Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Allocation in OFDMA Wireless Networks

Orthogonal frequency division multiple access (OFDMA) is becoming a widely deployed mechanism in broadband wireless networks due to its capability to combat the channel impairments and support high data rate. Besides, dealing with small units of spectrum, named sub-carriers, instead of whole spectrum, results in enhanced flexibility and efficiency of the resource allocation for OFDMA networks. Resource allocation and scheduling in the downlink of OFDMA networks supporting heterogeneous traffic will be considered in this thesis. The purpose of resource allocation is to allocate sub-carriers and power to users to meet their service requirements while maintaining fairness among users and maximizes resource utilization. To achieve these objectives, utility-based resource allocation schemes along with some state-of-the-art resource allocation paradigms such as power control, adaptive modulation and coding, sub-carrier assignment, and scheduling are adopted. On one hand, a utility-based resource allocation scheme improves resource utilization by allocating enough resources based on users’ quality of service (QoS) satisfaction. On the other hand, resource allocation based on utilities is not trivial when users demand different traffic types with convex and nonconvex utilities. The first contribution of the thesis is the proposing of a framework, based on joint physical (PHY) and medium access (MAC) layer optimization, for utility-based resource allocation in OFDMA networks with heterogeneous traffic types. The framework considers the network resources limitations while attempting to improve resources utilization and heterogeneous users’ satisfaction of service. The resource allocation problem is formulated by continuous optimization techniques, and an algorithm based on interior point and penalty methods is suggested to solve the problem. The numerical results show that the framework is very efficient in treating the nonconvexity problem and the allocation is accurate comparing with the ones obtained by a genetic search algorithm. The second contribution of the thesis is the proposing of an opportunistic fair scheduling scheme for OFDMA networks. The contribution is twofold. First, a vector of fair iii weights is proposed, which can be used in any scheduling scheme for OFDMA networks to maintain fairness. Second, the fair weights are deployed in an opportunistic scheduling scheme to compensate the unfairness of the scheduling. The proposed scheme efficiently schedules users by exploiting multiuser diversity gain, OFDMA resource allocation flexibility, and utility fair service discipline. It is expected that the research in the thesis contributes to developing practical schemes with low complexity for the MAC layer of OFDMA networks.
Identifying Features of Coherence for Evaluation of Automatic Summarization

This project compares previously proposed features of textual coherence from multiple disciplines, with the goal of developing an automatic evaluation method for coherence. Features are compared via a classification task wherein machine generated summaries from the Document Understanding Conference (DUC) are classified by coherence rating. Results suggest that entity grid features may be more valuable than previously thought. Stopped, stemmed and lemmatized variants show improvement over sentence cosine similarity features. Novel features based on TextTiling text segmentation are proposed and show promising results. Additionally, improvements to the manual evalution methods used by the DUC are suggested.
Distributional semantics and machine learning for statistical machine translation

In this work, we explore the use of distributional semantics and machine learning to improve statistical machine translation. For that purpose, we propose the use of a logistic regression based machine learning model for dynamic phrase translation probability modeling. We prove that the proposed model can be seen as a generalization of the standard translation probabilities used in statistical machine translation, and use it to incorporate context and distributional semantic information through lexical, word cluster and word embedding features. Apart from that, we explore the use of word embeddings for phrase translation probability scoring as an alternative approach to incorporate distributional semantic knowledge into statistical machine translation. Our experiments show the effectiveness of the proposed models, achieving promising results over a strong baseline. At the same time, our work makes important contributions in relation to bilingual word embedding mappings and word embedding based phrase similarity measures, which go beyond machine translation and have an intrinsic value in the field of distributional semantics. Language Analysis and Processing Distributional Semantics and Machine Learning for SMT iv/112 Language Analysis and Processing Distributional Semantics and Machine Learning for SMT v/112
Personal learning environments in K-12

Personalization is a trending topic in educational technology. The definition is so broad as to become a catch phrase to describe many new tools and transformation initiatives. While the concept of personalization is gaining traction, the actual application of student-constructed personal learning environments (PLEs) is currently limited in the K-12 literature and practice. This chapter explores existing research on PLEs and networked learning for adults and children. Research-based examples in K-12 are presented along with the processes required to support student-constructed personal learning environments. Implications for teaching practice, learning, and education policy are shared along with a call for additional research specific to K-12 students.
The Sound Bubble: A context-sensitive space in the space

The design of sonic environments is in need of more active strategies, taking into account not only the physical but also the social and sensorial aspects of a place. This implies abandoning traditional, mono-disciplinary responses in favour of interdisciplinary methods and approaches. In this study we explored the possibility of inserting context-sensitive sound textures to improve the experience of an activity-based office workplace. For this purpose, we developed the concept of the ‘sound bubble’ – a micro-space in which the user is embedded by a semi-transparent added sound environment that will operate as a subtle sound mask, attracting the attention without needing to hide the disturbing environment. This should help users (the workers) to stay in an ‘everyday listening’ mode. This means, not focusing on the environment in particular but on their tasks, while preserving the link with the surrounding space and activities.
Best Practices In Online Pedagogy: A Pearson Correlation Analysis Between Teaching Evaluation Scores And Transactional Distance Scores In Online Graduate Courses

The purpose of this quantitative study was to discover how four universally recognized best practices in online pedagogy affected teaching evaluation scores in online graduate courses. The four best practices in online pedagogy are: Course Organization and Presentation, Learning Objectives and Assessments, Instructor-Student Interpersonal Interaction, and the Appropriate Use of Video or Multimedia. The researcher modified these best practices from the Jaggars and Xu (2016) Online Course Quality Rubric. The study utilized Dr. Michael G. Moore’s (1973) theory of transactional distance to understand the relationship between teaching evaluation scores and transactional distance. University instructional designers assessed and rated how well the researcher incorporated the best practices and awarded each course a transactional distance score (TDS). The researcher used a Pearson’s correlation analysis to measure the strength of the relationship between teaching evaluation scores and TDS. A thorough literature review revealed a gap in research related to how best practices in online pedagogy affected teaching evaluation scores in online graduate courses. This research study added to the body of knowledge about the gap within the existing literature.
Big Data Modelling of the Knowledge Economy

A computer-generated 3D model is used to illustrate the advantages of in silico techniques in virtual reality. The model is derived from theoretical data available for SMEs in service industries and for example enables a business owner (or consultant) to identify where the particular organization is on this three-dimensional landscape and draw quantitative conclusions about fruitful future directions for the organization, as well as how high the benefits will be and what costs will be due along the journey. <br><br>Clearly this landscape map is of immense value for academics and practitioners alike, and is available “ready-to-go” in a generic and easily-applicable form. This Masterclass will go further and show participants the use of software tools to construct a contour landscape that is specific for their own client organization. <br><br>We will create the basic 3-dimensional fold in Maple and discuss the implications for SME growth and development. We will then move to Markov Chain Monte Carlo modelling, which - put simply - is throwing virtual balls down the basic fold to show how the fold can be used and manipulated to model and predict outcomes of “Knowledge Engineering”. In particular we will model; adding multi-skilled innovators, adding network input from the external environment, looking at the cost of controlling management by showing how much bad information gatekeeping at departmental manager level actually costs and investigating the value-adding effect of intellectual capital. <br><br>If time allows, participants will be presented with a small case study and – together with the class leader – will work through a simple solution thus illustrating the ease and applicability of the model in a practical and hands-on fashion
A knowledge-based approach to deriving logical structure from document images

An important application of artificial intelligence is document image understanding, specifically the analysis of document images to derive a symbolic description of the document structure and contents. This requires the segmentation of the different blocks of printed matter using standard image processing techniques, and the use of spatial domain knowledge to first classify these blocks (e.g., text paragraphs, photographs, etc.), then group these blocks into logical units (e.g., newspaper stories, magazine articles, etc.), and finally determine the reading order of the text blocks within each logical unit. The above steps describe the problem of converting the physical structure of the document into its logical structure with the use of domain knowledge about document layout. The objective of this work is to develop a computational model for the derivation of the logical structure of documents using certain formalisms designed for this task. In this model, a simple yet powerful rule-based control strategy utilizes the data obtained from the invocation of different types of operations on a digitized document image, and makes inferences about the document using a knowledge base of document layout rules. The domain knowledge about document structure is represented in a unified multi-level hierarchical form, and is used by reasoning processes to make inferences. The main issues investigated in this research are: the kinds of domain and control knowledge that are required, how to represent this knowledge in a globally integrated form, and how to devise a control strategy that efficiently utilizes this knowledge. A knowledge-based document logical structure derivation system (DeLoS) has been developed based on this model. The system consists of a hierarchical rule-based control system that guides the block classification, block grouping and read-ordering operations; a global data structure that stores the document image data and incremental inferences; and a domain knowledge base that encodes the rules governing document layout. Applications of this approach include use in digital libraries for the retrieval of relevant logical document information, as well as in comprehensive document understanding systems that can read document text and allow interactive querying of the syntactic and semantic information in a document.
Acoustic counter-sniper system

BBN has developed, tested, and fielded pre-production versions of a versatile acoustics-based counter-sniper system. This system was developed by BBN for the DARPA Tactical Technology Office to provide a low cost and accurate sniper detection and localization system. The system uses observations of the shock wave from supersonic bullets to estimate the bullet trajectory, Mach number, and caliber. If muzzle blast observations are also available from unsilenced weapons, the exact sniper location along the trajectory is also estimated. A newly developed and very accurate model of the bullet ballistics and acoustic radiation is used which includes bullet deceleration. This allows the use of very flexible acoustic sensor types and placements, since the system can model the bullet's flight, and hence the acoustic observations, over a wide area very accurately. System sensor configurations can be as simple as two small four element tetrahedral microphone arrays on either side of the area to be protected, or six omnidirectional microphones spread over the area to be monitored. Increased performance can be obtained by expanding the sensor field in size or density, and the system software is easily reconfigured to accommodate this at deployment time. Sensor nodes can be added using wireless network telemetry or hardwired cables to the command node processing and display computer. The system has been field tested in three government sponsored tests in both rural and simulated urban environments at the Camp Pendleton MOUT facility. Performance was characterized during these tests for various shot geometries and bullet speeds and calibers.
The A List

Session 2 (Tue Jan 24, 2017 12:10 PM 02:00 PM) The Mandeville Paradox: Private Vices, Public Benefits Mandeville, Fable of the Bees. Volume 1, pp. 1-181 [available in paperback, Liberty Fund] E.J. Hundert, The Enlightenment’s Fable, Chapter 1, “The foundations of a project” (Cambridge University Press, 1994) Albert Hirschman, The Passions and the Interests, pp. 1-66 (Princeton University Press, 1997) [available in paperback, Google Play, and Kindle]
Supervised Learning for Link Prediction Using Similarity Indices

The problem of link prediction gathered a lot of attention in the last few years, arising in different applications ranging from recommendation systems to social networks. In this paper, we will describe the most popular similarity indices, compare their performance in their ability to show links with the highest probability of being removed from initial network and describe the approach that allows to use them to predict missing links using supervised machine learning. We will show the accuracy of prediction of this method on examples of real networks.
Defect triangulation via demixing algorithms based on dictionaries with different morphological complexity

This work investigates the capabilities of methods based on morphological data demixing in application to anomaly detection and triangulation in solid media with significant structural complexity. The operating principle of morphological demixing is the decomposition of the spatiotemporal response of a structural system into two distinct contributions that are antithetical and complementary in terms of their morphology. While the bulk of the dynamic response can be generally represented in terms of smooth functions, the effect of nominally rare anomalies is captured through the projection of the data on a dictionary of sparse basis functions. The resulting sparse representation de facto distills the part of the response carrying the signature of the defects, thus promoting their agile triangulation. It has been shown that the triangulation of rare and weak anomalies can be enhanced through the adoption of dictionaries whose atoms feature, in addition to the above mentioned sparsity requirements, other morphological attributes that are germane to the scattered fields observed in the neighborhood of localized defects. This additional capability introduced through morphologically germane dictionaries results in a more robust discrimination between the signatures of true defects and the spurious response features due to other (often benign) structural elements. Our objective here is to explore the use of a dictionary constructed specifically to represent the anomalous response, whose atoms have morphological characteristics that mirror the structure produced in the response by a point excitation. The conclusions of our analysis are supported by numerical and laser-acquired experimental data.
Using a deductive approach in validating a measurement model

Information Systems researchers have employed a diversity of sometimes inconsistent measures of information system (IS) success, seldom explicating the rationale, thereby complicating the choice for future researchers. Moreover, less attention has been given to developing a standard measurement model that can be used across different context. Hence, not many researchers investigate the external validity of a measurement model, that is, to investigate the extent to which a theory or model can be generalized beyond the parameters of the previous research. In response to these and other issues, we re-validate and extend a measurement model that have been developed and validated in the Australian context to Malaysia. This measurement model is developed for measuring the impact of information systems to organisations. We aim to produce a robust and simple yet generalisable model that can be used across different contexts. This paper reported the findings of the IS-Impact model re-validation study at a state government in Malaysia with 77 users of a financial system that is currently being used at eleven state governments in Malaysia. A qualitative survey was employed to investigate the applicability of the IS-Impact model for the Malaysian context. A total of 278 impact statements or citations were extracted from the survey. Using a deductive approach, we coded or mapped these impact citations to the IS-Impact model which contains 37 perceptual measures along four dimensions for content validity. Findings indicate that 24 out of the 37 measures of the IS-Impact model were supported by the data. A new measure is identified and found reasonably appropriate to be added in the model. The outcome from the survey shows that the IS-Impact model can be used by both Malaysian and Australian organisations in measuring the success of their information systems. This study proved that a measurement model can be used across multiple contexts (e.g. types of IS, national, and language) even though some differences between contexts are applied.
New Developments in the Partial Control of Chaotic Systems

En esta tesis se hace un desarrollo ede un metodo de control llamado control parcial que se aplica a sistemas caoticos con escapes, con el fin de mantener la dinamica del sistema en una region de interes. Este metodo ha sido aplicado a diferentes modelos paradigmaticos en fisica no lineal, como el sistema de Lorenz, el mapa de Henon o el oscilador de Duffins. Diferentes modificaciones del metodo han sido desarrolladas asi como una generalizacion del mismo ue nos permite extender este esquema de control mas diversos.
Machine learning for image-based classification of Alzheimer's disease

Imaging biomarkers for Alzheimer’s disease are important for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable support for clinicians. This work investigates machine learning methods aimed at the early identification of Alzheimer’s disease, and prediction of progression in mild cognitive impairment. Data are obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL). Multi-region analyses of cross-sectional and longitudinal FDG-PET images from ADNI are performed. Information extracted from FDG-PET images acquired at a single timepoint is used to achieve classification results comparable with those obtained using data from researchquality MRI, or cerebrospinal fluid biomarkers. The incorporation of longitudinal information results in improved classification performance. Changes in multiple biomarkers may provide complementary information for the diagnosis and prognosis of Alzheimer’s disease. A multi-modality classification framework based on random forest-derived similarities is applied to imaging and biological data from ADNI. Random forests provide consistent similarities for multiple modalities, facilitating the combination of different types of features. Classification based on the combination of MRI volumes, FDG-PET intensities, cerebrospinal fluid biomarkers, and genetics out-performs classification based on any individual modality. Multi-region analysis of MRI acquired at a single timepoint is used to show volumetric differences in cognitively normal individuals differing in amyloid-based risk status for the development of Alzheimer’s disease. Reduced volumes in temporo-parietal and orbito-frontal regions in high-risk individuals from both ADNI and AIBL could be indicative of early signs of neurodegeneration. This suggests that volumetric MRI can reveal structural brain changes preceding the onset of clinical symptoms. Taken together, these results suggest that image-based classification can support diagnosis in Alzheimer’s disease and preceding stages. Future work may lead to more finely meshed prognostic data that may be useful clinically and for research.
An architecture for computer-based accounting information systems

The question addressed in this thesis is whether cost-effective, computer-based accounting systems can be used to generate "better" accounting information than existing transaction processing accounting systems. The first half of the thesis is devoted to gathering and summarizing information about how computer-based accounting systems work today, and what might constitute "better" accounting information. Data about present-day computerbased accounting systems was collected by mail questionnaires and personal reviews of widely-used packaged accounting software. Information about what constitutes "better" accounting information was collected, first, by reviewing the normative accounting literature, second, by reviewing the empirical literature on the inflation accounting experiments in the US (SFAS 33) and UK (SSAP 16), and third, by reviewing the academic literature on computer-based accounting, particularly the work of Ijiri, McCarthy, and Weber. To reconcile the apparent conflict between the empirical evidence that (a) inflation accounting is essential in times of very high inflation and (b) the empirical studies had found very little evidence of additional information content in SFAS 33 and SSAP 16 reports, it is suggested that the benefits of inflation accounting must only become apparent when general price-level changes exceed, say, 15%-20% p.a. Thus the ideas of the normative theorists are not rejected, and it is decided that a computer-based general ledger system