Graph Building Research Articles

Intelligent Fault Diagnosis for Aero-Engine Lubrication Systems Using Knowledge Graph and Deep Learning Techniques

Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.

Enhancing public opinion monitoring for social hot events with a time series neural network-based logic map

A method for constructing a commercial map of social hotspots based on time-series neural networks is proposed. It explores map construction through three stages: atomic event graph building, fusion, and public opinion analysis. The neural network captures event dynamics and public opinion evolution. An unsupervised learning approach trains the model to explore deep event connections. A time map reflects event trends and opinion changes. A sentence-level event extraction model provides rich information. An empirical mode decomposition method analyzes IMF data to understand intrinsic patterns. Time-series neural networks classify and reconstruct event graphs, constructing a knowledge graph. Experiments show this method reduces storage costs, simplifies prediction, and exhibits robustness. It offers new insights for commercial mapping and contributes to public opinion monitoring.

RAF-AG: Report analysis framework for attack path generation

Information sharing is a key practice in cybersecurity for coping with the ever-changing cyberattacks that are targeting computer systems. Thus, when cyber incidents happen, cyber threat intelligence (CTI) reports are prepared and shared among cybersecurity practitioners to help them get up-to-date information about those incidents. However, reading and analyzing the report text to comprehend the included information is a cumbersome process. Although techniques based on deep learning were proposed to speed up report analysis in order to obtain the enclosed essential information, such as attack path, training data insufficiency makes these methods inefficient in practical circumstances.This paper presents RAF-AG, a report analysis framework for attack path generation. To analyze CTI reports, RAF-AG utilizes the sentence dependency tree for entity and relation extraction, and a weak supervision approach for entity labeling. This is followed by graph building and graph alignment for generating the attack paths. Our approach resolves the data insufficiency problem in the cybersecurity domain by lowering the need for expert involvement. We evaluated RAF-AG by comparing the generated attack paths with those produced by AttacKG, a state-of-the-art automatic report analysis framework. RAF-AG was able to identify cyberattack steps by matching their appearance order inside the report, and link them with techniques from the MITRE ATT&CK knowledge base with an improved F1 score compared to AttacKG (0.708 versus 0.393).

Real-Time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics

We present a design methodology that enables the semi-automatic generation of a hardware-accelerated graph building architectures for locally constrained graphs based on formally described detector definitions. In addition, we define a similarity measure in order to compare our locally constrained graph building approaches with commonly used k-nearest neighbour building approaches. To demonstrate the feasibility of our solution for particle physics applications, we implemented a real-time graph building approach in a case study for the Belle II central drift chamber using Field-Programmable Gate Arrays (FPGAs). Our presented solution adheres to all throughput and latency constraints currently present in the hardware-based trigger of the Belle II experiment. We achieve constant time complexity at the expense of linear space complexity and thus prove that our automated methodology generates online graph building designs suitable for a wide range of particle physics applications. By enabling an hardware-accelerated preprocessing of graphs, we enable the deployment of novel Graph Neural Networks (GNNs) in first-level triggers of particle physics experiments.

A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.

Mapping automatic social media information disorder. The role of bots and AI in spreading misleading information in society.

This paper presents an analysis on information disorder in social media platforms. The study employed methods such as Natural Language Processing, Topic Modeling, and Knowledge Graph building to gain new insights into the phenomenon of fake news and its impact on critical thinking and knowledge management. The analysis focused on four research questions: 1) the distribution of misinformation, disinformation, and malinformation across different platforms; 2) recurring themes in fake news and their visibility; 3) the role of artificial intelligence as an authoritative and/or spreader agent; and 4) strategies for combating information disorder. The role of AI was highlighted, both as a tool for fact-checking and building truthiness identification bots, and as a potential amplifier of false narratives. Strategies proposed for combating information disorder include improving digital literacy skills and promoting critical thinking among social media users.

Arabic NER Evaluation: Pre-Trained Models via Contrastive Learning vs. LLM Few-Shot Prompting

Developing Natural Language Processing (NLP) tools for the Arabic language and its dialects is very challenging. Named Entity Recognition (NER) is one of these challenges, which serves as the core component in many NLP systems such as information extraction, question answering, machine translation and knowledge graph building. This paper sheds light on applying diferent approaches for Arabic NER (Flat and Nested) using a large and rich Arabic NER corpus, Wojood dataset, which consists of about 550K tokens annotated with 21 entity types. First, we apply the Wojood base model, AraBERTv2, along with various other Arabic BERT models such as MARBERTv2, CaMelBert, mBert, ..etc. Next, we utilize the Bi-Encoder Contrastive Learning (CL) approach, a framework developed by Microsoft, which maps candidate text spans and entity types into the same vector representation space. The primary challenge in this approach is distinguishing non-entity spans from entity mentions. This approach could achieve F1 score 91.25% for Flat and 91.40% for Nested NER. Additionally, for evaluating the predicted NER, we employ Few-Shot prompting on LLaMA, and GPT-3.5 using refined prompt-based strategy. Our findings reveal that LLaMA outperforms GPT3.5.

SMR: Spatial-Guided Model-Based Regression for 3D Hand Pose and Mesh Reconstruction

3D hand reconstruction is an important technique for human-computer interaction. Interactive experience depends on the accuracy, efficiency, and robustness of the algorithm. Therefore, in this paper, we first propose a balanced framework called spatial-aware regression (SAR) to achieve precise and fast reconstruction. SAR can bridge convolutional networks and graph-structure networks more effectively than existing frameworks to fully exploit extracted spatial information using a novel spatial-aware initial graph building module. In addition, SAR uses adaptive-GCN to make keypoints interact efficiently and effectively; and regresses 2.5D belief maps to characterize uncertainty. SAR is highly flexible because it can predict an arbitrary number of keypoints and apply pose-guided refinement for coarse to fine regression. To produce more rational results for challenging cases and mitigate 3D label reliance, we also propose a more robust model-based framework called spatial-guided model-based regression (SMR) that is based on SAR. There are two critical designs of SMR: 1) it uses SAR to enhance the features with pose information to help the regression of hand model parameters; and 2) it regresses parameters in a spatially aware manner that is similar to SAR. Experiments demonstrate that the proposed frameworks surpass existing fully-supervised approaches on the FreiHAND, HO-3D, RHD, and STB datasets. Also, the performances of the proposed frameworks under weakly/self-supervised settings outperform other competitors. Meanwhile, the proposed frameworks are accurate and efficient.

A novel local alignment algorithm for Multilayer networks

Multilayer Network (MN) is a complex network comprising a set of graphs (also referred to as layers) interconnected by edges or interlayer edges (or inter-edges) linking the nodes of different layers. In biology, a MN may model interactions among diseases, genes, and drugs, only using its structure. Recently, there has been a growing interest in comparing two MNs by revealing local regions of similarity as a counterpart of Network Alignment algorithms (NA) for simple networks. However, classical algorithms for NA such as Local NA (LNA) cannot be applied on multilayer networks, since they are not able to deal with interlayer edges. Therefore, there is a need for the introduction of novel algorithms. In this paper, we present MuLan, an algorithm for the local alignment of multilayer networks. MuLan is based on the building of a multilayer alignment graph starting from a set of seed nodes. Then it analyses such a graph by revealing conserved regions. We first show as proof of concept the performances of MuLaN on a set of synthetic multilayer networks. Then, we used as a case study a real multilayer network in the biomedical domain. Our results show that MuLaN can build high-quality alignments and can extract knowledge about the aligned multilayer networks. MuLaN is available at https://github.com/pietrocinaglia/mulan.

DESIGN AND CONSTRUCTION OF A KNOWLEDGE SERVICE SYSTEM FOR GLOBAL LAND COVER SPATIOTEMPORAL CHANGE

Abstract. Global land cover (GLC) change reflect the distribution and evolution process of various material types on the earth's surface. It is an important basic data for global natural resources survey and ecological environment research. Although many sets of GLC information service systems have been developed at home and abroad, but the knowledge stock of GLC has not been sufficiently mined, problems such as the knowledge sharing service of spatiotemporal change of GLC have not been solved. This paper reports how to design and construct a GLC spatiotemporal change knowledge service system, which provides users with accurate and in-time knowledge services for GLC spatiotemporal change through knowledge visualization, browsing and querying, knowledge recommendation, knowledge diagnosis, knowledge sharing, process simulation, knowledge graph building and spatiotemporal deduction.

최소비용 최대유량 알고리즘에 기반한 구급차의 환자이송 병원 배정 기법의 설계

This paper presents a design and analyzes the performance of a hospital assignment and reassignment scheme for emergency rescue services based on minimum cost maximum flow algorithm. It consists of flow graph building, link capacity updating, and allocation discovering steps. The efficiency of the algorithm makes it possible to reallocate hospitals even in case of dynamic changes in the number of patients or hospitals. The performance measurement result obtained from a prototype implementation shows that the proposed scheme can reduce the transport time requirement miss by up to 24%.

DEMOS: Clustering by Pruning a Density-Boosting Cluster Tree of Density Mounts

Most existing clustering algorithms require presetting cluster number and often fail to capture complex shapes. Herein, we propose a clustering algorithm by pruning a density-boosting cluster tree of density mounts—DEnsity MOuntains Separation clustering algorithm (DEMOS). A cluster is assumed to be a density-connected area with multiple (or a single) density mounts (i.e., single-peak clusters) and a relatively large dis-connectivity from density-connected areas of higher densities. Based on this assumption, DEMOS can easily detect the number of clusters and robustly reconstruct their complex shapes. It first builds the dataset into a peak graph, where each density peak represents a density mount. A multi-valley-link-based connectivity estimation method is embedded to efficiently estimate the connectivity between density peaks during peak graph building. Then, by applying a new linkage metric designed based on our assumption, DEMOS builds density mounts into a reasonably density-boosting cluster tree. After obtaining a robust center detection in a clarity-enhancing decision graph (i.e., a two-dimensional plot for detecting centers), DEMOS prunes the cluster tree into final clusters to finish clustering. Experimental results on both synthetic and real datasets demonstrated the effectiveness of DEMOS and its applicability to large-scale data clustering.

Ladder and zig-zag Feynman diagrams, operator formalism and conformal triangles

We develop an operator approach to the evaluation of multiple integrals for multiloop Feynman massless diagrams. A commutative family of graph building operators Hα for ladder diagrams is constructed and investigated. The complete set of eigenfunctions and the corresponding eigenvalues for the operators Hα are found. This enables us to explicitly express a wide class of four-point ladder diagrams and a general two-loop propagator-type master diagram (with arbitrary indices on the lines) as Mellin-Barnes-type integrals. Special cases of these integrals are explicitly evaluated. A certain class of zig-zag four-point and two-point planar Feynman diagrams (relevant to the bi-scalar D-dimensional “fishnet” field theory and to the calculation of the β-function in ϕ4-theory) is considered. The graph building operators and convenient integral representations for these Feynman diagrams are obtained. The explicit form of the eigenfunctions for the graph building operators of the zig-zag diagrams is fixed by conformal symmetry and these eigenfunctions coincide with the 3-point correlation functions in D-dimensional conformal field theories. By means of this approach, we exactly evaluate the diagrams of the zig-zag series in special cases. In particular, we find a fairly simple derivation of the values for the zig-zag multi-loop two-point diagrams for D = 4. The role of conformal symmetry in this approach, especially a connection of the considered graph building operators with conformal invariant solutions of the Yang-Baxter equation is investigated in detail.

Accelerating the Inference of the Exa.TrkX Pipeline

Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in reconstructing particle tracks in dense environments. It includes five discrete steps: data encoding, graph building, edge filtering, GNN, and track labeling. All steps were written in Python and run on both GPUs and CPUs. In this work, we accelerate the Python implementation of the pipeline through customized and commercial GPU-enabled software libraries, and develop a C++ implementation for inferencing the pipeline. The implementation features an improved, CUDA-enabled fixed-radius nearest neighbor search for graph building and a weakly connected component graph algorithm for track labeling. GNNs and other trained deep learning models are converted to ONNX and inferenced via the ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows integration with existing tracking software. We report the memory usage and average event latency tracking performance of our implementation applied to the TrackML benchmark dataset.

Neighbor Graph Based Tensor Recovery For Accurate Internet Anomaly Detection

Detecting anomalous traffic is a crucial task for network management. Although many anomaly detection algorithms have been proposed recently, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper uses the tensor model for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low-rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). Besides only detecting random anomalies, our algorithm can also effectively detect structured anomalies that appear as bursts. We have conducted extensive experiments using Internet traffic trace data Abilene and GÈANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-TR can achieve higher Accuracy and Recall.

Traversability Aware graph-based Subterranean Exploration with Unmanned Aerial Vehicles*

Subterranean exploration and mapping for search and rescue robotics have become an emerging research direction since the DARPA organized Subterranean Challenge. As part of development efforts within the team CoSTAR (Collaborative SubTerranean Autonomous Robots) in the Sub-T challenge, this work establishes a novel traversable graph-based exploration strategy that utilizes frontiers for local navigation and a fast collision risk-aware graph building for global navigation. The exploration strategy extracts frontiers in an unknown area that contribute to safe navigation while maximizing information gain for the robot. The exploration problem is further bifurcated into local and global exploration for faster decision-making at junctions with the goal of rapidly exploring the area. The local exploration guarantees collision-free straight-line paths to informative frontiers for rapid forward navigation, while global re-positioning utilizes a traversable graph subject to geometrical collision checks within the occupancy map. The pathfinding in a graph is addressed using a heuristic, which combines risk margins and travel costs to assist in short yet safe paths to the global frontier in case of a dead end in local exploration. The presented exploration strategy is developed with the goal of making exploration algorithms platform agnostic in order to be able to use it with aerial, as well as ground robots. The proposed method is also evaluated against different state-of-the-art exploration planners in simulated fixed-time budget-based exploration missions on an Unmanned Aerial Vehicle (UAV) in order to benchmark the capabilities and highlight the novelty.

RESTEP Into the Future: Relational Spatio-Temporal Learning for Multi-Person Action Forecasting

Multi-person action forecasting is an emerging topic in the computer vision field, and it is a pivotal step toward video understanding at a semantic level. This task is difficult due to the complexity of spatial and temporal dependencies. Yet, the state- of-the-art literature does not seem to be adequately responsive to this challenge. Hence, how to better foresee the forthcoming actions per actor has to be further pursued. Toward this end, we put forth a novel RElational Spatio-TEmPoral learning approach (RESTEP) for multi-person action forecasting. Our RESTEP explores the key that inherently characterizes actions from a perspective of incorporating the spatial and temporal information in a single pass (spatio-temporal dependencies) by extending rela- tional reasoning. As a result, the RESTEP enables simultaneously predicting the actions of all actors in the scene. Our proposal significantly differs from mainstream works that heavily rely on independently processing the spatial and temporal dependencies. The proposed RESTEP first perceives a graph building upon the historical observations, then reasons the relational spatio- temporal context to extrapolate future actions. In order to augment the comprehension of individual actions that might vary over time, we further delve deeper into the essence behind this point the evolution of spatio-temporal dependencies via optimizing the corresponding mutual information. We assess the RESTEP method on the large-scale Atomic Visual Actions (AVA) dataset, Activities in Extended Videos (ActEV/VIRAT) dataset and Joint-annotated Human Motion Data Base (J-HMDB). The experimental outcomes reveal that RESTEP can introduce con- siderable improvements with respect to recent leading studies.

Semi-supervised graph learning framework for apicomplexan parasite classification

Apicomplexan parasites cause diseases including malaria, toxoplasmosis, and babesiosis, affecting large parts of the world and hampering economic development to a considerable severity. Early recognition of pathogenic parasites can effectively constrain the spread of these infectious diseases. Recently, deep-learning-based computer-aided parasite recognition systems provide many practical tools for automated apicomplexan parasite recognition. However, existing research works are limited by the complicated and expensive manual annotating workload in supervised deep learning methods, which require many experts’ manually annotated samples. Moreover, they also ignore the significance of correlations among different parasite cells, which retain helpful information for identification. Therefore, this paper is the first to employ a semi-supervised learning strategy to reduce the manual labeling workload and introduce a hybrid graph learning algorithm into parasite recognition. Our approach has a superior parasite recognition capability with semi-supervised techniques and the graph-based model. Specifically, this research proposes a Semi-Supervised Graph Learning (SSGL) framework, composing CNN (Convolutional Neural Networks) feature embedding, learnable graph building, and graph feature learning components. To verify the performance of the SSGL model, the paper applies multiple evaluation indicators to appraise our model, and the approach can achieve high accuracy of 91.75%, AUC of 91.83%, the sensitivity of 91.75% and specificity of 97.25% with only a small amount of labeled data (20%). In conclusion, SSGL model provides high parasite recognition capability with minimal labels, greatly reduces the workload of relevant experts, and promotes the popularization of computer-aided parasite diagnosis tools.

A distributed joint extraction framework for sedimentological entities and relations with federated learning

Sedimentological knowledge graphs can be used to identify natural resources in earth layers, which may help geologists analyze the distribution of oil crude in earth, and therefore locating the oilfield that is unknown. The building of such knowledge graphs mainly counts on the methods of joint extraction for pairwise entities and the corresponding relations on large-scale data. However, the whole sedimentological data is fairly owned by the different parties with the possibly inconsistent format. Centralized processing on sedimentological data as a whole will be either securely or structurally impractical. Therefore, this paper proposes a framework of distributed joint extraction in order to harvest knowledge triplets on distributed sedimentological corpus that are from many disparate sources without data transmission. The experimental studies demonstrate our methods not only approach the previous state-of-the-art but also protect the data privacy and security for data holders.

Lifelong Topological Visual Navigation

Commonly, learning-based topological navigation approaches produce a local policy while preserving some loose connectivity of the space through a topological map. Nevertheless, spurious or missing edges in the topological graph often lead to navigation failure. In this work, we propose a sampling-based graph building method, which results in sparser graphs yet with higher navigation performance compared to baseline methods. We also propose graph maintenance strategies that eliminate spurious edges and expand the graph as needed, which improves lifelong navigation performance. Unlike controllers that learn from fixed training environments, we show that our model can be fine-tuned using only a small number of collected trajectory images from a real-world environment where the agent is deployed. We demonstrate successful navigation after fine-tuning on real-world environments, and notably show significant navigation improvements over time by applying our lifelong graph maintenance strategies.