nils-herrmann/clean_scirepeval huggingface dataset:
The emotional power of music: How music enhances the feeling of affective pictures

Music is an intriguing stimulus widely used in movies to increase the emotional experience. However, no brain imaging study has to date examined this enhancement effect using emotional pictures (the modality mostly used in emotion research) and musical excerpts. Therefore, we designed this functional magnetic resonance imaging study to explore how musical stimuli enhance the feeling of affective pictures. In a classical block design carefully controlling for habituation and order effects, we presented fearful and sad pictures (mostly taken from the IAPS) either alone or combined with congruent emotional musical excerpts (classical pieces). Subjective ratings clearly indicated that the emotional experience was markedly increased in the combined relative to the picture condition. Furthermore, using a second-level analysis and regions of interest approach, we observed a clear functional and structural dissociation between the combined and the picture condition. Besides increased activation in brain areas known to be involved in auditory as well as in neutral and emotional visual-auditory integration processes, the combined condition showed increased activation in many structures known to be involved in emotion processing (including for example amygdala, hippocampus, parahippocampus, insula, striatum, medial ventral frontal cortex, cerebellum, fusiform gyrus). In contrast, the picture condition only showed an activation increase in the cognitive part of the prefrontal cortex, mainly in the right dorsolateral prefrontal cortex. Based on these findings, we suggest that emotional pictures evoke a more cognitive mode of emotion perception, whereas congruent presentations of emotional visual and musical stimuli rather automatically evoke strong emotional feelings and experiences.
Effects of birth spacing on maternal, perinatal, infant, and child health: a systematic review of causal mechanisms.

This systematic review of 58 observational studies identified hypothetical causal mechanisms explaining the effects of short and long intervals between pregnancies on maternal, perinatal, infant, and child health, and critically examined the scientific evidence for each causal mechanism hypothesized. The following hypothetical causal mechanisms for explaining the association between short intervals and adverse outcomes were identified: maternal nutritional depletion, folate depletion, cervical insufficiency, vertical transmission of infections, suboptimal lactation related to breastfeeding-pregnancy overlap, sibling competition, transmission of infectious diseases among siblings, incomplete healing of uterine scar from previous cesarean delivery, and abnormal remodeling of endometrial blood vessels. Women's physiological regression is the only hypothetical causal mechanism that has been proposed to explain the association between long intervals and adverse outcomes. We found growing evidence supporting most of these hypotheses.
Review of Ontology Matching Approaches and Challenges

Ontology mapping aims to solve the semantic heterogeneity problems such as ambiguous entity names, different entity granularity, incomparable categorization, and various instances of different ontologies. The mapping helps to search or query data from different sources. Ontology mapping is necessary in many applications such as data integration, ontology evolution, data warehousing, e-commerce and data exchange in various domains such as purchase order, health, music and e-commerce. It is performed by ontology matching approaches that find semantic correspondences between ontology entities. In this paper, we review state of the art ontology matching approaches. We describe the approaches according to instancebased, schema-based, instance and schema-based, usage-based, element-level, and structure-level. The analysis of the existing approaches will assist us in revealing some challenges in ontology mapping such as handling ontology matching errors, user involvement and reusing previous match operations. We explain the way of handling the challenges using new strategy in order to increase the performance.
The Development of Honda Humanoid Robot

I n this paper. we present the mechanism, system cor$guration, basic control algorithm and integrated ,functions of the Nonda humanoid robot. Like its human counterpart, this robot lzas the ability to move forward and backward, sideways to the right or the left, as well as diagonally. I n addition, the robot can turn in any direction, walk up and down stairs continuously. Furthermore, due to its unique posture stability control, the robot is able to maintain its balance despite unexpected complications such as uneven ground su faces As a part of its integrated functions, this robot is able to inove on a planned path autonomously and to perform simple operations via wireless tele-operation.
The behavioral consequences of service quality: an empirical study in the Chinese retail pharmacy industry.

This study focuses on the impacts of service quality and examines the mediating effects of customer satisfaction and customer loyalty on willingness to pay more. The authors collected survey data from 479 actual retail pharmacy customers in China and used the structural equation modeling approach to test the hypotheses. The results reveal six dimensions of service quality and the differential impact of these dimensions on customer satisfaction and behavioral intentions. This study contributes to the existing literature by exploring the dimensionality of the service quality construct and mediating effects of customer satisfaction and customer loyalty in a non-Western setting.
An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition

ÐThis paper describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentationsymbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on real-life data show that the proposed approach can be successfully used for handwritten word recognition. Index TermsÐHandwriting modeling, preprocessing, segmentation, feature extraction, hidden Markov models, word recognition, rejection.
Automatic irrigation system based on wireless network

Considering the characteristic of irrigation in the rural area of China, this paper brings forward new devices based on wireless network, that are GSM (Global System Mobile) network and radio communication. Three levels are included in the system: the PC control platform or common cell phone for surveillance, the controller and the action unit. Simple GSM modules are available in the PC control platform and the controller. Orders can be sent from the PC control platform or cell phone to the controller and the information such as temperature, soil moisture and air humidity sampled by the controller can also be sent to the PC platform or cell phone by GSM message. Emitter and receiver of short-wave radio are embedded in the controller and the action unit respectively. Radio communication works between the controller and the action units. Database of spot information sampled can be analyzed and browsed by friendly interface in PC. The devices have been installed in some farms of Mentougou district in Beijing, capital of China, and Xinjiang, northwest of China.
Object Detection using Deep Learning

Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system . These systems recognize, classify and localize every object in an image by drawing bounding boxes around the object . These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real-world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.
Emotional intelligence and social and academic adaptation to school.

In a sample of 127 Spanish adolescents, the ability to understand and manage emotions, assessed by a performance measure of emotional intelligence (the MSCEIT), correlated positively with teacher ratings of academic achievement and adaptation for both males and females. Among girls, these emotional abilities also correlated positively with peer friendship nominations. After controlling for IQ and the Big Five personality traits, the ability to understand and manage emotions remained significantly associated with teacher ratings of academic adaptation among boys and peer friendship nominations among girls. Self-perceived emotional intelligence was unrelated to these criteria. These findings provide partial support for hypotheses that emotional abilities are associated with indicators of social and academic adaptation to school.
EXPLORING EMOTIONAL AND IMITATIONAL ANDROID-BASED INTERACTIONS IN AUTISTIC SPECTRUM DISORDERS

1 Interdepartmental Research Center “E. Piaggio”, University of Pisa, Italy 2 Scientific Institute Stella Maris (IRCCS), Pisa, Italy Individuals with Autistic Spectrum Disorders (ASDs) have impairments in processing of social and emotional information. To widen emotive responsiveness, the employment of robotic systems to engage proactive interactive responses in children with ASDs has been recently suggested. Understanding and teaching the processing of socio-emotional abilities is the inspiring principle of this novel approach and could be of tremendous clinical significance. Encouraging studies with robotic dolls, mobile robots and humanoids acting as social mediators have provided important insights and demonstrate the necessity of long term studies. In this study we report on a series of experiments on four subjects affected by ASDs as they interact with a biomimetic android. We assessed both their spontaneous behavior and reactions to therapist presses in correlation with the time course of the physiological and behavioral data, as well as the focusing of attention towards the android’s eye movements and the spontaneous ability to imitate gesture and facial expressions. Overall, subjects demonstrated a decrease in dysfunction in the areas of social communication, implying a marked improvement in these areas after interacting with the android.
3D-stackable crossbar resistive memory based on Field Assisted Superlinear Threshold (FAST) selector

We report the integration of 3D-stackable 1S1R passive crossbar RRAM arrays utilizing a Field Assisted Superlinear Threshold (FAST) selector. The sneak path issue in crossbar memory integration has been solved using the highest reported selectivity of 1010. Excellent selector performance is presented such as extremely sharp switching slope of <; 5mV/dec., selectivity of 1010, sub-50ns operations, > 100M endurance and processing temperature less than 300°C. Measurements on the 4Mb 1S1R crossbar array show that the sneak current is suppressed below 0.1nA, while maintaining 102 memory on/off ratio and > 106 selectivity during cycling, enabling high density memory applications.
Fast k-Means Based on k-NN Graph

In the big data era, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high when the data size and the cluster number are large. The processing bottleneck of k-means lies in the operation of seeking the closest centroid in each iteration. In this paper, a novel solution towards the scalability issue of k-means is presented. In the proposal, k-means is supported by an approximate k-nearest neighbors graph. In the k-means iteration, each data sample is only compared to clusters that its nearest neighbors reside. Since the number of nearest neighbors we consider is much less than k, the processing cost in this step becomes minor and irrelevant to k. The processing bottleneck is therefore broken. The most interesting thing is that k-nearest neighbor graph is constructed by calling the fast k-means itself. Compared with existing fast k-means variants, the proposed algorithm achieves hundreds to thousands times speed-up while maintaining high clustering quality. As it is tested on 10 million 512-dimensional data, it takes only 5.2 hours to produce 1 million clusters. In contrast, it would take 3 years for traditional k-means to fulfill the same scale of clustering.
Efficient Mean-shift Clustering Using Gaussian KD-Tree

Mean shift is a popular approach for data clustering, however, the high computational complexity of the mean shift procedure limits its practical applications in high dimensional and large data set clustering. In this paper, we propose an efficient method that allows mean shift clustering performed on large data set containing tens of millions of points at interactive rate. The key in our method is a new scheme for approximating mean shift procedure using a greatly reduced feature space. This reduced feature space is adaptive clustering of the original data set, and is generated by applying adaptive KD-tree in a high-dimensional affinity space. The proposed method significantly reduces the computational cost while obtaining almost the same clustering results as the standard mean shift procedure. We present several kinds of data clustering applications to illustrate the efficiency of the proposed method, including image and video segmentation, static geometry model and time-varying sequences
A test of the International Personality Item Pool representation of the Revised NEO Personality Inventory and development of a 120-item IPIP-based measure of the five-factor model.

There has been a substantial increase in the use of personality assessment measures constructed using items from the International Personality Item Pool (IPIP) such as the 300-item IPIP-NEO (Goldberg, 1999), a representation of the Revised NEO Personality Inventory (NEO PI-R; Costa & McCrae, 1992). The IPIP-NEO is free to use and can be modified to accommodate its users' needs. Despite the substantial interest in this measure, there is still a dearth of data demonstrating its convergence with the NEO PI-R. The present study represents an investigation of the reliability and validity of scores on the IPIP-NEO. Additionally, we used item response theory (IRT) methodology to create a 120-item version of the IPIP-NEO. Using an undergraduate sample (n = 359), we examined the reliability, as well as the convergent and criterion validity, of scores from the 300-item IPIP-NEO, a previously constructed 120-item version of the IPIP-NEO (Johnson, 2011), and the newly created IRT-based IPIP-120 in comparison to the NEO PI-R across a range of outcomes. Scores from all 3 IPIP measures demonstrated strong reliability and convergence with the NEO PI-R and a high degree of similarity with regard to their correlational profiles across the criterion variables (rICC = .983, .972, and .976, respectively). The replicability of these findings was then tested in a community sample (n = 757), and the results closely mirrored the findings from Sample 1. These results provide support for the use of the IPIP-NEO and both 120-item IPIP-NEO measures as assessment tools for measurement of the five-factor model.
Combinatorial Coverage Measurement Concepts and Applications

Empirical data demonstrate the value of t-way coverage, but in some testing situations, it is not practical to use covering arrays. However any set of tests covers at least some proportion of t-way combinations. This paper describes a variety of measures of combinatorial coverage that can be used in evaluating aspects of t-way coverage of a test suite. We also provide lower bounds on t-way coverage of several widely-used testing strategies, and describe a tool that analyzes test suites using the measures discussed in the paper.
Nursing Ethics: A Lifelong Commitment.

Over the past 30 years, the health-care context as well as the roles and responsibilities of nurses have drastically changed. Leaders in nursing around the world recognize that the health-care system is stressed and the well-being of the nursing workforce plagued by the pressures and challenges it faces in everyday practice. We do not intend to make a strong normative argument for why nursing ethics education should be done in a certain way, but instead show from where we have come and to where we can go, so that educators are positioned to address some of the current shortcomings in ethics education. Our goal is to provide an illustration of ethics education as an interwoven, ongoing, and essential aspect of nursing education and professional development. By developing professional identity as character, we hope that professional nurses are given the skills to stand in the face of adversity and to act in a way that upholds the core competencies of nursing. Ultimately, health-care organizations will thrive because of the support they provide nurses and other health-care professionals.
Gland Segmentation and Computerized Gleason Grading of Prostate Histology by Integrating Low-, High-level and Domain Specific Information

In this paper we present a method of automatically detecting and segmenting glands in digitized images of prostate histology and to use features derived from gland morphology to distinguish between intermediate Gleason grades. Gleason grading is a method of describing prostate cancer malignancy on a numerical scale from grade 1 (early stage cancer) through grade 5 (highly infiltrative cancer). Studies have shown that gland morphology plays a significant role in discriminating Gleason grades. We present a method of automated detection and segmentation of prostate gland regions. A Bayesian classifier is used to detect candidate gland regions by utilizing low-level image features to find the lumen, epithelial cell cytoplasm, and epithelial nuclei of the tissue. False positive regions identified as glands are eliminated via use of domain-specific knowledge constraints. Following candidate region detection via low-level and empirical domain information, the lumen area is used to initialize a level-set curve, which is evolved to lie at the interior boundary of the nuclei surrounding the gland structure. Features are calculated from the boundaries that characterize the morphology of the lumen and the gland regions, including area overlap ratio, distance ratio, standard deviation and variance of distance, perimeter ratio, compactness, smoothness, and area. The feature space is reduced using a manifold learning scheme (Graph Embedding) that is used to embed objects that are adjacent to each other in the high dimensional feature space into a lower dimensional embedding space. Objects embedded in this low dimensional embedding space are then classified via a support vector machine (SVM) classifier as belonging to Gleason grade 3, grade 4 cancer, or benign epithelium. We evaluate the efficacy of the automated segmentation algorithm by comparing the classification accuracy obtained using the automated segmentation scheme to the accuracy obtained via a user assisted segmentation scheme. Using the automated scheme, the system achieves accuracies of 86.35% when distinguishing Gleason grade 3 from benign epithelium, 92.90% distinguishing grade 4 from benign epithelium, and 95.19% distinguishing between Gleason grades 3 and 4. The manual scheme returns accuracies of 95.14%, 95.35%, and 80.76% for the respective classification tasks, indicating that the automated segmentation algorithm and the manual scheme are comparable in terms of achieving the overall objective of grade classification.
A Simplified Quaternion-Based Algorithm for Orientation Estimation From Earth Gravity and Magnetic Field Measurements

Orientation of a static or slow-moving rigid body can be determined from the measured gravity and local magnetic field vectors. Some formulation of the QUaternion ESTimator (QUEST) algorithm is commonly used to solve this problem. Triads of accelerometers and magnetometers are used to measure gravity and local magnetic field vectors in sensor coordinates. In the QUEST algorithm, local magnetic field measurements affect not only the estimation of yaw but also that of roll and pitch. Due to the deviations in the direction of the magnetic field vector between locations, it is not desirable to use magnetic data in calculations that are related to the determination of roll and pitch. This paper presents a geometrically intuitive 3-degree-of-freedom (3-DOF) orientation estimation algorithm with physical meaning [which is called the factored quaternion algorithm (FQA)], which restricts the use of magnetic data to the determination of the rotation about the vertical axis. The algorithm produces a quaternion output to represent the orientation. Through a derivation based on half-angle formulas and due to the use of quaternions, the computational cost of evaluating trigonometric functions is avoided. Experimental results demonstrate that the proposed algorithm has an overall accuracy that is essentially identical to that of the QUEST algorithm and is computationally more efficient. Additionally, magnetic variations cause only azimuth errors in FQA attitude estimation. A singularity avoidance method is introduced, which allows the algorithm to track through all orientations.
Software-Based Resolver-to-Digital Conversion Using a DSP

A simple and cost-effective software-based resolver-to-digital converter using a digital signal processor is presented. The proposed method incorporates software generation of the resolver carrier using a digital filter for synchronous demodulation of the resolver outputs in such a way that there is a substantial savings on hardware like the costly carrier oscillator and associated digital and analog circuits for amplitude demodulators. In addition, because the method does not cause any time delay, the dynamics of the servo control using the scheme are not affected. Furthermore, the method enables the determination of the angle for a complete 360deg shaft rotation with reasonable accuracy using a lookup table that contains entries of only up to 45deg. Computer simulations and experimental results demonstrate the effectiveness and applicability of the proposed scheme.
Music Cognition and the Cognitive Sciences

Why should music be of interest to cognitive scientists, and what role does it play in human cognition? We review three factors that make music an important topic for cognitive scientific research. First, music is a universal human trait fulfilling crucial roles in everyday life. Second, music has an important part to play in ontogenetic development and human evolution. Third, appreciating and producing music simultaneously engage many complex perceptual, cognitive, and emotional processes, rendering music an ideal object for studying the mind. We propose an integrated status for music cognition in the Cognitive Sciences and conclude by reviewing challenges and big questions in the field and the way in which these reflect recent developments.