Reviewerly/exHarmony huggingface dataset:
A Blockchain Solution for Secure Health Record Access with Enhanced Encryption Levels and Improvised Consensus Verification

Blockchain can be depicted as a permanent record, logging information in a decentralized way. This new innovation has been proposed to widen the horizons of information-driven spaces, including the medical information. Electronic medical records have recorded the course of event, improvement, and treatment of illnesses. So, it has high clinical worth. As healthcare information must be kept private and secure, information security and privacy preserving are the most crucial issues to be handled in healthcare. This paper analyzes and identifies the most pertinent security and privacy issues in existing healthcare systems. To resolve the privacy and security issues, blockchain innovation can act as a robust solution as blockchain technology uses cryptography and consensus verification as basic perspectives alongside decentralized architecture and immutable blocks of data. Various consensus and encryption algorithms of the blockchain technology have been studied in depth, and an overview has been presented in the paper. A novel blockchain-based patient health record (BB-PHR) system is proposed with improvised consensus mechanisms and enhanced encryption levels for medical data access control and protection in the paper. The system enforces consensus verification of patient and hospital administration both for maintaining privacy and tamper proof data access. Proposed system integrates encryption algorithms, access control mechanisms, and smart contract on a blockchain framework comprehensively to address the security and privacy vulnerabilities of a health record. With such a system, we can implement severe access and security control on healthcare information.
Design of Social Distance Monitoring Approach Using Wearable Smart Tags in 5G IoT Environment During Pandemic Conditions

Corona virus disease 2019 (COVID-19) is an enduring extensive pandemic disease globally that infected several million people, occasionally leads to fatal. COVID-19 transfers from one person to another, the prevention is possible only through maintaining social distance. This paper proposed Internet of Things (IoT)-based smart wearable tags for social distance monitoring to prevent COVID-19. The advantage of the proposed system is that it alarms the person with the proposed tag and also entries the data in the IoT ledger of the channel for further analysis. The proposed system responds adequately so that social distancing can be maintained and pandemics can be prevented from further spreading. The analysis has been carried out using an experimental setup, and Thingspeak is used as an IoT channel for data analytics. The result shows the successfulness of the developed system in the prevention of pandemics with social distancing monitoring.
Analysis of Visual Descriptors for Detecting Image Forgery

Dissemination of digital content over the Internet and social media sites has put forth major concerns about its authenticity and integrity. Specifically, the manipulation of images into their fake version has become common task. Identifying image forgery has become crucial in order to retain the confidence in numerous digital image processing applications. Our proposed method focuses on extracting multiple image attributes solitary and further using a deep ensemble approach to classify images as tampered or original content. In this paper, discriminative multi-queue classifier is fed with four different aspects of images having diversity in color space, edge details, texture, and moments. To achieve this target, different handcrafted image features were fed to the Convolutional Neural Network (CNN) model. Manipulated images are susceptible to duplication of image areas subjected to discrepancies in terms of underlying edge inconsistencies, change in contrast, or anomalies in texture. Despite the capability of CNN as a generic feature extractor to detect spatial patterns, we additionally extracted image characteristics to analyze images and detect various discrepancies. Trained CNN describes image splicing from a unified dataset from different approach and angles. The collaborative results of ensemble network outperformed single networks, thus improving the effectiveness and reliability of our approach with 92.8% accuracy and other performance parameters.
A Rule-Based Sentiment Analysis of WhatsApp Reviews in Telugu Language

One of the most active areas of research and discussion in the field of natural language processing is the concept of sentimental analysis or the concept of opinion mining. It has gained a lot of popularity in the past few years and the main cause behind that is the usefulness of the derived results. The methods and concepts can analyze the given text and provide results based on the nature of the text. This is very useful in terms of business growth as well as customer satisfaction and assistance. This rule-based approach was then used to train a machine learning model using a few parametric classifiers like K-nearest neighbors (KNN), XGBoost, and support vector machines (SVM). The classifiers also fetched a decent accuracy of 81%, 82%, and 78%, respectively, which indicated toward the good performance of the rule-based approach and its effectiveness with error counts of 0.296, 0.288, and 0.252 with TF-IDF and 0.285, 0.285, and 0.234 with bag of words. Along the process, manual observation was also used to compare the assigned sentiments to the sentence to find the errors in the method. The best performance with respect to results was given by SVM classifiers that returned an F1-score of 75%, and the lowest error count of 0.25 which is better among all the classifiers. The metrics which were used to judge these classifiers were the F1-scores as well as the mean squared error.
Hand Gesture-Controlled Simulated Mouse Using Computer Vision

Hand gestures are an essential component in our everyday lives for nodding and conversation. At this time, human–computer interactions have so many strategies. However, this type of interaction with a computer could create revolutions by not using complicated hardware. Using this technology, any individual can communicate with a computer in a more natural manner by simply using his/her hand to navigate throughout the computer screen. By virtually displaying our hand in front of the camera, we will be able to move the cursor and control our system. All sorts of human beings can use this system effectively and in an easy way to control computer systems and numerous gadgets. Based upon the above concept, this paper is staged. This paper clarifies the methodologies for providing numerous steps in color detection, and vision-based strategies for identifying a hand and controlling the virtual mouse.
Face Model Generation Using Deep Learning

Computer vision systems have embraced learning using networks in recent years. On the other hand, unsupervised learning with convolutional neural networks has received less attention. The proposed method will help to close the gap between convolutional networks' performance and that of other machine learning algorithms. The goal of this paper is to use deep convolutional generative adversarial networks, a type of convolutional neural network, to create fake face images. The research demonstrating deep convolutional generative adversarial networks outperform generative adversarial networks is used in this paper. By training on picture datasets, the deep convolutional adversarial pair learns a series of representations in both the discriminator and generator leading to realistic face images. At the end of this paper, the results obtained by using deep convolutional generative adversarial networks are shown.
Identification of Malayalam Stop-Words, Stop-Stems and Stop-Lemmas Using NLP

For the retrieval of information from different sources and formats, pre-processing of the collected information is the most important task. The process of stop-word elimination is one such part of the pre-processing phase. This paper presents, for the first time, the list of stop-words, stop-stems and stop-lemmas for Malayalam language of India. Initially, a corpus of Malayalam language was created. The total count of words in the corpus was more than 21 million out of which approximately 0.33 million were unique words. This was processed to yield a total of 153 stop-words. Stemming was possible for 20 words, and lemmatization could be done for 25 words only. The final refined stop-word list consists of 123 stop-words. Malayalam is a widely spoken language by people living in India and many other parts of the world. The results presented here are bound to be used by any NLP activity for this language.
Artificial Neural Network based Writer-Independent Offline Signature Verification

Since two or more person's signatures may appear to be identical, but a person's signature may differ reliant on the state, so, signature verification is a problematic research subject. The goal of this study is to see how well an Artificial-Neural-Network and a Local-Binary-Pattern feature set work together to create a Writer-Independent Offline-Signature verification system. The performance of system is evaluated using two datasets of signature, each with 260 and 100 writers. Authentic signatures of a person, as well as skilled-forgery, nonskilled-forgery, and random-forgery signs, are used to test the performance of the developed system, and authentic signatures, as well as skilled-forgery, nonskilled-forgery, and random-forgery signs, are taken into account in the development of the desired system. In this study, a false-acceptance-rate of 23.00 percent, 11.00 percent, and 0.00 percent was obtained for skilled-forgery signs, nonskilled-forgery signs, and random-forgery signs, respectively, while a false-rejection-rate of 0.00 percent was obtained for 15 reference signatures using a database of 260 writer&#x2019;s signatures.
Perceptions of knowledge management in Brazilian software development companies

The software development companies conduct activities that generate a considerable amount of knowledge. Knowledge Management (KM) allows working with the generated knowledge helping in organizational learning. However, professionals in software companies still face several challenges to articulate and leverage knowledge in the organization. We aim at providing evidence about how KM has been adopted in practical environments of software development. We designed a survey instrument that was distributed to Brazilian software development professionals. The survey results improved the current understanding of KM and how it manifests itself in practical software development environments.
Class-Controlling Copy-Paste Augmentation for Nuclear Segmentation

Building segmentation models that can deal with rare and small nuclear objects in hematoxylin and eosin (H&#x0026;E) stained pathologic images is a challenging task in digital pathology. Applying image augmentation can help alleviate this challenge. Hence, we propose new class-controlling copy-paste augmentation using a prepared nuclear objects set. Several image augmentations have been developed in computer vision for improving model performance; however, most of them are general-purpose methods and have not been designed for a specific domain. Our proposed method is appropriate for the pathology domain and provide strong regularization to make the model robust. In addition, it has the advantage of alleviating class imbalance problem, which is very common in histology datasets for nuclear segmentation. In our cross-validation experiments on a multi-tissue histology dataset, our method improves PQ and mPQ+ from 64.31 to 64.52 and 52.3 to 52.9, respectively.
Dynamic Super-Resolution in Particle Tracking Problems

Particle tracking in a live cell environment is concerned with reconstructing the trajectories, locations, or velocities of the targeting particles, which holds the promise of revealing important new biological insights. The standard approach of particle tracking consists of two steps: first reconstructing statically the source locations in each time step, and second applying tracking techniques to obtain the trajectories and velocities. In contrast to the standard approach, the dynamic reconstruction seeks to simultaneously recover the source locations and velocities from all frames, which enjoys certain advantages. In this paper, we provide a rigorous mathematical analysis for the resolution limit of reconstructing source number, locations, and velocities by general dynamical reconstruction in particle tracking problems, by which we demonstrate the possibility of achievingÂ super-resolution for the dynamic reconstruction. We show that when the location-velocity pairs of the particles are separated beyond certain distances (the resolution limits), the number of particles and the location-velocity pair can be stably recovered. The resolution limits are related to the cut-off frequency of the imaging system, signal-to-noise ratio, and the sparsity of the source. By these estimates we also derive a stability result for a sparsity-promoting dynamic reconstruction. In addition, we further show that the reconstruction of velocities has a better resolution limit which improves constantly as the particles moving. The result is derived by a crucial observation that the inherent cut-off frequency for the velocity recovery can be viewed as the total observation time multiplies the cut-off frequency of the imaging system, which may lead to a better resolution limit as compared to the one for each diffraction-limited frame. This is discovered for the first time and we hope it can inspire many new reconstruction algorithms that significantly improve the resolution of particle tracking in practice. In addition, we propose super-resolution algorithms for recovering the number and values of the velocities in the tracking problem and demonstrate theoretically or numerically their super-resolution capability.
Mutual Boost Network for Attributed Graph Clustering

Attributed graph clustering is an essential research topic on real-world data. However, the existing approaches are limited by the heterogeneous gap between node and structure features for discriminative representation. Besides, how to ensure the consistency of clustering assignments produced by the node and structure features is still a challenging problem, which always causes inferior clustering performance. To overcome these issues, we propose a novel dual-channel network for attributed graph clustering named Mutual Boost Network (MBN), which consists of auto-encoder and graph auto-encoder that can interact and learn from each other to achieve mutual boost of clustering performance. First, a novel representation enhancement module is proposed to disseminate the heterogeneous information from both node and structure features for learning comprehensive representation. Then, a consistency constraint by contrasting clustering assignments is devised to provide a mutual guide to make them tend to be consistent. Finally, the procedures of representation learning and clustering assignment are simultaneously optimized under a unified framework in a self-supervised manner. Extensive experiments on five public graph datasets show that the proposed MBN outperforms the state-of-the-art methods by a statistically significant margin. The code is available at: https://github.com/Xiaoqiang-Yan/MBN .
Pre-processing Transformation for Enhancing the Transferability of Adversarial Examples

Deep neural networks are susceptible to adversarial examples, which can cause misclassification of the models by adding some imperceptible perturbations to the original input images. Although most existing adversarial attack methods have achieved convincing success rates under white-box settings, they tend to exhibit weak transferability with the challenging black-box settings. Therefore, we propose a method based on input image pre-processing transformation named Shear & Pad Method (SPM) by optimizing the input diversity of the original images to generate adversarial examples. Meanwhile, it can alleviate the overfitting to improve the transferability of the adversarial examples. In addition, this method can be combined with related methods such as the family of fast gradient sign method to build stronger attack methods against the defense trained models. It can also be integrated into other transformation-based methods to generate more adversaries with better black-box transferability. Extensive experiments on the ImageNet dataset show that our proposed method has higher success rates than existing baseline attack methods both on a single model and an ensemble of models. Therefore, we hope that our method can be used as an effective benchmark for evaluating the robustness of deep network models.
Benchmarking Fairness Measures for Calibrated Recommendation Systems on Movies Domain

Collaborative Recommender Systems learn the users' preferences through their interaction history and use it to create personalized recommendation lists. Most recommender systems create the recommendation list based on the items most relevant/similar to the user's preferences. The emphasis on relevance can cause problems such as super specialization, popularity bias, or imbalance among the preferred classes. All these problems create a recommendation list with items that belong to only a few areas/classes of the user's preferences, i. e., generating a list that is not fair to the user's preferences. In this regard, the calibrated recommendation systems have attracted attention as a means of ensuring a degree of fairness. This type of recommender system is designed to recommend items that are simultaneously relevant and fair. Fairness is measured by comparing two distributions, one extracted from the user's preferences and another from the recommended list. Usually, as the distributions are less divergent, the recommendation becomes fairer. In this sense, this study benchmarks 57 fairness measures in order to determine the best one. The differences in the measure's functionality lead us to introduce a new way of extracting the distributions and measuring the relevance as infer changes on the trade-off balances proposed by the state-of-the-art. In total, our implementation produces 5928 unique calibrated recommendation systems. Each system is implemented to use two datasets, running 35 times. The results show that our proposals increase the precision and reciprocal rank.
An Autonomous Data Collection Pipeline for Online Time-Sync Comments

Time-Sync Comments (TSCs) are a sequence of comments associated with video contents at each timestamp. By applying textural analysis, researchers can transform the TSCs into labels that represent the semantic meaning of the original video content. Multiple studies have used the TSCs in video segmentation and tagging. TSCs can be either created by a single user or generated by various users. Thanks to the exploding of multimedia platforms, online comments have proved to be an efficient TSCs data source in multiple research since 2014. However, previous TSCs studies mainly focused on data sources targeting young non-English speaking audiences, potentially introducing data bias due to limited geographic regions and groups. This paper aims to solve this problem by proposing a universal data collection framework of TSCs generated by audiences worldwide from popular social media platforms. We first introduced an efficient data mining strategy for gathering such TSCs data in general. Then, we demonstrated how to build the autonomous pipeline and collected two large-scale TSCs datasets with different sets of keywords, namely LST-YF20 and LST-YT1000, directly from YouTube Lives. We also conducted an extensive experiment on the efficiency of our data pipeline with a group of fixed keywords. The result of our investigation suggests that our data pipeline could efficiently produce high-quality TSCs datasets while keeping a constrained budget. We believe our framework could further contribute to future research in the multimedia field.
Analysis on Deep Reinforcement Learning with Flappy Brid Gameplay

In recent years, machine learning has become a popular area in the IT field. In 2016, a game AI AlphaGo turned out to defeat many Go masters. The technology behind AlphaGo: the popularity of deep reinforcement learning is gradually increasing. This paper aims to use deep reinforcement learning algorithm to train a specific model which can play the Flappy Bird game autonomously. In this paper, two types of optimizers: Adam and RMSProp are used in the deep neural network during the training sessions and the testing results with the models are compared to figure out which model has a better performance when playing Flappy Bird. From the testing results, when the training rounds is insufficient, the model with Adam optimizer performs better than the model with RMSProp optimizer. However, when the training rounds are large enough, the performance of the model with RMSProp optimizer is almost 2 times better than the model with Adam optimizer. After comparison between the two models, this paper finds out that with the increasing of the training rounds, the performance of model with RMSProp optimizer will gradually exceed the model with Adam optimizer.
An experiment on how graduating students represent software designs

As a practice, software design seeks to contribute to developing quality software. For graduating students, the design is difficult to understand, and building a good design seems to require a certain level of cognitive development. In this study, we report an experiment to know how students habitually represent detailed design (what artifacts and ways of design representation they use) and if there is an effect on software quality when they deliver the design representation. We found that students design on a basic level, they do not achieve complete designs that combine several artifacts to model dynamic and static aspects. Also, the delivery of design representation did not improve software quality.
Comparative Research On Blockchain Consensus Algorithms

With the rapid rise of digital currency in recent years, blockchain technology has now gradually entered the public's field of vision. Bitcoin, as the first application of blockchain, is self-evidently hot. Among them, Consensus algorithm plays a crucial role in blockchain technology, and consensus algorithm as the core can directly affect the merits and demerits of the blockchain system. Therefore, different consensus algorithms are adopted for different blockchain systems, and different consensus algorithms have their own advantages and disadvantages. This paper introduces the most mainstream blockchain consensus algorithms in detail, reveals the characteristics of various algorithms, compares them in three aspects such as security, limitation, and decentralization degree, and summarizes the advantages and disadvantages of each. For higher efficiency, potential optimization schemes of various mainstream consensus algorithms proposed by researchers are also presented in, so that later researchers and developers can gain insight into designing and learning consensus algorithms and clarify future research directions as a way, so as to promote the development and application of blockchain technology.
Automation in Robotics with Artificial Intelligence

This document provides an introduction to man-made brain power, robots, and experimental streams that look at the financial and physiological consequences of this and other related innovations. We present a preliminary assessment of man-made knowledge and expertise to work on financial matters with writing managers and integrate existing methods adopted by investigators here. We look at the understanding of non-counterfeit fraud, mechanical autonomy, and the use of system sequences and robust systems, striving for the most remarkable commitment to these points by authoritative and technical analysts, as well as photographs of future experiments.