Reviewerly/exHarmony huggingface dataset:
Adversarial Attacks in Industrial Control Cyber Physical Systems

Machine learning and deep learning algorithms have been the frontier of artificial intelligence (AI) that reshape the current landscape of computing. However, the trustworthiness and reliability of AI models is a growing concern after they are being widely deployed. Adversarial attacks are cyber-related attacks that target machine learning and deep learning algorithms so that cause the training of the network to be fooled, and inaccurate predictions to be made. As result, adversarial attacks can affect critical AI systems, such as industrial control cyber-physical systems. They rely on machine learning models to perform day to day functions, this alone makes them prone to adversarial attacks since machine learning models are highly vulnerable to adversarial examples. Research on the consequences of these types of attacks can lead to insight on what can be done to prevent these malicious attacks if they were to be imposed on a vulnerable system. In this study, three adversarial cyber-attacks, specifically on power systems, are presented. Specifically, the Fast Gradient Sign Method, DeepFool, and Jacobian-Based Saliency Map Attacks were utilized to generate adversarial examples for machine learning and deep learning. The outcome of this study clearly indicates that adversarial attacks have negative implications on the performance of deep neural networks of cyber physical systems.
Detection of Face Features using Adapted Triplet Loss with Biased data

The task of classification of imbalanced data has recently become a major issue in pattern recognition and machine learning. The fundamental challenge with this type of data is that the smaller classes tend to be more valuable. However, standard classifiers have a bias towards the large classes and disregard the small ones. This results in a poor performance, especially in the minority class where accuracy is low. To demonstrate this problem, we used the VGGFace2 dataset, which is not biased. Therefore, we intentionally biased the data by distributing the images unequally within each class and address the problem of representation learning using triplet loss. We propose a model to obtain an informative feature embedding using the ResNet-18 network, and then use these learned embeddings for image classification. In addition, we use a method to improve the naive triplet loss named adapted triplet, to eliminate the bias resulting from the triplet selection process and to demonstrate the generalization on biased data. We implement this approach using the PyTorch framework. The experimental results show that the proposed approach achieves an accuracy of 90.01 % and 96.71 % for the triplet and adaptive triplet loss, respectively.
I-PRIDe: Video-based Person Re-Identification

The surveillance of infrastructures, strategic assets, and public places is a critical capability in preventing crimi-nal activities. The possibility of re-identifying recurrent actors contributes to severity assessment and activation of appropriate countermeasures. This paper presents a novel dataset and a baseline analysis for video-based person re-identification (Re-ID). Our baseline addresses three issues: the performance of OsNet [1] as a feature extractor, temporal modeling mechanisms to improve the overall Re-Idaccuracy, and an assessment of the effectiveness of the selected methods through different strategies in selecting frames from the videos for the temporal models. We make three distinct contributions. First, we introduce a new dataset I-PRIDe <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The dataset is available at https://datasets.vicomtech.org/di24-i-pride/ readme.txt that mitigates the lack of acquisition angles and poses variety in existing video-based person Re-Iddatasets. It counts with indoor and outdoor scenes, multiple camera angles (0°, 30°, 60°), and variety in the pose of the involved subjects. It contains 104 identities within 2,452 duly annotated sequences. Second, we justify that OsNet is well generalizable yet sufficiently discriminative to adapt to various perspectives and poses without fine-tuning. Third, we derive insights into evaluating the performance of temporal modeling techniques with specific frame selection settings. Notably, the rank-1 accuracy of our method reaches 89.86%, and the mean average precision achieves 85.71 %.
Method for Approximating RSA Prime Factors

The security of Prime-Number-Generators (PNGs) is measured by the difficulty to predict their output. A PNG is defined to be correctly implemented if the next output sequence cannot be predicted with more than 0.5 (50%) chance of success without knowing the initial seed value. However, it was recently questioned in [14] if this criterion is still sufficient to provide adequate security if the generated primes are used for RSA encryption. This paper reinforces this claim and demonstrates how the entropy of randomly generated RSA prime factors p and q could be reduced by predicting up to seven decimal digits through a statistical distance analysis. As a proof of concept, two billion generated prime pairs were evaluated based on N = p · q and . Upper and lower boundaries as well as prime subregions related to could be identified. This allows to predict the first digits of a generated RSA prime with a probability up to 60%. Additionally, the PNG implementation only generates primes within a certain upper and lower boundary depending on their bit size.
Wavelet Based Denoising and Cross Correlation for Identifying Features in CTG Traces

This work aims to demonstrate a method of pattern identification in CTG traces. Following de-noising using the discrete wavelet transformation, the zero normalized cross-correlation is applied between the trace and the pattern of interest. This study allows a custom pattern to be cross correlated with a CTG trace, and areas of maximal overlap are indicators of successful identification. The method has been tested with CTG records from Cork University Maternity Hospital to determine the prediction rate.
Hough Transform for Indirect Estimation of Wafer Placement Errors in Photoresist Spin Coating Processes

This paper discusses a low-cost, computer vision based approach to indirectly estimate on-chuck substrate (wafer) placement errors for photoresist spin coating processes in a semiconductor manufacturing environment. Placement errors are estimated by calculating the relative displacement vector between circles bounding the wafer and the photoresist region post edge bead removal (EBR) processing. On-chuck wafer placement is critical in maintaining concentric EBR performances and without a method of detection it is challenging to contain mechanical tool failures, incorrectly performed preventive maintenance (PM) or other human errors. The study revisits the Hough transform (HT) for circle detections from accuracy and computational viewpoints using synthetically generated images. The detection accuracy of HT is proven outstanding. However, processing times dramatically increase (hours) in case of high resolution, real wafer images despite adequate preprocessing. This drawback is compensated by processing only subsets of images relying on mechanical wafer position controls during the wafer scan although, this potentially undermines the overall accuracy of this classical approach.
Automatic Speech Recognition for Irish: testing lexicons and language models

A range of lexicons and language models were tested in the development of ASR for Irish. One problem, common among minority languages, is the multiplicity of dialects, with no one spoken standard. To address this challenge, in a hybrid ASR system two alternative cross-dialect lexicons are tested, which draw on research in dialect phonology. First, individual lexicons were built for the three main dialects of Ulster (Ul), Connaught (Co) and Munster (Mu). With these, a Multi-dialect lexicon incorporated all dialect-varying word forms. An alternative Global lexicon, essentially a trans-dialect lexicon, used abstract representations of dialect-varying forms (phoneme or morpheme sized units). These two cross-dialect lexicons were tested along with the three dialect-specific lexicons. Several different language models were also tested. Results for the Global and Multi-dialect lexicons were found to yield the highest performance, with the lowest overall WER for the latter. There were considerable differences in results for the individual dialect lexicons: this may reflect a bias in the datasets used or could be indicators of the linguistic distance between the dialects - competing hypotheses that will need more rigorous testing. Results showed a strong effect of the language model used. Error patterns show frequent substitutions involving inflected forms.
Towards Image Processing-based Quantification of White Particles in Reconstituted Infant Formula

When dissolving dairy-based powder in water, some particles do not ‘instantly’ dissolve and remain unreconstituted. Current tests for inspecting these particles are mainly prone to subjective interpretation and may not be reproducible. A reproducible quantitative detection can improve the quality assurance process and may permit a more accurate correlation of the defect with its underlying causes. In this study, a collaborative robot rehydrated twenty-nine commercial and pilot stage-1 infant formula powders and photographed the mixtures in a commercially available baby bottle. Although image processing using deep learning techniques is popular when large data sets are available, this study, which had a relatively small dataset, aimed to evaluate the efficacy of classical image processing techniques based on mean shift and sharpness. To compare our proposed method with commonly employed methods, a human rated the images using a number of developed reference pictures. In this preliminary evaluation, a Spearman correlation of 0.68 was observed between computer vision and human ratings for the same images. Further work is planned to evaluate the method, but this result suggests that the proposed method can detect and quantify undissolved particles in reconstituted stage-1 infant formula in a commercially available baby bottle, comparable to an end-user evaluation.
Extraction of Teletext Subtitles from Broadcast Television for Archival and Analysis

Teletext subtitles are broadcast in some regions of the world, providing a potential resource of textual information corresponding to television current affairs, talk and magazine shows. Such programming offers an insight into sentiment, and will be useful in studies of national and global events. This paper discusses how textual information may be derived from digital television broadcasts featuring teletext subtitles, and shows some basic examples of their analysis for keywords related to the COVID-19 pandemic. The method has further possibilities, such as for the development of a searchable national archive of broadcast materials for education and research, and development of datasets for topics such as machine learning in sign language and sentiment analysis.
Evaluating explainable artificial intelligence (XAI): algorithmic explanations for transparency and trustworthiness of ML algorithms and AI systems

Explainable Artificial Intelligence (XAI) is the capability of explaining the reasoning behind the choices made by the machine learning (ML) algorithm which can help understand and maintain the transparency of the decision-making capability of the ML algorithm. Humans make thousands of decisions every day in their lives. Every decision an individual makes, they can explain the reasons behind why they made the choices that they made. Nonetheless, it is not the same in the case of ML and AI systems. Furthermore, XAI was not wideley researched until suddenly the topic was brought forward and has been one of the most relevant topics in AI for trustworthy and transparent outcomes. XAI tries to provide maximum transparency to a ML algorithm by answering questions about how models effectively came up with the output. ML models with XAI will have the ability to explain the rationale behind the results, understand the weaknesses and strengths the learning models, and be able to see how the models will behave in the future. In this paper, we investigate XAI for algorithmic trustworthiness and transparency. We evaluate XAI using some example use cases and by using SHAP (SHapley Additive exPlanations) library and visualizing the effect of features individually and cumulatively in the prediction process.
Enhancing inference time and memory utilization for machine learning in resource constrained internet of battlefield things

Convolution Neural Network (CNN) models have demonstrated remarkable success for many applications including computer vision in recent years. However, the increase in accuracy achieved so far is at the cost of memory and complex computations. The CNN models are growing deeper and wider making it difficult to be fit in a single device that has limited resources. Moreover, the inference time of such models is large enough to be unfit to apply for the real-time mission critical applications like the Internet of Battlefield Things (IoBT). In IoBT, where the unmanned aerial vehicles (UAVs) flying in the battlefield zone and capturing images, require accurate learning and immediate inference. It becomes problematic if the learning model does not fit in a single UAV when it has limited resources. Considering the aforementioned issues, in this paper, we study a formal approach to improve the inference time and memory utilization in resource constrained IoBT. We consider that multiple UAVs are involved in the inference process in which we apply spatially parallel convolution and pooling operations for all the convolution layers and pooling layers as well as model parallelism for fully connected (FC) layers. Finally, we present the numerical results for varying number of participating UAVs, input data/image sizes, and communication speeds.
Assuring autonomous operations in aviation: is use of AI a good idea?

Autonomous systems, including self-driving cars and unmanned underwater/surface/ground/air/space vehicles, have caught the imagination of the press, the public, and personnel of the U.S. Department of Defense (DoD). Moreover, these systems increasingly harness the power of Artificial Intelligence and Machine Learning (AI/ML), more specifically Deep Learning (DL), for their operation. The stunning performance of deep learning algorithms in comparison to extant methods, including pattern matching, computational linguistics, statistical inferencing, and legacy machine-learning, has taken the world by storm. However, adoption of such systems in safety-critical applications has been the subject of intense debate and scrutiny, because of the propensity of these algorithms for erroneous operation, such as misdirection with seemingly innocuous signage, and their vulnerability to data poisoning and data sparsity attacks, leading to incorrect operation which can be both embarrassing and damaging. This has naturally led the DoD community to ask, “How do we harness this technology being unleashed upon the world, and yet keep our nation and warfighters safe?” Before we answer this question, however, it is important to note that trust is integral to DoD systems, including autonomous systems, and ensuring reliable system operations is paramount. Therefore, we need strategies to equip the DoD to design, build, deploy, and sustain autonomous systems that are trustworthy, secure, reliable, and dependable. In this paper, we investigate issues leading to poor performance and lack of robustness of autonomous systems based on machine learning, and discuss the state of the art for their mitigation.
PERFORMANCE ANALYSIS APPLİCATIONS OF SOME IMAGE ENCRYPTION TECHNIQUES

Data security is now the most vital and most important issue of governments, companies and individuals in the technology age we live in. Among the data types, images have a special importance because of the important information they contain. Transferring or storing images requires extra security measures. In this study, the performances of the image encryption methods were compared with each other by applying them to the most popular and most used images in the image processing area. Four different experiments were carried out. Performance of the seven particular encryption methods compared to each other and the obsevations and measurements was presented.
Enhancement of Target Feature Regions and Intention-Driven Visual Attention Selection in Traffic Scenes

Selective attention to specific areas and specific targets according to driving intention is of great significance for autonomous vehicles to efficiently obtain external environment information. In order to achieve efficient environmental perception, we propose an intention-driven visual attention selection model by simulating human active perception of the external environment. Meanwhile, in order to improve the integrity of the target category attention heatmap, a deep network training method with feature region enhancement is proposed. In this paper, FIMF Score-CAM which can fast integrate multiple features of local space is proposed. It generates intention-related target attention map by weighting the feature map extracted by forward convolution calculation, and combines spatial attention and feature attention to improve the ability of target category location. At the same time, the network is forced to pay more attention to the more comprehensive target-related region by using the guided random erasing in training process, which overcomes the deficiency that the model only pays attention to the most discriminative feature region, and achieves the purpose of feature region enhancement. Experiments on KITTI dataset show that the positioning integrity and accuracy of our model are significantly improved compared with other top-down attention models.
Comparison of Video-based Driver Gaze Region Estimation Techniques

Methods to estimate a driver’s visual attention from video images have received increased research interest. Such methods are especially important for detecting inattentive drivers in partially automated vehicles. The current study compares different driver gaze region estimation techniques, which may serve as a basis for detecting inattentive drivers. The accuracy of these techniques was evaluated on data from automated drives in a driving simulator. The examined techniques include a classical, state-of-the-art eye tracking approach, two data-driven approaches that rely on eye tracking data, a data-driven approach that only considers the driver’s facial configuration, and an end-to-end approach based on a convolutional neural network. The results showcase the advantages of data-driven approaches over a classical geometric interpretation of the eye tracking data. The results also highlight challenges regarding generalization for purely data-driven approaches and the benefits of data-driven approaches that operate on eye tracking data rather than video image data alone.
Spatial Optimization in Spatio-temporal Motion Planning

Motion Planning is one of the key modules in autonomous driving systems to generate trajectories for self-driving vehicles. Spatio-temporal motion planners are often used to tackle complicated and dynamic driving scenarios. While effective in dealing with temporal changes in the environment, the existing methods are limited to optimizing a particular family of cost functions defined based on decoupled longitudinal and lateral terms. However, the planning objectives can only be explained using coupled terms in some cases, e.g. closeness to the reference path, lateral acceleration, and heading rate. The limitation arises from expressing such objectives as linear and quadratic terms suitable for optimization. This paper proposes an approach with theoretical proofs to approximate the upper bound of a given couple, nonlinear cost term with a set of uncoupled terms, allowing for converting the planning optimization problem into a linear quadratic optimization. The effectiveness of the proposed approach is shown through a series of simulated scenarios. The proposed approach results in smoother and steadier trajectories in the spatial plane.
A Contrastive-Learning-Based Method for Alert-Scene Categorization

Whether it’s a driver warning or an autonomous driving system, ADAS needs to decide when to alert the driver of danger or take over control. This research formulates the problem as an alert-scene categorization one and proposes a method using contrastive learning. Given a front-view video of a driving scene, a set of anchor points is marked by a human driver, where an anchor point indicates that the semantic attribute of the current scene is different from that of the previous one. The anchor frames are then used to generate contrastive image pairs to train a feature encoder and obtain a scene similarity measure, so as to expand the distance of the scenes of different categories in the feature space. Each scene category is explicitly modeled to capture the meta pattern on the distribution of scene similarity values, which is then used to infer scene categories. Experiments are conducted using front-view videos that were collected during driving at a cluttered dynamic campus. The scenes are categorized into no alert, longitudinal alert, and lateral alert. The results are studied at both feature encoding, category modeling, and reasoning aspects. By comparing precision with two full supervised end-to-end baseline models, the proposed method demonstrates competitive or superior performance. However, it remains still questions: how to generate ground truth data and how to evaluate performance in ambiguous situations, which leads to future works.
A convolution-based grid map reconfiguration method for autonomous driving in highly constrained environments

This paper proposes a convolution-based method for reconfiguring highly constrained environments, which considers the contour and heading of an autonomous vehicle. The vehicle with possible different heading angles is taken as the kernels. The multiple convolutions between the kernels and the environment are performed to generate a three-dimensional grid map, which significantly improves the computational efficiency of the collision detection algorithm. Moreover, a hierarchical and multistage trajectory planning method based on the reconfigured grid map is proposed. The superiority of the proposed method is verified by comparative simulations and real-time experiments.
Recognition of Noises and Noise Speech Signals by Artificial Neural Networks

According to the studies, the most commonly used mathematical apparatuses for signal recognition tasks are Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Deep Neural Networks (DNNs). This paper presents an innovative approach related to the possibility for identification of accidental noise impacts and human speech with superimposed presence of noise by Backpropagation Neural Networks (BNNs) in different transfer functions. BPNs with linear, tangent-sigmoid and log-sigmoid transfer functions in the output layers are tested. A neural architecture for noise recognition in 6 neurons defined in the hidden layer with “tansig” activation output and achieved accuracy of 98.3% is selected. In the speech processing, an identical efficiency of 93.7% in 4, 3 and 4 hidden neurons for all types of output transfer functions was observed.