Reviewerly/exHarmony huggingface dataset:
Knowledge Engineering Using Natural Language Processing of User Reviews for Bahrain’s Mobile Government Applications

The Kingdom of Bahrain has launched various mobile government applications that work side by side with the national e-government portal by providing a range of government services, while services offered on mobile applications are still limited compared to the e-government portal, utilization of end users’ feedback is vital to improve and enhance functionality to ensure proper digital integration on the mobile environment. In this research, knowledge engineering using natural language processing is implemented to analyze 20,000 user reviews of the top four most reviewed google play mobile government applications in Bahrain. Two resampling techniques were used to under-sample and over-sample unbalanced datasets; Near-Miss and Synthetic Minority Oversampling combined with Edited Nearest Neighbor. The performance of three classifiers for data analysis was compared and assessed before and after data resampling. Results suggest that the Random Forest classifier outperformed Artificial Neural Network and LogitBoost.
Increasing Automatic Proctoring System Performance using Distributed Round-Robin Load Balancer

In the context of online examinations, ensuring a reliable and efficient proctoring system is crucial for maintaining academic integrity. The increasing demand for automatic proctoring services raises challenges related to handling growing traffic, server failures, and ensuring fair resource utilization. This study investigates the performance of an automatic proctoring load balancing system to address these challenges, focusing on key metrics such as response times, request distribution, and failover times. The system utilizes a Round Robin load balancing algorithm to effectively manage incoming requests and distribute them among multiple proctoring engines. The evaluation reveals a 22.22% decrease in response times, ensuring low latency and improved efficiency. Additionally, the algorithm evenly distributes requests across the proctoring engines, enabling fair resource utilization and adaptability to varying workloads. Despite an increase in failover times with the number of requests, the system maintains an average failover time of 700 ms, demonstrating resilience in handling server failures and ensuring uninterrupted service. Overall, the automatic proctoring load balancing system offers a more efficient, scalable, and resilient proctoring infrastructure capable of addressing the challenges of increasing traffic and server failures.
MapperPlus: Agnostic clustering of high-dimension data for precision medicine

One of the goals of precision medicine is to classify patients into subgroups that differ in their susceptibility and response to a disease, thereby enabling tailored treatments for each subgroup. Therefore, there is a great need to identify distinctive clusters of patients from patient data. There are three key challenges to three key challenges of patient stratification: 1) the unknown number of clusters, 2) the need for assessing cluster validity, and 3) the clinical interpretability. We developed MapperPlus, a novel unsupervised clustering pipeline, that directly addresses these challenges. It extends the topological Mapper technique and blends it with two random-walk algorithms to automatically detect disjoint subgroups in patient data. We demonstrate that MapperPlus outperforms traditional agnostic clustering methods in key accuracy/performance metrics by testing its performance on publicly available medical and non-medical data set. We also demonstrate the predictive power of MapperPlus in a medical dataset of pediatric stem cell transplant patients where a number of cluster is unknown. Here, MapperPlus stratifies the patient population into clusters with distinctive survival rates. The MapperPlus software is open-source and publicly available.
Detection of Physical Adversarial Attacks on Traffic Signs for Autonomous Vehicles

Current vision-based detection models within Autonomous Vehicles, can be susceptible to changes within the physical environment, which cause unexpected issues. Physical attacks on traffic signs could be malicious or naturally occurring, causing incorrect identification of the traffic sign which can drastically alter the behaviour of the autonomous vehicle. We propose two novel deep learning architectures which can be used as detection and mitigation strategy for environmental attacks. The first is an autoencoder which detects anomalies within a given traffic sign, and the second is a reconstruction model which generates a clean traffic sign without any anomalies. As the anomaly detection model has been trained on normal images, any abnormalities will provide a high reconstruction error value, indicating an abnormal traffic sign. The reconstruction model is a Generative Adversarial Network (GAN) and consists of two networks; a generator and a discriminator. These map the input traffic sign image into a meta representation as the output. By using anomaly detection and reconstruction models as mitigation strategies, we show that the performance of the other models in pipelines such as traffic sign recognition models can be significantly improved. In order to evaluate our models, several types of attack circumstances were designed and on average, the anomaly detection model achieved 0.84 accuracy with a 0.82 F1-score in real datasets whereas the reconstruction model improved performance of traffic sign recognition model from average F1-score 0.41 to 0.641.
Differentiable short-time Fourier transform with respect to the hop length

In this paper, we propose a differentiable version of the short-time Fourier transform (STFT) that allows for gradient-based optimization of the hop length or the frame temporal position by making these parameters continuous. Our approach provides improved control over the temporal positioning of frames, as the continuous nature of the hop length allows for a more finely-tuned optimization. Furthermore, our contribution enables the use of optimization methods such as gradient descent, which are more computationally efficient than conventional discrete optimization methods. Our differentiable STFT can also be easily integrated into existing algorithms and neural networks. We present a simulated illustration to demonstrate the efficacy of our approach and to garner interest from the research community.
Convolutional Neural Network-based Architecture for Detecting Face Mask in Crowded Areas

After the invasion of the Covid-19 virus, governments started containing the spread of the virus by forcing people to wear face masks in public places. Therefore, automatic face mask detection has become very important to limit the virus spread. Unfortunately, existing methods present limited performance in accurately detecting masks in crowded areas due to the significant number of faces per scene. In order to tackle this challenge, we propose a two-stage neural network-based architecture that can accurately detect face masks in crowded environments. Several simulations have been conducted to investigate the efficiency of the proposed architecture and the results show a high accuracy of detection that can reach up to 96.5%.
Extended Upscale and Downscale Representation with Cascade Arrangement

Smoothing filters are widely used in EEG signal processing for noise removal while preserving important features. Unlike common approaches in the time domain, a recent effective algorithm using the Upscale and Downscale Representation (UDR) technique has been introduced to process the signal in the image domain. The idea of UDR is to visualize the input with an appropriate line width, convert it to a binary image, and then smooth it by skeletonizing the signal object to a unit width and projecting it back to the time domain. We propose in this paper a cascaded UDR (CUDR) where the interested signal is filtered twice. CUDR’s performance is verified on simulated data with added white Gaussian noise and compared with the cascaded arrangement of some conventional techniques. Experimental results have demonstrated the outperformance of CUDR in terms of the fitting error when dealing with noisy signals, especially at a low signal-to-noise ratio.
A Data Efficient Vision Transformer for Robust Human Activity Recognition from the Spectrograms of Wearable Sensor Data

This study introduces the Data Efficient Separable Transformer (DeSepTr) architecture, a novel framework for Human Activity Recognition (HAR) that utilizes a light-weight computer vision model to train a Vision Transformer (ViT) on spectrograms generated from wearable sensor data. The proposed model achieves strong results on several HAR tasks, including surface condition recognition and activity recognition. Compared to the ResNet-18 model, DeSepTr outperforms by 5.9% on out-of-distribution test data accuracy for surface condition recognition. The framework enables ViTs to learn from limited labeled training data and generalize to data from participants outside of the training cohort, potentially leading to the development of activity recognition models that are robust to the wider population. The results suggest that the DeSepTr architecture can overcome limitations related to the heterogeneity of individuals’ behavior patterns and the weak inductive bias of transformer algorithms.
An Efficient Deep Network for Modulation Classification in Impaired MIMO-OFDM Systems

In this paper, an efficient automatic modulation classification for MIMO-OFDM signals is proposed for next generation wireless networks by exploiting cutting-edge deep learning techniques. Particularly, we design a deep network, namely OFDM modulation classification network (OMCNet), with asymmetric depthwise separable convolution, residual connection, and attention mechanism in a sophisticated design of processing blocks to reduce the overall complexity without sacrificing learning efficiency. Relying on the simulations, our deep network achieves over 92% for different delay spread models demonstrates the robustness of modulation classification under various channel impairments. Remarkably, compared with a baseline model, OMCNet reduces the network size by four times and computation cost by two times with the asymmetric depthwise separable while achieving a competitive accuracy thanks to residual connection and attention mechanism.
Federated Graph Learning for Low Probability of Detection in Wireless Ad-Hoc Networks

Low probability of detection (LPD) has recently emerged as a means to enhance the privacy and security of wireless networks. Unlike existing wireless security techniques, LPD measures aim to conceal the entire existence of wireless communication instead of safeguarding the information transmitted from users. Motivated by LPD communication, in this paper, we study a privacy-preserving and distributed framework based on graph neural networks to minimise the detectability of a wireless ad-hoc network as a whole and predict an optimal communication region for each node in the wireless network, allowing them to communicate while remaining undetected from external actors. We also demonstrate the effectiveness of the proposed method in terms of two performance measures, i.e., mean absolute error and median absolute error.
Deep Hand Gesture Recognition: A Wavelet Scattering Alternative to Convolutional Networks

Hand gesture recognition (HGR) is crucial for improving human-computer interaction, aiding people with disabilities, and enhancing industrial efficiency. Radars are a popular choice for HGR, as they can detect hand gestures in various lighting conditions, through obstructions, with low latency, and without line-of-sight. Deep Convolutional Neural Networks (DCNN) are commonly used to analyze radar signals and recognize complex hand gestures. However, training DCNN models requires significant computational resources, making real-time applications challenging. This study proposes using Wavelet Scattering Transform (WST) as a feature extractor to replace DCNN, while relying on lightweight traditional classifiers for identifying the class of hand movement. WST is a non-linear signal representation that preserves high levels of discriminability while maintaining stability under time-warping deformations. To compare DCNN against WST, the study used a publicly available database of ultra-wideband (UWB) impulse radar gestures, collected from eight participants performing twelve hand gestures. The results showed that WST can achieve an average accuracy of 95% across all subjects, making it a reliable, computationally efficient, and accurate alternative to DCNN. This is the first research demonstrating the effectiveness of WST against DCNN for radar HGR applications (to the best of the authors’ knowledge).
Improved Deterministic Usage of the Elliptic Curve Digital Signature Algorithm with Scrypt

In this paper, we propose an improved deterministic usage of the Elliptic Curve Digital Signature Algorithm (ECDSA) with the key derivation function scrypt. In particular, the scrypt function generates a batch of random bits where the random bits needed for the signing process are selected. As a certain number of bits is chosen from a bigger set, the reuse of the secret random number for each signing process is avoided, which is against fault and side-channel attacks. Numerical results are provided for five different-length messages and seventeen private keys considered as inputs for deterministic ECDSA and our proposed method. The random quality assessment using a statistical test suite of the National Institute of Standards and Technology (NIST) shows that our proposed method generates higher-quality random bit sequences, which can be seen clearly with one- and two-million-bit lengths respectively.
Cross Density Kernel for Nonstationary Signal Processing

This paper introduces the cross density kernel function (CDKF), a new positive-definite kernel that quantifies the statistical dependence between random processes, to address the challenge of applying time series prediction and modeling techniques to nonstationary signals. The paper highlights the limited applicability of the Wiener filter and Parzen’s autocorrelation reproducing kernel Hilbert spaces (RKHS) to stationary signals. CDKF extends these methods by capturing properties of probability density functions for random processes in the Hilbert space with a novel bidirectional recursion, and using two neural networks to optimize the kernel function based on realizations. The paper concludes by presenting experimental results that support the effectiveness of CDKF.
CSA-BERT: Video Question Answering

Convolutional networks are a key component of many computer vision applications. However, convolutions have a serious flaw. It only works in a small area, hence it lacks global information. The Attention method, on the other hand, is a new improvement in capturing long range interactions that has mostly been used to sequence modeling and generative modeling tasks. As an alternative to convolutions, we investigate the use of convolutions with an attention mechanism in a video question answering task. We present a unique self-attention mechanism based on convolutions that outperforms convolutions in the video question answering task. We discovered that combining convolutions with self-attention produces the greatest outcomes in experiments. As a result, we propose a hybrid idea, which combines convolutional operators with the self-attention mechanism. We combine convolutional feature maps with self-attention feature maps. Experiments show that convolution with self-attention improves video question answering tasks on the MSRVTT-QA dataset.
Dual-Input Type Convolutional Neural Networks Employing Color Normalized and Nuclei Segmented Data for Histopathology Image Classification

Improvements in Convolutional Neural Network (CNN) have been widely successful for histopathology image classification. However, color normalization for data preprocessing and nuclei segmentation for feature extraction should also be considered for further performance boost, data redundancy elimination, and provision of distinguishing information. These techniques are known to improve generalizability. However, there is a need to find ways to use the data obtained from color normalized and segmented data for training. In this work, dual-input CNN (DiCNN), concatenated-input CNN (CiCNN), and ensemble CNN (ECNN) are trained and tested with color normalized and nuclei segmented data. The normalization technique is chosen based on correlation and structural similarity. The segmentation method is chosen based on the best-performing normalization technique for consistency and generalizability. The results show that normalized and segmented inputs results in better binary classification with CiCNN outperforming other methods. However, for multiclass classification raw data training is advantageous for all approaches.
Semantic Communication for Partial Observation Multi-agent Reinforcement Learning

Effective cooperation and coordination among agents is essential for success in many real-world scenarios, particularly in reinforcement learning challenges. However, partial observation, where agents are not aware of all the observations made by other agents, creates a significant obstacle to coordination. To overcome this challenge, we propose the Shared Online Multi-agent Knowledge Exchange (SOME) framework, which allows agents to learn to anticipate each other’s observations and improve their local learning. In SOME, agents learn to anticipate the observations of other agents to improve their local learning, allowing for better coordination and cooperation. Additionally, using knowledge generators instead of full observations reduces communication costs. Our experimental evaluation demonstrates that agents trained with SOME can not only predict the next observations and actions of opponents and collaborators but also take appropriate actions, making it a promising approach for overcoming the partial observation challenge in multi-agent reinforcement learning.
Utilizing DETR model on SPECT image to assess remaining thyroid tissues post-thyroidectomy

In the management of thyroid cancer, subsequent to the excision of thyroid tissue through surgery, patients are administered with I-131 medication to eliminate residual thyroid tissue. An integral stage of the treatment process is the assessment of successful tissue removal. In this research, we present a model that aids in the automatic evaluation of a patient’s tissue removal using SPECT thyroid scan images. In instances where there is unsuccessful removal of tissue, the model is capable of automatically identifying residual thyroid tissue. For diagnostic purposes, the RSI is employed, and the detection model utilized is DETR, which effectively resolves the challenge of distance learning, thereby leading to desirable outcomes. Specifically, our proposed model achieves 0.91 on F1-score, mAP@0.5 with thyroid reaching 0.73, thereby attesting to its effectiveness. Therefore, our proposal is expected to contribute to the development of medical technology and improve the quality of diagnosis of thyroid nodule removal.
Differential privacy using Gamma distribution

The Laplace mechanism is a commonly employed approach that offers privacy guarantees within the framework of differential privacy. Nevertheless, the Laplace mechanism exhibits two limitations. Firstly, the privacy leakage of data can be exacerbated when the general differential private mechanism is accessed repeatedly with the same input owing to the sequential property of differential privacy. Secondly, the Laplace mechanism may not be suitable for some applications that solely involve positive samples as it can yield unwanted negative samples from the Laplace distribution.We address these issues by utilizing the Gamma distribution to handle database entries that must be consist of positive values ranging from 0 to infinity. In our approach, the epsilon parameter of our mechanism is determined by the value with noise according to the definition of differential privacy. Notably, the range of the noise is unbounded on the right thereby epsilon to approach infinity as the value with noise increases. To mitigate this, we impose constraints on the range of the noise in order to reasonably restrict the epsilon value of the mechanism. However, it should be noted that these constraints may impact the probability of ensuring epsilon-differential privacy and necessitate the imposition of a minimum boundary on the values of dataset. Additionally, we propose a new noise parameter that can be used to adjust the probability of ensuring differential privacy for a fixed epsilon.
Steganographic Stream Cipher Encryption Using True Random Number Generator

A novel, computationally simple method of hiding any message in the stream of random bits by using a secret key is presented together with a hardware-based true random number generator. The source of entropy in this generator is the random emission of low-energy electrons from the nuclear decay of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${}^{63}Ni$</tex> isotope.
Detection of Unoccupied Areas of the Road in Images from Mono Camera with Use of Multilevel Image Decomposition

The problem of detecting not occupied areas of the road is useful in many ADAS functions that include Lane Departure Warning (LDW), Lateral Control (LC), Adaptive Cruise Control (ACC). The problem may be in general defined as the ability to distinguish such areas from those that are occupied by other traffic members. The objective here is to provide data useful, for example, in planning vehicle's maneuvers. In this work, we propose a solution that supports the recognition of such areas. The proposed method is based on a procedure similar to the one used in Discrete Wavelet Transform (DWT) used in image decomposition. One of the introduced modifications relies on using simpler filters, whose structure facilitates their hardware implementation.
Power Analysis Attacks on the Customizable MK-3 Authenticated Encryption Algorithm

MK-3 is an authenticated encryption scheme based on the duplex sponge construction, suitable for both hardware and software. It provides broad factory and field customization features. The same security claims are valid for the original and all recommended customizations. Extensive security analyses of MK-3 were performed in our previous work: differential, linear, cube, and brute force attacks, as well as statistical analysis. In this work we report on new experiments involving Correlation Power Analysis (CPA), which is considered one of the most powerful side-channel attack (SCA) techniques. Two CPA attacks on MK-3 were developed: the first directly after the key absorption, and the second after the S-boxes in the first round of IV absorption. In the first attack, under strong assumptions about an attacker's capability to collect traces, we can recover 128 of the 512 state bits in a physical test on an FPGA. The second attack builds on top of the first one, but it assumes that special registers have been embedded after the S-boxes. Even under such ideal conditions, this attack can potentially reduce the brute-forcing difficulty only by an additional 88 to 194 bits. Overall, this gives the CPA attack no advantage over brute-forcing for the original 128-bit key. The previous and current results ensure that MK-3 and its customized versions effectively conceal its plaintext input.