Miracle-dz/UniFAR-FTUs huggingface dataset:
DISEÑO E IMPLEMENTACIÓN DE UN CLASIFICADOR MEDIANTE REDES NEURONALES PARA UN SISTEMA DE INSPECCIÓN INDUSTRIAL 3D

In this project, the design and implementation of a classi er based on convolutional neural networks is carried out with the aim of carrying out the labeling of 3D objects in order to feed an industrial inspection system. The proposed system aims to overcome the limitations of conventional classi ers currently implemented in the inspection system, where a set of concrete objects can present a very high ambiguity due to the three-dimensional reconstruction system that is used. In order to develop the system, the existing bibliography has been investigated in order to nd a neural network architecture oriented to the classi cation of three-dimensional objects, either through a multiview system or volumetric representations based on point clouds or voxels. A 3D object classi er is designed by means of a multiview system that is modi ed to be able to learn the scale of the objects based on the resolution of the image. This classi er is implemented using di erent architectures to determine which is the most appropriate. Finally, performance tests have been carried out generating a second dataset of defective parts, deforming the pieces of the original dataset, checking the response of the classi er to this type of inputs.
The Surviving Sepsis Campaign: Where have we been and where are we going?

The Surviving Sepsis Campaign develops and promotes evidence-based guidelines and performance-improvement practices aimed at reducing deaths from sepsis worldwide. The most recent guidelines, published in 2013, provide detailed management strategies for acute care, fluid resuscitation, and vasopressor use. In addition, the campaign has developed simple, short protocols for what to do within 3 and 6 hours of recognition of sepsis. These protocols are associated with reduced mortality rates. Evidence-based guidelines and performance-improvement practices will, it is hoped, reduce mortality from sepsis worldwide.
A Simple Theoretical Model of Importance for Summarization

Research on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notion of information Importance remaining latent. We argue that establishing theoretical models of Importance will advance our understanding of the task and help to further improve summarization systems. To this end, we propose simple but rigorous definitions of several concepts that were previously used only intuitively in summarization: Redundancy, Relevance, and Informativeness. Importance arises as a single quantity naturally unifying these concepts. Additionally, we provide intuitions to interpret the proposed quantities and experiments to demonstrate the potential of the framework to inform and guide subsequent works.
Beyond the Patchwise Classification: Spectral-Spatial Fully Convolutional Networks for Hyperspectral Image Classification

In recent years, patchwise classification methods are commonly adopted when dealing with the hyperspectral image (HSI) classification. Despite their promising results from the perspective of accuracy, the efficiency of these methods can hardly be ensured since there are redundant computations between adjacent patches. In this paper, we propose a spectral-spatial fully convolutional network for HSI classification with an end-to-end, pixel-to-pixel architecture. Compared with patchwise methods, the proposed framework can avoid the patch extraction and is more efficient. Since the training samples in HSIs are highly sparse, the training strategy in original fully convolutional networks is no longer feasible for HSIs. To solve this problem, we propose a novel mask matrix to assist the back-propagation in the training stage. Considering the importance of spectral and spatial features may vary for different objects and scenes, we combine both features with two weighting factors which can be adaptively learned during the network training. Besides, the dense conditional random field (CRF) is introduced into the framework to further balance the local and global information. Experiments on three benchmark HSI data sets demonstrate that the proposed method can yield competitive results with less time costs compared with patchwise methods.
Stochastic arithmetic implementations of neural networks with in situ learning

The implementation of artificial neural networks using stochastic arithmetic capable of in situ learning is described. Stochastic arithmetic uses values encoded as a pulse density, and allows addition, multiplication, and the nonlinearity to be implemented in a very small amount of digital hardware. A VLSI implementation of such a network is capable of processing 100000 training vectors per second. The performance of this architecture is demonstrated by two examples. <<ETX>>
Concise Definitive Review: Influenza

Influenza is a major concern for intensivists in all communities in the United States. While there is considerable concern whether or not the country will be ready for a pandemic influenza, even seasonal influenza poses a major challenge to hospitals. This concise review summarizes current knowledge about influenza. Seasonal Influenza Seasonal influenza, the influenza disease that occurs on a yearly basis, causes more than 200,000 hospitalizations and 41,000 deaths in the U.S. every year and is the seventh leading cause of death in the United States. [1] Despite this, 38% of unimmunized individuals feel they are not at risk for influenza and its related complications. [2] While this may be easy to attribute to the perceptions of the lay public, physicians’ attitudes are not much better; only 35–40% of health care workers are vaccinated annually; 40%of physicians believe that influenza is a benign disease that does not require treatment; and 29% believe that antiviral therapy decreases mortality, an efficacy that has never been shown in clinical trials. [3, 4] Intensivists need to recognize the importance of seasonal influenza as a cause of severe morbidity and mortality, and be well versed on diagnosis, complications, therapy, and infection control measures associated with this disease.
USE OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR PROGNOSTICS: NEW APPLICATION OF ROUGH SETS

The objective of this paper is to set the context for the potential application of rough sets in prognostics. Prognostics is a field of engineering, which deals with predicting faults and failures in technical systems. Engineering solutions to respective problems embrace the use of multiple Artificial Intelligence (AI) techniques. The authors, first, review selected AI techniques used in prognostics and then propose the application of rough sets to build the system health prognostication model.
Fiducial Point Location Algorithm for Automatic Facial Expression Recognition

We present an algorithm for the automatic recognition of facial features for color images of either frontal or rotated human faces. The algorithm first identifies the sub-images containing each feature, afterwards, it processes them separately to extract the characteristic fiducial points. Then Calculate the Euclidean distances between the center of gravity coordinate and the annotated fiducial points' coordinates of the face image. A system that performs these operations accurately and in real time would form a big step in achieving a human-like interaction between man and machine. This paper surveys the past work in solving these problems. The features are looked for in down-sampled images, the fiducial points are identified in the high resolution ones. Experiments indicate that our proposed method can obtain good classification accuracy.
Towards real-time object detection on edge with deep neural networks

Despite being a core topic for more than several decades, object detection is still receiving increasing attentions due to its irreplaceable importance in a wide variety of applications. Abundant object detectors based on deep neural networks have shown significantly revamped accuracies in recent years. However, it’s still the day one for these models to be effectively deployed to real world. In this dissertation, we focus on object detection models which tackle real world problems that are unavailable few years ago. We also aim at making object detectors on the go, which means detectors are not longer required to be run on workstations and cloud services which is latency unfriendly. To achieve these goals, we addressed the problem in two phases: application and deployment. We have done thoughtful research on both areas. Our contribution involves inter-frame information fusing, model knowledge distillation, advanced model flow control for progressive inference, and hardware oriented model design and optimization. More specifically, we proposed a novel cross-frame verification scheme for spatial temporal fused object detection model for sequential images and videos in a proposal and reject favor. To compress model from a learning basis and resolve domain specific training data shortage, we improved the learning algorithm to handle insufficient labeled data by searching for optimal guidance paths from pre-trained models. To further reduce model inference cost, we designed a progressive neural network which run in flexible cost enabled by RNN style decision controller during runtime. We recognize the awkward model deployment problem, especially for object detection models that require excessive customized layers. In response, we propose to use end-to-end neural network which use pure neural network components to substitute traditional post-processing operations. We also applied operator decomposition and graph level and on-device optimization towards real-time object detection on low power edge devices. All these works have achieved state-of-the-art performances and converted to successful applications. xi
Mutation Accumulation in Bacteria Exposed to UV Radiation

Reducing native complexity from living organisms has significance in several aspects such as academic application as model organism and industrial application as factory of useful material. In particular, reduced complexity is also interesting in the field investigating the minimal form of “life-as-we-knowit. ” However, subtracting or inactivating genes from the genome without growth defects is difficult due to the complexity of gene network and error proofing functions. In this study, using model bacteria Escherichia coli (E. coli), we designed a culture system using ultraviolet as a mutagen in order to select possible mutants growing rapidly with genomic inactivation. Here, we demonstrated that the culture system could accumulation of many mutations and preservation of growth ability. These results suggest that our method is effective to obtain functionally reduced genome of E. coli.
Semantic Content Extraction in Video Surveillance System

Recent increase in the video monitoring and surveillance has urged users to use video based application for extracting content and video semantics from it. The Results of such video monitoring and surveillance can be useful in various ways. Semantic analysis of videos has proven its worth in fields such as sports, national border security, suspicious activity monitoring along the LOC, etc. For fulfilling user's requirement such as an expected outcome in less time period and proper result with minimum storage space, the raw data and low level feature of videos are not sufficient. Though manual technique can prove handy in this regards, they are inefficient costly and time consuming. Increased demand for the video-based applications has revealed the need for extracting the content in video. We require automated system which moderate can surveillance and gives us information about important events happening in the premises. There are 3 layers of video data 1) Low level data (raw data) 2) Middle level data (content information) 3) High level Data (event information). The raw data contains the low level features of video like number of frames, length, pixel number, etc. these low level features are of very little or no importance to the user. The content, information consist information about object and occurrences of that object in video. The proposed system accepts videos as inputs which are divided into frames. Each frame is assigned to a particular number that gives event of frame in specific image from input video. These frames can be used for finding the position and speed of objects. Frame number plays the vital role in any video monitoring system.
The Costof Developing Large-ScaleSoftware

The workofsoftware costforecasting falls intotwo parts. First wemakewhatwecall structural forecasts, andthenwe calculate theabsolute dollar-volume forecasts. Structural forecasts describe thetechnology andfunction ofasoftware project, butnot itssize. We allocate resources (costs) overtheproject's life cycle fromthestructural forecasts. Judgment, technical knowledge, and econometric research should combine inmaking thestructural fore- casts. Amethodology basedona25X 7structural forecast matrix thathasbeenusedbyTRW withgoodresults overthepastfew years ispresented inthis paper. Withthestructural forecast inhand, we goontocalculate theabsolute dollar-volume forecasts. The general logic followed in"absolute" costestimating canbebased oneither amental process oranexplicit algorithm. Acostestimating algorithm ispresented andfive tradition methodsofsoftware cost forecasting aredescribed: top-down estimating, similarities and differences estimating, ratio estimating, standards estimating, and bottom-up estimating. Allforecasting methods suffer fromtheneed foravalid costdatabaseformanyestimating situations. Software information elements thatexperience hasshowntobeuseful in establishing suchadatabasearegiveninthebodyofthepaper. Majorpricing pitfalls areidentified. Twocasestudies arepresented thatillustrate thesoftware costforecasting methodology andhis- torical results. Topics forfurther workandstudy aresuggested. Index Terms-Computer programmer codeproductivity rates, cost databaseusedincostestimation mechanism, costofdeveloping customsoftware, incentive feestructure influence oncomputer soft- waredevelopment, principles ofpricing computer software proposals, resource allocation incomputer programdevelopment, software costestimating methods andgeneral logic, software costestimation algorithm, software development andtestlife cycle-cost impact, systems approach topricing large-scale software projects.
The Spaces Between Numbers: Getting International Data on Higher Education Straight (Extended Version)

1 INSTITUTE FOR HIGHER EDUCATION POLICY 01 acknowledgments The author first thanks two dozen individuals in 13 countries for the generosity of their time, information, and instruction in matters relating to both higher education data produced in their countries and comparative higher education data presentations. He is also grateful to the Association for Institutional Research (AIR) for featuring the topic addressed in these pages at the opening plenary session of its 2009 forum in Atlanta, Ga. , and the chance that forum presented for reviewing data issues and documents with international participants. Prior to the AIR forum, portions of this analysis were tried out in papers presented at the 2007 Forum of and the author expresses thanks to these organizations for those opportunities. At the Institute for Higher Education Policy (IHEP), the author appreciates the support and critiques of Alisa F. Cunningham, vice president of research and programs, the finely-honed feedback of Gregory Kienzl, director of research and evaluation, Jennifer Ramsey, research project manager, and Tia T. Gordon, communications and marketing consultant in whose fine hands the creation of all IHEP publications lie. This report was made possible through the generous support of the Lumina Foundation for Education, which, through its staff and Research Advisory Committee, provided spirited interchanges on topics both directly and indirectly related to this inquiry. Finally, the analysis, opinions, and conclusions expressed herein are those of the author, and do not necessarily represent the positions, opinions, or policies of either IHEP or the Lumina Foundation for Education.
Internet of Things: realising the potential of a trusted smart world

This report examines the policy challenges for the Internet of Things (IoT), and raises a broad range of issues that need to be considered if policy is to be effective and the potential economic value of IoT is harnessed. It builds on the Blackett review, The Internet of Things: making the most of the second digital revolution , adding detailed knowledge based on research from the PETRAS Cybersecurity of the Internet of Things Research Hub and input from Fellows of the Royal Academy of Engineering. The report targets government policymakers, regulators, standards bodies and national funding bodies, and will also be of interest to suppliers and adopters of IoT products and services. Challenges set out in the industrial strategy White – clean growth, the future of mobility and an ageing society – by enabling smart systems and greater resource efficiency, underpinning new business models in transport and driving innovations in health and care. and could speed or slow production meet daily targets. It also indicated how quickly employees were completing their respective stages of production. last mile solutions, wireless technologies, communications protocols and backbone services – requires improvements.
DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview

In talent recruitment, the job interview aims at selecting the right candidates for the right jobs through assessing their skills and experiences in relation to the job positions. While tremendous efforts have been made in improving job interviews, a long-standing challenge is how to design appropriate interview questions for comprehensively assessing the competencies that may be deemed relevant and representative for person-job fit. To this end, in this research, we focus on the development of a personalized question recommender system, namely DuerQuiz, for enhancing the job interview assessment. DuerQuiz is a fully deployed system, in which a knowledge graph of job skills, Skill-Graph, has been built for comprehensively modeling the relevant competencies that should be assessed in the job interview. Specifically, we first develop a novel skill entity extraction approach based on a bidirectional Long Short-Term Memory (LSTM) with a Conditional Random Field (CRF) layer (LSTM-CRF) neural network enhanced with adapted gate mechanism. In particular, to improve the reliability of extracted skill entities, we design a label propagation method based on more than 10 billion click-through data from the large-scale Baidu query logs. Furthermore, we discover the hypernym-hyponym relations between skill entities and construct the Skill-Graph by leveraging the classifier trained with extensive contextual features. Finally, we design a personalized question recommendation algorithm based on the Skill-Graph for improving the efficiency and effectiveness of job interview assessment. Extensive experiments on real-world recruitment data clearly validate the effectiveness of DuerQuiz, which had been deployed for generating written exercises in the 2018 Baidu campus recruitment event and received remarkable performances in terms of efficiency and effectiveness for selecting outstanding talents compared with a traditional non-personalized human-only assessment approach.
Deep Sketch-Shape Hashing With Segmented 3D Stochastic Viewing

Sketch-based 3D shape retrieval has been extensively studied in recent works, most of which focus on improving the retrieval accuracy, whilst neglecting the efficiency. In this paper, we propose a novel framework for efficient sketch-based 3D shape retrieval, i.e., Deep Sketch-Shape Hashing (DSSH), which tackles the challenging problem from two perspectives. Firstly, we propose an intuitive 3D shape representation method to deal with unaligned shapes with arbitrary poses. Specifically, the proposed Segmented Stochastic-viewing Shape Network models discriminative 3D representations by a set of 2D images rendered from multiple views, which are stochastically selected from non-overlapping spatial segments of a 3D sphere. Secondly, Batch-Hard Binary Coding (BHBC) is developed to learn semantics-preserving compact binary codes by mining the hardest samples. The overall framework is jointly learned by developing an alternating iteration algorithm. Extensive experimental results on three benchmarks show that DSSH improves both the retrieval efficiency and accuracy remarkably, compared to the state-of-the-art methods.
Extraction d’information de sous-catégorisation à partir des tables du LADL

Les tables du LADL (Laboratoire d’Automatique Documentaire et Linguistique) contiennent des données électroniques extensives sur les propriétés morphosyntaxiques et syntaxiques des foncteurs syntaxiques du français (verbes, noms, adjectifs). Ces données, dont on sait qu’elles sont nécessaires pour le bon fonctionnement des systèmes de traitement automatique des langues, ne sont cependant que peu utilisées par les systèmes actuels. Dans cet article, nous identifions les raisons de cette lacune et nous proposons une méthode de conversion des tables vers un format mieux approprié au traitement automatique des langues.
Graph-Based Routing, Broadcasting and Organizing Algorithms for Ad-Hoc Networks

The development of networks of low-cost, low-power, multi-functional devices has received increasing attention over the last ten years. These devices are small in size and able to process data, communicate with each other, typically over a radio channel, and even sense. Each device particpates in a self-configuring infrastureless network connected by wireless, called ad-hoc network. Since most of the individual node in ad-hoc networks is inherently resource constrained: limited processing speed, storage capacity, and communication range and energy, it is impossible to achieve application requirements by individual device or unattached devices. A number of devices within a network have to combine as an aggregate collaborating to achieve application requirements. However, such massive devices cooperation must be achieved by the necessary organizational structures without requiring human intervention.
A Parallel Framework for Large Spatial Microsimulations

This paper describes the concept of a versatile transport simulation framework to be used for the development of integrated transport simulation models. It is designed to improve modularization and thereby simplify the collaboration between scientists and engineers from different fields. Modularization is believed to be important due to the increasing complexity of the implementation task if models from different areas are to be integrated. In performance critical software projects this complexity is often further increased by the desire or the need for an implementation that can be run in parallel on multiple processors. Furthermore, developing an efficient parallel simulation is not trivial. In addition to the complexity of the modeling task as such one has to deal with communication delays and data availability issues. The idea of the presented framework is to handle this complexity by defining simple rules according to which user developed modules must act. These rules include certain minimum delays between the observation of a change in the system and the triggered reactions, limited vision, and limited traveling speed. To illustrate how the framework is to be used a simples modeling scenario is created and a possible implementation employing the framework is sketched. The example scenario consists of the integration of a pedestrian simulation with a load estimation module and a travel time estimator. Finally, an outlook on the next steps in the development of the framework is given.