Miracle-dz/UniFAR-FTUs huggingface dataset:
Biological Network Alignment Using Hybrid Genetic Algorithm and Simulated Annealing

This research demonstrates how we can improve the efficiency of protein-protein interaction (PPI) network alignment using soft computing. In Bioinformatics, biological network alignment is particularly important for its use in identifying cellular pathways, discovering new drugs, and detecting disease progression. Also, network alignment is used in social networks, ontology matching, pattern recognition, and natural language processing. In this regard, the main challenge is that the problem of finding the alignments in two graphs is NP-hard, therefore, accurate algorithms can only be used for very small instances. For real and relatively large cases, typically (meta)heuristic methods, which can find approximate solutions in reasonable time, are used. In this regard, we propose a new hybrid metaheuristic algorithm, called SAGA. The SAGA proposed method is applied the simulated annealing in the crossover operation of genetic algorithm. Concerning the integrated network alignment, SAGA first finds the local alignments and then it discovers the existing global network alignments. We implement the SAGA network aligner on python 3.6 and obtained experimental results on five eukaryotic species of the Biogrid dataset. The experimental results show that SAGA network aligner can achieve a better mapping than some of the state-of-the-art algorithms. Based on the experimental results, the proposed integrated network aligner can balance the functional quality and topological quality criteria that are significant in Bioinformatics.
Pipelines Bent, Pipelines Broken: Interdisciplinary Self-Reflection on the Impact of COVID-19 on Current and Future Research (Position Paper)

Among the many changes brought about by the COVID-19 pandemic, one of the most pressing for scientific research concerns user testing. For the researchers who conduct studies with human participants, the requirements for social distancing have created a need for reflecting on methodologies that previously seemed relatively straightforward. It has become clear from the emerging literature on the topic and from first-hand experiences of researchers that the restrictions due to the pandemic affect every aspect of the research pipeline. The current paper offers an initial reflection on user-based research, drawing on the authors’ own experiences and on the results of a survey that was conducted among researchers in different disciplines, primarily psychology, human-computer interaction (HCI), and visualization communities. While this sampling of researchers is by no means comprehensive, the multi-disciplinary approach and the consideration of different aspects of the research pipeline allow us to examine current and future challenges for user-based research. Through an exploration of these issues, this paper also invites others in the VIS—as well as in the wider—research community, to reflect on and discuss the ways in which the current crisis might also present new and previously unexplored opportunities.
Automatic Detection of Fake News Using Textual Entailment Recognition

Every day we access news from various sources such as social media, news blogs, online newspapers, and many other diverse sources. This has made the task of identifying trustworthy sources of news more and more critical.This paper describes techniques for utilizing textual entailment recognition in detecting fake news articles. At first, we discuss the architecture of the textual entailment recognizer and the steps for training the recognizer. We then describe the algorithms which uses the trained textual entailment recognizer for detecting fake news articles. We compare our proposed method with the popular state-of-the-art methods such as cosine similarity and word mover’s distance based methods. We also compare our work with other related works in this field. In our work, we train the textual entailment recognizer using the datasets from the Stanford Natural Language Inference corpus and show the effectiveness of using the trained textual entailment recognizer in detecting fake news on the FakeNewsAMT dataset. The proposed method achieves accuracy of more than 90% for fake news detection on the FakeNewsAMT dataset.
Two Nonparametric Direction-of-Arrival Estimation Approaches for Coprime Arrays via Iteration

Coprime arrays can highly improve resolution and increase degrees-of-freedom (DOFs) by exploiting the corresponding virtual array. In the context of direction-of-arrival (DOA) estimation using coprime arrays, a number of sparsity-based approaches have been developed, but the tedious process of selecting a regularization parameter is inevitable. To avoid this problem, in this paper, we apply two nonparametric and hyperparameter-free approaches named, respectively, iterative adaptive approach (IAA) and sparse iterative covariance-based estimation approach (SPICE) to DOA estimation. Numerical experiments validate the effectiveness of the two iterative algorithms when turning to virtual array domain, and their superiority is demonstrated in term of spatial spectrum, estimation accuracy and computational complexity.
Predicting Lysine Glutarylation Sites by Combining Multiple Feature Selection Methods

Lysine glutarylation is a dynamic and conserved post-translational modification of protein acylation. For identification of the molecular behavior of lysine glutarylation, it is necessary to identify the lysine glutarylation sites. Machine learning techniques help in the pathogenesis of biological mechanism processes to make a proper decision for developing glutarylated site identification. In this study, several feature encoding methods have been applied, i.e. amino acid factors (AAF), binary encoding (BE), and composition of k-spaced amino acid pairs (CKSAAP) to encode features from peptide sequences of lysine glutarylation modification database. Multiple feature selection methods have been used, i.e. Student's t-test, Kruskal-Wallis test, and minimum redundancy maximum relevance (mRMR) method to improve the prediction performance of the whole system reducing the number of features. A combined feature selection approach has been proposed. Moreover, due to the imbalance problem of the dataset, a biased support vector machine has been used as the classification model. The performance of different feature selection approaches has been measured according to the area under curve (AUC). Experimental studies show that the features that are selected by the combined approach provide better performance and efficiency than that of by individual approach.
MOSCAF – Specifying Data Quality Requirements according Web Functionalities

For any organization the data are considered as one of the main assets. Its strategic value leads to reconsider the importance of maintaining adequate levels of quality in data that is managed and used by applications, especially in Web Applications. The objective of this work is to propose a data quality model, it will guide to developers to the identification of data quality requirements versus web functionalities defined by the users, in order to avoid some problems related with inadequate levels of quality in data.
A Multiscale Characterization of Nanodielectrics: the Case of PI/Si3N4 Nanocomposite

Nanodielectrics are extensively investigated due to their improved properties attributed to the interphase formed between the nanoparticle and the matrix. The objective of this paper is to perform a multiscale characterization on polyimide (PI) / silicon nitride ($Si_{3}N_{4}$) nanocomposite (NC) in order to determine the impact of the interphase on macroscale properties. NC with untreated and silane treated particles are compared. On the macroscale, breakdown strength and conductivity measurements are performed. On the nanoscale, the interphase thickness and the charge injection and decay are measured using modes derived from the Atomic Force Microscopy (AFM). Results show a higher conductivity of the interphase area compared to the matrix and a direct link between local and macroscopic properties. In addition, the interphase region, with a lower permittivity than the matrix, may present a local enhancement of the applied electric field, leading to a lower breakdown strength for NC materials.
Deep Coupled Joint Distribution Adaptation Network: A Method for Intelligent Fault Diagnosis Between Artificial and Real Damages

Deep learning techniques have been widely applied for intelligent fault diagnosis. However, these techniques require large amounts of labeled data from a particular machine, which is demanding for real-world applications. Alternatively, models can be developed based on artificial damages and be applied for industrial data with real damages. In that case, a major challenge arises since the distributions of those artificial and real damages are greatly different, which results in severe performance degradation of conventional deep models. In this work, a model named deep coupled joint distribution adaptation network (DCJDAN) is proposed to address the large domain discrepancy between artificial and real damages. By utilizing two untied deep convolutional networks, the proposed method allows the source- and target-stream networks to focus on learning domain-representative features, providing flexibility for explicitly modeling the domain discrepancy. To ensure a more effective knowledge transferring, a regulation term is adopted to force the untied coupled networks to stay similar since the source domain and the target domain are related. The joint distribution adaptation module is further adapted to minimize the domain discrepancy, which considers both the marginal and conditional distribution differences and provides more accurate distribution matching. The effectiveness of the proposed method is evaluated based on three bearing data sets with artificial and real damages. As reported, the proposed method achieves an average accuracy of 98.17% for all tasks, which outperforms several state-of-the-art deep domain adaptation models and improves the diagnosis performance compared with the conventional deep learning models.
Liver Diseases Prediction using KNN with Hyper Parameter Tuning Techniques

The healthcare industry is producing massive volumes of data. The algorithms using ML can be used to discover hidden patterns for making diagnosis and critical decisions. In the past years, Liver disorders have increased persistently and it is the reason for a significant number of deaths in many countries like India. The number of patients with liver disease are rapidly increasing due to several reasons like over consumption of alcohol, breathing in injurious gases, eating unhygienic food, pickles and drugs. The aim of our work is to predict liver disease by Machine learning based prediction model trained with the dataset fetched from the northeast of Andhra Pradesh, India. Feature analysis is performed on data and checked for balanced/imbalanced, distribution of data and correlation between the features and between features and liver disease. Transformation techniques have been used to transform data into normal distribution. So before performing transformation, outliers are detected and removed using outlier removal and the best features are selected based on correlation matrix and feature selection approaches, which will transform the data set effectively. Grid Search techniques are used in Hyper parameter tuning. Performance is evaluated using various metrics such precision, recall, f1-score, precision, precision-recall curve (PRC) and Receiver operating curve (ROC). Eight models are proposed out of which the fine tuning parameters of K-NN using Grid search gives the better performance of 91% accuracy.
DIO-based robust fault estimation observer for discrete-time switched systems with unknown input*

In this paper, the robust fault estimation observer problem for discrete-time switched systems with unknown input is investigated. First of all, The state vector and fault vector of the initial system are transformed into the state vector of the augmented system, and then we can obtain the augmented system. Next, an unknown input observer(UIO) is designed based on the P-radius technique to estimate the state and fault of the augmented system, that can ensure the stability of the error system. In order to achieve the exothermic goal in this paper, some assumptions are given, and the observer is solved by using linear matrix inequality (LMI) technique and Schur complement lemma. At the last, an interval hull computation approximation algorithm of fault estimation is obtained. Compared with other methods, this method is more precise and efficient.
A Framework for Detection of Android Malware using Static Features

Android is an open source platform that permits users to take full benefit of mobile Operating Systems, but also raises an important issue associated with malicious applications. So it is essential to make an effective technique to detect the malicious applications. In this paper, we proposed an effective framework based on the combination of static features and employ machine learning classifiers to identify malware applications. We have extracted three types of static features i.e. API calls, permissions and intents. API calls are extracted from the classes.dex and permissions and intents are extracted from AndroidManifest.xml. Then these features are used for training and testing purpose to classify the applications. The test set outcomes indicate that the combination of features performs well in comparison to the individual features and also it is found that Random Forest and K-Nearest Neighbor provide the best accuracy (i.e. 95.9%) in classifying the Android applications.
Attributed Graph Clustering via Deep Adaptive Graph Maximization

Due to the increasing popularity of the social networks, the detection and discovery of the hidden building blocks and their community structures are considered as the primary tasks on the graph (network) based data structures. Graph clustering is considered as a challenging task as it requires contribution of input graph’s topological and content data jointly. Graph Convolutional Neural Networks (GCNs) have demonstrated remarkable power in the domain of graph representation learning by merging both structural and content information of networks. While GCN based clustering methods are being used as the state-of-the-art alternative solution for graph clustering, these methods fail to capture global structural information of networks, considering a local neighborhood of each node. Here we propose an integrated novel graph convolutional clustering approach that enables us to extract the local and global structures of the graph based data along with the nodes content. Experimental studies on three real-world benchmark information networks approve our approach and confirm that our proposed method outperforms baseline methods significantly in graph clustering and link prediction tasks.
Evaluation of Dental Prosthesis Colors Using a Neural Network

One of the most important tasks in the work of dental technicians is to reproduce the color of dental prostheses. Experienced dental technicians can correctly estimate the true color from a photograph of the patient’s teeth taken by the dentist, but this is a very difficult process for inexperienced dental technicians. In this study, using machine learning techniques, we aimed to develop an automatic color evaluation system that possesses the same skill as experienced dental technicians. We prepared 62 images of the patient’s teeth that were annotated by experienced dental technicians, and we attempted to estimate the true color by using a neural network. We confirmed that it is difficult to completely evaluate the true tooth color, but some color candidates could be obtained correctly.
Reconfigurable Microwave Filters Implemented Using Field Programmable Microwave Substrate

This article presents novel designs of reconfigurable microwave filters based on the recently introduced concept of field programmable microwave substrate (FPMS). Using reconfigurable FPMS substrate, significant tuning freedom of the designed microwave filters can be achieved. It allows implementation of smart filter designs with tunable center frequencies, operational bandwidths, and filter orders. Moreover, different microwave filter types are also realizable on one FPMS board with simple dynamic control. As a proof-of-concept, design steps are described for three filter types, including waveguide bandpass filter, quarter-wave-coupled bandpass filter, and waveguide bandstop filter. A good match between the simulated and measured results is presented, showing good tuning range for the center frequency and bandwidth of the bandpass filters around 2 GHz. In addition, the reconfigurability of the design allows it to switch to bandstop filters, which is a complete change in topology from a single physical design. The merits of the proposed design are reflected in the realized bandpass filter, exhibiting center frequency tuning of more than 20% along with bandwidth variability spanning three times its minimum value. Finally, a significant size reduction compared with the designs using conventional technologies is also demonstrated. With its inherent flexibility, low cost, and high degree of integration, FPMS filtering is suited to a wide variety of RF applications.
Detection of Arrhythmias for Multi-lead Electrocardiogram using Convolutional Neural Network

Recent researches indicated that one-dimensional convolutional neural network (1D-CNN) is a splendid approach to learn representative features from electrocardiogram (ECG) for arrhythmia detection. This study proposed an adaptive CNN combining 2D and 1D convolutional layers which applied 2D convolutional layers in shallow layers in order to share features learned among multilead of ECG since there are morphological similarities between different leads of ECGs. The study developed a multi-head CNN to classify various arrhythmias from 8-lead ECGs using a competition dataset comprised of over 20,000 ECG records. Compare the performance of the proposed adaptive CNN with the original 1D-CNN adapted for multiple channel input, experiments demonstrated that the proposed adaptive CNN can improve the correctness of diagnosis without increasing the model size.
Simulation study based performance projection of Negative capacitance FET

There are studies reasoning for moving from the MOSFET to Negative Capacitance Field Effect Transistor (NCFET) to produce steep switching devices. In this paper, we compare the simulation results of a simple Bulk MOSFET with NCFET. Simulation results show the considerable drop in Sub-threshold Swing (SS) for NCFET in comparison to MOSFET, which underscores the usefulness of NCFET in low power applications. We also demonstrate, using simulation, effect of parameters (Thickness of the Ferroelectric, Effective Oxide Thickness, Doping in Substrate, Drain Voltage) on performance of NCFET. The simulations are done with the help of Silvaco ATLAS tool and MATLAB (to solve the 1-D Landau–Khalatnikov equation of ferroelectrics). At drain voltage of 0.4V, we are able to observe the non-hysteresis characteristics at ferroelectric thickness 60nm and average SS of 53 mV/dec in simulation results.
Studies on Paralinguistic Speech Sounds

Virtual assistants smart interaction dialogue with human being there paralinguistic, emotional speech played very important role. In this paper a comprehensive view is presented on the research studies carried out so far on emotional speech and paralinguistic sounds. Different signal processing methods, acoustic features, and classification techniques explored so far in different studies are discussed. A comparison of performance and results is also presented along with the application domain. The novelty in this review paper is that features, methods, and classification results have been presented quite comprehensively using tables and graphical views. This study and review should be helpful towards faster development of smart assistant for diverse applications.
Scheduling across Multiple Applications using Task-Based Programming Models

Task-based programming models have shown their potential for efficiency and scalability in parallel and distributed systems. With such a model, a parallel application is broken down into a graph of tasks, which are subsequently scheduled for execution. Recently, implementations of task-based models have addressed distributed memory and heterogeneous systems with accelerators. However, the problem of scheduling tasks as well as allocating resources at runtime is still a challenge. In this paper, we propose coordinated and cooperative task scheduling across multiple applications. The main idea is to exploit the application's idle time e.g. from imbalance to serve tasks from another application. The experiments use Chameleon, a task-based framework for reactive tasking in distributed memory systems. In various example scenarios, we show improvements in CPU utilization of 5% – 15% by coordinated scheduling.
Knowledge Mapping of E-Commerce Platform Operation and Data Mining Based on The Method of Bibliometric

Due to the increasingly fierce market competition and the continued advancement of communication technology, enterprises and consumers are more willing to collect and exchange information through the Internet. With the application of advanced communication technologies, more and more people are beginning to share their shopping experience or the use of related products on social media or shopping platforms. As a result, many customers are accustomed to collecting reviews and related information on related products on the online platform before consumption. This study takes Thai consumers as the survey object. This research aims to provide a theoretical basis and practical suggestions for e-commerce platform operation and data mining based. After analyzing using SmartPLS 3, this study found that the source credibility of online reviews will have a positive and significant impact on consumers’ purchasing intentions for consumer electronics. At the same time, the source credibility of reviews will affect consumer electronics’ purchase intentions through consumers’ attitudes towards online reviews. Based on this finding, this research puts forward practical suggestions and the direction of follow-up research for the e-commerce platform operation and data mining based.
Android Malware Detection Amid COVID-19

Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious.