Miracle-dz/UniFAR-FTUs huggingface dataset:
Benchmarking and Performance Modelling of Dataflow with Cycles

Over the years, the popularity of iterative data-intensive applications such as machine learning applications has grown immensely. Unlike batch applications, iterative applications such as k-means, regression or classification algorithms require multiple access to the input data to train it sufficiently for convergence. In the context of big data, these applications are executed on distributed computing frameworks such as Apache Spark. These frameworks are simple to deploy and use, however, under the hood they are complex and highly configurable. To perform an exhaustive study of the impact of these ubiquitous parameters on application performance would be cumbersome due to the exponential amount of their combinations. In this paper, we group applications based on a common dataflow and communication pattern. We then present a multi-objective performance prediction framework to model the performance of these applications. The models can predict the execution time of a given application with high accuracy. The framework can be used to infer optimal configuration parameters to meet application execution deadlines. Based on these optimal configurable values, we recommend the best EC2 instances in terms of cost. The average error rate of the prediction results is ± 14% from the measured value.
Deep Learning-Based Models for Classification of Invasive Plant Species from Hyperspectral Remotely Sensed Data

Invasive plant species are plants, which spread extensively outside their native ecosystem. They pose a threat to the environment and economy at global and local scales, making effective mapping and detecting essential. This study aims to develop deep neural network-based classification models for hyperspectral data. The spectral reflectance of ten invasive plant leaves was collected using ASD FieldSpec4 standard Hi-Res device. Samples were collected from Dr. Babasaheb Ambedkar Marathwada university campus and Himayat Bagh garden in Aurangabad city, Maharashtra, India. Two types of deep neural networks (DNN) were applied: the first one is based on the one-dimensional convolutional neural network (1D CNN), and the second is based on the convolutional long short term memory (CNN-LSTM). We proposed and compare the performance of the two models with three existing models, multilayer perceptron (MLP), random forest (RF), and support vector machine (SVM). The CNN-LSTM model achieves the highest discrimination accuracy among all the other models with an overall test accuracy of 99.3% and an F1_score of 0.98. Moreover, the CNN model achieves a high classification accuracy of 97.3% and an F1 score of 0.97, which contains the inception layer. In the CNN-LSTM model, the convolutional layer extracts the input vector of spectral features and feeds them to the LSTM layer to capture contextual information. In 1D CNN, the inception layer concatenates the output of multiple kernel sizes and adds flexibility to the model to improve the classification performance. This study revealed that our proposed models provide better performance than traditional machine learning methods and simple deep learning ones.
A Review of Deep Learning IDS for DDoS Attacks in WLANs

An Intrusion detection system (IDS) is a critical security organ which serves as a gatekeeper against the ever-evolving cyber space attacks. Among the recent technological advancement, high speed communication networks and Artificial Intelligence are notably the key driving factors for the evolution of such IDS from the rule-based to classic machine learning and recently, the deep learning using either manual feature engineering or representation learning techniques. This paper is a methodological literature review of various generations of machine learning-based IDS to assess their effectiveness. The review result is a pretext and blueprint for a new method that attempts to combine representation and manual feature learning techniques to effectively detect Distributed Denial of Service attacks (DDoS) in Wireless Local Area Networks (WLANs).
Segmentation of Brain Tumor using Deep Learning Methods: A Review

In the world of medical imaging, detecting brain tumors is a difficult challenge. Manual segmentation of brain tumors from a vast number of MRI images for diagnosis was a complex and time-consuming task. Automatic brain tumor image segmentation serves a role. The process of detecting or identifying a brain tumor entails segmenting the brain image, extracting brain characteristics, and classifying abnormalities in the MRI brain image. The most popular method for diagnosing a brain tumor is an MRI. Deep learning algorithms make it possible to process vast amounts of MRI-based image data quickly. In this paper we have a look at field of brain tumor segmentation methods as applicable to deep neural network. We provide comprehensive coverage of the top approaches and summarize the categories, types and different architecture of deep learning architecture for segmentation.
Deep Learning Driven System to Analyze Reliability of COVID Infodemic and News Articles

In the modern era, social media and electronic news play an important role in the dissemination of information across the world. The ease of sharing news articles and information over the electronic media has made it challenging to prevent the outspread of fake news articles. Thus, it has been observed that in the present situation of a pandemic the misleading news specifically about COVID-19 are increasing day by day. Multiple research groups identified this challenge and proposed machine learning-based binary classifiers for categorizing news articles into fake and true classes. But, none of them addressed the challenge of identifying the misleading news. Also, the existing research works do not focus on examining the reliability of the news about COVID-19. Moreover, there is a lack of complete system that provide an integration of front-end and back-end where the users can check the reliability of news article and get recommendation of the sources for validation of news articles. The authors in this manuscript propose the Deep Learning-based tool with multiclass classifier for classifying the news articles into fake, true and misleading. This tool is an integration of front-end and back-end that is equally effective in assessing the reliability of news about COVID-19 and other events. The classifier of the tool has been trained on the integrated dataset comprising general new articles from the globe as well as news articles about COVID-19. The trained classifier reported an accuracy of 88% in classifying the news into Fake, Misleading, and True classes. The higher accuracy than state-of-the-art models validate the acceptance of the tool for real-life applications.
Diagnostics for characterisation of plasma actuators

The popularity of plasma actuators as flow control devices has sparked a flurry of diagnostic efforts towards their characterisation. This review article presents an overview of experimental investigations employing diagnostic techniques specifically aimed at AC dielectric barrier discharge, DC corona and nanosecond pulse plasma actuators. Mechanical, thermal and electrical characterisation techniques are treated. Various techniques for the measurement of induced velocity, body force, heating effects, voltage, current, power and discharge morphology are presented and common issues and challenges are described. The final part of this report addresses the effect of ambient conditions on the performance of plasma actuators.
Rule Based Question Generation for Arabic Text: Question Answering System

Question answering systems have evolved into a whole new domain. Question answering systems are of emense importance in the present scenario as well as for many purposes such as humanlike conversation, machine translation, summarization, etc. For the present work, the data is collected from the Arabic language textbook of the 5th standard of Yemen. The text is pre-processed for removing punctuation marks, stop words. The paragraphs are segmented first sentence-level and then word level. For the generation of questions, a rule based approach is used, which had a pre-requirement of properly tagged words. Thus pos tagger and NER relevant to the present domain is also developed considering linguistic aspect and rule based approach. A clear explanation of how wh-type questions are developed is given in detail. The main focus on which the work is done is types of nouns. The types of nouns have been used to generate wh-questions from the Arabic text.
Design of capacitance lubricating oil grains detecting system based on ring electrode

In abnormal operation,friction pair will produce large metal wear debris about tens of micron in size.When the impedance method is used to detect grains,very high sensitivity is necessary. So,a ring electrode capacitive sensor with a differential structure is designed. By replacing the complex signal processing circuit,capacitance-to-digital converter AD7746 is used to convert capacitance sensor output into digital signal directly,simplifying the electronics,and minimizing the interference of distributed and parasitic capacitance. Experiment result shows that the system has high sensitivity with resolution of 0.125 fF.
Tailoring Cardiac Synthetic Transcriptional Modulation Towards Precision Medicine

Molecular and genetic differences between individual cells within tissues underlie cellular heterogeneities defining organ physiology and function in homeostasis as well as in disease states. Transcriptional control of endogenous gene expression has been intensively studied for decades. Thanks to a fast-developing field of single cell genomics, we are facing an unprecedented leap in information available pertaining organ biology offering a comprehensive overview. The single-cell technologies that arose aided in resolving the precise cellular composition of many organ systems in the past years. Importantly, when applied to diseased tissues, the novel approaches have been immensely improving our understanding of the underlying pathophysiology of common human diseases. With this information, precise prediction of regulatory elements controlling gene expression upon perturbations in a given cell type or a specific context will be realistic. Simultaneously, the technological advances in CRISPR-mediated regulation of gene transcription as well as their application in the context of epigenome modulation, have opened up novel avenues for targeted therapy and personalized medicine. Here, we discuss the fast-paced advancements during the recent years and the applications thereof in the context of cardiac biology and common cardiac disease. The combination of single cell technologies and the deep knowledge of fundamental biology of the diseased heart together with the CRISPR-mediated modulation of gene regulatory networks will be instrumental in tailoring the right strategies for personalized and precision medicine in the near future. In this review, we provide a brief overview of how single cell transcriptomics has advanced our knowledge and paved the way for emerging CRISPR/Cas9-technologies in clinical applications in cardiac biomedicine.
Molecular Characterization of Severe Acute Respiratory Syndrome Coronavirus 2 Isolates From Central Inner Sardinia

Background The SARS-CoV-2 pandemic stimulated an outstanding global sequencing effort, which allowed to monitor viral circulation and evolution. Nuoro province (Sardinia, Italy), characterized by a relatively isolated geographical location and a low population density, was severely hit and displayed a high incidence of infection. Methods Amplicon approach Next Generation Sequencing and subsequent variant calling in 92 respiratory samples from SARS-CoV-2 infected patients involved in infection clusters from March 2020 to May 2021. Results Phylogenetic analysis displayed a coherent distribution of sequences in terms of lineage and temporal evolution of pandemic. Circulating lineage/clade characterization highlighted a growing diversity over time, with an increasingly growing number of mutations and variability of spike and nucleocapsid proteins, while viral RdRp appeared to be more conserved. A total of 384 different mutations were detected, of which 196 were missense and 147 synonymous ones. Mapping mutations along the viral genome showed an irregular distribution in key genes. S gene was the most mutated gene with missense and synonymous variants frequencies of 58.8 and 23.5%, respectively. Mutation rates were similar for the S and N genes with one mutation every ∼788 nucleotides and every ∼712 nucleotides, respectively. Nsp12 gene appeared to be more conserved, with one mutation every ∼1,270 nucleotides. The frequency of variant Y144F in the spike protein deviated from global values with higher prevalence of this mutation in the island. Conclusion The analysis of the 92 viral genome highlighted evolution over time and identified which mutations are more widespread than others. The high number of sequences also permits the identification of subclusters that are characterized by subtle differences, not only in terms of lineage, which may be used to reconstruct transmission clusters. The disclosure of viral genetic diversity and timely identification of new variants is a useful tool to guide public health intervention measures.
Electrochemical Sensors and Biosensors for the Analysis of Tea Components: A Bibliometric Review

Tea is a popular beverage all around the world. Tea composition, quality monitoring, and tea identification have all been the subject of extensive research due to concerns about the nutritional value and safety of tea intake. In the last 2 decades, research into tea employing electrochemical biosensing technologies has received a lot of interest. Despite the fact that electrochemical biosensing is not yet the most widely utilized approach for tea analysis, it has emerged as a promising technology due to its high sensitivity, speed, and low cost. Through bibliometric analysis, we give a systematic survey of the literature on electrochemical analysis of tea from 1994 to 2021 in this study. Electrochemical analysis in the study of tea can be split into three distinct stages, according to the bibliometric analysis. After chromatographic separation of materials, electrochemical techniques were initially used only as a detection tool. Many key components of tea, including as tea polyphenols, gallic acid, caffeic acid, and others, have electrochemical activity, and their electrochemical behavior is being investigated. High-performance electrochemical sensors have steadily become a hot research issue as materials science, particularly nanomaterials, and has progressed. This review not only highlights these processes, but also analyzes and contrasts the relevant literature. This evaluation also provides future views in this area based on the bibliometric findings.
COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.
Information Security in the Quantum Era. Threats to modern cryptography: Groverâs algorithm

Information security plays a major role in the dynamics of today’s interconnected world. Despite the successful implementation and eﬀectiveness of modern cryptographic techniques, their inherent limitations can be exploited by quantum computers. In this article we discuss Grover’s quantum searching algorithm and its impact on the security of modern symmetric ciphers. More speciﬁcally, we present its formal description and give an implementation of the algorithm using IBM’s Qiskit framework, which allows us to simulate and run the program on a real device
On the Aggression Diffusion Modeling and Minimization in Twitter

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.
The Effect of Electrical Stimulation–Induced Pain on Time Perception and Relationships to Pain-Related Emotional and Cognitive Factors: A Temporal Bisection Task and Questionnaire–Based Study

Pain has not only sensory, but also emotional and cognitive, components. Some studies have explored the effect of pain on time perception, but the results remain controversial. Whether individual pain-related emotional and cognitive factors play roles in this process should also be explored. In this study, we investigated the effect of electrical stimulation–induced pain on interval timing using a temporal bisection task. During each task session, subjects received one of five types of stimulation randomly: no stimulus and 100 and 300 ms of non-painful and painful stimulation. Pain-related emotional and cognitive factors were measured using a series of questionnaires. The proportion of “long” judgments of a 1,200-ms visual stimulus duration was significantly smaller with 300 ms painful stimulation than with no stimulus (P < 0.0001) and 100 ms (P < 0.0001) and 300 ms (P = 0.021) non-painful stimulation. The point of subjective equality (PSE) did not differ among sessions, but the average Weber fraction (WF) was higher for painful sessions than for no-stimulus session (P = 0.022). The pain fear score correlated positively with the PSE under 100 ms non-painful (P = 0.031) and painful (P = 0.002) and 300 ms painful (P = 0.006) stimulation. Pain catastrophizing and pain anxiety scores correlated significantly with the WF under no stimulus (P = 0.005) and 100 ms non-painful stimulation (P = 0.027), respectively. These results suggest that electrical stimulation–induced pain affects temporal sensitivity, and that pain-related emotional and cognitive factors are associated with the processing of time perception.
What Will Happen to This Dog? A Qualitative Analysis of Rehoming Organisations' Pre-adoption Dog Behaviour Screening Policies and Procedures

Rehoming organisations often undertake some type of behaviour evaluation to determine dogs' suitability for rehoming and/or the type of suitable home. Assessments can carry considerable weight in determining dogs' fates. Although evaluation of the validity and reliability of any test is important, a more fundamental consideration is if the nature of the information sought and the weight given to this in organisations' decision making is of more than anecdotal value. Therefore, this study's aim was to conduct a qualitative analysis of organisations' pre-adoption dog behaviour screenings and potential justifications, comparing this with the available scientific evidence. A written enquiry was sent electronically to rehoming organisations in the UK and US from February 2016-July 2017. Of 73 respondents, the majority conducted assessments for all dogs. Using a thematic analysis, nine themes and 71 sub-themes emerged concerning the types of information respondents aim to gather from assessments. The majority of respondents used, at least partially, pass/fail scoring, i.e., certain outcomes would lead to dogs being deemed unadoptable. Forty-one sub-themes and one theme were identified as potentially leading to a dog being deemed unadoptable. The evidence base for these factors was identified from the scientific literature relating to: increased risk for relinquishment, impact on a dog's quality of life, and human safety risk. Evidence supported 10 factors: “aggression towards people”, “aggression towards cats or other animals”, “aggression towards dogs”, “biting or snapping”, “resource guarding”, “activity level or exercise needs”, “destructiveness”, “housetrained”, “fearfulness”, and “knowledge of basic commands and/or general training”. Of those, seven were associated only with relinquishment risk, two (“resource guarding”, “knowledge of basic commands”) with human safety risk, and one (“fearfulness”) with both. Thus, for >85% of characteristics organisations deemed important for dogs' adoptability, scientific evidence to support this is lacking. More research is needed to investigate the value of behaviour assessments, especially concerning the assessment of factors that could pose a public safety risk. However, given the current lack of scientific support for many decisions regarding dogs' rehoming suitability and recognised pressure on resources, it is suggested that organisations should focus on pre-adoption adopter education and post-adoption support.