Reviewerly/exHarmony huggingface dataset:
A probably secure biometric‐based authentication and key agreement scheme for Internet of Drones

Abstract The Internet of Drones (IoD) is one of the world's most recent and innovative technology. Drones are most prominent IoT (Internet of Things) gadgets and their applications span from commercial to domestic. The sensitive data stored in drones has increased the demand for the security of communication in the IoD. As a result, numerous authentication and key agreement techniques have been developed to proffers secure communication among the entities of the IoD network. However, after scrutinizing the security of these protocols, many IoD protocols were observed to be susceptible to different cryptographic attacks. This has enhanced the necessity for an improved and efficient authentication scheme in IoD. In this article, we first investigate Zhang et al.'s approach and demonstrate that although it offers security verification, it has certain design problems. It is, furthermore, shown to be vulnerable to privileged insider, offline password guessing, and stolen smart device attacks. Second, we propose a novel biometric‐based scheme that includes countermeasures to protect against these flaws. The security of the mechanism is examined under real or random oracle model, informal security analysis and scyther simulation for numerous cryptographic assaults. Also, the performance analysis substantiates the competency of the proposed scheme corresponding to computation and communication cost with existing schemes.
Awareness of cybercrime among online banking users in Nigeria

This study aimed to investigate the awareness of cybercrime among online banking users in Nigeria and provide quantitative data on various aspects related to cybersecurity awareness. A comprehensive survey was conducted among 283 online banking users in Nigeria to gather data. The data were analyzed to identify trends and patterns in cybersecurity awareness among Nigerian online banking users. The study revealed that 82.0% of the respondents were aware of cybercrime, indicating a high level of awareness among Nigerian online banking users. Social media (37.5%) and friends (16.0%) were identified as the primary sources of knowledge about cybercrime. The majority of respondents employed multi-factor authentication methods, such as login + password + biometric, to secure their online banking accounts. However, there was room for improvement in password preferences, as some respondents still used simple passwords. The most used features of online banking were transferring money between accounts (42.4%), checking account balances (26.5%), and paying bills (11.0%). This study contributes new insights to the existing body of knowledge by providing a comprehensive analysis of cybersecurity awareness among Nigerian online banking users. It emphasizes the evolving landscape of cybercrime awareness, the influence of digital platforms in disseminating information, and the importance of targeted awareness campaigns and improved security measures.
Anomalies, 𝜼 , 𝜼’ as keys to glueballs.

Glueballs are the most straightforward prediction of QCD, yet while they have likely been produced, none has been unequivocally identified.We pursue a backdoor approach through anomalies, and singularly the and ' which brings light to this irritating situation.In particular, we advocate to consider the full decay chain / → , → ′ (into glue-rich states followed by glue-rich decays).We also suggest new BES III searches, namely for the 1 into ( ′ ) 0 , (this would be the partner of their recently observed 1 (1855)).Another useful investigation would be for other channels (or semi-inclusive) 0 (1500) decays (see last section)
Priority Recommendations for Residential Road Improvement Using the SMART Analysis Method

Roads are infrastructure for organizing transportation, which are places for traffic to flow both for people and goods to reach destinations safely, securely, comfortably, quickly, smoothly, orderly, and efficiently, especially roads in residential areas. Setting priorities for the road improvement program is the responsibility of the Public Housing, Settlement Areas, and Land Affairs Office, which handles technical planning, development, arrangement, supervision, and control of development in residential areas. Recommendations for road proposals for the currently running improvement program, based on an assessment of their physical condition, are carried out by experts. This prioritization certainly takes a long time because experts have to compare the physical conditions of the roads one by one to make a decision. A decision support system is specifically designed for the decision-making process that can be applied in various aspects of the decision-making field. Recommendations for alternative roads in the road improvement program were analyzed using the SMART method to find alternatives with the highest preference value and the advantage that they can be used for all weighting techniques. Accuracy testing shows that the priority recommendation output presented by the application has an accuracy rate of 80%. This value is obtained by comparing the results of recommendations from experts.
Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption compared to state-of-the-art deep learning algorithms. Our proposed system maintains two steps. At first, this work extracted the local features of an image by using a local binary pattern descriptor, and then we used deep learning to extract global features. The proposed approach has achieved excellent accuracy and high performance. The performance of the proposed method was tested on three benchmark datasets: the real-world masked faces dataset (RMFD), the simulated masked faces dataset (SMFD), and labeled faces in the wild (LFW). Performance metrics for the proposed technique were measured in terms of accuracy, precision, recall, and F1-score. Results indicated the efficiency of the proposed technique, providing accuracies of 99.86%, 99.98%, and 100% for RMFD, SMFD, and LFW, respectively. Moreover, the proposed method outperformed state-of-the-art deep learning methods in the recent bibliography for the same problem under study and on the same evaluation datasets.
Efficient automated error detection in medical data using deep-learning and label-clustering

Abstract Medical datasets inherently contain errors from subjective or inaccurate test results, or from confounding biological complexities. It is difficult for medical experts to detect these elusive errors manually, due to lack of contextual information, limiting data privacy regulations, and the sheer scale of data to be reviewed. Current methods for training robust artificial intelligence (AI) models on data containing mislabeled examples generally fall into one of several categories—attempting to improve the robustness of the model architecture, the regularization techniques used, the loss function used during training, or selecting a subset of data that contains cleaner labels. This last category requires the ability to efficiently detect errors either prior to or during training, either relabeling them or removing them completely. More recent progress in error detection has focused on using multi-network learning to minimize deleterious effects of errors on training, however, using many neural networks to reach a consensus on which data should be removed can be computationally intensive and inefficient. In this work, a deep-learning based algorithm was used in conjunction with a label-clustering approach to automate error detection. For dataset with synthetic label flips added, these errors were identified with an accuracy of up to 85%, while requiring up to 93% less computing resources to complete compared to a previous model consensus approach developed previously. The resulting trained AI models exhibited greater training stability and up to a 45% improvement in accuracy, from 69 to over 99% compared to the consensus approach, at least 10% improvement on using noise-robust loss functions in a binary classification problem, and a 51% improvement for multi-class classification. These results indicate that practical, automated a priori detection of errors in medical data is possible, without human oversight.
The Stretchy Strap: supporting encumbered interaction with guitars

Guitarists struggle to play their instruments while simultaneously using additional computing devices (i.e., encumbered interaction). We explored designs with guitarists through co-design and somaesthetic design workshops, learning that they (unsurprisingly) preferred to focus on playing their guitars and keeping their instruments’ material integrity intact. Subsequently, we devised an interactive guitar strap controller, which guitarists found promising for tackling encumbered interaction during instrumental transcription, learning and practice. Our design process highlights three strategies: considering postural interaction, applying somaesthetic design to interactive music technology development, and augmenting guitar accessories.
Risks' Attribute Values Evaluation in Software Engineering by Monte Carlo Simulation

It is a good practice in the industry to carry out a risk assessment even before the stages of software production because we can prevent the costs of both human and material resources. In the era of the success of artificial intelligence with machine learning, it is very important to have accumulated expert knowledge on the basis of which we fuel datasets. Usually, it is very difficult to obtain numerical values of risk attributes, and especially to determine their importance among others. The values of risk attributes can be balanced in various ways, one of which is Monte Carlo simulation, which is presented in the publication.
Ukrainian Architects of the Baroque Age: Algorithms of the Worldview Development

The article raises the problem of the artistic worldviews divergence of the artist (architect) and the commissioner of the construction. It is argued that the problem occurred due to the insufficient influence of aesthetic culture on the educational field in general and in particular, on primary and secondary education. In order to draw corresponding historical parallels, the subject matter of this paper is the Baroque era and the interaction of contemporary architects and commissioners. It is hypothesized that the commonality of artistic worldviews in this tandem was a key reason for its success. Therefore, identifying and investigating the formative factors of a common worldview of the educated circles of the Baroque era may be projected to modern times as an illustrative example of socially relevant architecture. A wide range of approaches was used asthe problem was considered within the axis of“culture—education—society” and in various aspects. The scope of identified factors determined the interdisciplinary nature of the study
Mixed Reality: A Tool for Investigating the Complex Design and Mechanisms of a Mechanically Actuated Digital Pump

Digital hydraulics is a discrete technology that integrates advanced dynamic system controls, digital electronics, and machine learning to enhance fluid power systems’ performance, overall efficiency, and controllability. A mechanically actuated inline three-piston variable displacement digital pump was previously proposed and designed. The inline three-piston pump incorporates complex mechanical and hydraulic subsystems and highly coupled mechanisms. The complexity of the utilized subsystems poses challenges when assessing the viability of the conceptual design. Therefore, this work focuses on designing, developing, and implementing a collaborative virtual platform involving a digitized module showcasing the internal mechanical structure of the digital pump utilizing mixed reality (MR) technology. MR technology is acknowledged as the forthcoming evolution of the human–machine interface in the real–virtual environment utilizing computers and wearables. This technology permits running simulations that examine the complexity of highly coupled systems, like the digital pump, where understanding the physical phenomenon is far too intricate. The developed MR platform permits multiple users to collaborate in a synchronized immersive MR environment to study and analyze the applicability of the pump’s design and the adequacy of the operated mechanisms. The collaborative MR platform was designed and developed on the Unity game engine, employing Microsoft Azure and Photon Unity Networking to set up the synchronized MR environment. The platform involves a fully interactive virtual module on the digital pump design, developed in multiple stages using Microsoft’s Mixed Reality Tool Kit (MRTK) for Unity and deployed in the synchronized MR environment through a HoloLens 2 MR headset. A research study involving 71 participants was carried out at Purdue University. The study’s objective was to explore the impact of the collaborative MR environment on understanding the complexity and operation of the digital pump. It also sought to assess the effectiveness of MR in facilitating collaboration among fluid power stakeholders in a synchronized digital reality setting to study, diagnose, and control their complex systems. Surveys were designed and completed by all 71 participants after experiencing the MR platform. The results indicate that approximately 75% of the participants expressed positive attitudes toward their overall MR platform experience, with particular appreciation for its immersive nature and the synchronized collaborative environment it provided. More than 70% of the participants agreed that the pump’s collaborative MR platform was essential for studying and understanding the complexity and intricacy of the digital pump’s mechanical structure. Overall, the results demonstrate that the MR platform effectively facilitates the visualization of the complex pump’s internal structure, inspection of the assembly of each of the involved subsystems, and testing the applicability of the complicated mechanisms.
Design of Virtual Reality for Gonggong Building as a Tourism Information

The rapid development of information media in line with the presence of technology increasingly provides convenience for humans so that information or messages can be conveyed properly, quickly, precisely, and usefully. One of the technological developments that support information media is Virtual Reality (VR). Information Media related to Tanjungpinang City Tourism with the object of research in the form of the Gonggong Building as the Tanjungpinang City Tourism Information Center, which has the problem of stopping the function of the Building as a City Tourism Information Center due to damage. The purpose of this research is to implement Virtual Reality on the Gonggong Building with the output of the VR website of the Gonggong Building and the presentation of Tanjungpinang City tourism information content delivered by the Chatbot Tour Guide. The SDLC method used in this research is Prototype. The research results are a VR website for the Gonggong Building, which contains information related to Tanjungpinang City Tourism, namely Typical Culinary Destinations, Nature Tourism, and Melayu Traditional Clothing of the Riau Islands. The result of testing methods with 25010 obtained excellent results with Grade B on performance GTmetrix in testing the efficiency of the system in getting a response time of 5,8s fully loaded time page. From the reliability test results, a summary is obtained in the form of successful session 25964, failed session 0, successful page 25966, failed pages 0, successful hits 51932, failed hits 0, and other errors 0. These results, if expressed in percentage form, amount to 100%.
Enabling Quantum Computer Simulations on AMD GPUs: a HIP Backend for Google's qsim

Quantum computer simulators play a critical role in supporting the development and validation of quantum algorithms and hardware. This study focuses on porting Google’s qsim, a quantum computer simulator, to AMD Graphics Processing Units (GPUs). We leverage the existing qsim CUDA backend and harness the HIPIFY tool to provide a qsim HIP backend tailored for AMD GPUs. Our performance analysis centers on evaluating the HIP backend’s capabilities, executed on a computing node equipped with the AMD MI250X GPU and the AMD EPYC Trento CPU. We use the Random Quantum Circuit (RQC) sampling benchmark, employing a circuit featuring 30 qubits. The qsim HIP backend on AMD GPU outperforms the CPU version by a remarkable margin, achieving seven to nine times faster speeds. Our investigation also compares qsim’s performance on the Nvidia A100 and AMD MI250X GPUs. The Nvidia A100 consistently outperforms the AMD MI250x counterpart, and this performance gap further widens with optimal gate fusion configurations. For instance, a two-gate fusion configuration exhibits a 5% difference, whereas a four-gate fusion setup reveals a large 44% performance gap. Our work highlights the substantial performance advantage of GPU-based quantum simulation over traditional CPU approaches. Despite a performance lag compared to the qsim CUDA backend, the AMD HIP qsim backend emerges as a competitive alternative poised for further optimization.
IMBoost: A New Weighting Factor for Boosting to Improve the Classification Performance of Imbalanced Data

Imbalanced datasets pose significant challenges in the field of machine learning, as they consist of samples where one class (majority) dominates over the other class (minority). Although AdaBoost is a popular ensemble method known for its good performance in addressing various problems, it fails when dealing with imbalanced data sets due to its bias towards the majority class samples. In this study, we propose a novel weighting factor to enhance the performance of AdaBoost (called IMBoost). Our approach involves computing weights for both minority and majority class samples based on the performance of classifier on each class individually. Subsequently, we resample the data sets according to these new weights. To evaluate the effectiveness of our method, we compare it with six well-known ensemble methods on 30 imbalanced data sets and 4 synthetic data sets using ROC, precision-eecall AUC, and G-mean metrics. The results demonstrate the superiority of IMBoost. To further analyze the performance, we employ statistical tests, which confirm the excellence of our method.
A bio-medical snake optimizer system driven by logarithmic surviving global search for optimizing feature selection and its application for disorder recognition

Abstract It is of paramount importance to enhance medical practices, given how important it is to protect human life. Medical therapy can be accelerated by automating patient prediction using machine learning techniques. To double the efficiency of classifiers, several preprocessing strategies must be adopted for their crucial duty in this field. Feature Selection (FS) is one tool that has been used frequently to modify data and enhance classification outcomes by lowering the dimensionality of datasets. Excluded features are those that have a poor correlation coefficient with the label class, i.e., they have no meaningful correlation with classification and do not indicate where the instance belongs. Along with the recurring features, which show a strong association with the remainder of the features. Contrarily, the model being produced during training is harmed, and the classifier is misled by their presence. This causes overfitting and increases algorithm complexity and processing time. The pattern is made clearer by FS, which also creates a broader classification model with a lower chance of overfitting in an acceptable amount of time and algorithmic complexity. To optimize the FS process, building wrappers must employ metaheuristic algorithms as search algorithms. The best solution, which reflects the best subset of features within a particular medical dataset that aids in patient diagnosis, is sought in this study using the Snake Optimizer (SO). The swarm-based approaches that SO is founded on have left it with several general flaws, like local minimum trapping, early convergence, uneven exploration and exploitation, and early convergence. By employing the cosine function to calculate the separation between the present solution and the ideal solution, the logarithm operator was paired with SO to better the exploitation process and get over these restrictions. In order to get the best overall answer, this forces the solutions to spiral downward. Additionally, SO is employed to put the evolutionary algorithms’ preservation of the best premise into practice. This is accomplished by utilizing three alternative selection systems – tournament, proportional, and linear – to improve the exploration phase. These are used in exploration to allow solutions to be found more thoroughly and in relation to a chosen solution than at random. These are Tournament Logarithmic Snake Optimizer (TLSO), Proportional Logarithmic Snake Optimizer, and Linear Order Logarithmic Snake Optimizer. A number of 22 reference medical datasets were used in experiments. The findings indicate that, among 86% of the datasets, TLSO attained the best accuracy, and among 82% of the datasets, the best feature reduction. In terms of the standard deviation, the TLSO also attained noteworthy reliability and stability. On the basis of running duration, it is, nonetheless, quite effective.
Technical, Economic, and Environmental Feasibility Assessment of Grid‐Connected Photovoltaic Power Generation Systems with Different Tracking Models in Hefei, China

Herein, the feasibility of a grid‐connected photovoltaic (PV) power generation system with five tracking models in Hefei is evaluated comprehensively. The comprehensive evaluation of grid‐connected PV power generation system mainly includes three aspects: technology, economy, and environment. The results show that the performance ratio of grid‐connected PV system with dual‐axis tracking device is the highest among the five kinds of PV systems with a value of 81.2%, while the performance ratio of grid‐connected PV system without tracking device is the lowest with a value of 77.3%. The grid‐connected PV system with dual‐axis tracking device is economically attractive, so users can be encouraged to purchase and install such systems to meet their electrical energy needs in Hefei. Furthermore, the grid‐connected PV system with dual‐axis tracking device is the most environmentally friendly and contributes to carbon emission reduction. Finally, it is concluded that the adoption of dual‐axis tracking grid‐connected PV power generation system in Hefei area is very attractive, and can provide a certain reference for the local promotion and use of such grid‐connected PV power generation system.