Reviewerly/exHarmony huggingface dataset:
Utilizing Analytical Parameterization for Job Scheduling and Virtual Machine Migration in Cloud Computing

How to ensure that tasks finish execution within predicted completion times utilizing the available resources’ bandwidth equally and effectively while recognizing the resource performance changes is a critical performance challenge in distributed decentralized systems like clouds. Historically, several models, including as reservation, immigration, and replication algorithms, have been developed under various scheduling strategies to address this problem; nonetheless, they each have their own unique set of challenges. To both meet the QoS needs of cloud users and get the most out of the resources available from service providers, this paper suggests a dynamic scheduling jobs simulation that uses job features to assign them to assets with the shortest possible execution time, taking into account the fair distribution of available bandwidth. The scheduling method considers the length, estimated execution time, and expected bandwidth of jobs in relation to the CPU, memory, and network bandwidth that are available. The basis of this scheduling approach is the generation of a standard value for each task that is proportionate to the degree to which the features of that job are connected to the characteristics of all other jobs. This should make their choice of virtual machine more accurately reflect their needs. In addition, it constructs a feedback technique for dealing with the reallocation of unsuccessful job applications that do not satisfy the mapping requirements.
Text Classification into Emotional States Using Deep Learning based BERT Technique

Classifying text into states of emotions is the most challenging task in processing natural languages, and it is even harder due to the absence of facial expressions in the sentences. Therefore, it is needed to determine what kind of emotions the texts or words communicate. Categorization of texts into sentimental text is extremely helpful and could be utilised across a variety of fields that include information mining, psychology, human-computer interaction, and many more. This research paper uses the BERT transformer from the hugging face library to classify and generate the results. The dataset comes from reviews collected from the google play store and is used to train the model; the model can be used to conclude which apps are better among the selected apps. Apart from this, the model may help developers to read the massive number of reviews written for a particular app and get new features or bugs written in the reviews.
Structured Approach for Relation Extraction in Legal Documents

Legal documents contain a wealth of valuable information, making them crucial for knowledge extraction and analysis. Extracting structured relationships between entities in legal texts can significantly enhance our understanding of legal concepts and facilitate the creation of knowledge graphs. In this paper, we propose a structured approach combining relation extraction, graph reasoning, and clustering methods to effectively extract and represent legal relationships. Firstly, we employ state-of-the-art relation extraction techniques to identify relevant entities and their relationships from unstructured legal documents. These techniques leverage natural language processing (NLP) and machine learning algorithms to recognize entity pairs and assign appropriate relationship labels. Next, we leverage graph reasoning to enhance the extracted relationships by incorporating additional contextual information. By representing the legal entities and their relationships as nodes and edges in a knowledge graph, we can apply reasoning algorithms to infer implicit relationships, resolve conflicts, and expand the graph based on the legal domain’s rules and regulations. Furthermore, we integrate clustering methods to organize related legal entities and relationships into meaningful clusters within the knowledge graph. By grouping semantically similar entities and relationships, we can uncover patterns, identify common themes, and enable more effective retrieval and analysis of legal information. Our experiments demonstrate that the structured approach, combining relation extraction, graph reasoning, and clustering methods, effectively extracts and represents legal relationships in a knowledge graph. The resulting graph provides a comprehensive and organized representation of legal concepts, facilitating legal research, decision-making, and knowledge discovery in the legal domain.
Comparative Analysis of Embedding Models for Keyphrase Extraction: A KeyBERT-Based Approach

Key phrase extraction is a fundamental task in information management, which is often used as a preliminary step in various information retrieval and natural language processing tasks. Embedding models achieve higher F-scores than graph-based models in key phrase extraction for short documents compared to longer documents. Therefore, they are suitable for real-time semantic processing of short textual data. With a vast set of embedding models dedicated to key phrase extraction, it becomes a tedious task to overview and compare the performance of each model. Hence, we have carried out an extensive comparative study of four prominent pre-trained embedding models, namely Sentence Transformers, Flair, spaCy, and Universal Sentence Encoder, that have been used in conjunction with Key-BERT. The results of the experiments conducted on this corpus show that the Sentence Transformer “all-MiniLM-L6-v2“ version outperforms the other embedding models listed above, with respect to BERT score evaluation and adaptability to transfer learning. Notably, spaCy exhibits fast processing while maintaining satisfactory accuracy. An extensive comparison of Key-BERT-based embedding models suited to texts of different sizes and a detailed qualitative analysis makes the case for our proposed methodology.
Reducing Complexity: A Comparative Analysis of Dimensionality-Reduction Techniques

Machine Learning (ML) is a modern fast-growing technology. It has extensive applications in disciplines like computer vision, bioinformatics, the medical field, finance, fraud detection, and so on. As we’ve seen, real-world data are used to train machine learning models, which can be used to process the data for purposes like prediction, classification, image processing, etc. The number of attributes in a dataset is known as its dimension. Real-world data sets have a lot of variables, which increases their dimension. It becomes increasingly challenging to envision and work on the training set as the number of variables increases. Sometimes the correlation between most of these factors makes them unnecessary. The benefits of using dimensionality are that when it comes to data compression and gaining less data space, dimensionality reduction is a great instrument. It drastically cuts down on computation time and it makes it simple to remove redundant characteristics from datasets. This project aims to provide a comparison of different dimensionality reduction algorithms. Dimensionality reduction uses a variety of techniques that yield varied outcomes. To compare and analyze the same, we have chosen four algorithms: The linear discriminant analysis algorithm (LDA), the CUR matrix decomposition algorithm, the singular value decomposition algorithm (SVD), and the principal component analysis algorithm (PCA). We are assessing the performance of these algorithms using the classifiers: Decision Tree classifier, Gaussian Naïve Bayes classifier, and the Random-Forest classifier, based on the parameter’s accuracy, recall, precision, and F-measure.
Development of Spell Corrector Model for Phrases

This paper presents a spelling correction system integrated into an intelligent tutor, facilitating natural language dialogue. The system combines rule-based and statistical approaches to identify and correct spelling errors. By considering word frequency and contextual information, it estimates the probability of a word being misspelled. It employs an efficient method for correcting misspelled words, utilizing additions, deletions, and substitutions, ranked by statistical models. To enhance robustness, the system employs adaptive learning, regularly updating its dictionary and models based on user input. Extensive testing across various domains confirms its accuracy, recall, and improved typing efficiency.
Augmented Reality-Based Visualization and Education for Biogas Plant Adoption in Rural Communities

Augmented Reality technology has experienced widespread adoption in various fields, including marketing, education, learning, and training. This paper highlights the utilization of an AR application to educate individuals about different types of biogas plants and the biomethane production process. The application incorporates AR Core technology, a Google-provided AR library that enables seamless detection and placement of biogas plant models onto suitable surfaces. Users have the ability to view and scale various biogas plant models in their immediate surroundings, aligning them with real-world dimensions. The application prioritizes optimization of models and graphics, ensuring a lightweight and efficient user experience. Notably, the models and animations showcased within the application are developed using Blender software, while the Unity Game Engine, with Android SDK support, facilitates the creation of the Android APK. Users can immerse themselves in highly interactive models, fostering a comprehensive understanding of the involved processes and mechanisms. Additionally, the application holds potential industrial benefits, as it allows industrial biogas plants to promote their products and drive wider adoption of their biogas plants.
Intelligent Deep Learning Framework for Breast Cancer Prediction using Feature Ensemble Learning

The mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. The diagnostic process can benefit from the application of machine-learning (ML) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. The input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer’s neurologist count and firing rate for each successive hidden layer. The most crucial data points have been elected utilizing three different feature-selection methods. The optimized DRNN was contrasted to five standard ML strategies: decision-tree (DT), support-vector-machine (SVM), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). The elected features have been applied to both standard ML models and optimized DRNN. The outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate.
Real-time Face Recognition Using an Enhanced VGGNET Convolutional Neural Network

Biometrics serves as the foundation for security systems and plays a pivotal role in implementing robust security measures. Among various biometric modalities such as iris, face, and fingerprint recognition, facial recognition stands out as the widely recognized and dependable option for identification and verification in security systems. Using VGGNET for classified incorporating face recognition as a biometric method offers immense potential, delivering high recognition rates while maintaining user-friendly operation. In this study, a real-time system was developed to recognize faces in a video stream obtained from a surveillance camera, specifically focusing on the face recognition approach in the presence of partial occlusion. The implemented system incorporated real-time face detection capabilities to enhance its performance. This approach not only mitigates the limitations associated with large training samples and supercomputers but also achieves an impressive 96% identification rate while maintaining acceptable performance levels and determined values.
Computer Vision and Voice Assisted Image Captioning Framework for Visually Impaired Individuals using Deep Learning Approach

Blind or visually challenged have considerable obstacles when obtaining visual material, restricting their capacity to derive meaningful information from pictures. This research proposing a novel framework combining computer vision with voice-based image captioning using deep learning techniques. The proposed system employs VGGNet-16 model for photo processing and long short-term memory networks (LSTMs) for natural language processing. The models used in this research were trained on Flickr8k, Flickr30k, and a bespoke dataset. Subsequently, these models were deployed on a Raspberry Pi 4B single-board computer that was equipped with a graphics processing unit. A two-fold approach is used, whereby the first phase entails exposing the input image to preprocessing using a pre-established VGGNet-16 model. This methodology enables the retrieval of relevant visual attributes, therefore capturing the intrinsic semantic content of the picture. The aforementioned attributes are fed into a language model based on LSTM to generate explanatory captions. To enhance communication with visually impaired people, the generated captions are converted into audible speech using text-to-speech synthesis technology. The effectiveness of the proposed architecture is evaluated via extensive experiments using benchmark datasets and real-time images obtained with a NoIR camera unit. The generated captions are evaluated using quantitative assessment criteria, namely BLEU and ROUGE scores. The developed VGGNet16 model has exceptional performance in terms of accuracy (95.620%), precision (96.928%), recall (87.879%), and F1 score (92.182%). The results suggest that using computer vision in conjunction with voice-based image captioning offers a promising solution for addressing the challenges faced by those with visual impairments in perceiving and comprehending visual content.
Cryptography, Code, Consensus and Currencies Exploring the Technological Evolution of Blockchain Networks

The concept of blockchain technology has been evolving for over four decades, truly gaining prominence with the advent of Bitcoin and its revolutionary consensus system. Since then, blockchain has grown beyond its initial scope, numerous academic studies and innovative projects have expanded the boundaries of blockchain’s potential applications. In this review paper, we aim to provide an up-to-date analysis of the most recent and significant technological advancements in the field of blockchain. Drawing upon a comprehensive review of the literature, we delve into the fundamental components of blockchain technology. Additionally, we offer a detailed technological overview of the ever evolving blockchain ecosystems and highlight their impact on the broader blockchain networks.
Digital Transformation Adoption in the Insurance Sector of Bangladesh: A Quantitative Study from the Perspective of Insurer

The process of digitization exerts a profound influence on societal behavior and culture, as well as on the operational procedures of the service sector and the financial framework and growth of a nation. The primary obstacle lies in the adoption of suitable digital transformation strategies, which can facilitate the development of innovative business models, processes and systems. The objective of this research was to investigate the current state of digitization within the insurance sector of Bangladesh, as well as to identify the challenges encountered by industry participants in their transition towards digitalization. The present study was conducted utilizing questionnaires as the primary research instrument. Questionnaires were developed with the purpose of gathering information from staff members at insurance companies in Bangladesh regarding the challenges faced by these companies in their digital transformation efforts. The insurance employee’s comments indicate that the barriers for insurance companies in Bangladesh to transition to digital platforms are significant. The marks assigned to all of the investigated barriers were determined to be above the average mark. The staff identified incorrect regulation as the most significant barrier, followed by a strict organizational structure, security concerns, and the complexity of offering products online. In contrast, customer willingness to use online platforms was found to be somewhat low, albeit still above average. This implies that the regulatory body plays a significant influence in influencing Bangladeshi insurance businesses to adopt digital technologies. Based on the analysis of the findings, it is recommended that the insurance oversight body in Bangladesh should support the implementation of digital transformation within insurance companies. Additionally, Bangladeshi insurance firms should consider adopting suitable digital platforms to effectively engage with their customers, particularly in order to efficiently manage the significant number of customers seeking online insurance services. If this trend continues, the insurance industry could experience significant growth, which could have a salutary effect on the economy as a whole over time.
Unmanned Surface Vehicle for Water Wastes Collection

In recent days, widespread floods and flash floods have been caused by heavy rains in various regions of Malaysia, particularly areas near the Klang Valley. It has been revealed through investigations that river flows are obstructed by overflowing garbage and solid wastes, further exacerbating the flood situation. This finding is further supported by the subpar performance of the SMART tunnel due to obstructions in irrigation and drainage channels. To address this critical issue, the development of an unmanned surface vehicle (USV) garbage collector, operated remotely or autonomously, is proposed to effectively clean Malaysian rivers. The prototype design includes four key subsystems: obstacle avoidance (SS1), coordinate detection (SS2), live-streaming video (SS3), and movement (SS4). These subsystems are crucial for efficient navigation and debris collection by the USV garbage collector. Furthermore, the prototype’s operations can be monitored and controlled by incorporating Internet of Things (IoT) technology, ensuring its effectiveness. This report delves into the detailed implementation and functionality of each subsystem and explores the potential positive impact of deploying the USV garbage collector. If successful, this innovative solution promises to mitigate flood risks, improve river health, and contribute to a cleaner and more resilient environment in Malaysia.
Chatbot Persona Selection Methodology for Emotional Support

Self-help groups are one way to cope with mental illness. Here, people in similar circumstances come together to provide peer support and help each other. However, some people are not comfortable using such groups. Therefore, it is necessary to investigate the possibility of using robots or other means of support. There are two types of identity: social identity and personal identity. Personal identity is defined as a kind of personality. Social identity is like a persona, a profile, such as a job or position. So far, we have suggested implementing personalities as chatbot identities and adapting the communication method to the user. However, using personalities alone would result in bland content. Therefore, we implement social identity in chatbots and use the chatbot’s personal information to enable the chatbot to speak in a way that is sympathetic to the user.
Lawful limits on freedom of expression for private communications ‘in public life’

This is a case note on Hamzy v Commissioner of Corrective Services NSW, a 2022 decision in which the Court of Appeal of the Australian state of New South Wales interpreted the right to freedom of expression, which is enshrined in international human rights law. The decision shows the difficulty of protecting choice of a language as part of freedom of expression both where the semantic import of that choice is undervalued when assessing the reasonableness of a State imposing a language choice and where the lawful exception for a State to restrict the freedom by mandating a language for its public interactions is unduly expanded. The case also reveals the vulnerability of a language speaker group to racialised linguistic discrimination without straightforward recourse.
Moving Object Extraction Based on the Accumulative Differences of Image Intensities and Gradient Orientations

The foreground extraction from the stationary background can be achieved by accumulating difference images (ADI) from a video sequence. We propose a novel method by applying the ADI technique to not only image intensities (INT) but also unit gradient vectors (UGV) of gray-scale images over time. We describe how these two approaches are fused. In addition, we discuss how to select the proper threshold values that are necessary for the ADI. Experimental results show the benefit of the fusion of the two approaches based on the F1 and F2 scores.
Analyzing the components of education quality in the Russian higher school

Subject. The article explores competition in the labor market, the problem of improving the level of professional training. Objectives. The purpose is to define areas for higher education development in Russia, taking into account the opinion of students about advantages and disadvantages of various forms of education. Methods. The study draws on methods of statistical and comparative analysis. Results. We systematized parameters that determine the quality of education. Most students support the use of blended learning in educational institutions, which involves a free schedule of attending classroom training, but at the same time, full-time education is more preferable compared to distance learning. Conclusions. The formation of the Russian model of higher education should not imply total rejection of principles of the Bologna Process (in particular, the rejection of bachelor's and master's degrees), which makes it possible to develop an interdisciplinary approach and change the "trajectory of learning".
Motion Planning for a Robotic Wheelchair with SLERP MPC Local Planner

This paper presents a motion planning approach for a four-wheeled robotic wheelchair that ensures safe and comfortable movement in an unknown scenario. To address this problem, a dual quaternion-based spherical linear interpolation (SLERP) model predictive control (MPC) local path planner is developed and implemented. First, the four-wheeled robotic wheelchair is described using the unicycle model. Our approach utilizes a simultaneous localization and mapping (SLAM) method to achieve the environment in which the robotic wheelchair operates. Additionally, we use a two-layer path planner to ensures smooth and comfortable motion for the wheelchair user, enhancing their overall experience. The A* algorithm is ultilized to generate global planning path in the established grid environment. Then, an online SLERP-MPC local planner is implemented for the navigation and control of robotic wheelchairs. The simulation environment were built within the Robot Operating System (ROS). Simulation results demonstrate the effectiveness of our proposed strategy, allowing the robotic wheelchair to move safely and smoothly from its initial position to a target location. In conclusion, our work presents a validated and effective motion planning design for robotic wheelchair navigation.