8333 research outputs found
Sort by
GDPR Compliance in the Metaverse
No full textAn exploration of existing research concerning the Metaverse revealed a lack of data relating to GDPR compliance by Metaverse applications. This research for this report was undertaken to address this gap by measuring the level of compliance of a sample of Metaverse applications with their GDPR obligations
Network-Specific Data Decryption Tool for Enhancing User Profile Security
No full textThe rationale for the outcome of this project was to develop an application that would enhance the security of data of the said profile with the guarantee that the decryption process will only be done with the involvement of the specific network that is deemed secure. This tool was meant to reduce such anxieties as data compromise to a specific platform; hence, the analyst could compute the user data safely in a particular context of a network. The objective was that decryption should only happen at the trusted network and if the host gets connected to an untrusted network, exposure must be prevented. It included dynamic network detection, authentication of the user and the reliable encryption. Several attributes’ names were established and cross tabulated with the secure values kept in Operating System Network parameters. User authentication motivated the user to input a username and password, which in turn was separated, hashed then stored in the system. Talking specifically about data encryption, the Fernet method, which is a type of the symmetric encryption that provides the confidentiality and considers the integrity of data, was used. The keys were well maintained ensuring that they met all the standards of secure management like key storage and frequent key renewal. Thus, the checks on the network were made complicated, and decryption was only done within the trusted network, and data was re-encrypted if the network was changed. Thus, intensive documentation and ideal error handling provided stability and keeping accurate records. Functional and efficacy as well as security tests were used to confirm the ability of the tool to work as intended
Anomaly Detection Method for OT/ICS Environment Using Ensemble Learning
No full textIn this research work we have looked into the current state of OT/ICS security, how important OT/ICS infrastructures are, what is their operational disruption impact and why it is important to secure them. Going forward we have delved into the literature review of relevant research papers, discussing challenges of OT/ICS space and approaches used for securing OT/ICS environment using different types of machine learning algorithms and techniques. This paper proposes an Anomaly Detection method based on ensemble learning model for securing OT/ICS environment. At the end we have discussed the implementation part carried out to develop an anomaly detection system, data transformation and discussion of the result and finally critically analysing the research and discussing the limitation
Harnessing evolving machine learning techniques for enhanced intrusion detection system
No full textThe overall design specification section determines the functional portion of the data execution proportion. This determines the functional evaluation of all the constructional approaches. The designing approach determines the involvement of Python coding. The designing approach introduces the functional section of the configuration of IDS. The implementation defines the introduction of the evaluation process by using data reading functionality. The supportable execution also provides the designing of the executional parameters. The evaluation defines the construction of various machine learning models such as KNN, SVM, Random Forest, Decision Tree, and ANN. The evaluation of those models supports the finding of the most suitable model for the detection of intrusion in the network. Future development supports the upgradation of this research process by the implementation of AI techniques and advanced methods
Factors influencing online shopping a comparative study of Mexican and Irish consumers
No full textThe purpose of the present research is to explore and compare between two samples from different contexts and backgrounds, Irish consumers and Mexican consumers the factors and some IT affordances of the social media that may influence online shopping.
The research questions that were addressed are focus on whether any the degree of difference in the degree of Perceived benefits, perceived risks and disadvantages, hedonic motivation, trust and security issues of online shopping, whether any the degree of difference in the degree of visibility, metavoicing, shopping guidance, social presence and interactivity affordances on social media shopping between Mexican and Irish consumers?
It is important to point out that differences were expected, however, this study is exploratory.
This work uses a qualitative research method and primary data, positivism and deductive approaches were adopted as research philosophy and research approach, respectively. The two samples that met the criteria and participated in the study were integrated by 128 participants in total, 64 Irish and 64 Mexican consumers. The data collection was carried out through surveys developed based on pre- validated instruments. All the collected data has been analysed using, excel for organising the information, the software SPSS statistics and the non-parametric test Mann-Whitney-U
The research has shown among its findings no significant differences between Irish and Mexican consumers for each of the factors evaluated, however, as it is explained in the proper chapter some variables were scored higher than others for each sample. In general, both samples consider the benefits perceived, the risks and disadvantages perceived and the metavoicing affordance on social media as the top three of the factors influencing online shopping. Unexpectedly, trust and security issues were the factors with the lowest score influencing the process of buying online.
This piece of work, may provide helpful insights for professionals in the field of marketing or e-commerce in order to develop strategies, improve and focus efforts to reach consumers in the better way
NLP for Smart Contracts
No full textThis research presents a way to determine how well an NLP algorithm can translate smart contracts that are in Solidity programming language to natural language that people can comprehend and vice versa. The approach is centered on enabling the improvements of smart contracts’ interpretability and scalability by applying NLP in the cryptocurrency context. It is possible to conclude that the goal of the discussed study is to introduce NLP algorithms into decision-making processes in smart contracts in a way that would be transparent and open to users. In the method, the literature research, the NLP algorithm for smart contract analysis and generation, and experiments to estimate the efficiency of the algorithms are described. A front-end HTML interface has been created; an API key has been created through OpenAI for translating Solidity code into English and back. Some experiments prove that the interface can take the translations, but to resolve present issues, optimisation is required. The expected outcomes include comprehending how NLP will be employed to translate smart contracts and how these contracts can be developed and enforced in the Case of Cryptocurrencies
A Deep Neural Network Approach Integrating CNN and BiLSTM-Transformer Architectures for Emotion Recognition from Speech
No full textThe goal of speech emotion recognition is to make human-computer interaction more efficient in several areas such as customer service, entertainment industry, human-computer interaction, healthcare, and education. Previous work in speech emotion analysis presented some issues like limited choice of features, model complexity, noise variability, and insufficient data samples, which negatively affected the prediction of emotions. This paper provides an in-depth study of speech emotion recognition using a hybrid deep neural network architecture that combines 1-D Convolutional Neural Network (CNN) and BiLSTM-Transformer models to analyze data from the Ravdess and Crema-D datasets. To make the datasets appropriate for emotion detection, all were preprocessed by means of librosa library to get rid of non-speech segments. Important sound characteristics such as Mel-Frequency Cepstral Coefficients(MFCC), Root Mean Square Energy (RMSE), and Zero Crossing Rate (ZCR) were extracted to get the spectral characteristics, intensity of feelings, and dynamic features present in the emotions. In order to improve model’s generalization and robustness noise injection, time stretching, time shifting as well as pitch shifting have been applied during data augmentation. The proposed model leverages the strengths of both CNN and BiLSTM-Transformer components. The proposed model’s 1-D CNN captures local patterns in sound whereas the BiLSTM-Transformer handles sequence data and complex hierarchical structures of audio. Various datasets such as Ravdess and Crema-D were used to train and test the performance of the model in the emotion classification task. To evaluate model’s performance, training-validation accuracy graph, confusion matrix and overall metrics which include precision, recall, and F1-score are used. Ravdess dataset achieved a high accuracy of 83.3%, surprise, angry, disgust and sad were among those emotions which this model identified with great accuracy. Crema-D dataset achieved 82.7% accuracy, and showed solid performance in detecting neutral, fear, and happy emotions. Accuracy plots between training and validation demonstrated good generalization for the unseen data and confusion matrices highlighted the emotion categories where improvement could be made
Global Culture and Corporate Social Responsibility in the Cosmetics Industry: Studying the Impact of Global Culture and Corporate Social Responsibility on Cosmetic Consumers’ Behaviour in Ireland
No full textCommunication technologies like social media have played a pivotal role in connecting individuals from across the globe. With increased connectivity, emerges a digital society, presenting a homogeneous global culture, where individuals share opinions, experiences, beliefs, and values online. Simultaneously, there is an increasing emphasis on the importance of battling societal and environmental issues. As a result, businesses are expected to contribute to positive change, by engaging in corporate social responsibility initiatives.
This paper examines global culture and corporate social responsibility as prevalent trends in international business and marketing and demonstrates how consumers frequently rely on these elements to produce opinions and preferences towards cosmetic brands.
The literature review conducted in this paper reveals that global culture and corporate social responsibility are radically impacting consumer behaviour and illustrates how companies must adapt to these changes with modern business and marketing strategies.
The primary research conducted by the author investigates how the global culture and corporate social responsibility are impacting cosmetic consumers’ behaviour in Ireland. The participants involved in this study are aged 18-30 years old and are active users of social media. The findings present valuable marketing information and unique insight from the participants who took part in a survey, which asked questions regarding their attitudes, purchasing habits, and personal experiences, in regard to cultural globalisation and corporate social responsibility in the cosmetic industry
Analysis of Film Industry After the Covid 19 Pandemic
No full textThe film and movie theater industry is an important and historic industry that operates worldwide and was impacted by the covid 19 pandemic as well as how the industry has emerged from the pandemic. The covid 19 pandemic impacted many industries globally so in this paper how covid impacted such an established and global industry will be analyzed. The industry has faced problems with film production being shut down, so it is going to be examined how an industry reacts when there is not enough demand to meet the supply. The industry has renet change with the introduction of online direct to consumer distribution method in streaming services, so we evaluate the impact that had to market and the disruption that was caused in the industry. With the covid 19 pandemic only recently occurring there has been very little research into how the film industry has been affected. the research question is Did the covid 19 pandemic negatively impact the film and movie theater industry and how the impact effect of the pandemic impact the distribution methods and consumers with consumers behavior in this market and affect consumers viewing habits?, with the aims of the research to find the effect that the covid 19 pandemic had on distribution methods, consumer beahviour and interest and price within the industry. To complete this analysis quantitative research will be used to examine the data on consumers viewing habits and behaviour to see how the have been changed since the pandemic and what changes that they have caused in the industry
The Impact of Social Media as a Marketing Communications in the Museums Industry from the Millennials’ Visitors Perspective in the Dublin
No full textMillennials are currently one of the most significant demographics. Cultural organizations are adapting their activities, the content on demonstration, and the method content is valued in response to millennials' influence, despite museums typically drawing an older audience. The utilization of social media through the museum environment is rapidly increasing. This study examines the correlation between millennial visitors and museums in Dublin by utilizing the visual social media site, Instagram.
The primary objective of this study is to investigate the effect of social media as a marketing communication tool in the museum industry, specifically from the perspective of millennial visitors in Dublin. The focus is on evaluating how Instagram influences the actions, behaviors, and preferences of this specific group of individuals.
A total of five comprehensive interviews were carried out with Millennial visitors who shared a mutual interest in museums, arts, and Instagram. This study employed an interpretivist, inductive methodology to accurately capture the perspectives of Millennial visitors. The perspectives were analyzed by the application of thematic coding. The research findings the necessity for museums to enhance their social media strategy