Reviewerly/exHarmony huggingface dataset:
An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care

Deep Learning (DL) models have received increasing attention in the clinical setting, particularly in intensive care units (ICU). In this context, the interpretability of the outcomes estimated by the DL models is an essential step towards increasing adoption of DL models in clinical practice. To address this challenge, we propose an ante-hoc, interpretable neural network model. Our proposed model, named double self-attention architecture (DSA), uses two attention-based mechanisms, including self-attention and effective attention. It can capture the importance of input variables in general, as well as changes in importance along the time dimension for the outcome of interest. We evaluated our model using two real-world clinical datasets covering 22840 patients in predicting onset of delirium 12 h and 48 h in advance. Additionally, we compare the descriptive performance of our model with three post-hoc interpretable algorithms as well as with the opinion of clinicians based on the published literature and clinical experience. We find that our model covers the majority of the top-10 variables ranked by the other three post-hoc interpretable algorithms as well as the clinical opinion, with the advantage of taking into account both, the dependencies among variables as well as dependencies between varying time-steps. Finally, our results show that our model can improve descriptive performance without sacrificing predictive performance.
Harnessing climate variables for predicting PV power output: A backpropagation neural network analysis in a subtropical climate region

This study examines the influence of weather conditions on the electricity production of photovoltaic (PV) panels in the Conghua district, a region in Southern China distinguished by its subtropical mountainous climate. The area's high humidity levels, high temperatures, and substantial rainfall often lead to predominantly cloudy skies, which alter solar irradiation patterns as a result. Utilizing the EnergyPlus simulation tool, the power generation of PV panels on rooftop application was simulated. A structured classification method is adopted to sort out weather conditions into categories of sunny, cloudy, and rainy. Through examining the influence of these varied weather scenarios on power generation, unique factors affecting electricity outputs are identified. As a result, this study illuminates the relationships between potential weather variables and PV power generation across each weather category. Subsequently, a back propagation neural network (BPNN) model is utilized to explore the relationship between weather categories and PV generation. The high coefficient of determination value (R2 > 0.95) from the comparison of simulated and predicted PV generation validates the model accuracy. This research enables the development of updated dataset and enriches the prediction of rooftop PV power generation. It equips designers and owners with a reliable, user-friendly, and real-time tool for PV power generation forecasting, and provides a working platform for PV performance evaluation during the design phase of PV projects. The insights derived from the BPNN model could guide the optimal design and implementation of PV systems in similar climates. Furthermore, these findings advance sustainable energy solutions in regions affected by complex weather patterns.
Do digital technologies pay off? A meta-analytic review of the digital technologies/firm performance nexus

The adoption of digital technologies (DTs) has become a predominant management practice in recent years. However, empirical findings on their performance outcomes are mixed and fragmented across cultures, contexts, and disciplines. Inspired by the resource-based view of the firm, resource orchestration theory, and contingency theory, this study aims to examine the extent to which four selected DTs—artificial intelligence (AI), big data analytics (BDAs), internet of things/cyber-physical system (IoT/CPS), and 3D printings (3DP)—impact the overall, financial, innovation, and operational performance of the firm through a meta-analysis. Employing a random-effects model and psychometric meta-analysis approach based on subgroup and meta-regression analyses, we examine 109 studies with 1,346,242 observations and 165 effect sizes from 2006 to 2021. The findings reveal that DTs have a positive and moderate effect on overall firm performance; however, the impact on innovation performance is the largest, followed by operational efficiency, and finally, financial performance. Moreover, the results show that the impact of AI on firm performance is the largest, while 3DP exerts the lowest impact. Furthermore, boundary-spanning conditions—contextual factors (i.e., firm size, firm age, sector, country development, sector technology intensity, time frame) and methodological factors (i.e., study type, sampling technique, type of performance measure)—moderate the focal relationship (i.e., account for between-study variance). The theoretical and managerial implications of these findings are highlighted.
A general skeleton-based action and gesture recognition framework for human–robot collaboration

Recognizing human actions is crucial for an effective and safe collaboration between humans and robots. For example, in a collaborative assembly task, human workers can use gestures to communicate with the robot, and the robot can use the recognized actions to anticipate the next steps in the assembly process, leading to improved safety and productivity. In this work, we propose a general framework for human action recognition based on 3D pose estimation and ensemble techniques, which allows to recognize both body actions and hand gestures. The framework relies on OpenPose and 2D to 3D lifting methods to estimate 3D joints for the human body and the hands, feeding then these joints into a set of graph convolutional networks based on the Shift-GCN architecture. The output scores of all networks are combined using an ensemble approach to predict the final human action. The proposed framework was evaluated on a custom dataset designed for human-robot collaboration tasks, named IAS-Lab Collaborative HAR dataset. The results showed that using an ensemble of action recognition models improves the accuracy and robustness of the overall system; moreover, the proposed framework can be easily specialized on different scenarios and achieve state-of-the-art results on the HRI30 dataset when coupled with an object detector or classifier.
Contrastive Hierarchical Clustering

Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the ground truth on popular benchmarks, the information contained in the flat partition is limited. In this paper, we introduce CoHiClust, a Contrastive Hierarchical Clustering model based on deep neural networks, which can be applied to typical image data. By employing a self-supervised learning approach, CoHiClust distills the base network into a binary tree without access to any labeled data. The hierarchical clustering structure can be used to analyze the relationship between clusters, as well as to measure the similarity between data points. Experiments demonstrate that CoHiClust generates a reasonable structure of clusters, which is consistent with our intuition and image semantics. Moreover, it obtains superior clustering accuracy on most of the image datasets compared to the state-of-the-art flat clustering models. Our implementation is available at https://github.com/MichalZnalezniak/Contrastive-Hierarchical-Clustering .
A novel unsupervised framework for time series data anomaly detection via spectrum decomposition

Time series is a common type of data that widely exists in various real-world scenarios, such as traffic flow data, network KPI, financial data, which can be regarded as time series. Anomaly detection in time series is an interesting research topic with a wide range of real-world applications, such as network intrusion detection, traffic situation monitoring and sensor error detection. In the real-world scenario, the frequency of anomalies is very low and little anomalous sample is available for analysis. Therefore, unsupervised methods are usually used for anomaly detection. In this paper, based on spectrum analysis and time series decomposition, an unsupervised deep framework for anomaly detection in time series data is designed. First, we decompose the original time series into trend series, seasonal series and residual series based on spectrum analysis. Then, prediction models based on long short-term memory (LSTM) networks and convolutional neural networks (CNNs) are designed to predict the trend series and seasonal series, respectively, and the residual series is reconstructed based on a Gaussian distribution. Next, the time series data are reconstructed by superimposing the predicted trend series and seasonal series and reconstructed residual series. Finally, we compare the original time series with the reconstructed time series and detect anomalies according to a certain threshold. The method proposed in this paper integrates prediction-based and reconstruction-based methods, and the experimental results on four datasets demonstrate the excellent performance of our method.
Stochastic video normality network for abnormal event detection in surveillance videos

Video Anomaly Detection (VAD) aims to automatically identify unexpected spatial–temporal patterns to detect abnormal events in surveillance videos. Existing unsupervised VAD methods either use a single proxy task to represent the prototypical normal patterns, or split the video into static and dynamic parts and learn spatial and temporal normality separately, which cannot model the inherent uncertainty of motion. In this regard, we propose for the first time to learn video normality via stochastic motion representation. Specifically, the proposed Stochastic Video Normality (SVN) network learns the prototypical local appearance patterns via deterministic multi-task learning and global motion patterns in a non-deterministic manner. The stochastic representation module uses recurrent networks to model historical motion as a conditional Gaussian distribution and infers motion explicitly with the learned prior. Besides, a mask auto-encoder is introduced to explore the relationship between appearance and motion, boosting the model to learn spatial–temporal normality. Experimental results show that our proposed SVN network performs comparably to the state-of-the-art methods. Extensive analysis demonstrates the effectiveness of multi-task learning-based appearance representation and stochastic motion representation for unsupervised video anomaly detection.
Sorting URLs out: seeing the web through infrastructural inversion of archival crawling

Web archives collections have become important sources for Internet scholars by documenting the past versions of web resources. Understanding how these collections are created and curated is of increasing concern and recent web archives scholarship has studied how the artefacts stored in archives represent specific curatorial choices and collecting practices. This paper takes a novel approach in studying web archiving practice, by focusing on the challenges encountered in archival web crawling and what they reveal about the web itself. Inspired by foundational work in infrastructure studies, infrastructural inversion is applied to study how crawler interactions surface otherwise invisible, background or taken-for-granted aspects of the web. This framework is applied to study three examples selected from interviews and ethnographic fieldwork observations of web archiving practices at the Danish Royal Library, with findings demonstrating how the challenges of archival crawling illuminate the web’s varied actors, as well as their changing relationships, power differentials and politics. Ultimately, analysis through infrastructural inversion reveals how collection via crawling positions archives as active participants in web infrastructure, both shaping and shaped by the needs and motivations of other web actors.
Multimodal Religiously Hateful Social Media Memes Classification based on Textual and Image Data

Multimodal hateful social media meme detection is an important and challenging problem in the vision-language domain. Recent studies show high accuracy for such multimodal tasks due to datasets that provide better joint multimodal embedding to narrow the semantic gap. Religiously hateful meme detection is not extensively explored among published datasets. While there is a need for higher accuracy on religiously hateful memes, deep learning-based models often suffer from inductive bias. This issue is addressed in this work with the following contributions. First, a religiously hateful memes dataset is created and published publicly to advance hateful religious memes detection research. Over 2000 meme images are collected with their corresponding text. The proposed approach compares and fine-tunes VisualBERT pre-trained on the Conceptual Caption (CC) dataset for the downstream classification task. We also extend the dataset with the Facebook hateful memes dataset. We extract visual features using ResNeXT-152 Aggregated Residual Transformations based Masked Regions with Convolutional Neural Networks (R-CNN) and Bidirectional Encoder Representations from Transformers (BERT) uncased for textual encoding for the early fusion model. We use the primary evaluation metric of an Area Under the Operator Characters Curve (AUROC) to measure model separability. Results show that the proposed approach has a higher AUCROC score of \(78\% \) , proving the model’s higher separability performance and an accuracy of \(70\% \) . It shows comparatively superior performance considering dataset size and against ensemble-based machine learning approaches.
Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs Using Confidence-Augmented Reinforcement Learning

Temporal knowledge graph completion (TKGC) aims to predict the missing links among the entities in a temporal knowledge graph (TKG). Most previous TKGC methods only consider predicting the missing links among the entities seen in the training set, while they are unable to achieve great performance in link prediction concerning newly-emerged unseen entities. Recently, a new task, i.e., TKG few-shot out-of-graph (OOG) link prediction, is proposed, where TKGC models are required to achieve great link prediction performance concerning newly-emerged entities that only have few-shot observed examples. In this work, we propose a TKGC method FITCARL that combines few-shot learning with reinforcement learning to solve this task. In FITCARL, an agent traverses through the whole TKG to search for the prediction answer. A policy network is designed to guide the search process based on the traversed path. To better address the data scarcity problem in the few-shot setting, we introduce a module that computes the confidence of each candidate action and integrate it into the policy for action selection. We also exploit the entity concept information with a novel concept regularizer to boost model performance. Experimental results show that FITCARL achieves stat-of-the-art performance on TKG few-shot OOG link prediction. Code and supplementary appendices are provided ( https://github.com/ZifengDing/FITCARL/tree/main ).
An Exploratory Study on Metaverse and SDGs

The new concept around technology is called the "Metaverse." It is described as the inevitable evolution of the internet in modern times. It is a vision shared by many in the computer industry who believe that the Metaverse is the next iteration of the internet—a single, immersive, shared 3D virtual space where human life experiences are not limited to the physical world. The Metaverse must provide a persistent, 3D virtual atmosphere in which any user can engage in everything (Dalgarno and Lee in Br J Educ Technol 41(1):10–3, 2010). Metaverse technology can be used as a resource to develop projects for Sustainable Development Goals (SDGs), which can help to formulate solutions to the world's current problems. The use of modern technology in the future will help achieve the successful completion of the United Nations' Sustainable Development Goals (SDGs). The SDGs are a collection of 17 interlinked objectives designed to serve as a "shared blueprint for peace and prosperity for people and the planet now and into the future" ( https://sdgs.un.org/ ). The Metaverse, one of the most prominent concepts of recent times, is one of the tools used to accomplish the SDGs, which are essential goals that affect our planet and humanity. People have been attempting to increase awareness by employing various technologies and concepts in several industries every day. The research aims to analyze how the concept of the Metaverse can be utilized in the achievement of Sustainable Development Goals.
Towards Practical Federated Causal Structure Learning

Understanding causal relations is vital in scientific discovery. The process of causal structure learning involves identifying causal graphs from observational data to understand such relations. Usually, a central server performs this task, but sharing data with the server poses privacy risks. Federated learning can solve this problem, but existing solutions for federated causal structure learning make unrealistic assumptions about data and lack convergence guarantees. $$\textsc {FedC}^{2}\textsc {SL}$$ is a federated constraint-based causal structure learning scheme that learns causal graphs using a federated conditional independence test, which examines conditional independence between two variables under a condition set without collecting raw data from clients. $$\textsc {FedC}^{2}\textsc {SL}$$ requires weaker and more realistic assumptions about data and offers stronger resistance to data variability among clients. FedPC and FedFCI are the two variants of $$\textsc {FedC}^{2}\textsc {SL}$$ for causal structure learning in causal sufficiency and causal insufficiency, respectively. The study evaluates $$\textsc {FedC}^{2}\textsc {SL}$$ using both synthetic datasets and real-world data against existing solutions and finds it demonstrates encouraging performance and strong resilience to data heterogeneity among clients.
$$\mathrm FG^2AN$$: Fairness-Aware Graph Generative Adversarial Networks

Graph generation models have gained increasing popularity and success across various domains. However, most research in this area has concentrated on enhancing performance, with the issue of fairness remaining largely unexplored. Existing graph generation models prioritize minimizing graph reconstruction's expected loss, which can result in representational disparities in the generated graphs that unfairly impact marginalized groups. This paper addresses this socially sensitive issue by conducting the first comprehensive investigation of fair graph generation models by identifying the root causes of representational disparities, and proposing a novel framework that ensures consistent and equitable representation across all groups. Additionally, a suite of fairness metrics has been developed to evaluate bias in graph generation models, standardizing fair graph generation research. Through extensive experiments on five real-world datasets, the proposed framework is demonstrated to outperform existing benchmarks in terms of graph fairness while maintaining competitive prediction performance.
Realizing a blockchain solution without blockchain? Blockchain, solutionism, and trust

Abstract Blockchain is employed as a technology holding a solutionist promise, while at the same time, it is hard for the promissory blockchain applications to become realized. Not only is the blockchain protocol itself not foolproof, but when we move from “blockchain in general” to “blockchain in particular,” we see that new governance structures and ways of collaborating need to be developed to make blockchain applications work /become real . The qualities ascribed to (blockchain) technology in abstracto are not to be taken for granted in blockchain applications in concreto . The problem of trust, therefore, does not become redundant simply through the employment of “trustless” blockchain technology. Rather, on different levels, new trust relations have to be constituted. In this article, we argue that blockchain is a productive force, even if it does not solve the problem of trust, and sometimes regardless of blockchain technology not implemented after all. The values that underpin this seemingly “trustless technology” such as control , efficiency , and privacy and the story that is told about these values co‐shape the actions of stakeholders and, to a certain extent, pre‐sort the path of application development. We will illustrate this by presenting a case study on the Red Button ( De Rode Knop ), a Dutch pilot to develop a blockchain‐based solution that enables people who are in debt to communicate to their creditors that they are, together with the municipality, working on improving their situation, thereby requesting a temporary suspension from debt collection.
Opposition-based Learning Cooking Algorithm (OLCA) for solving global optimization and engineering problems

This study introduces a new human-based meta-heuristic algorithm, the Learning Cooking Algorithm (LCA), based on the opposition-based learning (OBL) strategy, namely the Opposition-based Learning Cooking Algorithm (OLCA). The proposed OLCA algorithm consists of four stages: the first stage, where the OBL strategy is implemented to improve the initial population; the second stage, where children learn from their respective mothers; the third stage, where children and mothers learn from chefs; and the fourth stage, where OBL is applied again to update the population. The proposed OLCA has been examined over 23 test functions, and the OLCA outcomes are equated with several popular and top-performing optimization algorithms. The statistical outcomes, such as the average (Ave), standard deviation (Std), Wilcoxon rank-sum test, and [Formula: see text]-test, reveal that the outcomes of OLCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the proposed OLCA has been employed to solve three real-world engineering problems, such as the tension/compression spring problem, the gear train problem, and the three-bar truss problem. The results demonstrate the OLCA’s superiority and capability over other algorithms in solving engineering problems.
A MILP model on coordinated coverage path planning system for UAV-ship hybrid team scheduling software

Shipborne unmanned aerial vehicles (UAVs) are safer and more flexible for maritime missions, but frequent recharging is needed during long-term patrols. A coordinated system for path planning between ships and electric UAVs is necessary for efficient large-area coverage. A two-stage approach is proposed to minimize the makespan overall UAVs’ flight and the move distance overall ships combinationaly. First, the target space is trangularized corresponding to the UAV camera field of view for generating air waypoints. Second, a MILP model is designed to connect suitable air waypoints for UAVs and marine waypoints for ship(s) to form the optimal path for them coordinating the requirements of the area coverage and the UAV recharging. The simulation experiments show the proposed model works for the scenario of either the static or the dynamic motherships in a unified way. In the static mode, the vessels are not migrated and the number of vessels and ship calling points required is the same. In the dynamic model, the ship can be repositioned to recover and recharge the drone, and the task can be accomplished simply by repositioning the ship between waypoints. Dynamic models have better interaction patterns than static models.