·         Table of Contents (Volume #8, Issue #2)

·         Cover Page (Volume #8, Issue #2)

·         Editorial (Volume #8, Issue #2)

Paper #1

Lightweight Model for Occlusion Removal from Face Images

Sincy John and Ajit Danti

Abstract: In the realm of deep learning, the prevalence of models with large number of parameters poses a significant challenge for low computation device. Critical influence of model size, primarily governed by weight parameters in shaping the computational demands of the occlusion removal process. Recognizing the computational burdens associated with existing occlusion removal algorithms, characterized by their propensity for substantial computational resources and large model sizes, we advocate for a paradigm shift towards solutions conducive to low-computation environments. Existing occlusion riddance techniques typically demand substantial computational resources and storage capacity. To support real-time applications, it's imperative to deploy trained models on resource-constrained devices like handheld devices and internet of things (IoT) devices possess limited memory and computational capabilities. There arises a critical need to compress and accelerate these models for deployment on resource-constrained devices, without compromising significantly on model accuracy. Our study introduces a significant contribution in the form of a compressed model designed specifically for addressing occlusion in face images for low computation devices. We perform dynamic quantization technique by reducing the weights of the Pix2pix generator model. The trained model is then compressed, which significantly reduces its size and execution time. The proposed model, is lightweight, due to storage space requirement reduced drastically with significant improvement in the execution time. The performance of the proposed method has been compared with other state of the art methods in terms of PSNR and SSIM. Hence the proposed lightweight model is more suitable for the real time applications with less computational cost.

Keywords: Dynamic Quantization; Generative adversarial network (GAN); Occlusion; Pix2pix.

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Paper #2

The Proposal of Countermeasures for DeepFake Voices on Social Media Considering Waveform and Text Embedding

Yuta Yanagi, Ryohei Orihara, Yasuyuki Tahara, Yuichi Sei, Tanel Alumäe and Akihiko Ohsuga

Abstract: In recent times, advancements in text-to-speech technologies have yielded more natural-sounding voices. However, this has also made it easier to generate malicious fake voices and disseminate false narratives. ASVspoof stands out as a prominent benchmark in the ongoing effort to automatically detect fake voices, thereby playing a crucial role in countering illicit access to biometric systems. Consequently, there is a growing need to broaden our perspectives, particularly when it comes to detecting fake voices on social media platforms. Moreover, existing detection models commonly face challenges related to their generalization performance. This study sheds light on specific instances involving the latest speech generation models. Furthermore, we introduce a novel framework designed to address the nuances of detecting fake voices in the context of social media. This framework considers not only the voice waveform but also the speech content. Our experiments have demonstrated that the proposed framework considerably enhances classification performance, as evidenced by the reduction in equal error rate. This underscores the importance of considering the waveform and the content of the voice when tasked with identifying fake voices and disseminating false claims.

Keywords: natural language processing; neural networks; speech synthesis; voice processing.

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Paper #3

Wildfire Prediction in the United States Using Time Series Forecasting Models

Muhammad Khubayeeb Kabir, Kawshik Kumar Ghosh, Md. Fahim Ul Islam and Jia Uddin

Abstract: Wildfires are a widespread phenomenon that affects every corner of the world with the warming climate. Wildfires burn tens of thousands of square kilometres of forests and vegetation every year in the United States alone with the past decade witnessing a dramatic increase in the number of wildfire incidents. This research aims to understand the regions of forests and vegetation across the US that are susceptible to wildfires using spatiotemporal kernel heat maps and, forecast these wildfires across the United States at country-wide and state levels on a weekly and monthly basis in an attempt to reduce the reaction time of the suppression operations and effectively design resource maps to mitigate wildfires. We employed the state-of-the-art Neural Basis Expansion Analysis for Time Series (N-BEATS) model to predict the total area burned by wildfires by several weeks and months into the future. The model was evaluated based on forecasting metrics including mean-squared error (MSE)., and mean average error (MAE). The N-BEATS model demonstrates improved performance compared to other state-of-the-art (SOTA) models, obtaining MSE values of 116.3, 38.2, and 19.0 for yearly, monthly, and weekly forecasting, respectively.

Keywords: Deep learning; Forecasting; Time-series; Wildfires.

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Paper #4

A Torpor-based Enhanced Security Model for CSMA/CA Protocol in Wireless Networks

Abiodun Akinwale, John E. Efiong, Emmanuel Olajubu and Ganiyu Aderounmu

Abstract: Mobile wireless networks enable the connection of devices to a network with minimal or no infrastructure. This comes with the advantages of ease and cost-effectiveness, thus largely popularizing the network. Notwithstanding these merits, the open physical media, infrastructural-less attributes, and pervasive deployment of wireless networks make the channel of communication (media access) vulnerable to attacks such as traffic analysis, monitoring, and jamming. This study designed a virtual local area network (VLAN) model to circumvent virtual jamming attacks and other intrusions at the Media Access Control (MAC) layer of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. A Torpor VLAN (TVLAN) Data Frame Encapsulation and the algorithm for T-VLAN security in CSMA/CA were formulated and presented. A simulation experiment was conducted on the model using OMNeT++ software. The performance metrics used to evaluate the model were packet delivery ratio, network throughput, end-to-end channel delay, and channel load. The simulation results show that the TVLAN defence mechanism did not increase the channel load arbitrarily during TVLAN defence. similarly, the system throughput was shown to be 82% during TVLAN defence. Nevertheless, the network delay of the system during TVLAN defence was significantly high but the channel load was 297 when the TVLAN security mechanism was launched. These results demonstrate the model’s ability to provide a survivability mechanism for critical systems when under attack and add a security layer to the CSMA/CA protocol in wireless networks. Such a remarkable performance is required of a CSMA/CA infrastructure for improving the cybersecurity posture of a wireless network.

Keywords: IoT; IP; MAC address; MANET; Torpor VLAN; TVLAN.

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Paper #5

Enhancing Robot Navigation Efficiency Using Cellular Automata with Active Cells

Saleem Alzoubi and Mahdi H. Miraz

Abstract: Enhancing robot navigation efficiency is a crucial objective in modern robotics. Robots relying on external navigation systems are often susceptible to electromagnetic interference (EMI) and encounter environmental disturbances, resulting in orientation errors within their surroundings. Therefore, the study employed an internal navigation system to enhance robot navigation efficacy under interference conditions, based on the analysis of the internal parameters and the external signals. This article presents details of the robot’s autonomous operation, which allows for setting the robot's trajectory using an embedded map. The robot’s navigation process involves counting the number of wheel revolutions as well as adjusting wheel orientation after each straight path section. In this article, an autonomous robot navigation system has been presented that leverages an embedded control navigation map utilising cellular automata with active cells which can effectively navigate in an environment containing various types of obstacles. By analysing the neighbouring cells of the active cell, the cellular environment determines which cell should become active during the robot’s next movement step. This approach ensures the robot’s independence from external control inputs. Furthermore, the accuracy and speed of the robot’s movement have been further enhanced using a hexagonal mosaic for navigation surface mapping. This concept of utilising on cellular automata with active cells has been extended to the navigation of a group of robots on a shared navigation surface, taking into account the intersections of the robots’ trajectories over time. To achieve this, a distance control module has been used that records the travelled trajectories in terms of wheel turns and revolutions.

Keywords: Active cell; Cellular automata; Hexagonal mosaic; Motion trajectory; Navigation; Robot.

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