COMPUTER ASSISTED COMMUNICATION FOR THE HEARING IMPAIRED FOR AN EMERGENCY ROOM SCENARIO

While there has been research on computerized communication facilities for those with hearing impairment, issues still remain. Current approaches utilize an avatar based approach which lacks the ability to adequately use facial expressions which are an integral aspect to the communication process in American Sign Language (ASL). Additionally, there is a lack of research into integrating a system to facilitate communication with the hearing impaired into a clinical environment, namely an emergency room admission scenario. This research aims to determine if an alternate approach of using videos created by humans in ASL can overcome the understandability barrier and still be usable in the communication process

An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach

Phishing and spam detection is long standing challenge that has been the subject of much academic research. Large Language Models (LLM) have vast potential to transform society and provide new and innovative approaches to solve well-established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic-based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential for the potential of society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, our model based on fine-tuning the BERT family of models to specifically detect phishing and spam email. We demonstrate our fine-tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. This work serves as an important first step towards employing LLMs to improve the security of our information systems

Word Sense Disambiguation for Ontology Learning

Ontology learning aims to automatically extract ontological concepts and relationships from related text repositories and is expected to be more efficient and scalable than manual ontology development. One of the challenging issues associated with ontology learning is word sense disambiguation (WSD). Most WSD research employs resources such as WordNet, text corpora, or a hybrid approach. Motivated by the large volume and richness of user-generated content in social media, this research explores the role of social media in ontology learning. Specifically, our approach exploits social media as a dynamic context rich data source for WSD. This paper presents a method and preliminary evidence for the efficacy of our proposed method for WSD. The research is in progress toward conducting a formal evaluation of the social media based method for WSD, and plans to incorporate the WSD routine into an ontology learning system in the future

Heuristic Guided Evolution

Exploiting knowledge to guide the evolutionary process in evolutionary computing is a concept that has the potential to increase the performance of evolutionary algorithms. The research question of this paper is “Can heuristics derived from past experiences be incorporated into evolutionary computing in order to increase the performance?” In order to answer the research question the following hypothesis is developed: “A heuristically-guided mutation of decision trees will outperform randomly mutated decision trees in terms of classification accuracy.”The methodology for answering the hypothesis is an experiment that tests a knowledge-guided mutation of a decision tree using heuristics created from prior decision trees as a form of knowledge. This is compared with a random mutation of the same decision tree. This experiment supports the theory that using knowledge in the form of heuristics to guide mutation will produce a difference in the performance of the classification of data instances. This supports the need for further research into knowledge guided evolutionary algorithms

Feeling The Stock Market: A Study in the Prediction of Financial Markets Based on News Sentiment

Researchers are fascinated with predicting the stock market. Even though there is a large amount of supporting evidence that the dynamics of financial markets cannot be predicted, studies that employ creative prediction techniques continue to emerge. This study proposes a sentiment analysis model developed to infer the polarity of news articles related to a company. The process of collecting the dataset, as well as a diagram of the system architecture for the sentiment analysis engine used in this study is provided to readers. Insights from this research and experimental results are used to provide further proof that supports the Efficient Market Hypothesis

Ontologies and the Semantic Web for Digital Investigation Tool Selection

The nascent field of digital forensics is heavily influenced by practice. Much digital forensics research involves the use, evaluation, and categorization of the multitude of tools available to researchers and practitioners. As technology evolves at an increasingly rapid pace, the digital forensics field must constantly adapt by creating and evaluating new tools and techniques to perform forensic analysis on many disparate systems such as desktops, notebook computers, mobile devices, cloud, and personal wearable sensor devices, among many others. While researchers have attempted to use ontologies to classify the digital forensics domain on various dimensions, no ontology of digital forensic tools has been developed that defines the capabilities and relationships among the various digital forensic tools. To address this gap, this work develops an ontology using Resource Description Framework (RDF) and Ontology Web Language (OWL) which is searchable via SP ARQL ( an RDF query language) and catalogues common digital forensic tools. Following the concept of ontology design patterns, our ontology has a modular design to promote integration with existing ontologies. Furthermore, we progress to a semantic web application that employs reasoning in order to aid digital investigators with selecting an appropriate tool. This work serves as an important step towards building the knowledge of digital forensics tools. Additionally, this research sets the preliminary stage to bringing semantic web technology to the digital forensics domain as well as facilitates expanding the developed ontology to other tools and features, relationships, and forensic techniques

Hour of Code”: Can It Change Students’ Attitudes Toward Programming?

The Hour of Code is a one-hour introduction to computer science organized by Code.org, a non-profit dedicated to expanding participation in computer science. This study investigated the impact of the Hour of Code on students’ attitudes towards computer programming and their knowledge of programming. A sample of undergraduate students from two universities was selected to participate. Participants completed an Hour of Code tutorial as part of an undergraduate course. An electronic questionnaire was implemented in a pre-survey and post-survey format to gauge the change in student attitudes toward programming and their programming ability. The findings indicated the positive impact of the Hour of Code tutorial on students’ attitude toward programming. However, the students’ programming skills did not significantly change. The authors suggest that a deeper alignment of marketing, teaching, and content would help sustain the type of initiative exemplified by the Hour of Code

A New Framework for Securing, Extracting and Analyzing Big Forensic Data

Finding new methods to investigate criminal activities, behaviors, and responsibilities has always been a challenge for forensic research. Advances in big data, technology, and increased capabilities of smartphones has contributed to the demand for modern techniques of examination. Smartphones are ubiquitous, transformative, and have become a goldmine for forensics research. Given the right tools and research methods investigating agencies can help crack almost any illegal activity using smartphones. This paper focuses on conducting forensic analysis in exposing a terrorist or criminal network and introduces a new Big Forensic Data Framework model where different technologies of Hadoop and EnCase software are combined in an effort to promote more effective and efficient processing of the massive Big Forensic Data. The research propositions this model postulates could lead the investigating agencies to the head of the terrorist networks. Results indicate the Big Forensic Data Framework model is capable of processing Big Forensic Data

A New Framework for Securing, Extracting and Analyzing Big Forensic Data

Finding new methods to investigate criminal activities, behaviors, and responsibilities has always been a challenge for forensic research. Advances in big data, technology, and increased capabilities of smartphones has contributed to the demand for modern techniques of examination. Smartphones are ubiquitous, transformative, and have become a goldmine for forensics research. Given the right tools and research methods investigating agencies can help crack almost any illegal activity using smartphones. This paper focuses on conducting forensic analysis in exposing a terrorist or criminal network and introduces a new Big Forensic Data Framework model where different technologies of Hadoop and EnCase software are combined in an effort to promote more effective and efficient processing of the massive Big Forensic Data. The research propositions this model postulates could lead the investigating agencies to the head of the terrorist networks. Results indicate the Big Forensic Data Framework model is capable of processing Big Forensic Data

Awareness of Blockchain Usage, Structure, & Generation of Platform’s Energy Consumption: Working Towards a Greener Blockchain

Blockchain is a disruptive information technology innovation with energy consumption. As more organizations look to implement or embrace blockchain innovations, research must focus on making the blockchain greener. This research explores the current innovative blockchain usage, structure, generations, and energy consumption. An energy consumption comparison for consensus protocols is provided along with a list of recommendations for implementing green blockchains. This paper provides a significant impact upon previous literature and aids organizations considering implementing a green blockchain