Reviewerly/exHarmony huggingface dataset:
An Extended SSD-Based Cache for Efficient Object Store Access in SAP IQ

The cloud-native version of SAP IQ aims to reduce the storage and compute costs by storing data directly on object stores while benefiting from the greater elasticity and scale-out properties that are offered. In the cloud-native version of SAP IQ, the buffer manager has gone through a significant re-design for two major reasons. First, RAM is more expensive on the cloud; therefore, a buffer manager that relies exclusively on RAM would not have been a viable solution. Second, on object stores, read and write operations are generally associated with higher latencies. To counteract the impact of higher latencies without utilizing more RAM, we have extended SAP IQ's buffer manager with a second layer of cache, namely, the Extended Cache Manager (ECM). The ECM relies on fast solid state drives (SSDs). In this paper, we describe our experience with the design and implementation of the ECM, in particular, the design choices we had to make to overcome the fact that, when compared to object stores, SSDs have a much more limited I/O bandwidth.
Accurate and Scalable Graph Neural Networks for Billion-Scale Graphs

Graph Neural Networks (GNNs) have been success-fully applied to a variety of graph analysis tasks. Some recent studies have demonstrated that decoupling neighbor aggregation and feature transformation helps to scale GNNs to large graphs. However, very large graphs, with billions of nodes and millions of features, are still beyond the capacity of most existing GNNs. In addition, when we are only interested in a small number of nodes (called target nodes) in a large graph, it is inefficient to use the existing GNNs to infer the labels of these few target nodes. The reason is that they need to propagate and aggregate either node features or predicted labels over the whole graph, which incurs high additional costs relative to the few target nodes. To solve the above challenges, in this paper we propose a novel scalable and effective GNN framework COSAL. In COSAL, we substitute the expensive aggregation with an efficient proximate node selection mechanism, which picks out the most important <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> nodes for each target node according to the graph topology. We further propose a fine-grained neighbor importance quantification strategy to enhance the expressive power of COSAL. Empirical results demonstrate that our COSAL achieves superior performance in accuracy, training speed, and partial inference efficiency. Remarkably, in terms of node classification accuracy, our model COSAL outperforms baselines by significant margins of 2.22%, 2.23%, and 3.95% on large graph datasets Amazon2M, MAG-Scholar-C, and ogbn-papers100M, respectively. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code available at https://github.com/joyce-x/COSAL.
BlockOPE: Efficient Order-Preserving Encryption for Permissioned Blockchain

Permissioned blockchain is increasingly being used as a collaborative platform for sharing data. However, current blockchain-based data sharing is unable to balance privacy pro-tection and query functionality, limiting its application scenarios. Order-preserving encryption/encoding (OPE) allows encrypting data to prevent privacy leakage while still supporting efficient order-oriented queries on ciphertexts. But existing OPE schemes are constrained by limited use cases and inherent performance limitations that make them difficult to be adopted by permissioned blockchain where performance is a major concern. In this paper, we present BlockOPE, an efficient OPE scheme designed around the first study integrating OPE into blockchain systems. By supporting parallel processing with a conflict-reducing design, we argue that BlockOPE is feasible for permissioned blockchain, achieving orders-of-magnitude performance improvement while preserving the ideal OPE security. Additionally, we improve query processing by leveraging an adaptive lightweight client cache. Extensive experiment results and theoretical analysis illustrate the practicability of our approach.
Distributed Task-Based Training of Tree Models

Decision trees and tree ensembles are popular supervised learning models on tabular data. Two recent research trends on tree models stand out: (1) bigger and deeper models with many trees, and (2) scalable distributed training frameworks. However, existing implementations on distributed systems are IO-bound leaving CPU cores underutilized. They also only find best node-splitting conditions approximately due to row-based data partitioning scheme. In this paper, we target the exact training of tree models by effectively utilizing the available CPU cores. The resulting system called TreeServer adopts a column-based data partitioning scheme to minimize communication, and a node-centric task-based engine to fully explore the CPU parallelism. Experiments show that TreeServer is up to 10× faster than models in Spark MLlib. We also showcase TreeServer's high training throughput by using it to build big “deep forest” models.
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning (Extended Abstract)

Recently, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. However, the majority class of early GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more complex nodes and edges in heterogeneous graphs. Also, few heterogeneous GNNs can bypass the transductive learning scheme where all nodes must be known during training, highlighting the need for inductiveness. Furthermore, the training efficiency of most heterogeneous GNNs has been hindered by their sophisticated designs for extracting the semantics associated with each meta path or relation type. In this paper, we propose wide and deep message passing network (WIDEN) to cope with the aforementioned problems about heterogeneity, inductiveness, and efficiency that are rarely investigated together in graph representation learning. We propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes. Meanwhile, we present an innovative downsampling strategy to facilitate faster information propagation. Experiments on real-world heterogeneous graphs have further validated the performance of WIDEN from both effectiveness and efficiency perspectives.
GQP: A Framework for Scalable and Effective Graph Query-based Pricing

Data is increasingly being bought and sold online, and data market platforms have emerged to facilitate these activities. However, current mechanisms for pricing data mainly focus on traditional relational data. In this paper, we propose a framework GQP for pricing graph data on the data market platform. Specifically, given a set of graph price points and a graph query, we can efficiently compute the price of the query based on the graph price points. We first identify an important property (called arbitrage-free) GQP should satisfy with, such that GQP can effectively price the graph query. We then study the exact pricing problem (NP-completeness) and develop an efficient approximation algorithm to solve the problem. We also study the approximate pricing when the query cannot be answered by price points exactly. Furthermore, to avoid the expensive computing cost of updating graph price points, we study the dynamic query pricing and propose novel solutions to reuse the computed graph price points to reduce the computational complexity. Finally, we use real-life data and synthetic data to experimentally verify that the proposed algorithms are able to effectively and efficiently price large graph data based on the framework GQP.
Authentication model for mobile access subjects

The paper proposes a model for authenticating mobile access subjects in a critical system. The incident detection mechanism is based on training and classification of the user activity audit log dataset embedded in the underlying configuration of the content management system. The proposed mechanisms can be implemented in systems whose subjects have some terrain movement patterns (within a city, district). The proposed model eliminates the disadvantages of existing traditional authentication methods based on the use of explicit verification methods. KNN methods, SVM and random forest algorithm are used for classification. These classification methods are formalized mathematically in the context of the set authentication problem, and to confirm the effectiveness of the algorithm, the robustness of the model by Euclidean metric and Chebyshev metric is evaluated, as well as the information content of the input features that affect the classification by each of the considered methods.
Information Structures of the Kalman Filter for the Elastic Wave Equation

We study the Kalman Filter for the linear elastic wave equation over the real line with spatially distributed partial state measurements. The dynamics of the filter are described by a spatial convolution operator with asymptotic exponential spatial decay rate. This decay rate dictates how measurements from different spatial locations must be exchanged to implement the filter: faster spatial decay implies local measurements are more relevant and the filter is more “decentralized”; slower decay implies farther measurements also become relevant and the filter is more “centralized”. Using dimensional analysis, we demonstrate that this decay rate is a function of one dimensionless group defined from system parameters, such as wave speed and noise variances. We find a critical value of such dimensionless group for which the Kalman Filter is completely decentralized.
Forecasting Research on Long-term Solar Irradiance with An Improved Prophet Algorithm

To solve the problem of predicting solar irradiance in integrated energy systems (IES), this paper improves the Prophet algorithm by removing the trend and holidays terms, changing to the multiplicative form, and adding the monthly regressors. The improved Prophet algorithm is compared with the original and autoregressive integrated moving average (ARIMA) algorithms respectively, and the forecast accuracy is significantly improved, and the RMSE of the long-term forecast is 108.30% lower than that of ARIMA. The improved algorithm can be applied to forecast solar irradiance on long time scales with single information, providing a solution to the problem of PV array planning for IES. At the same time, the forecast data obtained by the algorithm can also be used as a reference value to optimize the day-ahead dispatch.
Enhancing IoT Data and Semantic Interoperability Based on Entity Tree Embedding Under an Edge–Cloud Framework

Internet of Things (IoT) devices and services have become increasingly ubiquitous in recent years as they greatly facilitate our daily life. To promote IoT data and semantic interoperability, we propose an edge–cloud framework. The edge end is responsible for handling customized data processing tasks and transferring the processed data, while the cloud end deals with semantic information processing. Additionally, we present an entity tree embedding algorithm at the cloud end to convert IoT entities and attributes into embedding vectors in a tree-structured way. Consequently, entity embeddings could reflect the semantic information at both Class and Property levels, which ameliorates our previous entity embedding method, leading to better embedding results and clustering effects. Finally, the entity tree embedding algorithm and the corresponding compression algorithm are evaluated. Results indicate that the Whitening algorithm is the best method to compress embedding vectors. More importantly, the entity tree embedding algorithm captures both the semantic and structural information of entities and attributes. Additionally, the clustering experiments show that the proposed embedding algorithm achieves better clustering results compared with the original entity embeddings and the uncompressed averaging method.
Behavior Cloning-Based Robot Active Object Detection With Automatically Generated Data and Revision Method

Active object detection (AOD), one of the greatest challenges in the robotics field, is the main focus of this article. Most current AOD methods are developed by reinforcement learning (RL) algorithms while they can be further improved in the aspects of training time, training efficiency, model performance, and model prediction. Therefore, different from the existing works, we propose an AOD method based on behavior cloning trained by automatically generated data. We transform the AOD task into an action classification problem to not only shorten the training time but also improve the training efficiency and model performance. As there is no available expert data for training the presented classification-based AOD model, we design an autonomous method of data generation to avoid the large amounts of manual annotations. We introduce a multiinput network for better obstacle avoidance and AOD performance, where the depth image is added to help the robot to perceive distance information of environments and objects. Moreover, we develop a revision method for model prediction to reduce the accumulation of compounding error, which improves the successful rate of the long path AOD tasks effectively. We extensively evaluate our method on an AOD dataset by the comparable experiments and the ablation study, proving that our approach outperforms other methods in AOD performance and efficiency. In addition, the AOD experiments in the real-world scenario with a TIAGo robot indicate the validity of our method.
Bidirectional Transformer for Video Deblurring

We present a bidirectional transformer network to exploit the long-range informative dependency both in temporal and spatial domains for video deblurring. Motivated by the fact that the optical flow is related to the latent frames rather than blurry ones in the degradation process, we first develop a pre-deblur module to generate initial latent frames so that we can use these initial latent frames to estimate optical flow for better exploring the temporal information. Then we propose an effective bidirectional transformer to explore the long-range information, where it first aggregates the temporal information of the whole sequence through backward and forward propagation with the estimated optical flow, then a recurrent pixel-wise neighbor transformer (PWNT) block is developed at the end of the module to extract the useful spatial information. We embed our bidirectional transformer into a deep convolutional neural network and evaluate it on the publicly available video deblurring benchmarks. Extensive experimental results show that the proposed method performs favorably against the state-of-the-art methods. The implementation is available in this website: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Rebeccaxq/pwnt</uri>
TONet: A Fast and Efficient Method for Traffic Obfuscation Using Adversarial Machine Learning

In this letter, we address the problem of privacy leakage in communications based on analysis of traffic patterns. We propose an efficient method of traffic obfuscation based on neural networks, that generates traffic distortions with minimal overhead and computational cost. Our experimental results show that the proposed method is orders of magnitude faster in implementation and has a higher obfuscation success rate with less perturbation on the traffic samples, compared to previously proposed adversarial machine learning-based traffic obfuscation methods.
Development of an Ontology for Smart Distributed Energy Systems *

The use of the ontological approach in the formation of information support for the automation of technical systems is becoming widespread. The application of ontology is especially relevant when designing control systems for smart distributed energy systems. The development of such systems is accompanied by different problems, primarily caused by the lack of a unified and sufficient information environment for describing the subject area. In this regard, it is important to ensure the advancement of the ontology for distributed energy systems using existing CIM standards and various extensions that include entities specific to the domain of decentralized energy. The paper describes an approach to the formation of the ontology of smart distributed energy systems, assesses the possibility of using existing ontologies to extend CIM, and considers the matter of integrating a CIM-based ontology with other domain ontologies. A description of an ontology update process is presented, which ensures the maintenance of the ontology in a form that corresponds to the needs of the digital platform stakeholders. The developed ontology is planned to be used to form a unified information model of the distributed energy domain when developing a Russian digital platform for managing distributed energy resources - the ∀Platform.
Out‐of‐bag stability estimation for <scp><i>k</i></scp>‐means clustering

Abstract Clustering data is a challenging problem in unsupervised learning where there is no gold standard. Results depend on several factors, such as the selection of a clustering method, measures of dissimilarity, parameters, and the determination of the number of reliable groupings. Stability has become a valuable surrogate to performance and robustness that can provide insight to an investigator on the quality of a clustering, and guidance on subsequent cluster prioritization. This work develops a framework for stability measurements that is based on resampling and OB estimation. Bootstrapping methods for cluster stability can be prone to overfitting in a setting that is analogous to poor delineation of test and training sets in supervised learning. Stability that relies on OB items from a resampling overcomes these issues and does not depend on a reference clustering for comparisons. Furthermore, OB stability can provide estimates at the level of the item, cluster, and as an overall summary, which has good interpretive value. This framework is extended to develop stability estimates for determining the number of clusters (model selection) through contrasts between stability estimates on clustered data, and stability estimates of clustered reference data with no signal. These contrasts form stability profiles that can be used to identify the largest differences in stability and do not require a direct threshold on stability values, which tend to be data specific. These approaches can be implemented using the R package bootcluster that is available on the Comprehensive R Archive Network.
Improved Random Grid-based Cheating Prevention Visual Cryptography Using Latin Square

Visual cryptography scheme is a method of encrypting secret image into n noiselike shares. The secret image can be reconstructed by stacking adequate shares. In the past two decades, many schemes have been proposed to realize the cheating prevention visual cryptography scheme (CPVCS). Significantly, Ren et al. [ 9 ] first introduced the idea of CPVCS with the help of Latin square. Inspired by their work, in this article, a new reliable scheme is proposed. More precisely, to facilitate the certification process, we embed meaningful characters into the randomly chosen authentication patterns in each divided blocks. Furthermore, we fix the security vulnerability in the stacked results of share S g and verification Ver g , where 1≤ g ≤ n . Since the improved scheme encrypts the secret image by utilizing random grids, the generated shares have no pixel expansion. Finally, theoretical analysis and experimental results are conducted to evaluate the efficiency and security of the proposed scheme.
Region‐based estimation of the partition functions for hybrid Bayesian network models

The partition function Ƶ ${\boldsymbol{Ƶ}}$ is a normalization constant for normalizing all the distributions in probabilistic inference. Ƶ ${\boldsymbol{Ƶ}}$ is closely related to the log probability of evidence ( log p ( e ) $\mathrm{log}\unicode{x0200A}p(e)$ ) for Bayesian networks (BNs), which plays an important role in many applications, such as parameter learning, classification, and clustering. However, evaluating Ƶ ${\boldsymbol{Ƶ}}$ and log p ( e ) $\mathrm{log}\unicode{x0200A}p(e)$ for BNs containing both discrete and continuous variables (known as hybrid Bayesian networks [HBNs]) is generally difficult for analytical and simulation-based solutions. This paper describes the work addressing the problem of estimating the partition function for HBNs when exact methods become inefficient or numerically unstable. We use dynamic discretization to provide a discretized version of the model and then perform region-based free energy optimization to obtain the log p ( e ) $\mathrm{log}\unicode{x0200A}p(e)$ for the HBN efficiently, which is called the DDJT-region partition function (RPR) algorithm. It is novel since the region-based methods have previously only been applied to discrete BNs. We show that the log p ( e ) $\mathrm{log}\unicode{x0200A}p(e)$ obtained by DDJT-RPR is exact for discretized models and with space complexity equal to that in marginal inference, thus generally applicable to a wide range of applications. To demonstrate these properties, we combine the DDJT-RPR algorithm with an improved expectation-maximization algorithm to learn Gaussian mixture models (GMMs), for applications requiring data only and with prior knowledge, which performed more robustly than conventional GMM learning algorithms.
Spatial attention contrastive network for scene text recognition

At present, most scene text recognition methods achieve good performance by training models on many synthetic data. However, many data lead to huge storage space and large amount of calculation. And there is a gap between synthetic and real data. To solve these problems, we use a few real data to train a novel proposed model named spatial attention contrastive network (SAC-Net). The SAC-Net consists of a background suppression network (BSNet), a feature encoder, an attention decoder (ADEer), and a feature contrastive network (FCNet). The BSNet based on U-Net is used to reduce the interference of background. For relatively low prediction accuracy brought by connectionist temporal classification, we design an ADEer to improve performance by using convolutional attention mechanism. Based on data augmentation, we design a FCNet which belongs to contrastive learning. Finally, our SAC-Net is almost equivalent to the state-of-the-art model trained on a few real data for word accuracy on six benchmark test datasets.
Explainable AI: Foundations, Applications, Opportunities for Data Management Research

Algorithmic decision-making systems are success-fully being adopted in a wide range of domains for diverse tasks. While the potential benefits of algorithmic decision-making are many, the importance of trusting these systems has only recently attracted attention. There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opacity of a model by explaining its behavior, its predictions or both, thus allowing humans to scrutinize and trust the model. A host of technical advances have been made and several explanation methods have been proposed in recent years that address the problem of model explainability. In this tutorial, we will present these novel explanation approaches, characterize their strengths and limitations, and enumerate opportunities for data management research in the context of XAI.