Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Prediction of Sepsis Considering Early Warning Scoring Systems

Sepsis is a noted cause of mortality in hospitalised patients, particularly patients in the ICU. Early prediction of sepsis facilitates a better targeted therapy which in turn reduces patient mortality rates. This study developed a methodology to allow automatic prediction of sepsis 6 hours prior to its clinical presentation. For this purpose, four vital signs comprising of HR, SBP, Temperature and respiratory rate, along with laboratory results for Platelets, WBC, Glucose and Creatinine are scored using Prehospital Early Sepsis Detection (PRESEP) and Sequential Organ Failure Assessment (SOFA) Early Warning Scoring (EWS) systems or screening tools and Systemic Inflammatory Response Syndrome (SIRS) criteria to allow under-sampling. The weighted scores obtained from the screening tools are also used to categorise patients into 4 groups with different probabilities of facing sepsis in ICU. The hourly data of each group is then trained through a KNN classifier to detect sepsis hours. The ensemble of classifiers are used to predict sepsis in all available dataset. The proposed model developed by UlsterTeam is trained on training setA and evaluated on training setB. The evaluation of the model on the training setB of the publically available dataset shows the Utility Score, accuracy, AUROC and AUPRC of the model are 0.27, 0.97, 0.71 and 0. 07 respectively.
Chapter 2 Energy Transfer Review

Excitation energy transfer of an excited state energy of the donor (D) to the ground state of the acceptor (A) is possible provided that the emission spectrum of the donor partially overlaps the absorption spectrum of the acceptor. This is a very important process, which occurs in a variety of situations. Excitation energy transfer can be grouped into heterotransfer versus homotransfer and radiative versus nonradiative transfer. Energy transfer from an excited donor (D ) to another that is chemically different, ground acceptor A, is called heterotransfer.
GradNet

Monte Carlo (MC) methods for light transport simulation are flexible and general but typically suffer from high variance and slow convergence. Gradientdomain rendering alleviates this problem by additionally generating image gradients and reformulating rendering as a screened Poisson image reconstruction problem. To improve the quality and performance of the reconstruction, we propose a novel and practical deep learning based approach in this paper. The core of our approach is a multi-branch auto-encoder, termed GradNet, which end-to-end learns a mapping from a noisy input image and its corresponding image gradients to a high-quality image with low variance. Once trained, our network is fast to evaluate and does not require manual parameter tweaking. Due to the difficulty in preparing ground-truth images for training, we design and train our network in a completely unsupervised manner by learning directly from the input data. This is the first solution incorporating unsupervised deep learning into the gradient-domain rendering framework. The loss function is defined as an energy function including a data fidelity term and a gradient fidelity term. To further reduce the noise of the reconstructed image, the loss function is reinforced by adding a regularizer constructed from selected rendering-specific features. We demonstrate that our method improves the reconstruction quality for a diverse set of scenes, and reconstructing a high-resolution image takes far less than one second on a recent GPU.
Aticle The Impact of Corporate Social Responsibility on Organisational Brand Image ; The Case of Les Brasseries du Cameroun

Purpose: The purpose of this study is to investigate the impact of corporate social responsibility on organizational brand image of Les Brasseries du Cameroun (SABC). The CSR levels used as variables here are Community, Workplace, Environment and Marketplace and it should be necessary to determine if it has either a positive or negative impact on the brand image. Design/Methodology/Approaches: The study made use of a Quantitative Research Design drawing from target population of 150 participants, out of which an actual sample size of 115 participants was successfully contacted as respondents. Through a structured questionnaire, data were collected and with the help of SPSS, analysis was made in this study. Findings: Findings of this study revealed that Brand image is considered the basic and significant important part of brand equity. The findings revealed that Corporate Social Responsibility has an influence on the brand image of the organization a level of significance of 5% was used and the overall results of the calculated F-statistics were 95% reliable. Specifically, the CSR Community and Workplace had a positive impact on brand image. Finding of this study also shows that CSR Environment and Marketplace had a negative impact on the brand image of Les Brasseries du Cameroun. Originality/Value: This research Topic, Questionnaire and methodology are new in the line of evaluating the impact of CSR on Organisational Brand Image especially in Brewery Company like Les Brasseries du Cameroun.
COMP 4801 Final Year Project 2018-19 Department of Computer Science , The University of Hong Kong Intermediate Report Project Title : A Motion Tracking Game Based On VR Student :

Virtual reality with motion tracking technology has been utilized in different industries around the world recently, from engineering simulation to gaming industry. Retrieved motion can be reconstructed inside the virtual world built by the software. It can then be used for simulating the interaction between users and computer-generated virtual world. However, the rudimentary implementation of such simulated interaction in video games can be improved further to elevate the whole gaming experience. In this paper, a new model named motiondriven player-game interaction is introduced as a conceptual solution to improve current virtual reality games with motion tracking devices. A detailed motion tracking algorithm will be designed and a demonstrative virtual reality game will be developed by the end of the project. HTC VIVE is chosen as the core virtual reality and motion tracking device in this project. The game is planned to be built in Unity Engine with the HTC VIVE. Currently, a motion tracking and pattern recognition algorithm is designed in order to efficiently detect and distinguish different postures and movements of the users and trigger the corresponding game events. The development of the demonstrative game in early January. and the project is slightly behind the schedule. Accuracy of the translated motion data and performance of the designed motion tracking algorithm are limited, but they could be improved with better algorithm implementation in the future work.
WSS 03 Applications , Products and Services of Web-based Support Systems 133

In this paper, a recommendation system for querying web pages is developed. When users query web pages through a search engine, the query keywords, browsed web pages, and feedback values are collected as user query transactions. A clustering algorithm based on bipartite graph analysis is designed to determine clusters of query keywords and the browsed web pages, called access preference clusters. Next, association rules of query keywords and web pages are mined for each access preference cluster. The feedback values of browsed web pages are incorporated into the calculation of the support and confidence for each association rule to reflect the subjective opinions. Based on the mined clusters and rules, the system applies the concept of collaborative filtering to recommend highly semantics relevant web pages in the access preference clusters partially covered by a user profile or a given query. The initial experiment result shows the system can improve the querying effect of
CONSTRUCTION Cross-sectional Analysis of Large International Engineering and Construction Firms

The construction industry generally plays a significant contribution to a country’s economy. Researches have shown that its output can represent as high as 15% of the gross domestic product (GDP) of a country (Mawhinney, 2001). With an estimated global market size of about $4 trillion in 2002, it stands as one of the largest industries in the world. However, unlike other large industries such as automobile and oil & gas, the construction industry remains very fragmented. This phenomenon accommodates various modes of survival and different configurations of competitive advantage. In the Engineering News Record’s “Top 225 International Contractors” ranking, among the most active engineering and firms are the European, the American, the Japanese and the Korean contractors – with the Chinese swiftly catching up with the trend in recent years. To date, limited research has been conducted comprehensively to analyze the performance of international engineering and construction (E&C) firms. Without some ground information that tracks the relative performance and competitiveness of these firms, other initiatives related to international management of E&C firms would be severely handicapped. As such, an objective assessment of the performance of E&C firms through fundamental and economic analyses is necessary. As a starting point, this article summarizes the pattern of growth of the global construction industry.
Research on the Application of GSR and ECG in the Usability Testing of an Aggregation Reading App

Usability testing is a very important step in improving App design and development. The traditional usability testing methods are based on users’expressions and behaviors, which hardly show users’ emotional experience and cognitive load in real time. The introduction of an electrophysiological technique can make up for the deficiency of the traditional usability testing methods. In this study, a usability testing was carried out with the old and the new version of an App software. The behavior and the subjective evaluation of the participants were recorded, and their GSR and ECG signals were collected. Then, 14 physiological characteristics, such as GSR-Mean, LF, HF, LF/HF, etc., were extracted from the GSR and ECG signals. These characteristics were analyzed, and a significance test of difference of the two versions was made. This research indicated that there is a certain application value of GSR and HRV in usability testing and evaluation of an App product. But the meanings of the physiological characteristics must be explained in combination with the behavior and subjective evaluation of users. The result can prove that physiological characteristics have obvious advantages in real-time monitoring users’ emotional changes, which can be helpful to find the usability problems of the product.
Bioleaching of a Complex Sulphide Ore by Mesophilic and Extremely Thermophilic Bacteria : Statistical Analysis of Data Using Ergun ' s Test

This study investigates the effects of bacterial strain, salinity and pH on the bioleaching of a complex ore using mesophilic and extremely thermophilic bacteria with the statistical analysis of the results using Ergun's test. The results showed that the ore was readily amenable to the selective extraction of zinc and the extreme thermophiles as confirmed by the statistical analysis displayed . superior kinetics of dissolution of zinc compared with the mesophiles. The bioleaching performance of the extreme thermophiles was found to improve in response to the increase in acidity (pH 2.0 to 1.0) while the activity of the mesophiles was adversely affected by decreasing the pH. The statistical analysis indicated that the effect of pH was insignificant in the range of pH 1.0-1.6 for the extreme thermophiles and pH I.6-2.0 for the mesophiles. The salinity was shown to have a suppressing effect on the mesophiles. However, the extreme thermophiles appear to be halophilic in character as they could operate efficiently under the saline conditions (1-4% CI").
The Meronomic Model of Cognitive Change and Its Application to Neolithic Çatalhöyük

The attempt to relate changes in human cognition to changes in human behaviour can be fruitful for cognitive science and archaeology alike. The process has the potential to enrich both our understanding of cognition, and interpretations of the archaeological evidence. A practical difficulty is the lack of clarity as to what cognitive change refers to. Characterisations vary considerably, even in regard to locus. Use of broad-brush terminology (e.g., `higher levels of consciousness') can add to the uncertainty. The present chapter argues that an effective way to understand cognitive change is in terms of conceptual structure, and the meronomic processes that apply. Building on the ontological distinction between meronomy and taxonomy, this approach allows cognitive change (by conceptual innovation) to be modeled precisely, while opening the way for quantification of conceptual creativity. This chapter shows how the model is derived, and explores some of the ways it might be connected with data from Catalhoyuk.
tw THE RELATIONSHIP BETWEEN GLOBALIZATION AND MILITARY EXPENDITURES IN G 7 COUNTRIES : EVIDENCE FROM A PANEL DATA ANALYSIS

This study explores the causal linkages between military expenditure and globalization in G7 countries (i.e. Canada, France, Germany, Italy, Japan, the UK, and the USA) by analyzing data for the period 1988-2011. Panel causality was examined to explain dependency and heterogeneity across countries. The results of one-way Granger causality show that globalization influenced military expenditures in Germany, and Japan. Moreover, there was no evidence that military expenditures caused globalization in any G7 country. The evidence from Italy shows interaction causality between globalization and military expenditure. Bootstrap panel Granger causality tests show that the causality between globalization and military expenditure varies across countries with different conditions. The findings of this study could provide important policy implications for the G7 countries under study.
Biologically Inspired Adaptive-Q Filterbanks for Replay Spoofing Attack Detection

Development of generalizable countermeasures for replay spoofing attacks on Automatic Speaker Verification (ASV) systems is still an open problem. Many countermeasures to date utilize bandpass filters to extract a variety of frequency band-based features. This paper proposes the use of adaptive bandpass filters, a concept adopted from human cochlear modelling to improve detection performance. Gains of filters used for subband based feature extraction are adaptively adjusted by varying their Q factors (Quality factor) as a function of input signal level to boost low amplitude signal components and improve the front-end’s sensitivity to them. This method is used to enhance information embedded in speech signals such as device channel effects which could be instrumental in distinguishing genuine speech signals from replayed ones. Three features extracted using the adaptive filter process yielded performance improvements over other auditory concepts-based baselines, showing the potential of using an adaptive filter mechanism for replay spoofing attack detection.
A Multi-Speaker Emotion Morphing Model Using Highway Networks and Maximum Likelihood Objective

We introduce a new model for emotion conversion in speech based on highway neural networks. Our model uses the contextual pitch, energy and spectral information of a source emotional utterance to predict the framewise fundamental frequency and signal intensity under a target emotion. We also incorporate a latent gender representation to promote cross-speaker generalizability. Our neural network is trained to maximize the error log-likelihood under an assumed Laplacian distribution. We validate our model on the VESUS repository collected at Johns Hopkins University, which contains parallel emotional utterances from 10 actors across 5 emotional classes. The proposed algorithm outperforms three state-of-the-art baselines in terms of the mean absolute error and correlation between the predicted and target values. We evaluate the quality of our emotion manipulations via crowd-sourcing. Finally, we apply our emotion morphing model to utterances generated by Wavenet to demonstrate our unique ability to inject emotion into synthetic speech.
The Dissemination and Pricing of Street Earnings

Investors rely on “street” earnings – earnings adjusted for consistency with analysts’ consensus forecasting basis and disseminated by forecast data providers (FDPs) – to measure quarterly earnings news. We examine an exogenous 2009 shock that significantly improved the timeliness of street earnings dissemination for a prominent FDP. We present evidence that this exogenous improvement in FDP timeliness increased the timeliness of investors’ response to earnings news. In the post-shock period, we find that, although both FDP dissemination and price discovery are concentrated to an intraday timescale, predictable information processing frictions explain the timeliness of the market reaction to street earnings, both directly and indirectly through their effect on FDPs. These frictions, along with after-hours liquidity constraints, limit investors’ ability to immediately self-process and trade on street earnings news.
Towards Large-Scale Hunting for Android Negative-Day Malware

Android malware writers often utilize online malware scanners to check how well their malware can evade detection, and indeed we can find malware scan reports that were generated before the major outbreaks of such malware. If we could identify in-development malware before malware deployment, we would have developed effective defense mechanisms to prevent malware from causing devastating consequences. To this end, we propose Lshand to discover undiscovered malware before day zero, which we refer to as negative-day malware. The challenge includes scalability and the fact that malware writers would apply detection evasion techniques and submission anonymization techniques. Our approach is based on the observation that malware development is a continuous process and thus malware variants inevitably will share certain characteristics throughout its development process. Accordingly, Lshand clusters scan reports based on selective features and then performs further analysis on those seemingly benign apps that share similarity with malware variants. We implemented and evaluated Lshand with submissions to VirusTotal. Our results show that Lshand is capable of hunting down undiscovered malware in a large scale, and our manual analysis and a third-party scanner have confirmed our negative-day malware findings to be malware or grayware.
Impact of Board Diversity on Corporate Social Responsibility (CSR) on Malaysian Public Listed Companies

Different theories and empirical research have been done and suggested to define and clarify how board diversity give impacts to corporate social responsibility (CSR). This study examines the impact of board diversity towards CSR based on the 50 Malaysian Public Listed Companies within the year 2010 to 2016. Board diversity examines the board gender, board age, board size, board independence and board tenure toward CSR. The methodology of the study used Ordinary Least Square (OLS) regression to determine the relationship of CSR and all variables under the board diversity. Various analysis being done starting from the Pearson Correlation Coefficient Test and Descriptive Statistics Analysis. Model determination in this study also being analysed by using The Breusch & Pagan Lagrange Multiplier (LM Test) and Hausman Test. Based on the findings, the regression results show the diversification of the board, and other control variables had a positive relationship with CSR. The results revealed that the impact of board diversity gives positive relationship towards the CSR performance among the companies except for board tenure that brings negative relationship towards CSR performance.
Creating a dynamically stable system in an agent based model using two contrasting modes of cognition

Cognition is a complex concept, many things have an influence on how we think and interact with our environment. Recently, a theory was developed regarding two contrasting modes of cognition, as framework for all cognition. The first mode is the coping mode, in which agents are inclined to make decisions that are focussed on their personal pressing need-fulfillment. The second is the co-creation mode, in which agents tend to direct their behaviour towards creating and maintaining an environment in which pressing needs are less likely to occur. According to theory, these two modes constitute what is known as core cognition, and they are present in every type of society, be it human or bacterial. This paper discusses a model built to test if, and under which conditions, it is possible that a multi-agent simulation will converge to a dynamically stable situation if it is populated by agents of both modes. The implementation is in NetLogo. Results showed that under many possible settings, the system converges to the same ratio of coping versus co-creation agents, and will remain dynamically stable from that point onwards.
Examining the Combination of Multi-Band Processing and Channel Dropout for Robust Speech Recognition

A pivotal question in Automatic Speech Recognition (ASR) is the robustness of the trained models. In this study, we investigate the combination of two methods commonly applied to increase the robustness of ASR systems. On the one hand, inspired by auditory experiments and signal processing considerations, multi-band band processing has been used for decades to improve the noise robustness of speech recognition. On the other hand, dropout is a commonly used regularization technique to prevent overﬁtting by keeping the model from becoming over-reliant on a small set of neurons. We hypothesize that the careful combination of the two approaches would lead to increased robustness, by preventing the resulting model to over-rely on any given band. To verify our hypothesis, we investigate various approaches for the combination of the two methods using the Aurora-4 corpus. The results obtained corroborate our initial assumption, and show that the proper combination of the two techniques leads to increased robustness, and to signiﬁcantly lower word error rates (WERs). Furthermore, we ﬁnd that the accuracy scores attained here compare favourably to those reported recently on the clean training scenario of the Aurora-4 corpus.
Investigating the effect of automatic MWE recognition on CCG parsing

We investigate the use of automatic Multiword Expressions (MWEs) recognition in parsing with Combinatory Categorial Grammar. We transform the representation of MWEs in CCGbank by collapsing them to one token. Our model significantly outperforms the baseline on the transformed gold standard showing the benefit of having this information at training time. It also performs significantly better on the transformed gold standard when the transformation is done before parsing as opposed to after parsing which shows that it can help the parser at prediction time. We conclude that despite the limited settings (our transformation algorithm is only able to deal with MWEs that do not cross constituent boundaries), our method can lead to improvements. We obtain different results with MWE recognisers that detect different types of MWE and therefore emphasize the need to experiment with different recognisers to find out which ones this method is best suited to.