Reviewerly/exHarmony huggingface dataset:
An Incentive Mechanism for Energy Internet of Things Based on Blockchain and Stackelberg Game

In the Internet of Everything era, the Energy Internet of Things (IoT), as a typical application of IoT technology, has been extensively studied. Meanwhile, blockchain technology and energy IoT can be coordinated and complementary. The energy IoT is diversified and has a high transaction demand. it is an issue worthy of research to discuss the impact of the energy IoT environment on the performance of blockchain consensus algorithms and guarantee blockchain stability in energy IoT environment. In the research, an incentive mechanism based on Stackelberg game is proposed for the network scenario involving multiple roadside units and user nodes. The proposed strategy is analyzed through the Matlab simulation platform. The simulation results show that the proposed scheme can effectively protect the interests of blockchain users and miners. It also can improve the security and stability of the blockchain-based energy IoT system. Moreover, the numerical results not only verify the model feasibility. It also shows that when there are many blockchain miners, the model performance is fine. However, when the number of miners reaches a certain value, there will be unobvious growth. Furthermore, it is also confirmed that the wireless energy IoT environment will also create a certain impact on the game model.
DETECTION OF FACE MASKS USING DEEP LEARNING

The COVID-19 pandemic has quickly disrupted global trade and travel, affecting our day-to-day lives. In its 209th report, released on August 16, 2020, The World Health Organization (WHO) reported that the acute respiratory syndrome (SARS-CoV2) coronavirus illness (COVID-19) has killed more than 379,941 people and infected more than 6000000 people worldwide. The custom of donning a safety mask has evolved. Many public service providers may soon need their clients to wear the necessary masks in order to use their services. In order to serve the worldwide society, face mask detection is becoming a key responsibility. The face mask alone can reduce the risk of contracting the infection by more than 70%. Infections have swiftly spread in some regions as a result of incorrect use of face masks. To solve this issue, we needed a dependable mask monitoring system. The several government agencies who are attempting to make wearing a face mask mandatory can benefit from face mask identification software. This study presents a concise deep learning approach for achieving this objective.
A NEURAL NETWORK METHOD TO PERFORM OPTICAL CHARACTER RECOGNITION OF PRINTED CHARACTERS

Text recognition has gained a lot of attention in the last few years. The Optical character recognition is one of the method used for text recognition .Also printed document with different font and also some low resolution images . This paper describes all the method used and also explanation for non-horizontal text which are used and explanation of historical of the text from old documents which have been digitalized. The Numerals and letters are recognized by Convolutional Neural Networks The results obtained by Convolutional Neural Networks seems to be promising in recognizing the characters in comparison to results obtained through other algorithms such as Support Vector Machines and Artificial Neural Networks. Characters are recognized by using Convolutional Neural Networks. The results obtained by Convolutional Neural Networks seem promising when it comes to detecting signs compared to the results obtained by other machine learning algorithms such as support vector machines and artificial neural networks.
Pengaruh Sumber Daya Manusia, Sistem Informasi Akuntansi, Dan Teknologi Informasi Terhadap Kecenderungan Kecurangan Akuntansi

The Research to how human resources, accounting information systems, and information technology affect the tendency of accounting fraud. Type research is quantitative, and the data used are primary data sourced from questionnaires distributed to manufacturing companies in Cilegon City according to the sample criteria using proposive sampling. The sample research is middle management and low management manufacturing companies located. Of the 120 questionnaires distributed, only 54 were processed. the data analyzed with IBM SPSS software version 23. The results obtained are Human Resources, Accounting Information Systems, and Information Technology partially have a positive and significant effect on the tendency of accounting fraud.
The Mediating Effects of Hedonie Value, Health Interest and Utilitarian Value on Continuous Intention to Use in Glucose Monitoring Devices

Continuous glucose monitoring (CGM) devices are medical devices that continuously measure and display blood sugar levels in real time. A small sensor that is implanted just beneath the skin, a transmitter that transmits data from the sensor to a display device, and a display that shows the blood sugar levels in real-time are the typical components of these devices, the user might be warned via alarms on some CGM devices if their blood sugar levels are too high or too low [1]. Examining the fundamental mediating effects of the hedonic value, health interest, and utilitarian values between significant antecedents and continuous intention to use CGM devices is the general goal of this study. By integrating several significant antecedents to the hedonic and utilitarian values research framework, the study expanded the hedonic and utilitarian behavioral decision research model by adding health interest variable. The key antecedents included are Safety, interactivity, Result Accuracy, User interface, Self Efficacy, and Personal Innovativeness. The study also intends to improve user's comprehension of CGM device adoption and continue intention to use, the methodology used in the study is suitable for using an online survey to collect data and answer the research questions, the methodologies are also conceptually and practically linked with previous research, the focus of this research is on CGM devices systems having qualities that enable users to foel upbeat while using them and to have a good attitude toward using them in the future. This study were to use a complete approach to examine the data in a quantitative analysis, an expanded hedonic and utilitarian behavioral decision research framework for the adoption and continues intention to use CGM device. 109 samples were gathered from an online survey and 100 valid responses were analyzed after eliminating 9 invalid responses, the structural equation modeling technique was adopted for this study, it enables simultaneous analysis. SmartPLS 4.0 and IBM SPSS version 23 were used for the analysis. The results of the analyses show that Safety, Interactivity, Self-efficacy, Result Accuracy, User Interface, and Personal Innovativeness have positive impacts on hedonic value, health value, and utilitarian value to adoption and continuous using CGM devices. The majority of the offered hypotheses are supported by this study model's adequate overall model fit indices.
Use of Common Media Client and Server Data to Improve Streaming Playback Performance

Smooth playback is critical for any streaming service. Naturally, viewers do not enjoy rebuffering events, but less frequent and shorter ones are preferred when they cannot be avoided. However, ensuring smoother playback becomes more challenging as more demanding types of services such as low-latency live (LLL) and 4K streaming become increasingly used. It has been shown many times that adaptive streaming clients compete with each other for available bandwidth and server capacity. The server’s priority in responding to the individual requests sent by these clients should be based on the clients’ time-varying playback buffer conditions. In an earlier article, we presented that the server could wisely allocate its output capacity among the incoming requests and largely mitigate the rebufferings suffered by the clients, provided that the clients informed the server about their buffer statuses using the Common Media Client Data (CMCD) standard. In a more recent study, we developed an alternative solution to the same problem using the Common Media Server Data (CMSD) standard. In this solution, the server’s response to a request that indicated a sufficient buffer level was delayed until the requests that indicated an insufficient buffer level were handled. The server attached a new CMSD parameter to the eventual response disclosing the length of the delay. This parameter avoided misinterpretation and the subsequent incorrect decision by the client’s rate-adaptation logic. Our experiments (for which we offer the source code) showed that the proposed CMSD parameter eliminated unnecessary rate shifting to lower bitrates while reducing the rebuffering rate as well as the rebuffering duration. This article summarizes these findings and illustrates other envisioned use cases for the CMSD standard.
Quantum computer-aided design for advanced superconducting qubit: Plasmonium

Complex quantum electronic circuits can be used to design noise-protected qubits, but their complexity may exceed the capabilities of classical simulation. In such cases, quantum computers are necessary for efficient simulation. In this work, we demonstrate the use of variational quantum computing on a transmon-based quantum processor to simulate a superconducting quantum electronic circuit and design a new type of qubit called “Plasmonium”, which operates in the plasmon-transition regime. The fabricated Plasmonium qubits show a high two-qubit gate fidelity of 99.58(3)%, as well as a smaller physical size and larger anharmonicity compared to transmon qubits. These properties make Plasmonium a promising candidate for scaling up multi-qubit devices. Our results demonstrate the potential of using quantum computers to aid in the design of advanced quantum processors.
Pregnancy outcomes of anti-TNF-alfa treatment during pregnancy: A mixed case series

The objective of this paper is to present and evaluate an approach to early-stage design product performance predicting that employs the “wisdom of the crowd” of artificial neural networks (ANN). The wisdom of the crowd has been used to gather insights, ideas, and problems by using responses from large numbers of people. In this work, 189 different ANN architectures will be used instead of human agents, thus creating an ANN Crowd. This research explores the question “should every agent in the crowd has the same voice?” To answer this, this research aims to broadly understand the sensitivity of influencing factors (training set selection, nodes, layers, and architecture) in the performance of the ANN Crowd. This will then support refinement to achieve generalizability for the ANN Crowds. The specific focus of this paper is to explore the effect of the training set selection on prediction accuracy. To do this, four different scenarios are explored (2 inputs x 2 outputs). ANN Crowds are created for these scenarios with different product training configurations to test how the training set influences the predictions (k-fold = 10). Results show that training set selection has a statistical and practical influence on prediction accuracy. Analyzing the presence of edge cases in training sets and the effects of training targets suggests further influence of training set selection. Future work includes a deeper exploration of the influence of edge cases within the training sets. Building on this foundation, the members of the ANN Crowd (different architectures) will be studied for their influence on prediction performance.
A Fast and Accurate Optical Flow Camera for Resource-Constrained Edge Applications

Oiptical Flow (OF) is the movement pattern of pixels or edges that is caused in a visual scene by the relative motion between an agent and a scene. OF is used in a wide range of computer vision algorithms and robotics applications. While the calculation of OF is a resource-demanding task in terms of computational load and memory footprint, it needs to be executed at low latency, especially in robotics applications. Therefore, OF estimation is today performed on powerful CPUs or GPUs to satisfy the stringent requirements in terms of execution speed for control and actuation. On-sensor hardware acceleration is a promising approach to enable low latency OF calculations and fast execution even on resource-constrained devices such as nano drones and AR/VR glasses and headsets. This paper analyzes the achievable accuracy, frame rate, and power consumption when using a novel optical flow sensor consisting of a global shutter camera with an Application Specific Integrated Circuit (ASIC) for optical flow computation. The paper characterizes the optical flow sensor in high frame-rate, low-latency settings, with a frame rate of up to 88 fps at the full resolution of 1124 by 1364 pixels and up to 240 fps at a reduced camera resolution of 280 by 336, for both classical camera images and optical flow data.
Adaptive Levy Flight Distribution Algorithm for Solving a Dynamic Model of an Electric Heater

This paper presents an improved version of Levy Flight Distribution (LFD) algorithm. The original LFD is formulated based on the random walk strategy. However, it suffers a premature convergence due to imbalance exploration and exploitation. Consequently, the algorithm produces unsatisfactory performance in terms of its final accuracy achievement. As a solution to the problem, an adaptive scheme of search agents step size is incorporated into the original LFD algorithm. Moreover, a mating strategy is also adopted to improve its stochastic nature throughout the search process. The algorithm is applied to optimize a nonlinear dynamic model of an electric water heater. A fuzzy-based Hammerstein structure is adopted to represent the heater model. It comprises a combination of both linear and nonlinear equations so that it can capture the dynamic behavior of the heater satisfactorily. The proposed adaptive LFD algorithm is compared with the original LFD algorithm. The result shows that the proposed algorithm has attained a better accuracy. It also has captured the dynamic behavior of the heater more adequately.
Almost sure convergence rates of stochastic proximal gradient descent algorithm

Stochastic proximal gradient descent (Prox-SGD) is a standard optimization algorithm for solving stochastic composite optimization problems in machine learning. However, the existing convergence rate analysis of Prox-SGD is mostly focused on convergence in expectation. To this end, we provide an almost sure convergence rate analysis of Prox-SGD in two optimization settings: convexity and strong convexity, showing that any implementation of the stochastic algorithm will converge with probability one. Specifically, we show that the average-iterates almost sure convergence rate of the function values for Prox-SGD is arbitrarily close to the optimal rate o(1/T) in the convex setting. For strongly convex objective functions, we obtain the almost sure convergence rate of the iterative sequence, which is also arbitrarily close to the optimal rate o(1/T). Last but not least, we establish the more challenging last-iterate convergence rate of the function values on convex problems, which contrasts with existing results on convergence for a weighted average.
Secure and Privacy in Healthcare Data Using Quaternion-based Neural Network Cryptography with the Blockchain Mechanism

In the healthcare industry, security and privacy are major concerns. For effective diagnosis the medical practitioners need to access the healthcare data of the patient and similarly, the patient also needs to access the data. Sharing such a sensitive health data needs to be secured and privacy-preserving is ensured. Before uploading and downloading private health data, the data should be secured. To ensure security and privacy-preserving of health data stored in the cloud by implementing the improved Quaternion-based neural network with the blockchain mechanism. Improved Quaternion neural network cryptography is executed for encrypting the shared health data. Encrypted data’s secret key is stored by utilizing blockchain which is converted into blocks and the SHA algorithm is employed for identifying key events in the blocks which are stored in cloud storage. The modified genetic algorithm is used to generate a key which would be used for encryption and decryption. The authorized patient or physician can access the medical data by using the secret key to decrypt and download the medical data. The proposed network is evaluated in terms of time taken and cost and compared with the existing study. The results show that the time taken for the encryption and decryption process and the transaction and execution cost are reduced compared with the existing study.
Online spike-based recognition of digits with ultrafast microlaser neurons

Classification and recognition tasks performed on photonic hardware-based neural networks often require at least one offline computational step, such as in the increasingly popular reservoir computing paradigm. Removing this offline step can significantly improve the response time and energy efficiency of such systems. We present numerical simulations of different algorithms that utilize ultrafast photonic spiking neurons as receptive fields to allow for image recognition without an offline computing step. In particular, we discuss the merits of event, spike-time and rank-order based algorithms adapted to this system. These techniques have the potential to significantly improve the efficiency and effectiveness of optical classification systems, minimizing the number of spiking nodes required for a given task and leveraging the parallelism offered by photonic hardware.
Fast Determination of Optimal Transmission Rate for Wireless Blockchain Networks: A Graph Convolutional Neural Network Approach

One of the primary challenges in wireless blockchain networks is to ensure security and high throughput with constrained communication and energy resources. In this paper, with curve fitting on the collected blockchain performance dataset, we explore the impact of the data transmission rate configuration on the wireless blockchain system under different network topologies, and give the blockchain a utility function which balances the throughput, energy efficiency, and stale rate. For efficient blockchain network deployment, we propose a novel Graph Convolutional Neural Network (GCN)-based approach to quickly and accurately determine the optimal data transmission rate. The experimental results demonstrate that the average relative deviation between the blockchain utility obtained by our GCN-based method and the optimal utility is less than 0.21%.
Pengaruh Motivasi dan Disiplin terhadap Prestasi Belajar Mahasiswa Jurusan Manajemen Universitas Sumatera Barat

This Research Aims to Know The Influence of Motivation and Discipline On Student Achievement Management Department, University of West Sumatra. Taking this title is based on the lack of learning motivation of students majoring in management compared to students in other majors so that discipline is not well implemented and student learning achievement when compared to students in other majors such as accounting, health etc. This study uses a type of quantitative research. The data taken is in the form of primary data by distributing questionnaires to students majoring in management at the University of West Sumatra. This study conducted research instrument tests, classical assumption tests, hypothesis testing using SPSS version 21. With the results of the research stating that seeing the results of the tests that have been carried out proves that partially or simultaneously both variables have a significant effect, and it can also be seen that Adjusted R Square is 0.522 or of 52.2% This means that 52.2% of learning achievement variables are influenced by motivation and discipline, the remaining 47.8% are influenced by other variables outside of this research variable.
A fine-tuned YOLOv5 deep learning approach for real-time house number detection

Detection of small objects in natural scene images is a complicated problem due to the blur and depth found in the images. Detecting house numbers from the natural scene images in real-time is a computer vision problem. On the other hand, convolutional neural network (CNN) based deep learning methods have been widely used in object detection in recent years. In this study, firstly, a classical CNN-based approach is used to detect house numbers with locations from natural images in real-time. Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7, among the commonly used CNN models, models were applied. However, satisfactory results could not be obtained due to the small size and variable depth of the door plate objects. A new approach using the fine-tuning technique is proposed to improve the performance of CNN-based deep learning models. Experimental evaluations were made on real data from Kayseri province. Classic Faster R-CNN, MobileNet, YOLOv4, YOLOv5 and YOLOv7 methods yield f1 scores of 0.763, 0.677, 0.880, 0.943 and 0.842, respectively. The proposed fine-tuned Faster R-CNN, MobileNet, YOLOv4, YOLOv5, and YOLOv7 approaches achieved f1 scores of 0.845, 0.775, 0.932, 0.972 and 0.889, respectively. Thanks to the proposed fine-tuned approach, the f1 score of all models has increased. Regarding the run time of the methods, classic Faster R-CNN detects 0.603 seconds, while fine-tuned Faster R-CNN detects 0.633 seconds. Classic MobileNet detects 0.046 seconds, while fine-tuned MobileNet detects 0.048 seconds. Classic YOLOv4 and fine-tuned YOLOv4 detect 0.235 and 0.240 seconds, respectively. Classic YOLOv5 and fine-tuned YOLOv5 detect 0.015 seconds, and classic YOLOv7 and fine-tuned YOLOv7 detect objects in 0.009 seconds. While the YOLOv7 model was the fastest running model with an average running time of 0.009 seconds, the proposed fine-tuned YOLOv5 approach achieved the highest performance with an f1 score of 0.972.