Reviewerly/exHarmony huggingface dataset:
Load Balancing Techniques in Cloud Environment - A Big Picture Analysis

Cloud computing is a cutting edge worldview to offer types of assistance through the Internet. Load balancing can chip away at the Service Quality (QoS) estimations, which also include reaction time, cost, throughput, execution and asset usage. Load Balancing is a normal issue in the cloud that makes it hard to stay aware of the introduction of the applications coterminous to the Service Quality (QoS) assessment and following the Service Level Agreement (SLA) report true to form from the cloud providers to adventures. Cloud suppliers battle to disperse equivalent responsibility among the servers. An investigation of writing on the undertaking booking and burden adjusting calculations is introduced. A comprehensive review of various burden adjusting strategies in a static, dynamic, and nature-jazzed up cloud environment to address the Data Center Response Time and by and large execution. Load balancing procedures are examined and the calculation characterized in the examination paper and its solidarity and shortcoming assuming any is talked about.
An Augmented Similarity Approach for Improving Collaborative Filtering based Recommender System

Collaborative Filtering (CF) is one of the earliest and extensively used techniques to provide recommendations to the users. The success of collaborative filtering can be attributed to its ability to find the similarity among users and identify a set of similar users known as neighborhood set. In this direction, many researchers have put their efforts to investigate and propose new and modified similarity measures. Some researchers have combined two measures or improved a traditional similarity measure with a method known as similarity modifier. In this paper, we analyze and evaluate various similarity measures and compare them with an augmented similarity measure approach. Our approach identifies the factor which improves the performance of a similarity measure. The experiments conducted on Yahoo! Movies dataset and the comparative results show that our proposed approach performs better than the state-of-the-art approaches in terms of Coverage, MAE and RMSE evaluation measures.
Predicting E-commerce Consumers’ Loyalty Through the Lens of Protection Motivation Theory

The issue of data breaches in the e-commerce industry has gotten a mountain of attention among online consumers. Despite the issue, this study investigates factors influencing e-commerce consumers’ loyalty. The protection motivation theory served as the underpinning theory. A total of 384 experienced online consumers in West Nusa Tenggara, Indonesia participated in this quantitative survey. Findings revealed that perceived severity, self-efficacy, and safety habit behaviour significantly influence online consumers’ loyalty in an e-commerce environment when a data breach occurs. Meanwhile, perceived cost and perceived vulnerability have no significant influence. Based on the findings, e-commerce providers’ efforts to fix data breaches will enable to increase in consumers’ loyalty toward online shopping and the likelihood of consumers’ repeat purchases. This study provides useful insights for e-commerce providers to improve their current and future e-commerce services and strategically allocate their resources to overcome the issue of data breaches toward resilient online shopping.
CoreMedi: Secure Medical Records Sharing Using Blockchain Technology

Individual medical records have a high priority of integrity and security, yet they are vulnerable to data breaches and manipulation. It’s vital to communicate these data in a secure and safe manner. To overcome this problem, the proposed solution named, CoreMedi, uses blockchain technology to store and share medical data. Data stored on blockchain is immutable, verifiable and transparent (based on accessibility). As data cannot be altered on blockchain, the rate of fraud may be drastically decreased. With the help of smart contracts which are small pieces of code that run on blockchain, a Decentralized Application (DApps) is designed and implemented that enables patients/individuals to store and share medical records securely. Users may share their medical history, medications, and personal data with a specific medical personnel on the same blockchain network using CoreMedi, a Decentralized Web application. The application is implemented with React as frontend and the backend infrastructure uses Ethereum solidity smart contracts. The process of running a blockchain application is not simple because the users must be willing to spend crytpocurrencies as gas fees, as this is the way to maintain the network with no single organization having control.
Detection of Customer Opinions with Deep Learning Method for Metaverse Collaborating Brands

In recent years, metaverse projects have been developed that both increase the number of users and bring a new concept to the use of the internet. With this development, collaborations are frequently established within the business world with metaverse projects that attract the attention of companies. In the study, the gains of the companies operating in the metaverse after these activities were examined. Thanks to the tweets collected before and after the companies participated in the metaverse, it was analyzed how potential users interpreted their participation in the metaverse. In this context, sentiment analysis experiments were conducted for five different clothing, sportswear, and retail companies (Adidas, Balenciaga, H&M, Nike, and Zara) serving in similar fields of activity. The BERT architecture, which is a language representation model, was used in the experiments, and it was seen that the positive shares on Twitter for companies increased greatly. After the companies transitioned to Metaverse, the biggest change in positive Twitter posts was seen in Nike, with 47%, and in second place, positive Twitter posts about Balenciaga increased by 42%. Experiments show that firms’ assets in the metaverse create a positive perception within one month.
Frameworks for Integrating Blockchain and Supply chain: A Survey

Current Supply Chain Management (SCM) practices focus too much on administrative processes among agencies of different countries, which leads to confusion and increased delivery time because of human error, bottlenecks in processing documents, and the necessity to comply with changing rules across foreign borders. With little to no changes in the administrative side of global distribution, the focus has shifted towards blockchain technology that can meet the demand of security, reliability, and accessibility while satisfying each requirement to conduct cross-border trade. In this paper, different blockchain frameworks were analyzed to assess common factors that could improve blockchain integration across other existing Supply Chain (SC) platforms traceability, intuitiveness, and security were found to be among the key aspects that positively influence the adoption of blockchain frameworks. While deploying security systems for multiple blockchains at different layers helps create a trusted environment, using a multi-chain approach improves traceability, which is critical for cross-border SC permits.
Hybrid Digital Image Cryptography Using Composition of Henon Map Transposition and Logistic Map Substitution

This paper proposes a new robust cryptography method based on the composition of the Henon map transposition function and the Logistic map substitution function. This method consists of encryption and decryption algorithms that randomly and simultaneously permute data position and modify data value. Both algorithms have been implemented and tested for digital images. The randomness of the encrypted images is performed using histogram, correlation, NPCR, UACI, and entropy analyses. The results of those analyses show that the proposed algorithm is robust against differential and statistical attacks. Whereas, the decrypted image quality analysis is carried out by MSE and PSNR measurements, where their results show the decrypted image is exactly the same as the original image.
A Study of Online Scams Associated with Age, Gender and Loss of Value in Thailand

Online scams have become a new normal risk for online shoppers, including for Thai people. This paper presents a study of online scams analyzed by age, gender and value loss. The dataset for this study is from the ChalaOhn system, a website developed by a state university and the metropolitan police for protection against online shopping scams in Thailand. The dataset was considered, analyzed and visualized before presenting the results. It was found that more than two thirds (69.8 %) of the victims are 20-39 years old, and more than two fifths (42.7 %) are 20-29 years old. 37.1 % of victims lost less than ${\$}$100, and the mode loss of value was ${\$}$28.6 (1,000 THB). These findings should be considered further to help online shoppers and reduce online scams in Thailand. Awareness raising based on these findings should focus on Thai people under 40 years old.
Real-Time Sign Language Detection Using CNN

Sign language is a system of communication using visual gestures and signs. Hearing impaired people and the deaf and dumb community use sign language as their only means of communication. Understanding sign language is so much difficult for a normal person. Therefore, the minority group has always faced many difficulties in communicating with the general population. In this research paper, we proposed a new deep learning-based approach to detect sign language, which can remove the barrier of communication between normal and deaf people. To detect real-time sign language first we prepared a dataset that contains 11 sign words. We used these sign words to train our customized CNN model. We did some preprocessing in the dataset before the training of the CNN model. In our findings, we see that the customized CNN model can achieve the highest 98.6% accuracy, 99% precision, 99% recall and 99% f1-score on the test dataset.
Blockchain Technology: Opportunities &amp; Challenges

As an emerging technology, Blockchain technology can efficiently be used for various tasks in various domains. It has become disruptive technology due to its secure, immutable, decentralized, persistent, anonymous, and auditable nature. Blockchain technology has a lot of opportunities to offer in various fields like cryptocurrency, risk management, supply-chain management, financial services, identity management, healthcare, cloud computing, and the Internet of things (IoT). Blockchain technology is relatively young and proliferating; thus, several challenges must be addressed to realize its full potential. These challenges are even more significant and require critical attention because of the financial aspect of blockchain-based solutions. This work attempts to present a holistic picture of Blockchain Technology by discussing an overview, style, reality, and actuality of Blockchain. It discusses various Blockchain components, including mining and consensus mechanisms. It also presents Blockchain layers, multiple applications, advantages, and disadvantages. This study also discusses various issues and challenges which are helpful for researchers to explore and advance multiple aspects of Blockchain Technology.
Face Recognition with Edge Detection and LBP Feature Extraction

The face is the main part of humans that is used as a recognition center in everyday life. With the different faces of many people, it will be increasingly difficult to remember by relying on human memory itself. Therefore we need a method called facial recognition technology. In its application, face recognition consists of two parts, namely face detection and face classification. However, this problem is different from computer recognition, facial recognition is still difficult because of different face shapes. So, in order for the computer to recognize it, a useful method is needed to reduce the complexity of the face. The methods used in face detection are the canny edge detection method and the LBP feature extraction method which will then continue to classify using SOM. This research was conducted with 2 types of datasets, namely full face and eye datasets, which will be converted into vector arrays and will be stored in the training model. From several stages carried out, this research succeeded in obtaining an evaluation accuracy rate of 17% for the full face dataset model and 11% for the eye dataset. The tests carried out were 20 different data, obtained results of 50% accuracy for the full face dataset and 25% for the eye dataset
Implementation of Association Rules with Apriori Algorithm in Determining Customer Purchase Patterns

Micro, Small, and Medium Enterprises in Indonesia Usaha Mikro, Kecil, Menengah (UMKM) have been affected by the COVID-19 pandemic. The barcode scanning system currently only helps support the buying and selling process and cannot determine the provision of stock or the creation of promotional packages. Website application development using the Association Rule Method with the Apriori Algorithm is the solution offered to produce a pattern of relationships between products that buy by customers. The goods relationship is the basis for making decisions by shop owners to determine the stock of interconnected goods or making promotional packages with the association method by calculating the value of support and confidence. The system was built using the PHP programming language and 4820 transaction data. The results of data analysis through the website using the Antoni store dataset show the results of association rules. Based on 50 experiments conducted by researchers, if the Antoni shop wants to produce two directions, it is better to use minimum confidence of 10% or 12% with minimum support of 2% or 4%. However, if you want to produce 1 rule, you should use minimum confidence of 14%, 16%, or 18% with minimum support of 2%, 4%, or 6%. The lift ratio value of each minimum belief and the recommended support are more significant than 1. Therefore, the combination of association rules results is solid and valid. It can be used that this algorithm is suitable for a collection of related items, so it is appropriate to be used in analyzing product sales patterns at Antoni Stores.
Comparison of Deep Learning Models for Body Cavity Fluid Cytology Images Classification

Disease detection is successfully performed in many medical fields using medical images and deep learning architectures. Deep learning architectures used in many fields such as brain images, gastroenterological images and chest x-rays support doctors in the detection of diseases. By using deep learning architectures, diseases can be detected more accurately and faster. With faster detection of diseases, treatment can be started more quickly. Early and accurate diagnosis of cytology images is also very important. After the cytology samples are taken from the patients in general health screenings, they should be interpreted by specialist doctors. The aim of this study is to detect benign and malignant cells from cytology images quickly and accurately. In this study, the Body Cavity Fluid Cytology Images dataset, which includes the data of a total of 21 patients with 14 malignant and 7 benign samples, was used. The dataset contains 693 images of 256x192 size. The images in the dataset are trained using the still popular ResNet50, GoogleNet and AlexNet classification architectures. The classification results obtained were compared with other studies using the same data set. As a result of the test processes, AlexNet architecture achieved 97.26%, GoogleNet architecture 98.12% and ResNet50 architecture 99.13% classification accuracy. The ResNet50 architecture with the best classification result achieved 99.25% specificity, 98.75% sensitivity and 99.13% accuracy. Training and testing in this study showed that the ResNet50 architecture classifies cytology images most successfully.
Dark Web Marketplace Monitoring - The Emerging Business Trend of Cybersecurity

Cyber threat intelligence (CTI) is vital for enabling effective cybersecurity decisions by providing timely, relevant, and actionable information about emerging threats. Monitoring the dark web to generate CTI is one of the upcoming trends in cybersecurity. As a result, developing CTI capabilities with the dark web investigation is a significant focus for cybersecurity companies like Deepwatch, DarkOwl, SixGill, ThreatConnect, CyLance, ZeroFox, and many others. In addition, the dark web marketplace (DWM) monitoring tools are of much interest to law enforcement agencies (LEAs). The fact that darknet market participants operate anonymously and online transactions are pseudo-anonymous makes it challenging to identify and investigate them. Therefore, keeping up with the DWMs poses significant challenges for LEAs today. Nevertheless, the offerings on the DWM give insights into the dark web economy to LEAs. The present work is one such attempt to describe and analyze dark web market data collected for CTI using a dark web crawler. After processing and labeling, authors have 53 DWMs with their product listings and pricing.
Taking Both the Modality and Class information for Visible Infrared Person Re-Identification

Visible infrared person re-identification (VI Re-ID) is an important and challenging task due to large cross-modality variations between visible and near-infrared images, which suffers from additional cross-modality discrepancy caused by different types of imaging systems. Existing research mainly focuses on expanding the difference between the features to solve the problem. However, there are few studies investigating the reduced differences between the modalities, which is important to this problem. In this paper, we propose a dual-constrained cross-modality (DCCM) loss to enhance the class and modal discrimination of features, and effectively reduce the difference between intra- and inter-modalities. Specifically, the DCCM loss supervises the discriminativeness of network learning features by constraining the distances from the same class and the same modality, the same class and the different modalities, the different classes and the same modality, the different classes and the different modalities. Under the joint supervision of cross-entropy loss and DCCM loss, the network is trained to achieve two important goals, namely class discrimination and modality discrimination. Besides, we propose a two-stream part-level network to learn local features representations of modality invariant. Extensive experimental results demonstrate that our method outperforms the state-of-the-art methods on two datasets.
Literature Review of Blockchain-based Cloud Computing: Data Security Issues and Challenges

Cloud computing has been widely accepted in a variety of areas of the information technology industry as a critical solution for handling infrastructure and data service demands at low cost, with minimal effort, and with a high degree of scalability. Even though the number of cloud computing users has increased significantly, security issues have not been adequately addressed. Cloud computing technologies can be considered the forerunners of blockchain technology. Because blockchain networks may be established under cloud security settings, they can play an essential role in blockchain deployments. Blockchain technology is frequently referred to as an incorruptible technology due to its rapid acceptance across a variety of industries. It is expected that blockchain technology might be able to solve the security issues associated with cloud applications. Most cloud computing’s benefits involve operational efficiency. Security must play a greater role in achieving blockchain’s benefits. While blockchain technology ensures data privacy and transactional traceability, it may increase the performance costs of cloud computing platforms. Before exploring how Blockchain could be used to safeguard Cloud Computing, this essay will investigate the numerous security considerations of Blockchain and Cloud Computing. Through a literature review, this study will examine cloud data security and how academics use blockchain technology to improve cloud data security.
Abnormal Wind Turbine Data Identification Using a Dirichlet Process Gaussian Mixture Model

A large amount of abnormal data will be generated during the actual wind turbine operation, thus the raw data can’t be directly applied to the subsequent work such as wind turbine power prediction and generation performance evaluation. This paper proposes an abnormal data identification method based on the Dirichlet Process Gaussian Mixture Model (DPGMM) to preprocess the raw data effectively. Firstly, all data points are allocated into corresponding power bins created in the horizontal power direction with a certain interval in the wind speed-power (V-P) coordinate system. And then, the DPGMM model that can adaptively determine the optimal number of Gaussian components is used to cluster the data points in each power bin. At last, combined with the parameters of each Gaussian component confidence ellipse and data points distribution characteristics in V-P coordinate system, the abnormal Gaussian components and their clustering, abnormal data can be accurately identified. Using actual wind turbine SCADA data, the proposed method is demonstrated to be effective.
Ship detection based on deep learning under complex lighting

In order to improve the real-time performance and detection rate of ship detection in a complicated marine lighting environment. An improved YOLOv3 algorithm is proposed. This paper first constructs a ship target data set under different light intensities. Then, the problem of poor detection of ship targets caused by the influence of illumination is addressed. The channel attention mechanism module is introduced into the network of the YOLOv3 algorithm. Generate weighting coefficients in the feature channel and enhance the feature extraction capabilities of the network. Finally, the accuracy of detection under complex lighting conditions is improved. The detection results show that the detection accuracy of the YOLOv3 network with the attention mechanism module has been significantly improved in the scene of changing lighting. It has laid a reliable foundation for the target tracking of ships at sea.
An Obstacle Avoidance Method Using Asynchronous Policy-based Deep Reinforcement Learning with Discrete Action

With the increasing application of mobile robots in manufacturing, service, and military fields, the demand of intelligent autonomous decision is also growing. In this paper, the state-of-the-art policy-based deep reinforcement learning (DRL) algorithm is applied to the mobile robot obstacle avoidance task. To solve the strong coupling in the existing DRL-based training process of mobile robot decision, an asynchronous decoupling architecture is proposed in this paper, which greatly improves the scalability and sample generation efficiency of the DRL algorithm. At the same time, based on the asynchronous decoupling architecture and Soft Actor-Critic (SAC) algorithm, we design an obstacle avoidance method named asynchronous dueling-based discrete action SAC (ADDSAC). Experiments show both action discretization and dueling network are simple and powerful techniques to improve obstacle avoidance performance. Finally, the reasons are also analyzed from different perspectives.