Miracle-dz/UniFAR-FTUs huggingface dataset:
Native language interference in learning a second language: Exploratory case studies of native language interference with target language usage

Regardless of the learning environment, the learner’s goal is mastery of the target language. The learner begins the task of learning a second language from point zero (or close to it) and, through the steady accumulation of the mastered entities of the target language, eventually amasses them in quantities sufficient to constitute a particular level of proficiency (Dulay, Burt & Krashen, 1982 and Ellis, 1984).
Classification of Animals with Different Deep Learning Models

The purpose of this study is that using different deep learning models for classification of 14 different animals. Deep Learning, an area of artificial intelligence, has been used in a wide range of recent years. Especially, it using in advanced level of image processing, voice recognition and natural language processing fields. One of the most important reasons for using a large field in image analysis is that it performs the feature extraction itself on the image and gives high accuracy results. It performs learning by creating at different levels representations for each image. Unlike other machine learning methods, there is no need of an expert for feature extraction on the images. Convolution Neural Network (CNN), which is the basic architecture of deep learning models, consists of different layers. These are Convolution Layer, ReLu Layer, Pooling Layer and Full Connected Layer. Deep learning models are designed using different numbers of these layers. AlexNet and VggNet models are used for classified of 14 different animals. These animals are Horse, Camel, Cow, Goat, Sheep, Wolf, Dog, Cat, Deer, Pig, Bear, Leopard, Elephant and Kangaroo respectively. Animals that are most likely to encounter when during driving road were selected. Because thinking this work to be a preliminary work for the control of autonomous vehicle driving. The images of animals are collected in color (RGB) on the internet. In order to increase the data diversity, images were also taken from the ready data sets. A total of 150 images were collected with 125 training and 25 test data for each animal. Two different data sets have been created, with each image having dimensions of 224x224 and 227x227. As a result of the study, the classification of the animals was realized with %91.2 accuracy with VggNet and %67.65 with AlexNet. The high error rate in AlexNet is due to the small number of layers in the network and the high selection of parameter values. For example, the filter size in the convolution layer in AlexNet architecture is 11x11 and the number of stride is 4. This situation causes data loss in transferring the information to the next layer. In contrast, VggNet has a filter size of 3x3 and a number of steps of 1, there is no data loss in the transfer to the next layer.
Optimal Cost Prediction in the Vehicle Routing Problem Through Supervised Learning

Machine learning techniques aim to train a model in such a way that it can approximate complex dynamics like the vehicle routing problem. In the recent years, combinatorial neural networks and deep learning methods have been used to predict the solution of routing problems. However, the approaches investigated so far in literature could be cumbersome to apply and replicate. Although such methods obtained good results in predicting the solutions of simple routing problems, their structures are complex and they do not consider any sort of precedence constraints, an aspect that is crucial in passenger transportation. The goal of this thesis is to apply supervised learning to predict the optimal cost of single-vehicle pick-up and delivery problem, leading to a simpler implementation compared with combinatorial neural networks. First, the most suitable machine learning model able to approximate problem is chosen as a neural network with one hidden layer and a ReLu activation function. Then, the input features that improve the prediction accuracy are searched. In particular, very good results are observed by feeding the solutions of heuristic algorithm as input to the neural network. Compared to baseline prediction method which returns the length of the route computed by greedy heuristic, an improvement of 40\% in prediction accuracy is obtained with the proposed approach. Finally, the model is improved to achieve better generalization properties with respect to a higher number of requests, by using the average optimal length as an additional input feature.
Spatial channel model and measurements for IMT-2000 systems

This paper presents a spatial channel model for IMT-2000 systems as well as measurement results for 1.2 MHz wide wireless channels using an eight element linear array at two different frequencies. The spatial channel model is a spatial extension of the commonly known IMT-2000 temporal (i.e., delay profile) model. The measurement results are used to characterize the spatial aspects of the wireless channel and are used to verify the model.
Two Teachers Implement One-to-One Computing: A Case Study

Research question: Given ubiquitous computing, how do teachers use computers in constructing curriculum and delivering instruction? In 2000, Maine's Governor announced a bold plan to make students in the state technologically literate. A task force was convened to study the concept, flesh out the vision, and address the barriers to the aims of the Maine Learning Technology Initiative (MLTI). Their vision was even bolder: Our schools are challenged to prepare young people to navigate and prosper in this world, with technology as an ally rather than an obstacle. The challenge is familiar, but the imperative is new: we must prepare young people to thrive in a world that doesn't exist yet, to grapple with problems and construct new knowledge which is barely visible to us today. It is no longer adequate to prepare some of our young people to high levels of learning and technological literacy; we must prepare all for the demands of a world in which workers and citizens will be required to use and create knowledge, and embrace technology as a powerful tool to do so. Research Institute was poised to evaluate the project on a statewide basis. The Institute offered a few grants for researchers who wished to study a slice of this innovative initiative. Since teachers can act as gatekeepers for student achievement, two faculty members at the University of Maine carried out a year-long case study, focusing on two teachers using the MLTI laptops. This research brief describes the study, which took place during 2002-2003, and is in the process of publication by the authors. Hillside Middle School is located in a rural area, not too far from the state university. (Except for the name of the state, pseudonyms are used.) When the study took place, Hillside housed approximately 380 students in grades six through eight. Across all subjects, for three years, the average school scores for the state standardized tests were remarkably close to the state averages. 1 The researchers used an iterative data analysis technique, which involved the transcription of audiotapes, and the coding of artifacts, observations, and interviews. They used inductive analysis in summarizing the data. Although the paired participants were "samples of convenience" they provided an intriguing look into how teachers worked with one-to-one computing. Rick, a science teacher from Hillside Middle School had just finished a master's program in instructional technology. He graciously offered to allow the researchers in …
Navigate as a Bat. Real-Time Echolocation System in Virtual Reality

Several studies provide evidence that blind people orient themselves using echolocation, transmitting signals with mouth clicks. Our previous study within embodiment in Virtual Reality (VR) showed the possibility to enhance a Virtual Body Ownership (VBO) illusion over a body morphologically different from human in the presence of agency. In this paper, we explore real-time audio navigation with echolocation in Virtual Environment (VE) in order to create a feeling of being a virtual bat. This includes imita-tion of the sonar system, which might help to achieve a stronger VBO illusion in the future, as well as build an echolocation training simulator. Two pilot tests were conducted using a within-subject study design, exploring time and traveled distance during spatial orientation in VE. Both studies, involved four conditions – early reﬂections, reverb, early reﬂections-reverb (with deprived visual cues) and ﬁ-nally vision. This resulted in preferred reﬂection pulses for the test subjects with musical background, while only reverberation features were favored by non-musicians, when being exposed to VE walking-based task.
A precision reference voltage source

With increasing temperature the base-emitter voltage of a transistor with a constant current decreases, while the difference in base-emitter voltages of two identical (integrated) transistors having a constant current ratio increases. From the sum of the two voltages a nearly temperature- independent output voltage is obtained if this sum equals the gap voltage of silicon. A reference voltage source of 10 V based on the principle is described. The reference part of the circuit is an integrated circuit, and thin-film resistors with a small relative temperature coefficient are used. An operational amplifier and a few resistors and capacitors complete the circuit. The source has a parabolic temperature characteristic and the temperature peak can be controlled by resistor adjustment. A change of /spl plusmn/10 K in respect of the peak temperature causes an output voltage change of -250 /spl mu/V, while a change of /spl plusmn/30 K causes a change of -2.2 mV. A long-term stability of 10 ppm/month was measured. The circuit can compete with the best available Zener diode sources, and has the added advantage that practically no selection is necessary.
Meervisage - A Community Based Annotation Tool

The social web has become one of the fastest growing areas of the internet. The increase in use of sites such as Facebook, MySpace, Flickr and other social network based Web sites has been phenomenal. The semantic web is the Web that contains information with underlying semantics, capable of being read automatically by software agents enabling information integration. This paper attempts to tackle the issues associated with the Semantic Web, and how it can be benefited from the incorporation and integration with the Social Web, adding a social aspect to semantics. By investigating the collaborative generation of metadata for the semantic web using social networks as a user base a system named ‘Meervisage’ is presented, capable of socially annotating web resources with semantic metadata.
Incorporating learning into theories of parsing

In their target article, Phillips and Ehrenhofer (2015; P&E) present a sketch of how concepts and methods from adult language processing could be used to illuminate first and second language acquisition. This is by no means the first attempt to find common ground between fields that often work independently of each other (e.g., for L1: Fodor, 1998a, 1998b; Frazier & De Villiers, 1990; O’Grady, 2005; Trueswell & Gleitman, 2004, for L1 & L2: MacWhinney & Bates, 1989). Regardless, one broad aim of psycholinguistics should be to integrate sub-disciplines, and in this sense the article is a welcome addition to the literature. In this vein, P&E argue that adult psycholinguistics can provide important and novel insights into the acquisition of grammars. In this commentary I concentrate on the converse of this argument: how insights from language acquisition can provide equally important contributions to adult psycholinguistics. My broad thesis is that processing considerations not only constrain learning, but that learning constrains the developing parser, such that we can see the footprints of acquisition in the end-state processing system. Due to space limitations I restrict my discussion to L1 acquisition.
Tablets for informal language learning: Student usage and attitudes

Mobile-assisted language learning (MALL), a relatively new area of CALL inquiry, is gaining more and more attention from language educators with the development of new mobile devices. Tablet computers—featuring high mobility, convenient network connectivity, and smart application extendibility—are part of a wave of the latest mobile inventions; how these new mobile devices enhance MALL and how students perceive them as a language learning tool have yet to be broadly explored. This action research project sought to investigate how students used tablet computers to learn English in informal settings outside of class and how to foster more effective usage of the tablet for independent language learning. The study shows that tablet computers are ideal tools for creating an interactive, collaborative, and ubiquitous environment for language learning, provided that the technological affordances of the device have been fully explored with the students. This investigation also reveals that students have a generally favorable attitude towards the usability, effectiveness, and satisfaction of tablet computers for MALL.
Calendar Mindset: How Scheduling Takes the Fun and Puts the Work in

Consumers often schedule their activities in an attempt to use their time more efficiently. Although the benefits of scheduling are well established, its potential downsides are not well understood. The authors examine whether scheduling uniquely undermines the benefits of leisure activities. In 13 studies using unambiguous leisure activities that consumers commonly schedule (e.g., movies, a coffee break), they find that scheduling a leisure activity (vs. experiencing it impromptu) makes it feel less free-flowing and more work-like. Furthermore, scheduling diminishes utility from leisure activities, in terms of both excitement in anticipation of the activities and experienced enjoyment. Importantly, the authors find that maintaining the free-flowing nature of the activity by “roughly scheduling” (without prespecified times) eliminates this effect, thus indicating that the effect is driven by a detriment from scheduling rather than by a boost from spontaneity. The reported findings highlight an important opportunity for marketers to improve consumers’ experiences and utility by leveraging scheduling behavior while also providing important implications for consumer well-being from leisure consumption.
Communication performance of 802.11 WLANs

Results from simulation model of an 802.11 WLAN system that suffers in performance due to multiple stations interference are presented. The system communication performance is studied by changing the data packet fragmentation thresholds and the amount of traffic sent, received, and retransmission attempts with and without RTS/CTS mechanism. Two scenarios, with three and five interfering stations have been analyzed and steps to minimize the interference suggested. Specifically, the uses of fragmentation thresholds and RTS/CTS mechanism to control the interference are illustrated via the simulation. It is shown that for the modeled WLAN communications systems, the threshold value should not exceed 1024 bytes causing minimal interference with least number of retransmissions and a higher throughput.
Investigating the use of Elliptic Curve Cryptography in Transport Ticketing

In this work we investigate the use of an Elliptic Curve Cryptography (ECC)based Public Key Infrastructure (PKI) in the transport ticketing environment. We propose use of ECC-based protocols within current ticketing systems, as well as within emerging EMVWave and Pay ticketing solutions, in an attempt to overcome potential system vulnerabilities and operational weaknesses. We present several ECC-based ticketing architectures, consider the ECC mechanisms put forward by EMVCo, and examine the feasibility of such proposals with regards to operational timing constraints.
Swish: a Self-Gated Activation Function

The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose a new activation function, named Swish, which is simply $f(x) = x \cdot \text{sigmoid}(x)$. Our experiments show that Swish tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9% for Mobile NASNet-A and 0.6% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.
Line-of-sight angle of arrival estimation in the outdoor multipath environment

Angle of arrival (AOA) estimation utilizing the MUltiple Signal Identification and Classification (MUSIC) algorithm is investigated for land vehicle location systems. A "virtual" antenna array is used to overcome problems inherent to MUSIC when operating in a highly correlated signal environment. Based on assumptions regarding the multipath radio environment, triangulation is possible by estimating the AOA of the line-of-sight (LOS) component in the received signal. Computer simulations are used to analyze the ability of techniques incorporating MUSIC to achieve this. Since triangulation is achieved using a narrowband signal, no new spectrum is required. Instead, the current cellular telephone bands as well as the existing basestation hardware could be exploited resulting in a cost-efficient and easily implemented system. A further cost reduction may be realized by processing received signal data while travelling along a nonlinear trajectory. In this way, an unambiguous angular spectrum is obtained with one antenna.
AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software Toolbox for Cloud/Cloud-Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery Eligible for Systematic ESA EO Level 2 Product Generation

The European Space Agency (ESA) defines as Earth observation (EO) Level 2 information product a single-date multi-spectral (MS) image corrected for atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud-shadow. ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem never accomplished to date in operating mode by any EO data provider at the ground segment. Herein, it is considered: (I) necessary not sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes. (II) Synonym of EO Analysis Ready Data (ARD) format. (III) Equivalent to a horizontal policy for background developments in Space Economy 4.0. In compliance with the GEO-CEOS Quality Assurance Framework for EO Calibration/Validation guidelines, to contribute toward filling an analytic and pragmatic information gap from multi-sensor EO big data to timely, comprehensive and operational EO value-adding information products and services, this work presents an innovative AutoCloud+ CV software toolbox for cloud and cloud-shadow quality layer detection in ESA EO Level 2 product. In vision, spatial information dominates color information. Inspired by this true-fact, the inherently ill-posed AutoCloud+ CV software was conditioned, designed and implemented to be “universal”, meaning fully automated (no human-machine interaction is required), near real-time, robust to changes in input data and scalable to changes in MS imaging sensor’s spatial and spectral resolution specifications.
Fault Diagnosis Research for Power System Based on Timing Constraint Fuzzy Directed Graph

By taking full use of the temporal information of alarm message for quick and accurate diagnosis is very important. This paper presents an effective method for fault diagnosis, based on the time constraint fuzzy directed graph (TCFDG), in order to solve the cases of incomplete alarm information. An improved recognition algorithm is proposed which can effectively recognize misreports, missing messages and timing inconsistencies etc. And false alarms are filtered to overcome the deficiencies of incomplete alarm information, and to provide protection for the precise positioning of faults. Then, a reasoning method based on TCFDG is proposed, and use min operator and multiplication operator to determine the fault source, and use incidence matrix reduction method, reducing the dimension of the matrix, improved operational efficiency. Finally, a typical power system compared with some other typical methods was shown for demonstrating the feasibility and efficiency of the proposed method.
Queueing Network-ACTR Modeling of Concurrent Tasks Involving Multiple Controlled Processes

An important research question of human performance modeling is how to model concurrent task performance and mental workload. In this paper, we introduce a new method to model concurrent tasks involving multiple controlled processes in need of dedicated cognitive resources, which previous methods have difficulties to model. The key concept is a filtering discipline implemented in Queueing Network-ACTR cognitive architecture that allows cognitive resources to be exclusively occupied by one of the concurrent tasks when necessary, instead of switching between the tasks as frequently as possible. In the simulations of dual-tasks involving diagnostic decision making and patient status tracking, we found that the new discipline is necessary to model human performance and mental workload. Implications and practical applications of Queueing Network-ACTR in system evaluation and design are discussed.
E-Progression of Nonprofit Organization Websites: U.S. Versus Thai Charities

ABSTRACT Nonprofit organizations are pivotal actors in society, working in various sectors attempting to fill gaps between government services and the market. This interdisciplinary research compared nonprofit websites in Thailand, a developing country, to nonprofit websites in the United States to determine progression through an adapted e-business stage model. A manual decoding process of websites and a web crawling analysis software, Sitebeam, were used to determine the nonprofits’ e-progression. Thai and U.S. nonprofits did not differ significantly from each other in terms of presence building content or interactivity, but the U.S. websites were more advanced in e-transactions than the Thai websites (55% vs. 37%). The acceptance of e-transactions was also found to be highly associated with greater online presence and interactivity, suggesting that there is significant room for advancement among organizations that do not accept e-transactions both in Thailand and the United States
Typologizing native language influence on intonation in a second language: Three transfer phenomena in Japanese EFL learners. Supplementary data to the doctoral dissertation of Aaron Lee Albin (2015).

See README.txt for a full description of the dataset, broken into the following sections: (1) Description of dissertation, (2) Description of dataset, (3) File list, (4) Contents of files, (5) Anticipated uses of dataset, and (6) Copyright and licensing information.
Distributed Cartesian Product

Ehrenwörtliche Erklärung Hiermit versichere ich, die vorliegende Diplomarbeit ohne Hilfe Dritter und nur mit den angegebenen Quellen und Hilfsmitteln angefertigt zu haben. Alle Stellen, die aus den Quellen entnommen wurden, sind als solche kenntlich gemacht worden. Diese Arbeit hat in gleicher oder ähnlicher Form noch keiner Prüfungsbehörde vorgelegen.