Miracle-dz/UniFAR-FTUs huggingface dataset:
2,5D building reconstruction from SAR and optical data using OOA : poster.

2.5D building modeling is of great interest for visualization, simulation and monitoring purposes. Recently, there have been several studies aiming at extraction of building information by merging SAR and optical imagery. Optical data mainly provides the shapes of the building footprints whereas SAR data is used for the improvement of the detected building footprints by fusion with optical data and providing height information. This research integrates the estimated building height from the high resolution spotlight mode TerraSAR-X SLC data, using SAR interferometry, and the building footprint, extracted from the very high resolution QuickBird data using Object-oriented analysis (OOA) technique, to reconstruct a 2.5D building model. The approach is tested on a study area in Delft, the Netherlands. For building height estimation, an InSAR DSM was generated from the two SLC images by calculating the SAR interferogram and different levels of external height information from Shuttle Topography Mission Digital Elevation Model (SRTM), LIDAR Digital Terrain Model (LIDAR DTM) and LIDAR Digital Surface Model (LIDAR DSM) were used for phase flattening and registration to investigate how they influence the overall accuracy of SAR interferometric processing. For the building footprint extraction, the QuickBird image was segmented with a region growing method and classified with the generated rule set by OOA. The InSAR DSM was used in this rule set. Then, the 2.5D building model was reconstructed by combining the extracted building footprint with the estimated building height. The accuracy was assessed for the building height. The building height was compared to the reference building heights from the LIDAR DSM and the field visit with a clinometer.
Altruism and Political Participation

Democratic government requires the participation of its citizens, but Downs shows that it is not in the self-interest of individuals to vote, or acquire political information. This chapter reviews the theoretical and empirical support for the three explanations for political participation: civic duty, expressive benefit, and altruism. The preponderance of evidence supports the civic duty and altruistic explanations for why people vote. But the civic duty explanation does not readily extend to the acquisition of political information, campaign contributions, or contributions to public interest groups. Additionally, neither civic duty nor expressive identification offers an explanation of why turnout increases in close elections or for strategic voting. Altruism incorporates probabilities in the determination of expected benefits from political participation and hence easily explains these phenomena. The preponderance of evidence, as indicated in this chapter, now favors the altruistic explanation for the different types of political participation.
Algorithmic Equity: A Framework for Social Applications

The primary focus of this report is understanding how to evaluate the equitable use of algorithms across a range of specific applications. The report outlines concepts of equity from philosophical, legal, and technical standpoints to draw insights that apply across algorithmic decisionmaking contexts for several domains, including auto insurance, recruiting, and criminal justice.
An Empirical Investigation of the Factors Affecting the Use of Social Networks in Human Resources Recruitment

ABSTRACT The main objective of this study is to investigate the factors affecting the use of social networks towards recruiting human resources for organizations. Through an in-depth review of the relevant literature, a questionnaire was designed and distributed among the sample and collected data were analyzed using PLS-Software. Results revealed that 50−75% of organizations use social networks in their organizations and all the identified effective factors in the use of social networks for human resources recruitment were confirmed, except for “hope for performance”. “Facilitating conditions” have the highest impact on the usage of social networks in recruiting, “Social influence” identified as one of the most important factors in people’s behavioral intention for the usage of social networks in recruitment and “Effort expectancy” had the least impact on behavioral intention. In addition, Gender and the level of education have no impact on the behavioral intention and the behavior of usage of social networking.
Service Mix, Market Competition, and Cost Efficiency: A Longitudinal Study of U.S. Hospitals

We examine the longitudinal impact of service mix on cost efficiency in U.S. acute care general hospitals. We propose two different dimensions of service mix, with the first dimension capturing internal emphasis on specific service lines (i.e., specialization) and the second dimension reflecting the deviation of a hospital from the average level of specialization for hospitals in the same service area (i.e., differentiation). We hypothesize that as the level of a hospital's specialization increases, hospital cost efficiency should improve at a decreasing rate. We also hypothesize that differentiation helps improve cost efficiency in competitive markets. Lastly, we examine whether acute care general hospitals can use service mix as an operational lever to improve cost efficiency when specialty hospitals are present in the same local markets. We find strong empirical support for the hypothesized relationships.
Learning from conflict in multi-agent, classical, and temporal planning

Conflict-directed learning has been one of the most significant advances in combinatorial optimisation in recent decades, but until very recently has not been directly applied to planning problems. Iterated compilations to conflict-directed solving technologies have seen significant success in planning, but these compilations do not learn from conflicts encountered during earlier iterations, seriously limiting their scalability and applicability to cost-optimal planning. This thesis introduces a number of novel algorithms that take into account information derived from conflict on some important variants of the planning problem. These approaches use combinations of conflict-directed encodings, relaxation-refinement procedures, and state-based search. Our results show that conflict-directed reasoning is a highly effective approach to cost-optimal planning when appropriate decompositions and explanations are known. Automatically deriving the most effective of these decompositions and explanations from unannotated problem descriptions will be a fruitful avenue of further work. We also highlight the unique potential for learning strongly generalisable knowledge from conflict within plan-space planning algorithms. This learned knowledge enables our plan-space search algorithms to compete with state-space search on some standard domains, and outperform it by orders of magnitude in appropriately structured industrial ones.
Navigation sur des cartes de connaissances suportées par un treillis de Galois

Devant le nombre, sans cesse croissant, de documents numeriques accessibles, un reel besoin de nouvelles techniques de representation emerge. En effet, la traditionnelle liste de resultats (type Google) ne suffit plus et des alternatives graphiques ont ete proposees, en particulier pour representer conjointement des vues globales et locales en selectionnant certains documents. Cependant pour pallier aux problemes lies a l'encombrement de l'ecran, il est necessaire mettre en place une assistance a la navigation basee sur la semantique des documents. Nous proposons d'utiliser la classification des donnees dans un treillis de Galois et des techniques de FCA - Formal Concept Analysis -, associees avec des techniques de visualisation traditionnelle pour suggerer des chemins de navigation sur une carte, en fonction de la selection de certains concepts du treillis. L'article est illustre avec un exemple concret de navigation sur une base de donnees de brevets industriels.
Do Managers Use Extension Elements Strategically in the SEC's Tagged Data for Financial Statements? Evidence from XBRL Complexity

This study examines whether firm use of XBRL extension elements increases the complexity of mandatory SEC filings. Using the ratio of extension elements to total elements in XBRL 10-K filings as the measure of XBRL complexity, we find that firms' XBRL filings are more complex when the firms are performing poorly, an effect that is more pronounced when firms are more complex. Furthermore, complex XBRL filings are associated with less (more) persistent positive (negative) earnings. Collectively, our results are consistent with managers using extension elements strategically to increase XBRL complexity, obfuscating XBRL-tagged financial information.
Algorithm-architecture adequacy of spiking neural networks for massively parallel processing hardware. (Adéquation algorithme-architecture de réseaux de neurones à spikes pour les architectures matérielles massivement parallèles)

Cette derniere decennie a donne lieu a la reemergence des methodes d'apprentissage machine basees sur les reseaux de neurones formels sous le nom d'apprentissage profond. Bien que ces methodes aient permis des avancees majeures dans le domaine de l'apprentissage machine, plusieurs obstacles a la possibilite d'industrialiser ces methodes persistent, notamment la necessite de collecter et d'etiqueter une tres grande quantite de donnees ainsi que la puissance de calcul necessaire pour effectuer l'apprentissage et l'inference avec ce type de reseau neuronal. Dans cette these, nous proposons d'etudier l'adequation entre des algorithmes d'inference et d'apprentissage issus des reseaux de neurones biologiques pour des architectures materielles massivement paralleles. Nous montrons avec trois contributions que de telles adequations permettent d'accelerer drastiquement les temps de calculs inherents au reseaux de neurones. Dans notre premier axe, nous realisons l'etude d'adequation du moteur BCVision de Brainchip SAS pour les plate-formes GPU. Nous proposons egalement l'introduction d'une architecture hierarchique basee sur des cellules complexes. Nous montrons que l'adequation pour GPU accelere les traitements par un facteur sept, tandis que l'architecture hierarchique atteint un facteur mille. La deuxieme contribution presente trois algorithmes de propagation de decharges neuronales adaptes aux architectures paralleles. Nous realisons une etude complete des modeles computationels de ces algorithmes, permettant de selectionner ou de concevoir un systeme materiel adapte aux parametres du reseau souhaite. Dans notre troisieme axe nous presentons une methode pour appliquer la regle Spike-Timing-Dependent-Plasticity a des donnees images afin d'apprendre de maniere non-supervisee des representations visuelles. Nous montrons que notre approche permet l'apprentissage d'une hierarchie de representations pertinente pour des problematiques de classification d'images, tout en necessitant dix fois moins de donnees que les autres approches de la litterature.
Classification of Gujarati Documents using Naïve Bayes Classifier

Objectives: Information overload on the web is a major problem faced by institutions and businesses today. Sorting out some useful documents from the web which is written in Indian language is a challenging task due to its morphological variance and language barrier. As on date, there is no document classifier available for Gujarati language. Methods: Keyword search is a one of the way to retrieve the meaningful document from the web, but it doesn’t discriminate by context. In this paper we have presented the Naive Bayes (NB) statistical machine learning algorithm for classification of Gujarati documents. Six pre-defined categories sports, health, entertainment, business, astrology and spiritual are used for this work. A corpus of 280 Gujarat documents for each category is used for training and testing purpose of the categorizer. We have used k-fold cross validation to evaluate the performance of Naive Bayes classifier. Findings: The experimental results show that the accuracy of NB classifier without and using features selection was 75.74% and 88.96% respectively. These results prove that the NB classifier contribute effectively in Gujarati documents classification. Applications: Proposed research work is very useful to implement the functionality of directory search in many web portals to sort useful documents and many Information Retrieval (IR) applications.
Numerical simulations of a multiscale model for maple sap exudation

Sap exudation refers to the process whereby trees such as sugar maple (Acer saccharum) and red maple (Acer rubrum) generate elevated stem pressure, which permits maple sap to be harvested as a viable agricultural product. There exists a vast literature on sap exudation and various hypotheses regarding the physical and biological mechanisms for internal pressure generation in trees but very limited mathematical modeling work. Here, we perform a careful parametric study of a model for sap exudation recently published by Graf et al. [J R Soc Interface 12:20150665, 2015] which consists of a nonlinear system of differential equations obtained by homogenizing the equations governing phase change and sap transport at the cellular level. We focus on the influence of realistic daily temperature oscillations on the resulting stem pressure and draw comparisons with experimental measurements on red and sugar maple saplings taken during the sap harvest season. This study demonstrates the limitations and capabilities of our model in terms of capturing realistic exudation behaviour.
Enabling acceptance and use of ecological intensification options through engaging smallholder farmers in semi-arid rural Limpopo and Eastern Cape, South Africa

ABSTRACT Ecological intensification is being promoted to address food security and environmental challenges. It has shown the potential to improve yields as well as adapt and mitigate the effects of climate variability and change. Despite the great potential, smallholder farmers continue to shun the latter. Apart from this, limited research has been conducted focussing on their acceptance and use in smallholder sub-Saharan African agricultural systems. In this study, a qualitative approach using the Unified Theory of Acceptance and Use of Technology (UTAUT) four constructs (performance, ease of use, social influence and enablers) was used to assess behavioral intention to accept and use ecological intensification options. A total of 97 smallholder farmers from diverse farm types in rural Limpopo and Eastern Cape, South Africa participated in focus group discussions to assess behavioral intention to accept and use ecological intensification options. Smallholder farmers revealed that they were exposed to a plethora of ecological intensification options. However, acceptance and utilization of these options were low, mainly due to lack of awareness, germplasm, and technical support. The four constructs of the UTAUT framework revealed locally relevant knowledge that must be considered for effective acceptance and use of ecological intensification options. Abbreviations: ACCESS: Alliance for Collaboration for Climate and Earth System Sciences; ADCI: African Climate Development Initiative; NRF: National Research Foundation; SSA: Sub Saharan Africa; UTAUT: Unified Theory of Acceptance and Use of Technology; WRC: Water Research Commission; FAO: Food and Agriculture Organisation; CA: Conservation Agriculture
The ideal companion: the role of mobile phone attachment in travel purchase intention

ABSTRACT Mobile phones have become a highly personal tool for individuals and have revolutionized many aspects of travellers’ lives. Indeed, mobile phones can be considered an integral part of travellers’ trip routines, thus engendering strong feelings of attachment to them. Following the stimulus–organism–response model, this paper analyses how certain stimuli (travellers’ addiction to their device, perceived control, perceived entertainment, and subjective norms) influence travellers’ mobile phone attachment (MPA) and intention to purchase travel using the device. The results of this model show the importance of personal and environmental factors in increasing MPA and reveal how this attachment positively influences intention toward mobile shopping for travel-related purchases. This work proposes a model in both the mobile and travel contexts with MPA as the central variable. As such, this paper contributes to the academic literature and yields several recommendations for business practice.
The influence of family businesses and women directors on innovation

ABSTRACT This work seeks to contribute to the debate about whether or not family businesses are more innovative than other kind of companies, and explore the specific effects women on boards have on innovation, comparing family and non-family businesses. Data collected from Spanish listed companies belonging to innovative sectors between 2003 and 2017 confirm that being a family business impacts positively on the number of registered patents and the proportion of women on boards exert a positive influence on R&D and patents. Moreover, the proportion of women on boards influences innovation in a different way when family and non-family businesses are compared, and depending on whether female directors belong or not to the family of control. Therefore, the results obtained suggest some relevant recommendations for research and practitioners, providing new insights into the debate on the benefits of family companies for innovation and of gender diversity of their boards.
Robustness analysis of the European air traffic network

The European air traffic network (ATN), consisting of a set of airports and area control centres, is highly complex. The current indicator of its performance, air traffic flow management delays, is insufficient for planning and management purposes. Topological analysis of ATNs of this kind has highlighted betweenness centrality (BC) as an indicator of network robustness, although such an indicator assumes no knowledge of actual traffic flows and the network's operational characteristics. This paper conducts topological and operational analyses of the European ATN in order to derive a more relevant and appropriate indicator of robustness. By applying a flow maximisation model to the network influenced by a range of capacity reductions at the local level, we propose a new index called the Relative Area Index (RAI). The RAI quantifies the importance of an individual node relevant to the performance of the entire network when it suffers from capacity reduction at a local scale. Air traffic data from three typical busy days in Europe are utilised to show that the RAI is more flexible and capable than BC in capturing the network impact of local capacity degradation. This index can be used to assess network robustness and provide a valuable tool for airspace managers and planners.
Analysis of long-term challenges for agricultural markets

The Long-term Agricultural Outlook model (LAO) is a long-run partial equilibrium trade model developed by the OECD as a complementary modelling tool to AGLINK-COSIMO, a more detailed partial equilibrium model used to develop ten-year projections for agricultural markets, as reported in the annual OECD-FAO Agriculture Outlook publication. LAO describes the drivers of structural changes in global supply and demand, provides a baseline of real agricultural prices, and can be used to situate short and medium term price projections in a longer-term context. The model is also suitable for performing scenario analysis of long-term issues concerning agriculture, such as the links between food security and climate change. The model structure is similar to that of AGLINK-COSIMO, which enhances comparability, but much simpler, with highly aggregated commodity and regional groupings. The simplified structure makes it possible to make use of data on productivity growth in a way not possible in more complex models, and to investigate the role of long term drivers of productivity growth, in particular research spending. It also facilitates a focus on key macro-economic drivers of agricultural market developments. This report details the motivation, structure, and development of the model, alongside initial outputs. The baseline results indicate a lowering of real agricultural prices in the long run, as growth in global supply outpaces growth in global demand.
RESTORASI GIGI ANTERIOR MENGGUNAKAN TEKNIK DIRECT KOMPOSIT (Kajian Pustaka)

Restorasi gigi anterior yang terlihat natural merupakan faktor estetik yang penting. Restorasi resin komposit adalah restorasi estetik di bidang konservasi gigi. Restorasi resin komposit direct paling umum dilakukan pada pasien karena langsung dikerjakan didalam mulut, menggunakan bahan yang sewarna dengan gigi aslinya dan cara manipulasi yang mudah. Restorasi direct pada gigi anterior dilakukan pada kasus diastema anterior, fraktur gigi anterior, karies kelas III, IV GV Black dan perubahan warna pada gigi. Teknik restorasi ini merupakan perawatan konservatif di kedokteran gigi.
Analysis of Criminal Case Judgment Documents Based on Deep Learning

In recent years, along with the improvement of population quality and the advancement of the rule of law society, the market for legal services in the middle and low-end markets has continued to expand, and legal advice has become widespread in daily life. In the process of legal services, the legal provisions play an important role in the lawyer's decisionmaking. Meanwhile, the historical cases can help the lawyers and the parties to draw lessons from similar cases. However, with the increasing number of judicial documents, it is becoming increasingly difficult to summarize and learn from history. Therefore, this paper proposes a sentencing interval prediction model of criminal cases based on convolutional neural network, and through the method of multi -core convolution, greatly enhances the generalization ability and prediction performance of the model. The experimental analysis of real criminal case verdict verifies that the model is more effective than other classification prediction algorithms. Keywords—convolutional neural network; multiple nuclear convolution; criminal case verdict document; prediction of sentencing interval