Miracle-dz/UniFAR-FTUs huggingface dataset:
Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation

ABSTRACT This paper presents a comprehensive review of high-level control techniques for upper-limb robotic training. It aims to compare and discuss the potentials of these different control algorithms, and specify future research direction. Included studies mainly come from selected papers in four review articles. To make selected studies complete and comprehensive, especially some recently-developed upper-limb robotic devices, a search was further conducted in IEEE Xplore, Google Scholar, Scopus and Web of Science using keywords (‘upper limb*’ or ‘upper body*’) and (‘rehabilitation*’ or ‘treatment*’) and (‘robot*’ or ‘device*’ or ‘exoskeleton*’). The search is limited to English-language articles published between January 2013 and December 2017. Valuable references in related publications were also screened. Comparative analysis shows that high-level interaction control strategies can be implemented in a range of methods, mainly including impedance/admittance based strategies, adaptive control techniques, and physiological signal control. Even though the potentials of existing interactive control strategies have been demonstrated, it is hard to identify the one leading to maximum encouragement from human users. However, it is reasonable to suggest that future studies should combine different control strategies to be application specific, and deliver appropriate robotic assistance based on physical disability levels of human users. GRAPHICAL ABSTRACT
Learning Success: A Comparative Analysis of a Digital Game-Based Approach and a Face-to-Face Approach

This study compares traditional face-to-face learning with digital game-based learning. A student group has to learn aspects of internet search in a face-to-face learning environment that combines lecture and tutorial and a further group has to learn the same aspects within a serious game. The comparison focuses on learning success. Measuring learning success is difficult and therefore the study includes questions to assess knowledge, motivation, fun and satisfaction in a preand post-tests. The results of the study show that the students evaluate game-based learning better in all surveyed categories.
Vector quantization for the efficient computation of continuous density likelihoods

In speech recognition systems based on continuous observation density hidden Markov models, the computation of the state likelihoods is an intensive task. The author presents an efficient method for the computation of the likelihoods defined by weighted sums (mixtures) of Gaussians. This method uses vector quantization of the input feature vector to identify a subset of Gaussian neighbors. It is shown that, under certain conditions, instead of computing the likelihoods of all the Gaussians, one needs to compute the likelihoods of only the Gaussian neighbours. Significant (up to a factor of nine) likelihood computation reductions have been obtained on various data bases, with only a small loss of recognition accuracy. <<ETX>>
A long history quantization approach to scalar and vector quantization of LSP coefficients

LHQ (long history quantization), when applied in conjunction with scalar quantization for the coding of LSP (line spectrum pair) coefficients in a CELP (code excited linear prediction) system, offers transparent quantization at an average bit rate of 25 bits/frame. Certain improvements to the LHQ-scalar quantization scheme are presented which further reduce the average LSP bit rate to about 22 bits/frame. In addition, a novel LHQ-vector quantization scheme is proposed, which allows the transparent quantization of LPC coefficients with only 19 bits/analysis frame. <<ETX>>
Beam search and partial traceback in the frame-synchronous two-level algorithm (TLBS)

The authors present and clarify some aspects of the frame-synchronous TL (two-level) algorithm. It is demonstrated that beam search and partial traceback can be integrated in the TL algorithm (two-level beam search or TLBS) so as to reduce the complexity of the original frame-synchronous TL. With this modification, TLBS decodes continuously and is well suited for word spotting. TLBS then becomes particularly attractive as it offers the potential for optimally solving the N-best decoding problem. The increased complexity of the TL is upper-bounded by the beam-search width and the traceback itself. The new TLBS algorithm behaves like the OP (one-pass) algorithm from a computational point of view. The complexity of TLBS becomes close to that of the OP for small search windows, and the window size is shown to be below two hundred frames (16 ms frame rate). <<ETX>>
eHealth WhatsApp Group for Social Support: Preliminary Results

Within groups that are starting a healthy lifestyle intervention together, there is potential for social e-support, as an addition to individual coaching. However, the support technology should be low-tech, low-threshold and preferably already omnipresent. A WhatsApp group was chosen as support tool, given the large variety of groups normally coached: from elderly, IT-phobic diabetics to highly educated young professionals. In this explorative pilot study, 11 young professionals volunteered. Despite their time-constrained schedules, 81 user inputs were generated in the first weeks, and the users valued the WhatsApp group as an attractive social support addition to the existing eTools and personal coaching which have a more functional focus on individual progress. Based on preliminary results: a) the WhatsApp group generated higher participation than most other social media, b) deploying social media use motives, c) possibly due to the relatively high ‘presence’ and ‘engagement’ attributes of WhatsApp, and d) contributing to healthy behaviours and health advocacy.
Speaker adaptation based on MAP estimation of HMM parameters

A number of issues related to the application of Bayesian learning techniques to speaker adaptation are investigated. It is shown that the seed models required to construct prior densities to obtain the MAP (maximum a posteriori) estimate can be a speaker-independent (SI) model, a set of female and male models, or even a task-independent acoustic model. Speaker-adaptive training algorithms are shown to be effective in improving the performance of both speaker-dependent and speaker-independent speech recognition systems. The segmental MAP estimation formulation is used to perform adaptive acoustic modeling for speaker adaptation applications. Tested on an RM (resource management) task, it was found that supervised speaker adaptation based on two gender-dependent models gave a better result than that obtained with a single SI seed. Compared with speaker-dependent training, speaker adaptation achieved an equal or better performance with the same amount of training/adaptation data. <<ETX>>
Motives to Form Alliances for Digital Innovation: The Case of Banks and Fintechs

In times of digitalization, the need for alliances among firms increases due to higher complexity and greater dynamics. Digital innovation leads to challenges for incumbent firms in adapting to changing rules set by new competitors and higher customer expectations. However, young firms providing technical solutions for the financial services industry (fintechs) also face difficulties; for instance, in meeting high regulatory requirements. Increasing alliances in the financial services industry can be observed due to shortcomings on the side of banks and fintechs. We conducted interviews to examine the motives of these alliance partners. The resulting motives are categorized as matching, complementary, and neutral. We derive practical implications in the form of recommendations for alliances, and deliver theoretical insights regarding criteria for digitalinnovationbased alliances.
IEEE Access Special Section Editorial: The Internet of Energy: Architectures, Cyber Security, and Applications

The energy crisis and carbon emissions have become two critical concerns globally. As a very promising solution, the concept of Internet of Energy has appeared to tackle these challenges. The Internet of Energy is a new power generation paradigm developing a revolutionary vision of smart grids into the Internet. The communication infrastructure is an essential component for implementing the Internet of Energy. A scalable and robust communication infrastructure is crucial in both operating and maintaining smart energy systems. The wide-scale implementation and development of Internet of Energy into industrial applications should take into account the following challenges:
Noise masking in a transform domain

A.P. Varga and K.M. Ponting (1989) have demonstrated that the noise masking algorithm can provide effective noise compensation, leading to robust speech recognition with almost undegraded performance down to signal-to-noise ratios as low as 3 dB in stationary pink noise. Noise compensation techniques have been limited to the log energy spectral domain, whereas recognition is more normally carried out in a transform domain. The authors describe the adaptation of the noise masking algorithm for use in a cepstral transform domain, providing performance which is not significantly different from masking in the log energy domain. On the task examined here the technique compares favourably with the more computationally demanding hidden Markov model decomposition approach, with the performances diverging significantly only for signal-to-noise ratios worse than 3 dB. It can be seen that the use of noise masking can provide a good degree of noise robustness in both the filter bank domain and the mel scaled frequency cepstral domain. <<ETX>>
Continuous mixture densities and linear discriminant analysis for improved context-dependent acoustic models

Linear discriminant analysis (LDA) experiments reported previously (ICASSP-92 vol.1, p.13-16), are extended to context-dependent models and speaker-independent large vocabulary continuous speech recognition. Two variants of using mixture densities are compared: state-specific modeling and the monophone-tying approach where densities are shared across the states relevant to the same phoneme. Results are presented on the DARPA Resource Management (RM) task for both speaker-dependent (SD) and speaker-independent (SI) parts. Using triphone models based on LDA and continuous mixture densities, significant improvements have been observed and the following word error rates have been achieved: for the SD part, 7.8% without grammar and 1.5% with word pair; and for the SI part, 17.2% and 4.6%, respectively. These scores are averaged over 1200 SD or SI evaluation sentences and are among the best published so far on the RM database. <<ETX>>
Central auditory model for spectral processing

Two central spectral processing models of the central auditory system for further processing the spatiotemporal firing pattern of the auditory nerve as outputs of the periphery auditory model are proposed. The temporal processing model capitalizes on the dominant periodicity in the firing pattern. The spatial processing model is based on the winner-take-all neural network mechanism and detects edges of discontinuities in the firing patterns. Experiments of noisy speech analysis showed that the proposed central auditory model can preserve prominent peaks at the formant resonances, while significantly reducing the output associated with the background noise. Noisy speech recognition experiments for both a small-scale classification task and a large-scale continuous speech recognition task showed that the proposed model provided a robust speech representation, and outperformed all other representations. <<ETX>>
On sources of error in beamforming arrays

Sources of error in practical beamforming arrays are identified and quantified. Three optimal beamforming techniques are compared and contrasted in the presence of these errors, and examples of their performance are given. The strengths and weaknesses of these techniques are identified and critiqued. The importance of incorporating calibration data into the beamforming algorithm is illustrated, and an effective technique for doing so is given. <<ETX>>
Enhanced video compression with standardized bit stream syntax

With the establishment of video coding standards and the expanding use of video codecs, it is necessary to consider the designs of encoders and decoders as two individual problems instead of the conventional concurrent design approach. The authors formulate the two separate problems and examine some possible ways to enhance performance over previous algorithms. Given the decoding process and the channel, the encoding problem can be formulated as a constrained optimization problem. The dual of the encoding problem, to decode the compressed video data generated by an existing encoder, can be formulated as an optimal estimation problem given the encoded bit stream. Examination of these two problems provides the pathway to the design of enhanced compatible encoding and decoding schemes. <<ETX>>
ICT supporting healthcare for elderly in China: A systematic mapping study

Bled eConference, organized by University of Maribor, Faculty of Organizational Sciences, has been shaping electronic interactions since 1988. Bled eConference is the oldest, most traditional and well renowned conference in the field with more than 30 years of tradition. The theme of this year’s conference is dedicated to “Digital Transformation – Meeting the Challenges”. The evolution of digital technologies and solutions has significantly impacted the way in which business is conducted and have big implications on our lives. Nowadays digital economy calls for transformation of businesses, governments, education and societies as whole. It also calls for enabling policies and politics for cross border and global digital business. In this year’s conference, we address various aspects of digital transformation and provide directions and guidelines for organizations to meet and overcome these challenges on their way towards successful digital transformation. Themes covered in the papers of these proceedings are focused on: digital transformation; business model innovation; blockchain and social media; big data, data science, and decision support systems; ehealth, digital wellness and wellbeing; new applications and organizational models; and novel approaches and cases in education in digital economy.
A non-metrical space search algorithm for fast Gaussian vector quantization

Three algorithms to speed up full covariance multivariate Gaussian vector quantizers are presented. The speed-up is achieved by avoiding the distance calculation for a considerable number of codebook classes at each input frame. In two cases, this pruning is guided by thresholds U/sub kappa lambda / which are computed for each class pair in a preprocessing stage. The computation of the U/sub kappa lambda /s from the codebook parameters by the gradient projection method leads to an admissible search strategy. Two of the proposed search procedures trace accuracy for speed. Both of them allow more than fivefold speed-up vector quantization at very low frame error rates, and without any degradation of word accuracy. <<ETX>>
Adaptive time-frequency transform

A matching pursuit decomposes any signal into a linear expansion of waveforms that belong to a dictionary of functions. These waveforms are selected in order to best match the signal structures. For dictionaries of functions that have a wide range of different time-frequency localizations, a matching pursuit yields an adaptive time-frequency transform. The authors derive a signal energy distribution in the time-frequency plane which does not include interference terms, unlike Wigner and Cohen class distributions. Signal components that are coherent with respect to the dictionary can be extracted. It is shown that time-frequency dictionaries yield adaptive decompositions where signal structures are represented by Gabor functions that match their time-frequency signature. The properties of the signal components are explicitly given by the scale, frequency, time, and phase indexes of the selected Gabor functions. <<ETX>>
Adaptive noise canceler for narrowband/wideband interferences using higher-order statistics

A higher-order statistics-based adaptive interference canceler is introduced to eliminate additive narrowband and wideband interferences in environments where the interference is non-Gaussian and a reference signal, which is highly correlated with the interference, is available. The proposed scheme is independent of Gaussian uncorrelated noise sources and insensitive to the reference signal statistics and the choice of step size. Simulations demonstrate that the higher-order statistics-based adaptive algorithm performs more effectively than the second-order statistics-based adaptive algorithm not only for single and multiple narrowband interferences with/without Gaussian uncorrelated noise sources but also for wideband (AM and FM) interferences. <<ETX>>
Improving the MS-TDNN for word spotting

An MS-TDNN (multistate time delay neural network) version of a word spotting system has been implemented and tested on two spontaneous continuous speech databases. The state-of-the-art MS-TDNN word spotter described here has shown its strength on the RoadRally database. With improvements, it has been adapted to the much more difficult Switchboard corpus with good results. This word spotting system has proved to be a viable alternative to the much larger full-vocabulary speech recognition systems. With relatively few parameters, one is able to achieve good performance and speed on noisy, telephone-quality spontaneous recordings. <<ETX>>
Perceived Intrusiveness of Personalized Marketing

Businesses collect vast amounts of customer data via forms that consumers fill in themselves and with automatic data collection tools. Advances of data collecting methods outpace consumers’ understanding of how their personal data is used. This paper investigates how the consumers perceive the pros and cons of marketers using their personal data for customized marketing messages and product offers. A qualitative study was conducted among Estonian consumers. In depth interviews with 18 consumers revealed surprisingly positive attitudes but also highlighted how consumers do the privacy calculus. Data based personalization is welcomed if the consumers perceive to receive benefits for disclosing their data. The results demonstrate that women are more willing to share their personal data for business purposes.
Implementation and evaluation of a 2400 bit/s mixed excitation LPC vocoder

The authors present an LPC (linear predictive coding) vocoder model with four features: mixed pulse and noise excitation, periodic or aperiodic pulses, pulse dispersion filter, and adaptive spectral enhancement. A 2400-bit/s speech coder based on this model has been implemented, as well as a 4800-bit/s coder which also includes Fourier series spectral information. Formal subjective test results show that this 2400-bit/s vocoder performs better than the government standard at the same bit rate, for both clean and noisy input speech. The quality of this coder approaches that of the higher-rate CELP (code-excited linear prediction) standard. The addition of Fourier magnitudes to the 4800-bit/s coder provides significant improvement, but both the bit rate and the computational requirements of the 4800-bit/s coder are much higher than for the 2400-bit/s vocoder. Therefore, this 4800-bit/s coder may not be as useful, but its performance does serve as a target for possible improvements to the parametric model. <<ETX>>
Adaptive detection and enhancement of closely spaced sinusoids using multirate techniques

A structure that is useful for the detection of closely spaced multiple sinusoids is presented. Cascaded adaptive notch filters are used between M-fold decimators and interpolators. The proposed multirate structure is a single-input, multiple-output adaptive filter. It takes multiple sinusoids corrupted by Gaussian noise and produces the individual sinusoids at each of the outputs. A closed-form expression for the steady-state mean square error of the variable coefficient has been obtained. By making use of the frequency enhancement properties of the decimation, the structure makes high-resolution sinusoidal detection possible. The variance of error for the enhanced sinusoid is shown. Results of computer simulation confirm the theoretical predictions. <<ETX>>
A dynamic cepstrum incorporating time-frequency masking and its application to continuous speech recognition

A dynamic cepstrum parameter that incorporates the time-frequency characteristics of auditory forward masking is proposed. A masking model is derived from psychological experimental results. A novel operational method using a lifter array is derived to perform the time-frequency masking. The parameter simulates the effective input spectrum at the front-end of the auditory system and can enhance the spectral dynamics. The parameter represents both the instantaneous and transitional aspects of a spectral time series. Phoneme and continuous speech recognition experiments demonstrated that the dynamic cepstrum outperforms the conventional cepstrum individually and in various combinations with other spectral parameters. The phoneme recognition results were improved for ten male and ten female speakers. The masking lifter with a Gaussian window provided a better performance than that with a square window. <<ETX>>
How to start with big data - a multiple case study

As part of an advancing digitalization, many enterprises feel the need to explore the possibilities big data may provide for their business. However, only a few companies use big data applications productively, despite its high expected potential. How companies examine the possibilities of big data, is therefore a highly interesting and relevant question. Based on a multiple case study we identify three different approaches: Companies either initially focus entirely on business aspects, or on a systematic build-up of a big data technology and data platform. Innovation adoption research is used as a theoretical basis
Theorems for discrete filtered modulated signals

Useful approximations to the responses of discrete linear systems and certain discrete nonlinear systems are developed for input complex AM-FM signals of the form s(m)=a(m) exp (j phi (m)). These are used to derive limits on simple AM-FM demodulation mechanisms related to the Teager-Kaiser operator. <<ETX>>
Analysis of local-camera-based shepherding navigation

ABSTRACT This study investigates group navigation with the aid of strong interaction between two kinds of agents: A shepherd drives a sheep group with a large population to a given goal position. Even though numerous studies have been performed on the realization of shepherd-like navigation, they are based on the condition that all sheep positions are given. This study examines the navigation of a sheep group using a local-camera-based approach, i.e. a shepherd perceives sheep using the shepherd's vision. Before testing local-camera-based navigation, we design a shepherd controller referred to as a farthest-agent targeting controller, in which the shepherd selects the sheep farthest from the goal. We demonstrate the validity of the proposed controller using statistical analysis and comparison with previous conventional controllers. After examining the effectiveness of this controller, we show that the controller works appropriately even if the shepherd cannot know all sheep positions. In addition, we show the robustness of the proposed controller for the positional errors of the sheep flock or for agent-lost cases to apply it to real-world situations. GRAPHICAL ABSTRACT