nils-herrmann/clean_scirepeval huggingface dataset:
Exhausted Parents: Development and Preliminary Validation of the Parental Burnout Inventory

Can parents burn out? The aim of this research was to examine the construct validity of the concept of parental burnout and to provide researchers which an instrument to measure it. We conducted two successive questionnaire-based online studies, the first with a community-sample of 379 parents using principal component analyses and the second with a community- sample of 1,723 parents using both principal component analyses and confirmatory factor analyses. We investigated whether the tridimensional structure of the burnout syndrome (i.e., exhaustion, inefficacy, and depersonalization) held in the parental context. We then examined the specificity of parental burnout vis-à-vis professional burnout assessed with the Maslach Burnout Inventory, parental stress assessed with the Parental Stress Questionnaire and depression assessed with the Beck Depression Inventory. The results support the validity of a tri-dimensional burnout syndrome including exhaustion, inefficacy and emotional distancing with, respectively, 53.96 and 55.76% variance explained in study 1 and study 2, and reliability ranging from 0.89 to 0.94. The final version of the Parental Burnout Inventory (PBI) consists of 22 items and displays strong psychometric properties (CFI = 0.95, RMSEA = 0.06). Low to moderate correlations between parental burnout and professional burnout, parental stress and depression suggests that parental burnout is not just burnout, stress or depression. The prevalence of parental burnout confirms that some parents are so exhausted that the term "burnout" is appropriate. The proportion of burnout parents lies somewhere between 2 and 12%. The results are discussed in light of their implications at the micro-, meso- and macro-levels.
3.5 A 56Gb/s NRZ-electrical 247mW/lane serial-link transceiver in 28nm CMOS

With the rapid growth of data traffic in data centers, data rates over 50Gb/s/signal (e.g., OIF-CEI-56G-VSR) will eventually be required in wireline chip-to-module or chip-to-chip communications [1-3]. To achieve better power efficiency than that of existing 25Gb/s/signal designs, a high-speed yet energy-efficient front-end is needed in both the transmitter and receiver. A receiver front-end with baud-rate architecture  has been successfully operated at 56Gb/s, but additional components such as eye-monitoring comparators, phase detectors, and clock recovery circuitry as well as a power-efficient transmitter are needed to build a complete transceiver.
Double-Quadrant State-of-Charge-Based Droop Control Method for Distributed Energy Storage Systems in Autonomous DC Microgrids

In this paper, a double-quadrant state-of-charge (SoC)-based droop control method for distributed energy storage system is proposed to reach the proper power distribution in autonomous dc microgrids. In order to prolong the lifetime of the energy storage units (ESUs) and avoid the overuse of a certain unit, the SoC of each unit should be balanced and the injected/output power should be gradually equalized. Droop control as a decentralized approach is used as the basis of the power sharing method for distributed energy storage units. In the charging process, the droop coefficient is set to be proportional to the nth order of SoC, while in the discharging process, the droop coefficient is set to be inversely proportional to the nth order of SoC. Since the injected/output power is inversely proportional to the droop coefficient, it is obtained that in the charging process the ESU with higher SoC absorbs less power, while the one with lower SoC absorbs more power. Meanwhile, in the discharging process, the ESU with higher SoC delivers more power and the one with lower SoC delivers less power. Hence, SoC balancing and injected/output power equalization can be gradually realized. The exponent n of SoC is employed in the control diagram to regulate the speed of SoC balancing. It is found that with larger exponent n, the balancing speed is higher. MATLAB/simulink model comprised of three ESUs is implemented and the simulation results are shown to verify the proposed approach.
The effectiveness of manual stretching in the treatment of plantar heel pain: a systematic review

Plantar heel pain is a commonly occurring foot complaint. Stretching is frequently utilised as a treatment, yet a systematic review focusing only on its effectiveness has not been published. This review aimed to assess the effectiveness of stretching on pain and function in people with plantar heel pain.



Medline, EMBASE, CINAHL, AMED, and The Cochrane Library were searched from inception to July 2010. Studies fulfilling the inclusion criteria were independently assessed, and their quality evaluated using the modified PEDro scale.



Six studies including 365 symptomatic participants were included. Two compared stretching with a control, one study compared stretching to an alternative intervention, one study compared stretching to both alternative and control interventions, and two compared different stretching techniques and durations. Quality rating on the modified Pedro scale varied from two to eight out of a maximum of ten points. The methodologies and interventions varied significantly between studies, making meta-analysis inappropriate. Most participants improved over the course of the studies, but when stretching was compared to alternative or control interventions, the changes only reached statistical significance in one study that used a combination of calf muscle stretches and plantar fascia stretches in their stretching programme. Another study comparing different stretching techniques, showed a statistically significant reduction in some aspects of pain in favour of plantar fascia stretching over calf stretches in the short term.



There were too few studies to assess whether stretching is effective compared to control or other interventions, for either pain or function. However, there is some evidence that plantar fascia stretching may be more effective than Achilles tendon stretching alone in the short-term. Appropriately powered randomised controlled trials, utilizing validated outcome measures, blinded assessors and long-term follow up are needed to assess the efficacy of stretching.
Are mindfulness-based interventions effective for substance use disorders? A systematic review of the evidence.

Mindfulness-based interventions (MBIs) are increasingly suggested as therapeutic approaches for effecting substance use and misuse (SUM). The aim of this article is to review current evidence on the therapeutic efficacy of MBIs for SUM. A literature search was undertaken using four electronic databases and references of retrieved articles. The search included articles written in English published up to December 2011. Quality of included trials was assessed. In total, 24 studies were included, three of which were based on secondary analyses of previously investigated samples. Current evidence suggests that MBIs can reduce the consumption of several substances including alcohol, cocaine, amphetamines, marijuana, cigarettes, and opiates to a significantly greater extent than waitlist controls, non-specific educational support groups, and some specific control groups. Some preliminary evidence also suggests that MBIs are associated with a reduction in craving as well as increased mindfulness. The limited generalizability of the reviewed findings is noted (i.e., small sample size, lack of methodological details, and the lack of consistently replicated findings). More rigorous and larger randomized controlled studies are warranted.
A Hierarchical Topology-Based Model for Handling Complex Indoor Scenes

This paper presents a topology-based representation dedicated to complex indoor scenes. It accounts for memory management and performances during modelling, visualization and lighting simulation. We propose to enlarge a topological model (called generalized maps) with multipartition and hierarchy. Multipartition allows the user to group objects together according to semantics. Hierarchy provides a coarse-to-fine description of the environment. The topological model we propose has been used for devising a modeller prototype and generating efficient data structure in the context of visualization, global illumination and 1 GHz wave propagation simulation. We presently handle buildings composed of up to one billion triangles.
Compact Wideband Open-End Slot Antenna With Inherent Matching

A compact wideband open-end slot antenna is proposed for DCS, PCS, UMTS, WLAN, and WiMAX band applications. The antenna is composed of an open-ended slot and a central line within the slot. The open-end slot decreases the antenna size in half, compared to a conventional slot-ring antenna. Furthermore, the narrowed slot and central line miniaturize the antenna and obtain wideband performance using the inherent impedance-matching network of the central line. The proposed antenna obtained an omnidirectional radiation pattern with an antenna gain of larger than 1 dBi and an antenna efficiency of greater than 50% over 1710-3660 MHz.
SR-SIM: A fast and high performance IQA index based on spectral residual

Automatic image quality assessment (IQA) attempts to use computational models to measure the image quality in consistency with subjective ratings. In the past decades, dozens of IQA models have been proposed. Though some of them can predict subjective image quality accurately, their computational costs are usually very high. To meet real-time requirements, in this paper, we propose a novel fast and effective IQA index, namely spectral residual based similarity (SR-SIM), based on a specific visual saliency model, spectral residual visual saliency. SR-SIM is designed based on the hypothesis that an image's visual saliency map is closely related to its perceived quality. Extensive experiments conducted on three large-scale IQA datasets indicate that SR-SIM could achieve better prediction performance than the other state-of-the-art IQA indices evaluated. Moreover, SR-SIM can have a quite low computational complexity. The Matlab source code of SR-SIM and the evaluation results are available online at http://sse.tongji.edu.cn/linzhang/IQA/SR-SIM/SR-SIM.htm.
A Variational Recurrent Neural Network for Session-Based Recommendations using Bayesian Personalized Ranking

This work introduces VRNN-BPR, a novel deep learning model, which is utilized in sessionbased Recommender systems tackling the data sparsity problem. The proposed model combines a Recurrent Neural Network with an amortized variational inference setup (AVI) and a Bayesian Personalized Ranking in order to produce predictions on sequence-based data and generate recommendations. The model is assessed using a large real-world dataset and the results demonstrate its superiority over current state-of-the-art techniques.
Introduction to Evolutionary Computation and Genetic Programming

Evolutionary computation is the study of non-deterministic search algorithms that are based on aspects of Darwin’s theory of evolution by natural selection . The principal originators of evolutionary algorithms were John Holland, Ingo Rechenberg, Hans-Paul Schwefel and Lawrence Fogel. Holland proposed genetic algorithms and wrote about them in his 1975 book [19]. He emphasized the role of genetic recombination (often called ‘crossover’). Ingo Rechenberg and Hans-Paul Schwefel worked on the optimization of physical shapes in fluids and, after trying a variety of classical optimization techniques, discovered that altering physical variables in a random manner (ensuring small modifications were more frequent than larger ones) proved to be a very effective technique. This gave rise to a form of evolutionary algorithm that they termed an evolutionary strategy [38, 40]. Lawrence Fogel investigated evolving finite state machines to predict symbol strings of symbols generated by Markov processes and non-stationary time series . However, as is so often the case in science, various scientists considered or suggested search algorithms inspired by Darwinian evolution much earlier. David Fogel, Lawrence Fogel’s son, offers a detailed account of such early pioneers in his book on the history of evolutionary computation [14]. However, it is interesting to note that the idea of artificial evolution was suggested by one of the founders of computer science, Alan Turing, in 1948. Turing wrote an essay while working on the construction of an electronic computer called the Automatic Computing Engine (ACE) at the National Physical Laboratory in the UK. His employer happened to be Sir Charles Darwin, the grandson of Charles Darwin, the author of ‘On the Origin of Species’. Sir Charles dismissed the article as a “schoolboy essay”! It has since been recognized that in the article Turing not only proposed artificial neural networks but the field of artificial intelligence itself [44].
Study and implementation of high frequency pulse LED driver with self-oscillating circuit

In this paper, the study and implementation of a high frequency pulse LED driver with self-oscillating circuit is presented. The self-oscillating half-bridge series resonant inverter is adopted in this LED driver and the circuit characteristics of LED with high frequency pulse driving voltage is also discussed. LED module is connected with full bridge diode rectifier but without low pass filter and this LED module is driven with high frequency pulse. In additional, the self-oscillating resonant circuit with saturable core is used to achieve zero voltage switching and to control the LED current. The LED equivalent circuit of resonant circuit and the operating principle of the self-oscillating half-bridge inverter are discussed in detail. Finally, an 18 W high frequency pulse LED driver is implemented to verify the feasibility. Experimental results show that the circuit efficiency is over 86.5% when input voltage operating within AC 110 ± 10 Vrms and the maximum circuit efficiency is up to 89.2%.
Autonomic Computing: Applications of Self-Healing Systems

Self -- Management systems are the main objective of Autonomic Computing (AC), and it is needed to increase the running system's reliability, stability, and performance. This field needs to investigate some issues related to complex systems such as, self-awareness system, when and where an error state occurs, knowledge for system stabilization, analyze the problem, healing plan with different solutions for adaptation without the need for human intervention. This paper focuses on self-healing which is the most important component of Autonomic Computing. Self-healing is a technique that aims to detect, analyze, and repair existing faults within the system. All of these phases are accomplished in real-time system. In this approach, the system is capable of performing a reconfiguration action in order to recover from a permanent fault. Moreover, self-healing system should have the ability to modify its own behavior in response to changes within the environment. Recursive neural network has been proposed and used to solve the main challenges of self-healing, such as monitoring, interpretation, resolution, and adaptation.
MMQA: A Multi-domain Multi-lingual Question-Answering Framework for English and Hindi

In this paper, we assess the challenges for multi-domain, multi-lingual question answering, create necessary resources for benchmarking and develop a baseline model. We curate 500 articles in six different domains from the web. These articles form a comparable corpora of 250 English documents and 250 Hindi documents. From these comparable corpora, we have created 5, 495 question-answer pairs with the questions and answers, both being in English and Hindi. The question can be both factoid or short descriptive types. The answers are categorized in 6 coarse and 63 finer types. To the best of our knowledge, this is the very first attempt towards creating multi-domain, multi-lingual question answering evaluation involving English and Hindi. We develop a deep learning based model for classifying an input question into the coarse and finer categories depending upon the expected answer. Answers are extracted through similarity computation and subsequent ranking. For factoid question, we obtain an MRR value of 49.10% and for short descriptive question, we obtain a BLEU score of 41.37%. Evaluation of question classification model shows the accuracies of 90.12% and 80.30% for coarse and finer classes, respectively.
Temporally adaptive estimation of logistic classifiers on data streams

Modern technology has allowed real-time data collection in a variety of domains, ranging from environmental monitoring to healthcare. Consequently, there is a growing need for algorithms capable of performing inferential tasks in an online manner, continuously revising their estimates to reflect the current status of the underlying process. In particular, we are interested in constructing online and temporally adaptive classifiers capable of handling the possibly drifting decision boundaries arising in streaming environments. We first make a quadratic approximation to the log-likelihood that yields a recursive algorithm for fitting logistic regression online. We then suggest a novel way of equipping this framework with self-tuning forgetting factors. The resulting scheme is capable of tracking changes in the underlying probability distribution, adapting the decision boundary appropriately and hence maintaining high classification accuracy in dynamic or unstable environments. We demonstrate the scheme’s effectiveness in both real and simulated streaming environments.
Using a suite of ontologies for preserving workflow-centric research objects

Scientific workflows are a popular mechanism for specifying and automating data-driven in silico experiments. A significant aspect of their value lies in their potential to be reused. Once shared, workflows become useful building blocks that can be combined or modified for developing new experiments. However, previous studies have shown that storing workflow specifications alone is not sufficient to ensure that they can be successfully reused, without being able to understandwhat theworkflows aim to achieve or to re-enact them. To gain an understanding of theworkflow, and how itmay be used and repurposed for their needs, scientists require access to additional resources such as annotations describing theworkflow, datasets used and produced by the workflow, and provenance traces recording workflow executions. In this article, we present a novel approach to the preservation of scientific workflows through the application of research objects—aggregations of data andmetadata that enrich theworkflow specifications. Our approach is realised as a suite of ontologies that support the creation of workflow-centric research objects. Their design was guided by requirements elicited from previous empirical analyses of workflow decay and repair. The ontologies developed make use of and extend existing well known ontologies, namely the Object Reuse and Exchange (ORE) vocabulary, the Annotation Ontology (AO) and the W3C PROV ontology (PROVO). We illustrate the application of the ontologies for building Workflow Research Objects with a case-study that investigates Huntington’s disease, performed in collaboration with a team from the Leiden University Medial Centre (HG-LUMC). Finally we present a number of tools developed for creating and managing workflow-centric research objects.
Revisiting AdaBoost for Cost-Sensitive Classification. Part II: Empirical Analysis

A lot of approaches, each following a different strategy, have been proposed in the literature to provide AdaBoost with cost-sensitive properties. In the first part of this series of two papers, we have presented these algorithms in a homogeneous notational framework, proposed a clustering scheme for them and performed a thorough theoretical analysis of those approaches with a fully theoretical foundation. The present paper, in order to complete our analysis, is focused on the empirical study of all the algorithms previously presented over a wide range of heterogeneous classification problems. The results of our experiments, confirming the theoretical conclusions, seem to reveal that the simplest approach, just based on cost-sensitive weight initialization, is the one showing the best and soundest results, despite having been recurrently overlooked in the literature.
Embedding Deep Networks into Visual Explanations

In this paper, we propose a novel explanation module to explain the predictions made by a deep network. The explanation module works by embedding a high-dimensional deep network layer nonlinearly into a low-dimensional explanation space while retaining faithfulness, so that the original deep learning predictions can be constructed from the few concepts extracted by the explanation module. We then visualize such concepts for human to learn about the high-level concepts that deep learning is using to make decisions. We propose an algorithm called Sparse Reconstruction Autoencoder (SRAE) for learning the embedding to the explanation space. SRAE aims to reconstruct part of the original feature space while retaining faithfulness. A pull-away term is applied to SRAE to make the explanation space more orthogonal. A visualization system is then introduced for human understanding of the features in the explanation space. The proposed method is applied to explain CNN models in image classification tasks, and several novel metrics are introduced to evaluate the performance of explanations quantitatively without human involvement. Experiments show that the proposed approach generates interesting explanations of the mechanisms CNN use for making predictions.
Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering

Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with users' analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the user's reasoning and intuition.