nils-herrmann/clean_scirepeval huggingface dataset:
Self-confidence and sports performance.

Self-confidence (SC) is one of the most cited factors thought to affect athletic performance. SC is said to playa critical role in athletes 'success; in contrast, lack ofSC seems to be closely associated with athletic failure. Thus, confidence is an importantfactor that distinguishes successful athletes from unsuccessful ones in terms of both their mental states as well as their performances. However, the precise nature of SC was quite unclear until the publication of Feltz's seminal chapter on this issue. The term self-confidence refers to one's belief that he or she can successfully execute a desired behavior (i.e., his or her belief of "T II get the job done"). Feltz argued that the exact relationships of SC and performance in sport have not been scientifically clarified in a satisfactory manner. To promote our understanding of the precise nature of these relationships, she first presented several definitions of SC and briefly discussed them with reference to some related terms, such as perceived ability, self-concept, self-esteem, and performance expectancies. Feltz presented three of the major theoretical approaches available at that time for studying these relationships: Bandura 's self-efficacy theory, Harter's perceived competence model, and Vealey 's concept of sport confidence. She not only described the theoretical approaches but also provided extensive research evidence in sport and reviewed some relevant criticisms. Feltz summarized and compared these three approaches and derived some important conclusions referring to the (at that time) updated scientific status of the relationships between SC and sport performance. This seminal chapter did not, of course, completely resolve the riddle of SC in sport. However, Feltz organized and presented the theoretical knowledge relevant to SC in sport at that time and undoubtedly had a profound influence on many researchers in this area, which is theoretically intriguing and has a substantial practical importance for those actively involved in sport.
Games with Incomplete Information Played by "Bayesian" Players, I-III: Part I. The Basic Model&

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The Alexa Meaning Representation Language

This paper introduces a meaning representation for spoken language understanding. The Alexa meaning representation language (AMRL), unlike previous approaches, which factor spoken utterances into domains, provides a common representation for how people communicate in spoken language. AMRL is a rooted graph, links to a large-scale ontology, supports cross-domain queries, finegrained types, complex utterances and composition. A spoken language dataset has been collected for Alexa, which contains ∼ 20k examples across eight domains. A version of this meaning representation was released to developers at a trade show in 2016.
Speed Complexity Pedestrian Driving Highway Lane-Keeping Parking Traffic Jam Urban Driving Racing Circuit

Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.
The Scripps Institution of Oceanography

DISSERTATION “Diversity and function from the ground up: Microbial mediation of wetland plant structure and ecosystem function via nitrogen fixation” Characterization of community diversity and activity of nitrogen-fixing microbes Evaluation of pathways for nitrogen transfer via plant-microbe interactions Functional assessments of disturbed and restored wetland ecosystems Selected technical skills: o Genetic fingerprinting (Terminal Restriction Fragment Length Polymorphism) of microbial communities via the nifH gene (encoding dinitrogenase reductase) o 15 N 2 isotopic enrichment studies tracing paths of fixed nitrogen in wetlands o Acetylene reduction assays and analyses with FID-equipped gas chromatograph
M^3RL: Mind-aware Multi-agent Management Reinforcement Learning

Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly learning a policy for each agent to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not want to do. For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together. The objective of the manager is to maximize the overall productivity as well as minimize payments made to the workers for ad-hoc worker teaming. To train the manager, we propose Mind-aware Multi-agent Management Reinforcement Learning (MRL), which consists of agent modeling and policy learning. We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents. The experimental results have validated the effectiveness of our approach in modeling worker agents’ minds online, and in achieving optimal ad-hoc teaming with good generalization and fast
Current Applications of Facial Volumization with Fillers.

After reading this article and watching the accompanying videos, the participant should be able to: 1. Assess patients seeking facial volumization and correlate volume deficiencies anatomically. 2. Identify appropriate fillers based on rheologic properties and anatomical needs. 3. Recognize poor candidates for facial volumization. 4. Recognize and treat filler-related side effects and complications.



Facial volumization is widely applied for minimally invasive facial rejuvenation both as a solitary means and in conjunction with surgical correction. Appropriate facial volumization is dependent on patient characteristics, consistent longitudinal anatomical changes, and qualities of fillers available. In this article, anatomical changes seen with aging are illustrated, appropriate techniques for facial volumization are described in the setting of correct filler selection, and potential complications are addressed.
Trajectory Data Mining: An Overview

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.
Mining Techniques and its Applications in Banking Sector

Data mining is becoming strategically important area for many business organizations including banking sector. It is a process of analyzing the data from various perspectives and summarizing it into valuable information. Data mining assists the banks to look for hidden pattern in a group and discover unknown relationship in the data. Today, customers have so many opinions with regard to where they can choose to do their business. Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations for the banking sector to avoid customer attrition. Customer retention is the most important factor to be analyzed in today’s competitive business environment. And also fraud is a significant problem in banking sector. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. This paper analyzes the data mining techniques and its applications in banking sector like fraud prevention and detection, customer retention, marketing and risk management.  Banking Sector, Customer Retention, Credit Approval, Data mining, Fraud Detection,
Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation

Character-based decoder does not outperform the word-based decoder but exhibits two promising   properties:   1) It takes much less time to compute the softmax layer 2) It can translate any word in a sentence ・Character-based decoder can translate the word “380” into “3 8 0” without copy mechanism (Ling et al., 2016), because their data are   included in the training dataset   ・UNK replacement is usually required by word-based decoder   but not by character-based decoder BLEU RIBES
Innovative public-private partnership to support Smart City : the case of “ Chaire REVES ”

French universities can play a key role in generating Smart City approach through an innovative Public-Private Partnership dedicated to urban transformation. We led an action-research study for five years with several research and pedagogic projects including users or citizens. The paper points out main factors of Smart City development. It also presents shared demonstrators’ characteristics including industrial scale, sustainability and citizens’ participation. University of Lorraine diversification strategy through the “Chaire REVES” supported by public and private partners. Social implications-At regional level, industrial-university-territorial partnerships could tackle both societal and economical issues “with”, “for”, and “by” citizens. Based on the Living Lab concept our case study shows a concrete regional university strategy involving: user-centric design, collaborative processes, citizens’ workshops and new financial and organizational answers enabling collaboration between private companies and public institutions. Our paper also argues that innovative public and private partnership involving users are necessary for developing smart cities. KeywordsSmart City, Living Lab, Public-Private Partnership, Collaborative innovation, sustainable urban transformation, shared demonstrator, diversification strategy
Thermal evolution of the Earth as recorded by komatiites T .

Komatiites are rare ultramafic lavas that were produced most commonly during the Archean and Early Proterozoic and less frequently in the Phanerozoic. These magmas provide a record of the thermal and chemical characteristics of the upper mantle through time. The most widely cited interpretation is that komatiites were produced in a plume environment and record high mantle temperatures and deep melting pressures. The decline in their abundance from the Archean to the Phanerozoic has been interpreted as primary evidence for secular cooling (up to 500‡C) of the mantle. In the last decade new evidence from petrology, geochemistry and field investigations has reopened the question of the conditions of mantle melting preserved by komatiites. An alternative proposal has been rekindled: that komatiites are produced by hydrous melting at shallow mantle depths in a subduction environment. This alternative interpretation predicts that the Archean mantle was only slightly (V100‡C) hotter than at present and implicates subduction as a process that operated in the Archean. Many thermal evolution and chemical differentiation models of the young Earth use the plume origin of komatiites as a central theme in their model. Therefore, this controversy over the mechanism of komatiite generation has the potential to modify widely accepted views of the Archean Earth and its subsequent evolution. This paper briefly reviews some of the pros and cons of the plume and subduction zone models and recounts other hypotheses that have been proposed for komatiites. We suggest critical tests that will improve our understanding of komatiites and allow us to better integrate the story recorded in komatiites into our view of early Earth evolution. 6 2004 Elsevier B.V. All rights reserved.
A CNN Accelerator on FPGA Using Depthwise Separable Convolution

Convolutional neural networks (CNNs) have been widely deployed in the fields of computer vision and pattern recognition because of their high accuracy. However, large convolution operations are computing intensive and often require a powerful computing platform such as a graphics processing unit. This makes it difficult to apply CNNs to portable devices. The state-of-the-art CNNs, such as MobileNetV2 and Xception, adopt depthwise separable convolution to replace the standard convolution for embedded platforms, which significantly reduces operations and parameters with only limited loss in accuracy. This highly structured model is very suitable for field-programmable gate array (FPGA) implementation. In this brief, a scalable high performance depthwise separable convolution optimized CNN accelerator is proposed. The accelerator can be fit into an FPGA of different sizes, provided the balancing between hardware resources and processing speed. As an example, MobileNetV2 is implemented on Arria 10 SoC FPGA, and the results show this accelerator can classify each picture from ImageNet in 3.75 ms, which is about 266.6 frames per second. The FPGA design achieves 20x speedup if compared to CPU.
Personality in cyberspace: personal Web sites as media for personality expressions and impressions.

This research examined the personality of owners of personal Web sites based on self-reports, visitors' ratings, and the content of the Web sites. The authors compared a large sample of Web site owners with population-wide samples on the Big Five dimensions of personality. Controlling for demographic differences, the average Web site owner reported being slightly less extraverted and more open to experience. Compared with various other samples, Web site owners did not generally differ on narcissism, self-monitoring, or self-esteem, but gender differences on these traits were often smaller in Web site owners. Self-other agreement was highest with Openness to Experience, but valid judgments of all Big Five dimensions were derived from Web sites providing rich information. Visitors made use of quantifiable features of the Web site to infer personality, and the cues they utilized partly corresponded to self-reported traits.
The Effects of Interleaved Practice

Previous research shows that interleaving rather than blocking practice of different skills (e.g. abcbcacab instead of aaabbbccc) usually improves subsequent test performance. Yet interleaving, but not blocking, ensures that practice of any particular skill is distributed, or spaced, because any two opportunities to practice the same task are not consecutive. Hence, because spaced practice typically improves test performance, the previously observed test benefits of interleaving may be due to spacing rather than interleaving per se. In the experiment reported herein, children practiced four kinds of mathematics problems in an order that was interleaved or blocked, and the degree of spacing was fixed. The interleaving of practice impaired practice session performance yet doubled scores on a test given one day later. An analysis of the errors suggested that interleaving boosted test scores by improving participants’ ability to pair each problem with the appropriate procedure.
A long-term study of children with autism playing with a robotic pet Taking inspirations from non-directive play therapy to encourage children ’ s proactivity and initiative-taking

!is paper presents a novel methodological approach of how to design, conduct and analyse robot-assisted play. !is approach is inspired by non-directive play therapy. !e experimenter participates in the experiments, but the child remains the main leader for play. Besides, beyond inspiration from non-directive play therapy, this approach enables the experimenter to regulate the interaction under speci"c conditions in order to guide the child or ask her questions about reasoning or a#ect related to the robot. !is approach has been tested in a longterm study with six children with autism in a school setting. An autonomous robot with zoomorphic, dog-like appearance was used in the studies. !e children’s progress was analyzed according to three dimensions, namely, Play, Reasoning and A#ect. Results from the case-study evaluations have shown the capability of the method to meet each child’s needs and abilities. Children who mainly played solitarily progressively experienced basic imitation games with the experimenter. Children who proactively played socially progressively experienced higher levels of play and constructed more reasoning related to the robot. !ey also expressed some interest in the robot, including, on occasion, a#ect.
Turn-taking cues in task-oriented dialogue

As interactive voice response systems become more prevalent and provide increasingly more complex functionality, it becomes clear that the challenges facing such systems are not solely in their synthesis and recognition capabilities. Issues such as the coordination of turn exchanges between system and user also play an important role in system usability. In particular, both systems and users have difficulty determining when the other is taking or relinquishing the turn. In this paper, we seek to identify turn-taking cues correlated with human–human turn exchanges which are automatically computable. We compare the presence of potential prosodic, acoustic, and lexico-syntactic turn-yielding cues in prosodic phrases preceding turn changes (smooth switches) vs. turn retentions (holds) vs. backchannels in the Columbia Games Corpus, a large corpus of task-oriented dialogues, to determine which features reliably distinguish between these three. We identify seven turn-yielding cues, all of which can be extracted automatically, for future use in turn generation and recognition in interactive voice response (IVR) systems. Testing Duncan’s (1972) hypothesis that these turn-yielding cues are linearly correlated with the occurrence of turn-taking attempts, we further demonstrate that, the greater the number of turn-yielding cues that are present, the greater the likelihood that a turn change will occur. We also identify six cues that precede backchannels, which will also be useful for IVR backchannel generation and recognition; these cues correlate with backchannel occurrence in a quadratic manner. We find similar results for overlapping and for non-overlapping speech.
Visual Analysis of Optical Coherence Tomography Data in Ophthalmology

Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina and thus, plays a fundamental role in detecting a wide range of ocular diseases. Despite OCT’s diagnostic value, managing and analyzing resulting data is challenging. We apply two visual analytics strategies for supporting retinal assessment in practice. First, we provide an interface for unifying and structuring data from different sources into a common basis. Fusing that basis with medical records and augmenting it with analytically derived information facilitates thorough investigations. Second, we present a tailored visual analysis tool for presenting, selecting, and emphasizing different aspects of the attributed data. This enables free exploration, reducing the data to relevant subsets, and focusing on details. By applying both strategies, we effectively enhance the management and the analysis of OCT data for assisting medical diagnoses.
Neural Abstractive Text Summarization

Abstractive text summarization is a complex task whose goal is to generate a concise version of a text without necessarily reusing the sentences from the original source, but still preserving the meaning and the key contents. We address this issue by modeling the problem as a sequence to sequence learning and exploiting Recurrent Neural Networks (RNNs). This work is a discussion about our ongoing research on abstractive text summarization, where our aim is to investigate methods to infuse prior knowledge into deep neural networks. We believe that these approaches can obtain better performance than the state-of-the-art models for generating well-formed and meaningful summaries.
Current working papers from the ' Performance of Australian Enterprises ' project

* This paper is the result of work being undertaken as part of a collaborative research program entitled ‘The Performance of Australian Enterprises: Innovation, Productivity and Profitability’. The project is generously supported by the Australian Research Council and the following collaborative partners: Australian Tax Office, Commonwealth Office of Small Business, IBIS Business Information Pty Ltd, Productivity Commission, and Victorian Department of State Development. The views expressed in this paper represent those of the authors and not necessarily the views of the collaborative partners. The author wishes to acknowledge helpful comments from Rob Phillips on an earlier draft of this paper.
Feature Selection with Data Balancing for Prediction of Bank Telemarketing

Nowadays, Telemarketing is an interactive technique of direct marketing that many banks apply to present a long term deposit to bank customers via the phone. Although the offering like this manner is powerful, it may make the customers annoyed. The data prediction is a popular task in data mining because it can be applied to solve this problem. However, the predictive performance may be decreased in case of the input data have many features like the bank customer information. In this paper, we focus on how to reduce the feature of input data and balance the training set for the predictive model to help the bank to increase the prediction rate. In the system performance evaluation, all accuracy rates of each predictive model based on the proposed approach compared with the original predictive model based on the truth positive and receiver operating characteristic measurement show the high performance in which the smaller number of features.