Miracle-dz/UniFAR-FTUs huggingface dataset:
MPop600: A Mandarin Popular Song Database with Aligned Audio, Lyrics, and Musical Scores for Singing Voice Synthesis

The purpose of singing voice synthesis (SVS) is to generate human-like singing voice from lyrics and the corresponding musical score. Nowadays, mainstream SVS approaches rely on neural networks (NNs) which can map linguistic and musical contextual factors to acoustic features for producing audio outputs. For SVS in Mandarin or other Chinese languages in particular, a sufficiently large and adequately labeled database has not been publicly available. To proceed with Mandarin SVS research, we built a singing voice database from scratch, with 600 pop songs sung by 2 male and 2 female vocalists. Each audio contains single vocal only, without any background music. This paper describes the recording of the dataset and necessary steps of data preprocessing for training NNs to perform SVS. Several simple neural network architectures were adopted so preliminary SVS performance can be compared. Both subjective and objective evaluations show that these networks could learn from the MPop600 database to generate singing voice with unseen musical scores. MPop600 is available in both the MIDI and the MusicXML formats. In the future, we believe that more advanced and recently developed networks can be applied to model the singing behaviors in this database and help advance research in Mandarin SVS.
Direction-of-arrival Estimation of Wideband Array with Differential Focusing Under Colored Noise

To solve the problem of poor accuracy and poor robustness of DOA estimation of wideband array in colored noise, this paper proposes an effective solution based on matrix difference algorithm and eigenvector space focusing algorithm. Firstly, the algorithm decomposes the difference covariance matrix, and takes the eigenvector corresponding to the positive eigenvalue part to reconstruct the observation model to eliminate the influence of colored noise and “false peak”. Secondly, for the new observation model, a new signal autocorrelation matrix is constructed, and the focusing matrix is solved based on the eigenvector signal subspace at different frequency points. Finally, according to the eigenvalue sequence, the eigenvector matrix is rearranged to reconstruct the focusing matrix. Theoretical analysis and simulation results show that the proposed method has high accuracy and good robustness, and does not need angle prediction.
An Exploratory Machine Learning Technique for Investigating Intrusion in Wireless Sensor Networks

Intrusion Detection System (IDS) is an efficient defense method against cyber-attacks in Wireless Sensor Networks (WSNs). A Novel paradigm of the Machine Learning technique called the Radial Bias technique has emerged recently for the improved detection rate and reduced false positives of intrusion over existing decision tree techniques. Malicious activities used in this work are Sleep Deprivation and Sinkhole attacks. Radial Basis Function (RBF) classifiers can detect novel attack types. The Radial Bias Function is used for timely and accurate detection of malicious activities. In this work, the performance of the Proposed Intrusion Detection System has been recorded in terms of an empirical result. The proposed RBF system's empirical results are more accurate than decision tree techniques.
Attention Aware and Multiple Granularity 3D Convolutional Neural Networks for Lung Nodule Classification on CT Image

The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules. In this paper, a novel 3D CNN is proposed to classify lung nodule. To make the feature map towards the most informative components of an input, spatial attention and channel attention are integrated with the 3D CNN backbone. To combine global and local information, a multiple granularity architecture is designed. We conduct a binary classification (benign and malignant) on two computed tomography (CT) datasets (public Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI) and private LIDC-TX dataset). It achieves a sensitivity of 0.962, specificity of 0.942 and error rate of 0.048 on LIDC-IDRI database. For the private LIDC-TX dataset, sensitivity, specificity and error rate are 0.718, 0.627 and 0.327, respectively. Experimental results demonstrate that the proposed method get a promising result compared with existing lung nodule classification methods.
Excitation Retrieval for Phased Arrays With Magnitude-Only Fields Measured at a Fixed Location

An excitation retrieval method designed for phased arrays using magnitude-only field data is proposed. The reconstruction of complex excitations is formulated as a phase retrieval problem. After converting the original quadratic constraint to a linear one, a semidefinite convex optimization method is applied to calculate the solution. By controlling the excitation phases of array elements via phase shifters, independent and phaseless field data can be provided. Required conditions of the optimization method are satisfied in a proper way at the same time. Owing to this control strategy, it requires the position-fixed receiving probe only. The measuring process can be further simplified and probe shifting errors can be avoided.
High Ability of a Reliable Novel TFET-Based Device in Detection of Biomolecule Specifies—A Comprehensive Analysis on Sensing Performance

Dielectrically-modulated Tunneling-Field-Effect-Transistor (TFET) based biosensor has been given a major deal of interest since it promises a high sensitivity but low current during the detection process. This paper has proposed a novel biosensor based on the TFET configuration to effectively increase both the sensitivity and current while conjugating the biomolecules. The proposed biosensor is configured by SiGe source along with the n+ doped pocket between the source and the channel region. Indeed, the material and structural engineering lending to modification of the bandgap and tunneling length have been introduced as the effective solutions that will help the biosensor detecting the biomolecule samples by high sensitivity which is immune to the possible noise. The proposed structure has been compared with a traditional biosensor and also a recently proposed biosensor including only a doped pocket inside the channel. Regarding the definition of the sensitivity of biosensor in term of the drain current, after performing theoretical analysis and making the comparisons, the proposed biosensor got a high sensitivity (76) and raised current (1.8E- $10~\mu \text{A} / \mu \text{m}$ ) during the hybridization of the biomolecule as compared to other structures. Also, practical issues in the cases of the TAT Trap-Assisted Tunneling (TAT) component and partial fill factor revealed that the proposed biosensor can be good selection. Biotin-Streptavidin binding as a specific biomolecule has been studied in order to show the power of the suggested biosensor in the detection. All the obtained results revealed that the proposed structure in this paper can be introduced as a suitable and excellent replacement for other counterparts.
Separation of superimposed PRPD Patterns by Signal Clustering

Partial discharges are one of the main reasons of insulation failure in transformers. Multiple partial discharge sources in HV equipment can be active at the same time. Diagnosis is complicated by superimposed or overlapping patterns in the PRPD patterns. In order to classify the fault source correctly, the patterns must be separated first. In this paper we propose a method to separate partial discharge signals from different sources by their impulse waveform. A feature-set is constructed by selecting relevant features from a pool of 700 identified possible features. Then, different sources are separated by clustering techniques. Centroid-based clustering methods like the k-means algorithm are compared to density-based approaches like DBSCAN. After clustering multiple PRPD patterns can be calculated – one for each source. This method is developed on artificial partial discharge data, measured in the laboratory. Four different typical defect types are considered. Through multiple case studies we show, that the separation process works on different kinds of PD problems. This method works for all kinds of PD faults. It is shown, that PD sources not present in the training data used for development can also be separated reliably.
Extraction of DNA by Reversed Micelles

Deoxyribonucleotide (DNA) from salmon testes was successfully extracted by reversed micelles that are formed by a cationic surfactant in isooctane. The degree of extraction of DNA was strongly influenced by the type of surfactant in the organic solvent and ionic strength in the material aqueous phase. The driving force of the DNA transfer was electrostatic interaction between cationic surfactants and negatively charged DNA. Cationic surfactants possessing two long-alkyl chains are one of the best candidates for the successful transfer of DNA from an aqueous solution to a reversed micellar solution.
Transfer Learning for Subject-Independent Stress Detection using Physiological Signals

The main objective of this work is to investigate the role of transfer learning for enhancing the performance of subject-independent stress detection models. Using Time Series Feature Extraction on basis of Scalable Hypothesis tests (tsfresh), features are extracted from raw Electrocardiogram (ECG) and Electrodermal Activity (EDA) data and Convolutional Neural Network (CNN) models are built for stress detection. We used benchmark datasets-ASCERTAIN and CLAS to conduct our experiments. The performance of CNN models (subject-independent models) are compared with and without transfer learning. Results show a 1-4 percentage (absolute) increase in transfer learning, so these results serves as the basis for future studies in this direction.
A Large-Scale Application Mapping in Reconfigurable Hardware Using Deep Graph Convolutional Network

Reconfigurable Computing (RC) systems are capable of hardware implementation for processing speedup with different reconfiguration features. They are key elements in nowadays High Performance Computing (HPC) systems with enormous demand of application execution. This paper aims to reduce the compilation time of RC applications by providing a hierarchical model in the mapping part. In this model, the application graph is clustered by using a Graph Convolutional Network (GCN). Merging information of neighborhood nodes in the layers of GCN, the network is trained to classify the nodes into least dependent clusters. To reduce the heavy computations of mapping operation, it is performed in independent steps, inter-cluster, and intra-cluster mappings. Intra-cluster mapping organizes logic blocks in small regions and inter-cluster mapping places these regions in the implementation area by using an average distance metric. Simulation results showed that high-quality solutions for the mapping problem have been achieved faster in comparison with previous works.
A Comparative Evaluation of the Deep Learning Algorithms for Pothole Detection

Potholes are a menace on roads and their presence compromises the safety of both drivers and pedestrians. In most developing countries, it is one of the major reasons for road accidents and loss of human life and property. Therefore, there is a need to consistently collect and update the data on the latest road conditions, so that the drivers can be advised for alternate routes and the concerned Government department can take immediate measures to fill up the potholes for the benefit of the commuters. A simple and efficient way to detect potholes on roads is through the application of object detection algorithms on images acquired from a smartphone camera. Therefore in this paper, we focus on evaluating the performance of state-of-the-art neural network algorithms such as YOLO and Faster R-CNN with VGG16 and ResNet-18 architectures for pothole detection that is both fast and accurate. Further, an improved YOLOv2 architecture is proposed to solve the class imbalance problem of "pothole" and "normal road" classes, and its performance is compared with other object detection techniques using precision, recall, intersection over union, and the number of frames processed per second (FPS). The results showed that the modified YOLOv2 architecture outperformed all the considered models with the lowest number of parameters (35 million) and the highest FPS (28), precision (0.87), and recall (0.89). This model can be deployed in autonomous vehicles for real-time geotagged pothole detection from photographs or video streams. The pothole detection application can also suggest potential alternate eco-friendly routes and guide the commuters in low light navigation.
Low Complexity Method for Parent Selection based on Rank Increase Enhancement in RPL routing protocol

6LoWPAN is regarded as the most prominent stack of protocols used for the Internet of Things or IoT and RPL has confirmed itself as the most commonly used network layer protocol for 6LoWPAN. But still, there are dozens of subjects open to research in RPL. One of these issues is the proper route establishment in a network of nodes. This task is preliminarily done by Objective Functions like OF0 and MRHOF. However, these OFs do not seem enough sophisticated since they do not take all the network metrics into consideration and consequently cause fast energy depletion of nodes or poor QoS properties. In this piece of research, we propose a method based on Fuzzy logic to modify the Objective Functions OF0 and MRHOf with regard to almost all the network parameters involved. Then our method is tested and simulated by the Cooja simulator that is based on Contiki OS to prove its effectiveness in comparison to the original OFs. By the results obtained from the simulations we show that our Fuzzy model has helped prolonging the life span of the networks by reducing energy consumption and other network properties like Throughput and PDR have also been improved.
A Design Framework of Automatic Deployment for 5G Network Slicing

Differential services driven user-end and operatorend challenges have been the main driving forces behind the 5G network, which is well perceived as an innovative platform for digital convergence of information, control and management. With both the network slicing (NS) and service slicing (SS) technologies, precious physical resources can thus be shared among multi-tenant mobile virtual network operators, such as over-the-top (OTT) service providers. This paper proposes a three-stage design for automatic slice deployment called LMA, namely (1) LCP: local charm provision for VNF services, (2) MSP: model-based slice planning for service chaining, and (3) ASD: automatic slice deployment for flexible and virtual resource allocation. The LMA is a model-based slice-specific platformneutral design framework for deploying the NS and SS, not only automatically deployable on both the x86-based desk-top computers and data-center bare-metal servers, but also on public or private clouds, as well as the 5G mobile edge. Our design framework adopts the Juju-as-a-Service and EurecomMosaic5G software technologies, where several customizable virtual network function (VNF) components can be flexibly chained together to form a desired NS or SS. This paper studies and presents two successful deployment showcases: a web-blogdatabase based SS and a virtual-evolved-packet-core based NS. Our preliminary results of performance benchmarking show a strong effect of the number of CPU cores on the average latency response of SS, in particular during congestions caused by concurrent user requests.
A Review of Neuromarketing Techniques and Emotion Analysis Classifiers for Visual-Emotion Mining

“People do well to express what they want, what they like, or how much they will pay for something. But they do not really know where that value comes from, or how and when it is influenced by such things as store demonstrations or products. ” With the growing trend of studying consumer behavior, a study called neuro-marketing can be applied on previous findings related to neuroscience to get an idea of marketing strategies. To obtain feedback about a product from various e-commerce sites sentiment and opinion mining of neurons can be carried out. This helps a user to predict emotions, to extract opinions from the internet and predict customer's preferences by applying various neuromarketing techniques. The paper provides a review of various neuromarketing and emotion analysis techniques to study marketing behavior.
An Angle Parameterization Method for the Perspective-n-Point Problem

This paper presents an angle parameterization method for the Perspective-n-Point (PnP) problem to improve efficiency. Via establishing two intermediate frames, the rotation matrix is represented by two angle parameters. Representing the translation vector by parameters in the rotation matrix, a nonlinear least-squares minimization problem is formulated. One angle parameter is solved by a seventh-order polynomial. Another angle parameter is then solved by a fourth-order polynomial. After retrieving the rotation matrix and the translation vector by these two angle parameters, one-step Gauss-Newton iteration is employed. This angle parameterization technique reduces the computational cost while the one-step Gauss-Newton iteration ensures the accuracy. Compared against the state-of-the-art methods, experimental results with synthetic data and real images indicate that the proposed method offers comparable accuracy but higher efficiency.
Palladium Complexes Bearing Novel Strongly Bent Trans-Spanning Diphosphine Ligands: Synthesis, Characterization, and Catalytic Activity

The new air-stable chelating diphosphine ligands 1,8-bis(diisopropylphosphino)triptycene (2) and 1-(diisopropylphosphino)-8-(diphenylphosphino)triptycene (3) were synthesized in 51−73% yield from readily available starting materials. The examination of their coordination modes in the palladium(II) complexes [PdCl2(2)] (4) and [PdCl2(3)] (5) by means of 1H, 13C, and 31P NMR is consistent with the formation of trans-spanned complexes. X-ray analysis of 4 and 5 confirmed the structure but disclosed a strong distortion of the palladium centers from the expected square-planar geometry toward a butterfly-like environment. Further investigation revealed that 2 can also behave as a binucleating ligand, forming the quasi-closed halogen-bridged dipalladium complex 6, featuring an unusual nonplanar Pd2(μ-Cl)2Cl2 core. The catalytic activity of the new ligands and complexes was tested in palladium-catalyzed cross-coupling reactions of aryl chlorides with phenylboronic acid.
Survey and Evaluation of RGB-D SLAM

The traditional visual SLAM systems take the monocular or stereo camera as input sensor, with complex map initialization and map point triangulation steps needed for 3D map reconstruction, which are easy to fail, computationally complex and can cause noisy measurements. The emergence of RGB-D camera which provides RGB image together with depth information breaks this situation. While a number of RGB-D SLAM systems have been proposed in recent years, the current classification research on RGB-D SLAM is very lacking, and their advantages and shortcomings remain unclear regarding different applications and perturbations, such as illumination transformation, noise and rolling shutter effect of sensors. In this paper, we mainly introduced the basic concept and structure of the RGB-D SLAM system, and then introduced the differences between the various RGB-D SLAM systems in the three aspects of tracking, mapping, and loop detection, and we make a classification study on different RGB-D SLAM algorithms according to the three aspect. Furthermore, we discuss some advanced topics and open problems of RGB-D SLAM, hoping that it will help for future exploring. In the end, we conducted a large number of evaluation experiments on multiple RGB-D SLAM systems, and analyzed their advantages and disadvantages, as well as performance differences in different application scenarios, and provided references for researchers and developers.
A Hybrid Framework for Detecting Repackaged Applications on the Android Market

Smartphones and tablets have become part of our daily lives. These devices run on two major competing software platforms: Apple’s iOS and the Google’s Android OS. The popular software design has implemented applications which are purchased by end users. Android apps are mostly purchased through the Google Play Store, while iOS apps are available on Apple’s App Store. The apps exclusively have given birth to a new market as well, attracting all kinds of interested users, hackers and cybercriminals. In this paper, we focus on the Android software being more susceptible to attacks through the purchase or download of malicious apps, also known as “repackaged apps. ” These are applications containing malicious code and phishing malware that are visually similar to the original app that the end user intended to purchase. The objective of this research is twofold: Our first research goal is to analyze the three state of the art malware detection mechanisms and identify their known vulnerabilities that can be exploited by the existing threats. Our second research goal is to develop a hybrid malware detection framework by combining the strong features of the preexisting schemes. In addition, we show the practicality of our proposed framework by presenting three case studies that demonstrate how different components of our hybrid solution will work together to maximize the malicious-code detection in repackaged applications.
New Politifact: A Dataset for Counterfeit News

Fake news is a fictitious article that is intentionally written to deceive people. So it is difficult to detect fake news based on the content of the news article. Online platforms publish these fabricated stories to make more money by enhancing the number of readerships. There is a need to detect fake news as early as possible. The lack of a standard dataset makes this task more challenging. So, In this paper, we proposed a new dataset that is scraped from Politifact websites. This dataset is larger in size in comparison to the existing datasets. We conducted extensive experiments by training state of the art machine learning and deep-learning algorithm. The results depict the effectiveness of our proposed dataset.
PI and Fuzzy Logic Control of Single Phase Grid Connected Inverter Serving Two PV Panels

In this paper, two serially connected solar photovoltaic panels operating under different irradiance and temperature levels are tied to the grid with a one phase transformerless inverter. The proposed method has been simulated in MATLAB with Proportional Integral Control and Fuzzy Logic Control scheme. Results of the two controllers has been compared with parameters such as PV voltage, PV current and PV power. The THD% for the grid current has also been compared for the two control schemes.