Reviewerly/exHarmony huggingface dataset:
BC-4Track: A Blockchain based System for Vehicle Tracking Management

Today, with the developments in Internet of Things, concepts like automation systems, novel communication and network technologies, machine-to-machine communication and the management of data generated through communication between objects have gained increasing significance. Particularly, in the domain of smart transportation systems, accurately detecting a vehicle’s technical specifications during stationary and driving states, as well as information related to the driver’s use of the vehicle, is critical for ensuring its proper functioning. Although several systems exist today to monitor events and data related to vehicles, most of these systems have a centralized structure that poses risks associated with single-point failure, trust management, transparency, and access control. Hence, obtaining reliable and accurate information during vehicle use is crucial for all stakeholders in the transportation ecosystem. The study presents a distributed and reliable blockchain based vehicle tracking management system called BC-4Track, that records all vehicle related events in an immutable and transparent manner. The proposed system will comprise a novel model that guarantees the registration of vehicle usage information and associated processes, as well as reconciliation among stakeholders. Accordingly, this study presents the preliminary design studies, system context and architectural requirements for the development of proposed system.
Detection of Malicious SQL Injections Using SVM and KNN Algorithms

One of the most destructive types of web application attacks is the SQL injection attack, which typically happens when the attacker or attackers alter, remove, read, and copy data from database servers. Confidentiality, integrity, and data availability are just a few of the security factors that can be affected if a SQL injection attack is successful. Structured query language, also known as SQL, is used to represent queries to database management systems. SQL injection attack detection and deterrence is not a new field of study, but it is still important because techniques from other fields can be used to enhance the attack’s detectability. Techniques from machine learning and artificial intelligence have been tried and tested to manage SQL injection attacks, with encouraging outcomes. During the model evaluation process, the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) models both obtained good accuracy ratings. The SVM model’s accuracy was approximately 99.08%, while the KNN model’s accuracy was roughly 99.01%.
Advancing Human-Machine Interaction: Speech and Natural Language Processing

In recent years, the realm of human-machine interaction has witnessed remarkable progress, largely attributed to the rapid evolution of speech and natural language processing (NLP) technologies. This paper delves into the latest developments in these fields and their profound impact on our daily lives. The integration of advanced machine learning algorithms and deep learning models, combined with the abundance of textual and audio data, has led to the transformation of the way humans interact with machines. Virtual assistants have become more capable and responsive, facilitating tasks ranging from scheduling appointments to providing personalized information. Moreover, this paper explores the applications of NLP, which have ushered in a new era of dialogue systems. These systems are now more context-aware, enabling them to engage in more natural and nuanced conversations. Such advances are especially beneficial in the realms of customer support, content recommendation, and information retrieval.
Comparison of YOLOv8 and Detectron2 on Crowd Counting techniques

With the increase of security and surveillance cameras in public spaces has also created a demand for analyzing them and Crowd counting has gained a lot of popularity in recent times as it is viewed as a one of the major components for ensuring public safety and monitoring as it is a very laborious task for a person to sit in front of a security footage. It can be a very challenging task to create a dataset for training an algorithm and laborious data annotation is required for training and testing of the algorithms. This research paper discusses the 2 popular approaches of crowd counting in use today and examines their key features. The techniques used here are YOLOvS and Detectron2 which can be used for object and image detection and segmentation. YOLO stands for”You Only Look Once” and it is a popular algorithm as it is suitable for a wide variety of object detection, image segmentation and image classification tasks.Detectron2 is a Facebook AI Research (FAIR)’s library for object detection and segmentation in images. In our research, we found YOLOvS performance is better than detectron2. Detectron2 has been performed better than YOLOvS in terms of overall accuracy, but it has been observed on the basis of the experiments conducted above that YOLOvS is able to detect more objects than Detectron
An Ontology Learning Framework for unstructured Arabic Text

Ontologies are widely regarded as valuable sources of semantics and interoperability in all artificially intelligent systems. Due to the rapid growth of unstructured data on the web, studying how to automatically get ontology from unstructured text is important. Therefore, ontology learning (OL) is an important process in the business world. It involves finding and extracting concepts from the text so that these concepts can be used for things such as information retrieval. Unfortunately, learning ontology is not easy for some reasons, and there has not been much research on how to automatically learn a domain-specific ontology from data.Ontology Studying Arabic text is not as developed as learning Latin text. There is almost no automated support for using Arabic literary knowledge in semantically enabled systems. Machine learning (ML) has proven beneficial in numerous fields, including text mining. By employing neural language models such as AraBERT, it is possible to obtain word embeddings as distributed word representations from textual input using machine learning. However, the application of machine learning to aid the development of Arabic ontology is largely unexplored. This research examines the performance of AraBERT for ontology learning tasks in Arabic. Early performance results as an application of Arabic ontology learning are promising. In this research, we provide a method for populating an existing ontology with instance information extracted from the input natural language text. This prototype has achieved an information extraction accuracy of 91%.
Implementing Generative AI and Large Language Models in Education

The recent advancements in Generative AI have been highlighted by the emergence of Large Language Models (LLMs) like ChatGPT. We track this evolution from the initial recurrent neural networks to the development of architectures like Transformers and Generative Pre-trained Transformers (GPT). ChatGPT, with its impressive ability to comprehend, process, and produce natural language, has piqued the interest of educators, students, and institutions within the education sector through its creation of high-quality textual responses.This marks the beginning of a new era in educational possibilities: we emphasize the beneficial effects of ChatGPT in learning environments, noting its utility in programming assistance, its clarity in concept explanation, and its role in enhancing automated learning processes. We also recognize potential drawbacks, including the risks of over-reliance, plagiarism, and the inherent constraints of these models in tackling mathematical and linguistic problem-solving tasks.In exploring the Paradox of Automation, we examine the implications of an over-reliance on AI in education. We seek to understand the importance of preserving critical thinking skills and ensuring that technology serves as a tool for augmenting human capabilities rather than supplanting them. Our analysis acknowledges that, while AI, including ChatGPT, can assist in content generation and problem-solving, it is essential for students to cultivate their abilities in analytical thinking, content verification, and error correction.
Cross-Lingual Sentiment Analysis in Literary Translation: A Case Study of the Novel Crystal Boys

Sentiment analysis, which is a subdomain of natural language processing (NLP), has attracted significant interest due to its extensive applications. However, its utilization in literary works, particularly in the context of translated novels, remains largely uncharted territory. This research introduces an innovative approach to exploring the emotional landscape of literary texts across different languages through the automated classification of emotion. This approach is applied to the translation of the novel Crystal Boys (孽子), which is a seminal work in lesbian, gay, bisexual and transgender (LGBT) literature authored by Pai Hsien-Yung (白先勇). The research employs a variant of Bidirectional Encoder Representations from Transformers (BERT), which is a multilingual language model, and fine-tunes it on the GoEmotions dataset. This dataset, which includes examples from 28 categories of emotion, is extended to include Chinese text via machine translation. The model is trained using a process with two stages: model training and sentiment analysis. The model was initially trained on the GoEmotions dataset, whose content was in English and machine-translated Chinese and represented a cross-language corpus. These data were divided into labeled and unlabeled sets to train and refine the model, respectively. The resulting model, which was capable of identifying a wide range of emotions in both English and Chinese text, was applied to the text of Crystal Boys. The sentiment analysis showed that the emotional profiles significantly varied between original Chinese novel and its English translation. The results provided a unique perspective on the emotional dynamics within the novel and highlight the potential for automated emotion classification to analyze literary works.
EventsVista: Enhancing Event Visualization and Interpretation

The surge in technological advancements in the last decade has given rise to massive amounts of data. Researchers have since developed a wide range of software to visualize extensive data sets. Data visualization techniques ensure an enhanced analysis of substantial amounts of information and bypass the difficulty in understanding the data they reflect. These techniques facilitate the collating of information and detecting the overall trends of events, which plays a critical role in different fields such as finance, medical recording, cybersecurity, and essential day-to-day tasks. However, information can be poorly translated when scaling from large datasets to representative graphics, either by causing information loss or over-crowding the audience receiving the visual in any application or software. Understanding and using the appropriate methodologies becomes critical for the authentic translation of data as well as the engagement of the audience. In this paper, we demonstrate the evolution of big data, the key elements of data visualization, and the operations conventionally used in visualizing the space-time cube. Moreover, we introduce our newly developed data visualization software, EventsVista, which displays and analyzes events based on the assessment of previous data visualization methods. EventsVista explicitly demonstrates the effect of merging conventional operations together, as well as adding novel ones based on the Degree of Freedom (DoF) strategy. Furthermore, EventsVista was tested on events taking place at the University of Southern Queensland, Toowoomba, Australia, prior to its launch for public user assessment. Positive user experience reflected the success of the DoF strategy in improving the event’s interpretation using our distinctive and publicly accessible visualization application.
Measuring the Rise and Fall of a Subreddit using Text Analytics: The Life Course of r/Antiwork

The subreddit r/Antiwork first attracted significant public attention during the Great Resignation of 2021, when millions of workers in the United States quit their jobs in response to psychological distress and the economic downturn arising from the COVID-19 pandemic. At the peak of its momentum, in January 2022, an r/Antiwork moderator was interviewed by a Fox News reporter who, caricaturing the moderator as emblematic of the r/Antiwork community, dismissed both as lazy, entitled, and misguided. The interview became a watershed moment for the community, instigating an influx of targeted attacks on r/Antiwork and the moderator herself, and calling attention to the issue of representation of an intentionally leaderless community with ideologically disparate members. In this paper, we aim to quantitatively capture the effects of the Fox News interview on the r/Antiwork community through examining posts contributed to the subreddit from 2020-2022. To do so, we analyze the metadata of the posts – including metrics for each post such as the number of comments received, the username of the author, and the date of publication – along with conducting a sentiment analysis over time, thereby aiming to identify observable metrics in r/Antiwork that signal changes in community dynamics. Although our quantitative results demonstrate stability in post metrics related to a thriving community, content analysis suggests these metrics are unsound and merit further qualitative scrutiny. Specifically, we argue, the forms of quantitative information available present a misleading picture of community dynamics when member perceptions suggest otherwise.
Two-factor authentication by combining PIN and biometrics Touch Dynamics

Mobile devices are becoming increasingly popular. The widespread use exposes individuals to unintentionally sharing sensitive information that could allow direct access to the mobile device. This paper implements and tests a user verification system based on touch dynamics biometrics while typing a Personal Identification Number (PIN). The proposed framework includes several features and feature selection. The classification problem has been considered in terms of one-class and binary classifiers. The proposed approach can outperform the current State of the art on the binary classification problem on the considered dataset, achieving an Equal Error Rate (EER) of 0.01%.
Decoupling Teaching Ability in Meta Knowledge Distillation

Knowledge distillation (KD) effectively solves the problem of difficulty in deploying and using pre-trained language models (PLMs) with numerous parameters for non-AI researchers such as sociologists. The integration of meta-learning and knowledge distillation enables teacher models to transfer their expertise to smaller student models with lower knowledge transfer loss. However, this approach introduces the challenge of excessive optimization parameters compared to vanilla distillation methods. To address this issue, we propose a parameter-efficient knowledge distillation framework that incorporates the Teacher Teaching Module/Student Feedback Module and gradient pruning to decouple the teaching ability and feature representation of the teacher model, allowing a more focused enhancement of teaching abilities without compromising feature representation capabilities. Additionally, we optimize a meta distillation method, which increases the optimization speed of the student model by about twice. Experimental evaluations conducted on the GLUE benchmark demonstrate that our proposed architecture achieves test scores comparable to previous methods while utilizing less than 2% of teacher parameter updates and 50% of student. Furthermore, we conduct experiments on select datasets relevant to sociological research, showcasing the ability of our compact model to provide higher results at a significantly reduced computational cost, thereby serving the needs of sociologists more efficiently.
Course Review Sentiment Analysis: A Comparative Study Of Machine Learning and Deep Learning Methods

In this ever-evolving world of education, one of the key tools for improving the quality of teaching and learning is understanding student sentiment and feedback through course reviews. This study analyzed more than 100,000 reviews of courses on the online learning platform Coursera, with the objective of comparing the efficacy of various sentiment analysis methods. The initial preprocessing steps involved data cleaning using different Natural Language Processing (NLP) techniques. The cleaned data subsequently underwent transformation into TF-IDF and word embeddings were created using a pre-trained model. Naive Bayes, Random Forest, and SVM models were trained on the vectorized TF-IDF data and fine-tuned using a grid search method. Meanwhile, the word embeddings were used to train the LSTM and GRU models, which were optimized through Bayesian methods. Additionally, the BERT model was incorporated for further comparison. The findings revealed that BERT outperformed all other models in metrics such as accuracy, precision, recall, and F1 score, making it the most effective for the sentiment analysis of course reviews in this study. This comprehensive analysis aims to offer valuable insight into the sentiments of course evaluation, ultimately striving to improve the educational experiences of students. It also discusses the significance of the findings and highlights potential areas of improvement for future research.
Knowledge Graph-Based Approach for Interactive Problem Solving with the 8D Method

The electrification of the powertrain is leading to profound changes in the automotive industry. However, manufacturing must continue to maintain high quality standards in the production of innovative product systems. Despite the widespread use of advanced methods and tools for preventive quality management, nonconformities-deviations from the defined target state or objective state-occur. The industry standard for managing nonconformities is problem solving with the 8D method. In the traditional, manual approach, the problem description in particular requires a great deal of effort, which means that analyses are often too perfunctory and take too long. This paper therefore presents a novel knowledge graph-based and interactive IT application for problem solving in 8D. For the technical implementation, we use OWL ontologies and SKOS taxonomies compliant with W3C standards. The proposed approach was evaluated at a Tier-l automotive supplier using a practical example in the area of electrified drive and mobility solutions.
Reducing Energy Consumption of Wearable Sensors by Efficient Piecewise Linear Approximation Algorithms

Due to higher demands on battery lifetimes of smart wearable sensor devices in the fitness, healthcare, industrial, and consumer markets, energy efficiency plays a crucial role. Especially when sensor data needs to be wirelessly transmitted, e.g., via Bluetooth Low Energy, a significant amount of electrical energy is consumed by the transmitter. A possible solution for applications where general signal shape information but not precise sensor values are needed, e.g., activity and gesture recognition, are data reduction techniques like piecewise linear approximation. In the paper at hand, we experimentally evaluate the effect of state-of-the-art piecewise linear approximation algorithms for sensor signals on a modern wearable sensor platform, i.e., Arduino Nicla Sense ME. Furthermore, we utilized the user programmable processing elements of modern sensor sub-systems to compute the piecewise linear approximation algorithms as part of the sensor firmware in terms of a virtual sensor driver. This way, sensor data is reduced as near to the signal source as possible. In our experiments we could achieve reductions in average device power consumption by up to 42.6 %.
Early Exiting with Compressible Activations for Efficient Neural Network Inference

Cloud-based Machine Learning (ML) incurs high latency and high sensitivity to connectivity failures. This can be improved by maximizing the ML processing on the local device. However, since these low-power devices typically have limited resources, the early-exit mechanism has been used to hierarchically split a neural network in parts over multiple devices, trading off computation with communication costs. But, the intermediate activations can be significantly large at the split point. In this paper, we present a novel entropy-based technique that learns to intelligently compress activations during training and efficiently encodes them during inference. We show that in an early-exit configuration, entropy regularization with Huffman coding can save up to 54% in communication cost, while keeping the classification accuracy of MobileNetV2 on CIFAR-10 above 85%.
The Problem of Conservatism and Development in the Russian School System

The article analyzes the processes and results of modernization of the Russian school system. It is shown, what factors were caused by goals of educational reforms in the 2000s: The devaluation and condemnation of the methods of teaching in the Soviet school during the Perestroika era, the neglect of the high performance of Soviet schoolchildren, the choice of a liberal direction in the modernization of the general education system related to the political objectives of WTO (World Trade Organization) accession, where education is a service sector. The data on unsatisfactory educational results of modern Russian schoolchildren are given. Conclusions about the need to preserve the conservative trend in modern general education have been drawn.
Health and mathematical modeling of biological processes in novel reality

Prediction is one of the activities that scientists engage in with great care, because they understand that models of biological processes are based on simplifying assumptions. This is true for medical systems as well. In the conditions of digital reality, new opportunities have opened up for processing and interpreting information of clinical interest. However, the risks of making erroneous conclusions based on “standardized” and unnecessarily unified approaches, including those based on neural network technologies, have also increased. Causal concepts in relation to complex biomedical systems can be realized only in relation to systems close to critical states. Approaches to personalized risk assessment of intravascular platelet activation are discussed.