Reviewerly/exHarmony huggingface dataset:
UPAFuzzySystems: A Python Library for Control and Simulation with Fuzzy Inference Systems

The main goal of control theory is input tracking or system stabilization. Different feedback-computed controlled systems exist in this area, from deterministic to soft methods. Some examples of deterministic methods are Proportional (P), Proportional Integral (PI), Proportional Derivative (PD), Proportional Integral Derivative (PID), Linear Quadratic (LQ), Linear Quadratic Gaussian (LQG), State Feedback (SF), Adaptative Regulators, and others. Alternatively, Fuzzy Inference Systems (FISs) are soft-computing methods that allow using the human expertise in logic in IF–THEN rules. The fuzzy controllers map the experience of an expert in controlling the plant. Moreover, the literature shows that optimization algorithms allow the adaptation of FISs to control different processes as a black-box problem. Python is the most used programming language, which has seen the most significant growth in recent years. Using open-source libraries in Python offers numerous advantages in software development, including saving time and resources. In this paper, we describe our proposed UPAFuzzySystems library, developed as an FISs library for Python, which allows the design and implementation of fuzzy controllers with transfer-function and state-space simulations. Additionally, we show the use of the library for controlling the position of a DC motor with Mamdani, FLS, Takagi–Sugeno, fuzzy P, fuzzy PD, and fuzzy PD-I controllers.
Deep learning and geometric deep learning: An introduction for mathematicians and physicists

In this expository paper, we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successful algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks. We go over the key ingredients for these algorithms: the score and loss function and we explain the main steps for the training of a model. We do not aim to give a complete and exhaustive treatment, but we isolate few concepts to give a fast introduction to the subject. We provide some appendices to complement our treatment discussing Kullback–Leibler divergence, regression, Multi-layer Perceptrons and the Universal Approximation theorem.
New Recommender System for Online Courses Using Knowledge Graph Modeling

Nowadays, customers can get recommendations for products and services that fit their anticipated needs in the context of a large body of data. Advances in this area provide novel concepts and enable technology for use in e-learning settings. The existing recommendation systems typically focus on improving the item side, but they don’t consider the user characteristics in the recommendation. Thus, they’re ineffective and hence not a good fit for a virtual classroom setting. This research focuses on applying semantics-based knowledge graph modeling with collaborative filtering (CF) to solve these issues. This proposed framework applies the users’ static features to aid in cold start issues for potential new users. The proposed framework significantly outperforms the existing models and helps maintain acceptable performance using better user-item interactions.
A Near-Term Path to Assured Aerial Autonomy

&lt;div&gt;Autonomy is a key enabling factor in uncrewed aircraft system (UAS) and advanced air mobility (AAM) applications ranging from cargo delivery to structure inspection to passenger transport, across multiple sectors. In addition to guiding the UAS, autonomy will ensure that they stay safe in a large number of off-nominal situations without requiring the operator to intervene. While the addition of autonomy enables the safety case for the overall operation, there is a question as to how we can assure that the autonomy itself will work as intended. Specifically, we need assurable technical approaches, operational considerations, and a framework to develop, test, maintain, and improve these capabilities.&lt;/div&gt; &lt;div&gt;We make the case that many of the key autonomy functions can be realized in the near term with readily assurable, even certifiable, design approaches and assurance methods, combined with risk mitigations and strategically defined concepts of operations. We present specific autonomy functions common to many civil beyond visual line of sight (BVLOS) operations and corresponding design assurance strategies, along with their contributions to an overall safety case. We provide examples of functions that can be certified under existing standards, those that will need runtime assurance (RTA) and those that will need to be qualified with statistical evidence.&lt;/div&gt;
Coherence filtration under strictly incoherent operations

We study the task of coherence filtration under strictly incoherent operations. The aim of this task is to transform a given state $\ensuremath{\rho}$ into another one ${\ensuremath{\rho}}^{\ensuremath{'}}$ whose fidelity with the maximally coherent state is maximal by using stochastic strictly incoherent operations. We find that the maximal fidelity between ${\ensuremath{\rho}}^{\ensuremath{'}}$ and the maximally coherent state is given by a multiple of the $\mathrm{\ensuremath{\Delta}}$ robustness of coherence, $R(\ensuremath{\rho}\ensuremath{\parallel}\mathrm{\ensuremath{\Delta}}\ensuremath{\rho}):=min{\ensuremath{\lambda}|\ensuremath{\rho}\ensuremath{\le}\ensuremath{\lambda}\mathrm{\ensuremath{\Delta}}\ensuremath{\rho}}$, which provides $R(\ensuremath{\rho}\ensuremath{\parallel}\mathrm{\ensuremath{\Delta}}\ensuremath{\rho})$ an operational interpretation. Finally, we provide a coherence measure based on the task of coherence filtration.
Large Language Models and Logical Reasoning

In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts. Instead, formal logical statements are preferable since they are subject to verifiability, reliability, and applicability. Another reason for this preference is that natural language is not designed for an efficient and reliable flow of information and knowledge, but is instead designed as an evolutionary adaptation as formed from a prior set of natural constraints. As a formally structured language, logical statements are also more interpretable. They may be informally constructed in the form of a natural language statement, but a formalized logical statement is expected to follow a stricter set of rules, such as with the use of symbols for representing the logic-based operators that connect multiple simple statements and form verifiable propositions.
Discriminative Angle Feature Learning for Open-Set Deep Fault Classification

Unknown faults may occur in practical applications, necessitating an open-set classifier that can classify known classes as well as recognize unknown faults.The current deep open-set classification methods are implicit in optimizing the intra-or inter-class distances, which may result in performance degradation when the number of unknown classes far exceeds that of the known.In this study, the discriminative angle deep features for vibration signals are investigated.A novel normalized one-versus-all classification loss with center and contrastive regularization is proposed.The trained network can explicitly optimize deep features to ensure intra-class compactness and inter-class divergence.In this case, such discriminative features can be used for open-set fault classification.Furthermore, the effectiveness of the proposed method is verified using field-measured motor bearing and gear vibration signals.The results demonstrate the evident advantages of our proposed method over other approaches in practical fault diagnosis scenarios.
Physical Consistent Path Planning for Unmanned Surface Vehicles under Complex Marine Environment

The increasing demand for safe and efficient maritime transportation has underscored the necessity of developing effective path-planning algorithms for Unmanned Surface Vehicles (USVs). However, the inherent complexities of the ocean environment and the non-holonomic properties of the physical system have posed significant challenges to designing feasible paths for USVs. To address these issues, a novel path planning framework is elaborately designed, which consists of an optimization model, a meta-heuristic solver, and a Clothoid-based path connector. First, by encapsulating the intricate nature of the ocean environment and ship dynamics, a multi-objective path planning problem is designed, providing a comprehensive and in-depth portrayal of the underlying mechanism. By integrating the principles of the candidate set random testing initialization and adaptive probability set, an enhanced genetic algorithm is devised to fully exploit the underlying optimization problem in constrained space, contributing to the global searching ability. Accounting for the non-holonomic constraints, the fast-discrete Clothoid curve is capable of maintaining and improving the continuity of the path curve, thereby promoting strong coordination between the planning and control modules. A thorough series of simulations and comparisons conducted in diverse ocean scenarios has conclusively demonstrated the effectiveness and superiority of the proposed path planning framework.
Central Bank Digital Currencies

The declining use of cash and the growing role of crypto assets have led central banks to contemplate CBDCs. A CBDC is a publicly issued digital money denominated in the national unit of account. It is issued and regulated by a country’s national authority or central bank. As such, it offers, in digital form, the unique advantages of central bank money: monetary anchor, settlement finality, liquidity and integrity. There are two main types: a retail CBDC is issued to the general public, such as individuals and businesses, and exhibits characteristics of cash (but in digital form), while a wholesale CBDC is issued primarily for interbank transactions and available only to selected financial institutions (similar to bank reserves). However, compared to bank reserves, a wholesale CBDC may be accessible to a wider range of counterparties and be interoperable with other payment systems (both domestic and foreign). CBDCs may be programmable in the form of smart contracts. Most developing country central banks are investigating both retail and wholesale CBDCs (Kosse and Mattei, 2022).
ST-YOLOA: a Swin-transformer-based YOLO model with an attention mechanism for SAR ship detection under complex background

A synthetic aperture radar (SAR) image is crucial for ship detection in computer vision. Due to the background clutter, pose variations, and scale changes, it is a challenge to construct a SAR ship detection model with low false-alarm rates and high accuracy. Therefore, this paper proposes a novel SAR ship detection model called ST-YOLOA. First, the Swin Transformer network architecture and coordinate attention (CA) model are embedded in the STCNet backbone network to enhance the feature extraction performance and capture global information. Second, we used the PANet path aggregation network with a residual structure to construct the feature pyramid to increase global feature extraction capability. Next, to cope with the local interference and semantic information loss problems, a novel up/down-sampling method is proposed. Finally, the decoupled detection head is used to achieve the predicted output of the target position and the boundary box to improve convergence speed and detection accuracy. To demonstrate the efficiency of the proposed method, we have constructed three SAR ship detection datasets: a norm test set (NTS), a complex test set (CTS), and a merged test set (MTS). The experimental results show that our ST-YOLOA achieved an accuracy of 97.37%, 75.69%, and 88.50% on the three datasets, respectively, superior to the effects of other state-of-the-art methods. Our ST-YOLOA performs favorably in complex scenarios, and the accuracy is 4.83% higher than YOLOX on the CTS. Moreover, ST-YOLOA achieves real-time detection with a speed of 21.4 FPS.
New infinite families of near MDS codes holding t-designs

In “Infinite families of near MDS codes holding t-designs, IEEE Trans. Inform. Theory, 2020, 66(9), pp. 5419-5428”, Ding and Tang made a breakthrough in constructing the first two infinite families of NMDS codes holding 2-designs or 3-designs. Up to now, there are only a few known infinite families of NMDS codes holding t-designs in the literature. The objective of this paper is to construct new infinite families of NMDS codes holding t-designs. We determine the weight enumerators of the NMDS codes and prove that the NMDS codes hold 2-designs or 3-designs. Compared with known t-designs from NMDS codes, ours have different parameters. Besides, several infinite families of optimal locally recoverable codes are also derived via the NMDS codes.
FCH, an incentive framework for data-owner dominated federated learning

Federated learning has gained prominence for its superior privacy-preserving properties. However, establishing an incentive framework that motivates participants to contribute fully is essential to mitigate opportunistic behaviour arising from information asymmetry. Current frameworks primarily incentivize data owners to contribute resources in scenarios where model owners are dominant. This article addresses the underexplored scenario where data owners are in charge and the potential unethical behaviours it may entail. We propose a new framework, the Fair Clearing House (FCH), that promotes balanced participation from data owners and model owners, optimizing their contributions to the learning process. Numerical results demonstrate that FCH outperforms existing frameworks under comparable conditions while reducing unethical behaviours.
C2F-TCN: A Framework for Semi- and Fully-Supervised Temporal Action Segmentation

Temporal action segmentation tags action labels for every frame in an input untrimmed video containing multiple actions in a sequence. For the task of temporal action segmentation, we propose an encoder-decoder style architecture named C2F-TCN featuring a “coarse-to-fine” ensemble of decoder outputs. The C2F-TCN framework is enhanced with a novel model agnostic temporal feature augmentation strategy formed by the computationally inexpensive strategy of the stochastic max-pooling of segments. It produces more accurate and well-calibrated supervised results on three benchmark action segmentation datasets. We show that the architecture is flexible for both supervised and representation learning. In line with this, we present a novel unsupervised way to learn frame-wise representation from C2F-TCN. Our unsupervised learning approach hinges on the clustering capabilities of the input features and the formation of multi-resolution features from the decoder's implicit structure. Further, we provide first semi-supervised temporal action segmentation results by merging representation learning with conventional supervised learning. Our semi-supervised learning scheme, called “Iterative-Contrastive-Classify (ICC)”, progressively improves in performance with more labeled data. The ICC semi-supervised learning in C2F-TCN, with 40% labeled videos, performs similar to fully supervised counterparts.
SexWEs: Domain-Aware Word Embeddings via Cross-Lingual Semantic Specialisation for Chinese Sexism Detection in Social Media

The goal of sexism detection is to mitigate negative online content targeting certain gender groups of people. However, the limited availability of labeled sexism-related datasets makes it problematic to identify online sexism for low-resource languages. In this paper, we address the task of automatic sexism detection in social media for one low-resource language -- Chinese. Rather than collecting new sexism data or building cross-lingual transfer learning models, we develop a cross-lingual domain-aware semantic specialisation system in order to make the most of existing data. Semantic specialisation is a technique for retrofitting pre-trained distributional word vectors by integrating external linguistic knowledge (such as lexico-semantic relations) into the specialised feature space. To do this, we leverage semantic resources for sexism from a high-resource language (English) to specialise pre-trained word vectors in the target language (Chinese) to inject domain knowledge. We demonstrate the benefit of our sexist word embeddings (SexWEs) specialised by our framework via intrinsic evaluation of word similarity and extrinsic evaluation of sexism detection. Compared with other specialisation approaches and Chinese baseline word vectors, our SexWEs shows an average score improvement of 0.033 and 0.064 in both intrinsic and extrinsic evaluations, respectively. The ablative results and visualisation of SexWEs also prove the effectiveness of our framework on retrofitting word vectors in low-resource languages.
The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States

Background: Diphenhydramine (DPH) is an antihistamine medication that in overdose can result in anticholinergic symptoms and serious complications, including arrhythmia and coma. We aimed to compare the value of various machine learning (ML) models, including light gradient boosting machine (LGBM), logistic regression (LR), and random forest (RF), in the outcome prediction of DPH poisoning. Materials and Methods: We used the National Poison Data System database and included all of the human exposures of DPH from January 01, 2017 to December 31, 2017, and excluded those cases with missing information, duplicated cases, and those who reported co-ingestion. Data were split into training and test datasets, and three ML models were compared. We developed confusion matrices for each, and standard performance metrics were calculated. Results: Our study population included 53,761 patients with DPH exposure. The most common reasons for exposure, outcome, chronicity of exposure, and formulation were captured. Our results showed that the average precision-recall area under the curve (AUC) of 0.84. LGBM and RF had the highest performance (average AUC of 0.91), followed by LR (average AUC of 0.90). The specificity of the models was 87.0% in the testing groups. The precision of models was 75.0%. Recall (sensitivity) of models ranged between 73% and 75% with an F1 score of 75.0%. The overall accuracy of LGBM, LR, and RF models in the test dataset was 74.8%, 74.0%, and 75.1%, respectively. In total, just 1.1% of patients (mostly those with major outcomes) received physostigmine. Conclusion: Our study demonstrates the application of ML in the prediction of DPH poisoning.
Starting to include references in DOI metadata for blog posts

Two weeks ago I started registering DOIs for blog posts included in the Rogue Scholar blog archive. It is an automated process but involves a lot of manual checks. So far I have registered 231 blog posts from 20 different science blogs, and I hope to finish the DOI registrations by the end of the month. Going forward these DOI registrations will happen automatically whenever one of the science blogs included in the Rogue Scholar publishes a new post. I do this by monitoring the RSS feeds of thes
Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression

Neural video codecs have demonstrated great potential in video transmission and storage applications. Existing neural hybrid video coding approaches rely on optical flow or Gaussian-scale flow for prediction, which cannot support fine-grained adaptation to diverse motion content. Towards more content-adaptive prediction, we propose a novel cross-scale prediction module that achieves more effective motion compensation. Specifically, on the one hand, we produce a reference feature pyramid as prediction sources and then transmit cross-scale flows that leverage the feature scale to control the precision of prediction. On the other hand, for the first time, a weighted prediction mechanism is introduced even if only a single reference frame is available, which can help synthesize a fine prediction result by transmitting cross-scale weight maps. In addition to the cross-scale prediction module, we further propose a multi-stage quantization strategy, which improves the rate-distortion performance with no extra computational penalty during inference. We show the encouraging performance of our efficient neural video codec (ENVC) on several benchmark datasets. In particular, the proposed ENVC can compete with the latest coding standard H.266/VVC in terms of sRGB PSNR on UVG dataset for the low-latency mode. We also analyze in detail the effectiveness of the cross-scale prediction module in handling various video content, and provide a comprehensive ablation study to analyze those important components. Test code is available at https://github.com/USTC-IMCL/ENVC.
Resource Aware Long Short-Term Memory Model (RALSTMM) Based On-Device Incremental Learning for Industrial Internet of Things

The interconnection of instruments (i.e., actuators and sensors) networked together for industrial applications brings about the Industrial Internet of Things (IIoT). This connectivity enables the collection, sharing, and analysis of data to enhance the efficiency and productivity of manufacturing. Machine learning models are popular methods for analyzing massive time-series data collected by industrial control systems. Classical Long Short-Term Memory (LSTM), which is a widely used time-series model, learns patterns by feeding the entire dataset at once, and the model remains fixed. However, real-world industrial control system nodes generate new data. This paper proposes Resource Aware Long Short-Term Memory Model (RALSTMM) based incremental learning for edge devices in the IIoT. In RALSTMM edge devices can collect and analyze data for various predictive applications. The proposed RALSTMM can be deployed on these tiny edge devices and can be updated to enhance existing knowledge using newly generated data. The RALSTMM gradually learns from newly collected data by leveraging crucial information from previously analyzed data, thereby minimizing the resources needed for training. Hence, previous data that has been processed earlier would not undergo further analysis as the model has already extracted the necessary knowledge from it. The performance of RALSTMM has been evaluated with Mean Squared Error (MSE), accuracy, recall, precision, information criteria and processing time using three IoT testbed datasets. A comparative experimental demonstration of the RALSTMM with the existing LSTM proves the effectiveness of the RALSTMM by reducing processing time and maintaining its performance.
An efficient image-denoising method using a deep learning technique

Numerous difficulties arise while denoising images in image processing at present. The article indicates a unique deep convolution neural network-based image denoising (DCNN). This article presents a DCNN to generate the noisy image as opposed to earlier learning-based methodologies. Therefore, it is possible to extract the clean latent image from the deformed image by removing the noise. Compared to other scenarios, this strategy is the most effective. Although some of the already existing systems included machine learning techniques in addition to filters such as wiener, median, and mean filters, they were unable to produce reliable results. Experimental results show that the suggested de-noising methodology outperforms the current denoising techniques. We may say that this strategy will aid in obtaining better results by looking at factors such as PSNR, MSE, training loss, and training accuracy. The conclusions also demonstrate that multiple noises with various noise levels can be suppressed using a single denoising model.