Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Web Certification uses Descriptive Analytics to standardize Data

Certification is an important component for a cryptocurrency project in terms of data security, networks and tokens or coins in the cryptocurrency network to attract investors to invest in a blockchain project. This is what encourages this research to conduct data analysis trials based on descriptive analysis for cryptocurrency project certification. In the data analysis used, the data analysis process includes using a pilot test to test the questionnaire so that the data obtained for testing the Abrar LMS system is maintained and its validity is continued. Pilot test is a test of the reliability and validity of research equipment. Before the survey was distributed to actual respondents, the survey was first tested on students and heads of university study programs. The validity of a test or a series of procedures indicates how well it measures what it is designed to measure. This interest can be proven by the results of respondents who have been analyzed using SPSS to conduct pilot tests, validity tests, and reliability tests. Testing the system on respondents using alpha to test the functionality of the system so that valid results are obtained from the combination of respondent data analysis.
Smart Crop Rotation For Sustainable Agriculture Against Climate Change Based On Local Wisdom

Rotation plant can in a manner effective reduce fragility system plant. Rotation plant need consider Lots factor. If happen error in election type plants, can lower results plant next. moment This rotation planting is done only based on estimate. exists artificial intelligence technology with use the Simple Multi Attribute Rating Technique (SMART) method that can used for optimization scheduling rotation plant. SMART is method weighting and calculation on determination scheduling rotation plant with determine ranking plant replacement in rotation plant based on criteria already determined. The criteria used namely botanical family, availability and needs water, and cost. Addition criteria based on culture on scheduling land agriculture as in a research pilot project in the region Java east use culture planting with “Pranata Mangsa” java’s calendar. Research results show application system supporters decision rotation plant use SMART method has accuracy by 90%. Ranking results sorted from percentage biggest until smallest.
Comparison of Simple Matching Coefficient and Euclidean Distance In K-Means Algorithm for Tourism Destination Classification

The tourism sector is currently experiencing accelerated growth after being slumped due to the Covid 19 pandemic for 3 years. government assistance continues to be disbursed to restore the tourism industry. this is responded by tourism businesses by developing tourist destinations. The development includes improving existing destinations, as well as by creating new tourist destinations. in various regions, the government and the private sector are competing to build new tourist attractions, usually located near tourist destinations that are well known to the public. These tourist destinations need to be recorded by the government and tourists. in collecting data, it is necessary to make groupings based on the characteristics of these tourist destinations. To assess a destination is done using the 6A framework, namely attractions, amenities, access, availability, activities, Ancillary. The data obtained is then clustered using the K-Means algorithm, using Simple Matching coefficient and Euclidean Distance. SMC uses the principle of similarity while Euclidean uses the principle of calculating the distance between data. from the calculation results of the two methods, the resulting calculator is the same but when the Davies Bouldin Index is calculated, Euclidean Distance shows better performance.
Aspect-based sentiment analysis on student reviews using the Indo-Bert base model

This study aims to gain a deeper understanding of online student reviews regarding the learning process at a private university in Indonesia and to compare the effectiveness of several algorithms: Naive Bayes, K-NN, Decision Tree, and Indo-Bert. Traditional Sentiment Analysis methods can only analyze sentences as a whole, prompting this research to develop an Aspect-Based Sentiment Analysis (ABSA) approach, which includes aspect extraction and sentiment classification. However, ABSA has inconsistencies in aspect detection and sentiment classification. To address this, we propose the BERT method using the pre-trained Indo-Bert model, currently the best NLP model for the Indonesian language. This study also fine-tunes hyperparameters to optimize results. The dataset comprises 10,000 student reviews obtained from online questionnaires. Experimental results show that the aspect extraction model has an accuracy of 0.890 and an F1-Score of 0.897, while the sentiment classification model has an accuracy of 0.879 and an F1-Score of 0.882. These results demonstrate the effectiveness of the proposed method in identifying aspects and sentiments in student reviews and provide a comparison between the four algorithms.
QIoTChain: Quantum IoT‐blockchain fusion for advanced data protection in Industry 4.0

Abstract The modern world has its foundation built on data. Data is generated all the time, and many aspects of the society and economy are built upon it. In today's world, data is employed in better decision‐making, innovation, and improving the efficiency of systems. It becomes essential to protect it from being tampered with or misused. Encryptions based on cryptographic systems are used in mainstream data protection these days. There have been newer iterations of mechanisms based on new techniques using blockchain and the zero trust model and even systems that employ fog computing along with content delivery networks. The advent of computation through quantum technologies also helped in developing data security with post‐quantum mechanisms. The critical conditions for the working of these mechanisms hinder their adoption primarily. This also leads current systems to lack one or more of these technologies. Thus, the importance of a unified system that can employ quantum technologies effectively becomes prominent and requires a definitive investigation. In this article, a Quantum‐IoT‐based data protection scheme that can be of value to Industry 4.0 is analyzed. Light is also shed on innovations in quantum computing that can help solve not just current problems but also the future.
A catalog of metrics at source code level for vulnerability prediction: A systematic mapping study

Industry practitioners assess software from a security perspective to reduce the risks of deploying vulnerable software. Besides following security best practice guidelines during the software development life cycle, predicting vulnerability before roll-out is crucial. Software metrics are popular inputs for vulnerability prediction models. The objective of this study is to provide a comprehensive review of the source code-level security metrics presented in the literature. Our systematic mapping study started with 1451 studies obtained by searching the four digital libraries from ACM, IEEE, ScienceDirect, and Springer. After applying our inclusion/exclusion criteria as well as the snowballing technique, we narrowed down 28 studies for an in-depth study to answer four research questions pertaining to our goal. We extracted a total of 685 code-level metrics. For each study, we identified the empirical methods, quality measures, types of vulnerabilities of the prediction models, and shortcomings of the work. We found that standard machine learning models, such as decision trees, regressions, and random forests, are most frequently used for vulnerability prediction. The most common quality measures are precision, recall, accuracy, and F $$ F $$ -measure. Based on our findings, we conclude that the list of software metrics for measuring code-level security is not universal or generic yet. Nonetheless, the results of our study can be used as a starting point for future studies aiming at improving existing security prediction models and a catalog of metrics for vulnerability prediction for software practitioners.
Regulating Cryptocurrency: A Challenge for the Central Government and Central Bank

A cryptocurrency is designed to be a currency, but it does not really function like a currency. Currency always has an issuer, usually a trusted entity like the sovereign.&nbsp; There are already indications that cross-border flows are taking place in cryptocurrencies. If this trend is regulated, a part of the flows related to trade payments, remittances or cross border investments would be made in these cryptocurrencies. Cryptocurrencies have specifically been developed to ignore the regulated financial system. These should be reason enough to treat them with caution. It is also seen that cryptocurrencies are not amenable to definition as a currency, asset or commodity; they have no underlying cash flows, they have no intrinsic value; that they are akin to Ponzi Schemes, and may even be worse. These should be reason enough to keep them away from the formal financial system. More significantly, they can destabilise the currency system, the monetary authority, the banking system, and in general Government’s ability to control the economy.
TSDet: A new method for traffic sign detection based on YOLOv5‐SwinT

Abstract In real scenarios, accurate and real‐time detection of traffic signs is of great significance to the automatic driving system. To meet the requirements of detection accuracy and speed, a new traffic sign detection method based on YOLOv5 and Swin‐Transformer is proposed in this paper. First, based on the traditional Focus structure, a lightweight shallow information enhancement module is designed. Second, to enhance the channel weights of useful information, an adjustable channel attention mechanism is proposed. Additionally, a Cross Stage Partial module based on Swin‐Transformer is designed to capture contextual information around traffic signs, thereby improving the detection accuracy of small‐scale traffic signs. Finally, to better fuse deep semantic features and shallow detail features, an adaptive feature fusion method is proposed. To verify the superiority of the proposed method, experimental verification was carried out on TT100K and DFG traffic sign detection datasets, and their mAP, AP 50 and FPS were (75.3, 94.8, 82) and (79.7, 85.9, 118), respectively. The experimental results show that the proposed traffic sign detection method has high accuracy and real‐time performance, and can meet the needs of traffic sign detection in the actual scene.
State‐Independent Geometric Quantum Gates via Nonadiabatic and Noncyclic Evolution

Abstract Geometric phases are robust to local noises and the nonadiabatic ones can reduce the evolution time, thus nonadiabatic geometric gates have strong robustness and can approach high fidelity. However, the advantage of geometric phase has not been fully explored in previous investigations. Here,a scheme is proposed for universal quantum gates with pure nonadiabatic and noncyclic geometric phases from smooth evolution paths. In the scheme, only geometric phase can be accumulated in a fast way, and thus it not only fully utilizes the local noise resistant property of geometric phase but also reduces the difficulty in experimental realization. Numerical results show that the implemented geometric gates have stronger robustness than dynamical gates and the geometric scheme with cyclic path. Furthermore, it proposes to construct universal quantum gate on superconducting circuits, with the fidelities of single‐qubit gate and nontrivial two‐qubit gate can achieve 99.97% and 99.87%, respectively. Therefore, these high‐fidelity quantum gates are promising for large‐scale fault‐tolerant quantum computation.
Weighted distances and distance transforms on the triangular tiling

Abstract Digital geometry is a field in the intersection of discrete mathematics and geometry having various applications including geographical information systems (GIS). In digital spaces, in grids, distances can be defined based on steps in paths in somewhat similarly as in graph theory. However, the grids have more definite structures, thus one may obtain more concrete results, for example, close formulae, than on arbitrary graphs. In this article, the weighted (also called chamfer) distances, and based on them, the distance transform are investigated on the regular triangular grid. Three types of neighborhood relations are used on the grid, and therefore, three weights are used to define a distance function. Natural conditions are used on the weights such as they are positive and a larger step (in the usual and also in the Euclidean sense) cannot have a smaller weight than a smaller one. Some properties of the weighted distances are discussed; for example, they are proven to be metrics. We also give algorithms and formulae that compute the weighted distance of any point pair on a triangular grid. Algorithm for weighted distance transform is provided based on wave‐front propagation. Therefore, these new distance functions are ready for further applications in GIS, in image processing tasks, in computer vision, in graphics, in networking, and also in other applied fields.
Bayesian acoustic emission time-difference method for locating the collision point of sweeping robot shell

Abstract The existing source localization based on acoustic emission technology often depends on the assumption of constant wave velocity inside the material. However, this assumption is hardly satisfied in actual engineering. The uncertainty of wave velocity can easily lead to low localization accuracy of sweeping robots. To overcome these deficiencies, a complete probability multi-directional measurement method based on the Bayesian inference mechanism is proposed. In the proposed method, based on the Bayesian probabilistic model, the extracted sensor time difference is subjected to probabilistic inference using the coordinate input model to determine the posterior distribution of the source’s position and wave velocity of the given arrival time. Compared with the traditional time-difference method, the proposed method achieves excellent results and outperforms the standard time-difference method in localization accuracy and anti-interference. In addition, the proposed method can conveniently, quickly, and effectively determine the location of the colliding point without considering the source emission time and wave velocity. The research in this paper provides an effective method for solving the collision localization problem of the sweeping robot shell under the acoustic emission time and wave velocity are unknown.
ASSESSMENT THE IMPACT OF SOLAR PANELS ON THE ENVIRONMENT

Solar energy is a renewable energy source that is becoming increasingly popular in providing electricity for households and industrial production. Using solar panels also brings many benefits to human life. In particular, solar panels are advantageous in providing electricity to remote and isolated areas that do not have access to the national grid. However, like any other technology, solar panels also have particular environmental impacts that need to be taken into account. From reference sources, this study aims to make statistics and evaluate the impacts of solar panels on the environment. The analyzed results show that the land occupancy index of solar power is the least compared to other power sources for the power plant's life of more than 25 years.
QuickNav: An Effective Collision Avoidance and Path-Planning Algorithm for UAS

Obstacle avoidance is a desirable capability for Unmanned Aerial Systems (UASs)/drones which prevents crashes and reduces pilot fatigue, particularly when operating in the Beyond Visual Line of Sight (BVLOS). In this paper, we present QuickNav, a solution for obstacle detection and avoidance designed to function as a pre-planned onboard navigation system for UAS flying in a known obstacle-cluttered environment. Our method uses a geometrical approach and a predefined safe perimeter (square area) based on Euclidean Geometry for the estimation of intercepting points, as a simple and efficient way to detect obstacles. The square region is treated as the restricted zone that the UAS must avoid entering, therefore providing a perimeter for manoeuvring and arriving at the next waypoints. The proposed algorithm is developed in a MATLAB environment and can be easily translated into other programming languages. The proposed algorithm is tested in scenarios with increasing levels of complexity, demonstrating that the QuickNav algorithm is able to successfully and efficiently generate a series of avoiding waypoints. Furthermore, QuickNav produces shorter distances as compared to those of the brute force method and is able to solve difficult obstacle avoidance problems in fractions of the time and distance required by the other methods. QuickNav can be used to improve the safety and efficiency of UAV missions and can be applied to the deployment of UAVs for surveillance, search and rescue, and delivery operations.
End-to-end image splicing localization based on multi-scale features and residual refinement module

The addition of objects to the original image in splice forgery results in a change in the semantics of the original image, and distribution of these spliced images may bring negative impacts. To solve this issue, many forgery detection methods based on convolutional neural networks are presented. However, they tend to extract deep features, but ignore the importance of shallow semantics. In addition, the complexity of the forgery localization task leads to insufficient accuracy in detecting smaller forged regions. Based on the above problems, an end-to-end image splicing localization network based on multi-scale features and a residual refinement module (RRM) is proposed in this work. Our approach can be roughly divided into two modules: the detection module and the RRM. First, shallow and deep features are extracted from a backbone network, and deep features are then processed by the deeper atrous spatial pyramid pooling (ASPP) module to extract multi-scale features. The deeper ASPP module uses a smaller dilation rate and light convolution, which is more suitable for detecting complex counterfeit images. Second, the shallow features are fused with the multi-scale features to complement the shallow semantic information, such as texture and edges, which further improve the robustness of the model. Finally, the detection network generates coarse prediction maps that are fed to the RRM, and the RRM optimizes these masks by smoothing the boundaries, filling small gaps, and enhancing the edge details, which improves the pixel-level segmentation results for forgery detection. Extensive experimental results on several public datasets show that this method outperforms other state-of-the-art methods in image forgery localization.
Development of AI-Based Tools for Power Generation Prediction

This study presents a model for predicting photovoltaic power generation based on meteorological, temporal and geographical variables, without using irradiance values, which have traditionally posed challenges and difficulties for accurate predictions. Validation methods and evaluation metrics are used to analyse four different approaches that vary in the distribution of the training and test database, and whether or not location-independent modelling is performed. The coefficient of determination, R2, is used to measure the proportion of variation in photovoltaic power generation that can be explained by the model’s variables, while gCO2eq represents the amount of CO2 emissions equivalent to each unit of power generation. Both are used to compare model performance and environmental impact. The results show significant differences between the locations, with substantial improvements in some cases, while in others improvements are limited. The importance of customising the predictive model for each specific location is emphasised. Furthermore, it is concluded that environmental impact studies in model production are an additional step towards the creation of more sustainable and efficient models. Likewise, this research considers both the accuracy of solar energy predictions and the environmental impact of the computational resources used in the process, thereby promoting the responsible and sustainable progress of data science.
Review Evaluation for Hotel Recommendation

With the prevalence of backpacking and the convenience of using the Internet, many travelers like sharing their experiences in online communities. The development of online communities has changed the decision-making process of consumer purchasing, especially for travel, i.e., some travelers reconsider their decisions because they believe that the reviews of online communities are more valuable than advertisements. However, these reviews are not completely reliable since most reviews are provided without specific author information and the review data are too large to be observed. In this paper, we propose a novel approach (named ET) to evaluate the trustworthiness of reviews in online travel communities. Our method considers three concepts, including the sentiment similarity of reviewers in the social network, features of the reviews, and behaviors of the reviewers. The experimental results demonstrate that our method is effective in evaluating the trustworthiness of reviews.
Magnetic resonance imaging based neurosurgical planning on hololens 2: A feasibility study in a paediatric hospital

The goal of this work is to show how to implement a mixed reality application (app) for neurosurgery planning based on neuroimaging data, highlighting the strengths and weaknesses of its design.Our workflow explains how to handle neuroimaging data, including how to load morphological, functional and diffusion tensor imaging data into a mixed reality environment, thus creating a first guide of this kind. Brain magnetic resonance imaging data from a paediatric patient were acquired using a 3 T Siemens Magnetom Skyra scanner. Initially, this raw data underwent specific software pre-processing and were subsequently transformed to ensure seamless integration with the mixed reality app. After that, we created three-dimensional models of brain structures and the mixed reality environment using Unity™ engine together with Microsoft® HoloLens 2™ device. To get an evaluation of the app we submitted a questionnaire to four neurosurgeons. To collect data concerning the performance of a user session we used Unity Performance Profiler.The use of the interactive features, such as rotating, scaling and moving models and browsing through menus, provided by the app had high scores in the questionnaire, and their use can still be improved as suggested by the performance data collected. The questionnaire's average scores were high, so the overall experiences of using our mixed reality app were positive.We have successfully created a valuable and easy-to-use neuroimaging data mixed reality app, laying the foundation for more future clinical uses, as more models and data derived from various biomedical images can be imported.