Miracle-dz/UniFAR-FTUs huggingface dataset:
Using NLP Techniques to Enhance Content Discoverability and Reusability for Adaptive Systems

The volume of digital content resources written as text documents is growing every day, at an unprecedented rate. Because this content is generally not structured as easy-to-handle units, it can be very difficult for users to find information they are interested in, or to help them accomplish their tasks. This in turn has increased the need for producing tailored content that can be adapted to the needs of individual users. A key challenge for producing such tailored content lies in the ability to understand how this content is structured. Hence, the efficient analysis and understanding of unstructured text content has become increasingly important. This has led to the increasing use of Natural Language Processing (NLP) techniques to help with processing unstructured text documents. Amongst the different NLP techniques, Text Segmentation is specifically used to understand the structure of textual documents. However, current approaches to text segmentation are typically based upon using lexical and/or syntactic representation to build a structure from the unstructured text documents. However, the relationship between segments may be semantic, rather than lexical or syntactic. Furthermore, text segmentation research has primarily focused on techniques that can be used to process text documents but not on how these techniques can be utilised to produce tailored content that can be adapted to the needs of individual users. In contrast, the field of Adaptive Systems has inherently focused on the challenges associated with dynamically adapting and delivering content to individual users. However, adaptive systems have primarily focused upon the techniques of adapting content, not on how to understand and structure this content. Even systems that have focused on structuring content are limited in that they rely upon the original structure of the content resource, which reflects the perspective of its author. Therefore, these systems are limited in that they do not deeply ? understand? the structure of the content, which in turn, limits their capability to discover and supply appropriate content for use in defined contexts, and limits the content? s amenability for reuse within various independent adaptive systems. In order to utilise the strength of NLP techniques to overcome the challenges of understanding unstructured text content, this thesis investigates how NLP techniques can be utilised in order to enhance the supply of content to adaptive systems. Specifically, the contribution of this thesis is concerned with addressing the challenges associated with hierarchical text segmentation techniques, and with content discoverability and reusability for adaptive systems. Firstly, this research proposes a novel hierarchical text segmentation approach, named C-HTS, that builds a structure from text documents based on the semantic representation of text. Semantic representation is a method that replaces keyword-based text representation with concept-based features, where the meaning of a piece of text is represented as a vector of…
A Horse Race in High Dimensional Space

In this paper, we study the predictive power of dense and sparse estimators in a high dimensional space. We propose a new forecasting method, called Elastically Weighted Principal Components Analysis (EWPCA) that selects the variables, with respect to the target variable, taking into account the collinearity among the data using the Elastic Net soft thresholding. Then, we weight the selected predictors using the Elastic Net regression coefficient, and we finally apply the principal component analysis to the new “elastically” weighted data matrix. We compare this method to common benchmark and other methods to forecast macroeconomic variables in a data-rich environment, dived into dense representation, such as Dynamic Factor Models and Ridge regressions and sparse representations, such as LASSO regression. All these models are adapted to take into account the linear dependency of the macroeconomic time series. Moreover, to estimate the hyperparameters of these models, including the EWPCA, we propose a new procedure called “brute force”. This method allows us to treat all the hyperparameters of the model uniformly and to take the longitudinal feature of the time-series data into account. Our findings can be summarized as follows. First, the “brute force” method to estimate the hyperparameters is more stable and gives better forecasting performances, in terms of MSFE, than the traditional criteria used in the literature to tune the hyperparameters. This result holds for all samples sizes and forecasting horizons. Secondly, our two-step forecasting procedure enhances the forecasts’ interpretability. Lastly, the EWPCA leads to better forecasting performances, in terms of mean square forecast error (MSFE), than the other sparse and dense methods or naive benchmark, at different forecasts horizons and sample sizes.
Roof Segmentation based on Deep Neural Networks

The given paper investigates deep neural networks (DNNs) for segmentation of roof regions in the context of 3D building reconstruction. Point clouds as well as derived depth and density images are used as input data. For 3D building model generation we follow a data driven approach, because it allows the reconstruction of roofs with more complex geometries than model driven methods. For this purpose, we need a preprocessing step that segments roof regions of buildings according to the orientation of their slopes. In this paper, we test three different DNNs and compare results with standard methods using thresholds either on gradients of 2D height maps or on point normals. For the application of a U-Net, we transform point clouds to structured 2D height maps, too. PointNet and PointNet++ directly accept unstructured point clouds as input data. Compared to classical gradient and normal based threshold methods, our experiments with U-Net and PointNet++ lead to better segmentation of roof structures.
A statistical model for the dual polarised MIMO land mobile satellite channel at S-band

This thesis explores channel modelling approaches to the land mobile satellite (LMS) channel in S-band, focussing on the implementation of multiple input multiple output techniques through the use of dual polarisation. An Enhanced Statistical Model is presented and the output of this model is analysed and compared to the two current state-of-the-art models that simulate the dual polarised LMS channel, i.e. the statistical Liolis-CTTC model and the geometric ray-tracing QuaDRiGa model. The enhanced model builds on the Liolis-CTTC model and presents solutions to a number of issues that arise in the statistical modelling process. The enhancements in the new model include imposing temporal correlation on the slow variations without unwanted high frequency components from low-pass filtering, introducing Doppler effects including Doppler shaping of the fast variations, implementing a smooth state transition process and also implementing an interpolation process to sample the channel at the required sub-symbol rate for transmission. In addition to the analysis of the three models, real channel measurements of the dual polarised LMS channel from the MIMOSA campaign are analysed. A statistical comparison between the models and the real measurement data for simulated journeys in a number of user environments is conducted through analysis of the timeseries, the cumulative density function (CDF), average fading duration (AFD) and level-crossing rate (LCR). Capacity analysis and eigenvalue analysis is also conducted and allows for validation of the enhanced model. The comparisons with the measurement data show good agreement between the real measurement data and the enhanced model.
An LCC-Compensated Multiple-Frequency Wireless Motor System

In this paper, a novel kind of wireless motors, namely, the LCC-compensated wireless switched reluctance motor, is proposed and implemented. The definite advantages are that there is no power converter at the motor side and particularly no switched-capacitor array at the transmitter side to realize the multiple-frequency operation. The key is to develop an LCC compensation involving an inductor and two capacitors (the so-called LCC) for a multiple-frequency wireless power transfer system. As a result, only one transmitter is needed to targetedly feed three receivers, which directly energize the three phase windings of the motor. Particularly, there is no need to control switched capacitors to change the resonant frequency of the transmitter. Meanwhile, the burst firing control method is employed to realize the speed control. Both calculation and experimental results are presented to validate the feasibility of the proposed system. In the system prototype, the transmission distance can reach up to 150 mm and the transmission efficiency can be achieved up to 80%.
RBA: Detection and Protection Analysis Using Region-Based Algorithm in Ultra-Dense Networks

Due to broadcast nature of wireless medium, increased density of users, base stations (BSs) and small cell size in the ultra-dense network (UDN), the users are susceptible to be attacked by malicious eavesdroppers (EDs). However, UDN is provisioned with exceptionally adequate performance for all diversity of users along with the appearance of low power small cells in abundance. This paper deals with the secure transmission in UDN for high speed (or vehicular) users under the influence of single or multiple EDs. Thereby to ensure secure communication, we design a detection algorithm based on pattern matching for high-speed users by computing the secrecy rate loss (SRL) and simultaneously estimating the ranks to determine the sensitive area for attack. We then also develop the mathematical approach through the formation of a correlation matrix. We formulate the secure and energy efficient algorithm called region-based algorithm (RBA), which will protect users by encrypting the information transmitted in the sensitive area of attack. Simulation results reveal that the proposed algorithm is energy efficient and secure. RBA outperforms the conventional approach of encryption and also helps in saving energy (reserved for encryption) more than 90%. We also propose the optimal utilization of saved encryption energy to further improve the secrecy rate of moving users in the region of attack. The simulation results verify our theoretical approach and proposed approach well meets the target QoS in the attacked zone, to overcome the impact of ED.
Improvement in transmission ultrasound tomography by refined dynamic programming and spatial filter

Ultrasound Computer Tomography (USCT) is a novel and a promising low-cost technology for breast cancer imaging. USCT can provide speed-of-sound, attenuation and reflectivity information about the analyzed area. Among USCT methods, the ray-based approach (transmission tomography) is faster and it can offer a-priori information as initialization for more complex USCT methods. To contribute to the generation of such initializations, this work presents, on simulated media, how the use of a spatial filter jointly with refraction can improve the image reconstructed by transmission USCT. The following were used: k-wave toolbox for data generation on a heterogeneous medium; a numerical phantom with objects whose speed of sound are between [1460−1570]m/s; all objects have equal density and attenuation; a Refraction Tomography as transmission reconstruction algorithm (with and without regularization), where the forward problem was solved by Dijkstra’s shortest-path algorithm and the inverse problem with Simultaneous Iterative Reconstruction Technique; an alternative of the Modified Median Filter (m-MMF) as a spatial filter. Two sets of data were generated using 100 and 192 transducers (400kHz) respectively. All transducers were uniformly distributed around the medium. Evaluations were based on Relative-Residual-Error (RRE), normalized-root-mean-square-error (NRMSE) and structural similarity (Q). Comparing the reconstructions, better performance (NRMSE < 0.01) was found when the filter was applied with the Refraction Tomography, regardless of the use or not of the regularization. As expected, the reconstructions improved their performance as the number of transducers was increased. The results suggest that it is possible to improve and obtain satisfactory reconstructions via Transmission reconstruction algorithm in conjunction with a spatial filter.
Efficiency of a production line by application of the Kanban method

This paper aims to present the study on the implementation of the Kanban system for the "bicycle wheel" benchmark in demand-side conditions. Due to variations in customer orders, production on the "Wheels" line is inconsistent, with work volume being also variable. In this way, dead times appear, production stagnating at some times, and in others being overstretched. There is a problem of finding a method of leveling the production, which also leads to a better performance of the Logistics department within the "Expedition" service. The work was carried out following a study with data provided by SC MECHROM INDUSTRY SRL, a member of the CAMPAGNOLO group, a bicycle manufacturing company, exclusively for performance sports.
Artificial Neural Network Approach to Prediction of Protein-RNA Residue-base Contacts

Protein-RNA complexes play essential roles in a cell, and are involved in the post-transcriptional regulation of gene expression. Therefore, it is important to analyze and elucidate structures of protein-RNA complexes and also contacts between residues and bases in their interactions. A method based on conditional random fields (CRFs) was developed for predicting residue-base contacts using evolutionary relationships between individual positions of a residue and a base. Further, the probabilistic model was modified to improve the prediction accuracy. Recently, many researchers focus on deep neural networks due to its classification performance. In this paper, we develop a neural network with five layers for predicting residue-base contacts. From computational experiments, in terms of the area under the receiver operating characteristic curve (AUC), the predictive performance of our proposed method was comparable or better than those of the CRF-based methods.
Joint TOA/DOA Estimation Using the SAGE Algorithm in OFDM Systems with Virtual Carriers

In this paper, we investigate the space-alternating generalized expectation-maximization (SAGE) algorithm with virtual carriers (VCs) in orthogonal frequency division multiplexing (OFDM) systems. The channel frequency response (CFR) at VCs cannot be estimated accurately due to edge effect after inverse discrete Fourier transform (IDFT). To solve the problem, an improved channel estimation method is introduced to minimize the errors of CFR via iterative technique. Then we apply the SAGE algorithm to estimate the direction of arrival (DOA) and time of arrival (TOA). The SAGE algorithm shows more excellent performance with higher resolution ability than subspace-based algorithms. Based on Monte Carlo trials, we test the performance of the improved method in terms of mean absolute error (MAE) at different signal-to-noise ratios (SNR). Simulation results indicate that the improved method behaves better at any SNR than the conventional DFT-based method.
Acoustic Scene Classification Based on Dense Convolutional Networks Incorporating Multi-channel Features

Motivated by the state-of-the-art performance of Dense Convolutional Networks (DenseNet) on highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN and ImageNet), this work presents improvements to the Acoustic Scene Classification (ASC) task of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2017) based on an optimized DenseNet model. Multi-channel Convolutional Neural Network (CNN) is also explored to extract features from different audio channels in an end-to-end manner. In the experiments, the proposed model is compared with the challenge baseline model of DCASE 2017 and several other state-of-the-art CNN architectures subject to the classification accuracy on the same open-source DCASE datasets. The results show that the proposed DenseNet-based architecture can achieve a superior performance in classification accuracy but with a lower model complexity in contrast with other models.
Computational Methods in Environmental and Resource Economics

Computational methods are required to solve problems without closed-form solutions in environmental and resource economics. Efficiency, stability, and accuracy are key elements for computational methods. This review discusses state-of-the-art computational methods applied in environmental and resource economics, including optimal control methods for deterministic models, advances in value function iteration and time iteration for general dynamic stochastic problems, nonlinear certainty equivalent approximation, robust decision making, real option analysis, bilevel optimization, solution methods for continuous time problems, and so on. This review also clarifies the so-called curse of dimensionality, and discusses some computational techniques such as approximation methods without the curse of dimensionality and time-dependent approximation domains. Many existing economic models use simplifying and/or unrealistic assumptions with an excuse of computational feasibility, but these assumptions might be able to be relaxed if we choose an efficient computational method discussed in this review.
An efficient virtual machine allocation algorithm for parallel and distributed simulation applications

Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs. Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification‐based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP‐based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang‐scheduling‐based, Makespan‐based, and Max‐Min‐based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication‐sensitive PADS applications, the advantage of ERPA is more significant.
A Hybrid MapReduce Implementation of PCA on Tianhe-2

“Big Data” has been a popular word anywhere. Researchers want the data processing more efficient. PCA algorithm is an effective data reduction algorithm applied to almost all big data fields. Meanwhile, there are many Machine Learning Algorithm Library applied to provide commonly-used algorithm, but these algorithms do not make good use of the resources of the supercomputer system. This paper uses MapReduce Model to design and implement PCA algorithm using MPI + OpenMP + SIMD hybrid accelerator programming tools on Tianhe-2 and get a significant speedup.
Establishing the Need to Use Mobile Phones in Distance Learning in Ghana

distances. The trend today, is young men and women of Ghana use a lot more mobile devices such as ipads, galaxy tabs, kindles, tablets PCs, Netbooks and the like for their day to day activities. Mobile phone has found a place as a permanent companion of the poor and the rich. It is no longer a device for showing off one’s prowess in
Programming with Implicit Values, Functions, and Control (or, Implicit Functions: Dynamic Binding with Lexical Scoping)

We introduce two new language features, called implicit functions and implicit control. Both generalize implicit values which are a typed implementation of dynamic binding. Implicit functions are bound dynamically but evaluated in the lexical scope of their binding. We show how this small generalization from regular implicit values leads to better abstraction. In particular, implicit functions encapsulate (side) effects at the definition site, as opposed to leaking them to the call site. Implicit control further generalizes implicit functions by adding the ability to return into the lexical scope of the binding or to resume to the call-site. We formalize the new features as an extension to Moreau’s calculus of dynamic binding (1998). Unifying all three language features in one framework guarantees that the interaction between implicit values, functions, and control is well-defined. We also show how our semantics correspond to a macro-translation into algebraic effect handlers.
Auto-Extraction and Integration ofMetrics forWeb User Interfaces

Metric-based assessment of web user interface (WUI) quality attributes is shifting from code (HTML/CSS) analysis to mining webpages’visual representations based on image recognition techniques. In our paper, we describe a visual analysis tool which takes a WUI screenshot and produces structured and machine-readable representation (JSON) of the interface elements’ spatial allocation. The implementation is based on OpenCV (image recognition functions), dlib (trained detector for the elements’ classiﬁcation), and Tesseract (label and content text recognition). The JSON representation is used to automatically calculate several metrics related to visual complexity, which is known to have major effect on user experience with UIs. We further describe a WUI measurement platform that allows integration of the currently dispersed sets of metrics from different providers and demonstrate the platform’s use with several remote services. We perform statistical analysis of the Journal of Web Engineering, Vol. 17 6&7, 561–590.
Return Instruction Identiﬁcation in Binary Code with Machine Learning

Binary code analysis is the main method for malware analysis. In this paper, the analysis is started by identifying return instructions to disassemble binary code. The return instruction identification problem is converted into a binary classification problem: is a byte in binary code the first byte of a return instruction? The 32 bytes around a byte in binary code are considered the feature of the byte. A multilayer perceptron is employed to build the classification model. Then, the model is trained with 1,383 binaries from Windows XP SP3. The evaluation results on several open sources show that our approach is feasible and has high accuracy.