nils-herrmann/clean_scirepeval huggingface dataset:
An exploration of affect factors and their role in user technology acceptance: Mediation and causality

Affect factors have gained researchers’ attention in a number of fields. The Information Systems (IS) literature, however, shows some gaps and inconsistencies regarding the role of affect factors in human–computer interaction. Building upon prior research, this study aims at a better understanding of affect factors by clarifying their relationships with each other and with other primary user acceptance factors. Two affect variables that are different in nature were examined: computer playfulness (CP) and perceived enjoyment (PE). We theoretically clarified and methodologically verified their mediating effects and causal relationships with other primary factors influencing user technology acceptance, namely perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BI). Quantitative data were analyzed using R.M. Baron and D. Kenny’s (1986) method for mediating effects and P.R. Cohen, A. Carlsson, L. Ballesteros, and R.S. Amant’s (1993) path analysis method for causal relationships.These analyses largely supported our hypotheses. Results from this research indicate that a PE→PEOU causal direction is favored, and PEOU partially mediates PE’s impacts on PU whereas PE fully mediates CP’s impact on PEOU. With the increased interest in various affect factors in user technology acceptance and use, our study sheds light on the role of affect factors from both theoretical and methodological perspectives. Practical implications are discussed as well.
Can Computer Playfulness and Cognitive Absorption Lead to Problematic Technology Usage?

Prior IS research demonstrates that computer playfulness, personal innovativeness and cognitive absorption during technology use lead to a number of positive behavioral and affective outcomes. While early studies have warned that these individual traits and psychological states can lead to certain negative effects, little IS research has empirically investigated this possibility. Building on recent findings in psychological research, this study represents an initial effort to fill this gap. Following prior IS work and using the World Wide Web as the target technology, our survey data revealed that innovative, playful and cognitively absorbed technology users are more likely to 1) exhibit problematic usage behavior, and 2) engage in higher daily usage. However, this increased overall usage is likely a result of increased use for social and leisure purposes, rather than for work and study purposes. Managerial and research implications of our findings are discussed.
Some Considerations on Learning to Explore via Meta-Reinforcement Learning

We interpret meta-reinforcement learning as the problem of learning how to quickly find a good sampling distribution in a new environment. This interpretation leads to the development of two new meta-reinforcement learning algorithms: E-MAML and E-RL. Results are presented on a new environment we call ‘Krazy World’: a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning. Further results are presented on a set of maze environments. We show E-MAML and E-RL deliver better performance than baseline algorithms on both tasks.
Knowledge-based graph document modeling

We propose a graph-based semantic model for representing document content. Our method relies on the use of a semantic network, namely the DBpedia knowledge base, for acquiring fine-grained information about entities and their semantic relations, thus resulting in a knowledge-rich document model. We demonstrate the benefits of these semantic representations in two tasks: entity ranking and computing document semantic similarity. To this end, we couple DBpedia's structure with an information-theoretic measure of concept association, based on its explicit semantic relations, and compute semantic similarity using a Graph Edit Distance based measure, which finds the optimal matching between the documents' entities using the Hungarian method. Experimental results show that our general model outperforms baselines built on top of traditional methods, and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks.
Improved English to Russian Translation by Neural Suffix Prediction

Neural machine translation (NMT) suffers a performance deficiency when a limited vocabulary fails to cover the source or target side adequately, which happens frequently when dealing with morphologically rich languages. To address this problem, previous work focused on adjusting translation granularity or expanding the vocabulary size. However, morphological information is relatively under-considered in NMT architectures, which may further improve translation quality. We propose a novel method, which can not only reduce data sparsity but also model morphology through a simple but effective mechanism. By predicting the stem and suffix separately during decoding, our system achieves an improvement of up to 1.98 BLEU compared with previous work on English to Russian translation. Our method is orthogonal to different NMT architectures and stably gains improvements on various domains.
A comparison of Under Frequency Relay based and Frequency Response model based Load Shedding scheme

The power system in Industrial plants is continually prone to unforeseeable contingencies which cause a disparity between power generated and the load demand. Such uncertainties make the system frequency to decay. In this work, frequency response due to load-generation mismatch is simulated. To forestall this dramatic event, under- frequency Load Shedding (UFLS) strategies are adopted. This paper focuses on the traditional Under Frequency Relay based load shedding and the adaptive System Frequency Response (SFR) model based load shedding. SFR model is developed in MATLAB-SIMULINK. Load shedding actions of both methods are simulated and compared in a test system in ETAP software to comprehend frequency response of an industrial system under transient conditions.
Artificial Intelligence – An Introduction to Robotics

This document gives a short introduction to the basics of robotics in the context of artificial intelligence. It describes the very basics of robotics like sensors and effectors, gives an overview on robotic history, and introduces some basic problems encountered in modern robotics. It describes possible solutions to those problems without going deeply into theory. The problems introduced are perception, basic pose description, transition and sensor models, localization as a special case of perception (Monte Carlo Localization, Extended Kalman Filter), representation of environment (workspace and configuration space), path planning (cell decomposition, potential fields, skeletonization, Voronoi diagrams, and probabilistic roadmaps), movement of robots, and some real-life examples. This document was created accompanying a talk in the context of the proseminar "Artificial Intelligence" in summer semester 2003 at the RWTH Aachen, Chair of Computer Science V, Knowledge-based Systems Group.
Commonsense Conceptions of Emergent Processes: Why Some Misconceptions Are Robust.

This article offers a plausible domain-general explanation for why some concepts of processes are resistant to instructional remediation although other, apparently similar concepts are more easily understood. The explanation assumes that processes may differ in ontological ways: that some processes (such as the apparent flow in diffusion of dye in water) are emergent and other processes (such as the flow of blood in human circulation) are direct. Although precise definition of the two kinds of processes are probably impossible, attributes of direct and emergent processes are described that distinguish them in a domain-general way. Circulation and diffusion, which are used as examples of direct and emergent processes, are associated with different kinds of misconceptions. The claim is that students’ misconceptions for direct kinds of processes, such as blood circulation, are of the same ontological kind as the correct conception, suggesting that misconceptions of direct processes may be nonrobust. However, students’ misconceptions of emergent processes are robust because they misinterpret emergent processes as a kind of commonsense direct processes. To correct such a misconception requires a re-representation or a conceptual shift across ontological kinds. Therefore, misconceptions of emergent processes are robust because such a shift requires that students know about the emergent kind and can overcome their (perhaps even innate) predisposition to conceive of all processes as a direct kind. Such a domain-general explanation suggests that teaching students the causal structure underlying emergent processes may enable them to recognize and understand a variety of emergent processes for which they have robust misconceptions, such as concepts of electricity, heat and temperature, and evolution. THE JOURNAL OF THE LEARNING SCIENCES, 14(2), 161–199
Mental Health of Transgender Veterans in US States With and Without Discrimination and Hate Crime Legal Protection.

To examine whether indicators of community- and state-level lesbian, gay, bisexual, and transgender equality are associated with transgender veterans' mental health.



We extracted Veterans Administration data for patients who were diagnosed with gender identity disorder, had at least 1 visit in 2013, and lived in a zip code with a Municipality Equality Index score (n = 1640). We examined the associations of whether a state included transgender status in employment nondiscrimination laws and in hate crimes laws with mood disorders; alcohol, illicit drug, and tobacco use disorders; posttraumatic stress disorder; and suicidal ideation or attempt.



Nearly half (47.3%) of the sample lived in states with employment discrimination protection, and 44.8% lived in states with hate crimes protection. Employment nondiscrimination protection was associated with 26% decreased odds of mood disorders (adjusted odds ratio [AOR] = 0.74; 95% confidence interval [CI] = 0.59, 0.93) and 43% decreased odds of self-directed violence (AOR = 0.57; 95% CI = 0.34, 0.95).



Understanding lesbian, gay, bisexual, and transgender social stressors can inform treatment and care coordination for transgender populations.
Computational Higher Type Theory II: Dependent Cubical Realizability

This is the second in a series of papers extending Martin-Löf’smeaning explanation of dependent type theory to account for higher-dimensional types. We build on the cubical realizability framework for simple types developed in Part I, and extend it to a meaning explanation of dependent higher-dimensional type theory. This extension requires generalizing the computational Kan condition given in Part I, and considering the action of type families on paths. We define identification types, which classify identifications (paths) in a type, and dependent function and pair types. The main result is a canonicity theorem, which states that a closed term of boolean type evaluates to either true or false. This result establishes the first computational interpretation of higher dependent type theory by giving a deterministic operational semantics for its programs, including operations that realize the Kan condition.
Global air quality and pollution.

The impact of global air pollution on climate and the environment is a new focus in atmospheric science. Intercontinental transport and hemispheric air pollution by ozone jeopardize agricultural and natural ecosystems worldwide and have a strong effect on climate. Aerosols, which are spread globally but have a strong regional imbalance, change global climate through their direct and indirect effects on radiative forcing. In the 1990s, nitrogen oxide emissions from Asia surpassed those from North America and Europe and should continue to exceed them for decades. International initiatives to mitigate global air pollution require participation from both developed and developing countries.
Drought-induced Accumulation of Soluble Sugars and Proline in Two Maize Varieties

Drought stress is a major factor in reduce growth, development and production of plants. Stress was applied with polyethylene glycol (PEG) 6000 and water potentials were: zero (control), -0.15 (PEG 10%), -0.49 (PEG 20%), -1.03 (PEG 30%) and -1.76 (PEG40%) MPa. The solutes accumulation of two maize (Zea mays L.) cultivars -704 and 301were determined after drought stress. In our experiments, a higher amount of soluble sugars and a lower amount of starch were found under stress. Soluble sugars concentration increased (from 1.18 to 1.90 times) in roots and shoots of both varieties when the studied varieties were subjected to drought stress, but starch content were significantly (p<0.05) decreased (from 16 to 84%) in both varieties. This suggests that sugars play an important role in Osmotic Adjustment (OA) in maize. The free proline level also increased (from 1.56 to 3.13 times) in response to drought stress and the increase in 704 var. was higher than 301 var. It seems to proline may play a role in minimizing the damage caused by dehydration. Increase of proline content in shoots was higher than roots, but increase of soluble sugar content and decrease of starch content in roots was higher than shoots.
Vision-based Localization and Robot-centric Mapping in Riverine Environments

Junho Yang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 e-mail: yang125@illinois.edu Ashwin Dani Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 e-mail: adani@illinois.edu Soon-Jo Chung Department of Aerospace Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 e-mail: sjchung@illinois.edu Seth Hutchinson Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 e-mail: seth@illinois.edu
Deep Canonically Correlated LSTMs

We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform correlation analysis on the outputs of these neural networks to find a correlated subspace through which we get our final representation via projection. This work follows from previous work done on Deep Canonical Correlation (DCCA), in which deep feed-forward neural networks were used to learn nonlinear transformations of data while maximizing correlation.
Acupuncture in patients with tension-type headache: randomised controlled trial.

To investigate the effectiveness of acupuncture compared with minimal acupuncture and with no acupuncture in patients with tension-type headache.



Three armed randomised controlled multicentre trial.



28 outpatient centres in Germany.



270 patients (74% women, mean age 43 (SD 13) years) with episodic or chronic tension-type headache.



Acupuncture, minimal acupuncture (superficial needling at non-acupuncture points), or waiting list control. Acupuncture and minimal acupuncture were administered by specialised physicians and consisted of 12 sessions per patient over eight weeks.



Difference in numbers of days with headache between the four weeks before randomisation and weeks 9-12 after randomisation, as recorded by participants in headache diaries.



The number of days with headache decreased by 7.2 (SD 6.5) days in the acupuncture group compared with 6.6 (SD 6.0) days in the minimal acupuncture group and 1.5 (SD 3.7) days in the waiting list group (difference: acupuncture v minimal acupuncture, 0.6 days, 95% confidence interval -1.5 to 2.6 days, P = 0.58; acupuncture v waiting list, 5.7 days, 3.9 to 7.5 days, P < 0.001). The proportion of responders (at least 50% reduction in days with headache) was 46% in the acupuncture group, 35% in the minimal acupuncture group, and 4% in the waiting list group.



The acupuncture intervention investigated in this trial was more effective than no treatment but not significantly more effective than minimal acupuncture for the treatment of tension-type headache.
Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests.

Statistical inference in psychology has traditionally relied heavily on p-value significance testing. This approach to drawing conclusions from data, however, has been widely criticized, and two types of remedies have been advocated. The first proposal is to supplement p values with complementary measures of evidence, such as effect sizes. The second is to replace inference with Bayesian measures of evidence, such as the Bayes factor. The authors provide a practical comparison of p values, effect sizes, and default Bayes factors as measures of statistical evidence, using 855 recently published t tests in psychology. The comparison yields two main results. First, although p values and default Bayes factors almost always agree about what hypothesis is better supported by the data, the measures often disagree about the strength of this support; for 70% of the data sets for which the p value falls between .01 and .05, the default Bayes factor indicates that the evidence is only anecdotal. Second, effect sizes can provide additional evidence to p values and default Bayes factors. The authors conclude that the Bayesian approach is comparatively prudent, preventing researchers from overestimating the evidence in favor of an effect.
Brain and cognitive reserve: Translation via network control theory

Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive "reserve," associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention.
Phytochemical Compounds and Protection from Cardiovascular Diseases: A State of the Art

Cardiovascular diseases represent a worldwide relevant socioeconomical problem. Cardiovascular disease prevention relies also on lifestyle changes, including dietary habits. The cardioprotective effects of several foods and dietary supplements in both animal models and in humans have been explored. It was found that beneficial effects are mainly dependent on antioxidant and anti-inflammatory properties, also involving modulation of mitochondrial function. Resveratrol is one of the most studied phytochemical compounds and it is provided with several benefits in cardiovascular diseases as well as in other pathological conditions (such as cancer). Other relevant compounds are Brassica oleracea, curcumin, and berberine, and they all exert beneficial effects in several diseases. In the attempt to provide a comprehensive reference tool for both researchers and clinicians, we summarized in the present paper the existing literature on both preclinical and clinical cardioprotective effects of each mentioned phytochemical. We structured the discussion of each compound by analyzing, first, its cellular molecular targets of action, subsequently focusing on results from applications in both ex vivo and in vivo models, finally discussing the relevance of the compound in the context of human diseases.
Habit and Intention in Everyday Life : The Multiple Processes by Which Past Behavior Predicts Future Behavior

Past behavior guides future responses through 2 processes. Well-practiced behaviors in constant contexts recur because the processing that initiates and controls their performance becomes automatic. Frequency of past behavior then reflects habit strength and has a direct effect on future performance. Alternately, when behaviors are not well learned or when they are performed in unstable or difficult contexts, conscious decision making is likely to be necessary to initiate and carry out the behavior. Under these conditions, past behavior (along with attitudes and subjective norms) may contribute to intentions, and behavior is guided by intentions. These relations between past behavior and future behavior are substantiated in a meta-analytic synthesis of prior research on behavior prediction and in a primary research investigation.
Psychological adjustment and sleep quality in a randomized trial of the effects of a Tibetan yoga intervention in patients with lymphoma.

Research suggests that stress-reduction programs tailored to the cancer setting help patients cope with the effects of treatment and improve their quality of life. Yoga, an ancient Eastern science, incorporates stress-reduction techniques that include regulated breathing, visual imagery, and meditation as well as various postures. The authors examined the effects of the Tibetan yoga (TY) practices of Tsa lung and Trul khor, which incorporate controlled breathing and visualization, mindfulness techniques, and low-impact postures in patients with lymphoma.



Thirty-nine patients with lymphoma who were undergoing treatment or who had concluded treatment within the past 12 months were assigned to a TY group or to a wait-list control group. Patients in the TY group participated in 7 weekly yoga sessions, and patients in the wait-list control group were free to participate in the TY program after the 3-month follow-up assessment.



Eighty nine percent of TY participants completed at least 2-3 three yoga sessions, and 58% completed at least 5 sessions. Patients in the TY group reported significantly lower sleep disturbance scores during follow-up compared with patients in the wait-list control group (5.8 vs. 8.1; P < 0.004). This included better subjective sleep quality (P < 0.02), faster sleep latency (P < 0.01), longer sleep duration (P < 0.03), and less use of sleep medications (P < 0.02). There were no significant differences between groups in terms of intrusion or avoidance, state anxiety, depression, or fatigue.



The participation rates suggested that a TY program is feasible for patients with cancer and that such a program significantly improves sleep-related outcomes. However, there were no significant differences between groups for the other outcomes.