Reviewerly/exHarmony huggingface dataset:
Social Media Sentiment Analysis Using Deep Learning Approach

Compared to more traditional social media channels, Facebook and Twitter are far more effective at spreading information. Social media has developed into a great data origin for businesses or researchers to create models to analyse this repository and harvest practical insights for marketing policy for word-of-mouth (WOM) trading. However, the vocabulary used in social media is rather condensed and includes specialised words and symbols. Such brief communications are not well suited for the majority of natural language processing (NLP) techniques, which concentrate on processing formal phrases. In this paper, we suggest a brand-new paradigm for social media sentiment analysis based on deep learning models. We gather information from which we create a dataset. We aim to create a semantic dataset after processing these particular phrases in order to support future study. Future applications will benefit greatly from the retrieved data. Several social media platforms have been crawled to gather the trial data.
Securing Outsourced Personal Health Records on Cloud Using Encryption Techniques

Due to the greater flexibility and accessibility of data outsourcing environments such as cloud computing environments, several healthcare organizations have implemented electronic Personal Health Records (PHRs) to ensure that individual patients have such resilience and scalability. It allows users to manage their health information in a safe environment. However, PHRs contain highly sensitive information where security and privacy issues are major concerns. PHR owners should also be able to securely define their own access policies for offsite data. In addition to basic authentication capabilities, existing commercial cloud platforms typically offer symmetric or public key encryption as an optional feature to keep tenants’ data confidential. However, such traditional encryption schemes are not suitable for data outsourcing environments due to the high key management overhead of symmetric encryption and the high maintenance costs of handling multiple copies of ciphertext for public key encryption solutions. The output of this study is to design and development of a secure, fine-grained access control scheme with lightweight updates to outsourced PHR access policies. The proposed scheme is based on Cipher Text Policy Attribute-Based Encryption (CP-ABE) and Proxy Re-Encryption (PRE). Additionally, this study introduces a policy versioning technique that supports full traceability of policy changes using the Elgamal technique and a performance evaluation that demonstrates the efficiency of the proposed scheme.
Video Content Analysis Using Deep Learning Methods

With the emergence of low-cost video recording devices, the internet is flooded with videos. However, most videos are uncategorized, necessitating video content analysis. This review effort addresses visual big data feature extraction, video segmentation, classification, and abstract video challenges. Exploring compressive sensing, deep learning (DL), and kernel methods for various tasks in video content analysis include video classification, clustering, dimension reduction, event detection, and activity recognition. DL is used to examine video footage recognition and classification. This study examines the algorithms' flaws and benefits when applied to datasets. The classification approaches used Naive Bayes, support vector machine (SVM), and Deep Convolution Neural Network (DCNN) with Deer Hunting Optimization (DHO). Other approaches have higher false discovery and alarm rates than the DCNNDHO algorithm.
Comparison of Advanced Encryption Standard Variants Targeted at FPGA Architectures

Digital communication of any form must provide data confidentiality as the threats are increasing in today’s rapid world. Data privacy and security are crucial factors as data is considered gold in the modern era. The 128-bit Advanced Encryption Standard algorithm, commonly known as AES, has been implemented in several designs, focusing on specific purposes and is used widely. The 256-bit variant uses the same fundamental cipher blocks as the 128-bit version but differs in key size, the key expansion function and the number of cipher rounds. This paper investigates the 256-bit AES algorithm targeted at FPGA-Field Programmable Gate Arrays architectures and compares it with the 128-bit implementation, reporting performance and resource utilization. Also, the security offered is discussed. The security is determined by the complexity of recovering the key using cryptanalytic attacks. Both encryption and decryption processes are handled by this implementation and are tested in Verilog language using the Xilinx Vivado software on the Xilinx Zynq-7000 (xc7z020-clg484-1) FPGA.
Multi-filter Wrapper Enhanced Machine Learning Model for Cancer Diagnosis

The classification accuracy of the high dimensional dataset degrades due to the redundant and irrelevant features. Feature selection (FS) is used to reduce the dimensionality of the dataset by removing the noisy features. Each filter has its statistical approach. So the feature selected by a single filter may ignore the important one. We have presented a multifilter (MF) wrapper hybrid model. The advantage of using the MF method is to select the important feature by one filter which one may ignore by the other. Here, we have used an aggregator approach to combine the most efficacious features among the four individual filter methods (information gain (IG), chi-square (Chi-sq), minimum redundancy maximum relevance (mRMR), and relief). The accuracy assessment is carried out in a multiple filter wrapper (Jaya-SVM, GA-SVM, PSO-SVM, and FA-SVM). The evaluation and prediction of the subset of features are carried out with four classifiers with excellent performance, such as the support vector machine (SVM), Naive Bayes (NB), decision tree (DT), and linear discriminant analysis (LDA) were tested respectively. Four (breast cancer, leukemia, ovarian, and central nervous system (CNS)) cancer datasets are used to implement the model. The performance of the MF wrapper is excellent in comparison to a single filter. According to the findings of this study, the proposed hybrid approach is a more efficient and trustworthy feature selection technique for selecting highly discriminative features.
A Decentralized and Immutable E-Voting System using Blockchain

Elections are an important event that is responsible for shaping democracies around the world, but still there are many populations around the world who do not fully trust the electoral system which has become one of the major concerns for democracies around the world. Even the world’s greatest democracies, such as India and Japan, have a flawed legal system. Vote rigging, EVM (Electronic Vote Machine) hacking, election tampering, booth capture squares, etc. are the main causes of the problems in the current voting method utilized in these countries. It has proven challenging to develop an electronic voting system that is both secure and safe and that offers a higher level of security, fairness, and privacy than current voting techniques while simultaneously offering the transparency and flexibility. Replacing the existing pen-andpaper (ballot-based voting) approach with a new election system offers the potential to reduce fraud and increase security while also increasing efficiency. Blockchain is a technology that promises to increase the overall robustness and security of various e-voting systems. The paper outlines a great attempt to develop a trustworthy and efficient voting system using blockchain’s properties, including its transparency and cryptographic foundations. The system offers end-to-end verifiability and satisfies the fundamental criteria for e-voting methods. The paper covers the electronic voting system and how it would work on the Multichain network.
Quantum walks on <i>N</i>-cycles with an absorbing wall

In this paper, we analyze quantum walks on cycles with an absorbing wall. We set the absorbing wall on cycles with N vertices (where N is an even number), and divide [Formula: see text] into two parts, [Formula: see text] and [Formula: see text]. Due to the periodicity of the cycles, the condition [Formula: see text] (or [Formula: see text]) is applied to [Formula: see text] and [Formula: see text], then the transmission probability [Formula: see text] and reflection probability [Formula: see text] at the absorbing wall [Formula: see text] at time t are obtained. Furthermore, we show that over time, the absorbing wall absorbs less and less.
Sentiment analysis on labeled and unlabeled datasets using BERT architecture

Sentiment analysis (SA) is the study of human perception in any subject of practice. It retrieves data from datasets using natural language processing (NLP) methodologies and algorithms that are either regulation-based, blended, or rely on machine learning approaches. SA is garnering fame for its capacity to fit in a large chunk of data with user evaluations, uncover a trend, and come to a consensus derived from real facts rather than hypotheses established on a limited number of observations. The flexible nature of sentiment gathering has helped in playing a critical role in both commercial and research applications in the last few years. This study presents new sentiment analysis models based on Bidirectional Encoder Representations from Transformers (BERT) for both labeled and unlabeled datasets. The labeled datasets using supervised learning are modeled in a hybrid architecture of fine-tuned BERT and interval Type—2 fuzzy sets. The inclusion of interval Type-2 fuzzy logic for handling reluctance or inaccuracy in data shows commendable results for the labeled datasets. For the prediction of sentiments in unlabeled datasets, they are embedded through a BERT tokenizer with the help of a threshold and activation functions. The coupling of a multi-layer perceptron with the BERT parser substantially decreases the time and complexity compared to supervised learning. Both the models have been implemented on multiple datasets and have outperformed existing state-of-the-art techniques in this field.
Overview: Real-Time Video Monitoring for Suspicious Fall Event Detection

Escorted by the advancement of technology, monitoring of real-time video data is playing an increasingly important role and massive impact on our lives. Therefore, correctly classifying the suspicious fall event detection during real-time video data monitoring is a highly appealing interest in the research area. In this paper, real-time events are considered for monitoring a person living in an indoor environment and performing their daily activity. Suspicious events are considered as detecting the falling of a person. Up to now, numerous techniques have been developed efficiently to classify suspicious fall event detection. An overview of numerous machine learning and deep learning techniques applied by several researchers for the detection of suspicious fall event detection in the monitoring of real-time video data has been considered. The paper also presents several affairs of machine learning and deep learning techniques that may assist focused researchers in working on monitoring video data.
Necessity of Self-explainability in AI: Insights from the Existing Social Projects

Self-explanatory artificial intelligence has lately gained prominence as researchers and practitioners strive to make their methodologies more understandable. It is not contentious to assert that researching how individuals explain to one another may be a useful beginning point for explanation in artificial intelligence, given that the majority of this research has focused on publicly explaining decisions or actions to a human observer. However, it is reasonable to say that the vast bulk of self-explanatory artificial intelligence research is based solely on the researchers’ subjective opinions of what constitutes a “good” explanation. Extensive and essential study in science and technology has been conducted on the definition, creation, selection, assessment, and presentation of explanations by humans. This study contends that people use certain cognitive biases and social expectations in the explanation process. This paper covers relevant studies from several fields and makes the case that self-explainable artificial intelligence should expand on some current research. It highlights several key results and explores how they may be combined with research on self-explainable artificial intelligence.
A Comparative Investigation of Deep Feature Extraction Techniques for Video Summarization

Video summarization refers to creating a temporally abridged version of video containing all the important highlights required for context understanding without the loss of original information. Segmentation and feature extraction are the two pre-processing tasks to be carried out on the input video sequence in any summarization framework. Segmentation divides the video into non-intersecting temporal segments while feature extraction process represents entire video in the form of feature vectors. This paper investigates video feature extraction using pre-trained deep neural networks, viz., GoogleNet, ResNet, and ResNeXt. These deep networks are employed to extract feature vectors from the video frames and the extracted features are used to summarize videos using summarization models. The efficacy of these deep networks for feature extraction step is then compared in terms of F1-scores of summarized videos. Our experimentation revealed that the performance of a deep network for feature extraction depends on nature of videos and summarization approaches. It is observed that GoogleNet is optimum choice for feature extraction in video summarization application.
Comparison of Blockchain Ecosystem for Identity Management

In recent years, blockchain technology is found to be the most significant discovery in this digital era, after the discovery of the Internet and Cloud Computing. Blockchain is a simple, distributed public ledger that contains all the user's transaction details in a block. The global copy of the block is then shared among all its peer-to-peer network users after validation by the blockchain miners. Once a block is validated and accepted, it cannot be altered by any users making it a trust-free transaction. It also resolves the problem of double-spending by using traditional cryptographic methods. Since the advent of Bitcoin, blockchain is the backbone for all its transactions. But in recent years, it has found its roots and uses in many fields like smart contracts, smart city management, healthcare, etc. Identity management against digital identity theft had become a major concern among financial and other organizations. To solve this digital identity theft, blockchain technology can be employed with existing identity management systems which maintain a distributed public ledger containing details of an individual's identity containing information such as digital birth certificates, citizenship number, bank details, voter details, and driving license in the form of blocks verified on the blockchain becomes time-stamped, unforgeable and publicly visible for any legitimate users. The main challenge in using blockchain technology for preventing digital identity theft is ensuring the pseudo-anonymity and privacy of the users. This survey paper will exert to study the blockchain concepts, consensus protocols, and various blockchain-based digital identity management systems with their research scope. This paper also discusses the role of blockchain in COVID-19 pandemic management through self-sovereign identity and supply chain management.
Evaluation of BERT Model for Aspect-Based Sentiment Analysis

Thanks to digital evaluations and ratings on E-commerce sites, people can now purchase items more effortlessly. Purchasers may share their stories on e-commerce giants like Amazon, Flipkart, and others, giving prospective buyers concrete proof of the product’s effects. One of the most difficult challenges in Sentiment Analysis (SA) is determining a customer's fine-grained opinion on a single component of a product. This research offers a machine learning model based on Bidirectional Encoder Representation Transformer (BERT) for performing Aspect-Based Sentiment Analysis (ABSA) of user evaluations. This paper proposes a BERT-based model for SA of three different datasets of user reviews obtained from Amazon, IMDB, and Tripadvisor, respectively. The proposed BERT model outperformed other classifiers when compared by varying learning rates, dropouts, and sentence lengths of user texts. The proposed model beats three baseline models in terms of accuracy of the classification and achieves state-of-the-art results.
Human Emotion Recognition Using Facial Characteristic Points and Discrete Cosine Transform with Support Vector Machine

This paper introduces a method for human emotion recognition using facial characteristic points (FCPs) and discrete cosine transform (DCT) coefficients as the feature vectors, extracted from the human emotion facial images. In order to classify the emotions based on the feature vectors, Support Vector Machine is used as a classifier. For experimentation purpose linear function, polynomial, non-homogenous polynomial, sigmoid, Gaussian, and radial basis functions are used as kernels. In this work two types of features as facial shape features and appearance features are used for facial emotion recognition. In the first type thirty FCPs are extracted as the facial shape features, whereas in the second type first hundred coefficients of DCT are used as the feature vectors. In order to extract the FCPs, the facial image is localized and divided into various regions. Performance of the proposed algorithm has been evaluated with the help of confusion matrices, average accuracy, and timing analysis on seven human emotion facial images from Japanese Female Facial Expression (JAFFE), and in-house generated databases independently. The databases is divided in two parts as train and test databases in the ratio of 70% and 30%, respectively. The performance is compared with the amount of confusion for the same number of the class created on the same database. The highest average accuracy obtained with the proposed algorithm for emotion recognition is 92.86%. The results show that the radial basis function of SVM is more suitable for person-associated situation and the linear function describes person-irrelevant problems better.
Novel SVD-DWT Based Video Watermarking Technique

In this paper, a novel Singular Value Decomposition (SVD)-Discrete Wavelet Transform (DWT) based video watermarking technique has been presented. The main purpose of using a combination of SVD and DWT is that it improves the imperceptibility and robustness of the proposed watermarking scheme. We embed the watermark information into singular values of SVD with entropy of singular value using additive method in low frequency sub-band of DWT. A blind detection technique is used in the extraction procedure. Several attacks are used, and various performance metrics are generated, to analyze the robustness and imperceptibility of the proposed watermarking system. The results show that the proposed technique is resistant to a variety of attacks and achieves a high level of invisibility.
The Role of Economics Factors in Driving Economic Growth: An Empirical Study in Iraq

This study examined the implications of inequality, human capital, innovation and entrepreneurship, social and political instability, and other factors on sustainable economic growth in Erbil-Kurdistan. Quantitative methods were applied, and information from 2006–2022 was analyzed. Data analysis included a correlation and regression test. It was observed that inequality, social discontent, and political instability were significantly connected to the opposite outcome (low economic growth), while human capital, innovation, and entrepreneurship were significantly related to high economic growth that could be sustained over time. The findings revealed that all developed research hypotheses are supported. The study highlights the importance of supporting innovation and entrepreneurship, as well as investing in people, for long-term economic growth in Erbil and Kurdistan. Inequality, as well as social and political instability, are addressed, and suggestions for policy change are offered. Further interdisciplinary study is required to properly understand the elements that contribute to Erbil and Kurdistan's sustained economic success.
DKSM: A Decentralized Kerberos Secure Service-Management Protocol for Internet of Things

Kerberos is a widely used authentication protocol that protects distributed services on the Internet of Things (IoT) and big data. In a distributed scenario, entities must prove their identity to a trusted third party using shared secrets, such as secret keys. Traditional schemes typically use a trusted central organization, like a Key Distribution Center, for identity authentication. However, Kerberos has some downsides, such as a single point of failure, vulnerability to replay attacks, and potential credential exposure, which can compromise system security. To address these problems, researchers have been working on various solutions, but most have their own drawbacks. In this paper, we propose a Decentralized Kerberos Secure Service-Management Protocol (DKSM) based on blockchain technology and Ciphertext-policy Attribute-based Encryption (CP-ABE) schema. Compared with existing protocols, DKSM fulfills decentralization, fine-grained access control with effective cost, and scalability simultaneously. DKSM uses AES and Fast Attribute-Based Encryption with Optimal Security (FABEO) as its cryptographic basis. We also discuss the security of DKSM and demonstrate how our protocol can defend against attacks. Finally, tests on the Ethereum testnet and FISCO consortium platform have shown that our designed protocol is efficient and cost-effective.
Cause of Death estimation from Verbal Autopsies: Is the Open Response redundant or synergistic?

Civil registration and vital statistics systems capture birth and death events to compile vital statistics and to provide legal rights to citizens. Vital statistics are a key factor in promoting public health policies and the health of the population. Medical certification of cause of death is the preferred source of cause of death information. However, two thirds of all deaths worldwide are not captured in routine mortality information systems and their cause of death is unknown. Verbal autopsy is an interim solution for estimating the cause of death distribution at the population level in the absence of medical certification. A Verbal Autopsy (VA) consists of an interview with the relative or the caregiver of the deceased. The VA includes both Closed Questions (CQs) with structured answer options, and an Open Response (OR) consisting of a free narrative of the events expressed in natural language and without any pre-determined structure. There are a number of automated systems to analyze the CQs to obtain cause specific mortality fractions with limited performance. We hypothesize that the incorporation of the text provided by the OR might convey relevant information to discern the CoD. The experimental layout compares existing Computer Coding Verbal Autopsy methods such as Tariff 2.0 with other approaches well suited to the processing of structured inputs as is the case of the CQs. Next, alternative approaches based on language models are employed to analyze the OR. Finally, we propose a new method with a bi-modal input that combines the CQs and the OR. Empirical results corroborated that the CoD prediction capability of the Tariff 2.0 algorithm is outperformed by our method taking into account the valuable information conveyed by the OR. As an added value, with this work we made available the software to enable the reproducibility of the results attained with a version implemented in R to make the comparison with Tariff 2.0 evident.