Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics

For decades, researchers have experimented with the possibility that machines can equal human linguistic capabilities. Recently, advances in the field of natural language processing (NLP) as well as a substantial increase in available naturally occurring linguistic data on social media platforms have made more advanced methodologies such as sentiment analysis (SA) gain substantial momentum on contemporary applications. This document compiles what the authors consider to be some of the most important concepts related to SA, as well as techniques and processes necessary for the various stages of its implementation. Furthermore, specific applications related to the extraction and classification of social media data using novel SA techniques are presented and quantified, with an emphasis on those used for the identification of mental health degradation during the COVID-19 pandemic. Finally, the authors present several conclusions highlighting the most prominent benefits and drawbacks of the methods discussed, followed by a brief discussion of possible future applications of certain methods of interest.
Three Layer Authentications with a Spiral Block Mapping to Prove Authenticity in Medical Images

Digital medical image has a potential to be manipulated by unauthorized persons due to advanced communication technology.Verifying integrity and authenticity have become important issues on the medical image.This paper proposed a self-embedding watermark using a spiral block mapping for tamper detection and restoration.The block-based coding with the size of 3×3 was applied to perform selfembedding watermark with two authentication bits and seven recovery bits.The authentication bits are obtained from a set of condition between sub-block and block image, and the parity bits of each sub-block.The authentication bits and the recovery bits are embedded in the least significant bits using the proposed spiral block mapping.The recovery bits are embedded into different sub-blocks based on a spiral block mapping.The watermarked images were tested under various tampered images such as blurred image, unsharp-masking, copy-move, mosaic, noise, removal, and sharpening.The experimental results show that the scheme achieved a PSNR value of about 51.29 dB and a SSIM value of about 0.994 on the watermarked image.The scheme showed tamper localization with accuracy of 93.8%.In addition, the proposed scheme does not require external information to perform recovery bits.The proposed scheme was able to recover the tampered image with a PSNR value of 40.45 dB and a SSIM value of 0.994.
Dynamics of quantum correlations in a quantum dot system with intrinsic decoherence effects

In this paper, we explore the dynamics of quantum correlations in an isolated physical quantum dot under the influence of intrinsic decoherence (ID). We characterize quantum correlations in the quantum dot system using Concurrence to capture the amount of entanglement, and we use Trace Distance Discord (TDD) and Local Quantum Uncertainty (LQU) to measure nonclassical correlations. Likewise, we investigate the effect of ID on the temporal evolution of these quantifiers. In particular, the behavior of the two discord-like quantifiers in terms of the system parameters and the ID rate is investigated and analyzed in detail. We found that TDD and LQU behave similarly for different parameters characterizing the considered system and that TDD is more resistant than Concurrence and LQU against the ID effects. Our results also show that the robustness of quantum correlations can be modulated by adjusting the ID rate, quantum dot physical properties and initial conditions.
Meanings of Machine Learning and Deepening Researches on Methods of Classifying and Recognizing Users Gender on Internet

In various firms from technology, manufacturing till digital, ICT firms, etc., they have to deal with big data of clients and if we can classify users based on gender, age, and other criteria, and it will help to facilitate our businesses. SVM is representing (short abrreviation) for Support Vector Machine that used as method for classification and regression analysis, and used in fields of statistics and computer science. Standard SVM will take advantage of data (input) and classifies it into two different classes. The study will perform Research and learn about gender prediction problem, present some gender prediction methods that have been studied before. Through learning and applying input data preprocessing tools, and researching and studying about the Support Vector Machine algorithm on two layers and many layers, it builds a program to get content from users’ posts on the social network Facebook.
Mix and Match: Learning-free Controllable Text Generationusing Energy Language Models

Recent work on controlled text generation has either required attribute-based fine-tuning of the base language model (LM), or has restricted the parameterization of the attribute discriminator to be compatible with the base autoregressive LM.In this work, we propose Mix and Match LM, a global score-based alternative for controllable text generation that combines arbitrary pre-trained black-box models for achieving the desired attributes in the generated text without involving any fine-tuning or structural assumptions about the black-box models.We interpret the task of controllable generation as drawing samples from an energy-based model whose energy values are a linear combination of scores from black-box models that are separately responsible for fluency, the control attribute, and faithfulness to any conditioning context.We use a Metropolis-Hastings sampling scheme to sample from this energy-based model using bidirectional context and global attribute features.We validate the effectiveness of our approach on various controlled generation and style-based text revision tasks by outperforming recently proposed methods that involve extra training, fine-tuning, or restrictive assumptions over the form of models.
Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs

Inductive link prediction for knowledge graph aims at predicting missing links between unseen entities, those not shown in training stage. Most previous works learn entity-specific embeddings of entities, which cannot handle unseen entities. Recent several methods utilize enclosing subgraph to obtain inductive ability. However, all these works only consider the enclosing part of subgraph without complete neighboring relations, which leads to the issue that partial neighboring relations are neglected, and sparse subgraphs are hard to be handled. To address that, we propose Subgraph Neighboring Relations Infomax, SNRI, which sufficiently exploits complete neighboring relations from two aspects: neighboring relational feature for node feature and neighboring relational path for sparse subgraph. To further model neighboring relations in a global way, we innovatively apply mutual information (MI) maximization for knowledge graph. Experiments show that SNRI outperforms existing state-of-art methods by a large margin on inductive link prediction task, and verify the effectiveness of exploring complete neighboring relations in a global way to characterize node features and reason on sparse subgraphs.
iLearning Model Approach in Creating Blockchain Based Higher Education Trust

Today, higher education presents challenges in terms of educational and industrial collaboration. Both theoretical and practical, formal and informal are also part of the application of blockchain in education. Moreover, the assessment is still quite difficult to do to measure the skill level of students so that they can compete for jobs in the future With the problem of the academic curriculum that still uses writing media on paper, problems often arise regarding the validity of reliable document validation. So it is necessary to compare the knowledge obtained to match their abilities. From these problems, the goal was created to improve the higher education curriculum in order to find a revolutionary solution for document validation beliefs. Evaluation of the iLearning learning system combined with blockchain technology (ledger) has the benefit of being able to support these problems. By using Blockchain technology, a new learning model innovation is created in the form of the SCi-B (Student-Centered iLearning Blockchain) framework. SCi-B is a new innovation in the learning model where all activities use Blockchain so that its existence is able to manage and store all transactions, competencies, and teaching that can provide intensive assessments through digital certificates for the academic world and the world of work. So that SCi-B has a significant impact on the confidence in the results that have been obtained. This paper aims to answer the challenges of the world of education which is currently getting wider, more open, and everywhere. The model in the SCi-B framework of this paper can be used for all training institutions because this model can adapt to the specific professional needs of the job sector. This model has been validated by the existence of a web application whose use is very satisfying.
During the COVID-19 pandemic participants prefer settings with a face mask, no interaction and at a closer distance

Peripersonal space is the space surrounding our body, where multisensory integration of stimuli and action execution take place. The size of peripersonal space is flexible and subject to change by various personal and situational factors. The dynamic representation of our peripersonal space modulates our spatial behaviors towards other individuals. During the COVID-19 pandemic, this spatial behavior was modified by two further factors: social distancing and wearing a face mask. Evidence from offline and online studies on the impact of a face mask on pro-social behavior is mixed. In an attempt to clarify the role of face masks as pro-social or anti-social signals, 235 observers participated in the present online study. They watched pictures of two models standing at three different distances from each other (50, 90 and 150 cm), who were either wearing a face mask or not and were either interacting by initiating a hand shake or just standing still. The observers' task was to classify the model by gender. Our results show that observers react fastest, and therefore show least avoidance, for the shortest distances (50 and 90 cm) but only when models wear a face mask and do not interact. Thus, our results document both pro- and anti-social consequences of face masks as a result of the complex interplay between social distancing and interactive behavior. Practical implications of these findings are discussed.
A Comparative Analysis of Deep Learning Models for Automated Cross-Preparation Diagnosis of Multi-Cell Liquid Pap Smear Images

Routine Pap smears can facilitate early detection of cervical cancer and improve patient outcomes. The objective of this work is to develop an automated, clinically viable deep neural network for the multi-class Bethesda System diagnosis of multi-cell images in Liquid Pap smear samples. 8 deep learning models were trained on a publicly available multi-class SurePath preparation dataset. This included the 5 best-performing transfer learning models, an ensemble, a novel convolutional neural network (CNN), and a CNN + autoencoder (AE). Additionally, each model was tested on a novel ThinPrep Pap dataset to determine model generalizability across different liquid Pap preparation methods with and without Deep CORAL domain adaptation. All models achieved accuracies >90% when classifying SurePath images. The AE CNN model, 99.80% smaller than the average transfer model, maintained an accuracy of 96.54%. During consecutive training attempts, individual transfer models had high variability in performance, whereas the CNN, AE CNN, and ensemble did not. ThinPrep Pap classification accuracies were notably lower but increased with domain adaptation, with ResNet101 achieving the highest accuracy at 92.65%. This indicates a potential area for future improvement: development of a globally relevant model that can function across different slide preparation methods.
Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition

Human monitoring applications in indoor environments depend on accurate human identification and activity recognition (HIAR). Single modality sensor systems have shown to be accurate for HIAR, but there are some shortcomings to these systems, such as privacy, intrusion, and costs. To combat these shortcomings for a long-term monitoring solution, an interpretable, passive, multi-modal, sensor fusion system PRF-PIR is proposed in this work. PRF-PIR is composed of one software-defined radio (SDR) device and one novel passive infrared (PIR) sensor system. A recurrent neural network (RNN) is built as the HIAR model for this proposed solution to handle the temporal dependence of passive information captured by both modalities. We validate our proposed PRF-PIR system for a potential human monitoring system through the data collection of eleven activities from twelve human subjects in an academic office environment. From our data collection, the efficacy of the sensor fusion system is proven via an accuracy of 0.9866 for human identification and an accuracy of 0.9623 for activity recognition. The results of the system are supported with explainable artificial intelligence (XAI) methodologies to serve as a validation for sensor fusion over the deployment of single sensor solutions. PRF-PIR provides a passive, non-intrusive, and highly accurate system that allows for robustness in uncertain, highly similar, and complex at-home activities performed by a variety of human subjects.
Arabic Aspect-Based Sentiment Classification Using Seq2Seq Dialect Normalization and Transformers

Sentiment analysis is one of the most important fields of natural language processing due to its wide range of applications and the benefits associated with using it. It is defined as identifying the sentiment polarity of natural language text. Researchers have recently focused their attention on Arabic SA due to the massive amounts of user-generated content on social media and e-commerce websites in the Arabic world. Most of the research in this fieldwork is on the sentence and document levels. This study tackles the aspect-level sentiment analysis for the Arabic language, which is a less studied version of SA. Because Arabic NLP is challenging and there are few available Arabic resources and many Arabic dialects, limited studies have attempted to detect aspect-based sentiment analyses on Arabic texts. Specifically, this study considers two ABSA tasks: aspect term polarity and aspect category polarity, using the text normalization of the Arabic dialect after making the classification task. We present a Seq2Seq model for dialect normalization that can serve as a pre-processing step for the ABSA classification task by reducing the number of OOV words. Thus, the model’s accuracy increased. The results of the conducted experiments show that our models outperformed the existing models in the literature on both tasks and datasets.
Semantic Connections in the Complex Sentences for Post-Editing Machine Translation in the Kazakh Language

The problems of machine translation are constantly arising. While the most advanced translation platforms, such as Google and Yandex, allow for high-quality translations of languages with simple grammatical structures, more morphologically rich languages still suffer from the translation of complex sentences, and translation services leave many structural errors. This study focused on designing the rules for the grammatical structures of complex sentences in the Kazakh language, which has a difficult grammar with many rules. First, the types of complex sentences in the Kazakh language were thoroughly observed with the use of templates from the FuzzyWuzzy library. Then, the correction of complex sentences was completed with parallel corpora. The sentences were translated into English and Russian by existing machine translation systems. Therefore, the grammar of both Kazakh–English and Kazakh–Russian language pairs was considered. They both used the rules specifically designed for the post-editing steps. Finally, the performance of the developed algorithm was evaluated for an accuracy score for each pair of languages. This approach was then proposed for use in other corpora generation, post-editing, and analysis systems in future works.
MetaOntology: Toward developing an ontology for the metaverse

Metaverse is now perceived as a celebrated future version of the internet. In this new anticipated virtual universe, interconnected digital platforms leveraged by augmented, extended, and virtual realities will elevate users' immersive experiences through multidimensional interactions. In particular, users will be offered a broad spectrum of digital activities within a newly immersive setting mediated by technology. This study aims to design a domain ontology (MetaOntology) for the metaverse to provide an explicit specification of relevant state-of-the-art technologies and infrastructure. A four-step methodological approach is followed to construct the designated ontology. Due to the immaturity of the metaverse, MetaOntology is not intended to furnish a complete outlook on the domain, rather it aims to establish a cornerstone so as to facilitate future efforts in building extant versions of this ontology considering the evolvement of relevant technologies.
Neural Architecture Search for Transformers: A Survey

Transformer-based Deep Neural Network architectures have gained tremendous interest due to their effectiveness in various applications across Natural Language Processing (NLP) and Computer Vision (CV) domains. These models are the de facto choice in several language tasks, such as Sentiment Analysis and Text Summarization, replacing Long Short Term Memory (LSTM) model. Vision Transformers (ViTs) have shown better model performance than traditional Convolutional Neural Networks (CNNs) in vision applications while requiring significantly fewer parameters and training time. The design pipeline of a neural architecture for a given task and dataset is extremely challenging as it requires expertise in several interdisciplinary areas such as signal processing, image processing, optimization and allied fields. Neural Architecture Search (NAS) is a promising technique to automate the architectural design process of a Neural Network in a data-driven way using Machine Learning (ML) methods. The search method explores several architectures without requiring significant human effort, and the searched models outperform the manually built networks. In this paper, we review Neural Architecture Search techniques, targeting the Transformer model and its family of architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Vision Transformers. We provide an in-depth literature review of approximately 50 state-of-the-art Neural Architecture Search methods and explore future directions in this fast-evolving class of problems.
Long-Short Temporal Contrastive Learning of Video Transformers

Video transformers have recently emerged as a competitive alternative to 3D CNNs for video understanding. However, due to their large number of parameters and reduced inductive biases, these models require supervised pretraining on large-scale image datasets to achieve top performance. In this paper, we empirically demonstrate that self-supervised pretraining of video transformers on video-only datasets can lead to action recognition results that are on par or better than those obtained with supervised pretraining on large-scale image datasets, even massive ones such as ImageNet-21K. Since transformer-based models are effective at capturing dependencies over extended temporal spans, we propose a simple learning procedure that forces the model to match a long-term view to a short-term view of the same video. Our approach, named Long-Short Temporal Contrastive Learning (LSTCL), enables video transformers to learn an effective clip-level representation by predicting temporal context captured from a longer temporal extent. To demonstrate the generality of our findings, we implement and validate our approach under three different self-supervised contrastive learning frameworks (MoCo v3, BYOL, SimSiam) using two distinct video-transformer architectures, including an improved variant of the Swin Transformer augmented with space-time attention. We conduct a thorough ablation study and show that LSTCL achieves competitive performance on multiple video benchmarks and represents a convincing alternative to supervised image-based pretraining.
Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology

Digital pathology has a crucial role in diagnostic pathology and is increasingly a technological requirement in the field. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond the microscopic slide and enable true integration of knowledge and expertise. There is clear potential for artificial intelligence (AI) breakthroughs in pathology and hematopathology. In this review article, we discuss the approach of using machine learning in the diagnosis, classification, and treatment guidelines of hematolymphoid disease, as well as recent progress of artificial intelligence in flow cytometric analysis of hematolymphoid diseases. We review these topics specifically through the potential clinical applications of CellaVision, an automated digital image analyzer of peripheral blood, and Morphogo, a novel artificial intelligence-based bone marrow analyzing system. Adoption of these new technologies will allow pathologists to streamline workflow and achieve faster turnaround time in diagnosing hematological disease.