Reviewerly/exHarmony huggingface dataset:
Efficient Gaussian Kernel Microcluster Real-Time Clustering Method for Industrial Internet of Things (IIoT) Streams

With recent advancements in Industrial Internet of Things (IIoT), the stream data generated in IIoT applications presents new characteristics: huge amount of data, ultrahigh computational complexity and large memory consumption, and the existence of concept drift leading to the ineffective distinction between real drift and anomalies. It is difficult for the current mainstream methods to cope with the above problems effectively. In this article, we propose an efficient Gaussian kernel microcluster real-time-clustering method for IIoT data streams (GKMC).The method uses a microcluster sketch structure instead of individual data sample points to participate in clustering directly, which solves the problem of not being able to store unlimited data in limited memory; it uses a Gaussian kernel function to calculate the local density of microclusters to enhance the detection of anomalies; in addition, using the gravity energy function recursively to update the microcluster online and using the relearning strategy to improve the detection ability of whether the outdated microcluster belongs to abnormal microcluster or has real concept drift, ensuring that the current microcluster is always the latest microcluster most closely related to the cluster. The theoretical analysis and sufficient comparison experiments on three data sets show that the proposed algorithm has a better clustering effect than the current mainstream stream clustering algorithms.
Sentiment analysis researches story narrated by topic modeling approach

The wild growth of user-generated content like websites, social media, and mobile apps, conducts individuals to create enormous masses of opinions and reviews about products, services, and every day events. Sentiment analysis (SA) embraces a powerful tool for businesses and researchers to explore and study community attitudes, interpretations and, insightful consequences for decision support. This paper brings forward a comprehensive study about main research topics, research trends, and comparisons of research topics” in the field of “sentiment analysis” through “social media” using topic modeling, in specific LDA. The findings of this paper prove that “machine learning” methods are among the most important topics the studies worked on in recent years. Also, various social media platforms such as “Twitter, Facebook, YouTube, and blog” are the SA infrastructures. Among the applications, transportation, spam detection, and decision making are important in terms of the normalized frequency. Finally, findings verify the concept “service improvement by sentiment analysis” indicates the important topic which concentrates quality improvement of firm's service through analysis of customer reviews and it permits researchers and practitioners and also managers have better visions about the hot era of “sentiment analysis”.
Dynamic Tuning of Fuzzy Membership Function for an Application of Soil Nutrient Recommendation

Successful soil nutrient management in the agriculture sector will enhance soil fertility and crop productivity. Laboratory soil testing procedure provides the nutrient ranges and recommends the fertilizers. After the chemical analysis, technicians recommend the nutrient ranges using the fertility table. The soil characteristics and conditions decide the fertility ranges. This paper proposes the fuzzy inference system-based soil nutrient recommendation system. Changing the fuzzy membership function (MF) ranges based on soil conditions is a tedious job in a fuzzy nutrient recommendation system. Therefore, the particle swarm optimization (PSO) technique dynamically updates the MF ranges in a fuzzy nutrient recommendation system. The final simulation result demonstrates the fuzzy PSO tuning gives finer performance over the typical fuzzy system for soil nutrient recommendation.
Design Of A Secure Virtual File Storage System On Cloud Using Hybrid Cryptography

As Security is becoming more and more useful in the field of computing, users would like to be sure of how secure their files are on a system, as security is one of the most crucial fields in networking and file storage. Dependable file storage and access establish several security issues in a cloud computing. This research designed and implemented virtual secure file storage system on cloud using hybrid cryptography. The cryptography method used for file encryption and decryption is AES and SHA-2 hash function. It is implemented using Cloud APIs with REST calls or client libraries in PHP. The system interfaces were developed using HTML, CSS and JAVASCRIPT. Back end development was done using PHP, MYSQL and GCP Cloud Storage Library then the file encryption and decryption was achieved through PHP classes which includes open_ssl_file_encryption and decryption (AES) and also MCRYPT function. The proposed virtual system is also compared with some latest related works.
Hierarchical Visualization for Exploration of Large and Small Hierarchies

This paper presents a comparison of visualization methods for hierarchical data, and extends it by demonstrating an interactive approach that uses a combination of hierarchical visualization methods to support exploration and analysis of high-dimensional data. Hierarchical visualization can provide a useful abstraction for investigation of large and complex datasets, but the range of options for visualization can make it challenging for users to determine which is the most suitable for their datasets. Existing evaluations of hierarchical visualization methods typically compare the representation of hierarchical structures rather than the representation of underlying data, despite the underlying data commonly being a crucial part of the analysis. Five different visualization methods for hierarchical data are compared in this paper, with focus on understanding the underlying data features, represented by size and colour, for two different sizes of hierarchical structures. The study results indicate that Sunburst and Circular Treemap perform best for larger hierarchical structures, while Treemap, Icicle plot and Circular Treemap performed best for smaller hierarchies. The results from the study, which were first presented in Macquisten et al. [1], are in this paper used to inform the design of a visualization approach for high-dimensional data exploration that combines hierarchical visualization methods. The approach is demonstrated through a use case analysis of data from a study of the ocean microbiome.
4D Facial Expression Recognition Using Geometric Landmark-based Axes-angle Feature Extraction

The primary goal of this paper is to describe a proposed framework for identifying human face expressions. A methodology has been proposed and developed to identify facial emotions using an axes-angular feature extracted from facial landmarks for 4D dynamic facial expression video data. The 4D facial expression recognition (FER) problem is modeled as an unbalanced problem using the full video sequence. The proposed dataset includes landmarks that are positioned to be fiducial features: around the brows, eyes, nose, cheeks, and lips. Following the initial facial landmark preprocessing, feature extraction is carried out. Input feature vectors from gamma axes and magnitudes in three-dimensional Euclidean space are constructed. This paper develops a new feature vector by concatenating the angle and magnitude features and compares them to a model created independently using angle features. In all three models, several filter-based feature selection techniques are used to estimate feature importance. The framework employs a multi-class support vector machine (SVM) on MATLAB, and the true positive rate (TPR) and average recognition rates (ARR) are used as performance metrics. In terms of average classification accuracy, the final results are compared to conventional state-of-the-art approaches. The results revealed a highly informative combined feature, demonstrating the efficiency of the proposed landmark-based strategy to classify facial expressions.
A Posit Based Multiply-accumulate Unit with Small Quire Size for Deep Neural Networks

Posit is a numerical representation that is especially focused on deep neural networks (DNNs). However, a specific register called quire is necessary for a posit multiply-accumulate (MAC) unit to ensure the calculation accuracy. In this paper, we proposed posit based MAC unit that can optimize quire size according to target applications. We also develop the DNN library to explore quire size of proposed MAC unit. Experimental result with ResNet-9 showed that we achieved the same level of accuracy as Deep Positron but with the area reduction of 43%.
Supply Chain Application of Blockchain-Based Solutions for Cyber-Physical Systems: Review and Prospects

Cyber-Physical Systems (CPS) are a new generation of systems that involves engineered computing and communicating systems interfacing the physical world, often interacting with a highly uncertain and adverse environment, including humans and other CPS. Flawless integration of these systems still faces various challenges, e.g., interoperability, decentralization, trust, privacy, and security. Thanks to its salient features, Blockchain is a promising technology that could efficiently address these challenges. This paper provides insights and explores the opportunities and challenges of incorporating CPS and Blockchain technology. As a propitious industrial application, this paper highlights blockchain-based CPS solutions for Supply Chain Management (SCM). We present opportunities, established projects, and some use cases. Finally, we point out some open research issues and outline future research directions.
Optimization for Interval Type-2 Polynomial Fuzzy Systems: A Deep Reinforcement Learning Approach

It is known that the interval type-2 (IT2) fuzzy controllers are superior compared to their type-1 counterparts in terms of robustness, flexibility, etc. However, how to conduct the type reduction optimally with the consideration of system stability under the fuzzy-model-based (FMB) control framework is still an open problem. To address this issue, we present a new approach through the membership-function-dependent (MFD) and deep reinforcement learning (DRL) approaches. In the proposed approach, the reduction of IT2 membership functions of the fuzzy controller is completing during optimizing the control performance. Another fundamental issue is that the stability conditions must hold subject to different type-reduction methods. It is tedious and impractical to resolve the stability conditions according to different type-reduction methods, which could lead to infinite possibility. It is more practical to guarantee the holding of stability conditions during type-reduction rather than resolving the stability conditions, the MFD approach is proposed with the imperfect premise matching (IPM) concept. Thanks to the unique merit of the MFD approach, the stability conditions according to all the different embedded type-1 membership functions within the footprint of uncertainty (FOU) are guaranteed to be valid. During the control processes, the state transitions associated with properly engineered cost/reward function can be used to approximately calculate the deterministic policy gradient to optimize the acting policy and then to improve the control performance through determining the grade of IT2 membership functions of the fuzzy controller. The detailed simulation example is provided to verify the merits of the proposed approach.
A Concise Review on Automatic Text Summarization

Today, data is the most important thing humanity needs, thus understanding the linguistics of such a large data is not practically possible so, text summarization is introduced as the problem in natural language processing (NLP). Text summarization is the technique to convert long text corpus such that the semantics of the text does not change. This paper provides a study of different text summarization methods till Q3 2020. Text summarization methods are broadly classified as abstractive and extractive. In this paper, more focus is given to abstractive summarization a review for most of the methods of text summarization to date is written concisely along with the evaluations and advantages-disadvantages also for each method. At the end of the paper, the challenges faced by researchers for this task are mentioned and what improvements can be done in every method for summarization is also written in a structured way.
A deep learning based approach for Arabic Sign language alphabet recognition using electromyographic signals

Unlike spoken languages, Sign languages are mainly based on gestures. This visual aspect makes their recognition a challenge for researchers. Despite the great advances in language processing, sign language recognition still requires a lot of work to achieve acceptable accuracy. On this work we aim to contribute on the improvement of the Arabic sign language recognition. In fact, we aim to exploit the electromyographic EMG signal produced by our muscles while making a gesture to recognize the gesture. Electromyographic signals are rich in information that can be used to calculate muscle activation. They present a potential technology for gesture recognition and classification. These signals are widely used to manufacture human-computer interaction (HMI) devices to control a prosthesis, a robot, or a computer to perform certain predefined tasks. The main objective of our research is to propose a new approach based on deep learning to recognize the Arabic sign language alphabet. We have proposed a novel convolutional neural network that uses LSTM to process feature dependencies. This model is applied to recognize 7 alphabetic characters of Arabic Sign Language. Compared to existing approaches, our approach shows a high accuracy of 97.5<sup>&#x0025;</sup>.
Charity Supervision Management System Based on Blockchain

The charity management system is a vital information management system. the information must be entirely open for society, and anyone has the right to supervise. Therefore, we use the characteristics of the blockchain to be tamper-proof, decentralized, autonomous, observable, verifiable, and information-sharing to develop a secure and robust charity information management system. Use the ring signature mechanism to protect the privacy of supervisors. A charity contract system was developed based on the Ethereum experimental environment, code the traditional charity text contract, storage and execution of contracts both are on the blockchain, which ensures the autonomy and reliability of the charity project and the trustworthiness and credibility. Our system is a novel innovation that can succeed the traditional public platform for charity.
The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights

Despite the encouraging outcomes of machine learning and artificial intelligence applications, the safety of artificial intelligence–based systems is one of the most severe challenges that need further exploration. Data set poisoning is a severe problem that may lead to the corruption of machine learning models. The attacker injects data into the data set that are faulty or mislabeled by flipping the actual labels into the incorrect ones. The word “robustness” refers to a machine learning algorithm’s ability to cope with hostile situations. Here, instead of flipping the labels randomly, we use the clustering approach to choose the training samples for label changes to influence the classifiers’ performance and the distance-based anomaly detection capacity in quarantining the poisoned samples. According to our experiments on a benchmark data set, random label flipping may have a short-term negative impact on the classifier’s accuracy. Yet, an anomaly filter would discover on average 63% of them. On the contrary, the proposed clustering-based flipping might inject dormant poisoned samples until the number of poisoned samples is enough to influence the classifiers’ performance severely; on average, the same anomaly filter would discover 25% of them. We also highlight important lessons and observations during this experiment about the performance and robustness of popular multiclass learners against training data set–poisoning attacks that include: trade-offs, complexity, categories, poisoning resistance, and hyperparameter optimization.
Human Factors in Electronic Health Records Cybersecurity Breach: An Exploratory Analysis.

The healthcare sector continues to be the industry suffering one of the highest costs of a data security breach. Healthcare lags behind other industries in cybersecurity preparedness despite advances in cybersecurity technologies. Technical safeguards to protect electronic health records must be combined with human behavioral interventions to promote a robust cybersecurity plan. Using data from the United States Department of Health and Human Services, we conducted an exploratory analysis of past data breaches in healthcare organizations from January 2015 to December 2020 to explore the extent to which human elements played a role in data security incidents. We found that a vast majority of health records were compromised due to poor human security. The mean number of records affected by a breach due to unintentional insider threats is more than twice that of breaches caused by malicious intent such as external cyberattacks and theft. Our findings also indicate that, on average, more patient records are compromised from falling for a phishing scam than any other reason. We argue that proper cybersecurity contingency plans in healthcare must include human behavioral interventions that go beyond technical controls.
Double decoupled network for imbalanced obstetric intelligent diagnosis

Electronic Medical Record (EMR) is the data basis of intelligent diagnosis. The diagnosis results of an EMR are multi-disease, including normal diagnosis, pathological diagnosis and complications, so intelligent diagnosis can be treated as multi-label classification problem. The distribution of diagnostic results in EMRs is imbalanced. And the diagnostic results in one EMR have a high coupling degree. The traditional rebalancing methods does not function effectively on highly coupled imbalanced datasets. This paper proposes Double Decoupled Network (DDN) based intelligent diagnosis model, which decouples representation learning and classifier learning. In the representation learning stage, Convolutional Neural Networks (CNN) is used to learn the original features of the data. In the classifier learning stage, a Decoupled and Rebalancing highly Imbalanced Labels (DRIL) algorithm is proposed to decouple the highly coupled diagnostic results and rebalance the datasets, and then the balanced datasets is used to train the classifier. This paper evaluates the proposed DDN using Chinese Obstetric EMR (COEMR) datasets, and verifies the effectiveness and universality of the model on two benchmark multi-label text classification datasets: Arxiv Academic Papers Datasets (AAPD) and Reuters Corpus1 (RCV1). Demonstrating the effectiveness of the proposed methods is an imbalanced obstetric EMRs. The accuracy of DDN model on COEMR, AAPD and RCV1 datasets is 84.17, 86.35 and 93.87% respectively, which is higher than the current optimal experimental results.
Partially Oblivious Neural Network Inference

Oblivious inference is the task of outsourcing a ML model, like neural-networks, without disclosing critical and sensitive information, like the model's parameters. One of the most prominent solutions for secure oblivious inference is based on a powerful cryptographic tools, like Homomorphic Encryption (HE) and/or multi-party computation (MPC). Even though the implementation of oblivious inference systems schemes has impressively improved the last decade, there are still significant limitations on the ML models that they can practically implement. Especially when both the ML model and the input data's confidentiality must be protected. In this paper, we introduce the notion of partially oblivious inference. We empirically show that for neural network models, like CNNs, some information leakage can be acceptable. We therefore propose a novel trade-off between security and efficiency. In our research, we investigate the impact on security and inference runtime performance from the CNN model's weights partial leakage. We experimentally demonstrate that in a CIFAR-10 network we can leak up to $80\%$ of the model's weights with practically no security impact, while the necessary HE-mutliplications are performed four times faster.
User Interface Design for AI-Based Clinical Decision-Support System : Preliminary Study

This paper presents a case study about the initial phases of the interface design for an artificial intelligence-based decision-support system for clinical diagnosis. The study presents challenges and opportunities in implementing a human-centered design (HCD) approach during the early stages of the software development of a complex system. These methods are commonly adopted to ensure that the systems are designed based on users' needs. For this project, they are also used to investigate the users' potential trust issues and ensure the creation of a trustworthy platform. However, the project stage and heterogeneity of the teams can pose obstacles to their implementation. The results of the implementation of HCD methods have shown to be effective and informed the creation of low fidelity prototypes. The outcomes of this process can assist other designers, developers, and researchers in creating trustworthy AI solutions.
AGRICULTURE 5.0 - REVIEW

Agriculture has always been an area of great interest when it comes to development and innovation. We are currently living in the age of agriculture 4.0 which is on the rise. That is why we will reach agriculture 5.0 which will be marked by the introduction of artificial intelligence, biotechnology and big data analysis, which will increase the performance, productivity and accuracy of automation.
Fraud Prevention Strategies: An Integrative Approach on the Role of Forensic Accounting

This study examined the role that forensic accounting play in aiding the success of fraud prevention strategies in combating frauds at both corporate and national levels. The study adopted the exploratory research design methodology involving extensive review of published articles, periodicals and other materials relevant to the subject matter. The study found that while the use and deployment of forensic accounting tools and techniques have made appreciable progress in the developed world, its appreciation and usage in emerging economies is still at the embryonic stage due to lack of political will, poor ethical tone set by management and dearth of skilled forensic accounting professionals. It therefore recommended that to ensure sustainable success of fraud prevention strategies, management of both public sector and corporate entities should demonstrate the needed political will and set the right ethical tone at the top through their actions and activities. Similarly, efforts should be made to continually upskill anti-fraud staffs (internal audit, forensic accountants, forensic investigators) through trainings and awareness programs on the latest fraud prevention methodologies. Also, that in view of the rising cases of cybercrimes, nation states should urgently consider the signing and implementation of legal treaties and frameworks to combat the scourge.