Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic table layout and formatting

In this thesis we address the problem of tabular layout with a focus on automatic table layout optimization. In a world where documents are produced in both physical format and also delivered electronically to an increasing range of smart devices, we draw attention to the fact that authoring tables is still difficult, frustrating, prone to errors and automated optimal table layout is basic or non-existent. We show through examples that while tables are well supported by document authoring tools, table layout suffers from many usability bottlenecks and inconsistencies. The current tools provide powerful features for controlling the presentation parameters but they provide limited or no features for controlling the logical table structure. In this context we survey table creation in several popular document authoring programs and review previous work on logical table models. We then present a combinatorial optimization modelling method for the table layout optimization problem, the problem of minimizing a table’s height subject to it fitting on a given page (width). We present three models of the problem for tables with spanning cells and inner tables and evaluate their performance using mixed integer programming, constraint programming and local search. When a table is too large to be displayed in one page, a multiple-objective optimization problem arises for automated table layout, finding suitable ways of splitting the table across multiple pages without affecting the meaning of the data. We present two mixed integer programming optimization models for finding arrangements of columns in pages such that the the number of pages is minimized and, at the same time, the changes to the column or row ordering or grouping is also minimized. To demonstrate our results, we developed a Table Drawing Tool prototype which implements an automated solution for the table layout optimization problem using a mathematical modelling method.
SUSTAINABILITY OF PUBLIC DEBT AND INEQUALITY IN A GENERAL EQUILIBRIUM MODEL

This study investigates the relationship between the sustainability of public debt and inequality in an endogenous growth model with heterogeneous agents. We show that the threshold for the sustainability of public debt is related to not only the relative size of public debt but also inequality. In addition, this study examines the effects of budget deficit and redistributive policies on the sustainability of public debt and inequality. We show that an increase in the deficit ratio or the redistributive tax makes public debt less sustainable. If the economy falls into the unsustainable region as a result of the policy change, both public debt and inequality continue to increase.
Valuing the Humanities

Humanities education and research have been a critical foundation of societies for centuries. However, societal change and the broadening of tertiary education over time have negatively affected the position and perception of the humanities, at least in relative terms. This article aims to redress this slide, informing discussion by bringing together new and existing evidence. We find that businesses, governments and societies in general benefit from humanities&#8208;educated individuals. We also find the humanities can improve students&#8217; job and earning prospects and equip them with a range of technical and transferrable skills.
Novel PV Panels Design Modeling to Support Smart Cities

Nowadays Photovoltaic (PV) became the new competitive energy resource. Therefore, it needs to be connected to the grid to reduce the demand from the authority, which means the flow of the load will be reduced. The production of the electricity as a solar energy will increase in the locations where the sun intensity is concentrated. The good location for solar energy is the six Gulf Cooperation Council (GCC) countries. These are six Middle Eastern countries; Kingdom of Saudi Arabia, Kuwait, the United Arab Emirates, Qatar, Kingdom of Bahrain, and Oman with high intensity of the solar radiation. The GCC countries have a good location for the solar energy Photovoltaic (PV) became the new competitive energy resource of the planet and it can be engaged in both Distribution and Transmission systems. In the present study, deriving the model's design of solar cell in five countries with the same house specifications. The Panel Generation Factor (PGF) considered satisfying the specification of the cells, which depends on the climate. This means that renewable energy provides many benefits for our climate, health and our economy. For the five countries the total (kWp) of PV panel capacity, number of PV panels needed for the design of 110 Wp PV module and Solar Charge Controller Rating are calculated based on PGF of each country.
Social Justice through Literacy: Integrating Digital Video Cameras in Reading Summaries and Responses.

Vicki A. Scullion, Ed. S. is a specialist in curriculum and instruction and a certified K-12 English teacher. Her current research centers on designing effective literacy lessons that incorporate technology for adult English for Academic Purposes students. ABSTRACT: Drawing data from an action-oriented research project for integrating digital video cameras into the reading process in pre-college courses, this study proposes using digital video cameras in reading summaries and responses to promote critical thinking and to teach social justice concepts. The digital video research project is founded on Vygotskian and semiotic theoretical approaches. A qualitative analysis of the method and a demonstration of its processes and benefits are provided using exemplar cases. Students practice critical thinking skills, including analysis, synthesis, and evaluation components, in multiple phases of the activity. Social justice issues are addressed through literacy as students analyze editorials from newspapers and respond to state a position and provide support. Synthesizing summaries and evaluating editorials in order to write a personal response can promote both critical thinking and awareness of social justice issues.
Granger Causality between Tourism and Income: A Meta-regression Analysis

In this article, we sum up the literature on the study of Granger causality relationships between tourism and income using a meta-regression analysis. We conclude that the acceptance of the tourism-led growth hypothesis is likelier the higher the countries’ degree of tourism specialization and population size. However, neither these variables nor the level of economic development is relevant for the explanation of the probability of acceptance of the hypothesis of reverse Granger causality. Notwithstanding these results, we find that several features of the research design are associated with the acceptance of both hypotheses.
Study on n-gram language models for topic and out-of-vocabulary words

Language models (LMs) are an important field of study in automatic speech recognition (ASR) systems. LM helps acoustic models find the corresponding word sequence of a given speech signal. Without it, ASR systems would not understand the language and it would be hard to find the correct word sequence. A data sparseness problem for modeling a language often occurs in LMs. The problem is caused by the insufficiency of training data, which in turn, makes the infrequent words have unreliable probability. In this research, we investigated a class LM based on a latent semantic analysis (LSA). A word-document matrix is commonly used to represent a collection of text (corpus) in LSA framework. This matrix tells how many times a word occurs in a certain document. In other words, this matrix ignores the word order in the sentence. We propose several word co-occurrence matrices that keep the word order. By applying LSA to these matrices, words in the vocabulary are projected to a continues vector space according to their position in the sentences. To support these matrices, we define a context dependent class (CDC) based ngram. Unlike traditional class-based n-gram LM, CDC LM distinguishes classes according to their context in the sentences. Experiments on Wall Street Journal (WSJ) corpus show that the word co-occurrence matrix works 3.62%-12.72% better in terms of perplexity than word-document matrix. Furthermore, the CDC improves the performance and achieves better perplexity than the traditional class-based n-gram LM based on LSA. When the model is linearly interpolated with the word-based 3-gram, it gives improvements about 2.01% for 3-gram model and 9.47% for 4-gram model on relative perplexity against a standard word-based 3-gram LM. During the past few years, researchers have tried to incorporate long-range dependencies into statistical word-based n-gram LMs. One of these long-range dependencies is topic. Unlike words, topic is unobservable. Thus, it is required to find the meanings behind the words to get into the topic. As the second part of this research, we proposed a new approach for a topic-dependent LM called topic dependent class (TDC) based n-gram, where the topic is decided in
Patent-to-Patent Similarity: A Vector Space Model

Current measures of patent similarity rely on the manual classification of patents into taxonomies. In this project, we leverage information retrieval theory and Big Data methods to develop a machine-automated measure of patent-to-patent similarity. We validate the measure and demonstrate that it significantly improves upon existing patent classification systems. Moreover, we illustrate how a pairwise similarity comparison of any and every two patents in the USPTO patent space can open new avenues of research in economics, management, and public policy. We make the data available for future scholarship through the Patent Research Foundation.
HOW DID AQ FACE THE EAST-WEST CHALLENGE? An Analysis of the AQ Family's Performance in the 2nd International Competition of Machine Learning Programs

The “East-West Challenge” is the title of the second international competition of machine learning programs, organized in the Fall 1994 by Donald Michie, Stephen Muggleton, David Page and Ashwin Srinivasan from Oxford University. The goal of the competition was to solve the “TRAINS problems”, that is to discover the “simplest” classification rules for train-like structured objects. The rule complexity was judged by a Prolog program that counted the number of various components in the rule expressed in the from of Prolog Horn clauses. There were 65 entries from several countries submitted to the competition. The GMU team’s entry was generated by three members of the AQ family of learning programs: AQ-DT, INDUCE and AQ17-HCI. The paper analyses the results obtained by these programs and compares them to those obtained by other learning programs. It also presents ideas for further research that were inspired by the competition. One of these ideas is a challenge to the machine learning community to develop a measure of knowledge complexity that would adequately capture the “cognitive complexity” of knowledge. A preliminary measure of such cognitive complexity, called C-complexity, different from the Prologcomplexity (P-complexity) used in the competition, is briefly discussed.
Sinkhole Vulnerabilities in Wireless Sensor Networks

Sinkhole attacks in wireless sensor networks occur when a compromised node tries to attract network traffic by advertising un-authorized/illegitimate routing updates. The victim node sends data to the compromised node rather than sending it to the node it was formerly using. Sinkhole attacks are typically used to launch other attacks on the network such as selective forwarding and wormhole attacks. Once the network is compromised it is very hard to predict the kind of attack that is to follow. For this reason, there is a need to strengthen the security of wireless sensor networks. In this paper, we first describe the challenges in detecting sinkhole attacks in wireless sensor networks, followed by an analysis of methods to prevent, detect and neutralize sinkhole attacks. The analysis will be based on discussing the advantages and limitations of the proposed solutions.
Cognitive Software-Defined Networking Using Fuzzy Cognitive Maps

Future networks are expected to provide improved support for several different kinds of applications and services. All these services will have diverse characteristics and requirements to be satisfied. A potential technology to upgrade efficiently and effectively current generation networks is virtualization via network “softwarization. ” This approach requires the combination of software-defined networking (SDN) and network function virtualization. Nevertheless, such a new complex network structure will raise further issues and challenges to be solved both reactively and proactively, without human intervention. In order to achieve that, academia and industry have identified the solution in the implementation and deployment of machine learning. Hence, very likely, 5G (and especially beyond 5G) networks will be cognitive virtualized networks. In that context, this paper proposes a cognitive SDN architecture based on fuzzy cognitive maps (FCMs). First, specific design modifications of FCMs are proposed to overcome some well-known issues of this learning paradigm. Second, the efficient integration with an SDN architecture is presented and analyzed. Finally, the emulation of a sample network scenario via Mininet is provided to validate the effectiveness and the potential of the new cognitive system and its capability to act and to adapt independently of human intervention.
RPAS IN THE SUPPORT FOR PHOTOGRAMMETRY EDUCATION: CASES IN TOPOGRAPHIC MAPPING AND DOCUMENTATION OF HISTORICAL MONUMENTS

Abstract. Currently on the world market there are several models and manufacturers of RPAS-UAV. This equipment made it possible to complete the entire flow of the photogrammetric mapping process in teaching for the students of the Cartographic Engineering course. In the Institute of Geosciences, this course already counted on the photogrammetric stations, equipped with hardware for 3D visualization (graphic card, high-speed monitors and stereoscopic vision glasses), photogrammetric software for data processing, and equipment for geodetic measurement control points. Before the acquisition of the RPAS, for the accomplishment of the academic works, it was necessary to buy or to look for in donations from companies of aerial photographs, that did not always correspond to an area of scientific interest. In addition, the scenes in the photos were not current, and it was impossible to have control points that were pre-signalized. From the availability of the RPAS in the University, the students began to have more interest by Photogrammetry in their professional end of coursework and throughout the disciplines. It became possible to carry out in practice all the processes involved in the mapping of nearby areas of interest, from the planning to the generation of photogrammetric products, such as the generation of DTM (Digital Terrain Model), DEM (Digital Elevation Model), orthophotomaps and extraction of features. In addition to products related to the 3D mapping of terrains, mainly through the RPAS of rotating wings, there was also the possibility of mapping and reconstructing 3D structures, such as buildings of historical monuments.
Visually Weighted Group-Sparsity Recovery for Compressed Sensing of Color Images with Edge-Preserving Filter

본 논문에서는 컬러 영상의 압축 센싱 복원 기술에 인지시각시스템의 특성을 접목해 복원 영상의 화질을 향상 시키는 방법을 연구하였다. 제안하는 그룹-희소성 최소화 기반 컬러 채널별 시각적 가중치 적용 방법은 영상의 성긴 특성뿐만 아니라 인지시각시스템의 특성을 반영할 수 있도록 설계되었다. 또한, 복원 영상에서의 잡음을 제거하기 위하여 설계한 경계보존 필터는 영상의 경계 부분에 대한 디테일을 보존함으로써, 복원 영상의 품질을 향상 시키는 역할을 한다. 실험 결과, 제안하는 방법이 최신의 그룹-희소성 최소화 기반 방법들보다 평균 0.56 ∼ 4dB 더 높은 PSNR을 달성함으로써, 객관적 성능을 향상시킬 수 있음을 확인하였으며, 주관적 화질 또한 기존 방법들에 비해 뛰어나다는 것을 복원된 영상 간 비교를 통해 확인하였다.
A Review on User Perception of Desktop and Mobile Service Website Using Webqual and User Experience Approach

As the online media dissemination, website has the advantages that it can be accessed freely, without limits, wherever, whoever, and whenever. However, the advantages of the website is sometimes not in line with its simplicity. So it becomes important that the organization can make an assessment of the quality of the website that they are used, whether its website has fulfilled what is perceived by the user. This paper reviews studies user perception of desktop and mobile service website used qualitative methods adapted to WebQual and User Experience approach. The expected outcome of this research is knowing the user's perception of the services and information available on the website along with the possibility of desktop and mobile gap arising from differences in the two services.
Experimental analyses of the factors affecting the gradience in sentence difficulty judgments

Although a reader's text-level comprehension is affected by the comprehension of individual sentences in a text, little attention has been paid to the difficulty of sentences. This study investigates whether measures (features) of text difficulty affect the gradience observed in sentence difficulty judgments. We examine two traditional features (sentence length and number of low-frequency words) and six nontraditional features (counts of clauses, dependent clauses, coordinate phrases, t-units, complex t-units, and Wh nominals). Five English language instructors participated in a sentence difficulty behavioral experiment. They had to judge how difficult the sentences were on a scale of 1 to 4, where '1' means very easy, '2' means easy, '3' means moderately difficult , and '4' means difficult . The scale of 1-4 allowed the subjects to treat perceived difficulty as a relative point on the scale rather than as categorical values ('easy' and 'difficult'). It was found that both traditional and nontraditional measures of text difficulty correlate with participants' perceived difficulty suggesting that both types of features play roles in the perception of sentence difficulty. In addition, a tree-based readability model was implemented using the same sentences and features. The preliminary data suggests that the traditional features are more important while classifying new observations.
Single Board Computers for Deep Machine Learning

This report examines the ability of a cluster of Raspberry Pi 3’s to be a viable platform to design, prototype, build and train a Deep Machine Learning System. The Raspberry Pi 3 cluster was cost and time compared against a Generic Desktop Computer and a General Purpose Graphics Processing Unit. The time comparison was conducted with a benchmarking machine learning dataset and a custom designed and created machine learning dataset. The time comparisons verified that Deep Machine Learning System was able to be trained and used on a Raspberry Pi 3 cluster. The results were validated against the other hardware platforms to ensure correct correlation. This parallel benefit was diminished beyond 4 Workers, with the likely cause being network communication bottlenecks. This research also showed experimentally the benefits of using multiple Raspberry Pi 3’s in a cluster to perform a Hyper Parameter Sweep to assist with design layouts, as an essential part of the design of a Deep Machine Learning System.
Improving query efficiency in high dimensional point indexes

: In this paper, we focus on the leaf level nodes of tree-like k-dimensional indexes that store the data entries, since those nodes represent the majority of the nodes in the index. We propose a generic node splitting approach that defers splitting when possible and instead favors merging of a full node with an appropriate sibling and then re-splitting of the resulting node. Our experiments with the hB-tree, show that the proposed splitting approach achieves high average node storage utilization regardless of data distribution,
Recent Developments and Applications of Acoustic Infrasound to Monitor Volcanic Emissions

Volcanic ash is a well-known hazard to population, infrastructure, and commercial and civil aviation. Early assessment of the parameters that control the development and evolution of volcanic plumes is crucial to effective risk mitigation. Acoustic infrasound is a ground-based remote sensing technique—increasingly popular in the past two decades—that allows rapid estimates of eruption source parameters, including fluid flow velocities and volume flow rates of erupted material. The rate at which material is ejected from volcanic vents during eruptions, is one of the main inputs into models of atmospheric ash transport used to dispatch aviation warnings during eruptive crises. During explosive activity at volcanoes, the injection of hot gas-laden pyroclasts into the atmosphere generates acoustic waves that are recorded at local, regional and global scale. Within the framework of linear acoustic theory, infrasound sources can be modelled as multipole series, and acoustic pressure waveforms can be inverted to obtain the time history of volume flow at the vent. Here, we review near-field (<10 km from the vent) linear acoustic wave theory and its applications to the assessment of eruption source parameters. We evaluate recent advances in volcano infrasound modelling and inversion, and comment on the advantages and current limitations of these methods. We review published case studies from different volcanoes and show applications to new data that provide a benchmark for future acoustic infrasound studies.
Matrix Algebra for GPU and Multicore Architectures (MAGMA) for Large Petascale Systems

The goal of the MAGMA project is to create a new generation of linear algebra libraries that achieve the fastest possible time to an accurate solution on hybrid Multicore+GPU-based systems, using all the processing power that future high-end systems can make available within given energy constraints. Our efforts at the University of Tennessee achieved the goals set in all of the five areas identified in the proposal: 1. Communication optimal algorithms; 2. Autotuning for GPU and hybrid processors; 3. Scheduling and memory management techniques for heterogeneity and scale; 4. Fault tolerance and robustness for large scale systems; 5. Building energy efficiency into software foundations. The University of Tennessee’s main contributions, as proposed, were the research and software development of new algorithms for hybrid multi/many-core CPUs and GPUs, as related to two-sided factorizations and complete eigenproblem solvers, hybrid BLAS, and energy efficiency for dense, as well as sparse, operations. Furthermore, as proposed, we investigated and experimented with various techniques targeting the five main areas outlined.
MODELING OF DISTRIBUTED MUTUAL EXCLUSION SYSTEM USING EVENT -B

The problem of mutual exclusion arises in distributed systems whenever shared resources are concurrently accessed by several sites. For correctness, it is required that shared resource must be accessed by a single site at a time. To decide, which site execute the critical section next, each site communicate with a set of other sites. A systematic approach is essential to formulate an accurate speciation. Formal methods are mathematical techniques that provide systematic approach for building and verification of model. We have used Event-B as a formal technique for construction of our model. Event-B is event driven approach which is used to develop formal models of distributed systems . It supports generation and discharge of proof obligations arising due to consistency checking. In this paper, we outline a formal construction of model of Lamport's mutual exclusion algorithm for distributed system using Event-B. We have considered vector clock instead of using Lam-port's scalar clock for the purpose of message's time stamping.