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1. Active shooter response training environment for a building evacuation in a collaborative virtual environment

   https://doi.org/10.2352/ISSN.2470-1173.2020.13.ERVR-223  

   Sharma, Sharad ; Bodempudi, Sri Teja ; Scribner, David ; Grazaitis, Peter ( July 2020 , Electronic Imaging)

   During active shooter events or emergencies, the ability of security personnel to respond appropriately to the situation is driven by pre-existing knowledge and skills, but also depends upon their state of mind and familiarity with similar scenarios. Human behavior becomes unpredictable when it comes to making a decision in emergency situations. The cost and risk of determining these human behavior characteristics in emergency situations is very high. This paper presents an immersive collaborative virtual reality (VR) environment for performing virtual building evacuation drills and active shooter training scenarios using Oculus Rift head mounted displays. The collaborative immersive environment is implemented in Unity 3D and is based on run, hide, and fight mode for emergency response. The immersive collaborative VR environment also offers a unique method for training in emergencies for campus safety. The participant can enter the collaborative VR environment setup on the cloud and participate in the active shooter response training environment, which leads to considerable cost advantages over large-scale real-life exercises. A presence questionnaire in the user study was used to evaluate the effectiveness of our immersive training module. The results show that a majority of users agreed that their sense of presence was increased when using themore »immersive emergency« less
2. Force-Aware Interface via Electromyography for Natural VR/AR Interaction

   https://doi.org/10.1145/3550454.3555461  

   Zhang, Yunxiang ; Liang, Benjamin ; Chen, Boyuan ; Torrens, Paul M. ; Atashzar, S. Farokh ; Lin, Dahua ; Sun, Qi ( December 2022 , ACM Transactions on Graphics)

   While tremendous advances in visual and auditory realism have been made for virtual and augmented reality (VR/AR), introducing a plausible sense of physicality into the virtual world remains challenging. Closing the gap between real-world physicality and immersive virtual experience requires a closed interaction loop: applying user-exerted physical forces to the virtual environment and generating haptic sensations back to the users. However, existing VR/AR solutions either completely ignore the force inputs from the users or rely on obtrusive sensing devices that compromise user experience. By identifying users' muscle activation patterns while engaging in VR/AR, we design a learning-based neural interface for natural and intuitive force inputs. Specifically, we show that lightweight electromyography sensors, resting non-invasively on users' forearm skin, inform and establish a robust understanding of their complex hand activities. Fuelled by a neural-network-based model, our interface can decode finger-wise forces in real-time with 3.3% mean error, and generalize to new users with little calibration. Through an interactive psychophysical study, we show that human perception of virtual objects' physical properties, such as stiffness, can be significantly enhanced by our interface. We further demonstrate that our interface enables ubiquitous control via finger tapping. Ultimately, we envision our findings to push forward researchmore »towards more realistic physicality in future VR/AR.« less
3. PassGAN: A Deep Learning Approach for Password Guessing

   https://doi.org/10.1007/978-3-030-21568-2_11  

   Hitaj, Briland ; Gasti, Paolo ; Ateniese, Giuseppe ; Perez-Cruz, Fernando ( May 2019 , International Conference on Applied Cryptography and Network Security)

   State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes. In addition to performing straightforward dictionary attacks, these tools can expand password dictionaries using password generation rules, such as concatenation of words (e.g., “password123456”) and leet speak (e.g., “password” becomes “p4s5w0rd”). Although these rules work well in practice, creating and expanding them to model further passwords is a labor-intensive task that requires specialized expertise. To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms. Instead of relying on manual password analysis, PassGAN uses a Generative Adversarial Network (GAN) to autonomously learn the distribution of real passwords from actual password leaks, and to generate high-quality password guesses. Our experiments show that this approach is very promising. When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools. However, in contrast with the other tools, PassGAN achieved this result without any a-priori knowledge on passwords or common password structures. Additionally, when we combined the output of PassGAN with the output of HashCat, we were ablemore »to match 51%–73% more passwords than with HashCat alone. This is remarkable, because it shows that PassGAN can autonomously extract a considerable number of password properties that current state-of-the art rules do not encode.« less
4. A Survey of DDOS Attack Detection Techniques for IoT Systems Using BlockChain Technology

   https://doi.org/10.3390/electronics11233892  

   Khan, Zulfiqar Ali ; Namin, Akbar Siami ( December 2022 , Electronics)

   The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by usingmore »blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.« less
5. Augmented Reality Computer-aided Design Education (ARCADE) Tool to Improve Student Motivation, Engagement, and Spatial Cognition

   Dakeev, U. ( July 2021 , 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference)

   Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) have become the worldwide huge technological innovations impacting human life significantly. While the VR is an enclosed environment separated completely from the real world, AR allows users to merge the digital and physical worlds and enable the interaction between them. The wide usage of AR has led researchers to investigate its potential capability in several areas including STEM-related fields. Previous research shows that AR assisted courses tend to enhance students’ learning, spatial cognition, increase the students’ motivation and engagement in the learning process. In this study, the researchers have developed an AR application to assist students with spatial cognition and remote course engagement independently. The ARCADE tool enables students to not only visualize the isometric product from its orthogonal views, but it also provides short tutorial clips on how a specific feature was developed and what tools were used. The students can perform basic modifications on the 3D part in the ARCADE such as section views, details views, scale, rotate and explode the assembly views. Although this project is a work in progress, the preliminary pretest and posttest results show there is a significant improvement in students’ spatial cognitionmore »when the proposed tool is used to assist the course.« less