Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Lexicon-Based Emotion Recognition of Text

Over the time, many lexicons have been developed for natural language processing. These are used as a baseline to learn the emotion recognition from texts. While most of them are annotated with the polarity of words, i.e. positive, negative or neutral for emotions recognition and sentiment analysis. However, they cover a limited number of words and even fewer lexicons can predict the harder task of emotions. “DepecheMood++” and “NRC” are currently the most comprehensive publicly available word-emotion lexicons for emotions that provide more detailed information on varied emotional parameters such as, happy, sad, fear, and angry. In this paper, we have investigated the performance by comparing the above two lexicons over a benchmark of the International Survey on Emotion Antecedents and Reactions (ISEAR) data set. Performance of aforementioned lexicals in an emotion recognition task is evaluated using F1-Measure. Also, machine learning classification algorithms such as “Naive Baye’s”, “Logistic Regression”, “K-Nearest Neighbours”, “Support Vector Machine”, and “Gaussian Naive Bayes” classifiers were utilized to compare the performance of the both lexicals. There are some notable differences between experimental results in the classification task.
Towards Deep Interpretable Features

The problem of interpretability for binary image classification is considered through the lens of kernel two-sample tests and generative modeling. A feature extraction framework coined Deep Interpretable Features is developed, which is used in combination with IntroVAE, a generative model capable of high-resolution image synthesis. Experimental results on a variety of datasets, including COVID-19 chest x-rays demonstrate the benefits of combining deep generative models with the ideas from kernel-based hypothesis testing in moving towards more robust interpretable deep generative models.
Expanding the current tacit/explicit knowledge dichotomy encompassing situated and theoretical/normative knowledge: a phenomenological perspective

Purpose This article introduces a model of knowledge creation in consciousness, the creation of explicit knowledge in six forms and its register and organisation in documents. Design/methodology/approach Assuming the premise of three realms of reference to knowledge and two forms of reference to entities, this article, through a phenomenological perspective, deduces a model of the creation of knowledge in consciousness and the creation of explicit knowledge in six forms and its register in documents. Findings Two basic types of knowledge are introduced: situated knowledge and theoretical/normative knowledge. Considering three realms of reference of knowledge – the space–time realm, subjectivity realm and linguistic realm – six general types of knowledge are deduced. Finally, three layers of knowledge organisation are presented: classification and mapping documents, theoretical/normative documents and documents of situations. Practical implications This article can contribute to the development of more efficient forms of creation of explicit knowledge, its register in documents and the development of more efficient knowledge organisation and management systems. Originality/value Relying on established perspectives of the realms where subjectivity is immersed, this article discusses how knowledge is created in consciousness and registered in documents. It also presents a novel perspective of types of knowledge through the combination of dimensions, realms of reference and forms of reference to entities.
Imperceptible Image Steganography Technique Using a Novel PIT-Based Technique

The rapid increase in Internet speed led to people exchanging a huge amount of information every moment, including sensitive and private information. Hence, confidentiality and privacy are a big concern. Image steganography is a tool used to ensure that sensitive information is securely shared by concealing it in a cover image. Many techniques have been proposed to protect the information, such as the Least Significant Bit (LSB), Pixel Value Differencing (PVD), Pixel Indicator Techniques (PIT), etc. This paper presents a new PIT -based technique for RGB images to enhance the requirement ofimperceptibility and security. This technique uses the LSB of the green channel as an indicator to hide and encode the secret information in the red and blue channels. The technique is developed in three versions wherein two LSBs, two to four LSBs, or four LSBs of the red and blue channels are utilised to encode up to six bits of the secret information. The experimental results of the proposed method are obtained using MATLAB 2022a. The experimental results showed promising results against standard images with higher Peak Signal-to-Noise Ratio (PSNR) values and Mean Square Error (MSE).
About Earthquake Prediction

The process of creating a mechanism for predicting earthquakes is identical to that of creating a perpetual motion machine. Everybody knows that it is impossible to create perpetual motion machine, but some scientists with fanatic persistence continue to invent different kinds of cunning mechanisms, which, as they selflessly believe, will help them to bypass fundamental laws of thermodynamics and glorify their names till the end of centuries. It would seem that construction of perpetuum mobile and the question of earthquake forecasting have been closed for a long time, but in serious scientific magazines articles of geophysicists appear now and then, where they present their developments, which allegedly lead to the possibility of earthquake forecasting. We have already written about such works and their authors and even wondered who are these knights-geophysicists, tirelessly fighting windmills? Are they really generous souls romantic who spend their life time for a phantom victory over underground element, or calculating cheaters, living on scientific grants, paid to them from taxpayers' pockets? We represent a small critical review of one of such papers where Don Quixotes from geophysics on the base of ionosphere anomalies analysis are trying once again convincing us in the possibility of earthquakes forecasting.
On the preferred extensions of argumentation frameworks: Bijections with naive sets

This paper deals with the problem of finding the preferred extensions of an argumentation framework by means of a bijection with the naive sets of another framework. First, we consider the case where an argumentation framework is naive-bijective: its naive sets and preferred extensions are equal. Recognizing naive-bijective argumentation frameworks is hard, but we show that it is tractable for frameworks with bounded in-degree. Next, we give a bijection between the preferred extensions of an argumentation framework being admissible-closed (the intersection of two admissible sets is admissible) and the naive sets of another framework on the same set of arguments. On the other hand, we prove that identifying admissible-closed argumentation frameworks is coNP-complete. At last, we introduce the notion of irreducible self-defending sets as those that are not the union of others. It turns out there exists a bijection between the preferred extensions of an argumentation framework and the naive sets of a framework on its irreducible self-defending sets. Consequently, the preferred extensions of argumentation frameworks with some lattice properties can be listed with polynomial delay and polynomial space.
A Comparative Review of Cryptoasset Products

Cryptoassets are an asset class recorded in a digital form that does not represent a financial claim or liability for an issuer or a custodian. This chapter provides a detailed review of various cryptoassets by comparing different characteristics, products, and listing exchanges and discusses criticisms of the crypto ecosystem. It also discusses cryptoasset features, methods of tokenization, and advances in decentralized, peer-to-peer exchanges. Another topic examined is the criticisms of cryptoasset exchanges and ongoing regulatory implications due to cryptocurrency's open-source nature. The chapter evaluates numerous types and trends of cryptoassets, including currency, utility, platform, and transactional tokens.
Quantifying the Expressive Capacity of Quantum Systems: Fundamental Limits and Eigentasks

The expressive capacity of quantum systems for machine learning is limited by quantum sampling noise incurred during measurement. Although it is generally believed that noise limits the resolvable capacity of quantum systems, the precise impact of noise on learning is not yet fully understood. We present a mathematical framework for evaluating the available expressive capacity of general quantum systems from a finite number of measurements, and provide a methodology for extracting the extrema of this capacity, its eigentasks. Eigentasks are a native set of functions that a given quantum system can approximate with minimal error. We show that extracting low-noise eigentasks leads to improved performance for machine learning tasks such as classification, displaying robustness to overfitting. We obtain a tight bound on the expressive capacity, and present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks. These results are supported by experiments on superconducting quantum processors. Our findings have broad implications for quantum machine learning and sensing applications.
Computational Charisma -- A Brick by Brick Blueprint for Building Charismatic Artificial Intelligence

Charisma is considered as one's ability to attract and potentially also influence others. Clearly, there can be considerable interest from an artificial intelligence's (AI) perspective to provide it with such skill. Beyond, a plethora of use cases opens up for computational measurement of human charisma, such as for tutoring humans in the acquisition of charisma, mediating human-to-human conversation, or identifying charismatic individuals in big social data. A number of models exist that base charisma on various dimensions, often following the idea that charisma is given if someone could and would help others. Examples include influence (could help) and affability (would help) in scientific studies or power (could help), presence, and warmth (both would help) as a popular concept. Modelling high levels in these dimensions for humanoid robots or virtual agents, seems accomplishable. Beyond, also automatic measurement appears quite feasible with the recent advances in the related fields of Affective Computing and Social Signal Processing. Here, we, thereforem present a blueprint for building machines that can appear charismatic, but also analyse the charisma of others. To this end, we first provide the psychological perspective including different models of charisma and behavioural cues of it. We then switch to conversational charisma in spoken language as an exemplary modality that is essential for human-human and human-computer conversations. The computational perspective then deals with the recognition and generation of charismatic behaviour by AI. This includes an overview of the state of play in the field and the aforementioned blueprint. We then name exemplary use cases of computational charismatic skills before switching to ethical aspects and concluding this overview and perspective on building charisma-enabled AI.
Safe Electronic Healthcare System with Innovative Blockchain Technology

Because of the Internet's revolutionary effect on the healthcare industry, patients now have constant, anywhere-and-anytime access to their own health data. Medical inconsistencies linked with Electronic Health Records (EHR) and concerns relating to privacy and security problems need to be resolved. Perhaps the largest difficulty of all was lack of uniformity and unfortunately it is not the only one with EHR. In order to solve the problems of interoperability and privacy, decentralised online ledgers using blockchain based platforms have been suggested and implemented. Other difficulties, though, persist. To be more specific, the technological issues of scalability, usability, and accessibility. The first technological obstacle is the need to keep medical records in their various formats off-chain while providing safe access control to them on-chain. The second difficulty is providing an accurate description of who owns what data and how that data is shared at times of medical urgency. Because formal verification of smart contracts takes a long time relative to processing a big block of transactions, accessibility remains an open problem. A Secure eHealth Framework (SeFra) is offered as a progressive temporal blockchain solution to the aforementioned problems.
An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image

The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype "Luminal A". It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis.
Pedagogical and research potential of a digital humanities laboratory: Language corpora as open educational resources (OER)

Digital humanities (DH) brings together the study and teaching of the humanities using digital technology, and the use of concepts and methodologies from within the humanities to study the digital. DH laboratories are therefore ideally suited to address the needs of the ‘Born Digital’ generation – the current generation of students who are very comfortable when navigating digital spaces and who process information differently and more quickly than their predecessors. In spite of this, however, DH laboratories are relatively unknown in Sri Lanka. This article, outlining the potential of a DH laboratory as a concept and site for teaching, learning and research, illustrates as a case study, the Sri Lanka English Newspaper Corpus (SLENC). This is a 31.8 million word corpus of Sri Lankan English newspaper articles under the aegis of the DH Lab of the Department of English, University of Colombo, funded by the AHEAD project of the World Bank. Corpora fit in well with the principles of digital humanities, particularly if they are hosted as open educational resources (OERs). We highlight the research potential of the SLENC, and affirm that DH laboratories are spaces that permit the exploration of the intersection between English Studies and the digital and promote research collaboration and community building.
The Impact of Social Media Marketing on Brand Equity Considering the Mediating Role of Brand Experience and Social Media Benefits

Aims and Scope | Author Guidelines | Current Issue | Archive | Indexing | Editorial Board | Contact Us Volume 5 | Issue 12 | 2022 Title: The Impact of Social Media Marketing on Brand Equity Considering the Mediating Role of Brand Experience and Social Media Benefits Author(S): Ghazale Asadi Damavandi; Reza Mohammadkazemi; Seyed Mojtaba Sajadi Corresponding Author Affiliation*: Faculty of … Continue reading "Journal of International Business and Management (JIBM)"
Performance Comparison of Credit Card Fraud Detection System using Machine Learning

Cyber security is becoming an integral part of modern life, tackling abnormal activities being the main challenge in the domain. To find and abort a fishy transaction is the procedure for maintain the security in credit cards. With the availability of past datasets, machine learning algorithms have made it possible to detect abnormal activities among the transactions. The aim of the paper is to create a balanced data from a pre-existing dataset and to apply six different machine learning algorithms like Decision tree, K-Nearest Neighbor, Logistic regression, SVM, Random Forest and XGBoost to detect fraud activities. Later, the results are compared and discussed on the basis of evaluation matrices which include precision, recall, f1-score and accuracy. All the algorithms are applied on three sets of data, where the ratio of fraud to valid transactions is varied. Three cases are considered with ratios as 1:100, 1:10 and 1:1. A balanced dataset showed greater performance in the terms of all evaluation matrices.
Enhancing the Protection of Information in Digital Voting Using the Fraud Application of Blockchain Technology

Elections are conducted electronically instead of using paper ballots to cut down on mistakes and discrepancies. Recently, it has been discovered that paper-based balloting fails owing to security and privacy difficulties, and the electronic balloting approach has been recommended as a replacement. For the sake of keeping your data safe, the authors have designed and developed a hashing algorithm based on SHA-256. The blockchain's adaptability is aided by the sealing of the block concept's incorporation. Consortium blockchain technology is employed to ensure that only the election commission has access to the blockchain database, which candidates and other outside parties cannot modify. When used in the polling method, the methodology discussed in this chapter can yield reliable findings. The authors used a hashing algorithm (SHA-256), block generation, data collection, and result declaration to get to this point.
Diagnosing the Leukemia using Faster Region based Convolutional Neural Network

It is difficult to building deep learning algorithms for identifying chronic diseases. One of the must difficulties facing the system of diagnosing leukemia is the irregular shape and twisted nucleus in white blood cells (WBCs) without cleaning and segmentation of cells by Appling filters. Moreover, it is challenge to identify and classify the WBC at once time which is considered the essential step of leukemia diagnosing. This paper proposed system only based on deep learning algorithms. The modified Faster R-CNN (Faster Region based-Convolutional Neural Networks) algorithm is used to detect and classify WBCs. The system is achieved a high accuracy training on the database tacked directly from microscope which used in [4].
A Review on Various MODI Text Recognition Techniques

MODI script is an ancient language of the Marathi people. MODI script is used to write the Marathi language, which is the mother language of Maharashtra, India. To understand this ancient language here we analyze text recognition techniques. MODI script was used primarily by administrative people to keep their accounts, as well as most of the revenue documents, were written in MODI language. For recognition of such text, number of image processing techniques are used. The official scriptures of Goa were previously written in this 17th-century Balbodh style of Devanagari, which is currently being restored. It is now a practical visual reminder of the former Maratha era and a specialized research skill; it is a technological key required primarily to access the Maratha state's empirical history through these archive resources. This paper explains the previous analysis of MODI text recognition. MODI text recognition system is well explained with the help of a generalized text recognition system model. The model includes image acquisition, normalization, binarization, segmentation, feature extraction, training, and classification lastly recognized image. Numerous optional strategies that have been used in various identification systems are available for each level. The history of the Maratha dynasty and other significant facts can be revealed in numerous MODI manuscripts by using these different techniques to identify MODI characters. Also, some applications of text recognition are explained in this paper.
Memory Storable Network Based Feature Aggregation for Speaker Representation Learning

Learning fixed-dimensional speaker representation using deep neural networks is a key step in speaker verification. In this work, we propose an auxiliary memory storable network (MSN) to assist a backbone network for learning discriminative features, which are sequentially aggregated from lower to deeper layers of the backbone. The proposed MSN has a similar architecture to the ResNet and contains a set of cascaded feature aggregation (FA) blocks. Each FA block first aggregates the multi-level features from the previous block and the features from the corresponding backbone layer. The output features of each intermediate layer within the backbone are then refined by the multi-level features of the corresponding FA block through masking and biasing operations. Finally, the features from the last layers of both MSN and the backbone are concatenated to form more discriminative speaker representations. Experimental results on five public datasets show significant and consistent improvements over conventional approaches. The effectiveness of the proposed method is also validated using ablation studies, showing a robust generalization capacity in combination with different backbone networks.