Miracle-dz/UniFAR-FTUs huggingface dataset:
Récepteurs itératifs pour systèmes multi-antennes

L'objectif des travaux realises dans le cadre de cette these est d'etudier l'association des techniques de transmission multi-antennes (MIMO) avec des schemas de reception iteratifs dans un contexte de transmission sans fil haut debit. A largeur de bande et qualite de reception equivalentes, l'utilisation d'antennes multiples en communications numeriques permet d'augmenter le debit utile de la transmission. Cependant sous l'hypothese d'une non connaissance du canal a l'emission, les potentialites du canal MIMO ne sont generalement exploitees qu'au prix d'une reception tres complexe, difficilement realisable au sein d'un circuit integre. Tout d'abord, afin de lutter contre la selectivite frequentielle et de simplifier le traitement en reception, nous choisissons d'associer la modulation multi-porteuses OFDM a des schemas de transmission MIMO. Par ailleurs, en etendant les resultats obtenus dans le domaine de la turbo-egalisation au contexte multi-antennes, nous etudions une technique de reception iterative adaptee au schema de transmission MIMO-OFDM. L'utilisation de filtres lineaires optimises au sens du critere Minimum Mean Square Error (MMSE) confere aux recepteurs une complexite raisonnable et conciliable avec les exigences d'implementation. Les recepteurs proposes sont ensuite associes a des techniques complementaires telles que le precodage lineaire ou le MC-CDMA. Il est demontre qu'en dimensionnant convenablement l'emission il est possible de tirer parti de toute la diversite du canal de propagation tout en exploitant de facon optimale sa capacite. Les performances de differents systemes sont enfin evaluees sur differents canaux theoriques puis realistes, en examinant notamment l'influence de la correlation entre antennes et en integrant la fonction d'estimation de canal. Dans ces differents contextes, la comparaison avec des systemes conventionnels montre l'interet d'utiliser une reception iterative.
User Behaviour-Based Access Control for Social Media with Qualitative Research and Bayesian Modelling

Access control systems protect against unauthorised access to resources, where security policies define what is allowed, and what is not. Traditional models focus on protecting access to files, directories, and processes; however, the rise of social media has brought about a need for a new type of model – one focused on sharing rather than protecting, and based on user behaviour rather than a technical specification. A methodology is proposed to perform what we call user behaviour-based access control, building access control policies through analysis of user behaviour. The process involves a combination of qualitative research practices and probabilistic reasoning to address the problems of uncertainty and diversity associated with the study of human behaviour. Data collection and analysis is achieved through semi-structured interviewing and grounded theory techniques. The results of the analysis are used to build a probabilistic model, in the form of a Bayesian network, which implements access control. Requests containing observations of the attribute values are pushed through the network to produce a probabilistic access control decision. The method has been evaluated on real social media users, and has been shown to be effective in capturing and mechanising user sharing preferences.
Efficient Optimal Overlap Removal: Algorithms and Experiments

Motivated by visualizing spatial data using proportional symbols, we study the following problem: given a set of overlapping squares of varying sizes, minimally displace the squares as to remove the overlap while maintaining the orthogonal order on their centers. Though this problem is NP‐hard, we show that rotating the squares by 45 degrees into diamonds allows for a linear or convex quadratic program. It is thus efficiently solvable even for relatively large instances.
Process Innovation and Improvement Using Business Object-Oriented Process Modelling (BOOPM) Framework

In the past decades, a number of methodologies have been proposed to innovate and improve business processes that play an important role in enhancing the operational efficiency of an organisation in order to attain business competitiveness. Traditional business process modelling (BPM) approaches are process-centric and focus on the workflow, ignoring the data modelling aspects that are essential for today’s data-centric landscape of modern businesses. Hence, a majority of BPM initiatives have failed in several organisations due to the lack of data-driven insights into their business performance. On the other hand, the information systems of today focus more on dataflows using object-oriented modelling (OOM) approaches. Even standard OOM approaches, such as unified modelling language (UML) methods, exhibit inherent weaknesses due to their lack of formalized innovation with business objects and the dynamic control-flows of complex business processes. In addition to these issues, both BPM and OOM approaches have been augmented with an array of complex software tools and techniques which have confused businesses. There is a lack of a common generalized framework that integrates the well-formalised control-flow based BPM approach and the dataflow based OOM approach that is suitable for today’s enterprise systems in order to support organisations to achieve successful business process improvements. This paper takes a modest step to fill this gap. We propose a framework using a structured six-step business process modelling (BPM) guideline combined with a business object-oriented methodology (BOOM) in a unique and practical way that could be adopted for improving an organisation’s process efficiency and business performance in contemporary enterprise systems. Our proposed business object-oriented process modelling (BOOPM) framework is applied to a business case study in order to demonstrate the practical implementation and process efficiency improvements that can be achieved in enterprise systems using such a structured and integrated approach.
Turkish Treebanking: Unifying and Constructing Efforts

In this paper, we present the current version of two different treebanks, the re-annotation of the Turkish PUD Treebank and the first annotation of the Turkish National Corpus Universal Dependency (henceforth TNC-UD). The annotation of both treebanks, the Turkish PUD Treebank and TNC-UD, was carried out based on the decisions concerning linguistic adequacy of re-annotation of the Turkish IMST-UD Treebank (Türk et. al., forthcoming). Both of the treebanks were annotated with the same annotation process and morphological and syntactic analyses. The TNC-UD is planned to have 10,000 sentences. In this paper, we will present the first 500 sentences along with the annotation PUD Treebank. Moreover, this paper also offers the parsing results of a graph-based neural parser on the previous and re-annotated PUD, as well as the TNC-UD. In light of the comparisons, even though we observe a slight decrease in the attachment scores of the Turkish PUD treebank, we demonstrate that the annotation of the TNC-UD improves the parsing accuracy of Turkish. In addition to the treebanks, we have also constructed a custom annotation software with advanced filtering and morphological editing options. Both the treebanks, including a full edit-history and the annotation guidelines, and the custom software are publicly available under an open license online.
Predicting MCI Status From Multimodal Language Data Using Cascaded Classifiers

Recent work has indicated the potential utility of automated language analysis for the detection of mild cognitive impairment (MCI). Most studies combining language processing and machine learning for the prediction of MCI focus on a single language task; here, we consider a cascaded approach to combine data from multiple language tasks. A cohort of 26 MCI participants and 29 healthy controls completed three language tasks: picture description, reading silently, and reading aloud. Information from each task is captured through different modes (audio, text, eye-tracking, and comprehension questions). Features are extracted from each mode, and used to train a series of cascaded classifiers which output predictions at the level of features, modes, tasks, and finally at the overall session level. The best classification result is achieved through combining the data at the task level (AUC = 0.88, accuracy = 0.83). This outperforms a classifier trained on neuropsychological test scores (AUC = 0.75, accuracy = 0.65) as well as the “early fusion” approach to multimodal classification (AUC = 0.79, accuracy = 0.70). By combining the predictions from the multimodal language classifier and the neuropsychological classifier, this result can be further improved to AUC = 0.90 and accuracy = 0.84. In a correlation analysis, language classifier predictions are found to be moderately correlated (ρ = 0.42) with participant scores on the Rey Auditory Verbal Learning Test (RAVLT). The cascaded approach for multimodal classification improves both system performance and interpretability. This modular architecture can be easily generalized to incorporate different types of classifiers as well as other heterogeneous sources of data (imaging, metabolic, etc.).
Identification of Two Missense Mutations in DUOX1 (p.R1307Q) and DUOXA1 (p.R56W) That Can Cause Congenital Hypothyroidism Through Impairing H2O2 Generation

Context: The DUOX/DUOXA systems play a key role in H2O2 generation in thyroid cells, which is required for iodine organification and thyroid hormone synthesis. DUOX2/DUOXA2 defects can cause congenital hypothyroidism (CH), but it is unknown whether DUOX1/DUOXA1 mutations can also cause CH. Objective: We aimed to identify DUOX1/DUOXA1 mutations and explore their role in the development of CH by investigating their functional impacts on H2O2 generation. Patients and Methods: Forty-three children with CH with goiter were enrolled, in whom all exons and flanking intronic regions of DUOX1/DUOXA1 were directly sequenced. We characterized the functional effects of identified mutations on the expression of DUOX1 and DUOXA1 and H2O2 generation. Results: We identified a heterozygous DUOX1 missense mutation (G > A base substitution at nucleotide 3920 in exon 31) that changed a highly conserved arginine to glutamine at residual 1307 (p.R1307Q) in patient 1. A heterozygous-missense mutation (c.166 C>T; p.R56W) was identified in DUOXA1 in patient 2. Functional studies demonstrated that both p.R1307Q mutant or p.R56W mutant decreased the DUOX1 expression at mRNA and protein levels, with a corresponding impairment in H2O2 generation (P < 0.01). The results also showed that intact DUOXA1 was required for full activity of DUOX1 and H2O2 generation. Conclusions: We have identified two heterozygous missense mutations in DUOX1 and DUOXA1 in two patients that can cause CH through disrupting the coordination of DUOX1 and DUOXA1 in the generation of H2O2. This study for the first time demonstrates that the DUOX1/DUOXA1 system, if genetically defective, can cause CH.
Whole Genome Sequencing of Extended-Spectrum Beta-Lactamase (ESBL)-Producing Escherichia coli Isolated From a Wastewater Treatment Plant in China

Background and Objectives Wastewater treatment plants (WWTPs) are one of the major reservoirs for antimicrobial resistant bacteria (ARB) and antimicrobial resistance genes (ARGs) in the environment. Thus, the investigation on ARB and ARGs from WWTPs has attracted increasing attention in recent years. In order to uncover the resistome in a WWTP treating effluents from a pharmaceutical industry in China, the extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli strains were isolated and their whole genome sequences were obtained and analyzed. Moreover, metagenomic sequencing was applied to give a comprehensive view of antibiotic resistance in this WWTP. Methods 18 ESBL-producing E. coli strains were isolated from a WWTP located in Taizhou, China on April, 2017. All strains were sequenced using Illumina HiSeq 2000 sequencer. The whole genome sequences were assembled using SPAdes software and annotated with RAST server. Sequence types (STs), plasmids, ARGs and virulence genes were predicted from the genomes using MLST, Plasmid Finder, ResFinder and Virulence Finder, respectively. Metagenomic DNA of the same sample was extracted and sequenced using Illumina Hiseq X Ten platform. Metagenomic sequences were assembled using SOAPdenovo software. Results All 18 ESBL-producing E. coli strains were resistant to ampicillin, cefazolin, and ceftriaxone. Analysis of their genomes revealed that all strains carried beta-lactamase encoding genes and the most prevalent type was blaCTX–M. Various virulence genes and ARGs confronting resistance to other types of antimicrobial agents were also predicted. Further investigation on the metagenomics data indicated 11 ARGs with high amino acid identities to the known ARGs. Five of these ARGs, aadA1, aac(6′)-lb-cr, flo(R), sul2 and sul1, were also present in the genomes of the ESBL-producing E. coli isolated from the same sample. Conclusion Our study revealed the resistome of a pharmaceutical WWTP by both culture-dependent and metegenomic methods. The existence of ESBL-producing E. coli strains, indicating that pharmaceutical WWTP can play a significant role in the emergence of ARB. The occurrence of ARGs annotated from the metagenomic data suggests that pharmaceutical WWTP can play a significant role in the emergence of ARGs. Our findings highlight the need for strengthening the active surveillance of ARB and ARGs from pharmaceutical industry.
A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents

Neural network simulation is an important tool for generating and evaluating hypotheses on the structure, dynamics, and function of neural circuits. For scientific questions addressing organisms operating autonomously in their environments, in particular where learning is involved, it is crucial to be able to operate such simulations in a closed-loop fashion. In such a set-up, the neural agent continuously receives sensory stimuli from the environment and provides motor signals that manipulate the environment or move the agent within it. So far, most studies requiring such functionality have been conducted with custom simulation scripts and manually implemented tasks. This makes it difficult for other researchers to reproduce and build upon previous work and nearly impossible to compare the performance of different learning architectures. In this work, we present a novel approach to solve this problem, connecting benchmark tools from the field of machine learning and state-of-the-art neural network simulators from computational neuroscience. The resulting toolchain enables researchers in both fields to make use of well-tested high-performance simulation software supporting biologically plausible neuron, synapse and network models and allows them to evaluate and compare their approach on the basis of standardized environments with various levels of complexity. We demonstrate the functionality of the toolchain by implementing a neuronal actor-critic architecture for reinforcement learning in the NEST simulator and successfully training it on two different environments from the OpenAI Gym. We compare its performance to a previously suggested neural network model of reinforcement learning in the basal ganglia and a generic Q-learning algorithm.
Genetic Detection of Lint Percentage Applying Single-Locus and Multi-Locus Genome-Wide Association Studies in Chinese Early-Maturity Upland Cotton

Upland cotton (Gossypium hirsutum L.) is the most important source of natural fiber in the world. Early-maturity upland cotton varieties are commonly planted in China. Nevertheless, lint yield of early-maturity upland cotton varieties is strikingly lower than that of middle- and late-maturity ones. How to effectively improve lint yield of early maturing cotton, becomes a focus of cotton research. Here, based on 72,792 high-quality single nucleotide polymorphisms of 160 early-maturing upland cotton accessions, we performed genome-wide association studies (GWASs) for lint percentage (LP), one of the most lint-yield component traits, applying one single-locus method and six multi-locus methods. A total of 4 and 45 significant quantitative trait nucleotides (QTNs) were respectively identified to be associated with LP. Interestingly, in two of four planting environments, two of these QTNs (A02_74713290 and A02_75551547) were simultaneously detected via both one single-locus and three or more multi-locus GWAS methods. Among the 42 genes within a genomic region (A02: 74.31–75.95 Mbp) containing the above two peak QTNs, Gh_A02G1269, Gh_A02G1280, and Gh_A02G1295 had the highest expression levels in ovules during seed development from 20 to 25 days post anthesis, whereas Gh_A02G1278 was preferentially expressed in the fibers rather than other organs. These results imply that the four potential candidate genes might be closely related to cotton LP by regulating the proportion of seed weight and fiber yield. The QTNs and potential candidate genes for LP, identified in this study, provide valuable resource for cultivating novel cotton varieties with earliness and high lint yield in the future.
Ocins for Food Safety

The food industry produces highly perishable products. Food spoilage represents a severe problem for food manufacturers. Therefore, it is important to identify effective preservation solutions to prevent food spoilage. Ocins (e.g., bacteriocins, lactocins, and enterocins) are antibacterial proteins synthesized by bacteria that destroy or suppress the growth of related or unrelated bacterial strains. Ocins represent a promising strategy for food preservation, because of their antagonist effects toward food spoilage microorganisms, high potency, and low toxicity. Additionally, they can be bioengineered. The most common and commercially available ocins are nisin, plantaracin, sakacin P, and pediocin. Several ocins have been characterized and studied biochemically and genetically; however, their structure-function relationship, biosynthesis, and mechanism of action are not understood. This narrative review focuses primarily on ocins and their relevance to the food industry to help prevent food spoilage. In particular, the applications and limitations of ocins in the food industry are highlighted.
Multiplatform MOOC Analytics: Comparing Global and Regional Patterns in edX and Edraak

While global massive open online course (MOOC) providers such as edX, Coursera, and FutureLearn have garnered the bulk of attention from researchers and the popular press, MOOCs are also provisioned by a series of regional providers, who are often using the Open edX platform. We leverage the data infrastructure shared by the main edX instance and one regional Open edX provider, Edraak in Jordan, to compare the experience of learners from Arab countries on both platforms. Comparing learners from Arab countries on edX to those on Edraak, the Edraak population has a more even gender balance, more learners with lower education levels, greater participation from more developing countries, higher levels of persistence and completion, and a larger total population of learners. This "apples to apples" comparison of MOOC learners is facilitated by an approach to multiplatform MOOC analytics, which employs parallel research processes to create joint aggregate datasets without sharing identifiable data across institutions. Our findings suggest that greater research attention should be paid towards regional MOOC providers, and regional providers may have an important role to play in expanding access to higher education.
Graph-Based Meaning Representations: Design and Processing

This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs. The tutorial will (a) briefly review relevant background in formal and linguistic semantics; (b) semi-formally define a unified abstract view on different flavors of semantic graphs and associated terminology; (c) survey common frameworks for graph-based meaning representation and available graph banks; and (d) offer a technical overview of a representative selection of different parsing approaches.
An Evaluation of the Impact of Automated Programming Hints on Performance and Learning

A growing body of work has explored how to automatically generate hints for novice programmers, and many programming environments now employ these hints. However, few studies have investigated the efficacy of automated programming hints for improving performance and learning, how and when novices find these hints beneficial, and the tradeoffs that exist between different types of hints. In this work, we explored the efficacy of next-step code hints with 2 complementary features: textual explanations and self-explanation prompts. We conducted two studies in which novices completed two programming tasks in a block-based programming environment with automated hints. In Study 1, 10 undergraduate students completed 2 programming tasks with a variety of hint types, and we interviewed them to understand their perceptions of the affordances of each hint type. For Study 2, we recruited a convenience sample of participants without programming experience from Amazon Mechanical Turk. We conducted a randomized experiment comparing the effects of hints' types on learners' performance and performance on a subsequent task without hints. We found that code hints with textual explanations significantly improved immediate programming performance. However, these hints only improved performance in a subsequent post-test task with similar objectives, when they were combined with self-explanation prompts. These results provide design insights into how automatically generated code hints can be improved with textual explanations and prompts to self-explain, and provide evidence about when and how these hints can improve programming performance and learning.
Perceived and Received Dimensional Support: Main and Stress-Buffering Effects on Dimensions of Burnout

Social support is an adaptive resource associated with lower levels of burnout in sport. The effects of social support on burnout have typically been demonstrated through (1) a main effects model (direct negative associations between social support and burnout) and (2) a stress-buffering model (social support buffering the negative effects of stress on burnout). While both models provide insights into functional adaptations to burnout and stress in sport, evidence for significant main and stress-buffering effects are inconsistent. Reasons for this is include: (1) testing of a singular perspective of support in empirical research, and (2) a lack of specificity when analyzing social support and burnout (e.g., adoption of global-level analyses). To address this, the purpose of the study was to test differing perspectives of social support (perceived availability of support and received support) in regards to the main and stress-buffering effects of dimensions of social support (emotional, esteem, informational, and tangible) on dimensions of burnout (reduced sense of accomplishment, devaluation, emotional and physical exhaustion). Cross-sectional data were collected from 222 athletes. Moderated hierarchical regression analyses revealed that: (1) higher levels of stress were associated with higher levels of burnout (all dimensions); (2) higher levels of perceived availability of support were associated with lower levels of reduced sense of accomplishment and devaluation (with the exception of perceived availability of emotional support upon devaluation), and (3) perceived availability of emotional support buffered the negative effects of high stress upon devaluation. There were no significant main or interactive effects for any dimensions of received support. The significant interaction suggests that higher levels of perceived availability of emotional support may result in a functional adaptation to higher stress such that individuals may be protected from higher levels of devaluation of sport.