Reviewerly/exHarmony huggingface dataset:
Aspect-Based Sentiment Analysis for Afaan Oromoo Movie Reviews Using Machine Learning Techniques

Aspect-based sentiment analysis (ABSA) is the subfield of natural language processing that deals with essentially splitting data into aspects and finally extracting the sentiment polarity as positive, negative, or neutral. ABSA has been widely investigated and developed for many resource-rich languages such as English and French. However, little work has been done on indigenous African languages like Afaan Oromoo both at the document and sentence levels. In this paper, ABSA for Afaan Oromoo movie reviews was investigated and developed. To achieve the proposed objective, 2800 Afaan Oromoo movie reviews were collected from YouTube using YouTube Data API. Following the data preprocessing, predetermined aspects of the Afaan Oromoo movie were extracted and labeled into positive or negative aspects by domain experts. For implementation, different machine learning algorithms including random forest, logistic regression, SVM, and multinomial naïve Bayes in combination with BoW and TF-IDF were applied. To test and measure the proposed system, accuracy, precision, recall, and f1-score were used. In the case of random forest, the accuracy obtained in combination with both BoW and TF-IDF was 88%. Using the SVM, the accuracy generated with BoW and TF-IDF was 88% and 87%, respectively. Applying logistic regression, the accuracy generated with both BoW and TF-IDF was 87%. Using multinomial naïve Bayes, the accuracy generated in combination with both BoW and TF-IDF was 88%. To improve the optimal performance evaluation parameters, different hyperparameter tuning settings were applied. The implementation result shows that the optimal values of models’ performance evaluation parameters were generated using different hyperparameter tuning settings.
Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

Nowadays, autonomous robots are expected to accomplish more complex tasks and operate in an open-world environment with uncertainties. Developing software for such robots involves the design of task planning paradigms and the implementation of robotic software architectures, making software development rather tricky and time-consuming. In recent decades, component-based software development approaches have been increasingly adopted in robotics to improve software development efficiency by reusing data and controlling flows between components. However, few works have tackled the more critical issue of reusing complex high-level task planning paradigms and robotic software architectures. To make up for the limitation, this paper first identifies the mainstream task planning paradigms and proposes a set of novel patterns for interaction pipelines between the robotic functions of sensing, planning, and acting. Then this paper presents a novel Behavior Tree (BT) based development framework Structural-BT, which provides a set of reusable BT structures that implement abstract interaction pipelines while maintaining interfaces for task-specific customization. The Structural-BT framework supports the modular design of structure functionalities and allows easy extensibility of the inner planning flows between BT components. With the Structural-BT framework, software engineers can develop robotic software by flexibly composing BT structures to formulate the skeleton software architecture and implement task-specific algorithms when necessary. In the experiment, this paper develops robotic software for diverse task scenarios and selects the baseline approaches of Robot Operating System (ROS) and classical BT development frameworks for comparison. By quantitatively measuring the reuse frequencies and ratios of BT structures, the Structural-BT framework has been shown to be more efficient than the baseline approaches for robotic software development.
The Cyborg Hand: Gesture, Technology, Disability and Interdisciplinarity in <i>Whatever Weighs You Down</i>

This article examines the collaborative genesis of a new work for solo performer co-composed by Neil Luck and the author, Whatever Weighs You Down (2022), with a focus on the use of MiMU sensor gloves, and the development of a work-specific gestural approach to this technology. This gestural approach was strongly influenced by a collaborator on the project, Deaf performance artist Chisato Minamimura. Minamimura’s expertise in BSL, dance, ‘sign mime’, and other approaches to movement allowed the creative team to collaboratively develop an approach to the gloves that explored the communicative dimensions of gesture, rather than simply maximising their functions as instruments. This collaboration demonstrates how new and innovative approaches to digital instruments can emerge through collaborations that include disabled artists.
Applications of Quantum Probability Amplitude in Decision Support Systems

Establishing various frameworks for managing uncertainties in decision-making systems have been posing many fundamental challenges to the system design engineers. Quantum paradigm has been introduced to the area of decision and control communities as a possible supporting platform in such uncertainty management. This paper presents an overview of how a quantum framework and, in particular, probability amplitude has been proposed and utilized in the literature to complement two classical probabilistic decision-making approaches. The first such framework is based in the Bayesian network, and the second is based on an element of Dempster–Shafer (DS) theory using the definition of mass function. The paper first presents a summary of these classical approaches, followed by a review of their preliminary enhancements using the quantum model framework. Particular attention was given on how the notion of probability amplitude is utilized in such extensions to the quantum-like framework. Numerical walk-through examples are combined with the presentation of each method in order to better demonstrate the extensions of the proposed frameworks. The main objective is to better define and develop a common platform in order to further explore and experiment with this alternative framework as a part of a decision support system.
Securing Healthcare IoT Data Using Multiple Blockchains at Fog and Cloud Layer

This paper proposes the use of blockchain at the fog and cloud layers to secure data generated from healthcare IoT. It addresses the challenges of storing healthcare data online, the benefits of IoT in healthcare, and the varying data protection standards across countries. The paper examines current solutions for complying with data protection protocols and the potential effects on patients in case of breaches. It concludes by highlighting the advantages of using blockchain technology to prevent unauthorized access, data tampering, and ensure the privacy and integrity of patient information.
Traffic Flow Analysis in Digital Forensics: Unveiling Patterns and Anomalies

The rapid advancement of digital technology and the increasing interconnection of systems has resulted in a complex network of digital activities. In this scene, examining organization traffic stream has arisen as a vital device in computerized criminology. This paper dives into the “Network Traffic Flow Analysis in the Field of Digital Forensics and Cybersecurity: Revealing Patterns and Deviations” domain. The objective of the study is to investigate the way in which traffic stream examination fills in as a strong procedure for unraveling the multifaceted computerized effects left behind by clients, gadgets, and potential danger entertainers. Forensic Data analysts acquire the ability to detect security breaches, data exfiltration, or unauthorized access by closely examining examples of information transmission, source-objective connections, and correspondence conventions within an organization. This analysis allows them to identify both regular patterns and irregular deviations. Methodologies for capturing and recording network packets, the role of flow data in summarizing complex interactions, and the use of intrusion detection systems (IDS) and intrusion prevention systems (IPS) to monitor real-time network activities are all examined in this paper. Also, the review dives into utilizing AI and man-made reasoning methods for prescient investigation and inconsistency identification inside network traffic.
Efficient Spam Email Classification Using Machine Learning Algorithms

In today's digital age, since email is the main form of communication, the identification of email spam is a critical issue. In addition to consuming a lot of time and money, email spam is also a security and privacy risk. In this paper, we provide a means for email spam detection that employes machine learning Algorithms. The required features for training the ML models have been engineered after analysis of the email dataset of content-based filtering obtained from Kaggle website. We tested a Several types of algorithms for machine learning and analyzed their level of performance using the dataset. Our findings demonstrate how effective is the suggested approach in identifying email spam with highest accuracy of 99.8% and Rmse of 0.2. Here we applied, the various ML classifier algorithm such as Decision tree, Voting Classifier, Random Forest, Logistic Regression and so on to our dataset, compared among each other and found which suits best for the dataset with the highest accuracy. This method can be useful in email clients or servers to detect spam emails automatically and enhanced
Recognition Of Facial Images Using Convolution Based Multilayer Perceptron Model

In this paper, C-MLP based Network is used to restore the image and recognize the facial images, for the purpose of resolving the face recognition issue when the facial images are severely occluded. Multilayer perceptrons (MLPs) provide the ability to effectively represent intricate, non-linear associations within datasets. In Face recognition techniques, faces can manifest diverse non-linear fluctuations as a consequence of factors such as variations in lighting conditions, facial emotions, and position. Multilayer perceptrons (MLPs) have the capability to acquire the ability to detect and represent these detailed patterns, rendering them well-suited for processing the intrinsic intricacy present in facial data. The ICA-NNMF feature extraction approach is employed in this model to extract features, and Convolution based MLP is deployed to recognize faces. Users must modify the reconstructing result in compliance to their particular interests or the properties of the restored image since the perceptual quality of the image is more or less subjective. With this approach, users can regulate the continuous transition between several objectives, such as the trade-off between noise reduction and detail preservation. This model ensures the normal distribution along with parametric optimization contributing to the high performance for restored face recognition. Thus, It is analyzed and described how well the framework performs when correctly identifying faces in the generated images.
Navigating Complex Multiclass Classification in High-Dimensional Spaces: A Hybrid Approach

Large-scale tabular data classification is a critical task and the complexity arises from the vast amount of structured data generated in these fields, coupled with the challenges of high dimensionality and limited sample sizes. To address these challenges, advanced machine learning algorithms are required to analyze and categorize instances within these datasets effectively. In this work, we propose the usage of a state-of-the-art classifier called XBNet, which combines the strengths of tree-based classifiers and neural networks to tackle large tabular datasets. Our methodology is validated on a dataset with 64 dimensions and 11 classes, showcasing the model's capability to detect patterns and extract relevant features automatically. Furthermore, we employ K-Fold cross-validation to assess the model's performance, achieving an impressive training accuracy of 61.9% and a validation accuracy of 31.7%. These results surpass those of competing algorithms, confirming the superiority of our proposed methodology in handling large-scale tabular data classification tasks.
Analysis of Crime Data Visualization and Clustering: PCA + K-Means vs. Feature Extraction

This research paper presents a novel approach for crime data analysis and visualization through the integration of Principal Component Analysis (PCA), K-Means clustering, and feature extraction techniques. Leveraging the inherent structure of crime datasets, an enhanced methodology is proposed that combines dimensionality reduction using PCA, followed by K-Means clustering to identify crime patterns. Additionally, feature extraction is used to provide a comparative study to understand which method is better for clustering which makes use of Silhouette scores The approach not only provides insightful crime data visualization but also identifies distinct crime clusters, aiding law enforcement agencies in efficient resource allocation and crime prevention strategies. Experimental results validate the effectiveness of the proposed method in uncovering intricate crime patterns within diverse datasets.
Hybrid Technology for Multilevel Feature Extraction in CBMIR

A new technique in the healthcare industry called Content-Based Medical Image Retrieval (CBMIR) gives medical personnel an effective way to search for and access pertinent medical images from huge databases. The successful representation and analysis of medical images is made possible by feature extraction techniques, such as the Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP) which are essential to CBMIR systems. As a result, rather than comparing all of the images directly pixel-wise, the retrieval method concentrates on comparing extracted features that capture the most important image attributes. The proposed system is an hybrid approach. A hybrid feature extraction technique combines LBP and DWT and normalizing the features. Next the reduction of the features is done using principal component analysis (PCA). The representation of the images is done using grey-level cooccurrence matrix (GLCM). The study uses a dataset of wrist images to extract the images, next is used to identify the fractures.
Decentralized Money Transaction Security System using HMBC algorithm

Blocakchain is now one of the most secure options for data protection. The rapid advancement of digital technology has brought forth new challenges in terms of data security. Organizations must adopt reliable authentication and key vaulting mechanisms to secure their data. It is advantageous to construct a secure data network due to the distributed ledger system's high level of security. Consumer goods and service providers secure and store customer data using blockchain technology. With the help of Blockchain, one of the most significant technological advancements of this century, we Although blockchain technology has high levels of security, there are still some difficulties that need to be resolved. To, solve this issue we proposed homomorphic blockchain Algorithm Secure money transactions. It can maintain our competitiveness without relying on the confidence or trust of other parties.More chances to compete with market services and consumer goods emerge as technology develops.With the development of various facets of the global economy, this technology will be employed more widely.
Enhancing Autonomous Vehicles: Robust Sign Board and Lane Detection

Despite the many obstacles that still need to be cleared, self-driving cars are gradually becoming a reality and have the potential to drastically alter our environment. It has the ability to recognize the elements and technology that must be utilized in order to navigate securely, autonomously, rapidly, and comfortably. The suggested system has the ability to recognize lanes, obstructions, sign boards, nearby cars, signals, etc. in almost real time. Face recognition in real-time video or images is accomplished using the object detection algorithm known as Haar's Algorithm. The system converts camera input pixels into driving commands by using edge or line detection characteristics, resulting in a level 2 autonomous vehicle. The network automatically picks up the most changeable aspects from the camera input, with little help from humans. This end-to-end strategy works well. Additionally, it works in places with hazy visual cues, including parking lots and gravel roadways. The Cascade Trainer GUI is used to implement this strategy, and the model has up to 98% accuracy.
NLP-Based Next-Word Prediction Model

Through the phases of the development of prediction model, there has been new advancements within a short time interval. It is also being modified and developed for the native languages. Long phrases might be tedious to write, but text prediction technology built into keyboards makes this simple. Another name for the next word prediction is language modelling. The task at hand is anticipating the first word that will be said. It has several applications and is one of the main tasks of human language technology. This approach employs letter to letter prediction and says that it predicts a letter when letter is used to build a word. Long short time memory equation can sense prior text and anticipate the words that may be beneficial again for users to surround phrases.
Deploy a Private Blockchain Network with a Power of Authority (PoA) Consensus Model Using GoEtherium in Amazon EC2

Emergence of blockchain technology has revolutionized various industries, offering unprecedented security, transparency and decentralized data management. Private block chain networks in particular have gained significant attention due to their enhanced privacy and control over network participants. This paper focuses on the deployment of a private blockchain network using GoEtherium with a Power of Authority (PoA) consensus model in the Amazon EC2 cloud computing environment. Study addresses the challenges associated with deploying a private blockchain network and provides practical insights. It explores design considerations, including GoEtherium selection and configuration, Amazon EC2 instance setup and component integration. Performance evaluation, security considerations and a use case analysis are conducted to demonstrate the effectiveness of the network. Findings contribute to the understanding of deploying private blockchain networks and provides guidance for organizations looking to leverage blockchain technology securely and efficiently.
AP-TRL: Augmenting Real-Time Personalization with Transformer Reinforcement Learning

In the digital era, understanding user behavior in real-time and providing immediate personalization can significantly enhance the user experience and engagement. This paper introduces a novel approach that integrates the transformer architecture with reinforcement learning (RL) for real-time user behavior tracking, and recommendation. This paper demonstrates how this hybrid model can efficiently categorize user actions, predict future behaviors, and personalize content in real-time. Experimental results show that our model outperforms traditional methods, with a marked improvement in accuracy and response time.
Dynamic Estimation of Obstacle Postion with Vision Sensing for Reactive Collision Avoidance of UAVs

It is very crucial for unmanned aerial vehicles to have autonomous obstacle detection and avoidance capability for their survivability during flight. This paper proposes and validates the application of extended Kalman filter for online obstacle position estimation with a vision based sensor and the usefulness of this information with two recently developed guidance algorithms for collision avoidance. The vision sensor is assumed to continuously sense the environment in front of the vehicle during flight. In case any obstacle is detected, the information from this sensor is then utilized in the filter to estimate the obstacle position online. Simultaneously, the collision cone approach is applied to predict any potential collision in future and, in case of a potential threat, to steer away the vehicle in order to avoid the collision. This is done by first computing a suitable ‘aiming point’ towards which the velocity vector of the vehicle must be aligned as soon as possible and then by using either of two recently proposed guidance laws, namely nonlinear geometric guidance and differential geometric guidance (which are identically same with appropriate gain correlation, but otherwise are different) to achieve this objective. Exhaustive simulation studies show that this overall strategy is fairly successful.
Determination of Whole Field Strain in Composite Reflector Using Photogrammetry

Conventional strain measurement techniques cannot be used to obtain the strains in composite reflectors under thermal environment. Though photogrammetry can be used to measure the deformations it cannot provide information on strains. In this work a methodology is developed to determine the strains from the displacements obtained from photogrammetry coordinate measurement. The strains are determined by applying equilibrium conditions and not from the gradient of the displacements. The results are verified using simulated data as well as experiment. It is demonstrated for a composite reflector of a typical spacecraft when subjected to thermal load. The methodology developed in this work helps in obtaining the strain distribution without using expensive digital image correlation equipment. This methodology can be used on wide variety of applications like inflatable structures, flexible structures etc., where strain gauge measurements are not possible to be carried out.
Smart Education and Hybrid Education in Students of the National University of Education in Times of Pandemic and Post-Pandemic

The pandemic caused by SARS-CoV-2 and its variants in the years 2020, 2021, and 2022 has led to reflections and research initiatives in the university. The world requires making decisions to mitigate global risks such as proper water resource management, energy conservation, waste recycling, addressing environmental damage, population growth, and others. Smart Education is needed to foster generations of knowledgeable citizens who can tackle the crises of the upcoming years, and the university has the obligation to be the main actor in this task. The research was conducted using a quantitative approach with a correlational design and aims to determine the relationship between Smart Education and learning in Hybrid Education among students of the Professional School of Electronics and Telecommunications at the National University of Education (UNE). Data was obtained from 99 students, excluding those in the first cycle. The results show that learning in hybrid education has a significant relationship with sustainable development and environmental protection, collaborative learning, student-centered learning, lifelong learning, and the varied learning methods of Smart Education.