Reviewerly/exHarmony huggingface dataset:
CLUSplus: A decision tree-based framework for predicting structured outputs

<h2>Abstract</h2> We present CLUSplus, a machine learning framework based on decision trees specialized for complex predictive modeling tasks. We provide the scientific community with an open source Java framework that unifies several major research directions in the machine learning field. The framework supports multi-target prediction, i.e., the simultaneous prediction of multiple continuous values, multiple discrete values, and hierarchically organized discrete values. Furthermore, CLUSplus enables state-of-the-art predictive performance via ensemble learning, exploitation of unlabeled data via semi-supervised learning, and data understanding via feature importance and building interpretable models. Out of a wide array of machine learning frameworks available today, very few support complex predictive modeling tasks and, to the best of our knowledge, none support all of the aforementioned functionalities.
Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants

As the world progresses toward a digitally connected and sustainable future, the integration of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) promises to become an essential tool in preserving water resources and assuring the continuous effectiveness of plants. When these complex and dynamic systems are coupled with limited historical anomaly data or complex anomalies, it is crucial to have powerful tools capable of detecting subtle deviations from normal behavior to enable the early detection of equipment malfunctions. To address this challenge, in this study, we analyzed five semi-supervised machine learning techniques (SSLs) such as Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OCSVM), Multilayer Perceptron Autoencoder (MLP-AE), and Convolutional Autoencoder (Conv-AE) for detecting different anomalies (complete, concurrent, and complex) of the Dissolved Oxygen (DO) sensor and aeration valve in the WWTP. The best results are obtained in the case of Conv-AE algorithm, with an accuracy of 98.36 for complete faults, 97.81% for concurrent faults, and 98.64% for complex faults (a combination of incipient and concurrent faults). Additionally, we developed an anomaly detection system for the most effective semi-supervised technique, which can provide the detection of delay time and generate a fault alarm for each considered anomaly.
Saturating systems and the rank-metric covering radius

Abstract We introduce the concept of a rank-saturating system and outline its correspondence to a rank-metric code with a given covering radius. We consider the problem of finding the value of $$s_{q^m/q}(k,\rho )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>s</mml:mi> <mml:mrow> <mml:msup> <mml:mi>q</mml:mi> <mml:mi>m</mml:mi> </mml:msup> <mml:mo>/</mml:mo> <mml:mi>q</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>k</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ρ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , which is the minimum $$\mathbb {F}_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> </mml:math> -dimension of a q -system in $$\mathbb {F}_{q^m}^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>F</mml:mi> <mml:mrow> <mml:msup> <mml:mi>q</mml:mi> <mml:mi>m</mml:mi> </mml:msup> </mml:mrow> <mml:mi>k</mml:mi> </mml:msubsup> </mml:math> that is rank- $$\rho $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ρ</mml:mi> </mml:math> -saturating. This is equivalent to the covering problem in the rank metric. We obtain upper and lower bounds on $$s_{q^m/q}(k,\rho )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>s</mml:mi> <mml:mrow> <mml:msup> <mml:mi>q</mml:mi> <mml:mi>m</mml:mi> </mml:msup> <mml:mo>/</mml:mo> <mml:mi>q</mml:mi> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>k</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ρ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> and evaluate it for certain values of k and $$\rho $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ρ</mml:mi> </mml:math> . We give constructions of rank- $$\rho $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ρ</mml:mi> </mml:math> -saturating systems suggested from geometry.
Boosted Additive Angular Margin Loss for breast cancer diagnosis from histopathological images

Pathologists use biopsies and microscopic examination to accurately diagnose breast cancer. This process is time-consuming, labor-intensive, and costly. Convolutional neural networks (CNNs) offer an efficient and highly accurate approach to reduce analysis time and automate the diagnostic workflow in pathology. However, the softmax loss commonly used in existing CNNs leads to noticeable ambiguity in decision boundaries and lacks a clear constraint for minimizing within-class variance. In response to this problem, a solution in the form of softmax losses based on angular margin was developed. These losses were introduced in the context of face recognition, with the goal of integrating an angular margin into the softmax loss. This integration improves discrimination features during CNN training by effectively increasing the distance between different classes while reducing the variance within each class. Despite significant progress, these losses are limited to target classes only when margin penalties are applied, which may not lead to optimal effectiveness. In this paper, we introduce Boosted Additive Angular Margin Loss (BAM) to obtain highly discriminative features for breast cancer diagnosis from histopathological images. BAM not only penalizes the angle between deep features and their target class weights, but also considers angles between deep features and non-target class weights. We performed extensive experiments on the publicly available BreaKHis dataset. BAM achieved remarkable accuracies of 99.79%, 99.86%, 99.96%, and 97.65% for magnification levels of 40X, 100X, 200X, and 400X, respectively. These results show an improvement in accuracy of 0.13%, 0.34%, and 0.21% for 40X, 100X, and 200X magnifications, respectively, compared to the baseline methods. Additional experiments were performed on the BACH dataset for breast cancer classification and on the widely accepted LFW and YTF datasets for face recognition to evaluate the generalization ability of the proposed loss function. The results show that BAM outperforms state-of-the-art methods by increasing the decision space between classes and minimizing intra-class variance, resulting in improved discriminability.
360° Video in teacher education: A systematic review of why and how it is used in teacher education

The recently developed technology of 360° video provides opportunities for teachers and teacher candidates to explore both the classroom environment and the teaching and learning process. In this paper, a systematic review has been conducted to better understand the use of 360° videos in teacher education. The study focuses on the reasons why and how 360° videos are used, as well as the impacts on the noticing abilities of teacher trainees. The review demonstrates how 360° videos are a preferred method of overcoming the limitations of standard video as they are able to provide a more realistic and authentic experience. The review employs a variety of videos of different duration that explore various research domains. It was concluded that 360° videos better support various skills, including teacher candidates’ noticing, when used in a virtual reality (VR) environment.
‘Co-creating’ experiential learning in the metaverse- extending the Kolb's learning cycle and identifying potential challenges

The metaverse has the potential to expand our understanding of the physical world by enabling seamless avatar-based interaction between users in both real and virtual contexts. Based on current insights, it is likely that learning and development methods for the management education in the metaverse will be transformed due to the blurring of lines between the virtual and real worlds. This study, based on systematic literature review, aims to present a framework to contextualize experiential learning in the metaverse for management education by highlighting the facilitators and potential challenges. The study also identifies 'co-creation' as a distinct dimension of the experiential learning cycle, which extends the existing Kolb's learning cycle to highlight the significance of interaction in the metaverse. The implications of this study are significant for both academics and practitioners, as the metaverse is still in its early stages, and discussions on management education via co-creation provide a rational perspective to consider for the future developmental needs.
Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions

Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain to costly and slow genetic tests. However, recent work has shown that relevant biomarkers can be extracted from these images using convolutional neural networks (CNNs). The CNN-based biomarkers predicted colorectal cancer patient outcomes comparably to gold standards. Extracting CNN-biomarkers is fast, automatic, and of minimal cost. CNN-based biomarkers rely on the ability of CNNs to recognize distinct tissue types from microscope whole slide images. The quality of these biomarkers (coined 'Deep Stroma') depends on the accuracy of CNNs in decomposing all relevant tissue classes. Improving tissue decomposition accuracy is essential for improving the prognostic potential of CNN-biomarkers. In this study, we implemented a novel training strategy to refine an established CNN model, which then surpassed all previous solutions . We obtained a 95.6% average accuracy in the external test set and 99.5% in the internal test set. Our approach reduced errors in biomarker-relevant classes, such as Lymphocytes, and was the first to include interpretability methods. These methods were used to better apprehend our model's limitations and capabilities.
Realization of quantum signal processing on a noisy quantum computer

Abstract Quantum signal processing (QSP) is a powerful toolbox for the design of quantum algorithms and can lead to asymptotically optimal computational costs. Its realization on noisy quantum computers without fault tolerance, however, is challenging because it requires a deep quantum circuit in general. We propose a strategy to run an entire QSP protocol on noisy quantum hardware by carefully reducing overhead costs at each step. To illustrate the approach, we consider the application of Hamiltonian simulation for which QSP implements a polynomial approximation of the time evolution operator. We test the protocol by running the algorithm on the Quantinuum H1-1 trapped-ion quantum computer powered by Honeywell. In particular, we compute the time dependence of bipartite entanglement entropies for Ising spin chains and find good agreements with exact numerical simulations. To make the best use of the device, we determine optimal experimental parameters by using a simplified error model for the hardware and numerically studying the trade-off between Hamiltonian simulation time, polynomial degree, and total accuracy. Our results are the first step in the experimental realization of QSP-based quantum algorithms.
Application of Image-To-Image Translation in Improving Pedestrian Detection

Application of deep learning techniques are limited in low-light scenarios. This is because lack of effective target regions makes it difficult to perform several visual functions in low intensity light. The objective of this work is to provide a framework for pedestrian recognition tasks in low-light conditions using image-to-image translation. The key idea behind is accumulation of high-quality information obtained by the combined use of infrared and visible images which make it possible to detect pedestrians even in low-light conditions. In this study, we are going to use deep learning-based models namely Pyramid pix2pixGAN and YOLOv7 to generate translated infrared images and detect pedestrians. The dataset used for training this model is LLVIP, the collection of visible-infrared image pairs for low light vision tasks. Our trained model is able to robustly detect pedestrians in low-light images and is able to beat previous state-of-the-art methods.
Recongo: Bounded Combinatorial Reconfiguration with Answer Set Programming

We develop an approach called bounded combinatorial reconfiguration for solving combinatorial reconfiguration problems based on Answer Set Programming. The general task is to study the solution spaces of source combinatorial problems and to decide whether or not there are sequences of feasible solutions that have special properties. The resulting recongo solver covers all metrics of the solver track in the most recent international competition on combinatorial reconfiguration (CoRe Challenge 2022). recongo ranked first in the shortest metric of the single-engine solvers track.
Lightweight multi-scale attention-guided network for real-time semantic segmentation

The wide application of small mobile devices makes the demand for lightweight real-time semantic segmentation algorithm become more and more intense, which makes it become one of the most popular research topics in the field of computer vision. However, some current methods blindly pursue low parameter numbers and high inference speeds, resulting in excessively low model accuracy and a loss of practical value. Therefore, a lightweight multi-scale attention-guided network for real-time semantic segmentation(LMANet) based on asymmetric encoder-decoder is proposed in this paper to solve the above dilemmas. In the encoder, we propose multi-scale asymmetric residual(MAR) modules to extract local spatial information and context information to enhance feature expression. In the decoder, we design an attention feature fusion(AFF) module and an attention pyramid refining (APR) module. AFF module guides the fusion of low-level and middle-level feature information through high-level semantic information, and finally refines the fusion result through APR module. In addition, we improve the segmentation performance of the model with the help of the attention modules in the network. Our network is tested on two complex urban road datasets. The experimental results show that LMANet achieves 70.6% mIoU and 66.5% mIoU on Cityscapes and Camvid datasets at 112FPS and 333FPS respectively, only 0.95 M parameters without any pre-training or pre-processing. Compared with most of existing state-of-the-art models, our network not only guarantees reasonable inference speed and parameter quantity, but also improves the accuracy as much as possible, which makes it more practical.
A numerically-stable trajectory generation and optimization algorithm for autonomous quadrotor UAVs

This paper introduces a novel trajectory generation and optimization algorithm (TGO) that enables agile and aggressive flight of quadrotor UAVs while considering various constraints associated with robot dynamics, actuator inputs, and flight environment. The TGO algorithm employs time-parametrized polynomial trajectories based on a predetermined sequence of waypoints to produce dynamically feasible and collision-free trajectories. This approach extends previous work that utilized differential flatness property and polynomial based trajectories by eliminating the need for iterative searching and computationally intensive sampling. One of the significant advantages of the TGO algorithm is its numerical stability for large number of waypoints and high-order polynomials. To address the ill-conditioned problem of Quadratic Programming (QP) based methods, the TGO algorithm reformulates the trajectory generation and optimization problem into an unconstrained quadratic programming (UCP) using the numerically stable null-space factorization method. The TGO algorithm produces minimum derivative trajectories and minimum waypoints arrival times, generating a wide range of aggressive trajectories that can leverage the full maneuvering capabilities of quadrotor robots. The proposed algorithm's numerical stability and computational advantages are demonstrated through various scenarios and comparisons. An animated simulation of the TGO algorithm is available at: https://youtu.be/MvvhBG14iIg.
Practical considerations when building concordances between English tests

Applicants must often demonstrate adequate English proficiency when applying to postsecondary institutions by taking an English language proficiency test, such as the TOEFL iBT, IELTS Academic, or Duolingo English Test (DET). Concordance tables aim to provide equivalent scores across multiple assessments, helping admissions officers to make fair decisions regardless of the test that an applicant took. We present our approaches to addressing practical (i.e., data collection and analysis) challenges in the context of building concordance tables between overall scores from the DET and those from the TOEFL iBT and IELTS Academic tests. We summarize a novel method for combining self-reported and official scores to meet recommended minimum sample sizes for concordance studies. We also evaluate sensitivity of estimated concordances to choices about how to (a) weight the observed data to the target population; (b) define outliers; (c) select appropriate pairs of test scores for repeat test takers; and (d) compute equating functions between pairs of scores. We find that estimated concordance functions are largely robust to different combinations of these choices in the regions of the proficiency distribution most relevant to admissions decisions. We discuss implications of our results for both test users and language testers.
Strongly Complete Axiomatization for a Logic with Probabilistic Interventionist Counterfactuals

Causal multiteam semantics is a framework where probabilistic notions and causal inference can be studied in a unified setting. We study a logic ( $$\mathcal {PCO}$$ ) that features marginal probabilities, observations and interventionist counterfactuals, and allows expressing conditional probability statements, do expressions and other mixtures of causal and probabilistic reasoning. Our main contribution is a strongly complete infinitary axiomatisation for $$\mathcal {PCO}$$ .
Robust Alternating-Time Temporal Logic

In multi-agent system design, a crucial aspect is to ensure robustness, meaning that for a coalition of agents A, small violations of adversarial assumptions only lead to small violations of A's goals. In this paper we introduce a logical framework for robust strategic reasoning about multi-agent systems. Specifically, inspired by recent works on robust temporal logics, we introduce and study rATL and rATL $$^*$$ , logics that extend the well-known Alternating-time Temporal Logic ATL and ATL $$^*$$ by means of an opportune multi-valued semantics for the strategy quantifiers and temporal operators. We study the model-checking and satisfiability problems for rATL and rATL $$^*$$ and show that dealing with robustness comes at no additional computational cost. Indeed, we show that these problems are PTime-complete and ExpTime-complete for rATL, respectively, while both are 2ExpTime-complete for rATL $$^*$$ .
Construction of an aspect-level sentiment analysis model for online medical reviews

Online medical services have become increasingly popular, and patient feedback can significantly influence other patients' medical decision-making. This study utilizes a double-layer domain ontology for conducting aspect-level sentiment analysis of reviews from online medical platforms. A double-layer aspect recognition model (OMR-ARM), aggregating the knowledge of the domain ontology, is built to identify the aspects of online medical reviews. The proposed model outperforms baseline models by up to 23.12%. Incorporating this model into a series of state-of-the-art models, the resultant OMR-ALSA model achieves a F1-score of 93.53% for aspect-level sentiment analysis of online medical reviews. Additionally, this study develops an object-aspect-sentiment knowledge graph of online medical reviews (OMR-KG) that can classify patients' sentimental polarities towards the different aspects of online medical reviews. The proposed model and constructed KG have the potential to provide reference and guidance to sentiment analysis research in the online medical review domain, thus contributing to more informed and personalized healthcare decision-making.
Comprehensive Study of Arabic Satirical Article Classification

A well-known issue for social media sites consists of the hazy boundaries between malicious false news and protected speech satire. In addition to the protective measures that lessen the exposure of false material on social media, providers of fake news have started to pose as satire sites in order to escape being delisted. Potentially, this may cause confusion to the readers as satire can sometimes be mistaken for real news, especially when their context or intent is not clearly understood and written in a journalistic format imitating real articles. In this research, we tackle the issue of classifying Arabic satiric articles written in a journalistic format to detect satirical cues that aid in satire classification. To accomplish this, we compiled the first Arabic satirical articles dataset extracted from real-world satirical news platforms. Then, a number of classification models that integrate a variety of feature extraction techniques with machine learning, deep learning, and transformers to detect the provenance of linguistic and semantic cues were investigated, including the first use of the ArabGPt model. Our results indicate that BERT is the best-performing model with F1-score reaching 95%. We also provide an in-depth lexical analysis of the formation of Arabic satirical articles. The lexical analysis provides insights into the satirical nature of the articles in terms of their linguistic word uses. Finally, we developed a free open-source platform that automatically organizes satirical and non-satirical articles in their correct classes from the best-performing model in our study, BERT. In summary, the obtained results found that pretrained models gave promising results in classifying Arabic satirical articles.