Reviewerly/exHarmony huggingface dataset:
A stochastic nonmonotone trust-region training algorithm for image classification

In this work, we consider the target of solving the nonlinear and nonconvex optimization problems arising in the training of deep neural networks. To this aim we propose a nonmonotone trust-region (NTR) approach in a stochastic setting under inexact function and gradient approximations. We use the limited memory SR1 (L-SR1) updates as Hessian approximations when the curvature information is obtained by several different strategies. We provide results showing the performance of the proposed optimizer in the training of residual networks for image classification. Our results show that the proposed algorithm provides comparable or better testing accuracy than standard stochastic trust-region depending on the adopted curvature computing strategy and outperforms the well-known Adam optimizer.
Exploiting Simplified Depth Estimation for Stereo-based 2D Object Detection

Stereo image inputs provide higher object detection accuracy than monocular images by enabling the detection of objects that are missed from one view while being detectable from another view. To take advantage of additional information from the secondary image, it is necessary to search for the corresponding region in the images of different views by projecting with depth information of the target object. However, most existing studies utilize highly complex computations to estimate the depth for simple 2D object detection. This complexity limits the potential for deploying the methods on platforms, such as unmanned aerial vehicles, that involve significant resource constraints. In this paper, we introduce a simplified depth approximation to obtain depth information by quantizing the depth values into a small number of representative values. With these values, the regions of interest are projected to the secondary image to concatenate the information from the additional image. We validate our method with the KITTI dataset. Our results show that while having very low complexity, our approximation method leads to greatly improved object detection performance in two out of three difficulty groups of the dataset, and comparable performance in the other difficulty group compared to use of monocular image input.
Detecting Physical Adversarial Patch Attacks with Object Detectors

Machine learning models are vulnerable to adversarial attacks which can cause integrity violations in real- world systems with machine learning components. Alarmingly, these attacks can also manifest in the physical world where an adversary can disrupt systems without gaining digital access. These attacks are becoming more concerning as safety-critical infrastructure such as healthcare and transportation increasingly rely on machine learning.This work is motivated by the need for safeguarding vision- based systems against physical adversarial pattern attacks—an important domain for autonomous vehicles. We propose the use of a separate detection module that can identify inputs that contain physical adversarial patterns. This approach allows for independent development of the defensive mechanism which can be updated without affecting the performance of the protected model. This methodology allows the model developers to focus on performance and leave security to a separate team. It is a practical approach that can provide security in cases where a model is acquired from a third party and cannot be re-trained.We perform experimentation demonstrating that we can detect unknown adversarial patterns with high accuracy using standard object detectors trained on datasets containing adversarial patches. A single detector is capable of detecting a variety of adversarial patterns trained from models with different datasets and tasks. Additionally, we introduce a new class of visually distinct adversarial patch attack we call GAN patches. Our experimentation shows that once observed the detection module can be updated to identify additional classes of patch attacks. Finally, we experiment with detectors trained trained on innocuous patches and examine how they can generalize to detecting a variety of known patch attacks.
BreastUS: Vision Transformer for Breast Cancer Classification Using Breast Ultrasound Images

Breast cancer is the most common and fatal cancer type in women, posing a serious threat to women’s health. Ultrasounds play an essential role in the testing and diagnosis of breast cancer. Currently, it is the golden standard for complimentary screening for dense breasts, which has the potential to develop tumours. This paper proposes a transformer model, namely BreastUS Transformer, for Breast ultrasound Image Classification (BUIC). BreastUS incorporates self-attention and enables the automatic classification of breast ultrasound images into normal, benign, and malignant cases. The performance of BrestUS is evaluated using standard performance metrics like accuracy, recall, precision, and the F1-score. The quantitative results are compared with five state-of-the-art convolutional neural networks (CNN), and at least a 4% performance boost is achieved compared to the best-performing CNN.
Breast Cancer classification using Neural networks

Nowadays, due to lack of awareness of breast cancer and its signs that show, as well as methods for prevention, causes them to be one of the most deadly types of cancer and the death rate has significantly increased. Hence, in order to stop the spread of cancer, early identification at a nearly stage is critical as well as important. Breast cancer is further classified in to two types, malignant and benign. This study used machine learning techniques and neural network methods to classify the breast cancer types. A system is automated to carry out its opinion that is also automated, for breast cancer. This approach uses DNN (deep neural network), CNN (Convolutional Neural Network) and ANN Artificial Neural Network) and RFE (recursive feature elimination) for feature selection. DNN is applied with a multitude of layers of functions processing is applied to categorize the breast cancer data set. The result shows DNN is comparatively more outperforming with an accuracy of 97%.
Performance Analysis of Various Machine Learning Classification Models Using Twitter Data: National Education Policy

With the exponential growth of social networking sites, people are using these platforms to express their sentiments on everyday issues. Collection and analysis of people's reactions to purchases of products, public services, etc. are important from a marketing and innovation perspective. Sentiment analysis also called opinion mining or emotion extraction is the classification of emotions in text. This technique has been widely used over the years to determine sentiment within given text data. Twitter is a social media platform primarily used by people to express their feelings about specific events. In this paper, collected tweets about National Education Policy which has been a hot topic for a while; and analyzed them using various machine learning algorithms such as Random Forest classifier, Logistic Regression, SVM, Decision Tree, XGBoost, Naive Bayes. This study shows that the Decision tree algorithm is performing best, compare to all the other algorithms.
Predicting Breast Cancer Using Classical Machine Learning and Deep Learning Algorithms

Cancer is the source of nearly one in six fatalities. There are numerous cancer types, breast cancer being one of them. IT can be easily detected by certain symptoms. Breast cancer affects not only women but also men; it affects around 12.5% of women in Europe, the United States, and 5% of women in India. We can detect the risk of Breast cancer by using some machine learning and deep learning algorithms. The accuracy that was obtained using the machine learning algorithms was good enough that is 92%. Thiswas obtained by the Random Forest Classifier. The other machine learning algorithms were also good, but this got the highest accuracy score. Whereas the deep learning algorithms had significantly higher scores than the machine learning algorithms. The RNN model scored 98%, which was the highest. Breast Cancer Wisconsin (Diagnostic) Set Of data is the set of information that was used.
A Survey on Copy Move Forgery Detection (CMFD) Technique

The advancement in technology has led effortlessly to tampering the digital images. The most typical method of image manipulation, in which a few segments of a digital image or objects are duplicated and pasted into the same image is termed to be “copy move forgery”. This type of image tampering has a particularly negative impact on the field of forensic copyright protection and poses a danger to society. In this study, research on copy-move image forgery that has been authored in reputable journals is reviewed. It also discusses several detection methods that may be used to spot copy-move forgeries. A comparison study is conducted to assist the researchers in creating new, more effective copy-move image detection algorithms.
Blockchain Enabled Autonomous Vehicle Based Vehicular IoT System

Blockchain technology is gaining its popularity in many areas with huge success which ultimately attracts all researchers to study this technology. And the result of that shows more and more research to be conducted. Which makes blockchain technology relevant in the field of transportation systems. It is expected that in near future their integration will achieve greater heights, as compared to other technology blockchain is still new and many more unknown things still need to be found out. The aim of this study is to find out how blockchain benefits in Autonomous vehicles. We have compared the blockchain integrated autonomous vehicle systems to get the picture of the present environment and the challenge of the future for this integration. AV's or we say intelligent transportation is the future. We are going to study the potential application of the blockchain which will help in improving this particular section of industry. Finally their limitations and challenges associated with this integration.
Implementation and Analysis of Various Kalman Filtering Techniques for Target Tracking

RADAR plays a crucial role in target tracking. A faulty tracking is vulnerable to fatal errors. Since RADAR signals contain noise in them, filters are used to remove the noise. This paper implements Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF) for radar target tracking. Each filter is exposed to the same environment and the results are observed. A radar target tracking model is proposed with an input chirp signal of 77 GHz. The turn rate is varied between 0.2 and 150. The simulations are carried out in MATLAB. Since every simulation generates random motions of the target, the filter can be gauged only on the basis of how well the estimated trajectory follows the true trajectory. The findings have shown that EKF works best for large turn rate and high noise, in comparison to UKF and CKF, thus making it an appropriate choice for such applications.
Classification of Breast Cancer Mammographic Images Using A Light-Weighted Convolutional Neural Network

Deep learning is a method demanded by radiologists to assist them interpret and classify medical images correctly. A Convolutional Neural Network (CNN) is the most widely used method for classifying and analysing images. In this paper, a light-weighted CNN is presented for breast cancer classification using a dataset of breast mammography images. The suggested methodology improves the classification of mammary cancer images to assist radiologists in the detection of mammary cancer. The application of the proposed model can help in the diagnosis of mammary cancer using digital mammograms without any preceding information about the existence of a cancerous lesion. The proposed CNN can categorize the input medical images as malignant or benign with an accuracy of 99.35% which is the highest accuracy achieved for such a large mammography dataset.
Triple Alliance Prototype Orthotist Network for Long-Tailed Multi-Label Text Classification

Text classification, is one of the key tasks for representing the semantic information of documents, multi-label text classification (MLTC) is an important branch of it. MLTC aims to tag the most relevant labels for the given document. Compared to the standard multi-class case where each document has only one label, it is considerably more difficulty to annotate new coming documents for multi-label text classification. Furthermore, it also suffers from the challenge of highly skewed long-tailed label distribution. i.e., a few labels are associated with a large number of documents (a.k.a. head labels), while a large fraction of labels are associated with a small number of documents (a.k.a. tail labels). Due to the relative infrequency of tail labels, this leads to an imbalance that biases towards predicting more head labels. As challenging as this task is, it is an essential task to tackle since it represents many real-world cases, such as text retrieval of news. To address the challenge, we propose a Triple Alliance Prototype Orthotist Network (TAPON) to build a generic meta-mapping from few-shot prototypes to many-shot classifier parameters, which aims to promote the generalizability of tail classifiers. To be specific, TAPON is a two-stage method. At the first stage focusing on head labels, TAPON obtains the meta-knowledge between many-shot classifier parameters and few-shot prototype of head labels. Head label classifiers are trained by many-shot documents. Meanwhile, the triple alliance prototype is obtained by adopting an Attentive Prototype with the aid of few-shot documents, label semantic information and label correlation. Additionally, a Prototype Orthotist module is especially designed to capture the meta-knowledge between the many-shot classifier and few-shot prototype. At the second stage of transferring, TAPON aims to transfer the generic meta-mapping from head labels to tail labels. It first uses Attentive Prototype to obtain triple alliance prototype for tail labels, and then uses the meta-knowledge obtained from the first stage to get many-shot classifiers for tail labels. By conducting extensive experiments on four benchmark datasets, we show that the proposed TAPON significantly outperforms other state-of-the-art methods for long-tailed multi-label text classification.
Design and Analysis of Posit Quire Processing Engine for Neural Network Applications

Multiplications-Accumulations (MACs) are the significant computations in deep learning networks that must be hardware efficient and result in higher inference accuracy for the given set of network layers. Typically, the processing engines (PEs) which comprise the MAC units are the most resource-intensive element in a Deep Neural Network (DNN) accelerator. This paper proposes efficient PEs and presents an architectural design, accuracy, and resources cost analysis for hardware-based parameterized Posit Quire Processing Engine (PQPE) supporting P(8,0), P(16,1), P(32,2), and P(64,3). PQPE performs the vector computations (dot products) on Posit numbers with a wide quire accumulator and has been enabled with instructions, commands, and responses. PQPE has been modelled in Python to evaluate the inference accuracy for various DNN kernels and datasets. LeNet-5, AlexNet, custom 5 layers and 4 layers CNN models have been adopted and evaluated for inference accuracy with MNIST, CIFAR-10, and Alphabet recognition datasets. The PQPE has been used for matrix computations in convolutions. P(16,1) without quire provides same accuracy as float32 with less hardware resources. P(16,1) with quire brings same accuracy level as without quire and hence quire is not advantageous for larger posit data widths. Posit (8,0) with / without quire results insignificant loss of 0.8% / 3.28% (LeNet + MNIST) to 3.8% / 10.1% (AlexNet + CIFAR-10) accuracy as compared to P(16,1)/float32. Further, PQPE was modelled using Verilog HDL, synthesized targeting Xilinx Virtex-7 FPGA using Xilinx-Vivado. PQPE (8,0) achieved a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f_{max}$</tex> of 372 MHz and utilized 77.6% fewer hardware resources as compared to float32 at the cost of insignificant accuracy loss for CIFAR-10 which is 0.8% on LeNet and 3.8% on AlexNet. PQPE (16,1) has used 6.5% less resources and achieved equivalent accuracy as compared to float32.
Virtual reality affordances for oral communication in English as a second language classroom: A literature review

This paper reviews 34 studies related to oral communication in English as a Second Language through Virtual Reality published from 2015 to 2022. Overall, Virtual Reality affordances, such as immersion, interaction, feedback, and creation, were perceived positively and found to be effective in terms of anxiety, motivation, confidence, cultural awareness, creativity, and willingness to communicate. However, learning effectiveness was still inconclusive, especially in terms of oral fluency. The insignificant learning outcomes were likely caused by greater cognitive load, equity issues, unpleasant experiences, motion sickness, technology challenges, and lack of instructional activities suited to the Virtual Reality environment. Several strategies to address the challenges of current Virtual Reality platforms identified in the literature review were suggested.
FR-HDNet: Faster RCNN based Haze Detection Network for Image Dehazing

Image dehazing plays a major role in several vision- based applications aiming to improve image quality to obtain rich textural information. This paper proposes a methodology to retain textural information after image enhancement for vision- based algorithms. The objective is first to detect the hazy regions in a hazy input image and then perform dehazing on these detected regions. This results in retaining the texture of haze- free regions and an enhanced view of hazy regions. In the first part of the proposed framework, a Faster RCNN-based haze detection network named FR-HDNet is proposed to identify the hazy regions in an input hazy image. In the second part, the detected hazy regions are dehazed. This results in an enhanced image optimally equipped with features that could aid vision- based algorithms. Finally, the enhanced dehazed image is fed into an object detection network. The experiments to validate the performance of the proposed framework are done on several benchmarked datasets like natural hazy benchmarked images frequently used in the literature, synthetic hazy images, indoor Synthetic Objective Testing Set (SOTS) images from REalistic Single Image DEhazing (RESIDE) dataset, outdoor SOTS images from RESIDE dataset, and real-world synthetic hazy images of Hybrid Subjective Testing Set (HSTS) from RESIDE dataset. The performance measures used to evaluate the quality of dehazed images are Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) index; and Lightness Order Error (LOE) and Naturalness Image Quality Evaluator (NIQE) as no- reference image quality metrics. The effectiveness of the proposed framework is compared with several benchmarked state-of-the- art dehazing methods. The comparison demonstrated that the proposed framework enhances image quality and results in better performance.
Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition

Wearing a face mask is one of the adjustments we had to follow to reduce the spread of the coronavirus. Having our faces covered by masks constantly has driven the need to understand and investigate how this behavior affects the recognition capability of face recognition systems. Current face recognition systems have extremely high accuracy when dealing with unconstrained general face recognition cases but do not generalize well with occluded masked faces. In this work, we propose a system for masked face recognition. The proposed system comprises two Convolutional Neural Network (CNN) models and two Transformer models. The CNN models have been fine-tuned on FaceNet pre-trained model. We ensemble the predictions of the four models using the majority voting technique to identify the person with the mask. The proposed system has been evaluated on a synthetically masked LFW dataset created in this work. The best accuracy is obtained using the ensembled models with an accuracy of 92%. This recognition rate outperformed the accuracy of other models and it shows the correctness and robustness of the proposed model for recognizing masked faces. The code and data are available at https://github.com/Hamzah-Luqman/MFR.
Robot Rotation Estimation Using Spherical Moments in Neural Networks

This work proposes a method to estimate mobile robot rotation using spherical moments and machine learning. A polynomial fisheye camera model has been used to generate each image considering flying camera along a pre-planned trajectory. A dataset with 2400 images is created and labeled using Blender. The images are processed before it is fed into the neural network using 20 masks to reduces errors caused by non-common parts of the edge in image. afterwards, the spherical moments are obtained, as well as the triplets and the basis. Finally the basis is converted into a quaternion as input of network. Because for a neural network, a smaller number of inputs means faster training and higher accuracy. For the training phase, a strategy of data augmentation is applied. The data set is divided into many fragments (folders) according to a folder size that can be adjusted. Among them, in each training set, a sliding window is used to expend combinations within a certain range to calculate the rotation between two images. In tne end, the model is tested with synthesized fisheye camera data from Blender. The used simulated environment contains different light and weather conditions like fog or bad lighting situation. The proposed method in this paper is compared with different feature-based methods like FAST, ORB, SURF etc., which cannot successfully extract the feature points, to prove the performance of this novel approach. The results show that the method proposed in this paper can still accurately estimate
An Evaluation of Federated Learning Techniques for Secure and Privacy-Preserving Machine Learning on Medical Datasets

Federated learning is a machine learning technique that trains a machine learning model across multiple decentralized edge devices or servers holding local data samples without revealing them. It is a technique that ensures the privacy of data stored (locally on a server/edge device) while training a machine learning model. This technique is particularly valuable for hospitals to collaboratively learn machine learning models without moving all the training data (e.g., patient records) to a centralized location due to federal laws. In this experimental study, we investigate how federated learning performs on different real-world medical datasets using a cluster of machines. We tested different deep learning models and federated optimization strategies on CloudLab, which is an experimental testbed for networking and distributed computing research. Our experiments showed that Inception-v3 and EfficientNetB0 were usually the top two performing models based on accuracy computed over the test set. For federated optimizations, FedAvg was the best strategy followed by FedAvgM.