Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic models for mining imbalanced relational data

Most data mining and pattern recognition techniques are designed for learning from flat data files with the assumption of equal populations per class. However, most real-world data are stored as rich relational databases that generally have imbalanced class distribution. For such domains, a rich relational technique is required to accurately model the different objects and relationships in the domain, which can not be easily represented as a set of simple attributes, and at the same time handle the imbalanced class problem. Motivated by the significance of mining imbalanced relational databases that represent the majority of real-world data, learning techniques for mining imbalanced relational domains are investigated. In this thesis, the employment of probabilistic models in mining relational databases is explored. In particular, the Probabilistic Relational Models (PRMs) that were proposed as an extension of the attribute-based Bayesian Networks. The effectiveness of PRMs in mining real-world databases was explored by learning PRMs from a real-world university relational database. A visual data mining tool is also proposed to aid the interpretation of the outcomes of the PRM learned models. Despite the effectiveness of PRMs in relational learning, the performance of PRMs as predictive models is significantly hindered by the imbalanced class problem. This is due to the fact that PRMs share the assumption common to other learning techniques of relatively balanced class distributions in the training data. Therefore, this thesis proposes a number of models utilizing the effectiveness of PRMs in relational learning and extending it for mining imbalanced relational domains. The first model introduced in this thesis examines the problem of mining imbalanced relational domains for a single two-class attribute. The model is proposed by enriching the PRM learning with the ensemble learning technique. The premise behind this model is that an ensemble of models would attain better performance than a single model, as misclassification committed by one of the models can be often correctly classified by others. Based on this approach, another model is introduced to address the problem of mining multiple imbalanced attributes, in which it is important to predict several attributes rather than a single one. In this model, the ensemble bagging sampling approach is exploited to attain a single model for mining several attributes. Finally, the thesis outlines the problem of imbalanced multi-class classification and introduces a generalized framework to handle
Semiparametric and nonparametric methods in data mining and statistical learning with applications in public health surveillance and personalized medicine

YINGQI ZHAO: SEMIPARAMETRIC AND NONPARAMETRIC METHODS IN DATA MINING AND STATISTICAL LEARNING WITH APPLICATIONS IN PUBLIC HEALTH SURVEILLANCE AND PERSONALIZED MEDICINE (Under the direction of Michael R. Kosorok) The field of statistical learning has been growing rapidly over the past few decades, with a diverse range of applications. In this dissertation, we develop methodology mainly using semiparametric and nonparametric statistical learning techniques for the areas of public health surveillance and personalized medicine. Surveillance, providing early warning for impending emergencies, is a key function of public health. In Chapter 2, we propose a semiparametric spatiotemporal method to model spatiotemporal lattice data via a local linear fitting combined with day-ofweek effects, in which both spatial and temporal information are taken into account. Detection of abnormal events are carried out using an ARMA time series technique for residuals combined with a resampling approach to determine the threshold for significance. We conduct simulations to assess the performance of the proposed method. Also, the method is illustrated using the data on daily asthma admissions collected through North Carolina emergency departments that occurred between 2006 and 2007. There is increasing interest in personalized medicine: the idea of tailoring treatment for each individual to optimize patient outcome. In Chapter 3, we focus on the singledecision setup. We show that estimating such an optimal treatment rule is equivalent to a classification problem where each subject is weighted proportional to his or her clinical outcome, although the true class labels, to which treatment group the patients belong
A tool set for distributed Ada programming

A tool set for a distributed Ada programmable testbed is described. The testbed includes an Ada compiler to produce distributed programs and a unique tool set to permit safe and rapid program partitioning. The testbed, and the tool set in particular, are designed to support rapid prototyping of distributed systems and to support research on software-fault-tolerant and distributed-program-construction techniques. <<ETX>>
Maturing the software communications architecture for JTRS

This paper summarizes the status of the Joint Tactical Radio System (JTRS) software communications architecture (SCA). As the result of steps taken by the JTRS Joint Program Office (JPO) and the Modular Software-programmable Radio Consortium (MSRC), the SCA is at a level of maturity that ensures the goals of the JTRS program can be met. The SCA defines the framework for JTRS implementations in a manner that provides for communications interoperability, portability of application programs, and the ability to leverage commercial development and product availability into JTRS procurements. Key features of the SCA are the core software framework interfaces defined in a standard interface definition language (IDL), communication waveform application program interfaces (APIs) and API building blocks, and requirements for embedded INFOSEC and for INFOSEC APIs. The JTRS JPO has contracted with multiple vendors to implement these elements to validate the architecture. The results have shown that the architecture can be implemented in multiple products providing interoperable communications and that waveforms can be ported between different implementations with greatly reduced effort compared to that required in current radio designs. The SCA is designed and expected to grow as systems are procured and experience is gained with fielded equipment. The efforts to date show that the SCA meets or exceeds the goals and expectations of the JTRS JPO in defining the framework on which to base those equipment designs.
Bis{4-[(2-hydroxy­benzyl­idine)hydra­zino]-8-(trifluoro­meth­yl)quinolinium} sulfate tetra­hydrate

The geometric parameters of the title compound, 2C17H13F3N3O+·SO42−·4H2O, are in the usual ranges. There are two organic cations in the asymmetric unit with intra­molecular dihedral angles of 10.94 (9) and 7.14 (8)° between their aromatic ring mean planes. The crystal packing is stabilized by numerous N—H⋯O, O—H⋯O and O—H⋯N hydrogen bonds.
Unsupervised Joint Training of Bilingual Word Embeddings

State-of-the-art methods for unsupervised bilingual word embeddings (BWE) train a mapping function that maps pre-trained monolingual word embeddings into a bilingual space. Despite its remarkable results, unsupervised mapping is also well-known to be limited by the original dissimilarity between the word embedding spaces to be mapped. In this work, we propose a new approach that trains unsupervised BWE jointly on synthetic parallel data generated through unsupervised machine translation. We demonstrate that existing algorithms that jointly train BWE are very robust to noisy training data and show that unsupervised BWE jointly trained significantly outperform unsupervised mapped BWE in several cross-lingual NLP tasks.
. The role of N-terminal pro-brain natriuretic peptide in the assessment of right ventricular dysfunction in patients with acute pulmonary embolism: Results from Serbian University Pulmonary Embolism Registry (Super)

Background: Right ventricular dysfunction (RVD) is a well-known predictor of early death in patients with acute pulmonary embolism and thus early identification of RVD is critical in the risk stratification or management of acute pulmonary embolism (PE). Aim of this study was to investigate a useful role of cardiac biomarker NTproBNP for predicting echocardiographic right ventricular dysfunction in patients with acute pulmonary embolism. Methods: A retrospective analysis was performed in 195 consecutive adult patients with pulmonary embolism from the Serbian University Pulmonary Embolism Registry (SUPER 2015-2019) created by six university clinics: Military Medical Academy (Belgrade), Institute of Pulmonary Diseases (Sremska Kamenica), Clinical Center (Nis), University Clinic Zvezdara, Clinical Center (Kragujevac) and University Clinical Centre of Republic of Srpska (Banja Luka). All patients were divided into RVD group and non-RVD group according to whether there was increase in systolic pressure in right ventricle (>40mmHg) on echocardiography. Odds ratios (OR) and 95% confidence intervals (CI) assessing the risk factors for RVD were assessed by multivariate logistic regression. The ability of the NT proBNP in predicting the RVD was described by the Receiving Operating Curves analysis. Results: The mean age is a strong predictor of echocardiographic RVD in patients with PE. The simple measurements of this cardiac biomarker could be helpful in clinical decision-making or risk stratification in patients with PE.
Performance trade-off analysis in bidirectional network beamforming.

This research examines a two-way relay network consisting of two transceivers and multiple parallel relays, which are equipped with single antennas and operate in a halfduplex mode. In this system, the two transceivers prefer to exchange their information via relays. It is assumed that the relays have the full instantaneous channel state information (CSI) and relay the signals using the amplify-and-forward (AF) method. The performance of two AF bi-directional network beamforming schemes, namely multiple access broadcast channel (MABC) strategy and time division broadcast channel (TDBC) protocol, under joint optimal power control and beamforming design are studied and compared. To do so, we first design a TDBC-based bi-directional network beamformers, through minimization of the total power consumed in the whole network subject to quality of service (QoS) constraints, for the case with a direct link between the two transceivers. The corresponding power minimization problem is carried out over the transceiver transmit powers as well as relay beamforming weights, thus resulting in a jointly optimal power allocation and beamforming approach. We devise optimal secondorder cone programming based solutions as well as fast gradient-based solutions to these problems. Then these solutions are exploited to compare the performance of the underlying TDBC-based approach to that of the MABC-based technique developed in [1]. This comparison is important because the TDBC approach appears to have certain advantages which can be exploited towards improving the performance of two-way network beamforming. These advantages include the additional degree of freedom as well as the possibility of benefitting from the availability of a direct link between the two transceivers. Interestingly, in the absence of a direct link between the two transceivers, we show that when the QoS constraints are imposed to meet certain given probabilities of un-coded error (or, equivalently, to meet certain signal-to-noise ratio constraints), these two schemes perform closely in terms of the minimum total transmit power. However, when the QoS
Provisão de qualidade de serviço em redes integradas LTE-EPON

A crescente demanda por acesso banda larga movel tem motivado a implantacao da tecnologia long term evolution (LTE) pelas operadoras de redes moveis (MNOs), permitindo o fornecimento de novos servicos que demandam alta largura de banda e requisitos rigorosos de qualidade de servico (QoS). Este aumento da demanda de banda passante leva a necessidade de um grande numero de estacoes radio base e ao aumento na quantidade de trafego injetado no backhaul da rede movel (MBH). Para lidar com os elevados custos do MBH, redes opticas passivas (PONs) podem ser usadas para reduzir os custos do MBH usando sistemas fiber to anywhere (FTTx) ja implantado bem como lidar com a crescente demanda por acesso banda larga a Internet por usuarios moveis. Alem disso, a estacao radio base LTE, tambem conhecida como evolved NodeB (eNB), pode ser integrada a unidade de rede optica (ONU) das PONs em um dispositivo unico, chamado ONU-eNB. A ONU-eNB compete pela largura de banda com outras ONUs em PONs, o que pode potencialmente prejudicar o fornecimento de QoS aos usuarios moveis. Esta dissertacao propoe um framework para provisao de QoS em redes moveis LTE que empregam backhaul baseado em redes opticas passivas Ethernet (EPONs). Propoe tambem, um escalonador para redes LTE com o objetivo de prover requisitos de QoS e melhorar a vazao total. O framework introduz uma arquitetura funcional para o dispositivo integrado ONU-eNB, um esquema de mapeamento de QoS e um metodo para permitir que os escalonadores LTE atuais possam levar em conta informacoes disponiveis da rede de acesso movel (LTE) bem com informacoes sobre a rede de backhaul (EPON), a fim de melhorar o desempenho total da rede, especialmente quando o enlace de backhaul esta congestionado. Mostra-se como a implementacao do framework proposto melhora a utilizacao da rede e a provisao de QoS em uma rede integrada, mesmo sob variacao da carga de trafego no backhaul e na rede LTE. Abstract
A hybrid genetic algorithm and support vector machine classifier for feature selection and classification of gene expression

Advancement in gene expression technology offers the ability to measure the expression levels of thousand of genes in parallel. Gene expression microarray data is expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. Key issues that need to be addressed under such circumstances are the efficient selection of a small subset of genes that might profoundly contribute to disease identification from the thousand of genes measured on microarrays that are inherently noisy. This research deals with finding a small subset of informative genes from gene expression data which maximizes the classification accuracy. This research proposed a hybrid between Genetic Algorithm and Support Vector Machine classifier for selecting an optimal small subset of informative genes and classifying the optimal subset. Two benchmark data sets were used to evaluate the usefulness of the approach for small and high dimension data. Although, the experimental results showed that the hybrid method performed better than some of the best previous methods on small dimensional data, its performance deteriorated significantly on the higher dimensional data. An improved version of the hybrid method was designed by introducing a new algorithm for features selection based on improved chromosome representation to replace the original algorithm on the hybrid method which appeared to perform poorly on high dimensional data. The results of the gene expression microarray classification demonstrated that the proposed method performed better than the original and the previous methods. The informative genes from the experiment results proved to be biologically plausible when compared with the biological results produced from biologist and computer scientist researches.
OBJECT TRACKING WITH A MOVING CAMERA An Application of Dynaiiiic Motion Analysis

The task of detect.ing and tracking moving 0bject.s is part.icularly challenging if it. niust be perfornied with a camera that is itself moving. Yet, in applications such as automated surveillalice and navigation, this task niust be perforiiied continuously, in real time, and using only niodest computing hardware. Dynaniic analysis techniques provide a key to real-time vision. Through strategies analogous to foveation and eye tracking in humans, these tecliniques direct. analysis to critical regions of a scene, and decompose tlie complex niotion problem into a sequence of relatively simple tasks.
A Rhombic Dodecahedron Topology for Human-Centric Banking Big Data

Banks are collecting an unprecedentedly large amount of data about their customers from difference sources, considering their cyber, physical, social activities. The focus of this paper is to study the problem of information sharing and lower the communication overhead among different nodes for a specific data mining approach in distributed big data architectures. This problem can be abstracted as how to efficiently search under a specific cluster node topology. This paper proposes a new design rule for topologies including: 1) low coordination number; 2) high packing density; and 3) having a 3-D structure. According to this rule, a rhombic dodecahedron topology is proposed. A distributed banking big data mining framework based on the proposed topology is implemented. The experiments based on multioptimization benchmark functions show the excellent searching ability of the proposed topology; and a banking customer feature reduction prototype has been implemented to showcase the practicality of the data mining framework.
A generalized approach for the fuzzy commitment scheme

ABSTRACT Commitment Scheme is the way to hide the information and that information is revealed to the receiver in the later stage. The commitment scheme uses witness value to open the commitment. The role of witness value differentiates the fuzzy commitment scheme from a commitment scheme. Fuzzy commitment scheme allows the use of corrupted witness. In this paper, a generalized fuzzy commitment scheme is introduced that has the capability to reconstruct the secret from corrupted committed message and corrupted witness. The paper presents the fuzzy commitment scheme with the codes and analyzes the scheme with BCH and RS codes.
Machine Learning Methods in Coherent Optical Communication Systems

The future demand for digital information will outpace the capabilities of current optical communication systems, which are approaching their limits due to fiber intrinsic nonlinear effects. Machine learning methods promise to find new ways of exploiting the available resources, and to handle future challenges in larger and more complex systems. The methods presented in this work apply machine learning on optical communication systems, with three main contributions. First, a machine learning framework combines dimension reduction and supervised learning, addressing computational expensive steps of fiber channel models. The trained algorithm allows more efficient execution of the models. Second, supervised learning is combined with a mathematical technique, the nonlinear Fourier transform, realizing more accurate detection of high-order solitons. While the technique aims to overcome the intrinsic fiber limitations using a reciprocal effect between chromatic dispersion and nonlinearities, there is a non-deterministic impairment from fiber loss and amplification noise, leading to distorted solitons. A machine learning algorithm is trained to detect the solitons despite the distortions, and in simulation studies the trained algorithm outperforms the standard receiver. Third, an unsupervised learning algorithm with embedded fiber channel model is trained end-to-end, learning a geometric constellation shape that mitigates nonlinear effects. In simulation and experimental studies, the learned constellations yield improved performance to state-of-the-art geometrically shaped constellations. The contributions presented in this work show how machine learning can be applied together with optical fiber channel models, and demonstrate, that machine learning is a viable tool for increasing the capabilities of optical communications systems.
Investigating and Comparing Communication Media Used in Higher Education

A variety of communication technologies are used in higher education classrooms, however, limited research has been conducted comparing the relative educational impact of these media. This paper explores four types of communication media: email, instant messaging, text messaging, and video chat. Comfort level, frequency of use, usefulness, reasons for using these media, and differences between peer-to-peer and peer-toinstructor interactions were examined. Over 90% of students were comfortable with email, instant messaging, and text messaging compared to a 50% comfort level using video chat. Email, instant messaging, and text messaging were used frequently and significantly more with peers compared to the instructor. Video chat was used infrequently with both peers and the instructor. Email was perceived as significantly more useful when used with the instructor vs. peers, while instant messaging was rated as significantly more useful when communicating with peers vs. the instructor. Text messaging was viewed as equally useful with peers and the instructor. Video chat was ranked as the least useful communication media with both peers and the instructor. Student comments described unique benefits and challenges for each type of communication medium.
Elements of a design theory of nano-viral messages: a case study of #solar nanovirals

ABSTRACT Viral messages reach a large number of people at almost no cost. However, the majority of viral messages are based on shocking or entertaining content. Is it possible to make other kinds of content go viral, such as science and technology news? I use conceptual blending analysis to analyze five representative, very small messages about solar technology that went viral (nanovirals). I identify four distinct viral strategies that vary according to the number of belief systems used, and whether the viral message confirmed or contradicted central beliefs. Finally, I use information systems modeling to depict a common viral mechanism underlying the strategies. I conclude with a practical heuristic to guide the design of nanoviral messages.
Assessing Student Learning: Are Business Schools Making the Grade?

In April 2003, some standards regarding accreditation from the Association to Advance Collegiate Schools of Business (AACSB) International changed. Under the 2003 standards, students had to demonstrate learning achievement; thus, the focus shifted from what teachers taught to what students learned. Survey data indicates that although progress has been made in meeting the new assurance of learning (AoL) standards, many schools still fall short. In particular, schools are lagging in master of business administration (MBA) assessment and in using assessment data to close the loop. In this article, the author draws on recent survey data to describe the current state of assessment practice in business schools. She provides examples on how assessment results can be used to improve curricula. She also provides insight to problems some schools have in meeting the AoL standards.
Motion capture data segmentation using Riemannian manifold learning

Due to the inherent nonlinear nature of data, traditional linear methods have some limitations in finding the intrinsic dimensions of motion capture (Mo‐cap) data. Mo‐cap data are more in line with the characteristics of the manifold. Assuming that the data are initially a low‐dimensional manifold and uniformly sampled in high‐dimensional Euclidean space, manifold learning recovers low‐dimensional manifold structures from high‐dimensional sampled data. This paper proposes an automatic segmentation method based on geodesics by introducing a Riemannian manifold. We convert Mo‐cap data from Euler angles into quaternions, calculate the intrinsic mean of the motion sequence, hemispherize quaternions, and use logarithmic and exponential mapping to calculate geodesic distances instead of quaternions. The experimental results show that the algorithms can achieve automatic segmentation and have a better segmentation effect.