Miracle-dz/UniFAR-FTUs huggingface dataset:
ARTIFICIAL NEURAL NETWORK WITH SPIDER MONKEY OPTIMIZATION ALGORITHM FOR CARDIOVASCULAR DISEASE PREDICTION

Globally, among the leading causes of death is coronary heart disease. Heart disease cases are rising quickly every day, thus early disease prediction is both important and dangerous. It is important to complete this diagnosis accurately and quickly because it is a challenging task. Specifically to improve diagnosis efficiency and accuracy, Machine Learning (ML) is increasingly popularity in the healthcare sector. By analyzing vast amounts of healthcare data, ML can predict diseases. Providing people and health professionals with the information they need to integrated content choices regarding preventing disease. However, the most difficult task in the area of clinical data analysis, is predicting a cardiac disease. Hence in order to solve these issues, Artificial Neural Network with Spider Monkey Optimization Algorithm for Cardio Vascular Disease Prediction is presented in this work. In a recent addition to the group of swarm intelligence-based optimization methods is the Spider Monkey optimization (SMO) algorithm. The combination of SMO and BSA (Bird Swarm optimization Algorithm) namely SMBS (Spider Monkey-Bird Swarm) will be used along with ANN (Artificial Neural Network) to predict the CVD (Cardio Vascular Disease. This System predicts the possibilities (i.e. presence, absence) of CVD. Parameters of prediction accuracy, precision, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) are used to assess the performance of the presented approach. This approach will achieve better results than earlier prediction models.
PERFORMANCE COMPARISON OF THREE DIFFERENT TYPES OF AUTOENCODERS USING RECOMMENDATION SYSTEMS

Recommendation system is one of the modern applications to solve information overload problem in a way to provide recommendations of interest to users. Websites such as Amazon, Netflix, Facebook, YouTube, and others apply recommendation system in recommending their products. This also includes recommending news to the readers. However, the systems suffer from some challenges such as high dimensional data, data sparsity, and cold start. To address these problems, deep learning techniques have recently been integrated with recommendation systems and achieve good performance. Autoencoder is one of the most widely used deep learning techniques in recommender systems, especially used for feature extraction, data dimensionality reduction, fast convergence, unsupervised learning, and data reconstruction. In this paper, a performance comparison between three different models of autoencoder is presented which applying in the recommendation systems to further improve the quality of recommendations provided to users. The models are Hybrid Collaborative Recommendation via Semi-AutoEncoder (HRSA), Recommendation via Dual-Autoencoder (ReDa), and Hybrid Collaborative Recommendation method via Dual-Autoencoder (HCRDa). These models work by retrieving the potential latent factors from the sparse rating matrix and predict the missing ratings. The performance is compared based on reconstruction loss that applies to the MovieLens 100K dataset with three different sets of training data: 70%, 80%, and 90%. As a result, it was found that the HCRDa is outperformed the other models in terms of reconstruction loss based on the RMSE evaluation metric and the use of side information in the model. Thus, it is the most effective technique in terms of enhancing the quality of user recommendations.
DETECTION OF FOREIGN OBJECT DEBRIS (FOD) USING CONVOLUTIONAL NEURAL NETWORK (CNN)

Foreign object damage (FOD) is a big problem in aviation maintenance industry that reduces the level of safety for an aircraft. Basically, FOD is known as foreign object (FO) that can cause severity and destruction to the aircraft such as engine failure and loss of human life. Nowadays there is no FOD detection system that can classify the type of FOD that is detected optimally. This study proposes the FOD classification approach using Deep Learning. The Deep Learning method that has significant results in image recognition is the Convolutional neural network (CNN). The purpose of this study is to determine the CNN algorithm model that has optimal accuracy in the FOD classification. Convolutional Neural Network (CNN) is a multilayer neural network with a supervised learning architecture that consists of two parts, namely feature reviewers and classifiers that can be trained. The research began by designing a dataset and CNN algorithm model, then proceed with Training and Testing data. Based on the research, CNN model that has optimal accuracy using a 64 x 64 input image, learning rate value of 0.001, filter size 3 x 3, number of epochs 100, number of training data of 1200 data and test data of 20 data. The number of convolution layers used is 1 layer. The algorithm created is able to classify FOD objects into 6 classes with 86.6% accuracy and provide the best classification results into 4 classes with an accuracy of up to 90%.
UvA-DARE (Digital Academic Repository) Evolution of the human immunodeficiency virus type 1 subtype-specific V3 domain is confined to a sequence space with a fixed distance to the subtype consensus

Human immunodeﬁciency virus type 1 (HIV-1) strains can be separated into genetic subtypes based on phylogenetic analysis of the envelope gene. Once it had been shown that population-wide intrasubtype genetic variation of HIV-1 strains increases in the course of the AIDS epidemic, it remained uncertain whether HIV-1 subtypes are phenotypic entities spreading as distinct virus populations. To examine this, we applied Eigen’s concepts of sequence geometry and ﬁtness topography to the analysis of intrasubtype evolution of the gp120 V3 domain of HIV-1 subtypes A, B, C, and D in the course of the global AIDS epidemic. We observed that despite the high evolution rate of HIV-1, the nonsynonymous distances to the subtype consensus of sequences obtained early in the epidemic are similar to those obtained more than 10 years later, in contrast to the synonymous distances, which increased steadily over time. For HIV-1 subtype B, we observed that the evolution rate of the individual sequences is independent of their distance from the subtype B consensus, but for the individual sequences most distant from the consensus evolution away from the consensus is constrained. As a result, individual HIV-1 genomes ﬂuctuate within a sequence space with ﬁxed distance to the subtype consensus. Our ﬁndings suggest that the evolution of the V3 domain of HIV-1 subtypes A, B, C, and D is conﬁned to an area in sequence space within a ﬁxed distance to the consensus of a respective subtype. This in turn indicates that each HIV-1 subtype is a distinct viral quasispecies that is well adapted to the present environment, able
Pharmacokinetics of Pharmacokinetics of 99m99m Tc-HMPAO in Isolated Perfused Rat Tc-HMPAO in Isolated Perfused Rat Lungs Lungs

Lung uptake of technetium-labeled hexamethylpropyleneamine oxime (HMPAO) increases in rat models of human acute lung injury, consistent with increases in lung tissue glutathione (GSH). Since 99m Tc-HMPAO uptake is the net result of multiple cellular and vascular processes, the objective was to develop an approach to investigate the pharmacokinetics of 99m Tc-HMPAO uptake in isolated perfused rat lungs. Lungs of anesthetized rats were excised and connected to a ventilation-perfusion system. 99m Tc-HMPAO (56 MBq) was injected into the pulmonary arterial cannula, a time sequence of images was acquired, and lung time-activity curves were constructed. Imaging was repeated with a range of pump flows and perfusate albumin concentrations and before and after depletion of GSH with diethyl maleate (DEM). A pharmacokinetic model of 99m Tc-HMPAO pulmonary disposition was developed and used for quantitative interpretation of the time-activity curves. Experimental results reveal that 99m Tc-HMPAO lung uptake, defined as the steady-state value of the 99m Tc-HMPAO lung time-activity curve, was inversely related to pump flow. Also, 99m Tc-HMPAO lung uptake decreased by ~65% after addition of DEM to the perfusate. Increased perfusate albumin concentration also resulted in decreased 99m Tc-HMPAO lung uptake. Model simulations under in vivo flow conditions indicate that lung tissue GSH is the dominant factor in 99m Tc-HMPAO retention in lung tissue. The approach allows for evaluation of the dominant factors that determine imaging biomarker uptake, separation of the contributions of pulmonary versus systemic processes, and application of this knowledge to in vivo studies.
Enriching Traceability with Context for Adaptive Information Security in the Cloud

– Cloud applications enjoy a diverse community of users to store and process a variety of data in different conditions in their execution environment. We refer to the attributes that determine these conditions as context. Therefore these applications have a variety of security requirements, the satisfaction of which depends on the application adapting on the users’ context. We call such adaptation capability Adaptive Information Security. The paper argues that one of the key prerequisites for adaptive information security in the cloud is the use of traceability as a means to reasoning the relationship between security requirements and the policies that satisfy those requirements. However, current approaches to traceability do not provide support for taking into account contextual attributes. This makes it challenging to reason about satisfaction of the security requirement at runtime. We propose an approach to traceability that addresses this challenge by making context explicit. Our approach uses entailment relationships to capture and enrich traceability links with context. We use these links to diagnose the violation of security requirements. We applied our approach to an open-source cloud application (ownCloud) which we re-engineered for adaptive access control.
Modelling the determinants of ignition in the Sydney Basin, Australia: implications for future management

Variations in area burnt by fire are governed by four processes: biomass growth, availability to burn, fire weather and ignitions. Insight into these how these processes are shaped by biophysical and human influences is required to underpin the development of effective management strategies. Patterns of natural and arson ignitions were examined within the densely populated Sydney region of south-eastern Australia to determine the extent to which management can alter the risk of ignition. Arson ignitions were more likely on ridges in association with human infrastructure, i.e. roads and houses. Lightning ignitions also occurred more frequently on ridges, but at greater distances from human infrastructure. These patterns are consistent with those reported in studies from forested regions in the northern hemisphere. Fuel age had a variable effect with lightning more likely in older fuels (>25 years) and arson more likely in younger fuels (<10 years). Probability of both ignition types increased under more severe fire weather. Climate change is predicted to increase the severity of fire weather and is therefore likely to result in an increase in ignition frequency in the Sydney Basin. Urban expansion is also likely to have significant effects on ignitions and resultant risks to people and property via an increase in the probability of arson ignitions.
Physiological observations validate ﬁ nite element models for estimating subject-speci ﬁ c electric ﬁ eld distributions induced by transcranial magnetic stimulation of the human motor cortex

Recent evidence indicates subject-speci ﬁ c gyral folding patterns and white matter anisotropy uniquely shape electric ﬁ elds generated by TMS. Current methods for predicting the brain regions in ﬂ uenced by TMS involve projecting the TMS coil position or center of gravity onto realistic head models derived from structural and functional imaging data. Similarly, spherical models have been used to estimate electric ﬁ eld distributions generated by TMS pulses delivered from a particular coil location and position. In the present paper we in-spect differences between electric ﬁ eld computations estimated using the ﬁ nite element method (FEM) and projection-based approaches described above. We then more speci ﬁ cally examined an approach for estimating cortical excitation volumes based on individualistic FEM simulations of electric ﬁ elds. We evaluated this approach by performing neurophysiological recordings during MR-navigated motormapping experiments. We recorded motor evoked potentials (MEPs) in response to single pulse TMS using two different coil orientations (45° and 90° to midline) at 25 different locations (5 × 5 grid, 1 cm spacing) centered on the hotspot of the right ﬁ rst dorsal interosseous (FDI) muscle in left motor cortex. We observed that motor excitability maps varied within and between subjects as a function of TMS coil position and orientation. For each coil position and orientation tested, simulations of the TMS-induced electric ﬁ eld were computed using individualistic FEM models and compared to MEP amplitudes obtained during our motormapping experiments. We found FEM simulations of electric ﬁ eld strength, which take into account subject-speci ﬁ c gyral geometry and tissue conductivity anisotropy, signi ﬁ cantly correlated with physiologically observed MEP amplitudes (r max = 0.91, p = 1.8 × 10 -5 r mean = 0.81, p = 0.01). These observations validate the implementation of individualistic FEM models to account for variations in gyral folding patterns and tissue conductivity anisotropy, which should help improve the targeting accuracy of TMS in the mapping or modulation of human brain circuits.
Efﬁciently Correlating Complex Events over Live and Archived Data Streams

Correlating complex events over live and archived data streams, which we call Pattern Correlation Queries (PCQs), provides many beneﬁts for domains which need real-time forecasting of events or identiﬁcation of causal dependencies, while handling data at high rates and in massive amounts, like in ﬁnancial or medical settings. Existing work has focused either on complex event processing over a single type of stream source (i.e., either live or archived), or on simple stream correlation queries (e.g., live events trigerring a database lookup). In this paper, we speciﬁcally focus on recency-based PCQs and provide clear, useful, and optimizable semantics for them. PCQs raise a number of challenges in optimizing data management and query processing, which we address in the setting of the DejaVu complex event processing system. More speciﬁcally, we propose three complementary optimizations including recent input buffering, query result caching, and join source ordering. Furthermore, we capture the relevant query processing tradeoffs in a cost model. An extensive performance study on synthetic and real-life data sets not only validates this cost model, but also shows that our optimizations are very effective, achieving more than two orders magnitude throughput improvement and much better scalability compared to a conventional approach.
University of Birmingham Sampling strategy for estimating human exposure pathways to consumer chemicals

Human exposure to consumer chemicals has become a worldwide concern. In this work, a comprehensive sampling strategy is presented, to our knowledge being the ﬁ rst to study all relevant exposure pathways in a single cohort using multiple methods for assessment of exposure from each exposure pathway. The selected groups of chemicals to be studied are consumer chemicals whose production and use are currently in a state of transition and are; per-and poly ﬂ uorinated alkyl substances (PFASs), traditional and “ emerging ” brominated ﬂ ame retardants (BFRs and EBFRs), organophosphate esters (OPEs) and phthalate esters (PEs). Information about human exposure to these contaminants is needed due to existing data gaps on human exposure intakes from multiple exposure pathways and relationships between internal and external exposure. Indoor environment, food and biological samples were collected from 61 participants and their households in the Oslo area (Norway) on two consecutive days, during winter 2013-14. Air, dust, hand wipes, and duplicate diet (food and drink) samples were collected as indicators of external exposure, and blood, urine, blood spots, hair, nails and saliva as indicators of internal exposure. A food diary, food frequency questionnaire (FFQ) and indoor environment questionnaire were also implemented. Approximately 2000 samples were collected in total and participant views on their experiences of this campaign were collected via questionnaire. While 91% of our participants were positive about future participation in a similar project, some tasks were viewed as problematic. Completing the food diaryand collection of duplicate food/drink portions were the tasks most frequent reported as “ hard ” / ” very hard ” . Nevertheless, a strong positive correlation between the reported total mass of food/drinks in the food record and the total weight of the food/drinks in the collection bottles was observed, being an indication of accurate performance of the participants despite the challenges of the sampling campaign. Copyright © 2016, The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).
Conceptual spaces as a framework for knowledge representation

: I focus on the distinction between sensation and perception. Perceptions contain additional information that is useful for interpreting sensations. Following Grush, I propose that emulators can be seen as containing (or creating) hidden variables that generate perceptions from sensations. Such hidden variables could be used to explain further cognitive phenomena, for example, causal reasoning.
A review of beached bird surveys within the Wadden Sea Trilateral Monitoring and Assessment Program (TMAP)

The Trilateral Monitoring and Assessment Programme (TMAP), established in 1994, is carried out in the framework of the Trilateral Cooperation on the Protection of the Wadden Sea, which includes Denmark, Germany, and the Netherlands. Its objective is integrated monitoring and assessment of the Wadden Sea ecosystem. Beached bird surveys have been carried out on southern North Sea coasts since the 1970s (Netherlands) or the mid-1980s (Germany and Denmark). The surveys use trilateral standardized procedures and are evaluated as part of the TMAP. The results are published at regular intervals in Wadden Sea Quality Status Reports by the Common Wadden Sea Secretariat. The results of the surveys within the Wadden Sea and adjoining regions indicate steady declines in pollution levels since the mid-1980s. For example, oiling rates (the proportion of beached birds that are oiled) of the Common Guillemot Uria aalge declined on German North Sea coasts to 62% in the period 1992/93–1994/95 from 82% (n = 620) during the three winters 1984/85–1986/87 (n = 967) and to 35.5% (n = 1024) in 2001/02–2003/04. The oiling rates of pelagic and offshore species are, however, still high on southern North Sea coasts. In the period 2001/02–2003/04, the oiling rates of the Red-throated Loon Gavia stellata, Common (Black) Scoter Melanitta nigra, and the Black-legged Kittiwake Rissa tridactyla on the German North Sea coast were 80% (n = 54), 50% (n = 357), and 22% (n = 137) respectively. An Ecological Quality Objective (EcoQO), phrased as “proportion of oiled Common Guillemots among those found dead or dying on beaches,” has been developed within the OSPAR Convention on the Protection of the Marine Environment of the North East Atlantic and is currently being implemented. Oiling rates for this species in the southern North Sea are currently much higher than the 10% level targeted by the EcoQO.
Knowledge Transfer in Vision Recognition: A Survey

In this survey, we propose to explore and discuss the common rules behind knowledge transfer works for vision recognition tasks. To achieve this, we firstly discuss the different kinds of reusable knowledge existing in a vision recognition task, then we categorize different knowledge transfer approaches depending on where the knowledge come from and where the knowledge go to. This viewpoint is different to previous surveys on knowledge transfer. We further show that some potential research directions could be induced based on our categorization methodology.
Discontinuity Assessment in Soils via Portable X-ray Fluorescence Spectrometry and Visible Near-Infrared

28 Lithologic discontinuity identification can be arduous and erroneous in instances where distinct 29 morphological differences between parent materials are absent. Often, investigators must wait for 30 laboratory data to help differentiate parent materials via physicochemical properties. This paper 31 uses visible near infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray 32 fluorescence (PXRF) spectrometry for establishing parent material differentia more quickly. Ten 33 pedons containing 135 samples were scanned in situ in the USA, Italy, and Hungary, 34 morphologically described by trained pedologists, then sampled for standard laboratory 35 characterization. Compared to laboratory data and/or morphologically described discontinuities, 36 PXRF data were associated with large, abrupt changes in standardized PXRF differences of 37 elements (DEs), noted in data plots as DE maxima and minima; areas of likely discontinuity. 38 Standardized VisNIR DRS calculated differences (CDs) in reflectance spectra (350-2500 nm) were 39 also associated with discontinuities based upon CD reflectance maxima and minima. Notably 40 within both types of data plots, lithologic discontinuities were not well captured by the proximal 41 sensors when CD or DE values fell in the data plot mid-section (e.g., not at maxima or minima 42 within the data plots). Across the pedons evaluated, PXRF was more useful for detecting 43 discontinuities than VisNIR DRS. Summarily, PXRF showed good alignment with 44 morphologically established discontinuities in eight out of ten pedons while VisNIR DRS showed 45
DNA SEQUENCE ANALYSIS FOR DISEASE PREDICTION AND TREATMENT BASED ON MACHINE LEARNING

Biomedical data management is crucial in biomedical systems. DNA sequence analysis performed to predict diseases. This paper reviews the most recent machine learning methods as employed in the medical field such as K-Nearest-Neighbor (KNN), Gaussian Process (GP) Classifier, Decision Tree (DT) Classifier, Random Forest (RF) Classifier, Multi-Layer Perceptron (MLP) Classifier, Ada Boost Classifier, Support Vector Machine (SVM) and Deep Learning (DL). The paper applies those methods on a standard DNA dataset. The paper proposes a biomedical data management framework. Then the paper introduces Bag-of-Words (BoW) Random Forest (RF), which achieved 100% accuracy compared to other machine learning methods. Furthermore, this paper introduces a treatment protocol recommendation based on DNA alignment and position matrix.