Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Microseismic Detection and Location Reveal the Seismic Characteristics and Causes in the Xiluodu Reservoir, China

ABSTRACT The Xiluodu reservoir, as the third reservoir developed in the lower Jinsha River, is the fourth largest reservoir in the world in terms of power generation. It is located in an area of historically high seismic intensity. A large amount of seismic activity has occurred in the reservoir area because the reservoir was impounded in 2013, but the mechanism of seismogenesis is still not clear. In this study, we collected continuous seismic records from July 2020 to October 2022 in the Xiluodu reservoir area, built a high-precision microseismic catalog for this region based on a deep learning seismic detection and location workflow called LOC-FLOW, and eventually obtained high-precision locations of 4924 earthquakes (five times more than the routine catalog). We sketched the main seismogenic structures based on the spatial and temporal distribution of the earthquakes in the catalog. According to the relationship between periodic variation of water level and seismic activity, seismicity in the reservoir area is active at the stage when the water level is filling to the highest point and starts to draw down. Especially, the sudden change in the rate of water level variation can easily trigger seismic activity. Combined with the spatiotemporal distribution of seismicity in each region and the previous results of numerical simulation, we concluded that the seismic activity in the reservoir head area and around the Manao fault is likely induced by the increase of normal stress and pore pressure diffusion caused by reservoir impoundment, whereas the ML 4.6 earthquake that occurred at the intersection of the Lianfeng fault and the Zhongcun fault was likely tectonic activity occurring on a concealed fault.
Legal View on Blockchain Technologies in Healthcare

This article presents an in-depth study of the legal landscape surrounding blockchain technology in the healthcare sector, with a special focus on case studies from European countries. Analyzing the existing legal framework and regulations, the research highlights the challenges and opportunities associated with the adoption of blockchain in healthcare. The most important research areas are data protection, security, consent, liability, and compliance. Through a comparative analysis of various European countries, the article illuminates the differences in legal approaches and points out possible areas of harmonization. The results clarify the legal aspects that must be addressed to ensure the integration of blockchain technology into healthcare systems, innovation while protecting patients' rights, and compliance with regulatory requirements.
SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples

Abstract We present SemOpenAlex , an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. SemOpenAlex is licensed under CC0, providing free and open access to the data. We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources. Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as exploratory semantic search via our website, large-scale scientific impact quantification, and other forms of scholarly big data analytics within and across scientific disciplines. Additionally, it enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. Finally, SemOpenAlex can serve for RDF query optimization benchmarks, creating scholarly knowledge-guided language models, and as a hub for semantic scientific publishing. Data and Services: https://semopenalex.org https://w3id.org/SemOpenAlex Code: https://github.com/metaphacts/semopenalex/ Data License: Creative Commons Zero (CC0) Code License: MIT License
TEC: Transparent Emissions Calculation Toolkit

Greenhouse gas emissions have become a common means for determining the carbon footprint of any commercial activity, ranging from booking a trip or manufacturing a product to training a machine learning model. However, calculating the amount of emissions associated with these activities can be a difficult task, involving estimations of energy used and considerations of location and time period. In this paper, we introduce the Transparent Emissions Calculation (TEC) toolkit, an open source effort aimed at addressing this challenge. Our contributions include two ontologies (ECFO and PECO) that represent emissions conversion factors and the provenance traces of carbon emissions calculations (respectively), a public knowledge graph with thousands of conversion factors (with their corresponding YARRRML and RML mappings) and a prototype carbon emissions calculator which uses our knowledge graph to produce a transparent emissions report. Resource permanent URL: https://w3id.org/tec-toolkit .
Dense Re-Ranking with Weak Supervision for RDF Dataset Search

Dataset search aims to find datasets that are relevant to a keyword query. Existing dataset search engines rely on conventional sparse retrieval models (e.g., BM25). Dense models (e.g., BERT-based) remain under-investigated for two reasons: the limited availability of labeled data for fine-tuning such a deep neural model, and its limited input capacity relative to the large size of a dataset. To fill the gap, in this paper, we study dense re-ranking for RDF dataset search. Our re-ranking model encodes the metadata of RDF datasets and also their actual RDF data—by extracting a small yet representative subset of data to accommodate large datasets. To address the insufficiency of training data, we adopt a coarse-to-fine tuning strategy where we warm up the model with weak supervision from a large set of automatically generated queries and relevance labels. Experiments on the ACORDAR test collection demonstrate the effectiveness of our approach, which considerably improves the retrieval accuracy of existing sparse models.
Optimizing SPARQL Queries with SHACL

We propose a set of optimizations that can be applied to a given SPARQL query, and that guarantee that the optimized query has the same answers under bag semantics as the original query, provided that the queried RDF graph validates certain SHACL constraints. Our optimizations exploit the relationship between graph patterns in the SPARQL queries and the SHACL constraints that describe those patterns in the RDF graph. We prove the correctness of these optimizations and show how they can be propagated to larger queries while preserving answers. Further, we prove the confluence of rewritings that employ these optimizations, guaranteeing convergence to the same optimized query regardless of the rewriting order.
Comparison of Knowledge Graph Representations for Consumer Scenarios

Abstract Knowledge graphs have been widely adopted across organizations and research domains, fueling applications that span interactive browsing to large-scale analysis and data science. One design decision in knowledge graph deployment is choosing a representation that optimally supports the application’s consumers. Currently, however, there is no consensus on which representations best support each consumer scenario. In this work, we analyze the fitness of popular knowledge graph representations for three consumer scenarios: knowledge exploration, systematic querying, and graph completion. We compare the accessibility for knowledge exploration through a user study with dedicated browsing interfaces and query endpoints. We assess systematic querying with SPARQL in terms of time and query complexity on both synthetic and real-world datasets. We measure the impact of various representations on the popular graph completion task by training graph embedding models per representation. We experiment with four representations: Standard Reification, N-Ary Relationships, Wikidata qualifiers, and RDF-star. We find that Qualifiers and RDF-star are better suited to support use cases of knowledge exploration and systematic querying, while Standard Reification models perform most consistently for embedding model inference tasks but may become cumbersome for users. With this study, we aim to provide novel insights into the relevance of the representation choice and its impact on common knowledge graph consumption scenarios.
Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.
Causal Inference-Based Debiasing Framework for Knowledge Graph Completion

The task of Knowledge Graph Completion (KGC) entails inferring missing relations and facts in a partially specified graph to discover new knowledge. However, the discrepancy in the targets between the training and inference phases might lead to in-depth bias and in-breadth bias during inference, potentially resulting in incorrect outcomes. In this work, we conduct a comprehensive analysis of these biases to determine their extent of impact. To mitigate these biases, we propose a novel debiasing framework called Causal Inference-based Debiasing Framework for KGC (CIDF) by formulating a causal graph and utilizing it for causal analysis of KGC tasks. The framework incorporates In-Depth Bias Mitigation to diminish the bias on feature representations by measuring the bias during inference, and In-Breadth Bias Mitigation to increase the distinguishability between feature representations by introducing a novel loss function. We evaluate the effectiveness of our proposed method on four benchmark datasets - WN18RR, FB15k-237, Wikidata5M-Trans, and Wikidata5M-Ind, achieving improvements of 2.5%, 0.9%, 3.2%, and 1.5% on Hit@1 respectively. Our results demonstrate that CIDF leads to significant improvements on these datasets, with more substantial gains observed in the biased settings on WN18RR achieving a 3.4% improvement in Hit@1.
Biomedical Knowledge Graph Embeddings with Negative Statements

A knowledge graph is a powerful representation of real-world entities and their relations. The vast majority of these relations are defined as positive statements, but the importance of negative statements is increasingly recognized, especially under an Open World Assumption. Explicitly considering negative statements has been shown to improve performance on tasks such as entity summarization and question answering or domain-specific tasks such as protein function prediction. However, no attention has been given to the exploration of negative statements by knowledge graph embedding approaches despite the potential of negative statements to produce more accurate representations of entities in a knowledge graph. We propose a novel approach, TrueWalks, to incorporate negative statements into the knowledge graph representation learning process. In particular, we present a novel walk-generation method that is able to not only differentiate between positive and negative statements but also take into account the semantic implications of negation in ontology-rich knowledge graphs. This is of particular importance for applications in the biomedical domain, where the inadequacy of embedding approaches regarding negative statements at the ontology level has been identified as a crucial limitation. We evaluate TrueWalks in ontology-rich biomedical knowledge graphs in two different predictive tasks based on KG embeddings: protein-protein interaction prediction and gene-disease association prediction. We conduct an extensive analysis over established benchmarks and demonstrate that our method is able to improve the performance of knowledge graph embeddings on all tasks.
Recognition of Human Interactions in Still Images using AdaptiveDRNet with Multi-level Attention

Human-Human Interaction Recognition (H2HIR) is a multidisciplinary field that combines computer vision, deep learning, and psychology. Its primary objective is to decode and understand the intricacies of human-human interactions. H2HIR holds significant importance across various domains as it enables machines to perceive, comprehend, and respond to human social behaviors, gestures, and communication patterns. This study aims to identify human-human interactions from just one frame, i.e. from an image. Diverging from the realm of video-based inter-action recognition, a well-established research domain that relies on the utilization of spatio-temporal information, the complexity of the task escalates significantly when dealing with still images due to the absence of these intrinsic spatio-temporal features. This research introduces a novel deep learning model called AdaptiveDRNet with Multi-level Attention to recognize Human-Human (H2H) interactions. Our proposed method demonstrates outstanding performance on the Human-Human Interaction Image dataset (H2HID), encompassing 4049 meticulously curated images representing fifteen distinct human interactions and on the publicly accessible HII and HIIv2 related benchmark datasets. Notably, our proposed model excels with a validation accuracy of 97.20% in the classification of human-human interaction images, surpassing the performance of EfficientNet, InceptionResNetV2, NASNet Mobile, ConvXNet, ResNet50, and VGG-16 models. H2H interaction recognition’s significance lies in its capacity to enhance communication, improve decision-making, and ultimately contribute to the well-being and efficiency of individuals and society as a whole.
Detection and Recognition of Obscured Traffic Signs During Vehicle Movement

The present study proposes an algorithm to extract obscured traffic sign information from road driving images and fuse it with the vehicle movement process by integrating the vehicle speed. Given the low recognition rate of obscured traffic signs, the proposed algorithm utilizes a traditional color-shape recognition approach to extract potential traffic sign regions. The fusion of traffic sign information from multiple consecutive frames into a single frame enhances the completeness and accuracy of the traffic sign information, bringing it closer to the original characteristics of the traffic sign. The experimental results demonstrate that fusing traffic sign information from the first and third frames improves the template matching similarity by 15.2%.And successfully identifies traffic signs that cannot be recognized by the YOLOV4 and YOLOV8 convolutional neural network when driving at vehicle speeds of 18 (km/h), 36 (km/h), and 54 (km/h). The findings highlight that the proposed algorithm can effectively fuse obscured traffic sign information during vehicle movement to obtain traffic signs with more similar geometric features to the original traffic signs.
Deep Video Stabilization via Robust Homography Estimation

Video stabilization can improve the visual quality of videos that have been captured on mobile devices or other handheld cameras, which are more prone to shaking and motion artifacts. Most of the existing deep video stabilization methods adopts optical flow-based, which produce artifacts and distortions caused by pixel-level warping and enquire expensive computation time. In this paper, we present a novel unsupervised deep video stabilization approach that addresses the influence of moving objects on video stabilization through robust homography estimation. Specifically, we design a foreground mask estimation module as a preprocessing step using a pre-trained semantic segmentation guided method to distinguish the foreground and background regions, enabling us to estimate camera motion via analyzing the background motion. Additionally, we design a low-level confidence feature extraction module to improve motion alignment loss and ensure robust motion estimation. By integrating the learned low-level confidence features with the foreground mask, we can then design a motion estimation module that captures the consistent spatial correspondence between frames through local and global feature extraction. At last, the learnt robust homography is leveraged to stabilize videos. Our method outperforms related state-of-the-art approaches in both quality and quantity on three public benchmarks while remaining computationally efficient.
Partial Homoscedasticity in Causal Discovery With Linear Models

Recursive linear structural equation models and the associated directed acyclic graphs (DAGs) play an important role in causal discovery. The classic identifiability result for this class of models states that when only observational data is available, each DAG can be identified only up to a Markov equivalence class. In contrast, recent work has shown that the DAG can be uniquely identified if the errors in the model are homoscedastic, i.e., all have the same variance. This equal variance assumption yields methods that, if appropriate, are highly scalable and also sheds light on fundamental information-theoretic limits and optimality in causal discovery. In this paper, we fill the gap that exists between the two previously considered cases, which assume the error variances to be either arbitrary or all equal. Specifically, we formulate a framework of partial homoscedasticity, in which the variables are partitioned into blocks and each block shares the same error variance. For any such groupwise equal variances assumption, we characterize when two DAGs give rise to identical Gaussian linear structural equation models. Furthermore, we show how the resulting distributional equivalence classes may be represented using a completed partially directed acyclic graph (CPDAG), and we give an algorithm to efficiently construct this CPDAG. In a simulation study, we demonstrate that greedy search provides an effective way to learn the CPDAG and exploit partial knowledge about homoscedasticity of errors in structural equation models.
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems

We propose a compositional approach to synthesize policies for networks of continuous-space stochastic control systems with unknown dynamics using model-free reinforcement learning (RL). The approach is based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">implicitly</i> abstracting each subsystem in the network with a finite Markov decision process with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unknown</i> transition probabilities, synthesizing a strategy for each abstract model in an assume-guarantee fashion using RL, and then mapping the results back over the original network with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximate optimality</i> guarantees. We provide lower bounds on the satisfaction probability of the overall network based on those over individual subsystems. A key contribution is to leverage the convergence results for adversarial RL (minimax Q-learning) on finite stochastic arenas to provide control strategies maximizing the probability of satisfaction over the network of continuous-space systems. We consider <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">finite-horizon</i> properties expressed in the syntactically co-safe fragment of linear temporal logic. These properties can readily be converted into automata-based reward functions, providing scalar reward signals suitable for RL. Since such reward functions are often sparse, we supply a potential-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reward shaping</i> technique to accelerate learning by producing dense rewards. The effectiveness of the proposed approaches is demonstrated via two physical benchmarks including regulation of a room temperature network and control of a road traffic network.
Lightweight blockchain solutions: Taxonomy, research progress, and comprehensive review

The proliferation of resource-constrained devices has become prevalent across various digital applications, including smart homes, smart healthcare, and smart transportation, among others. However, the integration of these devices brings many security issues. To address these concerns, Blockchain technology has been widely adopted due to its robust security characteristics, including immutability, cryptography, and distributed consensus. However, implementing blockchain within these networks is highly challenging due to the limited resources of the employed devices and the resource-intensive requirements of the blockchain. To overcome these challenges, a multitude of researchers have proposed lightweight blockchain solutions specifically designed for resource-constrained networks. In this paper, we present a taxonomy of lightweight blockchain solutions proposed in the literature. More precisely, we identify five areas within the “lightweight” concept, namely, blockchain architecture, device authentication, cryptography model, consensus algorithm, and storage method. We discuss the various methods employed in each “lightweight” category, highlighting existing gaps and identifying areas for improvement. Our review highlights the missing points in existing systems and paves the way to building a complete lightweight blockchain solution for networks of resource-constrained devices.
FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using Diffusion

Speech-driven 3D facial animation synthesis has been a challenging task both in industry and research. Recent methods mostly focus on deterministic deep learning methods meaning that given a speech input, the output is always the same. However, in reality, the non-verbal facial cues that reside throughout the face are non-deterministic in nature. In addition, majority of the approaches focus on 3D vertex based datasets and methods that are compatible with existing facial animation pipelines with rigged characters is scarce. To eliminate these issues, we present FaceDiffuser, a non-deterministic deep learning model to generate speech-driven facial animations that is trained with both 3D vertex and blendshape based datasets. Our method is based on the diffusion technique and uses the pre-trained large speech representation model HuBERT to encode the audio input. To the best of our knowledge, we are the first to employ the diffusion method for the task of speech-driven 3D facial animation synthesis. We have run extensive objective and subjective analyses and show that our approach achieves better or comparable results in comparison to the state-of-the-art methods. We also introduce a new in-house dataset that is based on a blendshape based rigged character. The code and the dataset will be publicly available on the project page1.