Miracle-dz/UniFAR-FTUs huggingface dataset:
MSPOT : Multi-Criteria Sequential Parmeter Optimization

Many relevant industrial optimization tasks feature more than just one quality criterion. State-of-the-art multi-criteria optimization algorithms require a relatively large number of function evaluations (usually more than 10) to approximate Pareto fronts. Due to high cost or time consumption this large amount of function evaluations is not always available. Therefore, it is obvious to combine techniques such as Sequential Parameter Optimization (SPO), which need a very small number of function evaluations only, with techniques from evolutionary multi-criteria optimization (EMO). In this paper, we show how EMO techniques can be efficiently integrated into the framework of the SPO Toolbox (SPOT). We discuss advantages of this approach in comparison to state-of-the-art optimizers. Moreover, with the resulting capability to allow competing objectives, the opportunity arises to not only aim for the best, but also for the most robust solution. Herein we present an approach to optimize not only the quality of the solution, but also its robustness, taking these two goals as objectives for multi-criteria optimization into account.
Nine Rules of Engagement: Reflections on Reflexivity

Wishing to be reflexive, to critically examine our assumptions, is easy. Doing it is less so. For researchers doing a study in their own professional field, it represents a particular challenge. In this essay, I explore this challenge using my own study as exemplar. I am researching workplace inclusion of workers with intellectual disability. As a professional, I have worked with and for people with intellectual disability for many years. The knowledge I bring to my inquiry – about the inabilities, vulnerabilities and needs ascribed to persons labelled thus – is deeply entrenched in common culture, as well as in my professional training. How can I handle this knowledge in my research? To what extent may, or must, it command my perceptions and interpretations? How may I challenge and look beyond it? Exploring this, I consult a handful of researchers who have examined pitfalls of involving informants with intellectual disability in research. I also call upon four giants of social science – Hans-Georg Gadamer, Charles Taylor, Pierre Bourdieu and Michel Foucault – as well as Brian Eno, British musician and producer extraordinaire. Departing from a concrete situation, the essay winds its way through nine discussions, each generating a “rule of engagement. ” Although the discussions are specific to my project, these nine methodological suggestions, ranging from seemingly trivial to decidedly non-trivial, are not. My belief is that they may hold some interest for any qualitative researcher.
Consumption-Based Asset Pricing with Loss Aversion

I incorporate loss aversion in a consumption-based asset pricing model with recursive preferences and solve for asset prices in closed-form. I find loss aversion increases expected returns substantially relative to the standard recursive utility model. This feature of my model improves the ability to match moments on asset prices. Further, I find loss aversion induces important nonlinearities into the expected excess returns as a function of the exposure to the consumption shocks. In particular, the elasticities of expected returns with respect to the exposure to the consumption shocks are greater for assets with smaller exposures to the shocks, thus generating interesting predictions for the cross-section of returns. I provide empirical evidence supporting this outcome. The model with loss aversion correctly predicts both a negative premium for skewness and a security market line, the excess returns as a function of the exposure to market risk, flatter than the CAPM.
Tempo Curves Considered Harmful

In the literature of musicology, computer music research and the psychology of music, timing or tempo measurements are mostly presented in the form of continuous curves. The notion of these tempo curves is dangerous, despite its widespread use, because it lulls its users into the false impression that a continuous concept of temporal flow has an independent existence, a musical or psychological reality, and that time can be perceived independent of events carrying it. But if one bases a transformation or manipulation of timing on the implied characteristics of such a notion, one is doomed to fail.
Skin detection based on thresholding in RGB and hue component

This paper presents a skin detection of color image using thresholding in hue component of HSV (Hue, Saturation, and Value) and RGB (Red, Green and Blue) color spaces. The reason for choosing hue component for thresholding compared to two other components is hue gives the best result for skin detection. The process of thresholding in hue component is done in order to get candidate pixels of skin regions. Then, the rectangular box of these blob regions are estimated and used to do thresholding in RGB color space where the mean and standard of each rectangular box is calculated. This method, is similar to adaptive thresholding technique in grayscale image processing, is able to detect the skin region of the test images accurately and with an acceptable robustness.
High-Definition Vector Imaging

■ High-definition vector imaging (HDVI) is a data-adaptive approach to synthetic-aperture radar (SAR) image reconstruction based on superresolution techniques originally developed for passive sensor arrays. The goals are to produce more informative, higher-resolution imagery for improving target recognition with UHF and millimeter-wave SAR and to aid the image analyst in identifying targets in radar imagery. Algorithms presented here include twodimensional minimum-variance techniques based on the maximum-likelihood method (Capon) algorithm and a two-dimensional version of the MUSIC algorithm. We use simulations to compare processing techniques, and we present results of wideband Rail SAR measurements of reflectors in foliage, demonstrating resolution improvement and clutter rejection. Results with airborne millimeter-wave SAR data demonstrate improved resolution and speckle reduction. We also discuss the vector aspect of HDVI, i.e., the incorporation of non-pointlike scattering models to enable feature detection. An example of a vector image is presented for data from an airborne UHF radar, using the broadside flash model to reveal greater information in the data.
Complex Semidefinite Programming Revisited and the Assembly of Circular Genomes

We consider the problem of arranging elements on a circle so as to approximately preserve specified pairwise distances. This problem is closely related to optimization problems found in genome assembly. The current methods for genome sequencing involve cutting the genome into many segments, sequencing each (short) segment, and then reassembling the segments to determine the original sequence. A useful paradigm has been using “mate pair” information, which, for a circular genome (e.g. bacterial genomes), generates information about the directed distance between non-adjacent pairs of segments in the final sequence. Specifically, given a set of equations of the form xv − yu ≡ duv (mod q), we study the objective of maximizing a linear payoff function that depends on how close the value xv − yu (mod q) is to duv. We apply the rounding procedure used by Goemans and Williamson for “complex” semidefinite programs. Our main tool is a simple geometric lemma that allows us to easily compute the expected distance on a circle between two elements whose positions have been computed using this rounding procedure.
Overcoming the Curse of Dimensionality

We study the behavior of pivot-based algorithms for similarity searching in metric spaces. We show that they are eeective tools for intrinsically high-dimensional spaces, and that their performance is basically dependent on the number of pivots used and the precision used to store the distances. In this paper we give a simple yet eeective recipe for practitioners seeking for a black-box method to plug in their applications. Besides, we introduce a new indexing algorithm that gives the minimum overall CPU search time for a given amount of memory, compared with other state-of-the-art approaches.
Designing modulation filters for improving speech intelligibility in reverberant environments

In this paper, we propose a new technique to design modulation filters to reduce degradation of speech intelligibility in reverberant environments. Using the inverse modulation transfer function, we design data-derived modulation filters for each speech frequency band. These filters preprocess speech signals between a microphone and a loudspeaker that radiates speech into a performance hall. Using our modulation filters, we conducted perceptual experiments with one hearingimpaired subject and two subjects with normal hearing. Test results indicate that our proposed method improves the intelligibility of reverberant speech.
The Effect of Dark Personality Traits and Cultural Logics on Counter-Productive Work Behaviors

Social Exchange Theory postulates that individuals build relationships on the basis that the parties involved are mutually engaging in reciprocity of benefits or rewards. Social exchange theory implies certain belief systems and dispositions shift expected ways of relating to organizations, with some more willing to exploit co-workers and organizations. The following study compared the bivariate and incremental importance of the Dark Triad (Psychopathy, Narcissism, and Machiavellianism) in relation to the Triadic Cultural Codes (Face, Honor, and Dignity) in predicting counterproductive work behaviors, both globally and broken down by interpersonal (CWB-I) and organizationally (CWB-O) directed acts. We found significant associations for all three Dark Traits with CWB but non-significant effects for all cultural codes. Both multiple and hierarchical regression confirm that cultural norms play no role in deviant work behavior. These findings suggest that character adaptations stemming from culture do not predict CWBs beyond the demonstrated relationship with dark personality traits that underlie more fundamental tendencies and dispositions. Post-hoc analyses show that Machiavellianism is predictive of CWB-O, which implies that Machiavellians tend to engage in CWBs directed at the organization (e.g. theft and sabotage) out of cynicism toward workplace politics. Narcissism was found to be predictive of CWB-I, which aligns with research that narcissists tend to utilize strategies that reinforce their perceived superiority over other individuals (e.g. bullying). Finally, psychopathy was found to be predictive of both CWB-O and CWB-I, which supports research implications that psychopaths tend to impulsively harm individuals and their organization’s property as a byproduct of their disinhibition.
Exploiting user interests for collaborative filtering: interests expansion via personalized ranking

In real applications, a given user buys or rates an item based on his/her interests. Learning to leverage this interest information is often critical for recommender systems. However, in existing recommender systems, the information about latent user interests are largely under-explored. To that end, in this paper, we propose an interest expansion strategy via personalized ranking based on the topic model, named iExpand, for building an interest-oriented collaborative filtering framework. The iExpand method introduces a three-layer, user-interest-item, representation scheme, which leads to more interpretable recommendation results and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues, such as the overspecialization and the cold-start problems. Finally, we evaluate iExpand on benchmark data sets, and experimental results show that iExpand outperforms state-of-the-art methods.
A novel method for calculating initial condition in harmonic balance analysis exploiting Volterra series

This paper presents a solution to one of the most troublesome sections of Newton algorithm for solving harmonic balance nonlinear system of equations which is choosing an appropriate initial condition. A good initial condition guaranties the convergence of the algorithm. Here, we have used Volterra series analysis to calculate the appropriate initial condition for the harmonic balance analysis. To demonstrate the performance of the Volterra assisted harmonic balance (VAHB) method we have compared the response of a typical nonlinear circuit with two other techniques. These techniques are implemented in the commercial ADS software. They exploit arbitrary initial condition and initial condition based on the transient analysis of the nonlinear circuit. Based on the calculated results the VAHB technique has a superior performance in terms of convergence with respect to other techniques.
A land and ocean microwave cloud classification derived from AMSU-A and-B , calibrated on MSG-SEVIRI infrared and visible observations

A statistical cloud classification and a cloud mask are developed based on AMSU-A and -B observations. The visible and infrared data from MSG-SEVIRI are used to calibrate the microwave classifier. Clear, low-, mediumand high-clouds can be retrieved over ocean and land with more than 80% confidence level. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean to discriminate clear situations and low clouds. This stand-alone AMSU-A and -B microwave cloud classification can be used efficiently in the retrieval of surface or atmospheric parameters from microwave satellite observations, over both ocean and land.
Polymorphic type, region and effect inference

Abstract We present a new static system which reconstructs the types, regions and effects of expressions in an implicitly typed functional language that supports imperative operations on reference values. Just as types structurally abstract collections of concrete values, regions represent sets of possibly aliased reference values and effects represent approximations of the imperative behaviour on regions. We introduce a static semantics for inferring types, regions and effects, and prove that it is consistent with respect to the dynamic semantics of the language. We present a reconstruction algorithm that computes the types and effects of expressions, and assigns regions to reference values. We prove the correctness of the reconstruction algorithm with respect to the static semantics. Finally, we discuss potential applications of our system to automatic stack allocation and parallel code generation.
Denoising and Time-window selection using Wavelet-based Semblance for improving ERP detection

Wavelet denoising has been successfully applied to Event-Related Potential (ERP) detection, but it usually works using channels information independently. This paper presents an adaptive approach to denoise signals taking into account the channels correlation in the wavelet domain. Moreover, we combine phase and amplitude information to automatically select a time window which increases ERP detection. Results on the P300 speller show that our algorithm has a better accuracy with respect to the VisuShrink wavelet technique and the XDAWN algorithm among 22 healthy subjects, and a better regularity than XDAWN.
Exponentially stabilizing continuous-time controllers for periodic orbits of hybrid systems: Application to bipedal locomotion with ground height variations

This paper presents a systematic approach for the design of continuous-time controllers to robustly and exponentially stabilize periodic orbits of hybrid dynamical systems arising from bipedal walking. A parameterized family of continuous-time controllers is assumed so that (1) a periodic orbit is induced for the hybrid system, and (2) the orbit is invariant under the choice of controller parameters. Properties of the Poincaré map and its first- and second-order derivatives are used to translate the problem of exponential stabilization of the periodic orbit into a set of bilinear matrix inequalities (BMIs). A BMI optimization problem is then set up to tune the parameters of the continuous-time controller so that the Jacobian of the Poincaré map has its eigenvalues in the unit circle. It is also shown how robustness against uncertainty in the switching condition of the hybrid system can be incorporated into the design problem. The power of this approach is illustrated by finding robust and stabilizing continuous-time feedback laws for walking gaits of two underactuated 3D bipedal robots.
Balancing in reverse engineering and in object-oriented systems engineering to improve reusability and maintainability

Whenever a reverse engineering method is applied to an old software system, the results are mainly limited to its degree of automation. Additional domain knowledge via a human expert helps to overcome this limitation. Modernizing old procedural software for object-oriented reuse requires domain knowledge as well us a balancing of the recovered information. We have developed a reverse engineering method for a gradual transition of procedural programs to an object-oriented architecture based on those aspects. This paper describes the balancing of the various generated models in our reverse engineering method and discusses this balancing in comparison to the balancing process in conventional object-oriented systems development. We also point out the links between these two engineering approaches. Furthermore the advantages of our reverse engineering process for reusability as well as maintainability of old procedural software based on object-oriented concepts are indicated. <<ETX>>
A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis

Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficients as the vocal tract parameterization of the speech signal. Mel Cepstral coefficients were never intended to work in a parametric speech synthesis framework, but as yet, there has been little success in creating a better parameterization that is more suited to synthesis. In this paper, we use deep learning algorithms to investigate a data-driven parameterization technique that is designed for the specific requirements of synthesis. We create an invertible, low-dimensional, noise-robust encoding of the Mel Log Spectrum by training a tapered Stacked Denoising Autoencoder (SDA). This SDA is then unwrapped and used as the initialization for a Multi-Layer Perceptron (MLP). The MLP is fine-tuned by training it to reconstruct the input at the output layer. This MLP is then split down the middle to form encoding and decoding networks. These networks produce a parameterization of the Mel Log Spectrum that is intended to better fulfill the requirements of synthesis. Results are reported for experiments conducted using this resulting parameterization with the ClusterGen speech synthesizer.
Femininity is not inferiority: women-led civil society organizations and “countering violent extremism” in Nigeria

ABSTRACT The United Nations Security Council Resolution 1325 on Women, Peace and Security, and the Millennium Development Goals, brought the inclusion of women in the security agenda into the international limelight. Although these global frameworks, and other international resolutions, underscore the importance of women’s participation in the politics of peace and security, Nigeria’s frameworks have not been inclusive. Extant literature has examined the role of women within the context of mainstream responsibilities for “counter-terrorism and counter-insurgency” that are held by the military and paramilitary forces. This study interrogates the role of women-led civil society organizations in “countering violent extremism” activities in Nigeria. Using a mixed-methods approach, it demonstrates that the participation of women-led civil society organizations in “countering violent extremism” activities has generated certain positive results for achieving women’s inclusion and gender equality in politics and society.
Patient, Carer and Professional Perspectives on Barriers and Facilitators to Quality Care in Advanced Heart Failure

Background Those with advanced heart failure (HF) experience high levels of morbidity and mortality, similar to common cancers. However, there remains evidence of inequity of access to palliative care services compared to people with cancer. This study examines patient, carer, and professional perspectives on current management of advanced HF and barriers and facilitators to improved care. Methods Qualitative study involving semi-structured interviews and focus groups with advanced HF patients (n = 30), carers (n = 20), and professionals (n = 65). Data analysed using Normalisation Process Theory (NPT) as the underpinning conceptual framework. Findings Uncertainty is ubiquitous in accounts from advanced HF patients and their caregivers. This uncertainty relates to understanding of the implications of their diagnosis, appropriate treatments, and when and how to seek effective help. Health professionals agree this is a major problem but feel they lack knowledge, opportunities, or adequate support to improve the situation. Fragmented care with lack of coordination and poor communication makes life difficult. Poor understanding of the condition extends to the wider circle of carers and means that requests for help may not be perceived as legitimate, and those with advanced HF are not prioritised for social and financial supports. Patient and caregiver accounts of emergency care are uniformly poor. Managing polypharmacy and enduring concomitant side effects is a major burden, and the potential for rationalisation exists. This study has potential limitations because it was undertaken within a single geographical location within the United Kingdom. Conclusions Little progress is being made to improve care experiences for those with advanced HF. Even in the terminal stages, patients and caregivers are heavily and unnecessarily burdened by health care services that are poorly coordinated and offer fragmented care. There is evidence that these poor experiences could be improved to a large extent by simple organisational rather than complex clinical mechanisms.
Potentially functional polymorphisms in PAK1 are associated with risk of lung cancer in a Chinese population

P21‐activated kinase 1(PAK1) plays an important role in the regulation of cell morphogenesis, motility, mitosis, and angiogenesis and has been implicated with tumorigenesis and tumor progression. We hypothesized that functional polymorphisms in PAK1 gene may modify the risk of lung cancer. We screened four potentially functional polymorphisms (rs2154754, rs3015993, rs7109645, and rs2844337) in PAK1 gene and evaluated the association between the genetic variants and lung cancer risk in a case–control study including 1341 lung cancer cases and 1982 cancer‐free controls in a Chinese population. We found that variant allele of rs2154754 was significantly associated with a decreased risk of lung cancer (OR = 0.85, 95% CI: 0.77–0.95, P = 0.004), meanwhile the result of rs3015993 was marginal (OR = 0.90, 95%CI: 0.81–1.00, P = 0.044). After multiple comparisons, rs2154754 was still significantly associated with the lung cancer risk (P < 0.0125 for Bonferroni correction). We also detected a significant interaction between rs2154754 genotypes and smoking levels on lung cancer risk (P = 0.042). Combined analysis of these two polymorphisms showed a significant allele‐dosage association between the number of protective alleles and reduced risk of lung cancer (Ptrend = 0.008). These findings indicate that genetic variants in PAK1 gene may contribute to susceptibility to lung cancer in the Chinese population.