Reviewerly/exHarmony huggingface dataset:
Visual Question Answering Optimized Framework using Mixed Precision Training

Thanks to the emergence and continued devel-opment of machine learning, particularly deep learning, the research on visual question and answer, also known as VQA, has advanced dramatically, with great theoretical research significance and practical application value. This field of study makes use of multimodal learning, computer vision, and natural language processing techniques. Except for a few academics who presented different types of optimized bi-linear fusion approaches that integrate text and image characteristics in an efficient way, there haven’t been many efforts to optimize the VQA framework. In order to optimize the VQA problem, we offer a unique Visual Question Answering framework in this research. Because both 16-bit and 32-bit floating points provide automatic mixed precision, deep learning architectures can now be optimized with less computation and execution time. Using the VQA 2.0 and CLEVR datasets, the proposed framework has been tested against two models. In terms of overall accuracy and execution time, the experimental findings demonstrated a significant improvement.
AI based mouse using Face Recognition and Hand Gesture Recognition

The computer mouse is one of the incredible inventions of Human-Computer Interaction. Wireless or Bluetooth mice we use currently are not free devices as they require batteries and dongles to plug into the Computer. Since computer vision is at its pinnacle and is used in many different aspects of day-to-day life, such as Face Recognition, Automatic car, and Color detection, we here are using it, to create an AI mouse by using hand tip detection and hand gestures. We also add face recognition using the Eigen face algorithm to revamp its security. The algorithm will first confirm the user’s authenticity by scanning their face once confirmed then one can access his computer through hand gestures, one can perform click and scroll the mouse without using the hardware mouse. The algorithm uses Eigenface and deep learning for the detection of hands.
Innovative Method for Earthquake Prediction System using Hybrid Convolutional Neural Network and SVM

The ability to estimate casualties from earthquakes is crucial for effective disaster response. Conventional forecasting techniques have stringent sample data requirements and several parameters that must be manually specified, which can lead to subpar outcomes with prediction accuracy as low and a slow rate of learning. In the suggested hybrid model, CNN is employed as an automatic feature extractor, while SVM is used as a binary classifier. Traditional CNN’s completely linked layers are swapped out for a support vector machine in this model to improve prediction accuracy. This proposed approach employs CNN for automatic feature extraction, and an SVM classifier for automatic classification. The experimental findings showed that compared to the CNN model (89%), our hybrid model was significantly more accurate at 98.5%
An Implementation of Hyperchaotic Encryption Based Steganography with XOR Operation in Wireless Transmission

Steganography is the art of employing digital graphics to conceal a message such that only the sender and the intended recipient can find out about its existence. The hyperchaotic encryption algorithm, which minimizes the computational complexity of the chaotic encryption technique and the foundation of the proposed system in this project to improve bit insertion. The suggested bitwise operation based on encryption changes the encryption phases from permutation to XOR and XOR to bit insertion. The next step is to create a four-dimensional hyperchaotic system that uses the chaotic system’s discrete time signal as the key generator and its necessary mapping. This result demonstrates how steganography may be used to increase security by monitoring the speed and quality of data sources. Additionally, an acceptable PSNR is obtained, which is higher than MSE (Mean-Square Error) and improves the quality of the steganosource.
Designing A Popular Game Framework Using Neat A Genetic Algorithms

The objective of conduct the current research is to understand the deep relationship between gaming along with how artificial intelligence (AI) is being incorporated into these fields. It is clear that gaming and AI have a natural connection, and the introduction of AI only strengthens this connection. This study aims to highlight the potential benefits of using AI in gaming and simulation, as well as the challenges that may arise in these contexts. The scope of the research includes exploring the various ways in which AI can be applied in gaming. In addition to optimizing programs and tasks, AI can be trained to work independently, reducing the need for human labour and potentially eliminating human error. Overall, this paper focuses on the diverse potential applications of combining AI with gaming and simulation. This research shows how AI can be learn to play independently with the help of Neuro Evolution of Augmenting Topologies (NEAT) a Genetic algorithm.
Facial Image Super Resolution and Feature Reconstruction using SRGANs with VGG-19-based Adaptive Loss Function

Image reconstruction and super resolution has various applications. Several deep learning techniques are being employed to constantly improve this space. The aim of this experiment is to showcase a unique deep learning approach to try and super resolve human faces from low resolution images. The experiment makes use of a machine learning framework designed to improve image quality called Super Resolution Generative Adversarial Neural (SRGANs) with a loss function based on the features accumulated from multiple layers of a trained Convolutional Neural Network named Visual Geometry Group-19 (VGG-19). The model super resolves lower quality image input and gives out image output of a superior quality
Text Analysis Tool

The automated analysis of electronic text is referred to as “text processing”. The amount of online textual data is increasing, and automatic text processing techniques have the potential to be tremendously beneficial because they can gather more meaningful information faster. Features like text summarization, language translation, emotion classifier and headline generation of news articles are some of the popular ways of text processing. The fundamental goal of text summarization is to extract the most important information from a text and deliver it in a concise and legible form. The practice of transforming written text from one language into another such that it may be easily understood is known as language translation. Emotion classifier analyses the emotions and categorizes the text into various emotions. Headline generation is the process of obtaining the headlines from various news articles. This paper is about developing a text processing tool based on the concepts of Machine Learning and Natural Language Processing. Implementation of this tool allows for the automation of text processing. This tool aims to improve productivity and efficiency of processed data by faster generation of precise and meaningful data to all the providers across all platforms.
Pehchaan: A Touchless Attendance System

With the Coronavirus pandemic taking its toll all over the world, and social distancing measures being adopted, there is an urgent need to digitise all the processes for the smooth functioning of organisations. Thus, Pehchaan presents a nocontact system for recording the attendance of entities by verifying face and voice. It makes use of low-cost ESP microcontrollers with a camera and microphone module to extract face measurements using a Deep Convolutional Neural Network and apply Mel Frequency Cepstrum techniques to the audio files. We verify the entities’ claim by comparing the similarity with the encodings stored in the database. We are using wifi networks to connect ESP with the backend server. Face and voice recognition together act as two-factor verification and an admin will be able to access the records of a particular day and time and thus would be able to capture the attendance without any manual effort.
Card Game to Demonstrate Quantum Key Exchange

This demonstration shows how the BB84 quantum key exchange works by using playing cards. The game is played with three players (students) and an oracle (the instructor). The three players represent three characters, Alice, Eve, and Bob. The game is played with and without the participation of Eve. Bob and Alice try to exchange a series of random bits and try to determine if Eve is participating (eavesdropping).
Educational cultural-semiotic and private environment of a developing person's life activity (towards the construction of semantic reality and ultimate ontology for the sphere of new education)

The article formulates the concept of an educational cultural-semiotic and private environment of a developing person's life activity. It is focused on new forms of education in the context of a change in the basic pedagogical paradigm and changes, which the author calls a "quiet revolution in education." The principles of the traditional and new paradigms of education are briefly characterized. The question is raised about the reality that will allow us to comprehend and link together the individual principles of the new paradigm: a student is a kind of centaur and a "social individual" and a "personality"; not individual characteristics, but different backgrounds and development trajectories; a combination of learning and independent activity (education that promotes in the future, the transition to self-education); search and formation of new forms of education; restoration of the meaningfulness of education in relation to goals and content; transition to reflexive contents. The author describes the image of culture that he developed in the process of many years of cultural studies and helped to see a new reality. But he called this reality not a culture, but an environment. The characteristics of such an environment are indicated, two levels of its constitution and description are distinguished - abstract (as reality) and more concrete (ontological). The latter is illustrated by the material of the problems of teaching mathematics. In conclusion, the criteria for demarcating competencies for new types of education are discussed: which area and tasks are responsible for, what forms of awareness take place, the nature and features of the current practice.
Future network requirements for extended reality applications

The availability of discrete, attractive and high-performing extended reality (XR) devices is key to the mass-market success of XR applications, and the process of creating them is well underway. Device developers are working intensively to evolve the device form factor from today's bulky head-mounted displays (HMDs) to lightweight and stylish glasses that will depend on computation offloading to a much greater extent.
Features of the use of active and interactive methods in the teaching of Oriental disciplines in a distance format

The object of this research is to study the organization of the educational process using active and interactive methods. The relevance of the topic is determined by the insufficient number of theoretical developments, research on the organization of interaction of these methods in a remote format. The purpose of this article was to clarify the features of the use of active and interactive methods in the conditions of distance learning and to present their own teaching methodology, the essence of which is the continuity and variability of the use of active and interactive teaching methods throughout the educational process by different teachers. The research methods are general philosophical methods of analysis and synthesis, as well as the method of systematization of materials, since the authors used various forms of classes, analyzed their effectiveness in terms of assimilation of the material and selected the most valuable options. In addition, the observation method and the survey method were used to obtain student reflection on the classes conducted. The novelty of the work lies in the development and implementation of the author's methodology for teaching Oriental disciplines in a classical university based on the continuity of courses and interdisciplinary interaction, which can be used both in full-time and in distance format. The cooperation of teachers of Oriental disciplines of IMOMI N.I. Lobachevsky State University is aimed at developing and achieving common goals, creating didactic and psychological conditions in which a student can show not only intellectual and cognitive activity, but also a personal social position, his individuality, express himself as a subject of learning. At the same time, teachers of Oriental disciplines carry out the continuity of courses not only from the point of view of the content of the material, but also from the point of view of the choice of teaching methods. The presented experience may be useful to teachers of other universities.
Automating telecom software deployment with GitOps

An always-up-to-date software (SW) base will be a huge advantage for communication service providers (CSPs) by making it possible to rapidly introduce new features and updates that open up new business opportunities, improve efficiency and proactively address security threats. To make it happen, greater automation of SW delivery and operational processes will be essential.
The role of forensic accountants in online dispute resolution: Benefits and challenges

The integration of technology into legal proceedings has become crucial. The potential of conducting trials, pleading, discovery, expert witnesses, and forensic accounting wholly or partially through online platforms is immense (Nasrallah et al., 2022). This paper aims to provide recommendations on the benefits and challenges of financial online dispute resolution. Through a comprehensive literature review, this paper identifies and explores the various benefits and challenges associated with incorporating forensic accountants into the online dispute resolution process. By examining the current landscape of online dispute resolution and the role of forensic accountants, this paper highlights the importance of having a robust technological framework and a well-trained team of forensic accountants to ensure the smooth functioning of online dispute resolution. The recommendations provided in this paper can serve as a valuable guide for policymakers, legal practitioners, and other stakeholders involved in the implementation of online dispute resolution systems. Overall, this paper contributes to the development of best practices for the use of forensic accountants in online dispute resolution and lays the foundation for future research in this field.
Beyond Locks and Keys: Structural Equation Modeling Based Framework to Explore Security Breaches Through the Lens of Crime Theories

The current evolution of the cyber-threat ecosystem shows that no organization can be considered invulnerable against security breach. It is therefore important to highlight exogenous factors other than those related to an organization’s security posture to predict security breach incidents. We present “Beyond Locks and Keys”, a framework that analyzes the projection of crime theories applied in science of victimology, to study the risk of victimization to security breach in cyber space. Factors that could be associated to information security breach in organizations are studied through hypotheses extracted from crime theories. Victimization risk analysis is built by creating a comprehensive profile for each organization, capturing its characteristics, and grouping them in constructs used to measure the likelihood of data breach. We use structural equation modeling and statistical hypothesis testing approach to build our framework. We evaluate the validity of our model on a dataset of 4868 organizations based in the United states (US) collected between the years 2018 and 2020. “Beyond Locks and Keys” highlights the importance of exogenous factors, besides the technical security ones, that contribute to the understanding of victimization risk to security breach.
Encrypted Search: Not Enough to Ensure Privacy

The success of most recent end-to-end encrypted messaging applications (such as whatsApp, signal, etc $$\ldots $$ ) is due to the fact that they rely on sound cryptographic primitives and protocols to provide a high level of data protection. The massive use of these applications leads to an abundance of encrypted messages that need to be stored and retrieved upon user requests. This leads to a heavy burden on mobile phones storage resources which could be solved by using a server store. However, the server store could not be trusted, and message encryption is not enough to ensure privacy protection. Indeed, even if the content is encrypted, the underlying metadata (such as the number of which a keyword has been searched, Timestamps, which user searched for which keyword) can reveal private and confidential information which can be exploited against the user will. In this work, we suggest an approach using the private information retrieval technique widely used in the area of information retrieval. Private Information Retrieval (PIR) provides a way to retrieve encrypted content from a server. Initially, an encrypted search query is sent to the server. Then, the server replies without knowing neither the keyword to be searched for, nor the identity of the user conducting the search. Thus, we adapt PIR technique to provide an algorithm for searching encrypted messages without revealing Metadata.
Visualization of the interaction dynamics between a two-level atom and a graphene sheet covered by a laser field

This study investigates the detection of two discrete quantum phenomena arising from the interaction between a single two-level atom, SU(1,1)quantum systems, and a graphene membrane in the presence of a laser field. We employ quantum skew information and second-order Rényi entropy as measures to discern non-classicality and entropy, respectively. Two distinct scenarios are considered addressing the system: first, when atomic frequency significantly surpasses laser coupling; second, when the atomic frequency is considerably lower than classical laser coupling. In the former case, both phenomena hinge upon initial preparation conditions for the SU(1,1)coherent cavity, whereas, in the second scenario, the non-classicality and entropy are influenced by the coherent graphene. Notably, we find that the detuning parameter enhances the information of the composite system when the intensities of the coherent cavity and graphene membrane are substantial.
Traffic perception from aerial images using butterfly fields

Drones are nowadays considered as a valuable solution to monitor urban traffic. Object detectors face numerous challenges when dealing with high-resolution aerial images captured by drones, due to variations in altitude, viewing angle, and weather conditions. To address these challenges, we present an object detector called Butterfly detector that is tailored to detect objects in aerial images. It is an anchor-free method that leverages field-based representations. We introduce Butterfly fields, a type of composite field that describes the spatial information of output features as well as the scale of the detected objects. We employ a voting mechanism between related Butterfly vectors pointing to the object center. We highlight the benefits of our method for urban traffic monitoring by (i) evaluating the recall/precision rate of our detector on two publicly available drone datasets (UAVDT and VisDrone2019), and (ii) measuring the error rate for flow estimations on our newly released EPFL roundabout dataset. We outperform the performance of previous methods while remaining real-time.
Subtask-masked curriculum learning for reinforcement learning with application to UAV maneuver decision-making

Unmanned Aerial Vehicle (UAV) maneuver strategy learning remains a challenge when using Reinforcement Learning (RL) in this sparse reward task. In this paper, we propose Subtask-Masked curriculum learning for RL (Sub Mas-RL), an efficient RL paradigm that implements curriculum learning and knowledge transfer for UAV maneuver scenarios involving multiple missiles. First, this study introduces a novel concept known as subtask mask to create source tasks from a target task by masking partial subtasks. Then, a subtask-masked curriculum generation method is proposed to generate a sequenced curriculum by alternately conducting task generation and task sequencing. To establish efficient knowledge transfer and avoid negative transfer, this paper employs two transfer techniques, policy distillation and policy reuse, along with an explicit transfer condition that masks irrelevant knowledge. Experimental results demonstrate that our method achieves a 94.8% success rate in the UAV maneuver scenario, where the direct use of reinforcement learning always fails. The proposed RL framework Sub Mas-RL is expected to learn an effective policy in complex tasks with sparse rewards.
Global information-aware argument mining based on a top-down multi-turn QA model

Argument mining (AM) aims to automatically generate a graph that represents the argument structure of a document. Most previous AM models only pay attention to a single argument component (AC) to classify the type of the AC or a pair of ACs to identify and classify the argumentative relation (AR) between the two ACs. These models ignore the impact of global argument structure of the documents, which is important, especially in some highly structured genres such as scientific papers, where the process of argumentation is relatively fixed. Inspired by this, we propose a novel two-stage model which leverages global structure information to support AM. The first stage uses a multi-turn question-answering model to incrementally generate an initial argumentative graph that identifies relations among ACs. At each turn, all ACs related to the query AC are generated simultaneously, such that the sibling global information between the answer ACs is considered. In addition, the partially constructed graph is used as global structure information to support the extension of the graph with additional ACs. After the whole initial graph structure has been determined, the second stage assigns semantic types to both the ACs and ARs among them, leveraging information from this initial graph as global structure information. We test the proposed methods on two scientific datasets (one is the AbstRCT dataset including 659 abstracts about cancer research and the other is the SciARG dataset that consists of 225 computer linguistic abstracts and 285 biomedical abstracts) and a student essay dataset PE with 402 essays. Our experiments show that our model improves the state-of-the-art performance on two scientific datasets for different AM subtasks, with average improvements of 1%, 2.41%, 1.1% for the ACC, ARI and ARC task respectively on the AbstRCT dataset, and 2.36%, 1.84%, 8.87% for the ACC, ARI and ARC task on the SciARG dataset. Our model also achieves comparative results on the PE datasets: 87.7% of F1 scores for the ACC task, 81.4% for the ARI task and 78.8% for the ARC task.