Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Analog Circuits Based on Graph Convolution Network

The determination of circuit type is an important prerequisite for automatic design of analog circuits. However, the same type of circuit will have some variants due to different design requirements or designers. In this paper, a graph convolution network framework used for analog circuit classification is proposed, which can effectively identify circuits and their variants. First, we convert the analog circuit netlist into the data of graph structure, and the circuit information is represented by features. By converting the data of graph structure into Fourier domain, we can convolute the circuit graph and propagate it linearly, and extract the characteristics of the circuit to identify the circuit type. We performed experiments on the data set of analog circuits, and the experimental results showed that the proposed graph convolutional network based method achieved the promising performance in identifying types of circuits and their variants.
Dual regularized feature extraction and adaptation for cross-subject motor imagery EEG classification

Bioinformatics theoretical-based methods have attracted great attention in rehabilitation-assisted motor imagery (MI) brain-computer interface (BCI) using EEG, and gained promising results to deal with cross-subject MI-EEG classification. However, most of the existing methods are either suffered from outliers during feature learning across subjects or inefficient in computing discriminative features. Thus, they may not be able to obtain an optimal feature representation across subjects, resulting in classification performance deterioration. This paper proposes a dual regularization-based MI-EEG feature learning framework, in which feature weighted common spatial pattern, joint probability distribution alignment, and minimum Mahalanobis distance are taken into account, thus facilitating cross-subject classification. Specially, we adopt the multi-source to single target domain adaptation strategy, which is more suitable for real-world MI-BCI scenarios. Empirical studies on three benchmark MI-EEG datasets reveal the effectiveness and efficiency of the proposed method, which achieves a great performance improvement with less time consumption.
Speech Emotion Recognition using a Novel Feature Vector based on Voiced Probability and Speech Characteristics

Emotion recognition in speech is one of the fast growing fields in artificial intelligence and machine learning. Feature selection is an essential process for speech emotion recognition and hence in this study a feature vector is proposed which is based on various characteristic of speech such as Zero Crossing rate, Fundamental Frequency and Mel Frequency Cepstrum Coefficients. The novelty that is introduced in this feature vector is using Probability of whether a frame is voiced or not This probability is concatenated with the rest of vector. The feature vector was tested on various classifiers such as CNN, SVM, Decision Tree, Logistic Regression. The result showed that the novel feature vector performed better as compared to conventional features used which is MFCC and its derivatives. Besides testing this feature vector against MFCC and its derivatives, feature vector (combined with a classifier) was also tested against other system of SER based on ML. The results showed that combination of feature vector and CNN were good and even outperformed some state of art techniques.
Evaluating the Level of Electromagnetic Interference Generated by the ESD Source in the TEM-Cell

We evaluated the induced electromagnetic interference (EMI) on a monopole and a microstrip line (MSL) generated by an electrostatic discharge (ESD) source in a small-sized TEM-cell. Analytical, quasi-static, and electrodynamic models are presented for calculating the waveforms and amplitudes of currents and voltages induced on the monopole and the MSL. These waveforms are presented for analytical, quasi-static, electrodynamic calculations, and measurements. The maximum deviations of analytical and electrodynamic calculations results for the monopole and the MSL are 25% and 9%. An approach is presented for using half of the coaxial chamber as a matching device between the ESD source and the TEM-cell. The results of calculations and measurements agree well, which indicates the possibility of using a small-sized TEM-cell and half of the coaxial chamber. These devices could be helpful to study the effect of ESD-generated EMI on integrated circuits (IC), electronic component base, and small devices in general.
Detection of Protein Coding Regions Using Fast Short Time Ramanujan Periodicity Transform

In this work a fast algorithm for the detection of protein coding regions in DNA sequences has been developed by modifying the Ramanujan Periodicity Transform. The performance of the algorithm has been evaluated on standard data sets. Significant reduction in computation time has been achieved, along with improvisation in the performance parameters for short exonic regions.
Adversarial Ensemble Modeling for Evasion Attack Detection

This article presents a hybrid method that statically evaluates several aspects regarding network-based model, text, as well as permissions, and monitors several activities dynamically; those activities include system call logs, memory usage, as well as the CPU usage. Thus, the designed machine learning-based model can easily distinguished, as well as identify Android ransomware among different and varied types of malware. According to the results, the machine learning-based approach proposed for Android ransomware classification and detection is able to classify unknown malware that exhibits the same static and dynamic behavior; it can prevent adversarial evasion attacks. Moreover, it was found that the data from permissions and system calls are more valuable to detect ransomware. These features and other considered static and dynamic features detect and classify Android Ransomware with high accuracy.
Key Factors Influencing Consumers' Behavioral Intention towards Adopting and Using Mobile Commerce Services Amid and Post COVID-19 Pandemic

Meeting customers' requirements and needs is con-sidered a cornerstone of the success of any type of business, regardless of its size. Therefore, engineering the products and services provided by those organizations should be made based on a clear understanding of such requirements. This in turn would increase their market penetration, competitiveness, and of course their profitability. Nowadays, with the emergence of COVID-19 pandemic, a big shift in the people's behavior towards using various electronic means, has been witnessed. Consequently, this had forced the business models to be shifted as well to cope with such changes in the behavior. In this research, we focused on exploring the main drivers of people's willingness to adopt mobile commerce services during times of pandemic. The research was conducted based on feedback collected from users of mobile commerce services in Jordan. The results showed that performance expectancy, personal innovativeness, and habit, had a positively significant impact on Jordanian customers' behavioral intention to adopt such services during the COVID-19 pandemic.
A Systematic Mapping Study in Intrusion Detection System for Unmanned Aerial Vehicles Security

With the increase in threats on Unmanned Aerial Vehicles (UAVs), there is a growing need for intrusion detection approaches that make it easier to defend against threats. However, the number of studies that address the existing Intrusion Detection System (IDS) for UAV security is scarce. This work presents a systematic mapping study about these IDS, bringing aspects of taxonomy, detected intrusions, machine learning approaches, exclusivity for UAV swarms, and standardization in development. A total of 56 studies were found about the research topic and reviewed, and from that, comparative analyzes were performed. Still, some future directions are indicated in the development of an IDS. Based on the search, this is the first systematic mapping of the subject.
Quantum Text Encoding for Classification Tasks

This paper explores text classification on quantum computers. Previous results have achieved perfect accuracy on an artificial dataset of 100 short sentences, but at the unscalable cost of using a qubit for each word. This paper demonstrates that an amplitude encoded feature map combined with a quantum support vector machine can achieve 62% average accuracy predicting sentiment using a dataset of 50 actual movie reviews. This is still small, but considerably larger than previously-reported results in quantum NLP.
RDDriver: A novel method based on multi-layer heterogeneous transcriptional regulation network for identifying pancreatic cancer biomarker

Pancreatic cancer is a malignant cancer with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding cancer biomarkers. Most of the existing network-based methods do not study RNA, rely on prior or mutation information, or can only achieve classification tasks. In this paper, wep ropose a method combining Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. We first construct a regulation network containing three types RNAs. Then, Relational Graph Convolutional Network is used to learn the node representation. Finally, we combine the idea of Deep Q-Network to prioritize each RNA with the network controllability theory. We train RDDriver on three simulated small networks, and calculate the average score after applying the model parameters to the regulation networks. To demonstrate the effectiveness of the method, we compare RDDriver with other eight methods based on the approximate cancer drivers benchmark RNAs.
IDIA: An Integrative Signal Extractor for Data-Independent Acquisition Proteomics

In proteomics, data-independent acquisition (DIA)has been shown to provide less biased and more reproducible results than data-dependent acquisition. Recently, many researchers have developed a series of methods to identify peptides and proteins by using spectrum libraries for DIA data. However, spectrum libraries are not always available for novel organisms or microbial communities. To detect peptides and proteins without a spectrum library, we developed IDIA, a library-free method using DIA data to generate pseudo-spectra that can be searched using conventional sequence database searching software. IDIA integrates two isotopic trace detection strategies and employs B-spline and Gaussian filters to help extract high-quality pseudo-spectra from the complex DIA data. The experimental results on human and yeast data demonstrated that our approach remarkably produced more peptide and protein identifications than the two state-of-the-art library-free methods, i.e., DIA-Umpire and Group-DIA. IDIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/IDIA.
Wearable ECG Devices: A State of the Art about Circuit, Software and Application

With the world's population aging and a greater focus on health in recent years, the implementation of everyday and wearable Electrocardiogram (ECG) signal detecting devices has become a hot issue. In terms of technology, the design solution should change depending on the application scenario and requirements. In this work, some fundamental investigations on front-end circuitry, chip selection, and algorithm application during back-end processing are introduced and analyzed. Specifically, this paper delves into certain common filter circuits and driven-right-leg circuit schematics in order to get enhanced front-end circuitry with lower noise and higher gain. Other optimization options, such as pseudo-ECG and chip selection, are also briefly discussed. Furthermore, this paper describes and contrasts a number of widely used algorithms for the back-end processing stage, which provides information obtained from ECG signals with varying degrees of accuracy and determines disease predictability. The applications based on wearable ECG devices are also discussed here. Hopefully, a well-designed ECG monitoring equipment may not only reliably and speedily record human body health parameters, but also assist doctors in diagnosing disease if necessary. With the description and analysis of substantial research presented in this work, it is believed that some future directions for wearable ECG device advancements can be identified.
Common Mode Noise Sources Analysis and Volume Optimized CM-EMI Filter Design for a TM-PFC

Electromagnetic interference (EMI) analysis for the grid and the connected equipment is essential to ensure that the defined EMI standards are not violated. In this paper, the common mode (CM) noise sources of low power boost power factor correction (PFC) converters operating in transition mode (TM) are investigated. The main portions of the propagated CM noise are due to the bridge rectifier diodes and the transformed differential mode (DM) noise to CM noise. Identifying these noise sources reveals noise reduction solutions. By applying these considerations, the maximum amount of noise at the critical frequency is determined. Passive filters tend to be bulky and their volume reaches up to 30% of the entire system volume. Thus, using an optimized approach, the filter with the minimum volume is dimensioned. A single-phase 80W TM-PFC with an exact model of operated controller IC is also simulated in LTspice to show the validity of the presented investigations.
Partial Discharge Monitoring and Analysis using Phase Resolve Partial Discharge Pattern

Motors and generators like other machineries, experience wear and ageing. The materials and components will deteriorate over time if nothing is done, and they will ultimately fail. Fortunately, most form of failures may be avoided with proper maintenances. To successfully schedule the maintenance, the plant operator must have reliable information on the current state of their machineries. This paper presents a technique and measuring system for PD monitoring of rotating machines using Phase Resolve Partial Discharge pattern. With this technique, the maintenance strategy and planning can be considerably improved. The proposed method was tested on asynchronous motor with 6.6 kV rated voltage. The measuring approach is based on the decoupling of PD pulses using inductive sensors mounted on terminals of the item to be tested and the handling of the sensor signals, which results in a measurement of the PD level in the test object. Since one of the most essential responsibilities of PD monitoring is to identify insulation degradation early, analyzing PRPD patterns is helpful for assessing the state of high voltage rotating machines so they may work reliably for a known period of time. The paper evaluates a case scenario of a machine and the PRPD results show the current condition of the inspected machine which is in good running condition.
On the role of shape features in metaphor generation for abstract images

In this study, we examined the role of shape features on metaphor generation for abstract images based on a simulation with a retrained convolutional neural network (CNN), which is in turn based on a pretrained CNN model (AlexNet). A computational experiment was conducted using three types of object recognition models, including a pretrained object recognition model (AlexNet) and recognition models that were retrained to recognize more or fewer shape features using edge-detected or blurred images from the ILSVRC-2012 dataset. A psychological experiment was conducted to collect metaphors that were used to explain the abstract images. The simulation results of the models for the abstract images were compared to examine how well they predicted the concepts used in the metaphors generated for the abstract images. The results of the computational experiment suggest that the model retrained to recognize fewer shape features performed best at predicting the generated metaphors. However, for some abstract images, the model retrained to recognize more shape features performed better. These results suggest that the role of shape features on metaphor generation differs depending on the types of abstract images.
On the Outage Performance of Uplink NOMA Inspired CR Network with CEEs and Imperfect SIC

In this article, a novel non-orthogonal multiple access (NOMA) inspired decode-and-forward cooperative relay (CR) network working in full-duplex mode is presented. The NOMA principle in uplink model is used to transmit symbols from the user to base station (BS) by employing a relay over Rayleigh fading channel. Outage probability (OP) expression is evaluated at the BS under channel estimation error (CEE) and imperfect successive interference cancellation (SIC) conditions. The article compares the presented model with the recently presented systems. We also investigate the impact of power allocation coefficient and CEEs on the OP of the presented scheme. Simulations are carried out to validate the analytical expressions.
Haplotype-phasing of long-read HiFi data to enhance structural variant detection through a Skip-Gram model

Haplotype-resolved assembly is the optimal approach to characterize structural variants (SVs) in the human genome. Current long-read technologies, such as high-fidelity (HiFi) reads provide both low sequencing error and long-range information which enable phased, diploid assembly. Here we introduce embSV, a haplotype-phasing-assisted diploid assembly-based SV detection tool, which takes advantage of a word-embedding model inspired by natural language processing to faithfully partition HiFi reads for local haplotype-specific assembly. The genome-wide haplotype-resolved assembly then allows us to comprehensively detect SVs. To thoroughly test the performance and robustness of embSV and compare it with existing SV callers, we have run several benchmarking experiments. Tuning different parameters that can affect breakpoint shift and alternate sequence similarity between called SVs and the gold standard reveals that embSV outperforms existing tools and is the most robust tool to parameter change.
Property-Controllable Generation of Quaternary Ammonium Compounds

Designing molecules with desired biological properties remains an outstanding challenge both in the wet and dry laboratories. Meeting this challenge promises great translational impacts across drug discovery, material sciences, biotechnology, and more. Recent momentum in deep learning promises to advance our computational capabilities on molecule generation. In particular, deep graph generative models which treat molecule design as a graph generation problem are allowing us to directly learn from existing databases of small molecules and generate novel, valid molecules. Currently, these models have many shortcomings, including poor controllability of desired molecular properties, especially in practical application where the training data is usually small, noisy, and incomplete. This paper focuses on equipping graph variational autoencoders with the ability to control for desired properties and its practical application in a practical application which is the generation of Quaternary Ammonium Compounds (QAC). Several controllable graph generation mechanisms are investigated for their effectiveness. A general framework is then proposed to extend these mechanisms by our newly proposed objective function to handle the challenges in practical applications where the property value annotations are usually censored and not fully available in all training samples. The experimental evaluation considers an experimentally-characterized dataset of antimicrobial small molecules with wet-lab characterized activity against antibiotic-resistant bacteria. Extensive experiments demonstrate the superiority of the proposed models and control of desired properties.
Modeling Flexible Parts for Robotic Assembly Simulations

The handling of flexible components, such as rubber sealings or cables, is an essential part of industrial manufacturing. Automating such processes through robotics is still a major challenge. Machine learning-based training of robots in combination with simulation has led to promising results in other areas of robotics. This approach can potentially be applied to assembly tasks as well. However, current simulation tools for robotics generally do not have capabilities for simulating flexible bodies. In this work, an approach for modeling such flexible bodies is proposed, which can be implemented with most common robotic simulation programs. Using the example of a rubber O-ring sealing, this approach was tested in such program and the results were compared with a professional multi-body physics engine. Realistic behavior of the O-ring was achieved with relatively low computation times.
Dynamical effects of confinement on atomic valence photoionization in Mg@C60

Himadri S. Chakraborty, Mohamed E. Madjet, Jan-M. Rost, and Steven T. Manson Department of Chemistry and Physics, Northwest Missouri State University, Maryville, Missouri 64468, USA Freie Universität, Institut für Chemie (Kristallographie), Takustrasse 6, D-14195 Berlin, Germany Max-Planck-Institut für Physik Komplexer Systeme, Nöthnitzer Strasse 38, D-01187 Dresden, Germany Department Physics and Astronomy, Georgia State University, Atlanta, Georgia 30303, USA Received 25 March 2008; published 15 July 2008
A Reciprocity-Based Method for Improving Community Detection on Directed Friendship Networks

This paper studies community detection on directed friendship networks. We propose a novel pre-processing method, Friendship Reciprocal Inclusivity-Emphasizing Never Does Backtrack Random Walk (FRIENDBRW), which is an extension of existing community detection algorithms to enhance results for sociological networks of friendship relations. This method is embarrassingly parallelizable and is motivated by the importance of information cycles and the reciprocal nature of friendships in forming communities. The algorithm can be adjusted to change how inclusive detected friend groups are, as measured by the prevalence of nonreciprocal edges. This scalable method is compared with the Louvain algorithm and the communities detected by our algorithm are more tightly-knit and realistically sized compared to empirical studies.
FPGA Hardware Implementation of the Yolo Subclass Convolutional Neural Network Model in Computer Vision Systems

The computing unit (CU) of the computer vision system (CVS) has been developed based on a system on a chip (SOC) with the Xilinx Field-Programmable Gate Array (FPGA). A new model of a convolutional neural network (CNN) tiny-YOLO-Inception-ResNet2 related to neural networks of the YOLO subclass was proposed. A feature of this model is the presence of two Inception-ResNet modules. The weight coefficients of the trained software-implemented CNN of the same model were used. Images from the Pascal VOC 2007 dataset were used to train and test these CNN models. The research of the CU effectiveness was carried out. Firstly, we study the detection time of one image which spent in each layer of the CNN model and of the whole model depending on the number of universal computing units in the CU. Also, the study of the detecting objects accuracy depending on the bit depth (16 or 32 bits) of floating-point numbers was carried out. It is concluded that it is necessary to perform calculations using 32-bit floating-point numbers. It is shown that the power consumption of the CU did not exceed 12 Watts during all experiments.