Reviewerly/exHarmony huggingface dataset:
A Novel Intelligent Blockchain Framework with Intelligence Replication

Nayak, Arijit Chowdhury, Deepraj De, Sourav Bhattacharyya, Siddhartha Muhammad, Khan Gorbachev, SergeyNowadays, blockchain technology is being adopted at a large scale due to its inherent characteristics that make data de-centralized and immune to intentional tampering. Different machine learning (ML) techniques are being integrated into the blockchain to make it capable of intelligently validating a transaction. Central to these ML techniques is the process of training a model that is both processor hungry and time consuming. Hence, the challenge is to make all the nodes in the blockchain intelligent by using the minimum processing capability of the network. In this paper, we propose an efficient mechanism to make an entire blockchain network intelligent using multilayer perceptron (MLP). At the time of the network formation, a single node trained as it has the model definition. After completing the training, both the model and the intelligence are replicated, so that every node becomes intelligent. A brand new node is also made part of a fully-functional blockchain network in a minimal time.
Improving online learning interactivity with 3D virtual classroom models

Abstract The world of education is experiencing a very significant impact of the Covid-19 pandemic. Online learning is a solution in overcoming learning limitations due to social distancing. Researchers developed a 3d virtual classroom using a Minecraft model to increase online learning interactivity. The 3 dimensional virtual classroom model design that has been developed has received respondents’ acceptance of 90.00% in the “Very Eligible” category. Testing the 3 virtual classroom features feature got a percentage of 90.83% with the “Very Eligible” category to produce a sense of presence. In the interactivity test, character interaction is 89.17% in the “Very Eligible” category. The effectiveness of online learning continuity using the 3D virtual classroom model tested gets 89.17% with the “Very Eligible” category. In addition, the test uses an average score of SUS (System Usability Scale) of 73.67, so it can be concluded that the 3D virtual classroom model is “acceptable” as an online learning medium.
Fast CU Size Decision Method Based on Just Noticeable Distortion and Deep Learning

With the development of broadband networks and high-definition displays, people have higher expectations for the quality of video images, which also brings new requirements and challenges to video coding technology. Compared with H.265/High Efficiency Video Coding (HEVC), the latest video coding standard, Versatile Video Coding (VVC), can save 50%-bit rate while maintaining the same subjective quality, but it leads to extremely high encoding complexity. To decrease the complexity, a fast coding unit (CU) size decision method based on Just Noticeable Distortion (JND) and deep learning is proposed in this paper. Specifically, the hybrid JND threshold model is first designed to distinguish smooth, normal, or complex region. Then, if CU belongs to complex area, the Ultra-Spherical SVM (US-SVM) classifiers are trained for forecasting the best splitting mode. Experimental results illustrate that the proposed method can save about 52.35% coding runtime, which can realize a trade-off between the reduction of computational burden and coding efficiency compared with the latest methods.
Shoulder Surfing Resistant Graphical Password Schema: Randomized Pass Points (RPP)

In this study, a pass-graph methodology that benefits from randomity and alternative pass-graphs derivation is proposed with the name of Randomized Pass Points. The proposed authentication methodology is scrutinized for its resistance against brute force and shoulder-surfing attacks. Evaluations prove that the proposed alternative is stronger than that of 8-digit 71-character-set password methodology against brute force attacks and it necessitates at least 5 valid log-ins be captured by the attacker to derive the pass-graph under given assumptions in shoulder-surfing attack.
Terrorism in Cyberspace : A Critical Review of Dark Web Studies under the Terrorism Landscape

Crime, terrorism, and other illegal activities are increasingly taking place in cyberspace. Crime in the dark web is one of the most serious challenges confronting governments around the world. Dark web makes it difficult to detect criminals and track activities, as it provides anonymity due to special tools such as TOR. Therefore, it has evolved into a platform that includes many illegal activities such as pornography, weapon trafficking, drug trafficking, fake documents, and more specially terrorism as in the context of this paper. Dark web studies are critical for designing successful counter-terrorism strategies. The aim of this research is to conduct a critical analysis of the literature and to demonstrate research efforts in dark web studies related to terrorism. According to result of study, the scientific studies related to terrorism activities have been minimally conducted and the scientific methods used in detecting and combating them in dark web should be varied. Advanced artificial intelligence, image processing and classification by using machine learning, natural language processing methods, hash value analysis, and sock puppet techniques can be used to detect and predict terrorist incidents on the dark web.
Research on TOF camera-based guided binocular fusion technology

As environment perception becomes increasingly necessary in human-computer interaction tasks, researchers are applying it to specific tasks, such as depth-of-field measurements by depth cameras. In order to improve the accuracy of depth information in-depth maps obtained by current depth cameras, this paper uses the complementary properties of TOF technology and binocular stereo vision technology to fuse TOF and binocular technology, guiding binocular stereo matching by matching TOF parallax maps with binocular parallax map feature points, and discussing the process of guided matching. Among them, the better Census matching algorithm is selected for experiments in the stereo matching process. The binocular matching after TOF guidance by this method significantly improves the matching time and the quality of the matched parallax map.
SDP-ML: An Automated Approach of Software Defect Prediction employing Machine Learning Techniques

Software Defect Prediction (SDP) method plays a vital role to ensure the software quality by predicting bugs in software development phase. In addition, this technique also assists developers to minimize the maintenance costs. In this paper, we proposed a model as SDP-ML that utilizes machine learning classification techniques to predict the faults in the software. In particular, the model uses three gradient boosting classification frameworks LightGBM (LGB), XGBoost (XGB), CatBoost (CB) for the prediction. The classification was performed on five publically available NASA Promise datasets, viz., CM1, JM1, PC1, KC1, KC2, and validated using ten-fold cross-validation techniques. Moreover, the remarkable evaluation techniques Precision, Recall, F1-score, and Accuracy were employed to evaluate the results. The outcomes demonstrated the dominant performance of LightGBM with GridSearchCV library (98%) than other algorithms considering the average F1-score. However, analysts can achieve free comprehension from this examination while choosing an automated research field for their planned application.
Stackelberg Game Based Resource Pricing and Scheduling in Edge-Assisted Blockchain Networks

Currently, the blockchain, as a key enabling technology of digital currency, has attracted lots of attention from both industry and academia. The blockchain mining process requires high computing power to solve a Proof-of-Work (PoW) puzzle, which is hard to implement on users’ mobile devices. So these miners may leverage the resources of the edge/cloud service providers (ESPs/CSP) to calculate the PoW puzzle. The existing edge-assisted blockchain networks simply assumed that all ESPs have a uniform propagation delay, which is not realistic. In this paper, we consider a more practical scene where ESPs with distributed geographic locations have diverse propagation delays when supporting the computation of the PoW puzzle. Additionally, the blockchain mining process generally involves the complicated competition and game among these ESPs and miners. Each ESP focuses on how to determine his resource price and to select the requests from the miners, so that he can maximize his utility. According to the set resource price, each miner concentrates on scheduling his resource requests for each ESP to maximize his individual utility which depends on ESPs’ resource price and propagation delays. We model such a resource pricing and scheduling problem as a multi-leader multi-follower Stackelberg game and aim at finding the joint maximization of the utilities of each ESP and each individual miner. We prove the existence and uniqueness of the Stackelberg equilibrium (SE) and meanwhile propose an algorithm to achieve the corresponding SE. Finally, extensive simulations are conducted to verify the significant performance of the proposed solution.
Automatic Thyroid Ultrasound Image Detection and Classification with Priori Knowledge

Medical ultrasonic imaging technology is currently the preferred method to detect and diagnose benign and malignant thyroid nodules, which is widely used because of their low cost and non-invasive damage to patients. But automatic lesion detection and classification on thyroid ultrasound image is quite challenging due to the poor image quality. To solve the problem, based on popular Faster R-CNN network for natural image detection, a ResAt-Faster R-CNN model was proposed in the paper according to the characteristics of thyroid ultrasound image, the residual module and attention mechanism. The medical prior knowledges such as location and distribution information are further introduced to constrain the model to reduce the interference of surrounding tissues. The experimental results demonstrated that our proposed method was effective in the discrimination of thyroid nodules.
An ensemble artificial intelligence<scp>‐enabled MIoT</scp> for automated diagnosis of malaria parasite

Abstract Rapid advancements in Information and Communication Technologies (ICT) and artificial intelligence (AI) applications permeating to all spheres of life, including medical prognosis, have led modern clinical systems to tread the path of advanced Internet of Medical Things (IoMT) by infusing advanced learning technologies, particularly deep learning. Automated diagnosis of malarial infection using AI‐enabled IoMT holds the promise of sustainable prognosis by reducing diagnosis error significantly with improved recognition accuracy. Existing automated diagnostic systems usually employ classical deep learning models wherein setting parameter values such as automatic learning rate selection, weight management etc. are a major concern. To address these issues, this paper proposes a collaborative ensemble AI‐enabled IoMT automated diagnosis model to classify malaria parasitized from microscopic images. The proposed model consists of two main stages. In the first stage, a Snapshot ensemble learning model is conjured upon by a combination of three distinct layers of Convolutional, Batch Normalization, and Relu networks; that alters the learning rate aggressively during training phase thus providing different network weights that gives multiple models by training a single model. In the second stage, an ensemble of three transfer learning models is constructed, and finally the average ensemble result is obtained. The learning rates at both these stages are empirically selected through Cosine Annealing. Experiment on the malaria parasite image dataset demonstrates the superiority of the proposed model with respect to a baseline algorithm.
Calibrating Privacy Budgets for Locally Private Graph Neural Networks

Graph neural networks have shown excellent performance in learning graph representations. In many cases, the graph structured data are crowd-sourced and may contain sensitive information, thus causing privacy issues. Therefore, privacy-preserving graph neural networks have spurred increasing interest nowadays. A promising approach for privacy-preserving graph neural networks is to apply local differential privacy (LDP). Though LDP provides protection against privacy attacks, the calibration of the privacy budget is not well understood and the relationship between privacy protection level and model utility is not well established. In this paper, we propose an evaluation method to characterize the trade-off between utility and privacy for locally private graph neural networks (LPGNNs). More specifically, we leverage the effect of attribute inference attacks as a privacy measurement to bridge the gaps among the model utility, privacy leakage, and the value of the privacy budget. Our experimental results show that the LPGNNs model may fulfill the promise of providing privacy protection against powerful opponents by providing poor model utility, and when it provides acceptable utility, it shows moderate vulnerability to the attribute inference attacks. Moreover, one of the direct applications of our method is visualizing the adjusting of privacy budgets and facilitating the deployment of LDP.
A Memory Saving Mechanism Based on Data Transferring for Pipeline Parallelism

The correctness and robustness of the neural network model are usually proportional to its depth and width. Currently, the neural network models become deeper and wider to cope with complex applications, which leads to high memory capacity requirement and computer capacity requirements of the training process. The multi-accelerator parallelism is a promising choice for the two challenges, which deploys multiple accelerators in parallel for training neural networks. Among them, the pipeline parallel scheme has a great advantage in training speed, but its memory capacity requirements are relatively higher than other parallel schemes. Aiming at solving this challenge of pipeline parallel scheme, we propose a data transfer mechanism, which effectively reduces the peak memory usage of the training process by real-time data transferring. In the experiment, we implement our design and apply it to Pipedream, a mature pipeline parallel scheme. The memory requirement of training process is reduced by up to 48.5%, and the speed loss is kept within a reasonable range.
An analysis of privacy policies of public<scp>COVID</scp>‐19 apps: Evidence from India

Governments around the world are utilizing their digital ecosystems to respond to the COVID‐19 pandemic. To increase awareness among the beneficiaries about privacy risks, they must proactively publish the data handling practices for their digital initiatives through appropriate privacy policies. This study analyzes the privacy policies of public COVID‐support mobile applications (apps) in the context of India. We found a total of 63 government initiated COVID‐support apps in India out of which 38% were found to have an app‐specific privacy policy. These policies were analyzed further to assess their coverage of key principles, such as “Purpose,” “Data Categories,” and “Data Retention,” derived from legal requirements. We also analyzed the extent of the specificity of policies with a high coverage. In India, only one nation‐wide app stood out to have both considerable coverage of key principles as well as a high level of specificity. Other national/regional apps fail to display the desired levels of coverage and/or specificity. The broader policy recommendations of this study are that the government should better address privacy concerns regarding its existing and future disaster management apps as well as its other digital initiatives by (a) establishing and enforcing a comprehensive legislative framework for data protection and (b) increasing privacy awareness among the beneficiaries.
The estimation and forecast of solar energy yield with Weibull distribution

Abstract In the first and second chapter the article provides an overview of the currently used energy sources in Hungary and the most popular renewable energies. In addition, the Weibull estimation is presented, too. The subsequent chapter looks at some of the research results about the solar energy optimization with Weibull distribution. The study presented is a mathematical solution of the solar energy optimization with distribution. The final chapter contains a brief explanation of the results. This publication briefly summarizes a prototype solution for an estimation and forecast of solar energy and yield with Weibull distribution.
Efficient FPGA Implementation of Parameterized Real Time Color Based Object Tracking

This paper presents an efficient color based tracking method applied on a sequence of live video frames for use in real time applications such as surveillance, video conferencing, and robot navigation. The proposed integrated system architecture consists of an attached camera that communicates with the FPGA through HDMI interface. The deployed computer vision algorithm in the FPGA can capture video frames at the rate of 60 fps with the large image sizes of up to 1280×1024 pixels. It then identifies the object based on the specified color, removes noise via spatial filtering and calculates the centroid allowing the object to be tracked during motion. The proposed algorithm leverages a reduction method to minimize the FPGA area as well as power consumption by averaging values over a range of several pixels; thus logarithmically reduces the design size. Our implementation is parameterized to be made as accurate or small as an application requires, with minimal error. The proposed tracking system is implemented on a Xilinx ZYNQ-7000 series XC7Z010 FPGA housed on Xilinx Zybo development board. The utilization reports show for a selected reduction rate of 16, 86.5% reduction in Slice LUTs and 81.3% in Slice registers with the maximum error of 1.5% in centroid calculation.
Method of coding dynamic sequence of frame-spline structures of provided frames in info-communications

This paper explains the general technological principle in the process of coding the sequence of predicted frames and their blocks in the spectrally differentiated space for coding methods of the H26 family *. There is the application of frame processing depending on their type in the stream and weight to video resource and their integrity. Problems for the existing technologies of processing a set of positional coordinates that describe the length of insignificant components of transformants are revealed. The main stages of the creation method of coding a dynamic sequence of frame-spline structures of the provided shots in info-communications are stated.
Low-Power Keyword Recognition Feature Extraction Circuit based on SRMFCC and Shared Multiplier for High Noise Background

In this paper, we proposes a precision- adaptive speech feature extraction module based on SRMFCC and shared multiplier for high-noise background. The main contributions of this paper include: (1) Based on the traditional Mel Frequency Cepstrum Coefficient (MFCC) feature extraction algorithm, a Simplified Rectangular MFCC(SRMFCC) algorithm is proposed. Compared with the traditional MFCC, the SRMFCC can maintain good recognition accuracy in noisy background under different signal-to-noise ratios(SNR). (2) For multiplication calculation in Fast Fourier Transform (FFT) module, a multi-quantization table shared multiplier is proposed, which can adapt to different calculation accuracy requirements. Compared with standard multiplication calculation, power consumption can be significantly reduced by 40%.
State Estimation of a Partially Observable Multi-Link System with No Joint Encoders Incorporating External Dead-Reckoning

This paper presents a technique for state estimation of a multi-link system having no joint encoders, which can only be partially observed by a camera. To fully observe the system without changing the current configuration, a gyroscope and an accelerometer are attached to each link as dead-reckoning sensors. Observations of the dead-reckoning sensors are associated with the states of the multi-link system such that the states are fully observable. The camera, which observes part of the system globally, is used as a global corrector in the framework of an extended Kalman filter to filter the dead-reckoning errors accumulated over time. Parametric studies in simulation have investigated and identified the efficacy of the proposed technique in estimating the state of the multilink system. Experimental validation using a two-link arm has demonstrated the applicability of the proposed technique to real-world multi-link systems.
3D surface measurement of static and dynamic objects in underwater environments based on the propagation and projection of CW sinusoidal signal at 532 nm

This work carries out both theoretical and experimental studies on three dimensional surface measurement of objects in underwater environments based on the use of the projection of continuous-wave laser-beam-based monochromatic structured light. The mathematical analysis and description of the generation and propagation of sinusoidal fringe patterns in air and underwater environments are presented, which provides the physical basis for the proposed method of measuring the underwater objects. In the experimental investigation we completed the in-door measurements of both static and dynamic objects located in a tank filled with real turbid seawater or lake water. Experimental results have indicated that the proposed approach is capable of yielding high-accuracy reconstructions for underwater 3D imaging of both static and dynamic objects, which shows the possibility of building a portable instrument based on the optical design and measuring technique presented in this work for carrying out high-accuracy in-situ underwater measurement.
API Adapter for Payment Gateway Variation Development in Software Product Line Engineering

A payment gateway is an e-commerce service that automatically processes and verifies money transactions. Many payment gateways can be integrated as a software feature, where each software product have their preferences. To accommodate that, we can use the Software Product Line Engineering (SPLE) paradigm, which maximizes the reusability and composition to develop a family of products with less production cost than conventional methods. We try to develop the payment gateway feature using SPLE-based development tools, which is ABS-Microservices Framework. We also develop the API Adapter during the development process, which is required to enable communication with the payment gateway provider. We then analyze our development by redeveloping the same features using the conventional method as a comparison. The analysis shows that the SPLE paradigm develops fewer lines of code than the conventional method, proving the production cost reduction, which is the benefit of the SPLE paradigm.
STEM Education

The purpose of the study is to analyze the key challenges in the implementation of STEM education in Russia and the largest economies in terms of employment promotion and increasing students’ competitiveness. The research is based on the system analysis aimed at assessing the current status of STEM education in Russia and highlighting its key problems, and the comparative analysis is focused on comparing the approaches to STEM education implementation in Russia and other countries. It has been shown that STEM approach is being most actively implemented in the American system of education, which attracts young people to STEM careers through state and non-formal STEM education programs. The research describes a scheme of cooperation and partnership between stakeholders and the federal coordinating body of the Russian Federation providing support for the STEM education and aimed at the interaction between state and public institutions.