Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Affecting Success of Serial Crowdfunding: From Heuristic and Systematic Perspectives

Much of the current crowdfunding literature focuses on revealing determinants of one-time crowdfunding performance. However, the impacts of existing platform cues on serial crowdfunding performance remain largely unexplored. Drawing heuristic-systematic model, this study examines how performance-based heuristics cues and opinion-based systematic cues exert differential impacts on subsequent crowdfunding performance. This paper will fill the research gap in the crowdfunding literature by examining how backers are processing performance-related and opinion-related information when making decisions in serial crowdfunding context.
A Controlled Environment to Analyze Dual-Polarimetric Features for Radar Remote Sensing Purposes

A controlled environment is designed to analyze polarimetric features and decomposition methods for radar remote sensing purposes. The environment is based on the anechoic chamber of Università degli Studi di Napoli Parthenope (UNP) and it exploits a quasi-monostatic antenna configuration that is obtained using a transmitting antenna and two receiving antennas that coherently receive the co- and cross-polarized signals. This allows emulating polarimetric radar measurements related to slecetd targets in a controlled enviroment and, therefore. testing polarimetric radar features and methods. In this abstract, both an incoherent and a coherent analysis are undertaken with the latter exploiting Stokes parameters and eigenvalue decomposition. Experiments undertaking using a man-made depolarizing target confirm the soundness of the approach.
Intelligent Decision Support for Pervasive Home Monitoring and Assisted Living

The current trend of population ageing leads to an increasingly larger population of older adults, who understandabl desire to continue living an independent and fulfilling life in their home and within their communities. While traditionally seen as a societal issue, we are currently at a point where advancement in science and technology enables us to augment human help with ambient assisted living solutions. This paper is the result of research and development undertaken within the framework of a European Union research project targeting the development of a cyber-physical system for assisted living and home monitoring. The system integrates an unobtrusive networkof wireless sensors with server software to provide ambient monitoring, location detection and real-time alerting. The present paper is focused on the system’s intelligent software components.The first is a business rules engine that can be configured to send real-time alerts in the case of certain ambient conditions or whe the location of the monitored person shows signification alteration from their usual movement patterns. The second component is a artificial intelligence-based location predictor, used to provide the monitored person’s location. Discrepancies between actual an expected location are used by the rule engine to trigger real-time alerts to caregivers.
An Efficient Region Proposal Method for Optical Remote Sensing Imagery

Region proposals are very important for several perfections of remote sensing applications such as vehicle detection, traffic surveillance and intelligent transport system. In this paper, an efficient region proposal approach has been proposed. The framework is organized into two key steps. The first step is based on extracting region proposals using Cascade system. The second step is based on the classification of extracted region proposals which is performed by transfer learning using Convolutional Neural Networks (CNN) and AlexNet architecture is utilized for transfer learning. The aim of this investigation is to evaluate the proposed method for vehicle detection. The selective search (SS) method is also briefly discussed for comparison. The results regarding vehicle detection are very promising.
Aerosol Optical Depth Characterization in Middle and Polar Latitudes

The Aerosol Optical Depth (AOD) is a very important atmospheric parameter which knowledge will help to understand the atmospheric processes and to propose different scenarios related with climate change. AOD plays a main role in the energy radiation balance in the atmosphere and in the aerosol radiative forcing. In this paper an analysis of time series of AOD, from MYD04_3K (MODIS Aqua Aerosols product gridded in a resolution of 3 kilometres), is analysed by means of Fourier Harmonics tool. Results shown in this paper are preliminary results because a more comprehensive study is envisaged where data coming from sun and lunar photometers installed in Artic stations will be analysed jointly.
Deep Multi-OMICs and Multi-Tissue Characterization in a Pre- and Postprandial State in Human Volunteers: The GEMM Family Study Research Design

Cardiovascular disease (CVD) and type 2 diabetes (T2D) are increasing worldwide. This is mainly due to an unhealthy nutrition, implying that variation in CVD risk may be due to variation in the capacity to manage a nutritional load. We examined the genomic basis of postprandial metabolism. Our main purpose was to introduce the GEMM Family Study (Genetics of Metabolic Diseases in Mexico) as a multi-center study carrying out an ongoing recruitment of healthy urban adults. Each participant received a mixed meal challenge and provided a 5-hours’ time course series of blood, buffy coat specimens for DNA isolation, and adipose tissue (ADT)/skeletal muscle (SKM) biopsies at fasting and 3 h after the meal. A comprehensive profiling, including metabolomic signatures in blood and transcriptomic and proteomic profiling in SKM and ADT, was performed to describe tendencies for variation in postprandial response. Our data generation methods showed preliminary trends indicating that by characterizing the dynamic properties of biomarkers with metabolic activity and analyzing multi-OMICS data it could be possible, with this methodology and research design, to identify early trends for molecular biology systems and genes involved in the fasted and fed states.
If You Are Happy and DON'T Know IT: Continuance? Analyzing Emotion Carry-Over Effects in Activity Tracking Continuance Decisions

Activity tracking devices and apps are positioned to enhance healthy behavior. Albeit positive outcomes are widely anticipated, many users abandon their devices and apps after short time which raises concerns about their effectiveness. Studies indicate that self-tracking can provoke –positive but particularly negative– emotions with which people have to cope. Though studying emotions in IS usage is gaining attention, the role of system-unrelated emotions have been largely neglected yet can play an important role in human behavior. This study theorizes how system-unrelated emotions ‚carry-over‘ into IS continuance decisions in the context of activity- tracking. Results of an experimental survey largely support the ‚carry-over‘ effect in IS continuance decisions – particularly for less experienced users. Our study contributes to the growing self-tracking literature but also to research on emotions in IS usage by highlighting the powerful role of system-unrelated emotions.
Research and Implementation of Oblique Photography Productions on the WEB3D Visualization of Digtal Earth

Three-dimensional WebGIS has become an important research direction of GIS, and the increasing maturity of oblique photography measurement technology makes 3D-city model more efficient and provides a large number of data sources for the three-dimensional WebGIS platform. However, the amount of data obtained directly from oblique photography products is quite large and can not be visualized on the Web side. In this paper, we establish a simplified algorithm of oblique photography products, reconstruct the LOD structure of products, establish an efficient spatial index model by using the quadtree algorithm, and improve the data scheduling mechanism.
Deriving High Performance Knowledge Sharing Culture (HPKSC): A Firm Performance & Innovation Capabilities Perspective

This paper intends to highlight the significance of ‘High Performance Work Practices (HPWPs)’ on employee performance and knowledge capabilities from the perspective of knowledge-sharing environment and shared work practices. By taking into account the underlying challenges governing the creation of ‘High Performance Knowledge Sharing Culture (HPKSC)’, this paper argues how specific set of Organizational factors (HPWPs) and Knowledge-Sharing factors can be applied and practiced in conjunction to result in HPKSC i.e. a flourishing Knowledge-sharing culture focused on delivering superior firm performance. Accordingly, a conceptual framework is developed using above set of factors that serves as a basis for deriving HPKSC, aimed at achieving firm performance and innovation capabilities.
Estimated Acute Effects of Ozone on Mortality in a Rural District of Beijing, China, 2005–2013: A Time-Stratified Case-Crossover Study

Studies have shown that ozone (O3) has adverse impacts on human health. In China, O3 levels have continued to increase since 2010. When compared to the large number of studies concerning the health effects of PM2.5 in China, there have been limited explorations of the effects of O3. The Beijing region has one of the highest O3 concentrations in the country, but there appear to be no published studies regarding the health effects of O3 in Beijing. In this study, we applied a time-stratified case-crossover design to explore the effects of O3 on cause-specific mortality for a rural location near Beijing over the period 2005–2013. For year-round effects, we found that for all-causes mortality, with a 10-unit increase in O3 concentration, the odds ratios (ORs) were in the range of 1.009–1.020 for different lag days. The ORs for cardiovascular mortality with a 10-unit increase in O3 concentration were in the range of 1.011–1.017 for different lag days. For warm season effects, the ORs with a 10-unit increase in O3 concentration for all-cause mortality were in the range of 1.025–1.031 for different lag days. The ORs for cardiovascular mortality with a 10-unit increase of O3 concentration were in the range of 1.020–1.024 for different lag days. Our findings fill a knowledge gap that has hitherto existed in studies regarding O3 health impacts, and our results will strengthen the rationale for O3 control in China.
Nonlinear Cook Distance for Anomalous Change Detection

In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach. A regressor between images is used to discover the most influential points in the observed data. Typically, the pixels with largest residuals are decided to be anomalous changes. In order to find the anomalous pixels we consider the Cook distance and propose its nonlinear extension using random Fourier features as an efficient nonlinear measure of impact. Good empirical performance is shown over different multispectral images both visually and quantitatively evaluated with ROC curves.
Classification of High Resolution Urban Remote Sensing Images Using Deep Networks by Integration of Social Media Photos

In recent decades, it is easy to obtain remote sensing images which have been successfully applied to various applications, such as urban planning, hazard monitoring, etc. In particular, high resolution (HR) remote sensing (RS) images can better monitor our living environment from a broader spatial perspective. However, raw remote sensing images provide no labeling information to train a classifier, which usually is exploited to generate remote sensing maps. Based on our previous work, in the paper, an automatic classification system is proposed to classify high resolution urban RS images using deep neural networks, in particular, convolutional neural networks and fully convolutional networks. The labeling information is assigned on the context of both social media photos and HR remote sensing images by significantly reducing the cost of manual labeling without the necessity of remote sensing experts. The experiments carried out on high resolution remote sensing images acquired in the city Frankfurt taken by the Jilin-1 satellites confirm the effectiveness of the proposed strategy compared to the state of the art.
Objective Estimation of Sensory Thresholds Based on Neurophysiological Parameters

Reliable determination of sensory thresholds is the holy grail of signal detection theory. However, there exists no assumption-independent gold standard for the estimation of thresholds based on neurophysiological parameters, although a reliable estimation method is crucial for both scientific investigations and clinical diagnosis. Whenever it is impossible to communicate with the subjects, as in studies with animals or neonates, thresholds have to be derived from neural recordings or by indirect behavioral tests. Whenever the threshold is estimated based on such measures, the standard approach until now is the subjective setting—either by eye or by statistical means—of the threshold to the value where at least a “clear” signal is detectable. These measures are highly subjective, strongly depend on the noise, and fluctuate due to the low signal-to-noise ratio near the threshold. Here we show a novel method to reliably estimate physiological thresholds based on neurophysiological parameters. Using surrogate data we demonstrate that fitting the responses to different stimulus intensities with a hard sigmoid function, in combination with subsampling, provides a robust threshold value as well as an accurate uncertainty estimate. This method has no systematic dependence on the noise and does not even require samples in the full dynamic range of the sensory system. We prove that this method is universally applicable to all types of sensory systems, ranging from somatosensory stimulus processing in the cortex to auditory processing in the brain stem.
Classification of Settlement Types from Tweets Using LDA and LSTM

Land use reflects the interrelation between the physically built environment and the activity patterns of people. It is indispensable information for decision-makes, but up-to-date and accurate land use information is often absent. Unlike approaches that make use of remote sensing data, in this work, we are interested in a novel data source, tweets, and explore its potential for land use classification in urban areas. Specifically, we propose a general framework for classifying settlement land-use types by extracting location, time, quantity and text features of twitter data. To do so, we apply latent Dirichlet allocation (LDA) and long short-term memory (LSTM) and then combines those features with spatial-temporal feature using Fused SVM and a two-stream convolutional neural network (CNN) for classification. For the case of classifying individual tweets by the land-use classes relevant in this study - residential, non-residential and mixed usage -, we reach overall accuracy (OA), average accuracy (AA), and Kappa coefficient with 72.35%, 73.76%, and 58.43%, respectively. As for the case of classifying block settlement types, we reach 61.90%, 63.33%, and 42.84%, respectively.
Automated Intracellular Calcium Profiles Extraction from Endothelial Cells Using Digital Fluorescence Images

Endothelial cells perform a wide variety of fundamental functions for the cardiovascular system, their proliferation and migration being strongly regulated by their intracellular calcium concentration. Hence it is extremely important to carefully measure endothelial calcium signals under different stimuli. A proposal to automate the intracellular calcium profiles extraction from fluorescence image sequences is presented. Digital image processing techniques were combined with a multi-target tracking approach supported by Kalman estimation. The system was tested with image sequences from two different stimuli. The first one was a chemical stimulus, that is, ATP, which caused small movements in the cells trajectories, thereby suggesting that the bath application of the agonist does not generate significant artifacts. The second one was a mechanical stimulus delivered by a glass microelectrode, which caused major changes in cell trajectories. The importance of the tracking block is evidenced since more accurate profiles were extracted, mainly for cells closest to the stimulated area. Two important contributions of this work are the automatic relocation of the region of interest assigned to the cells and the possibility of data extraction from big image sets in efficient and expedite way. The system may adapt to different kind of cell images and may allow the extraction of other useful features.
Vacuum emission in large-area nanogap fabricated by MEMS-controlled cleavage of single crystal silicon

Nanoscale thermotunneling emission (TTE) phenomenon, governed by tunneling of electrons across a nanogap, promises many applications, ranging from chip integratable solid state refrigeration to waste heat energy harvesting; however, inability of current technology to produce large emission area electrode pairs with uniform nanogaps across large overlapping areas remains the main challenge. Extreme distance sensitivity of emission current renders TTE energy exchange insignificant for bulk scale applications. We propose a micro-electromechanical system (MEMS) based controlled inter-planar cleaving method to fabricate large emission area Si electrode pairs to potentially overcome this problem. A silicon microbeam is cleaved across < 111 > planes using an integrated thermal actuator. Resulting cleaved planes that include a nanogap (~100 nm) between them are used as emission electrodes.
Manifold Regularized Low-Rank Representation for Hyperspectral Anomaly Detection

A novel method for hyperspectral anomaly detection based on low-rank representation with manifold regularization is proposed in this paper. Usually, a hyperspectral imagery can be modeled as a superposition of two parts: background part with low rank dimensionality and anomaly part described by a sparse matrix. Low-rank representation (LRR) can be used to find the lowest rank representation of all pixels jointly which represents the background part, then the anomaly part is contained in the residual of the original image. To learn a more discriminative representation, we incorporate the manifold regularization term into the original LRR model. An important advantage of the proposed method is that it can utilize the global low rank property and local geometrical structure jointly. The experimental results on both simulated and real hyperspectral datasets validate the effectiveness of the proposed method.
Topology Design for GEO Spaceborne-Airborne Multistatic SAR Using Multiobjective Optimization Algorithms

Geosynchronous (GEO) spaceborne-airborne multistatic SAR (GEO MulSAR) is flexible and accessible in remote sensing applications. Moreover, the information obtained by the multiple airborne receivers can be fused to enhance the spatial resolution. However, the fused spatial resolution significantly depends on the applied multistatic topology. In order to achieve the optimal fused spatial resolution by properly adjusting the imaging topology, a topology design method is proposed in this paper. Firstly, the spatial resolution model of GEO MulSAR is given and the dependance of the spatial resolution on the multistatic topology is analyzed. Then, a topology design method is proposed to obtain the best multistatic topology based on multiobjective optimization methods. Finally, the simulation results validate the effectiveness of the proposed method.
Hyperspectral Classification Via Spatial Context Exploration with Multi-Scale CNN

Spatial context has shown to be very useful in hyperspectral image processing. Existing convolutional neural network (CNN)-based methods for hyperspectral classification explore spatial context by single-scale convolution kernels in 2D or 3D shapes. However, such single-scale convolution may not be capable to explore the complex spatial context in a hyperspectral image. In this paper, we propose a multi-scale CNN, MS-CNN to explore the spatial context in different extents, in which adaptive spatial neighborhood convolution kernels are used to simultaneously extract multiple spectral-spatial features from spatial context of pixels. These features obtained by different spatial kernels are then concatenated and fused for further feature extraction and classification. Experimental results show that the proposed adaptive spatial neighborhood convolution are more effective to explore spatial context than traditional single-scale spatial convolution and the performance of the proposed MS-CNN outperforms several state-of-art CNNs for classification of hyperspectral images.
Wetland Classification Using Deep Convolutional Neural Network

The synergistic use of spatial features with spectral properties of satellite images enhances thematic land cover information. This study aims to address the lack of high-level features by proposing a classification framework based on convolutional neural network (CNN) to learn deep spatial features for wetland. In particular, a CNN model was used for classification of remote sensing imagery with limited number of training data by fine-tuning of a preexisting CNN (AlexNet). The classification results obtained by the deep CNN were compared with those based on well-known ensemble classifiers, namely Random Forest (RF), to evaluate the efficiency of CNN Experimental results demonstrated that CNN was superior to RF for complex wetland mapping even by incorporating the small number of input features (i.e., 3 features) for CNN compared to RF. The proposed classification scheme serves as a baseline framework to facilitate further scientific research using the latest state-of-art machine learning tools for processing remote sensing data.
Sentinel-1 Monitoring of Santorini Volcano Post-Unrest State

At the beginning of 2011 Santorini volcano underwent a volcanic unrest, accompanied by an increase of the microseismic activity. The unrest that lasted for over a year, demonstrated substantial amounts of uplift, justifying the volcanic origin of the activity. The continuation of the deformation monitoring after the unrest event was deemed necessary, in order to validate the post-unrest state of the volcano and to recognize potential ground deformation signals. The Copernicus Sentinel-1 mission is profoundly promising towards this direction, due to its systematic acquisition and short revisit. A multi-temporal SAR interferometric analysis was deliberated in a way to avoid significant phase changes and to retain high coherence. In this paper, DInSAR analysis was applied over Santorini to retrieve the actual deformation state of the volcano during the repose period 2014–2017. The results indicate subsidence rates on Nea Kameni of about 8–9 mm/yr, implying a gradual deflation of the magma chamber, while the rest of the area appears almost stable with negligible deformation.
Timing is Everything - Drought Classification for Risk Assessment

Drought is one of the most severe natural disasters with the highest risk for human livelihoods. Remote sensing based drought indices can identify dry periods using e.g. precipitation or vegetation information. Besides frequency, duration and intensity, the timing of a drought onset is an important variable to measure drought risk. This study classifies drought events based on the timing of drought onsets and duration. Drought and non-drought seasons are analyzed in two study sites in South Africa and Ukraine where drought characteristics are different. South Africa drought highly depends on the starting point and duration of rainfall, whereas in Ukraine soil moisture and temperature play key roles. Weighted linear combination is applied based on vulnerable growing stages in the seasonal phenology to classify droughts. By integration of socio-economic information this hazard information supports the quantification of the actual risk of a drought event.