Reviewerly/exHarmony huggingface dataset:
Impact of Web 3.0 on Blockchain Technology

The World-Wide-Web has become one of the major aspects of everyone’s existence around the globe practically. In this day and age where internet is incorporated in everyone’s lives, it is undoubtedly challenging to carry out today’s tasks without its utilization. In addition to this, the arrival of the global pandemic has significantly improved the priorities of the Internet in the times of unexpected unprecedented times of disaster.
Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets

Keyword extraction is a critical task that enables various applications, including text classification, sentiment analysis, and information retrieval. However, the lack of a suitable dataset for semantic analysis of keyword extraction remains a serious problem that hinders progress in this field. Although some datasets exist for this task, they may not be representative, diverse, or of high quality, leading to suboptimal performance, inaccurate results, and reduced efficiency. To address this issue, we conducted a study to identify a suitable dataset for keyword extraction based on three key factors: dataset structure, complexity, and quality. The structure of a dataset should contain real-time data that is easily accessible and readable. The complexity should also reflect the diversity of sentences and their distribution in real-world scenarios. Finally, the quality of the dataset is a crucial factor in selecting a suitable dataset for keyword extraction. The quality depends on its accuracy, consistency, and completeness. The dataset should be annotated with high-quality labels that accurately reflect the keywords in the text. It should also be complete, with enough examples to accurately evaluate the performance of keyword extraction algorithms. Consistency in annotations is also essential, ensuring that the dataset is reliable and useful for further research.
Morphology of Red Blood Cells Classification using Deep Learning Approach

Poikilocytosis is a condition in which the shape of RBC cells changes regularly. This condition is arising from several anemias and other diseases. It is critical to detect and classify abnormal RBC shapes as early as possible to diagnose and treat the condition. The proposed four different deep Convolutional Neural Networks (CNNs) are for RBC morphology classification, which is beneficial to hematologists. After using the data augmentation strategy, deep CNN produces good results and improves the performance of the models. The proposed deep CNN is tested on the recently available Erythrocytes IDB datasets. In Erythrocytes, there are three versions of IDB datasets IDB 1, IDB 2, and IDB 3. The proposed deep CNN models obtained an accuracy of 97.77 %, 96.28 %, 96.10 %, and 95.95 % and outperformed existing benchmark methods. The proposed CNN models produce good accuracy, speed, and classification rate results.
Applicability of deep learning optical flow estimation for PIV methods

Recently, there have been some attempts to introduce the method of deep learning-based optical flow(DL-based OF) into PIV measurements. These attempts have made it possible to estimate the flow field with higher accuracy and higher spatial resolution. The current mainstream deep-learning optical flow estimation method often requires a training set whose data distribution is closer to the real sample when completing the target task. However, there is a certain difference between the rigid body motion dataset commonly used in the field of computer vision and the fluid motion dataset in PIV measurements. Whether networks trained with rigid-body data can be directly applied to fluids has not been fully discussed. However, in existing attempts to introduce the DL-based OF method into the field of PIV measurement, the influence of the characteristics of the PIV particle image on the calculation results of the deep learning optical flow estimation (DL-based OF) has not been fully discussed. Therefore, the boundary conditions of the learning method for PIV calculation cannot be fully demonstrated. In this study, we selected the Flownet2 network, which is a classical baseline model on rigid body datasets, as a representative deep learning optical flow estimation method for experiments. Based on the characteristics of various particle images, this study generates a dataset with a wide range of parameters, such as particle density, particle diameter, sheet thickness, and maximum displacement, which are key to the generation of PIV simulation images and discusses the sensitivity of DL-based OF methods under extreme parameter conditions. This study compares (1) the model parameters trained using rigid body datasets such as Sintel, (2) the model parameters trained using synthetic PIV images, (3) the PIV-WIDIM method, and (4) the Optical Flow-Farneback method. The results reveal that the DL-based OF methods in (1) and (2) have a higher spatial resolution and estimation accuracy than the traditional methods in (3)] and (4). Training with the synthetic fluid dataset results in a smoother flow field in method (2)] compared with training with the rigid body dataset in method (1). In the case of limited training set parameters, Flownet2 can still be suitable for extreme conditions, such as small particle concentration, small particle diameter, insufficient particle brightness, or small particle displacement.
MARec: A multi-attention aware paper recommendation method

The paper recommender system has provided great convenience for people to obtain knowledge of what they are interested in. Existing recommendation works mainly rely on learning entities' feature representations (network embedding) from the Heterogeneous Information Networks (HINs) through meta-paths. Most of them suffer from the difficulty of designing and selecting meta-paths in HIN. Moreover, changes in papers' captive audiences and users' reading favor in the recent trends are always covered up by long-term trends. To this end, we introduce a multi-attention aware recommendation method, named MARec. Specifically, to efficiently design and select meta-paths, we propose an auxiliary node-driven HIN construction method and combine it with the graph attention network (GAT) to learn the entities' feature representations. To endow the recommendation results with timeliness, we design an attention-aware Bi-directional LSTM (Bi-LSTM) and a compensation mechanism to incorporate the recent trend changes. Furthermore, to take advantage of the importance of different papers/users, we utilize an attention mechanism to capture their importance by adaptively fusing the interactive historical data of those paper/user entities. We use extensive experiments on three real-world datasets to show that our proposed MARec outperforms current representative baselines by 11.49%, 11.40%, and 13.37% on average on three main metrics.
Efficient Denoising of Multi-modal Medical Image using Wavelet Transform and Singular Value Decomposition

Medical image denoising is a key step for reconstructing a high-quality image in image processing. Noises like Gaussian, Speckle etc. usually degrades the medical images while acquiring, transferring, and recovering from storing devices. So, research for an effective denoising techniques having high Peak Signal-Noise Ratio (PSNR) and low Mean Square Error (MSE) values is still going on. Wavelet Transform (WT) and Singular Value Decomposition (SVD) are powerful methods for removal of noise. In this paper, medical images like X-ray, CT and ultrasound images are used for experimentation. Noises like Gaussian and Speckle are applied on multi-modal images and denoised by SVD or wavelet transform. Denoising results of WT and SVD are compared on the basis of MSE and PSNR.
Blockchain Based Fake Item Identification System

Quick Response (QR) codes, an emerging technology for web use cases, offer a powerful method to combat the practice of counterfeiting items. When a product's QR code is connected to Blockchain, counterfeit goods can be found using a QR code scanner. Therefore, this system may be utilized to store product information and its produced unique code as database blocks. It requests the user's unique code, then checks it against records in the Blockchain database. If the code matches, it will provide the consumer with all the product's details; if not, nothing will be displayed, indicating that the goods is counterfeit. Keywords: Supply Chain, Blockchain, and Counterfeit.
Video Quality Measurement &amp; Control for Live Encoding

Video Quality (VQ) Measurement is essential in a variety of applications, e.g. to compare video codecs, to select bitrates or configurations, or to optimize encoder behavior. Many different VQ metrics have been developed over the past decades, both full-reference (comparing source and encoded videos) and no-reference (without access to the source) [3]. For most offline encoding use cases, these metrics typically suffice, and give a fairly accurate view of the subjective quality of compressed video streams. In live video distribution, however, VQ measurement becomes a lot more challenging, in particular when computational and cost constraints come into play. While several accurate metrics exist (such as MS-SSIM and VMAF), these are often too complex for real-time video compression and decision making. Faster metrics exist, but these typically lack accuracy.
Cloud-based Workflow for AVC Film Grain Synthesis

This paper describes and demonstrates a cloud-based workflow for bit-accurate film grain synthesis for providing a better viewing experience in streaming video applications. Film grain technology can be potentially used with video sources that do not have original film grain, as a compression aid tool to improve the subjective quality by subtly masking video coding artifacts at low bitrate encodings. This paper describes the key components which can enable streaming service providers and streaming client applications to build workflow and video playback applications using film grain technology. Some of the component software are made available for public use on popular open-source platforms like GitHub.
CP-Steering: CDN- and Protocol-Aware Content Steering Solution for HTTP Adaptive Video Streaming

In recent years, HTTP Adaptive Streaming (HAS)-based technologies, such as Dynamic Adaptive Streaming over HTTP (DASH), have become the predominant video delivery paradigm over the Internet. HAS-based content providers frequently employ multiple Content Delivery Network (CDNs) to distribute their content to the end users. Recently, Apple and DASH-IF standardization introduced content steering technique to enable content providers to switch the content source that a player utilizes at start-up or midstream. Due to diverse adaptive video streaming demands for latency-sensitive and/or bandwidth-sensitive streams, satisfying end users with a pleasant Quality of Experience (QoE) poses a new challenge for redesigning the current content steering strategies. This paper leverages the recent popular Quick UDP Internet Connections (QUIC) transport protocol and introduces a CDN- and Protocol-aware content Steering solution for HTTP Adaptive Streaming (CP-Steering). We discuss the details of CP-Steering strategy and then give directions for future work.
A New Forgery Image Dataset and its Subjective Evaluation

The aim of this research paper is to present a new forgery image dataset with a thorough subjective evaluation in detecting manipulated images, considering various parameters. The original images were obtained from public sources, and meaningful forgeries were produced using an image editing platform with three techniques: cut-paste, copy-move, and erase-fill. Both pre-processing and post-processing methods were used to generate fake images. The subjective evaluation revealed that the accuracy of manipulated image detection was affected by various factors, such as user type, image quantity, tampering method, and image resolution, which were analyzed using quantitative data.
EARR: Using rules to enhance the embedding of knowledge graph

Knowledge graphs have been receiving increasing attention from researchers. However, most of these graphs are incomplete, leading to the rise of knowledge graph completion as a prominent task. The goal of knowledge graph completion is to find missing relations in a knowledge graph. Knowledge graph embedding represents the entities and relations in a low-dimensional embedding space, simplifying operations and allowing for integration with knowledge graph completion tasks. Several popular embedding models, such as TransE, TransH, TransR, TuckER, RotatE, and others have achieved impressive results on knowledge graph completion tasks. However, most of these methods do not incorporate background knowledge that could enhance the quality of knowledge embedding. Logic rules are adaptable and scalable, which can enrich background knowledge, and separating the attributes of entities can improve the relevance of relations and facilitate the accuracy of logic rule extraction. Thus, we propose a novel method, named Entity–Attribute–Relation–Rule (EARR), which separates attributes from entities and uses logic rules to extend the dataset, improving the accuracy of knowledge graph completion tasks. We define a total of six rules in this paper, including Rule 1–3, Rule 5, and Rule 6 for entities, and Rule 4 for entities and attributes. We evaluate our method based on the task of link prediction through two kinds of experiments. In the basic experiment, we compare our method with three benchmark models, namely, TransE, TransH, and TransR. In the experiment with different size datasets, FB24K and CoDEx, we evaluate our method on different size datasets with different models, including TransE, TuckER, and RotatE. The experimental results indicate that EARR can improve the quality of knowledge graph embedding.
Energy vs Privacy: Estimating the Ecological Impact of Federated Learning

The increasing usage of edge devices and stricter data privacy regulations motivate the use of federated learning (FL). At the same time, more and more stakeholders are concerned about the ecological impact of machine learning and its associate network traffic. The current research in FL does not investigate the impact of different network constraints and privacy-enhancing techniques, such as differential privacy, on the network traffic and energy consumption of the clients. Most experiments run either on virtual machines or on one machine with simulated clients. In such environments, it is challenging to measure each client’s network and energy usage. Therefore, we built our "Distributed Edge Device Testbed" (DEDT) and evaluate a convolutional neural network trained on the MNIST data set under different network constraints on DEDT, with differential privacy and with an increasing amount of participating clients. For each experiment, we quantify the network traffic, energy consumption, and training time. The results show the importance of experiments on physically separated nodes and the need to improve software-based power monitoring. The estimated energy consumption deviates by up to 35 % from the measured ones. The accuracy of the estimated network traffic depends on the monitored network interface and gives an error of 18 % for virtual machines in combination with monitoring the Ethernet interface. The training time also increases linearly with the number of participating clients.
Attitude, Behavioral Intention and Adoption of AI Driven Chatbots in the Banking Sector

In this tech-savvy era, banks are investing in technologies such as artificial intelligence and the Internet of Things. Chatbots, which is one of the AI-driven technologies, are being used by banks to keep customers satisfied and retain their loyalty. Chatbots are used as a platform for promptly handling customers' requests, while also acting as a listening channel for banks to better understand customer reactions and offer tailored services. This study attempted to assess the attitude, behavioral intention, and adoption of AI-driven chatbots among banking customers. A modified version of the UTUAT framework was used to validate the factors of adoption of AI-driven chatbots in the banking system. The study resulted in a positive and significant relationship between attitude and behavioral intention towards the use of AI-driven chatbots in the banking system. Furthermore, a significant relationship was found between behavioral intention and adoption of AI-driven chatbots in banking. The perceived risk, performance expectations, and effect expectancy influenced the attitude of banking customers towards AI-driven chatbots. The subjective norms and facilitating conditions affected the behavioral intention to use the chatbots, which significantly influenced the adoption of chatbots in banking. The facilitating conditions of banks strongly influenced the behavioral intention of banking services users in developing countries to use chatbots. The study took place in tri-city of India, and a future study could be undertaken from a global perspective.
Highly secured and quickest image encryption algorithm based on trigonometric chaotic map and S-box

The purpose of this research is to develop a highly secured non-breakable fastest cryptosystem. A significant number of image encryption plans have been proposed during the past years. By far, most such plans arrive at a high-security level; be that as it may, their moderate velocities due to their complex phenomenon make them of no utilization progressively applications. Motivated by this, we propose another proficient and quick image encryption plan subject to the trigonometric turbulent guide. In contrast with most current plans, we utilize this basic map to create just a couple of arbitrary rows and columns in the form of S-boxes. Besides, to, moreover, accelerate, we raise the handling unit from the pixel level to the line/segment level. Security of the new plan is acquired through a replacement stage organization, where we implemented around the shift of lines and segments to break the strong relation between adjoining pixels and non-repeating sequences. By then, we join the Exclusive-OR operation with modulo capacity to cover the pixel esteems and avoid any spilling of information. High-security tests and reenactment examinations have been done to display that the plan is extremely safe and particularly speedy for continuous picture preparation at 80 fps (frames per second). The present research has developed a cryptosystem based on Exclusive-OR with S-box (Substitution Box) and trigonometric chaotic map (TCM) of image matrices. High security is achieved due to the non-repeating sequence of the TCM. Earlier, the author has already presented the efficacy of the TCM combined with the S-Boxes.
An improved linear prediction evolution algorithm based on nonlinear least square fitting model for optimization

The linear prediction evolution algorithm (LPE) is a recent addition in the field of optimization algorithm with few parameters and high exploration capability. LPE has shown excellent results in some real-world problems but still suffers from some issues such as slow convergence speed. To improve the performance of the LPE algorithm, this study presents an improved linear prediction evolution algorithm (ILPE) to enhance its exploration capability. The proposed ILPE algorithm treats the population series of evolutionary algorithms as a time series and uses the nonlinear least square fitting model as a reproduction operator to forecast the next generation. The performance of the proposed ILPE is verified using the CEC2014 and CEC2017 benchmark functions. The comparison results show that ILPE outperforms LPE, and it is highly competitive with other state-of-the-art optimization algorithms.
To improve the performance on disk load balancing in a cloud environment using improved Lion optimization with min-max algorithm

A key challenge was optimizing the speed of the network while maintaining impartiality. Power monitoring solutions have been developed to reduce these load imbalances. These techniques frequently need unique hardware or software on the participants' end. To accomplish the cloud computing system of load balancing, this study created an intelligent virtual machine programming scheme using a machine learning technique. The researchers introduced two methods that identify optimal probabilistic solutions to the issue after conducting an in-depth investigation. A min-max load balancing solution is generated by the second method, while the first approach reduces the load on the network's crowded Access Points (APs). Let's focus on the mapping of links in particular because the mapping of nodes was established a priori. This study provides an innovative and effective Improved Lion Optimization (ILO) with Min-Max Algorithm for enabling VNE in real systems consisting, breaking new ground in the research to employ Genetic Operators for parallelization. Load balancing & power conservation were two crucial goals that must be taken into account, and also the findings reduce the cost of processing time, the proposed system outperforms the sequentially one in terms of both goals. In addition, the adaptive capacity of the proposed algorithm was assessed across various substrate structural configurations. The load balancing goal was achieved; the typical data center usage was higher than that of other methods, reaching nearly 80%; the largest amount of virtual machine migration was reduced by 94.5%; the data center's maximum energy consumption was reduced by 49.13%.
The effect of anthropomorphic competence-warmth congruence of service robots on recommendation intention

Abstract Service robots are becoming more popular as artificial intelligence technology advances. Based on the theory of mind perception, this study divides the anthropomorphism of service robots into two dimensions (competence and warmth) and discusses its impact on consumer psychology from the perspective of dual matching. First, anthropomorphism of service robots has positive effect on consumers’ attachment to robots. Second, consumer attachment to service robots increased when anthropomorphic competence and warmth were congruent. Third, consumer attachment to service robots mediated the relationship between anthropomorphic competence and warmth (in)congruence of service robots and consumer recommendation intention. Fourth, consumer paradox mindset moderated the effect of anthropomorphic competence and warmth (in)congruence of service robots on consumer attachment to service robots. These research conclusions have made important theoretical contributions and practical guidance for the research of anthropomorphic service robots and consumer attitudes.
Committee-Based Blockchains as Games between Opportunistic Players and Adversaries

Abstract We study consensus in a protocol capturing in a simplified manner the major features of the majority of Proof of Stake blockchains. A committee is formed; one member proposes a block; and the others can check its validity and vote for it. Blocks with a majority of votes are produced. When an invalid block is produced, the stakes of the members who voted for it are “slashed.” Profit-maximizing members interact with adversaries seeking to disrupt consensus. When slashing is limited, free-riding and moral-hazard lead to invalid blocks in equilibrium. We propose a protocol modification producing only valid blocks in equilibrium. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.