Reviewerly/exHarmony huggingface dataset:
Simulating Quantum Hardware using Qiskit Metal

The process of building a quantum circuit often consists of several steps. We need a quantum framework that enables us to create these circuits and a simulation environment that enables us to analyse the circuit in order to execute these circuits. Qiskit Metal, an open-source framework made available by IBM, is the name of the quantum framework we used. Ansys Electronic Desktop is the modelling software used to evaluate these built-in circuits. In this paper, we go over how to build a quantum circuit and its simulation on Ansys’ electronic desktop. The conventional methods of modelling the circuit—manually by launching the project in Ansys or automatically through a code—don’t always work. This paper will examine the alternative ways of allowing simulations on quantum hardware projects.
From Templates to Transformers: A survey of Multimodal Image Captioning Decoders

Automated text-to-image (image synthesis) and image-to-text (image captioning) generation are two of the most challenging and cutting-edge fields of study in Computer Vision (CV) in conjunction with Natural Language Processing (NLP). The image-to-text synthesis, also known as Image Captioning (IC), has numerous applications in visual assistance, machine vision, remote security, healthcare, and remote sensing, among others. Since their origin, the IC frameworks have been constructed utilizing a two-subcomponent pipeline consisting of the visual feature extraction and natural language modeling subcomponents. The recent surge of interest in the applications of sequential deep learning models in machine translation has resulted in the development of efficient language modeling architectures. Deep sequential modeling architectures, such as Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRU), tackle the intricacies of the multi-modular space far better than existing machine learning-based translation approaches. The tremendous growth of deep language modeling architectures in IC necessitates a comprehensive review of its literature. In this survey, we conduct an exhaustive and analytical analysis of the language modeling architectures used in IC frameworks for caption generation, along with their training corpus datasets. We also identify a list of open research issues and potential research areas for future work.
Extraction of information from a cellular phone (mobile communication device) during investigative (search) actions

The relevance of the study is substantiated by the need to develop a system for procedural registration of information (statements) withdrawn from a mobile phone using hardware and software complexes, the content of which proves the involvement of a person in the commission of a criminal offence, since such a documentation mechanism has not been developed in practice. The purpose of the study was to highlight theoretical and applied approaches to the legal support of investigators’ actions to extract information from a mobile phone found at the incident scene. In accordance with the set goal and specifics of the subject of research, a set of the following methods was applied: formal logical, system and structural, hermeneutical, modelling, and generalisation. The study considers the actions of the investigator during the pre-trial investigation of criminal offences, when a cellular phone (mobile communication device) was found at the incident scene, which makes it necessary to use special knowledge. The profile and qualification of a specialist who needs to be involved in the inspection of a mobile phone are determined depending on the purpose and objectives of the investigative (search) action, established primary data on the nature of a criminal offense. A procedure for obtaining information (computer data) from a cellular phone (mobile communication device) is proposed, which provides for the creation of an “image”/electronic report of available information, which is recorded on a digital medium in the form of a file, fixed with an electronic label as a checksum. The practical value of the study lies in the procedural solution of the issue of extracting information (statements) from mobile phones, which is important during pre-trial investigation and documentation of digital information (computer data)
Energy Consumption Tokens for Blockchain-Based End-to-End Trading of Green Energy Certificates

In many regions of the world, energy attribute certificates (EACs) are created, stored and traded by means of centralized registries. These systems, which are often accused of being used for “greenwashing”, are usually country-specific and pose prohibitive administrative and economic barriers for small energy producers or prosumers. Blockchain-based approaches are widely considered an open, decentralized, transparent and more granular alternative. One of the technically mature solutions is Energy Web Origin. While this platform implements the concept of electricity generation devices, it does not provide any means of tying the use of an EAC to a point of consumption, such as a smart meter, a data center, or an industrial facility. To mitigate this limitation, we propose an extension of the Energy Web Origin system by an architecture that utilizes a consumption token based on the Ethereum ERC-721 standard. We assess the technical feasibility and scalability by measuring the amount of computing power (“gas”) required to run this extension on the Energy Web Chain.
Insights for the Next Solar Eclipse in Turkish Grid with Increased Solar Capacity

Solar panels are critical to future electricity generation, particularly as we transition from conventional to sustainable methods. While engineering applications can help address the disadvantages of solar panels in designing future power systems, managing grid operations during solar eclipses remains a challenge. During an eclipse, the obscuration of the sun reduces photovoltaic power generation. In this study, we analyze low- and high-resolution generation data from multiple panels to investigate production variance during a partial solar eclipse in Turkey in 2022. Our analysis provides insights into the impact of the next solar eclipse in 2027 and outlines necessary preparation steps. These findings can help address the challenge of managing grid operations during solar eclipses, which is critical for the successful integration of solar energy into the power grid.
Identifying Concept Drift with Supervised Algorithms in Smart Grids

Early and correct disclosure of anomality is critical for realiable and secure activity of the smart grid. Machine learning based outlier detection schemes could be helpful when there is no concept drift in the smart grid. Concept drift is considered an unforeseeable change in the distribution of the data in machine learning [4]. In this study, an anomaly detection is formulated as a machine learning problem and a robust detection algorithm is proposed. Numerical results demonstrate effectiveness of the proposed solution to detect anomaly and contingency besides integrity attacks in the smart grid.
Rhyme detection of Hindi and Rajasthani Poems using Statistical-Based Methods

Poetry is a kind of expression that may be used to convey our sentiments and emotions. Poetry makes up a significant portion of the canon of published works in each given language. Poetry can be clearly differentiated by humans even in the absence of any kind of specialist equipment, and this is one of its most important aspects of it. In the present article, the problem of poetry identification was accomplished using Hindi and Rajasthani corpora. Rhyme and wave frequencies are taken into consideration when developing corpora. The identification of poems is accomplished by comparing their textual content to that of trained corpora. The objective here is to accurately identify poems and extract poetic elements from them. This study first reviews the many rhyming detection methods already in use, followed by the introduction of our unique statistical-based approach, which is then evaluated against several supervised learning methods using MATLAB.
A Scoping review of Deep Reinforcement Learning methods in Visual Navigation

Reinforcement Learning (RL) is a subset of Machine Learning that trains an agent to make a series of decisions and take action by interacting directly with the environment. In this approach, the agent learns to attain the goal by the response from its action as rewards or punishment. Recent advances in reinforcement learning combined with deep learning methods have led to breakthrough research in solving many complex problems in the field of Artificial Intelligence. This paper presents recent literature on autonomous visual navigation of robots using Deep Reinforcement Learning (DRL) algorithms and methods. It also describes the algorithms evaluated, the environment used for implementation, and the policy applied to maximize the rewards earned by the agent. The paper concludes with a discussion of the new models created by various authors, their merits over the existing methods, and a briefing on further research.
Explainable IoT Forensics: Investigation on Digital Evidence

This research examines the relevance of digital forensics in the field of Internet of Things and describes how different forensics tools and software are used to investigate cybercrimes. It emphasizes the importance of IoT Forensics and how it’s used to tackle cybercrimes. It also discusses on the challenges faced by IoT forensics and gives an insight into the recent advancements in the field. It gives a walkthrough about how digital forensics investigation is done in 'data stolen' or 'data deleted' scenario. An outline of research potential and problems in IoT forensics is given in this chapter. The main details of IoT forensics are described. In all stages of a forensic investigation, issues linked to IoT are highlighted along with the potential that IoT presents for forensics. An illustration of an IoT forensics case is given with appropriate analytics. A brief research overview is provided, with information on the important research directions and a review of relevant articles. Future research proposals are included in the chapter's conclusion.
Using Smart Contracts on the Blockchain for IoMT in e-Healthcare

Healthcare facilities have seen major modifications in the last 20 years. With the aid of IoT-enabled devices, monitoring and analyzing patient diagnostic parameters is substantially facilitated. A paradigm change in healthcare technology is represented by IoMT. However, issues with patients’ right to privacy and data security continue to exist. Blockchain has permeated several scientific fields and is expected to grow dramatically. Blockchain smart contracts can eliminate intermediaries and increase contract execution. Blockchain and smart contracts in IoMT e-healthcare are discussed in this paper. The study analyses how decentralisation and smart contracts will affect the IoMT in e-healthcare, suggests a new architecture, and discusses the benefits, drawbacks, and future trends of integrating all three. The suggested architecture outperforms standard techniques in packet delivery ratio, latency, and energy efficiency.
Securing Data using Cryptography

In the information world, every piece of information travels through unidentified channels. Today, there are numerous simple techniques to hack data transferred to a destination. To keep the data protected from unknown third parties, it is encrypted and decrypted using the cryptography principle to keep it secure. There are many different algorithms that have been developed in cryptography, and numerous attacks render them vulnerable. The RSA algorithm is one of the best-known asymmetric cryptography algorithms. The different data security algorithms and data security in the area of blockchain applications are discussed here. Encryption time, decryption time, and other various data security characteristics have also been examined in a number of articles and discussed.
A Comprehensive Review of Image Steganography Techniques

Numerous photographs are shared on public channels due to the quick development of multimedia technology. As a result, there has been a lot of interest in offering secure image transmission across open channels. Multimedia data is frequently secured using encryption methods. The main motive of this study is to deliver a complete description of various steganography approaches & performance in terms of capacity, security, and robustness. The review includes a classification of the different steganography methods, including spatial domain, frequency domain, and transform domain techniques. Each method studied providing ability to go through any image processing operations, security, robustness, and ability to detect the hidden information. To assess the benefits and drawbacks of the various image steganography methods now in use, extensive comparisons have been made between them. For improved stego-medium, blockchain-based steganography approaches are also reviewed because now we are moving towards this field. In future it can be used to better understand the various techniques available and to make choices about which methods are best suited for their specific needs.
Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images

Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. This paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. This prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action.
Indian Coin Detection Algorithm Using MATLAB

The purpose of this essay is to classify Indian coins of various denominations. Identification of the coins and calculation of their total value in Indian National Rupees are the objectives (INR). The system uses image processing in the MATLAB tools section and edge detection technologies to identify coins. The properties of modern and historic coins of various denominations are taken into consideration for classification. Indian coins come in a variety of values and are categorized based on their shape, size, surface, weight, area, centroid, and other physical attributes. Other countries’ coins have the same specs but different values. This research is concerned with affine transformations, such as rotation, shearing, and straightforward grey level scaling. By using zooming techniques to increase the coin size in the photograph, the coins are clearly visible. To implement the coin categorization, MATLAB code is written, and it is tested using simulated results.
TSDRDL: Traffic Sign Detection and Recognition using Deep Learning Techniques

The recognition and detection of traffic-signs are crucial components of expert systems, including automated driving and traffic assistance systems. These systems rely on quickly detecting and identifying traffic-signs to ensure safe and efficient driving. This research proposes a novel method for accurately detecting and recognizing traffic-signs in real-world situations. The approach involves pre-processing images by converting them to grayscale and filtering them. The traffic-signs are then classified into subclasses using a convolutional neural network (CNN) with filtered input images and the same parameters as the detection stage. The CNN architecture parameters are optimized to achieve the highest recognition. The Experimental-results demonstrate the CNN architecture outperforms prior studies regarding accuracy. Overall, this research offers a promising approach to improving the reliability and efficiency of automated driving and traffic assistance systems.
Sentiment and Emotion Analysis of Significant Diseases in India and Russia

Healthcare organizations need this information to understand and treat the patient’s concerns. The motivation for this kind of analysis is how patients provide this information while wrapping it in their thoughts and emotions. It is less practicable to manually study all the free and abundant health-related knowledge accessible online to arrive at decisions that might contribute to an immediate and beneficial decision. Sentiment analysis methods perform this function through automated procedures with minimal human intervention. In this paper, an investigation is conducted to compare the region-wise, language-wise, and sentiment analysis of the tweets collected from Russia and India. The results obtained through research have shown some significant characteristics of the language models used for language detection. The inferenc and analysis obtained from the observations are included in this paper.
CPPER: A Controlled Partial Prioritized Experience Replay for Reinforcement Learning in its Multi- Agent extension

Multi-Agent Reinforcement Learning (MARL) in the recent past, has been used to model many real-life applications and perhaps even more recently. This Multi-Agent extension of Reinforcement Learning (RL) is heavily used to model those kinds of scenarios where the information regarding the environment is very limited. Off-policy RL and Off-policy MARL methods uses a memory to save the transitions (experiences) of the agent(s) and later samples them, in a particular strategy, for training the networks. Prioritized Experience Replay (PER) is a particular procedure which samples transitions on the account of the error(magnitude) a particular transition has during training. For a transition, the probability of it getting sampled for training gets higher with a higher error value. The method is effective in terms of giving better results early on and helps the model to learn policies quickly. In PER, it is important to strike a balance between priority and diversity of samples which it does by a prioritization factor. For our work in this paper, we propose and test a Controlled Partial PER(CPPER) where we sample transitions from the memory partly on the basis of PER and the remaining, uniformly with a control, as a result of which a further more diverse batch of transition is sampled. We performed experiments on Multi-Agent Particle Environment (MPE) for the proposed method and the results obtained were propitious.
Basketball Shot Prediction Using 2<sup>nd</sup> Order Polynomial Regression

Monitoring basketball is the most important and difficult functionalities in sport’s analysis because of how fast the game moves. The purpose of this study is to describe a computer vision-based method for detecting ball features, tracking the trajectory of ball, and estimating the likelihood that the ball will strike the basket. On the video frame depicting the sporting event, HSV-based color segmentation is utilized to locate and identify the basketball. The mask that contains the features of area and centroid is used to detect the shape of the ball. It is necessary to trace the path that the ball travels in order to gather information regarding the exact position of the ball in each respective frame. The second-order polynomial regression is utilized to produce predictions regarding the trajectory that is followed by the ball as well as the likelihood that it will contact the base. The actual path and the predicted path that was followed are almost identical. The accuracy of the proposed method is 97.98%.
Insider Threat Classification Using KNN Machine-Learning Technique

The insider threat has grown to be a broadly conventional problem, and it has become the most predominant demanding situation in the field of cybersecurity. This system shows that threats require a unique method of detection, techniques, and various tools that can simplify correct and speedy malicious insider detection. As insiders live at the back of the organizational level and regularly have access to the network, detection, and prevention of insider threats become very complicated. Later, a few issues are brought up based on the findings from the examined work, and new gaps and difficult circumstances are identified. This paper represents an up-todate review of all major machine learning algorithms employed for detecting insider threats. Moreover, various issues involved during the formulation of multiple algorithms for detecting insider threats are discussed.
Deploying NLP techniques in Twitch application to comprehend online user behaviour

Sentiment analysis of emotion entails identifying and analyzing subjective information from language, such as views and attitudes, and helps to improve data visualization by employing a variety of strategies, tactics, and tools. New media channels have significantly changed how people interact, exchange ideas, and share information. Numerous businesses have begun to mine this data, concentrating on social media since it is a popular platform for customers to voice their ideas about various brands or goods and because it gives users an audience, enhancing the visibility and potential effect of this input. So far, as the internet expands and modern technology advances, new avenues have emerged with a higher ability to offer businesses pertinent feedback on their goods. The goal of this study is to investigate the many forms of online behaviour by analyzing chat interactions from the well-known streaming service Twitch. Emotes were occasionally employed in place of letters, to get attention, or to communicate emotions. We propose a system that may take in chat logs from a certain stream, use a sentiment analysis algorithm to classify each message, and then display the data in a way that might permit users to analyze the results according to its polarity (positive message, negative message, or neutral message). This application must be sufficiently versatile to be used with any platform broadcast type and to handle the datasets at very huge level.
Hybrid Subset Feature Selection And Importance Framework

Feature selection algorithms are used in high-dimensional data to remove noise, reduce model overfitting, training and inference time, and get the importance of features. Features subset selection is choosing the subset with the best performance. This research provides a Hybrid subset feature selection and importance (HSFSI) framework that provides a pipeline with customization for choosing feature selection algorithms. The authors propose a hybrid algorithm in the HSFSI framework to select the best possible subset using an efficient exhaustive search. The framework is tested using the Bombay stock exchange IT index’s companies’ data collected quarterly for 16 years consisting of 71 financial ratios. The experimental results demonstrate that models created using 12 features chosen by the proposed algorithm outperform models with all features with up to 6% accuracy. The importance-based ranks of all features are generated using the framework calculated using 13 implemented feature selection techniques. All selected feature subsets are cross-validated using prediction models such as support vector machine, logistic regression, KNeighbors classffier, random forest, and deep neural network. The HSFSI framework is available as an open-source Python software package named ‘‘feature-selectionpy’’ available at GitHub and Python package index.