Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring CRM and SRM User Satisfaction in B2B Relationships Management

This paper explores CRM and SRM systems user satisfaction and its effect on Business to Business (B2B) relationships management. In the literature, a lot has been said about Customer Relationship Management (CRM) particularly in B2C context. However, fewer attempts to explore Supply Relationship Management (SRM) have been made. An inductive approach was adopted to explore CRM and SRM user satisfaction in achieving stronger B2B relationships. A single case study using observation, focus groups and interviews was implemented. Findings show that user involvement in systems design generates useR motivation to use the systems which in turn improves user satisfaction. This therefore, improves the company relationships with both suppliers and customers.
User Assistance for Health Care Information Systems

Health care is a domain characterized by low and slow adoption of IS compared to other domains, such as manufacturing or avionics. It seems to be promising to transfer existing (design) knowledge and solutions from these domains to the HIS context to increase effectiveness of HIS by, for example, improving usability of HIS and accessibility to assistive technology. Inattention to usability and accessibility of HIS will result in ineffective use of HIS, user dissatisfaction with HIS, the emergence of workarounds, and, ultimately, in a potentially decreased quality of care. One concept in IS research that can address these challenges is user assistance. With our research, we aim to enable users to better understand the functional capabilities of HIS, to effectively master their tasks using HIS, and, ultimately, to increase their performance.
Combination of linear power flow tools for voltages and power estimation on MV networks

European grid codes define new network management rules. To answer these decrees, it is important to estimate accurately the voltages and powers inside distribution networks. The intermittent nature of renewable sources leads to consider stochastic variables in power flow algorithms. A review of power flow methods and their ability to comply with these requirements is done, which shows that computationally demanding non-linear methods have to be discarded when dealing with stochastic data and considering limited calculation time. A combination of linear methods is proposed, for which average errors in power and voltage are quite low, when applied to a real-life distribution network. The validity domain of the method is also presented.
Aging Aware Retraining for Memristor-based Neuromorphic Computing

Memristor-based crossbars, which can achieve 1-2 orders of magnitude energy efficiency improvement over digital machines, have been introduced to accelerate the neural networks of machine learning tasks. Due to the high voltage pulses repeatedly applied onto memristors during programming and online tuning, the effective resistance ranges of the memristors actually decrease as a result of aging, which eventually impair the inference accuracy of the neural network running on the memristor-based crossbar. In this paper, we propose an algorithm-hardware co-design framework combining aging aware retraining and gradient sparsification to mitigate the impact of aging and extend the lifetime of the crossbar. Experimental results show that the proposed method can effectively increase the inference accuracy by up to 16% even with severe aging, while the crossbar lifetime can be extended by up to $2.7\times$.
Time Series Knee Joint Angle Analysis During Gait for Patients with Down Syndrome by 3d Pose Estimation

Down syndrome is the most common chromosomal abnormality. In recent years, the frequency of births with Down syndrome has been increasing in Japan. One of the reasons for this is the trend toward late childbearing. Many children with Down syndrome often have gait problems such as Genu valgum and Genu varum. In order to solve such gait problems, custom- made insoles need to be created. This is because the shape of the feet and the way of walking vary from patient to patient. In addition, it is necessary to investigate whether these custom-made insoles are suitable for the patients with Down syndrome or not. Currently, the evaluation is done visually by doctors and physical therapists, however the criteria for judgment are unclear. Therefore, we worked to develop a system to determine whether custom-made insoles would improve Genu valgum and Genu varum. Since the children with Down syndrome rarely walk straight when taking gait videos, we focused on performing gait analysis in 3D instead of 2D, which has been performed previously. In this study, we estimated the joint position coordinates of a person from a walking video using 3D pose estimation in order to quantitatively evaluate the gait condition of children with Down syndrome. Additionally, by detecting the time of knee joint loading and measuring the knee joint angle, we were able to propose a system that can confirm symptoms such as genu valgum and genu varum. This system is expected to be easier than the existing 3D analyzers for gait analysis of patients with Down syndrome. As a future work, the accuracy of the system itself needs to be evaluated using an existing 3D motion analyzer that measures with markers.
RAPSIM, a statistical toolbox for analysing and visualising large simulation data sets

Crop simulation models have the capacity to undertake extensive factorial pseudo-experiments over many years, generating large data sets [around 0.5 to 1 Gigabytes] of crop performance such as those available in “National Whopper Cropper” and “WA Wheat” derived from the APSIM crop simulation model. The analysis of such datasets is beyond the capacity of most commercial spreadsheet or standard statistical programs and generally proceeds by reducing the dataset to fit the capacity of the analytical program. RAPSIM is a collection of statistical functions which provide an easy approach to the analysis and presentation of large, structured data sets using the language of the R Statistical System. It has also been integrated as a menu within the R Statistical System on a PC computer running Windows XP. We show that the analysis and visualisation of simulated data from the large “WA Wheat” data set of crop performance using RAPSIM provides a structured approach to summarising and understanding the key sources of variability in the datasets generated by APSIM models.
Improving the quality of text clustering and cluster labeling

The abundance of available electronic information is rapidly increasing with the advancements in digital processing. Furthermore, huge amounts of textual data have given rise to the need for efficient techniques that can organize the data in manageable forms. In order to tackle this challenge, clustering algorithms try to group automatically similar documents. While clustering plays a significant role that helps to categorize documents, it owes intrinsic limits when it comes to allowing human users to understand the content of documents at a deeper level. This is where cluster labeling techniques come into the scene. The goal of cluster labeling is to label i.e., describe in an informative way clusters of documents according to their content. Document clustering and cluster labeling are two vital problems in the information retrieval domain because of their ability to organize increasing amount of texts and describe such the huge amount in a concise way. In this thesis, we have addressed these problems in four parts. In the first part, we investigate how we can improve the effectiveness of text clustering by summarizing some documents in a corpus, specifically the ones that are much significantly longer than the mean. The contribution in this part is twofold. First, we show that text summarization can improve the performance of classical text clustering algorithms, in particular, by reducing noise coming from long documents that can negatively affect clustering results. Moreover, we show that the clustering quality can be used to quantitatively evaluate different summarization methods. In the second part, we explore a multi-strategy technique that aims at enriching documents for improving clustering quality. Specifically, we use a combination of entity linking and document summarization, to determine the identity of the most salient entities mentioned in texts. We further investigate ensemble clustering in order to combine multiple clustering results, generated based on the combination of the specific set of features, into a single result of better quality. In the third part, we investigate the problem of cluster labeling whose quality obviously depends on the quality of document clustering. To this end, we first explore and categorize cluster labeling techniques, providing a thorough discussion of the relevant state-of-the-art literature. In the fourth part, we then present a fusion-based topic modeling approach to enrich documents’ vectors of corpus with the aim of improving the quality of text clustering. We further exploit such vectors through a fusion method for cluster labeling. Finally, we experimentally prove the effectiveness of our solutions, explained in four parts, in the clustering and cluster labeling problems with various datasets.
Advanced structural disaster damage assessment based on aerial oblique video imagery and integrated auxiliary data sources

Following natural and man-made disasters, comprehensive and reliable information on the nature, extent, and actual consequences of an event is required. Obtaining such information is particularly challenging following unheralded disasters, such as earthquakes or industrial accidents. In those situations, currently operational space-based sensors may not be able to provide timely data. In addition, even high spatial resolution satellites (< 1m) are limited in their capacity to reveal true 3D structural damage at a level of detail necessary for appropriate disaster response. In this study we investigated the use of oblique aerial video imagery for systematic quantitative damage assessment. Specifically, the following issues were addressed: (i) extraction of individual frames from aerial TV video data, and subsequent enhancement of frame information content using a synthetic aperture approach, and by stacking adjacent frames; (ii) spatial registration of individual frames based on automatically extracted positional information, and (iii) damage analysis based on HIS values and edge elements. We created a working environment that facilitates the video-based damage assessment process and integration with auxiliary data. The limited success of the automatic damage assessment was caused by poor image quality and empirically determined damage threshold values. We provide recommendations for improved image acquisition, and plan further work focusing on generic texture parameters and object geometry. As an illustration, we used aerial video data acquired in May 2000 over Enschede, the Netherlands, following the explosion of a fireworks factory that severely damaged or destroyed nearly 500 buildings and caused 22 fatalities.
Performance Evaluation of Feature Selection Algorithms in Educational Data Mining

: Educational Data mining(EDM)is a prominent field concerned with developing methods for exploring the unique and increasingly large scale data that come from educational settings and using those methods to better understand students in which they learn. It has been proved in various studies and by the previous study by the authors that data mining techniques find widespread applications in the educational decision making process for improving the performance of students in higher educational institutions. Classification techniques assumes significant importance in the machine learning tasks and are mostly employed in the prediction related problems. In machine learning problems, feature selection techniques are used to reduce the attributes of the class variables by removing the redundant and irrelevant features from the dataset. The aim of this research work is to compares the performance of various feature selection techniques is done using WEKA tool in the prediction of students’ performance in the final semester examination using different classification algorithms. Particularly J48, Naïve Bayes, Bayes Net, IBk, OneR, and JRip are used in this research work. The dataset for the study were collected from the student’s performance report of a private college in Tamil Nadu state of India. The effectiveness of various feature selection algorithms was compared with six classifiers and the results are discussed. The results of this study shows that the accuracy of IBK is 99.680% which is found to be high than other classifiers over the CFS subset evaluator. Also found that overall accuracy of CFS subset evaluator seems to be high than other feature selection algorithms. The future work will concentrate on the implementation of a proposed hybrid method by considering large dataset collected from many institutions.
Convolutional neural network optimization using genetic algorithms

CONVOLUTIONAL NEURAL NETWORK OPTIMIZATION USING GENETIC ALGORITHMS Name: Reiling, Anthony Joseph University of Dayton Advisor: Dr. Eric Balster This thesis proposes the use of a genetic algorithm (GA) to optimize the accuracy of a convolutional neural network (CNN). The GA modifies the structure of the CNN such as the number of convolutional filters, strides, kernel size, nodes, learning parameters, etc. Each modification of the network is trained and evaluated. Mutation of evolved networks create more successful networks over multiple generations. The final evolved network is 4.77% more accurate than a network proposed in the previous literature. Additionally, the evolved network is 13.4% less computationally complex.
A Method of Tracing Particles in Irregular Unstructured Grid System

Particle localization is commonly encountered in many areas of numerical computations. A method of tracing particles in irregular unstructured grid system is presented. The method introduced an additional set of indexical grid system to overlap the original irregular unstructured computational grid system. The particle tracing was first conducted in the indexical grid system to obtain which indexical grid cell the particle lies in, and then was carried out among the original irregular computation grid cells which were included in this localized indexical grid cell. The new method is easy to realize in numerical calculations and can apparently improve the efficiency of particle localization.
Compressive Sensing Based Hardware Design for Channel Estimation of Wideband Millimeter Wave Hybrid MIMO System

Channel estimation is a crucial issue for hybrid multiple-input multiple-output architecture of wideband wireless millimeter wave system. In this paper, we present a hardware implementation of channel estimation based on compressive sensing method. We exploit sparsity in channel space and take advantage of both the time domain and the frequency domain to effectively reduce the computational complexity. We then disassemble the sensing matrix into smaller dimensional matrices to further simply the sensing formula by utilizing the orthogonality of the DFT codebook. The sensing issue is solved by the generalized orthogonal matching pursuit with Cholesky decomposition techniques, which achieves a significant reduction in the number of computations. Finally, we evaluate the performance of the proposed method with the perfect channel state information, and hardware performance by fixed-point analysis and RTL design and synthesis results.
Orientation and Mobility Client Evaluation Tool (CET) Adult

Abstract A project team at Guide Dogs NSW/ACT, Australia designed, tested, and statistically validated over a five-year period, a client evaluation tool to assess the progress or achievement of adult clients (18+ years) who participate in orientation and mobility (O&M) programs (including Guide Dog programs). The tool is administered at the commencement of the client’s mobility program and again at its conclusion to provide a pre/post measure. The tool comprises two parts. Part 1, completed by the O&M specialist, measures the degree of vision impairment; client complexity, and the environmental complexity in which the training is being conducted. Part 2 includes the mobility specialist’s rating of the client’s skill and confidence level; and the client’s rating of their own skill and confidence as well as a rationale for their ratings. The tool appears effective in measuring client outcomes for all types of O&M program in any environment; is quick and easy to use, and non-invasive. The paper includes the results of the tool applied by 57 O&M specialists to 361 clients from July 2014 to July 2015 revealing significant improvements in skill and confidence post-program.
Partitioning Scheme and Performance Analysis of Distributed Digital Low-Dropout Regulators in SoC

This paper presents a partitioning scheme based on Fiduccia-Mattheyses (FM) Algorithm that targets at designing fully synthesizable spatially distributed digital low-dropout regulators (D-DLDRs) to power up a large digital system. Modeling methods of DLDRs are reviewed and applied to analyze dual and multiple distributed DLDRs. The analysis is confirmed by simulation results and suggests the advantages of using distributed DLDRs over stand-alone DLDRs.
Definition Extraction and Annotation in Sources of Law

Methods to identify, annotate and extract definitions from legal texts are investigated in this thesis. Literature suggests that there is no common model of definitory characteristics in general, and legal definitions in particular. Definitions in law tend to have some distinct properties such as a surface form, coverage and scope of applicability. Therefore, a novel model is proposed to capture these particular characteristics of legal definitions. A possible user interface to present definitions to end-users is also suggested. Definition extraction experiments are performed on a dataset of Dutch laws. This is firstly done using a rule-based approach where rules were formulated from another dataset. Supervised machine-learning using support vector machines was conducted afterwards in order to improve the rule-based results. The best setup achieved an F-score of 0.72 with precision 0.79 and recall 0.68. Varying the dataset size showed no significant improved in the overall scores.
A text to speech synthesis system for Maltese.

The subject of this thesis covers a considerably varied multidisciplinary area which needs to be addressed to be able to achieve a text-to-speech synthesis system of high quality, in any language. This is the first time that such a system has been built for Maltese, and therefore, there was the additional problem of no computerised sources or corpora. However many problems and much of the system designs are common to all languages. This thesis focuses on two general problems. The first is that of automatic labelling of phonemic data, since this is crucial for the setting up of Maltese speech corpora, which in turn can be used to improve the system. A novel way of achieving such automatic segmentation was investigated. This uses a mixed parameter model with maximum likelihood training of the first derivative of the features across a set of phonetic class boundaries. It was found that this gives good results even for continuous speech provided that a phonemic labelling of the text is available. A second general problem is that of segment concatenation, since the end and beginning of subsequent diphones can have mismatches in amplitude, frequency, phase and spectral envelope. The use of-intermediate frames, build up from the last and first frames of two concatenated diphones, to achieve a smoother continuity was analysed. The analysis was done both in time and in frequency. The use of wavelet theory for the separation of the spectral envelope from the excitation was also investigated. The linguistic system modules have been built for this thesis. In particular a rule based grapheme to phoneme conversion system that is serial and not hierarchical was developed. The morphological analysis required the design of a system which allowed two dissimilar lexical structures, (semitic and romance) to be integrated into one overall morphological analyser. Appendices at the back are included with detailed rules of the linguistic modules developed. The present system, while giving satisfactory intelligibility, with capability of modifying duration, does not include as yet a prosodic module.
Using the car in motion relative wind power for charging the car batteries

This paper explores the possibility of using the power of the wind created by a car-in-motion for charging the car batteries. When a vehicle is moving even in a still atmosphere, a wind will act on a turbine mounted on the vehicle, this wind is called further relative wind. We study the possibility of using only the power of relative wind. The main disadvantage of electric and hybrid cars is the limited capacity of their storage batteries which impedes them from covering long distances. The obvious advantages of electric cars are canceled by the limited amount of energy stored in their batteries. Also, it is known that the conversion of an energy into another always comes with losses; therefore, a perpetuum mobile is not possible. We do not try to violate this principle, but we try to increase the efficiency of a wind turbine mounted on a car in motion. The wind turbine must be mounted on the vehicle chassis in a way to satisfy the aerodynamic and aesthetic criterions. So, the turbine system shall charge the car propulsion battery without creating supplementary friction. Results regarding the impact of the relative wind on the car conduct are analyzed using software named ANSYS and experimental tests and they are exposed in present paper. The base of this research is to demonstrate if the relative wind is sustainable to charge the battery in motion even for small improve.
A Multi-class Classification Approach for Weather Forecasting with Machine Learning Techniques

Weather forecasting is vital as extreme weather events can cause damage and even death. The science of meteorology in recent decades has made spectacular progress resulting in more reliable forecasts. Although meteorologists now have adopted modern tools for accurate weather forecasting, extreme and sudden climate changes in the atmosphere have posed accurate weather forecasting even more valuable. In this research paper, we present a multi-class classification methodology from machine learning (ML) in order to predict the five classes of weather conditions. Specifically, the One-Against-One (OAO) and One-Against-All (OAA) strategies are evaluated under Support Vector Machine (SVM) and Logistic Regression (LR) assuming, for comparison, Random Forest (RF) and k-Nearest Neighbours (k-NN). The prevailing model is linear SVM under the OAO method achieving the average Accuracy, Precision, Recall, F-Measure and Area Under Curve (AUC) of 96.64%, 96.8%, 96.6%, 96.6% and 98.5%, respectively.
Unstructured Weight Pruning in Variability-Aware Memristive Crossbar Neural Networks

Pruning is a process of removing unwanted neurons from neural network computations. In Neural Networks, pruning creates sparse information processing, which can improve the overall generalisation and energy efficiency of the network. By excluding redundant weight values which are not contributing significantly to the system performance, hardware complexity can be reduced while maintaining the recognition accuracy. This paper evaluates the effectiveness of unstructured pruning on memristive crossbar based neural nodes in Artificial Neural Network (ANN) and Binary Weighted Neural Network (BWNN) architectures. The impact of pruning is analysed in terms of inference accuracy, energy consumption and area efficiency. The robustness of the pruned system is validated under the influence of conductance variability, bit errors, and multiply and accumulate errors.