Reviewerly/exHarmony huggingface dataset:
A State of Art Survey of Nano Technology: Implementation, Challenges, and Future Trends

Nanotechnology is a field of study that aims to make our lives easier, safer, and more environmentally friendly. With current upgrades and alterations to available networking and communication paradigms, incorporating Wireless Nano Sensor Networks (WNSN) with various products, sensors, and devices would introduce new network paradigms. The Internet of Nano Things is a term for this concept (IoNT), To achieve seamless interconnection between Nano networks and existing communication networks and the Internet, many topologies and communication paradigms must be developed while technological hurdles are addressed. The amount of data accessible limits how much research and decision-making can be done. This research visualizes a wide range of nanotechnology applications. The goal of this project was to show in more than way this research method may be used to information recommendation services. The routing protocol is critical in WNSN and IoNT because of the many nanoscale constraints. While ensuring the flow of data and information, this routing protocol must take into account the specific features of nanoscale communication.This research aims to provide insight into the WNSN (Wireless Nano Sensor Networks) and IoNT (The Internet of Nano Things) paradigms, as well as a detailed assessment of a large number of current routing protocols that are tailored to the characteristics and features of nano communication. Big data applications with their features and characteristics in general also use cloud computing. This paper explains different hands based on neural networks and implemented on FPGAs (which is Field-Programmable Gate Array) and other genetic algorithms and neural networks. Many more approaches and models compare.
Facebook Oversight Board's decision on the indefinite suspension of Donald Trump's account

On May 5, 2021 Facebook Oversight Board, a global body created by the company to act as an independent appeals mechanism for Facebook's content moderation, issued its highly awaited decision concerning the "indefinite suspension" of former US President Donald Trump's account. The suspension was originally imposed by Facebook between January 6 and 7, 2021 for Trump's posts related to the attacks on Capitol Hill in Washington, DC. This case note shows the Oversight Board's almost exclusive reliance on international human rights standards in deciding the case. It sees it as a positive development. Additionally, the author argues that the decision also highlights Facebook's struggle and potential lack of willingness to develop a unified approach to its treatment of political leaders and the company's reluctance to grapple with the way its business model has potentially contributed to violent events, including the attacks on the Capitol Hill on January 6, 2021.
SPPD: A Novel Reassembly Method for 3D Terracotta Warrior Fragments Based on Fracture Surface Information

As one of China′s most precious cultural relics, the excavation and protection of the Terracotta Warriors pose significant challenges to archaeologists. A fairly common situation in the excavation is that the Terracotta Warriors are mostly found in the form of fragments, and manual reassembly among numerous fragments is laborious and time-consuming. This work presents a fracture-surface-based reassembling method, which is composed of SiamesePointNet, principal component analysis (PCA), and deep closest point (DCP), and is named SPPD. Firstly, SiamesePointNet is proposed to determine whether a pair of point clouds of 3D Terracotta Warrior fragments can be reassembled. Then, a coarse-to-fine registration method based on PCA and DCP is proposed to register the two fragments into a reassembled one. The above two steps iterate until the termination condition is met. A series of experiments on real-world examples are conducted, and the results demonstrate that the proposed method performs better than the conventional reassembling methods. We hope this work can provide a valuable tool for the virtual restoration of three-dimension cultural heritage artifacts.
A novel hybrid decision-based filter and universal edge-based logical smoothing add-on to remove impulsive noise

This paper presents a novel hybrid filter along with a universal extension to remove salt and pepper noise even at a very high noise density. The proposed filter initially specifies a threshold and then denoises the image using a combination of linear, nonlinear, and probabilistic techniques. Furthermore, to improve the quality, a universal add-on is presented which uses edge detection and smoothening techniques to brush out fine details from the restored image. To evaluate the efficacy, the proposed and existing filtering techniques are implemented in MATLAB and simulated with benchmark images. The simulation results show that the proposed filter is able to restore image details even at the extremely high noise density of 99%. Moreover, the proposed filter provides admirable results on natural as well as medical images from very low to very high noise density. Finally, it is observed that, on average, the proposed filter improves the PSNR by 11% over the state-of-the-art technique.
A Survey of Finding Trends in Data Mining Techniques for Social Media Analysis

Social media have become very popular in the last few decades. Users rely on social network sites like Twitter, Facebook, YouTube, and LinkedIn for both information and entertainment needs. Social media analytics with data mining technology could be an analysis axis centered on extracting trends, patterns, and rules from the social media pool, to serve the people and organizations to have optimum choices concerning many disciplines. The traditional media analytical techniques appear obsolete and inadequate to gratify this immense array of unstructured social media knowledge characterized by three key problems namely; size, noise, and dynamism, predominantly shifting from the batch scale to the streaming one. The objective of this study is to investigate the data mining techniques that were used by social media networks during the years 2010 and 2020. The effort is a systematic review of content analysis in studies within the field of social media analytics that was published in principal databases. 125 articles were reviewed in this paper. Content analysis was implemented based on their approach, tools utilized, language, the dataset used, country, year, and nature of the experiment. The review discovered that 22 data mining techniques were employed with social media data while frequently used in Artificial Neural Network (ANN), Bayesian networks (BN) and Support Vector Machine (SVM), K-means Clustering, and Neuro-Fuzzy Logic Approach. The study has focused to assist the involved analyzers and educators to capture the research trends and problems associated with the Social media analytics process with future research initiatives.
A Multi-Objective Genetic Programming Approach with Self-Adaptive α Dominance to Uncertain Capacitated Arc Routing Problem

The Uncertain Capacitated Arc Routing Problem (UCARP) has a variety of real-world applications. Genetic Programming Hyper-heuristic (GPHH) is considered a promising technique to handle UCARP. Many scholars have shown the power of GPHH of evolving effective routing policies. However, the size of the evolved routing policies is ignored. Typically, smaller routing policies can have better interpretability and generalisation. Thus, it is necessary to optimise the size along with the effectiveness. The objective selection bias issue arises as the size is much easier to be optimised than effectiveness. The Pareto front is biased to the size gradually during the evolutionary process. To address this issue, we develop an α dominance criteria based Multi-Objective GP with a self-adaptive α scheme (αMOGP-sa). The basic idea of the α dominance criteria is to set tradeoff rates between objectives. For different instances, the search space can be very different. In this case, the self-adaptive α scheme is employed to automatically tuning the α value during the evolutionary process so that we can identify a valid α value for different instances. This paper examines the proposed algorithm in eight different problem instances. The experimental results showed that αMOGP-sa could effectively handle the objective selection bias issue, and evolve much better Pareto front on Hyper-Volume and Inverted Generational Distance than the current state-of-the-art MOGP approach for UCARP in terms of effectiveness and size on all instances. Also, αMOGP-sa can evolve much smaller routing policies than the state-of-art single-objective GPHH without sacrificing effectiveness.
A comprehensive survey of imbalanced learning methods for bankruptcy prediction

In practical datasets used for supervised learning, the uneven distribution of the amounts of data between classes is known as the class imbalance problem, and can reduce the performance of basic classifiers. The class imbalance problem arises in various areas such as medical diagnosis, spam filtering, and fraud detection. Bankruptcy prediction is of particular research interest due to the current economic upheaval. In a bankruptcy prediction dataset, there are two classes containing bankrupt and normal companies, and this problem can therefore be solved with binary classification methods. Several advanced models for handling the class imbalance problem in bankruptcy prediction have been developed to improve the predictability performance on a Korean bankruptcy (KB) dataset. To give an overview of imbalanced learning methods for bankruptcy prediction, this study first reviews several state-of-the-art approaches for handling this problem in bankruptcy prediction, including an oversampling-based framework, a cost-sensitive method (the CBoost algorithm), a combination of resampling techniques and a cost-sensitive framework, and an ensemble-based model (the XGBS algorithm). We also conduct empirical experiments to evaluate the methods surveyed here in terms of two performance metrics, the area under the receiver operating characteristic (ROC) curve and the geometric mean. The results show that the ensemble-based model outperforms other methods in terms of bankruptcy prediction on the KB dataset.
Blockchain Network Propagation Mechanism Based on P4P Architecture

Blockchain is a mainstream technology in which many untrustworthy nodes work together to maintain a distributed ledger with advantages such as decentralization, traceability, and tamper-proof. The network layer communication mechanism in its architecture is the core of the networking method, message propagation, and data verification among blockchain nodes, which is the basis to ensure blockchain’s performance and key features. When blocks are propagated in peer-to-peer (P2P) networks with gossip protocol, the high propagation delay of the protocol itself reduces the propagation speed of the blocks, which is prone to the chain forking phenomenon and causes double payment attacks. To accelerate the propagation speed and reduce the fork probability, this paper proposes a blockchain network propagation mechanism based on proactive network provider participation for P2P (P4P) architecture. This mechanism first obtains the information of network topology and link status in a region based on the internet service provider (ISP), then it calculates the shortest path and link overhead of peer nodes using P4P technology, prioritizes the nodes with good local bandwidth conditions for transmission, realizes the optimization of node connections, improves the quality of service (QoS) and quality of experience (QoE) of blockchain networks, and enables blockchain nodes to exchange blocks and transactions through the secure propagation path. Simulation experiments show that the proposed propagation mechanism outperforms the original propagation mechanism of the blockchain network in terms of system overhead, rate of data success transmission, routing hops, and propagation delay.
THE IMPLEMENTATION OF ONLINE LEARNING IN ENGLISH LANGUAGE TEACHING DURING PANDEMIC: THE TEACHERS VOICE

This study aims to explore the implementation of website that used in MAN 2 Bandarlampung. To overcome the problem structurally, the theoretical framework by discussing about theory and approach was conducted. The theories applied helped in finding out the implementation of this website. Meanwhile the approach was emphasized on one approach that is online media learning in the learning process during pandemic. Moreover, this analysis applied descriptive qualitative method to depict clearly about the problem by using the words. The result of this analysis shows that the use of online learning, especially Rumah Belajar brings some factors for the teachers in deliver the material. First factor is from the technology, it has some benefits and also problems for the teacher. The second factor is the teachers behavior in using this website. It can be concluded this website is quite good to help the teacher to do the learning process during pandemic because this website has covered the teachers need, but the school has not explained some features, so it hinders the teacher from doing their best. The implementation of Rumah Belajar during pandemic must have sufficient training to help teachers do their teaching during e-learning maximally. As a result Rumah Belajar during pandemic can assist in the implementation of online learning.
A Framework for Secure Storage and Sharing of Electronic Health Records using Blockchain Technology

Public Healthcare issues have been the main focus of any governing body for the last decade. Each day a large amount of Healthcare data is generated by individual and Medical Organizations. This information is considered highly sensitive and private, which needed to be securely stored and protected against unauthorized access. In a traditional system, all the data is stored in a centralized system which comes with lots of drawbacks. This where blockchain technology comes in because of its Peer-To-Peer nature and Security. Several studies highlight the importance of blockchain and how it can be implemented in Electronic Medical Record(EMR) systems which face problems regarding privacy,security,decentralization and confidentiality.In this paper, We explain how blockchain technology may be utilised to improve EMR systems and how it could be a solution to these problems. We offer a framework for implementing blockchain technology in the healthcare industry for EMR.The aim of our proposed framework is first to implement blockchain technology for EHR and secondly to provide secure storage of electronic records by defining granular access rules for the users of the proposed framework. Moreover,this framework provides the EHR system with the benefits of a blockchain-based solution that is scalable, secure, and integrated.
Image Classification Using CNN

Content Based Image Retrieval Technique(CBIR) is used to retrieve images from a database by adding some algorithms. The images are initially stored in the database and then retrieved on the basis of different features and techniques. User can extract images based on different search results. Still, there are various algorithms which are unable to find some specific criteria. Users directly write any name and get relevant results based on that. But there were lots of challenges which were solved by using various algorithms. The algorithms used in CBIR must be optimized for good results as well as higher accuracy and recall rate. Image classification is a technique in which the images are classified into different classes. Image classification is used to accurately classify the images based on different categories and based on different techniques the images are been set to a particular class. If an image belongs to the class A, then the algorithm must ensure that it must classify it as class A image. Convolutional neural network(CNN) is a technique which we can use for the image classification. This paper will show how the image classification works in case of cifar-10 dataset. We used the sequential method for the CNN and implemented the program in jupyter notebook. We took 3 classes and classify them using CNN. The classes were aeroplane, bird and car.We presente d the classification by using CNN and we took batch size as 64. We got 94% accuracy for the 3 classes used in cifar-10 dataset.
The internet-of-vehicle traffic condition system developed by artificial intelligence of things

An Internet-of-Vehicle (IoV) system primary transmits traffic information and various kinds of emergency notices through Vehicle-to-Vehicle (V2V) or Vehicle-to-Infrastructure (V2I); however, the transmission of multimedia enables drivers to control route conditions better, such as road obstacles and the range of a construction site. Additionally, car accidents usually require relevant video records of the scene for investigation; surrounding cars could transfer the accident scene videos to help the police restore the detailed situation. Meanwhile, the multimedia messages of IoV need to go through security verification and privacy protection for the system to deliver push notifications and multimedia messages to social groups instantly. The study aims to construct an IoV traffic condition system developed by Artificial Intelligence of Things (AIoT); the data transmitting method of this research is via the 6th Generation Network (6G Network), which has advantages of high transmission speed and Quality of Service (QoS) guarantee. Furthermore, the suggested system employs federated learning to ensure message security and privacy. The features of the researched system are: 1. Use Faster Region-based Convolutional Neural Networks (R-CNN) to recognize the objects in cameras and judge if there are road obstacles and any constructions; 2. Capture car accident videos through federated learning, and send the encrypted evidence to relevant legal units; 3. Use push notifications to send multimedia messages to social groups instantly, marking the locations and the road conditions to help drivers control the conditions with the surroundings. This study expects to delivering videos and Global Positioning System (GPS) data for road condition recognition, improving driving safety. The features of the approach developed in this article are different from those IoV alarms presented in past research that requires drivers to enter messages for notifying nearby cars. Instead, this research utilizes Faster R-CNN to recognize road conditions and transmit information to base stations, and the base stations will pass the information to other vehicles. The federated learning technique in this article can enhance the Faster R-CNN model's accuracy in each car.
Smart contracts for decentralized vehicle services

Internet of Vehicles (IoV), whose basic concepts are like those of the Internet of Things (IoT), refers to the ability of vehicles to communicate intelligently with each other through the exchange of information. To guarantee the services offered by IoV, it is necessary to consider the exchange of a large amount of information without human intervention and in a decentralized manner. To overcome these challenges, the use of blockchain technology is required. This technology is supported by the concept of smart contracts and implements the notion of decentralized applications (DApp). Our objective is, then, to design a decentralized application that brings together all the services accessible by vehicles. The user, depending on the type of service, can filter the search result and benefit from the appropriate service. Via this paper, we present the basic concepts to be used in the development of our platform such as blockchain, smart contracts, DApp and their relationship with IoV. We model the communication between the different actors of the system as well as the design of the proposed framework.
An intelligent <scp>IoMT</scp> enabled feature extraction method for early detection of knee arthritis

Abstract Osteoarthritis and rheumatoid are most common form of arthritis disorder, affecting millions of people worldwide. This article presents a computer aided detection system (CAD) for early knee osteoarthritis and rheumatoid detection using X‐ray images and machine learning classifiers. This work also proposed a novel feature extractor from X‐ray images of knee to assist in detection and classification, called explainable Renyi entropic segmentation with Internet of Things (IoT) framework. The proposed method later utilizes model agnostic algorithm using post hoc explainability for extracting relevant information from prediction of knee joint segmentation. CAD system is integrated with an IoT framework and can be used remotely to assist medical practitioners in treatments of knee arthritis. The presented results show commendable improvement over different existing feature extractors in combination with different classifiers. The best result of proposed extractor method was obtained when combined with random forest classifier having Euclidean hyperparameter that gave an accuracy of 95.23%, among all the evaluators. The obtained results show the effectiveness of proposed feature extractor model to determine relevant features from knee and describe the suitable knee disorders.
An Improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math>-Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>k</mi> </math> -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.
Dig into Multi-modal Cues for Video Retrieval with Hierarchical Alignment

Multi-modal cues presented in videos are usually beneficial for the challenging video-text retrieval task on internet-scale datasets. Recent video retrieval methods take advantage of multi-modal cues by aggregating them to holistic high-level semantics for matching with text representations in a global view. In contrast to this global alignment, the local alignment of detailed semantics encoded within both multi-modal cues and distinct phrases is still not well conducted. Thus, in this paper, we leverage the hierarchical video-text alignment to fully explore the detailed diverse characteristics in multi-modal cues for fine-grained alignment with local semantics from phrases, as well as to capture a high-level semantic correspondence. Specifically, multi-step attention is learned for progressively comprehensive local alignment and a holistic transformer is utilized to summarize multi-modal cues for global alignment. With hierarchical alignment, our model outperforms state-of-the-art methods on three public video retrieval datasets.