nils-herrmann/clean_scirepeval huggingface dataset:
The science of interpersonal touch: An overview

Surprisingly little scientific research has been conducted on the topic of interpersonal touch over the years, despite the importance of touch in our everyday social interactions from birth through to adulthood and old age. In this review, we critically evaluate the results of the research on this topic that have emerged from disciplines, such as cognitive and social psychology, neuroscience, and cultural anthropology. We highlight some of the most important advances to have been made in our understanding of this topic: For example, research has shown that interpersonal tactile stimulation provides an effective means of influencing people's social behaviors (such as modulating their tendency to comply with requests, in affecting people's attitudes toward specific services, in creating bonds between couples or groups, and in strengthening romantic relationships), regardless of whether or not the tactile contact itself can be remembered explicitly. What is more, interpersonal touch can be used to communicate emotion in a manner similar to that demonstrated previously in vision and audition. The recent growth of studies investigating the potential introduction of tactile sensations to long-distance communication technologies (by means of mediated or 'virtual' touch) are also reviewed briefly. Finally, we highlight the synergistic effort that will be needed by researchers in different disciplines if we are to develop a more complete understanding of interpersonal touch in the years to come.
Humanoid robot indoor navigation based on 2D bar codes: application to the NAO robot

In this paper, we put forward a method for humanoid robot indoor localization and navigation, using 2D bar codes, running on embedded hardware. Our approach is based on camera image processing and the detection of 2D bar codes stuck to the walls. The particularities of our approach are: it works without odometry, fast localization (≈1s) that allows for kidnapping and falls, and it does not require the manual edition of a map (thus no extra computer is needed). Results of an experimental study conducted with three different NAO robots in our office show that the proposed system is operational and usable in domestic environments. The NAO robots are able to navigate through a corridor of 5m in under 86s on average. Moreover using our system the robots manage to map and navigate a complex environment with multiple rooms that includes doors and furniture. Taken together our results demonstrate that our navigation system could become a standard feature for our robots.
SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics

Data analysts often build visualizations as the first step in their analytical workflow. However, when working with high-dimensional datasets, identifying visualizations that show relevant or desired trends in data can be laborious. We propose SeeDB, a visualization recommendation engine to facilitate fast visual analysis: given a subset of data to be studied, SeeDB intelligently explores the space of visualizations, evaluates promising visualizations for trends, and recommends those it deems most "useful" or "interesting". The two major obstacles in recommending interesting visualizations are (a) scale: evaluating a large number of candidate visualizations while responding within interactive time scales, and (b) utility: identifying an appropriate metric for assessing interestingness of visualizations. For the former, SeeDB introduces pruning optimizations to quickly identify high-utility visualizations and sharing optimizations to maximize sharing of computation across visualizations. For the latter, as a first step, we adopt a deviation-based metric for visualization utility, while indicating how we may be able to generalize it to other factors influencing utility. We implement SeeDB as a middleware layer that can run on top of any DBMS. Our experiments show that our framework can identify interesting visualizations with high accuracy. Our optimizations lead to multiple orders of magnitude speedup on relational row and column stores and provide recommendations at interactive time scales. Finally, we demonstrate via a user study the effectiveness of our deviation-based utility metric and the value of recommendations in supporting visual analytics.
National Sleep Foundation's sleep time duration recommendations: methodology and results summary.

The objective was to conduct a scientifically rigorous update to the National Sleep Foundation's sleep duration recommendations.



The National Sleep Foundation convened an 18-member multidisciplinary expert panel, representing 12 stakeholder organizations, to evaluate scientific literature concerning sleep duration recommendations. We determined expert recommendations for sufficient sleep durations across the lifespan using the RAND/UCLA Appropriateness Method.



The panel agreed that, for healthy individuals with normal sleep, the appropriate sleep duration for newborns is between 14 and 17 hours, infants between 12 and 15 hours, toddlers between 11 and 14 hours, preschoolers between 10 and 13 hours, and school-aged children between 9 and 11 hours. For teenagers, 8 to 10 hours was considered appropriate, 7 to 9 hours for young adults and adults, and 7 to 8 hours of sleep for older adults.



Sufficient sleep duration requirements vary across the lifespan and from person to person. The recommendations reported here represent guidelines for healthy individuals and those not suffering from a sleep disorder. Sleep durations outside the recommended range may be appropriate, but deviating far from the normal range is rare. Individuals who habitually sleep outside the normal range may be exhibiting signs or symptoms of serious health problems or, if done volitionally, may be compromising their health and well-being.
LineUp: Visual Analysis of Multi-Attribute Rankings

Rankings are a popular and universal approach to structuring otherwise unorganized collections of items by computing a rank for each item based on the value of one or more of its attributes. This allows us, for example, to prioritize tasks or to evaluate the performance of products relative to each other. While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings. Advanced visual exploration tools are needed to make this process efficient. In this paper we present a comprehensive analysis of requirements for the visualization of multi-attribute rankings. Based on these considerations, we propose LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. It enables users to interactively combine attributes and flexibly refine parameters to explore the effect of changes in the attribute combination. This process can be employed to derive actionable insights as to which attributes of an item need to be modified in order for its rank to change. Additionally, through integration of slope graphs, LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations. We evaluate the effectiveness of the proposed multi-attribute visualization technique in a qualitative study. The study shows that users are able to successfully solve complex ranking tasks in a short period of time.
Multiband Compact Printed Dipole Antennas Using NRI-TL Metamaterial Loading

A compact, fully-printed negative-refractive-index transmission-line (NRI-TL) metamaterial-loaded dipole antenna is proposed, which exhibits multiband resonant characteristics that are not harmonically related. The multiband behavior is achieved by loading a host dipole antenna with series capacitive gaps and shunt inductive strips, to form a two unit-cell NRI-TL loaded antenna. By modeling the right-handed dipole sections as biconical transmission lines, the rich dispersion properties of the NRI-TL structure can be exploited, while still creating an effective radiator. An equivalent circuit model of the NRI-TL antenna is developed, and it is shown that its performance agrees well with the results obtained using full-wave simulations. It is also shown that the resonant behavior of the antenna can be adjusted by changing its geometrical parameters, which in turn tune the loading parameters of each NRI-TL unit cell. As such, both dual-band and triband versions of the antenna are designed and fabricated. The dual-band version exhibits three distinct resonances at 1.15, 2.88, and 3.72 GHz, with measured -10 dB bandwidths of 37 and 1150 MHz. The lowest resonance at 1.15 GHz corresponds to a 47% reduction in the resonant frequency compared to that of a reference unloaded dipole antenna, which translates into a size miniaturization factor of approximately two. The antenna exhibits stable dipolar radiation patterns with a linear electric field polarization, and has a total size of 50 × 10 × 0.79 mm3 or 0.19λ0 × 0.04λ0 × 0.003λ0 at 1.15 GHz. Throughout the operating bands the measured gain and radiation efficiency vary from 0.11 to 3.26 dBi and 49.5% to 95.6%, respectively.
Incorporating Expert Knowledge into Keyphrase Extraction

Keyphrases that efficiently summarize a document’s content are used in various document processing and retrieval tasks. Current state-of-the-art techniques for keyphrase extraction operate at a phrase-level and involve scoring candidate phrases based on features of their component words. In this paper, we learn keyphrase taggers for research papers using token-based features incorporating linguistic, surfaceform, and document-structure information through sequence labeling. We experimentally illustrate that using withindocument features alone, our tagger trained with Conditional Random Fields performs on-par with existing state-of-the-art systems that rely on information from Wikipedia and citation networks. In addition, we are also able to harness recent work on feature labeling to seamlessly incorporate expert knowledge and predictions from existing systems to enhance the extraction performance further. We highlight the modeling advantages of our keyphrase taggers and show significant performance improvements on two recently-compiled datasets of keyphrases from Computer Science research papers.
Touch projector: mobile interaction through video

In 1992, Tani et al. proposed remotely operating machines in a factory by manipulating a live video image on a computer screen. In this paper we revisit this metaphor and investigate its suitability for mobile use. We present Touch Projector, a system that enables users to interact with remote screens through a live video image on their mobile device. The handheld device tracks itself with respect to the surrounding displays. Touch on the video image is "projected" onto the target display in view, as if it had occurred there. This literal adaptation of Tani's idea, however, fails because handheld video does not offer enough stability and control to enable precise manipulation. We address this with a series of improvements, including zooming and freezing the video image. In a user study, participants selected targets and dragged targets between displays using the literal and three improved versions. We found that participants achieved highest performance with automatic zooming and temporary image freezing.
Learning effective human pose estimation from inaccurate annotation

The task of 2-D articulated human pose estimation in natural images is extremely challenging due to the high level of variation in human appearance. These variations arise from different clothing, anatomy, imaging conditions and the large number of poses it is possible for a human body to take. Recent work has shown state-of-the-art results by partitioning the pose space and using strong nonlinear classifiers such that the pose dependence and multi-modal nature of body part appearance can be captured. We propose to extend these methods to handle much larger quantities of training data, an order of magnitude larger than current datasets, and show how to utilize Amazon Mechanical Turk and a latent annotation update scheme to achieve high quality annotations at low cost. We demonstrate a significant increase in pose estimation accuracy, while simultaneously reducing computational expense by a factor of 10, and contribute a dataset of 10,000 highly articulated poses.
On Estimation and Selection for Topic Models

This article describes posterior maximization for topic models, identifying computational and conceptual gains from inference under a non-standard parametrization. We then show that fitted parameters can be used as the basis for a novel approach to marginal likelihood estimation, via block-diagonal approximation to the information matrix, that facilitates choosing the number of latent topics. This likelihood-based model selection is complemented with a goodness-of-fit analysis built around estimated residual dispersion. Examples are provided to illustrate model selection as well as to compare our estimation against standard alternative techniques.
Shielded coil structure suppressing leakage magnetic field from 100W-class wireless power transfer system with higher efficiency

In this paper, the shielded coil structure using the ferrites and the metallic shielding is proposed. It is compared with the unshielded coil structure (i.e. a pair of circular loop coils only) to demonstrate the differences in the magnetic field distributions and system performance. The simulation results using the 3D Finite Element Analysis (FEA) tool show that it can considerably suppress the leakage magnetic field from 100W-class wireless power transfer (WPT) system with the enhanced system performance.
The Tyranny of the Balanced Scorecard in the Innovation Economy

The balanced scorecard (BSC) has developed as a very useful managerial tool from the mid1990s, and has met with general (and often enthusiastic) acceptance in both business and academic circles. In the knowledge-networked innovation economy of the early 21 century, which is increasingly characterized by globally integrated supply and demand chains, outsourcing of traditional business competencies (even innovation itself), and an emphasis on intellectual capital in contrast to physical capital, the BSC is now showing serious deficiencies. The tyranny of the BSC as a measurement ‘straightjacket’ is beginning to jeopardize the survival of firms, and hinders much-needed business ecosystem innovation, thereby negatively affecting customer value rejuvenation, shareholders’ benefits, and other stakeholders’ as well as societal benefits in general. This article traces the rationale, features, development and application of the BSC in the past ten years, and then provides a critical review of its key problematic effects on firms and their stakeholders in today’s changing business environment. Five major problem areas are identified and discussed, with selected business examples. An alternative to the BSC is proposed and motivated, involving drastic change in both the underlying assumptions of the BSC and moving from a systematic, single enterprise focus to a systemic, dynamic framework – a systemic management system, including a systemic scorecard.
Transfer Learning to Infer Social Ties across Heterogeneous Networks

Interpersonal ties are responsible for the structure of social networks and the transmission of information through these networks. Different types of social ties have essentially different influences on people. Awareness of the types of social ties can benefit many applications, such as recommendation and community detection. For example, our close friends tend to move in the same circles that we do, while our classmates may be distributed into different communities. Though a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of predicting social ties across multiple heterogeneous networks.
 In this work, we develop a framework referred to as TranFG for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a factor graph model, which effectively improves the accuracy of predicting the types of social relationships in a target network by borrowing knowledge from a different source network. We also present several active learning strategies to further enhance the inferring performance. To scale up the model to handle really large networks, we design a distributed learning algorithm for the proposed model.
 We evaluate the proposed framework (TranFG) on six different networks and compare with several existing methods. TranFG clearly outperforms the existing methods on multiple metrics. For example, by leveraging information from a coauthor network with labeled advisor-advisee relationships, TranFG is able to obtain an F1-score of 90% (8%--28% improvements over alternative methods) for predicting manager-subordinate relationships in an enterprise email network. The proposed model is efficient. It takes only a few minutes to train the proposed transfer model on large networks containing tens of thousands of nodes.
Tools for Privacy Preserving Data Mining

Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacy-preserving data mining problems.
Evolving designs by generating useful complex gene structures

This chapter presents two examples of work for evolving designs by generating useful complex gene structures. where the first example uses a genetic engineering approach whereas the other uses a growth model of form. Both examples have as their motivation to overcome the combinatorial effect of large design spaces by focussing the search in useful areas. This focussing is achieved by starting with design spaces defined by low-level basic genes and creating design spaces defined by increasingly more complex gene structures. In both cases the low-level basic genes represent simple design actions which when executed produce parts of design solutions. Both works are exemplified in the domain of architectural floor plans.
Metacognition and the spacing effect : the role of repetition , feedback , and instruction on judgments of learning for massed and spaced rehearsal

Although memory performance benefits from the spacing of information at encoding, judgments of learning (JOLs) are often not sensitive to the benefits of spacing. The present research examines how practice, feedback, and instruction influence JOLs for spaced and massed items. In Experiment 1, in which JOLs were made after the presentation of each item and participants were given multiple study-test cycles, JOLs were strongly influenced by the repetition of the items, but there was little difference in JOLs for massed versus spaced items. A similar effect was shown in Experiments 2 and 3, in which participants scored their own recall performance and were given feedback, although participants did learn to assign higher JOLs to spaced items with task experience. In Experiment 4, after participants were given direct instruction about the benefits of spacing, they showed a greater difference for JOLs of spaced vs massed items, but their JOLs still underestimated their recall for spaced items. Although spacing effects are very robust and have important implications for memory and education, people often underestimate the benefits of spaced repetition when learning, possibly due to the reliance on processing fluency during study and attending to repetition, and not taking into account the beneficial aspects of study schedule.
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth

Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of which may substantially reduce the number of combinations to be examined. However, still encounters problems when a sequence database is large and/or when sequential patterns to be mined are numerous and/or long. In this paper, we propose a novel sequential pattern mining method, called PrefixSpan (i.e., Prefix-projected Sequential pattern mining), which explores prefixprojection in sequential pattern mining. PrefixSpan mines the complete set of patterns but greatly reduces the efforts of candidate subsequence generation. Moreover, prefix-projection substantially reduces the size of projected databases and leads to efficient processing. Our performance study shows that PrefixSpan outperforms both the -based GSP algorithm and another recently proposed method, FreeSpan, in mining large sequence
Realising Benefits from Current ERP and CRM Systems Implementations: An Empirical Study

In this study we report on the benefits achieved from enterprise systems implementations. Building on the existing research this paper uses the expectation-benefits framework as an analytical lens to identify and understand realised benefits. The study comprises 14 empirical case studies that focus on projects to extend existing ERP and CRM implementations. The work serves two key objectives. First to gain greater insights into the range and scope of realised benefits and second to further validate the exp-ben framework. Benefits are identifed in all four areas of the exp-ben framework. Most notable is the continued focus on achieving integration and in the improvement of information quality and better business intelligence.
Automated vehicle control developments in the PATH program

The accomplishments to date on the development of automatic vehicle control (AVC) technology in the Program on Advanced Technology for the Highway (PATH) at the University of California, Berkeley, are summarized. The basic prqfiiples and assumptions underlying the PATH work are identified, ‘followed by explanations of the work on automating vehicle lateral (steering) and longitudinal (spacing and speed) control. For both lateral and longitudinal control, the modeling of plant dynamics is described first, followed by development of the additional subsystems needed (communications, reference/sensor systems) and the derivation of the control laws. Plans for testing on vehicles in both near and long term are then discussed.