nils-herrmann/clean_scirepeval huggingface dataset:
Human Activity Recognition Using Spatiotemporal 3 D Body Joint Features with Hidden Markov Models
Venue: KSII Trans. Internet Inf. Syst.

Video based human activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart home healthcare, and human computer interactions. The essential goals of a video based activity recognition system include the provision of behavior based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human activity recognition, whereby human body joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained activity HMMs are used for depth video activity recognition.
The DialPort Portal Grouping Diverse Types of Spoken Dialog Systems

This paper describes a new spoken dialog portal that connects systems produced by the spoken dialog academic research community and gives them access to real users. We introduce a distributed, multi modal, multi agent prototype dialog framework that affords easy integration with various remote resources, ranging from end to end dialog systems to external knowledge APIs. To date, the DialPort portal has one chatbot and has successfully connected to the multi domain spoken dialog system at Cambridge University, the NOAA (National Oceanic and Atmospheric Administration) weather API and the Yelp API.
Structural built up of cement based materials used for 3D printing extrusion techniques

Additive manufacturing and digital fabrication bring new horizons to concrete and cement based material construction. 3D printing inspired construction techniques that have recently been developed at laboratory scale for cement based materials. This study aims to investigate the role of the structural build up properties of cement based materials in such a layer by layer construction technique. As construction progresses, the cement based materials become harder with time. The mechanical strength of the cement based materials must be sufficient to sustain the weight of the layers subsequently deposited. It follows that the comparison of the mechanical strength, which evolves with time (i.e. structural build up) with the loading due to layers subsequently deposited, can be expected to provide the optimal rate of layer by layer construction. A theoretical framework has been developed to propose a method of optimization of the building rate, which is experimentally validated in a layer wise built column.
Gamification for health and wellbeing: A systematic review of the literature
Venue: Internet interventions

Compared to traditional persuasive technology and health games, gamification is posited to offer several advantages for motivating behaviour change for health and well being, and increasingly used. Yet little is known about its effectiveness.  We aimed to assess the amount and quality of empirical support for the advantages and effectiveness of gamification applied to health and well being.  We identified seven potential advantages of gamification from existing research and conducted a systematic literature review of empirical studies on gamification for health and well being, assessing quality of evidence, effect type, and application domain.  We identified 19 papers that report empirical evidence on the effect of gamification on health and well being. 59% reported positive, 41% mixed effects, with mostly moderate or lower quality of evidence provided. Results were clear for health related behaviours, but mixed for cognitive outcomes.  The current state of evidence supports that gamification can have a positive impact in health and wellbeing, particularly for health behaviours. However several studies report mixed or neutral effect. Findings need to be interpreted with caution due to the relatively small number of studies and methodological limitations of many studies (e.g. a lack of comparison of gamified interventions to non gamified versions of the intervention)
Femtosecond harmonic mode locking of a fiber laser at 3.27 GHz using a bulk like, MoSe2 based saturable absorber.
Venue: Optics express

We experimentally demonstrate the use of a bulk like, MoSe2 based saturable absorber (SA) as a passive harmonic mode locker for the production of femtosecond pulses from a fiber laser at a repetition rate of 3.27 GHz. By incorporating a bulk like, MoSe2/PVA composite deposited side polished fiber as an SA within an erbium doped fiber ring cavity, mode locked pulses with a temporal width of 737 fs to 798 fs can be readily obtained at various harmonic frequencies. The fundamental resonance frequency and the maximum harmonic resonance frequency are 15.38 MHz and 3.27 GHz (212th harmonic) respectively. The temporal and spectral characteristics of the output pulses are systematically investigated as a function of the pump power. The output pulses exhibited Gaussian temporal shapes irrespective of the harmonic order, and even when their spectra possessed hyperbolic secant shapes. The saturable absorption and harmonic mode locking performance of our prepared SA are compared with those of previously demonstrated SAs that are based on other transition metal dichalcogenides (TMDs) To the best of the authors' knowledge, the repetition rate of 3.27 GHz is the highest frequency that has ever been demonstrated regarding the production of femtosecond pulses from a fiber laser that is based on SA induced passive harmonic mode locking.
The effects of surface roughness on varnish adhesion strength of wood materials
Venue: Journal of Coatings Technology and Research

In this study, the effects of surface roughness of wood material on the adhesion strength of varnish layers have been tested. For this purpose, test samples of beech (Fagus orientalis L. Scots pine (Pinus sylvestris L. and cherry (Prunus cerasus) wood species were prepared and sanded with 80, 120, and 180 grit abrasives. The surface roughness values of specimens were determined using a stylus type profilometer TIME TR 200 according to the ISO 4287 standards. Then, water based, polyurethane, and acrylic varnish were applied to the surfaces of the samples in accordance with ASTM D 3023. Layer adhesion strength was determined to the TS EN ISO 4624 standards. As a result, the highest adhesion strength value (2.52 N/mm2) was found in cherry, while the Scots pine had the lowest value (2.32 N/mm2) For the varnish types, polyurethane varnish types gave the highest value (3.15 N/mm2) while the lowest value was obtained from the water based varnish. Looking at interactions between adhesion strength and surface roughness, the water based varnish had the strongest correlation with adhesion strength (69% and acrylic varnish also had a similar result (67% while polyurethane varnish had the weakest result (33% in this interaction.
Impact of Disease Duration on Vascular Surrogates of Early Atherosclerosis in Childhood Onset Systemic Lupus Erythematosus
Venue: Arthritis rheumatology

To determine whether longer disease duration negatively impacts carotid intima media thickness (CIMT) flow mediated dilation (FMD) and pulse wave velocity (PWV) in a cohort of patients with childhood onset systemic lupus erythematosus (SLE) and to compare CIMT, FMD, and PWV in patients with childhood onset SLE with those in healthy children and explore determinants of vascular test results in childhood onset SLE.
Mean platelet volume is a marker of inflammation but not a marker of disease activity in children with juvenile SLE

To study the mean platelet volume (MPV) in children with juvenile onset SLE (Jo SLE) and whether MPV can be used as a biologic marker for disease activity or flare.  Twenty nine patients from the rheumatology outpatient clinic, Pediatric Cairo University Hospitals and age 36 and gender matched healthy controls were included in the study. The MPV was determined within 4 h of blood sampling in all study populations. Recent routine laboratory investigations for Jo SLE patients were obtained. Disease activity was estimated using SLE disease activity index (SLEDAI) Results 29 Jo SLE patients had a mean age of 12.8 2.9 years and disease duration of 3.5 3 year. The most frequent clinical manifestations were photosensitivity, malar rash, followed by arthritis and serositis. The MPV in Jo SLE patients was 8.2 2.1 femtoliters (fL) compared with 5.6 0.9 fL in healthy controls (p  Results of the present study confirm the association between MPV and inflammation, but do not support the use of MPV as an indicator for monitoring disease activity or flare in juvenile SLE. Further longitudinal studies with larger numbers of patients are warranted to unveil the possibility of using MPV as a biologic marker of disease activity.
Leitfaden zur Erfassung des psychopathologischen Befundes. Halbstrukturiertes Interview anhand des AMDP Systems

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An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation
Venue: Front. Neurosci.

Currently, network oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1 minimization Approach (CLIME) which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non neurophysiological effects. An R package "DensParcorr" can be downloaded from CRAN for implementing the proposed statistical methods.
From functional to structural connectivity using partial correlation in neuronal assemblies.
Venue: Journal of neural engineering

Our goal is to re introduce an optimized version of the partial correlation to infer structural connections from functional effective ones in dissociated neuronal cultures coupled to microelectrode arrays.  We first validate our partialization procedure on in silico networks, mimicking different experimental conditions (i.e. different connectivity degrees and number of nodes) and comparing the partial correlation's performance with two gold standard methods: cross correlation and transfer entropy. Afterwards, to infer the structural connections in in vitro neuronal networks where the ground truth is unknown, we propose a thresholding heuristic approach. Then, to validate whether the partialization process correctly reconstructs macroscopic features of the network structure, we extract a modularity index from segregated in silico and in vitro models. Finally, as a case study, we apply our partialization procedure to analyze connectivity and topology on spontaneous developing and electrically stimulated in vitro cultures.  In simulated networks, partial correlation outperforms cross correlation and transfer entropy at low and medium connectivity degrees, not only in relatively small (60 nodes) but also in larger (120 240 nodes) assemblies. Furthermore, partial correlation correctly identifies interconnected neuronal sub populations and allows one to derive network topology in in vitro cortical networks.  Our results support the idea that partial correlation is a good method for connectivity studies and can be applied to derive topological and structural features of neuronal assemblies.
A Study of Message Communication Method Using Attribute Based Encryption in IoT Environment

Many countries, especially ICT powers, are supporting IoT based technology at a national level and this technology is actively being researched in the businesses and research institutes in an aim to develop technology and create an ecosystem. Roads in the Seoul city are building public facilities based on IoT to provide various services and conveniences for the users. However, for the full fledged introduction and development of IoT, there are many cases where infringement on security and privacy and threat for life and safety happen. Also, as the IoT environment includes various environment technologies such as the existing sensor network, heterogeneous communication network, and devices optimized for the IoT environment, it inherits the existing security threat and various attack techniques. This paper researches the attribute based encryption technology for safe communication in the IoT environment. The data collected from the device is transmitted utilizing the attribute based encryption and by designing the key generation protocol, grades and authorities for the device and users are identified to transmit safe messages.
Sparse matrix factorization in the implicit finite element method on petascale architecture

The performance of the massively parallel direct multifrontal solver Watson Sparse Matrix Package (WSMP) for solving large sparse systems of linear equations arising in implicit finite element method on unstructured (free) meshes in solid mechanics was evaluated on one of the most powerful supercomputers currently available to the open science community the sustained petascale high performance computing system of Blue Waters. We have performed full scale benchmarking tests up to 65,536 cores using assembled global stiffness matrices and load vectors ranging from 11 to 40 million unknowns extracted from "real world" commercial implicit finite element analysis (FEA) applications. The results show that a direct multifrontal factorization method with a hybrid parallel implementation in WSMP performs exceedingly well on a petascale high performance computing (HPC) system, and delivers superior factorization time and parallel scalability, thus opening the door for the high fidelity modeling of complex industrial structures and assemblies in real scale.
HOFEM: Higher order finite element method simulator for antenna analysis
Venue: 2016 IEEE Conference on Antenna Measurements Applications (CAMA)

This document presents the application of a new electromagnetic in house parallel EM simulator to the antenna analysis. The simulator is included in the commercial software HOBBIES. HOFEM makes use of a weak formulation based on double curl vector wave equation discretized with the higherorder isoparametric curl conforming tetrahedral and triangular prismatic finite element rigorous implementations of Nedelec's first family of elements. These implementations together with an appropriate hp adaptivity technique provide very accurate field approximations. A non standard mesh truncation technique, FE IIEE (Finite Element Iterative Integral Equation Evaluation) for open region problems is also implemented providing an arbitrarily exact radiation boundary condition while retaining the original sparse structure of the FEM matrices. A numerical example is presented showing the reliability, performance and versatility of the solver for the antenna analysis.
How do People Make Sense of Unfamiliar Visualizations? A Grounded Model of Novice's Information Visualization Sensemaking
Venue: IEEE Transactions on Visualization and Computer Graphics

In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e. a parallel coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think aloud sessions and semi structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvice's information VIsualization Sensemaking (NOVIS model) which consists of the five major cognitive activities: 1 encountering visualization, 2 constructing a frame, 3 exploring visualization, 4 questioning the frame, and 5 floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.
Sustainable Load Balancing Scheme for Inter Sensor Convergence Processing of Routing Cooperation Topology

Recent advancements in Information Technology (IT) have sparked the creation of numerous and diverse types of devices and services. Manual data collection measurement methods have been automated through the use of various wireless or wired sensors. Single sensor devices are included in smart devices such as smartphones. Data transmission is critical for big data collected from sensor nodes, such as Mobile Sensor Nodes (MSNs) where sensors move dynamically according to sensor mobility, or Fixed Sensor Nodes (FSNs) where sensor locations are decided by the users. False data transfer processing of big data results in topology lifespan reduction and data transfer delays. Hence, a variety of simulators and diverse load balancing algorithms have been developed as protocol verification tools for topology lifespan maximization and effective data transfer processing. However, those previously developed simulators have limited functions, such as an event function for a specific sensor or a battery consumption rate test for sensor deployment. Moreover, since the previous load balancing algorithms consider only the general traffic distribution and the number of connected nodes without considering the current topology condition, the sustainable load balancing technique that takes into account the battery consumption rate of the dispersed sensor nodes is required. Therefore, this paper proposes the Sustainable Load balancing Scheme (SLS) which maximizes the overall topology lifespan through effective and sustainable load balancing of data transfer among the sensors. SLS is capable of maintaining an effective topology as it considers both the battery consumption rate of the sensors and the data transfer delay.
Crew injuries in container vessel accidents

This paper investigates determinants of the probability of non fatal and fatal crew injuries in container vessel accidents. The determinants are deduced from an estimated probit regression equation based upon 2001 2008 U.S. Coast Guard container vessel accident data. The estimation results suggest that a crew member is: (1) less likely to have a non fatal injury in containership and ro ro container vessel accidents if the vessel has a steel hull and the vessel accident occurs at nighttime, (2) more likely to have a fatal injury if fire is involved in containership and ro ro container vessel accidents, and (3) less likely to have a fatal injury in containership and ro ro container vessel accidents if the vessel is U.S. flagged, steel hulled, and powered by a diesel engine. These results are critical in developing new policies in reducing non fatal and fatal crew injuries in container vessel accidents.