Reviewerly/exHarmony huggingface dataset:
RADAR: Reactive Concept Drift Management with Robust Variational Inference for Evolving IoT Data Streams

The accuracy and performance of Machine Learning (ML) models can gradually or even suddenly degrade when the underlying statistical distribution of data streams changes over time; this is known as concept drift. This phenomenon could adversely affect the IoT data management and analysis landscape that relies intensely on data-driven cognitive technologies. Therefore, concept drift should be detected immediately, which is challenging due to the increasing number of dimensional features and lack of ground truth. Its adaptive countermeasures also become difficult to design when data streams are being generated frequently and require latency-sensitive responses. The uncertainty and time dependencies characteristics of IoT data streams further intensify the complexity of concept drift management. This work proposes a reactive drift management framework named RADAR for streaming IoT applications that can simultaneously detect and react to concept drift using two novel methods: temporal discrepancy measure, and intensity-aware analyser. Collectively, these methods help to determine the adaptation decision to ensure reliable performance, thereby limiting the scope of the frequent ML model update. Experiments conducted using synthetic and real-world setups comprising end-to-end systems demonstrate that RADAR outperforms other benchmarks in achieving better improvement of the performance with the best F-score of 0.86, and obtaining efficient runtime with large data streams.
Efficient Multi-GPU Graph Processing with Remote Work Stealing

Graph algorithms support a broad spectrum of big data applications. A typical approach to scale graph algorithms is to run in a distributed and parallel setting with multiple processing devices. The approach requires balanced and effective utilization of computation, memory, and communication resources across devices. To address the problem, a large number of studies have been conducted, such as graph partitioning and asynchronous computation. However, there are still many outstanding issues yet to be solved. For example, the workloads can be skewed differently across devices, and between iterations, even with the state-of-the-art graph partitioners. As the graph partitions are typically static, they fall short in capturing the dynamic characteristics with different algorithms, inputs, and progress, leading to poor utilization of resources. Recently, GPUs have been increasingly used to accelerate various graph algorithms. Their highly efficient interconnection technologies, such as NVLink, open new opportunities for us to achieve better resource utilization. In this paper, we analyze the dynamic load-imbalance (DLB) problem and the long tail (LT) problem in multi-GPUs and solve them by adaptive remote work stealing on-the-fly. We first introduce a frontier stealing algorithm to solve the DLB problem, then an ownership stealing algorithm to solve the LT problem. Based on these two algorithms, we developed Gum — a multi-GPU graph processing system with high device utilization. We evaluated Gum on four typical graph algorithms (BFS, WCC, PR, SSSP). The results show that Gum can run up to an order of magnitude faster than Gunrock and Groute, with fewer stragglers and less synchronization overhead.
A Secure and Efficient Framework for Outsourcing Large-scale Matrix Determinant and Linear Equations

Large-scale matrix determinants and linear equations are two basic computational tools in science and engineering fields. However, it is difficult for a resource-constrained client to solve large-scale computational tasks. Cloud computing service provides additional computing resources for resource-constrained clients. To solve the problem of large-scale computation, in this article, a secure and efficient framework is proposed to outsource large-scale matrix determinants and linear equations to a cloud. Specifically, the proposed framework contains two protocols, which solve large-scale matrix determinant and linear equations, respectively. In the outsourcing protocols of large-scale matrix determinants and linear equations, the task matrix is encrypted and sent to the cloud by the client. The encrypted task matrix is directly computed by using LU factorization in the cloud. The computed result is returned and verified by the cloud and the client, respectively. The computed result is decrypted if it passes the verification. Otherwise, it is returned to the cloud for recalculation. The framework can protect the input privacy and output privacy of the client. The framework also can guarantee the correctness of the result and reduce the local computational complexity. Furthermore, the experimental results show that the framework can save more than 70% of computing resources after outsourcing computing. Thus, this article provides a secure and efficient alternative for solving large-scale computational tasks.
Collaborative Representation Based Fisher Discrimination Dictionary Learning For Image Classification

Recent developments of deep neural networks based image classification have attracted much attention because of their top performance. However, the volume of data for training seriously affected the classification effect of neural networks, thus it remains an interesting problem to estimate the accuracy of classification efficiently and accurately on a small-size dataset. In this article, a novel collaborative representation dictionary learning is presented, named collaborative representation based fisher discrimination dictionary learning (CRFDDL), in which under collaborative representation, this work solves the coefficients efficiently so that efficient learning model can be obtained. In consideration of the fact that both of the reconstruction residual and coefficients of each sample are discriminative, the classification scheme for fusing these two discriminative clues is naturally adopted for the image classification. Experiments shows that the proposed CRFDDL achieves competitive recognition rate compared with the existing classic image classification methods.
Retracted: Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games

[This retracts the article DOI: 10.1155/2021/4674140.].
Looking Back on the Past: Active Learning with Historical Evaluation Results : Extended Abstract

Active learning is effective for tasks with limited labeled data by annotating a small set of data actively. It utilizes the current trained model to evaluate all unlabeled samples and annotates the best samples scored by a specific query strategy to update the underlying model iteratively. Most active learning approaches rely on only the current evaluation score but ignore the results from previous iterations. In this paper, we propose using more historical evaluation results which can provide additional information to help better select samples. First, we apply two heuristic features of the historical evaluation results, i.e. the weighted sum and the fluctuation of history sequences. Next, to make fuller use of the information contained in the historical results, we design a query strategy that learns to select samples based on the history sequence automatically. Our proposed idea is general and can be combined with both basic and state-of-the-art query strategies to achieve improvements. Experimental results show that our methods significantly promote existing methods.
Research and Application of Knowledge Graph in Airport Emergency Plan

Airport emergency plan is a scheme that airport emergency management team can deal with corresponding emergency affairs scientifically, effectively and quickly. The traditional emergency plan was tried to find information from books and prepare the plan manually, which brought a series of problems, such as slow speed, narrow reference materials and so on. This paper applies knowledge graph in airport emergency plan scheme, knowledge graph of airport emergency plan is established. According to safety characteristics of the airport, with knowledge graph of airport emergency plan, the scheme makers are recommended the plans of similar airports with the most relative materials to assist the plan makers in order to improve the efficiency and accuracy. In detail, this paper proposes a content recommendation algorithm based on knowledge representation algorithm and the unified embedded content recommendation algorithm of airport emergency. The research of making the airport emergency plan based on knowledge graph realizes the recommendation of ideal plan making content for airport. The experimental results verify the process and results of the plan making of airport emergency plan, which greatly improves the efficiency of the emergency plan.
Clutch &amp; Grasp: Activation gestures and grip styles for device-based interaction in medical spatial augmented reality

Presenting medical volume data using augmented reality (AR) can facilitate the identification of anatomical structures, the perception of their spatial relations and the development of mental maps compared to more commonly used monitors. However, interaction methods explored in these conventional settings may not be applicable in AR environments, or perform differently. In terms of mode activation, gestural interaction was shown to be a viable, touchless alternative to traditional input devices, which is desirable in sterile medical use cases. Therefore, we present a user study (n = 21) comparing hand and foot gestures with voice commands for the activation of interaction modes within a projector-based, spatial AR prototype to visualize medical volume data. Interaction itself was performed via hand movements captured by a data glove. Consistent, statistically significant results across measured variables suggest advantages of voice commands. In addition, a second experiment (n = 17) compared the hand-based interaction with two motion-sensitive devices held in power and in precision grip respectively. All modes were activated using voice commands. No considerable differences between tested grip styles could be determined. The findings suggest that the choice of preferable interaction devices is user and use case dependent.
Graph Neural Networks for Tabular Data Learning

Deep learning-based approaches to Tabular Data Learning (TDL) have shown promising performance compared to their conventional counterparts. However, these methods often fail to account for the latent correlation among data instances and feature values. Recently, graph neural networks (GNNs) have gained attention across various application domains, including TDL, for their ability to model relations and interactions between different data entities. By creating appropriate graph structures from the input tabular data and employing GNNs for learning, the performance of TDL can be improved significantly. In this tutorial, we systematically introduce the methodologies of designing and applying GNNs to TDL. Our discussion covers the foundations and overview of GNN-based TDL methods, with a focus on formulating TDL as different graph structures. We also provide a comprehensive taxonomy of constructing graph structures and representation learning in GNN-based TDL methods. We describe the TDL model training framework, which includes different auxiliary tasks and supports open-world learning. Additionally, we discuss how to apply GNNs to various TDL application scenarios and tasks. Finally, we outline the limitations of current research and future directions for this field.
Matrix Factorization with Landmarks for Spatial Data

Matrix factorization (MF) is widely adopted to learn from data, e.g., for data representation and recommendation as well as many database applications such as data imputation and repairing. While it works for numerical values in general, for spatial data, without considering the locality w.r.t. the spatial information, the learned features could vary in spatial distribution. Even if smoothness in terms of close neighbors could be considered in the objective function to leverage the spatial information, the learned features are still uncontrolled in locations, and thus do not help much in learning from the data that are geographically distant. Therefore, in this study, we propose to introduce landmarks to control the locations of learned features and make them geographically close to the data observations. The proposed SMFL, Spatial Matrix Factorization with Landmarks, benefits from landmarks in more accurate learned features, along with better interpretability, and reduced computation cost. Our major contributions include (1) introducing landmarks to guide the locations of learned features and enhance the performance as well as the interpretability of the MF model, (2) proposing the SMFL method that cooperates landmarks with NMF and spatial regularization, for better utilizing the spatial information, and (3) devising updating rules with landmarks and proving the convergence for the proposed method. Experiments on real-world datasets highlight the advance of our proposal in various applications.
Retracted: Multirobot Collaborative Pursuit Target Robot by Improved MADDPG

[This retracts the article DOI: 10.1155/2022/4757394.].
Retracted: Application of Mathematical Models in Economic Variable Input and Output Models under the Scientific Visualization

[This retracts the article DOI: 10.1155/2022/6269358.].
A Graph Convolutional Network for Session Recommendation Model Based on Improved Transformer

Graph convolutional networks are widely used for session-based recommendation (SBR) of products, aimed at solving anonymous sequence recommendation problems. However, currently almost all SBR models only focus on the current session, ignoring item transitions in other sessions. The paper introduces DA-GCN, a session-based recommendation model that utilizes graph convolutional networks. DA-GCN learns item embeddings from two perspectives, the global graph and the session graph: (1) The global graph updates the adjacency matrix through the shortest path algorithm, transforming the adjacency matrix from a single 0/1 information element to a complex dynamic graph with weights, and the global item embeddings are learned recursively through a session-aware attention mechanism; (2) The session graph learns session-level item embeddings by considering the item transitions in the current session graph and introduces an improved Transformer network when aggregating node information in the graph. The improved Transformer uses reverse position encoding to simulate the historical interests of the current session, while considering the correlation with global item embeddings. The DA-GCN model adopts an auxiliary loss function to supervise the historical interest extraction process, and then further models the correlation between the historical interests of the current session and the global item embeddings using attention mechanisms. The research uses three real-world datasets to demonstrate the effectiveness of the proposed method, and the results show an average improvement of 4.06% on the core metric P@20.
Retracted: Classification of Breast Cancer Images by Implementing Improved DCNN with Artificial Fish School Model

[This retracts the article DOI: 10.1155/2022/6785707.].
Identifying Identifying

Day 1 of my five-week experiment to elaborate on FAIR-enabling services, and I already find myself fallen flat on my face. I had wanted to go through motions of brainstorming concepts related to the service of identifying, partition them into concepts, attributes, and relationships in the sense of Sequeda and Lassila’s1 “Knowledge Report” intermediate representation – for each, draft a table to name it, provide an alternative name or two, a definition, an identifier for...
Retracted: English Speech Recognition System Model Based on Computer-Aided Function and Neural Network Algorithm

[This retracts the article DOI: 10.1155/2022/7846877.].
Learning Efficient Transformer Representation for Siamese Tracker to UAV

In the last few years, there has been growing recognition of the vital links between visual tracking and unmanned aerial vehicle (UAV). Questions have been raised about the feasibility of CNN-based and transformer-based trackers on UAVs. However, deep neural network modules or self-attention modules can be adversely affected when employing on UAVs for their complex architecture. In this paper, we propose an efficient transformer tracker (ET2) with a lightweight network. The study set out to examine the usability of a transformer-based tracker. Our tracker performs at frame rates far surpassing real-time, achieving the balance of precision and running speed.
An Ontology Approach to Support the Body-in-White Connection Points Documentation in an Automotive Industry

Due to constant pressure from the regulatory organizations and continuously stricter security tests applied in newly designed vehicles, the automotive industries must respond with new technologies and solutions to ensure the survivability of the passengers. The application of crash test simulations al-lows the engineers to analyze and solve structural deformation issues during the early development of the vehicle, however, all the documentation of the vehicle’s parts and connection points must first be officialized by the product engineering team. Since this officialization must be done manually, in short amounts of time due to competition pressure, and since it is a highly complex and extensive task, human error is expected, and it causes re-works. To create a tool that can assist the employee in performing the vehicle’s connection documentation correctly the first time, it was applied AI (Artificial Intelligence) focused on ontology and knowledge representation in a real industry case. The results obtained show that the ontology can join dense information from different databases into a single tool with a simple and intuitive interface while also making new assertions through its reasoner, allowing the detection of documentation inconsistencies that the existing industry’s quality checks could not find.
An Interactive Showcase of RCSketch: Sketch, Build, and Control Your Dream Vehicles

We present RCSketch, the award-winning interactive system that lets anyone sketch their dream vehicles in 3D, build moving structures of those vehicles, and control them from multiple viewpoints. Visitors to this interactive showcase are able to use our system and design vehicles of their own and perform a wide variety of realistic movements across the vast digital landscape onboard their vehicles.
Retracted: Network Blockchain Security Sharing Model Based on Fuzzy Logic

[This retracts the article DOI: 10.1155/2022/1509000.].
Retracted: Visual Resolve of Modern Educational Technology Based on Artificial Intelligence under the Digital Background

[This retracts the article DOI: 10.1155/2022/1924138.].
Exploring digital competencies domain and elements for information technology graduates in Malaysia

&lt;span lang="EN-US"&gt;This paper explores the reliability of an instrument to evaluate digital competencies domain and elements for polytechnics’ information technology (IT) graduates in Malaysia towards future industrial revolution using exploratory factor analysis (EFA). This study thereby obtained 146 items from the previous phase (industries experts’ interview) which was later developed according to the study’s objectives. Experts validated the items, and after that, a pilot study was executed with 102 randomly chosen Polytechnics’ IT lecturers from four Malaysian Polytechnics as the respondents for this study. Four domains had been decided which are Personal Effective Competencies, Functional Competencies, Essential Competencies, and Industrial Revolution 4.0 (4IR) Competencies. Elements for each domain were created using the EFA, in which internal reliability was achieved for all construct dimensions. There were 15 elements gained through EFA for those four domains.&lt;/span&gt;
CEAT: Curvature Feature Extractor Using Action Based Triplet Learning for Action Segmentation

With the continued growth of untrimmed videos on the internet, there is an increasing demand for advanced action segmentation methods, capable of accurately and semantically localizing sequences within lengthy videos. Traditional approaches have attempted to overcome the prevalent issue of over-segmentation by smoothing the predictions of consecutive frames. However, this technique can potentially overlook important spatio-temporal characteristics. Other common strategies include the incorporation of supplementary temporal data, which can be difficult to obtain in practical real-world scenarios. To more effectively address these problems, we propose a novel approach that constructs a geometric curve based on frame-wise embeddings and extracts curvature features. This procedure allows us to leverage the curvature information of embedded vectors and seamlessly integrate spatio-temporal information into existing action segmentation models. Our investigation reveals that our novel curvature-based approach enriches embedding representations, making them more suitable for action segmentation. It effectively brings closely together the representations of similar actions from different videos while appropriately distancing dissimilar action frames from the same video. Consequently, our experimental results provide substantial evidence that incorporating curvature information into various existing action segmentation models can significantly enhance action segmentation performances.
Visualization Recommendation Through Visual Relation Learning and Visual Preference Learning

Visualization recommendation (VisRec) is to automatically generate the most relevant visualization for a table of interest to a user. In this paper, we present a novel machine learning-based VisRec method, VisFormer, which solves VisRec in three stages: 1) Table representation learning, which is to learn accurate column-level representations for a table. To achieve it, we resort to Transformer, a powerful language model that can learn accurate word embeddings by modeling context. Specifically, we propose a hierarchical Transformer-based architecture to learn expressive column representations by capturing two types of context, intra-column context and cross-column context; 2) Visual Relation Learning, which is to capture column relations. To achieve it, we regard each visualization as a relation tuple with a special relation, visual relation, between the columns. Then for each visual relation, we use a neural network to evaluate the corresponding visualizations; 3) Visual Preference Learning, which is to extract visual preference features that can affect users’ decision from a visualization. To achieve so, we use a Convolution Neural Network to extract such features and explore how to use them to refine the recommendation results. We conduct experiments to compare with three state-of-the-art ML-based methods on a large real-world dataset, Plotly community feed. The experimental results show that compared with the most competitive baseline, the relative improvements of VisFormer on Recall@1, Recall@2, and Recall@3 are 8.8%, 20.6%, and 21.0%, respectively.