The classification of statements provided by individuals during police interviews is a complex and significant task within the domain of natural language processing (NLP) and legal informatics. The lack of extensive domain-specific datasets raises challenges to the advancement of NLP methods in the field. This paper aims to address some of the present challenges by introducing a novel dataset tailored for classification of statements made during police interviews, prior to court proceedings. Utilising the curated dataset for training and evaluation, we introduce a fine-tuned DistilBERT model that achieves state-of-the-art performance in distinguishing truthful from deceptive statements. To enhance interpretability, we employ explainable artificial intelligence (XAI) methods to offer explainability through saliency maps, that interpret the model’s decision-making process. Lastly, we present an XAI interface that empowers both legal professionals and non-specialists to interact with and benefit from our system. Our model achieves an accuracy of 86%, and is shown to outperform a custom transformer architecture in a comparative study. This holistic approach advances the accessibility, transparency, and effectiveness of statement analysis, with promising implications for both legal practice and research.
Background: With global food demand increasing, novel agricultural practices are critical for increasing productivity and minimising waste. Water, a crucial agricultural resource, must be used efficiently, which needs modern irrigation systems.Objective: The article investigates the possibility of 5G networks in changing conventional irrigation systems into intelligent, highly responsive, and managed infrastructures. The research integrates Internet of Things (IoT) sensors and machine learning algorithms to construct an intelligent irrigation model that uses 5G technology’s quick data transfer, low latency, and widespread connection.Methodology: The study included year-long field tests that monitored soil moisture, rainfall, and crop health across various plants with varying water requirements. Data was gathered and delivered over a 5G network to a centralised control system that used machine learning techniques to change watering schedules dynamically.Results: Compared to traditional irrigation systems, the intelligent irrigation model resulted in a 30% reduction in water use, a 15% increase in crop output, and a 25% reduction in energy consumption.Conclusions: According to the study, 5G-enabled intelligent irrigation systems have the potential to revolutionise agricultural practises by providing more efficient, sustainable, and flexible water management solutions. This breakthrough is becoming more critical as climate change exacerbates water shortage issues. The article discusses how 5G networks might help to achieve global sustainability objectives by revolutionizing the agricultural industry and laying the groundwork for future research and development in intelligent farming technology.
Background: Technology in education has given birth to online examination systems (OES), which provide a simple and risk-free testing technique across several networks. The OES overcomes concerns inherent in manual check techniques, such as processing delays, registration, and record filtering issues.Objective: This article introduces an automated, web-based Open Educational System (OES) that is adaptable to any educational level or discipline, explaining its essential features, functions, and the technology that underpins it. The current study investigates how the OES improves test administration efficiency while protecting the privacy and security of students’ scores.Methods: The OES has a client-server architecture, leveraging PHP and MySQL, and functions across many networks, allowing for remote access and exam management. It includes features such as automatic test production, an analytical system, and configurable tests, all while maintaining safe and reliable test administration.Results: The OES implementation has considerably decreased the time and effort necessary for giving exams and reporting results. The system’s flexibility in test customisation and quick result tabulation underlines its efficiency, while its security features protect the examination process’s integrity.Conclusion: The OES provides a disruptive approach to digital exams, providing educational institutions with a safe, trustworthy, and efficient alternative to conventional testing techniques. The system’s flexibility and automation capabilities highlight its promise to transform testing operations by solving difficulties related to dependability, productivity, and security inherent in manual testing methods.
In the domain of character-based approximate string matching, edit distances such as Levenshtein have remained predominant despite their quadratic time complexity. This reality has prompted the adoption of more efficient metrics like Jaro and Jaro-Winkler. However, these methods often overlook the significance of character order within the matching window, which can adversely affect accuracy.For the first time, we introduce a novel class of character-based approximate string matching algorithms that leverage a convolutional kernel, surpassing the performance of existing state-of-the-art unsupervised character-based approximate string matching algorithms. This paper presents Convolutional Jaro (ConvJ) and Convolutional Jaro-Winkler (ConvJW), innovative similarity metrics designed to overcome these shortcomings. ConvJ and ConvJW utilize a convolutional approach with Gaussian weighting to effectively capture the positional proximity of matching characters, resulting in a more precise similarity evaluation. This method not only achieves computational efficiency comparable to that of Jaro and Jaro-Winkler but also surpasses the state-of-the-art in terms of F1-score, demonstrating faster execution times compared to the conventional Jaro and Jaro-Winkler implementations across various datasets.Our extensive experimental analysis highlights the exceptional performance of ConvJ and ConvJW across a range of datasets. Remarkably, ConvJ exhibits a 7x faster execution time than the fast Jaro implementation and exceeds the state-of-the-art F1-score by a significant margin of 10% more than Jaro. By setting a new benchmark in unsupervised character-based approximate string matching, our research shows the new way for future exploration and development in this field. The ConvJ and ConvJW algorithms, characterized by their quasilinear time complexity and improved accuracy, provide a solid foundation for the advancement of string matching techniques. These developments hold promise for a broad spectrum of applications in data mining, bioinformatics, and related areas.
Background: As wireless communication systems progress, there is a more significant requirement for high data transmission rates, requiring the investigation of practical multicarrier modulation approaches.Objective: The article’s objective is to develop and compare two popular multicarrier modulation algorithms, Coded Orthogonal Frequency Division Multiplexing (COFDM) and Orthogonal Frequency Division Multiplexing (OFDM), utilising the TMS320C6713 digital signal processor and the MATLAB Simulink platform.Methods: The article goes deeply into the theoretical and practical aspects of COFDM and OFDM. We assess both methods’ spectrum and energy efficiency using MATLAB Simulink simulations, considering the real-world constraints they may encounter.Results: The results show that COFDM has improved spectral efficiency, particularly in frequency-selective fading channels, and higher resistance to interference. As a result, COFDM is an excellent choice for applications requiring high data rates and continuous transmission under challenging situations. On the other hand, OFDM has higher energy efficiency, making it the modulation of choice for situations with strict power limits.Conclusion: The current study provides critical insights into the practical performance of COFDM and OFDM, supporting stakeholders in identifying the best modulation approach for various communication applications. The insights reached are critical in pushing the creation of digital communication systems adapted to individual needs.
