nils-herrmann/clean_scirepeval huggingface dataset:
Creature features: online motion puppetry for non-human characters

We present a novel real-time motion puppetry system that drives the motion of non-human characters using human motion input. We aim to control a variety of creatures whose body structures and motion patterns can differ greatly from a human's. A combination of direct feature mapping and motion coupling enables the generation of natural creature motion, along with intuitive and expressive control for puppetry. First, in the design phase, direct feature mappings and motion classification can be efficiently and intuitively computed given crude motion mimicking as input. Later, during the puppetry phase, the user's body motions are used to control the target character in real-time, using the combination of feature mappings generated from the design phase. We demonstrate the effectiveness of our approach with several examples of natural puppetry, where a variety of non-human creatures are controlled in real-time using human motion input from a commodity motion sensing device.
Issues in Complex Event Processing Systems

Many Big Data technologies were built to enable the processing of human generated data, setting aside the enormous amount of data generated from Machine-to-Machine (M2M) interactions. M2M interactions create real-time data streams that are much more structured, often in the form of series of event occurrences. In this paper, we provide an overview on the main research issues confronted by existing Complex Event Processing (CEP) techniques, as a starting point for Big Data applications that enable the monitoring of complex event occurrences in M2M interactions.
Exhaustive search algorithms to mine subgroups on Big Data using Apache Spark

Subgroup discovery is a well-known technique for the extraction of patterns, with respect to a variable of interest in the data. However, the explosion in data gathering has hampered the performance of traditional algorithms to discover interesting relationships between different objects in a set with respect to a specific property which is of interest to the user. In this regard, our goal is to propose a set of efficient techniques to mine subgroups on Big Data by means of Apache Spark. On this matter, AprioriK-SD-OE and PFP-SD-OE are proposed as fast exhaustive search algorithms to discover subgroups on Big Data using Apache Spark. The experimental study includes more than 70 datasets considering search spaces bigger than $$10^{15}$$ 10 15 subgroups. The scalability of our proposals are analyzed by considering datasets with 200 million of instances demonstrating the usefulness of using Spark to tackle Big Data.
Vehicular Networks: A New Challenge for Content-Delivery-Based Applications

A significant number of promising applications for vehicular ad hoc networks (VANETs) are becoming a reality. Most of these applications require a variety of heterogenous content to be delivered to vehicles and to their on-board users. However, the task of content delivery in such dynamic and large-scale networks is easier said than done. In this article, we propose a classification of content delivery solutions applied to VANETs while highlighting their new characteristics and describing their underlying architectural design. First, the two fundamental building blocks that are part of an entire content delivery system are identified: replica allocation and content delivery. The related solutions are then classified according to their architectural definition. Within each category, solutions are described based on the techniques and strategies that have been adopted. As result, we present an in-depth discussion on the architecture, techniques, and strategies adopted by studies in the literature that tackle problems related to vehicular content delivery networks.
Partitioning into Colorful Components by Minimum Edge Deletions

The NP-hard Colorful Components problem is, given a vertex-colored graph, to delete a minimum number of edges such that no connected component contains two vertices of the same color. It has applications in multiple sequence alignment and in multiple network alignment where the colors correspond to species. We initiate a systematic complexity-theoretic study of Colorful Components by presenting NP-hardness as well as fixed-parameter tractability results for different variants of Colorful Components. We also perform experiments with our algorithms and additionally develop an efficient and very accurate heuristic algorithm clearly outperforming a previous min-cut-based heuristic on multiple sequence alignment data.
A comparison of methods for estimating directions in egocentric space.

A central issue for researchers of human spatial knowledge, whether focused on perceptually guided action or cognitive-map acquisition, is knowledge of egocentric directions, directions from the body to objects and places. Several methods exist for measuring this knowledge. We compared two particularly important methods, manual pointing with a dial and whole-body rotation (body heading), under various conditions of sensory or memory access to targets. In two experiments, blindfolded body rotation resulted in the greatest variability of performance (variable error), while the manual dial resulted in greater consistent bias (constant error). The variability of performance with body rotation was no greater than that of the dial when subjects' memory loads for directions to targets was reduced by allowing them to peek at targets in between trials, point to concurrent auditory targets, or point with their eyes open. In both experiments, errors with the manual dial were greater for directions to targets that were further from the closest orthogonal axis (ahead, behind, right, left), while errors with body rotation with restricted perceptual access were greater for directions to targets that were further from an axis straight ahead of subjects. This suggests that the two methods will produce evidence of different organizational frameworks for egocentric spatial knowledge. Implications for the structures and processes that underlie egocentric spatial knowledge, and are involved in estimating directions, are discussed, as is the value of decomposing absolute errors into variable and constant errors.
HANDEXOS : TOWARDS A SUPPORT DEVICE FOR HAND ACTIVITIES AND

HANDEXOS is an exoskeleton device for supporting th e human hand and performing teleoperation activities. It could be used to opera te both in remote-manipulation mode and directly in microgravity environments. In manipulation mode, crew or operators within the space ship could tele-control the endeffector of a robot in the space during the executi on of extravehicular activities (EVA) by means of an advanced upper limb exoskeleton. The ch oice of an appropriate man-machine interface (MMI) is important to allow a correct and dexterous grasp of objects of regular and irregular shapes in the space. Many different t chnologies have been proposed, from conventional joysticks to exoskeletons, but the ari sing number of more and more dexterous space manipulators such as Robonaut  or Eurobot  leads researchers to design novel MMIs with the aim to be more capable to exploit all functional advantages offered by new space robots. From this point of view exoskeletons better suite for execution of remote control-task than conventional joysticks, facilitat ing commanding of three dimensional trajectories and saving time in crew’s operation a nd training [3]. Moreover, it’s important to point out that in micro gravity environments the astronauts spend most time doing motor exercises, so HANDEXOS can be useful in supporting such motor practice, assisting human operators in overco ming physical limitations deriving from the fatigue in performing EVA. It is the goal of this paper to provide a detailed description of HANDEXOS mechanical design and to present the results of the preliminar y simulations derived from the implementation of the exoskeleton/human finger dyna mic model for different actuation solutions.
Sex bias in graduate admissions: data from berkeley.

Examination of aggregate data on graduate admissions to the University of California, Berkeley, for fall 1973 shows a clear but misleading pattern of bias against female applicants. Examination of the disaggregated data reveals few decision-making units that show statistically significant departures from expected frequencies of female admissions, and about as many units appear to favor women as to favor men. If the data are properly pooled, taking into account the autonomy of departmental decision making, thus correcting for the tendency of women to apply to graduate departments that are more difficult for applicants of either sex to enter, there is a small but statistically significant bias in favor of women. The graduate departments that are easier to enter tend to be those that require more mathematics in the undergraduate preparatory curriculum. The bias in the aggregated data stems not from any pattern of discrimination on the part of admissions committees, which seem quite fair on the whole, but apparently from prior screening at earlier levels of the educational system. Women are shunted by their socialization and education toward fields of graduate study that are generally more crowded, less productive of completed degrees, and less well funded, and that frequently offer poorer professional employment prospects.
What triggers human remembering of events? A large-scale analysis of catalysts for collective memory in Wikipedia

Going beyond its role as an encyclopedia, Wikipedia becomes a global memory place for high-impact events, such as, natural disasters and manmade incidents, thus influencing collective memory, i.e., the way we remember the past. Due to the importance of collective memory for framing the assessment of new situations, our actions and value systems, its open construction and negotiation in Wikipedia is an important new cultural and societal phenomenon. The analysis of this phenomenon does not only promise new insights in collective memory. It is also an important foundation for technology, which more effectively complements the processes of human forgetting and remembering and better enables us to learn from the past. In this paper, we analyse the long-term dynamics of Wikipedia as a global memory place for high-impact events. This complements existing work in analysing the collective memory negotiation and construction process in Wikipedia directly following the event. In more detail, we are interested in catalysts for reviving memories, i.e., in the fuel that keeps memories of past events alive, interrupting the general trend for fast forgetting. For this purpose, we study the trigger of revisiting behavior for a large set of event pages by exploiting page views and time series analysis, as well as identify of most important catalyst features.
Advances in Artificial Intelligence: From Theory to Practice

This paper aims at the annotation of movement phrases in Vietnamese folk dance videos that were mainly gathered, stored and used in teaching at art schools and in preserving cultural intangible heritages (performed by different famous folk dance masters). We propose a framework of automatic movement phrase annotation, in which the motion vectors are used as movement phrase features. Movement phrase classification can be carried out, based on dancer’s trajectories. A deep investigation of Vietnamese folk dance gives an idea of using optical flow as movement phrase features in movement phrase detection and classification. For the richness and usefulness in annotation of Vietnamese folk dance, a lookup table of movement phrase descriptions is defined. In initial experiments, a sample movement phrase dataset is built up to train k-NN classification model. Experiments have shown the effectiveness of the proposed framework of automatic movement phrase annotation with classification accuracy at least 88%.
Combining Health Data Uses to Ignite Health System Learning.

In this paper we aim to characterise the critical mass of linked data, methods and expertise required for health systems to adapt to the needs of the populations they serve - more recently known as learning health systems. The objectives are to: 1) identify opportunities to combine separate uses of common data sources in order to reduce duplication of data processing and improve information quality; 2) identify challenges in scaling-up the reuse of health data sufficiently to support health system learning.



The challenges and opportunities were identified through a series of e-health stakeholder consultations and workshops in Northern England from 2011 to 2014. From 2013 the concepts presented here have been refined through feedback to collaborators, including patient/citizen representatives, in a regional health informatics research network (www.herc.ac.uk).



Health systems typically have separate information pipelines for: 1) commissioning services; 2) auditing service performance; 3) managing finances; 4) monitoring public health; and 5) research. These pipelines share common data sources but usually duplicate data extraction, aggregation, cleaning/preparation and analytics. Suboptimal analyses may be performed due to a lack of expertise, which may exist elsewhere in the health system but is fully committed to a different pipeline. Contextual knowledge that is essential for proper data analysis and interpretation may be needed in one pipeline but accessible only in another. The lack of capable health and care intelligence systems for populations can be attributed to a legacy of three flawed assumptions: 1) universality: the generalizability of evidence across populations; 2) time-invariance: the stability of evidence over time; and 3) reducibility: the reduction of evidence into specialised sub-systems that may be recombined.



We conceptualize a population health and care intelligence system capable of supporting health system learning and we put forward a set of maturity tests of progress toward such a system. A factor common to each test is data-action latency; a mature system spawns timely actions proportionate to the information that can be derived from the data, and in doing so creates meaningful measurement about system learning. We illustrate, using future scenarios, some major opportunities to improve health systems by exchanging conventional intelligence pipelines for networked critical masses of data, methods and expertise that minimise data-action latency and ignite system-learning.
Shoulder pain in swimmers: a 12-month prospective cohort study of incidence and risk factors.

To investigate shoulder pain incidence rates and selected risk factors for shoulder pain in competitive swimmers.



12-month prospective cohort study.



Five swimming clubs in Melbourne, 
74 (37 M, 37 F) competitive swimmers ranging in age from 11 to 27 years and performing at least five swim sessions per week.


ASSESSMENT OF RISK FACTORS
Swimmers completed a baseline questionnaire regarding demographics, anthropometric features, swimming characteristics and training and injury history. Active shoulder internal (IR) and external rotation (ER) range of motion and passive joint laxity were measured.



Shoulder pain was self-reported over 12 months with significant interfering shoulder pain (SIP) defined as pain interfering (causing cessation or modification) with training or competition, or progression in training. A significant shoulder injury (SSI) was any SIP episode lasting for at least 2 weeks.



28/74 (38%) participants reported SIP while 17/74 (23%) reported SSI. Exposure-adjusted incidence rates were 0.3 injuries and 0.2 injuries per 1000 swim km for SIP and SSI, respectively. Swimmers with both high and low ER range were at 8.1 (1.5, 42.0) and 12.5 (2.5, 62.4) times greater risk of sustaining a subsequent SIP, respectively and 35.4 (2.8, 441.4) and 32.5 (2.7, 389.6) times greater risk of sustaining a SSI, respectively than those with mid-range ER. Similarly swimmers with a history of shoulder pain were 4.1 (95% CI: 1.3, 13.3) and 11.3 (95% CI: 2.6, 48. 4) times more likely to sustain a SIP and SSI, respectively.



Shoulder pain is common in competitive swimmers. Preventative programs should be particularly directed at those swimmers identified as being at risk of shoulder pain.
Texting while driving: the development and validation of the distracted driving survey and risk score among young adults.

Texting while driving and other cell-phone reading and writing activities are high-risk activities associated with motor vehicle collisions and mortality. This paper describes the development and preliminary evaluation of the Distracted Driving Survey (DDS) and score.



Survey questions were developed by a research team using semi-structured interviews, pilot-tested, and evaluated in young drivers for validity and reliability. Questions focused on texting while driving and use of email, social media, and maps on cellular phones with specific questions about the driving speeds at which these activities are performed.



In 228 drivers 18-24 years old, the DDS showed excellent internal consistency (Cronbach's alpha = 0.93) and correlations with reported 12-month crash rates. The score is reported on a 0-44 scale with 44 being highest risk behaviors. For every 1 unit increase of the DDS score, the odds of reporting a car crash increases 7 %. The survey can be completed in two minutes, or less than five minutes if demographic and background information is included. Text messaging was common; 59.2 and 71.5 % of respondents said they wrote and read text messages, respectively, while driving in the last 30 days.



The DDS is an 11-item scale that measures cell phone-related distracted driving risk and includes reading/viewing and writing subscores. The scale demonstrated strong validity and reliability in drivers age 24 and younger. The DDS may be useful for measuring rates of cell-phone related distracted driving and for evaluating public health interventions focused on reducing such behaviors.
Umbilical venous catheters placement evaluation on frontal radiogram: application of a simplified flow-chart for radiology residents

Umbilical Venous Catheter (UVC) are commonly used in neonatal period; they can be not correctly positioned and could be associated with complications. The purpose of this article is to suggest a flow-chart to evaluate the placement of UVC, testing it in young radiologists-in-training. We developed a simple flow-chart to asses, steps by step, UVC placement considering its course and tip location (ideally placed in the atriocaval junction). We tested the flow-chart impact asking to 20 residents to evaluate the placement of 10 UVC before and after they familiarized with the flow-chart and the anatomical findings of a newborn. The agreement among the 20 students was evaluated too. The number of correct characterizations was different due to the administration of the flow-chart. One hundred and six correct UVC assessments at the beginning switched to 196 after the administration of the flow-chart (p = 0.0001). The observed agreement among the twenty radiology residents was statistically significant, both before (kappa = 0.41, p < 0.001) and after (kappa = 0.37, p < 0.001) the flow-chart administration. The developed flow-chart demonstrated to be useful in increasing residents performance in UVC placement assessment.
Oows: A method to develop web applications from web-oriented conceptual models

This work presents an OO software production method that provides conceptual modelling extensions (in terms of models and abstraction primitives) to facilitate the web application specification and development process. It also discusses a strategy to automatically go from the problem space (conceptual models) to the solution space (final software product) in an automatic way, by implementing a set of mappings between conceptual primitives and their corresponding software representations. To include a practical view of the approach, the work describes a real case-study of a University Department Web System that has been developed with this method.
A signaling game model for moving target defense

Incentive-driven advanced attacks have become a major concern to cyber-security. Traditional defense techniques that adopt a passive and static approach by assuming a fixed attack type are insufficient in the face of highly adaptive and stealthy attacks. In particular, a passive defense approach often creates information asymmetry where the attacker knows more about the defender. To this end, moving target defense (MTD) has emerged as a promising way to reverse this information asymmetry. The main idea of MTD is to (continuously) change certain aspects of the system under control to increase the attacker's uncertainty, which in turn increases attack cost/complexity and reduces the chance of a successful exploit in a given amount of time. In this paper, we go one step beyond and show that MTD can be further improved when combined with information disclosure. In particular, we consider that the defender adopts a MTD strategy to protect a critical resource across a network of nodes, and propose a Bayesian Stackelberg game model with the defender as the leader and the attacker as the follower. After fully characterizing the defender's optimal migration strategies, we show that the defender can design a signaling scheme to exploit the uncertainty created by MTD to further affect the attacker's behavior for its own advantage. We obtain conditions under which signaling is useful, and show that strategic information disclosure can be a promising way to further reverse the information asymmetry and achieve more efficient active defense.
Internet of Things-enabled multiagent system for residential DC microgrids

This paper proposes an Internet of Things (IoT)-enabled multiagent system (MAS) for residential DC microgrids (RDCMG). The proposed MAS consisting of smart home agents (SHAs) aims to cooperate each other to alleviate the peak load of the RDCMG and to minimize the electricity costs for smart homes. These are achieved by agent utility functions and the best operating time algorithm (BOT) in the MAS. Moreover, IoT-based efficient and cost-effective agent communication method is proposed, which applies message queuing telemetry transport (MQTT) publish/subscribe protocol via MQTT brokers. The proposed IoT-enabled MAS and smart home models are implemented in five Raspberry pi 3 boards and validated by experimental studies for a RDCMG with five smart homes.
Dhwani: secure peer-to-peer acoustic NFC

Near Field Communication (NFC) enables physically proximate devices to communicate over very short ranges in a peer-to-peer manner without incurring complex network configuration overheads. However, adoption of NFC-enabled applications has been stymied by the low levels of penetration of NFC hardware. In this paper, we address the challenge of enabling NFC-like capability on the existing base of mobile phones. To this end, we develop Dhwani, a novel, acoustics-based NFC system that uses the microphone and speakers on mobile phones, thus eliminating the need for any specialized NFC hardware. A key feature of Dhwani is the JamSecure technique, which uses self-jamming coupled with self-interference cancellation at the receiver, to provide an information-theoretically secure communication channel between the devices. Our current implementation of Dhwani achieves data rates of up to 2.4 Kbps, which is sufficient for most existing NFC applications.