Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing Heterogeneity in Model-Based Systems Engineering of Cyber-Physical Systems

Model-based Systems Engineering plays a pivotal role in the design of distributed embedded systems by enabling early virtual integration of the different parts of the system. Traditionally, the system model is composed of subsystem models at the same level of abstraction and with one particular view. However, in some cases the system model may comprise sub-system models at different levels of abstraction. Integration of these different abstraction level models imposes some important drawbacks which hinder the overall system simulations. These drawbacks need to be addressed to facilitate the simulation of systems composed with multi-level subsystem models. In this paper we report on modelling techniques for embedded and distributed systems to deal with this heterogeneity. We describe a methodology to (semi-)automatically generate an executable multi-level system simulation model starting from an abstract system architecture of the system. A platooning system example is used to demonstrate the new modelling techniques.
Economic Analysis of the Production of Amylases and Other Hydrolases by Aspergillus awamori in Solid-State Fermentation of Babassu Cake

Amylases are one of the most important industrial enzymes produced worldwide, with their major application being in ethanol manufacturing. This work investigated the production of amylases by solid-state fermentation of babassu cake, using the filamentous fungus Aspergillus awamori IOC-3914. Lab-scale experiments were carried out to generate input data for simulations of an industrial plant for amylase production. Additionally to the target enzymes, other hydrolases (cellulases, xylanases, and proteases) were also produced, enriching the final product. The most suitable fermentation time was 144 hours, when exoamylase and endoamylase activities of 40.5 and 42.7 U g−1 were achieved, respectively. A first evaluation showed a large impact of the inoculum propagation medium on production costs. Therefore, five propagation media were compared, and PDA medium presented the best cost-benefit ratio. The credits obtained from sales of fermented cake as a coproduct enabled a significant decrease in the production cost of the enzyme product, down to 10.40 USD kg−1.
VAPC, an Human Endogenous Inhibitor for Hepatitis C Virus (HCV) Infection, Is Intrinsically Unstructured but Forms a “Fuzzy Complex” with HCV NS5B

Nearly 200 million people are infected by hepatitis C virus (HCV) worldwide. For replicating the HCV genome, the membrane-associated machinery needs to be formed by both HCV non-structural proteins (including NS5B) and human host factors such as VAPB. Recently, the 99-residue VAPC, a splicing variant of VAPB, was demonstrated to inhibit HCV replication via binding to NS5B, thus acting as an endogenous inhibitor of HCV infection. So far, the structure of VAPC remains unknown, and its interaction with NS5B has not been biophysically characterized. In this study, we conducted extensive CD and NMR investigations on VAPC which led to several striking findings: 1) although the N-terminal 70 residues are identical in VAPC and VAPB, they constitute the characteristic β-barrel MSP fold in VAPB, while VAPC is entirely unstructured in solution, only with helical-like conformations weakly populated. 2) VAPC is indeed capable of binding to NS5B, with an average dissociation constant (Kd) of ∼20 µM. Intriguingly, VAPC remains dynamic even in the complex, suggesting that the VAPC-NS5B is a “fuzzy complex”. 3) NMR mapping revealed that the major binding region for NS5B is located over the C-terminal half of VAPC, which is composed of three discrete clusters, of which only the first contains the region identical in VAPC and VAPB. The second region containing ∼12 residues appears to play a key role in binding since mutation of 4 residues within this region leads to almost complete loss of the binding activity. 4) A 14-residue mimetic, VAPC-14 containing the second region, only has a ∼3-fold reduction of the affinity. Our study not only provides critical insights into how a human factor mediates the formation of the HCV replication machinery, but also leads to design of VAPC-14 which may be further used to explore the function of VAPC and to develop anti-HCV molecules.
Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm

Linguistic fuzzy modeling in high dimensional regression problems is a challenging topic since conventional linguistic fuzzy rule-based systems suffer from exponential rule explosion when the number of variables and/or data examples becomes high. A good way to face this problem is by searching for a good and simple global structure within the same process, in order to consider the relationships among the different components defining the final linguistic model. In this contribution, we propose an effective multi-objective evolutionary algorithm that based on the data base learning a priori (involved variables, granularities and slight uniform displacements of the fuzzy partitions) allows a fast derivation of simple and quite accurate linguistic models, making use of some effective mechanisms in order to ensure a fast convergence. The good results obtained in several large-scale regression problems demonstrate the effectiveness of the proposed approach.
Family gene based Cloud Trust model

Inspired by the biologic gene technique, we present a Cloud Trust model based on Family Gene which is different from the traditional PKI (Public Key Infrastructure) model, and give the formal definition of model and correlation conception of family gene, cloud family, trust relation, gene identification, and gene assignment. We depict how the model works. The experimental results show the model has steady and excellent real-time performance on the realization and possess characteristic of simple and convenient identification. It is good way for the cloud trust solution.
Age and gender differences in employee cyberloafing behavior

Cyberloafing at the workplace, a form of deviant behavior, refers to the use of organization’s Internet access by employees during work for non-work purposes. Recently, researchers have an increased interest in cyberloafing on the job among employees as they realize the negative outcomes of this behavior. This study examined whether there were differences in cyberloafing among younger and older employees as well as males and females. Using survey questionnaires, data were gathered from 260 Malaysian employees from a public sector organization. We found that irrespective of age, males engaged in cyberloafing more than females. Implications for organizations and research are presented.
A vowel-based method for vocal tract control in clarinet pedagogy

Our review of scientific literature shows that the activity inside the clarinetist’s vocal tract (VT) affects pitch and timbre, while also facilitating technical exercises. Clarinetists adapt their VT intuitively and, in some cases, may compensate an inadequate VT configuration through unnecessary pressure, resulting in technical blockage, fatigue, and impeding progress. The pedagogical literature sometimes disregards the explicit use of the VT, but common trends exist, using specific vowel formations to model VT configurations. To understand the actual approach of the VT in practice, we interviewed 12 renowned clarinetists from different continents. While all interviewees consider VT configurations important, some find VT manipulation an intuitively acquired skill. Most instructors use vowels to teach VT configurations. We propose a normalized system: /a/ for chalumeau, /ɔ/ for throat register, /i/ for clarion, /ø/ for altissimo, /o/ as a base vowel, /ɔ/–/i/ for ascending leaps, /i/–/ɔ/ for descending intervals. We have applied this method to clarinet teaching at the conservatory with very encouraging results, as students easily understand the vowel system, and in relatively short time have achieved better sound quality and technical ease. Students have gained a conscious control of their VT and consequently have acquired a technique that they now use intuitively.
Statistical Methods for Assessing Genetic Association in the Presence of Linkage

This thesis is concerned with the analysis of association between genetic markers and disease. We consider a scenario where it is known that a genetic region of interest has a tendency to be transmitted intact from parent to ospring. The region is said to be linked. The hope is that a mutation involved in the causal pathway of the disease is contained in the linked region, and that we can pinpoint its exact location through association analysis. We describe and assess existing methodologies, parametric and non-parametric, for the testing and estimation of association in the presence of linkage. Many genetic association studies have complex ascertainment schemes. We develop a novel score test of association in the presence of linkage for binary traits that takes ascertainment, as well as population stratification, into account.
Mr. Scan: Extreme scale density-based clustering using a tree-based network of GPGPU nodes

Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most wellknown density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points.
A supervised singular value decomposition for independent component analysis of fMRI

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive tech- nique for studying the brain activity. The data acquisition process results a tempo- ral sequence of 3D brain images. Due to the high sensitivity of MR scanners, spikes are commonly observed in the data. Along with the temporal and spatial features of fMRI data, this artifact raises a challenging problem in the statistical analysis. In this paper, we introduce a supervised singular value decomposition technique as a data reduction step of independent component analysis (ICA), which is an effective tool for exploring spatio-temporal features in fMRI data. Two major advantages are discussed: first, the proposed method improves the robustness of ICA against spikes; second, the method uses the fMRI experimental designs to guide the fully data-driven ICA, yielding a more computationally efficient procedure and highly interpretable results. The advantages are demonstrated using spatio-temporal sim- ulation studies as well as a data analysis.
Parallel image segmentation using multi-threading and k-means algorithm

In the present era of mechanization, image segmentation has brought revolutionary changes in the field of image processing. There are many image segmentation methods in use but most of the prevalent techniques are not very efficient. In our current work, a method is proposed to segment images using multi-threaded programming and k-means clustering. Parts of images are used as a thread and k-means clustering is used to segment them on every thread. An efficient image segmentation technique using the multithreaded program is done in this paper. These results are compared with segmentation results without multithreading. The obtained results are very much encouraging.
PyMEF — A framework for exponential families in Python

Modeling data is often a critical step in many challenging applications in computer vision, bioinformatics or machine learning. Gaussian Mixture Models are a popular choice in many applications. Although these mixtures are powerful enough to approximate complex distributions, they may not be the best choice for some applications. Usual software mixtures libraries are often limited to a particular kind of distribution, which makes difficult to change the distribution and so to choose the best one. In this paper we focus on a particular class of distributions, the exponential families (which contains a lot of usual distributions like Gaussian, Rayleigh or Gamma). We present pyMEF, a Python framework to manipulate, learn and simplify mixtures of exponential families.
GIR Experiments with Forostar at GeoCLEF 2007

In this paper we describe our Geographic Information Retrieval experiments with Forostar, our GIR application on the GeoCLEF 2007 corpus and query set. We compare the results from orthogonal text with no geographic entities and only geographic entities with standard text retrieval and combined text and geographic relevance methods. The text and named entity analysis and retrieval methods of Forostar are described in detail. We also detail our placename disambiguation and geographic relevance ranking methods. The paper concludes with an analysis of our results including significance testing where we show our baseline method, in fact, to be best. Finally we identify weaknesses in our approach and ways in which the system could be optimised and improved.
Nugget-Based Computation of Graded Relevance

We propose a simple method for assigning graded relevance values to documents judged during the course of a retrieval experiment. In making this proposal, we aim to avoid the potential for ambiguity and greater cognitive load associated with standard graded relevance judgments. Under our proposal, we first decompose a retrieval topic into a number of informational nuggets. For each document, a binary judgment is made with respect to each nugget. The ratio of relevant nuggets to total nuggets becomes the graded relevance value assigned to that document. To provide support for this idea, we turn to test collections created for the TREC Web Track. Along with the usual graded relevance judgments required by traditional effectiveness measures, these test collections include topic decompositions created for the purpose of evaluating novelty and diversity. By exploiting these test collections for our own purposes, we demonstrate a clear relationship between our proposed method and traditional graded relevance. In addition to supporting our proposal, our experiments suggest that informational nuggets can provide a unified approach to relevance assessment, supporting both traditional effectiveness measures and newer measures of novelty and diversity.
Reporting Experiences on Group Activities in Cross-Device Settings

Even though mobile devices are ubiquitous and users often own several of them, using them in concert to achieve a common goal is not well supported and remains a challenge for HCI. In this paper, we report on our observations of cross-device usage within groups when they engaged in a dyadic collaborative sensemaking task. Based on our findings, we discuss limitations of a state-of-the-art cross-device setting and present a set of design recommendations. We then propose an alternative design that aims for greater flexibility when using mobile devices to enable a free configuration of workspaces depending on users’ current activity.
Efficiency of Machine Learning Techniques in Bankruptcy Prediction

Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms’ impending failure is indeed desirable. The scope of the research reported here is to investigate the efficiency of machine learning techniques in such an environment. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 150 failed and solvent Greek firms in the recent period 2003-2004. It was found that learning algorithms could enable users to predict bankruptcies with satisfying accuracy long before the final bankruptcy.
Towards a user-adapted information environment on the Web

Due to the tremendously increasing popularity of the World-Wide Web, hypermedia is going to be the leading online information medium for some years to come and will most likely become the standard gateway for citizens to the "information highway". Already today, visitors of web sites are generally heterogeneous and have different needs. The aim of the AVANTI project is to cater hypermedia information to these individual needs by adapting the content and the presentation of web pages to each individual user. The special needs of elderly and disabled users are also partly considered. A model of the characteristics of user groups, individual users, usage environments, and a domain model are exploited in the adaptation process. One aim of this research is to verify that adaptation and user modeling techniques that were hitherto mostly used for catering interactive software systems to normal- abled users also prove useful for adaptation to users with special needs. The evaluation of the effectivity, efficiency, and acceptance concerning the individualized presentation of information is also part of the project. Another original aspect is the development of a network-wide user modeling server that can concurrently accommodate the user modeling needs of several applications and instances of an application within a distributed computing environment.
A co-design strategy for embedded Java applications based on a hardware interface with invocation semantics

As programmable hardware technology gathers momentum, the partitioning of applications into hardware and software will prove to be an increasingly important research area. Co-design technologies that achieve this partitioning typically adopt a strategy in which a high level specification is used to synthesise both hardware and software. This paper proposes an alternative approach by which equivalencies between hardware and software components are defined, thereby providing a common interface between them. This allows logic to be moved between hardware and software while retaining the functional properties of the application. An investigation is carried out to derive equivalencies between software elements of the Java language and hardware components by appropriate wrapping of the latter. By developing a framework that captures these equivalencies, this paper shows how hardware/software partitioning of a system can be relegated to a late stage of system development and include both application and virtual machine logic.
Simultaneous localization and mapping using monocular direct method

In this paper, we accomplish simultaneous localization and mapping using the monocular LSD-SLAM. This method is different with feature method, estimating the accurate and reconstructing the large scale environment map. Using the direct image alignment, the environment can be mapped in pose-graph of key frames semi-dense maps. The LSD-SLAM contains two advantage. A novel direct tracking method can definitely estimate the scale-drift. In addition, the probabilistic estimation can optimize the effect of noisy depth values on tracking. The experiment showed the LSD-SLAM is reliability, robustness and real-time
TEXTUAL ENTAILMENT USING LEXICAL AND SYNTACTIC SIMILARITY

A two-way Textual Entailment (TE) recognition system that uses lexical and syntactic features has been described in this paper. The TE system is rule based that uses lexical and syntactic similarities. The important lexical similarity features that are used in the present system are: WordNet based uni-gram match, bi-gram match, longest common sub-sequence, skip-gram, stemming. In the syntactic TE system, the important features used are: subject-subject comparison, subject-verb comparison, object-verb comparison and cross subject-verb comparison. The system has been separately trained on each development corpus released as part of the Recognising Textual Entailment (RTE) competitions RTE-1, RTE-2, RTE-3 and RTE-5 and tested on the respective RTE test sets. No separate development data was released in RTE-4. The evaluation results on each test set are compared with the RTE systems that participated in the respective RTE competitions with lexical and syntactic approaches.
A Real-Time Adaptive Learning Method for Driver Eye Detection

This paper presents an adaptive learning method for real-time driver eye detection, which could be used for monitoring a driverpsilas vigilance level while he/she is operating a vehicle on road. To adapt to the variances in the eye shape and size of different individuals, a learning mode is introduced at the early stage of eye positioning to build the sample learning library. Face detection is firstly performed to narrow the search region. Then contour detection and heuristic rules are used to identify the region of interest (ROI) of the eye. By learning the eye region, a set of images that satisfy the pre-set rules are obtained to form the eye templates. When the number of successful learning exceeds a pre-set threshold, the algorithm switches to the non-learning mode, in which the template matching and a distance factor are used for eye detection. By extracting the skeleton curve and the corner of the eye, the closing level of the eye is calculated which could be used as indicators to the driverpsilas vigilance level. The validation results show that the method could achieve a high level of accuracy.
NRWRH for Drug Target Prediction ∗

Drug-target interaction prediction is an important problem for the development of novel drugs and human medical improvement. Many supervised and semi-supervised methods are proposed to uncover the relation between drugs and targets. Under the hypothesis that similar drugs target similar target proteins and the framework of Random Walk with Restart, the method of Networkbased Random Walk with Restart on the Heterogeneous network (NRWRH) is proposed to infer potential drug-target relationship. This method integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction network) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of drug-target proteins interaction data including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves the previous methods in terms of cross validation.