Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized inexact proximal algorithms : habit ’ s formation with resistance to change , following worthwhile changes

This paper shows how, in a quasi metric space, an inexact proximal algorithm with a generalized perturbation term appears to be a nice tool for Behavioral Sciences (Psychology, Economics, Management, Game theory,. . . ). More precisely, the new perturbation term represents an index of resistance to change, defined as a “curved enough” function of the quasi distance between two successive iterates. Using this behavioral point of view, the present paper shows how such a generalized inexact proximal algorithm can modelize habit’s formation in a striking way. This idea comes from a recent “variational rationality approach” of human behavior which unifies a lot of different theories of stability (habits, routines, equilibrium, traps,. . . ) and changes (creations, innovations, learning and destructions,. . . ) in Behavioral Sciences and a lot of concepts and algorithms in Variational Analysis. In this variational context, the perturbation term represents a specific instance of the very general concept of resistance to change, which is the desutility of some costs to change. Central to the analysis are the original variational concepts of “worthwhile changes” and “marginal worthwhile stays”. At the behavioral level, this paper advocates that proximal algorithms ∗The author was supported in part by CNPq Grant 471815/2012-8, Project CAPES-MES-CUBA 226/2012, PROCAD-nf-UFG/UnB/IMPA and FAPEG/CNPq. IME-Universidade Federal de Goiás, Goiânia-GO 74001-970, BR (Email: glaydston@mat.ufg.br) †Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS, FR, antoine.soubeyran@gmail.com
Ensemble Classifier based Approach for Classification of Examination Questions into Bloom’s Taxonomy Cognitive Levels

The concept of Bloom’s taxonomy cognitive domain has been broadly used as a guideline in preparing a reasonable examination paper that consists of questions belonging to various cognitive levels which are helpful in evaluating different capabilities of students. Currently, academicians identify Bloom’s taxonomy cognitive level manually, but that is a tedious and a time-consuming task. Therefore, the use of automatic classification technique based on Bloom’s taxonomy cognitive levels is highly needed. Several studies have been carried out to fulfill this task, but most of these studies have failed to address the overlapping keyword problem among Bloom’s taxonomy cognitive levels, and most of these studies have not considered the semantic structure of the examination questions. To overcome these problems, this study proposes a question classification model using an ensemble classifier approach by combining four different classifiers; namely rule based, support vector machine, k-nearest neighbor and Naive Bayes.The results of four different classifiers are integrated to derive the final corresponding Bloom’s taxonomy cognitive level, using majority voting and WordNet similarity values. WordNet similarity is used to explore the semantic structure of the examination questions. A sample of first year programming examination questions of University of Colombo School of Computing was used for the evaluation. Four domain experts confirmed identified Bloom’s taxonomy cognitive levels of the questions in the dataset. The experimental results indicate that the proposed ensemble classifier approach yields much better accuracy than the accuracy of the individual classifiers.
External-Memory Search Trees with Fast Insertions

This thesis provides both experimental and theoretical contributions regarding externalmemory dynamic search trees with fast insertions. The first contribution is the implementation of the buffered repository B-tree, a data structure that provably outperforms B-trees for updates at the cost of a constant factor decrease in query performance. This thesis also describes the cache-oblivious lookahead array, which outperforms B-trees for updates at a logarithmic cost in query performance, and does so without knowing the cache parameters of the system it is being run on. The buffered repository B-tree is an external-memory search tree that can be tuned for a tradeoff between queries and updates. Specifically, for any ǫ ∈ [1/ lg B, 1] this data structure achieves O((1/ǫB)(1+ logB(N/B))) block transfers for Insert and Delete and O((1/ǫ)(1 + logB(N/B))) block transfers for Search. The update complexity is amortized and is O((1/ǫ)(1+ logB(N/B))) in the worst case. Using the value ǫ = 1/2, I was able to achieve a 17 times increase in insertion performance at the cost of only a 3 times decrease in search performance on a database with 12-byte items on a disk with a 4-kilobyte block size. This thesis also shows how to build a cache-oblivious data structure, the cacheoblivious lookahead array, which achieves the same bounds as the buffered repository B-tree in the case where ǫ = 1/ lg B. Specifically, it achieves an update complexity of O((1/B) log(N/B)) and a query complexity of O(log(N/B)) block transfers. This is the first data structure to achieve these bounds cache-obliviously. The research involving the cache-oblivious lookahead array represents joint work with Michael A. Bender, Jeremy Fineman, and Bradley C. Kuszmaul. Thesis Supervisor: Charles E. Leiserson Title: Professor of Computer Science and Engineering Thesis Supervisor: Bradley C. Kuszmaul Title: Research Scientist
Mechanistic modelling of bioavailability : putting soil microbiology in its pore scale context

The soil-microbe complex was investigated in terms of the relationship between the physical soil structure and related functional characteristics. The use of CT scanning technology provided a means of obtaining high-resolution digital reconstructions of the internal soil structures in three dimensions thus allowing, for the first time, the internal architecture of soil to be investigated. The investigation of soil structure was facilitated through the development of a theoretical modelling framework that acted as a virtual laboratory. A suite of metrics was implemented within the modelling framework to allow the quantification of the physical soil structure. The analysis of a range of CT imaged soil structures via the quantification metrics showed that the micro-structure of soil can span a range of porosities and textures. The metric values obtained for individual structures characterise the features of the structures that play an influential role in mediating the functional value of a sample. A model that simulates soil structure in three dimensions from two-dimensional data is shown to reproduce the key structural characteristics of soil at the micro-scale. Data extracted from the CT imaged soil samples were extracted to parameterise the model and thus the simulated soil structures were validated against the original CT imaged structure using the quantification metrics. In general, the model adequately reconstructed the variation found across the CT imaged samples. However, for low porosity samples the complex pore architectures were not captured. The soil structures were used to simulated oxygen transport through the microenvironment to microbial active sites thus allowing the examination of the impact of physical structure on microbial respiration. The results presented show that soil structure is the determining factor in bulk respiration of the soil microenvironment.
No . 34 / 07 EU Trade Policies : Benchmarking Protection in a General Equilibrium Framework

This paper deals with the EU’s trade policy with two objectives: on the one hand, we study the performance of EU's preferential agreements in granting their partners improved market access; on the other hand, we assess the extent to which domestic sectors are effectively protected. As far as the first objective is concerned, we construct bilateral indicators of protection based on the applied tariffs faced by each exporter. In order to do this, an index of trade policy restrictiveness is computed, using the Mercantilistic Trade Restrictiveness Index as the tariff aggregator. We also analyze the protection granted to each sector by the existing tariff structure. In this respect, we compute effective rates of protection that overcome the well-known theoretical shortcomings of the traditional definition (Output Effective Rate of Protection). The analysis is based on a comparative static applied general equilibrium model (Global Trade Analysis Project) and on the most recent version (release 6) of the related database. Results are obtained with reference to the situation existing in 2001, but the assessment of protection is carried out for the enlarged EU. Overall, it appears that notwithstanding the rhetoric about preferential access, several developing countries are the ones facing the highest hurdles in getting into the EU markets. Both bilateral protection and effective protection rates are broadly consistent with the evolution of the WTO negotiations: the strongest demands from developing countries in terms of market access in the EU have less to do with the overall applied MFN tariffs on industrial products than the reduction of distortions affecting trade in agriculture.
Composition, Spatial Distribution, and Trophic Structure of the Zooplankton Community in San Jorge Gulf, Southwestern Atlantic Ocean

PERMISSIONS Oceanography (ISSN 1042-8275) is published by The Oceanography Society, 1 Research Court, Suite 450, Rockville, MD 20850 USA. ©2018 The Oceanography Society, Inc. Permission is granted for individuals to read, download, copy, distribute, print, search, and link to the full texts of Oceanography articles. Figures, tables, and short quotes from the magazine may be republished in scientific books and journals, on websites, and in PhD dissertations at no charge, but the materials must be cited appropriately (e.g., authors, Oceanography, volume number, issue number, page number[s], figure number[s], and DOI for the article). Republication, systemic reproduction, or collective redistribution of any material in Oceanography is permitted only with the approval of The Oceanography Society. Please contact Jennifer Ramarui at info@tos.org. Permission is granted to authors to post their final pdfs, provided by Oceanography, on their personal or institutional websites, to deposit those files in their institutional archives, and to share the pdfs on open-access research sharing sites such as ResearchGate and Academia.edu. Oceanography THE OFFICIAL MAGAZINE OF THE OCEANOGRAPHY SOCIETY
Antenne réseau cylindrique pour station sol GPS différentiel

Les multitrajets du a la presence d'obstacles au voisinage du sol restent une source d'erreurs difficile a supprimer dans les techniques differentielles de localisation par satellite. Nous proposons une antenne constituee d'un reseau cylindrique vertical a forte coupure au voisinage du sol permettant de reduire l'influence de ces multitrajets. La synthese de ce reseau pose des problemes specifiques lies a un pas d'echantillonnage plus faible que la demi longueur d'onde et a la necessite d'avoir un gain quasi constant aux elevations voisines de 90° alors que le gain doit rester faible pour les elevations negatives. Un autre probleme specifique est lie au couplage entre elements qui impose un reseau de repartition specialement adapte pour conserver l'omnidirectionnalite en azimut et une bonne purete de polarisation circulaire.
RESEARCH OUTPUTS / RÉSULTATS DE RECHERCHE

We present ArtVis, an advanced user interface combining state-of-the-art visualisation techniques and tangible interaction to explore the Web Gallery of Art digital artwork collection consisting of almost 28000 European artworks. The graphical ArtVis interface contains three separate visualisation panels that allow users to temporally, semantically and geographically investigate the digital artwork collection. The goal of ArtVis is that users can gain new insights by visually analysing and exploring a large data set rather than to only provide detailed information about individual artworks. This explorative ArtVis interaction style is further fostered by a tangible user interface where physical artefacts can be used as simple handles to drive the faceted browsing of the rich digital information space.
Track-Aligned Extents: Matching Access Patterns to Disk Drive Characteristics

Track-aligned extents (traxtents) utilize disk-specific knowledge to match access patterns to the strengths of modern disks. By allocating and accessing related data on disk track boundaries, a system can avoid most rotational latency and track crossing overheads. Avoiding these overheads can increase disk access efficiency by up to 50% for mid-sized requests (100–500 KB). This paper describes traxtents, algorithms for detecting track boundaries, and some uses of traxtents in file systems and video servers. For large-file workloads, a version of FreeBSD’s FFS implementation that exploits traxtents reduces application run times by up to 20% compared to the original version. A video server using traxtent-based requests can support 56% more concurrent streams at the same startup latency and buffer space. For LFS, 44% lower overall write cost for track-sized segments can be achieved.
Combining the Description Features of UML-RT and CSP + T Specifications Applied to a Complete Design of Real-Time Systems

UML is a collection of notations for capturing a software system specification. These notations have a specific syntax defined by the Object Management Group (OMG), but many of their constructs only present informal semantics. They are primarily graphical, with textual annotation. The inadequacies of standard UML as a vehicle for complete specification and implementation of real-time embedded systems has led to a variety of competing and complementary proposals. The Real-time UML profile (UML-RT), developed and standardized by OMG, defines a unified framework to express the time, scheduling and performance aspects of a system. We present in this paper a framework approach aimed at deriving a complete specification of a real-time system. Therefore, we combine two methods, a semiformal one, UML-RT, which allows the visual modeling of a realtime system and a formal one, CSP+T, which is a design language including the specification of real-time requirements. As to show the applicability of the approach, a correct design of a real-time system with hard real time constraints by applying a set of mapping rules is obtained. Keywords—CSP+T, formal software specification, process algebras, real-time systems, Unified Modeling Language.
Scientific Utopia: II - Restructuring Incentives and Practices to Promote Truth Over Publishability

An academic scientist’s professional success depends on publishing. Publishing norms emphasize novel, positive results. As such, disciplinary incentives encourage design, analysis, and reporting decisions that elicit positive results and ignore negative results. Prior reports demonstrate how these incentives inflate the rate of false effects in published science. When incentives favor novelty over replication, false results persist in the literature unchallenged, reducing efficiency in knowledge accumulation. Previous suggestions to address this problem are unlikely to be effective. For example, a journal of negative results publishes otherwise unpublishable reports. This enshrines the low status of the journal and its content. The persistence of false findings can be meliorated with strategies that make the fundamental but abstract accuracy motive – getting it right – competitive with the more tangible and concrete incentive – getting it published. We develop strategies for improving scientific practices and knowledge accumulation that account for ordinary human motivations and self-serving biases.
Organized Behavior Classification of Tweet Sets using Supervised Learning Methods

During the 2016 US elections Twitter experienced unprecedented levels of propaganda and fake news through the collaboration of bots and hired persons, the ramifications of which are still being debated. This work proposes an approach to identify the presence of organized behavior in tweets. The Random Forest, Support Vector Machine, and Logistic Regression algorithms are each used to train a model with a data set of 850 records consisting of 299 features extracted from tweets gathered during the 2016 US presidential election. The features represent user and temporal synchronization characteristics to capture coordinated behavior. These models are trained to classify tweet sets among the categories: organic vs organized, political vs non-political, and pro-Trump vs pro-Hillary vs neither. The random forest algorithm performs better with greater than 95% average accuracy and f-measure scores for each category. The most valuable features for classification are identified as user based features, with media use and marking tweets as favorite to be the most dominant.
Automatical Composition of Lyrical Songs

We address the challenging task of automatically composing lyrical songs with matching musical and lyrical features, and we present the first prototype, M.U. SicusApparatus, to accomplish the task. The focus of this paper is especially on generation of art songs (lieds). The proposed approach writes lyrics first and then composes music to match the lyrics. The crux is that the music composition subprocess has access to the internals of the lyrics writing subprocess, so the music can be composed to match the intentions and choices of lyrics writing, rather than just the surface of the lyrics. We present some example songs composed by M.U. Sicus, and we outline first steps towards a general system combining both music composition and writing of lyrics.
Wavelet Transform in Biomedical Image Segmentation and Classification

Segmentation, feature extraction and classification stand for important topics of biomedical image processing with further use in microscopy and cell analysis. The paper presents the use of wavelet transform for signal or image decomposition and reconstruction allowing time scale signal analysis, image de-noising, resolution changes and feature extraction. The main part of the paper is devoted to image regions boundary detection and wavelet decomposition of resulting boundary signals to find features for the following segments classification. Results of classification achieved by self-organizing neural networks using features obtained by the discrete wavelet transform are compared with that obtained by the discrete Fourier transform. Proposed algorithms have been verified for simulated structures and then used for analysis of biomedical magnetic resonance images of the brain slice.
Future Generation Computer

The wide-spread embracement and integration of Internet of Things (IoT) has inevitably lead to an explosion in the number of IoT devices. This in turn has led to the generation of massive volumes of data that needs to be transmitted, processed and stored for efficient interpretation and utilization. Edge computing has emerged as a viable solution which complements cloud thereby enabling the integrated edge–cloud paradigm to successfully satisfy the design requirements of IoT applications. A vast majority of existing studies have proposed scheduling frameworks for individual tasks and only very few works have considered the more challenging problem of scheduling complex workloads such as workflows across edge–cloud environments. Workflow scheduling is an NP hard problem in distributed infrastructures. It is further complicated when scheduling framework needs to coordinate workflow executions across resource constrained and highly distributed edge–cloud environments. In this work, we leverage Deep Reinforcement Learning for designing a workflow scheduling framework capable of overcoming the aforementioned challenges. Different from all existing works we have designed a novel hierarchical action space for promoting a clear distinction between edge and cloud nodes. Coupled with this a hybrid actor–critic based scheduling framework enhanced with proximal policy optimization technique is proposed to efficiently deal with the complex workflow scheduling problem in edge– cloud environments. Performance of the proposed framework was compared against several baseline algorithms using energy consumption, execution time, percentage of deadline hits and percentage of jobs completed as evaluation metrics. Proposed Deep Reinforcement Learning technique performed 56% better with respect to energy consumption and 46% with respect to execution time compared to time and energy optimized baselines, respectively. This was achieved while also maintaining the energy efficiency in par with the energy optimized baseline and execution time in par with the time optimized baseline. The results thus demonstrate the superiority of the proposed technique in establishing the best-trade off between the conflicting goals of minimizing energy consumption and execution time. © 2022PublishedbyElsevierB.V.
NanoCaller for accurate detection of SNPs and small indels from long-read sequencing by deep neural networks

Background : Variant detection from high-throughput sequencing data remains an important, unresolved yet often overlooked problem. Long-read sequencing technologies, such as Oxford Nanopore and PacBio sequencing, present unique advantages to detect SNPs and small indels in genomic regions that short-read sequencing cannot reliably examine (for example, only ~80% of genomic regions are marked as "high-confidence regio n” to have SNP/indel calls in the Genome In A Bottle project). However, existing software tools for short-read data perform poorly on long-read data; instead, several recent studies showed promising results in variant detection on long-read data by deep learning. Methods : Here we present NanoCaller, a computational method that integrates haplotype structure in deep convolutional neural network for the detection of SNPs/indels from long-read sequencing data. NanoCaller uses long-range information to generate predictions for each candidate variant site by considering pileup information of other candidate sites sharing reads. Subsequently, it performs read phasing and carries out local realignment on each set of phased reads to call indels. Results : We evaluate NanoCaller on multiple human genomes (NA12878/HG001, NA24385/HG002, NA24149/HG003, NA24143/HG004 and HX1), by cross-genome, cross-chromosome, cross-reference genome, and cross-platform benchmarking tests. Our results demonstrate that NanoCaller performs competitively against other long-read variant callers. In particular, NanoCaller can generate SNP/indel calls in complex genomic regions that are removed from variant calling by other software tools. Conclusions : In summary, NanoCaller enables the detection of genetic variants from genomic regions that are previously inaccessible to genome sequencing, and may facilitate the use of long-read sequencing in finding disease variants in human genetic studies.