Miracle-dz/UniFAR-FTUs huggingface dataset:
Enactive Systems & Computing Mapping the terrain for Human-Computer Interaction research

While the concept of enaction is not new, recent trends on the use of contemporary devices to interact with computational systems have attracted the attention in the field of interaction design. A quick look in the literature reveals different meanings for an enactive system, depending on the knowledge domain. This paper explores the subject by conducting a Systematic Literature Review to construct a big picture on the subject. Results reveal the paths being traced and issues still unexplored that offer opportunities for investigation in the human computer interaction field.
AUTOMATED EXTRACTION OF LARGE-SCALE DATA FROM ELECTRONIC HEALTH RECORDS IN TERTIARY HOSPITAL

Background: Cardiovascular diseases are the major cause of mortality globally, as well as in India. Large volumes of data are continuously generated from clinical and diagnostic studies cataloged in EHRs. The collection and storage of a large amount of unstructured and heterogeneous data from various sources are not easily proceeded as in simple databases; therefore, we aimed to develop a fast and efficient algorithm to establish a research platform from the EHRs System for prediction of cardiovascular disease in South India. Methods : A retrospective study was designed to access the echocardiography reports from EHRs, who underwent transthoraci c echocardiography at JSS Hospital. The Macro VBA algorithm was applied to 16580 records of patients with a total of 55 attributes. During data cleansing, repetitive records, spelling errors and unrealistic data are detected and removed by using Macro VBA. The missing values in the data are identified and replaces them with the mean value of the data. Box and Whisker plot distribution of raw quantitative echocardiographic parameters, Multivariate identity, and extreme outliers are existing in the data series. Results: The study presents the automated extraction of non-standardized transthoracic echocardiographic reports from EHRs by using the Macro VBA algorithm and it was applied to 6891 outpatients and 9689 inpatients records at a single academic medical center in the year 2016. The total time taken by Macro VBA for extracting 16580 echocardiography reports and generating output in a structured format was less than an hour on a personal laptop, this algorithm will serve to decrease the burden of obtaining large scale data in a structured format. Conclusion: The study presents the automated extraction of non-standardized transthoracic echocardiographic reports into a structured data format from electronic health records by using Macro VBA.
Spatial distribution of manganese concentration and load in street dust in Mexico City.

OBJECTIVE To obtain a first indication of the distribution and extent of manganese (Mn) contamination in Mexico City. Mn concentration and load in street dust were analyzed in order to reveal the most contaminated areas. MATERIALS AND METHODS 482 samples of street dust were analyzed through inductively coupled plasma-optical emission spectroscopy. The contamination factor (CF), the geoaccumulation index (Igeo) and the spatial interpolations of the kriging indicator were calculated. RESULTS A slight influence of anthropogenic activities is detected on the Mn content of street dust. The highest levels of pollution by concentra- tion (Igeo=uncontaminated to moderately contaminated) are grouped towards the city's north (industrial) and center (commercial and high traffic) areas. The areas with the high- est Mn load were located towards the east and northwest areas (Igeo=moderately contaminated). CONCLUSIONS These findings will serve as a baseline to assess future variations in Mn content in Mexico City's environment.
Astroturf lobbying in the EU: The case of shale gas exploration

This paper seeks to shed light on astroturf lobbying, a strategy that recently invaded the European public. Its purpose is to simulate citizen support for a specific issue whilst keeping its identity secret. The public sphere is envisaged as a constellation of issues around which gravitate interest groups that try to influence the debate, and doing so by carefully frame their messages. In the case of the shale gas debate in the EU, the question that emerged is to see whether astroturf groups convey the economic frames used by the oil and gas companies they represent, or if they mobilised environmental frames such as shale gas opponents. Results show that the astroturf group mostly emphasized the safety of hydraulic fracturing and tried to counter the environmental frames of competing NGOs.
University of Birmingham Effects of pH manipulation, CatSper stimulation and Ca2+-store mobilisation on [Ca2+]i and behaviour of human sperm

Study question: How do the alkaline pH, progesterone and 4-aminopyridine interact in their effects 20 on human sperm? What is known already? CatSper channels of human sperm, which are central to generation of 24 sperm [Ca 2+ ] i signals and induction of hyperactivated motility, are activated by intracellular 25 alkalinisation and progesterone. Progesterone (P4) is much less effective than 4-aminopyridine (4-AP) 26 (which mobilises stored Ca 2+ but also raises pH i ) as an inducer of hyperactivation. Study design, This was a laboratory study, spanning approximately 18 months 28 that used 15 sperm donors and involved more than 100 separate experiments. 2+ ] i responses of suspended cell 31 populations examined relationship between [Ca 2+ ] i (fluorescence of fluo4) and proportion of hyperactivated cells, 44 irrespective of pH o, agonist or dose, was fitted by a single curve (2 nd order polynomial; R 2 =0.96). Similar analysis of curvilinear velocity (VCL) and amplitude of lateral head displacement (ALH) 46 showed a linear relationship to [Ca 2+ ] i (R 2 >0.9).
Supplementary Material : Reaching the quantum limit of sensitivity in pulsed Electron Spin Resonance spectroscopy

The sample consists of a natural silicon (100) substrate on which a 700 nm-thick isotopically enriched 99.95% Si epitaxial layer was grown. Bismuth dopants were subsequently implanted into the epitaxial layer and activated by thermal annealing (see [1] for more details). The implantation profile, measured via Secondary Ion Mass Spectroscopy (SIMS) is shown in 1 of the main text. The activation step consists in an anneal to 800◦C for 20min under nitrogen atmosphere. An electrical activation yield of 60% has been measured using a Hall effect measurement system under similar implantation conditions [1].
SEM Image Analysis of CNT Interconnect Vias

Carbon-based nanostructures such as carbon nanotubes (CNTs) and carbon nanofibers (CNFs) are candidate materials for next-generation integrated circuit technologies due to their high current-carrying capacities and excellent electrical, thermal, and mechanical properties [1,2]. Since the via resistance depends on the number of conductive CNTs inside the via, it is important to investigate not only the characteristics of the via but also the individual CNTs inside [3]. In this paper, we report the use of a SEM imaging technique to detect defective CNT vias and identify CNTs contributing to the conducting process.
Achieving CCA1-security in homomorphic encryption

This paper proposes the combination of homomorphic encryption and verifiable computation to avoid key recovery attacks and achieve CCA1-secure constructions of Somewhat Homomorphic Encryption (SHE) schemes described in the literature. We also provide concrete parameters, based on the bestattack analysis, concluding that the AGCD [van Dijk et al. 2010] family of SHE schemes may be the best implementation choice under certain circumstances. Resumo. Este artigo propõe a combinação de encriptação homomórfica e computação verificável para evitar ataques de recuperação de chaves e obter segurança CCA1 em construções de esquemas parcialmente homomórficos descritos na literatura. Além disso, são propostos parâmetros concretos, baseados na análise do melhor ataque, concluindo que a famı́lia AGCD [van Dijk et al. 2010] de esquemas SHE pode ser consideradas a melhor escolha em determinadas circunstâncias.
A Comparison of Collaborative Filtering-based Recommender Systems

: The proliferation of Internet has made people to rely on virtual recommendations. Recommender systems help out in giving important recommendations. Collaborative filtering is the most successful and widely used approach in designing recommender systems since the introduction of the concept of recommender systems. This approach uses the known tastes and preferences of a set of users to make predictions or generate recommendations about the unknown tastes and preferences of the target user. This paper discusses various works which use collaborative filtering approach to design recommender systems. The paper also gives a comparison of these approaches
Factors Influencing the Vulnerability of Female Domestic Workers in Nairobi County

Abstract: The purpose of this study was to explore factors th at influenced the vulnerability of female domestic workers in Nairobi County. The objectives of the study were to determi ne how vocational training or the lack of it; awareness of rights among the workers; their socioeconomic backgrounds and prior work experience infl uenced the vulnerability experienced by the female domestic workers. A cross-sectional surv ey design was applied in the study. The target population was adolescent female domestic wo rkers from ten estates scattered in Nairobi County. A sample size of 47 respondents was picked using purposive and snowballing sampling techniques. Data was collected using a questionnair e. After categorizing the data, simple descriptive statistics were used to analyze the dat a. The study found that domestic work was still viewed by many as difficult, dirty, and demeaning a d that domestic workers were paid low wages and worked long hours. The study findings cal l for implementation of decent work legislation for domestic workers as envisaged in IL O conventions.
Analysis of crustal deformation due to slip across faults using finite element method

We conducted time dependent block fault modeling for southeast Japan. The result shows that slip deficit on the Philippine sea plate which takes block motion into account becomes smaller than that which does not take into account block motion. In particular, slip deficit of the Philippine Sea plate in the Tokai inland area disappears, as was confirmed in previous studies by many researchers. Introduction It is very important to estimated interplate coupling precisely so that we can get information about whereabout and moment magnitude of the expected large subduction earthquakes. In particular, it is an urgent task to prepare for the Tokai and Tonakai earthquakes along the Suruga and Nankai troughs. Under this circumstance, many studies have been conducted about the interplate coupling in southeast Japan. However, spatial and temporal interplate coupling has not been treated in detail. In this study, we developed an analytical code of time dependent block fault modeling and applied it to southeast Japan. Analytical Procedure Hashimoto et al(2000) conducted block fault modeling of Japan, using GPS data. In this study, we used the block model geometry of Hashimoto et al. (2000). That is we used rectangular faults in inland area defined by Hashimoto et al. (2000). The plate boundary in a subdcution area was represented by superposition of spline functions. By using this geometry, we estimated interplate coupling in inland area and subduction zones. We used EW, NS, UD position time series at approximately 500 GSP sites. The period was set at 2008-2009 Results and Discussion Time dependent block fault modeling shows that interplate coupling on the Philippine Sea plate decreases when we take into account a block motion. In Tokai region, coupling on the Philippine Sea plate disappears in deep inland area. time dependent inversion, block fault modeling , souteast Japan SSS32-P05 Japan Geoscience Union Meeting 2016 ©2016. Japan Geoscience Union. All Right Reserved. SSS32-P05
Human Grasp Classification for Reactive Human-to-Robot Handovers

Transfer of objects between humans and robots is a critical capability for collaborative robots. Although there has been a recent surge of interest in human-robot handovers, most prior research focus on robot-to-human handovers. Further, work on the equally critical human-to-robot handovers often assumes humans can place the object in the robot’s gripper. In this paper, we propose an approach for human-to-robot handovers in which the robot meets the human halfway, by classifying the human’s grasp of the object and quickly planning a trajectory accordingly to take the object from the human’s hand according to their intent. To do this, we collect a human grasp dataset which covers typical ways of holding objects with various hand shapes and poses, and learn a deep model on this dataset to classify the hand grasps into one of these categories. We present a planning and execution approach that takes the object from the human hand according to the detected grasp and hand position, and replans as necessary when the handover is interrupted. Through a systematic evaluation, we demonstrate that our system results in more fluent handovers versus two baselines. We also present findings from a user study (N = 9) demonstrating the effectiveness and usability of our approach with naive users in different scenarios. More information can be found at http://wyang.me/handovers.
Dynamic Transformation of Prior Knowledge Into Bayesian Models for Data Streams

We consider how to effectively use prior knowledge when learning a Bayesian model from streaming environments where the data come endlessly and sequentially. This problem is highly important in the era of data explosion and rich sources of valuable external knowledge such as pre-trained models, ontologies, Wikipedia, etc. We show that some existing approaches can forget any knowledge very fast. We then propose a novel framework that enables to incorporate the prior knowledge of different forms into a base Bayesian model for data streams. Our framework subsumes some existing popular models for time-series/dynamic data. Extensive experiments show that our framework outperforms existing methods with a large margin. In particular, our framework can help Bayesian models generalize well on extremely short text while other methods overfit. An implementation of our framework is available at http://github.com/bachtranxuan/TPS.
Image Simulations for Strong and Weak Gravitational Lensing

Gravitational lensing has been identified as a powerful tool to address fundamental problems in astrophysics at different scales, ranging from exoplanet identification to dark energy and dark matter characterization in cosmology. Image simulations have played a fundamental role in the realization of the full potential of gravitational lensing by providing a means to address needs such as systematic error characterization, pipeline testing, calibration analyses, code validation, and model development. We present a general overview of the generation and applications of image simulations in strong and weak gravitational lensing.
LIBRE: The Multiple 3D LiDAR Dataset

In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of manufacturers, models, and laser configurations. Data captured independently from each sensor includes three different environments and configurations: static targets, where objects were placed at known distances and measured from a fixed position within a controlled environment; adverse weather, where static obstacles were measured from a moving vehicle, captured in a weather chamber where LiDARs were exposed to different conditions (fog, rain, strong light); and finally, dynamic traffic, where dynamic objects were captured from a vehicle driven on public urban roads, multiple times at different times of the day, and including supporting sensors such as cameras, infrared imaging, and odometry devices. LIBRE will contribute to the research community to (1) provide a means for a fair comparison of currently available LiDARs, and (2) facilitate the improvement of existing self-driving vehicles and robotics-related software, in terms of development and tuning of LiDAR-based perception algorithms.
PointINS: Point-Based Instance Segmentation

In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features. Differentiating multiple potential instances within a single PoI feature is challenging, because learning a high-dimensional mask feature for each instance using vanilla convolution demands a heavy computing burden. To address this challenge, we propose an instance-aware convolution. It decomposes this mask representation learning task into two tractable modules as instance-aware weights and instance-agnostic features. The former is to parametrize convolution for producing mask features corresponding to different instances, improving mask learning efficiency by avoiding employing several independent convolutions. Meanwhile, the latter serves as mask templates in a single point. Together, instance-aware mask features are computed by convolving the template with dynamic weights, used for the mask prediction. Along with instance-aware convolution, we propose PointINS, a simple and practical instance segmentation approach, building upon dense one-stage detectors. Through extensive experiments, we evaluated the effectiveness of our framework built upon RetinaNet and FCOS. PointINS in ResNet101 backbone achieves a 38.3 mask mean average precision (mAP) on COCO dataset, outperforming existing point-based methods by a large margin. It gives a comparable performance to the region-based Mask R-CNN K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in Proc. IEEE Int. Conf. Comput. Vis. , 2017, pp. 2980–2988 with faster inference.
Learning Graph Embedding with Limited Labeled Data: An Efficient Sampling Approach

Semi-supervised graph embedding methods represented by graph convolutional network has become one of the most popular methods for utilizing deep learning approaches to process the graph-based data for applications. Mostly existing work focus on designing novel algorithm structure to improve the performance, but ignore one common training problem, i.e., could these methods achieve the same performance with limited labelled data? To tackle this research gap, we propose a sampling-based training framework for semi-supervised graph embedding methods to achieve better performance with smaller training data set. The key idea is to integrate the sampling theory and embedding methods by a pipeline form, which has the following advantages: 1) the sampled training data can maintain more accurate graph characteristics than uniformly chosen data, which eliminates the model deviation; 2) the smaller scale of training data is beneficial to reduce the human resource cost to label them; The extensive experiments show that the sampling-based method can achieve the same performance only with 10$\%$-50$\%$ of the scale of training data. It verifies that the framework could extend the existing semi-supervised methods to the scenarios with the extremely small scale of labelled data.
BuildSenSys: Reusing Building Sensing Data for Traffic Prediction With Cross-Domain Learning

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperforms all baseline methods with up to 65.3 percent accuracy improvement (e.g., 2.2 percent MAPE) in predicting nearby traffic volume. We believe that this work can open a new gate of reusing building sensing data for urban traffic sensing, thus establishing connections between smart buildings and intelligent transportation.
The Evolution of the Star-Forming Interstellar Medium Across Cosmic Time

Over the past decade increasingly robust estimates of the dense molecular gas content in galaxy populations between redshift 0 and the peak of cosmic galaxy/star formation from redshift 1-3 have become available. This rapid progress has been possible due to the advent of powerful ground-based, and space telescopes for combined study of several millimeter to far-IR, line or continuum tracers of the molecular gas and dust components. The main conclusions of this review are: 1. Star forming galaxies contained much more molecular gas at earlier cosmic epochs than at the present time. 2. The galaxy integrated depletion time scale for converting the gas into stars depends primarily on z or Hubble time, and at a given z, on the vertical location of a galaxy along the star-formation rate versus stellar mass "main-sequence" (MS) correlation. 3. Global rates of galaxy gas accretion primarily control the evolution of the cold molecular gas content and star formation rates of the dominant MS galaxy population, which in turn vary with the cosmological expansion. A second key driver may be global disk fragmentation in high-z, gas rich galaxies, which ties local free-fall time scales to galactic orbital times, and leads to rapid radial matter transport and bulge growth. Third, the low star formation efficiency inside molecular clouds is plausibly set by super-sonic streaming motions, and internal turbulence, which in turn may be driven by conversion of gravitational energy at high-z, and/or by local feedback from massive stars at low-z. 4. A simple 'gas regulator' model is remarkably successful in predicting the combined evolution of molecular gas fractions, star formation rates, galactic winds, and gas phase metallicities.