Reviewerly/exHarmony huggingface dataset:
Scaling A Blockchain System With Layered Architecture

Blockchain Technology (BCT), characterized by its decentralized and distributed ledger structure, records transactions securely using cryptographic methods. As BCT continues to evolve rapidly, it’s becoming increasingly integral to distributed system architectures. However, scalability remains a critical challenge for researchers, especially as transaction volume, block size, or node count grows, potentially hindering the development of distributed ecosystems. This paper introduces a federated layered model aimed at creating a scalable Blockchain system. This model ensures immediate, atomic updates across all nodes, including cross-zone transactions. The proposed system’s architecture is not only scalable but also flexible and manageable, effectively addressing issues like network congestion and asymmetric node distribution. When compared to traditional blockchain models, this new architecture significantly enhances scalability.
17th International Workshop on Models and Evolution Special Theme: Sustainability (ME 2023)

Model artifacts are subject to constant evolution throughout the lifecycle of systems. The evolutionary pressure emerges from various technical and business factors throughout the overall software/system engineering endeavor. Pertinent examples of such factors include changing requirements, changing environment, and changing user base, all of which give rise to unique evolutionary challenges in various system artifacts from architecture to implementation, and even in informal artifacts, such as documentation. These challenges, if left unmanaged, may lead to deteriorating quality attributes, and in severe cases inconsistent artifacts or even incorrect artifacts, preventing the system from operating as intended. Therefore, proper support for efficient and effective evolution is required. The Models and Evolution workshop promotes novel theories, techniques, and tools to support evolution. To this end, the workshop brings together researchers and practitioners to discuss the latest developments on the topic.
Generalizations of Matrix Multiplication can solve the Light Bulb Problem

In the light bulb problem, one is given as input vectors $x_{1}, \ldots, x_{n}, y_{1}, \ldots, y_{n} \in\{-1,1\}^{d}$ which are all uniformly random. They are all chosen independently except for a planted pair $\left(x_{i^{*}}, y_{j^{*}}\right)$ which is chosen to have correlation $\rho$ for some constant $\rho\gt 0$. The goal is to find the planted pair. The light bulb problem was introduced over 30 years ago by L. Valiant, and is known to have many applications in data analysis, statistics, and learning theory. The naive algorithm runs in $\Omega\left(n^{2}\right)$ time, and algorithms based on Locality-Sensitive Hashing approach quadratic time as $\rho \rightarrow 0$. In 2012, G. Valiant gave a breakthrough algorithm running in time $O\left(n^{(5-\omega)} /(4-\omega)\right)\lt O\left(n^{1.615}\right)$, no matter how small $\rho\gt 0$ is, by making use of fast matrix multiplication. This was subsequently refined by Karppa, Kaski, and Kohonen in 2016 to running time $O\left(n^{2 \omega / 3}\right)\lt $ $O\left(n^{1.582}\right)$, but is essentially the only known approach for this important problem. In this paper, we propose a new approach based on replacing fast matrix multiplication with other variants and generalizations of matrix multiplication, which can be computed faster than matrix multiplication, but which may omit some terms one is supposed to compute, and include additional error terms. Our new approach can make use of a wide class of tensors which previously had no known algorithmic applications, including tensors which arise naturally as intermediate steps in border rank methods and in the Laser method. We further show that our approach can be combined with locality-sensitive hashing to design an algorithm whose running time improves as $\rho$ gets larger. To our knowledge, this is the first algorithm which combines fast matrix multiplication with hashing for the light bulb problem or any closest pair problem, and it leads to faster algorithms for small $\rho\gt 0$. We then focus on tensors for “multiplying“ $2 \times 2$ matrices; using such small tensors is typically required for practical algorithms. In this setting, the best prior algorithm, using Strassen’s algorithm for matrix multiplication, yields a running time of only $O\left(n^{1.872}\right)$. We introduce a new such low-rank tensor we call $T_{2112}$, which has omissions and errors compared to matrix multiplication, and using it, we design a new algorithm for the light bulb problem which runs in time $O\left(n^{1.797}\right)$. We also explain why we are optimistic that this approach could yield asymptotically faster algorithms for the light bulb problem.
Scalable Blockchain Through Prioritised Sharding

Blockchain Technology (BCT) relies on trust assumptions within distributed computing, involving independent nodes. Addressing scalability issues in Blockchain remains a continuous endeavor for researchers. In this research paper, authors introduced Prioritised Sharding (P-Sharding), a novel approach that enhances the scalability of Blockchain systems by prioritizing nodes based on their performance. We accomplish this through the use of advanced algorithms and real-time analytics. Our empirical findings demonstrate marked enhancements in aspects such as response times to queries, the accessibility of data, and the total throughput of the system. Moreover, our approach successfully diminishes the common bottlenecks imputed to the conventional sharding methods. This research significantly adds to the discussion regarding tactics for scalable data management, offering practical solutions for extensive, data-centric enterprises.
Research on Health Status Evaluation of Measurement and Control Devices by Fusing Chip-Board-Device Information

Measurement and control devices are important secondary equipment in substations, and their health status can provide guidance for equipment condition maintenance and troubleshooting, and it is crucial to ensure the health of devices for safe and stable operation of substations. However, the existing secondary equipment assessment focuses on equipment or board-level information and lacks an overall consideration of chip-board-device. This paper establishes a chip-board-complete three-level device health evaluation system covering the core modules of the measurement and control device and proposes a fuzzy comprehensive rating method. Specifically, it is as follows: firstly, the typical structure of the measurement and control device is introduced; secondly, the three-level equipment health evaluation system of the measurement and control device incorporating chip-board-complete machine information is given; again, based on the normal-type affiliation function, the fuzzy comprehensive evaluation method of the measurement and control device is given; finally, the effectiveness of the method is verified by combining with examples.
Multi-view Self-supervised Object Segmentation

Robots often operate in open-world environments, where the capability to generalize to new scenarios is crucial for robotic applications such as navigation and manipulation. In this paper, we propose a novel multi-view self-supervised framework (MVSS) to adapt off-the-shelf segmentation methods in a self-supervised manner by leveraging multi-view consistency. Pixel-level and object-level correspondences are established through unsupervised camera pose estimation and cross-frame object association to learn feature embeddings that the same object are close to each other and embeddings from different objects are separated. Experimental results show that it only needs to observe the RGB-D sequence once without any annotation, our proposed method is able to adapt existing methods in new scenarios to achieve performance close to that of supervised segmentation methods.
The Development of a Visual Servo Simulator Based on ROS2 and MuJoCo

In developing a visual servoing scheme, simulation is important for testing robot behavior and control algorithms in different surrounding environments. Robot Operating System 2 (ROS2) is an important framework and tool for quickly building robot systems, while Multi-Joint dynamics with Contact (MuJoCo) is an important robot simulation engine. However, ros2 officially does not currently provide an interface for MuJoCo simulation. This paper presents the development of a simulator tailored for visual servoing scenarios and 7-degree-of-freedom (DOF) robots, utilizing ROS2 and MuJoCo. Our purpose is to facilitate the testing of visual servoing schemes and robot controllers. Specifically, We have utilized this simulator to implement position-based visual servoing (PBVS) and image-based visual servoing (IBVS) to verify the functionality and performance of the designed simulator. Finally, the experiment shows that the simulator can complete visual servo tasks and demonstrate good stability.We anticipate that this simulator can be utilized to develop different visual servo algorithms in the future.
Memory Enabled Segmentation of Terrain for Traversability based Reactive Navigation

This article presents a novel 2D traversability image estimation for local reactive navigation, that attributes the fusion of a novel Convolutional Neural Network (CNN) for coarse semantic segmentation on terrain roughness, with surface geometric normals. The proposed segmentation model consists of a U-Net based Encoder-Decoder architecture with a MobileNet V3 Large backbone for real-time performance. At the bottom layer, the bottleneck block commonly found in a U-Net has been enhanced with an Atrous Spatial Pyramid Pooling (ASPP) block. In addition, a SEResNet based decoder instead of the classical stacked convolution blocks of U-Net has been implemented, while a concatenation layer has been added at the output. Moreover, the development of a novel memory module to dynamically update the semantic segmentation image based on certainty heat maps is also shown. The efficacy of the proposed scheme has been evaluated in real-life environments such as indoors, outdoors and subterranean (SubT) environments on a Pioneer 3AT mobile robot.
Heuristic Online Path Planning for Mobile Air Purification and Disinfection Robot In Regulating Indoor Air Quality

In light of past epidemics, people have become more aware of the possibility of viruses on object surfaces and in the air. As a result, there is an increasing demand for robots capable of autonomously disinfecting surfaces and purifying the air. This paper focuses on a mobile robot equipped with air purification and UV-C LED disinfection to combat airborne pathogens and environmental pollution. Through the onboard sensors, the proposed algorithm uses gaussian process model to predict the high potential air pollution area in real-time without the requirement of a distributed array of sensors, and it avoids environmental modifications. Then we propose the hybrid artificial potential field (HAPF) by integrating the predicted result with the artificial potential field method, to generate a heuristic path that can navigate the robot to the affected areas with obstacle avoidance. Experimental testing in simulated and real-world environments demonstrates the effectiveness of the proposed approach in rapidly planning purification paths, marking significant progress in automated air purification, and disinfection technology.
Inverse Reinforcement Learning with Attention-based Feature Extraction from Video Demonstrations

The sparse reward problem makes the robot difficult to get effective feedback during learning skills with reinforcement learning, which will seriously affect the training efficiency. In this paper, we propose an inverse reinforcement learning (IRL) method to understand expert intent from video demonstrations and further provide dense rewards for robots to learn the task. We learn representations related to task progress by matching different videos in the embedding space based on their temporal alignment and incorporating attention mech-anisms to focus on the task-relevant features. Based on this, we design an efficient and robust dense reward function that enhances the efficiency of the reinforcement learning process. We validated our method in robotic manipulation tasks, and the experimental results showed that our approach exhibits faster convergence speed and stability and have the generalization ability in skill learning with video demonstrations from unseen embodiment.
Exhaustiveness Does Not Necessarily Mean Better: Selective Task Planning for Multi-robot Systems

The performance of multi-robot systems heavily relies on efficient task allocation and motion coordination. However, for a group of a large number of robots, finding the optimal solution is inevitably time-consuming and may become impossible. Recognizing that tasks vary in their impact on system performance, our main idea is to identify their critical subset that significantly influences the entire system, and enhance task allocation efficiency by optimally planning critical tasks while distributing the remaining tasks randomly or with simple strategies. We call this approach Selective Multi-robot Task Planning (SMTP), which contributes to significantly reducing the computational requirements and solution time, and in the meanwhile, maintaining the system performance. In addition, by implementing a filtering mechanism based on the conditional expectation to eliminate less essential tasks, SMTP shows high extendability, maximizes task allocation efficiency, and balances computational efficiency and solution quality. Massive simulation and real experiments demonstrate that our algorithm decreases the computation time and maintains the properties of the base system. Large-scale experiments show that our approach only takes 11% computation time to reach 80% optimization objective and 94.8% traffic balance performance.
An Efficient and Robust Tightly Coupled Framework for Lidar-IMU Localization and Mapping

In this article, we propose a framework for tightly-coupled lidar inertial odometry, which can achieve highly accuracy in real-time ego-motion estimation and map building of the robot. The estimated motion from inertial measurement unit (IMU) preintegration de-skews point clouds. Then, an initialization method based on MAP estimation is used to align the Lidar frame with the world frame and provide reliable initial values for the system. In order to make the scan matching more accurate, we track the state of each frame, and transform the feature points to the reference frame to build a local map for matching with key frames. To ensure high performance in real-time, an efficient sliding window was used to optimize the keyframes we chose. After obtaining the optimized pose of the keyframe, we further optimize the pose of the normal frame by pose constraints. In addition, loop detection is integrated into our algorithm framework to eliminate accumulated errors, making our algorithm to obtain a globally consistent estimate. The experiment results demonstrate that our method can achieve high accuracy in a variety of indoor and outdoor environments even under fast motion conditions or with insufficient features.
MultiRoboLearn: An open-source Framework for Multi-robot Deep Reinforcement Learning

It is well known that it is difficult to have a reliable and robust framework to link multi-agent deep reinforcement learning algorithms with practical multi-robot applications. To fill this gap, we propose and build an open-source framework for multi-robot systems called MultiRoboLearn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . This framework builds a unified setup of simulation and real-world applications. It aims to provide standard, easy-to-use simulated scenarios that can also be easily deployed to real-world multi-robot environments. Also, the framework provides researchers with a benchmark system for comparing the performance of different reinforcement learning algorithms. We demonstrate the generality, scalability, and capability of the framework with two real-world scenarios using different types of multi-agent deep reinforcement learning algorithms in discrete and continuous action spaces.
Optimal Testing of Generalized Reed-Muller Codes in Fewer Queries

A local tester for an error correcting code $C\subseteq\Sigma^{n}$ is a tester that makes Q oracle queries to a given word $w\in\bar{\Sigma}^{n}$ and decides to accept or reject the word w. An optimal local tester is a local tester that has the additional properties of completeness and optimal soundness. By completeness, we mean that the tester must accept with probability 1 if $w\in C$. By optimal soundness, we mean that if the tester accepts with probability at least $ 1-\varepsilon$ (where $\varepsilon$ is small), then it must be the case that w is $O(\varepsilon/Q)$-close to some codeword $c\in C$ in Hamming distance. We show that Generalized Reed-Muller codes admit optimal testers with $Q=(C_{p}q)^{\lceil\frac{d+1}{q-1}\rceil+O(1)}$ queries for $C_{p}=(2p-1)^{\frac{1}{p-1}}$. Here, for a prime power $q=p^{k}$, the Generalized Reed-Muller code, $\operatorname{RM}[n, q, d]$, consists of the evaluations of all n-variate degree d polynomials over $\mathbb{F}_{q}$. As $p,q$, and d go to infinity, Q matches the known lower bound of $q^{\frac{d+1}{q-1}}$ up to a multiplicative factor of 1. Previously, no tester achieving this query complexity was known, and the best known testers due to Haramaty, Shpilka and Sudan [21] (which is optimal) and due to Ron-Zewi and Sudan [33](which was not known to be optimal) both required $q^{\lceil\frac{d+1}{q-q/p}\rceil}$ queries. Our tester achieves query complexity which is polynomially better than by a power of $p/(p-1)$, which is nearly the best query complexity possible for generalized Reed-Muller codes. The tester we analyze is constructed using the same framework of Ron-Zewi and Sudan, and in fact our analysis shows that their tester is optimal as well. More generally, our methods allow us to prove that a wide class of testers, which follow the form of the Ron-Zewi and Sudan tester, are optimal. This result applies to testers for all affine-invariant codes (which are not necessarily generalized Reed-Muller codes).
Experimental Work on Competence Building of a University Teacher in Inclusive Education by Means of Digital Technology

&lt;p&gt;The article presents the results of experimental work on the formation of the competence of university teachers in inclusive education by means of information technology in additional professional education. Information technology is implemented on the basis of two components: information and technology. The didactic digital complex acts as an information component, the technological component provides the personification of the educational process, the use of a set of forms and methods of training for intensifying the process of additional professional education using information and communication technologies, creating conditions for the formation of pedagogical communities for the exchange of experience and interactive solving of the professional tasks in. 353 teachers from 6 educational universities were recruited for this study. The effectiveness of the study was confirmed by the method of mathematical statistics (Pearson's criterion).&lt;/p&gt;
A dual-modal dynamic contour-based method for cervical vascular ultrasound image instance segmentation

&lt;abstract&gt;&lt;p&gt;&lt;italic&gt;Objectives:&lt;/italic&gt; We intend to develop a dual-modal dynamic contour-based instance segmentation method that is based on carotid artery and jugular vein ultrasound and its optical flow image, then we evaluate its performance in comparison with the classic single-modal deep learning networks. &lt;italic&gt;Method:&lt;/italic&gt; We collected 2432 carotid artery and jugular vein ultrasound images and divided them into training, validation and test dataset by the ratio of 8:1:1. We then used these ultrasound images to generate optical flow images with clearly defined contours. We also proposed a dual-stream information fusion module to fuse complementary features between different levels extracted from ultrasound and optical flow images. In addition, we proposed a learnable contour initialization method that eliminated the need for manual design of the initial contour, facilitating the rapid regression of nodes on the contour to the ground truth points. &lt;italic&gt;Results:&lt;/italic&gt; We verified our method by using a self-built dataset of carotid artery and jugular vein ultrasound images. The quantitative metrics demonstrated a bounding box detection mean average precision of 0.814 and a mask segmentation mean average precision of 0.842. Qualitative analysis of our results showed that our method achieved smoother segmentation boundaries for blood vessels. &lt;italic&gt;Conclusions:&lt;/italic&gt; The dual-modal network we proposed effectively utilizes the complementary features of ultrasound and optical flow images. Compared to traditional single-modal instance segmentation methods, our approach more accurately segments the carotid artery and jugular vein in ultrasound images, demonstrating its potential for reliable and precise medical image analysis.&lt;/p&gt;&lt;/abstract&gt;
Russian Publications in Web of Science: A bibliometric study

This article presents a bibliometric study of 1.38 million Russian publications indexed in Web of Science as of May 2022 without any restrictions as to document types, time periods, scientific disciplines, etc. From this perspective, the present analysis reflects Russian research’s true presence and visibility in the most prestigious scientific literature database. The main results obtained are: a) There was a rapid increase in research production in the 2010s, but the share of the Russian output in the global research production is still below 3%. b) International collaborative publications account for about 30% of Russian papers but around 70% of Russian citations. c) Physics, chemistry, and engineering are the most productive Russian research areas, but their citation impact is below the world average in those respective fields. d) The most frequently collaborating countries are the United States, Germany, and France, but Canada and Switzerland consistently contribute to the greatest relative citation impact of collaborative papers in the top ten research areas.
Visual Representations for Data Analytics: User Study

One of the characteristics of big data is its internal complexity and also variety manifested in many types of datasets that are to be managed, searched, or analyzed. In their natural forms, some of the data entities are unstructured, such as texts or multimedia objects, while some are structured but too complex. In this paper, we have investigated how visualizations of various complex datasets perform in the role of universal data representations for both human users and deep learning models. In a user study, we have evaluated several visualizations of complex relational data, where some proved their superior performance with respect to the precision and speed of classification by human users. Moreover, the same visualizations also led to effective classification performance when used with deep learning models.
Metaverse Retail and Sustainability: A Thematic Review

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