Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Multithreaded Object Tracker through Occlusions for Spatial Augmented Reality

A spatial augmented reality (SAR) system enables a virtual image to be projected onto the surface of a real‐world object and the user to intuitively control the image using a tangible interface. However, occlusions frequently occur, such as a sudden change in the lighting environment or the generation of obstacles. We propose a robust object tracker based on a multithreaded system, which can track an object robustly through occlusions. Our multithreaded tracker is divided into two threads: the detection thread detects distinctive features in a frame‐to‐frame manner, and the tracking thread tracks features periodically using an optical‐flow‐based tracking method. Consequently, although the speed of the detection thread is considerably slow, we achieve real‐time performance owing to the multithreaded configuration. Moreover, the proposed outlier filtering automatically updates a random sample consensus distance threshold for eliminating outliers according to environmental changes. Experimental results show that our approach tracks an object robustly in real‐time in an SAR environment where there are frequent occlusions occurring from augmented projection images.
Second language development theories and technology-mediated language learning

Information and communication technologies have never been more interesting due in large part to their intimate integration into everyday life. Second and foreign language researchers and educators have long recognized the potential of technology to provide access to input and rehearsal (recordings, tutorials and drills), to amplify possibilities for personal expression (text and media processing), to extend existing and enable new opportunities for interpersonal communication (synchronous and asynchronous messaging), and most recently, considerable interest has been focused on social media and social networking environments that have de facto become fused with the activity of real (and not merely ‘virtual’) life.
Introducing Locus: a NIME for Immersive Exocentric Aural Environments

Locus is a NIME designed specifically for an interactive, immersive high density loudspeaker array environment. The system is based on a pointing mechanism to interact with a sound scene comprising 128 speakers. Users can point anywhere to interact with the system, and the spatial interaction utilizes motion capture, so it does not require a screen. Instead it is completely controlled via hand gestures using a glove that is populated with motion-tracking
Understanding the Intention to Adopt XBRL: An Environmental Perspective

ABSTRACT : Based on a survey of 52 U.S.-based organizations, this study investigates the influence of perceptions of environmental factors on an organization's intention to adopt XBRL, after controlling for the SEC mandate. Reflecting the view of XBRL as both a technical innovation and a standard for financial reporting, the selection of environmental factors was derived from institutional theory and from the standards adoption literature. The results suggest that perceived mimetic and normative pressures, as well as perceptions of network technologies and related technologies that facilitate the use of XBRL, exert a significant positive influence on XBRL adoption even after controlling for the effects of the SEC mandate.
PRO-ACTIVE CONNECTION MAINTENANCE IN AODV AND MAODV

I ABSTRACT A major aspect of ad-hoc networks is that the nodes can move randomly, which requires the routing protocols in ad-hoc network to quickly respond to the network topology change in order to guarantee successful data packet delivery. A link state prediction method can predict the exact link breakage time of an active link before the breakage actually occurs. So by using link state prediction, a new route can be constructed before the old route becomes unavailable, thus avoiding data packet loss. In this thesis, we first added the link state prediction method to the reactive unicast protocol AODV. The source can smoothly update the currently used route to avoid any soon-to-be-broken link. Simulation results demonstrate that this pro-active route maintenance can significantly reduce packet loss (between 32% and 72%) with slight overhead increase (between 4% and 49%). We also examine the link state prediction method in the tree-based multicast protocol MAODV to maintain the multicast tree in advance and avoid any branch breakage. Simulation results show the throughput is greatly improved to above 85% from around 70% with overhead increase below 12%.
Low-complexity, low-area computer architectures for cryptographic application in resource constrained environments

RCE (Resource Constrained Environment) is known for its stringent hardware design requirements. With the rise of Internet of Things (IoT), low-complexity and low-area designs are becoming prominent in the face of complex security threats. Two low-complexity, low-area cryptographic processors based on the ultimate reduced instruction set computer (URISC) are created to provide security features for wireless visual sensor networks (WVSN) by using field-programmable gate array (FPGA) based visual processors typically used in RCEs. The first processor is the Two Instruction Set Computer (TISC) running the Skipjack cipher. To improve security, a Compact Instruction Set Architecture (CISA) processor running the full AES with modified S-Box was created. The modified S-Box achieved a gate count reduction of 23% with no functional compromise compared to Boyar’s. Using the Spartan-3L XC3S1500L-4-FG320 FPGA, the implementation of the TISC occupies 71 slices and 1 block RAM. The TISC achieved a throughput of 46.38 kbps at a stable 24MHz clock. The CISA which occupies 157 slices and 1 block RAM, achieved a throughput of 119.3 kbps at a stable 24MHz clock. The CISA processor is demonstrated in two main applications, the first in a multilevel, multi cipher architecture (MMA) with two modes of operation, (1) by selecting cipher programs (primitives) and sharing crypto-blocks, (2) by using simple authentication, key renewal schemes, and showing perceptual improvements over direct AES on images. The second application demonstrates the use of the CISA processor as part of a selective encryption architecture (SEA) in combination with the millions instructions per second set partitioning in hierarchical trees (MIPS SPIHT) visual processor. The SEA is implemented on a Celoxica RC203 Vertex XC2V3000 FPGA occupying 6251 slices and a visual sensor is used to capture real world images. Four images frames were captured from a camera sensor, compressed, selectively encrypted, and sent over to a PC environment for decryption. The final design emulates a working visual sensor, from on node processing and encryption to back-end data processing on a server computer.
Children's information retrieval: beyond examining search strategies and interfaces

The study of children's information retrieval is still for the greater part untouched territory. Meanwhile, children can become lost in the digital information world, because they are confronted with search interfaces, both designed by and for adults. Most current research on children's information retrieval focuses on examining children's search performance on existing search interfaces to determine what kind of interfaces are suitable for children's search behaviour. However, to discover the true nature of children's search behaviour, we state that research has to go beyond examining search strategies used with existing search interfaces by examining children's cognitive processes during information-seeking. A paradigm of children's information retrieval should provide an overview of all the components beyond search interfaces and search strategies that are part of children's information retrieval process. Better understanding of the nature of children's search behaviour can help adults design interfaces and information retrieval systems that both support children's natural search strategies and help them find their way in the digital information world.
Visual saliency and eye movement : modeling and applications

Humans are capable of narrowing their focus on the highlights of visual information in a fraction of time in order to handle enormous mass of data. Akin to human, computers should deal with a tremendous amount of visual information. To replicate such a focusing mechanism, computer vision relies on techniques that filter out redundant information. Consequently, saliency has recently been a popular subject of discussion in the computer vision community, though it is an old subject matter in the disciplines of cognitive sciences rather than computer science. The reputation of saliency techniques – particularly in the computer vision domain – is greatly due to their inexpensive and fast computation which facilitates their use in many computer vision applications, e.g., image/video compression, object recognition, tracking, etc. This study investigates visual saliency modeling, which is the transformation of an image into a salience map such that the identified conspicuousness agrees with the statistics of human eye movements. It explores the extent of image and video processing to develop saliency techniques suitable for computer vision, e.g., it adopts sparse sampling scheme and kernel density estimation to introduce a saliency measure for images. Also, it studies the role of eye movement in salience modeling. To this end, it introduces a particle filter based framework of saccade generation incorporated into a salience model. Moreover, eye movements and salience are exploited in several applications. The contributions of this study lie on the proposal of a number of salience models for image and video stimuli, a framework to incorporate a model of eye movement generation in salience modeling, and the investigation of the application of salience models and eye movements in tracking, background subtraction, scene recognition, and valence recognition.
Mitigating the Problems of SMT using EBMT

Statistical Machine Translation (SMT) typically has difficulties with less-resourced languages even with homogeneous data. In this thesis we address the application of Example-Based Machine Translation (EBMT) methods to overcome some of these difficulties. We adopt three alternative approaches to tackle these problems focusing on two poorly-resourced translation tasks (English–Bangla and English–Turkish). First, we adopt a runtime approach to EBMT using proportional analogy. In addition to the translation task, we have tested the EBMT system using proportional analogy for named entity transliteration. In the second attempt, we use a compiled approach to EBMT. Finally, we present a novel way of integrating Translation Memory (TM) into an EBMT system. We discuss the development of these three different EBMT systems and the experiments we have performed. In addition, we present an approach to augment the output quality by strategically combining EBMT systems and SMT systems. The hybrid system shows significant improvement for different language pairs. Runtime EBMT systems in general have significant time complexity issues especially for large example-base. We explore two methods to address this issue in our system by making the system scalable at runtime for a large example-base (English–French). First, we use a heuristic-based approach. Secondly we use an IR-based indexing technique to speed up the time-consuming matching procedure of the EBMT system. The index-based matching procedure substantially improves run-time speed without affecting translation quality.
Factors Influencing Learners' Cognitive and Affective Processes in Visual Learning

This chapter focuses on the cognitive and affective factors that may influence learners’ performance in visual learning. Both cognitive and affective factors were identified with cognitive factors aiming to reduce cognitive load, making meaningful learning through schema connection and activation. The affective factors focused on meeting the psychological and self-fulfillment needs in visual learning. Discussions were made on the implications of the chapter in terms of supporting professionals and educators in their design of effective visual learning in education.
LOWER SIX STUDENTS’ PREFERRED MODE OF FEEDBACK FOR ESSAY REVISION

ESL students value feedback to their essay drafts. Through feedback and by doing multiple revisions, they learn to develop and express complex ideas clearly and effectively. Accordingly, feedback is seen as a way to improve the communicative effectiveness of a given written piece by correcting and guiding students so that their ideas are effectively communicated to the reader. This study determined the students’ preference towards feedback type during essay revision. The quasi-experimental study used the counter-balanced research design. For comparison, the students in this study were exposed to two different treatments, teacher-written feedback and online-automated feedback for twelve weeks. The results revealed that the students preferred teacher-written feedback to online-automated feedback although both the experimental groups showed improvement in their essay writing based on both modes of feedback.
Text mining to detect indications of fraud in annual reports worldwide

The research described in this thesis examined the contribution of text analysis to detecting indications of fraud in the annual reports of companies worldwide. A total of 1,727 annual reports have been collected, of which 402 are of the years and companies in which fraudulent activities took place, and which have an impact on the information disclosed in the annual report. A method for the automatic extraction of information from annual reports has been proposed to obtain the data needed to compile this data set. The approach has also been used to extract the Management Discussion & Analysis (MD&A) section, which is the part of the annual report the research in this thesis focuses on. The first models developed for the research described in this thesis, analyze the texts by counting the words (unigrams). This representation of the text is the input of the machine learning algorithms Naive Bayes (NB) and Support Vector Machine (SVM). These algorithms learn patterns to classify the annual reports as `fraud' or `no fraud'. Subsequently, the NB and SVM models based on unigrams are expanded with the linguistic features of the text found to be informative in the previous research concerning the detection of fraud or deception in text. We subdivided the linguistic features into six categories: consecutive words (bigrams), descriptive, complexity, grammatical, readability, psychological and grammatical relations. Finally, a Convolutional Neural Network (CNN) model has been applied. With this model, each word in the text is represented by vectors (word embeddings). Word embeddings aim to include the semantic relationships between words, in addition to the representation of the individual words. The results show that text analysis can contribute to the detection of indications of fraud. However, it desirable to further enhance the performance of the models.
Overview of DUC 2005

The focus of DUC 2005 was on developing new evaluation methods that take into account variation in content in human-authored summaries. Therefore, DUC 2005 had a single user-oriented, question-focused summarization task that allowed the community to put some time and effort into helping with the new evaluation framework. The summarization task was to synthesize from a set of 25-50 documents a well-organized, fluent answer to a complex question. The relatively generous allowance of 250 words for each answer reveals how difficult it is for current summarization systems to produce fluent multi-document summaries.
Wireless Backhaul Architectures for 5G Networks

This thesis investigates innovative wireless backhaul deployment strategies for dense small cells. In particular, the work focuses on improving the resource utilisation, reliability and energy efficiency of future wireless backhaul networks by increasing and exploiting the flexibility of the network. The wireless backhaul configurations and topology management schemes proposed in this thesis consider a dense urban area scenario with static users as well as an ultra-dense outdoor small cell scenario with vehicular traffic (pedestrians, bus users and car users). Moreover, a diverse range of traffic types such as file transfer, ultra-high definition (UHD) on-demand and real-time video streaming are used. In the first part of this thesis, novel dynamic two-tier Software Defined Networking (SDN) architecture is employed in backhaul network to facilitate complex network management tasks including multi-tenancy resource sharing and energy-aware topology management. The results show the proposed architecture can deliver efficient resource utilisation, and QoS guarantee. The second part of the thesis presents wireless backhaul architectures that serve ultra-dense outdoor small cells installed on street-level fixtures. The characteristics of vehicular communications including diverse mobility patterns and unevenly distributed traffic are investigated. The system-level performance of two key technologies for 5G backhaul are compared: massive MIMO backhaul using sub-6GHz band and millimetre (mm)-wave backhaul in the 71 – 76 GHz band. Finally, innovative wireless backhaul architectures delivered from street fibre cabinets for ultra-dense outdoor small cells with vehicular traffic is proposed, which can effectively minimise the need for additional sites, power and fibre infrastructure. Multi-hop backhaul configurations are presented in order to bring in an extra level of flexibility, and thus, improve the coverage of a street cabinet mm-wave backhaul network as well as distribute traffic loads.
Effectiveness of structured teaching program on knowledge of health promotional activities among home makers of a selected rural community in Mangalore , Karnataka

Introduction: Health promotion is the process of helping people to gain control over the determinants of health and thereby improve their health. Objectives: Objectives of the study were to determine the effectiveness of structured teaching program in terms of gain in knowledge of homemakers on health promoting activities and to determine the association between health promotional activities of homemakers and selected variables. Methods: A pre-experimental design was used for the study and 60 participants were drawn through simple random sampling technique. The structured interview schedule was used to collect the data. Results: The findings of the study revealed that maximum number of respondents 39 (65%) had average knowledge on health promotional activities. The mean post-test knowledge scores (91.35) of subjects were significantly higher than their mean pre-test knowledge scores (44.70) and calculated the ‘t’ value was 22.477, (p<0.05) which indicated that structured teaching program was an effective means in improving the knowledge of homemakers on health promotional activities. Conclusion: Findings of the study revealed that the majority of the homemakers had average knowledge on health promotional activities. The structured teaching program helped them to gain knowledge on different areas of health promotional activities.
A comparison of convolutive blind source separation algorithms applied to speech signals

Blind source separation aims at estimating a number of unobserved source signals from several observed mixtures of those source signals. In the case of acoustic applications, the sources are people speaking, and the mixtures are microphone recordings. Many different algorithms have been proposed to solve this problem. There is, however, a need for a more involved comparison of the performance of the different algorithms. This dissertation examines the performance of several blind source separation algorithms applied to speech signals.
Structural Relationship of the Mental Toughness on Athletes’ Success in Thai Contact Sport

Mental toughness (MT) is an ability in controlling and responding to properly mind of moodes, concentration, commitment and attention in various situation. Originally, foreign countries have studied it continually and sequenaly for understanding of their appropriate structures explaining of athlete mental toughness and a success in a sport, especially in the professional or champion’s level of sport. For Thai athletes, an extending of this knowledgeable to deeply and specify is undoubtedly important and needs to be continued as well, such as a contact event of sport. The objectives were to examine the MT structural relationships on athlete success and analyze their norm scores for Thai contact sport athletes. The samples were screened for 917 from the whole sample of 2,030 Thai athletes who were participated in the National Games and were Thai athletes’ representative. There were youth males (65.0 %), and females (35.0 %) from other one previously study by the authors (Choosakul and Julavanichpong, 2014). They were chosen through random sampling. The sample size was specified by considering at the lowest test's criteria of structural relationships and invariance of mental toughness, 20 times more items than those in the inventory (Tabachnick & Fidell, 1996). The tools used in this study were MT Inventory (MTI) (Middleton, 2005a) and Athlete Success Inventory (ASI) (Choosakul and Julavanichpong, 2014), both had been proved as good content and construction validities and reliabilities. The data was analyzed by basic statistics including mean, standard deviation, and percentage for presenting any basic data. Structural equation modeling (SEM) technique was employed for the measurement of Thai contact sport athletes' MT and relation of the success in sport. Fit Indices were used X, CFI, TLI, RMSEA, path coefficient, df, df diff and X diff for appropriate and coherent data analysis of the model. Finally, T-score and percentile was used for inspecting Thai contact sport athletes norms of MT ability. The research results found that the MTI shown it concurrent criterion-related validity and could predict the athletes' MT for Thai contact sport athletes by the model fit indices (X= 4,347.60, df = 548, p = .00 CFI = .98, TLI = .98, 1 Faculty of Education, Mahasarakham University, THAILAND 2 Faculty of Sport Science, Burapha University, THAILAND Journal of Education, Mahasarakham University 319 Volume 12 Number 3 July-September 2018 RMSEA = .069 and RMSEA = .069, (3). Norm scores of the Thai athletes' MT for Thai contact sport athletes was found in both of the whole and separated group of the factors of MT. In conclusion, the MTI was a strong measure and effective. In terms of reliability, construct validity, concurrent validity that could be predicted and measured the success for Thai contact sport athletes, which was very important to develop their mental ability to a success in the future.