Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of captioning videos used for foreign language listening activities

This study investigated the effects of captioning during video-based listening activities. Second- and fourth-year learners of Arabic, Chinese, Spanish, and Russian watched three short videos with and without captioning in randomized order. Spanish learners had two additional groups: one watched the videos twice with no captioning, and another watched them twice with captioning. After the second showing of the video, learners took comprehension and vocabulary tests based on the video. Twenty-six learners participated in interviews following the actual experiment. They were asked about their general reactions to the videos (captioned and noncaptioned). Results from t-tests and two-way ANOVAs indicated that captioning was more effective than no captioning. Captioning during the first showing of the videos was more effective for performance on aural vocabulary tests. For Spanish and Russian, captioning first was generally more effective than captioning second; while for Arabic and Chinese, there was a trend toward captioning second being more effective. The interview data revealed that learners used captions to increase their attention, improve processing, reinforce previous knowledge, and analyze language. Learners also reported using captions as a crutch.
Through-life change prediction and management

Changes occur at all stages in the product life. This paper presents a methodology to support designers managing changes through the entire lifecycle of a system, by providing them with a means of assessing potential knock-on changes across a system using a series of related product connectivity models. It is centred on the use of the risk-based Change Prediction Method (CPM), which is extended beyond its original use in the design stage of product development to cover all life-stages of a system. Conceptual design is supported by helping designers identify areas in a system that are highly susceptible to change and evaluate solution alternatives. During detail design, this methodology facilitates design freeze planning. Operational life-stages are supported through predicting knock-on effects of environmental changes and upgrades.
Analysis of spectral reflectance of mucous membrane for endoscopic diagnosis

Using color of mucous membrane is extremely important in endoscopic diagnosis. We have developed an endoscopic spectroscopy system (ESS) to clarify spectral characteristics of gastric and colon cancer, and a large amount of spectral reflectance data of mucous membrane has been measured by ESS. This paper proposes a method to find the spectral features of lesions considering the light scattering phenomena. We suppose that the influence of mucous membrane, i.e., tumor mainly appear in the spectral data of short wavelength, because the tumor of early cancer is usually present in superficial cell where short wavelength light is scattered more strongly than long wavelength light. To reveal the features in the short wavelength components, the spectral reflectance is divided by the same reflectance of a certain long wavelength. Then, the effect of individual variation on the mucosal color is reduced by subtracting the reflectance of normal region from that of adjacent tumor region. We apply principal component analysis and statistical discriminant analysis to the subtracted spectral reflectance data; the difference between the tumor and normal tissue is revealed.
Multifractal Characterization of Urban Form and Growth: The Case of Beijing

Urban form takes on properties similar to random growing fractals and can be described in terms of fractal geometry. However, a model of simple fractals is not effectual enough to characterize both the global and local features of urban patterns. In this paper multifractal measurements are employed to model urban form and analyze urban growth. The capacity dimension D0, information dimension D1, and correlation dimension D2 of a city's pattern can be estimated utilizing the box-counting method. If D0 > D1 > D2 significantly, the city can be treated as a system of multifractals, and two sets of fractal parameters, including global and local parameters, can be used to spatially analyze urban growth. In this case study, multifractal geometry was applied to Beijing city, China. The results based on the remote-sensing images taken in 1988, 1992, 1999, 2006, and 2009 show that the urban landscape of Beijing bears multiscaling fractal attributes. The dimension spectrum curves show several abnormal aspects, especially the upper limit of the global dimension breaks through the Euclidean dimension of embedding space and the local dimension fails to converge in a proper way. The geographical features of Beijing's spatiotemporal evolution are discussed, and the conclusions may be instructive for spatial optimization and city planning in the future.
Keyword Relation Analysis Using Concept Graph Toward Automatic Categorization of Online Novels

—Contents of user-generated media (UGM) such as YouTube are popular in recent years. The authors are interested in online novel services as a UGM on the Web. A large number of novels are being uploaded, and a few novels become major. Because there are no professional editor or no trained librarian concerning to UGM, neither quality evaluation nor categorization of UGM contents is clear. Moreover, the creator of a novel gives keywords without control, and they often generate new words, then it is difficult to use keyword for categorization. Toward automatic categorization, we have to understand relation between keywords. In this paper, we focused on analysis of keywords relation using concept graph. The concept graph is based on statistical analysis of word frequency and co-occurrence. We also show several interesting relations on concept graph.
A neural network controller by adaptive interaction

We propose an approach to neural network controllers by using a new adaptation algorithm. The algorithm is derived from the theory of adaptive interaction. The principle behind the adaptation algorithm is a simple but efficient methodology to perform gradient descent optimization in the parametric space. Unlike the approach based on the backpropagation algorithm, this approach does not require the plant to be converted to its neural network equivalent (a major obstacle in early approaches). By applying this adaptive algorithm, the same adaptation as the backpropagation algorithm is achieved without the need of backward propagating the error throughout a feedback network. This important property makes it possible to adapt the neural network controller directly. Control of second and third order plants are simulated to demonstrate the effectiveness of the algorithm.
Dynamic modelling of a smart material robot

Three dynamic models of a smart material robot are presented. First, Hamilton's approach is used to derive an accurate model described by partial differential equations (PDEs), which is not suitable for controller design. Based on the PDEs model, the assumed modes method (AMM) and the finite element method (FEM) are employed to derive two finite dimensional models in the forms of ordinary differential equations which are readily usable for the controller design. All of the models show that a smart material robot cannot be simply treated the same as that of a pure flexible robot. Comparative studies between AMM model and FEM model are carried out in both time and frequency domains to analyze the performance of the system and verify the correctness of the models.
Revisiting Random Utility Models

This thesis explores extensions of Random Utility Models (RUMs), providing more exible models and adopting a computational perspective. This includes building new models and understanding their properties such as identi ability and the log concavity of their likelihood functions as well as the development of estimation algorithms. A special case of RUMs that has received signi cant attention is the Luce model, for which there are fast inference methods for maximum likelihood estimation. This thesis introduces RUMs including those with exponential family utility distributions, mixture of RUMs, and non-parametric RUMs. Fast inference is achieved through the Monte-Carlo ExpectationMaximization (MC-EM) algorithm. Results on both real-world and simulated data provide support for the ability of these models to better capture heterogeneity in data and for scalable model estimation. A class of Generalized Method-of-Moments (GMM) algorithms for computing parameters of the Luce model and RUMs is also proposed. The technique is based on breaking full rankings into pairwise comparisons, and then computing parameters that satisfy a set of generalized moment conditions. The conditions for the output of GMM to be unique are identi ed, leading to a class of pairwise consistent and inconsistent breakings. Theoretical and empirical results show that the algorithms run signi cantly faster than the classical Minorize-Maximization (MM) and MC-EM approaches, while achieving competitive statistical e ciency. I propose two preference elicitation scheme for generalized RUMs, in which the utilities can also depend on attributes of agents and alternatives. An empirical study shows that the
Personalized Content Provision for Virtual Learning Environments via the Semantic Web

Abstract In this paper we discuss how we may personalize e-learning along three distinct axes, namely: teaching and learning pedagogical philosophies, personalized educational processes to taste and the coordination of these processes during execution. In doing so we are concerned with supporting users’ choices of educational options in course delivery via the Web services. In the work presented here, we assess the practical needs of learners and tutors and then the main research problems are analysed from a practical and pragmatic point of view. Following on from this the design of an intelligent virtual learning environment (VLE) is described to map a set of extensive didactic paradigms, which is represented by a system model and architecture. In this system, the semantic information of learning units and processes (e.g. the relationships among units) can be described and integrated in terms of various requirements of our users. As a result instructional materials with a wide variety of executional options and conditions can be built. Furthermore, through reassembling the semantics of learning content according to users’ new demands, our target audience (both student and content deliverers) can change their particular educational experience dynamically. This VLE can provide high-powered pedagogy-layered personalization - thus enabling new managed e-learning Web services and applications.
Efficient Image Segmentation using GPU Computing

- In applications such as pattern recognition, the whole image cannot be processed directly as it is inefficient and unpractical. Hence several image segmentation algorithms were proposed to segment an image. Image segmentation is a part of image processing, which classify digital image into multiple segments according to the feature of image for example pixel values. This is useful in pattern recognition and computer vision. Pattern recognition is a process of recognition of patterns and regularities in data and is used in medical diagnosis. Computer vision is a field of understanding images to produce numerical or symbolic information. This field theme is to duplicate the abilities of human vision by understanding and electronically perceiving an image. Level Set Method (LSM) had been used widely for image segmentation. Partitioning the input image into multiple segments efficiently using LSM is the main purpose of this paper. The paper implements an image segmentation algorithm. First it preprocess input color image (size can be upto 2MB). It deals with converting of input image into gray-scale image and then noise removal using Gaussian filter. This is followed by segmentation, which is implemented using LSM with edge, region and 2D histogram information. LSM enables to easily handle complex shapes and topological changes such as merging and splitting. But LSM is computationally expansive. This computational complexity of LSM based image segmentation approach is significantly reduced by the use of extremely parallelizable method called Lattice Boltzmann Method (LBM), with a body force to solve the Level Set Equation (LSE). Nvidia GPU (GeForce GTX) is used to take full advantage of LBM parallelism nature. CUDA C programming is used to implement algorithm on GPU. An Image
Interactive exploration and model analysis for coreference annotation

I present the design and implementation of an interactive visualizationand explorationframework for coreference annotations. It is designed to meet the needs of multiple different users on a modern and multifaceted graphical exploration tool. To demonstrate its suitability for these various needs I outline several use cases and how the framework can help users in their individual tasks. It offers the user different views on the data with additional functionality to compare several annotations. Complex analysis of annotated corpora is supported by means of a search engine which lets the user construct queries both in a graphical and textual form. Both qualitative and quantitative result breakdowns are available and the implementation features specialized visualizations to aggregate complex search results. The framework is extensible in many ways and can be customized to handle additional data formats.
E-differentiation for analyzing scalability of parallel algorithms on parallel architectures

The E-differentiation, which is derived from the idea of isoefficiency analysis and used for analyzing scalability of parallel algorithms on parallel architectures, is proposed in this paper. The basic philosophy of E-differentiation is that scalability analysis should be based on the ratio of the change of E (efficiency of processors) vs. W (Workload), E/sub w//sup '/, and E vs. P (number of processors), E/sub p/'. E-differentiation can overcome the incompletion of other metrics for analyzing scalability with assumptions which confine the manner of the increasement of W w.r. t. p; and it is easy to give the equivalent expression of these metrics in terms of E-differentiation so that relationships between these metrics can be studied more definitively. Because E-differentiation does not the restrict of isoefficiency, which confines the generality of the isoefficiency analysis, it can measure the scalability of any parallel algorithms on any parallel architectures, and can predict the change of performance of parallel algorithms when change manner of W w.r. t. p does not follow isoefficiency function.
Computer Game Controlled by Eye Movements

Sundry years ago people played video games for fun merely. Nowadays, video games are correlated toÂ education, medicine, and re- searches. In this paper, we have addressed a computer game which takes input from aÂ video camera by detecting the user looking direction as well as eye gestures. Since webcam is easily accessible, weÂ have carefully weighed it as a video input device. We have tried to use the eye movements as the human computerÂ interaction (HCI) tool, which would be used instead of a mouse. In general, this furnishes much easier and fasterinterac- tion with computer for everyone, especially elders, children, and disabled people who cannot use mice. Â We have also evinced that eye tracking and eye gaze estimation can be very easy and fast way to obtain HCI. Â Experimental results show the potential usages of our proposed game.
Segmenting dispensers: Factors in selecting open‐canal fittings

The trend is clear. More clinicians and patients are choosing open-canal (OC) fitting hearing aids as a treatment option for hearing impairments. Now, just a few years after GN ReSound’s launch of an OC device called the ReSoundAIR, nearly every manufacturer has a similar product. In a very real sense, an OC is more or less a small behind-the-ear (BTE) device with a tube fitting, although some products classified as “open” have the receiver in the ear canal and use a wire rather than a tube to connect with the ear canal. Last year, BTE sales in the USA increased significantly (no doubt largely because of the OC product), jumping to 33% of all hearing aid sales. In the second quarter of 2006, BTEs accounted for 44% of the domestic market and a large portion of these were OC devices. Based on OC products’ cosmetic appeal and their use in fitting mild to moderate high-frequency hearing losses, one might assume that the OC aid’s closest competitor would be the completely-in-the canal (CIC) style. However, according to the Hearing Industries Association (HIA) data,1 the surge in BTE sales is coming at the expense of all three custom hearing aid styles. Sales for the in-the-ear (ITE) styles have fallen from 41% of the market in 2003 to 32% this year, while the in-the-canal (ITC) and CIC styles have fallen from 21% and 14% of the total market, respectively, in 2003 to 15% and 11% in 2006. The OC device is clearly shaking up the hearing aid market.
Exploring Twitter by Combining Structured and Unstructured Information

In this paper we show how it is possible to extract useful knowledge from Twitter structured information that can improve the results of a NLP task. Tweets are short and low quality and this makes it difficult to apply classical NLP techniques to this kind of texts. However, Twitter offers more than 140 characters in their messages to work with. In Twitter ecosystem there are many objects (tweets, hashtags, users, words, .) and relationships between them (co-occurrence, mentions, re-tweets, .) that allow us to experiment with alternative processing techniques. In this paper we have worked with a tweet classification task. If we only use knowledge extracted from the relationship Follow we achieve similar results to those of a classifier based on bags of words. When we combine the knowledge from both sources we improve the results in more than 13 percentual points with respect to the original models. This shows that structured information is not only a good source of knowledge but is also complementary to the content of the messages. We also have applied the same philosophy to the task of collecting the corpus for our classification task. In this case we have use a dynamic retrieval technique based on relationships between Twitter entities that allows us to build a collection of more representative tweets.
Potential Game for Dynamic Spectrum Management in Cognitive Radio Networks

Dynamic spectrum management is one of the key technologies in cognitive radio networks, and more and more researchers have been paying much attention to algorithms and models for Spectrum sharing. In this paper, we present a dynamic spectrum management application model based on potential game. Firstly, the mapping relationship is constructed for sections in cognitive radio network and game theory model. Game participants include primary network users and cognitive network users. Game process is defined as participants want to gain personal best interests and use different strategies in game. Secondly, we present a model for dynamic spectrum management in cognitive radio networks. In accordance with the definition and existence theorem of Nash equilibrium, we describe the users’ behaviors in cognitive radio networks. In accordance with the definition and existence theorem of Nash equilibrium, we describe the users’ behaviors in cognitive radio networks. Lastly, we simulate the realistic scenarios with the presented model and the results show that it can solve the problem for dynamic spectrum management with lower efficiency and non-flexibility.
Systolic architecture for 2-D rank order filtering

The proposed systolic design for 2-D rank order filtering has a wide variety of applications in image processing. It derives its architecture mainly from a systolic design for 1-D rank order filtering proposed previously. The adopted systolic design, called the sample oriented rank order filter design, takes advantage of the evaluated rank values in the current window for the evaluation of the rank values in the next window without explicitly sorting the data in the window. This makes it possible to convert a 2-D windowed data sequence into a 1-D windowed data sequence with multiple data sample exchange at each window movement, and the 1-D systolic design is thus applicable. By cascading many such 1-D systolic arrays into a 2-D array, and supplied with simple parallel-in-serial-out (PISO) logic, this architecture can achieve the maximally available parallelism with nearly 100% efficiency if either the pipeline interleaving or processor sharing technique is used. The design does not require the preloading of the whole 2-D data array, and line scanned images can be processed with negligible time delay. <<ETX>>
Question Generation from Knowledge Graphs

In this thesis we present a novel approach for generating natural language questions, using factual information from a knowledge graph and automatically assessing their difficulty. Our work elicits a further utilization of the knowledge captured in knowledge graphs that could find applications in research, education and leisure. In general, coming up with question manually can be a resource consuming endeavor. An automatic approach can therefore provide an alternative that substantially reduces the required effort. Established methods for question generation have used document corpora as their main source of information. However, the utilization of knowledge graphs for this purpose has received far less attention. Furthermore, to the best of our knowledge, no previous work has examined question difficulty in the context of question generation. We specify a framework for a system staged in quiz setting that can test the domain knowledge of players. The framework is implemented as an end-to-end system that expects a human to specify a topic and a difficulty level. The resulting output is a question in natural language, that abides the input criteria. The challenges we address along the way include the principled selection of the contents of the question, the verbalization of these contents into natural language, and the creation of an automated question difficulty estimation scheme. We empirically show the effectiveness of our approach and conduct user studies to demonstrate the correlation between our automated difficulty judgments and those made by human annotators.
Implementations of iterative algorithms in Hadoop and Spark

Facing the challenges of large amounts of data generated by various companies (such as Facebook, Amazon, and Twitter), cloud computing frameworks such as Hadoop are used to store and process the Big Data. Hadoop, an open source cloud computing framework, is popular because of its scalability and fault tolerance. However, by frequently writing and reading data from the Hadoop Distributed File System (HDFS), Hadoop is quite slow in many applications. Apache Spark, a new cloud computing framework developed at AMPLab of UC Berkeley, solves this problem by caching data in memory. Spark develops a new abstraction called resilient distributed dataset (RDD) which is both scalable and fault-tolerant. In this thesis, we describe the architecture of Hadoop and Spark and discuss their differences. Properties of RDDs and how they work in Spark are discussed in detail, which gives a guide on how to use them efficiently. The main contribution of the thesis is to implement the PageRank algorithm and Conjugate Gradient (CG) method in Hadoop and Spark, and show how Spark out-performs Hadoop by taking advantage of memory caching.