Reviewerly/exHarmony huggingface dataset:
The Future of Cryptocurrency Blockchains in the Quantum Era

Cryptocurrency are decentralized digital money systems built on blockchain technology. The transactions are secured in these system using digital signatures based on public-key cryptography. Public-key cryptographic algorithms used for digital signatures, such as ECDSA, are vulnerable to quantum attacks. With over 2 trillion dollars market capitalization, the cryptocurrency industry will be at high risk in the Quantum Era. This paper evaluates the security of today's cryptocurrency blockchains under quantum attacks. It also reviews some of the proposed solutions to protect blockchains in the Quantum Era. Moreover, it presents a number of post-quantum digital signature schemes used to build quantum-safe blockchains. The result of this study shows that only a small portion of cryptocurrency can resist quantum attacks, while most of the cryptocurrencies used today are vulnerable to quantum attacks, with more than 99.8% of the total cryptocurrency market cap remains at risk. Useful recommendations are provided as a result of this study.
Voronoi Games Using Geodesics

In this paper, we study the single-round geodesic Voronoi games on various classes of polygons and polyhedra for two players. We prove some tight bounds on the payoffs, that is, the number of clients served by both the first player, Alice, and the second player, Bob, for orthogonal convex polygons and polyhedra for the $$\mathbb {L} _1$$ metric.
Transformer-Based Bidirectional Encoder Representations for Emotion Detection from Text

Social media influences internet users to share their sentiments, feelings, or emotions about entities. In particular, sentiment analysis classifies a text into positive, negative, or neutral. It does not capture the state of mind of an individual like happiness, anger, and fear. Therefore, emotion detection plays an important role in user-generated content for capturing the state of mind. Moreover, researchers adopted traditional machine learning and deep learning models to capture emotions from the text. Recently, transformers-based architectures achieve better results in various natural language processing tasks. Therefore, we propose a transformer-based emotion detection system, which uses context-dependent features and a one-cycle learning rate policy for a better understanding of emotions from the text. We evaluate the proposed emotion detection model using error matrix, learning curve, precision, recall, F1-score, and their micro and macro averages. Our results indicate that the system achieves a 6 % accuracy over existing models.
iPUG for Multiple Graphcore IPUs: Optimizing Performance and Scalability of Parallel Breadth-First Search

Parallel graph algorithms have become one of the principal applications of high-performance computing besides numerical simulations and machine learning workloads. However, due to their highly unstructured nature, graph algorithms remain extremely challenging for most parallel systems, with large gaps between observed performance and theoretical limits. Further-more, most mainstream architectures rely heavily on single instruction multiple data (SIMD) processing for high floating-point rates, which is not beneficial for graph processing which instead requires high memory bandwidth, low memory latency, and efficient processing of unstructured data. On the other hand, we are currently observing an explosion of new hardware architectures, many of which are adapted to specific purposes and diverge from traditional designs. A notable example is the Graphcore Intelligence Processing Unit (IPU), which is developed to meet the needs of upcoming machine intelligence applications. Its design eschews the traditional cache hierarchy, relying on SRAM as its main memory instead. The result is an extremely high-bandwidth, low-latency memory at the cost of capacity. In addition, the IPU consists of a large number of independent cores, allowing for true multiple instruction multiple data (MIMD) processing. Together, these features suggest that such a processor is well suited for graph processing. We test the limits of graph processing on multiple IPUs by implementing a low-level, high-performance code for breadth-first search (BFS), following the specifications of Graph500, the most widely used benchmark for parallel graph processing. Despite the simplicity of the BFS algorithm, implementing efficient parallel codes for it has proven to be a challenging task in the past. We show that our implementation scales well on a system with 8 IPUs and attains roughly twice the performance of an equal number of NVIDIA V100 GPUs using state-of-the-art CUDA code.
Effect of Using Embodied Avatars on Turn-taking during Conversational Activities in a Social VR Space

This study aims to explore a learner's embodiment and movements during discussion which are represented on an avatar in a social virtual reality (VR) space. From a user study, we demonstrated the validity of social VR space in an active learning (AL) setting. It is unclear whether such information represented on an avatar affects turn-taking and facilitates group discussion during AL activities. Therefore, this study conducted an experiment and clarified that turn-taking in the social VR space occurs more than on a monitor, leading to a better discussion.
Improving Online Teaching Based on Knowledge Tracing Model

During the hybrid teaching, knowledge tracing plays an important role in constructing adaptive teaching system. This study models the students' knowledge status by mining a large number of exercise records based on the improved dynamic key-value memory network (DKVMN), which is a knowledge tracing model with two external memory modules. Furthermore, the features of students' behavior are extracted to improve the prediction results of DKVMN. Since the model could depict the evolving knowledge state of students, the visualized results are displayed to both students and teachers. It could encourage students to learn the concepts that have not been mastered. On the other hand, it could help teachers to conduct teaching interventions on the high-risk students.
A K-means Optimization Algorithm Suitable for Fast Clustering of WebGIS Massive Data

Abstract K-means has the advantage of fast speed and is suitable for clustering large-scale data of WebGIS geographic information. However, due to the random selection of K-means initial clustering centers, the clustering results are unstable and the clustering accuracy is poor. Some current research documents have solved the problems of clustering accuracy and stability, but the clustering time has been greatly increased. The article proposes a grid-based K-means improved algorithm GBK-means, which is based on an adaptive grid method to obtain initial clustering centers. Firstly, the parameters of the grid division are obtained by judging the distribution state of the sample data; then, the interconnected areas of each dense grid are obtained and the cluster centers are obtained, and on this basis, the initial cluster centers are obtained. The experimental results on the real data set of WebGIS show that GBK-means has better clustering effect and faster clustering speed than K-means, K-means++, literature [2], and literature [3]. The average value of its F value, accuracy rate and adjusted Rand coefficient (ARI) is 10.9%, 11% and 11.2% higher than that of K-means. The average clustering time is 75.4%, 52.3%, 85.1%, 91.1% faster than K-means, K-means++, literature [2], and literature [3].
Supervised Noise Reduction for Clustering on Automotive 4D Radar

In the automotive industry, radar technology is an essential component for object identification due to its low cost and robust accuracy in harsh weather conditions. Clustering, an unsupervised machine learning technique, groups together individual radar responses to detect objects. Because clustering is a significant step in the automotive object identification pipeline, cluster quality and speed are especially critical. To that extent, density-based clustering algorithms have made significant progress due to their ability to operate on data sets with an unknown quantity of clusters. However, many density-based clustering algorithms such as DBSCAN remain unable to deal with inherently noisy radar data. Furthermore, many existing algorithms are not adapted to operate on state-of-the-art 4D radar systems. Thus, we propose a novel pipeline that utilizes supervised machine learning to predict noisy points on 4D radar point clouds by leveraging historical data. We then input noise predictions into two proposed cluster formation approaches, respectively involving dynamic and fixed search radii. Our best performing model performs roughly 153 percent better than the baseline DBSCAN in terms of V-Measure, and our quickest model finishes in 75 percent less time than DBSCAN while performing 130 percent better in terms of V-Measure.
SEAL: Semantically Enriched Authoring in LATEX—A Model for Scientific Discourse

Semantic tags can enrich citation graphs by inter-connecting papers with citation reasons. One of the best sources of knowledge to tell the reason for citation is the author himself. Integrating these reasons in an authoring system can help authors to choose a reason while citing. We examined various Human and Automatic authoring systems for integration of citation reasons. However, to the best of our knowledge, no such system exists that facilities authors to integrate the reasons while citing. Same is the case with Cite Packages. This research proposes integration of <i>CCRO</i>:Citations&#x2019; Context and Reasons Ontology&#x2019;s (Ihsan and Qadir, 2019) taxonomic hierarchy of reasons within document. We have developed a <i>CCRO</i> Package to semantically tag citations with reasons and to create an intra-discourse relation between research articles. Furthermore, embedding these structures within <i>RDF</i> Data Store enables the creation of knowledge graphs that become a foundation artifact for the Semantic &#x0026; Scientific Discourse.
Lightweight Urine Sediment Image Recognition Network Based on Deep Separable Residual Structure

The size of urine sediment image is small, different categories are easy to be confused, and feature extraction is difficult. This paper proposes an automatic recognition method of urine sediment images based on hourglass residual structure and super-resolution image reconstruction. First, annotate and preprocess the urine sediment image to generate a urine sediment data set. Then, the super-resolution reconstruction technology is used to reconstruct the small-size urine sediment image to adapt to the input of the deep learning model. Finally, an hourglass residual network is constructed to automatically extract the features of the urine sediment image to realize the classification and recognition of the urine sediment image. The experimental results show that the overall accuracy of the method for the recognition of 13 kinds of urine sediment images can reach 99.05%. This method is lightweight enough while maintaining the depth of the network. The number of parameters is 0.73M, which is conducive to porting to mobile devices. This paper proposes a new intelligent recognition method for urine sediment images, which has a good prospect for engineering applications.
Trojan Attacks and Defense for Speech Recognition

Mobile devices commonly employ speech recognition (SR) techniques to facilitate user interaction. Typical voice assistants on mobile devices detect a wake word or phrase before allowing users to use voice commands. While the core functionality of contemporary SR systems relies on deep learning, researchers have shown that deep learning suffers from various security issues. Among these security threats, Trojan attacks in particular have attracted great interest in the research community. To conduct a Trojan attack, an adversary must stealthily modify a target model, such that the compromised model will output a predefined label whenever presented with a trigger. Most work in the literature has focused on Trojan attacks for image recognition, and there is limited work in the SR domain. Due to the increasing use of SR systems in daily devices, such as mobile phones, Trojan attacks for SR pose a great threat to the public and is therefore an important topic of concern to mobile internet security. Despite its growing importance, there has not been an extensive review conducted on Trojan attacks for SR. This paper fills this gap by presenting an overview of existing techniques of conducting Trojan attacks and defending against them for SR. The purpose is to provide researchers with an in-depth comparison of current methods and the challenges faced in this important research area.
EIoVChain: Towards Authentication and Secure Communication Based Blockchain for Internet of Vehicles (IoV)

As the backbone of smart cities, the IoV has the potential to greatly enhance transportation effectiveness, passenger and driver safety, reduction of energy use, and road accidents. However, due to the fact that the vehicle, Road-Side-Units (RSU), and Edge Server use wireless channels to communicate, the risk of leaking or falsifying information is greatly improved. We offer EIoVChain, a reliable registration and authentication, secure information sharing based on the IoV edge server blockchain schema with minimal latency and efficient storage to address these issues. EIoVChain works with a novel primary node allocation algorithm that selects primary nodes based on their decentralized trust and a block verification process that identifies the transaction's validity of that block. The consensus algorithm follows the practical Byzantine fault tolerance model (PBFT) which works even when opposing nodes are active in EIoVChain. We have implemented EIoVChain on Raspberry pi 4. Experimental results show that EIoVChain outperforms conventional solutions with 66% verification delay and 49% communication overhead.
A Novel Method for Signature Verification Using Deep Learning

In general, signatures are used to authenticate an individual's authenticity. A solid way of certifying the legitimacy of a signature is still waiting for. The approach offered in this paper will allow people to identify signatures to determine whether a signature is forged or genuine. We have tried to automate the signature verification procedure using Convolutionary Neural Networks in our system. In computer applications such as picture registration and identification of objects, image classification and recovery, feature detectors and descriptors have a key role to play. This article discusses the performance analysis of numerous characteristic and descriptor detectors like SIFT, SURF, ORB, BRIEF, BRISK, FREAK. The number of matching points in areas overlapping between images and subjective stitching accuracy were analysed in terms of the number of features. The removal of varied symmetrical characteristics of the image enhances the possibility that different scene views can reliably satisfy the requirements. One experiment result show that the maximum number of detected key points is AGAST, FAST and BRISK detection, but the lesser number of extracted key points is STAR, AKAZE and MSER. In addition, each algorithm's speed is recorded.
Path Extraction and Planning for Intelligent Battlefield Preparation Using Particle Swarm Optimization, Gravitational Search Algorithm, and Genetic Algorithm

This paper proposes a nature-inspired computation scheme using Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Genetic Algorithm (GA) (A. Elshamli, H. A. Abdullah and S. Areibi: Genetic algorithm for dynamic path planning. In: Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513), IEEE Publications, pp. 677–680, vol. 2, https://doi.org/10.1109/CCECE.2004.1345203 , Canada (2004).) to find the shortest obstacle free path between two given points on a satellite dataset. The proposed anticipatory system finds the shortest and the best feasible path of attack on the enemy base station. The designed system is an improvement over the traditional decision-making techniques currently employed on the battlefield. Automation using bio-inspired soft computing techniques to predict the shortest path to enemy stronghold facilitates commanders in the battlefield to make more informed choices for better battlefield preparation. The scheme is implemented and results are compiled on two natural terrains, viz., plain desert region of Alwar and the hilly region of Mussourie. The proposed method improved the shortest path for Alwar and Mussourie datasets by 34% and 17% by pixel length in comparison to the extended BBO-PSO-ACO hybrid in ( C. E. Thomas, M. A. C. Pacheco, M.M. and B.R. Vellasco: Mobile Robot Path Planning Using Genetic Algorithms. In: Foundations and tools for neural modeling, vol. 1606/1999, Springer Berlin/ Heidelberg, ISBN: 3–540-66,069–0, pp. 671–679 (1999).; Wu, J., Feng, S.: Improved biogeography-based optimization for the traveling salesman problem. In: 2nd IEEE International Conference on Computational Intelligence Applications (ICCIA 2017), China, pp. 166–171. https://doi.org/10.1109/CIAPP.2017.8167201 (2017).;).
Accurate Graph-Based PU Learning without Class Prior

How can we classify graph-structured data only with positive labels? Graph-based positive-unlabeled (PU) learning is to train a binary classifier given only the positive labels when the relationship between examples is given as a graph. The problem is of great importance for various tasks such as detecting malicious accounts in a social network, which are difficult to be modeled by supervised learning when the true negative labels are absent. Previous works for graph-based PU learning assume that the prior distribution of positive nodes is known in advance, which is not true in many real-world cases. In this work, we propose GRAB (Graph-based Risk minimization with iterAtive Belief propagation), a novel end-to-end approach for graph-based PU learning that requires no class prior. GRAB models a given graph as a Markov network and runs the marginalization and update steps iteratively. The marginalization step estimates the marginals of latent variables, while the update step trains a classifier network utilizing the computed priors in the objective function. Extensive experiments on five datasets show that GRAB achieves state-of-the-art accuracy, even compared with previous methods that are given the true prior.
Environmental Visual Features Based Place Recognition in Manufacturing Environment

This paper presents a visual features based place recognition method to be used in the computer vision or robotics research fields especially in the localization and navigation algorithm. Many research studies have been conducted on the navigation method for mobile robot (or AGV in the context of manufacturing environment), and introduced precise and accurate place recognition methods. However, we believe that in some situation, rather than precise and accurate recognition, identifying the place through an uncomplicated yet robust recognition method should be good enough for a mobile robot (or AGV) to move and perform its tasks in a manufacturing environment. In our proposed method, the recognition method depends only on the visual features which could be extracted from the environment, and evaluated by using a neural network. Experimental results demonstrate the effectiveness of our proposed method.
Systems and Methods of Document Delivery in the Foreign Libraries

In the modern digital society, innovative technologies are coming to the fore in the activities of libraries; with their help the libraries can focus on the value of their role in culture and education, meet information needs in the context of digital transformation. At the same time, libraries try to facilitate the work of their employees as much as possible and simplify the efforts of readers in obtaining the necessary literature. This can be achieved by automation of the process of delivery and return of library materials ordered by users within the premises of the library. As a rule, for such purposes there is used telelift — an automated monorail handling system designed for the delivery of goods up to 50 kg (documents, books, magazines, etc.) inside buildings and premises. Telelift is installed in many large libraries, including national libraries in many countries. In addition to telelifts, national libraries use some other document delivery systems. So, the national library of the Netherlands, which is located in one building, has three reading rooms and several storages in the same building, uses the automated storage and retrieval systems of Swisslog company. The article is based on studying the materials of websites of the national libraries, the appeals to the library virtual reference services Ask a Librarian, correspondence with employees of the departments of storage and library and information services of national libraries, as well as additional questionnaire surveys of libraries on the issues related to the storage of library materials. The questionnaire covered the following topics: conducting complete or partial audit, types of audit, use of RFID tags, delivery of material to reading rooms, including, from the remote repositories, the preferred format for issuing a document to the user if this document has a copy (microfilm, microfiche, electronic copy, etc.), as well as statistics of binding and restoration work, ordering necessary materials by readers, etc. The article presents the questionnaire survey responses of the national libraries of Switzerland, Korea, the Royal Library of Netherlands, the British Library, as well as the the Library and Archives of Canada.
Spatio-Temporal Transformer for Online Video Understanding

Abstract Leading methods in the field of online video understanding try to extract useful information from the spatial and temporal dimensions of an input video. But they are suffering from two problems: (1) These methods can only extract local video information, and cannot relate to the important features of the temporal context in the video. (2) Although some methods can quickly process the information of each frame in the video, the processing efficiency of the whole video is not good, so this type of method cannot be applied to online video understanding tasks. This article introduces a Transformer-based network, which considers spatial and temporal content, and can quickly process each video at the same time. Our approach can efficiently handle up to 170 videos with hundreds of frames per second for action classification. Our method achieve 10 to 90 times faster than existing methods on the action classification datasets.
Multi-Feature Integration and Occlusion Detect Mechanism Based Object Tracking Algorithm

Tracking failure happens in complex scenes such as background clutter and occlusion. Aiming at improving performance of the correlation filter based tracking algorithm, an object tracking algorithm that integrates multi-feature and occlusion detect mechanism is proposed. In this paper, multi-feature is utilized in the construction of object appearance model and background context model for better representing object and discriminating object from disturbance. In addition, an occlusion detection mechanism is introduced to effectively estimate whether the object is occluded or seriously disturbed, avoiding the useless model updating in the occlusion scene. Experimental results on the OTB-100 dataset show that the success rate score of the proposed algorithm is 0.631 and the precision rate score is 0.865 that better than other compared algorithms in many complex scenes.