Data Mining - Concepts and Techniques.

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

Matrix Factorization Techniques for Recommender Systems

Self-Directed Learning, A Guide for Learners and Teachers

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

/pdf/a-review-on-genetic-algorithm-past-present-and-future-2o5y8ru178.pdf

A review on genetic algorithm: past, present, and future

The market leaders of Cloud Computing try to leverage the parallel-processing capability of GPUs to provide more economic services than traditions. As the cornerstone of enterprise applications, database systems are of the highest priority to be improved for the performance and design complexity reduction. It is the purpose of this paper to design an in-memory database, called CUDADB, to scale up the performance of the database system on GPU with CUDA. The details of implementation and algorithms are presented, and the experiences of GPU-enabled CUDA database operations are also shared in this paper. For performance evaluation purposes, SQLite is used as the comparison target. From the experimental results, CUDADB performs better than SQLite for most test cases. And, surprisingly, the CUDADB performance is independent from the number of data records in a query result set. The CUDADB performance is a static proportion of the total number of data records in the target table. Finally, this paper comes out a concept of turning point that represents the difference ratio between CUDADB and SQLite.

https://ir.nctu.edu.tw:443/bitstream/11536/16582/1/000307276000006.pdf

Scaling database performance on GPUs

Security is a crucial factor for the appropriate functioning of fog/edge computing. Secured mutual authentication in networks has become key demand as per the current security standard. Several applications are in its requirements like wireless sensor network (WSN), Distributed Systems, Micro-Cloud, Smart City, Smart Industry 4.0. Problem statement is “Design and implementation of Fog servers and edge devices to dynamically interconnect with each other using secured mutual authentication”, which is an NP complete problem. Implementation of secured mutual authentication protocol (SMAP) using techniques of pseudo-random number generator, time-stamps and hash functions can only be considered to evaluate the best performance for connecting large number of smart devices. Our protocol avoids storing master secret keys and repetition of session keys, which makes it more secure and carries no overhead. The experimental results show that the secured mutual authentication system is efficient in comparison to recent benchmarks.

/pdf/smap-fog-edge-a-secure-mutual-authentication-protocol-for-2ymm0tqco6.pdf

SMAP Fog/Edge: A secure mutual authentication protocol for fog/Edge

Face recognition and hand gesture recognition technologies have been developed separately for many years. Usually they are treated as independent systems. In this paper, we integrate the face and hand gesture recognition. We claim that the face recognition rate can be improved by hand gesture recognition. Also, we propose a security elevator scenario. Finally, we simulate this security elevator scenario by PCA method, based on the ORL database, and show that the face recognition rate and overall accuracy is improved after integration. We believe that this is a general method to integrate two recognition engines, not only for face and hand gesture recognition.

https://ir.nctu.edu.tw:443/bitstream/11536/1364/1/000262290400055.pdf

Integration of Face and Hand Gesture Recognition

The development and acceptance pace of Mobile Learning Technologies are increasing rapidly as there are abundant applications for mobile learning technologies in education, from the ability to convey learning modules as well as facilitating learners to communicate and learn "on-the-go". Mobile Learning in Chinese has become to play a role in the attainment of Chinese proficiency, therefore, International Students face many issues related to the Chinese Language itself. This paper explains the development of a UI-based mobile learning system for learning Chinese as a second language. QT SDK Framework was used to develop this system that works on mobile phones. Ten Chinese Lessons were developed for this system. International Students at National Chiao Tung University tried out this system using their mobile phones for 5 weeks. Surveys were used to evaluate the usability and effectiveness of the system.

A Cross-Platform Mobile Learning System Using QT SDK Framework

In this study, we propose using a thermal imaging camera (TIC) with a deep learning model as an intelligent human detection approach during emergency evacuations in a low-visibility smoky fire scenarios. We use low-wavelength infrared (LWIR) images taken by a TIC qualified with the National Fire Protection Association (NFPA) 1801 standards as input to the YOLOv4 model for real-time object detection. The model trained with a single Nvidia GeForce 2070 can achieve >95% precision for the location of people in a low-visibility smoky scenario with 30.1 frames per second (FPS). This real-time result can be reported to control centers as useful information to help provide timely rescue and provide protection to firefighters before entering dangerous smoky fire situations.

/pdf/using-deep-learning-with-thermal-imaging-for-human-detection-3o41g5di.pdf

Shyan-Ming Yuan

Papers

Scaling database performance on GPUs

SMAP Fog/Edge: A secure mutual authentication protocol for fog/Edge

Integration of Face and Hand Gesture Recognition

A Cross-Platform Mobile Learning System Using QT SDK Framework

Using Deep Learning with Thermal Imaging for Human Detection in Heavy Smoke Scenarios