Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Designs for Environmental Valuation with Choice-Experiments: A Monte-Carlo Investigation

We review the practice of experimental design in the environmental economics literature concerned with choice experiments. We then contrast this with advances in the field of experimental design and present a comparison of statistical efficiency across four different experimental designs evaluated by Monte Carlo experiments. Two different situations are envisaged. First, a correct a priori knowledge of the multinomial logit specification used to derive the design and then an incorrect one. The data generating process is based on estimates from data of a real choice experiment with which preference for rural landscape attributes were studied. Results indicate the D-optimal designs are promising, especially those based on Bayesian algorithms with informative prior. However, if good a priori information is lacking, and if there is strong uncertainty about the real data generating process - conditions which are quite common in environmental valuation - then practitioners might be better off with conventional fractional designs from linear models. Under misspecification, a design of this type produces less biased estimates than its competitors.
Understanding Mobile Payment Adaption with the Integrated Model of UTAUT and MOA Model

This study focuses on mobile payment adaption and proposes a new technology acceptance model validated by Partial Least Squares (PLS) method. (1) Based on UTAUT and the Motivation-opportunity-Ability (MOA) model, this study proposes an integrated technology acceptance model: the actual mobile payment adoption behavior is affected by behavior intention, cash payment habit, and coverage of mobile payment context (CMPC). (2) The results indicate that behavior intention, cash payment habit and CMPC have significant impact on actual behavior, and CMPC has a positive moderating effect on the influence of behavior intention on actual behavior. (3) With high coefficient of determination (0.77), this new model shows better prediction of adaption behavior and emphasizes the importance of habits and context to behavior in addition to behavior intention.
Team and Members’ Contribution to Inventions: Diversity from Individual and Team

Effective team building is widely considered to be an essential factor of research and development. The purpose of this paper is to explore how team profiles and individual profiles affect team performance, respectively. We focus on technological diversities as a vital profile of both teams and individuals. We examine the relationship between diversity and team performance using patent bibliographic data and patent citation analysis on U.S. patents. Our results show that both interpersonal diversity and generalist inclusion affect teams’ inventive performance. From the perspective of team construction, the result indicates that management could flexibly apply interpersonal diversity or generalist inclusion. We hope that our findings could be helpful for team management and human resource in research and development.
Microwave surface resistance of MgB2

The microwave power and frequency dependence of the surface resistance of MgB2 films and powder samples were studied. Sample quality is relatively easy to identify by the breakdown in the ω2 law for poor-quality samples at all temperatures. The performance of MgB2 at 10 GHz and 21 K was compared directly with that of high-quality YBCO films. The surface resistance of MgB2 was found to be approximately three times higher at low microwave power and showed an onset of nonlinearity at microwave surface fields ten times lower than the YBCO film. It is clear that MgB2 films are not yet optimized for microwave applications.
Gamma Gamma Turbulence and Nonzero Boresight Spatial Jitter Influence at the Outage Performance of THz Links

The recent increasing requirements for high speed and robust wireless communication links have given rise to the development of THz wireless communication systems (0.3-10 THz), as a very promising alternative to their MMW and FSO counterparts. However, the performance of THz wireless systems is subject to free space path loss, signal attenuation due to humid air, atmospheric turbulence and pointing errors between transmitter and receiver terminals. Thus, in this work we first introduce the stochastic impact of generalized PEs with nonzero boresight in the THz area, while atmospheric turbulence is modeled through the suitable gamma gamma distribution. Considering also the deterministic impact of free space path loss and signal attenuation, an outage probability analysis is performed for a typical line of sight THz link. In this context, novel closed form expressions are derived. Their analytical results demonstrate the joint influence of these effects on THz signal transmissions, while simulation results validate the accuracy of our analysis.
An Exploratory Study of Creativity in Business Domain: A Systematic Literature Review

This paper reports on a systematic literature review of creativity in the business domain to develop the study of individual and organizational creativity in innovation management. A transition of study of essential topics in business creativity was observed, qualitatively organized and discussed. Using the SCOPUS database, this paper studied 197 articles, reviews, or conference papers on business creativity with over 50 citations, out of 3,073 published by 2020. We have found the following. The research of creativity in the business domain began to occur in the 1970s and has attracted much interest from the latter half of the 1980s. In addition, we have confirmed that there was high interest in the research topics such as entrepreneurial creativity, creative leadership, creativity management, and creative thinking processes. That research was conducted by cross-sectional study and literature review. Alternatively, there are few long-term studies through careers of businessperson across educational institutions and multiple organizations. This paper provides implications for future business creativity research and significantly change the way recruitment and organizing for innovation by discovering a research void that the research period is a snapshot in time, and there are few long-term studies toward specific individuals or organizations.
On the turbulent mixing of compressible free shear layers

For over a quarter of a century it has been recognized that turbulent shear flows are dominated by large-scale structures. Yet the majority of models for turbulent mixing fail to include the properties of the structures either explicitly or implicitly. The results obtained using these models may appear to be satisfactory, when compared with experimental observations, but in general these models require the inclusion of empirical constants, which render the predictions only as good as the empirical database used in the determination of such constants. Existing models of turbulence also fail to provide, apart from its stochastic properties, a description of the time-dependent properties of a turbulent shear flow and its development. In this paper we introduce a model for the large-scale structures in a turbulent shear layer. Although, with certain reservations, the model is applicable to most turbulent shear flows, we restrict ourselves here to the consideration of turbulent mixing in a two-stream compressible shear layer. Two models are developed for this case that describe the influence of the large-scale motions on the turbulent mixing process. The first model simulates the average behaviour by calculating the development of the part of the turbulence spectrum related to the large-scale structures in the flow. The second model simulates the passage of a single train of large-scale structures, thereby modelling a significant part of the time-dependent features of the turbulent flow. In both these treatments the large-scale structures are described by a superposition of instability waves. The local properties of these waves are determined from linear, inviscid, stability analysis. The streamwise development of the mean flow, which includes the amplitude distribution of these instability waves, is determined from an energy integral analysis. The models contain no empirical constants. Predictions are made for the effects of freestream velocity and density ratio as well as freestream Mach number on the growth of the mixing layer. The predictions agree very well with experimental observations. Calculations are also made for the time-dependent motion of the turbulent shear layer in the form of streaklines that agree qualitatively with observation. For some other turbulent shear flows the dominant structure of the large eddies can be obtained similarly using linear stability analysis and a partial justification for this procedure is given in the Appendix. In wall-bounded flows a preliminary analysis indicates that a linear, viscous, stability analysis must be extended to second order to derive the most unstable waves and their subsequent development. The extension of the present model to such cases and the inclusion of the effects of chemical reactions in the models are also discussed.
A 5-Gb/s PAM4 Voltage Mode Transmitter with Current Mode Continuous Time Linear Equalizer

This work presents a 5 Gb/s PAM4 hybrid voltage-mode transmitter with current mode continuous-time linear equalization. The transmitter achieves a large output signal swing with a PAM4 CMOS output driver. CTLE is embedded in the transmitter’s output stage to compensate a range of channel loss without reducing the signal swing at the receiver front end. Fabricated in 65 nm CMOS process, the transmitter dissipates 20.4 mW in the output driver at 5 Gb/s while transmitting 1.1 V peak-to-peak differential output swing across 2 m UTP cable.
Printed wireless battery-free sensor tag for structural health monitoring of natural fiber composites

This paper presents a fully printed wireless sensor tag for measuring temperature and strain. The sensor concept was developed in the spirit of sustainability and in line with the European Green Deal, without the use of a battery or a chip. This makes it a promising candidate for integration in natural fiber reinforced composites. Measurement results show the function of the wireless read-out over a distance of 2.5 mm. In the case of both sensors, the measurement data follows a linear dependence and is in good agreement with literature and reference data. The temperature sensor shows a TCR of 1.61*10-3 K-1, the strain sensor a sensitivity of 16.5 kHz per 0.1 % strain.
Attacking and Defending DNP3 ICS/SCADA Systems

The highly beneficial contribution of intelligent systems in the industrial domain is undeniable. Automation, supervision, remote control, and fault reduction are some of the various advantages new technologies offer. A protocol demonstrating high utility in industrial settings, and specifically, in smart grids, is Distributed Network Protocol 3 (DNP3), a multi-tier, application layer protocol. Notably, multiple industrial protocols are not as securely designed as expected, considering the highly critical operations occurring in their application domain. In this paper, we explore the internal vulnerabilities-by-design of DNP3, and proceed with the implementation of the attacks discovered, demonstrated through 8 DNP3 attack scenarios. Finally, we design and demonstrate a Deep Neural Network (DNN)-based, multi-model Intrusion Detection Systems (IDS), trained with our experimental network flow cyberattack dataset, and compare our solution with multiple machine learning algorithms used for classification. Our solution demonstrates a high efficiency in the classification of DNP3 cyberattacks, showing an accuracy of 99.0%.
Automatic Extraction of Muscle Fascicle Pennation Angle from Raw Ultrasound Data

Compact, wearable, wireless ultrasound (US) sensing systems are promising devices for the observation of human muscle dynamics, offering low power, non-invasive continuous monitoring of contracting muscles. Plane wave imaging is an ideal imaging modality to meet the high frame rate requirements of fast contracting muscles. However, the low power requirements of wearable wireless US constrain the data transfer rates from the probe to the host computer, and limit the maximum number of transducer channels, at the same time discouraging beamforming on the probe. Therefore, it is crucial to extract physiological parameters directly from raw US data for applications demanding fast imaging speeds (like monitoring muscles and tendons in motion). Machine Learning (ML) methods can be employed to effectively extract such features. Although a few recent works demonstrated A-mode US for motion and force prediction, the automatic extraction of structural muscle features from raw data is still in its infancy. This paper demonstrates the feasibility of extracting pennation angles from raw US data on a small dataset of contracting medial gastrocnemius muscles. Automatically extracted labels from US images are used as ground truth to train ML algorithms that predict pennation angles directly from raw US data, without the need for image reconstruction. We employ statistical features, Principle Components Analysis (PCA) and Covolutional Autoencoder (AE) for feature extraction and evaluate Random Forest (RF), Gradient Boosting (XGBoost) and Convoltional Neural Network (CNN) as regressors. Experimental results show that the best method (AE + XGBoost) achieves a mean absolute error of ~ 0.43° that is consistent with the variability of the manually annotated pennation angles reported in the literature, with a memory footprint smaller than 400 kB and less than 5 ms execution time.
The Anatomy of a Corporate Venture Builder : Factors influencing Failure

Digital transformation opens numerous opportunities for companies. To seize such opportunities, corporations look at startups and entrepreneurship, integrating new organizational forms and practices into their existing innovation management capabilities. Corporate startup programs, accelerators, incubators, venture builders, and CVC have gained momentum. This paper explores factors that affect the unsuccessful development of a corporate venture builder (CVB). CVB is an organizational unit that builds new ventures combining internal resources and external entrepreneurs. Relying on an in-depth case study of the experience of a Brazilian software firm during the three-year life of its CVB, which ended in 2019, we analyze which aspects of the company's innovation management capabilities affected the unsuccessful outcome. Our findings suggest that the lack of alignment between the CVB initiative and the company's top management was a critical determinant for discontinuity. While the new businesses management unit that has pulled the CVB aimed at rather explorative, long-term growth-oriented objectives, the Board of the company and its C-level expected relatively short-term results. This case provides insights into how current innovation management capabilities and related organizational forms deter the development of corporate venturing initiatives and interact with the entrepreneurial behavior of founders that participate in such endeavors.
A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy

The locations and users’ information can be shared and interacted in the IoV (Internet of Vehicles), which provides sufficient data for traffic deployment and behavior pattern analysis. However, privacy issues had become more severe since personal or sensitive information is inclined to be revealed in a big data environment. In this work, a novel differential privacy-based algorithm named DPTD (Differentially Private Trajectory Database) is proposed for trajectory database releasing. Firstly, a 3-dimensional generalized trajectory dataset is established by considering the time factor. Then, the trajectory space is divided into several planes through the timestamps, and the set of the locations on each plane is further processed by clustering and generalizing to re-form new trajectories, that is, the trajectories to be released. This method is quite favorable to prefix-tree releasing because the spatiotemporal characteristics of the trajectories can be captured and spareness problem is fixed. Besides, a Markov assumption-based prediction method is suggested in order to reduce the cost of adding noise. Unlike the traditional method that the noise is added layer by layer, the noise is only added to the odd layers based on the prediction through spatio-temporal correlation, saving approximately 50% of the privacy budget. Theoretical analysis and experimental results show that the proposed algorithm has better data availability than the compared algorithms while guaranteeing the expected privacy level.
Weighted Token-Level Virtual Adversarial Training in Text Classification

Text Classification is the process of classifying text into categories. Among the contextualized architecture of the pretrained model proposed, the Bidirectional Encoder Representations from Transformers (BERT) helps models learn bidirectional contexts of words, making it possible to classify text much more efficiently and accurately. Although BERT and its variance have led to impressive gains on many natural language processing (NLP) tasks, one of the problems of BERT is overfitting. When training data is limited, BERT model overemphasizes certain words and ignores the whole context of the sentence. This makes it difficult for the model to make accurate predictions on the test data. In this paper, we propose weighted token-level virtual adversarial training, which combines two-level perturbations: (1) the sentence-level perturbation and (2) the weighted token perturbation to create a more granular perturbation than traditional virtual adversarial training with only sentence-level perturbation. Our approach can help models learn more about the key and important tokens in sentences when trained with virtual adversarial examples. The experiments in the General Language Understanding Evaluation (GLUE) benchmark showed that our approach can achieve the average score of 79.5%, which outperforms BERTbase model. Our approach can also reduce the overfitting problem especially when datasets are small.
Dynamic Variable Ordering during Algebraic Backward Rewriting for Formal Verification of Multipliers

Recently, many efforts have been made to verify the multiplier circuits using Symbolic Computer Algebra (SCA) techniques. One important SCA technique is Algebraic Backward Rewriting, using which one can deduce the input specification of a multiplier circuit from known output specification. Although the resulting input specification has a smaller number of monomials, the intermediate monomials may explode due to the complexity of circuits. However, state-of-the-art tools use adder tree detection techniques to overcome the monomial explosion problem. The adder tree detection technique is limited to the circuit structure and does not work for algebraic unfriendly multipliers. This paper proposes and implements a fanout-free-based xor detection, level promotion, and order selection method to overcome such challenges. We have reduced the internal monomials up to 24.86% and verified up to 512 - bits array multiplier circuits using our unique method.
E2SRI: Learning to Super-Resolve Intensity Images From Events

An event camera reports per-pixel intensity differences as an asynchronous stream of events with low latency, high dynamic range (HDR), and low power consumption. This stream of sparse/dense events limits the direct use of well-known computer vision applications for event cameras. Further applications of event streams to vision tasks that are sensitive to image quality issues, such as spatial resolution and blur, e.g., object detection, would benefit from a higher resolution of image reconstruction. Moreover, despite the recent advances in spatial resolution in event camera hardware, the majority of commercially available event cameras still have relatively low spatial resolutions when compared to conventional cameras. We propose an end-to-end recurrent network to reconstruct high-resolution, HDR, and temporally consistent grayscale or color frames directly from the event stream, and extend it to generate temporally consistent videos. We evaluate our algorithm on real-world and simulated sequences and verify that it reconstructs fine details of the scene, outperforming previous methods in quantitative quality measures. We further investigate how to (1) incorporate active pixel sensor frames (produced by an event camera) and events together in a complementary setting and (2) reconstruct images iteratively to create an even higher quality and resolution in the images.
Production Flow Analysis in the Era of Industry 4.0 : How Digital Technologies can Support Decision-Making in the Factory of the Future

In the context of Industry 4.0, manufacturing companies have been increasingly adopting digital technologies such as Internet of Things, data analytics and cyber-physical systems to seize opportunities for productivity improvements. At the same time, established manufacturing philosophies such as Group Technology have assisted companies in managing the complexity of production processes for decades. To support manufacturing management with more informed decision-making tools, the literature has been proposing new approaches that exploit the potential of digital technologies to enhance the effectiveness of traditional manufacturing techniques. This study focuses on Production Flow Analysis (PFA) as an established approach for Group Technology. Although the existing literature has been focusing on Artificial Intelligence (AI) based approaches to plan the change to Group Technology for decades, few studies rely on production data directly extracted from the factory floor. This is partly due to the fact that technologies such as sensors and data analytics have been increasingly adopted in recent years, and this has led to an increasing amount of data that can be exploited to develop models that can support decisions. In particular, in the context of Industry 4.0, process mining has gained increasing interest, as it provides a data-driven methodology to capture real production processes. The goal of this study is to explore how PFA has evolved in the last decade thanks to the adoption of digital technologies, and to investigate potential synergies between PFA and process mining. This study uses a structured literature review to map advances in industrial applications of PFA in relation to digital technologies, as well as process mining applications in manufacturing to present a future research agenda. This provides manufacturing managers with a structured overview of existing industrial applications and the digital technologies adopted to enhance decision-making tools.
Most precise single redshift bound to Delta alpha/alpha

Verification of theoretical predictions of an oscillating behavior of the fine-structure constant alpha with cosmic time requires high precision Delta alpha/alpha measurements at individual redshifts, while in earlier studies the mean Delta alpha/alpha values averaged over wide redshift intervals were usually reported. This requirement can be met via the single ion differential alpha measurement (SIDAM) procedure proposed in Levshakov et al. (2005). We apply the SIDAM to the FeII lines associated with the damped Ly-alpha system observed at z=1.15 in the spectrum of HE0515-4414. The weighted mean calculated on base of carefully selected 34 FeII pairs {1608,X} (X = 2344, 2374, and 2586 A) is = (-0.07+/-0.84) 10^{-6} (1sigma C.L.). The precision of this estimate improves by a factor 2 the previous one reported for the same system by Quast et al. (2004). The obtained result represents an absolute improvement with respect to what has been done in the measurements of Delta alpha/alpha.