Miracle-dz/UniFAR-FTUs huggingface dataset:
Compensating the Effect of Signal-processing Delay on Objective Intelligibility Prediction

Previous work showed the negative influence due to signal-processing delay on objective intelligibility prediction. As most intelligibility indices were designed primarily with either temporal-envelope or spectral-detail information, this work compared the temporal-delay-caused effects on intelligibility prediction by the temporal-envelope and spectral-detail based intelligibility indices, and introduced new schemes to compensate the effect of signal-processing delay on intelligibility prediction. Subjective intelligibility scores from normal-hearing listeners obtained under 72 conditions were used to evaluate the performance of intelligibility prediction when the processed and probe speech waveforms were temporally misaligned. Results showed that marked time delay had a greater effect on the short-time spectral-detail-based index (i.e., the coherence-based intelligibility index, or CSII) than on the temporal-envelope-based index (i.e., the normalized covariance metric, or NCM). The negative influence of time delay on intelligibility prediction could be compensated by employing a small range of envelope modulation frequency and a long segmentation duration in NCM and CSII, respectively.
Pedestrian Detection Based on Laplace Operator Image Enhancement Algorithm and Faster R-CNN

Pedestrian detection is an important branch of computer vision. Many car manufactures have used this technology in real situation. Recently, deep learning has become the best method of pedestrian detection, and the advantage of deep neural network is that it can use statistical method to extract high-level features from raw sensory data and to obtain effective feature. Currently, Faster R-CNN is a typical framework, which commonly be used in the field of image processing. However, in order to achieve better performance in pedestrian detection, Faster R-CNN requires a large number of high-quality training samples. Due to the change of light and pedestrian density, the quality of the collected image is poor. Based on this problem, our research introduces Laplacian operator, it can enhance local image comparison. By introducing the Laplace operator, the proposed method can effectively preprocess the samples of the Faster R-CNN. The real data experiments verify the effectively of this algorithm as well as good robustness to the interference.
Multiple Periods Location and Capacity Optimization of Charging Stations for Electric Vehicle

With rapid growth of electric vehicles, the development of charging stations has to keep up. This paper proposes a multi-period location and capacity optimization model of charging stations, and introduces congestion rate to control the queuing situation. As the model is a NP-hard problem, two kinds of greedy algorithms are proposed, which are proved to be effective by lots of computational experiments.
A New Singular Value Decomposition Watermarking Algorithm Based on Directionlet

A new singular value decomposition watermarking algorithm based on Directionlet was proposed. Singular Value Decomposition (SVD) is able to resist various geometric attacks when images are subjected to small disturbances. At first, the transform direction of Directionlet transform is positioned by using canny operate detection and Hough line detection technology; Then, selecting the blue region of the color original image to perform the Directionlet transform and where the first frequency subband is obtained to carry out SVD; Finally, the binary watermark image after performing SVD is embedded the singular matrix of the first frequent subband image. When extracting the watermark, we compare the difference between original image and watermarked image to obtain the binary watermark. Experimental results prove that the embedded watermarking color images has a good resistance against to common attacks such as cropping, rotation and noise etc.
Forecasting Retweet Count during Elections Using Graph Convolution Neural Networks

A retweet refers to sharing a tweet posted by another user on Twitter and is primary way information spreads on the Twitter network. Political parties use Twitter extensively as a part of their campaign to promote their presence, announce their propaganda, and at times debating with opponents. In this work we consider the problem of early prediction of the final retweet count using information from the network during the first several minutes after a post is made. Such predictions are useful for ranking and promoting posts and also can be used in combination with fake news detection. From a machine learning perspective, the task can be viewed as a regression problem. We introduce a novel graph convolution neural network for forecasting retweet count that combines network level features through graph convolution layers as well as tweet level features at a higher dense layer in the network. We first will provide an overview of the graph convolution network architecture and then perform several experiments on Twitter data collected during presidential elections in South Africa (2014) and Kenya (2013). We show that the model outperforms baseline models including a feed forward neural network and the popular point process based model SEISMIC.
Method of Assigning Spreading Factor to Improve the Scalability of the LoRaWan Wide Area Network

In modern world Low Power Wide Area Network (LPWAN) technologies have become the key enabler for a sheer diversity of the Internet of Things (IoT) applications involving massive deployment of resource-limited devices. In this paper we address the problem of improving the network scalability for one of the most widely used today licensee-free-band LPWAN technologies, named LoRaWAN. We show that the conventional method for assigning the spreading factor (SF) parameter to the devices in a LoRaWAN network, which effectively minimizes the consumption of individual devices, has some drawbacks when this comes to the scalability of the network as whole. Therefore, in this paper we propose another method of assigning the SFs to the nodes, which improves the probability of data delivery in an LPWAN at a cost of minor increase of the devices' consumption. The results of the conducted simulations confirm and characterize the utility of the proposed method.
FROM HYBRID SIMULATION TO HYBRID SYSTEMS MODELLING

Hybrid Simulation (HS) is the combined application of simulation approaches like SD, DES and ABS in the model implementation stage of a simulation study. Its objective is to better represent the system under scrutiny. Hybrid Systems Modelling (HSM), on the other hand, is the combined application of simulation with methods and techniques from disciplines such as Applied Computing, Computer Science, Engineering and the wider OR. HSM can be applied to multiple stages of a simulation study. In this paper, we present a classification of HS and extend it to include HSM approaches which use simulation with other OR techniques. The paper contributes to the debate on what constitutes HS and offers a unifying conceptual representation for mixing simulation approaches with HSM methods and techniques.
Data-Based Testing for Nonlinearity in Dynamical Systems: The Use of Surrogate Data

The use of Fourier-based surrogates for data-based testing for nonlinearity within system identification is introduced. It is shown that the presented method handles multi-input multi-output systems as easily as single-input single-output systems. In addition, the approach can be readily applied as a tool for validating nonlinear models by residual analysis. Furthermore, an extensive empirical analysis shows that the proposed methodology appears to be the most reliable alternative for detecting nonlinearity in general dynamical systems.
Music Emotions Recognition Based on Feature Analysis

Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, psychology, arts and affective computing. In this paper, music emotions are classified into four types known as exciting, happy, serene and sad. MER is formulated as a classification problem in cognitive computing where music features are extracted. And, the feature sets are input into Support Vector Machine (SVM) and Convolutional Neural Networks to classify the music emotion. It can be seen that the best accuracy of 88.2% in VGG16 where Chirplet has been turned into features images. The results show that the feature graph is feasible for music emotion classification.
Physical Layer Security of Hybrid Satellite-FSO Cooperative Systems

In this paper, we study the physical layer secrecy performance of a hybrid satellite and free-space optical (FSO) cooperative system. The satellite links are assumed to follow the shadowed-Rician fading distribution, and the channel of the terrestrial link between the relay and destination is assumed to experience the gamma–gamma fading. For the FSO communications, the effects of different types of detection techniques (i.e., heterodyne detection and intensity modulation with direct detection) as well as the pointing error are considered. We derive exact analytical expressions for the average secrecy capacity and secrecy outage probability (SOP) for both cases of amplify-and-forward (AF) and decode-and-forward (DF) relaying. The asymptotic analysis for the SOP is also conducted to provide more insights on the impact of FSO and satellite channels on secrecy performance. It is found that with the AF with fixed gain scheme, the secrecy diversity order of the investigated system is only dependent on the channel characteristics of the FSO link and the FSO detection type, whereas the secrecy diversity is zero when the relay node employs DF or AF with variable-gain schemes.
Linear lJ-nonparallel support vector machine for pattern classification

In this paper, we propose a novel nonparallel linear hyperplane classifier called linear $\nu $-nonparallel support vector machine ($ L_{1}-\nu $-NPSVM) for binary classification. Based on $L_{1}-$ NPSVM (Linear Nonparallel Support Vector Machine), and combining the $\nu $-support vector classification and $\nu $-support vector regression together, the primal problem of $ L_{1}-\nu $-NPSVM is obtained. Compared to $L_{1}$-NPSVM, $ L_{1}-\nu $-NPSVM has the following advantages: (1) By introducing a new parameter $\nu $ to effectively control the number of support vectors, the model's generalization ability and accuracy can be improved; (2) By introducing a new parameter v, we can eliminate one of the other free parameters of the $L_{1}$-NPSVM to reduce the difficulty of selecting parameters. Moreover, experimental results on data sets show the effectiveness of our method.
Bitcoin Price Forecasting Using Time Series Analysis

Over the past few years, Bitcoin has been a topic of interest of many, from academic researchers to trade investors. Bitcoin is the first as well as the most popular cryptocurrency till date. Since its launch in 2009, it has become widely popular amongst various kinds of people for its trading system without the need of a third party and also due to high volatility of Bitcoin price. In this paper, we propose a suitable model that can predict the market price of Bitcoin best by applying a few statistical analysis. Our work is done on four year's bitcoin data from 2013 to 2017 based on time series approaches especially autoregressive integrated moving average (ARIMA) model and the work finally could acquire an accuracy of 90% for deciding volatility in weighted costs of bitcoin in the short run.
Synthesis of Quasi-Fractal Hemispherical Dielectric Resonator Antennas

The paper considers the models quasi-fractal dielectric resonator antenna (DRA) based on a hemispherical. Thus, the influence of the depth of the overlap of the constituent elements in the spatial-frequency characteristics DRA. The interaction of the antennas of non-Euclidean geometry with radio waves is described difficult. It is therefore advisable to perform the synthesis of these antennas by mathematical modelling using the Ansoft HFSS package. To simplify the analysis of the characteristics of the antennas, their arrangement is limited geometrical structure, which has five elements on one horizontal surface. Evaluation and comparison of antennas held by the following characteristics: amplitude-frequency response, beam pattern and voltage standing wave ratio.
Robust Infrared Pedestrian Detection via GMR and Logistic Regression Based ROIs Extraction

Real-time detection of persons via infrared camera is a potential task for advanced driver assistance systems (ADAS). In this paper, we develop a simple and effective pedestrian detection strategy. To robustly locate the region of interest (ROI), we first design a gradient based descriptor which called gradient magnitude ratio (GMR) to depict the pedestrian target with very low computing cost. Then a logistic regression (LR) classifier is trained to identify the ROIs. After this coarse detection step, about one thousand ROIs is obtained in the experiments. Finally, the detected candidates are feed into a traditional HOG-SVM based classifying framework to gain the fine detection results. To furtherly enhance the real-time performance of the proposed system, PCA based dimension reduction is performed on the original HOG feature. Experiment results show that the proposed method could detect pedestrians effectively with realtime speed under challenging circumstances such as dynamic background, low contrast and various scales.
Model Adaptation and Personalization for Physiological Stress Detection

Stress and accompanying physiological responses can occur when everyday emotional, mental and physical challenges exceed one's ability to cope. A long-term exposure to stressful situations can have negative health consequences, such as increased risk of cardiovascular diseases and immune system disorder. It is also shown to adversely affect productivity, well-being, and self-confidence, which can lead to social and economic inequality. Hence, a timely stress recognition can contribute to better strategies for its management and prevention in the future. Stress can be detected from multimodal physiological signals (e.g. skin conductance and heart rate) using well-trained models. However, these models need to be adapted to a new target domain and personalized for each test subject. In this paper, we propose a deep reconstruction classification network and multi-task learning (MTL) for domain adaption and personalization of stress recognition models. The domain adaption is achieved via a hybrid model consisting of temporal convolutional and recurrent layers that perform shared feature extraction through supervised source label predictions and unsupervised target data reconstruction. Furthermore, MTL based neural network approach with hard parameter sharing of mutual representation and task-specific layers is utilized to acquire personalized models. The proposed methods are tested on multimodal physiological time-series data collected during driving tasks, in both real-world and driving simulator settings.
High-Gain Optical Injection Locking Amplifier in Phase-Coherent Optical Frequency Transmission

Optical amplification, which is necessary for extending the distance of optical frequency transfer, has a significant impact on the transmission performance. In this paper, we propose a high-gain optical injection locking amplifier (OILA) with low phase noise based on an optical injection phase-locked loop for the phase-coherent optical frequency transmission. The high-gain OILA can provide more than 75-dB gain and ensure that the input carrier frequency fractional stability can reach 8.2 × 10−20 at an averaging time of over 100 s. We then transfer a commercial narrow-linewidth laser over a 220-km fiber link with only one amplification step placed at the remote end. After eliminating the noise induced in the fiber link, the fractional frequency instability of the transferred frequency can reach 9.0 × 10−16 at 1 s and 7.1 × 10−20 at 20 000 s.
A Review on Adopting Personalized Gamified Experience in the Learning Context

Gamification illustrates motivation and encouragement that can be gained by people during using gamified experience. Gamification technology has been successfully applied in several domains including health and fitness, marketing, daily activities and education. It is the strategy which uses heterogeneous game elements within nonentertainment context. As Millennial Generation students have their own needs and preferences, the necessity of having personalized learning is increased recently to boost the effectiveness of the education system. Thus, many researches have investigated how dissimilarities between students can influence their suitable game elements. Since 2010 the published studies on gamification area increased; moreover, some attempts have been made to study the personalization in the gamification model. In this paper, we conducted a review of personalization in gamified learning experience to answer two review questions. We collect, review and synthesize studies related to gamified learning and personalization published from 2010 to 2017. The review strategy included six stages to identify the articles. 13 studies were identified which discussed adopting personalization and gamified learning experience in education. The results show that the studies that apply personalization within gamified learning approach accelerated during the last 3 years. This review reveals the trends in personalization parameters used in the studies, such as student's learning style and personality type. Moreover, most studies have shown high student performance while using the personalized gamified learning experience.
Citizen Contributions and Minor Heritage: Feedback on Modeling and Visualising an Information Mash-up

The citizen science paradigm and the practices related to it have for the last decade called a wide attention, beyond academics, in many application fields with as a result a significant impact on discipline-specific research processes and on information sciences as such. Indeed, in the specific context of minor heritage (tangible and intangible cultural heritage assets that are left aside from large official heritage programs), citizen-birthed contributions appear as a major opportunity in the harvesting and enrichment of data sets (notwithstanding data quality and heterogeneity issues). In parallel, it seems we have today reached a moment when the acquisition and analysis of spatio-historical information appears "easier" since citizens are seen as potential (and legitimate) sensors. But is it really "easier"? And if so, at what cost? Having a closer look on practical challenges behind the curtain can avoid turning the above mentioned opportunity into a lost one. In this contribution we present a research initiative that aims at better circumscribing the difficulties one has to foresee when wanting to harvest and visualize pieces of data on minor heritage collections, and then to derive from them spatial, temporal, and thematic knowledge. The contribution focuses on three aspects: a short analysis of citizen contributions in the context of minor heritage, a description of the case study and of the data modeling bottlenecks we are facing, and an exemplification of the visual analysis solutions we experiment in order to portray and question our understanding of collections. The case study acts as a test bench helping to investigate data harvesting and modeling challenges, as well as potential added-value of the visualization step.
Compact Self-Diplexing Antenna Using Dual-Mode SIW Square Cavity

This letter presents a substrate-integrated-waveguide-based compact, dual-mode self-diplexing antenna. A single square cavity with a rectangular ring slot is used for both dual-mode excitation and radiation. A prototype diplexing antenna is fabricated that radiates linear, orthogonal polarization at 9.5 and 10.5 GHz. The measured peak gains at the two frequencies are 5.75 and 5.95 dBi, respectively. Intermodal coupling between the modes provides two transmission zeros, which are utilized to achieve port isolation better than 29 dB. The proposed technique can be used for closely spaced radiating frequencies. As no external filter is used, the structure is compact. Furthermore, because of the slot loading effect, the antenna is smaller than a recently published antenna claimed as the most compact antenna. Its working principle is discussed in detail. Finally, design guidelines are suggested.
End To End Model For Keyword Spotting With Trainable Window Function And Densenet

In this paper, we propose an end to end model for keyword spotting(KWS) with densely connected convolutional network(DenseNet) and the integration of Short Time Fourier Transform(STFT) which aims to extract utterances from raw waveform directly. Furthermore, we investigate the efficiency of adaptive and trainable window function in the task of keywords spotting. Using the recently-released Google Speech Commands Dataset as our benchmark. Our DenseNet implementation significantly outperforms previous neural networks based KWS system in terms of accuracy.
Color Converted White Light-Emitting Diodes With 637.6 MHz Modulation Bandwidth

High bandwidth GaN-based white light-emitting diode (LED) was fabricated using GHz bandwidth blue LED chip combined with CdSe/ZnS semiconductor quantum dots (QDs). The maximum optical power of the blue LED chip can reach 7.2 mW. By changing the optical power ratio of the LED chip to the white LED, we have investigated the modulation properties and established a frequency response function of QD-converted white LED. The behaviors of the measured and simulated bandwidths using the function are in good agreement with each other. After optimizing the optical power ratio and decreasing the chip carrier lifetime, the 637.6 MHz bandwidth of QD-converted white LED was achieved, which can support 675 Mb/s real-time non-return-to-zero on–off-keying signal at a low bit error rate of $\textsf {4.35}\times \textsf {10}^{-\textsf {8}}$ without optical filters. Our findings can help to design high bandwidth white LEDs for visible light communication applications in free space.
Analysis of the Technologies and Methodologies of Data Transmission in Distributed Information Systems

In this paper the main methods and technologies of data distribution and delivery are presented. A lot of algorithms which allow selecting the optimum route and server for the content requests of users were described. Also monitoring methods that always allow having the latest information about the status of any server and making optimum load distribution between all edge servers were depicted.
EFFORT ALLOCATION AND STATISTICAL INFERENCE FOR 1-DIMENSIONAL MULTISTART STOCHASTIC GRADIENT DESCENT

Multistart stochastic gradient descent methods are widely used for gradient-based stochastic global optimization. While these methods are effective relative to other approaches for these challenging problems, they seem to waste computational resources: when several starts are run to convergence at the same local optimum, all but one fail to produce useful information; when a start converges to a local optimum worse than an incumbent solution, it also fails to produce useful information. For problems with a one-dimensional input, we propose a rule for allocating computational effort across starts, Most Likely to Succeed (MLS), which allocates more resources to the most promising starts. This allocation rule is based on a novel Gaussian-Process-based statistical model (SGD-GP) for a start’s limiting objective value. Unlike previously proposed statistical models, ours agrees with known convergence rates for SGD. Numerical results show our approach outperforms equal allocation of effort across starts and a machine learning method.