Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Cost Sparse Subspace Tracking Algorithm based on Shear and Givens Rotations

In this paper, we consider the problem of tracking the signal subspace under a sparsity constraint over the weight basis matrix. In the same spirit of our previous work [1, 2], we propose a new low cost algorithm which has at every iteration two stages. First, it estimates adaptively an orthogonal basis for the signal subspace. Then, it uses the Shear and Givens rotations to minimize the sparsity criterion. The proposed algorithm has a approximately the same performance compared to our previous best proposition in [2]. However, the new algorithm has a low (linear) computational complexity. Moreover, simulation results show that it outperforms the state of art solutions.
IMAGE-BASED VEHICLE TRACKING FROM ROADSIDE LIDAR DATA

Abstract. Vehicle tracking is of great importance in urban traffic systems, and the adoption of lidar technologies – including on-board and roadside systems – has significant potential for such applications. This research therefore proposes and develops an image-based vehicle-tracking framework from roadside lidar data to track the precise location and speed of a vehicle. Prior to tracking, vehicles are detected in point clouds through a three-step procedure. Cluster tracking then provides initial tracking results. The second tracking stage aims to provide more precise results, in which two strategies are developed and tested: frame-by-frame and model-matching strategies. For each strategy, tracking is implemented through two threads by converting the 3D point cloud clusters into 2D images relating to the plan and side views along the tracked vehicle’s trajectory. During this process, image registration is exploited in order to retrieve the transformation parameters between every image pair. Based on these transformations, vehicle speeds are determined directly based on (a) the locations of the chosen tracking point in the first strategy; (b) a vehicle model is built and tracking point locations can be calculated after matching every frame with the model in the second strategy. In contrast with other existing methods, the proposed method provides improved vehicle tracking via points instead of clusters. Moreover, tracking in a decomposed manner provides an opportunity to cross-validate the results from different views. The effectiveness of this method has been evaluated using roadside lidar data obtained by a Robosense 32-line laser scanner.
Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model

We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8% F1 in the DRS parsing shared task.
Gender‐Based Differential Prediction by Curriculum Samples for College Admissions

A longstanding concern about admissions to higher education is the underprediction of female academic performance by admission test scores. One explanation for these findings is selection system bias, that is, not all relevant KSAOs that are related to academic performance and gender are included in the prediction model. One solution to this problem is to include these omitted KSAOs in the prediction model, many of these KSAOs are 'noncognitive' and “hard‐to‐measure” skills in a high‐stakes context. An alternative approach to capture relevant KSAOs is using representative performance samples. We examined differential prediction of first year‐ and third year academic performance by gender based on a curriculum‐sampling test that was designed as a small‐scale simulation of later college performance. In addition, we examined differential prediction using both frequentist and Bayesian analyses. Our results showed no differential prediction or small female underprediction when using the curriculum‐sampling tests to predict first year GPA, and no differential prediction for predicting third year GPA. In addition, our results suggest that more comprehensive curriculum samples may show less differential prediction. We conclude that curriculum sampling may offer a practically feasible method that yields minimal differential prediction by gender in high‐stakes operational selection settings.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATION

Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Monetary Policy and Defaults in the United States

This paper uses a structural VAR model to study the effect of monetary policy on the delinquency rate of business loans and consumer credit. The VAR is identified using, jointly, several external instruments that reflect different approaches from the literature. Delinquency rates, defined as the rate of loans with overdue repayments relative to total loans, are found to decrease in response to an exogenous monetary expansion. The results are consistent with a general equilibrium effect formalized in the paper using a standard model of optimal defaults. According to both the theoretical model and the reported empirical evidence, the decrease in defaults is driven by the fact that monetary expansions increase aggregate demand and push up profits and income, thereby improving the repayment possibility of borrowers.
Context-based publication search paradigm in literature digital libraries

This thesis identifies two problems with the task of searching literature digital libraries: (a) there are no effective paper-scoring and ranking mechanisms. Without a scoring and ranking system, users are often forced to scan a large and diverse set of publications listed as search results and potentially miss the important ones. (b) Topic diffusion is a common problem: publications returned by a keyword-based search query often fall into multiple topic areas, not all of which are of interest to users. As a response to the problems listed above, this thesis proposes a new literature digital library search paradigm, called context-based search, which effectively ranks search outputs and controls the topic diversity of keyword-based search query outputs. Our approach can be summarized as follows. During pre-querying, publications are classified to pre-specified ontology-based contexts, and query-independent context scores are attached to papers with respect to their assigned contexts. When a query is posed, relevant contexts are selected, search is performed within the selected contexts, context scores of publications are revised into relevancy scores with respect to the query at hand and the context that they are in, and query outputs are ranked within each relevant context. With the context-based search approach, (1) query output topic diversity is minimized, (2) query output size is reduced, (3) user time spent scanning query results is decreased, and (4) query output ranking accuracy is increased. In addition to keyword-based search, one important feature in searching literature digital libraries is to find “related publications” of a given publication. Existing approaches do not take into account publication topics in the relatedness computation, allowing topic diffusion to permeate across query output publications. In this thesis, we propose a new way to measure “relatedness” by incorporating “contexts” of publications. We define three ways of context-based relatedness, namely, (a) relatedness between two contexts (context-to-context relatedness) by using publications that are assigned to the contexts and the context structures in the context hierarchy, (b) relatedness between a context and a paper (paper-to-context relatedness), which is used to rank the relatedness of contexts with respect to a paper, and (c) relatedness between two papers (paper-to-paper relatedness) by using both paper-to-context and context-to-context relatedness measurements. Using existing biomedical ontology terms as contexts for genomics-oriented publications, our experiments indicate that the context-based approach is highly accurate and effectively solves the topic diffusion problem across search results.
SEMANTIC INDEXING FOR QUESTION ANSWERING SYSTEM

With the vast growth of various forms of digital data, automated indexing has become very important so that it enables the needs of the current users to be fulfilled. Keywords based indexing has failed to accommodate to the needs of the present demands. The representation of the document content as well as the indexing process is a crucial factor that ensures the success of retrieval process. Therefore, this research introduces a new approach in creating semantic indexing that uses Skolem representation which automatically indexes multiple documents into a single knowledge representation. This knowledge representation will then be used by the proposed question answering system in retrieving the answers as well as pointing to the documents the answer contains based on the user’s query. The system managed to achieve 93.84% of recall and 82.92% of precision.
Generating a non-sexist corpus through gamification for automatic sexism detection

Most social media platforms allow users to freely express their opinions, feelings, and beliefs. However, in recent years the growing propagation of hate speech, offensive language, racism and sexism on the social media outlets have drawn attention from individuals, companies, and researchers. Today, sexism both online and offline with different forms, including blatant, covert, and subtle lan- guage, is a common phenomenon in society. A notable amount of work has been done over identifying sexist content and computationally detecting sexism which exists online. Although previous efforts have mostly used peoples’ activities on social media platforms such as Twitter as a public and helpful source for collecting data, they neglect the fact that the method of gathering sexist tweets could be biased towards the initial search terms. Moreover, some forms of sexism could be missed since some tweets which contain offensive language could be misclassified as hate speech. Further, in existing hate speech corpora, sexist tweets mostly express hostile sexism, and to some degree, the other forms of sexism which also appear online was disregarded. Besides, the creation of labeled datasets with manual exertion, relying on users to report offensive comments with a tremendous effort by human annotators is not only a costly and time-consuming process, but it also raises the risk of involving discrimination under biased judgment. This thesis generates a novel sexist and non-sexist dataset which is constructed via "UnSexistifyIt", an online web-based game that incentivizes the players to make minimal modifications to a sexist statement with the goal of turning it into a non-sexist statement and convincing other players that the modified statement is non-sexist. The game applies the methodology of "Game With A Purpose" to generate data as a side-effect of playing the game and also employs the gamification and crowdsourcing techniques to enhance non-game contexts. When voluntary participants play the game, they help to produce non-sexist statements which can reduce the cost of generating new corpus. This work explores how diverse individual beliefs concerning sexism are. Further, the result of this work highlights the impact of various linguistic features and content attributes regarding sexist language detection. Finally, this thesis could help to expand our understanding regarding the syntactic and semantic structure of sexist and non-sexist content and also provides insights to build a probabilistic classifier for single sentences into sexist or non-sexist classes and lastly find a potential ground truth for such a classifier.
TCPOP : Valuation and Optimal Strategy for Option Contracts in the Shipping Industry

This thesis is an analysis of a special type of option contract often found in the shipping industry, a time charter with purchase option (TCPOP). The TCPOP is a time charter (TC) agreement with an embedded purchase option. The freight rate is modeled using the two mean reverting stochastic processes: Ornstein Uhlenbeck (OU) and Geometric Mean Reversion (GMR). Both models are estimated from historical spot freight rate data on Suezmax tankers, using OLS. Based on the stochastic processes I specify a one factor model for vessel values. The model is calibrated to historical prices for 5 year old vessels, by approximating the risk premium, using a numerical least squares method. GMR seems to perform better than OU in predicting the distribution of future freight rates and vessel values. Using Monte Carlo simulation and applying the least square Monte Carlo approach (LSM) proposed by Longstaff & Schwartz (2001), I specify procedures for approximating values of option contracts with different complexity, where a Suezmax vessel is the underlying asset. GMR consistently predicts higher vessel values and option values than OU. Acknowledgments I would like to thank my supervisor, Professor Steinar Ekern, for providing invaluable advice during the process. Ekern, together with Professor Petter Bjerksund, should also be acknowledged for their early work on contingent claims in shipping, which has been an important building block in this thesis. I would also like to thank Tom Johan Austrheim, CFO at Kristian Gerhard Jebsen Skipsrederi, for introducing me to the Bermudan TCPOP. In addition, I would like to thank Roar Adland for providing me with data.
Super Resolution Image Reconstruction Using Iterative Regularization Method and Feed –Forward Neural Networks

Super resolution is one of the best existing procedures to acquire high resolution image due to its effortlessness and extensive variety of use in numerous fields of science and engineering. There are various methods exist for super resolution but, this work made an effort by combining the iterative regularization method with neural network and obtained a good result comparatively from the previous method because of its high compatible nature with less and user friendly approach. It takes care of the noise in the initial stage and gets a concrete result when neural network is introduced. In addition to the noise it controls the vulnerable parameters and gets a highly super resolution image as compare to the literature.
A Review of Question Answering Systems

Question Answering (QA) targets answering questions deﬁned in natural language. Question Answering Systems offer an automated approach to procuring solutions to queries expressed in natural language. A lot of QA surveys have classiﬁed QuestionAnswering systems based on different criteria such as queries inquired by users, features of data bases used, nature of generated answers, question answering approaches and techniques. To fully understand QA systems, how it has grown into its current QA needs, and the need to scale up to meet future expectations, a broader survey of QA systems becomes essential. Hence, in this paper, we take a short study of the generic QA framework vis a vis Question Analysis, Passage Retrieval and Answer Extraction and some important issues associated with QA systems. These issues include Question Processing, Question Classes, Data Sources for QA, Context and QA, Answer Extraction, Real time Question Answering, Answer Formulation, Multilingual (or cross-lingual) question answering, Advanced reasoning for QA, Interactive QA, User proﬁling for QA and Information clustering for QA. Finally, we classify QA systems based on some identiﬁed criteria in literature. These include Application domain, Question type, Data source, Form of answer generated, Language paradigm and Approaches. We subsequently made an informed judgment of the basis for each classiﬁcation criterion through literature on QA systems.
“You Just Sit There and Be Quiet”: Latino/A Bilingual Educators in Texas

ABSTRACT A shortage of Spanish/English bilingual educators exists in Texas with large numbers of emergent bilingual students concentrated in high poverty areas. The goal of this study was to examine how six Latino/a bilingual teachers narrated their professional lives while negotiating the power relations within the school system. Data was collected via semi-structured interviews. Findings indicate that these teachers were plagued by limited resources, marginalization, and educational practices that set them up for failure. Suggestions for addressing gaps in the training of administrators and mainstream teachers are provided.
Sharing Secrets with Robots

This paper presents initial findings from a large-scale study that evaluated levels of student disclosure on sensitive topics. Four different conditions of survey delivery were applied and follow up interviews were undertaken. Non-parametric tests were used due to the data not satisfying the assumptions of parametric statistical tests; Wilcoxon Signed Ranks tests were conducted to examine the differences further. Preliminary data suggest that students disclosed additional information to the chatbot on more sensitive topics when the length of engagement was increased, but that this effect could be negated by the inclusion of the depth of engagement questions. Such findings suggest that the sensitivity of the student-chatbot conversation is critical in determining the influence of the chatbot, and that particular care should be taken when designing contextually-relevant ‘icebreaker’ questions.
A cluster-based topology control algorithm for wireless sensor networks

Wireless sensor networks and limitation of their energy resources are big challenges for the researchers at present. Topology control is one of the mechanisms for improving energy consume of these networks. The protocol which is presented is a Neighbor-based Clustering Topology Control Algorithm which is called NCTC. NCTC uses centralized and distributed algorithms. For this reason, it is regarded as a hybrid approach. In addition, it doesn’t need local information for topology construction. NCTC consumes low energy and also it has low complexity message. Simulation results show that in NCTC algorithm network lifetime is prolonged significantly.
Enhancing the Reliability of Functional MRI and Magnetoencephalography for Presurgical Mapping

ix List of Abbreviations and Symbols Used . x Acknowledgements . xii Chapter
AppBeach: A Static Behavior Checker for iOS Mobile Applications

AppBeach standing on App Behavior Checker is a new service to reconstruct and report behaviors of iOS mobile applications, using static binary analysis to reveal embedded functions within the executables. AppBeach adopts a distributed algorithm on call sequence counting via the hadoop framework, achieving a scalable static syntax analysis on executables of modern apps. The main idea is syntactically counting call sequences that are embedded in iOS executable. This is done by distributing routines to mappers with the assembly tool that resolves explicit and implicit system method calls that are embedded in the iOS executables. The reducer then collects the counting from mappers to characterize the behaviors of apps. We learn patterns of malicious behaviors from the difference of pairs of normal and malicious apps, and report the probability of potential behaviors of commercial apps by matching these patterns to their call sequence counts.
Testing the pecking order theory of Capital structure: Kazakhstan Experience

The aim of this work is to test whether publicly available companies from Kazakhstan follow the pecking order theory of capital structure in their financial decisions. We defined the importance of the financial leverage ratio as a key coefficient demonstrating financial stability of the company. The financial reports of the biggest companies were used to identify the independent variables which become the determinants of capital structure choice. Results of econometric analysis partially confirmed the predicted hypotheses of the relationship between leverage ratio and determinants. Overall, the outcomes of this work assist financial managers in terms of financial risk management and strategical decision making in defining the capital structure of the firm.
Genetic Algorithm based CFS and Naive Bayes Algorithm to Enhance the Predictive Accuracy

For better classification accuracy in the area of data mining, feature selection techniques are applied to medical data sets. In this workgenetic algorithm and Particle Swarm Optimization search techniques and correlation based feature selection is used for evaluation and naive bayes classifier for classification purpose. The hepatitis data set, taken from the UCI machine learning repository, is applied in this work. Accuracy and time is the outcome of the classification model and also various measures like sensitivity, specificity, precision and recall are also calculated.
Does peer assessment promote student learning? A meta-analysis

Abstract In recent years, there has been an increasing use of peer assessment in classrooms and other learning settings. Despite the prevailing view that peer assessment has a positive effect on learning across empirical studies, the results reported are mixed. In this meta-analysis, we synthesised findings based on 134 effect sizes from 58 studies. Compared to students who do not participate in peer assessment, those who participate in peer assessment show a . 291 standard deviation unit increase in their performance. Further, we performed a meta-regression analysis to examine the factors that are likely to influence the peer assessment effect. The most critical factor is rater training. When students receive rater training, the effect size of peer assessment is substantially larger than when students do not receive such training. Computer-mediated peer assessment is also associated with greater learning gains than the paper-based peer assessment. A few other variables (such as rating format, rating criteria and frequency of peer assessment) also show noticeable, although not statistically significant, effects. The results of the meta-analysis can be considered by researchers and teachers as a basis for determining how to make effective use of peer assessment as a learning tool.