised Ranking Model for Entity Coreference Resolution

Coreference resolution is one of the first stages in deep language understanding and its importance has been well recognized in the natural language processing community. In this paper, we propose a generative, unsupervised ranking model for entity coreference resolution by introducing resolution mode variables. Our unsupervised system achieves 58.44% F1 score of the CoNLL metric on the English data from the CoNLL-2012 shared task (Pradhan et al., 2012), outperforming the Stanford deterministic system (Lee et al., 2013) by 3.01%.
Surprise! Dopamine signals mix action, value and error

Two studies invite us to reconsider the nature of striatal dopamine signals. Accumbens dopamine appears to signal the value of overt action and prediction errors arise from deviations in these signals. The neuromodulator dopamine (DA) is crucial to motivation, learning and action; its dysfunc-tion is implicated in motor disorders, addiction and apathy. However, how dopamine operates across these domains remains poorly understood. On one hand, a large amount of literature focuses on the role of dopamine in motivation to exert physical effort to obtain a reward 1 or the incentive-motivating properties of stimuli that drive 'wanting' 2. On the other hand, an influential theory proposes that phasic changes in dopamin-ergic signals reflect reward prediction errors (RPEs), or outcomes that are better or worse than expected 3 ; these are used to potentiate corticos-triatal synapses, thereby implementing a form of reinforcement learning. Although both theories have enjoyed much empirical support 4 , two new studies 5,6 recording dynamics of accumbens DA concentration during instrumental learning suggest that an integration of the two (Fig. 1a) may be more appropriate. Syed et al. 6 tested the effect of overt action selection on DA signals in an instrumental task. Rats were trained to distinguish cues indicating whether they would be rewarded for selecting an action (pressing a lever) or for withholding action (staying put until a fixed delay). The unexpected appearance of a cue indicated a change in expected future reward and was therefore expected to lead to a phasic increase in DA, independently of the required action. Using fast-scan cyclic voltammetry, the authors reported that DA levels varied with RPEs, but only in those trials requiring overt action to obtain reward. These signals were strongly diminished or absent for cues signaling the need to inhibit action-despite equivalent behavioral success and equivalent delays between cues and rewards. In those trials , phasic increases in DA related to reward expectation were nevertheless observed following movement initiation to collect the reward, after successful waiting triggered by the cue, and phasic decreases in DA were observed when the animals failed to withhold responses and cues indicated that rewards would not be obtained. Thus bidirectional RPEs were observed, but only in anticipation of, or following, active actions. Dopamine generally increases motor activity, especially in rewarding contexts (Pavlovian approach), so DA release could lead to maladaptive learning when action needs to be inhibited to obtain a reward 7. Thus, …
Reduction in injury rates in nursing personnel through introduction of mechanical lifts in the workplace.

Health care workers incur frequent injuries resulting from patient transfer and handling tasks. Few studies have evaluated the effectiveness of mechanical lifts in preventing injuries and time loss due to these injuries.



We examined injury and lost workday rates before and after the introduction of mechanical lifts in acute care hospitals and long-term care (LTC) facilities, and surveyed workers regarding lift use.



The post-intervention period showed decreased rates of musculoskeletal injuries (RR = 0.82, 95% CI: 0.68-1.00), lost workday injuries (RR = 0.56, 95% CI: 0.41-0.78), and total lost days due to injury (RR = 0.42). Larger reductions were seen in LTC facilities than in hospitals. Self-reported frequency of lift use by registered nurses and by nursing aides were higher in the LTC facilities than in acute care hospitals. Observed reductions in injury and lost day injury rates were greater on nursing units that reported greater use of the lifts.



Implementation of patient lifts can be effective in reducing occupational musculoskeletal injuries to nursing personnel in both LTC and acute care settings. Strategies to facilitate greater use of mechanical lifting devices should be explored, as further reductions in injuries may be possible with increased use.
Power Factor Correction Using PIC Microcontroller

In recent years, the power quality of the ac system has become great concern due to the rapidly increased numbers of electronic equipment, power electronics and high voltage power system. Most of the commercial and industrial installation in the contry has large electrical loads which are severally inductive in nature causing lagging power factor which gives heavy penalties to consumer by electricity board. This situation is taken care by PFC. Power factor correction is the capacity of absorbing the reactive power produced by a load. In case of fixed loads, this can be done manually by switching of capacitors, however in case of rapidly varying and scattered loads it becomes difficult to maintain a high power factor by manually switching on/off the capacitors in proportion to variation of load within an installation. This drawback is overcome by using an APFC panel. In this paper measuring of power factor from load is done by using PIC microcontroller and trigger required capacitors in order to compensate reactive power and bring power factor near to unity.
Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error

Deep neural networks (DNNs) have achieved great success, but the applications to mobile devices are limited due to their huge model size and low inference speed. Much effort thus has been devoted to pruning DNNs. Layer-wise neuron pruning methods have shown their effectiveness, which minimize the reconstruction error of linear response with a limited number of neurons in each single layer pruning. In this paper, we propose a new layer-wise neuron pruning approach by minimizing the reconstruction error of nonlinear units, which might be more reasonable since the error before and after activation can change significantly. An iterative optimization procedure combining greedy selection with gradient decent is proposed for single layer pruning. Experimental results on benchmark DNN models show the superiority of the proposed approach. Particularly, for VGGNet, the proposed approach can compress its disk space by 13.6× and bring a speedup of 3.7×; for AlexNet, it can achieve a compression rate of 4.1× and a speedup of 2.2×, respectively.
Enhancing Detection of SSVEPs for a High-Speed Brain Speller Using Task-Related Component Analysis

This study proposes and evaluates a novel data-driven spatial filtering approach for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high-speed brain-computer interface (BCI) speller.  Task-related component analysis (TRCA), which can enhance reproducibility of SSVEPs across multiple trials, was employed to improve the signal-to-noise ratio (SNR) of SSVEP signals by removing background electroencephalographic (EEG) activities. An ensemble method was further developed to integrate TRCA filters corresponding to multiple stimulation frequencies. This study conducted a comparison of BCI performance between the proposed TRCA-based method and an extended canonical correlation analysis (CCA)-based method using a 40-class SSVEP dataset recorded from 12 subjects. An online BCI speller was further implemented using a cue-guided target selection task with 20 subjects and a free-spelling task with 10 of the subjects.  The offline comparison results indicate that the proposed TRCA-based approach can significantly improve the classification accuracy compared with the extended CCA-based method. Furthermore, the online BCI speller achieved averaged information transfer rates (ITRs) of 325.33 ± 38.17 bits/min with the cue-guided task and 198.67 ± 50.48 bits/min with the free-spelling task.  This study validated the efficiency of the proposed TRCA-based method in implementing a high-speed SSVEP-based BCI.  The high-speed SSVEP-based BCIs using the TRCA method have great potential for various applications in communication and control.
Widely Used but also Highly Valued? Acceptance Factors and Their Perceptions in Water-Scrum-Fall Projects

Agile methodologies like Scrum propose drastic changes with respect to team hierarchies, organizational structures, planning or controlling processes. To mitigate the level of change and retain some established processes, many organizations prefer to introduce hybrid agile-traditional methodologies that combine agile with traditional development practices. Despite their importance in practice, only a few studies have examined the acceptance of such methodologies, however. In this paper, we present the results of a qualitative study that was conducted at a Swiss bank. It uses Water-Scrum-Fall, which combines Scrum with traditional practices. Based on the Diffusion of Innovations theory, we discuss several acceptance factors and investigate how they are perceived. The results indicate that, compared to traditional development methodologies, some aspects of Water-Scrum-Fall bring relative advantages and are more compatible to the way developers prefer to work. Yet, there also exist potential acceptance barriers such as a restricted individual autonomy and increased process complexity.
Montreal Neural Machine Translation Systems for WMT'15

Neural machine translation (NMT) systems have recently achieved results comparable to the state of the art on a few translation tasks, including English→French and English→German. The main purpose of the Montreal Institute for Learning Algorithms (MILA) submission to WMT’15 is to evaluate this new approach on a greater variety of language pairs. Furthermore, the human evaluation campaign may help us and the research community to better understand the behaviour of our systems. We use the RNNsearch architecture, which adds an attention mechanism to the encoderdecoder. We also leverage some of the recent developments in NMT, including the use of large vocabularies, unknown word replacement and, to a limited degree, the inclusion of monolingual language models.
3D printing in neonatal care

In recent years additive manufacturing, or threedimensional (3D) printing, it is becoming increasingly widespread and used also in the medical and biomedical field . 3D printing is a technology that allows to print, in plastic or other material, solid objects of any shape from its digital model. The printing process takes place by overlapping layers of material corresponding to cross sections of the final product. The 3D models can be created de novo, with a 3D modeling software, or it is possible to replicate an existing object with the use of a 3D scanner. In the past years, the development of appropriate software packages allowed to generate 3D printable anatomical models from computerized tomography, magnetic resonance imaging and ultrasound scans [2,3]. Up to now there have been 3D printed objects of nearly any size (from nanostructures to buildings) and material. Plastics, metals, ceramics, graphene and even derivatives of human tissues. The so-called “bio-printers”, in fact, allow to print one above the other thin layers of cells immersed in a gelatinous matrix. Recent advances of 3D bioprinting enabled researchers to print biocompatible scaffolds and human tissues such as skin, bone, cartilage, vessels and are driving to the design and 3D printing of artificial organs like liver and kidney . Dentistry, prosthetics, craniofacial reconstructive surgery, neurosurgery and orthopedic surgery are among the disciplines that have already shown versatility and possible applications of 3D printing in adults and children [2,5]. Only a few experiences have instead been reported in newborn and infants. 3D printed individualized bioresorbable airway splints have been used for the treatment of three infants with severe tracheobronchomalacia, ensuring resolution of pulmonary and extrapulmonary symptoms [6,7]. A 3D model of a complex congenital heart defects have been used for preoperative planning of intraoperative procedures, allowing surgeons to repair a complex defect in a single intervention . As already shown for children with obstructive sleep apnea and craniofacial anomalies [9]. personalized 3D printed masks could improve CPAP effectiveness and comfort also in term and preterm neonates. Neonatal emergency transport services and rural hospitals could also benefit from this technology, making possible to print medical devices spare parts, surgical and medical instruments wherever not readily available. It is envisaged that 3D printing, in the next future, will give its contribute toward the individualization of neonatal care, although further multidisciplinary studies are still needed to evaluate safety, possible applications and realize its full potential.
Are expressive suppression and cognitive reappraisal associated with stress-related symptoms?

Emotion dysregulation is thought to be critical to the development of negative psychological outcomes. Gross (1998b) conceptualized the timing of regulation strategies as key to this relationship, with response-focused strategies, such as expressive suppression, as less effective and more detrimental compared to antecedent-focused ones, such as cognitive reappraisal. In the current study, we examined the relationship between reappraisal and expressive suppression and measures of psychopathology, particularly for stress-related reactions, in both undergraduate and trauma-exposed community samples of women. Generally, expressive suppression was associated with higher, and reappraisal with lower, self-reported stress-related symptoms. In particular, expressive suppression was associated with PTSD, anxiety, and depression symptoms in the trauma-exposed community sample, with rumination partially mediating this association. Finally, based on factor analysis, expressive suppression and cognitive reappraisal appear to be independent constructs. Overall, expressive suppression, much more so than cognitive reappraisal, may play an important role in the experience of stress-related symptoms. Further, given their independence, there are potentially relevant clinical implications, as interventions that shift one of these emotion regulation strategies may not lead to changes in the other.
Interrupted time series regression for the evaluation of public health interventions: a tutorial

Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
Effects of Conversational Agents on Human Communication in Thought-Evoking Multi-Party Dialogues

This paper presents an experimental study that analyzes how conversational agents activate human communication in thought-evoking multi-party dialogues between multi-users and multi-agents. A thought-evoking dialogue, which is a kind of interaction in which agents act on user willingness to provoke user thinking, has the potential to stimulate multi-party interaction. In this paper, we focus on quiz-style multi-party dialogues between two users and two agents as an example of a thought-evoking multi-party dialogue. The experiment results showed that the presence of a peer agent significantly improved user satisfaction and increased the number of user utterances. We also found that agent empathic expressions significantly improved user satisfaction, raised user ratings of a peer agent, and increased user utterances. Our findings will be useful for stimulating multi-party communication in various applications such as educational agents and community facilitators.
Cryptographic Enforcement of Role-Based Access Control

Many cryptographic schemes have been designed to enforce information flow policies. However, enterprise security requirements are often better encoded, or can only be encoded, using role-based access control policies rather than information flow policies. In this paper, we provide an alternative formulation of role-based access control that enables us to apply existing cryptographic schemes to core and hierarchical role-based access control policies. We then show that special cases of our cryptographic enforcement schemes for role-based access control are equivalent to cryptographic enforcement schemes for temporal access control and to ciphertext-policy and key-policy attribute-based encryption schemes. Finally, we describe how these special cases can be extended to support richer forms of temporal access control and attributebased encryption.