Miracle-dz/UniFAR-FTUs huggingface dataset:
Free Wil l without Metaphysics

What does it mean to have free will? When asked, people widely believe that they have free will (Baumeister, Crescioni, & Alquist, 2010), and free will is commonly asserted as a critical underpinning for moral and legal responsibility (Greene & Cohen, 2004). But for such a seemingly widespread and important concept, there is remarkable confusion over its definition and use. Philosophers and theologians have debated the question of free will for millennia. Today, neuroscientists and psychologists have joined philosophers in trying to answer some nagging questions about free will: Is it an illusion (Wegner, 2002)? Is it incompatible with determinism (Nichols, 2011)? Can people be morally responsible without it (Greene & Cohen, 2004)? However, what is the “it” in each of these questions? The “it” is the folk concept of free will. It is this concept that is suspected to be an illusion, incompatible with determinism, and required for moral responsibility. Unfortunately, scholars know very little about what constitutes this ordinary concept of free will. We therefore need clarity both on the concept and the underlying phenomenon, and in doing so we must go beyond philosophers’ and scientists’ intuitions. We must empirically examine ordinary people’s conceptualization of free will and their application of this concept in everyday life. Without taking seriously the actual folk concept of free will, any theory of free will is at “risk of having nothing more than a philosophical fiction as its subject matter” (Mele, 2001, 27). Science and philosophy might discover facts that suggest revisions to the folk concept of free will; but without knowing what the concept is we can hardly revise it. OUP UNCORRECTED PROOF – FIRSTPROOFS, Tue May 06 2014, NEWGEN
Improving BER Performance of Massive MIMO Scheme over Rayleigh Fading Channel

In massive multiple input multiple output (mMIMO) scheme, the system capacity can be improved without additional bandwidth or transmit power by using a huge antenna array at base station with as much separation between antenna elements as possible. Unfortunately, its performance depends on having a perfect channel state estimate between the base state and the users. In this paper, the bit error rate (BER) performance of a mMIMO scheme is improved using genetic algorithm-based optimization with simulation performed in MATLAB software environment. The genetic algorithm used selects the best signal required for effective transmission. Four different antenna configurations in the order of 2x2, 4x4, 8x8 and 16x16 were considered for the simulation. The encoding and decoding were done using an STBC coded. Also, filter bank multicarrier-offset quadrature amplitude modulation (FBMC-OQAM) scheme was used and simulation was carried out for 4-FBMC-OQAM, 16 FBMC-OQAM, and 64 FBMC-OQAM order. The BER is computed for both the optimized and un-optimized mMIMO schemes, and the performance of both schemes is compared. Simulation results show a significant improvement in the BER of the optimized mMIMO compared to the normal (coded) MIMO scheme. The overall results show that the optimized mMIMO experience a reduced BER when compared to the normal mMIMO. In both cases, the BER reduces gradually as the number of antenna increases.
Relationship and Impact of Service Quality, Destination Image, on Customer Satisfaction and Revisit Intention to Syariah Destination in Madura, Indonesia

Madura is one of the sharia tourist attractions in Indonesia, so many local and foreign tourists come to Madura Island to carry out sharia tours by visiting Islamic places such as mosques, tombs of the great Madurese clerics, and halal culinary places. In the conditions of the COVID-19 pandemic which has been going on for one year, government policy has closed these sharia tourist spots. However, since the number of Covid 19 cases, especially on Madura Island, has decreased significantly, the government has allowed opening these sharia tourist spots with the provisions of health protocols. The purpose of this study is to examine the relationship between service quality, destination image, and customer satisfaction on the revisit intention of tourists to tour destination religious in Madura. Data were collected from a questionnaire survey and empirical studies were conducted using structural equation modeling (SEM) to test the hypotheses, as well as the structural relationships between these factors. The results of empirical studies confirm that service quality and destination image make a positive contribution to tourist satisfaction, then that the service quality contribute to the revisit intention, but the destination image does not contribute to the revisit intention, and tourist satisfaction contributes positively to the revisit intention. Furthermore, this study also contributes to the managers of sharia tourist destinations in Madura to analyze the relationship between service quality attributes and destination image to revisit intention by improving the services provided, thereby contributing to increasing the number of tourists, and implementing sustainable service quality policies.
Factors Related to Testing Location during the 2020–2021 School Year

Learning environments during the 2020–2021 school year looked very different across the country due to varied responses to the COVID-19 pandemic. Research on school opening plans has pointed to political and demographic variables as driving school instructional modes, rather than COVID infection rates (Valant, 2020; Harris & Oliver, 2021; Lehrer-Small, 2021). In this study, we use a student-level measure of testing location to provide more fine-grained insight into which students were in school, remote, or hybrid during the 2020–2021 school year.
Historical changes in semantic weights of sub-word units

In this chapter, we present a computational study on how the weight of sub-word units in determiningwordmeanings evolves chronologically in different languages. Sub-word units, e.g., morphemes, syllables etc., play variable roles in determining word meanings. Some morphemes in English have standalone lexical meanings (e.g., the root) while others function more as morpho-syntactic markers (e.g., the bound morphemes such as -ness etc.) The semantic weight of sub-word units changes over time; for instance, some ancient characters in Chinese or ancient prefixes in English no longer carry clear semantic meanings. The goal of this chapter is to characterize such a change with computational methods. The semantic weight of sub-word units can be captured by word embedding models (and their variants).
Growth Mindset in Mathematics among Ninth-Grade Students via 5Ps Learning Model

This paper determined the effects of implementing the Prepare, Perform, Process, Ponder, and Practice (5Ps) learning model in teaching ninth-grade students’ growth mindset in mathematics. This study employed the quasi-experimental design and mixed-method research approach to answer the research questions with 60 ninth-grade students at a public secondary high school in the Philippines. The study administered a growth mindset questionnaire, informal interviews, learning journals, and Focus Group Discussions (FGD) on identifying learning experiences and mindsets of the students. Findings presented that employing the 5Ps learning model significantly influences students' mindset in mathematics. Meanwhile, traditional teaching does not significantly affect students' mindset in mathematics. The implementation of the 5Ps learning model has a significant positive effect on students' growth mindset in mathematics. The results of the study are limited merely to the participants included in the study; similar research utilizing the 5Ps learning model to other learning areas with a larger sample is recommended for more generalizable results.
Adaptive limit analysis using deviatoric fields

Accurate estimates of limit loads for difficult stability problems in geotechnical engineering can rarely be obtained from a single finite element limit analysis without using an excessive number of elements. Therefore, efficient adaptive strategies which maximize the solution accuracy using minimum number of elements in the mesh are of great interest. The key to obtaining accurate solutions lies in accurately capturing the areas of plasticity within the problem domain, as their pattern and intensity characterize the solution. Several approaches have been practiced so far including those based on plastic multipliers, strain and power dissipation fields employed as control variables. All these schemes work quite well for cohesive or cohesive-frictional materials, but for purely frictional soils their performance stalls as e.g. plastic multipliers have substantially high values for all zero stress points on the surface of soil domain, therefore cannot indicate reliably plastic areas. Similar conclusion can be made about performance of schemes based on power dissipation or strain rates. This study explores the possibility of using the internal dissipation calculated from deviatoric stresses and strain rates as suitable control field for purely frictional materials. The performance observed for considered set of problematic for other adaptive schemes geotechnical examples is very promising. Moreover, the proposed approach works very well also for cohesive and cohesive frictional materials, suggesting its use as general engine for adaptive mesh refinement.
MD-07-2019-1006_proof 638..652

Purpose – In a work environment marked by unprecedented complexity, volatility and ambiguity, managers must accomplish their objectives while navigating many challenges. This paper aims to investigate potential interrelations among environmental transformations, cognitive biases and strategic decisions. In particular, the purpose of the study is to crystallize the state of art on the impact of cognitive biases on strategic decisions, in the context of environmental transformations. Design/methodology/approach – The authors have conducted a systematic literature review to identify existing relevant work on this topic and to detect potential avenues for future research. Findings –The findings highlight how decision-making is influenced and enabled by internal (e.g. perception) and external factors (e.g. digitalization). Specifically, the strategic role of cognitive biases appears to be crucial when investigating the related impact on strategic decisions in times of environmental transformation. Practical implications – Implications are drawn for scholars and practitioners interested in evaluating the role of specific decision-making determinants for the formation and implementation of strategic decisions. In this sense, we stress that decision-makers need to manage their cognitive biases and select the right information out of a wide data set in order to adapt to environmental transformations. Originality/value – By systematizing the literature review, potential interrelations among environmental transformations, cognitive biases and strategic decisions are identified. Furthermore, the primary phases that drive the decision-making process are proposed (analysis, decision, onboarding and control).
Increasing understanding and confidence in THM simulations of Engineered Barrier Systems

Previous studies on the modelling of coupled thermo-hydr barrier systems (EBSs) showed the sensitivity of the output the effects of uncertainties on the modelling results, to imp repository near field and to gain in-depth understanding o Department of Geotechnical Engineering, Civil Engineering College, Tongji Univeristy, Shanghai, China Alessio Ferrari PhD École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Università degli Studi di Palermo, Palermo, Italy Lyesse Laloui PhD École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Anders Sjöland PhD Swedish Nuclear Fuel and Waste Management Company, Stockholm, Sweden
U-Net Neural Network for Locating Midpoint of Insertion Zone of Transcatheter Aortic Valves in CTA Images

Identifying the insertion zone of transcatheter heart valves can be time-consuming and suffers from variability and reproducibility problems. We present a deep leaning approach in CTA images to locate the midpoint of the insertion zone. A U-Net neural network is implemented to automatically segment the aortic valve on axial projection. The insertion zone midpoint is calculated based on the range of slices with the more concentrated area of activated pixels. We found a very low systematic error with a median computed error of 0.38mm and interquartile range of 0.15 – 0.75mm. The proposed model was shown to be a robust and powerful tool to automatically locate the insertion zone midpoint and we believe it will play a critical role on automated assessment of aortic stenosis.
THE INFLUENCE OF SUSTAINABLE INNOVATION ON FINANCIAL ENTREPRENEURSHIP PERFORMANCE: GROWTH AND PREDICTION IN AN EMERGING MARKET

How to cite this paper: Hamdallah, M. E. , & Srouji, A. F. (2022). The influence of sustainable innovation on financial entrepreneurship performance: Growth and prediction in an emerging market. Journal of Governance & Regulation, 11(1), 27–37. https://doi.org/10.22495/jgrv11i1art3 Copyright © 2022 The Authors This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). https://creativecommons.org/licenses/by/
Genetic diversity and species richness patterns in Baetidae (Ephemeroptera) in theMontsenyMountain range

Genetic diversity and species richness patterns in Baetidae (Ephemeroptera) in the Montseny Mountain range (NorthEast Iberian Peninsula) This study aimed to describe patterns of diversity of Baetidae (Ephemeroptera) at the community and population levels within the Montseny Mountain range (North-East Iberian Peninsula). We studied both the distribution of 4 species of baetids in 20 sites among three catchments along the altitudinal gradient (350-1700 masl); and the genetic diversity of the mtDNA cytochrome c oxidase subunit I (cox1) gene of the two common species Baetis alpinus and Baetis rhodani. We found a gradual replacement of the dominant species along the altitudinal gradient. Baetis alpinus inhabited sites at high-altitudes, and this species was replaced by B. rhodani when the altitude decreased. Baetis melanonyx and Alainites muticus attained low abundance at all river sections, and no clear altitudinal trend appeared. Our hypothesis at the population level was that genetic structuring is associated with geographic distance and limited by drainage boundaries among the three studied catchments because of the short-time dispersion of adults. Unexpectedly, analyses of molecular variance (AMOVA) and isolation-bydistance (IBD) showed genetic diversity was unstructured by distance for both species, which may be explained by the relatively short spatial scale studied and small topographic barriers among the three catchments. The Generalized Mixed Yule-Coalescent (GMYC) model showed that B. rhodani had two differentiated genetic lineages that co-occurred in all sites. Overall, diversity of baetids was structured at the community level along the altitudinal gradient, whereas it was unstructured at the population level within the Montseny Mountain range.
Transient-Fault Robust Systems Exploiting Quasi-Delay Insensitive Asynchronous Circuits

Recent deep-submicron technology-based ICs are significantly more vulnerable to transient faults. The arisen errors are thus also more critical than they have ever been before. This thesis presents a further novel benefit of the Quasi-Delay Insensitive (QDI) asynchronous circuits in terms of reliability: their strong natural ability to mitigate longduration transient faults that are severe in modern synchronous circuits. A methodology to evaluate comparatively the transient-fault effects on synchronous and QDI asynchronous circuits is presented. Furthermore, a method to obtain the transient-fault mitigation ability of the QDI circuits' memory elements (i.e., the C-elements) is also proposed. Finally, mitigation techniques are suggested to increase even more the Celements' transient-fault attenuation, and thus also the QDI asynchronous systems' robustness.
The Literalist Fallacy & the Free Energy Principle: Model-building, Scientific Realism and Instrumentalism

: Disagreement about how best to think of the relation between theories and the realities they represent has a longstanding and venerable history. We take up this debate in relation to the free energy principle (FEP) - a contemporary framework in computational neuroscience, theoretical biology and the philosophy of cognitive science. The FEP is very ambitious, extending from the brain sciences to the biology of self-organisation. In this context, some find apparent discrepancies between the map (the FEP) and the territory (target systems) a compelling reason to defend instrumentalism about the FEP. We take this to be misguided. We identify an important fallacy made by those defending instrumentalism about the FEP. We call it the literalist fallacy : this is the fallacy of inferring the truth of instrumentalism based on the claim that the properties of FEP models do not literally map onto real-world, target systems. We conclude that scientific realism about the FEP is a live and tenable option.
Efficient state synchronisation in model-based testing through reinforcement learning

Model-based testing is a structured method to test complex systems. Scaling up model-based testing to large systems requires improving the efficiency of various steps involved in testcase generation and more importantly, in test-execution. One of the most costly steps of model-based testing is to bring the system to a known state, best achieved through synchronising sequences. A synchronising sequence is an input sequence that brings a given system to a predetermined state regardless of system’s initial state. Depending on the structure, the system might be complete, i.e., all inputs are applicable at every state of the system. However, some systems are partial and in this case not all inputs are usable at every state. Derivation of synchronising sequences from complete or partial systems is a challenging task. In this paper, we introduce a novel Q-learning algorithm that can derive synchronising sequences from systems with complete or partial structures. The proposed algorithm is faster and can process larger systems than the fastest sequential algorithm that derives synchronising sequences from complete systems. Moreover, the proposed method is also faster and can process larger systems than the most recent massively parallel algorithm that derives synchronising sequences from partial systems. Furthermore, the proposed algorithm generates shorter synchronising sequences.
Supporting High-Stakes Investigations with Expert-Led Crowdsourcing

My dissertation introduces the concept of expert-led crowdsourcing (ELC) to support expert investigators who increasingly face limits on their time and attention. ELC combines experts’ domain knowledge and experience with the speed and scale of crowds. I study ELC in two investigative domains: journalism and law enforcement. Through four studies, I show: 1) how novice crowds can effectively augment expert investigators’ work practice; 2) the ethical tensions in conducting an ELC investigation for real-world, sensitive investigations; 3) how capture-the-flag competitions increase inter-team collaboration in ELC investigations; and 4) how different teamwork structures affect intra-team collaboration in ELC investigations.
A sinusoidal signal reconstruction method for the inversion of the mel-spectrogram

The synthesis of sound via deep learning methods has recently received much attention. Some problems for deep learning approaches to sound synthesis relate to the amount of data needed to specify an audio signal and the necessity of preserving both the long and short time coherence of the synthesised signal. Visual time-frequency representations such as the log-mel-spectrogram have gained in popularity. The log-mel-spectrogram is a perceptually informed representation of audio that greatly compresses the amount of information required for the description of the sound. However, because of this compression, this representation is not directly invertible. Both signal processing and machine learning techniques have previously been applied to the inversion of the log-mel-spectrogram but they both caused audible distortions in the synthesised sounds due to issues of temporal and spectral coherence. In this paper, we outline the application of a sinusoidal model to the ‘inversion’ of the log-mel-spectrogram for pitched musical instrument sounds outperforming state-of-the-art deep learning methods. The approach could be later used as a general decoding step from spectral to time intervals in neural applications.
CROWDSOURCING: A NOVEL GROUP-LEVEL MECHANISM STRUCTURES CHROMATIN AND FOSTERS GENE-COMPLEX ACTIVATION

Mammalian gene expression is often regulated by distal enhancers. However, little is known about higher order functional organization of enhancers. Using ~100K P300bound regions as candidate enhancers, we investigated their correlated activity across 72 cell types based on DNAse hypersensitivity (DHS). We found widespread correlated activity between enhancers, which decreases with increasing inter-enhancer genomic distance. We found that correlated enhancers tend to share common transcription factor (TF) binding motifs, and several chromatin modification enzymes preferentially interact with these TFs. Presence of shared motifs in enhancer pairs can predict correlated activity with 73% accuracy. Also, genes near correlated enhancers exhibit correlated expression and share common function. Correlated enhancers tend to be spatially proximal. Interestingly, weak enhancers tend to correlate with significantly greater numbers of other enhancers relative to strong enhancers. Furthermore, strong/weak enhancers preferentially correlate with strong/weak enhancers respectively. We constructed enhancer networks based on shared motif and correlated activity and show significant functional enrichment in their putative target gene clusters. Overall, our analyses shows extensive correlated activity among enhancers and reveals clusters of enhancers whose activities are coordinately regulated by multiple potential mechanisms involving shared TF binding, chromatin modifying enzymes and 3D chromatin structure, that ultimately co-regulate functionally linked genes.
GCWSNet: Generalized Consistent Weighted Sampling for Scalable and Accurate Training of Neural Networks

We propose using "powered generalized min-max'' (pGMM) hashed (linearized) via the "generalized consistent weighted sampling'' (GCWS) for training (deep) neural networks (hence the name "GCWSNet''). The pGMM and several related kernels were proposed in 2017. We demonstrate that pGMM hashed by GCWS provide a numerically stable scheme for applying power transformation on the original data, regardless of the magnitude of p and the data. Our experiments show that GCWSNet often improves the accuracy. It is also evident that GCWSNet converges substantially faster, reaching reasonable accuracy with merely one epoch of the training process. This property is much desired because many applications, such as advertisement click-through rate (CTR) prediction models, or data streams (i.e., data seen only once), often train just one epoch. Another beneficial side effect is that the computations of the first layer of the neural networks become additions instead of multiplications because the input data become binary and highly sparse. Empirical comparisons with (normalized) random Fourier features (NRFF) are provided. We also propose to reduce the model size of GCWSNet by count-sketch and develop the theory for analyzing the impact of using count-sketch on the accuracy of GCWS. Our analysis shows that an "8-bit'' strategy should provide the good trade-off between accuracy and model size. There are other ways to take advantage of GCWS. For example, one can apply GCWS on the last layer to boost the accuracy of trained deep neural nets.