Miracle-dz/UniFAR-FTUs huggingface dataset:
Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms

Machine Learning and Data Mining are two key components in decision making systems which can provide valuable in-sights quickly into huge data set. Turning raw data into meaningful information and converting it into actionable tasks makes organizations profitable and sustain immense competition. In the past decade we saw an increase in Data Mining algorithms and tools for financial market analysis, consumer products, manufacturing, insurance industry, social networks, scientific discoveries and warehousing. With vast amount of data available for analysis, the traditional tools and techniques are outdated for data analysis and decision support. Organizations are investing considerable amount of resources in the area of Data Mining Frameworks in order to emerge as market leaders. Machine Learning is a natural evolution of Data Mining. The existing Machine Learning techniques rely heavily on the underlying Data Mining techniques in which the Patterns Recognition is an essential component. Building an efficient Data Mining Framework is expensive and usually culminates in multi-year project for the organizations. The organization pay a heavy price for any delay or inefficient Data Mining foundation. In this research, we propose to build a cost effective and efficient Data Mining (DM) and Machine Learning (ML) Framework on cloud computing environment to solve the inherent limitations in the existing design methodologies. The elasticity of the cloud architecture solves the hardware constraint on businesses. Our research is focused on refining and enhancing the current Data Mining frameworks to build an Enterprise Data mining and Machine Learning Framework. Our initial studies and techniques produced very promising results by reducing the existing build time considerably. Our technique of dividing the DM and ML Frameworks into several individual components (5 sub components) which can be reused at several phases of the final enterprise build is efficient and saves operational costs to the organization. Effective Aggregation using selective cuboids and parallel computations using Azure Cloud Services are few of many proposed techniques in our research. Our research produced a nimble, scalable portable architecture for enterprise wide implementation of DM and ML frameworks. INDEX WORDS: FinTech,Big Data, Data Mining, Machine Learning, Cloud Computing, Enterprise Architecture ENTERPRISE DATA MINING & MACHINE LEARNING FRAMEWORK ON CLOUD COMPUTING FOR INVESTMENT PLATFORMS
Big Data & Macroeconomic Nowcasting: Methodological Review

This paper is concerned with an introduction to big data which can be potentially used in nowcasting the UK GDP and other key macroeconomic variables. We discuss various big data classifications and review some indicative studies in the big data and macroeconomic nowcasting literature. A detailed discussion of big data methodologies is also provided. In particular, we focus on sparse regressions, heuristic optimisation of information criteria, factor methods and textual-data methods.
THE INFLUENCE OF SERIES COMPENSATING FACTS DEVICES ON SHORT-CIRCUIT BEHAVIOR

The number of applications of FACTS de- vices (FD) has increased in the recent years. But whereas numerous tools for the determination of FD's impact on power flow and stability are already existing, no algorithm for the calculation of the fault level in networks with series compensating FD is available. Conventional methods can- not be used, because the equivalent serial reactance of a converter-based FD whose knowledge is indispensable for fault level calculations depends on the short-circuit current itself. Therefore a new solution is presented in the paper which uses an iterative algorithm to stepwise calculate the equivalent reactance and the short-circuit current. The dependency between those two values is given by the FACTS model described in the paper. In order to verify the described method the application to various test cases in a real power system is included. Furthermore the poten- tial to specifically influence the fault level at critical nodes is pointed out.
Distributing Tourists among POIs with an Adaptive Trip Recommendation System

Traveling is part of many people leisure activities and an increasing fraction of the economy comes from the tourism. Given a destination, the information about the different attractions, or points of interest (POIs), can be found on many sources. Among these attractions, finding the ones that could be of interest for a specific user, who may have different constraints—such as the available time or budget—represents a challenging task. Travel recommendation systems deal with this type of problems. Despite the vast literature on this topic, most of the solution does not take into account the impact of the suggestions on the level of crowding of POIs. This paper considers the trip planning problem focusing on user balancing among the different POIs. To this aim, we consider the effects of the previous recommendations, as well as estimates based on historical data, while devising a new recommendation. The problem is formulated as a multi-objective optimization problem, and a recommendation engine has been designed and implemented for exploring the solution space in near real-time, through a distributed version of the Simulated Annealing approach. We test our solution using a real dataset of users visiting the POIs of a touristic city, and we show that we are able to provide high quality recommendations, yet maintaining the attractions not overcrowded.
Spatio-Temporal Pedestrian Accident Analysis to Improve Urban Pedestrian Safety: The case of the Eskisehir Motorway

Determining pedestrian accident hotspots on road segments is a crucial part of the pedestrian safety assessment as it is used to prioritize problematic parts of a road network for in particularly planning and implementation strategies. Moreover, the spatial pattern of the pedestrian accidents may change over time due to several factors. In order to better understand pedestrian safety conditions, pedestrian accident patterns have to be analysed with regard to both space and time. This paper adapts such a spatio-temporal hotspot detection method for the analysis of pedestrian accidents. In this study, 189 traffic accidents involving pedestrians that resulted in injury or fatality on the Eskisehir motorway (Turkey) between the years of 2005 and 2010 are mapped with their spatial and temporal information. Network-based Kernel Density Estimation is used to examine the hotspots of pedestrian accidents and their changes over the years. Then, the significances of the results are evaluated by using Network-based Nearest Neighbor Distance and the K-function methods. The impact of land use change and taken measures are evaluated based on spatio-temporal hotspot analysis.
A Control Theoretic Approach to

Modern control and estimation theory is used to establish a framework for the analysis of manned-vehicle systems. By assuming that the human behaves L'optiially" in some sense, subject to his inherent psychophysical limitations, a quantitative model is developed for the response characteristics of the human operator. The resultant model can be used to predict task per- formance and human control characteristics. The model is described in detail and is used to predict quantities measured experimentally in both simple and reasonably complex manual control tasks. Remarkable agreement between measured and predicted quantities is obtained, demonstrating the value and potential of the optimiza- tion approach to manned-vehicle systems analysis.
RGML: A Specification Language that Supports the Characterization of Requirements Generation Processes

Despite advancements in requirements generation models, methods and tools, low quality requirements are still being produced. One potential avenue for addressing this problem is to provide the requirements engineer with an interactive environment that leads (or guides) him/her through a structured set of integrated activities that foster “good” quality requirements. While that is our ultimate goal, a necessary first step in developing such an environment is to create a formal specification mechanism for characterizing the structure, process flow and activities inherent to the requirements generation process. In turn, such specifications can serve as a basis for developing an interactive environment supporting requirements engineering. Reflecting the above need, we have developed a markup language, the Requirements Generation Markup Language (RGML), which can be used to characterize a requirements generation process. The RGML can describe process structure, flow of control, and individual activities. Within activities, the RGML supports the characterization of application instantiation, the use of templates and the production of artifacts. The RGML can also describe temporal control within a process as well as conditional expressions that control if and when various activity scenarios will be executed. The language is expressively powerful, yet flexible in its characterization capabilities, and thereby, provides the capability to describe a wide spectrum of different requirements generation processes.
Characterization of Ripening Bananas by Monitoring the Amino Acid Composition by Gas Chromatography–Mass Spectrometry With Selected Ion Monitoring and Star Pattern Analysis

Abstract Bananas are a well-known healthy food that contain various functional amino acids (AAs) whose concentrations may vary during ripening. Thus, amino acid profiling analysis was performed at different stages of ripeness in bananas by gas chromatography–mass spectrometry (GC-MS) using selected ion monitoring (SIM) with ethoxycarbonyl/tert-butyldimethylsilyl (EOC/TBDMS) derivatives. The variability of the amino acids in ripening was subsequently explored by star pattern analysis after normalization to the corresponding mean values of the unripe bananas. Significant alterations of amino acids were found in ripe bananas following 1, 6, and 10 days of storage. The star graphic patterns were different in the unripe and ripe bananas. In particular, glutamine and asparagine were present as two major components making up 65.68 to 71.75% and 10.86 to 14.94% of the total amino acids. The compositions (%) of valine, leucine, γ-aminobutyric acid, and glutamic acid were significantly increased by 65–210% in ripe bananas following 10 days of storage. In addition, the total composition (%) of essential amino acids was significantly increased at 10 days by comparison with those of 1 and 6 days. Therefore, the present amino acid profiling protocol combined with star pattern analysis has been shown to be useful for the monitoring of the ripening of bananas.
Sharp Analytical Capacity Upper Bounds for Sticky and Related Channels

We study natural examples of binary channels with synchronization errors. These include the duplication channel, which independently outputs a given bit once or twice, and geometric channels that repeat a given bit according to a geometric rule, with or without the possibility of bit deletion. We apply the general framework of Cheraghchi (Journal of the ACM, 2019) to obtain sharp analytical upper bounds on the capacity of these channels. Previously, upper bounds were known via numerical computations involving the computation of finite approximations of the channels by a computer and then using the obtained numerical results to upper bound the actual capacity. While leading to sharp numerical results, further progress on the full understanding of the channel capacity inherently remains elusive using such methods. Our results can be regarded as a major step toward a complete understanding of the capacity curves. Quantitatively, our upper bounds sharply approach, and in some cases surpass, the bounds that were previously only known by purely numerical methods. Among our results, we notably give a completely analytical proof that, when the number of repetitions per bit is geometric (supported on the non-negative integers) with mean growing to infinity, the channel capacity remains substantially bounded away from 1.
Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence

The development of rational techniques to discover new proteins for use in variety of applications ranging from agriculture to biotechnology remains an outstanding materials design problem. The key barrier is to design a sequence to fold into a predictable structure to achieve a certain material function. Focused on alpha-helical proteins, we report a Multi-scale Neighborhood-based Neural Network (MNNN) model to learn how a specific amino acid sequence folds into a protein structure. The algorithm predicts the protein structure without using a template or co-evolutional information at a maximum error of 2.1 Å. We find that the prediction accuracy is higher than other models and the prediction consumes less than six orders of magnitude time than ab initio folding methods. We demonstrate that MNNN can predict the structure of an unknown protein that agrees with experiments, and our model hence shows a great advantage in the rational design of de novo proteins.
An Efficient Data Compression Scheme for Reduction of Cloud Storage Space

Important challenge concerning cloud storage services is data management of rising volume. Convergent encryption will makes sure of secured data in the process of deduplication. Process of convergent encryption was proposed to put into effect data privacy while making of deduplication practicable. For protecting of security in an effective means our work will make an initial attempt to deal with difficulty of authorized data deduplication. Although data deduplication will bring several issues of benefits, security as well as privacy takes place as user sensitive data is vulnerable to attacks. Differential privileges of users are measured in duplicate check in addition to data itself and this is different from the de-duplication methods of earlier ones. In our work we sort out authorization difficulty of deduplication above data within public cloud and also deal with the privacy preserving problem of deduplication within cloud computing. We put forward a novel de-duplication system supporting for Differential Authorization and Authorized Duplicate Check. Keywords: Convergent encryption; De-duplication; Privacy preserving; Cloud computing; Differential privileges; Data management; Cloud storage
A High-Sensitivity Resonant Tangential E-Probe With Loaded Improved Dipole and Embedded Integrated Balun

In this paper, a high-sensitivity resonant probe for capturing tangential <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-field component is fabricated on a four-layer printed circuit board. The resonant probe consists of an improved dipole employed to further enhance sensitivity, a resonator made up of open- and short-circuit stubs achieving specific resonance, and an integrated Marchand balun used to transform the differential-mode voltage to the single-end output and match the instrument port impedance match. Compared with the referenced broadband probe, the relative sensitivity of the proposed resonant probe in terms of <inline-formula> <tex-math notation="LaTeX">$\vert S_{12}\vert $ </tex-math></inline-formula> is enhanced by 13.21 and 14.82 dB for simulation and measurement, respectively. The absolute sensitivity in terms of the noise-equivalent field strength (NEFS) is 26.4 <inline-formula> <tex-math notation="LaTeX">${\mathrm{ dB}} \mu {\mathrm{ V}} ({\mathrm{ Hz}})^{1/2} $ </tex-math></inline-formula>. The measurements of the passive circuits (microstrip lines and coupled lines) and active circuit module are conducted to validate the resonant probe.
Secure Semantic Smart HealthCare (S3HC)

Healthcare is a signiﬁcant domain having a huge knowledge base, a signiﬁcant part which comes from medical, diagnostic and imaging devices and sensors. The health status of patients may be monitored and managed remotely by performing reasoning over this knowledge base. Specialists in HealthCare facilities are required to handle large quantity of data generated and make decisions. However, the heterogeneous and complex nature and the huge amount of data generated; the way it is represented and presented; and the security challenges may overburden the core abilities of thinking and reasoning of even highly skilled and knowledgeable experts putting the lives of patients at risk. The situation may become even worse when data is coming from various healthcare devices and sensors which are themselves characterized by a number of representation and serialization formats. To address the various challenges in healthcare, this paper tries to represent and hence exchange the data collected by healthcare devices meaningfully and securely. This allows all healthcare devices to operate in conjunction with each other facilitating deeper insights and enabling generation of intelligent recommendations.
Natal dispersal in the Southern Ground Hornbill Bucorvus leadbeateri

Dispersal is of ecological and evolutionary importance for population biology because it affects a species’ ability to expand its range, to colonise new favourable habitats and to increase the likelihood of population persistence. Male birds are mostly philopatric, delaying dispersal or dispersing closer to their natal territories than females. Females tend to disperse further than their male siblings to reduce the chance of inbreeding. Dispersal in the cooperative breeding Southern Ground Hornbill Bucorvus leadbeateri is poorly understood. It is not known how far individuals disperse, or what the difference in dispersal age and distance is between males and females. We investigated dispersal events using field observations of ringed individuals within groups in a study population and resightings in surrounding areas. A total of 113 individuals from 19 groups were ringed during 2005–2015. Of these, 14 were breeding adults who never dispersed. Of the remaining 99, 27 were still present in their natal groups at the end of the study, four deaths were confirmed, 50 disappeared from their group and were never seen again (excluded from further analysis), and 18 were confirmed to have dispersed by direct observations in secondary groups. Median dispersal distance was similar for females and males (22 km and 25 km, respectively). Females dispersed younger than males (eight months post-fledging and 44 months post-fledging, respectively). One male delayed dispersal beyond nine years and all females dispersed before three years post-fledging. These data provide insights into the dispersal dynamics and dispersal propensity of this endangered species. Ways to improve data collection and the study of dispersal in this species are discussed.
Pharmacokinetics, pharmacogenetics and optimisation of treatment with non-nucleoside reverse transcriptase inhibitors in HIV-infected African children

ii Contribution to the field . iii Full length original articles . iii Conference presentations. iii Declaration . v Table of contents . x List of figures . xvii List of tables . xix List of appendices . xxii List of abbreviations and acronyms . xxiii Chapter 1: Introduction . 1 1.1 Context . 2 1.2 Hypothesis: . 7 1.3 Key research questions: . 7 1.4 Outline of thesis . 8 1.5 Coherence of thesis . 10 CHAPTER 2: Background and literature review . 11 2.1 Human immunodeficiency virus – epidemiology, disease and treatment . 12 2.1.1 Epidemiology of HIV infection . 12 2.1.2 Pathogenesis of HIV infection . 13 2.1.3 Pharmacological approach to HIV treatment . 14 2.1.4 Treatment of paediatric HIV . 16 2.2 Pharmacological considerations and treatment optimisation . 19 2.3 Pharmacology of non-nucleoside reverse transcriptase inhibitors . 21 2.3. 1 Efavirenz . 21
Implications of Generation Bidding on the Mexican Wholesale Electricity Market

Different rules are established in the electricity markets to define bidding formats. A usual strategy is the piecewise linear approximation (PWL) of the quadratic cost curves of the generating units. However, it is important to keep in mind that various approaches can be obtained from the PWL methods, which lead to different results. This article presents a comparative study of the effect on Unit Commitment (UC) and their level of dispatch when three different PWL methods are applied under Mexican electricity market rules and different scenarios. The purpose of these numerical tests is to show the way in which the methodology to calculate the PWL offers affects the order and priority in the UC.
Towards Robust and Accurate Single-View Fast Human Motion Capture

This paper proposes a new method for fast human motion capture based on a single RGB-D sensor. By leveraging the human pose detection results for reinitializing the ICP-based sequential human motion tracking algorithm when tracking failure happens, our system achieves a highly robust and accurate human motion tracking performance even for fast motion. Moreover, the calculation and utilization of semantic tracking loss enable body-part-level motion tracking refinement, which is better than whole-body refinement. Finally, a simple yet effective post-processing method, semantic bidirectional motion blending, for single-view human motion capture is proposed to further improve the tracking accuracy, especially under severe occlusions and fast motion. The results and experiments demonstrate that the proposed method achieves highly accurate and robust human motion capture performance in a very efficient way. Applications include AR&VR, human motion analysis, movie, and gaming.
2122-P: System x(c)- Is a Key Molecule in Oxidative Stress-Induced ß-Cell Damage

Pancreatic β-cells are vulnerable to oxidative stress, which is related to β-cell failure in type 2 diabetes. System x(c)- is a sodium-independent cystine/glutamate antiporter that mediates the exchange of extracelluar L-cystine and intracellular L-glutamate. The import of L-cystine through this transporter is the limiting state of intracellular glutathione (GSH) synthesis that has a major role in defends against oxidative stress. Our previous study reported that 2-deoxy-D-ribose (dRib) induces oxidative damage through GSH depletion in pancreatic β-cells. The aim of the present study was to elucidate the mechanism underlying oxidative stress-induced β-cell damage. We measured intracellular L-[14C] cysteine uptake, GSH content and reactive oxygen species (ROS) levels, cytotoxicity and apoptosis in rat insulinoma cell line (RINm5F cells). Stimulation with dRib dose- and time-dependently decreased intracellular L-[14C] cystine uptake and GSH content and increased intracellular ROS levels, cytotoxicity and apoptosis. The addition of 2-mercaptoethanol (2-ME), a cystine uptake enhancer, sodium-independently recovered the dRib-induced decreases in L-[14C] cystine uptake, GSH content and cell viability. To determine clearly whether system x(c)- deficiency mediate the oxidative stress-induced β-cell damage, we overexpressed xCT, the substrate-specific subunit of the system x(c)-, by using a lentiviral vector in RINm5F cells. Overexpression of xCT fully recovered the dRib-induced decreases in L-[14C] cystine uptake and GSH content and prevented the dRib-induced ROS rises, cytotoxicity and apoptosis. In summary, overexpression of xCT showed protective effects against oxidative damage in RINm5F cells. It suggests that system x(c)- deficiency play a critical role in oxidative stress-induced β-cell damage. Disclosure S. Yoo: None. J. Bae: None. H. Chin: None. G. Koh: None.