Reviewerly/exHarmony huggingface dataset:
PENCAPAIAN KOMPETENSI LITERASI NUMERASI SISWA DENGAN MODEL PROBLEM BASED LEARNING

Numerical literacy is an ability that needs and is very important to improve. So it is necessary to do research to improve students' numeracy literacy skills with the PBL model. This research is a quantitative research. The research method is pretest-posttest design with purposive sampling technique. Samples were selected randomly from the population. The population of this study were high school students in the South coast district. The sample was selected from schools that have A accreditation because seen from the data obtained, there are more schools that are accredited B and others. The conclusion is obtained by looking at the results of the given numeracy literacy skills which have previously been validated by experts in their fields. The conclusion obtained is that the achievement of the control class using PBL can effectively improve students' numeracy literacy skills, while the control class has not provided an effective increase in students' numeracy literacy abilities.
Bionic Artificial Self-recovery and Artificial Intelligence

Artificial self- recover puts forward the concept of “self- recovery mechanism”, expands the field of Bionics research. AI is the simulation of the conscious thinking of the human brain to control the purposeful behavior, AI will make machines smarter; AS is a simulation of the self-recovery mechanism of the body's unconscious mind, AS can make the machine healthier. AI lays a foundation for the generation and development of AS. Based on the research of CPS, the three-body model of Artificial intelligence cyber physics system AI-CPS and Artificial self-recovery cyber physics system AS-CPS are proposed.
How blockchain technology can impact agriculture

Blockchain is a special type of distributed ledger technology (DLT) that enables verifying and recording peer-to-peer transactions in a distributed, append-only and immutable fashion. In this chapter, we describe applications of blockchain technology which focus on the current challenges or issues in some key sectors of agriculture as well as how blockchain technology could be beneficial in addressing those challenges. Finally, we discuss some limitations of blockchain technology.
Digital Rights in Nigeria: Through the Cases

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsIfeoma E. NwaforIfeoma E. Nwafor is a lecturer in Law at Godfrey Okoye University, Nigeria and an EU Eramus+ Scholar, Faculty of Business, Economics and Law, TH Köln, Germany. She holds a PhD in Cyber Law from the University of Nigeria. Nwafor is the author of the book titled ‘Cybercrime and the Law: Issues and Developments in Nigeria’, published in March 2022. She is a member of several regional and international organisations and committees, including the United Nations Office on Drugs and Crime (UNODC), African Women in Cyber Professional Group; Governing Council of the Digital Rights Lawyer’s Initiative, the African Women in Security Network and the Cyber Security Experts Association of Nigeria. She was a Finalist, Women in Cybersecurity Awards Africa 2020 (Woman Data Privacy and Law Professional of the Year Category).
Patient Monitoring System using Blockchain and IoT Technology

Background: Recently, the construction of a smart city has accelerated due to developing technologies such as 5G and the Internet of things (IoT). The IoT is gaining access day by day in the area of the remote monitoring system. Along with smart city concepts, intelligent patient monitoring systems and intelligent health are being developed. Combining the two, here, patients have a nurse navigation system and a doctor emergency call system. IoT improves the effectiveness of medical equipment by providing real-time monitoring of patient health, in which sensors collect data from patients with less human errors. In IoT, the patient's parameters get transmitted through medical devices via a gateway to the centralized system, where it is stored and analyzed. Objective: In this paper, we have proposed an intelligent patient monitoring system for monitoring the patients' health condition automatically through sensors-based connected links. The system is more intelligent that it can be able to detect the critical condition of a patient by processing sensor data and instantly provides a notification to doctors/nurses as well as hospital in-charge personnel. The doctors and nurses get benefited from this system by observing their corresponding patients remotely without visiting to the particular seat of the patient. Methods: An idea has been proposed to use the centralized processing of IoT devices along with the rigid security and privacy of the classical blockchain to overcome the computation expense challenge with blockchain and the security and privacy threat of the IoT systems. We are developing a system in the field of medical care to enhance the functioning of Medicare systems and provide patients with more ease and hassle-free procedures of the Medicare system, i.e., hospitals, laboratories, dispensaries, chemists, druggists, etc. Results: This model helps in getting rid of this issue faced by all of us. There is a separate blockchain of different verified medical stores linked within the networks, which will be managed by the central hub based on their ratings, reviews, and stock availability. Conclusion: The exponential expansion of blockchain technology in healthcare is also astonishing and has a tremendous impact on the industry. By assessing prior activities, the idea is proposed to use a centralized database system with more privacy and security to gap between the healthcare industry and blockchain technologies has been addressed in this paper.
Multi-Kernel based Adaptive Support Vector Machine for Scalable Predictive Maintenance

Application of data-driven solutions across an industry is challenging, since the data are often stored locally, and increasing privacy and security concerns restrict access to the data. Because it is highly unlikely that all potential data patterns are captured in a single data source, machine learning (ML) models developed from a single source cannot be robust enough. An alternative is to train local ML model at each source and at the central location combine all the local models to generate a global model. In this work, we develop a proof-of-concept of distributed machine learning model, federated transfer learning, using a multi-kernel-based adaptive support vector machine. For federated learning, the multikernel approach enables feature-specific model aggregation under data heterogeneity; whereas for transfer learning the adaptive model enables utilization of an aggregated model from a different task. The proposed approach is validated using nuclear power plant vertical motor-driven pump data to predict the health condition of vertical motor-driven pumps as an anomaly detection. The efficiency of the proposed approach is also quantified and compared with neural network.
[Cascaded multi-level medical image registration method based on transformer].

In deep learning-based image registration, the deformable region with complex anatomical structures is an important factor affecting the accuracy of network registration. However, it is difficult for existing methods to pay attention to complex anatomical regions of images. At the same time, the receptive field of the convolutional neural network is limited by the size of its convolution kernel, and it is difficult to learn the relationship between the voxels with far spatial location, making it difficult to deal with the large region deformation problem. Aiming at the above two problems, this paper proposes a cascaded multi-level registration network model based on transformer, and equipped it with a difficult deformable region perceptron based on mean square error. The difficult deformation perceptron uses sliding window and floating window techniques to retrieve the registered images, obtain the difficult deformation coefficient of each voxel, and identify the regions with the worst registration effect. In this study, the cascaded multi-level registration network model adopts the difficult deformation perceptron for hierarchical connection, and the self-attention mechanism is used to extract global features in the basic registration network to optimize the registration results of different scales. The experimental results show that the method proposed in this paper can perform progressive registration of complex deformation regions, thereby optimizing the registration results of brain medical images, which has a good auxiliary effect on the clinical diagnosis of doctors.在基于深度学习的图像配准中，图像中具有复杂解剖结构的形变区域是影响网络配准精度的重要因素，然而现有方法很难关注到图像的复杂解剖区域。同时，卷积神经网络的感受野受其卷积核大小的限制，难以学习空间位置距离较远的体素之间的关系，使其难以处理较大区域形变问题。针对以上两个问题，本文提出了一种基于视觉变换器（Transformer）的级联多阶层配准网络模型，并配备了一种基于均方误差的困难形变感知机。困难形变感知机使用滑动窗口和浮动窗口技术在配准图像中进行检索，得到每个体素的困难形变系数，识别出配准效果最差的区域。本研究中，级联多阶层配准网络模型采用困难形变感知机进行阶层连接，在基础配准网络中凭借自注意力机制提取全局特征，对不同尺度的配准结果进行优化。实验结果证明，本文提出的方法可以对复杂形变区域进行渐进配准，从而优化脑部医学影像的配准结果，对医生的临床诊断工作有良好的辅助作用。.
A novel scheme to manage the e-healthcare system using cloud computing and the internet of things

The unification of cloud computing and the Internet of Things (IoT) has become a trend in the e-healthcare system. A lot of research work has been done that tries to explore correct and systematic advanced technologies, such as sensors, big data analysis, and data mining, for healthcare products that can trace, hold the location of a patient’s health in daily life, and also trace misshaping or emergency accidental situations. It also tries to reduce the gap between the healthcare industry, clinical staff, doctors, and patients by using these technologies. In this paper, IoT-DHS (distributed healthcare system) and cloud computing are discussed which have to build a crucial effect on healthcare organizations, but still, several open challenges and issues are there that need to be focused on and evaluated to find solutions. In cloud computing and IoT-based DHS, all medical-related resources are interconnected with each other to supply accurate and efficient healthcare services to everyone. This paper provides a novel scheme to manage the e-healthcare system by using IoT and cloud computing, and a detailed study of their IoT and cloud computing build to realize the maintainability or sustainability of numerous healthcare systems. The open challenges and issues are also discussed with existing IoT and cloud computing. This paper also discusses how IoT and cloud computing are beneficial to all healthcare system aspects and how patients, doctors, IoT, cloud computing, and other computing techniques are held together to find solutions.
Reading and Writing Skills in Developmental Language Disorder: Parental Perception

Reading and writing skills appear as one of the most persistent difficulties that people with Developmental Language Disorder (DLD) present throughout their schooling.The objective of this study was to know the parents' perception of these deficits.The sample consisted of 45 families of children and adolescents with DLD.The participants were evaluated through the Celf 5 questionnaire for parents on linguistic competence that collects data on the following areas: listening, speaking, reading and writing.This study describes reading and writing data.The sample consisted of 45 families of children and adolescents with DLD.The results indicate that most of parents manifest problems in all reading areasv(especially reading comprehension) and writing (format and content) bareas, which influence their academic development.These data support the importance of working on Reading and writing skills in all educational stages.The perception of families is very similar to what the scientific literature affirms.In addition, these data highlight the importance of the work of families in the educational development of people with DLD.
Application and Research of Monte Carlo Sampling Algorithm in Music Generation

Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment.Algorithmic composition aims to develop algorithms for music composition.Recently, algorithmic composition using artificial intelligence technologies received considerable attention.In particular, computational intelligence is widely used and achieves promising results in the creation of music.This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm.First, transform the MIDI music format files to digital data.Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern.Except for time series, the converted data also includes duration, pitch, and velocity.Second, using MC simulation to deal with them summed up their distribution law respectively.The two main control parameters are the value of discrete sampling and standard deviation.Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.
Rmap+: Autonomous Path Planning for Exploration of Mobile Robot Based on Inner Pair of Outer Frontiers

Exploration of mobile robot without prior data about environments is a fundamental problem during the SLAM processes.In this work, we propose improved version of previous Rmap algorithm by modifying its Exploration submodule.Despite the previous Rmap's performance which significantly reduces the overhead of the grid map, its exploration module costs a lot because of its rectangle following algorithm.To prevent that, we propose a new Rmap+ algorithm for autonomous path planning of mobile robot to explore an unknown environment.The algorithm bases on paired frontiers.To navigate and extend an exploration area of mobile robot, the Rmap+ utilizes the inner and outer frontiers.In each exploration round, the mobile robot using the sensor range determines the frontiers.Then robot periodically changes the range of sensor and generates inner pairs of frontiers.After calculating the length of each frontiers' and its corresponding pairs, the Rmap+ selects the goal point to navigate the robot.The experimental results represent efficiency and applicability on exploration time and distance, i.e., to complete the whole exploration, the path distance decreased from 15% to 69%, as well as the robot decreased the time consumption from 12% to 86% than previous algorithms.
Implementation of Secure Data Storage and Retrieval Approach

Implementation of a data storage and retrieval approach to achieve the best results is a challenge. The approach is proposed where access control is assured and a minimum of data information is revealed. On the other side special features of every cloud in a multi-cloud storage system make implementation more complicated. In this paper, the authors will present important problems, solutions, algorithms, comparison results, and details of the implementation of secure data storage and retrieval approach in a multi-cloudIoT system.
SigVM: enabling event-driven execution for truly decentralized smart contracts

This paper presents SigVM, the first blockchain virtual machine that extends EVM to support an event-driven execution model, enabling developers to build truly decentralized smart contracts. Contracts in SigVM can emit signal events, on which other contracts can listen. Once an event is triggered, corresponding handler functions are automatically executed as signal transactions. We build an end-to-end blockchain platform SigChain and a contract language compiler SigSolid to realize the potential of SigVM. Experimental results show that our benchmark applications can be reimplemented with SigVM in a truly decentralized way, eliminating the dependency on centralized and unreliable mechanisms like off-chain relay servers. The development effort of reimplementing these contracts with SigVM is small, i.e., we modified on average 3.17% of the contract code. The runtime and the gas overhead of SigVM on these contracts is negligible.
Automatic Template Detection for Camera Calibration

Camera calibration is the process of extract the intrinsic and extrinsic parameters of a camera. Those parameters guide the 3-dimensional localization into relation to the 2-dimensional space from the images acquired by the camera. The 3-dimensional correlation can be generated with an object with known measures, being the most common checkerboard for this purpose. From these checker- boards, the usual approach extracts the position of the inner points, equivalent to the corners of the squares, to generate this correlation. A broad range of algorithms tries to ﬁnd those points on the image. Still, usually, they require previous knowledge about the dimensions of the image, the pattern distribution, or even the pattern type. In some scenario, maybe is difﬁcult, or impossible, to implement such precise solution, targeting these limitations our work proposes a two-step end-to-end convolutional neural network architecture that processes the corner detection on a unique ﬂow. Our proposal is agnostic to checkerboard size, pattern disposal, and positioning. In our work, ﬁrst, a segmentation CNN extracts only the checkerboard from the input image (CheckerNet); from the extracted checkerboard, we extract the corner points with a corner detection CNN (Point- Net). The PointNet also works as a segmentation CNN, and the generated points are heatmaps related to points on the checkerboard corners. We performed post-processing with a K-Means-based clustering to convert those heatmaps into single positions (x,y) from the image. We compare our proposed method with the other well-known convolutional neural networks used for corner detection MATE and CCDN. For the evaluation, two datasets were used: GoPro e uEye. Our method provides better results in both datasets, reducing missed corners, double detections, false positives, and competitive results on pixel accuracy.
Enhancing the Performance of Association Rule Generation over Dynamic Data using Incremental Tree Structures

To discover a novel and dynamic approach for frequent itemsets generation and also for generating association rules is an imperative aspect in data mining. With the fast increase in databases, new transactions added, the incremental mining is acquainted to resolve the issues of maintaining association rules in updated databases. Earlier algorithms focused on this problem which consumed more time and costly to mine. This paper intends to analyze the tree construction like Frequent Pattern-tree(FP),PreOrderCoded(POC) tree and PrePostCoded(PPC) tree for sinking overheads and time constraints. To overcome theissueof updating association rules when new transactions addition this paper proposes a dynamic frequent itemsets mining approach using Incremental PreOrderCoded (IPOC)tree. This will reduce computational and scanning overheads of original dataset against addition of new transaction items and also works in an optimized way. An analysis was done on existing algorithms and compares time complexities for various standard datasets. The proposed approach shown better performance against existed ones over time and efficiency.
A Fast HDR Image TMO based on a Simplified Eye Sensitivity Model

Tone mapping is essential for displaying High Dynamic Range (HDR) images on Low Dynamic Range (LDR) screens. The quality performance of these algorithms has improved greatly over recent times, however their computational complexity has also considerably increased. Real-time applications are constrained because of this limitation in their deployment. Therefore, a fast tone-mapping algorithm is presented that maintains the same quality level as state-of-the-art techniques but has a considerably lower computational complexity. Based on an existing well-known Threshold vs. Intensity (TVI) model, a simplified human eye sensitivity model is derived that leads to a non-iterative method of TMO construction. The proposed model is used to construct a histogram of the HDR image such that the bins vary in size in the spatial domain but each of them covers the same amount of change in the luminance perceived by the human eye. Moreover, a simple yet effective solution is suggested to the over-enhancement and over-compression problems faced by histogram-based techniques. The proposed architecture is computationally light, hardware-friendly, produces superior visual results, and works significantly faster than state-of-the-art techniques.
MStereoNet: A Lightweight Stereo Matching Network Using MobileNet

Deep learning model-based approaches to stereo matching challenges are more accurate than conventional feature-based techniques created by hand. This leads to the issue that deploying applications on devices with restricted resources is not friendly to employing complicated networks and total cost space to increase performance. To minimize processing effort without sacrificing matching accuracy, we propose MStereoNet in this study, a more effective stereo network. It has been demonstrated experimentally that the network in this research significantly lowers the requirement for computing power. The network in this article also employs a multiscale loss to enhance the reliability of the detail. The approach in this study delivers a performance comparable to the best existing algorithms in the Sceneflow dataset when compared to other low-cost dense stereo depth estimation techniques. Research demonstrates that the network suggested in this study can reduce up to 72.5% and 87% of the parameters and operations than the largest volume of the methods involved in the comparison. Our network also marginally outperforms other lightweight binocular matching networks in terms of accuracy.
Fast Deep Multi-patch Progressive Network for Low-light Image Enhancement in Intelligent Transportation Systems

Intelligent transportation systems (ITS) have been widely used in transportation service systems such as roads, railways and waterways. However, the complex low-light imaging environment will lead to the unnatural visual phenomena of over-exposure or under-exposure in the images collected by the visual acquisition equipment of ITS. This paper proposes a fast deep multi-patch progressive network (FDMPN) that aggregates features from multiple image patches in different spatial parts of low-light images with fewer network parameters. Specifically, we propose a multi-receptive field attention module (MRFAM) to enhance local and global feature information perception. To improve the generalization ability of the network, we randomly scramble the smallest feature patches into the MR-FAMs, thereby improving the feature extraction ability of each MRFAM. Experimental results demonstrate that our method can efficiently extract valuable features from complex low-light backgrounds and generate naturally illuminated enhanced images in ITS. Quantitative evaluation results on multiple standard datasets validate that FDMPN outperforms many existing state-of-the-art methods, showing great effectiveness and potential in transportation service systems. The source code is available at https://github.com/LouisYuxuLu/FDMPN.