Miracle-dz/UniFAR-FTUs huggingface dataset:
Scaling the EOS namespace – new developments, and performance optimizations

EOS is the distributed storage solution being developed and deployed at CERN with the primary goal of fulfilling the data needs of the LHC and its various experiments. Being in production since 2011, EOS currently manages around 256 petabytes of raw disk space and 3.4 billion files across several instances. Nowadays, EOS is increasingly being used as a distributed filesystem and file sharing platform, which poses scalability challenges on its legacy namespace subsystem, tasked with keeping track of all file and directory metadata on a particular instance. In this paper we discuss said challenges, and present our solution which has recently entered production. We made several architectural improvements to the overall system design, the most important of which was introducing QuarkDB, a highly-available datastore capable of serving as the metadata backend for EOS, tailored to the needs of the namespace. We also describe our efforts in providing comparable latency and performance to the legacy in-memory implementation, both when reading through the use of extensive caching and prefetching, and when writing through the use of latencyhiding techniques involving a persistent, back-pressured local queue for batching writes towards the QuarkDB backend.
Multi-Level Policy and Reward-Based Deep Reinforcement Learning Framework for Image Captioning

Image captioning is one of the most challenging tasks in AI because it requires an understanding of both complex visuals and natural language. Because image captioning is essentially a sequential prediction task, recent advances in image captioning have used reinforcement learning (RL) to better explore the dynamics of word-by-word generation. However, the existing RL-based image captioning methods rely primarily on a single policy network and reward function—an approach that is not well matched to the multi-level (word and sentence) and multi-modal (vision and language) nature of the task. To solve this problem, we propose a novel multi-level policy and reward RL framework for image captioning that can be easily integrated with RNN-based captioning models, language metrics, or visual-semantic functions for optimization. Specifically, the proposed framework includes two modules: 1) a multi-level policy network that jointly updates the word- and sentence-level policies for word generation; and 2) a multi-level reward function that collaboratively leverages both a vision-language reward and a language-language reward to guide the policy. Furthermore, we propose a guidance term to bridge the policy and the reward for RL optimization. The extensive experiments on the MSCOCO and Flickr30k datasets and the analyses show that the proposed framework achieves competitive performances on a variety of evaluation metrics. In addition, we conduct ablation studies on multiple variants of the proposed framework and explore several representative image captioning models and metrics for the word-level policy network and the language-language reward function to evaluate the generalization ability of the proposed framework.
Can Auctions Select People by Their Level-K Types?

In this paper, we seek to determine if auctions can be used to select players according to their level-k types. To do so, we embed auctions into the p-beauty contest game. We find that by using different designs, we can get the auction winners to be either the lower level-k types or the higher level-k types. In particular, when the value of winning the auction is increasing in the level-k types of all the players, higher level-k players bid higher. When the value of winning the auction is decreasing in the level-k types of all the players, the lower level-k players bid higher. Taken together, our experiment confirms that we can use auctions to select players by their level-k types. This shows that auctions can allow an economic designer to affect the outcome of a game through the selection of level-k types entering to play the game.
Technology-Aided Instruction & Intervention (TAII). EBP Brief Packet.

1. Overview: A quick summary of salient features of the practice, including what it is, who it can be used with, what skills it has been used with, and settings for instruction. 2. Evidence-base: The TAII Evidence-base details the NPDC criteria for inclusion as an evidence-based practice and the specific studies that meet the criteria for this practice. 3. Step-by-Step Guide: Use the TAII Step-by-Step Practice Guide as an outline for how to plan for, use, and monitor TAII. Each step includes a brief description as a helpful reminder while learning the process. 4. Implementation Checklist: Use the TAII Implementation Checklist to determine if the practice is being implemented as intended. 5. Data Collection Sheets: Use the data collection sheets as a method to collect and analyze data to determine if progress is being made for a learner with ASD. 6. Tip Sheet for Professionals: Use the TAII Tip Sheet for Professionals as a supplemental resource to help provide basic information about the practice to professionals working with the learner with ASD. 7. Parent Guide: Use the TAII Parent Guide to help parents or family members understand basic information about the practice being used with their child. 8. Additional Resources: Use the Additional Resources to learn more about the practice. 9. CEC Standards: A list of CEC Standards that apply specifically to TAII. 10. Module References: A list of numerical References utilized for the TAII module.
Ontology-Based RDF Integration of Heterogeneous Data

The proliferation of heterogeneous data sources in many application contexts brings an urgent need for expressive and efficient data integration mechanisms. There are strong advantages to using RDF graphs as the integration format: being schemaless, they allow for flexible integration of data from heterogeneous sources; RDF graphs can be interpreted with the help of an on-tology, describing application semantics; last but not least, RDF enables joint querying of the data and the ontology. To address this need, we formalize RDF Integration Systems (RIS), Ontology Based-Data Access mediators, that go beyond the state of the art in the ability to expose, integrate and flexibly query data from heterogeneous sources through GLAV (global-local-as-view) mappings. We devise several query answering strategies, based on an innovative integration of LAV view-based rewriting and a form of mapping saturation. Our experiments show that one of these strategies brings strong performance advantages, resulting from a balanced use of mapping saturation and query reformulation.
Patch based Semi-supervised Linear Regression for Face Recognition

To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to [1,1, ··· ,1] T . The solutions of all the mapping matrices are integrated into an overall objective function, which uses ℓ 2 , 1 -norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion.
Fruit Disease Categorization based on Color, Texture and Shape Features

Nowadays digitization and automation of machine in agriculture field plays prominent role. In this paper, we have proposed method to classify fruit as diseased and non-diseased. Firstly, we used K means clustering method for segmentation of diseased regions. Later, we used to extract shape, color and texture features on segmented diseased regions. We have collected fruit diseased images from internet to create dataset and totally we have collect 2500 images from 10 fruit classes. We have conducted extensive experimentation using Artificial Neural Network and results shows that proposed method gives better performance compared to SVM and KNN.
GeOASDVN: Um protocolo geocast ciente de obstáculos baseado em Redes Veiculares Definidas por Software

As Redes Veiculares (Vehicular Ad-hoc Networks-VANETs) são tecnologias que permitem a utilização de serviços de rede por motoristas e passageiros em veículos. A heterogeneidade das VANETs, que podem utilizar diferentes tecnologias sem-fio, representa um desafio para o desenvolvimento de protocolos de comunicação. Neste contexto, o paradigma de Redes Veiculares Definidas por Software (Software Defined Vehicular Network-SDVN) surge como uma alternativa promissora que permite a criação de protocolos flexíveis e adaptáveis. Portanto, este trabalho apresenta um protocolo de disseminação geocast que utiliza informações do ambiente e dos veículos para realizar a disseminação de mensagens de forma inteligente. Os resultados obtidos através de simulações mostram que o protocolo apresenta um comportamento mais eficiente que os adversários, tanto em ambientes que não consideram construções (prédios, casas, etc.) quanto em ambientes que construções são consideradas (realístico).
Geometrical Structure Classification of Target HRRP Scattering Centers Based on Dual Polarimetric $H/\alpha$ Features

Polarimetric high resolution range profile (HRRP) contains the geometrical structural information along radar line-of-sight and has shown great potentials in target recognition. In existing researches, <inline-formula> <tex-math notation="LaTeX">$H / \alpha $ </tex-math></inline-formula> decomposition has been applied to exploit global polarimetric features of a single HRRP from its spatially averaged coherent matrix, which loses target local scattering information. In this paper, we propose to apply the <inline-formula> <tex-math notation="LaTeX">$H / \alpha $ </tex-math></inline-formula> decomposition along the slow time dimension in a dual polarimetric HRRP sequence. The <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> features are extracted for each target scattering center by averaging its samples in different observations of the HRRP sequence, which provides both target local high resolution and polarimetric scattering characteristics. We also design a novel six-zone <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> plane for geometrical structure classification of target scattering centers. Simulation and experimental results show the effectiveness of the proposed method and display its good potentials for practical applications.
A Theory of Timing Effects in a Self-Organizing Model of Sentence Processing

Many theories of sentence processing are based on the idea that a discrete, symbolic grammar defines all of the structures relevant for parsing, effectively supervising the parser as it selects from those structures the one that best fits the input. However, local coherence effects, where people’s parsing behavior suggests they are entertaining locally viable but globally impossible structures, suggest that this may not always be the case. We introduce a self-organized sentence processing (SOSP) model of local coherence effects and use it to demonstrate how predictions about timing effects (a major source of psycholinguistic data and a shortcoming of many previous dynamical parsers) can be derived directly from a harmony (wellformedness) function covering both grammatical and ungrammatical structures. This framework allows us to simulate the processing of any set of lexical features and attachment links, making it widely applicable to psycholinguistic phenomena.
Gender Classification and its Applications

- The ongoing development in web-based social networking and the social stage has offered ascend to expanding measure of utilization. The programmed classification of age and gender has turned out to be significant for an expanding solicitation of utilizations. Gender Identification is one of the real segments for creating gender-subordinate acoustic modules for discourse acknowledgment and so forth.
ParaDis and Démonette - From Theory to Resources for Derivational Paradigms

This article traces the genesis of the French derivational database Démonette v 2 and shows how current architecture and content of derivational morphology resources result from theoretical developments in derivational morphology and from the users’ need. The development of this large-scale resource began a year ago and is part of the Demonext project (ANR-17-CE23-0005). Its conception is adapted from theoretical approaches of derivational morphology where lexemes, units of analysis, are grouped into families that are organized into paradigms. More precisely, Démonette v 2 is basically an implementation of ParaDis , a paradigmatic model for representing morphologically complex lexical units, formed by regular processes or presenting discrepancies between form and meaning. The article focuses on the principles of morphological, structural and semantic encoding that reﬂect the methodological choices that have been made in Démonette v 2 . Our proposal will be illustrated with various examples of non-canonical word formations.
Flood Management in Aqala through an Agent-Based Solution and Crowdsourcing Services in an Enterprise Geospatial Information System

Propagating crowdsourcing services via a wireless network can be an appropriate solution to using the potential of crowds in crisis management processes. The present study aimed to deploy crowdsourcing services properly to spatial urgent requests. Composing such atomic services can conquer sophisticated crisis management. In addition, the conducted propagated services guide people through crisis fields and allow managers to use crowd potential appropriately. The use of such services requires a suitable automated allocation method, along with a proper approach to arranging the sequence of propagating services. The solution uses a mathematical framework in the context of a GIS (Geospatial Information System) in order to construct an allocation approach. Solution elements are set out in a multi-agent environment structure, which simulate disaster field objects. Agents which are dynamically linked to objects in a crisis field, interact with each other in a competitive environment, and the results in forming crowdsourcing services are used to guide crowds in the crisis field via the crowdsourcing services. The present solution was implemented through a proper data schema in a powerful geodatabase, along with various users with specialized interfaces. Finally, a solution and crowdsourcing service was tested in the context of a GIS in the 2019 Aqala flood disaster in Iran and other complement scenarios. The allocating performance and operation of other system elements were acceptable and reduced indicators, such as rescuer fatigue and delay time. Crowdsourcing service was positioned well in the solution and provided good performance among the elements of the Geospatial Information System.
Performance Analysis for Clusters of BLE Devices with Heterogeneous Parameters

—As one of the most promising technologies, Bluetooth low energy has been widely utilized for supporting various Internet of Things services and applications. To create connection between BLE devices, neighbor discovery process is an essential procedure. However, because of the effect of signal collision problem between BLE devices, the neighbor discovery performance is degraded seriously. In this paper, we consider clustering BLE devices, where the devices in same cluster own same parameter settings. We analyze the signal collision probability, and the discovery delay of these clustering devices during the discovery process by simulator. The simulation result shows the relationship between discovery performance and the rate of the number devices in each cluster.
Progressive Web App (PWA) for Organization System

: Now a days use our mobile devices a lot and a large amount of that time is spent using apps. However, the remainder of that time is spent browsing and searching the web from the mobile device. PWAs aim to capitalize on this time by marrying the experience of an app with the immediate accessibility of visiting a website. As smart phones get used in large amount, the number of apps are also increased. But the problem with these native web apps is it cannot work properly when network is poor or unreliable. Also these apps require large memory space and they cannot work on offline mode. So for overcoming all these problems, the concept of Progressive Web Apps comes in frame which can work on any platform. The application here is online tier shopping system for user and owner.
Neuromorphic Event-based Action Recognition

An action can be viewed as spike trains or streams of events when observed and captured by neuro-morphic imaging hardware such as the iniLabs DVS128. These streams are unique to each action enabling them to be used to form descriptors. This paper describes an approach for detecting speciﬁc actions based on space-time template matching by forming such descriptors and using them as comparative tools. The developed approach is used to detect symbols from the popular RoShambo (rock, paper and scissors) game. The results demonstrate that the developed approach can be used to correctly detect the motions involved in producing RoShambo symbols.
A Comparison of Face Verification with Facial Landmarks and Deep Features

Face verification is a key task in many application fields, such as security and surveillance. Several approaches and methodologies are currently used to try to determine if two faces belong to the same person. Among these, facial landmarks are very important in forensics, since the distance between some characteristic points of a face can be used as an objective measure in court during trials. However, the accuracy of the approaches based on facial landmarks in verifying whether a face belongs to a given person or not is often not quite good. Recently, deep learning approaches have been proposed to address the face verification problem, with very good results. In this paper, we compare the accuracy of facial landmarks and deep learning approaches in performing the face verification task. Our experiments, conducted on a real case scenario, show that the deep learning approach greatly outperforms in accuracy the facial landmarks approach. Keywords–Face Verification; Facial Landmarks; Deep Learning; Surveillance; Security.
An Approach to Classify Heritage Sites Architecture Using Convolution Neural Networks

: In this project, we have explored the use of Convolution Neural Networks for semantic classification of heritage site architectures like dome, bell tower, glass, and gargoyle. We have tested our proposed system with two different learning modalities. Besides conventional training from scratch, we have resorted to pre-trained networks that have been fine-tuned on a huge amount of data in order to avoid overfitting problems and to reduce design/computing time. Experiments on a dataset we have built by ourselves from Heritage repository show that with the help of this step of fine-tuning, one of the simplest (and oldest) networks able to produce similar results than more sophisticated ones as ResNet which is pretty 3 times more consuming in computing time, mainly due to the number of the layer it uses. With this model, we have been able to show that our model can classify in the right way more than 98% of the images belonging to the dataset test. From now, this model can be used to classify the full dataset of the Heritage repository.
Hydrogen sulphide synthesis (version 2019.4) in the IUPHAR/BPS Guide to Pharmacology Database

Hydrogen sulfide is a gasotransmitter, with similarities to nitric oxide and carbon monoxide. Although the enzymes indicated below have multiple enzymatic activities, the focus here is the generation of hydrogen sulphide (H2S) and the enzymatic characteristics are described accordingly. Cystathionine β-synthase (CBS) and cystathionine γ-lyase (CSE) are pyridoxal phosphate (PLP)-dependent enzymes. 3-mercaptopyruvate sulfurtransferase (3-MPST) functions to generate H2S; only CAT is PLP-dependent, while 3-MPST is not. Thus, this third pathway is sometimes referred to as PLP-independent. CBS and CSE are predominantly cytosolic enzymes, while 3-MPST is found both in the cytosol and the mitochondria. For an authoritative review on the pharmacological modulation of H2S levels, see Szabo and Papapetropoulos, 2017 [4].
Machine Learning Approach for Identification of Diseases through Gene Mapping

: Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. Genetic mapping is based on the use of Genetic Techniques to construct maps showing the positions of genes and other sequence features on genome. The early applications of machine learning to population genetics demonstrate that they outperform traditional approaches. Potentially important disease biomarkers have been revealed by the use of machine learning methods on gene expression data, where algorithms learn to differentiate between different disease phenotypes. Genetic testing can be considered as the perfect field for machine learning applications in many ways, considering the enormous amount of data that these programs need to contend with.
Calibration-less parallel imaging compressed sensing reconstruction based on OSCAR regularization

Over the last decade, the combination of parallel imaging (PI) and compressed sensing (CS) in magnetic resonance imaging (MRI) has allowed to speed up acquisition while maintaining a good signal-to-noise ratio (SNR) for millimetric resolution. Self-calibrating techniques such as 1-ESPiRIT have emerged as a standard approach to estimate the coil sensitivity maps that are required at the reconstruction stage. Although straightforward in Cartesian acquisitions, these approaches become more computationally demanding in non-Cartesian scenarios especially for high resolution imaging (e.g. 500 µm in plane). Instead, calibration-less techniques no longer require this prior knowledge to perform multi-channel image reconstruction from undersampled k-space data. In this work, we introduce a new calibration-less PI-CS reconstruction method that is particularly suited to non-Cartesian data. It leverages structure sparsity of the multi-channel images in a wavelet transform domain while adapting to SNR inhomogeneities across receivers thanks to the OSCAR-norm regularization. Comparison and validation on 8 to 20-fold prospectively accelerated high-resolution ex-vivo human brain MRI data collected at 7 Tesla shows that the subbandwise OSCAR-norm regularization achieves the best trade-off between image quality and computational cost at the reconstructions stage compared to other tested versions (global, scalewise and pixel-wise). This approach provides slight to moderate improvement over its state-of-the-art competitors (self-calibrating 1-ESPIRiT method and calibration-less AC-LORAKS and CaLM methods) in terms of closeness to the Cartesian reference magnitude image. Importantly, it also preserves much better phase information compared to other approaches.