Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional Analysis and Trends in Music Development

Today’s world is exploding with new music and attributing to its diversity, it has become the most popular past time ever. Even though there are over ninety seven million songs on the internet, a descriptive study of emotions and trends in music development is still missing. Trends in music development represent how the music industry has grown throughout the years. In this paper, we use the million songs dataset, a publicly available dataset that contains audio features and metadata about a million songs, to dig deeper into the musical trends our world has experienced in the past few decades. By finding out the most popular genres and how their popularity has changed throughout time, we seek to provide concrete evidence of influence of technological advancements on music sector. Sentiments and emotions in songs are analyzed to see which genre promotes more positivity or negativity in its music, providing data to aid current music recommendation systems. Furthermore, we also analyzed the business values of various genres of music to shed some light on which genres are most profitable.
The confinement effect of BNNT nanofillers on the thermal conductivity of PE from molecular simulation

The key point of improving the thermal conductivity of polymer is to increase the orientation of the chains and reduce the disorder conformation. Recently, the nanoring structures are incorporated into polymers to tune the chain dynamics. In this work, the wide diameter (4~7 nm) but short length (~10 nm) BN nantube as nanoring incorporated into polyethylene. The thermal conductivity of nanocomposite can be increased by about 3 times from the nonequilibrium molecular dynamics calculation. Structurally, the polyethylene chains are aligned along the nanoring. In addition, the confinement of nanoring reduces the mobility of polyethylene chains by 40% and enhances the interatomic and interaction, which results in red shift of low frequency phonons. The results provide valuable guidance for improving the thermal conductivity of polymers by doping nanoring.
Design and Analysis of Voltage Control Function Generator Using Highly Linear CCII

In this paper, a voltage control function generator using highly linear second generation current conveyor (CCII) is presented. The current architecture of function generator is based on band pass filter (BPF) which simultaneously provides sinusoidal, square and triangular voltage outputs. The proposed circuit implementation utilizes only five CCII and four grounded capacitors. The tunable frequency range of proposed circuit is 25MHz to 47MHz. All circuits have been evaluated using Spectre simulation in UMC 130nm CMOS technology at 1.2V supply voltage.
Persian Text Based Traffic sign Detection with Convolutional Neural Network: A New Dataset

Recently, traffic panel detection has attracted both academic and industrial attention. However, there are a few works that studied text based traffic panels. This is because there are many challenges in this kind of traffic panels. To obtain an appropriate accuracy in text recognition in the text based traffic panels, we need to detect the panel. Since there is no public text based traffic panels dataset, we collected a new dataset included the Persian text based traffic panels in the streets of Tehran-Iran for the first time. Our dataset contains two sets of figures. The first set has 9294 pictures and the second set has 3305 pictures. The second dataset is more uniform than the first dataset. Therefore, we exploit the first set as an additional dataset and use the second one as the main dataset. Accordingly, we pretrain the network by the additional dataset and train it by the main dataset. We use the tiny YOLOv3 (You Only Look Once version three) algorithm to pretrain, train, and test the dataset. The algorithm is fast and has low complexity. We use K-fold cross validation method to appraise efficiency of the algorithm. From the results section we could see that Precision is 0.973, Recall is 0.945, and Fmeasure is 0.955.
An Enhanced Stability Criterion for Strict Passive Analysis of Digital Filters

The present study establishes a very-strict passivity stability condition for digital filters subjected to external disturbance employing saturation nonlinearities. It is shown that proposed criterion is more relaxed than a few existing works in the literature. When external disturbance goes to zero, a result for asymptotic stability of the digital filter employing saturation overflow arithmetic is made available. The established conditions are in linear matrix inequality framework, hence easily tractable. Numerical examples are put in order to demonstrate the efficacy of the formulated stability conditions.
DOA Estimation with High Precision for Multiple Mainlobe Jammings

With the increase of the spatial degree of freedom (DOF), the radar system achieves multi-channels data processing and improves the survivability of the radar in the complex jamming environment. When the direction of arrival (DOA) of the jamming is close to that of the target, the conventional method will be ineffectiveness. In this paper, a DOA estimation method with high precision for multiple mainlobe jammings based on sparse reconstruction is proposed. The steering vectors are estimated based on the maximize signal-noise-ratio (MSNR) criterion. Besides, the oblique projection with ℓ1-norm singular value decomposition (OP- ℓ1-SVD) is performed to accomplish the jamming suppression and DOAs estimation of the target. The proposed method can be applied to the uniform linear array (ULA) model of a practical radar system and can resist at least two mainlobe jammings.
PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications

Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named “Probabilistic Evolution based Version Recommendation Model (PEVRM)” integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple’s iTunes App Store3, for revealing its promising performance.
The Role of Filler Interface in Suppressing the Erosion of Silicone Rubber Under DC Voltage

The presented paper aims to highlight the role of fillerpolymer interface on the DC erosion resistance of silicone rubber (SiR) outdoor insulators. SiR composites filled with nano-sized boron nitride and fumed silica have been prepared in this study. Electrical and thermal experimental tools of the inclined plane tracking-erosion test, simultaneous thermogravimetric-differential thermal analyses, and thermal conductivity measurements are used to analyze the prepared nanocomposites. The analysis primarily investigates whether the composite’s enhancement of thermal conductivity or filler interface would be more influential on erosion suppression. Literature reports that the boron nitride filler improves SiR’s thermal conductivity, whereas fumed silica provides an exceptional interaction with SiR at the filler interface. SiR was filled with boron nitride at 5wt% (percent by weight) and 10wt%, while fumed silica was loaded with 5wt% and used as a reference composite in the study. The obtained results clearly indicate a superior erosion resistance of the silica filled SiR over the boron nitride filled one. An observation which seemingly could give more significant role of the filler-polymer interface on the erosion resistance of SiR used for high voltage DC outdoor insulators.
A Machine Learning Based Bike Recommendation System Catering To User’s Travel Needs

Bike sharing system has emerged as a popular transportation system. Each ride accumulates data which includes bike id, trip information, station information, etc. Prevailing weather conditions also play a major role in bike sharing systems. This paper attempts to infer from the public bike sharing dataset and weather data to help the riders in selecting a suitable bike for their travel demands. For this, we propose a novel bike recommendation system using machine learning techniques to cluster bikes with similar behavioural patterns and predict the best cluster for user’s travel needs. A strategy is further proposed to identify actively used bikes in predicted cluster. We evaluate the proposed method using real world bike sharing dataset from Divvy Bike System at Chicago, North America. Results show that the method effectively infers bike trip patterns along with the weather conditions to suggest the best cluster of bikes suitable for every individual trip.
Fast and Accurate Genomic Minisatellites Disclosure

Minisatellites are genomic sequences comprised of short monomers which are successively repeated many times in the same direction. They are highly variable sequences in terms of their monomer building blocks, number of repeats, and their location on the genomic sequences. This mutability has made them excellent genetic markers for both linkage analysis and forensic science. This paper presents an accurate and highly efficient computer method for identifying all minisatellites in a given DNA, gene, or genomic sequence. It is based on a new indexing method which is intelligently used in such a way that does not use any main memory or secondary storage for storing search-keys. Furthermore, for each search-key value, which is not stored, a pointer points to a list occurrences of the search-key value in the sequence. Potential minisatellites are detected from these lists and actual ones are recognized. The software is capable of discovering all minisatellites of very large sequences such as human genome with 3.2 Giga base pairs in a very short time. The minimum number of repeats of the motif is set to be 3. An advantage of the software is in the detection of overlapped minisatellites that some state of the art software cannot detect. With respect to the time, looking for minisatellites with the proposed approach would be faster than TRF, Mreps and Dot2dot.
A method for domestic audio event recognition based on attention-CRNN

To monitor the equipment in the power grid’s communication room intelligently, so as to find the equipment operation failure in time, a method for domestic audio event recognition based on attention-CRNN is proposed. The method is divided into 3 steps: (1) Extracting Mel Frequency Cepstral Coefficients (MFCC) from audio data; (2) Using convolutional neural network (CNN) to extract audio features, and using this feature as the input of a recurrent neural network Bi-GRU (Bidirectional Gated Recurrent Unit), and assigning attention weights to the features in time series; (3) Putting the time series features into the fully connected layer for forward propagation, using the Softmax function as classifier and the cross-entropy as loss function, and using adam gradient descent method to reversely update the model weights. We use the international competition DCASE 2018 Task 5 domestic audio data set for experiments. The experimental results show that the f1_score value of this method is 89.6%, which is an increase of 3.9% compared to the 85.7% of the official baseline given by the competition.
Predicting the association between fine-grained attributes and black-boxed perceptual performance*

Per-image performance of a certain visual perception algorithm is the combination of per-task performances in the image. Based on the black box test, we address to discover and explain the potential shortness of the evaluated intelligent algorithms/systems at the fine-grained task-level by human knowledge. By assuming the domain knowledge in visual tasks could be represented by a latent vector which is a sparse embedding of the catenated object-level and image-level features, we propose a latent dictionary learning framework for joint latent knowledge representation and knowledge-output regression at task level. In this way, so we can use semantic concepts to explain the relationship between test cases and test results. The experiments validate the idea of task-level explainable AI evaluation initially as well as the effectiveness of proposed method.
Exposing DeepFakes Using Convolutional Neural Networks and Transfer Learning Approaches

Advancements in Artificial Intelligence – oriented computing power and the ever-growing reach of social media have proven to be catalysts in emergence and spread of a new vein of AI generated fake videos known as ’DeepFake’ videos. Such videos are synthesized using generative machine learning models like Generative Adversarial Networks or Variational AutoEncoders and they can achieve high degrees of realism. Spread of sensitive political or obscene content in form of such videos may lead to social distress to the target entity(s). This paper presents a study pertinent to the detection of DeepFake videos using Convolutional Neural Networks (CNNs) with transfer learning. A comparative study of the performance of various models in the detection of tampered videos has been presented. These models are trained (fine-tuned) and tested on a custom dataset encompassing randomly selected labelled frames from videos in the DeepFake Detection Dataset by Google AI and FaceForensics++ dataset.
Modeling Android Security using an Extension of Knowledge Discovery Metamodel

Due to the exponential growth of the number of mobile users and applications, mobile application development, especially on the Android platform, has received increasing interest in recent years. Since many applications interact with sensitive data, the security aspects of an Android application is attracting growing attention from the research community. However, security aspects have been underestimated by many existing Android applications, and they may need to be redeveloped to include essential security factors. Taking advantage of Model Driven Reverse Engineering (MDRE) into account, we can employ Knowledge Discovery Metamodel (KDM), the most popular metamodel for reverse engineering, to model Android application enhancement. Since KDM lacks Android concepts, it is unable to represent all the details of an existing Android application. To the best of our knowledge, no extension of this metamodel is proposed for modeling the security aspects of an Android application. To address this problem, in this paper, we introduce a heavyweight extension of KDM for the security aspects of the Android application. Our experimental results show that the extended metamodel will facilitate analyzing the security issues of an existing Android application.
Integrated Access Backhaul Node supporting 5G and IoT Access

BHARATNET, the world's largest rural broadband project, aims to provide broadband connectivity to 2.5 lakhs gram panchayats across India. In chaining with BHARATNET OFC installation, FCC has created a runway for millimeter-wave researchers to launch 5G technologies with fiber-fast capabilities to reach broadband to end home users. The IAB technology is further formulated and standardized by 3GPP in Release 16 and 17 for the realization of FCC's creation of the use of millimeter-wave for small cell deployment. Full duplex (FD) technology is the promising technology for IAB with improved capacity but at the cost of Self Interference (SI). SI needs to be cancelled to improve system performance and reception of Signal of Interest (SOI). Dynamic spectrum sharing (DSS) is also helping a lot for improving system performance. The authors are engaged in development of 28 GHz IAB communication system with FDD mode and FD Mode.
Coordinated Control of Voltage and Frequency in a Three-Area Multisource System Integrated with SMES Using FOI-FOPDF Controller

This paper highlights the significance of superconducting magnetic energy storage (SMES) device and non-linearity constraints on the multi-area multisource interconnected power system. The fractional-order integral minus fractional-order proportional-derivative with filter (FOI-FOPDF) controller for the automatic load frequency control loop and proportional-integral-derivative with filter controller for the automatic voltage regulator loop is proposed. The controller parameters are optimized at nominal system loading by a new metaheuristic algorithm name as artificial flora algorithm (AFA). The proposed controller (FOI-FOPDF) is superior to the traditional controller in terms of peak overshoot, undershoot, and settling time. The dynamic response of system is enhanced in the presence of SMES and deteriorated due to the system non-linearities. The sensitivity analysis reveals that the optimized controller parameters at nominal system loading does not need to be reset repeatedly for a wide variation of the system loading. Moreover, it is also observed the supremacy of AFA by its convergence.
Diagnosis of Parkinson’s Disease using Computer Aided Tool based on EEG

Parkinson’s Disease (PD) is a disorder affecting cortical and sub-cortical regions of the brain. The EEG recorded from these regions show variations in their patterns in patients with PD compared to that of healthy controls. Initially, Kolmogorov Complexity, Sample Entropy and Approximate Entropy are extracted from the epochs of 20 EEG electrodes collected from 18 subjects. These features are fed to the Back-Propagation Multi-Layer Perceptron (BP MLP) Neural Network and trained to perform diagnosis which results in 90% Accuracy, 100% Precision, 83% Sensitivity and 100% Specificity. Secondly, the above features from a set of EEG electrodes Fz, F1 and F2 that are placed close to cortical regions are considered and are fed to the BP MLP Neural Network, resulting in 97% Accuracy, 100% Precision, 96% Sensitivity and 100% Specificity. Therefore, PD can be effectively diagnosed by EEG readings recorded close to cortical regions thereby reducing the storage space and computation time.
Driver Drowsiness Detection using Knowledge Distillation Technique for Real Time Scenarios

Deep Learning algorithms have proven to be highly accurate for feature detection tasks in images. However, the large size of the deep learning models makes it very difficult for them to be deployed in real time applications because of the high memory consumption and delayed response. Detecting driver drowsiness poses a need for high accuracy, along with high speed and lesser memory requirement. In order to utilize the highly accurate deep learning models for solving the problem, we incorporate compression of deep learning network using knowledge distillation technique. Using knowledge distillation technique, we train the small size student network using the complex teacher network to increase the accuracy of the student network. A modified version of VGG19 is incorporated as teacher network involving 85 million parameters and student network is a reduced VGG-16 network having only 2 million parameters, which finally gives an accuracy of 95% on the ZJU dataset.
Stock Market Reaction to Chief Data or Digital Officers Appointments

The chief digital or data officer (CDO) manages big data, analytics, and new information strategies in organizations. CDO appointments are strategic, and we ask the question on whether announcing such appointments affect a broader market reaction. Our results show that although the stock market reaction is generally neutral to newly created CDO positions, it does react positively under certain conditions. The reaction is more positive when firms have strong growth prospects and do not have existing chief information officers (CIOs). The market also reacts positively to an outsider CDO when CIOs are absent. Using these findings, we argue firms need to effectively communicate the need and value of CDO positions and to ensure that they do not muddle CDO and CIO responsibilities.
Single-Wavelength and Single-Photodiode 700 Gb/s Entropy-Loaded PS-256-QAM and 200-GBaud PS-PAM-16 Transmission over 10-km SMF

We demonstrate the transmission over 10.2-km SMF of intensity-modulated and direct-detected (IM-DD) signals at record 700.4 Gb/s line rate and record 538.8 Gb/s net rate with entropy loaded PS-256-QAM signals, and 200-GBaud PS-PAM-16 signals with 650 Gb/s line rate and 484.5 Gb/s net rate.
50Gb/s Hybrid Integrated Si-Photonic Optical Link in 16nm FinFET

We present an EAM based single-mode optical link. The TX uses over-peaking to improve modulation efficiency and relax TIA's noise requirement. The RX uses a 3-stage TIA with T-coils to improve bandwidth. The link achieves $BER < 10^{-12}$ while consuming 4.31pJ/bit at 50Gb/s with 2dB link margin.
A Reverse Optimization Algorithm for the Thermoseed Mediated Magnetic Induction Hyperthermia Preoperative Treatment Plan

With a lot of researches focusing on hyperthermia treatment technology, thermoseed mediated magnetic induction hyperthermia (MIH) has the potential to become a clinical intervention for tumor. While, quantitative thermoseed distribution in tumor is a key technical problem for thermoseed mediated MIH treatment plans. In order to solve this problem, a reverse optimization algorithm (ROA) is proposed to use a minimum number of thermoseeds to achieve the desired temperature distribution which meets the treatment needs. An improved simulated annealing algorithm is applied to generate conformal thermoseed distribution with that the tumor achieves irreversible thermal damage and the temperature distribution in normal tissues is controlled in a safe range. Because of the specialty of the thermoseed distribution problem, initial temperature, stop condition, evaluation function, annealing strategy, and specific calculation steps are redefined. Finally, computer simulation was used to model the tumor region and generate the thermoseed implant model as well as the temperature distribution. According to the application in MIH planning system, the feasibility of ROA to solve the problem of thermoseed distribution was verified. This research can promote the application of the magnetic induction hyperthermia computation method to clinical trials, which will improve the safety, efficacy, and accuracy of clinical trials in the future.
Machine Learning at Resource Constraint Edge Device Using Bonsai Algorithm

In the worldwide billions of devices connected each other to interact with the surrounding environment to collect the data based on the context. Using machine learning algorithm intelligence can be incorporated in these Internet of Things (IoT) devices to get valuable insights from these data for accurate predictions. Machine learning model is deployed onto the devices for making the decisions locally. This enables fast, accurate prediction within few milliseconds by evading data transmission to the cloud and makes perfectly applicable for real time applications. In this paper, the experiment is conducted with publicly available dataset with Bonsai algorithm. This algorithm is implemented in Linux environment with core is processor in python 2.7 and achieved 92% accuracy with model size of 6.25KB, which can be easily deployed on resource constraint IoT devices.