Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Semantic Textual Similarity

Measuring semantic similarity and relatedness between textual items (words, sentences, paragraphs or even documents) is a very important research area in Natural Language Processing (NLP). In fact, it has many practical applications in other NLP tasks. For instance, Word Sense Disambiguation, Textual Entailment, Paraphrase detection, Machine Translation, Summarization and other related tasks such as Information Retrieval or Question Answering. In this masther thesis we study different approaches to compute the semantic similarity between textual items. In the framework of the european PATHS project1, we also evaluate a knowledge-base method on a dataset of cultural item descriptions. Additionaly, we describe the work carried out for the Semantic Textual Similarity (STS) shared task of SemEval-2012. This work has involved supporting the creation of datasets for similarity tasks, as well as the organization of the task itself. http://www.paths-project.eu/
An Epigenetic Approach to Semantic Categories

It is argued that early language learning in children emerges from five primary knowledge structures: 1) space; 2) objects; 3) actions; 4) number; and 5) events. These structures constitute the basis for the semantic domains that are used to form categories that represent the meanings of early words. The domains are naturally modeled in conceptual spaces that are based on geometric notions rather than on symbolic representations. It is shown how these semantics domains can be used to generate an epigenetic model of language acquisition. The four first primary knowledge systems are used for prepositions, nouns, verbs, and quantifiers, respectively, while events form the meanings of declarative sentences. This semantic model leads to direct recommendations for how the model can be implemented in robots and other artificial systems.
Bayesian Programming

Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreter that allows readers to experiment with this new approach to programming. Principles and Modeling Only requiring a basic foundation in mathematics, the first two parts of the book present a new methodology for building subjective probabilistic models. The authors introduce the principles of Bayesian programming and discuss good practices for probabilistic modeling. Numerous simple examples highlight the application of Bayesian modeling in different fields. Formalism and Algorithms. The third part synthesizes existing work on Bayesian inference algorithms since an efficient Bayesian inference engine is needed to automate the probabilistic calculus in Bayesian programs. Many bibliographic references are included for readers who would like more details on the formalism of Bayesian programming, the main probabilistic models, general purpose algorithms for Bayesian inference, and learning problems. FAQsAlong with a glossary, the fourth part contains answers to frequently asked questions. The authors compare Bayesian programming and possibility theories, discuss the computational complexity of Bayesian inference, cover the irreducibility of incompleteness, and address the subjectivist versus objectivist epistemology of probability. The First Steps toward a Bayesian ComputerA new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps toward reaching that goal. It encourages readers to explore emerging areas, such as bio-inspired computing, and develop new programming languages and hardware architectures.
A Portfolio Theory Approach to Software Vendor Selection

: The paper presents a minimum risk model, inspired from the financial portfolio theory, for the selection of a software vendor. The performance of the software products offered by potential software vendors is evaluated by several experts regarding several criteria. The minimum risk model has several constraints. One of the constraints is a complementarity constraint. Other constraints are connected with the available budget and the expected performance of the software products. A procedure for solving the minimum risk model is presented and a numerical example is analyzed.
Comparison of the Incidence of Postoperative Hyponatremia after Infusion of Hypotonic Versus Isotonic Intravenous Solutions in Children

Introduction: Hyponatremia is the most common electrolyte disorder in patients following surgical interventions (19-50%). Hospital acquired hyponatremia is often due to using hypotonic solution s and can be lethal. Materials and Methods: Between January and December 2014, 190 children (1 month to 12 years) who were admitted in the urology department of Children’s Hospital Medical Center for elective surgical procedures were enrolled in the study. The patients were randomly divided into two groups: group I received 50 mEq/L sodium and 20 mEq/L potassium in D/W 5% and group II received 154 mEq/l sodium and 20 mEq/L potassium in D/W 5% at the maintenance dose for a period of 6 hours following the operation. The patients did not have any oral fluid intake 6 hours postoperatively. The incidence of hyponatremia before and after maintenance IV fluid therapy was analyzed. Other characteristics of the patients such as age, gender, duration of hospitalization, other concomitant electrolyte disturbances, and symptoms of hypervolemia were also evaluated. The incidence of fluid-IV therapy-induced hyponatremia was investigated and analyzed in different categories of patients. Results: One hundred and ninety patients were enrolled . The mean age was 3.75 years (ranging from 1 month to 12years). One hundred and thirty-three patients (70%) were boys. The incidence of hyponatremia before and after maintenance IV fluid therapy was 9.5% and 36%, respectively. After the therapy, the incidence of hyponatremia was 54% and 17% in hypotonic and isotonic groups, respectively. Final multivariate logistic analysis showed that hyponatremia was common in patients that received hypotonic solution after surgery. Conclusions: Hyponatremia was markedly induced in patients receiving hypotonic solution after surgery. It seems isotonic fluid therapy after surgery protects the patients from hyponatremia. Keywords: Hyponatremia; Isotonic solutions; Hypotonic solutions; Child.
Automatic Extraction and Validation of Lexical Ontologies from Text

Nowadays, semantic information plays an important role in natural language processing, more specifically describing and representing “the meanings of the words” crucial for understanding the human language. In the last two decades, there have been efforts to create a large database that represents lexical knowledge, where the words and their meanings are represented along with connections held between them. However, in most of the cases, this resources are created manually. For instance Princeton WordNet is considered the standard model of a lexical ontology for the English language. Besides that, also for Portuguese there have been some attempts to create a broad-coverage ontology, also created manually and not publicly available. Still, they are not public available for download, and also all of them were manually created. Despite being less prone to errors, the problem is that the manual creation of these resources takes a lot of time consuming and requires a team, and researchers specialised in the area. Nevertheless, in the last years, some efforts have been made to develop computational tools to reduce the need of manual intervention, such as some authors that propose lexico-semantic patterns to find semantic relations between terms in text. This kind of approach should be considered as an alternative and subject of research, in order to avoid impractical human work in the construction of these resources. Having this in mind, the work expected in this project is the creation of a system capable of automatically acquire semantic knowledge from any kind of Portuguese text. The extraction method is based on lexico-syntactic patterns, that indicate a relation of interest, and also by a inference method to extract hypernymy relations from compound nouns. Also, different kinds of textual resources are used to test and improve our system. Furthermore, this work analyses the benefits from applying similarity distributional metrics based on the occurrence of words in documents to our system outputs. The quality and the utility of the knowledge extracted from the various textual resources, will be compared against another Portuguese knowledge-base. In the end of this research, important contributions for the computational processing of Portuguese language are provided, such as computational tools capable of extracting and inferring lexico-semantic information from text, methodologies to automatically validate these knowledge, and also compare knowledge-bases. Finally, the experiments outcomes and conclusions are published in important conferences for the area.
Leading Innovation through Knowledge Sharing and HRM practices

Innovation is one of the most important factors for economic growth in every company. Previous studies have shown that innovation is highly dependent to the knowledge. On the other hand knowledge sharing is an influential factor can create knowledge. Therefore, knowledge sharing can be applied to increase innovation (product/service, process, marketing, and organizational innovation). Training, staffing, reward system, performance appraisal, and participation are the most applicable HRM practices that have potential to affect knowledge sharing and thereby innovation. This paper attempts to justify the relationship between HRM practices, knowledge sharing, and innovation. At the end, this study proposes a framework which any linkage of it is being supported by extant research.
Knowledge in the making : prototyping and human-centred design practice

This thesis presents an enquiry into the nature and role of prototyping within human-centred design practice, examining the capabilities and limitations of emerging prototyping technologies within this context. A contextual review explores the significance of the human element in design. This leads to the proposal of a paradigm statement for human-centred design which informs the theoretical and practical research activity undertaken in the course of this investigation. A critical review of literature aimed at the design and engineering professions identifies a rhetoric celebrating the virtualisation of design processes. Here, advocates of emerging virtual prototyping technologies argue computer-based simulation techniques may reduce or replace physical prototype iterations, thereby greatly increasing the speed and efficiency of new product development processes. This thesis questions the extent to which virtual prototyping can replace physical human input in design. A counter argument to the designer's total immersion in the virtual design world is that valuable creative opportunities may be revealed through discovery-oriented physical prototyping. Furthermore, it may not be possible to adequately describe all aspects of a design proposal using virtual methods alone. This is demonstrated in practical investigations in which designers sought to exploit tactile qualities as essential features in design, and also in cases involving complex structural behaviour. Despite significant advances in virtual prototyping technologies, there remain some types of design problem which may only be identified and addressed through the making and testing of physical models. Moreover, this thesis argues that the valuable practical knowledge which may be derived through hands-on engagement and manipulation of physical prototypes and materials must be retained as an essential human element in design.
Mass Deportations Would Impoverish US Families and Create Immense Social Costs

Executive Summary1 This paper provides a statistical portrait of the US undocumented population, with an emphasis on the social and economic condition of mixed-status households - that is, households that contain a US citizen and an undocumented resident. It is based primarily on data compiled by the Center for Migration Studies (CMS). Major findings include the following: • There were 3.3 million mixed-status households in the United States in 2014. • 6.6 million US-born citizens share 3 million households with undocumented residents (mostly their parents). Of these US-born citizens, 5.7 million are children (under age 18). • 2.9 million undocumented residents were 14 years old or younger when they were brought to the United States. • Three-quarters of a million undocumented residents are self-employed, having created their own jobs and in the process, creating jobs for many others. • A total of 1.3 million, or 13 percent of the undocumented over age 18, have college degrees. • Of those with college degrees, two-thirds, or 855,000, have degrees in four fields: engineering, business, communications, and social sciences. • Six million undocumented residents, or 55 percent of the total, speak English well, very well, or only English. • The unemployment rate for the undocumented was 6.6 percent, the same as the national rate in January 2014.2 • Seventy-three percent had incomes at or above the poverty level. • Sixty-two percent have lived in the United States for 10 years or more. • Their median household income was $41,000, about $12,700 lower than the national figure of $53,700 in 2014 (US Census Bureau 2015). Based on this profile, a massive deportation program can be expected to have the following major consequences: • Removing undocumented residents from mixed-status households would reduce median household income from $41,300 to $22,000, a drop of $19,300, or 47 percent, which would plunge millions of US families into poverty. • If just one-third of the US-born children of undocumented residents remained in the United States following a mass deportation program, which is a very low estimate, the cost of raising those children through their minority would total $118 billion. • The nation's housing market would be jeopardized because a high percentage of the 1.2 million mortgages held by households with undocumented immigrants would be in peril. • Gross domestic product (GDP) would be reduced by 1.4 percent in the first year, and cumulative GDP would be reduced by $4.7 trillion over 10 years. CMS derived its population estimates for 2014 using a series of statistical procedures that involved the analysis of data collected by the US Census Bureau's American Community Survey (ACS). The privacy of all respondents in the survey is legally mandated, and, for the reasons listed in the Appendix, the identity of undocumented residents cannot be derived from the data. A detailed description of the methodology used to develop the estimates is available at the CMS website.3
Generalized Probabilistic Topic and Syntax Models for Natural Language Processing

Generalized Probabilistic Topic and Syntax Models for Natural Language Processing William M. Darling University of Guelph, 2012 Advisor: Professor Fei Song This thesis proposes a generalized probabilistic approach to modelling document collections along the combined axes of both semantics and syntax. Probabilistic topic (or semantic) models view documents as random mixtures of unobserved latent topics which are themselves represented as probabilistic distributions over words. They have grown immensely in popularity since the introduction of the original topic model, Latent Dirichlet Allocation (LDA), in 2004, and have seen successes in computational linguistics, bioinformatics, political science, and many other fields. Furthermore, the modular nature of topic models allows them to be extended and adapted to specific tasks with relative ease. Despite the recorded successes, however, there remains a gap in combining axes of information from different sources and in developing models that are as useful as possible for specific applications, particularly in Natural Language Processing (NLP). The main contributions of this thesis are two-fold. First, we present generalized probabilistic models (both parametric and nonparametric) that are semantically and syntactically coherent and contain many simpler probabilistic models as special cases. Our models are consistent along both axes of word information in that an LDA-like component sorts words that are semantically related into distinct topics and a Hidden Markov Model (HMM)-like component determines the syntactic parts-of-speech of words so that we can group words that are both semantically and syntactically affiliated in an unsupervised manner, leading to such groups as verbs about health care and nouns about sports. Second, we apply our generalized probabilistic models to two NLP tasks. Specifically, we present new approaches to automatic text summarization and unsupervised part-of-speech (POS) tagging using our models and report results commensurate with the state-of-the-art in these two sub-fields. Our successes demonstrate the general applicability of our modelling techniques to important areas in computational linguistics and NLP.
Robotic 3D Plant Perception and Leaf Probing with Collision-Free Motion Planning for Automated Indoor Plant Phenotyping

Various instrumentation devices for plant physiology study such as chlorophyll fluorimeter and Raman spectrometer require leaf probing with accurate probe positioning and orientation with respect to leaf surface. In this work, we aimed to automate this process with a Kinect V2 sensor, a high-precision 2D laser profilometer, and a 6-axis robotic manipulator in a high-throughput manner. The relatively wide field of view and high resolution of Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. Given the number of plants, the location and size of each plant were estimated by K-means clustering. A real-time collision-free motion planning framework based on Probabilistic Roadmap was adopted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from top with the short-range profilometer to obtain a high-precision point cloud where potential leaf clusters were extracted by region growing segmentation. Each leaf segment was further partitioned into small patches by Voxel Cloud Connectivity Segmentation. Only the small patches with low root mean square values of plane fitting were used to compute probing poses. To evaluate probing accuracy, a square surface was scanned at various angles and its centroid was probed perpendicularly with a probing position error of 1.5 mm and a probing angle error of 0.84 degrees on average. Our growth chamber leaf probing experiment showed that the average motion planning time was 0.4 seconds and the average traveled distance of tool center point was 1 meter.
Next-generation sequencing: emerging technology in the area of hematologyand blood disorder.

In the modern era, whole world has experienced tremendous boost in the field of molecular diagnosis by use of DNA sequencing technology. The human genome contains more than 3 billion base pairs that contain all the information of about our health and well-being. The first whole genome sequence of human was published successfully before decades [1,2]. It was very expensive and paid billion dollars to complete. The cost paid for, was much more worthy as it providing the first fundamental understanding of the structure and biology of human genome and relation with diseases [3]. Initially NGS technology was generated huge amount data of human variant later it was proved that this NGS technology is much more effective in the diagnosis human diseases by use of bioinformative tools to select pathogenic variants. In present days, sequencing costs have dramatically decline and therefore it is now routinely using for diagnosis of many rare inherited diseases including hematology and blood disorder. Genomewide sequence analysis is already playing an important role in the hematology field [4]. Numbers of review article has been published on genome sequencing and its impact on hematology, which provide a state-of-the-art snapshot of this rapidly moving field [5]. Many researchers in this modern world did a big job of preparing reader to uncovered variations in the human genome through NGS. This new sequencing technology is going to solve the challenges that researchers in the field of hematology are going forward. Now a day’s researcher are making disease specific targeted NGS panel, which is helping more quickly and precise diagnosis of specific disease in the field of hematology. Keeping in mind the growing research in the area of molecular diagnosis, how genome-wide analysis has unlocked new avenues of research, diagnosis, and therapy for benign hematologic disorders [6]. Recent advances in molecular technologies, mainly nextgeneration sequencing, inspire us to apply these technologies as a first-line approach for the identification of potential mutations and to determine the novel causative genes in patients with blood disorders. Researcher have started preparing targeted NGS panels for diagnosis of hematologic malignancies, Red cell congenital hemolytic anemia for diagnosis of all rare cause of hemolytic anemia which covers around 70-80 genes associated with hemoglobinopathies, which will cover gene related alpha (HBA1/2) and beta (HBB) globin gene locus analysis, HBD (Δ-globin) sequence analysis, gene related to RBC membrane protein disorders, RBC enzymopathies genes, congenital dyserythropoietic anemia (CDA) and the inherited bone marrow failure syndromes (IBMFS) are a group of rare genetic blood disorders in which there is usually some form of aplastic anemia (failure of the bone marrow to produce blood), associated with a family history of the similar disorder. (Includes sequence analysis of C15orf41, CDAN1, GATA1, KIF23, KLF1, SEC23B), Diamond-Blackfan anemia panel and genes related to bone marrow failure syndromes [7].
Elective Neck Dissection Versus Observation in Early‐Stage (cT1/T2N0) Oral Squamous Cell Carcinoma

Whether to perform elective neck dissection (END) or apply the observation (OBS) policy in patients with early‐stage oral squamous cell carcinoma (OSCC) without clinical evidence of cervical lymph node metastasis (cT1/T2N0) remains uncertain. The two most recent meta‐analyses include many studies published before the widespread availability of CT scanning in the 1990s. With the rapid advancement in imaging studies since 1990, the early clinical detection of cervical node metastasis has become more reliable without the need for END or pathological staging. Thus, we conducted a systematic review and meta‐analysis of studies comparing survival outcomes between END and OBS in patients with cT1/T2N0 OSCC.