Reviewerly/exHarmony huggingface dataset:
Lost in Translation: AI-based Generator of Cross-Language Sound-squatting

Sound-squatting is a phishing attack that tricks users into accessing malicious resources by exploiting similarities in the pronunciation of words. It is an understudied threat that gains traction with the popularity of smart-speakers and the resurgence of content consumption exclusively via audio, such as podcasts. Defending against sound-squatting is complex, and existing solutions rely on manually curated lists of homophones, which limits the search to a few (and mostly existing) words only. We introduce Sound-squatter, a multi-language AI-based system that generates sound-squatting candidates for proactive defense that covers over 80% of exact homophones and further generating thousands of high-quality approximated homophones. Sound-squatter relies on a state-of-art Transformer Network to learn transliteration. We search for Sound-squatter generated cross-language sound-squatting domains over hundreds of millions of emitted TLS certificates comparing with other types of squatting candidates. Our finding reveals that around 6% of generated sound-squatting candidates have emitted TLS certificates, compared to 8% of other types of squatting candidates. We believe Sound-squatter uncovers the usage of multilingual sound-squatting phenomenon on the Internet and it is a crucial asset for proactive protection against sound-squatting.
Hyperspectral image destriping and denoising from a task decomposition view

The generalized mathematical model for HSI denoising or destriping lacks stability and uniqueness properties, failing to accurately portray the distribution and effects of stripes. Solutions following such a model would inevitably result in excessive destriping of strip-free areas, leading to the loss of texture detail. To remedy the above deficiencies, we reformulate the destriping task and introduce a novel solution from the task decomposition view. It is broken down into auxiliary sub-tasks involving stripe mask detection, stripe intensity estimation, and HSI restoration, which greatly reduces the difficulty of solving such an ill-posed problem. Based on this, we adopt a sequential multi-task learning framework and propose a stripes location-dependent restoration network, termed SLDR, which integrates the distribution and intensity features of stripes to achieve accurate destriping and high-fidelity restoration. Furthermore, we design a stripe attribute-aware estimator and a weighted total variation loss function to capture the unique properties of stripes and adaptively adjust the restoration weights of striped and non-striped regions. Extensive evaluation and comprehensive ablation studies on synthetic and practical scenes show the effectiveness and superiority of our model and architecture.
A Usability Study on the Adaptation of Virtual Reality for UiTM Melaka Immersive Walkthrough

Universiti Teknologi MARA Melaka Branch (UiTM Melaka) has many buildings accommodating thousands of students for many activities. With the large surrounding, new students or visitors may find difficulties in searching specific buildings. Several techniques have been developed to allow people to walk in larger virtual spaces than permitted by the physical extent of the virtual reality (VR) hardware and provide a new way of thinking about the user’s relationship to the virtual environment, guiding the user’s continuous and direct input within both space and time allowing a more narrative presentation. The paper then presents the details of how this analogy was applied to the VR experience. The immersive walkthrough (IW) could be done in VR, where people can try to play immersive environment to improve their understanding of UiTM Melaka and help them to navigate the facilities and find the specific area easily by searching it in the building. This study comprises five phases in developing IW UiTM Melaka: analysis, design, development, implementation, and evaluation (ADDIE). We evaluate the result by applying usability testing on effectiveness, efficiency, and satisfaction for the correctness of the IW. The questionnaire was distributed using a survey method involving 43 participants. Based on the survey analysis, we received a percentage of 89% effectiveness, 88% of efficiency and 89% of satisfaction. As a result, the usability test of this study achieved 89%. We conclude that the system is good and accepted, where each feature is easily accessible and used by the user, and the navigation is not complex.
Long Short-Term Memory-based Static and Dynamic Filipino Sign Language Recognition

Filipino Sign Language (FSL) is a distinctive form of sign language with its own set of pose, gestures, and grammar, which can cause a challenge in terms of identifying it. Earlier researches have identified three categories of sign language recognition methods, these are the gloves-based, vision-based and hybrid. Existing studies are only limited to recognizing static FSL and only include limited phrases or words for dynamic FSL recognition. Since the existing studies for dynamic FSL are only capable of recognizing limited words and phrases, this could limit the communication. Thus, adding more phrases or words for dynamic FSL recognition is significant. The objective of this study is to create a FSL recognition model using Long Short-term Memory and MediapPipe Holistic pipeline. A recurrent neural network type called Long Short-Term Memory (LSTM) is capable of recognizing sign language gestures, including FSL, because it can handle long-term dependencies. Using 11,070 sequences, this study trained a model to recognize 24 static Filipino sign languages including alphabets and 17 dynamic Filipino sign languages including common Filipino words, greetings, and phrases. Since a lot of people in the Philippines are not familiar with FSL, this study is useful to improve communication, and making it easier for people who do not understand FSL to understand Filipino deaf or speech impaired people in a certain communication. The recognition model produced by the proponents achieved a 99.72% model accuracy score using MediaPipe and LSTM, and can accurately detect and interpret static and dynamic Filipino sign language gestures in real-time.
Traffic Sign Classification based on Deep Learning

In the context of 5G communication, technologies such as the Internet of Things, big data, and supercomputers have been further developed, and artificial intelligence technology has also become one of the hot topics at present. In recent years, the image recognition technology of artificial intelligence has been pursued by most academic enthusiasts and has been rapidly developed. With the development of computer vision, a series of target detection algorithms have emerged in recent years. This paper classifies traffic signs based on the target detection algorithm of YOLOv5 and make use of the advantages of the accuracy and real-time performance of the YOLO algorithm. To restore the real traffic scene, different shooting angles and different shooting locations are prepared as data sets. Finally, to realize the detection and recognition of traffic signs and optimize the algorithm to improve the recognition rate.
Improving Rice Farming Productivity through Solar-Powered Electronic Stirring with SMS Notifications

This research project aims to address the challenges farmers face in drying paddy rice to produce high-quality paddy grain due to a lack of manpower. The study presents an Electronic Stirring Rice Paddy system with SMS Notification and Solar Charging that stirs the rice every thirty minutes until it is dried, with features that notify the user of potential obstacles and changing weather conditions. The study used the Engineering Design Process and experimental method to gather data and test the system’s performance in terms of air temperature, solar panel charge rate, duration, effectiveness in paddy moisture, and cost benefits. Results indicate that the developed system is effective in stirring rice, has a solar panel charge rate of 3.83% per thirty minutes, and there is a significant difference in dry paddy moisture compared to the traditional method. The cost-benefit analysis revealed a return on investment after five months.
GA Based Autonomous Self Localization and Mapping for Bomb Disposal Robot

With the advancement of technology, the police and military personnel begin to rely on technology to perform tasks which in the past require manual intervention. Detecting bomb under the car has been one of the routines for police and military personnel during their mission to protect important individual from bomb threat. In this study, the localization, navigation and mapping of vehicle undercarriage are simulated. Navigation simulation includes the perimeter navigation, path planning and manual threat search. Perimeter navigation navigates the perimeter of the vehicle to construct a boundary condition for threat search. Path planning allows for automated threat search and three different methods were used to construct the path on random spot generated within the perimeter of the vehicle. The methods are heuristic (Greedy Method), GA, and GA optimized heuristic. Manual threat search allows user to define search area within the perimeter of the vehicle. Mapping of vehicle undercarriage is done by sweeping under the vehicle while scanning the undercarriage of the vehicle using distance sensor.
Comparison of GWO, BAT, and CLAHE in Image Contrast Enhancement

There are several CE algorithm techniques for contrast enhancement such as Contrast Limited Adaptive Histogram Equalization (CLAHE). However, these methods suffer from over-enhancement issue. Therefore, in this paper applied adaptive intelligent filters BA or GWO as enhance the contrast of color image on different number of brightness density and compare the proposed algorithm with Contrast-limited adaptive histogram equalization (CLAHE) fitter. The proposed methods performance evaluated using PSNR and MSE measures. The experimental results show that the suggested intelligent filters (BA, GWO) were found best, among the other traditional filter (AHE, CLAHE). In comparison to the other methods employed, the experimental results showed that the GWO outperforms other filters in terms of PSNR and MSE values. The simulating results the PSNR performance of proposed method (38.713, 33.890, 25.162), MSE equal (47.887, 112.520, 527.989) compared with bat algorithm and the filter of CLAHE (Contrast-limited adaptive histogram equalization).
Image Forgery Detection Using Low Dimensional Texture Feature Vector

The emergence of strong program for digital image editing that allows you to easily change the contents of images without leaving any trace signs of such alterations, therefore the authenticity of a digital image is in significant danger. To authenticate a digital image, many digital image forgery detection (DIFD) techniques have been developed. Machine learning is one of the greatest technologies for addressing the issue and assisting in the development of such a system. In this paper, the proposed DIFD has been separated into three steps. The first step involves image processing, followed by feature vector extraction of two handcrafted features from images. Finally, for training these feature vectors, the Gaussian Discriminant Analysis (GDA) classifying model is utilized to distinguish between authentic and fake images. The experiment findings indicate that in the GDA classifier, the (Tamura) texture feature outperformed the (LBP) texture features, as a result, it is the best texture property for fake detection. A comparison of the suggested approach with other with additional modern methods is displayed using the CASIA V2.0 freely accessible dataset.
Forecasting Cost Saving Through Solar System Installation

The benefits of installing solar panels for electricity generation are not widely understood. Thus, this study aims to raise awareness by analyzing solar energy generation and simulating potential cost savings based on solar irradiance data. A Linear Regression model was developed by identifying the correlation between actual solar energy generation and solar irradiance data at a specific location. The study employs MSE, RMSE, and R-squared metrics to evaluate prediction accuracy. The developed model has a low MSE and RMSE value of 5.9 and 2.43, respectively, and a high R-squared value of 0.75, indicating high prediction accuracy. This model can predict solar power generation at specific locations based on solar irradiance data, enabling the estimation of cost savings from reduced electricity bills and maximum power generated by the solar panels.
Passive Forgery Detection Techniques:A Survey

The practice of altering images without authorization is referred to as digital image forgery (DIF). Digital manipulation is now a reasonably simple technique. The primary research topics for the detection of image forgeries are two categories of image forgery: copy-move and image splicing forgery. A piece of an input image is copied, and then another section of the same digital image has the copied section pasted over it this manipulation is called copy-move forgery. While splicing is a form of manipulation that merges various sections from the same or different sources to produce a composite forged image. However, this paper compared and analyzed two forgery techniques and attempted to describe recent findings in both kinds by gathering and organizing them according to the qualities that were employed.
Simulation of Real-Time Frontier Exploration in Confined &amp; Cluttered Environment

This paper presents a real-time simulation of a frontier exploration robot using a nonholonomic mobile robot’s kinematic model. In MATLAB, three heuristic-based frontier selection methods, namely Randomized Histogram Sector (RHS), Informed Randomized Point (IRP), and Histogram Clustering (HC), were simulated within two constrained space maps, one of which (Map 1) contained more obstacles than the other (Map 2). For the suggested frontier detection, the percentage of successfully mapped area against the ground truth map was determined to be 97.5% for Map 1 and 98.3% for Map 2. These results indicate that the proposed method yields great results. The HC approach is the most effective of the three proposed methods for both maps in terms of the time required to explore the maps and the efficiency of navigation inside the maps.
Efficient-Low Memory Path Planning Algorithm Based on Adaptive Thresholding

Aiming at the problem of low efficiency and large memory consumption in mobile robot path planning, this paper proposes an improved version of the RRT*Fixed Nodes( RRT*FN )algorithm based on the adaptive threshold, named A-RRT*FN. The algorithm constructs a sampling tree in the two directions of the starting point, and the target point and adaptively sets the threshold according to the environment on both sides to improve the environmental adaptability and search speed of the algorithm. Secondly, a node elimination strategy is proposed to quickly locate low-performance nodes and reduce the number of iterations of the algorithm and the memory occupation of low-performance nodes. Finally, the path smoothing strategy is used to smooth the obstacle-free path to obtain a path more suitable for robot tracking control. The proposed algorithm is compared with RRT* FN, Fast-RRT, and Improved Bi-RRT* algorithms under three different maps of simple, narrow, and complex. The results show that the proposed algorithm has better performance in convergence efficiency and memory consumption.
Air-Gap Electromagnetic Covert Channel

Air-gapped systems are isolated from the Internet due to the sensitive information they handle. This paper introduces a covert channel attack that leaks sensitive information over the air from highly isolated systems. The information emanates from the air-gapped computer over the air and can be picked up by a nearby insider or spy with a mobile phone or laptop. Malware on an air-gapped computer can generate radio waves by executing crafted code on the target system. The malicious code exploits the dynamic power consumption of modern computers and manipulates the momentary loads on CPU cores. This technique allows the malware to control the computer's internal utilization and generate low-frequency electromagnetic radiation in the 0 - 60 kHz band. Sensitive information (e.g., files, encryption keys, biometric data, and keylogging) can be modulated over the emanated signals and received by a nearby mobile phone at a max speed of 1000 bits/sec. We show that a smartphone or laptop with a small $1 antenna carried by a malicious insider or visitor can be used as a covert receiver. Notably, the attack is highly evasive since it executes from an ordinary user-level process, does not require root privileges, and is effective even within a virtual machine (VM). We discuss the attack model and provide technical details. We implement air-gap transmission of texts and files and present signal generation and data modulation. We test the covert channel and show evaluation results. Finally, we present a set of countermeasures to this air-gap attack.
A Novel Chase Kötter-Vardy Algorithm

In this paper, we first formulate a novel Chase Guruswami-Sudan algorithm which list corrects not only all errors among the Chase flipping symbols but also the number of errors up to the enhanced Johnson bound among remaining positions, for Reed-Solomon and q-ary BCH codes. The enhanced Johnson bound is induced by shortening the code without those Chase flipping symbols. We next devise a novel Chase Kötter-Vardy algorithm for soft decision decoding of Reed-Solomon codes. We show that the a posteriori probabilities of two branches of one of the least unreliable symbols which undergo Chase flipping shall be merged and assigned to the correct branch out of the two (it does not matter anyway if neither is correct), based on the rationale that one of exhaustive Chase flipping patterns must hit the genie pattern among the flipped symbols. During the exhaustive trial-and-error Chase decoding, each flipping pattern is treated as the genie and thus the associated branches of each flipping symbol are assigned with the merged a posteriori probabilities while its alternative branch is amortized. The multiplicity matrix is constructed using the state-of-the-art Kötter-Vardy transformation. We show by both theoretical analysis and simulations that the proposed Chase Kötter-Vardy algorithm significantly outperforms the original Kötter-Vardy algorithm under a similar complexity. We then extend this approach to a coded Chase decoding scheme wherein the Chase pattern set comes from a covering code. Surprisingly, simulations show that the best performance is typically achieved by concatenating the exhaustive Chase flipping on a number of the most reliable secondary indexes and a coded Chase flipping on the remaining secondary most reliable secondary indexes. We also employ a novel preprocessing technique that enforces a zero constant term for the message polynomial, by equivalently sacrificing one parity symbol. It effectively eradicates spurious candidate bivariate polynomials by merely computing and checking their constant terms, and thus dramatically reduces the number of candidate bivariate polynomials that are actually interpolated and factorized.
Optical Neural Network in Free-Space and Nanophotonics

The explosive data growth has resulted in increased computing costs. As Moore’s Law is increasingly slowing down, the traditional computing approach based on the von Neumann architecture is gradually becoming unable to fulfill future computing needs. However, optical neural networks have emerged as a potential solution because of their high speed, high bandwidth, and capability to subdue the bottleneck problem of computing power. With the development of optics and nanophotonics, it is possible to implement complex optical neural networks in free-space and nanophotonic platforms. This article reviews the research progress of optical neural networks. Firstly, various methods of implementing optical matrix calculations are described. Secondly, the construction method of optical neural network in free space and nanophotonic platform is introduced respectively. In free space, based on 4f system and diffractive optical elements and in nanophotonic, relied on optical waveguide devices such as microring resonator or Mach-Zehnder Interferometer. Thirdly, we introduce the methods in training and nonlinear activity. Finally, we summarized the current research status and challenges of optical neural networks. In the future, optical neural networks have great application value.
Advanced First-Order Optimization Algorithm With Sophisticated Search Control for Convolutional Neural Networks

As the performance of computing devices such as graphics processing units (GPUs) has improved dramatically, many deep neural network models, especially convolutional neural networks (CNNs), have been widely applied to various applications such as image classification, semantic segmentation, and object recognition. However, effective first-order optimization methods for CNNs have rarely been studied, although many CNN models have been successfully developed. Accordingly, this paper investigates various advanced adaptive solution search methods and proposes a new first-order optimization algorithm for CNNs called Adam-ASC. Our approach uses four sophisticated adaptive solution search methods to adjust its search strength in the complicated large-dimensional weight solution space spanned by a loss function. At the same time, we explain how they can be combined compensatively to form a complete optimizer with a detailed implementation. From the experiments, we found that our Adam-ASC can significantly improve the image recognition performance of practical CNNs in both the image classification and segmentation tasks. These experimental results show that the four fundamental methods of Adam-ASC and their compensative combination strategy play a crucial role in training CNNs by effectively finding their optimal weights.
Toward High-Quality Real-Time Video Denoising With Pseudo Temporal Fusion Network

With the increasing availability of high-resolution video recording and streaming, there is a need for fast and high-quality video denoising methods that can handle high-resolution videos. However, many existing methods fail to achieve high-quality denoising performance and lightweight at the same time. This paper proposes a video denoising network, Pseudo Temporal Fusion Network (PTFN), that satisfies these requirements. PTFN adopts a new Pseudo Temporal Fusion (PTF) module that captures pseudo-temporal relationships between video frames in combination with the Temporal Shift Module. PTFN also adopts a modern ConvBlock paradigm that breaks away from the classical ConvBlock paradigm, contributing to denoising performance and lightweight. PTFN achieves better performance than existing video denoising methods in terms of both video quality and lightweight. Specifically, PTFN has only about 16.7% of the computational cost of existing lightweight state-of-the-art methods, while it improves denoising performance. PTFN is also superior in terms of memory consumption. It can process 1080p videos with a GPU with 24 GB RAM. In addition, a lighter version (PTFN Half) can process 2K videos at high speed under the same conditions.