Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of word prediction & symbol-supported writing software on written output of students with Down syndrome

This study examined the effectiveness of two types of assistive technology for writing instruction of students with Down syndrome in British Columbia. Students received either Clicker 5, a symbol-supported writing software program; or Co:Writer, a word prediction software program designed to support written output. Data collection was conducted between January-June 2007 (Year 1) and October-May 2008 (Year 2). Clicker 5 was provided to 43 students in Year 1 (17 of whom also participated in the study during Year 2) and was designed to support early and emergent literacy development. Co:Writer was provided to 18 students in Year 1 (2 of whom also participated in the study during Year 2) and was designed to support text writing. Each month during both school years, teachers were asked to complete an on-line survey with questions related to their impressions of the impact of the technology and other variables. Students in the Clicker group produced 10-minute monthly writing samples about a selected topic using a Clicker grid designed by the research team. Students in the Co:Writer group produced one handwritten and one Co:Writer-supported 10minute writing sample every month about the specified topic. Data were analyzed with regard to writing rate, spelling accuracy (Co:Writer group only), and quality (measured both analytically and holistically). Results for dependent measures of writing for the Clicker group were variable but provided some support for the use of symbol-supported writing software for producing meaningful written output. The Co:Writer group was more accurate with regard to spelling and grammar while using Co:Writer compared to handwriting. The results are discussed in terms of the practical implications, limitations, and areas for future research.
Improving Nursing Documentation via Reducing the Written Work Load

In this world, the healthcare has become increasingly demanding. The complex environment of the healthcare system and high customer demands creates challenging task for the healthcare professionals. In this situation hospitals endeavor to contemplate on patient care and increased patient altitude of satisfaction. One of the major vicinities of patient care is “nursing care” where nurses’ available 24 hours to full fill patient requirements. With this belief, nursing profession has gain more responsibility as they are the frontline of healthcare delivery. They are linked with direct delivering of patient centered care and high level of outcomes and patient satisfaction. However, the turn over rate has increased for varous factors which lead to nursing turnover rate and effect patient care.
Social-Aware Optimal Electric Vehicle Charger Deployment on Road Network

With the increasing awareness towards protecting environment, people are paying more attention to the electric vehicles (EVs). Accompanying the rapid growing number of EVs, challenges raise at the same time about how to place EV chargers (EVC), within a city, to satisfy multiple types of charging demand. To provide a better EVC station deployment plan to benefit the whole society, we propose a problem called Social-Aware Optimal Electric Vehicle Charger Deployment (SOCD) on road network. The SOCD problem is hard and different from existing work in three aspects, 1) we assume that the charging demand should be satisfied not only in urban areas but also in relatively rural areas; 2) our work is the first one that considers an EVC station should have multiple types of charging plugs, which is more reasonable in real world; 3) different from the regional deployment solutions in previous literature, our SOCD directly works on a real road network and EVC stations are placed at appropriate POIs laying on the road network. We show that the SOCD problem is NP-hard. To deal with the hardness, we design two heuristic algorithms whose efficiency and effectiveness can be experimentally demonstrated. Furthermore, we investigate the incremental case, that is, given an existing EVC station deployment plan and extra more budget, we need to decide where and how many to place more chargers. Finally, we conduct extensive experiments on real road network of Shanghai to demonstrate both effectiveness and efficiency of our algorithms.
Crowdsourcing and Aggregating Nested Markable Annotations

One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation. Markable identification is typically carried out semi-automatically, by running a markable identifier and correcting its output by hand–which is increasingly done via annotators recruited through crowdsourcing and aggregating their responses. In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector. One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task and language-independent and are thus applicable to a variety of other annotation scenarios.
Feature extraction via deep learning for emotion estimation

Optimizing the extraction of feature sets for classification tasks is still a fundamental and challenging problem in the area of machine learning. For Mammals to perform classification tasks we use hierarchical features and efficient coding in our visual pathway. Information in visual system is encoded using distributed coding schemes and later the sparse coding is utilized. We propose multiple architectures to extract features for emotional analysis that encode the information according to a specific activation profile. We show how our models much like the visual system, can learn distributed coding in lower layers and sparse coding in higher layers for emotional analysis. Feature extraction for emotion related tasks must ensure high accuracy in locality and activation. Our models can dynamically extract features and perform classification and ranking for many commonly used datasets. As for emotional classification through facial expressions, we introduce regularization through noisy training that helped internal representation of learned features to emerge and increased sparsity of hidden units. This regularization also improved the network generalization through automatic structuration. For our ranking models that estimate emotion intensity, they extracted the sequential relationship in the temporal domain that appears due to the natural change of facial expression during an emotional transition. The output ranking score can be directly used for intensity estimation for all emotional states. This allows an early detection of an emotional transition and estimation of intensity of an emotion considering temporal information. Beside facial expressions for emotion analysis, we proposed an eye tracking that can be used to understand person emotions through pupil movement. Through Gabor filter, we detected the intensity change of the gaze edge by applying Gabor filter and its second moment matrix. Eigenvalues inequality of this matrix with a given local intensity-based threshold defines the prevailing directions of the neighboring gradients and the amount by which these directions are coherent at a certain point. This allows accurate estimation of the gaze direction. We find that all CNN features can be used for knowledge representation purposes both by their activation and locality, doubling the information a single CNN feature may provide. We also study the levels of importance for these features, and propose approaches and structures to discard most of it. All these insights have a direct application to the generation of CNN embedding spaces.
CogNet: A Large-Scale Cognate Database

This paper introduces CogNet, a new, large-scale lexical database that provides cognates -words of common origin and meaning- across languages. The database currently contains 3.1 million cognate pairs across 338 languages using 35 writing systems. The paper also describes the automated method by which cognates were computed from publicly available wordnets, with an accuracy evaluated to 94%. Finally, it presents statistics about the cognate data and some initial insights into it, hinting at a possible future exploitation of the resource by various fields of lingustics.
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media

Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. In this paper, we highlight the importance of contextualizing social information, capturing how this information is disseminated in social networks. We use Graph Convolutional Networks, a recently proposed neural architecture for representing relational information, to capture the documents’ social context. We show that social information can be used effectively as a source of distant supervision, and when direct supervision is available, even little social information can significantly improve performance.
Constructing a Reference Genome in a Single Lab: The Possibility to Use Oxford Nanopore Technology

The whole genome sequencing (WGS) has become a crucial tool in understanding genome structure and genetic variation. The MinION sequencing of Oxford Nanopore Technologies (ONT) is an excellent approach for performing WGS and it has advantages in comparison with other Next-Generation Sequencing (NGS): It is relatively inexpensive, portable, has simple library preparation, can be monitored in real-time, and has no theoretical limits on reading length. Sorghum bicolor (L.) Moench is diploid (2n = 2x = 20) with a genome size of about 730 Mb, and its genome sequence information is released in the Phytozome database. Therefore, sorghum can be used as a good reference. However, plant species have complex and large genomes when compared to animals or microorganisms. As a result, complete genome sequencing is difficult for plant species. MinION sequencing that produces long-reads can be an excellent tool for overcoming the weak assembly of short-reads generated from NGS by minimizing the generation of gaps or covering the repetitive sequence that appears on the plant genome. Here, we conducted the genome sequencing for S. bicolor cv. BTx623 while using the MinION platform and obtained 895,678 reads and 17.9 gigabytes (Gb) (ca. 25× coverage of reference) from long-read sequence data. A total of 6124 contigs (covering 45.9%) were generated from Canu, and a total of 2661 contigs (covering 50%) were generated from Minimap and Miniasm with a Racon through a de novo assembly using two different tools and mapped assembled contigs against the sorghum reference genome. Our results provide an optimal series of long-read sequencing analysis for plant species while using the MinION platform and a clue to determine the total sequencing scale for optimal coverage that is based on various genome sizes.
Voltage/Current Large Transient Suppression in DC Microgrids Using Local Information and Active Stabilizing Capability

This paper presents a nonlinear control strategy for the suppression of large voltage–current transients in a dc microgrid (dc-MG) with a high penetration of constant power loads (CPLs). The voltages and currents in a dc-MG experience significant transients during large changes in the CPLs with their nonlinear voltage–current characteristics. Herein, an auxiliary nonlinear stabilizer is designed for a super-capacitor (SC) module for actively stabilizing a dc-MG. Due to the limitations in the energy of the SC module; a fuel cell (FC) stack is paralleled with the SC module in the form of a hybrid SC/FC unit to provide the steady-state power demand of dc-MG. The proposed controller is designed using Lyapunov theory and is robust with respect to the output current transients imposed on power converters from the rest of the dc-MG. In the presented control strategy, only one-generation unit is designed and implemented in the form of a hybrid SC/FC unit to suppress the large transients. The proposed stabilizer in the SC module operates based on local information. The effectiveness of the proposed control strategy has been validated in a multisource dc-MG using time domain simulations carried out in MATLAB/Simulink software environment.
Effective Adversarial Regularization for Neural Machine Translation

A regularization technique based on adversarial perturbation, which was initially developed in the field of image processing, has been successfully applied to text classification tasks and has yielded attractive improvements. We aim to further leverage this promising methodology into more sophisticated and critical neural models in the natural language processing field, i.e., neural machine translation (NMT) models. However, it is not trivial to apply this methodology to such models. Thus, this paper investigates the effectiveness of several possible configurations of applying the adversarial perturbation and reveals that the adversarial regularization technique can significantly and consistently improve the performance of widely used NMT models, such as LSTM-based and Transformer-based models.
Towards practical neural networkmeta-modeling

This thesis primarily focuses on the topic of efficient automated procedures for convolutional neural network (CNN) architecture search. We first introduce a novel approach for CNN architecture architecture using Q-learning, a popular value iteration algorithm from the reinforcement learning community for sequential decision problems. On the task of object classification, the Q-learning agent outperforms all human crafted models that are similar to those in the search space. By analysing the underlying weights of the agent, we are also able to uncover some of the design principles that the agent learned during the search process. Reinforcement learning is generally very sample inefficient; in the case of architecture search most approaches require thousands of unique models to be trained. In the second part of this thesis we introduce simple sequential regression models (SRM) to predict final performance of a candidate CNN from partially observed learning curves. We use these performance predictors and empirical variance estimates for practical early stopping of online optimization procedures. Our SRMs are state-of-the-art in performance prediction and early stopping.
Resource Allocation, User Association, and User Scheduling for OFDMA-based Cellular Networks

Current advances in wireless communication are driven by an increased demand for more data and bandwidth, mainly due to the development of new mobile platforms and applications. Ever since then the network operators are overwhelmed by the rapid increase in mobile user subscriptions and the amount of average data volume per subscription, which is primarily fueled by more viewing of data-intensive content. Furthermore, according to the statistics, the ratio of downlink and uplink data traffic demands have changed drastically as they are observed to be significantly asymmetric even over small time periods. In recent years, different solutions, based on topological and architectural innovations of the conventional cellular networks, have been proposed to address the issues related to the increasing data requirements and uplink/downlink traffic asymmetries. The most trivial solution is to scale the network capacity through network densification, i.e., by bringing the network nodes closer to each other through efficient spectrum sharing techniques. The resulting dense networks, also known as heterogeneous networks, can address the growing need for capacity, coverage, and uplink/downlink traffic flexibility in wireless networks by deploying numerous low power base stations overlaying the existing macro cellular coverage. However, there is a need to analyze the interplay of different network processes within these heterogeneous networks, which has not been studied in detail due to its complexity. In the first part of this thesis, we analyze the performance of the most fundamental network process, i.e., the user scheduling process and understand its interplay with other network processes, which is more challenging in a heterogeneous network setting. Since, we need to propose a global optimization framework that allows us to obtain the throughput performance of a heterogeneous network when the network processes are optimized jointly. This is viable under a fixed network setting, where the parameters like the channel gains and the number of users are fixed and assumed to be known. Using this framework, we have been able to study different choices of resource allocation, which has allowed us to provide a number of important engineering insights on the throughput performance of different traffic scenarios and the resource allocation schemes. The heterogeneous networks are often characterized by complex user dynamics and interference patterns, which are known to present difficulties in their design and performance
Learning to be Competent

The thesis presents a new approach for the study of competent cognitive behavior. The approach, learning to be competent, suggests that learning phenomena and the competencies attributed to intelligence should be studied together. Instead of requiring omniscience or otherwise optimal performance, we claim that the tasks and success criteria should be de ned behaviorally; that is, a system is competent if it functions well in its environment. We further suggest that competent behavior should only be expected in light of a learning experience in the same or similar environment, and that the solutions exhibited should be computationally e cient. These ideas are presented in a formal setting, so that the various tasks and their proposed solutions can be studied and analyzed. Thus, one contribution of this approach is in formalizing the problem in a form that is amenable to analysis, while at the same time being cognitively and computationally plausible. The learning to reason framework is used to study the problem of logical reasoning in propositional domains. We consider a variety of possible interfaces for learning, and describe learning algorithms that interact with them, thus demonstrating the robustness of this approach. The results show that learning to reason is possible even in cases where the traditionally separate problems, namely concept learning and reasoning by proving assertions, do not have e cient solutions. In the course of studying reasoning tasks, we develop a model based representation, the set of characteristic models, which supports e cient solutions for several forms of logical reasoning. This representation is utilized in the learning to reason framework, and helps in deriving the above mentioned results. The representation is also shown to have other applications, in the theory of relational databases, and in computational tasks that arise in the design of such databases. The task of acting in a dynamic world in order to achieve some goals is studied in the learning to act framework. We present results on supervised learning of action strategies in the form of production rule systems. The framework and the results combine features from the area of symbolic computation and that of reactive agents, which have been previously seen as opposed if not contradictory, and thus advance our understanding of the problems. iii
Multi-Task Learning for Semantic Relatedness and Textual Entailment

Recently, several deep learning models have been successfully proposed and have been applied to solve different Natural Language Processing (NLP) tasks. However, these models solve the problem based on single-task supervised learning and do not consider the correlation between the tasks. Based on this observation, in this paper, we implemented a multi-task learning model to joint learn two related NLP tasks simultaneously and conducted experiments to evaluate if learning these tasks jointly can improve the system performance compared with learning them individually. In addition, a comparison of our model with the state-of-the-art learning models, including multi-task learning, transfer learning, unsupervised learning and feature based traditional machine learning models is presented. This paper aims to 1) show the advantage of multi-task learning over single-task learning in training related NLP tasks, 2) illustrate the influence of various encoding structures to the proposed single- and multi-task learning models, and 3) compare the performance between multi-task learning and other learning models in literature on textual entailment task and semantic relatedness task.
An Experimental Study on the Effects of Communication, Credibility, and Clustering in Network Games

Abstract We examine how preplay communication and clustering affect play in a challenging hybrid experimental game on networks. Free-form chat is impressively effective in achieving the nonequilibrium efficient outcome, but restricted communication has little effect. We support this result with a model about the credibility of cheap-talk messages. We also offer a model of message diffusion that correctly predicts more rapid diffusion without clustering. We show an interaction effect of network structure and communication technologies. A remarkable result is that restricted communication is quite effective in a network stag hunt but not in our extended game.
Deep Iris: Deep Learning for Gender Classification Through Iris Patterns

Introduction: One attractive research area in the computer science field is soft biometrics. Aim: To Identify a person’s gender from an iris image when such identification is related to security surveillance systems and forensics applications. Methods: In this paper, a robust iris gender-identification method based on a deep convolutional neural network is introduced. The proposed architecture segments the iris from a background image using the graph-cut segmentation technique. The proposed model contains 16 subsequent layers; three are convolutional layers for feature extraction with different convolution window sizes, followed by three fully connected layers for classification. Results: The original dataset consists of 3,000 images, 1,500 images for men and 1,500 images for women. The augmentation techniques adopted in this research overcome the overfitting problem and make the proposed architecture more robust and immune from simply memorizing the training data. In addition, the augmentation process not only increased the number of dataset images to 9,000 images for the training phase, 3,000 images for the testing phase and 3,000 images for the verification phase but also led to a significant improvement in testing accuracy, where the proposed architecture achieved 98.88%. A comparison is presented in which the testing accuracy of the proposed approach was compared with the testing accuracy of other related works using the same dataset. Conclusion: The proposed architecture outperformed the other related works in terms of testing accuracy.