Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pregroup Representation of Word Order Alternation Using Hindi Syntax

Pregroup calculus has been used for the representation of free word order languages (Sanskrit and Hungarian), using a construction called precyclicity. However, restricted word order alternation has not been handled before. This paper aims at introducing and formally expressing three methods of representing word order alternation in the pregroup representation of any language. This paper describes the word order alternation patterns of Hindi, and creates a basic pregroup representation for the language. In doing so, the shortcoming of correct reductions for ungrammatical sentences due to the current apparatus is highlighted, and the aforementioned methods are invoked for a grammatically accurate representation of restricted word order alternation. The replicability of these methods is explained in the representation of adverbs and prepositional phrases in English.
Ultrasonic Guided Wave Annular Array Transducers for Structural Health Monitoring

The work presented in this paper utilizes the physics of guided wave propagation for structural health monitoring (SHM) transducer designs. Both the theoretical and experimental studies illustrated the importance of guided wave mode selection for SHM applications. Guided wave mode control is realized with an annular array transducer design on a PVDF polymer piezoelectric film. A sample problem on a 1mm thick aluminum plate is presented. Numerical calculations of the wave structures and guided wave power flow distribution inside the plate provide quick guidelines for the wave mode selection in structural health monitoring. Experimental study illustrates the importance of mode control with the comparison of PVDF annular array transducers and PZT ceramic disc transducers. The characteristics of wave mode reflections to defect depth and the defect sizing effect are also discussed in this paper.
Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks

Automated extraction methods are widely available for vowels, but automated methods for coding rhoticity have lagged far behind. R-fulness versus r-lessness (in words like park, store, etc.) is a classic and frequently cited variable, but it is still commonly coded by human analysts rather than automated methods. Human-coding requires extensive resources and lacks replicability, making it difficult to compare large datasets across research groups. Can reliable automated methods be developed to aid in coding rhoticity? In this study, we use Neural Networks/Deep Learning, training our model on 208 Boston-area speakers.
A Note on DL-Lite with Boolean Role Inclusions

We discuss the complexity of reasoning and ontology-mediated query answering with the logics from the DL-Lite family extended by various types (Horn, Krom and Boolean) of role inclusions. We compare the expressive power of those logics with 1-and 2-variable fragments of ﬁrst-order logic. Our most interesting ﬁndings show that binary disjunctions on roles do not change the complexity of satisﬁability if disjunction is allowed on concepts, while still causing undecidability of UCQ answering
Navegação e Determinação de Atitude em Aeronaves Através de Múltiplos Receptores GNSS

The development over recent decades of GNSS’s, in particular the GPS, and augmentation systems, such as EGNOS and WAAS, made possible the use of this kind of navigation systems for applications in the civil aviation industry, while new developments are still expected in the coming decades, such as the GALILEO service. In order to use these now available systems, to full capacity, new applications are being developed. Among them aircraft attitude determination methods are especially prominent since they could replace the existing radio navigation aids systems and the expensive and complex, inertial navigation systems. This thesis presents a possible solution to the attitude determination problem using exclusively the estimated GNSS positioning of four on-board independent receivers. It also presents the models used in the navigation equation solution and details about the GNSS systems used, GPS and GALILEO. The presented method evaluation was done in a computer simulation environment executed in MATLAB.
A Taxonomy for User Feedback Classifications

Online user feedback contains information that is of interest to requirements engineering (RE). Natural language processing (NLP) techniques, especially classification algorithms, are a popular way of automatically classifying requirements-relevant contents. Research into this use of NLP in RE has sought to answer different research questions, often causing their classifications to be incompatible. Identifying and structuring these classifications is therefore urgently needed. We present a preliminary taxonomy that we constructed based on the findings from a systematic literature review, which places 78 classifications categories for user feedback into four groups: Sentiment, Intention, User Experience, and Topic. The taxonomy reveals the purposes for which user feedback is analyzed in RE, provides an initial harmonization of the vocabulary in this research area, and may inspire researchers to investigate classifications they had previously not considered. This paper intends to foster discussions among NLP experts and to identify further improvements to the taxonomy.
The Role of Morality in Digital Piracy: Understanding the Deterrent and Motivational Effects of Moral Reasoning in Different Piracy Contexts

Digital piracy has been a chronic issue in intellectual property protection. With the prevalence of online technologies, digital piracy has become even more rampant, as digital resources can now be accessed and disseminated easily through the Internet. While the antecedents of piracy behaviors have been studied for years, previous studies often focus on a specific type of behavior or pirated content and the findings are far from conclusive. They do not paint a coherent picture of the impacts of antecedents. In this study, we focus on the role of morality by revealing the different levels of moral reasoning that can both deter and motivate users’ piracy intentions. Furthermore, we differentiate between two types of piracy behaviors (unauthorized copying/downloading vs. unauthorized sharing) and two categories of digital products (application software vs. music/movies), so that the differential impacts of the various antecedents can be assessed and articulated more clearly. We empirically evaluated the models in the four piracy contexts using a sample of 3,426 survey participants from a sizable IT-literate society. Our findings indicate the conflicting roles of morality in piracy intention and demonstrate its differential impacts across the two types of piracy behaviors, which can be generalized across the two categories of digital products. Our study sheds new light on end users’ considerations in accessing and disseminating unauthorized digital content. It also informs the design of copyright protection policies and sanction measures with different levels of specificity.
Chasing Sets: How to Use Existential Rules for Expressive Reasoning (Extended Abstract)

We propose that modern existential rule reasoners can enable fully declarative implementations of rule-based inference methods in knowledge representation, in the sense that a particular calculus is captured by a fixed set of rules that can be evaluated on varying inputs (encoded as facts). We introduce Datalog(S) -- Datalog with support for sets -- as a surface language for such translations, and show that it can be captured in a decidable fragment of existential rules. We then implement several known inference methods in Datalog(S), and empirically show that an existing existential rule reasoner can thus be used to solve practical reasoning problems.
Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models

Building causal models of complicated phenomena such as food insecurity is currently a slow and labor-intensive manual process. In this paper, we introduce an approach that builds executable probabilistic models from raw, free text. The proposed approach is implemented through three systems: Eidos, INDRA, and Delphi. Eidos is an open-domain machine reading system designed to extract causal relations from natural language. It is rule-based, allowing for rapid domain transfer, customizability, and interpretability. INDRA aggregates multiple sources of causal information and performs assembly to create a coherent knowledge base and assess its reliability. This assembled knowledge serves as the starting point for modeling. Delphi is a modeling framework that assembles quantified causal fragments and their contexts into executable probabilistic models that respect the semantics of the original text, and can be used to support decision making.
Answer Set Programs Challenged by Ontologies

We introduce resilient logic programs that couple a nonmonotonic logic program and a first-order theory or description logic (DL) ontology. Unlike previous hybrid languages, where the interaction between the program and the theory is limited to consistency or query entailment tests, in RLPs answers must be ‘resilient’ to the models of the ontology, allowing non-output predicates to respond differently to different models. RLPs can elegantly express ∃∀∃-QBFs, disjunctive ASP, and configuration problems under incompleteness of information. To guarantee decidability, we use a novel relaxation of DL-safeness that safeguards rules via predicates whose extensions can be inferred to have a finite bound. We present algorithms and tight complexity results for the case where ontologies are written in some prototypical DLs.
Taxonomic revision of Sphaerotrichia divaricata (Ectocarpales, Phaeophyceae), with a reappraisal of S. firma from the north-west Pacific

Abstract Specimens of Sphaerotrichia in Japan can be classified into two morphotypes, based on their mode of branching and anatomy: (1) specimens that have an obvious (traceable) main axis associated with a relatively high number of first-order branches, and a relatively constant distance between branches; and (2) specimens that branch more or less divaricately, with shorter distances between branches in the distal portion. Molecular analyses of Sphaerotrichia from the North Atlantic and North Pacific using ribosomal RNA (ITS1–5.8S–ITS2 and intragenic spacer regions between 26S and 5S rDNA) and Rubisco (almost complete rbcL gene and the spacer between rbcL and rbcS) gene sequences revealed two major genetic groups, Group-1 and Group-2. The sequence length variation in ITS1 suggested subdivisions of Group-2 into two subgroups: Group-2Atlantic and Group-2Pacific. Specimens belonging to Group-1 were generally epilithic, and morphologically corresponded to S. firma. Specimens belonging to Group-2 were epiphytic or epilithic and morphologically corresponded to S. divaricata.
Genetic polymorphism of angiotensin I-converting enzyme (ACE-I) and prostate cancer risk

genotypes among the patients with prostate cancer were 8.3%, 47.9%, and 43.8%, respectively; and among the control subjects, they were 23.5%, 35.3%, 41.2%, respectively. Although we found that patients have a higher level of DD genotype, the distribution of the ACE geno
Agglomeration and the Location of Foreign Direct Investment in Portugal

Abstract In urban economics, and more recently in the international economics literature, agglomeration has been offered as a principal determinant of new investment. Yet agglomeration has rarely been subject to precise statistical tests. In this paper, the availability of detailed urban and regional data for Portugal allowed for a close study of the spatial choices for newly created foreign-owned plants. It appears that agglomeration economies are decisive location factors. Service agglomeration has a notably strong effect, while industry-level localization economies and urbanization externalities are verifiable location determinants as well. Distance from the principal cities is statistically significant, but there is no evidence that local labor costs matter.
Temporal DL-Lite over Finite Traces (Preliminary Results)

We transfer results on the temporal DL-Lite family of logics, moving from infinite models of linear time to the case of finite traces. In particular, we investigate the complexity of the satisfiability problem in various fragments of TUDL-Litebool, distinguishing the case of global axioms from the case where axioms are interpreted locally. We also consider satisfiability on traces bounded by a fixed number of time points.
The impact of parameter optimization of ensemble learning on defect prediction

Machine learning algorithms have configurable parameters which are generally used with default settings by practitioners. Making modifications on the parameters of machine learning algorithm is called hyperparameter optimization (HO) performed to find out the most suitable parameter setting in classification experiments. Such studies propose either using default classification model or optimal parameter configuration. This work investigates the effects of applying HO on ensemble learning algorithms in terms of defect prediction performance. Further, this paper presents a new ensemble learning algorithm called novelEnsemble for defect prediction data sets. The method has been tested on 27 data sets. Proposed method is then compared with three alternatives. Welch’s Heteroscedastic F Test is used to examine the difference between performance parameters. To control the magnitude of the difference, Cliff’s Delta is applied on the results of comparison algorithms. According to the results of the experiment: 1) Ensemble methods featuring HO performs better than a single predictor; 2) Despite the error of triTraining decreases linearly, it produces errors at an unacceptable level; 3) novelEnsemble yields promising results especially in terms of area under the curve (AUC) and Matthews Correlation Coefficient (MCC); 4) HO is not stagnant depending on the scale of the data set; 5) Each ensemble learning approach may not create a favorable effect on HO. To demonstrate the prominence of hyperparameter selection process, the experiment is validated with suitable statistical analyzes. The study revealed that the success of HO which is, contrary to expectations, not depended on c ©2019 by Muhammed Maruf Öztürk
Graph-Convolution-Based Classification for Ontology Alignment Change Prediction

Finding alignments between ontologies is a challenging and time-consuming task. When the aligned ontologies change, these alignments need to be changed as well. A recent approach to this problem proposes using embeddings as a representation for classifying changes. In this work, we compare embedding-based approaches to a neural network architecture built for node classification in knowledge graphs, namely relational graph convolutional networks. In our evaluation on two datasets from the biomedical domain, the best-performing embedding-based methods are RDF2Vec and TransE. The Graph convolution approach achieves similar results as the best-performing embedding based methods on a smaller dataset but outperforms all other approaches in standard classification metrics on a bigger dataset.
Exploring users perception on security and satisfaction requirements of context-aware applications: An Online Survey

Our lives are being transformed by innovative software applications with important social, environmental, and economic implications. For instance, context-aware software systems can be found in different domains such as health-care, telecommunication, transportation, etc. It is expected that in the near future software-intensive systems will behave autonomously thanks to the continuous sensing and monitoring. In this proposal, given the complexity of this kind of systems, and the social implications behind emerging wearable sensing technologies, we aim to empirically investigate some quality attributes that contribute to the social sustainability from a consumer perspective. In a previous work [3], experts recognised that security and satisfaction requirements are the cornerstone to get healthy social implications of software applications. However, do end-users perceive the same importance?
Mixed-World Reasoning with Existential Rules under Active Domain Semantics (Abstract)

In this paper, we study reasoning with existential rules in a setting where some of the predicates may be closed (i.e., their content is fully specified by the data instance) and the remaining open predicates are interpreted under active-domain semantics. We show, unsurprisingly, that the main reasoning tasks (satisfiability and certainty / possibility of Boolean queries) are all intractable in data complexity in the general case. However, several positive (PTIME data) results are obtained for the linear fragment, and interestingly, these tractability results hold also for various extensions, e.g., with negated closed atoms and disjunctive rule heads. This motivates us to take a closer look at the linear fragment, exploring its expressivity and defining a fixpoint extension to approximate non-linear rules.
Deep Adversarial Learning for NLP

Adversarial learning is a game-theoretic learning paradigm, which has achieved huge successes in the field of Computer Vision recently. Adversarial learning is also a general framework that enables a variety of learning models, including the popular Generative Adversarial Networks (GANs). Due to the discrete nature of language, designing adversarial learning models is still challenging for NLP problems. In this tutorial, we provide a gentle introduction to the foundation of deep adversarial learning, as well as some practical problem formulations and solutions in NLP. We describe recent advances in deep adversarial learning for NLP, with a special focus on generation, adversarial examples & rules, and dialogue. We provide an overview of the research area, categorize different types of adversarial learning models, and discuss pros and cons, aiming at providing some practical perspectives on the future of adversarial learning for solving real-world NLP problems.
Expectation and Locality Effects in the Prediction of Disfluent Fillers and Repairs in English Speech

This study examines the role of three influential theories of language processing, viz., Surprisal Theory, Uniform Information Density (UID) hypothesis and Dependency Locality Theory (DLT), in predicting disfluencies in speech production. To this end, we incorporate features based on lexical surprisal, word duration and DLT integration and storage costs into logistic regression classifiers aimed to predict disfluencies in the Switchboard corpus of English conversational speech. We find that disfluencies occur in the face of upcoming difficulties and speakers tend to handle this by lessening cognitive load before disfluencies occur. Further, we see that reparandums behave differently from disfluent fillers possibly due to the lessening of the cognitive load also happening in the word choice of the reparandum, i.e., in the disfluency itself. While the UID hypothesis does not seem to play a significant role in disfluency prediction, lexical surprisal and DLT costs do give promising results in explaining language production. Further, we also find that as a means to lessen cognitive load for upcoming difficulties speakers take more time on words preceding disfluencies, making duration a key element in understanding disfluencies.
Morphological and Biochemical Responses of Helianthus annuus L. to Chromium Contaminated Soil from Sukinda Mining Area (Odisha, India)

Helianthus annuusL.is a crop of interest because of its shorter growing season. However the yield potential is affected by chromium toxicity. The chromium toxicity also affects its morphological and biochemical parameters. Sunflower is highly chromium sensitive plant and acts as chromium toxicity indicator. In our study an attempt has been taken to investigate the effect of chromium contaminated soil of Sukinda mining area of Odisha, on morphological and biochemical responses of Helianthus annuusL. Different concentration of contaminated soil along with control soil were taken as a measure of chromium toxicity on plants. Among the parameters taken, it was showed that the accumulation of chromium was more in roots than shoots, because chromium translocated into the shoots from the roots. May be it was the main cause for poor results obtained in physical and biochemical parameters. In almost all treatments and days of respective observations taken, the physical, biochemical parameters are declining with the increase in chromium contamination (i.e. from control to 25%, 50% and pure contaminated soil).
Profiling outdoor leadership

This paper examines the role outdoor recreation and education plays in the development of generic leaders who have a positive relationship to the natural world. Three questionnaires (Multifactor Leadership Questionnaire - MLQ; the New Ecological Paradigm Scale - NEP; and the Connectedness to Nature Scale - CNS) were administered online to 104 international outdoor leaders through five online networks. The three instruments assessed the nexus of transformational leadership theory and outdoor leadership. A descriptive analysis of early findings from the project are outlined in this paper. The results can be viewed as an appropriate platform for understanding outdoor recreation and education leaders’ ecological perspectives and the generic, transformational leadership skills.
Circumnuclear Rings and Lindblad Resonances in Spiral Galaxies

In order to study the location of circumnuclear rings (CNR) and their possible relation with the inner Lindblad resonances (ILR), we investigate a sample of spiral galaxies. For this purpose, we have obtained and analyzed medium resolution spectra of five spiral galaxies in the range 6200–6900 Å. Through the Hα emission line, we constructed the radial velocity curves, and then the rotation curves. By fitting them, considering two or three components of an axisymmetric Miyamoto–Nagai gravitational potential, we constructed the angular velocity and Lindblad curves. In addition, we determined the CNR radius using the 2D spectra and generating the Hα spatial emission radial profiles. We determined the position of the resonances and we calculated the angular velocity pattern, which are in the range of 26–47 km s−1 kpc−1 for the galaxies of the sample. According to our results, the CNR are located between the inner ILR (iILR) and the outer ILR (oILR), or between the center of the galaxy and the ILR, when the object has only one of such resonance, in agreement with previous results. In addition, we calculated the dimensionless parameter defined as = RCR/Rbar, being in the range 1.1–1.6, in agreement with previous results found in the literature.