Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- 1.2 COMPUTER AND INFORMATION SCIENCE (1)
- 2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING (1)
- Aerospace (1)
- Artificial Intelligence (1)
- Black-Box DeepLearning (1)
-
- Covid-19 pandemic (1)
- Cybersecurity (1)
- DCT circuitry (1)
- Data anonymization (1)
- Differential privacy (1)
- Digital signal processors (1)
- Discrete Fourier Transforms (1)
- Education Technology (1)
- Explainability (1)
- Image compression techniques (1)
- Interpretability (1)
- K-anonymity (1)
- Knowledge Graphs (1)
- Knowledge Infusion (1)
- Maritime transportation (1)
- Mental Healthcare (1)
- Mentorship and Leadership (1)
- Navigation (1)
- Neuro-Symbolic AI (1)
- Parking management (1)
- Privacy preservation (1)
- Recommendation service (1)
- Recommender system (1)
- Satellites (1)
- Smart parking (1)
Articles 1 - 5 of 5
Full-Text Articles in Engineering
Aessa Young Professionals Forum Webinar “Technologies And Skills That Will Gearup The Aerospace Industry Post Pandemic” - A Global Perspective With An Emphasis On South Africa October 2021, Linda Vee Weiland
Publications
A webinar presentation for AeSSA Young Professionals.
Cross Domain Iw Threats To Sof Maritime Missions: Implications For U.S. Sof, Gary C. Kessler, Diane M. Zorri
Cross Domain Iw Threats To Sof Maritime Missions: Implications For U.S. Sof, Gary C. Kessler, Diane M. Zorri
Publications
As cyber vulnerabilities proliferate with the expansion of connected devices, wherein security is often forsaken for ease of use, Special Operations Forces (SOF) cannot escape the obvious, massive risk that they are assuming by incorporating emerging technologies into their toolkits. This is especially true in the maritime sector where SOF operates nearshore in littoral zones. As SOF—in support to the U.S. Navy— increasingly operate in these contested maritime environments, they will gradually encounter more hostile actors looking to exploit digital vulnerabilities. As such, this monograph comes at a perfect time as the world becomes more interconnected but also more vulnerable.
Parking Recommender System Privacy Preservation Through Anonymization And Differential Privacy, Yasir Saleem Shaikh, Mubashir Husain Rehmani, Noel Crespi, Roberto Minerva
Parking Recommender System Privacy Preservation Through Anonymization And Differential Privacy, Yasir Saleem Shaikh, Mubashir Husain Rehmani, Noel Crespi, Roberto Minerva
Publications
Recent advancements in the Internet of Things (IoT) have enabled the development of smart parking systems that use services of third-party parking recommender system to provide recommendations of personalized parking spot to users based on their past experience. However, the indiscriminate sharing of users’ data with an untrusted (or semitrusted) parking recommender system may breach the privacy because users’ behavior and mobility patterns could be inferred by analyzing their past history. Therefore, in this article, we present two solutions that preserve privacy of users in parking recommender systems while analyzing the past parking history using k-anonymity (anonymization) and differential privacy …
Reduced Multiplicative Complexity Discrete Cosine Transform (Dct) Circuitry, Sirani Kanchana Mututhanthrige Perera
Reduced Multiplicative Complexity Discrete Cosine Transform (Dct) Circuitry, Sirani Kanchana Mututhanthrige Perera
Publications
System and techniques for reduced multiplicative complexity discrete cosine transform (DCT) circuitry are described herein. An input data set can be received and, upon the input data set, a self-recursive DCT technique can be performed to produce a transformed data set. Here, the self-recursive DCT technique is based on a product of factors of a specified type of DCT technique. Recursive components of the technique are of the same DCT type as that of the DCT technique. The transformed data set can then be produced to a data consumer.
Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth
Semantics Of The Black-Box: Can Knowledge Graphs Help Make Deep Learning Systems More Interpretable And Explainable?, Manas Gaur, Keyur Faldu, Amit Sheth
Publications
The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively. The DL models utilizing massive computing power and enormous datasets have significantly outperformed prior historical benchmarks on increasingly difficult, well-defined research tasks across technology domains such as computer vision, natural language processing, signal processing, and human-computer interactions. However, the Black-Box nature of DL models and their over-reliance on massive amounts of data condensed into labels and dense representations poses challenges for interpretability and explainability of the system. Furthermore, DLs have not yet been proven in their ability to …