Asila AlHarmali1, Saqib Ali1, Waqas Aman1, Omar Hussain2, 1Department of Information Systems, Sultan Qaboos University, Muscat, Oman, 2School of Business, University of New South Wales, Canberra, Australia
Cyber-Physical Systems (CPS) integrate physical and embedded systems with information and communication technology systems, monitoring and controlling physical processes with minimal human intervention. The connection to information and communication technology exposes CPS to cyber risks. It is crucial to assess these risks to manage them effectively. This paper reviews scholarly contributions to cyber risk assessment for CPS, analyzing how the assessment approaches were evaluated and investigating to what extent they meet the requirements of effective risk assessment. We identify gaps limiting the effectiveness of the assessment and recommend real-time learning from cybersecurity incidents. Our review covers twentyeight papers published between 2014 and 2023, selected based on a three-step search. Our findings show that the reviewed cyber risk assessment methodologies revealed limited effectiveness due to multiple factors. These findings provide a foundation for further research to explore and address other factors impacting the quality of cyber risk assessment in CPS.
cyber risk assessment, Cyber-Physical Systems (CPS), real-time learning, cybersecurity incidents, effectiveness of risk assessment.
Abhik Choudhury, IBM Corporation, Exton, PA, USA
Fuzzy logic provides a framework for dealing with uncertainty and imprecision, making it particularly useful in natural language processing (NLP) applications. A critical subset of fuzzy logic is fuzzy search, which enhances search capabilities by allowing approximate matches rather than requiring exact ones. This paper explores the integration of fuzzy search techniques within the context of wholesale pharma distribution, a field that demands high accuracy in data retrieval due to its impact on public health and safety. We investigate two distinct case studies where each demonstrates specific fuzzy search techniques tailored to address unique challenges in data retrieval. Through a Python code implementation, we illustrate how these techniques can be practically applied to improve the accuracy and efficiency of searches within large datasets common in wholesale pharma distribution environments. Our findings underscore the potential of fuzzy logic as a transformative tool for enhancing information retrieval systems. By providing practical insights and technical guidance, this research aims to empower stakeholders in the pharmaceutical industry to leverage fuzzy search techniques effectively, ultimately contributing to better data management practices and improved decision-making processes.
Fuzzy logic, Fuzzy search, NLP, Levenshtein Distance, TF-IDF Vectorization, Cosine Similarity, Wholesale Drug Distribution, Chemical Composition Search, String Matching Algorithms, Data Preprocessing, Information Retrieval, Robust Search Techniques.
Tian Zhan, Austin Amakye Ansah
Capturing aesthetically pleasing photographs can be challenging for amateur photographers due to the complexityof factors such as lighting, composition, and contrast. To address this issue, we propose a mobile applicationpowered by deep learning models and regression analysis. This application analyzes real-time image frames usingapre-trained MobileNet backbone and a custom classification layer [8]. By leveraging the Aesthetics and Attributesdatabase, the app calculates an aesthetic score for each photograph, providing instant feedback to users. Challenges encountered during development, including interfacing with machine learning models and implementingcamera functionalities, are addressed. Through experiments, we evaluate dif erent training approaches andcompare our methodology with existing research. Our solution aims to empower users to capture high-qualityphotographs by assisting them in understanding and applying fundamental principles of photography.
Machine Learning, Mobile, Tensorflow, Flutter.
YongQin Zeng1, Roy Chun2, 1Woodbridge high school, 2 Meadowbrook, Irvine, CA 92604, 2Computer Science Department, California State Polytechnic University, Pomona, CA 91768
I made this program so I can predict and deliver ESG scores of various companies to people [1]. I conducted an experiment to test out the accuracy of my sentiment determination system for posts and got about half correct. Three important systems in my program are the posting system, the authentication system, and the user interface. I used Scraper API to retrieve the posts l need for the posting system [2]. An alternative API is SmartProxy. I used Google Firebase for my authentication system [3]. Another one I could have used is Parse. I used Flutter to build the user interface system. An alternative is React Native.
ESG Scores, Sentiment Analysis, Scraper API, Flutter UI.
