nils-herrmann/clean_scirepeval huggingface dataset:
Effects of music therapy on prosocial behavior of students with autism and developmental disabilities

Effects of Music Therapy on Prosocial Behavior of Students with Autism and Developmental Disabilities by Catherine L. de Mers Dr. Matt Tincani, Examination Committee Chair Assistant Professor o f Special Education University o f Nevada, Las Vegas This researeh study employed a multiple baseline across participants design to investigate the effects o f music therapy intervention on hitting, screaming, and asking o f three children with autism and/or developmental disabilities. Behaviors were observed and recorded during 10-minute free-play sessions both during baseline and immediately after musie therapy sessions during intervention. Interobserver agreement and procedural fidelity data were collected. Music therapy sessions were modeled on literature pertaining to music therapy with children with autism. In addition, social validity surveys were eollected to answer research questions pertaining to the social validity of music therapy as an intervention. Findings indicate that music therapy produced moderate and gradual effects on hitting, screaming, and asking. Hitting and sereaming deereased following intervention, while asking increased. Intervention effects were maintained three weeks following
Pattern Recognition and Machine Learning Solutions to the Exercises : Web-Edition

This is the solutions manual (web-edition) for the book Pattern Recognition and Machine Learning (PRML;  2006). It contains solutions to the www exercises. This release was created September 8, 2009. Future releases with corrections to errors will be published on the PRML web-site (see below). The authors would like to express their gratitude to the various people who have provided feedback on earlier releases of this document. In particular, the " Bishop Reading Group " , held in the Visual Geometry Group at the University of Oxford provided valuable comments and suggestions. The authors welcome all comments, questions and suggestions about the solutions as well as reports on (potential) errors in text or formulae in this document; please send any such feedback to
Evolution of Complex Hierarchical Societies

One of the greatest puzzles of human evolutionary history concerns the how and why of the transition from small-scale, ‘simple’ societies to large-scale, hierarchically complex ones. This paper reviews theoretical approaches to resolving this puzzle. Our discussion integrates ideas and concepts from evolutionary biology, anthropology, and political science. The evolutionary framework of multilevel selection suggests that complex hierarchies can arise in response to selection imposed by intergroup conflict (warfare). The logical coherency of this theory has been investigated with mathematical models, and its predictions were tested empirically by constructing a database of the largest territorial states in the world (with the focus on the preindustrial era).
A brief review of current scientific evidence involving aromatherapy use for nausea and vomiting.

The objective of this study was to compile existing scientific evidence regarding the effects of essential oils (EOs) administered via inhalation for the alleviation of nausea and vomiting.



CINAHL, PubMed, and EBSCO Host and Science Direct databases were searched for articles related to the use of EOs and/or aromatherapy for nausea and vomiting. Only articles using English as a language of publication were included. Eligible articles included all forms of evidence (nonexperimental, experimental, case report). Interventions were limited to the use of EOs by inhalation of their vapors to treat symptoms of nausea and vomiting in various conditions regardless of age group. Studies where the intervention did not utilize EOs or were concerned with only alcohol inhalation and trials that combined the use of aromatherapy with other treatments (massage, relaxations, or acupressure) were excluded.



Five (5) articles met the inclusion criteria encompassing trials with 328 respondents. Their results suggest that the inhaled vapor of peppermint or ginger essential oils not only reduced the incidence and severity of nausea and vomiting but also decreased antiemetic requirements and consequently improved patient satisfaction. However, a definitive conclusion could not be drawn due to methodological flaws in the existing research articles and an acute lack of additional research in this area.



The existing evidence is encouraging but yet not compelling. Hence, further well-designed large trials are needed before confirmation of EOs effectiveness in treating nausea and vomiting can be strongly substantiated.
Fake News Detection using Stacked Ensemble of Classifiers

Fake news has become a hotly debated topic in journalism. In this paper, we present our entry to the 2017 Fake News Challenge which models the detection of fake news as a stance classification task that finished in 11th place on the leader board. Our entry is an ensemble system of classifiers developed by students in the context of their coursework. We show how we used the stacking ensemble method for this purpose and obtained improvements in classification accuracy exceeding each of the individual models’ performance on the development data. Finally, we discuss aspects of the experimental setup of the challenge.
Bayesian Optimization in High Dimensions via Random Embeddings

Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration. Despite these successes, the approach is restricted to problems of moderate dimension, and several workshops on Bayesian optimization have identified its scaling to high dimensions as one of the holy grails of the field. In this paper, we introduce a novel random embedding idea to attack this problem. The resulting Random EMbedding Bayesian Optimization (REMBO) algorithm is very simple and applies to domains with both categorical and continuous variables. The experiments demonstrate that REMBO can effectively solve high-dimensional problems, including automatic parameter configuration of a popular mixed integer linear programming solver.
Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination

Effective crisis management has long relied on both the formal and informal response communities. Social media platforms such as Twitter increase the participation of the informal response community in crisis response. Yet, challenges remain in realizing the formal and informal response communities as a cooperative work system. We demonstrate a supportive technology that recognizes the existing capabilities of the informal response community to identify needs (seeker behavior) and provide resources (supplier behavior), using their own terminology. To facilitate awareness and the articulation of work in the formal response community, we present a technology that can bridge the differences in terminology and understanding of the task between the formal and informal response communities. This technology includes our previous work using domain-independent features of conversation to identify indications of coordination within the informal response community. In addition, it includes a domain-dependent analysis of message content (drawing from the ontology of the formal response community and patterns of language usage concerning the transfer of property) to annotate social media messages. The resulting repository of annotated messages is accessible through our social media analysis tool, Twitris. It allows recipients in the formal response community to sort on resource needs and availability along various dimensions including geography and time. Thus, computation indexes the original social media content and enables complex querying to identify contents, players, and locations. Evaluation of the computed annotations for seeker-supplier behavior with human judgment shows fair to moderate agreement. In addition to the potential benefits to the formal emergency response community regarding awareness of the observations and activities of the informal response community, the analysis serves as a point of reference for evaluating more computationally intensive efforts and characterizing the patterns of language behavior during a crisis.
Personalized mobile advertising : Its key attributes , trends , and social impact

Article history: Received 4 January 2011 Received in revised form 1 August 2011  August 2011  Advertising media are a means of communication that creates different marketing and communication results among consumers. Over the years, newspaper, magazine, TV, and radio have provided a one-way media where information is broadcast and communicated. Due to the widespread application of the Internet, advertising has entered into an interactive communications mode. In the advent of 3G broadband mobile communication systems and smartphone devices, consumers' preferences can be pre-identified and advertising messages can therefore be delivered to consumers in a multimedia format at the right time and at the right place with the right message. In light of this new advertisement possibility, designing personalized mobile advertising to meet consumers' needs becomes an important issue. This research uses the fuzzy Delphi method to identify the key personalized attributes in a personalized mobile advertising message for different products. Results of the study identify six important design attributes for personalized advertisements: price, preference, promotion, interest, brand, and type of mobile device. As personalized mobile advertising becomes more integrated in people's daily activities, its pros and cons and social impact are also discussed. The research result can serve as a guideline for the key parties in mobile marketing industry to facilitate the development of the industry and ensure that advertising resources are properly used.
AndroTracker : Creator Information based Android Malware Classification System

Thousands of malicious applications targeting mobile devices, including the popular Android platform, are created every day. A large number of those applications are created by a small number of professional underground actors, however previous studies overlooked such information as a feature in detecting and classifying malware, and in attributing malware to creators. Guided by this insight, we propose a method to improve on the performance of Android malware detection by incorporating the creator’s information as a feature and classify malicious applications into similar groups. We developed a system called AndroTracker that implements this method in practice. AndroTracker enables fast detection of malware by using creator information such as serial number of certificate. Additionally, it analyzes malicious behaviors and permissions to increase detection accuracy. AndroTracker also can classify malware based on similarity scoring. Finally, AndroTracker shows detection and classification performance with 99% and 90% accuracy respectively.
Neural pathways link social support to attenuated neuroendocrine stress responses

It is well established that a lack of social support constitutes a major risk factor for morbidity and mortality, comparable to risk factors such as smoking, obesity, and high blood pressure. Although it has been hypothesized that social support may benefit health by reducing physiological reactivity to stressors, the mechanisms underlying this process remain unclear. Moreover, to date, no studies have investigated the neurocognitive mechanisms that translate experiences of social support into the health outcomes that follow. To investigate these processes, thirty participants completed three tasks in which daily social support, neurocognitive reactivity to a social stressor, and neuroendocrine responses to a social stressor were assessed. Individuals who interacted regularly with supportive individuals across a 10-day period showed diminished cortisol reactivity to a social stressor. Moreover, greater social support and diminished cortisol responses were associated with diminished activity in the dorsal anterior cingulate cortex (dACC) and Brodmann's area (BA) 8, regions previously associated with the distress of social separation. Lastly, individual differences in dACC and BA 8 reactivity mediated the relationship between high daily social support and low cortisol reactivity, such that supported individuals showed reduced neurocognitive reactivity to social stressors, which in turn was associated with reduced neuroendocrine stress responses. This study is the first to investigate the neural underpinnings of the social support-health relationship and provides evidence that social support may ultimately benefit health by diminishing neural and physiological reactivity to social stressors.
Mesh denoising via cascaded normal regression

We present a data-driven approach for mesh denoising. Our key idea is to formulate the denoising process with cascaded non-linear regression functions and learn them from a set of noisy meshes and their ground-truth counterparts. Each regression function infers the normal of a denoised output mesh facet from geometry features extracted from its neighborhood facets on the input mesh and sends the result as the input of the next regression function. Specifically, we develop a filtered facet normal descriptor (FND) for modeling the geometry features around each facet on the noisy mesh and model a regression function with neural networks for mapping the FNDs to the facet normals of the denoised mesh. To handle meshes with different geometry features and reduce the training difficulty, we cluster the input mesh facets according to their FNDs and train neural networks for each cluster separately in an offline learning stage. At runtime, our method applies the learned cascaded regression functions to a noisy input mesh and reconstructs the denoised mesh from the output facet normals.
 Our method learns the non-linear denoising process from the training data and makes no specific assumptions about the noise distribution and geometry features in the input. The runtime denoising process is fully automatic for different input meshes. Our method can be easily adapted to meshes with arbitrary noise patterns by training a dedicated regression scheme with mesh data and the particular noise pattern. We evaluate our method on meshes with both synthetic and real scanned noise, and compare it to other mesh denoising algorithms. Results demonstrate that our method outperforms the state-of-the-art mesh denoising methods and successfully removes different kinds of noise for meshes with various geometry features.
Cooperative intersection collision-warning system based on vehicle-to-vehicle communication

Intersection-collision warning systems use vehicle-to-infrastructure communication to avoid accidents at urban intersections. However, they are costly because additional roadside infrastructure must be installed, and they suffer from problems related to real-time information delivery. In this paper, an intersection-collision warning system based on vehicle-to-vehicle communication is proposed in order to solve such problems. The distance to the intersection is computed to evaluate the risk that the host vehicle will collide at the intersection, and a time-to-intersection index is computed to establish the risk of a collision. The proposed system was verified through simulations, confirming its potential as a new intersection-collision warning system based on vehicle-to-vehicle communication.
PIVO: Probabilistic Inertial-Visual Odometry for Occlusion-Robust Navigation

This paper presents a novel method for visual-inertial odometry. The method is based on an information fusion framework employing low-cost IMU sensors and the monocular camera in a standard smartphone. We formulate a sequential inference scheme, where the IMU drives the dynamical model and the camera frames are used in coupling trailing sequences of augmented poses. The novelty in the model is in taking into account all the cross-terms in the updates, thus propagating the inter-connected uncertainties throughout the model. Stronger coupling between the inertial and visual data sources leads to robustness against occlusion and feature-poor environments. We demonstrate results on data collected with an iPhone and provide comparisons against the Tango device and using the EuRoC data set.
On pressure and velocity boundary conditions for the lattice Boltzmann BGK model

Pressure~density! and velocity boundary conditions are studied for 2-D and 3-D lattice Boltzmann BGK models~LBGK! and a new method to specify these conditions is proposed. These conditions are constructed in consistency with the wall boundary condition, based on the idea of bounceback of the non-equilibrium distribution. When these conditions are used together with the incompressible LBGK model @J. Stat. Phys.81, 35 ~1995!# the simulation results recover the analytical solution of the plane Poiseuille flow driven by a pressure ~density! difference. The half-way wall bounceback boundary condition is also used with the pressure ~density! inlet/outlet conditions proposed in this paper and in Phys. Fluids 8, 2527~1996! to study 2-D Poiseuille flow and 3-D square duct flow. The numerical results are approximately second-order accurate. The magnitude of the error of the half-way wall bounceback boundary condition is comparable with that of other published boundary conditions and it has better stability behavior.
Corporate Social Responsibility and Access to Finance

In this paper, we investigate whether superior performance on corporate social responsibility (CSR) strategies leads to better access to finance. We hypothesize that better access to finance can be attributed to a) reduced agency costs due to enhanced stakeholder engagement and b) reduced informational asymmetry due to increased transparency. Using a large cross-section of firms, we find that firms with better CSR performance face significantly lower capital constraints. Moreover, we provide evidence that both of the hypothesized mechanisms, better stakeholder engagement and transparency around CSR performance, are important in reducing capital constraints. The results are further confirmed using several alternative measures of capital constraints, a paired analysis based on a ratings shock to CSR performance, an instrumental variables and also a simultaneous equations approach. Finally, we show that the relation is driven by both the social and the environmental dimension of CSR.
Relative Effects of Real-world and Virtual-World Latency on an Augmented Reality Training Task: An AR Simulation Experiment

In augmented reality (AR), virtual objects and information are overlaid onto the user’s view of the physical world and can appear to become part of the real-world. Accurate registration of virtual objects is a key requirement for an effective and natural AR system, but misregistration can break the illusion of virtual objects being part of the real-world and disrupt immersion. End-to-end system latency severely impacts the quality of AR registration. In this research, we present a controlled study that aims at a deeper understanding of the effects of latency on virtual and real-world imagery and its influences on task performance in an AR training task. We utilize an AR simulation approach, in which an outdoor AR training task is simulated in a high-fidelity virtual reality (VR) system. The real and augmented portions of the AR training scenarios are simulated in VR, affording us detailed control over a variety of immersion parameters and the ability to explore the effects of different types of simulated latency. We utilized a representative task inspired by outdoor AR military training systems to compare various AR system configurations, including optical see-through and video see-through setups with both matched and unmatched levels of real and virtual objects latency. Our findings indicate that users are able to perform significantly better when virtual and real-world latencies are matched (as in the case of simulated video see-through AR with perfect augmentation-to-real-world registration). Unequal levels of latency led to reduction in performance, even when overall latency levels were lower compared to the matched case. The relative results hold up with increased overall latency.
FANNC: A Fast Adaptive Neural Network Classifier

In this paper, a fast adaptive neural network classifier named FANNC is proposed. FANNC exploits the advantages of both adaptive resonance theory and field theory. It needs only one-pass learning, and achieves not only high predictive accuracy but also fast learning speed. Besides, FANNC has incremental learning ability. When new instances are fed, it does not need to retrain the whole training set. Instead, it could learn the knowledge encoded in those instances through slightly adjusting the network topology when necessary, that is, adaptively appending one or two hidden units and corresponding connections to the existing network. This characteristic makes FANNC fit for real-time online learning tasks. Moreover, since the network architecture is adaptively set up, the disadvantage of manually determining the number of hidden units of most feed-forward neural networks is overcome. Benchmark tests show that FANNC is a preferable neural network classifier, which is superior to several other neural algorithms on both predictive accuracy and learning speed.
ICDAR 2005 text locating competition results

This paper describes the results of the ICDAR 2005 competition for locating text in camera captured scenes. For this we used the same data as the ICDAR 2003 competition, which has been kept private until now. This allows a direct comparison with the 2003 entries. The main result is that the leading 2005 entry has improved significantly on the leading 2003 entry, with an increase in average f-score from 0.5 to 0.62, where the f-score is the same adapted information retrieval measure used for the 2003 competition. The paper also discusses the Web-based deployment and evaluation of text locating systems, and one of the leading entries has now been deployed in this way. This mode of usage could lead to more complete and more immediate knowledge of the strengths and weaknesses of each newly developed system.