Miracle-dz/UniFAR-FTUs huggingface dataset:
Tikhonov Regularization Based Modeling and Sidereal Filtering Mitigation of GNSS Multipath Errors

In Global Navigation Satellite System (GNSS) relative positioning applications, multipath errors are non-negligible. Mitigation of the multipath error is an important task for precise positioning and it is possible due to the repeatability, even without any rigorous mathematical model. Empirical modeling is required for this mitigation. In this work, the multipath error modeling using carrier phase measurement residuals is realized by solving a regularization problem. Two Tikhonov regularization schemes, namely with the first and the second order differences, are considered. For each scheme, efficient numerical algorithms are developed to find the solutions, namely the Thomas algorithm and Cholesky rank-one update algorithm for the first and the second differences, respectively. Regularization parameters or Lagrange multipliers are optimized using the bootstrap method. In experiment, data on the first day are processed to construct a multipath model for each satellite (except the reference one), and then the model is used to correct the measurement on the second day, namely following the sidereal filtering approach. The smoothness of the coordinates calculated using the corrected measurements is improved significantly compared to those using the raw measurement. The efficacy of the proposed method is illustrated by the actual calculation.
Error Source Analysis and Correction of GF-3 Polarimetric Data

The GaoFen-3 (GF-3) satellite is the first polarimetric synthetic aperture radar (PolSAR) satellite in China. With a designed in-orbit life of 8 years, it will provide large amounts of PolSAR data for ocean monitoring, disaster reduction, and many other applications. The polarimetric data quality is essential for all these applications, so the analysis and calibration of the polarimetric error sources are very important for GF-3. In this study, we established a full-link error model for GF-3 PolSAR system. Based on this model, we comprehensively analyzed the quantitative effects of the main error sources including the composition, figured out characteristics of the phase imbalance introduced by the antenna, and pointed out the error sources which have to be corrected. Furthermore, the polarimetric correction method for GF-3 PolSAR system is proposed. Finally, assisted by several external calibration experiments, polarimetric errors of GF-3 data are efficiently corrected during in-orbit-test phase.
Structural and Spectral Analysis of Cereal Canopy Reflectance and Reflectance Anisotropy

The monitoring of agricultural areas is one of the most important topics for remote sensing data analysis, especially to assist food security in the future. To improve the quality and quantify uncertainties, it is of high relevance to understand the spectral reflectivity regarding the structural and spectral properties of the canopy. The importance of understanding the influence of plant and canopy structure is well established, but, due to the difficulty of acquiring reflectance data from numerous differently structured canopies, there is still a need to study the structural and spectral dependencies affecting top-of-canopy reflectance and reflectance anisotropy. This paper presents a detailed study dealing with two fundamental issues: (1) the influence of plant and canopy architecture changes due to crop phenology on nadir acquired cereal top-of-canopy reflectance, and (2) the anisotropic reflectance of cereal top-of-canopy reflectance and its inter-annual variations as affected by varying contents of biochemical constituents and changes on canopy structure across green phenological stages between tillering and inflorescence emergence. All of the investigations are based on HySimCaR, a computer-based approach using 3D canopy models and Monte Carlo ray tracing (drat). The achieved results show that the canopy architecture significantly influences top-of-canopy reflectance and the bidirectional reflectance function (BRDF) in the VNIR (visible and near infrared), and SWIR (shortwave infrared) wavelength ranges. In summary, it can be said that the larger the fraction of the radiation reflected by the plants, the stronger is the influence of the canopy structure on the reflectance signal. A significant finding for the anisotropic reflectance is that the relative row orientation of the cereal canopies is mapped in the 3D-shape of the BRDF. Summarised, this study provides fundamental knowledge for improving the retrieval of biophysical vegetation parameters of agricultural areas for current and upcoming sensors with large FOV (field of view) with respect to the quantification of uncertainties.
Evaluation of the SPARSE Dual-Source Model for Predicting Water Stress and Evapotranspiration from Thermal Infrared Data over Multiple Crops and Climates

Using surface temperature as a signature of the surface energy balance is a way to quantify the spatial distribution of evapotranspiration and water stress. In this work, we used the new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) based on the Two Sources Energy Balance (TSEB) model rationale which solves the surface energy balance equations for the soil and the canopy. SPARSE can be used (i) to retrieve soil and vegetation stress levels from known surface temperature and (ii) to predict transpiration, soil evaporation, and surface temperature for given stress levels. The main innovative feature of SPARSE is that it allows to bound each retrieved individual flux component (evaporation and transpiration) by its corresponding potential level deduced from running the model in prescribed potential conditions, i.e., a maximum limit if the surface water availability is not limiting. The main objective of the paper is to assess the SPARSE model predictions of water stress and evapotranspiration components for its two proposed versions (the “patch” and “layer” resistances network) over 20 in situ data sets encompassing distinct vegetation and climate. Over a large range of leaf area index values and for contrasting vegetation stress levels, SPARSE showed good retrieval performances of evapotranspiration and sensible heat fluxes. For cereals, the layer version provided better latent heat flux estimates than the patch version while both models showed similar performances for sparse crops and forest ecosystems. The bounded layer version of SPARSE provided the best estimates of latent heat flux over different sites and climates. Broad tendencies of observed and retrieved stress intensities were well reproduced with a reasonable difference obtained for most of the points located within a confidence interval of 0.2. The synchronous dynamics of observed and retrieved estimates underlined that the SPARSE retrieved water stress estimates from Thermal Infra-Red data were relevant tools for stress detection.
Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.
Animation Techniques in Human-Robot Interaction User Studies

There are many different ways a robot can move in Human-Robot Interaction. One way is to use techniques from film animation to instruct the robot to move. This article is a systematic literature review of human-robot trials, pilots, and evaluations that have applied techniques from animation to move a robot. Through 27 articles, we find that animation techniques improves an individual’s interaction with robots, improving the individual’s perception of qualities of a robot, understanding what a robot intends to do, and showing the robot’s state or possible emotion. Animation techniques also help people relate to robots that do not resemble a human or robot. The studies in the articles show further areas for research, such as applying animation principles in other types of robots and situations, combining animation techniques with other modalities, and testing robots moving with animation techniques over the long term.
Metodologías del diseño en la promoción de aprendizaje organizacional

El diseño como disciplina fenomenológica surge en el escenario actual como mecanismo de creación de relaciones sinérgicas entre empresas y universidades. Muchas veces estas relaciones tienen sus bases en una revisión de los procesos internos de las organizaciones. En este contexto, las metodologías del diseño pueden establecer nuevos parámetros de aprendizaje organizacional, creando formas de participación fluida y creativa a través de sectores y competencias. Para ilustrar esta perspectiva, presentamos un modelode aprendizaje basado en la educación a través de la práctica del proyecto reflexivo utilizando la herramienta del Workshop, notable instrumento metodológico de aprendizaje creativo del Diseño Estratégico. De esta manera, este trabajo presenta el estudio de casos de experiencias proyectuales de los Workshops llevados a cabo en colaboración con la marca de calzado de plástico Melissa. Los resultados fueron soluciones innovadoras, tanto en el ámbito de la educación organizacional, como en el campo de aplicación de nuevosparámetros de comprensión del diseño para los diseñadores de la marca.
Viewpoint: Toward Involvement of Caregivers in Suicide Prevention Strategies; Ethical Issues and Perspectives

Suicide prevention research faces specific challenges related to characteristics of suicide attempts and attempters (Wasserman, 2004). Suicide attempters have been described as poorly adhering to intensive treatment over time, and delivery of interventions in the emergency department can be difficult, where psychiatric staff availability is often limited or absent. The post-discharge period constitutes a critical challenge for emergency and mental health care services both in the short and long terms (Hunt et al., 2009). Given these issues, there has been growing interest in assessing the efficacy of post discharge intervention after a suicide attempt. For example, brief contact interventions (BCIs) are low resources interventions seeking to maintain long-term contact with patients after a suicide attempt (Milner et al., 2015). These interventions intent to reinforce the health care networking around the patient but only rely on mental health and emergency services. Depression, drug misuse, family and social situations are well established suicide risk factors (Zalsman et al., 2016). Recent findings also showed the correlation between sleep disturbances and suicidality from a clinical point of view (Pompili et al., 2013). Including protective factors in suicide risk management might be of great interest. However, prevention strategies often exclude form the preventative procedure an essential preventative component of patients social network: the caregiver (Mann et al., 2005). Indeed, studies showed the potential positive impacts of caregivers in the management of important suicide risk factors as depression (Joling et al., 2012), and social isolation (Chatterjee et al., 2014).
The Evolution of Sexual Fluids in Gymnosperms From Pollination Drops to Nectar

A current synthesis of data from modern and fossil plants paints a new picture of sexual fluids, including nectar, as a foundational component of gymnosperm reproductive evolution. We review the morpho-anatomical adaptations, their accompanying secretions, and the functional compounds involved. We discuss two types of secretions: (1) those involved in fertilization fluids produced by gametophytes and archegonia of zooidogamous gymnosperms, i.e., Ginkgo and cycads, and (2) those involved in pollen capture mechanisms (PCMs), i.e., pollination drops. Fertilization fluids provide both liquid in which sperm swim, as well as chemotactic signals that direct sperm to the egg. Such fertilization fluids were probably found among many extinct plants such as ancient cycads and others with swimming sperm, but were subsequently lost upon the evolution of siphonogamy (direct delivery of sperm to the egg by pollen tubes), as found in modern gnetophytes, conifers, and Pinaceae. Pollination drops are discussed in terms of three major types of PCMs and the unique combinations of morphological and biochemical adaptations that define each. These include their amino acids, sugars, calcium, phosphate and proteins. The evolution of PCMs is also discussed with reference to fossil taxa. The plesiomorphic state of extant gymnosperms is a sugar-containing pollination drop functioning as a pollen capture surface, and an in ovulo pollen germination medium. Additionally, these drops are involved in ovule defense, and provide nectar for pollinators. Pollination drops in anemophilous groups have low sugar concentrations that are too low to provide insects with a reward. Instead, they appear to be optimized for defense and microgametophyte development. In insect-pollinated modern Gnetales a variety of tissues produce sexual fluids that bear the biochemical signature of nectar. Complete absence of fluid secretions is restricted to a few, poorly studied modern conifers, and is presumably derived. Aspects of pollination drop dynamics, e.g., regulation of secretion and retraction, are reviewed. Lastly, we discuss pollination drops’ control of pollen germination. Large gaps in our current knowledge include the composition of fertilization fluids, the pollination drops of Podocarpaceae, and the overall hydrodynamics of sexual fluids in general.
Identification and Evaluation of Neuropsychological Tools Used in the Assessment of Alcohol-Related Cognitive Impairment: A Systematic Review

Background: Neuropsychological assessment is central to identifying and determining the extent of Alcohol-Related Cognitive Impairment (ARCI). The present systematic review aimed to synthesize and discuss the evidence appraising the neuropsychological tests used to assess ARCI in order to support clinicians and researchers in selecting appropriate tests for use with this population. Methods: We searched for studies investigating the psychometric, diagnostic and practical values of tools used in the screening, diagnosis, and assessment of Korsakoff's Syndrome (KS), Alcohol-Related Dementia (ARD), and those with a specific diagnosis of Alcohol-Related Brain Damage (ARBD). The following databases were searched in March 2016 and again in August 2018: MEDLINE, EMBASE, Psych-INFO, ProQuest Psychology, and Science Direct. Study quality was assessed using a checklist designed by the authors to evaluate the specific factors contributing to robust and clearly reported studies in this area. A total of 43 studies were included following the screening of 3646 studies by title and abstract and 360 at full-text. Meta-analysis was not appropriate due to heterogeneity in the tests and ARCI samples investigated in the studies reviewed. Instead, review findings were narratively synthesized and divided according to five domains of assessment: cognitive screening, memory, executive function, intelligence and test batteries, and premorbid ability. Effect sizes (d) were calculated to supplement findings. Results: Overall, several measures demonstrated sensitivity to the cognitive deficits associated with chronic alcoholism and an ability to differentiate between gradations of impairment. However, findings relating to the other psychometric qualities of the tests, including those important for the accurate assessment and monitoring of ARCI (e.g., test-retest reliability), were entirely absent or limited. Additionally, the synthesis of neuropsychological outcomes presented here supports the recent impetus for a move away from discrete diagnoses (e.g., KS, ARD) and the distinctions between them toward more broad and inclusive diagnostic conceptualizations of ARCI, thereby recognizing the heterogeneity in presentation. Conclusions: Based on the evidence reviewed, provisional recommendations for appropriate tests in each domain of assessment are presented, though further validation of most tests is warranted. Review findings can support efficient and evidenced-based test-selection and guide future research in this area.
Pause-Based Phrase Extraction and Effective OOV Handling for Low-Resource Machine Translation Systems

Machine translation is the core problem for several natural language processing research across the globe. However, building a translation system involving low-resource languages remains a challenge with respect to statistical machine translation (SMT). This work proposes and studies the effect of a phrase-induced hybrid machine translation system for translation from English to Tamil, under a low-resource setting. Unlike conventional hybrid MT systems, the free-word ordering feature of the target language Tamil is exploited to form a re-ordered target language model and to extend the parallel text corpus for training the SMT. In the current work, a novel rule-based phrase-extraction method, implemented using parts-of-speech (POS) and place-of-pause in both languages is proposed, which is used to pre-process the training corpus for developing the back-off phrase-induced SMT. Further, out-of-vocabulary (OOV) words are handled using speech-based transliteration and two-level thesaurus intersection techniques based on the POS tag of the OOV word. To ensure that the input with OOV words does not skip phrase-level translation in the hierarchical model, a phrase-level example-based machine translation approach is adopted to find the closest matching phrase and perform translation followed by OOV replacement. The proposed system results in a bilingual evaluation understudy score of 84.78 and a translation edit rate of 19.12. The performance of the system is compared in terms of adequacy and fluency, with existing translation systems for this specific language pair, and it is observed that the proposed system outperforms its counterparts.
Automatic Embedding of Social Network Profile Links into Knowledge Graphs

Recent Knowledge Graphs (KGs) like Wikidata and YAGO are often constructed by incorporating knowledge from semi-structured heterogeneous data resources such as Wikipedia. However, despite their large amount of knowledge, these graphs are still incomplete. In this paper, we posit that Online Social Networks (OSNs) can become prominent data resources comprising abundant knowledge about real-world entities. An entity on an OSN is represented by a profile; the link to this profile is called a social link. In this paper, we propose a KG refinement method for adding missing knowledge to a KG, i.e., social links. We target specific entity types, in the scientific community, such as researchers. Our approach uses both scholarly data resources and existing KG for building knowledge bases. Then, it matches this knowledge with OSNs to detect the corresponding social link(s) for a specific entity. It uses a novel matching algorithm, in combination with supervised and unsupervised learning methods. We empirically validate that our system is able to detect a large number of social links with high confidence.
Evaluating the security levels of the Web-Portals based on the standard ISO/IEC 15408

Evaluating the security level of the Web-Portal is an urgent need, but it is not yet paid enough attention. A quantitative method is a key factor in analyzing security level evaluation. The formal model of the standard ISO/IEC 15408 and some other security standards cannot be directly applied to Web-Portals due to the generality and the abstraction of the model. The prestigious model of OWASP (Open Web Application Security Project) provides many best practices for Web application, but it is sill not enough for a quantitative evaluation and it is hardly applicable to compare the security level of different Web applications. This paper proposes a model and a quantitative method for evaluating the security levels of the Web-Portals based on the standard ISO/IEC 15408, which is highly feasible in the practice.
Random ensemble oblique decision stumps for classifying gene expression data

Cancer classification using microarray gene expression data is known to contain keys for addressing the fundamental problems relating to cancer diagnosis and drug discovery. However, classification gene expression data is a difficult task because these data are characterized by high dimensional space and small sample size. We investigate random ensemble oblique decision stumps (RODS) based on linear support vector machine (SVM) that is suitable for classifying very-high-dimensional microarray gene expression data. Our classification algorithms (called Bag-RODS and Boost-RODS) learn multiple oblique decision stumps in the way of bagging and boosting to form an ensemble of classifiers more accurate than single model. Numerical test results on 50 very-high-dimensional microarray gene expression datasets from Kent Ridge Biomedical repository and Array Expression repositories show that our proposed algorithms are more accurate than the-state-of-the-art classification models, including k nearest neighbors (kNN), SVM, decision trees and ensembles of decision trees like random forests, bagging and adaboost.
Novel metric for performance evaluation of routing in MANETs

Mobility of nodes in mobile ad hoc network constitutes a big challenge in the design of efficient routing protocol. Such issues reside in frequent and unpredictable links failure, which impact the overall network performance. Research community has proposed several scenarios to minimize the impact of mobility and deliver the quality of service required by MANET applications. However, efficiency of routing protocols must be assessed before their deployment. Actually, routing performances are evaluated using four main metrics: latency or end-to-end delay, Throughput, Packet delivery ratio and routing overhead. The purpose of this paper is the introduction of two new metrics that are devoted to evaluate how well routing protocol act in case of link failure triggered generally by high mobility. The first metric is path change factor (PCF), which characterize mobility adaptiveness and the second metric is route repair influence (RRI) that represent the effectiveness of route repair scheme. In addition we implemented the new metrics using an AWK script that uses as input NS2 trace file format.