Reviewerly/exHarmony huggingface dataset:
Wireless Sensor Network Topology Theory for Data Collection and Analysis of Sports Training Human Body

This paper is based on wireless sensor network topology for sports training human data collection, and the collected data are studied and analyzed in depth. According to the application requirements of sports training, a sports training system consisting of an embedded data collection terminal, and a database server is designed using wireless sensor network technology. The hardware is designed with sensor nodes and base stations to collect athletes’ motion parameters in real time. The software is designed with node and base station control software and sports database management system to realize the receiving, storing, and analyzing of sports parameters. And the system experiments were conducted, and the experimental results show that this system meets the application requirements of sports training and provides an effective tool for scientific training decision research. The needs of designing sports training systems under wireless sensor networks are analyzed, and the system is designed and implemented. Our results confirm that the use of wireless sensor network technology in the design of the sports training system improves the system application performance by 16%. And the interactivity of the sports training system in practice has increased by 8%. All of these show that the design of the sports training system under the wireless sensor network meets the actual system application requirements and has a positive impact. The design of base station control, node control, and sports database software is implemented in the software system, which can effectively realize the collection, storage, and analysis of sports parameters. Finally, the designed wireless sensor network-based sports training system is tested, and the test results indicate that the system designed in this paper can meet the needs of sports training use.
Impact of Saving Attacks on Blockchain Consensus

Blockchain consensus, which enables nodes on a peer-to-peer network to agree on the same ledger history, is the core element of blockchain systems.In many blockchain systems, a node chosen as a block proposer, in accordance with the consensus protocol, generates a block, and each node chooses a chain to extend by a fork-choice rule.This study introduces saving attacks, a new kind of attack that prevents nodes from reaching a consensus.In saving attacks, the adversary "saves" its rights to propose blocks during a temporal consensus failure and utilizes them later to cause another consensus failure.As a result, the blockchain suffers from poor performance and high latency to block finalization.We study the effect of saving attacks on various fork-choice rules, including those that Ethereum 2.0 plans to employ.We simulate saving attacks on the longest-chain rule, Greedy Heaviest-Observed Sub-Tree (GHOST), latestmessage-driven (LMD) GHOST, and fresh-message-driven (FMD) GHOST.We show that the saving attack has a very large negative impact on the consensus.For example, we observe that under a certain condition, an adversary with 30% of the total stake that has saved its blocks for 32 minutes succeeds in preventing a consensus against LMD GHOST for 83 minutes in the context of Ethereum 2.0.We also show that FMD GHOST decreases the attack duration to approximately 6.4 minutes under the same conditions.Our results are applicable to all slot-based proof-of-stake blockchains, not just Ethereum 2.0.
A Survey on Arabic Handwritten Script Recognition Systems

The optical character recognition (OCR) system is still an active research field in pattern recognition. Such systems can identify, recognize and distinguish electronically between characters and texts, printed or handwritten. They can also do a transformation of such data type into machine-processable form to facilitate the interaction between user and machine in various applications. In this paper, we present the global structure of an OCR system, with its types (on-line and off-line), categories (printed and handwritten) and its main steps. We also focused on off-line handwritten Arabic character recognition and provided a list of the main datasets publicly available. This paper also presents a survey of the works that have been carried out over recent years. Finally, some open issues and potential research directions have been highlighted
A Three-Stage Annealing Method Solving Slot-Placement Problems Using an Ising Machine

Ising machines are promising alternatives to solve combinatorial optimization problems, which search for their quasi-optimal solutions with high speed and high accuracy. However, the obtained solution much depends on the initial spin states, since the computation time is finite. Moreover, due to their probabilistic nature, they cannot always satisfy the constraints given to combinatorial optimization problems. In this paper, we propose a three-stage annealing method, targeting a slot-placement problem as a typical but difficult example of combinatorial optimization problems. The proposed method is composed of an initial process, an annealing process, and a correction process. The initial process and the correction process are executed by a classical computer while the annealing process is executed by an Ising machine. In the initial process, we give initial spin values that lead to a relatively good solution to the combinatorial optimization problem, which satisfies the given constraints. Then, the annealing process is executed by an Ising machine, and the solution obtained by the annealing process is further corrected to satisfy the constraints. The experimental results demonstrate that the proposed method reduces a minimum total weighted wiring length by 0.0898%–2.45% on average depending on the initial process methods used, compared to the existing method. The mean total weighted wiring length is reduced by 2.79%–7.08% on average depending on the initial process methods used.
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions

Multi-text applications, such as multidocument summarization, are typically required to model redundancies across related texts.Current methods confronting consolidation struggle to fuse overlapping information.In order to explicitly represent content overlap, we propose to align predicate-argument relations across texts, providing a potential scaffold for information consolidation.We go beyond clustering coreferring mentions, and instead model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents.Our setting exploits QA-SRL, utilizing question-answer pairs to capture predicate-argument relations, facilitating laymen annotation of cross-text alignments.We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset.Analyses show that our new task is semantically challenging, capturing content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks.
ROBOVAC-Automatic Floor Cleaning Robot

In this technological era, currently people have taken a liking to develop new designs related to low-cost flooring robots. One of the challenges has been to reduce the number of sensors as it contributes considerably to the cost of the robot. Robovac - automatic floor cleaning robot is designed in such a way that the robot can navigate and clean a floor at the simultaneously. It involves hardware construction and software implementation. The four wheels help the robot to move in different and desired direction. Cleaning brush is placed in front of the cleaning bot to perform necessary cleaning process. ATMEGA 328 microcontroller and a L293D motor driver are incorporated into the cleaning robot for processing sensors signals and controlling DC motors. LCD Display provides the status of the robot. The ultrasonic sensor is mounted on the cleaning robot for obstacle avoidance during its navigation. A RF module is installed on the main board to enable wireless control of the robot. In the control process fuzzy logic techniques has been put into practice. The main reasons for using the fuzzy logic are that, fuzzy logics are easy to put into practice and it is flexible such that it can be used in incomplete and inaccurate data. The other reasons are it is cost - efficient to develop, operating conditions are wider and can be customized easily in understandable language. The robot is small and compact in size. Robovac - automatic floor cleaning robot can be employed in places difficult for humans to clean. This is where the bot comes handy. When compared with the manpower, robots are cost efficient when it comes to industries. Adding to this are time efficiency, smooth running, modernized, thus making the bot perfect choice for floor cleaning.
Fuzzy-logic-based control

We consider a simple fuzzy logic control methodology that is applicable for most systems. It was proven and utilized for the first time in renewable energy systems and wind turbine control systems. We also look at fuzzy control preliminaries, fuzzy controller heuristics, fuzzy logic controller design, and industrial fuzzy control supervision and scheduling of conventional controllers.
Oriented R-CNN for Object Detection

Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes. This diminishes the detectors' speed, thereby becoming the computational bottleneck in advanced oriented object detection systems. This work proposes an effective and simple oriented object detection framework, termed Oriented R-CNN, which is a general two-stage oriented detector with promising accuracy and efficiency. To be specific, in the first stage, we propose an oriented Region Proposal Network (oriented RPN) that directly generates high-quality oriented proposals in a nearly cost-free manner. The second stage is oriented R-CNN head for refining oriented Regions of Interest (oriented RoIs) and recognizing them. Without tricks, oriented R-CNN with ResNet50 achieves state-of-the-art detection accuracy on two commonly-used datasets for oriented object detection including DOTA (75.87% mAP) and HRSC2016 (96.50% mAP), while having a speed of 15.1 FPS with the image size of 1024$\times$1024 on a single RTX 2080Ti. We hope our work could inspire rethinking the design of oriented detectors and serve as a baseline for oriented object detection. Code is available at this https URL.
Hamming Distance of Constacyclic Codes of Length <i>p<sup>s</sup> </i> Over F<sub> <i>p<sup>m</sup> </i> </sub>+<i>u</i>F<sub> <i>p<sup>m</sup> </i> </sub>+<i>u</i>²F<sub> <i>p<sup>m</sup> </i> </sub>

Let p be any prime, s and m be positive integers. In this paper, we completely determine the Hamming distance of all constacyclic codes of length ps over the finite commutative chain ring Fpm + uFpm +u2Fpm (u3 = 0). As applications, we identify all maximum distance saparable codes (i.e., optimal codes with respect to the Singleton bound) among them.
Distribution-free conditional median inference

We consider the problem of constructing confidence intervals for the median of a response Y∈R conditional on features X∈Rd in a situation where we are not willing to make any assumption whatsoever on the underlying distribution of the data (X,Y). We propose a method based upon ideas from conformal prediction and establish a theoretical guarantee of coverage while also going over particular distributions where its performance is sharp. Additionally, we prove an equivalence between confidence intervals for the conditional median and confidence intervals for the response variable, resulting in a lower bound on the length of any possible conditional median confidence interval. This lower bound is independent of sample size and holds for all distributions with no point masses.
Generic Framework for Key-Guessing Improvements

We propose a general technique to improve the key-guessing step of several attacks on block ciphers. This is achieved by defining and studying some new properties of the associated S-boxes and by representing them as a special type of decision trees that are crucial for finding fine-grained guessing strategies for various attack vectors. We have proposed and implemented the algorithm that efficiently finds such trees, and use it for providing several applications of this approach, which include the best known attacks on Noekeon, GIFT, and RECTANGLE.
Edge Computing Assisted an Efficient Privacy Protection Layered Data Aggregation Scheme for IIoT

The emergence of edge computing has improved the real time and efficiency of the Industrial Internet of Things. In order to achieve safe and efficient data collection and application in the Industrial Internet of Things, a lot of computing and bandwidth resources are usually sacrificed. From the perspective of low computing and communication overhead, this paper proposes an efficient privacy protection layered data aggregation scheme for edge computing assisted IIoT by combining the Chinese Remainder Theorem (CRT), improved Paillier homomorphic algorithm, and hash chain technology (edge computing assisted an efficient privacy protection layered data aggregation scheme for IIoT, EE-PPDA). In EE-PPDA, first, a layered aggregation architecture based on edge computing is designed. Edge nodes and cloud are responsible for local aggregation and global aggregation, respectively, which effectively reduces the amount of data transmission. At the same time, EE-PPDA achieves data confidentiality through improved Paillier encryption, ensuring that neither attackers nor semitrusted nodes (e.g., edge nodes and clouds) can know the private data of a single device, and it can resist by simply using hash chains to resist tampering and pollution attacks ensure data integrity. Second, according to the CRT, the cloud can obtain the fine-grained aggregation results of subregions from the global aggregation results, thereby providing fine-grained data services. In addition, the EE-PPDA scheme also supports fault tolerance. Even if some IIoT devices or communication links fail, the cloud can still decrypt incomplete aggregated ciphertexts and obtain the expected aggregation results. Finally, the performance evaluation shows that the proposed EE-PPDA scheme has less calculation and communication costs.
Synthesis of Reversible and Quantum Circuit Using ROCBDD and Mixed-Polarity Toffoli Gate

In the last decade, the synthesis of reversible logic circuits has become a trending topic because of its future necessity and importance. Many methods have been studied and proposed, for instance, transformation-based, search-based, cycle-based, ESOP-based and BDD-based methods. Each of them has its limitation related to time processing, ancilla and garbage line, quantum cost. This study develops an algorithm that could synthesize quantum circuits based on mixed-polarity Toffoli gate and a variant of binary decision diagram (BDD) called reduced-ordered-complemented edge-bdd (ROCBDD). It is an optimized method of BDD to reduce nodes in the representation of Boolean functions, which leads to adding more lines and quantum gates in the reversible circuit. First, the differences between synthesis using ROCBDD, the traditional BDD and others methods are introduced. Then, we define a new structure consisting of mixed-polarity Toffoli gates relied on nodes of ROCBDD. An efficient algorithm to match BDD representation to the reversible logic circuit is also mentioned. Finally, the experimental results show that our algorithm has better synthesizing costs than previous BDD-based methods.
Study of the Error Caused by Camera Movement for the Stereo-Vision System

The stereo-vision system plays an increasingly important role in various fields of research and applications. However, inevitable slight movements of cameras under harsh working conditions can significantly influence the 3D measurement accuracy. This paper focuses on the effect of camera movements on the stereo-vision 3D measurement. The camera movements are divided into four categories, viz., identical translations and rotations, relative translation and rotation. The error models of 3D coordinate and distance measurement are established. Experiments were performed to validate the mathematical models. The results show that the 3D coordinate error caused by identical translations increases linearly with the change in the positions of both cameras, but the distance measurement is not affected. For identical rotations, the 3D coordinate error introduced only in the rotating plane is proportional to the rotation angle within 10° while the distance error is zero. For relative translation, both coordinate and distance errors keep linearly increasing with the change in the relative positions. For relative rotation, the relationship between 3D coordinate error and rotation angle can be described as the nonlinear trend similar to a sine-cosine curve. The impact of the relative rotation angle on distance measurement accuracy does not increase monotonically. The relative rotation is the main factor compared to other cases. Even for the occurrence of a rotation angle of 10°, the resultant maximum coordinate error is up to 2000 mm, and the distance error reaches 220%. The results presented are recommended as practice guidelines to reduce the measurement errors.
Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects

We present state-of-the-art results on morphosyntactic tagging across different varieties of Arabic using fine-tuned pre-trained transformer language models.Our models consistently outperform existing systems in Modern Standard Arabic and all the Arabic dialects we study, achieving 2.6% absolute improvement over the previous state-of-the-art in Modern Standard Arabic, 2.8% in Gulf, 1.6% in Egyptian, and 8.3% in Levantine.We explore different training setups for fine-tuning pre-trained transformer language models, including training data size, the use of external linguistic resources, and the use of annotated data from other dialects in a low-resource scenario.Our results show that strategic fine-tuning using datasets from other high-resource dialects is beneficial for a low-resource dialect.Additionally, we show that high-quality morphological analyzers as external linguistic resources are beneficial especially in low-resource settings.
Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning

In Federated Learning (FL), a common approach for aggregating local solutions across clients is periodic full model averaging. It is, however, known that different layers of neural networks can have a different degree of model discrepancy across the clients. The conventional full aggregation scheme does not consider such a difference and synchronizes the whole model parameters at once, resulting in inefficient network bandwidth consumption. Aggregating the parameters that are similar across the clients does not make meaningful training progress while increasing the communication cost. We propose FedLAMA, a layer-wise adaptive model aggregation scheme for scalable FL. FedLAMA adjusts the aggregation interval in a layer-wise manner, jointly considering the model discrepancy and the communication cost. This fine-grained aggregation strategy enables to reduce the communication cost without significantly harming the model accuracy. Our extensive empirical study shows that, as the aggregation interval increases, FedLAMA shows a remarkably smaller accuracy drop than the periodic full aggregation, while achieving comparable communication efficiency.
Blockchain-Based Authentication with Optional Privacy Preservation for Internet of Vehicles

With the rapid development of the mobile internet and intelligent technology of in-vehicle equipment, the Internet of Vehicles (IoV), centered on intelligent connected cars, has gradually entered people’s lives. However, these technologies also bring serious privacy risks and security issues in terms of data transmission and storage. In this article, we propose a blockchain-based authentication system to provide vehicle safety management. The privacy and security attributes of various vehicle authentication transactions are based on high-level cryptographic primitives, realizing temporary and formal authentication methods. At the same time, a fair blockchain consensus mechanism Auction of block generation Rights (AoR) is proposed. To demonstrate the feasibility and scalability of the proposed scheme, security and performance analyses are presented. The relevant experimental results show that the scheme can provide superior decentralized management for IoV.
An Automatic Classification Method of Sports Teaching Video Using Support Vector Machine

There are many different types of sports training films, and categorizing them can be difficult. As a result, this research introduces an autonomous video content classification system that makes managing large amounts of video data easier. This research provides a video feature extraction approach using a support vector machine (SVM) video classification algorithm and a mix of video and audio dual-mode characteristics. It automates the classification of cartoons, ads, music, news, and sports videos, as well as the detection of terrorist and violent moments in films. To begin, a new feature expression scheme, the MPEG-7 visual descriptor subcombination, is proposed based on an analysis of the existing video classification algorithms, with the goal of addressing the problems in these algorithms. This is accomplished by analyzing the visual differences of the five video classification algorithms. The model was able to extract 9 descriptors from the four characteristics of color, texture, shape, and motion, resulting in a new overall visual feature with good results. The results suggest that the algorithm optimizes video segmentation by highlighting disparities in feature selection between different categories of films. Second, the support vector machine’s multivideo classification performance is improved by the enhanced secondary prediction method. Finally, a comparison experiment with current related similar algorithms was conducted. The suggested method outperformed the competition in the accuracy of video classification in five different types of videos, as well as in the recognition of terrorist and violent incidents.
A Brief Survey of Question Answering Systems

Question Answering (QA) is a subfield of Natural Language Processing (NLP) and computer science focused on building systems that automatically answer questions from humans in natural language. This survey summarizes the history and current state of the field and is intended as an introductory overview of QA systems. After discussing QA history, this paper summarizes the different approaches to the architecture of QA systems -- whether they are closed or open-domain and whether they are text-based, knowledge-based, or hybrid systems. Lastly, some common datasets in this field are introduced and different evaluation metrics are discussed.
GERAKAN LITERASI SEKOLAH DASAR

School Literacy Action in Primary Schools. This article aims to develop the manners of learners through literacy ecosystem civilizing schools so that they become lifelong learners. The goal was all the school community. School Literacy Action is more than just reading and writing but include thinking skills in accordance with the stages and components of literacy. While in good practices need to emphasize the principles of school literacy action. In order for schools to be the front line in the culture of literacy, it is necessary to use several strategies for implementation. There are some technical concept of literacy in schools, among others, on a daily, weekly, monthly and each semester. This event is held to foster interest in reading for students so that knowledge can be mastered as well. Reading material contains budu character values, local knowledge, national and global, delivered according to the development of learners. Monitoring and evaluation activities of GLS (School Literacy Action) using the indicators of achievement of each stage.