Miracle-dz/UniFAR-FTUs huggingface dataset:
The Business Model of Sustainable Competitive Advantage through Strategic Leadership Capabilities and Knowledge Management Processes to Overcome COVID-19 Pandemic

In recent decades, particularly during the outbreak of COVID-19, businesses organizations have focused on knowledge management processes in order to attain a sustainable competitive advantage by creating human and social capital as strategic leadership capabilities. This paper aims to develop an integrated conceptual and theoretical framework that connects strategic leadership capabilities, knowledge management processes, and sustainable competitive advantage for the public and private organizations within the service industry. The model approach has been used to build a theoretical framework that predicts the relationships between the variables. To support the link between them, a systematic literature review of traditional and contemporary theoretical and empirical research studies is conducted. Through the combination of the related literature, propositions are established. This conceptual research is primarily comprised of a model and assertions concerning strategic leadership capabilities, knowledge management processes, and sustainable competitive advantage. The outcomes of this study reflect that strategic leadership capabilities can accomplish sustainable competitive advantage. They also have the greatest potential to impact sustainable competitive advantage via engaging the knowledge management processes. Particularly, there are significant implications of this study. Regarding the theoretical implication, it contributes to the resource-based view, knowledge-based view, and knowledge creation theory in supporting strategic leadership capabilities, knowledge management processes, and sustainable competitive advantage. To the practical implication, the study linked the concepts of research variables for public and private enterprises within the service industry. It can also enable strategic leaders and businesses owners to participate in real-time opportunities and determine threats for achieving a sustainable competitive advantage in light of the COVID-19 pandemic. This is a preliminary study of the sustainable competitive advantage literature that includes the concept of strategic leadership as a knowledge management processes enabler for achieving a sustainable competitive advantage. Until now, there has not been any work like it in the literature, save in the qualitative style.
Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems

Polar codes are featured by their low encoding/decoding complexity for symmetric binary input-discrete memoryless channels. Recently, flexible generic Successive Cancellation List (SCL) decoders for polar codes were proposed to provide different throughput, latency, and decoding performances. In this paper, we propose to use polar codes with flexible fast-adaptive SCL decoders in Digital Video Broadcasting (DVB) systems to meet the growing demand for more bitrates. In addition, they can provide more interactive services with less latency and more throughput. First, we start with the construction of polar codes and propose a new mathematical relation to get the optimized design point for the polar code. We prove that our optimized design point is too close to the one that achieves minimum Bit Error Rate (BER). Then, we compare the performance of polar and Low-Density Parity Check (LDPC) codes in terms of BER, encoder/decoder latencies, and throughput. The results show that both channel coding techniques have comparable BER. However, polar codes are superior to LDPC in terms of decoding latency, and system throughput. Finally, we present the possible performance enhancement of DVB systems in terms of decoding latency and complexity when using optimized polar codes as a Forward Error Correction (FEC) technique instead of Bose Chaudhuri Hocquenghem (BCH) and LDPC codes that are currently adopted in DVB standards.
Adaptive Convolutional Neural Network for Text-Independent Speaker Recognition

In text-independent speaker recognition, each speech is composed of different phonemes depending on spoken text. The conventional neural networks for speaker recognition are static models, so they do not reﬂect this phoneme-varying characteristic well. To tackle this limitation, we propose an adaptive convolutional neural network (ACNN) for text-independent speaker recognition. The utterance is divided along the time axis into short segments with small ﬂuctuating phonemes. Frame-level features are extracted by applying input-dependent kernels adaptive to each segment. By applying time average pooling and linear layers, utterance-level embeddings extraction and speaker recognition are performed. Adaptive VGG-M using 0.356 seconds segmentation shows better speaker recognition performance than baseline models, with a Top-1 of 86.51% and an EER of 5.68%. It extracts more accurate frame-level embeddings for vowel and nasal phonemes compared to the conventional method without overﬁtting and large parameters. This framework for text-independent speaker recognition effectively utilizes phonemes and text-varying characteristic of speech.
Fair Voice Biometrics: Impact of Demographic Imbalance on Group Fairness in Speaker Recognition

Speaker recognition systems are playing a key role in modern online applications. Though the susceptibility of these systems to discrimination according to group fairness metrics has been recently studied, their assessment has been mainly focused on the difference in equal error rate across groups, not accounting for other fairness criteria important in anti-discrimination policies, deﬁned for demographic groups characterized by sensitive attributes. In this paper, we therefore study how existing group fairness metrics relate with the balancing settings of the training data set in speaker recognition. We conduct this analysis by operationalizing several deﬁnitions of fairness and monitoring them under varied data balancing settings. Experiments performed on three deep neural architectures, evaluated on a data set including gender/age-based groups, show that balancing group representation positively impacts on fairness and that the friction across security, usability, and fairness depends on the fairness metric and the recognition threshold.
Non-Verbal Vocalisation and Laughter Detection Using Sequence-to-Sequence Models and Multi-Label Training

Non-verbal vocalisations (NVVs) such as laughter are an important part of communication in social interactions and carry important information about a speaker’s state or intention. There remains no clear deﬁnition of NVVs and there is no clearly deﬁned protocol for transcribing or detecting NVVs. As such, the standard approach has been to focus on detecting a single NVV such as laughter and map all other NVVs to an “other” class. In this paper we hypothesise that for this task such an approach hurts performance, and that giving more information by using more classes is beneﬁcial. To address this, we present studies using sequence-to-sequence deep neural networks where we include multiple NVV classes rather than mapping them to “other” and allow more than one label per sample. We show that this approach yields better performance than the standard approach on NVV detection. We also evaluate the same model on laughter detection using frame-based and utterance-based metrics and show that the proposed approach yields state-of-the-art performance on the ICSI corpus.
Tangent barrier Lyapunov function‐based constrained control of flexible manipulator system with actuator failure

This article puts forward an adaptive neural network fault‐tolerant control scheme under the state constraints for the flexible manipulator system with uncertain terms. The dynamic model of the system is described by partial differential equations. The tangent barrier Lyapunov functions are chosen in the design process for the sake of ensuring that all states in the system satisfy the constrained conditions. The uncertainties resulted from load mass, hub inertia, and bending stiffness in the system are approximated by using the universal approximation property of neural networks. The adaptive method is used to counteract the influence of joint motor actuator failure. At the same time, combining with the backstepping design framework to design effective controllers to assure that all signals in the closed‐loop system are bounded. Lyapunov stability analysis method is used to prove the stability of the system. Finally, the simulation results prove the availability of the raised control method.
Work availability types and well-being in Germany – a latent class analysis among a nationally representative sample

ABSTRACT Employees who remain available for work outside regular work hours often experience strain and work–home conflicts. This study clusters employees in distinct availability types based on different aspects of unregulated extended work ability, which are contacting frequency, availability expectations and perceived legitimacy of availability. Moreover, we examined covariates of class membership and relationships with employees’ well-being. We used data from 17,410 employees who took part in a representative survey of the German working population. Latent class analysis with double cross-validation revealed three availability types. Satisfaction with work–life balance was higher and internal work–home interference was lower in the “rarely available”-class than in the “legitimate available”-class and the “illegitimate available”-class. Members of the “illegitimate available”-class reported worse subjective health, more psychovegetative health complaints, and higher levels of exhaustion than members of the “legitimate available”-class and the “rarely available”-class. Several socio-demographic variables, job characteristics, and factors associated with boundary management predicted class membership. Overall, the study highlights the risks for employees’ well-being associated with unregulated extended work availability – particularly when it is perceived as illegitimate – and points towards implications on the individual, organisational, and political level that may help reduce and better manage extended work ability.
The Effect of Work-Life Balance and Work Stress on Turnover Intention with Job Satisfaction as A Mediator at Anti-Corruption Institution in Indonesia

The number of corruption cases in Indonesia that have been revealed from every year has given to the workload of employees to take action on the cases to completion in restoring financial state losses that is caused from corruption. The job characteristic that requires employees to be available at all times has also affected the balancing of two domains: work and non-work, especially employees who work in the office of deputy for enforcement. This research aims to examine the relationship between work life balance and work stress on turnover intention and the effect of job satisfaction in mediating relationships between these variables among employees of Deputy for Enforcement of AntiCorruption Institution in Indonesia. This research was conducted by using quantitative method through the distribution of questionnaires. An amount of 218 respondents were obtained as a sample of a population of 427 specialist and administrative employees. Hypotheses were tested with structural equation modeling (SEM) using Amos 23. The results found that there were positive influences on the relationships between work stress and turnover intention, both directly and indirectly through the mediation of job satisfaction. The results also revealed that there was no direct effect on work-life balance and turnover intention relationship, but indirect effect was found through the mediation of job
Constraints on the Cycling of Iron Isotopes From a Global Ocean Model

Although iron (Fe) is a key regulator of primary production over much of the ocean, many components of the marine iron cycle are poorly constrained, which undermines our understanding of climate change impacts. In recent years, a growing number of studies (often part of GEOTRACES) have used Fe isotopic signatures (δ56Fe) to disentangle different aspects of the marine Fe cycle. Characteristic δ56Fe endmembers of external sources and assumed isotopic fractionation during biological Fe uptake or recycling have been used to estimate relative source contributions and investigate internal transformations, respectively. However, different external sources and fractionation processes often overlap and act simultaneously, complicating the interpretation of oceanic Fe isotope observations. Here we investigate the driving forces behind the marine dissolved Fe isotopic signature (δ56Fediss) distribution by incorporating Fe isotopes into the global ocean biogeochemical model PISCES. We find that distinct external source endmembers acting alongside fractionation during organic complexation and phytoplankton uptake are required to reproduce δ56Fediss observations along GEOTRACES transects. δ56Fediss distributions through the water column result from regional imbalances of remineralization and abiotic removal processes. They modify δ56Fediss directly and transfer surface ocean signals to the interior with opposing effects. Although attributing crustal compositions to sedimentary Fe sources in regions with low organic carbon fluxes improves our isotope model, δ56Fediss signals from hydrothermal or sediment sources cannot be reproduced accurately by simply adjusting δ56Fe endmember values. This highlights that additional processes must govern the exchange and/or speciation of Fe supplied by these sources to the ocean.
Age-Invariant Training for End-to-End Child Speech Recognition Using Adversarial Multi-Task Learning

Automatic speech recognition for children’s speech is a challenging task mainly due to scarcity of publicly available child speech corpora and wide inter-and intra-speaker variability in terms of acoustic and linguistic characteristics of children’s speech. We propose a framework for age-invariant training of the acoustic model of end-to-end speech recognition systems based on adversarial multi-task learning. We use age information additionally to just differentiating between the child and adult domains and thus force the acoustic model to learn age invariant features. Our results on publicly available data sets show that this leads to better leveraging of existing data during training. We further show that usage of adversarial multi-task learning should not necessarily be regarded as a substitute for traditional feature space adaptation methods, but that both should be used together for best performance.
A New Algorithm for Dynamic Vestibular System Analysis with Wearable Pressure and Motion Sensors

Objective: Balance is a complex state that requires integrating information from many systems into various levels of the nervous system and the actions of commands from the central nervous system and the musculoskeletal system. Postural control is the ability to maintain body position against forces that threaten the individual’s orientation or balance. In our study, a mobile prototype system was used to create equilibrium data of subjects and generate a set of normal values, diseases and fuzzy decisions. Methods: This study included 106 adults (55 females, 51 males) with no vestibular, neurologic, orthopaedic and eye problems to create the normalisation data. Also, 60 patients (37 females, 23 males) with different vestibular pathologies (23 benign paroxysmal positional vertigo, 12 Meniere’s disease, 10 vestibular neuritis, 15 unilateral vestibular hypofunction) comprised the patient group. Results: With the newly developed system, step length and symmetry, distance between feet, gait symmetry, ankle mobility and symmetry and phases of each step and step symmetry were collected. Significant differences were found between the normal and pathological groups in the data obtained from the dynamic balance and pressure sensors on the sole of the foot. Conclusion: Dynamic balance and walking analysis by our system are considered important in audiology, orthopaedics and neurology. It can contribute clinically by collecting data from several patients with different pathologies
State-of-the-Art in Automated Story Generation Systems Research

ABSTRACT This paper presents a review of research works from the last several years in automated story generation systems. These systems are categorised into interactive story generation systems and non-Interactive story generation systems. Interactive systems are those that collaborate with a user/author during the process of creating and/or executing the story. The extent of user interaction varies across systems but remains an integral part of the creation and/or the unfolding of the story. Non-Interactive systems concentrate on complete automation of the creative process involved in narrative generation to create diverse and interesting stories. Interactive story generators specifically designed for video game narratives are reviewed as a separate sub-class of interactive story generation systems. Also reviewed are the methods used for evaluation of story generation systems as a way to explore the possibility of having standard methods of evaluation within the research community. The paper includes a discussion of trends and directions of the research discipline.
Optimum design of middle stage tool geometry and addendum surfaces in sheet metal stamping processes using a new isogeometric-based framework

An efficient isogeometric-based framework is presented to integrate optimum design and formability analysis of sheet metal forming processes. To assess the quality of the formed parts, several objective functions such as fracture, wrinkling, thickness variation, and stretching are studied. In this framework, geometric parameters of addendum surfaces and middle tools are considered as design variables, the objective functions are calculated using the recently developed one-step and multi-step inverse isogeometric methods, and the optimum design variables are obtained using the genetic global optimization algorithm. The major advantage of employing the inverse methods is to analyze the formability of the parts with a low computation time. In this research, the effects of altering addendum surfaces and/or middle tools on the quality of the formed parts are simultaneously observed since modeling, formability analysis, and optimization stages of sheet metal forming simulation are integrated using the NURBS functions. To evaluate the performance of the inverse isogeometric models in calculation of the studied objective functions, the results obtained by these models are compared to those of experiment and forward FEM. Comparisons of the results indicate that these models predict the objective functions with acceptable accuracy at a low computation time. For instance, in sheet metal forming analysis of a rectangular box with three different addendum surfaces, the maximum error in prediction of minimum thickness using the one-step inverse model is approximately 4.65% more than forward FEM, while the solution time of forward FEM is around 40 times greater. Finally, the presented optimization procedure is applied to design addendum surfaces in forming of a rectangular box and the middle tools in a two-stage drawing of a square box. The results of these problems confirm the credibility of the present approach in rapid optimum design of addendum surfaces and intermediate tools with acceptable accuracy.
Wearable supercapacitor based on polyaniline supported by graphene coated polyester textile

Wearable supercapacitors have broad application prospects in emerging portable devices. To realize the large‐scale application, it is necessary to develop a low‐cost and large‐scale method to fabricate wearable electrode with high electrochemical performance. Here, polyaniline supported by graphene coated polyester textile (PANI/GPT) electrode is prepared through a simple and low‐cost method. First, graphene ink with high concentration is prepared, and then the polyester textile is coated by graphene through a repeatedly soaking‐drying process. Subsequently, PANI/GPT electrode is prepared by electrochemical deposition of polyaniline (PANI) on graphene coated polyester textile. The ratio of PANI in the textile electrode has been systematically modified and the obtained textile electrode possesses good energy storage performance with specific capacitance reaching 896.50 F g−1 and good rate capability. The assembled flexible supercapacitor also owns excellent flexibility with capacitance retention of 95.10% after bending for up to 5000 cycles. Such high‐performance textile electrodes and the low‐cost method can be widely applied to develop other wearable energy storage devices.
Sport prosumer networks: exploring prosumption value in Twitter conversations during COVID-19

ABSTRACT Purpose Value within prosumption systems such as Twitter is underexplored. We adopt an economic sociology perspective to measure prosumption value, using the #ProjectRestart campaign as football looked to resume following COVID-19. Design We use social network analysis to analyse 21,000 tweets involving 10,810 Twitter users using the #ProjectRestart hashtag. Specifically, we apply network theory measures, community clustering, betweenness, domain prestige and proximity prestige to explore how prosumption value can be measured. Findings Our empirical findings demonstrate how value can be perceived within prosumption systems. Specifically, it shows how developing cohesive prosumer networks is vital in exploiting prosumer capital, creating value in the virtual space, which is imperative in negotiating through times of uncertainty, like COVID-19. Practical Implications The practical implications encourage the industry to think of value in the virtual space differently, embedding this into future management strategies. Research Contribution This research provides a theoretical contribution of prosumer value, blending prosumption and economic sociology theories. Empirically, it demonstrates how actors in the football world used prosumer networks to create value during the COVID-19 pandemic.
Design and Optimization of Surface Plasmon Resonance Spectroscopy for Optical Constant Characterization and Potential Sensing Application: Theoretical and Experimental Approaches

The best surface plasmon resonance (SPR) signal can be generated based on several factors that include the excitation wavelength, the type of metal used, and the thickness of the metal layer. In this study, the aforementioned factors have been investigated to obtain the best SPR signal. The excitation wavelength of 633 nm and gold metal with thickness of 50 nm were required to generate the SPR signal before the SPR was used for optical constant characterization by fitting of experimental results to the theoretical data. The employed strategy has good agreement with the theoretical value where the real part refractive index, n value, of the gold thin film was 0.1245 while the value for the imaginary part, k, was 3.6812 with 47.7 nm thickness. Besides that, the optical characterization of nanocrystalline cellulose (NCC)-based thin film has also been demonstrated. The n and k values found for this thin film were 1.4240 and 0.2520, respectively, with optimal thickness of 9.5 nm. Interestingly when the NCC-based thin film was exposed to copper ion solution with n value of 1.3333 and k value of 0.0060 to 0.0070 with various concentrations (0.01–10 ppm), a clear change of the refractive index value was observed. This result suggests that the NCC-based thin film has high potential for copper ion sensing using SPR with a sensitivity of 8.0052°/RIU.