Reviewerly/exHarmony huggingface dataset:
Applications of blockchain in government education sector: a comprehensive review and future research potentials

Purpose With the time and increase in usage of information technology (IT), blockchain technology is gaining immense attention from researchers, academicians, and practitioners because of its distinctive features such as transference, security and data reliability. The purpose of this study is to endow with a systematic review of literature on blockchain in context to the government education sector in terms of its usage, benefits, obstacles and practical implementation in future areas in education. Design/methodology/approach The study adopted a bibliometric visualization tool to classify data in yearly publications, highly cited journals, prominent authors, leading publications in countries and institutions and highly cited papers – the data was extracted from the SCOPUS database by using relevant keywords. Thus, the following research questions were developed: How has blockchain technology been used in the government educational sector? What are the benefits examined in the field of education? What were the problems/obstacles faced using the technology in a government education structure? Findings The findings identify and provide a comprehensive review of the technique regarding the present research stream in terms of highest publication, author, journal, subject-wise and relevance of the technology in government education structure. Thus, the future research potential of the technology in the education sector is much more as it is in the initiation stage. A lot of opportunities and benefits need to be extracted at large. Research limitations/implications The present findings of the study provide a base work for government education institutions, policy developers and researchers to investigate other areas where the technology can be implemented. Finally, more technology applications will develop strategies for proper data management and cost-effective decisions. Originality/value This study explains the relevance of technology in education through bibliometric visualization. The study adopted the review and significance of blockchain technology in the government education sector by identifying its benefits, current scenario, application and future research potential areas.
Human-Computer Interaction With Big Data Analytics

Big Data has been playing a vital role in almost all environments such as healthcare, education, business organizations and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Big data consists of huge unstructured data that require advance real-time analysis. Thus, nowadays many of the researchers are interested in developing advance technologies and algorithms to solve the issues when dealing with big data. Big Data has gained much attention from many private organizations, public sector and research institutes. This chapter provides an overview of the state-of-the-art algorithms for processing big data, as well as the characteristics, applications, opportunities and challenges of big data systems. This chapter also presents the challenges and issues in human computer interaction with big data analytics.
Machine Learning Techniques for Medical Image Processing

In last decade, machine learning (ML) techniques increased the capability to automatically learn the experience without being explicitly programmed. Different machine learning methods are used for a number of tasks such as image processing, predictive analytics, data mining, and are used for classification, regression, clustering, and dimensionality reduction. ML is widely utilized in medical imaging research field. This paper introduces a survey on ML used in medical image processing and it focuses on two main types (supervised and unsupervised learning) to importance them in medical image processing with explains the foremost important algorithms of machine learning, discussing the most important advantages and drawbacks of applying Machine Learning techniques in medical imaging. In addition, some common algorithms were applied like k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Logistic Regression and Random Forest on medical dataset in order to check the efficiency of algorithms.
Effect of self‐learning media based on 360° virtual reality for learning periodontal instrument skills

Of 360° Virtual Reality (VR) is possibly produced and sufficiently effective as a consumer-friendly VR learning medium. Therefore, it is also expected to be useful in the dental practice field, as a self-learning medium for non-face-to-face skill training during the ongoing pandemic (COVID-19). Accordingly, this study was conducted to assess 360° VR self-learning media for a periodontal instrument operation.We recruited 30 participants who had never experienced instrument training. We offered basic education and initial assessment (IA), then divided them into three groups: 1) PAPER: trained only with paper handouts; 2) 2D: trained with 2D video; 3) VR: trained with 360° VR. Each group performed self-learning and mid-term assessment (MA). Subjects then implemented home self-learning with the same media for one week, which was then followed by a final assessment (FA).Analysis of IA-to-FA improvement scores showed that VR and 2D video were significantly higher than the PAPER groups. Meanwhile, analysis of MA-to-FA improvement scores showed that only VR was substantially higher than the PAPER group. Although VR and 2D video groups were not considerably different, VR scores were numerically higher than 2D video in all improvement score analyses.Both 2D video and 360° VR training were helpful to participants for an effective self-learning and also had good portability and accessibility as online-based learning methods. 360° VR showed higher learning efficiency than regular 2D video, possibly due to its autonomy, 360° visual information and physical and immersive characteristics, which positively affected self-training.Our findings showed the potential of 360° VR learning media and, further, suggest its usefulness as a novel self-learning method in future dental education.
Tunable Trajectory Planner Using G<sup>3</sup> Curves

Trajectory planning is commonly used as part of a local planner in autonomous driving. This paper considers the problem of planning a continuous-curvature-rate trajectory between fixed start and goal states that minimizes a tunable trade-off between passenger comfort and travel time. The problem is an instance of infinite dimensional optimization over two continuous functions: a path and a velocity profile. We propose a simplification of this problem that facilitates the discretization of both functions. This paper also proposes a method to quickly generate minimal-length paths between start and goal states based on a single tuning parameter: the second derivative of curvature. Further, we discretize the set of velocity profiles along a given path into a selection of longitudinal jerk way-points along the path. Finally, we repeatedly solve the path and velocity profiles in an iterative fashion. Numerical examples are provided to illustrate the benefits of the proposed methods.
Towards an Autonomous, Visual Inspection-aware 3D Exploration and Mapping System for Water Ballast Tanks of Marine Vessels

While there exist several approaches for autonomous exploration of confined spaces, when it comes to inspection tasks, there are not many works that take into account the actual classification of defects in the robot&#x2019;s view. Such approaches may result in less than optimal observations &#x2013;even miss corrosion areas&#x2013; and, therefore, can hinder the fulfillment of the purpose of the exploration, which is the identification of defects. In this paper, the first steps towards the unification of the exploration and inspection procedures, are theorized and experimentally evaluated. The next best view algorithm is augmented with information stemming from the defect classification. The system is tested within a computer model of a water ballast tank, from a double sided cargo carrier, infused with corrosion areas. The initial results indicate that the UAV is able to successfully maneuver through tight entrance ways to explore and map the different compartments of a ballast tank while observing a higher percentage of corrosion than &#x201C;vanilla&#x201D; next best view.
Declarative machine learning systems

The future of machine learning will depend on it being in the hands of the rest of us.
Improved Generalization in Signal Identification With Unsupervised Spiking Neuron Networks for Fiber-Optic Distributed Acoustic Sensor

More and more machine learning techniques are involved in fiber-optic distributed acoustic sensor (DAS) to improve its intelligent recognition ability. However, the supervised methods need sufficient and comprehensive labeled data, or they may present poor generalization when the test and the training data are inconsistent. Thus, in this paper a novel unsupervised Spiking Neural Network (SNN) is introduced into DAS for the first time to improve its generalization in different scenes and time-varying backgrounds. The theoretical mechanism, construction and optimization of the new network are first discussed in details; then its effectiveness is validated through three groups of field tests, using small dataset A with balanced, typical and consistent data, bad datasets B and C full of inconstant and atypical data, and full imbalanced dataset D respectively. It shows that the proposed SNN using the unsupervised learning mechanism, spiking-timing dependent plasiticity (STDP), is more stable than the mainstream supervised CNN when trained with small number of samples or imbalanced dataset, what often happens in practice. Especially in the most challenging case full of atypical or inconsistent samples, its generalization and anti-overfitting abilities are proved to be much better, where the average accuracy in the random test using bad datasets B and C has been increased by 18.0% and 14.4%. In the field test with full dataset, its recognition perform is also stably better than the compared CNN but with a longer time.
Optical information encryption based on partially-update iterative system with azimuth multiplexing

This paper introduces an optical information encryption method based on partially-update iteration system with azimuth multiplexing. Multiple high-definition pictures are stored in multiple QR codes as amplitude constraints, and then encrypted in Fresnel domain by azimuth multiplexing. Due to the proposal of the enhancement system with partial update of the phase mask, the number of pictures can be encrypted increases. The security of the system is guaranteed by azimuth multiplexing, and the multiplexing capacity is jointly guaranteed by phase partial update system and QR code. The decoded QR code can extract the encrypted information without much quality loss. Simulation results show that the scheme has a large-capacity information storage, high security, robustness and practicability.
Formal metatheory of second-order abstract syntax

Despite extensive research both on the theoretical and practical fronts, formalising, reasoning about, and implementing languages with variable binding is still a daunting endeavour - repetitive boilerplate and the overly complicated metatheory of capture-avoiding substitution often get in the way of progressing on to the actually interesting properties of a language. Existing developments offer some relief, however at the expense of inconvenient and error-prone term encodings and lack of formal foundations. We present a mathematically-inspired language-formalisation framework implemented in Agda. The system translates the description of a syntax signature with variable-binding operators into an intrinsically-encoded, inductive data type equipped with syntactic operations such as weakening and substitution, along with their correctness properties. The generated metatheory further incorporates metavariables and their associated operation of metasubstitution, which enables second-order equational/rewriting reasoning. The underlying mathematical foundation of the framework - initial algebra semantics - derives compositional interpretations of languages into their models satisfying the semantic substitution lemma by construction.
Radiation avoiding algorithm for nuclear robot path optimization

Improved elephant herding-ant colony hybrid algorithm (IEHO-ACO) is proposed for the nuclear robot path planning problem under the dual constraints of distance and radiation in the radiation environment. In this algorithm, the particle swarm algorithm speed update method and "hybrid operation" are introduced to increase the ability of the elephant herding optimization to jump out of the local optimum. The shortest path obtained by the improved elephant herding optimization is converted into the increment of the initial pheromone distribution of the ant colony optimization to increase the convergence speed of the algorithm and reduce the "blindness" of the ants. At the same time, the "Z" transformation strategy is introduced to dynamically adjust the combination of pheromone heuristic factor and expectation heuristic factor, so as to avoid the occurrence of premature stagnation and falling into local optimum in the search process. Taking full account of the particularity of the radiation environment, the way heuristic factors and pheromone updates in the ant colony algorithm are adapted. Also, the attraction of the target point to the ants is increased when the fallback strategy solves the deadlock problem. The simulation results show that the paths obtained by IEHO-ACO are more reliable and converge faster, and it can be applied to complex environments, especially in environments containing concave obstacles, IEHO-ACO shows good search performance.
Nonlinear Depth Quantization Using Piecewise Linear Scaling for Immersive Video Coding

Moving Picture Experts Group (MPEG) is developing a standard for immersive video coding called MPEG Immersive Video (MIV) and is releasing a reference software called Test Model for Immersive Video (TMIV) in the standardization process. The TMIV efficiently compresses an immersive video comprising a set of texture and depth views acquired using multiple cameras within a limited 3D viewing space. Moreover, it affords a rendered view of an arbitrary view position and orientation with six degrees of freedom. However, the existing depth quantization applied to depth atlas in TMIV is insufficient since the reconstructed depth is crucial for achieving the required quality of a rendered viewport. To address this issue, we propose a nonlinear depth quantization method that allocates more codewords to a depth subrange with a higher occurrence of depth values located at edge regions, which are important in terms of the rendered view quality. We implement the proposed nonlinear quantization based on piecewise linear scaling considering the computational complexity and bitstream overhead. The experimental results show that the proposed method yields PSNR-based Bjøntegaard delta rate gains of 5.2% and 4.9% in the end-to-end performance for High- and Low-bitrate ranges, respectively. Moreover, subjective quality improvement is mainly observed at the object boundaries of the rendered viewport. The proposed nonlinear quantization method has been adopted into the TMIV as a candidate standard technology for the next MIV edition.
Blockchain-based Decentralized Hybrid P2P Energy Trading

With increasing local energy generation consumers are converting into prosumers by selling their excess generation with their peers. This transaction of locally generated energy is called Peer-to-Peer (P2P) energy trading. For P2P energy trading, a community system is widely endorsed but causes an increase in overall system cost as both prosumer and consumer need to pay charges to the community manager. In this context, a hybrid P2P energy trading market is implemented through a decentralized blockchain-based mechanism. It permits market participants to trade energy directly without involving an additional entity. Contracts play a crucial role in blockchain-based energy trading mechanisms. Thus, smart contracts are designed for the efficient implementation of this trading mechanism. These contracts are designed using the Solidity platform on REMIX IDE. Prosumer and consumer interact among themselves through the main smart contract to execute P2P energy trading till market-clearing time. Afterwards, they interact with the grid through Peer-to-Grid (P2G) smart contracts for additional sell/purchase of energy. Results show that the proposed approach leads to a smart, secure and economical mechanism of energy trading.
Machine Learning in Healthcare: Operational and Financial Impact

Machine learning is revolutionizing healthcare management due to the exponential increase in health records' digitization and computing power. Predictive analytics are utilized across multiple applications and different hospital departments to accelerate workflow and improve medical decision-making. Machine learning is decisively altering diagnostic processes such as imaging, as computer vision continues to be adopted in radiology. In this chapter we highlight some of the many machine learning applications with direct impact on operations and/or financial outcomes within a hospital setting. We also address the important topics of fairness and transparency in healthcare modeling.
ANN Design Model to Recognize The Direction of Multi-Robot AGV

Automatic Guided Vehicle (AVG) Robot is a kind of mobile robot. This robot serves to transport goods from one place to a certain place. One type of robot that is currently being developed and researched is the multi robot. This robot is more focused in terms of communication between robots, so that the robots will not collide with each other. In its implementation, multi-robots differentiate from each other by communicating using camera sensors, so that image processing will be carried out. In this study, an Artificial Neural Network (ANN) model will be implemented which can differentiate between two robots. There are six input which are R1L, R1SR, R1SL, R2Ls, R2SR and R2 SL. The number of data sets entered is 300 data which is divided into 225 train data and 75 test data. The activation used are RelU and Softmax. The optimizer used is the Adam optimizer with a learning rate of 0.003, epoch used 50 with a batch size of 25. The result shows that the accuracy of the ANN model was 96%.
Blockchain Application for Central Bank Digital Currencies (CBDC)

Central Bank Digital Currency (CBDC) is a digital version of domestic currency with the unit of account equivalent to its domestic currency. Blockchain can be used to implement CBDC to execute and settle peer-to-peer transactions. With the emergence of private money such as cryptocurrencies and stable coins and the growing use of digital payments to lessen the global pandemic spread, CBDC is an active research area among the central banks worldwide. Many central banks started their CBDC projects by building proofs of concept (PoCs) to replicate wholesale payment systems and expand their investigation into other use cases such as delivery versus Payment (DvP) and cross-border remittance. PBoC, China Central Bank, has already started a pilot testing of their digital currency. This paper discusses the application of blockchain for CBDC by presenting CBDC projects by central banks. Moreover, this paper analyses issues, identify challenges, and discusses future works in this rapidly evolving field.
Document Graph for Neural Machine Translation

Previous works have shown that contextual information can improve the performance of neural machine translation (NMT).However, most existing document-level NMT methods only consider a few number of previous sentences.How to make use of the whole document as global contexts is still a challenge.To address this issue, we hypothesize that a document can be represented as a graph that connects relevant contexts regardless of their distances.We employ several types of relations, including adjacency, syntactic dependency, lexical consistency, and coreference, to construct the document graph.Then, we incorporate both source and target graphs into the conventional Transformer architecture with graph convolutional networks.Experiments on various NMT benchmarks, including IWSLT English-French, Chinese-English, WMT English-German and Opensubtitle English-Russian, demonstrate that using document graphs can significantly improve the translation quality.Extensive analysis verifies that the document graph is beneficial for capturing discourse phenomena.
Vehicle-to-vehicle distance estimation using artificial neural network and a toe-in-style stereo camera

In road traffic, adjusting the tracking distance between your vehicle and the one in front of your vehicle is necessary for safe driving. The systems developed for this purpose must detect object distance with high reliability. Accordingly, this article presents a stereo-vision-based method for estimating the distance between a given vehicle and the vehicle in front of it. In the proposed method, cameras are positioned such that their optical axes are equally tilted toward and intersect each other (i.e., the toe-in style). The tilt of the optical axes toward each other makes the different horizontal positions of the front vehicle important in the proposed distance estimation. Images of different horizontal deviation values at specified distances from the front vehicle are used to develop an artificial neural network (ANN) model. The results obtained from the test data show that, the proposed methodology is effective for determining the distance between vehicles.
Generalization of Bangla Sign Language Recognition Using Angular Loss Functions

Sign Language provides the means of conveying messages for deaf and mute people. Effective communication with the masses is a great challenge for the deaf and mute community, as Sign Language is not commonly understood. Many researchers have done numerous works in foreign language datasets like English, French, Japanese, etc. However, for Bangla, one of the most widely spoken languages, much significant work has not been done yet. Most of the works on Bangla Sign Language are executed on small datasets and report satisfactory performance. However, when small datasets are evaluated from the perspective of generalizability, particularly when using deep learning based solutions, these models fail to reproduce to the same performance. Therefore, this paper poses inter-dataset evaluation as the main evaluation criteria and evaluates several deep learning based models. This evaluation is done for Bangla by leveraging two popular datasets of Bangla Sign Language. Unsurprisingly, the inter-dataset performance is inferior, and several approaches to improve are explored and documented, including the use of angular margin based loss functions. The results demonstrate the importance of such an evaluation and also show that one of the proposed approaches shows promising performance, albeit with significant room for improvement. This raises the need for a standardized dataset to overcome this issue of generalization for real-life applications, as well as the need to encourage future works to concentrate on challenging evaluations instead of pursuing deceptively good intra-dataset performance.