Reviewerly/exHarmony huggingface dataset:
Efficient SpMM with Kernel Switching on GPUs for Graph Neural Networks

Graph neural networks are frameworks for deep learning on graph-structured data. When updating node information, graph neural networks need to aggregate information from peripheral node, which can be represented mathematically as a sparse matrix multiplied by a dense matrix. Because many calculations in graph neural networks are based on SpMM-like operations, accelerating SpMM is significant for overall acceleration of graph neural network training. However, due to the highly irregular memory access caused by the sparse matrix, the data locality of this computing kernel is poor. To address this issue, we have designed a method to accelerate SpMM on GPU. Our method adopts two sparse matrix storage formats and two task partitioning methods for different types of sparse matrices. Because the sparse matrix storage format is commonly used, no additional data conversion is required. The row splitting method is based on the CSR format, while the non-zero splitting method is based on the COO format. The threshold for the non-zero splitting method is set according to the average number of non-zero entries per row of the sparse matrix, and the optimal calculation kernel is selected for different sparse matrices. Experimental evaluation using real-world graphs from the SuiteSparse collection shows that our technique achieves an average speedup of 1.43x (up to 2.26x) for SpMM over the vendor-optimized library cuSPARSE[1] by NVIDIA on an NVIDIA V100 GPU.
Design and Implementation of External Storage Large-Scale Graph Computing System

With the rise of big data, graph computing has become prevalent in many fields. To effectively address such problems, large-scale graph computing systems have emerged. Most existing systems adopt memory-based computing frameworks. However, the rapid growth of data scale often leads to insufficient memory storage. To tackle these issues, an external storage-based graph computing system called DCGraph has been designed and implemented. The bottleneck of large-scale graph computing systems based on single-machine external storage usually lies in external storage I/O. Hence, DCGraph has been optimized for external storage I/O. In the preprocessing stage, graph data is compressed and transformed into a two-dimensional CSC format. Each block records the offset of its data. This guarantees that data of the same target vertex is continuously stored in external storage within the same block. DCSC format is selected for compressing data blocks with many vertices of zero in-degree. Additionally, a jump-out calculation mode suitable for the CSC format is designed for specific graph calculation algorithms. During data reading, it is determined whether to skip the current record based on its subscript and offset. Selective scheduling is used to skip inactive data blocks during each layer iteration to reduce unnecessary data reading and processing. This approach reduces both the number and total amount of I/Os. Experimental results show that compared to GridGraph, DCGraph achieves a speedup ratio of 1.5 to 2.8 times while reducing the total amount of I/Os in calculation to less than half of that of GridGraph.
A Method of Generating and Tracing Unmarked Text Watermarking Based on Thesaurus

Text watermarking technology has been applied broadly to trace the source of data leakage and achieve copyrights protection. However, modifying text format or text structure, the two essential methods of watermarking technology, lack resistance in the application of OCR and other identification technologies, for it removes both the format and the structure of the text. Therefore, this paper proposes an OCR-resistant text watermarking technique independent of the structure and the format in Chinese, and explores the tracing watermarking of such unmarked text. Firstly, binary watermark sets are generated based on parity check. Then, the dictionary set of N groups is established on synonym pairs denoted by 0 and 1 respectively. Lastly, the text to be encoded is sequentially iterated over and the keywords in the text are replaced by the corresponding synonyms in the thesaurus according to the binary watermark bit. The method presented in this paper can effectively resist the invalid watermark caused by the modification of the format and the structure. Even after the text is scanned or reformatted by OCR, the watermark still exists. At the same time, invisible tracing watermarking secures watermark protection and text tracing because parity check can enhance the error detection capability and separated word database used to generate index can improve the operational performance. Experiments show that this method has good invisibility and high resistance to various text attacks, including format transformation, structure transformation, and retyping the text.
Challenges in Combining Big Data and Computer Vision Technologies in Current Context from an Algorithmic Experiment Perspective

The combination of big data and computer vision technology is an essential way to monetize the value of big data and has been widely used in traffic violation video identification and processing. This paper first introduces the combination of big data and computer vision technology, theoretically analyzes their application fields, and sets up experiments using the OTB2015 dataset's 9 groups of test video sequences to test the proposed algorithm's performance. From the comparison of experimental results, the difficulties faced by the combination of the two in major fields are identified, and the critical dependencies for solving these difficulties are proposed.
Cohort-based Federated Learning Credit Evaluation Method in the Metaverse

Considered the development paradigm of the next generation of the Internet after the network and mobile Internet revolution, the metaverse has received extensive attention. However, potential security problems in the metaverse hinder its further development. Among them, federated learning (FL), as a distributed learning framework that shares the important asset User Generated Content (UGC) in the meta-universe, is unable to withstand hidden poisoning attacks and is still threatened by security vulnerabilities. Therefore, this paper proposes a cohort-based credit evaluation method for federated learning (CoCE) to address the privacy and integrity deficiencies of traditional federal learning. In this article, by constructing cohorts, we alleviate the problem of uneven data distribution in federated learning and achieve multitask learning. In addition, we combine cohort mechanisms to evaluate the comprehensive credit of clients in terms of the number of customers, data distribution, and model preferences, and to screen out potential attackers in the course of federated learning. Finally, we have carried out experiments on open datasets to verify the robustness and superiority of CoCE.
Semantic Deep Mining Method for Recommendation Based on Small-World Graphs

Currently, the mainstream recommendation techniques mainly include categorized directory, keyword search, and collaborative filtering. However, due to the complex and non-uniform classification rules of various information, users of categorized directory methods are prone to problems such as too large or too small a range. The keyword-based search techniques, on the other hand, can hardly avoid the dilemma of falling into local information and ignoring other keywords or other fields of information. For collaborative filtering techniques, again, problems such as "cold start" and "Matthew effect" cannot be avoided. Therefore, in this paper, we propose a semantic deep mining-based recommendation method HRS, using a vector retrieval method based on navigable small-world graphs, and a semantic model based on the data from the technical specialized lexicon of the constructed requirement knowledge base. When node data is inserted into the navigable small-world graph, node lookup is performed, i.e., nodes that are close nearest neighbors to the node to be inserted are found, and subsequently the node to be looked up is connected to the first n close nearest neighbors to complete the node insertion. Finally, experimental results are used to compare the effectiveness of the method in terms of search accuracy, search completion rate and F1 value. The reliability of the recommended method proposed in this topic is demonstrated through experiments.
Design of Tile-Based VR Transcoding and Transmission System for Metaverse

Watching panoramic videos is the embryonic form of metaverse experience in recent years. In order to reduce the cost of user access to the metaverse services, developers are starting to experiment with ways to watch panoramic videos on mobile devices. Therefore, how to guarantee high bit-rate and low latency when watching panoramic video becomes a very critical issue. This paper develops a complete system for watching panoramic videos, aiming at the problem of watching high bit-rate panoramic videos on mobile devices. The system consists of three main parts: the video processing part, the video watching part, and the data analysis part. The system is based on a web application, and users can use the system by a web browser. In order to guarantee high bit-rate and low latency when watching, the system uses MPEG-DASH for playing tiled panoramic video with the Adaptive Bit-Rate (ABR) algorithm. The video is divided into six tiles corresponding to the six faces of the 3D scene. The player can automatically change the bit-rate of different tiles of the video according to the user watching direction and buffer area. Using A-frame to create a 3D scene on a website allows users to watch the video with or without a VR headset.
An Online Incremental Learning Framework for HPC Job Power Consumption Prediction

This paper proposes a high-performance computing (HPC) job power consumption prediction framework based on online incremental learning. The framework combines traditional offline models with online incremental update mechanisms, allowing it to quickly adapt to the uncertainty and dynamic changes of power consumption behaviors in HPC jobs. We use NBeats, LSTM, Seq2seq, and GRU models as our offline models and combine them with the proposed online update mechanisms to achieve online incremental updates. We conducted experiments using real HPC job data, and the results show that our online incremental model can predict more accurately than traditional offline models. In addition, We propose an update frequency control mechanism that reduces the running time by about 30 while guaranteeing the accuracy of the prediction. This indicates that the HPC job power consumption prediction framework based on online incremental learning proposed in this paper has a wide range of applications in practical use.
Blockchain and Elections: Opportunity and Peril

This chapter discusses the opportunities and perils associated with adopting blockchain-based systems to solve longstanding problems in elections. The chapter covers some of the historical problems in elections and how they were solved, as well as some of the benefits of blockchain technologies. Several attempts to apply blockchain solutions to the election domain are analyzed and potential paths forward are suggested.
Recurrent Event Networks Based on Subgraph and Attention Enhancement

Temporal knowledge graph (TKG) reasoning, as an essential research direction in natural language processing, focuses on capturing the dynamic changes of entities and relationships over time. However, the inference task of predicting potential future events faces significant challenges as it has to deal with uncertainty, complexity, and missing data. To this end, this paper proposes a new TKG extrapolation model SubRE-NET based on the Recurrent Event Network(RE-NET). The model performs reasoning by aggregating local and global information and introduces subgraph in the encoding stage to enhance the ability to capture local correlations and temporal features of events within a time window. At the same time, the attention mechanism is introduced to solve the problem that the original model cannot distinguish the importance of nodes and relationships, and the subgraph is further enhanced by cropping technology. In the aggregation stage, an extended Relational Graph Gonvolutional Network(RGCN) is adopted to overcome the limitation of the original model that cannot capture temporal information when locally and globally aggregated. Experimental results show that compared with the baseline model RE-NET, our SubRE-NET model has achieved significant performance improvements on three event-based datasets and two public knowledge graph datasets, with an average MRR performance improvement of 11.49%. At the same time, The average performance of the Hits@1 metric is also improved by 12.38%.
Two-Stage Billet Identification Number Recognition Using Label Distribution

With the progressive automation of factories, the demand for deep learning methods capable of recognizing characters is rising. A billet identification number (BIN) is a string of characters that contains all information about the billet, but it is often oriented arbitrarily. Because each plant has different features of data, it requires time and effort to secure enough data to train the model that can be applied to each plant. In addition, the existing BIN recognition model confuses characters with similar shapes when rotated because it shares a feature extractor for angle estimation and character recognition. In this study, we propose a method to solve the problems and improve the BIN recognition performance. We separate the two parts of extracting angles and characters, allowing each module to independently focus on the features of the data. Label distribution is used to enhance the angle estimation accuracy with a small dataset, and the triangular distribution results in the highest accuracy. Finally, to train rotated characters, a large amount of data that are randomly rotated is required, but by separating the angle and character module, the variation within classes is reduced, resulting in high BIN recognition performance even with a small dataset.
Iterated Greedy

The iterated greedy algorithm generates a sequence of solutions by iterating over a constructive heuristic using destruction and construction phases. In the last few years, it has been employed to solve a considerable number of optimization problems, including some diversity and dispersion problems. The first part of this chapter is devoted to the revision of the basic components for the design of this metaheuristic. It also presents the changes and extensions to the original iterated greedy methodology that have been recently explored to provide advanced implementations being able to achieve high-quality solutions to difficult optimization problems. With the aim of providing additional results and insights on the application of iterated greedy to face diversity and dispersion problems, the second part of the chapter is dedicated to contribute with a perturbation-based iterated greedy for the large-scale MaxMin diversity problem (an extremely complex optimization problem, since it joins high dimensionality with a max-min objective function). Extensive experiments verify that the proposal can achieve better solution quality than the state-of-the-art optimizer for this diversity maximization problem and other competing algorithms.
Unsupervised Multimodal Remote Sensing Image Registration via Domain Adaptation

Registration of multi-modal remote sensing images with geometric distortions is one of the fundamental applications, but it remains difficult since multi-modal remote sensing images have significant differences in both radiometric and geometric features. One of the challenges is the disregarding of modality-specific information, which hinders the model from focusing on the content information of structure and texture due to differences in radiometric features. In this paper, an unsupervised Content-focused Hierarchical Alignment Network (CHA-Net) is proposed, which is constructed based on the theory of domain adaptation. The kernel idea of CHA-Net is to weaken the style differences among different modal images and achieve non-rigid multi-modal remote sensing image registration. CHA-Net is a hierarchical refinement model, where different scales of features are aligned respectively by utilizing the field calibration module and gradually generating the registration field. To be specific, CHA-Net consists of two structures: the Siamese Feature Decoupling (SFD) structure and the Hierarchical Refinement Alignment (HRA) structure. The SFD aims at reducing the style differences caused by cross-modal differences and developing a shared-weight Siamese network to map images to content feature space. The HRA enhances the ability of the network by capturing global distortions based on the Transformer model. Experiments on public datasets indicate that compared with other methods, CHA-Net performs better when geometric and radiometric distortions appear.
X, Bitcoin, Decentralised Information

Some people don’t see how decentralisation could possibly “work”, in the sense of solving important problems such as resource allocation. Whether the economic system as a whole or a single business enterprise, they favour a system of centralisation and control. On the other side are those who have glimpsed the information-transmission function of markets and the burden of computation, incentivisation, and communication that would be required by any mechanism designed to replicate it. These two groups engaged in one of the great debates of 20th century economics. All the main players would have been awed by the decentralisation of Bitcoin and the information-communication flowing through X. Is it any surprise that some people want to abolish Bitcoin and control information on X while another group holds the exact contrasting position? For those familiar with the mechanism design debate, these battlegrounds have an air of inevitability. Each step of the way, more and more intellectual and technological firepower was brought to bear. Economic argument in prose. In mathematics. In more sophisticated math. Computer science. Information theory (i.e., Shannon). Information economics. Decision theory. Game theory. And now the themes resurface in the context of a digital commodity and a social media platform. Let us put it all into perspective.
Private Blockchain Envisioned Access Control System for Securing Industrial IoT-Based Pervasive Edge Computing

The Industrial Internet of Things (IIoT) is able to connect machines, analytics and people with IoT smart devices, gateway nodes and edge devices to create powerful intuitivenesses to drive smarter, faster and effective business agreements. IIoT having interconnected machines along with devices can monitor, gather, exchange, and analyze information. Since the communication among the entities in IIoT environment takes place insecurely (for instance, wireless communications and Internet), an intruder can easily tamper with the data. Moreover, physical theft of IoT smart devices provides an intruder to mount impersonation and other attacks. To handle such critical issues, in this work, we design a new private blockchain-envisioned access control scheme for Pervasive Edge Computing (PEC) in IIoT environment, called PBACS-PECIIoT. We consider the private blockchain consisting of the transactions and registration credentials of the entities related to IIoT, because the information is strictly confidential and private. The security of PBACS-PECIIoT is significantly improved due to usage of blockchain as immutability, transparency and decentralization along with protection of various potential attacks. A meticulous comparative analysis exhibits that PBACS-PECIIoT achieves greater security and more functionality features, and requires low costs for communication and computational as compared to other pertinent schemes.
Multi-Step Training Framework Using Sparsity Training for Efficient Utilization of Accumulated New Data in Convolutional Neural Networks

With the accumulation of large amounts of data that have the most significant impact on network performance and the development of hardware accelerators that can support such data processing, convolutional neural networks (CNNs) have achieved remarkable advances in various applications such as computer vision. In particular, in recent years, various new data suitable for user environments have been obtained from numerous mobile devices, and accordingly, methods have to be developed to train CNNs with only new data on the existing pre-trained network without access to the data used for the pre-training process on server platforms. Nevertheless, only a few studies have considered efficient training schemes for new data. In this study, we propose a multi-step training framework that efficiently utilizes accumulated new data for CNN training. In detail, to significantly improve the performance of CNNs, the proposed method creates unimportant filters while preserving knowledge of previous data through selective sparsity training and can effectively utilize these filters for CNN training of new data through diverse re-initialization and adaptive online distillation techniques. In addition, the proposed multi-step sparsity training with multi-step loss enables iterative network training of new data. The results of extensive experiments performed on various datasets show that the proposed method outperforms many existing methods, including fine-tuning.
A Tiny Transformer-Based Anomaly Detection Framework for IoT Solutions

The widespread proliferation of Internet of Things (IoT) devices has pushed for the development of novel transformer-based Anomaly Detection (AD) tools for an accurate monitoring of functionalities in industrial systems.Despite their outstanding performances, transformer models often rely on large Neural Networks (NNs) that are difficult to be executed by IoT devices due to their energy/computing constraints.This paper focuses on introducing tiny transformer-based AD tools to make them viable solutions for ondevice AD.Starting from the state-of-the-art Anomaly Transformer (AT) model, which has been shown to provide accurate AD functionalities but it is characterized by high computational and memory demand, we propose a tiny AD framework that finds an optimized configuration of the AT model and uses it for devising a compressed version compatible with resource-constrained IoT systems.A knowledge distillation tool is developed to obtain a highly compressed AT model without degrading the AD performance.The proposed framework is firstly analyzed on four widely-adopted AD datasets and then assessed using data extracted from a real-world monitoring facility.The results show that the tiny AD tool provides a compressed AT model with a staggering 99.93% reduction in the number of trainable parameters compared to the original implementation (from 4.8 million to 3300 or 1400 according to the input dataset), without significantly compromising the accuracy in AD.Moreover, the compressed model substantially outperforms a popular Recurrent Neural Network (RNN)-based AD tool having a similar number of trainable weights as well as a conventional One-Class Support Vector Machine (OCSVM) algorithm.