Reviewerly/exHarmony huggingface dataset:
Modeling and Analysis of Proof-Based Strategies for Distributed Consensus in Blockchain-Based Peer-to-Peer Networks

Blockchain technology has a wide range of applicability in the fields of transportation infrastructure construction and maintenance, transportation big data analysis and application, expressway toll collection, and logistics. The core technology lies in the distributed, decentralized, immutable, and programmable features brought about by consensus. This paper studies the dynamic analytical modeling of Proof-Based Consensus (PBC) strategies in blockchain systems, focusing on basic strategies, including Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Luck (PoL), which can be extended to other PBC models. We focus on modeling these typical strategies and discuss their solution characteristics in terms of algorithmic mechanisms and principles. The relevant results can be used for quantitative analysis and evaluation of distributed consensus based on the model.
Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework

Machine learning-based (ML) systems are becoming the primary means of achieving the highest levels of productivity and effectiveness. Incorporating other advanced technologies, such as the Internet of Things (IoT), or e-Health systems, has made ML the first choice to help automate systems and predict future events. The execution environment of ML is always presenting contrasting types of threats, such as adversarial poisoning of training datasets or model parameters manipulation. Blockchain technology is known as a decentralized network of blocks that symbolizes means of protecting block content integrity and ensuring secure execution of operations.Existing studies partially incorporated Blockchain into the learning process. This paper proposes a more extensive secure way to protect the decision process of the learning model. Using smart contracts, this study executed the model’s decision by the reversal engineering of the learning model’s decision function from the extracted learning parameters. We deploy Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers decision functions on-chain for more comprehensive integration of Blockchain. The effectiveness of this proposed approach is measured by applying a case study of medical records. In a safe environment, SVM prediction scores were found to be higher than MLP. However, MLP had higher time efficiency.
Multi-level Federated Learning for Industry 4.0 - A Crowdsourcing Approach

Federated learning is one of the emerging areas of research in computer science. It has shown great potential in some application areas and we are witnessing evidence of new approaches where millions or even billions of IoT devices can contribute collectively to achieve a common goal of machine learning through federation. However, existing approaches are primarily suitable for single-task learning with a single objective in a single task owner where it is assumed that the majority of devices contributing to federated learning have a similar design or device type and restrictions. We argue that the true potential of federated learning can only be realised if we have a dynamic and open ecosystem where devices, industrial units, machine manufacturers, non-governmental agencies, and governmental entities can contribute toward learning for multiple tasks and objectives in a crowdsourced manner. In this article, we propose a multi-level framework that shows how federated learning, IoT, and crowdsourcing can come hand-in-hand with each other to make a robust ecosystem of multi-level federated learning for Industry 4.0. This helps build future intelligent applications for Industry 4.0 such as predictive maintenance and fault detection for systems in smart manufacturing units. In addition, we also highlight several use-cases of multi-level federated learning where this approach can be implemented in Industry 4.0. Moreover, if the approach is implemented successfully, besides enhancement in performance it will also help towards a greater common goal e.g. UN Sustainable Goal No 13 i.e. reduction in carbon footprint.
Design and implementation of an SLA and energy-aware VM placement policy in green cloud computing

The pay-per-use model of cloud computing is booming in every industry. Power-hungry large heterogeneous data centers are providing these cloud services for 24× 7. Green cloud computing offers an energy-efficient and environment-friendly mechanism to offer cloud services while maintaining the quality of service (QoS). QoS is defined in terms of service-level agreement (SLA). VM Consolidation is one of the widely used frameworks that involve Virtual Machine (VM) migration and VM Placement to reduce energy consumption and maintain better QoS. Using the VM Placement policy VMs are allocated to suitable physical machines (PM). Efficient VM placement with better resource management can conserve electricity and provide better QoS. A new SLA and energy-aware VM placement policy have been developed in this research. The VMs are placed in a PM having the least energy consumption and SLA violation (SLAV). Further, the proposed VM placement policy has been implemented using CloudSim and it has been shown that the proposed VM placement policy outperforms state-of-art VM placement policies like PEBFD, MBFD, MFPED, and PEFED.
Underwater Image Enhancement Approaches: A Current Perspective

The submerged pictures are gaining significance as the exploration of the world covered up in the profundity of the sea has to be known. There is extensive research work going on in this area and for that, pictures are significant because of the different components. It is somewhat hard to get accurate and specific pictures due to the submerged environment and the obstacles undersea. This poses a lot of challenges in the investigations of submerged life and finding distinguished proof of marine creatures. There are loads of cameras used to capture the images of the ocean bottom, but they are fitted within the robots which can go submerged or the sensors that can work submerged. As there are numerous issues with the cameras and sensor due to the submerged environment, so it should be taken care of. Since light changes its property as per the profundity of the submerged world and the biotic action on the ocean bottom, the captured pictures are commonly influenced by the dimness and grains because of dispersing. Besides, many more things make a commotion in the picture. Over the years, various procedures are presented and tested which can either capture better pictures or process post-captured ones for improvements. In this paper, we review various procedures which can do target improvement sought for some specific purpose. It comprises shading recreation and checking the improvement of pictures through various measures. The review work presents a comparative outcome of recent works in the area of underwater image enhancements and future scopes in this field.
Adversarial Blur-Deblur Network for Robust UAV Tracking

Unmanned aerial vehicle (UAV) tracking has been widely applied in real-world applications such as surveillance and monitoring. However, the inherent high maneuverability and agility of UAV often lead to motion blur, which can impair the visual appearance of the target object and easily degrade the existing trackers. To overcome this challenge, this work proposes a tracking-oriented adversarial blur-deblur network (ABDNet), composed of a novel deblurrer to recover the visual appearance of the tracked object, and a brand-new blur generator to produce realistic blurry images for adversarial training. More specifically, the deblurrer progressively refines the features through pixel-wise, spatial-wise, and channel-wise stages to achieve excellent deblurring performance. The blur generator adaptively fuses an image sequence with a learnable kernel to create realistic blurry images. During training, ABDNet is plugged into the state-of-the-art real-time trackers and trained with blurring-deblurring loss as well as tracking loss. During inference, the blur generator is removed, while the deblurrer and the tracker can work together for UAV tracking. Extensive experiments in both public datasets and real-world testing have validated the effectiveness of ABDNet.
Instant Function Calls Using Synchronized Cross-Blockchain Smart Contracts

Most current cross-blockchain approaches focus on exchanging or transferring tokens between networks. While some concepts foster smart contract invocations across blockchains, they require multiple transactions and operate asynchronously. We present a concept enabling instant smart contract calls by creating synchronized client contracts on arbitrary blockchains. Other smart contracts can query these client contracts on the target blockchain for retrieving information without requiring cross-chain message queues. With this, we reduce the dependency of smart contracts on their host blockchain, as remote contracts become available as read-only instances. The synchronization process does not require trust in the executing intermediary since Merkle proofs based on shared state roots are utilized to guarantee correct execution. We propose a novel concept called transition proofs for efficiently proving the correctness of state updates. The prototypical implementation permits smart contract synchronization between EVM-compatible blockchains. Our evaluation shows the approach’s applicability regarding execution costs and delay. Further, we conduct a case study by synchronizing one of the largest decentralized exchanges deployed to the Ethereum network.
Dynamic Programming Principles for Mean-Field Controls with Learning

Multiagent systems—such as recommendation systems, ride-sharing platforms, food-delivery systems, and data-routing centers—are areas of rapid technology development that require constant improvements to address the lack of efficiency and curse of dimensionality. In the paper “Dynamic Programming Principles for Mean-Field Controls with Learning,” we show that multiagent systems with mean-field approximation and learning can be recast as general forms of reinforcement learning problems, where the state variable is replaced by the probability distribution. This reformulation paves the way for developing efficient value-based and policy-based algorithms for mean-field controls with learning. It is also the first step toward future theoretical development of learning problem with mean-field controls.
Lint-Based Warnings in Python Code: Frequency, Awareness and Refactoring

Python is a popular programming language characterized by its simple syntax and easy learning curve. Like many languages, Python has a set of best practices that should be followed to avoid bugs and improve other quality attributes (such as maintenance and readability). In this context, non-compliance to these practices can be detected by using linting tools. Previous work conducted studies to better understand the frequency of a class of problems that can be found using Python linters: warnings, here named as lint-based warnings. However, they either rely on small datasets or focus on few domains, such as machine learning or web-systems projects. In this paper, we provide a mixed-method study where we analyze the frequency of six lint-based warnings in 1,119 different open-source general-purpose Python projects. To go further, we also conduct a survey to check whether developers are aware of the lint-based warnings we study here. In particular, we intend to check whether they are able to identify the six lint-based warnings. To remove the lint-based warnings, we suggest the application of simple refactorings. Last but not least, we evaluate the suggestions by submitting pull requests to remove lint-based warnings from open-source projects. Our results show that 39% of the 1,119 projects have at least one lint-based warning. After analyzing the survey data, we also show that developers prefer Python code without lint-based warnings. Regarding the pull requests, we achieve a 71.8% of acceptance rate.
Big-O Time Complexity Analysis Of Algorithm

Time complexity describes the amount of time taken by the computer to run a code by counting the number of operations performed in an algorithm. Algorithms with optimistic logic tend to have less time complexity. Usually, the time complexity is computed theoretically or mathematically. Big O notation describes the worst-case time complexity of the algorithm using algebraic terms. This work calculate the Big O time complexity using two methods: brute force approach and machine learning approach. The Machine learning Approach determines the algorithm's time complexity more rapidly and without executing the code. Lexer is used to extract features from source code in machine learning approach. It is outlined that significant improvement can be achieved in the machine learning approach by giving importance to some known features and increasing dataset size. These two methods are benchmarked on the bubble sort algorithm. Experimental results show that the machine learning approach speeds up time complexity estimate by a factor of up to 97.51% compared to the brute force approach..
Malicious Webpage Classification Based on Web Content Features using Machine Learning and Deep Learning

Cybercrime has been on the rise in recent years. Cybercriminals target Internet users and commit illegal acts. Malicious web pages are an easy tool to obtain sensitive personal information from Internet users, install malware on their computers, and expose them to cyberspace. More advanced techniques are used by the attackers to inject the malicious code into the normal legitimate webpages or create a web page with illegal content and then spread the URL through various possible media to perform the intended action. Internet users who click the link will be in their nest. Identifying such URLs or web pages can be a daunting task for internet users. So, the researchers using different machine/deep learning methods with different set features that are derived from URL, content, and server-based information of the web page to detect or classify the malicious web pages. Most attacks, including drive-by downloads, phishing, and injections, conceals the maliciously intended code or link in the content of legitimate or purportedly legitimate webpage. Many current research work uses the URL based features due to its risk-free processing. Only less attention is given to the content-based features. That hides the vital area of features to better classification. To address this issue, this paper focus on web content-based features for malicious web page classification. The result shows, the proposed method performs better in terms of accuracy.
SECURE MACHINE LEARNING BASED TRANSACTION SYSTEM USING FINGERPRINT AUTHENTICATION

Human fingerprints have a wealth of minutiae, or minute characteristics, that can be used as identity marks for fingerprint verification. Our term project is to investigate fingerprint identification and transaction systems based on minutiae-based matching, which is widely utilized in fingerprint algorithms and methodologies. The approach taken in this project entails extracting minutiae points from fingerprint photos and then doing fingerprint matching between two fingerprints. If yes, the database is queried for additional information, and the information is displayed as validated. In this instance, we'll have to deal with the user's requirements. This project was created using Visual Studio.
Incorporating Augmented Reality Tools into an Educational Pilot Plant of Chemical Engineering

Chemical Engineering courses are often designed to be divided into two types of lessons: lecture and experimental. In the second one, students develop the knowledge in a hands-on way by attending a pilot plant equipped with different instruments. Hence, a thorough understanding of the different unit operations is needed and, therefore, the implications of changing the operational variables in a process. In this context, the use of new digital technologies is emerging as support tools with the aim of both improving the learning and the motivation of students. Specifically, Augmented Reality (AR) provides a modified physical environment overlaid with multimedia content in the form of text, graphics, video and/or audio. Thus, the incorporation of AR systems in the learning of science has proven to be useful, because it can present multiple benefits for students and teachers related to the improvement of spatial abilities, the increase of memory retention, the decrease of cognitive overload, and a boost in student motivation. This study has carried out a search for resources, projects, software, and applications to implement AR-based tools in the experimental sessions of a Chemical Engineering educational pilot plant. Based on all the information found, several AR projects were proposed by the teachers. Later, some of them were selected according to previously defined criteria and implemented as educational tools for students in the course called Experimentation in Chemical Engineering I. Finally, this tool was evaluated through subsequent post-surveys, being very positively rated by both students and teachers, mainly in the items related to helping to understand concepts or the operating procedures of the equipment.
Network representation learning via improved random walk with restart

Real-world networks are usually sparse and high-dimensional representations that occupy considerable storage space and increase the computational complexity when performing network tasks. Recently, various methods of network representation learning that can effectively learn dense and low-dimensional network representations have been proposed. However, while these methods consider either the global or local structure of the network, the structures are not considered comprehensively. To address this problem, we propose a novel network representation learning method based on random walk with restart (NRL-RWR), which effectively captures the global and local structures of a network by means of an improved random walk with restart strategy. First, the random walk with restart is improved by an attention mechanism that assigns weights to edges by measuring the similarity between nodes. Second, the improved random walk with restart strategy has the ability to obtain the probability distribution of nodes, and always returns to the starting position with a certain probability in the process of walking, so that more global and local structures of the network can be retained. Finally, we construct an objective function based on Kullback–Leibler divergence, and we adopt negative sampling and edge sampling to optimize the objective function and reduce the computational complexity of network representation learning. Experimental results based on six real-world networks show that NRL-RWR outperforms state-of-the-art network representation learning methods in obtaining low-dimensional vector representations of networks.
Usability Evaluation on Educational Chatbot Using the System Usability Scale (SUS)

Chatbot is a medium that can be used in education, especially learning, students seem to communicate through chat with educators about learning materials. To assist the learning in computer hardware, AI-based was used to build the chatbot. This research was to asses the effectiveness of myHardware chatbot. The research started with the determination of research object, data used and the scale evaluation in the utility system. The method consisted of scenario determination, data collection, and calculation. Based on the results, the assesment score obtained was 61.03. With a description of marginal acceptability, a grade D scale, with ok rating of adjective and the application had sufficient usability value. Based on the final System Usability Scale (SUS) score, it is possible to conclude that the myHardware chatbot is acceptedable by the students and can function well.
A high-accuracy online calibration method for structured light 3D measurement

Traditional offline calibration of a structured light 3D measurement system is cumbersome, ineffective, and dependent on calibration targets, whereas state-of-the-art (SoTA) online calibration methods are often haunted by low-precision matching points and thus exhibit poor accuracy and reliability. To this end, we present an automated and high-precision online calibration approach for structured light probes from a multi-camera-projector structured light system(MCPSLS). At the core of the approach, a new criterion for matching points filtration is constructed based on local point set topological similarity, which demonstrates higher accuracy and more robustness than typical RANSAC to outliers. Moreover, the fundamental matrix is optimized based on trinocular epipolar constraints, which further prevents the extrinsic parameter solution from being inaccurate and unreliable due to the small number of filtered matching points. Besides, to achieve a simultaneous and automated online calibration, the MCPSLS’s registration objects are served as spatial constraint fields to solve scale factors. Finally, all calibration parameters are nonlinearly optimized for their global optimal solutions. Experimental results show that the proposed approach outperforms SoTA online methods in robustness and accuracy and achieves comparable performance as the traditional offline calibration method.
A comparative study of word embedding techniques for SMS spam detection

E-mail and SMS are the most popular communication tools used by businesses, organizations and educational institutions. Every day, people receive hundreds of messages which could be either spam or ham. Spam is any form of unsolicited, unwanted digital communication, usually sent out in bulk. Spam emails and SMS waste resources by unnecessarily flooding network lines and consuming storage space. Therefore, it is important to develop high accuracy spam detection models to effectively block spam messages, so as to optimize resources and protect users. Various word-embedding techniques such as Bag of Words (BOW), N-grams, TF-IDF, Word2Vec and Doc2Vec have been widely applied to NLP problems, however a comparative study of these techniques for SMS spam detection is currently lacking. Hence, in this paper, we provide a comparative analysis of these popular word embedding techniques for SMS spam detection by evaluating their performance on a publicly available ham and spam dataset. We investigate the performance of the word embedding techniques using 5 different machine learning classifiers i.e. Multinomial Naive Bayes (MNB), KNN, SVM, Random Forest and Extra Trees. Based on the dataset employed in the study, N-gram, BOW and TF-IDF with oversampling recorded the highest F1 scores of 0.99 for ham and 0.94 for spam.
Colorization Of Grayscale Images Using Deep Learning

With the advent of high-resolution photographs, the task of transforming monochrome photos into full color remains difficult. Black and white images are muddy and frustrating to work with. Therefore, numerous methods of colorizing the original black-and-white photos will be employed in this project. Adobe Photoshop and MATLAB are only two examples of software that can transform monochrome photos into full color. Adobe Photoshop presents a challenge since coloring just one image might take as long as one month. It calls for a lot of studies. Up to twenty separate layers of red, green, and blue hues are required to get the desired effect on a single face. In addition, a MATLAB expert is required for this task. Both approaches require the expertise of trained individuals. The proposed algorithm aims to efficiently color the image so that it may be used by everyone. The combination of image processing and deep learning had been used to create a model that can instantly identify colors in monochrome photos. As videos are essentially a series of still photos being animated, the technique employed here may be used to add color to those as well in the future.