Miracle-dz/UniFAR-FTUs huggingface dataset:
GAMLS: a generalized framework for associative modular learning systems

Learning a large number of simple local concepts is both faster and easier than learning a single global concept. Inspired by this principle of divide and conquer, a number of modular learning approaches have been proposed by the computational intelligence community. In modular learning, the classification/regression/clustering problem is first decomposed into a number of simpler subproblems, a module is learned for each of these subproblems, and finally their results are integrated by a suitable combining method. Mixtures of experts and clustering are two of the techniques that are describable in this paradigm. In this paper we present a broad framework for Generalized Associative Modular Learning Systems (GAMLS). Modularity is introduced through soft association of each training pattern with every module. The coupled problems of learning the module parameters and learning associations are solved iteratively using deterministic annealing. Starting at a high temperature with only one module, GAMLS framework automatically evolves the required number of modules through a systematic growing and pruning technique. Each phase begins by splitting every module in the previous phase into two, updating these new modules and then pruning and merging any redundant modules. A phase transition is induced by temperature decay. A number of existing modular learning problems, both unsupervised (clustering, mixture model density, mixture of principal components) and supervised (mixture of experts, radial basis function networks), can be effectively tackled in GAMLS. Case studies for clustering and regression using mixture of experts are provided for a number of datasets showing the efficacy of the GAMLS framework in evolving the right number of modules, inducing interpretable localizations among modules and robustness of the solution obtained. More importantly, this framework provides a unifying view for understanding and characterizing modular learning methods.
Browsing and Visualizing Semantically Enriched Information Resources

We are developing an approach to organizing bookmarks and other information resources by annotating them with metadata in the form of synsets taken from WordNet. This paper shows how a collection of annotated bookmarks can be semantically enriched by adding hyper-/hyponym relations from WordNet. It then illustrates how the semantically enriched bookmark collection can be browsed and visualized using the Longwell faceted browser. Our initial investigation suggests that the rich semantics of WordNet synsets combined with Longwell’s faceted browsing provide users with a useful aid for navigating and comprehending complex annotated information resources.
Mechanistic Pharmacokinetic Modeling for the Prediction of Transporter-Mediated Disposition in Humans from Sandwich Culture Human Hepatocyte Data

With efforts to reduce cytochrome P450-mediated clearance (CL) during the early stages of drug discovery, transporter-mediated CL mechanisms are becoming more prevalent. However, the prediction of plasma concentration-time profiles for such compounds using physiologically based pharmacokinetic (PBPK) modeling is far less established in comparison with that for compounds with passively mediated pharmacokinetics (PK). In this study, we have assessed the predictability of human PK for seven organic anion-transporting polypeptide (OATP) substrates (pravastatin, cerivastatin, bosentan, fluvastatin, rosuvastatin, valsartan, and repaglinide) for which clinical intravenous data were available. In vitro data generated from the sandwich culture human hepatocyte system were simultaneously fit to estimate parameters describing both uptake and biliary efflux. Use of scaled active uptake, passive distribution, and biliary efflux parameters as inputs into a PBPK model resulted in the overprediction of exposure for all seven drugs investigated, with the exception of pravastatin. Therefore, fitting of in vivo data for each individual drug in the dataset was performed to establish empirical scaling factors to accurately capture their plasma concentration-time profiles. Overall, active uptake and biliary efflux were under- and overpredicted, leading to average empirical scaling factors of 58 and 0.061, respectively; passive diffusion required no scaling factor. This study illustrates the mechanistic and model-driven application of in vitro uptake and efflux data for human PK prediction for OATP substrates. A particular advantage is the ability to capture the multiphasic plasma concentration-time profiles for such compounds using only preclinical data. A prediction strategy for novel OATP substrates is discussed.
A System-Oriented Approach to Full-Text Search on Encrypted Cloud Storage

Today's Cloud market offers a wide variety of online storage services which provide reliable and inexpensive storage accessible from almost every device at almost any location. Still, it proofs to be difficult to satisfy the user's confidence concerning the confidentiality and integrity of the own data in the Cloud. This is especially true since the user is forced to take a tradeoff between security and functionality. Storage services which allow the adoption of self-selected and self-controlled security mechanisms provide a high security level but miss on the other side a set of useful operations such as e.g. a full-text search or a synchronization across distinct devices. Such features require that the server-side is capable of accessing the data in unencrypted form for processing purposes. This paper presents an system-oriented approach to this issue by introducing an architecture that allows to integrate a full-text search operation with file-based Cloud storage services in the presence of user-selected and user-controlled security. Different instances of the presented architecture allow furthermore for a more fine-grained balance between the level of security and the provided functionality by the Cloud storage service.
Automatic ECG Beat Arrhythmia Detection

Background: In recent years automated data analysis techniques have drawn great attention and are used in almost every field of research including biomedical. Artificial Neural Networks (ANNs) are one of the Computer- Aided- Diagnosis tools which are used extensively by advances in computer hardware technology. The application of these techniques for disease diagnosis has made great progress and is widely used by physicians. An Electrocardiogram carries vital information about heart activity and physicians use this signal for cardiac disease diagnosis which was the great motivation towards our study. Methods: In this study we are using Probabilistic Neural Networks (PNN) as an automatic technique for ECG signal analysis along with a Genetic Algorithm (GA). As every real signal recorded by the equipment can have different artifacts, we need to do some preprocessing steps before feeding it to the ANN. Wavelet transform is used for extracting the morphological parameters and median filter for data reduction of the ECG signal. The subset of morphological parameters are chosen and optimized using GA. We had two approaches in our investigation, the first one uses the whole signal with 289 normalized and de-noised data points as input to the ANN. In the second approach after applying all the preprocessing steps the signal is reduced to 29 data points and also their important parameters extracted to form the ANN input with 35 data points. Results: The outcome of the two approaches for 8 types of arrhythmia shows that the second approach is superior than the first one with an average accuracy of %99.42.
Intelligent Control of Wind Energy Conversion Systems

Wind turbines form complex nonlinear mechanical systems exposed to uncontrolled wind profiles. This makes turbine controller design a challenging task (Athanasius & Zhu, 2009). As such, control of wind energy conversion systems (WECS) is difficult due to the lack of systematic methods to identify requisite robust and sufficiently stable conditions, to guarantee performance. The problem becomes more complex when plant parameters become uncertain. Fuzzy control is one of the techniques which deal with this class of systems. The stability of fuzzy systems formed by a fuzzy plant model and a fuzzy controller has recently been investigated. Various stability conditions have been obtained through the employment of Lyapunov stability theory (Schegner & La Seta, 2004; Tripathy, 1997), fuzzy gainscheduling controllers (Billy, 2011a, 2011b; Iescher et al., 2005), switching controllers (Lescher et al., 2006) and by other methods (Chen & Hu, 2003; Kamal et al., 2008; Muljadi & Edward, 2002). Nonlinear controllers (Boukhezzar & Siguerdidjane, 2009; Chedid et al., 2000; Hee-Sang et al., 2008) have also been proposed for the control of WECS represented by fuzzy models. In addition to stability, robustness is also an important requirement to be considered in the study of uncertain nonlinear WECS control systems. Robustness in fuzzy-model-based control has been extensively studied, such as stability robustness versus modelling errors and other various control techniques for Takagi–Sugeno (TS) fuzzy models (Kamal et al., 2010; Uhlen et al., 1994). In order to overcome nonlinearity and uncertainties, various schemes have been developed in the past two decades (Battista & Mantz, 2004; Boukhezzar & Siguerdidjane, 2010; Prats et al., 2000; Sloth et al., 2009). (Battista & Mantz, 2004) addressing problems of output power regulation in fixed-pitch variable-speed wind energy conversion systems with parameter uncertainties. The design of LMI-based robust controllers to control variable-speed, variable-pitch wind turbines, while taking into account parametric uncertainties in the aerodynamic model has been presented (Sloth et al., 2009). (Boukhezzar & Siguerdidjane, 2010) comparing several linear and nonlinear control strategies, with the aim of improving wind energy conversion systems. (Prats et al., 2000) have also investigated fuzzy logic controls to reduce uncertainties faced by classical control methods. Furthermore, although the problem of control in the maximization of power generation in variable-speed wind energy conversion systems (VS-WECS) has been greatly studied, such
Functional Divergence in the Genus Oenococcus as Predicted by Genome Sequencing of the Newly-Described Species, Oenococcus kitaharae

Oenococcus kitaharae is only the second member of the genus Oenococcus to be identified and is the closest relative of the industrially important wine bacterium Oenococcus oeni. To provide insight into this new species, the genome of the type strain of O. kitaharae, DSM 17330, was sequenced. Comparison of the sequenced genomes of both species show that the genome of O. kitaharae DSM 17330 contains many genes with predicted functions in cellular defence (bacteriocins, antimicrobials, restriction-modification systems and a CRISPR locus) which are lacking in O. oeni. The two genomes also appear to differentially encode several metabolic pathways associated with amino acid biosynthesis and carbohydrate utilization and which have direct phenotypic consequences. This would indicate that the two species have evolved different survival techniques to suit their particular environmental niches. O. oeni has adapted to survive in the harsh, but predictable, environment of wine that provides very few competitive species. However O. kitaharae appears to have adapted to a growth environment in which biological competition provides a significant selective pressure by accumulating biological defence molecules, such as bacteriocins and restriction-modification systems, throughout its genome.
Deep Learning with K-Means Applied to Community Detection in Networks

Identifying communities, or clusters, in graphs is a task of great importance when analyzing network structures. A telecommunications provider, for instance, would like to identify communities of customers that place a large amount of calls to each other, in order to create more effective, directed marketing campaigns. Another example would be a financial institution, trying to identify and understand communities of customers that have a high amount of financial transactions between each other. There is also a wide applicability of community detection in life sciences research as well: for example, when studying protein-protein interaction networks. There is a wide range of algorithms and methods that can be applied, in order to identify communities in network structures, such as Spectral Clustering, Modularity Maximization, and Hierarchical Clustering. Spectral Clustering is one of the traditional algorithms that is suitable for network clustering because it first map the original data points into a lower-dimensional space, where the clustering properties of the graph tend to be much more evident, allowing for a subsequent application of standard clustering techniques such as K-Means. Despite its good results, Spectral Clustering presents some computational challenges when applied to very large networks. Recent research shows that techniques and algorithms from Deep Learning, such as layered neural networks-based auto-encoders, are suitable for the task of mapping data points into lower-dimensional spaces, which can be useful for a variety of further tasks such as data clustering and classification. There is also recent research showing that a K-Means based approach is also suitable for a lower-dimensional data representation that can replace traditional neural networks-based auto-encoders in a Deep Learning pipeline. In this project we will create a new processing pipeline for non-overlapping community detection in network structures based entirely on K-Means. We will show that this approach is similar to a traditional Deep Learning auto-encoder in its ability to learn useful representations of the original data in a lower-dimensional space, making the data clustering task easier to accomplish. We will then test its applicability for the specific challenges of community detection in networks and compare its performance with the traditional
Predicting Student Earnings After College

Many students see college as an investment to help them earn more and live better lives after graduation. While it is true that college graduates earn more on average than those without a degree, large numbers of students today are graduating with worrying amounts of debt, calling into question the assumption that attending college is always the wisest investment. It has become more important then to understand the factors that contribute to post-graduation earnings and the ability to repay student loans.
The Impact of Data Placement Strategies on ReorganizationCosts in Parallel

In this paper, we study the data placement problem from a reorganization point of view. EEective placement of the declustered fragments of a relation is crucial to the performance of parallel database systems having multiple disks. Given the dynamic nature of database systems, the optimal placement of fragments will change over time and this will necessitate a reorganization in order to maintain the performance of the database system at acceptable levels. This study shows that the choice of data placement strategy can have a signiicant impact on the reorganization costs. Until now, data placement heuristics have been designed with the principal purpose of balancing the load. However , this paper shows that such a policy can be beneecial only in the short term. Long term database designs should take reorganization costs into consideration while making design choices.
Low Complexity Constraints for Energy and Performance Management of Heterogeneous Multicore Processors using Dynamic Optimization

Optimization in multicore processor environment is significant in real world dynamic applications, as it is crucial to find and track the change effectively ov er time, which requires an optimization algorithm. In massively parallel processing multicore processor a rchitectures, like other population based metaheuri stics Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling can be effectively implemented. In this study we discuss possible appr oaches to parallelize CBFPSO in multicore system, which uses different constraints; to exploit parall elism are explored and evaluated. Due to the abilit y of keeping good balance between convergence and maintenance, for real world applications, among the various algorithms for parallel architecture optimi zation CBFPSOs are attracting more and more attentions in recent years. To tackle the challenges of parall el architecture optimization, several strategies ha ve been proposed, to enhance the performance of Particle Swarm Optimization (PSO) and have obtained success on various multicore parallel architecture optimizatio n problems. But there still exist some issues in mu lticore architectures which require to be analyzed carefull y. In this study, a new Constraint based Bacterial Foraging Particle Swarm Optimization (CBFPSO) scheduling for multicore architecture is proposed, which updates the velocity and position by two bacterial behaviours, i.e., reproduction and elimination disp ersal. The performance of CBFPSO is compared with the simulation results of GA and the result shows that the proposed algorithm has pretty good performance on almost all types of cores compared to GA with respec t to completion time and energy consumption.
Developable surface processing methods for three-dimensional meshes

Developable surfaces are of great significance in computer graphics as they play a key role in many applications involving planar surface parameterization. Industries where 3D objects are constructed from sheets of material such as fabric or metal typically employ these surfaces throughout their design processes. This work introduces a new descriptor for developable mesh surfaces, which provides the means for creating simple and robust tools for detecting, measuring, and approximating developable surface charts in meshes. Based on this descriptor two novel algorithms are proposed. D-Charts, an algorithm for mesh segmentation into (nearly) developable charts, and DCS approximation, an algorithm for approximation of meshes with developable surfaces. D-Charts uses the proposed descriptor to segment meshes into nearly developable charts for texture atlas generation. By bounding the distortion directly during the segmentation stage, the generated atlases exhibit less distortion for the same number of charts compared to those created using state-of-the-art techniques. In addition to texture atlases, we demonstrate the practicality of this method for industrial applications using the patterns produced by the algorithm to make fabric and paper copies of popular computer graphics models. The DCS approximation method increases surface developability by modifying the geometry, while at the same time keeping the deformed surface as close as possible to the input surface. The method was developed in the context of virtual fabric design, and was combined with a sketching interface to provide a novel design system. The algorithm modifies non-developable meshes generated from sketches into piecewise developable surfaces. These allow straightforward computation of distortion-free texture mapping and automatic generation of 2D patterns for sewing real replicas of the designed garments.
Incremental CFG Parsing with Statistical Lexical Dependencies

Incremental parsing with a context free grammar produces partial syntactic structures for an initial fragment on the word-by-word basis. Owing to the syntactic ambiguity, however, too many structures are produced, and therefore its parsing speed becomes very slow. This paper describes a technique for efficient incremental parsing using lexical information. The probability concerning dependencies between words, as the lexical information, is automatically acquired from a large-scale corpus with syntactic structures. A process for discarding syntactic structures which will not be likely has been integrated into the incremental chart parsing. That is, partial syntactic structures whose dependency probabilities are not high will be removed from the chart. Our technique proposed in this paper can also be considered as a kind of practical methods of incremental disambiguation. An experiment using Penn Treebank has shown our technique to be feasible and efficient.
Lifespan and popularity measurement of online content on social networks

With rapid development and increased popularity of social networks, more interests have been made in obtaining information from such social networking websites. Analysis on the popularity of online contents is one of the hottest interests which has triggered intensive research. Our research focuses on measuring the popularity of online posts within specified topics, such as “drug abuse”, as it can be used to detect the crime and discover potential drug abusers. We measure the lifespan and the popularity of drug related posts in order to know the level of influence they made. Identifying popular posts online can help us reveal the trend, and also detect the latent danger and may prevent future crime. In this paper, the Comment Arrival Model is proposed to identify the lifespan and the comment frequency pattern of posts, which are considered the main factors to define post popularity. And we present 4 general models to measure the popularity of posts which can be applied in different social network platforms. We also did experiments and evaluated the performance of models in two popular social networks in Hong Kong, HK Discussion and Twitter.
Systemic abnormalities of psoriatic patients: a retrospective study

Background Psoriasis is a chronic immune-mediated inflammatory disease related to the metabolic syndrome, cardiovascular disease, and other comorbidities. However, so far there has been no specific research concerning systemic abnormalities in psoriatic patients. Objective A retrospective study was conducted focusing on the detailed systemic abnormalities in psoriatic patients. Methods Psoriatic inpatients data was collected from July 2009 to September 2015. The inclusion criteria were first-time hospitalization and without administration of systemic drug therapy or exposure to phototherapy for psoriasis for at least 1 month. Detailed systemic indexes were mainly evaluated. Results The abnormality rates of blood routine examination, urine examination, blood biochemical examination and chest X-ray of 43 psoriatic patients were significantly higher than those of 44 non-psoriasis controls, and psoriasis patients significantly had higher absolute values of leukocytes and neutrophils, and significantly lower values of lymphocytes. Compared with psoriasis vulgaris, erythrodermic psoriasis had significantly higher abnormality rates of blood biochemical examination and serum electrolyte analysis. Erythrodermic psoriasis had significantly higher absolute values of blood leukocytes, neutrophils, and lower serum calcium compared with those of psoriasis vulgaris. The neutrophil-to-lymphocyte ratio of controls was significantly lower than that of psoriatic patients, and neutrophil-to-lymphocyte ratio of erythrodermic psoriasis was significantly higher in comparison with psoriasis vulgaris. Conclusion This study is the first report in relation to a detailed assessment of systemic abnormalities in psoriatic patients prior to onset of systemic treatment. The systemic condition of psoriatic patients should be observed by clinicians before systemic therapy.