Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning, Probability and Logic: Toward a Unified Approach for Content-Based Music Information Retrieval

Within the last fifteen years, the field of Music Information Retrieval (MIR) has made tremendous progress in the development of algorithms for organizing and analyzing the ever-increasing large and varied amount of music and music-related data available digitally. However, the development of content-based methods to enable or improve multimedia retrieval still remains a central challenge. In this perspective paper, we critically look at the problem of automatic chord estimation from audio recordings as a case study of content-based algorithms, and point out several bottlenecks in current approaches: expressiveness and flexibility are obtained to the expense of robustness and vice-versa; available multimodal sources of information are little exploited; modeling multi-faceted and strongly interrelated musical information is limited with current architectures; models are typically restricted to short-term analysis that does not account for the hierarchical temporal structure of musical signals. Dealing with music data requires the ability to handle both uncertainty and complex relational structure at multiple levels of representation. Traditional approaches have generally treated these two aspects separately, probability and learning being the standard way to represent uncertainty in knowledge, while logical representation being the standard way to represent knowledge and complex relational information. We advocate that the identified hurdles of current approaches could be overcome by recent developments in the area of Statistical Relational Artificial Intelligence (StarAI) that unifies probability, logic and (deep) learning. We show that existing approaches used in MIR find powerful extensions and unifications in StarAI, and we explain why we think it is time to consider the new perspectives offered by this promising research field.
Signal Processing for Optical Communication System Assisted by Computer Vision Techniques

We review the recent progress on the application of image processing techniques to optical communication systems. The focus is placed mainly on the implementation complexity and performance of the techniques for optical performance monitoring and the compensation of common phase error. We also briefly introduce several applications where machine learning algorithms could be beneficial to fiber-optic transmission system.
Joint Time and Power Allocation in Multi-Cell Wireless Powered Communication Networks

This paper investigates the joint time and power allocation in a multi-cell wireless powered communication network (WPCN) with successive interference cancellation. In the WPCN, the hybrid access points broadcast wireless energy to all users and then the users utilize the harvested energy to transmit information simultaneously in a spectrum-sharing fashion. The max–min fairness throughput optimization and max-sum throughput optimization problems are formulated to analyze the system performance. However, both of the two optimization problems are nonconvex due to the complicated co-channel interference and the coupled time and power variables. To deal with this challenge, efficient two-stage approaches are proposed to transform them into more tractable ones. Specifically, for the max–min fairness throughput optimization problem, we first convert the power allocation problem into geometric programming (GP) problem for given time allocation and then optimize the time allocation. For the max-sum throughput optimization problem, we first transform the power allocation problem into a signomial programming problem with fixed time allocation, which is further approximated as a GP one. Then, time allocation is optimized. The simulation results validate the effectiveness of the two proposed approaches. In addition, it is shown that the “doubly near-far” issue can be well addressed by the max–min fairness throughput optimization.
Hybrid Calculation Architecture of Cyber Physical Power System Based on Correlative Characteristic Matrix Model

With the close integration of cyber system and physical system in smart grid, traditional isolated modeling and calculation methods for information and communication system and power system cannot meet the needs of the analysis and control of the Cyber Physical Power System (CPPS). In this paper, a hybrid calculation architecture, which is on demand response and layered, is proposed to realize the hybrid calculation of the CPPS model and the security analysis of CPPS. Case study verifies the effectiveness of this CPPS hybrid calculation architecture.
Bacterial Colonization of the Hospitalized Newborn: Competition Between Staphylococcus aureus and Staphylococcus epidermidis.

BACKGROUND In adults, Staphylococcus epidermidis and Staphylococcus aureus compete for colonization of the nasal mucosa and S. epidermidis strains that produce the Esp serine protease eradicate S. aureus nasal colonization. Whether similar phenomena are seen in newborn infants is unknown. METHODS Nasal swabs were obtained on admission and discharge from newborn infants (n = 90 and 83, respectively) in the neonatal intensive care unit at UC Davis Children's Hospital. Swabs were cultured for S. aureus and S. epidermidis. S. epidermidis isolates were tested for Esp expression, overall secreted protease activity and biofilm inhibition. RESULTS No infant had S. aureus on admission. S. epidermidis colonization was rare on admission in inborn infants (2.5%), but common in infants transferred from referring hospitals (50%). At discharge, most infants (96%) were colonized by staphylococci. S. aureus colonization was less common in infants with S. epidermidis colonization (9%) and more common in infants without S. epidermidis (77%) (relative risk of S. aureus colonization in infants colonized with S. epidermidis 0.18, 95% confidence interval: 0.089-0.34, P < 0.0001). Compared with S. epidermidis strains from infants without S. aureus, S. epidermidis from infants co-colonized with S. aureus had lower total proteolytic enzyme activity and decreased biofilm inhibition capacity, but did not have lower frequency of Esp positivity. CONCLUSIONS In hospitalized neonates, S. epidermidis colonization has a protective effect against S. aureus colonization. Secretion of proteases by S. epidermidis is a possible mechanism of inhibition of S. aureus colonization; however, in this cohort of neonates, the source of major protease activity is likely other than Esp.
Estimation of SoC of Batteries Using Terminal Sliding-Mode Observer

This paper presents a new real-time estimation algorithm for the SoC of a Li-ion battery using the sliding mode observer. A RC equivalent circuit model is utilized to express a Li-ion battery. A terminal sliding mode observer is developed to estimate the SoC of a battery accurately. Compared to the existing sliding mode observer used for SoC estimation of a Li-ion battery, the internal uncertainties are taken into consideration during the design of observer. The design of terminal sliding-mode observer attenuates chattering existed in traditional sliding-mode observers. The estimation results validate the proposed method.
A Study on Phalangeal Joint Reference Line Detection for Finger Vein Images

Region of interest extraction is crucial for both the finger vein recognition system and the finger vein based cryptography system. Accurate phalangeal joint reference line detection is a key step in many ROI extraction methods for finger vein images and the position of the reference line is usually detected by a sliding line or rectangle window. Initiated by the brighter irregular shape of the finger joint region, an ellipse sliding window is used in our proposed method. In order to find out the most suitable ellipse window, a series of ellipse windows with different width is studied in our experiment. Based on our proposed contour similarity distance evaluation method, the performances of finger joint reference line detection by various ellipse windows are evaluated. And the experimental results show that the ellipse whose ratio of width to height is 0.85 has the best overall performance.
Combined epidural adhesiolysis and balloon decompression can be effective in intractable lumbar spinal stenosis patients unresponsive to previous epidural adhesiolysis

Abstract Moderate evidence exists regarding percutaneous epidural adhesiolysis (PEA) being an effective treatment for lumbar spinal stenosis (LSS). Although PEA is successfully performed using balloon-less epidural catheters, many patients with severe adhesions cannot obtain satisfactory results. Combined treatment with balloon-inflatable catheters for PEA and balloon decompression recently demonstrated sufficient pain relief and functional improvement in patients with intractable LSS. We compared the effects of PEA and balloon decompression in patients with intractable LSS who did not undergo PEA and those who were unresponsive to previous PEA with a balloon-less catheter. We examined 315 patients who underwent PEA and balloon decompression with balloon-inflatable catheters. Patients with intractable LSS were divided into those without previous PEA (No-PEA) and those unresponsive to previous PEA using balloon-less catheters (Prev-PEA). The numeric rating scale, Oswestry disability index, and global perceived effect of satisfaction scale were measured at 0, 1, 3, and 6 months after the intervention. Responder analysis was performed based on changes in measured scales and indices. A successful treatment response was observed at 1, 3, and 6 months after the intervention in 56.4%, 42.7%, and 32.9%, respectively, of the No-PEA group and in 48.9%, 37.8%, and 25.6%, respectively, of the Prev-PEA group. No significant between-group differences were detected. Pain intensities and functional status improved and were maintained throughout follow-up after PEA with balloon decompression using balloon-inflatable catheters. This modality may represent a useful alternative to overcome the limitations of preexisting adhesiolysis procedures.
Using Airborne Lidar, Multispectral Imagery, and Field Inventory Data to Estimate Basal Area, Volume, and Aboveground Biomass in Heterogeneous Mixed Species Forests: A Case Study in Southern Alabama

Airborne light detection and ranging (lidar) has proven to be a useful data source for estimating forest inventory metrics such as basal area (BA), volume, and aboveground biomass (AGB) and for producing wall-to-wall maps for validation of satellite-derived estimates of forest measures. However, some studies have shown that in mixed forests, estimates of forest inventory derived from lidar can be less accurate due to the high variability of growth patterns in multispecies forests. The goal of this study is to produce more accurate wall-to-wall reference maps in mixed forest stands by introducing variables from multispectral imagery into lidar models. Both parametric (multiple linear regression) and non-parametric (Random Forests) modeling techniques were used to estimate BA, volume, and AGB in mixed-species forests in Southern Alabama. Models from Random Forests and linear regression were competitive with one another; neither approach produced substantially better models. Of the best models produced from linear regression, all included a variable for multispectral imagery, though models with only lidar variables were nearly as sufficient for estimating BA, volume, and AGB. In Random Forests modeling, the most important variables were those derived from lidar. The following accuracy was achieved for linear regression model estimates: BA R2 = 0.36, %RMSE = 31.26, volume R2 = 0.45, %RMSE = 35.30, and AGB R2 = 0.41, %RMSE = 31.31. The results of this study show that the addition of multispectral imagery is not substantially beneficial for improving estimates of BA, volume, and AGB in mixed forests and suggests that the investigation of other variables to explain forest variability is necessary.
Real Time Writing Reproduction by Robot Arm

Robot writing has been concerned due to the development of industry technique. Previous studies focused on the writing by inputting the predefined motion or characters to the robot, and few work has been done on the real time writing due to the challenges. In this paper we control the robot to write in real time with a 3D camera Kinect. During the implementation, we design an algorithm to accurately track the movement of hand. As the real time requirement, the gap exists between the tracked motion and the writing trajectory. To combat it, we propose a real time interpolation algorithm to trade off the real time and accuracy. Experimental results verify the effectiveness of this system.
Wideband Linearly-Polarized and Circularly-Polarized Aperture-Coupled Magneto-Electric Dipole Antennas Fed by Microstrip Line With Electromagnetic Bandgap Surface

Wideband aperture-coupled magneto-electric (ME) dipole antennas fed by microstrip line are proposed in this paper. The aperture-coupled microstrip-line-fed method was utilized in the proposed configuration to replace the conventional $\Gamma $ -shaped probe to excite the ME dipole antenna. Particularly, a printed mushroom-like electromagnetic bandgap (EBG) surface was mounted below the microstrip line to effectively eliminate the back radiation caused by the aperture on the ground plane. Two configurations of ME dipole antennas with linear polarization (LP) and circular polarization (CP) have been proposed for adequately studying the effect of the EBG surface. Both the simulation and measurement of the two prototypes had been carried out for verification. Based on the measured results, wide impedance bandwidth (SWR < 2) of 54.9% and 58% can be obtained for both the proposed LP and CP ME dipole antennas, respectively. Besides, the performances of the stable broadside radiation pattern with the enhanced front-to-back ratio (FBR) can also be achieved for both configurations.
Coordinated Multicast Precoding for Multi-Cell Massive MIMO Transmission Exploiting Statistical Channel State Information

This paper considers coordinated multi-cell multicast precoding for massive multiple-input-multiple-output transmission where only statistical channel state information of all user terminals (UTs) in the coordinated network is known at the base stations (BSs). We adopt the sum of the achievable ergodic multicast rate as the design objective. We first show the optimal closed-form multicast signalling directions of each BS, which simplifies the coordinated multicast precoding problem into a coordinated beam domain power allocation problem. Via invoking the minorization-maximization framework, we then propose an iterative power allocation algorithm with guaranteed convergence to a stationary point. In addition, we derive the deterministic equivalent of the design objective to further reduce the optimization complexity via invoking the large-dimensional random matrix theory. Numerical results demonstrate the performance gain of the proposed coordinated approach over the conventional uncoordinated approach, especially for cell-edge UTs.
Microporous Formation Mechanism of Biaxial Stretching PA6/PP Membranes with High Porosity and Uniform Pore Size Distribution

The low porosity and wide pore size distribution of biaxial stretching PP microporous membranes continue to be the primary impediments to their industrial application. To solve this problem, there is a critical and urgent need to study the micropore-forming mechanism of PP membranes. In this research, the interfacial micropore formation mechanism of PA6/PP membranes during biaxial stretching was investigated. PA6/PP membranes containing spherical PA6 and fibrillar PA6 were found to exhibit different interfacial micropore formation mechanisms. Numerous micropores were generated in the PA6/PP membranes, containing PA6 spherical particles via the interface separation between the PP matrix and PA6 spherical particles during longitudinal stretching. Subsequent transverse stretching further expanded the two-phase interface, promoting the breakdown and fibrosis of the PP matrix and forming a spider-web-like microporous structure centered on spherical PA6 particles. In PA6/PP membranes with PA6 fibers, fewer micropores were generated during longitudinal stretching, but the subsequent transverse stretching violently separated the PA6 fibers, resulting in a dense fiber network composed of PA6 fibers interwoven with PP fibers. Crucially, the PA6/PP biaxial stretching of microporous membranes presented an optimized pore structure, higher porosity, narrower pore size distribution, and better permeability than β-PP membranes. Furthermore, this study explored a new approach to the fabrication of high-performance PA6/PP microporous membranes, with good prospects for potential industrial application.
Parsimonious Tensor Discriminant Analysis

: Discriminant analyses of multidimensional array data (i.e., tensors) are of substantial interest in numerous statistics and engineering research problems, such as signal processing, imaging, genetics, and brain–computer interfaces. In this study, we consider a multi-class discriminant analysis with a tensor-variate predictor and a categorical response. To overcome the high dimensionality and to exploit the tensor correlation structure, we propose the discriminant analysis with tensor envelope (DATE) model for simultaneous dimension reduction and classiﬁcation. We extend the notion of tensor envelopes from regression to discriminant analysis and develop two complementary estimation procedures: DATE-L is a likelihood-based estimator that is shown to be asymptotically efﬁcient when the sample size goes to inﬁnity and the tensor dimension is ﬁxed; DATE-D is a novel decomposition-based estimator suitable for high-dimensional problems. Interestingly, we show that DATE-D is still root-n consistent, even when the tensor dimensions on each model grow arbitrarily fast, but at a similar rate. We demonstrate the robustness and efﬁ-ciency of our estimators using extensive simulations and real-data examples.
An improved user-based collaborative filtering algorithm

The collaborative filtering algorithm[1] proposed by Grouplens[2] is one of the most commonly used methods for personalized recommendation in recommendation systems [3] [4] [5] [6], and the core component of User-based collaborative filtering is the similarity measure. The traditional user similarity measurement method does not consider the influence of factors such as frequent user interest transfer and content popularity degree difference on the accuracy of the algorithm, and the existing improvement strategies cannot comprehensively consider these two factors. Based on the traditional similarity algorithm, this paper introduces influential factors such as user interest decline over time and content popularity, so as to improve the existing user similarity algorithm and to compare the actual data to prove the improved algorithm.
Improved CSMA/CA Algorithm Based on Alternative Channel of Power Line and Wireless and First-Time Idle First Acquisition

Wireless and power line communication hybrid networking can complement each other, save construction costs, and satisfy the needs of different services. Few studies have investigated the MAC layer mechanism when the two networks are mixed. This paper proposes an improved CSMA/CA algorithm suitable for power line and wireless hybrid networking scenarios. The algorithm is compatible with the CSMA/CA algorithm in the existing standards, namely, IEEE 1901 and 802.11, and adopts a two-choice method of the power line and wireless first idle. As long as one of the wireless and power line channels is in the idle state, it can be levied to transmit a data packet. Based on the decoupling hypothesis, this paper establishes a system analysis model of the algorithm, uses simultaneous nonlinear equations to calculate the throughput and collision probability performance of the algorithm, and provides a uniqueness analysis of the solution. This paper also determines the latency of the algorithm. Finally, simulation verifies the effectiveness and reliability of the algorithm model and analyzes the performance in terms of throughput, time delay, and collision probability. Compared with the existing wireless and power line CSMA/CA algorithm, the proposed algorithm can improve the throughput and ensure the effectiveness of the network.
Web Browsing Requirements of Older Adults

This paper presents a study aimed at understanding issues related to older persons' interaction with web browser. The study is a questionnaire-based study that focused on web browser use in general, functions used and assistive features requested. The results revealed that page search was considered the most important function, undesired content was the biggest problem, and stopping pop-up windows was the most requested assistive feature. The results can inform design of ageing-friendly web browser.
Designing Neural Networks Using Novel Water Wave Optimization Based Memetic Algorithm

Searching neural networks' (NN) optimal parameters can be seen as a multi-modal optimization problem. This paper proposes a memetic water wave optimization algorithm (WWOMA) to determine the optimal weights of NN. In the proposed WWO-based MA (WWOMA), we employ WWO to perform global search by both individual improvement and population co-evolution, and then employ several local search components to enhance its local refinement ability. Furthermore, a Meta-Lamarckian learning strategy is utilized to choose a proper local refinement component to concentrate computational efforts on more promising solutions. We carry out numerical experiments on six well-known NN designing benchmark problems, the empirical results demonstrate both the feasibility and effectiveness by applying WWOMA to design NN.
The Effect of Service Quality on Community Satisfaction of Public Service Mall Users in Sidoarjo Regency

This article aims to empirically test the influence of service quality on the satisfaction of the community users of Sidoarjo Regency Public Service Mall. The problem is focused on the quality of service contained in the Sidoarjo Regency Public Service Mall. To approach this problem, theories from Zeithaml, Berry, and Parasuraman are used about 5 (five) indicators of service quality, including tangible, reliability, responsiveness, assurance, and empathy that are analyzed quantitatively. The data analysis techniques applied in this study use instrument validity and reliability tests, variable descriptive statistical analysis tests, inferential statistical analysis tests, simple linear regression analysis tests, determination coefficient (R2) tests, t statistical tests. This study shows that the effect of service quality on the satisfaction of the community of users of the Sidoarjo Regency Public Service Mall gives a significance value of 0,000 and the value is smaller than 0.05. The data showed that the quality of service had a positive effect on community satisfaction, which made Ha accepted and Ho rejected. The results also showed an R Square value of 0.171 or 17.10%. This explains that the contribution of service quality related to the community satisfaction variable is worth 17.10%. While the rest of the calculation is worth 82.90% due to the influence of other factors that are not included in this study.