Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of total antioxidant status, superoxide dismutase and malondialdehyde in apparently healthy active tobacco smokers in Nnewi Metropolis, South-East, Nigeria

Oxidative stress has been implicated in the pathogenesis of many diseases. Daily cigarette consumption and duration of smoking were obtained through a semi-structured questionnaire. Information on other risk factors like alcohol consumption were also obtained using the same tool. A total of 120 subjects were used; 60 tests subjects and 60 control subjects. The test group were subdivided into 3 groups based on duration of smoking (1- 5 years, 6 -10 years, 11 years and above). Daily cigarette consumption/number of sticks smoked was also used to subdivide the test group into 3 groups (1 – 5 sticks/day, 6 – 10 sticks/day, 11 and above sticks/day). Total antioxidant status (1148.0660 ± 171.97974 µmol/L) and Superoxide Dismutase activity (8.5360 ± 3.98112IU/L) were significantly reduced in current cigarette smokers compared with control (1310.4080 ± 230.85928µmol/L, 12.6640 ± 2.26849IU/L respectively, p<0.05). This outcome correlated with duration of smoking (TAC = <0.001 SOD =<0.001) but not with the rate of cigarettes smoking. Furthermore, MDA levels (1.6620 ± .72868µg/L) were significantly elevated in current cigarette smokers compared with control group (nonsmokers) (1.2660 ± .28400µg/L, p<0.05) and correlated with duration of smoking (<0.001) which maybe suggestive of lipid peroxidation. These findings may indicate that smoking cigarette no matter the quantity depletes the antioxidant pool of the body exposing the body to effects of reactive oxygen species and other free radicals. Furthermore, the increase in the index of lipid peroxidation (MDA) could be suggestive of cell/DNA damage.
Deep Structured Models for Large Scale Object Co-detection and Segmentation

Structured decisions are often required for a large variety of image and scene understanding tasks in computer vision, with few of them being object detection, localization, semantic segmentation and many more. Structured prediction deals with learning inherent structure by incorporating contextual information from several images and multiple tasks. However, it is very challenging when dealing with large scale image datasets where performance is limited by high computational costs and expressive power of the underlying representation learning techniques. In this thesis, we present efficient and effective deep structured models for context-aware object detection, co-localization and instance-level semantic segmentation. First, we introduce a principled formulation for object co-detection using a fullyconnected conditional random field (CRF). We build an explicit graph whose vertices represent object candidates (instead of pixel values) and edges encode the object similarity via simple, yet effective pairwise potentials. More specifically, we design a weighted mixture of Gaussian kernels for class-specific object similarity, and formulate kernel weights estimation as a least-squares regression problem. Its solution can therefore be obtained in closed-form. Furthermore, in contrast with traditional co-detection approaches, it has been shown that inference in such fully-connected CRFs can be performed efficiently using an approximate mean-field method with high-dimensional Gaussian filtering. This lets us effectively leverage information in multiple images. Next, we extend our class-specific co-detection framework to multiple object categories. We model object candidates with rich, high-dimensional features learned using a deep convolutional neural network. In particular, our max-margin and directloss structural boosting algorithms enable us to learn the most suitable features that best encode pairwise similarity relationships within our CRF framework. Furthermore, it guarantees that the time and space complexity is O(n ∗ t) where n is the total number of candidate boxes in the pool and t the number of mean-field iterations. Moreover, our experiments evidence the importance of learning rich similarity measures to account for the contextual relations across object classes and instances. However, all these methods are based on precomputed object candidates (or proposals), thus localization performance is limited by the quality of bounding-boxes. To address this, we present an efficient object proposal co-generation technique that leverages the collective power of multiple images. In particular, we design a
Data Attribute Selection with Information Gain to Improve Credit Approval Classification Performance using K-Nearest Neighbor Algorithm

Credit is one of the modern economic behaviors. In practice, credit can be either borrowing a certain amount of money or purchasing goods with a gradual payment process and within an agreed timeframe. Economic conditions that are less supportive and high community needs make people choose to buy goods with this credit process. Unfortunately the high needs sometimes are not in line with the ability to make payments in accordance with the initial agreement. Such condition causes the payment process to be disrupted or also called the term “bad credit”. This research uses public data of credit card dataset from UCI repository and private data that is dataset of credit approval from local banking. The information gain algorithm is used to calculate the weights of each of the attributes. From the calculation results note that all attributes have different weights. This study resulted in the conclusion that not all data attributes influence the classification result. Suppose attribute A1 to UCI dataset as well as loan type attribute on local dataset that has information gain weight 0 (zero). The result of classification using K-Nearest Neighbors algorithm shows that there is an increase of 7.53% for UCI dataset and 3.26% for local dataset after feature selection on both datasets.
Optimal Prediction of Weather Conditon Based on C4.5 Classification Technique

In this paper focuses, an implementing the classification technique of C4.5 algorithm can be analyzed for the performance and accuracy of technique which can be used in weather prediction of training data under the various regions such as Tamil Nadu, Andra Pradesh, Gujarat and Odhisa. Finally, weather condition can be predicted on various monsoons seasonally on the respective class label of climate range.
nvasion status of Asiatic red-bellied squirrels in Argentina erónica

The only known introduction of a squirrel species into South America is the case of the Asiatic red-bellied tree squirrel Callosciurus erythraeus that was introduced in the Pampas Region of Argentina in 1970. To inform management programmes, we analysed the current distribution, expansion rate, and abundance of the red-bellied squirrel in Argentina, and identified invasion pathways. Apart from the first and main focus of invasion, three other invasion foci have originated as a consequence of intentional releases between 1995 and 2000. The main invasion focus already extends over >1300 km2, where estimated −1 bundance and distribution
Absolute Frequency Measurement of the 4 2 S 1 / 2 – 3 2 D 5 / 2 Quadrupole Transition in 40 Ca + Ions

The National Institute of Information and Communications Technology is developing an optical frequency standard using the 4 S1/2 3 D5/2 quadrupole transition of single Ca ions. After our first transition observation, we improved vacuum pressure of the chamber and introduced a pulse-light sequence system to the spectroscopy. We observed the Zeeman components of the quadrupole transition with linewidths of <25 kHz. The absolute frequency of the unperturbed line was calculated from the symmetrical pairs of the Zeeman components by canceling the first-order Zeeman shift. It was preliminarily evaluated to be 411 042 129 779.2 ±1.5 kHz.
Wavelet Entropy and Complexity Analysis for Drinkers ’ EEG

This paper investigates the influence of alcohol on brain complexity. Considering electroencephalogram (EEG) has the nonlinear dynamics characteristic of time-varying and non-stationary, the wavelet entropy (WE) analysis is introduced. The EEG data of drinkers' and normal people's is analyzed using the wavelet entropy. The results show that the EEG wavelet entropy of drinkers' is markedly greater than the EEG wavelet entropy of normal people's, The EEG complexity of drinkers' is higher and the brain of drinkers' is in a more chaotic state. Copyright © 2013 IFSA.
Briareos - A Modular Framework for Elastic Intrusion Detection and Prevention

Current intrusion detection systems are mainly based on signature detection running on top of highly optimized parallel engines. While other approaches exist, there is no unified intrusion detection architecture that is able to support them under a unified framework. Given the highly dynamical nature of networked attacks, there is a growing shift to a multi-disciplinary approach as a way to uncover novel algorithms, systems and techniques, including but not limited to, symbolic execution, machine learning and graph mining. We propose a new intrusion detection and prevention approach capable of preventing unknown attacks by correlating network traffic with the operating system behavior. We implemented these concepts in a module extensible framework called Briareos. The Briareos Host Component is composed of pipelines for traffic processing, whose nodes contain modules. It supports multiple processing modes: inline, parallel and distributed. We have implemented a distributed system capable of performing heavy tasks, which can be a plus for detecting unknown attack vectors. The distributed system is composed of multiple clusters of workers and a broker that receive tasks using a Least Recently Used (LRU) queue. We also created a worker manager to automatically start or stop worker instances according to workload metrics. Each processing mode of the Briareos Distrbuted System has advantages and disadvantages in terms of transfer rate. The distributed mode is the fastest mode in both low-processing rate and high-processing rate scenarios. The inline mode was the slowest mode, since it offers prevention and not just detection. The Briareos Distributed System was capable of adapting to workload changes. We achieved a superlinear speedup while comparing the performance with an elastic number of workers and a fixed number of workers. We also have built modules for unknown attack detection and experimentally tested a subset of vulnerable binaries with real-world vulnerabilities from Capture The Flag (CTF) security competitions. We detected and prevented 100% of the 5 exploitation attempts with a 95% confidence interval of [80%-100%]. Briareos is a disruptive framework that changes the way intrusion detection and prevention is currently performed. Users are given full control over the inspection processing flow.
Ais-Psmaca: Towards Proposing an Artificial Immune System for Strengthening Psmaca: An Automated Protein Structure Prediction using Multiple Attractor Cellular Automata

Predicting the structure of proteins from their amino acid sequences has gained a remarkable attention in recent years. Even though there are some prediction techniques addressing this problem, the approximate accuracy in predicting the protein structure is closely 75%. An automated procedure was evolved with MACA (Multiple Attractor Cellular Automata) for predicting the structure of the protein. Artificial Immune System (AIS-PSMACA) a novel computational intelligence technique is used for strengthening the system (PSMACA) with more adaptability and incorporating more parallelism to the system. Most of the existing approaches are sequential which will classify the input into four major classes and these are designed for similar sequences. AIS-PSMACA is designed to identify ten classes from the sequences that share twilight zone similarity and identity with the training sequences with mixed and hybrid variations. This method also predicts three states (helix, strand, and coil) for the secondary structure. Our comprehensive design considers 10 feature selection methods and 4 classifiers to develop MACA (Multiple Attractor Cellular Automata) based classifiers that are build for each of the ten classes. We have tested the proposed classifier with twilight-zone and 1-high-similarity benchmark datasets with over three dozens of modern competing predictors shows that AIS-PSMACA provides the best overall accuracy that ranges between 80% and 89.8% depending on the dataset.
Do payroll tax subsidies reduce undeclared work ? Evidence from Korea

Using administrative panel data sets on over 300,000 firms in Korea, we examine the effects of subsidizing social security contributions on the number of workers covered by the social security. Exploiting variations in location and size of firms which affected eligibility for the program, we implement a difference-in-differences (DD) and a difference-in-difference-in-differences (DDD) estimation in order to identify the effect of subsidies. Our DD estimates show that the subsidy program increased the number of covered workers by 1.34 percent while the DDD estimates indicate an increase in the number of covered workers by 0.71 percent. These estimates are smaller in size than those reported in previous studies implying massive fiscal drains from the subsidy program. The minimal effects and large deadweight losses of the subsidy scheme may be explained by the design features of the subsidy program in Korea.
Massively Parallel Algorithm for Solving the Eikonal Equation on Multiple Accelerator Platforms

The research presented in this thesis investigates parallel implementations of the Fast Sweeping Method (FSM) for Graphics Processing Unit (GPU)-based computational platforms and proposes a new parallel algorithm for distributed computing platforms with accelerators. Hardware accelerators such as GPUs and co-processors have emerged as general-purpose processors in today’s high performance computing (HPC) platforms, thereby increasing platforms’ performance capabilities. This trend has allowed greater parallelism and substantial acceleration of scientific simulation software. In order to leverage the power of new HPC platforms, scientific applications must be written in specific lower-level programming languages, which used to be platform specific. Newer programming models such as OpenACC simplifies implementation and assures portability of applications to run across GPUs from different vendors and multi-core processors. The distance field is a representation of a surface geometry or shape required by many algorithms within the areas of computer graphics, visualization, computational fluid dynamics and more. It can be calculated by solving the eikonal equation using the FSM. The parallel FSMs explored in this thesis have not been implemented on GPU platforms and do not scale to a large problem size. This thesis addresses this problem by designing a parallel algorithm that utilizes a domain decomposition strategy for multi-accelerated distributed platforms. The proposed algorithm applies first coarse grain parallelism using MPI to distribute subdomains across multiple nodes and then fine grain parallelism to optimize performance by utilizing accelerators. The results of the parallel implementations of FSM for GPU-based platforms showed speedup greater than 20× compared to the serial version for some problems and the
CANDU 6 Nuclear Power Plant : Reactor building floor response spectra considering seismic wave incoherency

Within the scope of the refurbishment project of a CANDU 6 nuclear power plant (NPP), the new equipmen ts should be designed to sustain a new seismic demand characterised by a uniform hazard spectra (UHS) whi ch exhibits larger spectral ordinates in the high-freq uency range. This paper presents a procedure develo ped at Hydro-Quebec for generating floor response spectra (FRS) using ambient vibrations calibrated detailed 3D finite element model. These FRS are based on ground motion time histories compatible with the mean UHS. Becau se the reactor building is founded on a large circular raft, it is possible to consider the effect of the seismic wave incoherency to filter out the high-frequency conten t, mainly above 10 Hz, using the incoherency transf er function "ITF" method proposed by EPRI (2006) and i mplemented herein into the software ABAQUS. This allows reducing significantly the non-necessary con servatism in resulting FRS which is an important is sue for an existing NPP.
Exploiting Packet Semantics in Real-time Multimedia Streaming

In this paper, we propose packet selection and significance based interval allocation algorithm for real-time streaming service. In real-time streaming of inter-frame (and layer) coded video, minimizing packet loss does not imply maximizing QoS. It is true that packet loss adversely affects the QoS but one single packet can have more impact than several other packets. We exploit the fact that the significance of each packet loss is different from the frame type it belongs to and its position within GoP. Using packet dependency and PSNR degradation value imposed on the video from the corresponding packet loss, we find each packet’s significance value. Based on the packet significance, the proposed algorithm determines which packets to send and when to send them. The proposed algorithm is tested using publicly available MPEG-4 video traces. Our scheduling algorithm brings significant improvement on user perceivable QoS. We foresee that the proposed algorithm manifests itself in last mile connection of the network where intervals between successive packets from the source and to the destination are well preserved.
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1 DEDICATION . 3 ACKNOWLEDGEMENTS . 4 CONTRIBUTORS . 5
Merging Event Logs for Process Mining with Hybrid Artificial Immune Algorithm

Current process mining techniques and tools are based on a single log file. In an actual business environment, however, a process may be supported by different computer systems. So it is necessary to merge the recorded data into one file, and this mission is still challenging. In this paper, we present an automatic technique for merging event logs with an artificial immune algorithm combed with simulated annealing. Two factors that we used in the affinity function, occurrence frequency and temporal relation can express the characteristics of matching cases more accurate than some factors used in other solutions. By introducing simulated annealing selection and an immunological memory to strengthen the diversity of population, the proposed algorithm has the ability to deal with local optima due to pre-mature problem of artificial immune algorithms. The proposed algorithm has been implemented in the ProM platform. The test results show a good performance of the algorithm in merging logs.
FACULTY-PERCEIVED CONSTRAINTS TOWARDS EMBRACING TRANSFORMATIVE eLEARNING TECHNOLOGIES AT A SOUTH AFRICAN TERTIARY INSTITUTION

This paper examines the constraints that derail the intensive uptake of eLearning programmes in a particular higher education institution. The study adopted an inductive research paradigm that followed a qualitative research strategy. Data were collected by means of one-on-one in-depth interviews from selected faculty members at a nominated institution of higher learning. Data were iteratively and reflexively analysed, leading to the emergence of four themes. Notably, the scepticism towards the implementation of transformative eLearning was ascribed to complex initiation procedures, inadequate training and support, an incoherent e-policy at the institution as well as resistance to change. In lieu of this, the paper advocates for the incremental adoption of fully-fledged eLearning strategies and policies among academic institutions as well as the effusive use of blended learning approaches. Thus, as opposed to merely enabling academic faculty to refine their teaching, eLearning strategies could possibly alter the manner in which faculty members conduct their teaching and assessment activities.
EFFECTIVENESS OF EMOTION FREEDOM TECHNIQUE ON PREGNANT WOMEN ' S PERCEIVED STRESS AND RESILIENCE

The current study intended to investigate the effectiveness of Emotion Freedom Technique (EFT) on pregnant women's perceived stress and resilience. This research utilized a quasi-experimental method with pretest-posttest and control group. Using convenience sampling, 30 pregnant women referring to Parsa hospital were selected, and they were randomly assigned into two groups. First, both groups completed Perceived Stress Questionnaire and Resilience Questionnaire. Then, the experimental group received Emotion Freedom Technique intervention for eight sessions. But, the control group received no intervention. After the intervention sessions, both groups were assessed once more. The data were analyzed using one-way analysis of covariance. Findings suggested the efficacy of emotion freedom technique on resilience and perceived stress; namely, the experimental group‘s scores on perceived stress have significantly decreased, while its scores on resilience have significantly increased. Effect size of this effectiveness, being 0.90 for both variables, indicates the power of this technique. Therefore, this technique can be used to decrease the problems related to stress and resilience increase of the pregnant women.
Seismic Response of offshore wind turbine foundations aper Title Line 1

This paper investigates the response of wind turbines founded on suction caissons with due account of non-linear soil-structure interaction. The models are subjected to static cyclic and earthquake loading in order to parametrically explore the role of potential non-linear interface behavior materialized through sliding between the caisson skirt and the soil or gap formation. It is shown that interface failure may substantially reduce the capacity of such foundations, while the effect becomes more intense as the caisson depth decreases. When subjected to earthquake shaking, imperfect interface conditions may limit the tower bending but produces irrecoverable displacement on the nacelle level; a direct result of the accumulated rotation at the foundation. This undesirable rotation may be more effectively prevented by increasing the caisson diameter rather than its depth of embedment.
A Space-Time Analysis of LTE and Wi-Fi Interworking

Cooperative inter-working of the Long Term Evolution (LTE) and the Wireless Fidelity (Wi-Fi) networks have drawn much attention recently, and several strategies have been proposed to enhance their network capacity. In this paper, we propose a new framework to analyse the network performance of several inter-working strategies for the LTE and the Wi-Fi. The proposed framework considers both the LTE and the Wi-Fi systems, both the downlink (DL) and the uplink (UL) transmissions, and the generated interference in both the time and the spatial domains. Based on such framework, we theoretically analyse for the first time the performance of a Wi-Fi network, taking into account the intra-cell time efficiency, and the signal and inter-cell interference with spatial randomness. Moreover, we study the performance of i) a coexisting architecture where Wi-Fi coexists with an ideal CSMA duplex (ID-CSMA) system, which represents an upper bound performance for the LTE Release 14 licensed assisted access (LAA) network, and ii) a brand-new architecture that allows UL on LTE and DL on Wi-Fi, referred to as the Boost architecture. We derive analytical results for both the DL and the UL network performance in terms of the signal quality distribution and the total area system throughput (AST) in these two architectures, and quantify their performance gain compared with the traditional disjoint LTE Wi-Fi architecture. Simulation results validate our analysis results, and show that in a typical outdoor scenario, the coexisting architecture and the Boost architecture can respectively increase the total AST up to 11% and 25%, compared with the traditional disjoint LTE Wi-Fi.