Miracle-dz/UniFAR-FTUs huggingface dataset:
A low-complexity min-sum decoding algorithm for LDPC codes

This paper proposes a low-complexity LDPC decoding algorithm with simplified check nodes updating. The proposed algorithm simplifies the result of the second-minimum in check nodes based on the first-minimum computation instead of computing it directly. In order to obtain the approximate result, effective corrected coefficients are utilized, which can reduce the complexity by eliminating the complex computations. Complexity analysis is provided and the results indicate that the complexity of the proposed algorithm is much lower than the general NMS algorithm and MS-based simplified algorithms. Simulation results show that the performance of the proposed algorithm can closely match the NMS algorithm with the same number of iterations.
Automatic Enrichment of Terminological Resources: the IATE RDF Example

This publication is supported by a research grant from Science Foundation Ireland, SFI/12/RC/2289 (Insight), a research visit grant from Universidad Politecnica de Madrid, ´ by the Spanish Datos4.0 project (TIN2016-78011-C4-4-R) and by the EU’s Lynx project (H2020 Research and Innovation Programme under GA num 780602).
PERFORMANCE-BASED OPTIMIZATION AND SEISMIC COLLAPSE SAFETY ASSESSMENT OF STEEL MOMENT FRAMES

The main aim of the present study is to optimize steel moment frames in the framework of performance-based design and to assess the seismic collapse capacity of the optimal structures. In the first phase of this study, four well-known metaheuristic algorithms are employed to achieve the optimization task. In the second phase, the seismic collapse safety of the obtained optimal designs is evaluated by conducting incremental dynamic analysis and generating fragility curves. Three illustrative examples including 3-, 6-, and 12-story steel moment frames are presented. The numerical results demonstrate that all the performance-based optimal designs obtained by the metahuristic algorithms are of acceptable collapse margin ratio.
Anti-3D Weapon Model Detection for Safe 3D Printing Based on Convolutional Neural Networks and D2 Shape Distribution

With the development of 3D printing, weapons are easily printed without any restriction from the production managers. Therefore, anti-3D weapon model detection is necessary issue in safe 3D printing to prevent the printing of 3D weapon models. In this paper, we would like to propose an anti-3D weapon model detection algorithm to prevent the printing of anti-3D weapon models for safe 3D printing based on the D2 shape distribution and an improved convolutional neural networks (CNNs). The purpose of the proposed algorithm is to detect anti-3D weapon models when they are used in 3D printing. The D2 shape distribution is computed from random points on the surface of a 3D weapon model and their geometric features in order to construct a D2 vector. The D2 vector is then trained by improved CNNs. The CNNs are used to detect anti-3D weapon models for safe 3D printing by training D2 vectors which have been constructed from the D2 shape distribution of 3D weapon models. Experiments with 3D weapon models proved that the D2 shape distribution of 3D weapon models in the same class is the same. Training and testing results also verified that the accuracy of the proposed algorithm is higher than the conventional works. The proposed algorithm is applied in a small application, and it could detect anti-3D weapon models for safe 3D printing.
Monitoring System for Patients Using Multimedia for Smart Healthcare

The use of multimodal inputs in a smart healthcare framework is promising due to the increase in accuracy of the systems involved in the framework. In this paper, we propose a user satisfaction detection system using two multimedia contents, namely, speech and image. The three classes of satisfaction are satisfied, not satisfied, and indifferent. In the proposed system, speech and facial image of the user are captured, transmitted to a cloud, and then analyzed. A decision on the satisfaction is then delivered to the appropriate stakeholders. Several features from these two inputs are extracted from the cloud. For speech, directional derivatives of a spectrogram are used as features, whereas for image, a local binary pattern of the image is used to extract features. These features are combined and input to a support vector machine-based classifier. It is shown that the proposed system achieves up to 93% accuracy in detecting satisfaction.
Annotating Opinions and Opinion Targets in Student Course Feedback

In this paper, we present a student course feedback corpus, a novel resource for opinion target extraction and sentiment analysis . The corpus is developed with the main aim of summarizing general feedback given by students on undergraduate - level courses . In this corpus, opinion targets, opinion expressions, and polarities of the opinion expressions towards the opinion targets are annotated . Opinion targets are basically the important key points in feedback that the students have shown their sentiment towards, such as “ Lecture Slides ” , and “ Teaching Style ”. The uniqueness of the corpus, annotation methodology, difficulties faced during annotating, and possible usages of the corpus are discussed in this paper .
Automatic Thesaurus Construction for Modern Hebrew

Automatic thesaurus construction for Modern Hebrew is a complicated task, due to its high degree of inflectional ambiguity. Linguistics tools, including morphological analyzers, part-of-speech taggers and parsers often have limited in performance on Morphologically Rich Language s (MRLs) such as Hebrew. In this paper, we adopted a schematic methodology for generating a co-occurrence based thesaurus in a MRL and extended the methodology to create distributional similarity thesaurus. We explored three alternative levels of morphological term representations, surface form, lemma, and multiple lemmas, all complemented by the clustering of morphological variants. First, we evaluated both the co-occurrence based method and the distributional similarity method using Hebrew WordNet as our gold standard. However, due to Hebrew WordNet's low coverage, we completed our analysis with a manual evaluation. The results showed that for Modern Hebrew corpus-based thesaurus construction, the most directly applied statistical collection, using linguistics tools at the lemma level, is not optimal.
Spatially inhomogeneous population dynamics: beyond the mean field approximation

We propose a novel method for numerical modeling of spatially inhomogeneous moment dynamics of populations with nonlocal dispersal and competition in continuous space. It is based on analytically solvable decompositions of the time evolution operator for a coupled set of master equations. This has allowed us—for the first time in the literature—to perform moment dynamics simulations of spatially inhomogeneous birth-death systems beyond the mean-field approach and to calculate the inhomogeneous pair correlation function using the Kirkwood superposition ansatz. As a result, we revealed a number of new subtle effects, possible in real populations. Namely, for systems with short-range dispersal and mid-range competition, strong clustering of entities at small distances followed by their deep disaggregation at larger separations are observed in the wavefront of density propagation. For populations in which the competition range is much shorter than that of dispersal, the pair correlation function exhibits long-tail asymptotics. Remarkably, the latter effect takes place only due to the spatial inhomogeneity and thus was completely unknown before. Moreover, the both effects get stronger in the direction of propagation. All these types of behaviour are interpreted as a trade-off between the dispersal and competition in the coexistence of reproductive pair correlations and the inhomogeneity of the density of the system.
Is empowerment a route to improving mental health and wellbeing in an urban regeneration (UR) context?

Urban regeneration (UR) programmes are recognised as a type of Population Health Intervention (PHI), addressing social and health inequalities. Policy recommends programmes involve communities through engagement and empowerment. Whilst the literature has started to link empowerment with health improvement, this has not been within an UR context. As part of broader research on the economic evaluation of community empowerment activities, this paper examines how health gains can be generated through promoting empowerment as well as identifying whether feelings of empowerment are associated with residents personal characteristics or perceptions of their neighbourhood. Using 2011 Community Health and Wellbeing Survey (GoWell) cross-sectional data, ordinal logistic regression and simple linear regression analysis of 15 Glasgow neighbourhoods undergoing regeneration with 4302 adult householders (≥16 years old) was completed. Analyses identified strong associations (P≥ 0.05) between empowerment and the mental health subscale of the SF12v2 and with several items of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS) scale. Furthermore, residents’ who felt more empowered reported more positive attitudes towards their surroundings and housing providers. This concurs with recent evidence of the importance of residents’ psychological investments in their neighbourhood influencing their sense of place attachment. Such analyses present initial evidence of the value of investing resources within UR programmes to activities geared towards increasing residents’ empowerment as a means of producing those health gains often sought by more costly aspects of the programmes.
A Fully-Automated Subcortical and Ventricular Shape Generation Pipeline Preserving Smoothness and Anatomical Topology

In this paper, we present a fully-automated subcortical and ventricular shape generation pipeline that acts on structural magnetic resonance images (MRIs) of the human brain. Principally, the proposed pipeline consists of three steps: (1) automated structure segmentation using the diffeomorphic multi-atlas likelihood-fusion algorithm; (2) study-specific shape template creation based on the Delaunay triangulation; (3) deformation-based shape filtering using the large deformation diffeomorphic metric mapping for surfaces. The proposed pipeline is shown to provide high accuracy, sufficient smoothness, and accurate anatomical topology. Two datasets focused upon Huntington's disease (HD) were used for evaluating the performance of the proposed pipeline. The first of these contains a total of 16 MRI scans, each with a gold standard available, on which the proposed pipeline's outputs were observed to be highly accurate and smooth when compared with the gold standard. Visual examinations and outlier analyses on the second dataset, which contains a total of 1,445 MRI scans, revealed 100% success rates for the putamen, the thalamus, the globus pallidus, the amygdala, and the lateral ventricle in both hemispheres and rates no smaller than 97% for the bilateral hippocampus and caudate. Another independent dataset, consisting of 15 atlas images and 20 testing images, was also used to quantitatively evaluate the proposed pipeline, with high accuracy having been obtained. In short, the proposed pipeline is herein demonstrated to be effective, both quantitatively and qualitatively, using a large collection of MRI scans.
Slow Passage Through a Hopf Bifurcation: From Oscillatory to Steady State Solutions

This paper investigates the slow passage through a supercritical Hopf bifurcation from a branch of slowly varying periodic solutions to a branch of slowly varying steady states. This analysis is motivated by a recent numerical study of bursting oscillations in an enzymatic system. It was found that the transition from oscillations to steady states is delayed even if the rate of change of the control parameter is extremely small.The delay due to the slow passage is characterized by determining the amplitude of the oscillations at the bifurcation point. Defining $\varepsilon $ as the rate of change of the bifurcation parameter, it is shown that the amplitude is an $O( \varepsilon^{1/4} )$ quantity as $\varepsilon \to 0$. In addition, a particular class of equations leading to relaxation oscillations is considered. It is assumed that frequency $\omega $ of the oscillations at the Hopf bifurcation can be controlled using a second parameter distinct from the bifurcation parameter. It is then shown that the ampl.
Using Provenance for Security and Interpretability

System security is somewhat stymied because it is difficult, if not impossible, to design system defenses that address the full complexity of a system’s interaction. Interestingly, this problem has parallels in understanding how machine learning (ML) algorithms make predictions. Both of these problems require a structured, comprehensive understanding of what a system/model is doing. My dissertation addresses these seemingly disparate problems by exploiting data provenance, which provides just such a solution. I exploit provenance both to design intrusion detection systems and to explain how ML algorithms arrive at their predictions.
Bacterial Growth Efficiency , Respiration and Secondary Production and in the Cross River Estuary , Nigeria and the Near Coast Antai

Bacterial growth efficiency (BGE) can be a major factor in estimation of energy transfer within the food web, but its controlling factors are poorly understood. It also regulates measurements of bacterial production and respiration [BGE = BP/(BP + BR)]. BGE values in natural bacterial assemblage were determined over 12 months in a tropical estuary of Cross River and adjacent Gulf of Guinea. BGE values ranged from 20.80% to 52.40% with a mean value of 36.16% and correlated positively with bacterial production (BP) (r = 0.676, p>0.001). Much of the BGE variation could be attributed to BP because BGE correlated poorly with BR (r = 0.322, p<0.001) which had a conservative tendency. Variations of BGE will reflect the trophic status of the ecosystem and may be influenced by combination of factors associated with seasons. Therefore a constant value of BGE in assessing bacterial trophodynamics can be fraught with error.
Enriching a Lexicon of Discourse Connectives with Corpus-based Data

We present the results of the effort of enriching the pre-existing resource LICO, a Lexicon of Italian COnnectives retrieved from lexicographic sources (Feltracco et al., 2016), with real corpus data for connectives marking contrast relations in text. The motivation beyond our effort is that connectives can only be interpreted when they appear in context, that is, in a relation between the two fragments of text that constitute the two arguments of the relation. In this perspective, adding corpus examples annotated with connectives and arguments for the relation allows us to both extend the resource and validate the lexicon. In order to retrieve good corpus examples, we take advantage of the existing Contrast-Ita Bank (Feltracco et al., 2017), a corpus of news annotated with explicit and implicit discourse contrast relations for Italian according to the annotation scheme proposed in the Penn Discourse Tree Bank (PDTB) guidelines (Prasad et al., 2007). We also use an extended -non contrast annotated-version of the same corpus and documents from Wikipedia. The resulting resource represents a valuable tool for both linguistic analyses of discourse relations and the training of a classiﬁer for NLP applications.
A Parallel Corpus of Arabic-Japanese News Articles

Much work has been done on machine translation between major language pairs including Arabic–English and English–Japanese thanks to the availability of large-scale parallel corpora with manually veriﬁed subsets of parallel sentences. However, there has been little research conducted on the Arabic–Japanese language pair due to its parallel-data scarcity, despite being a good example of interestingly contrasting differences in typology. In this paper, we describe the creation process and statistics of the Arabic–Japanese portion of the TUFS Media Corpus, a parallel corpus of translated news articles collected at Tokyo University of Foreign Studies (TUFS). Part of the corpus is manually aligned at the sentence level for development and testing. The corpus is provided in two formats: A document-level parallel corpus in XML format, and a sentence-level parallel corpus in plain text format. We also report the ﬁrst results of Arabic– Japanese phrase-based machine translation trained on our corpus.
Concordance of Interests in Dynamic Models of Social Partnership in the System of Continuing Professional Education.

A dynamic game theoretic model of concordance of interests in the process of social partnership in the system of continuing professional education is proposed. Non-cooperative, cooperative, and hierarchical setups are examined. Analytical solution for a linear state version of the model is provided. Nash equilibrium algorithms (for non-cooperative and cooperative setups) are identified. H.Stakelberg's1t algorithms of equilibrium solution of the game in hierarchical setup are described (in the general case). A method of building the precise discrete analogue of a continuous model is used for examining the hierarchical setup. Examples of test calculations for different data sets are provided; content interpretation of the results is discussed.
Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model

Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy. Predicting the popularity of a petition — commonly measured by its signature count — based on its textual content has utility for policymakers as well as those posting the petition. In this work, we model this task using CNN regression with an auxiliary ordinal regression objective. We demonstrate the effectiveness of our proposed approach using UK and US government petition datasets.
Title The Chinese classroom paradox : A cross-cultural comparison ofteacher controlling behaviors

Chinese classrooms present an intriguing paradox to the claim of self-determination theory that autonomy facilitates learning. Chinese teachers appear to be controlling but Chinese students do not have poor academic performance in international comparisons. The present study addressed this paradox by examining the cultural differences in students’ interpretation of teacher controlling behaviors. Affective meanings of teacher controlling behaviors were solicited from 158 Chinese 5 graders and 115 American 5 graders. It was found that the same teacher controlling behaviors had different affective meanings for different cultural groups (Chinese vs. American) and groups with different levels of social-emotional relatedness with teachers (High vs. Low). Chinese children perceived the behaviors as less controlling than American children and in turn reported more motivated in their teachers’ class than American children. Regardless of culture, children with high social-emotional relatedness with teachers perceived the behaviors as less controlling than children with low social-emotional relatedness with teachers. It was also found that internalization mediated the relation between social-emotional relatedness and children’s learning motivation in both cultures. The findings revealed cultural differences as well as similarities in the psychological process of internalization.
The ACoLi CoNLL Libraries: Beyond Tab-Separated Values

We introduce the ACoLi CoNLL libraries, a set of Java archives to facilitate advanced manipulations of corpora annotated in TSV formats, including all members of the CoNLL format family. In particular, we provide means for (i) rule-based re-write operations, (ii) visualization and manual annotation, (iii) merging CoNLL ﬁles, and (iv) data base support. The ACoLi CoNLL libraries provide command-line interface to these functionalities. The following aspects are technologically innovative and exceed beyond the state of the art: We support every OWPL (one word per line) corpus format with tab-separated columns, whereas most existing tools are speciﬁc to one particular CoNLL dialect. We employ established W3C standards for rule-based graph rewriting operations on CoNLL sentences. We provide means for the heuristic, but fully automated merging of CoNLL annotations of the same textual content, in particular for resolving conﬂicting tokenizations. We demonstrate the usefulness and practicability of our proposed CoNLL libraries on well-established data sets of the Universal Dependency corpus and the Penn Treebank.
Hydroxytyrosol and olive leaf extract exert cardioprotective effects by inhibiting GRP78 and CHOP expression

Myocardial infarction triggers massive biochemical changes, even cardiac cell death. Endoplasmic reticulum stress is involved in the pathology of myocardial infarction-mediated apoptosis. In the present study, myocardial cell line H9c2 cells were treated with cobalt chloride (CoCl2) to induce hypoxia. Isoproterenol was used for two successive days to induce myocardial infarction in SD rats. The cardioprotective effect of olive leaf extract (OLE) and its main constituent hydroxytyrosol and the underlying mechanisms were evaluated. The results showed that hydroxytyrosol markedly protected H9c2 cells against CoCl2-induced apoptosis. Hydroxytyrosol could reduce the mRNA and protein expression of GRP78 and CHOP induced by CoCl2 in vitro. In vivo, the decreased ejection fraction and fractional shortening, increased heart weight/body ratio, the formation of infarction, disordered cardiac muscle fibers and infiltration of inflammatory cells induced by isoproterenol could be significantly ameliorated by pretreatment with OLE for a month. Similarly, OLE could also reverse the increase of GRP78 and CHOP expression induced by isoproterenol. Therefore, OLE and hydroxytyrosol exert a cardioprotective effect through endoplasmic reticulum stress, which could be a new target for the prevention and treatment of cardiovascular diseases.
Understanding payment card fraud through knowledge extraction from neural networks using large-scale datasets

A novel approach to knowledge extraction from neural network classifiers when applied to payment card fraud detection is proposed. Existing Fraud Management Systems (FMS) use neural network classifiers but do not have the ability to explain their learnt patterns of fraud. Rule extraction from such classifiers with a high level of abstraction and linguistic simplicity is proposed. Decompositional knowledge extraction methods are found to be too reliant on the architecture of the fraud classifer and current pedagogical rule extraction methods produce rules that are not sufficiently comprehensible. In this thesis the Sparse Oracle-based Adaptive Rule (SOAR) pedagogical extraction algorithm is proposed to extract generalising rules that explain patterns of fraud. SOAR uses sensitivity analysis to avoid the exhaustive searches of other pedagogical methods. By projecting into discretised space, polytopes are formed by SOAR covering the class convex hull of the classifier surface. A methodological and verifiable empirical evaluation on publicly available datasets in various domains is undertaken. These results show that SOAR extracts comprehensible rules that are sound from a deep learning neural network. When SOAR is applied to large datasets provided by payment card issuers it discovered new fraud types that were of key interest to payment risk/fraud analysts. SOAR provides an improved understanding of fraud vectors that will lead to a more secure payment process through informed payment fraud prevention steps and this work could therefore alter how fraud management is undertaken in the future.