Reviewerly/exHarmony huggingface dataset:
Transformer Dissolved Gas Analysis for Highly-Imbalanced Dataset Using Multiclass Sequential Ensembled ELM

Dissolved gas analysis (DGA) has been a critical technique for transformer diagnosis. DGA is a typical multiclass imbalance problem where most of the samples correspond to healthy state transformers or units. Though numerous works have been carried out on this issue, the diagnosis accuracy is still unsatisfactory when the status of health and multiple faults are considered. Multiclass imbalance problem is also a tough task from the view of algorithm development. Previous works underestimate this issue in some sakes such as lacking investigation of the highly imbalanced dataset and lacking consideration of health data. This article presents a comprehensive study of the mentioned issues. A novel algorithm called sequential ensembled extreme learning machine (SE-ELM) is proposed. SE-ELM adopts a novel multiclass undersampling strategy followed by a sequentially updated ensemble, which achieves both accuracy and efficiency. The proposed method is validated on both an open international electrotechnical commission (IEC) dataset and a highly imbalanced private dataset. The comparison with popular algorithms proves the efficiency of SE-ELM.
Towards DEMO Model-Based Automatic Generation of Smart Contracts

The production of self-executing computational agreements in the form of smart contracts remains a manual coding endeavour that hinders the widespread adoption of solutions that run on top of a distributed ledger technology such as blockchain. We explore the automatic generation of smart contracts based on a visual composition of reusable action rule specifications and other elements from the action model of the DEMO methodology. Several design and implementation considerations entail this choice of SC generation, all of which motivated a smart contract-enabled extension of DEMO’s way of modelling. The main research contribution is a foundation of synergistic knowledge accompanied by an extension proposal involving DEMO and smart contracts that can be built upon in future business cases where enterprise interoperability supported by blockchain technology is a requirement.
Implementing Blockchain Technology in Healthcare Systems utilizing Machine learning Techniques

With the ongoing development in many fields, healthcare systems can be improved to a larger extent by utilizing emerging technologies. Blockchain technology has become a very important technology playing vital role in many healthcare applications. The health care industries are striving for secured, reliable and most advanced system; which this novel blockchain technology can provide. Health data breaches, because of lack of security systems can be avoided in the best possible way by storing the health database on the blockchain platform. The decentralized database of blockchain provides extreme privacy to patient's health records and data files. The aim of this system is to provide a secured, privacy protected and reliable platform which can lead to smart health systems, as a better solution for traditional health record systems. Healthcare individuals can have access to the blockchain network, where all the medical records of the patient are made available and Machine learning algorithms are used to predict the diseases based on the medical symptoms provided by the patients to doctors. In this paper a user defined Blockchain for the implementation utilizes advances of SHA-256 (Secure Hashing Algorithm) to maintain security of the transactions carried out. The paper also discusses how the Machine learning algorithm can provide clinical suggestions to doctors for more better diagnosis. The best possible ML algorithm used for prediction here is Naive Bayes classifier with an higher accuracy of 97.236%. Thus, by integrating the latest emerging technologies, the medical system will have improved security, reduced costs and decentralized smart healthcare systems.
Retracted: A Machine Learning Method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector Machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses carried out for this study led the researchers to the conclusion that hyperboles may be found in unbiased datasets, which helps improve sarcasm identification as well.
A.I. based obstacle Avoidance and Signal Management Autonomous Car with Less Collision

The proposed autonomous vehicle is designed and developed to operate on the road without any driver. It is a self-driven remote vehicle to avoid obstacles and reduces accidents or collisions. It is developed using different kinds of IoT and sensor devices and implemented using the Raspbian buster real-time operating system with a Raspberry Pi microcontroller. It can also detect the traffic signal using the camera and decide according to the signal light. The main motto of this work is to reduce traffic rule violations and accidents due to carelessness. The proposed vehicle will also detect the traffic signs and signals and act according to the respective situations.
Crowd Anomaly Detection Using Machine Learning Techniques

Convolution neural networks (CNNs) are used to perform the image recognition and other visual system job. Their implementation in the detection systems of anomaly will essentially upgrade observation of correctness, which will usually reduce the calamity of anomaly and reduce the environmental and community consequences. But, the CNN-founded systems of detection of anomaly mainly concerned with their performance in earlier existence inspection system, Because of their big reflection and estimation demand for intervention. This paper article, introduce a real, natural, and computation effectively construction of CNN architecture for anomaly recognition, isolation, and semantic interpretation of the location of the fire. Here utilize little Convolution center and consists thin, completely associated sheets, which assist to maintain a minimum mathematical essential. Regardless of its less mathematical requirements, the investigator announce conclude that our presented suspension outcome correctness that are similar to another, high multiplex representation, in the main because of its enlarged floor.
Swin-Conv-Dspp and Global Local Transformer for Remote Sensing Image Semantic Segmentation

Compared with the traditional method based on hand-crafted features, deep neural network has achieved a certain degree of success on remote sensing (RS) image semantic segmentation. However, there are still serious holes, rough edge segmentation, and false detection or even missed detection due to the light and its shadow in the segmentation. Aiming at the above problems, this article proposes a RS semantic segmentation model SCG-TransNet that is a hybrid model of Swin transformer and Deeplabv3+, which includes Swin-Conv-Dspp (SCD) and global local transformer block (GLTB). First, the SCD module which can efficiently extract feature information from objects at different scales is used to mitigate the hole phenomenon, reducing the loss of detailed information. Second, we construct a GLTB with spatial pyramid pooling shuffle module to extract critical detail information from the limited visible pixels of the occluded objects, which alleviates the problem of difficult object recognition due to occlusion effectively. Finally, the experimental results show that our SCG-TransNet achieves a mean intersection over union of 70.29 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on the Vaihingen datasets, which is 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> higher than the baseline model. It also achieved good results on POSDAM datasets. These demonstrate the effectiveness, robustness, and superiority of our proposed method compared with existing state-of-the-art methods.
On Bridges and Software

Lately, I have been thinking about how the lessons of traditional engineering disciplines may be leveraged to develop more secure software. In particular, I have been thinking about how designing bridges to avoid failure may help us to develop software in a manner that avoids introducing exploitable flaws.
Inventory Big Data Management for Internet of Things based on Privacy Preserving

RFID and cloud computing technologies have been widely applied in warehouses to improve the efficiency and accuracy of inventory management based on the above excellent features. Besides the detection of missing RFID tags, a number of efficient schemes are proposed in recent studies, in which the security and privacy aspects are less concerned. In real-world applications, the integrity of tag data is an important factor. To address these problems, we propose an inventory data integrity detection scheme for cloud-based Internet of things warehouse systems. For the purpose of evaluation, we conduct experiments to compare the proposed detection protocol and tag data update protocols with existing schemes in simulations. The results show that the proposed scheme has better performance, as it not only identifies the missing tags in a secure and efficient way but also enables dynamic tag data integrity management quickly.
A CNN-Based Novel Approach for The Detection of Compound Bangla Handwritten Characters

The identification of handwritten characters has been a major focus of research, owing to the ease of use and improved language comprehension provided by technological advancements. In this study, we present a model for detecting compound Bangla characters from handwritten images. Of the almost 320 compound characters in the Bangla language, 171 have been previously studied by other researchers. We focused on the remaining 50 characters that have not been studied before. Our model's accuracy is dependent on the quality and comprehensiveness of the dataset, which we collected by gathering data on sheets and converting it to JPG format. To process the images, we developed a box detection algorithm in Python open CV, along with other processing techniques. Our machine learning model used a multilayer CNN architecture comprising convolution, maxpooling, dense, and dropout layers. The model achieved an accuracy of 88.48%, which is considered good. This study contributes to the field of handwritten identification for the Bangla language and demonstrates the efficacy of our approach in detecting previously unstudied characters.
Impact Analysis and Performance Model of Social Engineering Techniques

The field of information security is rapidly growing discipline. Although the effectiveness of security measures to protect sensitive information is increasing, the human factor that are open to manipulation remains the weakest link in the chain of security. Security of information is vital for organizations and governments. Also development of safeguards against illegal access to information is an area of increasing interest of researchers. Technology alone is not an adequate protection against information theft; human is often the weakest link in the chain of information security. The "art" of exposing people's vulnerabilities for sensitive information is known as social engineering. At the same time, the process of exploiting personal vulnerabilities is known as a social engineering attack. There are different kinds of this kinds of attacks. Targeting human weakness, social engineering attack uses various routing techniques to obtain sensitive information. In this work, the factors of the success of phishing, which is one of the social engineering attacks, were investigated. Some hypotheses have been developed according to these factors, and the accuracy of the hypotheses was shown with a questionnaire. A model that is used to calculate these parameters mathematically is proposed and the importance of being conscious to prevent such attacks is emphasized.
Comparative Analysis of Password Storage Security using Double Secure Hash Algorithm

Passwords are generally used to keep unauthorized users out of the system. Password hacking has become more common as the number of internet users has extended, causing a slew of issues. These problems include stealing the confidential information of a company or a country by adversaries which harm the economy or the security of the organization. Hackers often use password hacking for criminal activities. It is indispensable to protect passwords from hackers. There are many hacking methods such as credential stuffing, social engineering, traffic interception, and password spraying for hacking the passwords. So, in order to control hacking, there are hashing algorithms that are mostly used to hash passwords making password cracking more difficult. In this proposed work, different hashing algorithms such as SHA-1, MD-5, Salted MD-5, SHA-256, and SHA-512 have been used. And the MySQL database is used to store the hash values of passwords that are generated using various hash functions. It is proven that SHA is better than MD-5 and Salted MD-5. Whereas in the SHA family, SHA-512 and SHA-256 have their own benefits. Four new hashing functions have been proposed using the combination of existing algorithms like SHA-256, and SHA-512 namely SHA-256_with_SHA-256, SHA-256_ With_SHA-512,SHA-512_With_SHA-512,and SHA-512_ With_SHA-256. They provide strong hash value for passwords by which the security of passwords increases, and hacking can be controlled to an extent.
Sentiment Analysis of Handwritten and Text Statement for Emotion Classification using Intelligent Techniques: A Novel Approach

Traditionally, many people still wish to write on pen and paper. However, it has some drawbacks, like accessing and storing physical documents efficiently, searching through them, and sharing them efficiently. Handwriting to Text Conversion (HTC) classifies and converts an individual’s handwriting into digital form. However, HTC removes all the mentioned problems as storing, retrieving, and using the text as and when required is easier. Emotions are a basic and particularly important aspect of one’s life. To understand this important aspect of an individual’s life, we must detect emotions using affect data like text, voice, and image. We have used text as the effect data for this work. We can find a person’s emotions behind his text by sentiment analysis. Sentiment recognition and analysis is a topic with wide research as many brands, companies, and even famous personalities are very much interested in getting feedback and thus do the evaluation of their performance and knowing what people think about them around the world. Authors have proposed a model where they collected data from social media reviews and classified it into three broad categories, which are positive, negative, and neutral. Find the emotions category viz. happiness, sadness, shame, anger, disgust, fear, surprise, or neutral from three types of classified sentiments. The proposed model combined machine learning, deep learning, and natural language processing techniques to achieve the best outcome.
Automated Alarm System for Student Anomalous Action Detection in Examination Based on Video Surveillance Using ML Techniques

Determining students' anomalous behavior in the examination room is an important issue to consider. Video surveillance is one of the foremost machinery for identifying the aberrant behavior of the students in test room. This method performs best when it is used with a compact camera and focuses on every student. Monitoring students during exams is a tedious task, with rapid development of technology there is a need for a system that effectively performs the function of the invigilator during the exams i.e., to monitor students for misconduct during exams. The system is designed to detect unique patterns for actions of concern in real-time, such as passing of notes, using cellphones & earphones etc., during exam hours. This detection is based on YOLOv3 object detection algorithm. The paper explores ways to discover the unusual behavior of the students in a test room that helps to avoid cheating in the test room and helps to reduce the duty of invigilator and provide evidence of cheat with an alarm sound.
Text Representation for Sentiment Analysis: From Static to Dynamic

Text representation in a vector, known as embedding, is crucial for various classification tasks including sentiment analysis. It helps to process and understand natural language text more effectively. It has evolved from static approaches, such as bag-of-words and n-grams, to more dynamic approaches that consider the context and meaning of words, such as word embeddings and contextualized embeddings. Word embeddings use neural networks to learn vector representations of words based on their co-occurrence patterns in large text corpora. On the other hand, contextualized embeddings, such as BERT, consider the context of each word within a sentence or document to generate more nuanced representations. Numerous researchers have suggested modifying the original Word2Vec and BERT embeddings to include sentiment information. This paper provides a comprehensive overview of these methods by including a detailed discussion of various evaluation techniques. The paper also outlines several challenges related to embeddings that can be addressed in order to improve the results of sentiment analysis.
Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19

As findings on the epidemiological and genetic risk factors for coronavirus disease-19 (COVID-19) continue to accrue, their joint power and significance for prospective clinical applications remains virtually unexplored. Severity of symptoms in individuals affected by COVID-19 spans a broad spectrum, reflective of heterogeneous host susceptibilities across the population. Here, we assessed the utility of epidemiological risk factors to predict disease severity prospectively, and interrogated genetic information (polygenic scores) to evaluate whether they can provide further insights into symptom heterogeneity. A standard model was trained to predict severe COVID-19 based on principal component analysis and logistic regression based on information from eight known medical risk factors for COVID-19 measured before 2018. In UK Biobank participants of European ancestry, the model achieved a relatively high performance (area under the receiver operating characteristic curve ~90%). Polygenic scores for COVID-19 computed from summary statistics of the Covid19 Host Genetics Initiative displayed significant associations with COVID-19 in the UK Biobank ( p -values as low as 3.96e-9, all with R 2 under 1%), but were unable to robustly improve predictive performance of the non-genetic factors. However, error analysis of the non-genetic models suggested that affected individuals misclassified by the medical risk factors (predicted low risk but actual high risk) display a small but consistent increase in polygenic scores. Overall, the results indicate that simple models based on health-related epidemiological factors measured years before COVID-19 onset can achieve high predictive power. Associations between COVID-19 and genetic factors were statistically robust, but currently they have limited predictive power for translational settings. Despite that, the outcomes also suggest that severely affected cases with a medical history profile of low risk might be partly explained by polygenic factors, prompting development of boosted COVID-19 polygenic models based on new data and tools to aid risk-prediction.
Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence auto-encoding reconstruction (VSAR), which performs latent sequence inference given an observed text. To leverage information from text pairs, we additionally introduce a novel supervised model we call dual directional learning (DDL), which is designed to integrate with our proposed VSAR model. Combining VSAR with DDL (DDL+VSAR) enables us to conduct semi-supervised learning. Still, the combined model suffers from a cold-start problem. To further combat this issue, we propose an improved weight initialisation solution, leading to a novel two-stage training scheme we call knowledge-reinforced-learning (KRL). Our empirical evaluations suggest that the combined model yields competitive performance against the state-of-the-art supervised baselines on complete data. Furthermore, in scenarios where only a fraction of the labelled pairs are available, our combined model consistently outperforms the strong supervised model baseline (DDL) by a significant margin (p <.05; Wilcoxon test). Our code is publicly available at "https://github.com/jialin-yu/latent-sequence-paraphrase".