Reviewerly/exHarmony huggingface dataset:
PATNet: Propensity-Adjusted Temporal Network for Joint Imputation and Prediction Using Binary EHRs With Observation Bias

Predictive analysis of electronic health records (EHR) is a fundamental task that could provide actionable insights to help clinicians improve the efficiency and quality of care. EHR are commonly recorded in binary format and contain inevitable missing data. The nature of missingness may vary by patients, clinical features, and time, which incurs observation bias. It is essential to account for the binary missingness and observation bias or the predictive performance could be substantially compromised. In this paper, we develop a propensity-adjusted temporal network (PATNet) to conduct data imputation and predictive analysis simultaneously. PATNet contains three subnetworks: 1) an imputation subnetwork that generates the initial imputation based on historical observations, 2) a propensity subnetwork that infers the patient-, feature-, and time-dependent propensity scores, and 3) a prediction subnetwork that produces the missing-informative prediction using the propensity-adjusted imputations and the missing probabilities. To allow the propensity scores to be inferred from data, we use the expectation-maximization (EM) algorithm to learn the imputation and propensity subnetworks and incorporate a low-rank constraint via PARAFAC2 approximation. Extensive evaluation using the MIMIC-III and eICU datasets demonstrates that PATNet outperforms the state-of-the-art methods in terms of binary data imputation, disease progression modeling, and mortality prediction tasks.
Towards a Research Agenda for Understanding and ManagingUncertainty in Self-Adaptive Systems

Despite considerable research efforts on handling uncertainty in self-adaptive systems, a comprehensive understanding of the precise nature of uncertainty is still lacking. This paper summarises the findings of the 2023 Bertinoro Seminar on Uncertainty in Self- Adaptive Systems, which aimed at thoroughly investigating the notion of uncertainty, and outlining open challenges associated with its handling in self-adaptive systems. The seminar discussions were centered around five core topics: (1) agile end-toend handling of uncertainties in goal-oriented self-adaptive systems, (2) managing uncertainty risks for self-adaptive systems, (3) uncertainty propagation and interaction, (4) uncertainty in self-adaptive machine learning systems, and (5) human empowerment under uncertainty. Building on the insights from these discussions, we propose a research agenda listing key open challenges, and a possible way forward for addressing them in the coming years.
A Hybrid Discrete Artificial Bee Colony Algorithm Based on Label Similarity for Solving Point-Feature Label Placement Problem

The artificial bee colony algorithm (ABC) is a promising metaheuristic algorithm for continuous optimization problems, but it performs poorly in solving discrete problems. To address this issue, this paper proposes a hybrid discrete artificial bee colony (HDABC) algorithm based on label similarity for the point-feature label placement (PFLP) problem. Firstly, to better adapt to PFLP, we have modified the update mechanism for employed bees and onlooker bees. Employed bees learn the label position of the better individuals, while onlooker bees perform dynamic probability searches using two neighborhood operators. Additionally, the onlooker bees’ selection method selects the most promising solutions based on label similarity, which improves the algorithm’s search capabilities. Finally, the Metropolis acceptance strategy is replaced by the original greedy acceptance strategy to avoid the premature convergence problem. Systematic experiments are conducted to verify the effectiveness of the neighborhood solution generation method, the selection operation based on label similarity, and the Metropolis acceptance strategy in this paper. In addition, experimental comparisons were made at different instances and label densities. The experimental results show that the algorithm proposed in this paper is better or more competitive with the compared algorithm.
Enhancing Industrial Anomaly Detection Using Edge Image Reconstruction with Neighbor Masked Convolutional Transformer Block

Anomaly detection has become increasingly popular in recent years, where reconstruction-based approaches have been widely explored in. This approach usually assumes that the model can reconstruct normal patterns well but fails in anomalies, so that anomalies can be detected by evaluating the reconstruction error. However, it is usually difficult to control the model generalization boundaries in practice, because models with too much generalization ability will reconstruct the anomalous regions well, making them indistinguishable, and models with poor generalization ability fail to reconstruct the normal regions. In order to solve the above problems, we propose a new network that will reconstruct the original RGB image from edges of the image grayscale values by fusing the improved Neighbor Masked Convolutional Transformer Block, which achieves new and excellent results on the industrial anomaly detection MVTec-AD dataset.
HLC-KEPLM: Hierarchical Label Classification Based on Knowledge-Enhanced Pretrained Language Model for Chinese Telecom

The task of hierarchical label classification is currently an important task in the field of natural language processing (NLP), especially how to leverage the relationship between tags is a challenge. Most of the previous pretrained language models are based on contextual information modelling, ignoring the knowledge information, which is later injected into the language representation model. Inspired by the fact that knowledge can cover a diverse range of information and contribute to semantic understanding. We propose an improved model of hierarchical label classification based on knowledge-enhanced pretrained language model for domain of Chinese telecom (HLC-KEPLM), which learns with multiple local classifiers on top of pretrained language model with knowledge fusion in text classification. The core of our work is knowledge-aware encoder and multiple local classifiers fine-tuning. In the knowledge-aware encoder, we propose incorporating sememe knowledge through the HowNet knowledge base to fit the Chinese language features. In the fine-tuning stage, we exploit the relationship information between labels using the share encoder layer. We pretrained and fine-tuned on real data from the Chinese telecom domain and verified the effect resulting in an accuracy improvement of 6.4%.
Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic

In Malaysia, during the early stages of the COVID-19 pandemic, the negative impact on mental health became noticeable. The public's psychological and behavioral responses have risen as the COVID-19 outbreak progresses. A high impression of severity, vulnerability, impact, and fear was the element that influenced higher anxiety. Social media data can be used to track Malaysian sentiments in the COVID-19 era. However, it is often found on the internet in text format with no labels, and manually decoding this data is usually complicated. Furthermore, traditional data-gathering approaches, such as filling out a survey form, may not completely capture the sentiments. This study uses a text mining technique called Latent Dirichlet Allocation (LDA) on social media to discover mental health topics during the COVID-19 pandemic. Then, a model is developed using a hybrid approach, combining both lexicon-based and Naïve Bayes classifier. The accuracy, precision, recall, and F-measures are used to evaluate the sentiment classification. The result shows that the best lexicon-based technique is VADER with 72% accuracy compared to TextBlob with 70% accuracy. These sentiments results allow for a better understanding and handling of the pandemic. The top three topics are identified and further classified into positive and negative comments. In conclusion, the developed model can assist healthcare workers and policymakers in making the right decisions in the upcoming pandemic outbreaks.
A Blockchain-Based Framework for Rural Property Rights Transactions

Currently, there are several issues in China’s rural property rights trading, including poor credit reporting, low transaction efficiency, non-standardized transaction contracts, and high costs for rights protection. However, blockchain technology, known for its convenience, security, and traceability, is highly compatible with the requirements of standardization, convenience, and agility in rural property rights trading. Therefore, we propose a system framework for rural property rights trading based on the Hyperledger Fabric consortium blockchain. This framework significantly enhances transaction efficiency, improves the security of rural property rights trading, effectively resolves contract disputes in property rights transactions, and promotes rural revitalization and development. Furthermore, we design the information data structure on the blockchain, present the information flow, and develop smart contracts that utilize automation capabilities to automatically summarize, analyze, and issue alerts based on on-chain data, enabling risk assessment for both buyers and sellers. Additionally, we introduce an improved PBFT consensus algorithm called CA-PBFT, which is based on the PBFT consensus protocol and integrates a scoring model and simplified consensus protocol. CA-PBFT efficiently reduces transaction response latency and improves information throughput. Finally, we develop a prototype system for rural property rights trading and perform a performance comparison and analysis of CA-PBFT against other PBFT-based consensus algorithms in this prototype system. Experimental results demonstrate the feasibility of the prototype system framework for rural property rights trading.
A More Context-aware Approach for Textual Adversarial Attacks using Probability Difference-guided Beam Search

Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness. Previous context-aware attack models suffer from several limitations. They generally rely on out-of-date substitutes, solely consider the gold label probability, and use the greedy search when generating adversarial examples, often limiting the attack efficiency. To tackle these issues, we propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MC-PDBS</b> , a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ore <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ontext-aware textual adversarial attack model using <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</b> robability <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ifference-guided <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> eam <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> earch. MC-PDBS generates substitutes using the newest perturbed text sequences in each attack iteration, enabling the generation of more context-aware adversarial examples. The probability difference is an overall consideration of the probabilities of all class labels, which is more effective than the gold label probability in guiding the selection of attack paths. In addition, the beam search enables MC-PDBS to search attack paths from multiple search channels, thereby avoiding the limited search space problem. Extensive experiments and human evaluation demonstrate that MC-PDBS outperforms previous best models in a series of evaluation metrics, particularly bringing up to a +19.5% attack success rate. Extensive analyses further confirm the effectiveness of MC-PDBS.
Social Media as a Platform for Acquiring Medical English: Measuring Perceptions of Non-native Healthcare Providers

The primary goal of this study is to measure the perceptions of non-native healthcare providers towards the role of social media in acquiring medical English and enhancing language learning autonomy, and the features of social media that help them acquire medical vocabulary. To this end, a questionnaire was constructed and disseminated to 350 Egyptian healthcare providers working at Magdi Yacoub Foundation (MYF), Aswan Heart Centre (AHC), Egypt. For data triangulation, interviews were conducted with a subset of the questionnaire takers. Findings of the study reveal that social media platforms tremendously assist non-native healthcare providers in acquiring medical English vocabulary and enhancing language learning autonomy. Among the features of social media that proved to be beneficial to Egyptian healthcare providers are easiness of usage, free-of-charge availability, ability to edit, copy and share, ability to store data forever, emoticons to express feelings, choice to join private conversations, and choice to join public discussions and debates.
O<sup>2</sup>DCA: Online Outlier Detection and Classification Approach for WSN

Today's scientific and corporate communities are highly interested in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). This kind of network consists of sensors with low resources that gather information for various real-life applications (healthcare, industrial, security, etc.), with streaming data requiring online processing. However, since outliers may occur in sensors collected data, it is necessary to identify and classify them into errors and events using online outlier detection and classification techniques suitable for the WSNs real-life applications. In this paper, we propose a centralised method for online outlier detection and classification in WSN. Our approach can differentiate between errors caused by malfunctioning sensors and errors caused by events. We also consider the spatial-temporal connection between sensor data vectors and nearby sensor nodes. Our approach, titled O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DCA, for Online Outlier Detection and Classification Approach, combines the benefits of the Fixed Width Clustering (FWC) and the Inter-Cluster Distance (ICD) algorithms for clustering outlier detection, respectively. For classification, we use the Inverse Distance Weighting (IDW) method, which allows us to classify outliers into errors that will be discarded and relevant events for which a necessary decision must be taken. We show through simulation using both synthetic and real-world datasets that our novel online approach is suitable for working with real-life applications where the Detection Rate (DR) performance metric stays stable and better than the offline approach.
Superposing compass states for asymptotic isotropic sub-Planck phase-space sensitivity

Compass states deliver a sub-Planck phase-space structure in the sense that sensitivity to phase-space displacement is superior to the sensitivity of displacing the vacuum state in any direction, but this sensitivity is anisotropic: better sensitivity for some directions of phase-space displacement versus others. Here we introduce generalized compass states as superpositions of $n$ compass states, with each oriented by $\ensuremath{\pi}/2n$ with respect to its predecessor. Specifically, we derive Wigner functions for these generalized compass states and approximate closed-form expressions for overlaps between generalized compass states and their displaced counterparts. Furthermore, we show that generalized compass states, in the limit $n\ensuremath{\rightarrow}\ensuremath{\infty}$, display isotropic sensitivity to phase-space displacement in any direction.
Revisiting Mehrotra and Nichani’s Corner Detection Method for Improvement with Truncated Anisotropic Gaussian Filtering

In the early 1990s, Mehrotra and Nichani developed a filtering-based corner detection method, which, though conceptually intriguing, suffered from limited reliability, leading to minimal references in the literature. Despite its underappreciation, the core concept of this method, rooted in the half-edge concept and directional truncated first derivative of Gaussian, holds significant promise. This article presents a comprehensive assessment of the enhanced corner detection algorithm, combining both qualitative and quantitative evaluations. We thoroughly explore the strengths, limitations, and overall effectiveness of our approach by incorporating visual examples and conducting evaluations. Through experiments conducted on both synthetic and real images, we demonstrate the efficiency and reliability of the proposed algorithm. Collectively, our experimental assessments substantiate that our modifications have transformed the method into one that outperforms established benchmark techniques. Due to its ease of implementation, our improved corner detection process has the potential to become a valuable reference for the computer vision community when dealing with corner detection algorithms. This article thus highlights the quantitative achievements of our refined corner detection algorithm, building upon the groundwork laid by Mehrotra and Nichani, and offers valuable insights for the computer vision community seeking robust corner detection solutions.
Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. The primary objective of this work is to propose a novel approach for the detection of lung cancer using histopathological images. In this work, the histopathological images underwent preprocessing, followed by segmentation using a modified approach of KFCM-based segmentation and the segmented image intensity values were dimensionally reduced using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Algorithms such as KL Divergence and Invasive Weed Optimization (IWO) are used for feature selection. Seven different classifiers such as SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without feature selection and hyperparameter tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%.
An Improved Binary Quadratic Interpolation Optimization for 0-1 Knapsack Problems

This paper presents a new binary optimization technique for solving the 0–1 knapsack problem. This algorithm is based on converting the continuous search space of the recently proposed quadratic interpolation optimization (QIO) into discrete search space using various V-shaped and S-shaped transfer functions; this algorithm is abbreviated as BQIO. To further improve its performance, it is effectively integrated with a uniform crossover operator and a swap operator to explore the discrete binary search space more effectively. This improved variant is called BIQIO. Both BQIO and BIQIO are assessed using 20 well-known knapsack instances and compared to four recently published metaheuristic algorithms to reveal their effectiveness. The comparison among algorithms is based on three performance metrics: the mean fitness value, Friedman mean rank and computational cost. The first two metrics are used to observe the accuracy of the results, while the last metric is employed to show the efficiency of each algorithm. The results of this comparison reveal the superiority of BIQIO over the classical BQIO and four rival optimizers.
Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research

Visualization for machine learning (VIS4ML) research aims to help experts apply their prior knowledge to develop, understand, and improve the performance of machine learning models. In conceiving VIS4ML systems, researchers characterize the nature of human knowledge to support human-in-the-loop tasks, design interactive visualizations to make ML components interpretable and elicit knowledge, and evaluate the effectiveness of human-model interchange. We survey recent VIS4ML papers to assess the generalizability of research contributions and claims in enabling human-in-the-loop ML. Our results show potential gaps between the current scope of VIS4ML research and aspirations for its use in practice. We find that while papers motivate that VIS4ML systems are applicable beyond the specific conditions studied, conclusions are often overfitted to non-representative scenarios, are based on interactions with a small set of ML experts and well-understood datasets, fail to acknowledge crucial dependencies, and hinge on decisions that lack justification. We discuss approaches to close the gap between aspirations and research claims and suggest documentation practices to report generality constraints that better acknowledge the exploratory nature of VIS4ML research
ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional Data

Exploration and analysis of high-dimensional data are important tasks in many felds that produce large and complex data, like the fnancial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specifc application, limiting their applicability in other felds. However, as diverse as these felds are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires signifcant engineering efforts. We developed ManiVault, a fexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workfows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum fexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workfows and results. A copy of this paper and all supplemental material is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">osf.io/9k6jw</uri> , and source code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">github.com/ManiVaultStudio</uri> .