Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Social Media to Assess User Satisfaction with Transport for London’s Oyster

ABSTRACT What items contribute to user satisfaction with large ubiquitous systems? This article reports on studies that seek answers to this question through the context of London Transport’s Oyster system. Two studies are presented. In the first, an existing quantitative instrument (End-User Computing Satisfaction, or EUCS), originally developed to evaluate satisfaction with mandatory use systems, is used to measure satisfaction through a small survey. In the second, Twitter data are used to demonstrate how broader items contribute to user satisfaction. The first study demonstrates that the EUCS constructs of Content, Accuracy, Format, Ease of use, and Timeliness are still statistically valid, although discriminant problems exist. However, the second study, the analysis of Twitter activity, reveals that Content and Format are not relevant to Oyster users, and that Accuracy and Timeliness are conceptualized very differently. Moreover, Ubiquity emerges as an important item contributing to satisfaction, or in some cases dissatisfaction.
Lecturer Workload Optimization Applying Interactive Visualization

Lecturers are required to perform three basic activities to be accomplished each semester, i.e. teaching, performing research, and participating in community services. However, capturing lecturer productivity for the evaluation purposes is a complex problem. This research aims to design and develop a tool to visualize and conceive lecturer workload. Instead of analysing text-based big data distribution, this research adopted interactive visualization approach that offers interaction to support lecturer workload exploratory data analysis. This approach consists of overview, zoom and filter, and detail on demand. The result is able to show the workload of each lecturer in three different level: lecturer level, head of department level, and institution level, thus made it easier to examine each lecturer activity and productivity. The contribution of this research is to provide a visualization tool for determining fair and reasonable workload for lecturers to optimize their productivity and evaluate their work rate distribution.
“I’m really sorry about what I said”

This paper extends the concept of local grammar to speech act studies, focusing specifically on apologising in English. It aims primarily to demonstrate the usefulness of a local grammar approach to account for speech acts and ultimately to contribute to the on-going development of corpus pragmatics. Apology expressions in a corpus of scripted TV conversations are first automatically extracted and then manually examined in order to make sure that all remaining instances have the illocutionary force of apologising and thus qualify for further analysis. The subsequent local grammar analyses facilitate the establishment of a local grammar of apology, comprising 14 local grammar patterns. The analyses show that it is promising to develop a set of local grammars to account more adequately for speech acts in general. The relationship between local grammars, functional grammars, and general grammars is further discussed, which suggests that local grammars can be an alternative approach to functional-pragmatic studies of language and discourse. Directions for future research are outlined; and implications and applications are briefly discussed.
Modelling Long-distance Node Relations for KBQA with Global Dynamic Graph

The structural information of Knowledge Bases (KBs) has proven effective to Question Answering (QA). Previous studies rely on deep graph neural networks (GNNs) to capture rich structural information, which may not model node relations in particularly long distance due to oversmoothing issue. To address this challenge, we propose a novel framework GlobalGraph, which models long-distance node relations from two views: 1) Node type similarity: GlobalGraph assigns each node a global type label and models long-distance node relations through the global type label similarity; 2) Correlation between nodes and questions: we learn similarity scores between nodes and the question, and model long-distance node relations through the sum score of two nodes. We conduct extensive experiments on two widely used multi-hop KBQA datasets to prove the effectiveness of our method.
The Financial Narrative Summarisation Shared Task (FNS 2020)

This paper presents the results and findings of the Financial Narrative Summarisation shared task (FNS 2020) on summarising UK annual reports. The shared task was organised as part of the 1st Financial Narrative Processing and Financial Narrative Summarisation Workshop (FNP-FNS 2020). The shared task included one main task which is the use of either abstractive or extractive summarisation methodologies and techniques to automatically summarise UK financial annual reports. FNS summarisation shared task is the first to target financial annual reports. The data for the shared task was created and collected from publicly available UK annual reports published by firms listed on the London Stock Exchange (LSE). A total number of 24 systems from 9 different teams participated in the shared task. In addition we had 2 baseline summarisers and additional 2 topline summarisers to help evaluate and compare against the results of the participants.
Arabic Curriculum Analysis

Developing a platform that analyzes the content of curricula can help identify their shortcomings and whether they are tailored to specific desired outcomes. In this paper, we present a system to analyze Arabic curricula and provide insights into their content. It allows users to explore word presence, surface-forms used, as well as contrasting statistics between different countries from which the curricula were selected. Also, it provides a facility to grade text in reference to given grade-level and gives users feedback about the complexity or difficulty of words used in a text.
Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.
Enhancing 3D Visual Odometry with Single-Camera Stereo Omnidirectional Systems

Enhancing 3D Visual Odometry with Single-Camera Stereo Omnidirectional Systems by Carlos Jaramillo Advisor: Prof. Jizhong Xiao We explore low-cost solutions for e ciently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task – as it is called when using computer vision to estimate egomotion – is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions presented in this thesis. To deliver the portability goal with a single o↵-the-shelf camera, we have taken two approaches: The first one, and the most extensively studied here, revolves around an unorthodox camera-mirrors configuration (catadioptrics)
Development of a modeling methodology for circuits with memristors

Since the seminal paper of Prof. Chua in the early 70’s when the concept of the memristor was introduced, a period of nearly 40 years has passed in order to have a real memristor device in 2008 at the HP Labs. Nowadays, this emerging device has been already used in novel applications that span from the development of modern memories to analog circuit applications in combination with traditional electronics to obtain novel functionalities. As a direct consequence, especially suited models for the memristor are needed that can be used in circuit simulation applications to help the design flow of memristive circuits and systems. This work introduces a novel modeling methodology in order to generate semi-symbolical expressions in the form of circuit simulation-oriented memristor models in the DC and time domains. The generated models are expressed in the form of an i-v constitutive branch relationship, i.e. a functional memristor model. Special attention during the generation process has been devoted to verify that the fingerprints of the memristor are fulfilled, which means that the proposed models posses the main properties stipulated for a device to be considered a memristor. The models have been thoroughly characterized versus several parameters of the device assuming the HP Lab values. In the last step of the methodology, the models are recast in functional blocks that can be used within industrial IC simulation frameworks such as SPICE. The generated memristor models have been tested in a series of bench-mark circuits with excellent results.
Secure Techniques of Data Anonymization for Privacy Preservation

Abstract: Now a day's there is an extensive use of internet , the data present on it should be made available in a way that an individual's privacy is not affected. Recently, many organizations are accumulating gigantic amounts of data which are stored in huge databases. Data publisher gather data from data holders, and publicize this data to data recipient for mining, statistical analysis etc. The released data can reveal secret information of an individual. For providing security to the data, many anonymization techniques have been designed for privacy preserving and micro data publishing. This paper discusses various anonymization techniques such as generalization, bucketization, slicing and also provide a methodology for enhancing security in the slicing technique. Further, a comparative analysis of the proposed method with existing techniques is discussed.
Semantic Analysis and Computational Modeling of Legal Documents

The number of legal documents is continuously increasing but technology insufficiently supports knowledge-intensive processes on these documents. This work shows the potentials of data and text mining for semantic analysis, in particular natural language processing (NLP), of legal documents. The analysis uses powerful and adapted software architectures, e.g. Apache UIMA and Apache Spark, to facilitate interpretation of statutory texts and the creation of executable models in meta-model-based information systems (IS).
The Effects of the Parent Training Program for Obesity Reduction on Health Behaviors of School-Age Children

The purposes of the study were to evaluate the effectiveness of the Parent Training Program for Obesity Reduction (PTPOR) on health behaviors of school-age children. An Ecological Systems Theory (EST) was approached the study and a randomized control trial was used in this study. Participants were school-age overweight or obese children and their parents. One hundred and one parent-child dyads were recruited and random assigned into the PTPOR (N=30), Educational Intervention or EI (N=32), and control group (N=39). The parents in the PTPOR group participated in five sessions including an educational session, a cooking session, aerobic exercise training, 2-time group discussion sessions, and 4-time telephoned counseling sessions. Repeated Measure ANCOVA was used to analyze data. The results presented that the outcomes of the PTPOR group were better than the EI and the control groups at 1, 8, and 32 weeks after finishing the program such as child exercise behavior (F(2,97) = 3.98, p = . 02) and child dietary behavior (F(2,97) = 9.42, p = . 00). The results suggest that nurses and health care providers should utilize the PTPOR for child weight reduction and for the health promotion of a lifestyle among overweight and obese children. Keywords—Parent training program for obesity reduction, child health behaviors, school-age children.
A Point Cloud Approach to Object Slicing for 3D Printing

xi . CHAPTER ONE: INTRODUCTION 1 . Methods of Manufacturing 2 . Subtractive Manufacturing 3 . Additive Manufacturing 5 . A Brief History 6 . The Emergence of Rapid Prototyping 7 . The Birth of 3D Printing 9 . Advantages 12 . Motivation for this Research 14 . Organization of the Dissertation 16 . Terminology in this Document 17 . CHAPTER TWO: 3D PRINTING HARDWARE 19 . Processes 20 . Vat Photopolymerization 20 . Material Extrusion 22 . Powder Bed Fusion 25 . Sheet Lamination 28 . Binder Jetting 30 . Material Jetting 34 . Directed Energy Deposition 36 . Other Methods 38 . Current Trends and Research 39 . Accuracy and Quality Control 39 . Functional Printing 40 . Nontraditional Additive Manufacturing 40 . Larger Scale 3D Printing 42 . Bioengineering 43 .
A Dichotomy for Homomorphism-Closed Queries on Probabilistic Graphs

We study the problem of probabilistic query evaluation (PQE) over probabilistic graphs, namely, tuple-independent probabilistic databases (TIDs) on signatures of arity two. Our focus is the class of queries that is closed under homomorphisms, or equivalently, the infinite unions of conjunctive queries, denoted UCQ^\infty . Our main result states that all unbounded queries in UCQ^\infty are #P-hard for PQE. As bounded queries in UCQ^\infty are already classified by the dichotomy of Dalvi and Suciu [16], our results and theirs imply a complete dichotomy on PQE for UCQ^\infty queries over arity-two signatures. This dichotomy covers in particular all fragments in UCQ^\infty such as negation-free (disjunctive) Datalog, regular path queries, and a large class of ontology-mediated queries on arity-two signatures. Our result is shown by reducing from counting the valuations of positive partitioned 2-DNF formulae (#PP2DNF) for some queries, or from the source-to-target reliability problem in an undirected graph (#U-ST-CON) for other queries, depending on properties of minimal models.
FPT Inapproximability of Directed Cut and Connectivity Problems

(see paper for full abstract) Cut problems and connectivity problems on digraphs are two well-studied classes of problems from the viewpoint of parameterized complexity. After a series of papers over the last decade, we now have (almost) tight bounds for the running time of several standard variants of these problems parameterized by two parameters: the number $k$ of terminals and the size $p$ of the solution. When there is evidence of FPT intractability, then the next natural alternative is to consider FPT approximations. In this paper, we show two types of results for several directed cut and connectivity problems, building on existing results from the literature: first is to circumvent the hardness results for these problems by designing FPT approximation algorithms, or alternatively strengthen the existing hardness results by creating "gap-instances" under stronger hypotheses such as the (Gap-)Exponential Time Hypothesis (ETH).
The null-timelike boundary problems of linear wave equations in asymptotically anti-de Sitter spacetime

In this paper, we study the initial-boundary problem of the massive wave equation which is conformal regular on an asymptotically anti-de Sitter spacetime, where the initial-boundary data are given on an outgoing null hypersurface and a timelike hypersurface, and the asymptotic information is given on the future null infinity. After a conformal rescaling, this problem will reduce to a null-timelike boundary problem of linear wave equation on the conformal compactificated spacetime, where the initial data is given on a null hypersurface and the boundary data are given on two timelike hypersurfaces. We demonstrate well posedness for the associated null-timelike boundary problems. The proofs rely on energy estimates (weighted energy estimates for null-timelike boundary problems) and local existence of solution for initial-boundary problems. This is a toy model motivated by AdS/CFT correspondence.
Token Economics in Real-Life: Cryptocurrency and Incentives Design for Insolar’s Blockchain Network

The study of how to set up cryptocurrency incentive mechanisms and to operationalize governance is token economics. Given the $250 billion market cap for cryptocurrencies, there is compelling need to investigate this topic. In this paper, we present facets of the token engineering process for a real-life 80-person Swiss blockchain startup, Insolar. We show how Insolar used systems modeling and simulation combined with cryptocurrency expertise to design a mechanism to incentivize enterprises and individual users to use their new MainNet public blockchain network. The study showed subsidy pools that incentivize application developers to develop on the network does indeed have the desired positive effect on MainNet adoption. For a startup like Insolar whose success hinge upon how well their model incentivizes various stakeholders to participate on their MainNet network versus that of numerous alternatives, this token economics simulation analysis provides invaluable insights.
Relationship Explainable Multi-objective Optimization Via Vector Value Function Based Reinforcement Learning

Solving multi-objective optimization problems is important in various applications where users are interested in obtaining optimal policies subject to multiple, yet often conflicting objectives. A typical approach to obtain optimal policies is to first construct a loss function that is based on the scalarization of individual objectives, and then find the optimal policy that minimizes the loss. However, optimizing the scalarized (and weighted) loss does not necessarily provide a guarantee of high performance on each possibly conflicting objective. In this paper, we propose a vector value based reinforcement learning approach that seeks to explicitly learn the inter-objective relationship and optimize multiple objectives based on the learned relationship. In particular, the proposed method is to first define relationship matrix, a mathematical representation of the inter-objective relationship, and then create one actor and multiple critics that can co-learn the relationship matrix and action selection. The proposed approach can quantify the inter-objective relationship via reinforcement learning when the impact of one objective on another is unknown a prior. We also provide rigorous convergence analysis of the proposed approach and present a quantitative evaluation of the approach based on two testing scenarios.
Zooming method (× 2.0) of digital mammography vs digital magnification view (× 1.8) in full-field digital mammography for the diagnosis of microcalcifications

The purpose of this study was to determine whether the interpretation of microcalcifications assessed on images zoomed (× 2.0) from digital mammograms is at least equivalent to that from digital magnification mammography (× 1.8) with respect to diagnostic accuracy and image quality. Three radiologists with different levels of experience in mammography reviewed each full-field digital mammography reader set for 185 patients with pathologically proven microcalcification clusters, which consisted of digital magnification mammograms (MAGs) with a magnification factor of 1.8 and images zoomed from mammograms (ZOOM) with a zoom factor of 2.0. Each radiologist rated their suspicion of breast cancer in microcalcific lesions using a six-point scale and the image quality and their confidence in the decisions using a five-point scale. Results were analysed according to display methods using areas under the receiver operating characteristic curves (Az value) for ZOOM and MAGs to interpret microcalcifications, and the Wilcoxon matched pairs signed rank test for image quality and confidence levels. There was no statistically significant difference in the level of suspicion of breast cancer between the ZOOM and MAG groups (Az = 0.8680 for ZOOM; Az = 0.8682 for MAG; p = 0.9897). However, MAG images were significantly better than ZOOM images in terms of visual imaging quality (p < 0.001), and the confidence level with MAG was better than with ZOOM (p < 0.001). In conclusion, the performance of radiologists in the diagnosis of microcalcifications using ZOOM was similar to that using MAGs, although image quality and confidence levels were better using MAGs.
Feature map size selection for fMRI classification on end-to-end deep convolutional neural networks

The emergence of convolutional neural networks (CNN) in various fields has also paved numerous ways for advancement in the field of medical imaging. This paper focuses on functional magnetic resonance imaging (fMRI) in the field of neuroimaging. It has high temporal resolution and robust to control or non-control subjects. CNN analysis on structural magnetic resonance imaging (MRI) and fMRI datasets is compared to rule out one of the grey areas in building CNNs for medical imaging analysis. This study focuses on the feature map size selection on fMRI datasets with CNNs where the selected sizes are evaluated for their performances. Although few outstanding studies on fMRI have been published, the availability of diverse previous studies on MRI previous works impulses us to study to learn the pattern of feature map sizes for CNN configuration. Six configurations are analyzed with prominent public fMRI dataset, names as Human Connectome Project (HCP). This dataset is widely used for any type of fMRI classification. With three set of data divisions, the accuracy values for validation set of fMRI classification are assessed and discussed. Despite the fact that only one slice of every 118 subjects' temporal brain images is used in the study, the validation of classification for three training-excluded subjects known as validation set, has proven the need for feature map size selection. This paper emphasizes the indispensable step of selecting the feature map sizes when designing CNN for fMRI classification. In addition, we provide proofs that validation set should consist of distinct subjects for definite evaluation of any model performance.
Animat Navigation Using Landmarks Navigation of Simulated Animals Inspired by Bees

Machine Learning techniques have made large advancements in previously challenging problems. A common problem with these methods is that they are very specialised on their specific field problem and it requires a lot of work to apply the methods on a new problem. A solution to this general issue is Artificial General Intelligence( AGI). This is an AI that is able to identify any problem and find a solution. Animals often needs to navigate complex environments to survive. This master thesis tries to implement a homing navigation model, used by bees to find food by remembering places relative positions to landmarks, in the general animat model. The model consists of using sensors that detect landmarks relative position to the animat. The model shows a small improvement when compared against Q-learning. The result suggests that the animat model can produce more sophisticated methods of navigation but further research needs to be conducted to explore its limits.