Reviewerly/exHarmony huggingface dataset:
Automated Facial Recognition based Attendance System using OpenCV in Python

Face detection and recognition systems work by detecting faces present in an image or in a video frame and identifying the person in the image. In this work, we are interested in face detection to achieve an automatic attendance system. This work is implemented using Python and can be operated from any standalone device. This automated system stores the attendance records of students/employees with proper timestamps in the local Secondary Memory. All these records are stored date-wise. The implementation of an Automated Facial Recognition based Attendance System can help in identifying and verifying a person’s identity from a digital source in real-time. Accurate attendance records are very important for classroom evaluation in schools and colleges. It also helps to keep track of the attendance of the employees in different organizations. The traditional system of manual attendance tracking in institutes can result in errors and missed or duplicated entries. The adoption of the Face Recognition-based attendance system could help eliminate these problems and shortcomings of the classical manual attendance system. The proposed work is tested with different persons with different age groups and genders in real time. The system successfully identifies the proper persons with significant accuracy and records their attendance.
Introduction of Reinforcement Learning and Its Application Across Different Domain

In the modern era of rapid development in Deep Neural Networks, Reinforcement Learning (RL) has evolved into a pivotal and transformative technology. RL, a learning process where these machine agent interacts with several unknown environment through trial and error. The agent, responsive to the learning machine, go through these interaction, and start receiving feedback in the form of positive rewards or negative rewards like penalties from the environment, and constantly refines its behavior. This research paper offers an in-depth introduction to the foundational concepts of RL, focusing on Markov Decision Processes and various RL algorithms.&lt;/p&gt; &lt;p&gt;Machine Learning (ML) is a subset of Artificial Intelligence, which deals with ‘‘the question of how to develop software agents (Machine) that improve automatically with experience’’. The basic three categories of Machine Learning are.&lt;/p&gt; &lt;ol&gt; &lt;li&gt;Supervised Learning&lt;/li&gt; &lt;li&gt;Unsupervised Learning&lt;/li&gt; &lt;li&gt;Reinforcement Learning&lt;/li&gt; &lt;/ol&gt; &lt;p&gt;RL method is that in any situation the agent has to choose between using its acquired knowledge of the environment i.e. using an action already tried or performed previously or exploring actions never tried before in that situation.&lt;/p&gt; &lt;p&gt;In this review paper, we will discuss the most used learning algorithms in games robotics and healthcare, autonomous control as well as communication and networking, natural language processing.[1]&lt;/p&gt;
Neural Network Surrogate Modeling of a Synthetic Bi-Physical Second Order Low-Pass Filter

In this work, an initial step forward to address the development of multi-physics neural modeling is presented. The initial step consists of a prove of concept to determine if a neural network model (NN) is enough accurate to represent the bi-physical response (electrical and thermal) of a second order low-pass filter. Three different learning algorithms were used to train the neural network, being the Bayesian Regularization the model which exhibits the best generalization performance to represent the EM responses subject to changes in the circuit temperature.
VoteCoin : A Blockchain Perspective on E-Voting Infrastructure

The implementation of a dependable and equitable decision-making process is of paramount importance and carries substantial influence within any organizational context. At present, decision-making procedures frequently depend on either traditional manual approaches or sophisticated technological resolutions. There are several challenges associated with these approaches, which encompass a dearth of transparency, low rates of participation, the possibility of manipulation, skepticism towards the decision-making body, counterfeiting of unique identification, prolonged delays in the dissemination of results, and significant security concerns. When contemplating the integration of a digital decision-making system, security throughout the entire process is invariably the foremost consideration. In light of the seriousness of the matters under consideration, it is indisputable that the system must ensure data security and prevent potential breaches. One plausible solution for mitigating these security challenges entails the utilization of blockchain technology. Blockchain, an implementation of decentralized ledger technology, enables the decentralized exchange of digital assets in a secure and transparent manner. In this regard, distributed ledger technology signifies a substantial progression; every block comprises an exhaustive ledger of every transaction. The defining features of blockchain technology are anonymity, decentralization, security, and transparency. The convergence of smart contracts and blockchain technology presents a profoundly auspicious prospect in the realm of constructing decision-making systems that place utmost importance on transparency, safety, and security. A prototype decision-making application was effectively developed and evaluated in this research endeavor by leveraging smart contracts on the Ethereum network. By integrating digital wallets, blockchain technology, and the Solidity programming language, this accomplishment was realized. A finite quantity of tokens, analogous to gas, is assigned to each user within their wallet. By depleting these tokens upon user participation in the decision-making process, duplicate actions are ensured to be prevented. In addition to examining the advantages and disadvantages of blockchain technology, the study presents a working prototype of a web application that is specifically engineered to optimize the process of making decisions. This highlights the intrinsic constraints of the system.
Silencing the Scammers: Effective Strategies in Credit Card Fraud Detection

The amount of fraud has recently grown. This fraud includes your personal information or sensitive data, which someone can steal. Financial fraud is increasing significantly with the development of modern technology. Companies and financial institutions lose large amounts due to fraud. Fraud analytics combines big data analysis techniques with human interactions to help detect fraud. Credit card fraud is a pervasive and costly problem for individuals, businesses, and financial institutions worldwide. It involves the unauthorized use of someone's credit card information to make fraudulent transactions, leading to financial losses and potential harm to the cardholder's credit score. Detecting and preventing credit card fraud is a critical concern in the modern financial ecosystem, and it relies on advanced technologies and data analysis techniques.
Quantum Key Distribution in Optical Fibers: A Comprehensive View Beyond Transmission

This paper reports a network operator point of view about the introduction of Quantum Key Distribution in optical networks to secure the data plane and/or specific applications, focusing on the aspects that go beyond pure transmission.
Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a human-inspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future.
Feature Fusion and Augmentation based on Manifold Ranking for Image Classification

Despite the great advances in the field of image classification, the association of ideal approaches that can bring improved results, considering different datasets, is still an open challenge. In this work, a novel approach is presented, based on a combination of compared strategies: features extraction for early fusion; rankings based on manifold learning for late fusion; and feature augmentation applied in a long short-term memory (LSTM) algorithm. The proposed method aims to investigate the effect of feature fusion (early fusion) and rankings fusion (late fusion) in the final results of image classification. The experimental results showed that the proposed strategies improved the accuracy of results in different tested datasets (such as CIFAR10, Stanford Dogs, Linnaeus5, Flowers102, and Flowers17) using a fusion of features from three convolutional neural networks - CNNs (ResNet152, VGG16, DPN92) and its respective generated rankings. The results indicated significant improvements and showed the potential of the approach proposed for image classification.
Improving Image Classification using Triple-GAN: A Fusion of Generative Adversarial Networks and Transfer Learning

Generative neural networks, particularly Generative Adversarial Networks (GANs), have gained a significant attention for their ability to generate new data. Moreover, transfer learning, known for its efficient processing and promising research outcomes, has emerged as a popular approach for feature extraction. This paper explores the application of the Triple-GAN model, which combines the advantages of GANs with transfer learning, particularly in the context of image classification. The study focuses on utilizing the Triple-GAN model combined with a pre-trained ResNet101 model for image classification. The evaluation of the proposed approach is conducted on the CIFAR-100 dataset, employing specific metrics to assess the model’s efficacy. The results highlight the promising nature of the proposed model, showcasing its potential for accurate image classification. Our model exhibits a remarkable level of accuracy reaching above 71.52 % outperforming other conventional ANN models. The significance of this research lies in its potential to advance image classification techniques by employing the Triple-GAN model with transfer learning as a viable alternative to traditional classification methods.
Self-Supervised Regression for Query Performance Prediction on Image Retrieval

Content-Based Image Retrieval (CBIR) systems are currently a widely used solution for image retrieval tasks with various applications. Despite the advances achieved, one of the central issues is the need for methods capable of handling the scarcity or absence of labeled data. In this scenario, Query Performance Prediction (QPP) approaches represent a successful technique in the effectiveness estimation of retrieval results. In this work, we propose a novel self-supervised framework, named Regression for Query Performance Prediction Framework - RQPPF, which is flexible and can be used with different regression models. Among the contributions, our training relies only on synthetic data and rank-based features. An experimental evaluation was conducted on 4 different retrieval datasets, considering 14 visual features and 11 regression models. The results indicate highly effective predictions and most of them are greater than recent baselines.
Advancing Trustworthy Knowledge Engineering through User-Centered AI-based Systems: A Systematic Review

This document is a model and instructions for LATEX. This and the IEEEtran.cls file define the components of your paper [title, text, heads, etc.]. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or Abstract.
CO-TRIS: Container Orchestration – Transforming container using Resource Inspection System

Traditional approaches to container orchestration, such as distributed threshold-based policies, often result in isolated or underutilized resources. This can negatively affect application availability and performance. This paper presents a new orchestration framework that solves the cloud-computing resource-allocation problem, focusing on containerized environments. This framework focuses on the runtime phase of the container lifecycle. It uses visualized resource information to optimize resource reallocation in cloud services, and introduces a systematic orchestration approach that enables efficient resource utilization and real-time allocation. Its goal is to improve the smooth operation and quality of service (QoS) of containers and microservices in a cloud environment. Our method improved the experimental allocation from 24 container blocks to 67. The results improved linearly until 30k epochs and then plateaued.
Impact of Metaverse in Healthcare based on Architecture, Challenges and Opportunities

The Metaverse represents a rapidly-evolving archetype of the next generation of digital worlds, that seeks to create a fully engaging, hyper-spatiotemporal, shared space for people to work and play. Recent advances in emerging technologies such as virtual reality (VR) and augmented reality (AR), artificial intelligence, and blockchain are driving the metaverse from a hypothetical iteration to an upcoming reality. In the wake of the Covid-19 pandemic, healthcare systems worldwide have faced significant limitations, making it imperative to explore alternative healthcare solutions. This paper provides a comprehensive overview of how healthcare is evolving with the metaverse across six domains: telemedicine, clinical care, education, mental health, physical fitness, and pharmaceuticals. We examine the applications of the metaverse in these domains, discuss the technical issues involved, and propose solutions for developing future-proof medical systems using metaverse technology. The architecture is presented for implementing the metaverse in the healthcare sector and identifies key challenges that are currently impeding the global adoption of the metaverse in healthcare. Finally, we suggest open research directions for building healthcare systems using metaverse technology,
A Data Sharing and Access Control Model Based on Blockchain

In the era of data-centric driving enterprise business, how to effectively control data access and securely share data between enterprises has become a common concern. While blockchain's distributed ledger solves this problem in some ways, blockchain's asymmetric encryption is only for one-to-one secure transmissions. Therefore, this paper uses attribute-based encryption to propose a data sharing and access control model for enterprise data. Designed to achieve more precise access control and secure sharing. After comparison, the model can better solve the problem of data access control within enterprises and data sharing between enterprises.
SSDAEE: Semi-Supervised Data-Augmented Event Extraction for Transfer Learning

Although the general event extraction model has achieved good results in the standard data sets, there is a gap in the extraction results in low-resource environments. The general event extraction model still has room for improvement in the performance of special fields. Therefore, this paper proposes a semi-supervised reading comprehension event extraction model adapted to transfer learning, based on improved UDA for data enhancement, called SSDAEE, which can increase the accuracy of reading comprehension event type recognition. SSDAEE has a good performance on the public opinion data set. It can not only enhance a small number of supervised data in low-resource fields with unsupervised data but also improve the generalization ability of the model. Experiments show that when only 5% unsupervised data is used, the F1 score of SSDAEE on the public opinion data set is increased by 9% compared with the fully supervised model, and by 2% compared with other semi-supervised models.
Multi-Task Learning for Game Review Classification with Emotion and Sarcasm Detection

Sentiment analysis is a widely used technique to identify the emotion tendency of a piece of text. As the number of video games and players continues to increase, the task of automating emotion computing for game reviews is essential for both game platforms and developers. The former can recommend good and suitable games to users, while the latter can provide game developers with suggestions for improvement. However, there are few affective computing studies and models in the field of game reviews. Therefore, this paper investigates the performance of several traditional sentiment classification models on game review datasets and deduces several possible reasons for model performance degradation based on misclassified samples. Based on these preliminary studies, we believe that sarcasm, game context, and comparison are the three main reasons leading to the decline in classifier performance. We conclude that a satisfactory model requires the following characteristics: sarcasm detection, game contextualization, and key emotion capture. Based on this, we design a multi-tasking game review classification model integrating sarcasm detection and emotion analysis modules. Experiments on two real datasets from the Steam platform demonstrate that our approach has superior performance compared to mainstream sentiment classification models.
Multi-Modal Knowledge Representation: A Survey

A modality refers to the way people receive information, such as vision, hearing, tactile sense, etc. If a task or data set involves multiple modalities, it is called multi-modal. In recent years, there has been more and more research in the field of multi-modal. Especially in the area of vision and language, many studies combine the excellent work of CV and NLP. With the development of knowledge graphs and representation, many works have also begun utilizing external knowledge to improve performance. To explore how multi-modal information and knowledge are utilized, this paper provides a comprehensive survey on multi-modal knowledge representation, which has never been systematically discussed. To facilitate the discussion of how the models combine and utilize multi-modal information and knowledge, we categorize multi-modal knowledge representation methods into two frameworks: explicit multi-modal knowledge representation and implicit multi-modal knowledge representation. For each framework, we introduce some typical models in vision-and-Ianguage, to help new researchers in this field understand the multi-modal knowledge representation model better. In addition, we introduce some downstream tasks and propose some important directions for future work.
DACTI: a Generation-Based Data Augmentation Method for Cyber Threat Intelligence

Methods of data augmentation are extensively utilized to boost the efficacy of machine learning, given their superior capability in promoting generalization performance. In this paper, we address the issue of data scarcity in the field of cyber threat intelligence and propose a method for generating high-quality synthetic data by training a language model on linearized labeled phrases. Subsequently, we design a relational classification model to classify relational entities related to cyber threat intelligence in the created synthetic data. We conduct extensive experiments on datasets of different sizes, and the experimental results show that our proposed method outperforms the baseline methods, especially when the amount of gold data provided is insufficient, and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> score of our model is improved by up to 1.94 points.
Survey on Fuzzing Techniques in Deep Learning Libraries

The irreversible tendency to empower the industry with deep learning (DL) capabilities is rising new security challenges. A DL-based system will be vulnerable to serious attacks if the vulnerabilities of underlying DL libraries (e.g. Tensorflow, PyTorch) are exploited. It is crucial to test the DL library to bridge the gap between security requirements and deployment urgency. This paper provides a comprehensive overview of fuzzing techniques used in deep learning (DL) libraries. It elucidates the evolution of these techniques, discussing various methodologies including CARDLE, Audee, LEMON, Muffin, DocTer, FreeFuzz, DeepREL, IvySyn, and the more recent, TitanFuzz. While these methods have significantly enhanced the reliability of DL libraries, several challenges persist. Most notably, there is a need for more diverse API sequences and improved handling of sequences of chained APIs, which often induce bugs. We also discuss the potential of large language models in generating input programs for fuzzing DL libraries, while noting the need for further research to fully explore their generalizability and efficacy. The paper concludes with an emphasis on the importance of advancing fuzzing techniques to enhance the robustness of DL libraries, a critical factor in driving the next wave of advancements in artificial intelligence and deep learning.
A Novel Concept-Driven Negative Sampling Mechanism for Enhancing Semanticity and Interpretability of Knowledge Graph Completion Task

In recent years, it has witnessed the revival of knowledge engineering, which is characterized by the rapid growth of Knowledge Graph (KGs). Currently, Knowledge Graph Representation (KGR) and Completion (KGC) methods all use the methodology of scoring triple ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$e_{h},r, e_{t}$</tex> ) to judge its correctness. Wherein, in the procedure of conventional Negative Sampling (NS) and reasoning, it is easy to be affected by the low-quality and uncertainty of negative triples related to the given KG, resulting in some unpredictable and inexplicable wrong results that do not conform to commonsense. On the contrary, human beings with commonsense could easily solve this problem. For this reason, this paper proposes a novel mechanism to automatically capture commonsense (concept emphasized here) associated with current KG to provide more deterministic and interpretable semantic information from the fact triple, and further designs a concept-driven negative sampling strategy for commonsense understanding. Overall, a hybrid reasoning approach jointly driven by concept constraints (inter with KG) and fact triple guides (intra with KG), is presented to promote more effective learning and training procedure of KG's representation and completion. Experiments on real-world datasets, show that the proposed work could be used as a flexible plug-in unit to enhance the previous KG representation and completion models.