Miracle-dz/UniFAR-FTUs huggingface dataset:
DIBS: Diversity inducing Information Bottleneck in Model Ensembles

Although deep learning models have achieved state-of-the art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research. Bayesian approaches including Bayesian Neural Nets (BNNs) do not scale well to modern computer vision tasks, as they are difficult to train, and have poor generalization under dataset-shift. This motivates the need for effective ensembles which can generalize and give reliable uncertainty estimates. In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning the stochastic latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. We evaluate our method on benchmark datasets: MNIST, CIFAR100, TinyImageNet and MIT Places 2, and compared to the most competitive baselines show significant improvements in classification accuracy, under a shift in the data distribution and in out-of-distribution detection. over 10% relative improvement in classification accuracy, over 5% relative improvement in generalizing under dataset shift, and over 5% better predictive uncertainty estimation as inferred by efficient out-of-distribution (OOD) detection.
The Influence of Knowledge-Based HRM Practices on Productivity of Knowledge Workers: A Study on Malaysian Universities

Knowledge workers have contributed significantly to the development of nations. There are various researches that extensively recognized the positive impact of knowledge-based HRM practices on knowledge worker's productivity. While there are studies regarding knowledge-based HRM practices across different countries and industries, however, there is dearth of research regarding the impact of knowledge-based HRM practices on universities' knowledge workers. Therefore, the aim of this current study was to fill up the gap by analysing the influence of various knowledge-based HRM practices on the productivity of knowledge workers in universities. This study investigated HRM practices namely knowledge-based recruiting practices, knowledge-based training and development practices, knowledge-based performance appraisal practices and knowledge-based compensation practices on the quality and quantity of the productivity of knowledge workers. Herzberg's Two-Factor Theory, Expectancy Theory, and MacGregor's Theory X and Theory Y were used as underpinning theories to support the proposed conceptual model. Eight hypotheses were developed based on the proposed research model and standard instrument was used to obtain data. By employing non-probability sampling method, a total of 129 knowledge workers in Selangor and Kuala Lumpur participated in survey. The data were collected at one point of time across the sample population. The data obtained were assessed using SPSS and Partial Least Square Structural Equation Modelling (PLS-SEM). Results revealed the positive and significant influence of knowledge-based recruiting practices and knowledge-based compensation practices on quality and quantity of knowledge workers' productivity. Knowledge-based training and development practices were found to have their positive and significant impact on quantity of knowledge workers' productivity but not on quality of knowledge workers' productivity. Additionally, knowledge-based performance appraisal was not found to have its positive as well as significant impact on quality and quantity of knowledge worker's productivity.
Content Detection for Continuous and Efficient Production of Solid Rocket Fuel by Peristaltic Mixer

In recent years, expectations for low-cost and high-frequency rocket launches for space exploration have increased. Solid fuel rockets are small, inexpensive, and easy to handle. However, in the production of solid fuel, the mixing process and the transportation process are separate batch processes, leading to an increase in maintenance costs and disposal costs. In addition, being a manual process, it is difficult to manufacture large amounts simultaneously in a safe method. The authors have developed a mixing and transportation device that simulates the movement of the intestinal tract by using an elastic duct and a low pneumatic drive. In addition, actual fuel production has been carried out in a mixing and transporting experiment using this device. The effectiveness of this device has been exhibited from the combustion test of the produced fuel. In this paper, we present a high-quality and efficient method of mixing and transporting solid propellant material. This is a mixing of solid and liquid achieved by peristaltic movement done in real-time by adjusting the mixing degree of the mixture inside the device. The degree of mixing is determined by the solid propellant’s volume and viscosity change. Therefore, we first investigate whether the content volume can be detected when the rigid bodies with different volumes are inserted. Next, we focus on the change in the viscosity of the mixture that occurs during the mixing process. We also examine the viscosity of the contents when fluids having different viscosities are inserted.
Acquisition of L2 Mandarin Rhythm By Russian and Japanese Learners

For Chinese as the second language (CSL) learners with different mother tongues (L1), the developments of their speech rhythm received little attention. Based on the interval-based acoustic rhythm metrics, we compared the speech productions of L2 Mandarin by 15 Japanese and 15 Russian learners with different proficiency level. The data included 103 sentences in read speech by each speaker (3605 sentences in total). Preliminary results showed: a. )During the progress from beginners toward intermediate level, the durational variability decreased in both groups of learners, which indicated acquisition of L2 Mandarin rhythm followed similar developmental paths from more stress-timed toward more syllable-timed; b. )During the progress from intermediate toward advanced level, Russian learners kept kind of stress-timed rhythm, Japanese learners appeared mora-timed rhythm, it indicated the transfer effects were influential at this learning stages.
Parametric Study of the Design Variables Involved in the EMF Process of Sheet Metal

This study carries out a numerical parametric analysis on some key variables of an electromagnetic forming (EMF) system designed for free bulging of circular metal sheets, aiming a high and adequate distribution of electromagnetic pressure produced by the EMF system using a spiral actuator coil with a single-layer. The proposed methodology considers fully coupled electric-magnetic problems but uncoupled with the mechanical phenomenon, and solves the equations related to the coupled electrical circuits, identifying the influence of their parameters on the EMF process. Further, a calculation method is presented for the time-dependent electromagnetic pressure intensities and distributions along the radial direction, regarding their dependency in relation to the EMF system parameters. The research methodology is suitable to aid in the EMF systems design, evidencing the parametric influences, considering the pulse unit, and the EMF system geometry. The numerical results show that there exists an optimum time for the maximum electromagnetic pressure to occur producing higher sheet displacements and this optimum time is related to the spiral coil geometry, and mainly to pulse unit parameters. In other words, there exists an optimum discharge current frequency for each EMF process.
Comparing Native Chinese Listeners' Speech Reception Thresholds for Mandarin and English Consonants

The presence of noise can greatly affect listeners' speech perception. Previous studies have demonstrated that nonnative listeners' speech perception performance is reduced more than natives' in noise conditions. Most previous studies have focused on the effects of different noise types on non-native speech perception, and using a fixed signal to noise ratio level in different perception tasks. However, the masking effect of noise may be different for individual speech sounds, therefore leaving an incomplete picture of non-native speech perception in noise conditions. The current study applies an adaptive procedure to dynamically adjust the signal to noise ratio to measure listeners' Speech Reception Threshold (SRT) in noise conditions. More specifically, a group of native Chinese listeners' SRTs for Mandarin and English consonants in Speech Shaped Noise were measured and compared. The results showed that Chinese listeners' mean SRT for Mandarin consonants was 3.6dB lower than that for English consonants, indicating a general native language advantage. However, detailed analysis has revealed the mean SRT for the 5 most noise-tolerable consonants in Mandarin was 2.6dB higher than that in English. This result suggests that non-native speech perception in noise conditions may not always be more difficult than native ones. The acoustic features of different sounds could affect their intelligibility in noise conditions.
On a Class of Markov Order Estimators Based on PPM and Other Universal Codes

We investigate a class of estimators of the Markov order for stationary ergodic processes which form a slight modification of the constructions by Merhav, Gutman, and Ziv in 1989 as well as by Ryabko, Astola, and Malyutov in 2006 and 2016. All the considered estimators compare the estimate of the entropy rate given by a universal code with the empirical conditional entropy of a string and return the order for which the two quantities are approximately equal. However, our modification, which we call universal Markov orders, satisfies a few attractive properties, not shown by the mentioned authors for their original constructions. Firstly, the universal Markov orders are almost surely consistent, without any restrictions. Secondly, they are upper bounded asymptotically by the logarithm of the string length divided by the entropy rate. Thirdly, if we choose the Prediction by Partial Matching (PPM) as the universal code then the number of distinct substrings of the length equal to the universal Markov order constitutes an upper bound for the block mutual information. Thus universal Markov orders can be also used indirectly for quantification of long memory for an ergodic process.
Analysis and Design of Connected Slot Arrays with Artificial Dielectrics

Connected arrays of slots loaded with artificial dielectric layers have been proposed to realize wideband phased arrays with large scan capability. The role of the artificial dielectric superstrate is to provide a wide angle impedance matching transformation from the impedance at the feeding port of the unit cell to the free space impedance. This wideband transformer also reduces the negative effects of the ground plane on the impedance matching. Unlike dielectric superstrates, artificial dielectrics can avoid the propagation of surface waves and the occurrence of scan blindness over very wide scan volumes and large frequency ranges. The analytical method used for the modeling of the unit cell is described. The analysis is based on closed-form expressions for both the connected array and the artificial dielectric, thus it can be used to simulate the main array parameters with minimal computation resources. The method is then used to design a 5:1 connected array with scanning capability up to 60 degrees in all azimuthal directions.
Coordination Methods for Entropy-Based Multi-Agent Exploration Under Sparsity Constraints

This paper is an extension of a previous work that examined a decentralized approach to evaluate the uncertainty of estimating a spatial process using guided model-based multi-agent exploration. The model is a superposition of fixed kernel functions, with each kernel playing the role of a feature. The measurements, collected by the agents, are then used to collectively estimate the weights of the features under sparsity constraints and derive the corresponding spatial uncertainty distribution to optimally guide the agents to reduce the uncertainty. This paper extends these results in several respects. First, we investigate different coordination strategies, which all aim to efficiently optimize the exploration criterion in a distributed multiagent setting. Second, we compare different features, specifically radial basis functions (RBFs), Lanczos kernels, Legendre polynomials, and discrete cosine functions. Third, we conduct hardware-in-the-loop experiments to validate the proposed coordination strategies using real robots. Results show that the coordination strategy together with the selected feature has a significant influence on the exploration performance.
Vectorial Nature in Nonlinear Multimode Interference Based Ultrafast Fiber Lasers

Recent research on nonlinear multimode interference (NL-MMI) based mode-locking method provides a new approach for ultrafast all-fiber lasers. Here, we report on the polarization dynamics of group velocity locked vector solitons (GVLVSs) and noise-like vector pulses (NLVPs) in a graded-index multimode fiber (GIMF) based all-fiber laser. Due to the non-polarization sensitive characteristic of the NL-MMI, emitted pulses automatically evolve into the vector multi-pulse complexes constituting two orthogonally polarized components. Beyond the traditional polarization resolved measurement, dispersive Fourier transform (DFT) and polarization beam splitting techniques are jointly adopted to enable insights into the transient polarization dynamics of the vector solitons. It is found in real time that the two orthogonally polarized components oppositely shift their central wavelengths to compensate the polarization dispersion. In addition, stationary and pulsating NLVPs are respectively unfolded by the DFT based polarization resolved measurement. Especially, the pulsating NLVPs before and after polarization resolved measurement are accompanied with the identical periodic spectral breathing process. All these findings reveal the vectorial nature in NL-MMI based ultrafast fiber lasers, as well as highlight the transient polarization dynamics and the long-term pulsation in dissipative systems.
Cross-band spectrum prediction based on deep transfer learning

Spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data. In practice, for a given spectrum band of interest, when facing relatively scarce historical data, spectrum prediction based on traditional learning methods does not work well. Thus, this paper proposes a cross-band spectrum prediction model based on transfer learning. Firstly, by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping, the similarity between spectrum bands has been verified. Next, the features, which mainly affect the performance of transfer learning in the crossband spectrum prediction, are explored by leveraging transfer component analysis. Then, the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated. Further, experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-the-art models when the historical spectrum data is limited.
Parallel Capsule Neural Networks for Sound Event Detection

In this work, we propose a sound event detection system based on a parallel capsule neural network. The system takes advantage of the capability of capsule neural networks in the detection of overlapping objects. It further develops a parallel architecture and uses the kernel design of different shapes and sizes to effectively utilize the feature information to increase the detection accuracy. The experimental results show that the performance of the proposed system is as low as 52.34% measured by the error rate, which is even lower than the rank 1 system in DCASE2017 challenge.
Factors associated with violence against Peruvian children at school

Our objective was to determine factors associated with violence against children at school in Peru. A secondary analysis of the National Survey of Social Relations (ENARES) of 2015 was carried out. Our sample consisted of 1559 primary school students, whose age ranged from 9 to 11 years old. Raw and adjusted prevalence ratios (PR) were estimated using Poisson regression with robust variance. Regarding domestic violence, it was found that having previously been victims of violence at home, living in a home where constant fights or arguments between parents or adults occur, and coming from a single parent family were all factors associated with a higher risk of violence. Meanwhile, the factors associated with lower risk of suffering violence at home included; that children age and become adults and classrooms with a higher proportion of females.
Scalable and Fair Resource Sharing Among 5G D2D Users and Legacy 4G Users: A Game Theoretic Approach

We propose a game theoretic approach to the problem of resource allocation in 5G device to device (D2D) communications underlaying cellular networks, where modern 5G D2D users (DUs) share channel resources of legacy 4G cellular users (CUs). Though such sharing improves scalability as more and more users can be served, the individual throughput obtained by a CU may get reduced when its channel resource is shared by a DU pair. Thus our aim is to increase the overall system throughput while ensuring effective user fairness among the DUs and CUs served. Our approach provides a tradeoff between the two objectives thereby ensuring no legacy CUs are compromised heavily while encouraging modern DUs. We have addressed this fairness problem by thresholding the reduction of the throughput of a CU with respect to its ideal throughput if not reused. We have proposed a game theoretic model in order to make strategic decision for selecting the preferred action configuration in order to meet this objective. Together with this game model and a validation process, we aim to improve scalability, overall system throughput without compromising on the fairness of the users. We have also compared the user fairness achieved by our approach with an existing approach to demonstrate the effectiveness of our game theoretic approach.
Giant Detectivity of ZnO-Based Self-Powered UV Photodetector by Inserting an Engineered Back Gold Layer Using RF Sputtering

The realization of high-responsivity, self-powered and low-cost ultraviolet (<italic>UV</italic>) photodetector (<italic>PD</italic>) based on eco-friendly and earth-abundant compounds, remains far from satisfactory for future optoelectronic applications. In this paper, we demonstrated a new high-performance <italic>UV</italic>-<italic>PD</italic> based on planar <italic>ZnO</italic> thin-film, efficiently operating without any power supply. The proposed device was elaborated by evaporating an engineered back metallic layer onto the glass substrate and then depositing <italic>ZnO</italic> thin layer through <italic>RF</italic> sputtering technique. The sensor structural and optical properties were systematically analyzed by the techniques of <italic>X</italic>-ray diffraction (<italic>XRD</italic>) and <italic>UV</italic>–<italic>Vis</italic> absorbance spectrometry. The resulted <italic>ZnO UV-PD</italic> showcased a clear and distinctive photovoltaic behavior. Interestingly, it also demonstrated a high responsivity of <italic>0.38A/W</italic> and a giant detectivity exceeding <italic>10</italic><sup><italic>14</italic></sup> <italic>Jones</italic> at zero bias, which is much higher than other reported self-powered <italic>UV-PD</italic> despite the use of an <italic>All-ZnO</italic> structure. The device photodetecting mechanism in self-driven mode was discussed using the energy band diagram, where the key role of the engineered back metallic layer in modulating the electric field distribution within the <italic>ZnO</italic> active region to effectively achieve an asymmetric behavior is emphasized. Therefore, the presented work offers a novel pathway to design high-responsivity self-powered <italic>UV-PDs</italic> based on a simple <italic>All-ZnO</italic> structure.
On the Design of a Front-face Grid for Shielding Enclosure Using Evolutionary Computations

The increased demand for protection quality in recent years, causing a severe problem for electronic compatibility for electric and electronic devices. Numerous electronic devices need to be quality shielded against electromagnetic interference. Especially image sensors that are a vital part of security cameras have the highest level of sensitivity to the electromagnetic field. However, the design of a high-quality shield is a difficult task even for experts, and it is impossible to test every possible solution. Therefore, the design can be conducted utilizing techniques adopted from the field of artificial intelligence. More specifically, the design will be carried on by selected evolutionary algorithms, and their found designs results are compared to find the best fit solution.
Comparative Study of Deep Learning Based and Traditional Single-Channel Noise-Reduction Algorithms

Deep neural networks (DNN) have been applied to the problem of noise reduction and promising results have been reported widely, leading to the impression that the traditional techniques based on blind noise estimation may no longer be needed. However, there lacks comprehensive and rigorous evaluation and comparison between DNN based and traditional noise-reduction algorithms for their pros and cons. In this work, we attempt to evaluate some widely used DNN based noise-reduction algorithms and compare them to a traditional noise-reduction method. We also evaluate a method that straightforwardly combines a DNN based regression method with the optimal filtering technique. Through experiments, it is observed that: 1) DNN based methods have advantages over the traditional methods in scenarios with non-stationary noise and low signal-to-noise ratios (SNRs); 2) generalization remains a challenging issue with DNN based methods; for noise types unseen in the training data, which happen often in practical environments, DNN based methods do not show any advantage over the traditional technique; 3) combining DNN-based regression and the optimal filtering technique shows some potential in improving the noise-reduction performance as well as system generalization.
Multimode wideband waveform for Software Defined Radio based on hybrid CPM-CDMA concept

Future Software Defined Radio (SDR) waveforms require increasing throughputs and at the same time security and anti-jamming capabilities to support wide variety of applications for military, commercial and civilian applications. It requires combination of spectral efficiency and anti-jamming capabilities. To meet this requirements, a multi-mode wideband waveform for software defined radio based on hybrid Continuous Phase Modulation & Code Division Multiple Access (CPM-CDMA) concept is proposed in this paper. The proposed waveform has three modes of operation and maximum achievable throughput of 1.33 Mbps. The user throughput of the proposed hybrid waveform is compared with the classical CDMA scheme using the same bandwidth. Due to higher spectral efficiency provided by CPM, it is shown that the proposed waveform achieves higher throughput for appropriate choice of pulse length L and modulation index h as compared to the CDMA based waveform. At the end BER performance of hybrid CPM-CDMA proposed mode is analyzed and is compared with traditional CDMA based waveform.