Data science, machine learning, and distributed computational models have evolved dramatically over the last decade. Cloud and cluster computing is full-fledged and ready for processing big data. Data driven research and decision have become the trend in multiple disciplines. However, very few organizations have experienced the full impact or competitive advantage from their advanced data analytics initiatives despite significant investments in data science and machine learning. There are a number of issues resulting in such a phenomenon including difficult to maintain and configure a cluster, complex transition from a platform to another, sophisticated programming interfaces to machine learning libraries, network congestion, and most importantly lake of well-trained personnel to sanitize and analyze data. We propose a flexible heterogeneous computing cluster with off-the-shelf computers and a Blockly programming interface for multidisciplinary users such as cybersecurity ana-lyst, biologist, geologist, musician, and choreographer.
The work described in this paper consists of a temperature tracking system that follows a Client-Server architecture. A Raspberry Pi, a System-on-a-Chip (SoC) device, is responsible for sensing the temperature and streaming it to a server, the readings then are displayed in a mobile android application. For this system, a python application was developed to sense and stream the temperature, a servlet was developed to read and store the temperature in a SQLite database, and a mobile Android application was developed to read and display the temperature readings from the server. The initial versions of the project used the SoC device as a server (storing temperature readings into a local SQLite database), and both the SoC device and the mobile device needed to be connected in a local area network. However, the project was further developed to separate the server responsibilities from the SoC device. The system now supports user authentication, and both devices are connected through the Internet. This implementation allows the temperature readings to be viewed and displayed anytime from anywhere in the world since the database is hosted on a server which can be accessed over the internet. Also, this solution allows multiple SoC devices to stream temperatures to the server, to different mobile clients using the same database. The Android client application was also implemented to graphically show the temperature readings recorded by Raspberry Pi using Restful architecture. Moreover, an alert message notification was implemented in Android application so that a user is notified whenever the temperature reading reaches the preset threshold. On the other hand, the smart chair system has brilliant commercial prospects, which can be helpful to build health care products with the help of wearable sensors, intelligent refrigerator/oven temperature tracking system and etc.
Quantum-based Machine Learning (QML) combines quantum computing (QC) with machine learning (ML), which can be applied in various sectors, and there is a high demand for QML professionals. However, QML is not yet in many schools' curricula. We design labware for the basic concepts of QC, ML, and QML and their applications in science and engineering fields in Google Colab, applying a three-stage learning strategy for efficient and effective student learning.
In this paper, we describe an online tutoring system currently under development. The online tutoring system is designed with a goal to help nontraditional students learn subjects that require a significant amount of assistance from tutors, who may only be available on campus. The subjects include but not limited to Calculus, Algebra, Computer Science, Physics, and the like. This system makes it possible for students to request instant help from tutors online anytime anywhere. An accounting subsystem allows instructors to monitor students' performance and tutors' efficiency. A Latex interface is built to ease input special notations and Mathematical equations.
Hardware components have been designated as required academic content for colleges to be recognized as a center of academic excellence in cyber operations by the National Security Agency (NSA). To meet the hardware requirement, computer science and information technology programs must cover hardware concepts and design skills, topics which are less emphasized in existing programs. This paper describes a new pedagogical model for hardware based on network intrusion detection taught at college and graduate levels in a National Center of Academic Excellence in Information Assurance Education Program (CAE/IAE). The curriculum focuses on the fundamental concepts of network intrusion detection mechanisms, network traffic analysis, rule-based detection logic, system configuration, and basic hardware design and experiments. This new course enriches students with the latest developments in computer security and hands-on projects to better prepare them for their information security and assurance careers.
Machine Learning (ML) analyzes, and processes data and discover patterns. In cybersecurity, it effectively analyzes big data from existing cybersecurity attacks and develop proactive strategies to detect current and future cybersecurity attacks. Both ML and cybersecurity are important subjects in computing curriculum, but using ML for cybersecurity is not commonly explored. This paper designs and presents a case study-based portable labware experience built on Google's CoLaboratory (CoLab) for a ML cybersecurity application to provide students with hands-on labs accessing from anywhere and anytime, reducing or eliminating tedious installations and configurations. This approach allows students to focus on learning essential concepts and gaining valuable experience through hands-on problem solving skills. Our preliminary results and student evaluations are reported for a case-based hands-on regression labware in cyber fraud prediction using credit card fraud as an example.
Video Steganalysis is the forensic study of dealing with steganography within a video after a video has been modified by a steganographer. Stego-videos have been used by hackers to exfiltrate data silently out of corporate networks through services such as YouTube and Vimeo. Legal consequences of exfiltration can lead to loss of data confidentiality from within a network and millions of dollars lost in damages. Within the massive frames of a video, passive steganalysis, or statistical detection of video modifications, can be timely, difficult, or costly, because a hacker may reduce the amount of information hidden within a video to fool the steganalyst, or she or he may hide the information in incomplete video regions. In the past, researchers have focused on passive steganalysis, but recently researchers have used an active approach to disarm the modifications done to the video, and this is computationally easier than the passive warden. We demonstrate a novel active steganalysis algorithm, and with it we devise a practical framework using both the active and existing passive approach to prevent and detect possible data exfiltration in video. We find our complete approach improves on existing steganalysis schemes for online video.
As technology continues to develop, there is a growing need to find sustainable solutions in all industries, including cryptocurrency. Due to the high energy consumption that cryptocurrencies are known for, there have been efforts to reduce waste consumption and in turn minimize the carbon footprint. We support the trends for creating an environment-friendly crypto token using the ERC-20 standard on the Ethereum blockchain. We outline the various aspects that make a token more sustainable and highlight the potential benefits of such tokens. Our proposal involves the design of a smart contract that incorporates eco-friendly features such as lower energy consumption, carbon offsetting, and more efficient methods or algorithms. We also discuss the importance of transparency and accountability in the design and implementation of such tokens. This paper discusses not only the practical tools and steps necessary in creating a crypto token but also highlights the challenges associated with creating a more sustainable token.
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