Reviewerly/exHarmony huggingface dataset:
The Impact of Data Injection on Predictive Algorithm Developed within Electrical Manufacturing Engineering in the Context of Aerospace Cybersecurity

Cybersecurity plays a relevant role in the new digital age within the aerospace industry. Predictive algorithms are necessary to interconnect complex systems within the cyberspace. In this context, where security protocols do not apply, challenges to maintain data privacy and security arise for the organizations. Thus, the need for cybersecurity is required. The four main categories to classify threats are interruption, fabrication, modification, and interception. They all share a common thing, which is to soften the three pillars that cybersecurity needs to guarantee. These pillars are confidentiality, availability, and integrity of data (CIA). Data injection can contribute to this event by the creation of false indicators, which can lead to error creation during the manufacturing engineering processes. In this paper, the impact of data injection on the existing dataset used in manufacturing processes is described. The design model synchronizes the following mechanisms developed within machine learning techniques, which are the risk matrix indicator to assess the probability of producing an error, the dendrogram to cluster the dataset in groups with similarities, the logistic regression to predict the potential outcomes, and the confusion matrix to analyze the performance of the algorithm. The results presented in this study, which were carried out using a real dataset related to the electrical harnesses installed in a C295 military aircraft, estimate that injection of false data indicators increases the probability of creating an error by 24.22% based on the predicted outcomes required for the generation of the manufacturing processes. Overall, implementing cybersecurity measures and advanced methodologies to detect and prevent cyberattacks is necessary.
Frequency Agile Anti-Interference Technology Based on Reinforcement Learning Using Long Short-Term Memory and Multi-Layer Historical Information Observation

In modern electronic warfare, radar intelligence has become increasingly crucial when dealing with complex interference environments. This paper combines radar agile frequency technology with reinforcement learning to achieve adaptive frequency hopping for radar anti-jamming. Unlike traditional reinforcement learning with Markov decision processes (MDPs), the interaction between radar and jammers occurs within the partially observable Markov decision processes (POMDPs). In this context, the partial observation information available to the agent does not strictly satisfy the Markov property. This paper uses multiple layers of historical observation information to solve this problem. Historical observations can be viewed as a time series, and time-sensitive networks are employed to extract the temporal information embedded within the observations. In addition, the reward function is optimized to facilitate the faster learning of the agent in the jammer sweep environment. This simulation shows that the optimization of the agent state, network structure, and reward function can effectively help the radar to resist jamming.
Supervised Learning Algorithms

Numerous domains now employ learning algorithms. It has distinct performance metrics appropriate for them.. Based on a predetermined set of paired input-output training samples, a machine learning paradigm known as “Supervised Learning” is used to gather information about a system's input-output relationship. An input-output training sample is also known as supervised or labeled training data because the output is regarded as the input data or supervision label. Supervised learning aims to build an artificial system that can learn the mapping between input and output and predict the system's output, given new information. The learned mapping results in the classification of the input data if the output takes a limited set of discrete values representing the input's class labels. Regression of the information occurs if the output takes continuous values. The chapter details the various algorithms, technologies used and their applications.
Deep Learning Approach for Deduction of 3D Non-Rigid Transformation Based on Multi-Control Point Perception Data

In complex measurement systems, scanning the shape data of solid models is time consuming, and real-time solutions are required. Therefore, we developed a 3D non-rigid transformation deduction model based on multi-control point perception data. We combined a convolutional neural network (CNN), gated recurrent unit (GRU), and self-attention mechanism (SA) to develop the CNN-GRU-SA deduction network, which can deduce 3D non-rigid transformations based on multiple control points. We compared the proposed network to several other networks, with the experimental results indicating that the maximum improvements in terms of loss and root-mean-squared error (RMSE) on the training set were 39% and 49%, respectively; the corresponding values for the testing set were 48% and 29%. Moreover, the average deviation of the inference results and average inference time were 0.55 mm and 0.021 s, respectively. Hence, the proposed deep learning method provides an effective method to simulate and deduce the 3D non-rigid transformation processes of entities in the measurement system space, thus highlighting its practical significance in optimizing entity deformation.
A secure food supply chain solution: blockchain and IoT-enabled container to enhance the efficiency of shipment for strawberry supply chain

The supply chain systems in the food industry are complex, including manufacturers, dealers, and customers located in different areas. Currently, there is a lack of transparency in the distribution and transaction processes of online food trade. The global food supply chain industry has enormous hurdles because of this problem, as well as a lack of trust among individuals in the sector and a reluctance to share information. This study aims to develop a blockchain-based strawberry supply chain (SSC) framework to create a transparent and secure system for tracking the movement of strawberries from the farm to the consumer. Using Ethereum smart contracts, the proposed solution monitors participant interactions, triggers events, and logs transactions to promote transparency and informed decision-making. The smart contracts also govern interactions between vendors and consumers, such as monitoring the status of Internet of Things (IoT) containers for food supply chains and notifying consumers. The proposed framework can be extended to other supply chain industries in the future to increase transparency and immutability.
Explainability of Automated Fact Verification Systems: A Comprehensive Review

The rapid growth in Artificial Intelligence (AI) has led to considerable progress in Automated Fact Verification (AFV). This process involves collecting evidence for a statement, assessing its relevance, and predicting its accuracy. Recently, research has begun to explore automatic explanations as an integral part of the accuracy analysis process. However, the explainability within AFV is lagging compared to the wider field of explainable AI (XAI), which aims at making AI decisions more transparent. This study looks at the notion of explainability as a topic in the field of XAI, with a focus on how it applies to the specific task of Automated Fact Verification. It examines the explainability of AFV, taking into account architectural, methodological, and dataset-related elements, with the aim of making AI more comprehensible and acceptable to general society. Although there is a general consensus on the need for AI systems to be explainable, there a dearth of systems and processes to achieve it. This research investigates the concept of explainable AI in general and demonstrates its various aspects through the particular task of Automated Fact Verification. This study explores the topic of faithfulness in the context of local and global explainability. This paper concludes by highlighting the gaps and limitations in current data science practices and possible recommendations for modifications to architectural and data curation processes, contributing to the broader goals of explainability in Automated Fact Verification.
Insights into software development approaches: mining Q &amp;A repositories

Abstract Context Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q &amp;A forums. Objective We conducted an empirical study to analyze developers’ discussions on Q &amp;A forums to gain insights into software development approaches in practice. Method We analyzed 13,903 developers’ posts across Stack Overflow (SO), Software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings We identified 15 frequently mentioned software development approaches topics on Q &amp;A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop.
A Natural-Language-Processing-Based Method for the Clustering and Analysis of Movie Reviews and Classification by Genre

Reclassification of massive datasets acquired through different approaches, such as web scraping, is a big challenge to demonstrate the effectiveness of a machine learning model. Notably, there is a strong influence of the quality of the dataset used for training those models. Thus, we propose a threshold algorithm as an efficient method to remove stopwords. This method employs an unsupervised classification technique, such as K-means, to accurately categorize user reviews from the IMDb dataset into their most suitable categories, generating a well-balanced dataset. Analysis of the performance of the algorithm revealed a notable influence of the text vectorization method used concerning the generation of clusters when assessing various preprocessing approaches. Moreover, the algorithm demonstrated that the word embedding technique and the removal of stopwords to retrieve the clustered text significantly impacted the categorization. The proposed method involves confirming the presence of a suggested stopword within each review across various genres. Upon satisfying this condition, the method assesses if the word’s frequency exceeds a predefined threshold. The threshold algorithm yielded a mapping genre success above 80% compared to precompiled lists and a Zipf’s law-based method. In addition, we employed the mini-batch K-means method for the clustering formation of each differently preprocessed dataset. This approach enabled us to reclassify reviews more coherently. Summing up, our methodology categorizes sparsely labeled data into meaningful clusters, in particular, by using a combination of the proposed stopword removal method and TF-IDF. The reclassified and balanced datasets showed a significant improvement, achieving 94% accuracy compared to the original dataset.
Binocular Vision-Based Pole-Shaped Obstacle Detection and Ranging Study

(1) Background: In real road scenarios, various complex environmental conditions may occur, including bright lights, nighttime, rain, and snow. In such a complex environment for detecting pole-shaped obstacles, it is easy to lose the feature information. A high rate of leakage detection, false positives, and measurement errors are generated as a result. (2) Methods: The first part of this paper utilizes the improved YOLOv5 algorithm to detect and classify pole-shaped obstacles. Then, the identified target frame information is combined with binocular stereo matching to obtain more accurate distance information. (3) Results: The experimental results demonstrate that this method achieves a mean average precision (mAP) of 97.4% for detecting pole-shaped obstacles, which is 3.1% higher than the original model. The image inference time is only 1.6 ms, which is 1.8 ms faster than the original algorithm. Additionally, the model size is only 19.0 MB. Furthermore, the range error of this system is less than 7% within the range of 3–15 m. (4) Conclusions: Therefore, the algorithm not only achieves real-time and accurate identification and classification but also ensures precise measurement within a specific range. Meanwhile, the model is lightweight and better suited for deploying sensing systems.
Fusion of monocular height maps for 3D urban scene reconstruction from uncalibrated satellite images

ABSTRACTWith the increased availability of multi-view satellite images, the number of investigations on 3D urban scene reconstruction from multiple satellite images is also increasing. Conventional Multi-View Stereo (MVS) pipelines require the calibrated pose information of the satellite cameras to determine the epipolar geometry and the 3D structure of the stereo correspondences. In this study, we propose a novel Monocular Height estimation and Fusion (MHF) method for 3D reconstruction from uncalibrated multi-view satellite images. By employing a learned monocular depth network, the proposed method first obtains the height map of each satellite image. Second, all height maps obtained from the multi-view images are fused to a refined height map in each image plane. To fuse the height maps, all maps are affine transformed to a virtual reference coordinate system and the transformed maps are then projected to the image plane of each camera coordinate system. The monocular depth network was trained and evaluated on the Data Fusion Contest 2019 (DFC19) dataset including Jacksonville, FL, and Omaha, NE. We also evaluate the ATL-SN4 dataset covering Atlanta, GA to test on untrained new urban scenes.KEYWORDS: Monocular height map3D urban scene reconstructionDeep learningPhotogrammetryUncalibrated satellite image Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Ministry of Science and ICT, South Korea [2021R1A2C2009722,2021R1A6A1A03043144].
FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems

In this paper, we present a comparative study of modern semantic segmentation loss functions and their resultant impact when applied with state-of-the-art off-road datasets. Class imbalance, inherent in these datasets, presents a significant challenge to off-road terrain semantic segmentation systems. With numerous environment classes being extremely sparse and underrepresented, model training becomes inefficient and struggles to comprehend the infrequent minority classes. As a solution to this problem, loss functions have been configured to take class imbalance into account and counteract this issue. To this end, we present a novel loss function, Focal Class-based Intersection over Union (FCIoU), which directly targets performance imbalance through the optimization of class-based Intersection over Union (IoU). The new loss function results in a general increase in class-based performance when compared to state-of-the-art targeted loss functions.
A Certificateless Online/Offline Aggregate Signcryption Scheme against Collusion Attacks Based on Fog Computing

The certificateless online/offline aggregate signcryption scheme combines the characteristics of the certificateless aggregate signcryption scheme and the online/offline encryption scheme, which can increase efficiency while simultaneously reducing consumption. Some schemes can meet the requirements of confidentiality and real-time transmission of the data in ad hoc networks (VANETS). However, they are unable to withstand collusion attempts. A brand-new certificateless aggregate signcryption approach is suggested to overcome this problem. First, combining fog computing with online/offline encryption (OOE) technology can increase efficiency while simultaneously reducing consumption. Second, we may achieve effective information authentication and vehicle identification using aggregation and vehicle pseudonym systems. Third, the anti-collusion component is suggested as a viable defense against collusion assaults since certain methods are unable to withstand such attacks. Additionally, it is demonstrated that the technique has unforgeability and secrecy, and can fend off collusion attacks using the random oracle model. The findings demonstrate that our system can not only ensure the confidentiality and the real-time transmission of data but also resist collusion attacks without raising computational costs.
Statistical Validation of a Physical Prime Random Number Generator Based on Quantum Noise

Random prime numbers are an essential resource for many asymmetric cryptographic protocols. However, despite the emerging popularity of quantum random number generators (QRNGs) as sources of secure randomness, physical prime number generators have not yet been explored. In this work, we experimentally implement and characterize a vacuum-based probabilistic prime number generation scheme with an error probability of 3.5×10−15. By removing the quantum source (QS), an additional scheme based on electronic noise is derived, and a comparative analysis for increasing prime lengths is made. We observed that the QS significantly outperforms the classical scheme for small prime generation, where increases up to 585.0% in the diversity of unique primes obtained are seen. Moreover, we propose a length-agnostic statistical test for prime number sequences and apply it to the output of the uniformized randomness source, which was successful in revealing underlying biases in the output prime distributions. The resultant sequences were subsequently submitted to the NIST statistical test suite, where the quantum and classical sources passed, respectively, 86.96% and 45.34% of the total test set applied.
Epistemological role of human reasoning in data-informed decision-making

Visual analytics was introduced in 2004 as a “grand challenge” to build an interdisciplinary “science of analytical reasoning facilitated by interactive visual interfaces”. The goal of visual analytics was to develop ways of interactively visualizing data, information, and computational analysis methods that augment human expertise in analysis and decision-making. In this paper, we examine the role of human reasoning in data analysis and decision-making, focusing on issues of expertise and objectivity in interpreting data for purposes of decision-making. We do this by integrating the visual analytics perspective with Decision Intelligence, a cognitive framework that emphasizes the connection between computational data analyses, predictive models, actions that can be taken, and predicted outcomes of those actions. Because Decision Intelligence models factors of operational capabilities and stakeholder beliefs, it necessarily extends objective data analytics to include intuitive aspects of expert decision-making such as human judgment, values, and ethics. By combining these two perspectives we believe that researchers will be better able to generate actionable decisions that ideally effectively utilize human expertise, while eliminating bias. This paper aims to provide a framework of how Decision Intelligence leverages visual analytics tools and human reasoning to support the decision-making process.
Analyzing public sentiments on the Cullen Commission inquiry into money laundering: harnessing deep learning in the AI of Things Era

This study employs deep learning methodologies to conduct sentiment analysis of tweets related to the Cullen Commission’s inquiry into money laundering in British Columbia. The investigation utilizes CNN, RNN + LSTM, GloVe, and BERT algorithms to analyze sentiment and predict sentiment classes in public reactions when the Commission was announced and after the final report’s release. Results reveal that the emotional class “joy” predominated initially, reflecting a positive response to the inquiry, while “sadness” and “anger” dominated after the report, indicating public dissatisfaction with the findings. The algorithms consistently predicted negative, neutral, and positive sentiments, with BERT showing exceptional precision, recall, and F1-scores. However, GloVe displayed weaker and less consistent performance. Criticisms of the Commission’s efforts relate to its inability to expose the full extent of money laundering, potentially influenced by biased testimonies and a narrow investigation scope. The public’s sentiments highlight the awareness raised by the Commission and underscore the importance of its recommendations in combating money laundering. Future research should consider broader stakeholder perspectives and objective assessments of the findings.
The exponential logic of sequentialization

Linear logic has provided new perspectives on proof-theory, denotational semantics and the study of programming languages. One of its main successes are proof-nets, canonical representations of proofs that lie at the intersection between logic and graph theory. In the case of the minimalist proof-system of multiplicative linear logic without units (MLL), these two aspects are completely fused: proof-nets for this system are graphs satisfying a correctness criterion that can be fully expressed in the language of graphs. For more expressive logical systems (containing logical constants, quantifiers and exponential modalities), this is not completely the case. The purely graphical approach of proof-nets deprives them of any sequential structure that is crucial to represent the order in which arguments are presented, which is necessary for these extensions. Rebuilding this order of presentation - sequentializing the graph - is thus a requirement for a graph to be logical. Presentations and study of the artifacts ensuring that sequentialization can be done, such as boxes or jumps, are an integral part of researches on linear logic. Jumps, extensively studied by Faggian and di Giamberardino, can express intermediate degrees of sequentialization between a sequent calculus proof and a fully desequentialized proof-net. We propose to analyze the logical strength of jumps by internalizing them in an extention of MLL where axioms on a specific formula, the jumping formula, introduce constrains on the possible sequentializations. The jumping formula needs to be treated non-linearly, which we do either axiomatically, or by embedding it in a very controlled fragment of multiplicative-exponential linear logic, uncovering the exponential logic of sequentialization.
Wavelets: Mathematical Tools for Image Analysis

Mathematics is very human—we all enjoy using our brains, thinking, making connections, and checking to see if our conclusions are true. For me, mathematics is also a way to experience creativity, which is what makes me happy. In this article, I want to share this delight with you, through the story of wavelets. Wavelets are mathematical tools that my colleagues and I developed, which are very useful in analyzing images and other signals like medical scans or audio files. I hope that, by the end of this article, you will see that mathematics is much more interesting and diverse than memorizing formulas and performing calculations. It is a creative endeavor that advances society and can make our lives richer and more beautiful.