Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive on-line software aging prediction based on machine learning

The growing complexity of software systems is resulting in an increasing number of software faults. According to the literature, software faults are becoming one of the main sources of unplanned system outages, and have an important impact on company benefits and image. For this reason, a lot of techniques (such as clustering, fail-over techniques, or server redundancy) have been proposed to avoid software failures, and yet they still happen. Many software failures are those due to the software aging phenomena. In this work, we present a detailed evaluation of our chosen machine learning prediction algorithm (M5P) in front of dynamic and non-deterministic software aging. We have tested our prediction model on a three-tier web J2EE application achieving acceptable prediction accuracy against complex scenarios with small training data sets. Furthermore, we have found an interesting approach to help to determine the root cause failure: The model generated by machine learning algorithms.
Learning Compact Binary Codes for Hash-Based Fingerprint Indexing

Compact binary codes can in general improve the speed of searches in large-scale applications. Although fingerprint retrieval was studied extensively with real-valued features, only few strategies are available for search in Hamming space. In this paper, we propose a theoretical framework for systematically learning compact binary hash codes and develop an integrative approach to hash-based fingerprint indexing. Specifically, we build on the popular minutiae cylinder code (MCC) and are inspired by observing that the MCC bit-based representation is bit-correlated. Accordingly, we apply the theory of Markov random field to model bit correlations in MCC. This enables us to learn hash bits from a generalized linear model whose maximum likelihood estimates can be conveniently obtained using established algorithms. We further design a hierarchical fingerprint indexing scheme for binary hash codes. Under the new framework, the code length can be significantly reduced from 384 to 24 bits for each minutiae representation. Statistical experiments on public fingerprint databases demonstrate that our proposed approach can significantly improve the search accuracy of the benchmark MCC-based indexing scheme. The binary hash codes can achieve a significant search speedup compared with the MCC bit-based representation.
Analysis of the quantization error in digital multipliers with small wordlength

The analysis of the quantization error in fixed-point arithmetic is usually based on simplifying assumptions. The quantization error is modelled as a random variable which is independent of the quantized variable. This contribution investigates the wordlength reduction of a digital multiplier in greater detail. The power spectrum of the quantization is expressed by the power spectrum of the multiplier input. The analytical results agree with measurements of the quantization error. The presented error model is shown to be superior to the simplified one for wordlengths in the range of eight bit.
Traffic sign recognition using convolutional neural networks

Traffic sign recognition (TSR) represents an important feature of advanced driver assistance systems, contributing to the safety of the drivers, pedestrians and vehicles as well. Developing TSR systems requires the use of computer vision techniques, which could be considered fundamental in the field of pattern recognition in general. Despite all the previous works and research that has been achieved, traffic sign detection and recognition still remain a very challenging problem, precisely if we want to provide a real time processing solution. In this paper, we present a comparative and analytical study of the two major approaches for traffic sign detection and recognition. The first approach is based on the color segmentation technique and convolutional neural networks (C-CNN), while the second one is based on the fast region-based convolutional neural networks approach (Fast R-CNN).
Enhancing collective entity resolution utilizing Quasi-Clique similarity measure

Entity resolution(ER) is the problem of identifying duplicate references that refer to the same real world entity. It is a critical component of data integration and data cleaning. Attribute-based entity resolution is the traditional approach where similarity is computed for each pair of references based on their attributes. More recently, context-base entity resolution has been proposed which considers the attributes of the related references. In this paper, we present a collective entity resolution approach which using Quasi-Clique similarity to improve the accuracy. It complements the traditional methodology by reducing the number of false positive. An experimental evaluation on several datasets shows high recall and precision rates, which validate the method's efficiency.
Efficient sparse Bayesian learning via Gibbs sampling

Sparse Bayesian learning (SBL) has been used as a signal recovery algorithm for compressed sensing. It has been shown that SBL is easy to use and can recover sparse signals more accurately than the well-known Basis Pursuit (BP) algorithm. However, the computational complexity of SBL is quite high, which limits its use in large-scale problems. We propose herein an efficient Gibbs sampling approach, referred to as GS-SBL, for compressed sensing. Numerical examples show that GS-SBL can be faster and perform better than the existing SBL approaches.
Tracing Value-Added and Double Counting in Gross Exports

This paper proposes a framework for gross exports accounting that breaks up a country's gross exports into various value-added components by source and additional double counted terms. By identifying which parts of the official trade data are double counted and the sources of the double counting, it bridges official trade (in gross value terms) and national accounts statistics (in value added terms). Our parsimonious framework integrates all previous measures of vertical specialization and value-added trade in the literature into a unified framework. To illustrate the potential of such a method, we present a number of applications including re-computing revealed comparative advantages and the magnifying impact of multi-stage production on trade costs.
Endothelial Cell Capture of Heparin-Binding Growth Factors under Flow

Circulation is an important delivery method for both natural and synthetic molecules, but microenvironment interactions, regulated by endothelial cells and critical to the molecule's fate, are difficult to interpret using traditional approaches. In this work, we analyzed and predicted growth factor capture under flow using computer modeling and a three-dimensional experimental approach that includes pertinent circulation characteristics such as pulsatile flow, competing binding interactions, and limited bioavailability. An understanding of the controlling features of this process was desired. The experimental module consisted of a bioreactor with synthetic endothelial-lined hollow fibers under flow. The physical design of the system was incorporated into the model parameters. The heparin-binding growth factor fibroblast growth factor-2 (FGF-2) was used for both the experiments and simulations. Our computational model was composed of three parts: (1) media flow equations, (2) mass transport equations and (3) cell surface reaction equations. The model is based on the flow and reactions within a single hollow fiber and was scaled linearly by the total number of fibers for comparison with experimental results. Our model predicted, and experiments confirmed, that removal of heparan sulfate (HS) from the system would result in a dramatic loss of binding by heparin-binding proteins, but not by proteins that do not bind heparin. The model further predicted a significant loss of bound protein at flow rates only slightly higher than average capillary flow rates, corroborated experimentally, suggesting that the probability of capture in a single pass at high flow rates is extremely low. Several other key parameters were investigated with the coupling between receptors and proteoglycans shown to have a critical impact on successful capture. The combined system offers opportunities to examine circulation capture in a straightforward quantitative manner that should prove advantageous for biologicals or drug delivery investigations.
Enhancing the Thermoelectric Power Factor by Using Invisible Dopants

Nanoparticle dopants that are invisible to conduction electrons and have sharp dips in their electron scattering rate versus electron energy close to the Fermi level. Replacement of such dopants with traditional impurities results in simultaneous enhancement of the Seebeck coefficient and the electron mobility and therefore a large enhancement in the thermoelectric power factor can be achieved.
SIRO: On-line semantic information retrieval using ontologies

The huge number of available documents on the Web makes finding relevant ones challenging. Thus, searching for information becomes more and more complex because of the growing volume of data and of its lack of structure. The quality of results that traditional full-text search engines provide is still not optimal for many types of user queries. Especially, the ambiguities of natural languages and abstract concepts are handled inadequately by full-text search engines. Ontologies provide a solution to these problems. They can help a user to find documents of a specific domain. This paper proposes a new retrieval system based on ontologies. This system integrates results from traditional full-text engines, and thus supports a gradual transition from classical full-text search engines to ontology-based ones.
Elimination of Harmonics in a Five-Level Diode-Clamped Multilevel Inverter Using Fundamental Modulation

In this study, elimination of harmonics in a five- level diode-clamped multilevel inverter (DCMLI) has been implemented by using fundamental modulation switching. The proposed method eliminates harmonics by generating negative harmonics with switching angles calculated for selective harmonic elimination method. In order to confirm the proposed method, first Matlab/Simulink and PSIM simulation results are given. Then the proposed method is also validated by experiments with Opal-RT controller and a 10 kW three- phase, five-level DCMLI prototype.
Cloning and Characterization of Benzoate Catabolic Genes in the Gram-Positive Polychlorinated Biphenyl DegraderRhodococcus sp. Strain RHA1

ABSTRACT Benzoate catabolism is thought to play a key role in aerobic bacterial degradation of biphenyl and polychlorinated biphenyls (PCBs). Benzoate catabolic genes were cloned from a PCB degrader,Rhodococcus sp. strain RHA1, by using PCR amplification and temporal temperature gradient electrophoresis separation. A nucleotide sequence determination revealed that the deduced amino acid sequences encoded by the RHA1 benzoate catabolic genes, benABCDK, exhibit 33 to 65% identity with those of Acinetobacter sp. strain ADP1. The gene organization of the RHA1 benABCDKgenes differs from that of ADP1. The RHA1 benABCDK region was localized on the chromosome, in contrast to the biphenyl catabolic genes, which are located on linear plasmids. Escherichia coli cells containing RHA1 benABCD transformed benzoate to catechol via 2-hydro-1,2-dihydroxybenzoate. They transformed neither 2- nor 4-chlorobenzoates but did transform 3-chlorobenzoate. The RHA1 benA gene was inactivated by insertion of a thiostrepton resistance gene. The resultant mutant strain, RBD169, neither grew on benzoate nor transformed benzoate, and it did not transform 3-chlorobenzoate. It did, however, exhibit diminished growth on biphenyl and growth repression in the presence of a high concentration of biphenyl (13 mM). These results indicate that the cloned benABCD genes could play an essential role not only in benzoate catabolism but also in biphenyl catabolism in RHA1. Six rhodococcal benzoate degraders were found to have homologs of RHA1benABC. In contrast, two rhodococcal strains that cannot transform benzoate were found not to have RHA1 benABChomologs, suggesting that many Rhodococcus strains contain benzoate catabolic genes similar to RHA1 benABC.
Using virtual impedance network to improve the control performances of LCL-type grid-connected inverter under the weak grid condition

Under the high penetration of renewable energy, the power grid may features as the weak grid, which contains a large set of grid impedance values and varies of harmonics. The grid-connected inverter can be greatly challenged by this weak grid condition when it is operated as the interface to inject the generated power into the grid. Particularly, the stability problem may be aroused by the grid impedance and the injected grid current can be greatly distorted by the grid voltage harmonics. Luckily, the output impedance of the inverter can give an insight into the stable robustness and the harmonic rejection ability of control system, so relationships between them are investigated in this paper. Based on which, the virtual impedance network is proposed to provide additional degrees of freedom for the output impedance optimization. With this method, the grid-connected inverter exhibits high robustness against the grid-impedance variation and also performs strong rejection ability against grid background harmonics at the same time. Experimental results from a LCL-type single-phase grid-connected inverter confirm the theoretical expectations and the effectiveness of the proposed solutions.
BEHAVIOURAL FINANCE : A BACKROUND BRIEFING Professor

The principal purpose of this paper is to provide a background briefing on behavioural finance (BF) for those unfamiliar with this significant paradigm shift underway in finance scholarship. The paper synthesises and summarises a rapidly burgeoning literature on BF which, presently, is not as accessible as that concerned with modern finance theory. Attention is first given to what is meant by the rationality of financial agents, and the possibility is introduced that financial agents in an uncertain real world may be less than strictly rational. Thereafter, the various heuristics and cognitive biases that may characterise financial decision-making in an uncertain real world are catalogued and explained. The paper finishes with an assessment of the potential for BF to transform scholarship in finance. The economist may attempt to ignore psychology, but it is sheer impossibility for him to ignore human nature. If the economist borrows his conception of man from the psychologist, his constructive work may have some chance of remaining purely economic in character. But if he does not, he will not thereby avoid psychology. Rather, he will force himself to make his own, and it will be bad psychology. Clark (1918, p. 4) Introduction Modern Finance Theory (MFT), as presented in textbooks and taught in universities around the world, has evolved over more than 50 years. Seminal works in the earlier stages of this evolution include Dean (1951) on capital budgeting, Markowitz (1952, 1959) on portfolio theory, Modigliani and Miller (1958, 1963) on capital structure decisions, Miller and Modigliani (1961) on dividend policy decisions, Sharpe (1964) and Lintner (1965a, 1965b) on capital asset pricing, Fama (1970) on capital market efficiency, Black and Scholes (1973) on option pricing, Jensen and Meckling (1976) on agency theory, Ross (1976) on arbitrage
Clustered Mutations in HIV-1 Gag Are Consistently Required for Escape from Hla-B27–Restricted Cytotoxic T Lymphocyte Responses

The immune response to HIV-1 in patients who carry human histocompatibility leukocyte antigen (HLA)-B27 is characterized by an immunodominant response to an epitope in p24 gag (amino acids 263–272, KRWIILGLNK). Substitution of lysine (K) or glycine (G) for arginine (R) at HIV-1 gag residue 264 (R264K and R264G) results in epitopes that bind to HLA-B27 poorly. We have detected a R264K mutation in four patients carrying HLA-B27. In three of these patients the mutation occurred late, coinciding with disease progression. In another it occurred within 1 yr of infection and was associated with a virus of syncytium-inducing phenotype. In each case, R264K was tightly associated with a leucine to methionine change at residue 268. After the loss of the cytotoxic T lymphocyte (CTL) response to this epitope and in the presence of high viral load, reversion to wild-type sequence was observed. In a fifth patient, a R264G mutation was detected when HIV-1 disease progressed. Its occurrence was associated with a glutamic acid to aspartic acid mutation at residue 260. Phylogenetic analyses indicated that these substitutions emerged under natural selection rather than by genetic drift or linkage. Outgrowth of CTL escape viruses required high viral loads and additional, possibly compensatory, mutations in the gag protein.
Learning on Graph with Laplacian Regularization

We consider a general form of transductive learning on graphs with Laplacian regularization, and derive margin-based generalization bounds using appropriate geometric properties of the graph. We use this analysis to obtain a better understanding of the role of normalization of the graph Laplacian matrix as well as the effect of dimension reduction. The results suggest a limitation of the standard degree-based normalization. We propose a remedy from our analysis and demonstrate empirically that the remedy leads to improved classification performance.
Learning EMG control of a robotic hand: towards active prostheses

We introduce a method based on support vector machines which can detect opening and closing actions of the human thumb, index finger, and other fingers recorded via surface EMG only. The method is shown to be robust across sessions and can be used independently of the position of the arm. With these stability criteria, the method is ideally suited for the control of active prosthesis with a high number of active degrees of freedom. The method is successfully demonstrated on a robotic four-finger hand, and can be used to grasp objects
Structural Stability Monitoring of a Physical Model Test on an Underground Cavern Group during Deep Excavations Using FBG Sensors

Fiber Bragg Grating (FBG) sensors are comprehensively recognized as a structural stability monitoring device for all kinds of geo-materials by either embedding into or bonding onto the structural entities. The physical model in geotechnical engineering, which could accurately simulate the construction processes and the effects on the stability of underground caverns on the basis of satisfying the similarity principles, is an actual physical entity. Using a physical model test of underground caverns in Shuangjiangkou Hydropower Station, FBG sensors were used to determine how to model the small displacements of some key monitoring points in the large-scale physical model during excavation. In the process of building the test specimen, it is most successful to embed FBG sensors in the physical model through making an opening and adding some quick-set silicon. The experimental results show that the FBG sensor has higher measuring accuracy than other conventional sensors like electrical resistance strain gages and extensometers. The experimental results are also in good agreement with the numerical simulation results. In conclusion, FBG sensors could effectively measure small displacements of monitoring points in the whole process of the physical model test. The experimental results reveal the deformation and failure characteristics of the surrounding rock mass and make some guidance for the in situ engineering construction.
Investigating the effects of Social and Organizational Structures on Information spread: a simulation-based Approach

Organizations rely on access to information to deal with environmental uncertainty and to support their decision making processes. Consequently, they implement structural mechanisms and information processing capabilities to enhance the information flow, such as integrated information systems and formal hierarchies. Particularly in environments that are strongly influenced by information and communication technology, the ability of an organization to provide decision makers with useful and trusted information is also dependent on social interactions outside of the organizational hierarchy. This research therefore jointly analyses the effects of social and organizational network structures on information spread for archetypical organizational configurations. Drawing upon literature on information diffusion in social networks and on organizational design, a conceptual model of information spread in organizations is developed. Based on this model, a simulation is built, validated, and a series of simulation experiments is performed. First, the simulation is able to replicate observations from other studies. Second, the results indicate that information spreading patterns in organizations differ significantly, depending on the organizational configuration, the position of a person within this configuration, and the employed communication strategy. Implications and next steps for further, more detailed analyses are eventually discussed.
Characterization of binary composites by X-ray, thermal and microscopic studies

Anisotropic mechanical of the composite alloys: Al-Sn; Al-Bi and Al-Pb determined at macro levels and their relationship with anisotropic microstructural parameters in variable growth rates of solidification is the basis of the current finding. The mechanical parameters are found generating an identical form of Weibull distribution curve with the variation of anisotropic growth. It follows from the physical understanding of the curve that anisotropic microstructural parameters follow the Weibull distribution. Evidentially, there occurs a relationship between mechanical and microstructural parameters. The relationship is critically analyzed in view of the curve which has two cut-off points corresponding to a lower strength limit in the slow and fast growth regions and an upper strength limit in the moderate growth region. The latter is equivalent to the theoretical strength of lamellae, as microstructural parameters obey Gauss distribution, in the absence of any surface flaws which are responsible for the reduced strength. The physical signification to be drawn from the experimental results is that the variation of an anisotropic mechanical property over the entire experimental range of growth velocity, furnishes an evidence of its dependence as linear, nonlinear and linear respectively, in the slow, moderate and fast growth regions of solidification. Moderate super anisotropic growth velocity (~2.90 x 10-7m3s-1) strengthens the microstructural of binary composite phases two to three fold stronger than those resulting in from their respective isotropic growth observed in an icebath (~273 K) and manifold superior to its constituent phases irrespective of growth mode. Growth habits and thermal stability of composite phases are ascertained using SEM and DSC. X-ray diffraction studies affirm composite alloy to be a terminal solidus solution of physically distinct and mechanical separable phases
Diffusion and adoption of dynamic electricity tariffs: An agent-based modeling approach

This paper proposes an agent-based modeling (ABM) approach to study the diffusion and adoption of dynamic electricity tariffs. We discuss the difference between opinions and decisions of electricity consumers regarding dynamic pricing. By means of a simple ABM, we provide a plausible explanation for the observed in retail electricity markets discrepancy between the relatively high willingness to adopt dynamic tariffs and the actual low adoption rate.