Reviewerly/exHarmony huggingface dataset:
Hardware-Accelerated Hypergraph Processing with Chain-Driven Scheduling

Beyond ordinary graphs, hypergraphs are a graph representation to flexibly express complex multilateral relationships between entities. Hypergraph processing can be used to solve many real-world problems, e.g., machine learning, VLSI design, and image retrieval. Existing hypergraph processing systems handle a hypergraph in order of its hyperedge and vertex indices. This makes processing hypergraphs on generalpurpose architectures suffer significantly from excessive offchip memory accesses, most of which however are redundant in frequently accessing overlapped hyperedges and vertices, but the index-ordered scheduling destroys this potential locality.In this paper, we propose a novel Generate-Load-Apply (GLA) execution model to improve locality in hypergraph processing. The key insight of GLA is to use a concept of chain to characterize the overlapped feature of a hypergraph, exposing data reuse opportunities missed in existing hypergraph systems. The precondition of driving GLA model is to generate expected chains on the fly, but the software solution is so expensive that its overheads may outweigh the benefits achieved from the chain-driven scheduling. We further present ChGraph, the first hardware-accelerated hypergraph processing engine near each core. ChGraph is specialized in accelerating the chain generation and the chain-guided data loading (to hide memory access latency) while the general-purpose cores are responsible only for handling the apply operations of GLA. We evaluate ChGraph against a state-of-the-art hypergraph processing system Hygra on six hypergraph algorithms using five large real-world hypergraphs. Results on a simulated 16core system show that ChGraph reduces the number of offchip memory accesses by up to 4.56&#x00D7; and achieves up to 4.73&#x00D7; speedup while introducing only 0.26% area overhead.
Ballot Blockchain System Design and Development on Ethereum Platform

Ballots are often hold for fair decisions such as party theme selecting, however, the existing traditional ballot has some problems involving amount of human resources, cost of places, equipment, time and traffic, and repeated procedures. In order to solve the issues aforementioned, a ballot blockchain system is designed and implemented based on the smart contract of Ethereum. It is designed on the core blockchain technologies of the decentralized ledger technology, using a secure hash algorithm, anonymous user, incorruptible data, and adopting a public blockchain. The ballot blockchain system is implemented based on the MetaMask verification and the Remix interface development environment. The smart contract plays the role of the decision-maker for controlling ballot activities instead of numerous human tasks. All ballot transactions are recorded in the ballot blockchain permanently when the ballot completed. The aim of the ballot blockchain system is to achieve a fair, less time-consuming, secured, and transparent environment.
Informatics and the Development of Global Studies

Globalization has become one of the characteristic features of our epoch. Different kinds of global problems are constantly discussed by the world community; global research in modern science is expanding uninterruptedly. Thus, global studies have gradually formed as a direction in scientific thought. They organically combine the features of differentiation and integration of scientific cognition. One possible perspective of subsequent development of global studies is their transformation in a particular sphere of knowledge. The information aspects of the global analysis of reality are bound with the progress of informatics; the present-day pandemic confirms this fact once more.
Video-Based 3D pose estimation for residential roofing

Residential roofers are often exposed to awkward postures and motions in a prolonged time, which may not only reduce their body stability and increase fall potential, but also increase the risk of musculoskeletal disorders (MSDs). To assess their risks of fatal and musculoskeletal injuries, it is crucial to capture 3D body poses of workers during roofing tasks. In this paper, we proposed a novel two-stage motion estimation approach based on a convolution neural network to estimate residential roofer’s body poses using three-view video data. Our approach includes two stages: (1) use of an offline multi-view model to estimate the 3D pose in a single frame; (2) use of a multi-frame model to apply temporal convolutions to refine the multi-view outputs. The performance of the approach was evaluated by comparing our estimation with the gold-standard marker-based 3D human pose during one of the common residential roofing tasks – shingle installation. The evaluation results show that the proposed multi-frame model can effectively improve the accuracy of the coordinate sequence. Moreover, these results prove that the proposed video-based motion estimation approach can efficiently and accurately locate 3D body joints and pave the way for future onsite motion analysis during roofing activities.
Deep learning-based multi-object association retrieval

Advances in computer technology and artificial intelligence have led to a surge in the volume and complexity of multimedia data, and content-based image retrieval systems (CBIR) have become widely popular to extract useful information from this data. Traditional CBIR systems are implemented by inter-image features and cannot perform associative image retrieval. Therefore, in this paper, we propose a new method for associative image retrieval by mimicking the human brain mechanism. By using an improved deep residual network to extract different kinds of image features, and using a loss function to maximize the feature distance loss between non-associative image groups and minimize the loss between associative image groups, and finally using the trained model to achieve multi-objective associative retrieval.
EduML: An explorative approach for students and lecturers in machine learning courses

Due to its achievements in recent years, machine learning (ML) is now used in a wide variety of domains. Educating ML has hence become an important factor in academia and industry. We argue that students learning about machine learning will need, in addition to theoretical knowledge, approaches to interactively explore Machine Learning models and their parameters. This paper introduces EduML &#x2013;an interactive approach for lecturers to teach and for students or professionals to study and explore the fundamentals of machine learning. EduML allows users to experiment with data preparation, dimensionality reduction and a wide range of classifiers on different data sets. These data sets can be analysed in order to understand the complexity of the classification problem. The classifiers can be autonomously fitted to the training data or the effect of manually altering model hyperparameters can be explored. Additionally, to get started with programming own ML pipelines, Python and R source code of configured ML pipelines can be extracted. EduML has been used in a lecture as an interactive demo or by students in lab sessions. Both scenarios were evaluated with a user survey.
The Cognitive Force Equations

The knowledge spectrum provides information about pragmatic knowledge types, which are placed on a spectrum based on their potential cognitive force. Based on the relationship between insight and Dretske’s three knowledge types, a conceptual framework for the equations is provided. These equations provide a shorthand method for determining the cognitive force of a specific pragmatic knowledge type. Together, the graphical and mathematical techniques of knowledge microanalysis, which is based on the knowledge chemistry approach, will be calibrated and refined. Two tools will be used to estimate cognitive force. These tools will enable organisations to develop models of their existing and proposed knowledge-intensive processes for analysis and refinement.
ScaleServe

We present, ScaleServe, a scalable multi-GPU machine learning inference system that (1) is built on an end-to-end open-sourced software stack, (2) is hardware vendor-agnostic, and (3) is designed with modular components to provide users with ease to modify and extend various configuration knobs. ScaleServe also provides detailed performance metrics from different layers of the inference server which allow designers to pinpoint bottlenecks.
A Novel Soft Color Generative Adversarial Network for Image-to-Image Translation

In the past, training neural networks was a complex manual work. A large amount of data had to be collected, sorted, and labeled. And then put the data into the neural work for training. But collecting data in a specific field is difficult and expensive. On the contrary, using unpaired datasets and unsupervised architectures for image-to-image translation not only increase the dataset, but also add variety to the dataset. Several studies have proved this data preprocessing method. For example, they adjusted colors and hue distribution but retained the regular attributes as well. This shows that the actual color of the object is crucial, because it affects the final accuracy of the entire model and the final training result. Therefore, the study about image-to-image translation related technology is in demand. For data preprocessing in deep learning, the approach of increasing dataset can enhance accuracy of training.rnThe study used the image style transfer technology as the basic framework of neural networks, which combined with the loss function of unsupervised cross-domain transfer and migration technology. The feature of one domain was transferred to the feature of another domain. One can flexibly learn from what has been learned from another domain, and then the features were connected together. Therefore, under the condition of one domain, the potential variables could be strengthened and controlled. Specific samples were generated by this transfer technology. The result proved that the output retained more input RGB color features, and the colors became softer than the one produced only by using the image style transfer technology.
An indirect network acquisition system based on Weibo user data

The theory of the six degrees of separation states that there are no more than six people between someone and any stranger, meaning that someone can meet any stranger through no more than six intermediaries. As one of the most popular social media applications in China, Weibo and its user data can be employed as the testing tool for the practical significance the theory holds. In this paper, an indirect network acquisition system containing two subsystems: Weibo user data acquisition system and user relationship analysis system has been established by Web crawler, and the system’s availability and effectiveness have been tested by system service test, which shows the possibility of the Indirect network acquisition system.
Multilabel causal variable discovery in multisource

Abstract Multilabel causal feature selection, as a well‐known and effective approach in dealing with high‐dimensional multilabel data, is a popular topic. Amount of causal feature selection algorithms have achieved a great deal of success in classification and prediction tasks. However, the descriptive information of data is collected from different data sources in many practical applications. While few researches focus on the causal variable discovery in multisource environments due to the complex causal relationships. To address these problems, we propose a causal feature selection framework in multisource environments to solve the above problems. Firstly, we mine the causal mechanism with respect to the class attribute under the assumption that only a single data source is included. Secondly, by utilizing the concept of causal invariance in causal inference, we formulate the problem of causal feature selection with multiple data sources as a search problem for an invariant set across data sources. In addition, we give the upper and lower bounds of the causal invariant set. Finally, we design a novel multisource multilabel causal feature selection (MMCFS) algorithm. To verify the effectiveness of the proposed algorithm, we compare it with 12 feature selection methods on synthetic datasets. Experiment results show that the classification performance of MMCFS achieves highly competitive performance against other comparing algorithms.
An Adaptable Indexing Pipeline for Enriching Meta Information of Datasets from Heterogeneous Repositories

Dataset repositories publish a significant number of datasets continuously within the context of a variety of domains, such as biodiversity and oceanography. To conduct multidisciplinary research, scientists and practitioners must discover datasets from various disciplines unfamiliar with them. Well-known search engines, such as Google dataset and Mendeley data, try to support researchers with cross-domain dataset discovery based on their contents. However, as datasets typically contain scientific observations or collected data from service providers, their contextual information is limited. Accordingly, effective dataset indexing can be impossible to increase the Findability, Accessibility, Interoperability, and Reusability (FAIRness) based on their contextual information. This paper presents an indexing pipeline to extend contextual information of datasets based on their scientific domains by using topic modeling and a set of suggested rules and domain keywords (such as essential variables in environment science) based on domain experts’ suggestions. The pipeline relies on an open ecosystem, where dataset providers publish semantically enhanced metadata on their data repositories. We aggregate, normalize, and reconcile such metadata, providing a dataset search engine that enables research communities to find, access, integrate, and reuse datasets. We evaluated our approach on a manually created gold standard and a user study.
A robust three-stage central difference Kalman filter for nonlinear discrete-time systems considering faults and unknown inputs

In this study, a novel robust three-stage central difference Kalman filter (ThSCDKF) is proposed for nonlinear discrete-time systems involving unknown inputs (uncertainties) and fault terms in the state and measurement equations. It is assumed that the evolution models of the fault and unknown inputs are not available and there is no prior information about the statistical properties of these factors. The proposed method is developed by decoupling an Augmented CDKF (ACDKF) via U-V transformation. By modifying the measurement stage, a robust ThSCDKF is proposed to achieve a robust state estimation when we do not have prior knowledge about the statistical model of the fault or uncertainties. The stability of the proposed filters is proved by the Lyapunov theory. The proposed methods have been applied to the on-board calibration of a star sensor. Finally, the effectiveness of the proposed filters is demonstrated by conducting different simulations.
Anti-UAV High-Performance Computing Early Warning Neural Network Based on PSO Algorithm

In order to effectively solve the problem that the radar detection system is difficult to detect the “low, small, slow” UAV, the high-performance computing early warning neural network is used to recognize the air UAV in real time and extract the target category and image space location information; the PSO algorithm is used to optimize the parameters of the anti-UAV to ensure that the anti-UAV not only relies on factors but also fully combines the dependence of the visual field factor to quickly obtain the optimal solution through analyzing the high-performance computing early warning neural network in this paper. This algorithm is used to initialize the anti-UAV resources and improve the global optimization capability of the algorithm proposed in this paper. Finally, the experimental results show that the proposed PSO algorithm has better high-performance computing early warning performance to meet the actual needs of network high-performance computing early-warning neural networks.
Learning quantum graph states with product measurements

We consider the problem of learning N identical copies of an unknown n-qubit quantum graph state with product measurements. These graph states have corresponding graphs where every vertex has exactly d neighboring vertices. Here, we detail an explicit algorithm that uses product measurements on multiple identical copies of such graph states to learn them. When n ≫ d and N = O(d log(1/ϵ) + d <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> log n), this algorithm correctly learns the graph state with probability at least 1 – ϵ. From channel coding theory, we find that for arbitrary joint measurements on graph states, any learning algorithm achieving this accuracy requires at least Ω(log(1/ϵ) + d log n) copies when $d = o\left( {\sqrt n } \right)$. We also supply bounds on N when every graph state encounters identical and independent depolarizing errors on each qubit.
Efficient Complete Coverage Path Planning for Collaborative Survey Tasks by AUVs

The need for survey tasks by AUVs is constantly increasing due to heavily used port facilities and the need for environmental monitoring. The use of teams of AUVs can significantly accelerate the complete coverage but requires greater planning effort. This paper describes approaches to plan and adapt collaborative survey tasks in real time by decomposing the work space with computational geometry methods into more easily solvable sub-problems. The order is then determined and the sub-problems are then distributed among the agents through various methodologies such as auction procedures.
Colorectal segmentation in medical images

Colorectal cancer is one of the digestive tract malignant tumors in life. In recent years, the incidence rate of rectal cancer has increased significantly in China. The segmentation of CT images of colorectal tumors is a tedious, time-consuming and expensive job.When diagnosing rectal cancer, it would be helpful to accurately segment the rectal tumor region from CT images so that doctors could make more accurate diagnoses and thus have better results for the disease. Based on the literature review, this paper presents medical image segmentation algorithms, including traditional algorithms based on energy minimization, and evaluates them according to the metrics used. The paper also describes the public datasets available for use in the study. In this paper, various segmentation methods are analyzed in detail. Their theoretical and practical characteristics are described for each group of methods, and the most advanced and promising methods are proposed. This paper can help understand the available colorectal segmentation methods, making developing new and more effective and improving existing methods easier. The method is simple to interact with, robust and accurate, and can effectively assist physicians in achieving diagnosis and surgical planning.
Extracting events from Daily Drilling Reports using Fuzzy String Matching

Continuous monitoring of oil drilling operations reduces process interruptions and equipment failure. It also contributes to the development of Key Performance Indicators, which leads to more efficient resource management. Daily Drilling Reports (DDRs) have long been the primary way of recording noticeable events, such as stuck pipe. DDRs came to constitute a valuable information base for most oil drilling companies. However, the task of extracting knowledge from DDRs can be costly and time-consuming. This work proposes an approach to recognise drilling events in DDRs using a rule-based language processing method called Fuzzy String Matching (FSM). We applied the FSM algorithm to search for a set of predefined keywords and key phrases to extract possible Invisible Lost Time (ILT) events from DDRs that may indicate risks or low operational efficiency. The fuzzy part of the algorithm allows the identification of terms or expressions that match the pre-established ones approximately rather than exactly, accounting for typos and different suffixes or prefixes. The proposed solution was applied on a data set of 392 real-world DDR records from a drilling company using a set of six ILT event’s key phrases annotated by Subject Matter Specialists. This process can be readily replicated to other events. The results show that in 116 reports tagged as normal, 92 records were identified as possible ILT events, which represents, in hours, 56% of the total drill normal time. Such promising results can lead to very significant improvements in identifying and extracting drilling events within DDRs.
Health assessment method of regional power grid topology based on deep neural network

With the rapid development of social economy, users have higher requirements for power quality. As the last level of power system, the reliability research of distribution network has been paid more and more attention. However, due to the random failure of system components, some or even all of the system functions are lost, which brings more and more huge economic and social losses to the normal production and life of modern society. This paper analyzes the topology of distribution network by using deep neural network, reveals the influence of network topology similarity on reliability from the perspective of network graph theory, and obtains regular conclusions. On this basis, the failure rate level of key branches, grid structure and access location of distributed power generation are further evaluated. The mathematical relationship between network topology and reliability is described with the index as the medium. Finally, the linkage optimization of reliability index is realized through the optimization of the above indexes, which solves the unclear optimization objectives in traditional planning. It pursues local optimization and ignores global optimization. Experiments show that the research content of this paper can effectively evaluate the health of power grid structure, and provide a reference for the research of distribution network graph theory.
Construction and application of knowledge graph for fault diagnosis of turbine generator set based on ontology

Abstract Aiming at the problems of complex structure and multi-source heterogeneity, imperfect knowledge representation, single knowledge extraction method, and difficulty of sharing and reuse in the knowledge field of turbine generator set fault diagnosis. The construction of knowledge graph is studied from multiple dimensions such as experts, fault characteristics, diagnosis techniques, research results, and solutions in the field for fault diagnosis knowledge of turbine generator set. And an ontology model of fault diagnosis knowledge for the turbine generator set is constructed. The entities, attributes, and relationships of the fault diagnosis knowledge graph for the turbine generator set are represented based on the model. The knowledge graph data are stored by the Neo4j graph database. The problems caused by multi-source and heterogeneous knowledge, fuzzy knowledge, and difficulty sharing, was solved in this field. The knowledge search system and automated quiz system based on knowledge graph are developed using the B/S framework. Many functions are realized by the knowledge graph, such as knowledge correlation, intelligent retrieval, visual display, and automated quiz, which improves the service and sharing ability of fault diagnosis knowledge for turbine generator set. Finally, the effectiveness and superiority of the system are verified by an example of a turbine generator set.