nils-herrmann/clean_scirepeval huggingface dataset:
Brief mindfulness meditation training alters psychological and neuroendocrine responses to social evaluative stress

To test whether a brief mindfulness meditation training intervention buffers self-reported psychological and neuroendocrine responses to the Trier Social Stress Test (TSST) in young adult volunteers. A second objective evaluates whether pre-existing levels of dispositional mindfulness moderate the effects of brief mindfulness meditation training on stress reactivity.



Sixty-six (N=66) participants were randomly assigned to either a brief 3-day (25-min per day) mindfulness meditation training or an analytic cognitive training control program. All participants completed a standardized laboratory social-evaluative stress challenge task (the TSST) following the third mindfulness meditation or cognitive training session. Measures of psychological (stress perceptions) and biological (salivary cortisol, blood pressure) stress reactivity were collected during the social evaluative stress-challenge session.



Brief mindfulness meditation training reduced self-reported psychological stress reactivity but increased salivary cortisol reactivity to the TSST, relative to the cognitive training comparison program. Participants who were low in pre-existing levels of dispositional mindfulness and then received mindfulness meditation training had the greatest cortisol reactivity to the TSST. No significant main or interactive effects were observed for systolic or diastolic blood pressure reactivity to the TSST.



The present study provides an initial indication that brief mindfulness meditation training buffers self-reported psychological stress reactivity, but also increases cortisol reactivity to social evaluative stress. This pattern may indicate that initially brief mindfulness meditation training fosters greater active coping efforts, resulting in reduced psychological stress appraisals and greater cortisol reactivity during social evaluative stressors.
A grounded theory of information sharing behavior in a personal learning space

This paper presents a grounded theory of information sharing behavior of the users of a personal learning space. A personal learning space is an environment consisted of weblog, ePortfolio, and social networking functionality. It is primarily used within education as a tool to enhance learning, but is also used as a knowledge management tool and to develop communities of practice. Our results identify privacy as a main concern for users of a personal learning space and illustrate challenges users face in ensuring privacy of their information and strategies they employ to achieve the desired level of privacy. We then identify factors that affect users' decisions regarding disclosure of their personal artifacts to various people and groups in a personal learning space. The three main themes as emerged in our study include current stage in the information life cycle, the nature of trust between the owner and the receiver of information, and the dynamics of the group or community within which the information is being shared. Together, these themes portrayed a clearer picture of users' perspective on the privacy of their information in a personal learning space. The findings offer some ideas about how to create privacy management mechanisms for personal learning spaces that are based on users' mental model of information privacy. Practical implications of the results are also discussed.
Acquiring a Dictionary of Emotion-Provoking Events

This paper is concerned with the discovery and aggregation of events that provoke a particular emotion in the person who experiences them, or emotion-provoking events. We first describe the creation of a small manually-constructed dictionary of events through a survey of 30 subjects. Next, we describe first attempts at automatically acquiring and aggregating these events from web data, with a baseline from previous work and some simple extensions using seed expansion and clustering. Finally, we propose several evaluation measures for evaluating the automatically acquired events, and perform an evaluation of the effectiveness of automatic event extraction.
Variational free energy and the Laplace approximation

This note derives the variational free energy under the Laplace approximation, with a focus on accounting for additional model complexity induced by increasing the number of model parameters. This is relevant when using the free energy as an approximation to the log-evidence in Bayesian model averaging and selection. By setting restricted maximum likelihood (ReML) in the larger context of variational learning and expectation maximisation (EM), we show how the ReML objective function can be adjusted to provide an approximation to the log-evidence for a particular model. This means ReML can be used for model selection, specifically to select or compare models with different covariance components. This is useful in the context of hierarchical models because it enables a principled selection of priors that, under simple hyperpriors, can be used for automatic model selection and relevance determination (ARD). Deriving the ReML objective function, from basic variational principles, discloses the simple relationships among Variational Bayes, EM and ReML. Furthermore, we show that EM is formally identical to a full variational treatment when the precisions are linear in the hyperparameters. Finally, we also consider, briefly, dynamic models and how these inform the regularisation of free energy ascent schemes, like EM and ReML.
Learning visual balance from large-scale datasets of aesthetically highly rated images

The concept of visual balance is innate for humans, and influences how we perceive visual aesthetics and cognize harmony. Although visual balance is a vital principle of design and taught in schools of designs, it is barely quantified. On the other hand, with emergence of automantic/semi-automatic visual designs for self-publishing, learning visual balance and computationally modeling it, may escalate aesthetics of such designs. In this paper, we present how questing for understanding visual balance inspired us to revisit one of the well-known theories in visual arts, the so called theory of “visual rightness”, elucidated by Arnheim. We define Arnheim’s hypothesis as a design mining problem with the goal of learning visual balance from work of professionals. We collected a dataset of 120K images that are aesthetically highly rated, from a professional photography website. We then computed factors that contribute to visual balance based on the notion of visual saliency. We fitted a mixture of Gaussians to the saliency maps of the images, and obtained the hotspots of the images. Our inferred Gaussians align with Arnheim’s hotspots, and confirm his theory. Moreover, the results support the viability of the center of mass, symmetry, as well as the Rule of Thirds in our dataset.
A Delay Model and Speculative Architecture for Pipelined Routers

This paper introduces a router delay model that accurately models key aspects of modern routers. The model accounts for the pipelined nature of contemporary routers, the specific flow control method employed, the delay of the flowcontrol credit path, and the sharing of crossbar ports across virtual channels. Motivated by this model, we introduce a microarchitecture for a speculative virtual-channel router that significantly reduces its router latency to that of a wormhole router. Simulations using our pipelined model give results that differ considerably from the commonlyassumed ‘unit-latency’ model which is unreasonably optimistic. Using realistic pipeline models, we compare wormhole  and virtual-channel flow control [4]. Our results show that a speculative virtual-channel router has the same per-hop router latency as a wormhole router, while improving throughput by up to 40%.
On Cognitive Computing

Inspired by the latest development in cognitive informatics and contemporary denotational mathematics, cognitive computing is an emerging paradigm of intelligent computing methodologies and systems, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. This article presents a survey on the theoretical framework and architectural techniques of cognitive computing beyond conventional imperative and autonomic computing technologies. Theoretical foundations of cognitive computing are elaborated from the aspects of cognitive informatics, neural informatics, and denotational mathematics. Conceptual models of cognitive computing are explored on the basis of the latest advances in abstract intelligence and computational intelligence. Applications of cognitive computing are described from the aspects of autonomous agent systems and cognitive search engines, which demonstrate how machine and computational intelligence may be generated and implemented by cognitive computing theories and technologies toward autonomous knowledge processing. [Article copies are available for purchase from InfoSci-on-Demand.com]
Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems

In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending customer-driven vehicles with a taxi service. In our system, a customer arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to “rebalance the rebalancers” by letting them travel back to the popular destinations with a customer. In this paper we study how to optimally route the rebalancing vehicles and drivers so that the number of waiting customers remains bounded while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. We determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.
Multimodal Hierarchical Dirichlet Process-based Active Perception

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback–Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive an efficient Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The results support our theoretical outcomes.
Integrated web services platform for the facilitation of fraud detection in health care e-government services

Public healthcare is a basic service provided by governments to citizens which is increasingly coming under pressure as the European population ages and the ratio of working to elderly persons falls. A way to make public spending on healthcare more efficient is to ensure that the money is spent on legitimate causes. This paper presents the work of the iWebCare project where a flexible, on-line, fraud detection, Web services platform was designed and developed. It aims to help those in the healthcare business, minimize the loss of funds to fraud. The platform is able to detect erroneous or suspicious records in submitted health care data sets, ensuring homogeneity and consistency and promoting awareness and harmonization of fraud detection practices across health care systems in the EU. Critical objectives included, the development of an ontology of health care data associated with semantic rules, implementation and initial population of an ontology and rules repository, development of a fraud detection engine and implementation of a data mining module. The potential impact of this work can be substantial. More money on healthcare mean better healthcare. Living conditions and the trust of citizens in public healthcare will be improved.
USMART: An Unsupervised Semantic Mining Activity Recognition Technique

Recognising high-level human activities from low-level sensor data is a crucial driver for pervasive systems that wish to provide seamless and distraction-free support for users engaged in normal activities. Research in this area has grown alongside advances in sensing and communications, and experiments have yielded sensor traces coupled with ground truth annotations about the underlying environmental conditions and user actions. Traditional machine learning has had some success in recognising human activities; but the need for large volumes of annotated data and the danger of overfitting to specific conditions represent challenges in connection with the building of models applicable to a wide range of users, activities, and environments. We present USMART, a novel unsupervised technique that combines data- and knowledge-driven techniques. USMART uses a general ontology model to represent domain knowledge that can be reused across different environments and users, and we augment a range of learning techniques with ontological semantics to facilitate the unsupervised discovery of patterns in how each user performs daily activities. We evaluate our approach against four real-world third-party datasets featuring different user populations and sensor configurations, and we find that USMART achieves up to 97.5% accuracy in recognising daily activities.
Beyond Recommender Systems: Helping People Help Each Other

The Internet and World Wide Web have brought us into a world of endless possibiliti es: interactive Web sites to experience, music to li sten to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other’s experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations.
Evaluation of a 900 V SiC MOSFET in a 13.56 MHz 2 kW resonant inverter for wireless power transfer

This paper demonstrates the performance of a 900 V silicon carbide (SiC) power MOSFET operating at 13.56 MHz in a 2 kW resonant inverter targeted for wireless power transfer (WPT) systems. Operating at multi-MHz switching frequency leads to smaller passive components and has the potential to improve power density and reduce weight for weight-sensitive applications. In addition, the advent of wide band gap (WBG) power semiconductors with higher breakdown voltages than silicon (Si) allows the design of high-power (2 kW or more) inverters even when operating at MHz frequencies. Previous work described the design and implementation of a class Φ2 inverter using an enhancement mode gallium nitride (eGaN) FET at output power of 1.3 kW and switching at 13.56 MHz . While eGaN FETs have promising electrical characteristics for high-frequency operation, SiC MOSFETs are becoming available with higher temperature ratings, higher breakdown voltages and higher avalanche ratings to operate at tens of MHz switching frequency. In this work, we study and evaluate the electrical characteristics of a 900 V, 7L D2PAK SiC MOSFET and demonstrate the design of a 2 kW single-ended resonant inverter operating at 13.56 MHz.
An algorithm to find optimal attack paths in nondeterministic scenarios

As penetration testing frameworks have evolved and have become more complex, the problem of controlling automatically the pentesting tool has become an important question. This can be naturally addressed as an attack planning problem. Previous approaches to this problem were based on modeling the actions and assets in the PDDL language, and using off-the-shelf AI tools to generate attack plans. These approaches however are limited. In particular, the planning is classical (the actions are deterministic) and thus not able to handle the uncertainty involved in this form of attack planning. We herein contribute a planning model that does capture the uncertainty about the results of the actions, which is modeled as a probability of success of each action. We present efficient planning algorithms, specifically designed for this problem, that achieve industrial-scale runtime performance (able to solve scenarios with several hundred hosts and exploits). These algorithms take into account the probability of success of the actions and their expected cost (for example in terms of execution time, or network traffic generated). We thus show that probabilistic attack planning can be solved efficiently for the scenarios that arise when assessing the security of large networks. Two "primitives" are presented, which are used as building blocks in a framework separating the overall problem into two levels of abstraction. We also present the experimental results obtained with our implementation, and conclude with some ideas for further work.
Analysis of brushless permanent magnet synchronous motors

A brief design review of the Permanent Magnet Synchronous Motors has been presented. A procedure has been developed to predict the steady state and dynamic performances of a brushless permanent magnet synchronous motor. Finite element analysis has been combined with a lumped parameter circuit model in order to provide satisfactory engineering information. To this end, two coordinated steps are involved. One is to develop a unified lumped parameter circuit model for both steady state and dynamic analysis. The second step is to extract the individual lumped parameters from finite element solutions based on corresponding equivalent circuits, each with a pre-determined topology. The proposed techniques have been experimentally verified in a laboratory permanent magnet synchronous motor.
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora

Active learning (AL) has been proven effective to reduce human annotation efforts in NLP. However, previous studies on AL are limited to applications in a single language. This paper proposes a bilingual active learning paradigm for relation classification, where the unlabeled instances are first jointly chosen in terms of their prediction uncertainty scores in two languages and then manually labeled by an oracle. Instead of using a parallel corpus, labeled and unlabeled instances in one language are translated into ones in the other language and all instances in both languages are then fed into a bilingual active learning engine as pseudo parallel corpora. Experimental results on the ACE RDC 2005 Chinese and English corpora show that bilingual active learning for relation classification significantly outperforms monolingual active learning.
I/O efficient Core Graph Decomposition at web scale

Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are big and may not reside in memory. In the literature, there is only one work for I/O efficient core decomposition that avoids loading the whole graph in memory. However, this approach is not scalable to handle big graphs because it cannot bound the memory size and may load most parts of the graph in memory. In addition, this approach can hardly handle graph updates. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. This model works well in many web-scale graphs. We propose a semi-external algorithm and two optimized algorithms for I/O efficient core decomposition using very simple structures and data access model. To handle dynamic graph updates, we show that our algorithm can be naturally extended to handle edge deletion. We also propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost by investigating some new graph properties. We conduct extensive experiments on 12 real large graphs. Our optimal algorithm significantly outperform the existing I/O efficient algorithm in terms of both processing time and memory consumption. In many memory-resident graphs, our algorithms for both core decomposition and maintenance can even outperform the in-memory algorithm due to the simple structures and data access model used. Our algorithms are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory.