Reviewerly/exHarmony huggingface dataset:
Explicit Message-Passing Heterogeneous Graph Neural Network

Graph neural network (GNN) has shown its prominent performance in representation learning of graphs but it has not been fully considered for heterogeneous graphs which contain more complex structures and rich semantics. The rich semantic information of heterogeneous graph can be usually revealed by meta-paths. Therefore, most of the existing GNN models designed for heterogeneous graphs utilize the meta-path based neighborhood sampler to divide a heterogeneous graph into multiple homogeneous subgraphs according to various meta-paths so that the homogeneous GNN can be applied to investigate heterogeneous graphs. Nevertheless, the way of embedding semantic information of meta-paths into multiple homogeneous graphs is <b>implicit</b> and ineffective, which cannot accurately capture the semantics of heterogeneous graphs. In this paper, we propose a novel semi-supervised GNN model named <u>E</u>xplicit <u>M</u>essage-<u>P</u>assing Heterogeneous Graph Neural Network (EMP), which executes the process of <b>explicit</b> message-passing along the meta-paths. Besides, we also propose a split method for meta-paths and consider mutual effect between various meta-paths in advance in the proposed model, so that the semantic information of the whole set of meta-paths can be captured accurately. Extensive experiments conducted on three real-world datasets demonstrate the superiority of the proposed model.
Retrofitting LBR Profiling to Enhance Virtual Machine Introspection

Cloud attack provenance is a well-established industrial practice for assuring transparency and accountability for a service provider to tenants. However, the multi-tenancy and self-service nature coupled with the sheer size of a cloud implies many unique challenges to cloud forensics. Although Virtual Machine Introspection (VMI) is a powerful tool for attack provenance due to the privilege isolation, the stealthiness of state-of-the-art attacks and the lack of precise information make existing attack provenance solutions difficult to fulfill real-time forensics when tracking enormous suspicious behaviors. To this end, we propose an instruction-level tracing framework for inspecting the presence of attacks by dynamically tracking shared processor hardware event patterns and analyzing the attack traces. To overcome the challenges of real-time detection and provenance, we advocate Last Branch Record (LBR) profiling, to extract the suspicious execution flows. With the hardware assistance and software-based virtualization introspection, we show that the framework can provide an effective response to threats in different cases, thereby enabling a quick attack provenance with high fidelity. The evaluation shows that our prototype introduces negligible performance penalties.
Imitation Learning for Autonomous Vehicle Driving: How Does the Representation Matter?

Autonomous vehicle driving is gaining ground, by receiving increasing attention from the academic and industrial communities. Despite this considerable effort, there is a lack of a systematic and fair analysis of the input representations by means of a careful experimental evaluation on the same framework. To this aim, this work proposes the first comprehensive, comparative analysis of the most common inputs that can be processed by a conditional imitation learning (CIL) approach. With more details, we considered the combinations of raw and processed data—namely RGB images, depth (D) images and semantic segmentation (S)—to be assessed as inputs of the well-established Conditional Imitation Learning with ResNet and Speed prediction (CILRS) architecture. We performed a benchmark analysis, endorsed by statistical tests, on the CARLA simulator to compare the considered configurations. The achieved results showed that RGB outperformed the other monomodal inputs, in terms of success rate on the most popular benchmark NoCrash. However, RGB did not generalize well when tested on different weather conditions; overall, the best multimodal configuration was a combination of the RGB image and semantic segmentation inputs (i.e., RGBS) compared to the others, especially in regular and dense traffic scenarios. This confirms that an appropriate fusion of multimodal sensors is an effective approach in autonomous vehicle driving.
A Benchmark of Parsing Vietnamese Publications

In recent decades, digital transformation has received growing attention worldwide, that has leveraged the explosion of digitized document data. In this paper, we address the problem of parsing publications, in particular, Vietnamese publications. The Vietnamese publications are well-known with high variant, diverse layouts, and some characters are equivocal in the visual form due to accent symbols and derivative characters that pose many challenges. To this end, we collect the UIT-DODV-Ext dataset: a challenging Vietnamese document image including scientific papers and textbooks with 5,000 fully annotated images. We introduce a general framework to parse Vietnamese publications containing two components: page object detection and caption recognition. We further conduct an extensive benchmark with various state-of-the-art object detection and text recognition methods. Finally, we present a hybrid parser which achieves the top place in the benchmark. Extensive experiments on the UIT-DODV-Ext dataset provide a comprehensive evaluation and insightful analysis.
Degeneracy-Free Particle Filter: Ensemble Kalman Smoother Multiple Distribution Estimation Filter

We propose the ensemble Kalman smoother multiple distribution estimation filter (EnKS-MDEF) for nonlinear state estimation problems. The EnKS-MDEF is an example of the multiple distribution estimation filter (MDEF), which is a particle filter (PF) that estimates the filtered state probability density function (pdf) using multiple conditional state pdfs. The one step behind (OSB) smoothed state pdf used for calculating the filtered state pdf of the MDEF is approximated by the ensemble Kalman smoother (EnKS). Then, the particle weights for the EnKS-MDEF remain equal during the filter execution, which indicates that the EnKS-MDEF is a degeneracy-free PF. Since, the MDEF and the EnKS-MDEF, both estimate the OSB smoothed state pdf prior to calculating the filtered state pdf, these filters provide a simultaneous estimation of filtered and OSB smoothed states. The examples of the EnKS-MDEF are the EnKS-extended and unscented Kalman multiple distribution estimation filters, and their filtering and OSB smoothing performances are evaluated and compared with those for the representative filters and smoothers using a benchmark simulation problems.
Prototyping and Control of an Automatic Ceramic Tableware Finishing Device

This paper presents a first prototype of an automated system that will be applied in stoneware tableware ceramics finishing, being developed in the scope of STC 4.0 HP project. The main objective of this prototype is to test different alternatives to obtain a precise finish on ceramic pieces produced by GRESTEL - PRODUTOS CERÂMICOS S.A, improving the production of irregular pieces that until now are finished using manual labor. This is why the implementation of a closed-loop control of the rotation speed of a finishing sponge and its applied force control is proposed. The mechanical structure of the devised solution was prototyped using a FDM based technology. A 3D printer was used for the manufacturing of the structural parts to support the rotating sponge and measurement sensors. In addition a PID based control is used to control the system. Once the prototype has been designed and assembled a series of tests and measurements were carried out leading to the conclusion that the proposed approach is adequate to meet the design requirements for this prototype.
A General Framework for Class Label Specific Mutual Information Feature Selection Method

Information theory-based feature selection (ITFS) methods select a single subset of features for all classes based on the following criteria: 1) minimizing redundancy between the selected features and 2) maximizing classification information of the selected features with the classes. A critical issue with selecting a single subset of features is that they may not represent the feature space in which individual class labels can be separated exclusively. Existing methods fail to provide a way to select the feature space specific to the individual class label. To this end, we propose a novel feature selection method called class-label specific mutual information (CSMI) that selects a specific set of features for each class label. The proposed method maximizes the information shared among the selected features and target class label but minimizes the same with all classes. We also consider the dynamic change of information between selected features and the target class label when a candidate feature is added. Finally, we provide a general framework for the CSMI to make it classifier-independent. We perform experiments on sixteen benchmark data sets using four classifiers and found that the CSMI outperforms five traditional, two state-of-the-art ITFS (multi-class classification), and one multi-label classification methods.
Dynamic Security Assessment of Small-Signal Stability for Power Grids Using Windowed Online Gaussian Process

The online small-signal stability assessment of electrical power grids is typically a challenging problem due to uncertainties and parameter variations of power system dynamics as well as the incurred high computational complexity. This paper proposes a novel theoretical framework for dynamic small-signal stability assessment of power grids by estimating the region of attraction (ROA) for operating states in real time. By analyzing the latest sampling data of power grids in a fixed time window, an up-to-date training set is constructed with the aid of converse Lyapunov function, which enables us to develop an online learning approach based on Gaussian Process (GP) to assess the stability level of power grids. As a result, an iteration algorithm is designed to update the assessment parameters by learning the input-output pairs in the training set. Theoretical analysis is conducted to ensure the existence of converse Lyapunov function for differential-algebraic system that serves to describe power system dynamics, as well as to estimate the region of attraction for operating states with a given confidence level. In particular, a practical method is proposed to leverage time series of phasor measurement unit (PMU) measurements including voltage/current magnitude, phase and frequency (i.e., PMU data) of real power grids for validating the online GP approach. Moreover, validations are taken to substantiate the proposed approach by using PMU data of real smart-grid infrastructure and IEEE test cases. The proposed assessment approach contributes to situational awareness of human operators in the control station, thereby taking proactive remedial actions prior to emergencies. Note to Practitioners—This paper was motivated by the problem of online assessment of power systems security but it also applies to other industrial control systems that have stable state trajectories. Existing approaches to security assessment of power systems generally focus on the adoption of various machine-learning algorithms by treating power grid as a “black box,” which ignores the intrinsic characteristics of power systems and thus restricts the inference performance. This paper proposes a new approach using limited sampling data and system dynamics to construct a domain of stability for power grids, which can reflect the evolution of security zones and provide more accurate predictions. In this work, we mathematically characterize the domain of stability for a practical power grid by analyzing and learning its state trajectories. Then we show how the proposed approach can be efficiently implemented online, which can timely alert human operators to abnormalities. Preliminary validations suggest that this approach is feasible and effective. In future research, we will incorporate it into an energy management system and test it in other industrial processes.
An Antijamming and Lightweight Ship Detector Designed for Spaceborne Optical Images

Ship detection in spaceborne optical images is a challenging task because ships have various orientations and scales, especially complex backgrounds, i.e., ships are easily obscured by various jamming. Moreover, most accurate object detectors have enormous computation and parameter numbers, which are unsuitable for resource-bounded spaceborne platforms that contain restrictive memory access and computation. In this paper, in order to cope with the influence of complex backgrounds and jamming on detection, and improve the practicality of detection algorithms, a new satellite optical image dataset and a novel ship detector are proposed. We have collected a new dataset from the satellite, which contains images of different time periods, different illuminations, and different levels of jamming. The proposed dataset is different from the widely used public remote sensing datasets, it is more practical and challenging. The proposed ship detector can deal with various images well and is robust to various complex backgrounds. Specifically, a feature refining module is designed to extract features effectively, which can improve detection performance significantly. An anti-jamming module is proposed to highlight the features of objects in the whole feature map. In contrast to mainstream ship detectors, the proposed method is effective and lightweight. It can also predict objects with oriented bounding boxes. Extensive experiments demonstrate that the proposed detector is efficient and robust to various complex backgrounds, and the new dataset CBSD is more suitable for application scenarios and is quite challenging.
Adaptive Sampling-Based Moving Obstacle Avoidance for Cable-Driven Parallel Robots

The ability to avoid the moving obstacle is critical to cable-driven parallel robots (CDPRs) operating in real-world environments. However, the moving obstacle raises extraordinary challenges for CDPRs due to various constraints introduced by cables. In this article, an adaptive sampling-based path planning method is presented for CDPRs to find collision-free trajectories under dynamic scenarios. In detail, the suggested method is based on the rapidly exploring random tree algorithm; it allows the robot to deal with complex constraints, such as cable collision and dynamic feasible workspace. To handle dynamic environments, partial motion planning with forward simulation is employed to construct an optimal time-based tree, and the predicted collision is checked in a closed-loop system. In particular, the swept volume caused by the time interval is considered, and the Gilbert–Johnson–Keerthi algorithm is applied for collision detection. Finally, a vision system with double webcams is designed to track the moving obstacle; the image processing based on morphological operations is introduced to segment the moving objects and automatically initialize a tracking box. Simulation results show that the proposed method can efficiently avoid the moving obstacle; these results are, then, validated experimentally using a built eight-cable-driven parallel robot with a drone as the moving obstacle.
BitAnalysis: A Visualization System for Bitcoin Wallet Investigation

Bitcoin is gaining ever increasing popularity. However, professional skills are required if people want to check bitcoin transaction information from the blockchain. As pointed out in a recent study, there is a lack of tools to support effective interactive investigation of bitcoin transactions. Therefore, we present a novel visualization system, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BitAnalysis</i> , for interactive bitcoin wallet investigation. The analytical and visualization functions of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BitAnalysis</i> are defined and developed by following the advice and requirements of a group of entrepreneurs and regulators of bitcoin-related business. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BitAnalysis</i> provides a rich set of functions and intuitive visual interfaces for the users, such as law-enforcement officers and regulators, to effectively visualize and analyze the transactions of a bitcoin wallet (i.e., a cluster of bitcoin addresses) and its related wallets, to track the flow of bitcoins, and to identify wallet correlation using our novel clustering functions. To achieve these functions, we have designed new visualization techniques for presenting bitcoin transactions information and introduced the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">connection diagram</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bitcoin flow map</i> as new ways of analyzing, tracking and monitoring the trading activities of a cluster of closely related wallets. We also present an extensive user study that validated the effectiveness and usability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BitAnalysis</i> .
Federated Clouds for Efficient Multitasking in Distributed Artificial Intelligence Applications

Distributed cloud/edge resources are needed to execute pervasive artificial intelligence tasks, collectively. The AI workload and data sets have variable multitasking granularity, privacy constraints, and communication latency concerns. This article presents a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated cloud/edge</i> (FCE <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">)</i> framework, illustrated by distributed medical image processing across multiple hospital sites. This federated cloud system appeals to train many machine learning models efficiently with workload balancing and reduced communication overheads. We tested the FCE model on a multi-cloud platform recently built at the Chinese University of Hong Kong in Shenzhen. We claim three distinct advantages in using the FCE system. First, our federated cloud system results in 41.3% reduction in total AI processing time in large-scale ML/DL experiments. Second, high machine model accuracy was achieved at 87% level in telemedicine experiments. The virtual graph helps reduce internode traffic latencies to avoid ML inference slowdowns. Third, the system can tolerate multiple cloud failures to enter a graceful degradation mode in case of node failures. The scalable performance gains in AI processing speed, model accuracy, and fault tolerance make our federated clouds a truly viable approach to solving massive AI multitasking problems in pervasive AI applications.
A Survey of PSO Contributions to Water and Environmental Sciences

Particle Swarm Optimization (PSO) is a nature-inspired optimizer that has attracted a lot of attention since its inception in 1995 due to its ease of application and promising results. The inspiration of PSO is the collaborative swarm behavior of biological populations—a noted computational intelligence technique. There is no doubt that PSO has made outstanding contributions to vast water and environmental science problems in the real world thus far. The standard PSO has been improved many times over the past decades, leading to various hybrid, multi-objective, and improved versions. This chapter aims to present a comprehensive overview of PSO application in solving such problems, initially discussing how PSO works and obtains optimal solution. Subsequently, different versions of PSO are presented followed by their respective contributions to the water and environmental problems. Our survey revealed that PSO has been employed both solely and collaboratively with other approaches like machine learning techniques and simulation software to solve single- and multiple-objective problems of different sectors from surface water to renewable energy generation.
Self-Orthogonality Matrix and Reed-Muller Codes

Kim et al. (2021) gave a method to embed a given binary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k]$ </tex-math></inline-formula> code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {C}\,\,(k = 3, 4)$ </tex-math></inline-formula> into a self-orthogonal code of the shortest length which has the same dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> and minimum distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d' \ge d(\mathcal {C})$ </tex-math></inline-formula> . We extend this result by proposing a new method related to a special matrix, called the self-orthogonality matrix <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SO_{k}$ </tex-math></inline-formula> , obtained by shortening a Reed-Muller code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathcal R}(2,k)$ </tex-math></inline-formula> . Using this approach, we can extend binary linear codes to many optimal self-orthogonal codes of dimensions 5 and 6. Furthermore, we partially disprove the conjecture (Kim et al. (2021)) by showing that if <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$31 \le n \le 256$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n\equiv 14,22,29 \pmod {31}$ </tex-math></inline-formula> , then there exist optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n], [5]$ </tex-math></inline-formula> codes which are self-orthogonal. We also construct optimal self-orthogonal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n], [6]$ </tex-math></inline-formula> codes when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$41 \le n \le 256$ </tex-math></inline-formula> satisfies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n \ne 46, 54, 61$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n \equiv \!\!\!\!\!/~7, 14, 22, 29, 38, 45, 53, 60 \pmod {63}$ </tex-math></inline-formula> .
Cryptoassets and Property

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI