Significance of Semantic Reconciliation in Digital Forensics

Digital forensics (DF) is a growing field that is gaining popularity among many computer professionals, law enforcement agencies and other stakeholders who must always cooperate in this profession. Unfortunately, this has created an environment replete with semantic disparities within the domain that needs to be resolved and/or eliminated. For the purpose of this study, semantic disparity refers to disagreements about the meaning, interpretation, descriptions and the intended use of the same or related data and terminologies. If semantic disparity is not detected and resolved, it may lead to misunderstandings. Even worse, since the people involved may not be from the same neighbourhood, they may not be aware of the existence of the semantic disparities, and probably might not easily realize it. The aim of this paper, therefore, is to discuss semantic disparity in DF and further elaborates on how to manage it. In addition, this paper also presents the significance of semantic reconciliation in DF. Semantic reconciliation refers to reconciling the meaning (including the interpretations and descriptions) of terminologies and data used in digital forensics. Managing semantic disparities and the significance of semantic reconciliation in digital forensics constitutes the main contributions of this paper. Keywords: Digital forensics, semantic disparity, managing semantic disparity, semantic reconciliation, significance of semantic reconciliatio

Exposing knowledge: providing a real-time view of the domain under study for students

With the amount of information that exists online, it is impossible for a student to find relevant information or stay focused on the domain under study. Research showed that search engines have deficiencies that might prevent students from finding relevant information. To this end, this research proposes a technical solution that takes the personal search history of a student into consideration and provides a holistic view of the domain under study. Based on algorithmic approaches to assert semantic similarity, the proposed framework makes use of a user interface to dynamically assist students through aggregated results and wordcloud visualizations. The effectiveness of our approach is finally evaluated through the use of commonly used datasets and compared in line with existing research

A Model for Recommending Related Research Papers: A Natural Language Processing Approach

The volume of information generated lately has led to information overload, which has impacted researchers’ decision-making capabilities. Researchers have access to a variety of digital libraries to retrieve information. Digital libraries often offer access to a number of journal articles and books. Although digital libraries have search mechanisms it still takes much time to find related research papers. The main aim of this study was to develop a model that uses machine learning techniques to recommend related research papers. The conceptual model was informed by literature on recommender systems in other domains. Furthermore, a literature survey on machine learning techniques helped to identify candidate techniques that could be used. The model comprises four phases. These phases are completed twice, the first time for learning from the data and the second time when a recommendation is sought. The four phases are: (1) identify and remove stopwords, (2) stemming the data, (3) identify the topics for the model, and (4) measuring similarity between documents. The model is implemented and demonstrated using a prototype to recommend research papers using a natural language processing approach. The prototype underwent three iterations. The first iteration focused on understanding the problem domain by exploring how recommender systems and related techniques work. The second iteration focused on pre-processing techniques, topic modeling and similarity measures of two probability distributions. The third iteration focused on refining the prototype, and documenting the lessons learned throughout the process. Practical lessons were learned while finalising the model and constructing the prototype. These practical lessons should help to identify opportunities for future research.Thesis (MA) -- Faculty of Engineering, the Built Environment, and Technology, 202

A model for recommending related research papers: A natural language processing approach

The volume of information generated lately has led to information overload, which has impacted researchers’ decision-making capabilities. Researchers have access to a variety of digital libraries to retrieve information. Digital libraries often offer access to a number of journal articles and books. Al though digital libraries have search mechanisms it still takes much time to find related research papers. The main aim of this study was to develop a model that uses machine learning techniques to recommend related research papers. The conceptual model was informed by literature on recommender systems in other domains. Furthermore, a literature survey on machine learning techniques helped to identify candidate techniques that could be used. The model comprises four phases. These phases are completed twice, the first time for learning from the data and the second time when a recommen dation is sought. The four phases are: (1) identify and remove stopwords, (2) stemming the data, (3) identify the topics for the model, and (4) measuring similarity between documents. The model is implemented and demonstrated using a prototype to rec ommend research papers using a natural language processing approach. The prototype underwent three iterations. The first iteration focused on under standing the problem domain by exploring how recommender systems and related techniques work. The second iteration focused on pre-processing techniques, topic modeling and similarity measures of two probability dis tributions. The third iteration focused on refining the prototype, and docu menting the lessons learned throughout the process. Practical lessons were learned while finalising the model and constructing the prototype. These practical lessons should help to identify opportunities for future research.Thesis (MIT) -- Faculty of Engineering the Built Environment and Technology, Information Technology, 202

A model for recommending related research papers: A natural language processing approach

The volume of information generated lately has led to information overload, which has impacted researchers’ decision-making capabilities. Researchers have access to a variety of digital libraries to retrieve information. Digital libraries often offer access to a number of journal articles and books. Al though digital libraries have search mechanisms it still takes much time to find related research papers. The main aim of this study was to develop a model that uses machine learning techniques to recommend related research papers. The conceptual model was informed by literature on recommender systems in other domains. Furthermore, a literature survey on machine learning techniques helped to identify candidate techniques that could be used. The model comprises four phases. These phases are completed twice, the first time for learning from the data and the second time when a recommen dation is sought. The four phases are: (1) identify and remove stopwords, (2) stemming the data, (3) identify the topics for the model, and (4) measuring similarity between documents. The model is implemented and demonstrated using a prototype to rec ommend research papers using a natural language processing approach. The prototype underwent three iterations. The first iteration focused on under standing the problem domain by exploring how recommender systems and related techniques work. The second iteration focused on pre-processing techniques, topic modeling and similarity measures of two probability dis tributions. The third iteration focused on refining the prototype, and docu menting the lessons learned throughout the process. Practical lessons were learned while finalising the model and constructing the prototype. These practical lessons should help to identify opportunities for future research.Thesis (MIT) -- Faculty of Engineering the Built Environment and Technology, Information Technology, 202

A Model for Recommending Related Research Papers: A Natural Language Processing Approach

The volume of information generated lately has led to information overload, which has impacted researchers’ decision-making capabilities. Researchers have access to a variety of digital libraries to retrieve information. Digital libraries often offer access to a number of journal articles and books. Although digital libraries have search mechanisms it still takes much time to find related research papers. The main aim of this study was to develop a model that uses machine learning techniques to recommend related research papers. The conceptual model was informed by literature on recommender systems in other domains. Furthermore, a literature survey on machine learning techniques helped to identify candidate techniques that could be used. The model comprises four phases. These phases are completed twice, the first time for learning from the data and the second time when a recommendation is sought. The four phases are: (1) identify and remove stopwords, (2) stemming the data, (3) identify the topics for the model, and (4) measuring similarity between documents. The model is implemented and demonstrated using a prototype to recommend research papers using a natural language processing approach. The prototype underwent three iterations. The first iteration focused on understanding the problem domain by exploring how recommender systems and related techniques work. The second iteration focused on pre-processing techniques, topic modeling and similarity measures of two probability distributions. The third iteration focused on refining the prototype, and documenting the lessons learned throughout the process. Practical lessons were learned while finalising the model and constructing the prototype. These practical lessons should help to identify opportunities for future research.Thesis (MA) -- Faculty of Engineering, the Built Environment, and Technology, 202

Cybersecurity: Past, Present and Future

The digital transformation has created a new digital space known as cyberspace. This new cyberspace has improved the workings of businesses, organizations, governments, society as a whole, and day to day life of an individual. With these improvements come new challenges, and one of the main challenges is security. The security of the new cyberspace is called cybersecurity. Cyberspace has created new technologies and environments such as cloud computing, smart devices, IoTs, and several others. To keep pace with these advancements in cyber technologies there is a need to expand research and develop new cybersecurity methods and tools to secure these domains and environments. This book is an effort to introduce the reader to the field of cybersecurity, highlight current issues and challenges, and provide future directions to mitigate or resolve them. The main specializations of cybersecurity covered in this book are software security, hardware security, the evolution of malware, biometrics, cyber intelligence, and cyber forensics. We must learn from the past, evolve our present and improve the future. Based on this objective, the book covers the past, present, and future of these main specializations of cybersecurity. The book also examines the upcoming areas of research in cyber intelligence, such as hybrid augmented and explainable artificial intelligence (AI). Human and AI collaboration can significantly increase the performance of a cybersecurity system. Interpreting and explaining machine learning models, i.e., explainable AI is an emerging field of study and has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Self-organizing distributed digital library supporting audio-video

The StreamOnTheFly network combines peer-to-peer networking and open-archive principles for community radio channels and TV stations in Europe. StreamOnTheFly demonstrates new methods of archive management and personalization technologies for both audio and video. It also provides a collaboration platform for community purposes that suits the flexible activity patterns of these kinds of broadcaster communities