Miracle-dz/UniFAR-FTUs huggingface dataset:
Convolutional Neural Networks Approach for Music Genre Classification

In recent years, the complexity of music production has gradually decreased results in many people create music and upload their created music to streaming media. The huge music streaming media has caused people to spend much time searching for specific music. Therefore, the technique of quick classification of music genres is very important in today's society. As machine learning and deep learning technologies maturing, the Convolutional Neural Networks (CNN) are applied to many fields, and various CNN-based variants have emerged one after another. The traditional music genre classification requires relevant professional knowledge to manually extract features from time series data. Deep learning has been proven to be effective and efficient in time series data. In order to save the user's time when searching for different styles of music, we applied CNN's advantages and characteristics in audio to implement a music genre classification model. In the pre-processing, we use Librosa is used to convert the original audio files into their corresponding Mel spectrums. The converted Mel spectrum is then fed into the proposed CNN model for training. The majority voting is applied to the decisions made by the 10 classifiers, and the average accuracy obtained on the GTZAN dataset is 84%.
Research of Various Approaches to Forming Book Covers Based on Semantic Text Analysis

the article represents the use of various methods of text analysis in natural language processing tasks and the extraction of key information from it. In addition, various approaches to image generation using machine learning technologies are discussed, as well as the use of various methods for subsequent image styling. The research aims are to develop an algorithm and technology for generating the cover of a printed book based on the analysis of the text of the work. The research is divided into several main stages. The first stage is searching for the necessary information. The next step was to use text analysis methods to extract the main information from it. Next, generating images using machine learning technologies or parsing images for further placement on the cover. In the end, images are edited and stylized. Different approaches were applied for each of the stages, and the results were used to select the most appropriate one.
Hoechst33342 losp primitive hemopoietic stem cells revealed by pyronin Y

Objective. The aim of this study was to determine the function of primitive hematopoietic stem cells (PHSC) at phases G 0 and G 1 of the cell cycle. Materials and Methods. A combination of supravital dyes rhodamine123 (Rh), Hoechst33342 (Ho), and pyronin (PY) was used to isolate the G 0 and G 1 subsets of PHSC. A competitive re- population assay was used to evaluate their in vivo function. Results. We confirmed that the Rh lo LinKitSca-1 � PHSC were relatively quiescent when com- pared with the more mature Rh hi LinKitSca-1 � HSC and Rh hi LinKitSca-1 � progenitors. In addition, cells with Rh lo LinKitSca-1 � , Rh lo Ho lo LinSca-1 � , or Rh lo Ho sp LinSca-1 � pheno- types identified the same cell population. We further subfractionated the Rh lo Ho losp LinSca-1 � PHSC using PY into PY lo and PY hi subsets. Limiting dilution analysis revealed that the fre- quency of long-term in vivo competitive repopulating units (CRU) of the PY lo Rh lo Ho losp PHSC was 1 in 10 cells, whereas there was at least a three-fold lower frequency in those isolated at the G 1 phase (PY hi ). We found a dose-dependent PY-mediated cytotoxicity that at moderate concen- tration affected most of the murine hematopoietic compartment but spared the early HSC com- partment. Conclusion. Our data confirm that the HSC compartment is hierarchically ordered on the basis of quiescence and further extend this concept to PY-mediated cytotoxicity. PY supravital dye can be used to reveal functional heterogeneity within the Rh lo Ho losp PHSC population but is of limited
Evaluation of the recognition of Cobalt-Rich Manganese Crusts based on Deep Learning Networks with physical phantoms

As one of the important submarine mineral resources, Cobalt-Rich manganese Crusts (CRCs) are a kind of crustaceous sediment that could occur throughout the Pacific on seamounts, ridges, and plateaus. As a key step of the exploiting work of CRCs, recognizing areas of the seafloor that consist of CRCs can prevent false conclusions being made over areas that do not consist of crusts. In this paper, we built a Programmable Phased Parametric Array Acoustic Probe (PPPAAP) for the in-situ CRCs recognition task. PPPAAP is designed with the dual-channel for receiving the primary and secondary signal, which lays a foundation for the recognition algorithm based on Deep Learning Networks. Finally, we carry out the preliminary experiment based on the several physical phantoms that have similar acoustic properties with CRCs and the sediments to evaluate the effectiveness of the recognition algorithm.
Lost at Sea: Multi-Searcher Multi-Target Search

This paper addresses the multi-searcher multi-target search problem in open ocean environments. It is a challenging problem as the searchers need to trade-off between the capture rate and capture time while accounting for uncertainties due to dynamic ocean environment in a large search region. We formulate the searcher to target allocation problem as an iterative bipartite task assignment problem with the cost function as a combination of the probability of finding the target and the time to travel to it. Additionally, we implement three search strategies which the searchers dynamically select based on the different phases of their search mission. These search strategies include, inward spiral search, outward spiral search and patrolling the circumference. We validate our proposed multi-searcher multitarget search algorithm in simulation with real-world environmental data for wind and current. Our experimental results suggest at least 98.5% of targets will be found within first 12 hours of the searchers' deployment.
Relative Position Estimation Between Two UWB Devices With IMUs

For a team of robots to work collaboratively, it is crucial that each robot have the ability to determine the position of their neighbors, relative to themselves, in order to execute tasks autonomously. This letter presents an algorithm for determining the three-dimensional relative position between two mobile robots, each using nothing more than a single ultra-wideband transceiver, an accelerometer, a rate gyro, and a magnetometer. A sliding window filter estimates the relative position at selected keypoints by combining the distance measurements with acceleration estimates, which each agent computes using an on-board attitude estimator. The algorithm is appropriate for real-time implementation, and has been tested in simulation and experiment, where it comfortably outperforms standard estimators. A positioning accuracy of less than 1 m is achieved with inexpensive sensors.
A Bed-Exit and Bedside Fall Warning System Based on Deep Learning Edge Computing Techniques

According to the hospital's old adult wards' clinical needs, we propose a deep learning edge computing-based two-stage early bed-exit warning and bedside fall notification system for aged patients with a high risk of falls. The proposed system can effectively help the hospital reduce bed fall incidence and increase bed-exit and bedside fall notification rates. As a result, the rate of injury from falling bed-exit can be reduced.
Investigation of Mobile Machine Learning Models to Preserve the Effectiveness of User Privacy

Machine Learning (ML) has become one of the dominant technologies in the research world. It is being applied without exception in every field where automation and future predictions are required such as cyber security, computer vision, data science, search engines and various other disciplines. The application of ML in search engines creates a high risk of breaching user’s privacy because this involves using data gathered from user’s browsing history, purchase transactions, searching videos and queries. The user’s information gathered from the search engine queries stored in computers, mobiles, other handheld devices is privately uploaded to a centralised cloud location and is then utilised in designing various ML models. As most ML models use a trained model that requires large datasets, user data gathered this way plays an important role in the development of the ML models. This however creates a significant privacy issue for individuals who may not want to reveal their personal information for ML training yet, prevention of this is beyond their access and control. In this article, we focus on the use of ML in mobile devices and address privacy concerns that can be raised by practising ML in mobile devices. The primary area of study in this research is the comparison of ML on mobile devices with ML on the cloud and figuring out its feasibility of becoming an essential ML for preserving user’s privacy. Sequentially, this study first explores the need for using the ML algorithm to address privacy issues. Next, a pre-chosen ML algorithm will be tested on mobile devices and cloud to get the comparison outcome that justifies the adoption of privacy-preserving ML model on mobile devices to preserve the user’s privacy.
Power Exponent Based Weighting Criterion for DNN-Based Mask Approximation in Speech Enhancement

In this letter, a novel weighted mean square error (WMSE) is proposed to improve the DNN-based mask approximation method for speech enhancement, in which the weighting is closely related to the power exponent about noisy spectrum amplitude (NSA) base. The power exponents 0 and 2 separately reflect ideal amplitude masking (IAM) without any clippings and the indirect mapping (IM) on short-time spectral amplitude (STSA), and it is highly related to the enhanced spectrum and the performance of the enhanced signal based on the tests. Also, the experimental results show that the outstanding weighting is the noisy spectrum base with the power exponent 1 for the phase-unaware masking and results in better harmonic structure restoration. The objective function with the WMSE on the NSA (WMSE-NSA) can averagely improve 0.1 on the test of perceptual evaluation of speech quality (PESQ) and 1.7% on the test of short-time objective intelligibility (STOI) compared with the MSE-based mask approximation methods.
Practices for modeling oil shale pyrolysis and kinetics

Abstract Oil shale is one of the largest, relatively undeveloped natural fossil fuel resources in the world and so an important potential source of energy. The organic matter of oil shale is present as a complex combination of carbon, hydrogen, sulfur and oxygen named kerogen. Pyrolysis-gas chromatography-mass spectroscopy affords the opportunity to chemically characterize the main structural skeleton in this kerogen and is a favorable method to study the structural characteristics of kerogen at a molecular level. The thermal degradation of oil shale kerogen is a complex chemical process, accompanied by the wide variety of products obtained, which poses difficulties in the determination of the kinetics and mechanism of pyrolysis. Understanding the kinetics of kerogen decomposition to oil is critical to design a viable retorting process. Comprehensive kinetic data are also essential for accurate mathematical modeling of various oil shale processes. Classic graphical methods cannot unambiguously measure and estimate kinetic parameters due to the mathematical complexity. Advanced isoconversion methods would be appropriate for the calculation of the distribution of activation energies for multiple reactions involved in the decomposition of complex material such as kerogen to products. The range of variability in the principal activation energy is from about 200 to 242 kJ mol−1, with most samples being in the middle half of that range, while the range of frequency factors most likely in the 1012–1016 s−1 range, with most values within the middle half of that range. The review presents the complexity of the oil shale pyrolysis mechanism and pyrolysis kinetics along with the challenges in experimental procedures and modeling of oil shale pyrolysis kinetics.
Design and Simulation of Ultrasonic Triangular Blades for Honeycomb Structures Cutting

Honeycomb materials, composite and aluminum, are widely used in aerospace, automotive and other fields due to their outstanding characteristics such as high strength, rigidity, low density and good thermal insulation properties. At the same time, the processing of honeycomb materials is associated with a number of difficulties, and one of the technologies that allows effectively solving the layout problems for honeycomb materials is ultrasonic cutting. In this work triangular blades for ultrasonic cutting of honeycomb materials with operating frequency 20 kHz with different geometric parameters for different variants of honeycomb structures are analyzed. Eigenfrequency properties are studied for ultrasonic triangular blade’s shape and length and experimental results for a number of deigns are presented.
RSS-Based Cooperative Localization and Orientation Estimation Exploiting Antenna Directivity

In this paper, we propose a factor-graph-based cooperative positioning algorithm that uses RSS radio measurements and accounts for the directivity of the antennas. This is achieved by modeling the directivity with a parametric antenna pattern and jointly estimating position and orientation of the agents. We propose two different approaches whereas the first one uses a continuous representation of the orientation state and the second one a discrete representation. We validate our proposed methods with simulations and measurements in a static sensor network with more than 900 agents in an indoor environment and show that the positioning accuracy can be improved significantly by considering the influence of orientations.
A Comparative Study on Detection of Combustion Vehicles and Electric Vehicles in New Era

To fulfil the Government of India’s vision of 100% e-mobility by 2030, all the vehicles should be replaced by e-vehicles. Adoption of EVs under “Make in India” programme promote indigenous manufacture of electric vehicles and create a roaring market for auxiliary services. Collection of traffic data about E-vehicle in the city is utmost required for well organised traffic management, implementation of smart City, extension of essential services such as use of bus lanes, reservation of parking centres in cities, given concession at toll zones and traffic junctions. To control air pollution caused by conventional vehicle and for sustainable development, promotion of electric vehicle is required at national and international levels. Advanced technologies and awareness on pollution control have given E-vehicle a tag to rapid progress in future mobility. Implementation of Intelligent Transport Systems (ITS) is essential to monitor and get correct data of traffic in real time basis for traffic management, planning and conveyance safety [1]. So, in that situation which is very necessary in future to identify both electric vehicles and combustion vehicles. Keeping that aim in mind we need to detect which vehicle is electric vehicle and which vehicle is combustion vehicle. For that purposes different techniques are currently in use. The aim of this paper is to discuss different techniques/methods available currently to detect electric and combustion vehicles (EVs) and their comparative study to find the superior method of detection.
Analysis of Nonlinear Distortions in Transmitarrays

In this study, an analysis of nonlinear distortions in transmitarrays is presented. The simplified model of a unit cell with PIN diode as a switching element was simulated to estimate the level of generated nonlinear distortions. A 10x10 unit cell transmitarray was analyzed to predict a level of the second harmonic and third-order intermodulation products in the far-field zone. As a result of the calculation, radiation patterns of the transmitarray at frequencies of the 2nd harmonic and IM3 products were obtained.
Multi-Digit Recognition using Image Processing and Neural Network

Digitization has become one of the most important aspects in today’s era. One such application is in the local markets where many vendors still use handwritten bills to provide for the customer. To solve this real time issue the work proposes a new method for digitizing handwritten bills. In the proposed method, image processing and neural networks are used to convert handwritten bills into digital format using image processing and neural networks. The proposed method is very convenient and easy for the vendor as all he has to do is to click a photograph of the handwritten bill.
Research on the Machine Vision Positioning Method Applied in the Docking System

The docking stage is a key for the entering of the AUV into the docking station. In this work, the monocular and binocular positioning methods that can solve the three-dimensional pose information of the AUV are applied during AUV docking process. The accuracy and real-time performance of the two machine vision positioning methods are verified in the pool experiment. Then we combined the two positioning methods for docking experiment of the ROV-shaped AUV. The results show that the monocular and binocular positioning methods can perform well in the docking stage.
Using deep learning improve the aerial engine nondestructive radiographic tests

This paper aim use of deep convolutional neural networks (CNNs) with generative adversarial networks for aircraft engine X-ray cracks image classification and detection posed. On the basis of the CNNs approach requires large amounts of X-ray defect imagery data. Those data facilitate a cracks image segmentation and tracking on multiple defect of aircraft engine defection by edge detection feature extraction and classification process. The use of the deep CNNs approach deep learning model seeks to augment and improve existing automated nondestructive testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers insufficient types of X-ray aircraft engine defect data samples can thus pose another problem in support vector machine (SVM) model accuracy. To overcome this issue, we employ a deep learning paradigm of generative adversarial network such that a pre-trained deep CNNs. We are primarily trained for aircraft engine defect X-ray image classification eight types where sufficient training data exists. This result are empirically show that deep learning net complex with the pre-tuned model features also more yield superior performance to human crafted features on object identification tasks. Overall the achieve result get more then 90% accuracy based on the DetectNet features model retrained with 8 types of composite material defect classifiers.
Broadband Dual-Polarized Endfire Array With Compact Magneto-Electric Planar Yagi Antenna for mm-Wave Terminals

Based on a newly proposed planar Yagi antenna with magnetic and electric dipoles, a compact <inline-formula> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> broadband dual-polarized (DP) array with endfire radiation is designed in this paper. The magnetic Yagi antenna operates on the vertical polarization while the electric one operates on the horizontal one. The compact configuration of <inline-formula> <tex-math notation="LaTeX">$0.5\,\,\lambda _{0}\times 0.63\,\,\lambda _{0}\times 0.09\,\,\lambda _{0}$ </tex-math></inline-formula> benefits from the novel half-mode magnetic Yagi antenna and the arrangement with the electric Yagi antenna. The broadband characteristic comes from the directors of the magnetic and electric Yagi antennas and the reflector of magnetic Yagi antenna. The simulation and the measurement verify this design. The measured common bandwidth of <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -10$ </tex-math></inline-formula> dB for both polarization ports is 23.8–30.1 GHz (23.7%), and the gains vary between 3 dBi and 4 dBi within 24.2–30 GHz band range. The <inline-formula> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> DP array with <inline-formula> <tex-math notation="LaTeX">$0.5~\lambda _{0}$ </tex-math></inline-formula> element space has the beam scanning range of ±43 ° at the endfire direction and the gain of higher than 7.1 dBi. The scanning performances are experimentally validated by two phase-shift feed networks for 0 ° and 30 ° main beam directions. The proposed endfire DP array has the good features of compact structure, a low profile and no clearance requirement, which is easy to integrate in 5G terminals.
USV Path-Following Control Based On Deep Reinforcement Learning and Adaptive Control

This paper presents a guidance and control scheme for an unmanned surface vehicle. The approach combines a deep reinforcement learning based guidance law that can learn the dynamics of vessel with an adaptive sliding mode controller to achieve path-following. The guidance implements a deep deterministic policy gradient algorithm to obtain the desired heading command, whereas the adaptive control drives the heading and surge speed. The proposed guidance has self-learning ability based on evaluative feedback, which does not require any prior knowledge of the dynamic system, and the controller exhibits robustness against bounded uncertainties and perturbations, control gain non-overestimation, and chattering reduction. Simulation results show that the proposed guidance and control law achieves fast convergence and small overshoot, and improved performance when compared against line-of-sight based guidance laws.
Treatment with low-dose sorafenib in combination with a novel benzimidazole derivative bearing a pyrolidine side chain provides synergistic anti-proliferative effects against human liver cancer

Hepatocellular carcinoma (HCC) is one of the most prevalent malignancies and deadliest cancers in the world. Currently, sorafenib is the only drug that has been approved by the U.S. FDA for patients with advanced HCC. However, its improvement on patient outcomes is modest, and the median survival time is only prolonged 2–3 months. In addition, the application of sorafenib is limited because of its high cost and severe adverse side-effects. Therefore, developing more effective novel agents and reducing the dosage of sorafenib are urgently needed for HCC therapy. Here, a novel benzimidazole derivative (4a) bearing a pyrolidine side chain (9a) was synthesized. The treatments of compounds 4a, 9a and sorafenib either alone or in combination on the inhibition of liver cancer cells proliferation were measured using alamarBlue cell viability and trypan blue staining assay. Intracellular signaling pathway activities were assessed by Western blot, Q-PCR and IHC staining. The HuH7 xenograft model was used to examine antitumor activity in vivo. Adverse effects (e.g., changes in body weight, serum parameters, liver function and pathology) of mice treated with 9a were also evaluated. Compound 9a significantly inhibited HCC cell proliferation compared with 4a. In addition, 9a strongly synergized with a low dose of sorafenib in suppressing HCC cell proliferation. Regarding the activities of the signaling pathways, sorafenib did not suppress AKT signaling; however, 9a inhibited AKT and its downstream phosphorylation of p70S6K. In addition, treatment with either 9a alone or in combination with sorafenib led to the inhibition of JNK phosphorylation. However, there were no effects on the inhibition of apoptosis. The in vivo HuH7 xenograft model showed that the administration of 9a plus a low dose of sorafenib significantly decreased expression of the HCC markers α-fetoprotein, glypican-3 and survivin as well as suppressed tumor growth. Finally, there were no adverse effects in mice treated with 9a. In conclusion, co-treatment with a novel benzimidazole derivative bearing a pyrolidine side chain in combination with a low dose of sorafenib exerted significant antitumor activity in preclinical HCC models, which potentially suggests its use as a novel therapeutic strategy for patients with HCC.