Reviewerly/exHarmony huggingface dataset:
Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling

This paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD. The developed DJSD method incorporates the Simulated Annealing operators into the conventional Jellyfish Search Algorithm in the exploration stage, in a competitive manner, to enhance its ability to discover more feasible regions. This combination is performed dynamically using a fluctuating parameter that represents the characteristics of a hammer. The disruption operator is employed in the exploitation stage to boost the diversity of the candidate solutions throughout the optimization operation and avert the local optima problem. A comprehensive set of experiments is conducted using thirty classical benchmark functions to validate the effectiveness of the proposed DJSD method. The results are compared with advanced well-known metaheuristic approaches. The findings illustrated that the developed DJSD method achieved promising results, discovered new search regions, and found new best solutions. In addition, to further validate the performance of DJSD in solving real-world applications, experiments were conducted to tackle the task scheduling problem in cloud computing applications. The real-world application results demonstrated that DJSD is highly competent in dealing with challenging real applications. Moreover, it achieved gained high performances compared to other competitors according to several standard evaluation measures, including fitness function, makespan, and energy consumption.
Decentralized, not Dehumanized in the Metaverse: Bringing Utility to NFTs through Multimodal Interaction

User Interaction for NFTs (Non-fungible Tokens) is gaining increasing attention. Although NFTs have been traditionally single-use and monolithic, recent applications aim to connect multimodal interaction with human behavior. This paper reviews the related technological approaches and business practices in NFT art. We highlight that multimodal interaction is a currently under-studied issue in mainstream NFT art, and conjecture that multimodal interaction is a crucial enabler for decentralization in the NFT community. We present a continuum theory and propose a framework combining a bottom-up approach with AI multimodal process. Through this framework, we put forward integrating human behavior data into generative NFT units, as "multimodal interactive NFT." Our work displays the possibilities of NFTs in the art world, beyond the traditional 2D and 3D static content.
Relevance-based label distribution feature selection via convex optimization

In label distribution learning, high dimensionality is one of the most prominent characteristics of the data, which increases the model complexity and computational cost. Feature selection is an efficient technique to mitigate the "curse of dimensionality". The current label distribution feature selection approach based on mutual information employs the heuristic search algorithm to identify the most discriminative features. However, this method can be time-consuming and fall into local optimum. Motivated by this, in this paper, an approach named relevance-based label distribution feature selection via convex optimization is proposed, which takes both feature relevance and label relevance (i.e., label correlation) into account. For features, the relevance to labels calculated by mutual information is considered into a convex optimization function to guide the feature selection process. Compared with the heuristic search, the learning framework for optimization purposes is more conducive to reducing repetitive computations and avoiding the local optimum. For labels, the Pearson correlation coefficient is exploited to describe the correlation information between labels, aiming to enhance the generalization ability of the learning model. Comprehensive experiments are conducted on twenty datasets, and the results demonstrate the effectiveness of the proposed method compared with eight state-of-the-art methods in terms of six evaluation metrics.
A Bayesian game-enhanced auction model for federated cloud services using blockchain

Industrial applications often require federated cloud services from multiple providers to improve reliability and flexibility. Traditional selection methods through auctions usually involve a centralized auctioneer to coordinate the auction procedure. Blockchain and smart contracts provide a decentralized mechanism to automate the cloud auction process; however, existing solutions fail in the selection of the most suitable providers and the violation detection of the signed auction agreements, which are also known as service-level agreements (SLAs). To tackle these problems, we propose an integrated auction model using Bayesian game theory and blockchain techniques. The proposed model is enhanced with two Bayesian Nash Equilibriums (BNEs); the first BNE enables the selection of cost-effective providers to construct the federated cloud services, while the second BNE ensures consistent and trustworthy monitoring of federated SLAs. Moreover, a timed message submission (TMS) algorithm is proposed to protect the auction privacy during the message submission phase. This paper validates the equilibrium results of two BNEs and implements the proposed model on the Ethereum blockchain. The analytical and experimental results demonstrate the feasibility, trustworthiness, and cost-effectiveness of our model.
Image encryption algorithm based on digital filters controlled by 2D robust chaotic map

This paper introduces a new cryptosystem algorithm based on digital filters; as an encryption operation, second-order and third-order IIR digital filters are used, and as a decryption operation, second-order and third-order FIR digital filters are used: Chaos in digital filters is caused by the overflow non-linearity function. Both IIR and FIR digital filters are controlled by the first random sequence of zeros and ones produced by the 2D robust chaotic map, while the filters coefficients are generated by the second sequence, so that each pixel is encrypted and decrypted with different coefficients. Fisher-Yates shuffle has been used to obtain a random permutation of the position of the pixels of the original image, so that, the rows are shuffled first, then the columns, based on random numbers obtained by a hyperchaotic map 2D-LICM and 2D-SLIM respectively. Another algorithm based only on a single digital IIR/FIR filter is also presented in this work to allow us to demonstrate the development and high security of the proposed approach. Simulation and experimental results with performance analysis are carried out to demonstrate that our algorithm has high security and can resist various attacks such as differential attacks and statistical attacks.
A machine learning-based procedure for leveraging clickstream data to investigate early predictability of failure on interactive tasks

Early detection of risk of failure on interactive tasks comes with great potential for better understanding how examinees differ in their initial behavior as well as for adaptively tailoring interactive tasks to examinees' competence levels. Drawing on procedures originating in shopper intent prediction on e-commerce platforms, we introduce and showcase a machine learning-based procedure that leverages early-window clickstream data for systematically investigating early predictability of behavioral outcomes on interactive tasks. We derive features related to the occurrence, frequency, sequentiality, and timing of performed actions from early-window clickstreams and use extreme gradient boosting for classification. Multiple measures are suggested to evaluate the quality and utility of early predictions. The procedure is outlined by investigating early predictability of failure on two PIAAC 2012 Problem Solving in Technology Rich Environments (PSTRE) tasks. We investigated early windows of varying size in terms of time and in terms of actions. We achieved good prediction performance at stages where examinees had, on average, at least two thirds of their solution process ahead of them, and the vast majority of examinees who failed could potentially be detected to be at risk before completing the task. In-depth analyses revealed different features to be indicative of success and failure at different stages of the solution process, thereby highlighting the potential of the applied procedure for gaining a finer-grained understanding of the trajectories of behavioral patterns on interactive tasks.
A Hybrid Deep Learning and Visualization Framework for Pushing Behavior Detection in Pedestrian Dynamics

Crowded event entrances could threaten the comfort and safety of pedestrians, especially when some pedestrians push others or use gaps in crowds to gain faster access to an event. Studying and understanding pushing dynamics leads to designing and building more comfortable and safe entrances. Researchers-to understand pushing dynamics-observe and analyze recorded videos to manually identify when and where pushing behavior occurs. Despite the accuracy of the manual method, it can still be time-consuming, tedious, and hard to identify pushing behavior in some scenarios. In this article, we propose a hybrid deep learning and visualization framework that aims to assist researchers in automatically identifying pushing behavior in videos. The proposed framework comprises two main components: (i) Deep optical flow and wheel visualization; to generate motion information maps. (ii) A combination of an EfficientNet-B0-based classifier and a false reduction algorithm for detecting pushing behavior at the video patch level. In addition to the framework, we present a new patch-based approach to enlarge the data and alleviate the class imbalance problem in small-scale pushing behavior datasets. Experimental results (using real-world ground truth of pushing behavior videos) demonstrate that the proposed framework achieves an 86% accuracy rate. Moreover, the EfficientNet-B0-based classifier outperforms baseline CNN-based classifiers in terms of accuracy.
Resource-constrained FPGA implementation of YOLOv2

Abstract Progress is being made to deploy convolutional neural networks (CNNs) into the Internet of Things (IoT) edge devices for handling image analysis tasks locally. These tasks require low-latency and low-power computation on low-resource IoT edge devices. However, CNN-based algorithms, e.g. YOLOv2, typically contain millions of parameters. With the increase in the CNN’s depth, filters are increased by a power of two. A large number of filters and operations could lead to frequent off-chip memory access that affects the operation speed and power consumption of the device. Therefore, it is a challenge to map a deep CNN into a low-resource edge IoT platform. To address this challenge, we present a resource-constrained Field-Programmable Gate Array implementation of YOLOv2 with optimized data transfer and computing efficiency. Firstly, a scalable cross-layer dataflow strategy is proposed which allows on-chip data transfer between different types of layers, and offers flexible off-chip data transfer when the intermediate results are unaffordable on-chip. Next, a filter-level data-reuse dataflow strategy together with a filter-level parallel multiply-accumulate operation computing processing elements array is developed. Finally, multi-level sliding buffers are developed to optimize the convolutional computing loop and reuse the input feature maps and weights. Experiment results show that our implementation has achieved 4.8 W of low-power consumption for executing YOLOv2, an 8-bit deep CNN containing 50.6 MB weights, using low-resource of 8.3 Mbits on-chip memory. The throughput and power efficiency are 100.33 GOP/s and 20.90 GOP/s/W, respectively.
Assessment of artificial neural network to identify compositional differences in ultrahigh-resolution mass spectra acquired from coal mine affected soils

This study assessed the applicability of artificial neural networks (ANNs) as a tool to identify compounds contributing to compositional differences in coal-contaminated soils. An artificial neural network model was constructed from laser desorption ionization ultrahigh-resolution mass spectra obtained from coal contaminated soils. A good correlation (R2 = 1.00 for model and R2 = 0.99 for test) was observed between the measured and predicted values, thus validating the constructed model. To identify chemicals contributing to the coal contents of the soils, the weight values of the constructed model were evaluated. Condensed hydrocarbon and low oxygen containing compounds were found to have larger weight values and hence they were the main contributors to the coal contents of soils. In contrast, compounds identified as lignin did not contribute to the coal contents of soils. These findings were consistent with the conventional knowledge on coal and results from the conventional partial least square method. Therefore, we concluded that the weight interpretation following ANN analysis presented herein can be used to identify compounds that contribute to the compositional differences of natural organic matter (NOM) samples.
Multi-strategy mutual learning network for deformable medical image registration

Deformable medical image registration plays a vital role in clinical diagnosis, monitoring treatment, and postoperative recovery. Nevertheless, the existing registration algorithms rely on a single network or training strategy to complete the registration task. Even the most advanced registration algorithms cannot capture sufficiently compelling feature information by a single network or strategy.This paper proposes a new unsupervised and deformable medical image registration network framework to get the compelling feature. The network uses 2D sub-images of 3D images as additional constraint information to supplement the training process. Therefore, the network’s two-dimensional and three-dimensional data images can interactively learn each other’s characteristic information. In addition, we propose to perform secondary registration on the concentrated part of the registration area according to the characteristics of the input image. It enables the complete image and critical regions to learn their characteristic information interactively. This paper uses the image pyramid to integrate the two mutual learning strategies, thus proposing a multi-strategy mutual learning network(MMLN) and conducting many evaluation tests on the public data set OASIS and LPBA40. The test results show that the network can achieve better registration performance than other learning-based methods and traditional algorithms.
A proportional, integral and derivative differential evolution algorithm for global optimization

The proportional, integral, and derivative differential evolution algorithm (PID-DE) is proposed as a new type of interdisciplinary metaheuristic evolutionary algorithm in this paper. The inspiration of PID-DE is derived from the classical proportional, integral, and derivative control method in engineering, and it is used in the framework of the differential evolution (DE) algorithm. To begin, the mathematical models of proportional search, integral search, and derivative search are established as the fundamental search operations. Five different types of optimizers that use a combination of these three basic operations and an additional mutation operation are presented. The selection and crossover methods in DE are then modified to maintain population diversity while also improving global search capacity, and a feedback strategy is established to adaptively adjust the subgroup member of each optimizer. Following that, an integrated high-accuracy, rapid-convergence, and stable metaheuristic is invented using the comprehensive information utilization principle and flexible parameter determination method. Five groups of experiments are studied to assess the overall performance of the proposed algorithm. The first test comprises 12 standard benchmark functions with minimum optima. In Tests 2 and 3, the 52 functions of Congress on Evolutionary Computation (CEC) 2014 and CEC 11 are evaluated using PID-DE under standard test conditions. Besides, three classic real-world engineering design problems and the CEC 2020 test suit are studied for the constrained optimization test. The experimental tests validate PID-DE’s higher accuracy and faster convergence speed in numerical optimization when compared with the representative approaches and top algorithms in the CEC competitions.
Deterministic and probabilistic analysis of different empirical models to estimate monthly mean diffuse solar radiation for composite climatic region of India

Abstract This research compares the performance of the models to estimate monthly mean diffuse solar radiation (DSR) on a horizontal surface for composite climatic region of India. The goal is to identify the most accurate model using deterministic and probabilistic analysis through two levels of uncertainty in daily sunshine period to examine the accuracy of the model that will be used to estimate DSR in the region under consideration. The meteorological data were collected from Indian Meteorological Department for the city of New Delhi (28°34′N, 77°12′E) which comes under composite climatic region prescribed by the Energy Conservation Building Code for India. From the literature, 150 typical models were chosen and categorized into three categories correlating diffuse fraction to sky‐clearness index and relative sunshine period. The models are statistically evaluated using well‐known statistical indicators in a unique way. In addition, the global performance indicator (GPI) is calculated using scaled values of indicators. The GPI of the chosen models ranged from −2.2912 to 0.2584, with the greatest value indicating the best model. Following that, the models are then ranked in decreasing order of GPI. Finally, the performances of the models are also checked for different locations having similar climatic conditions. Thus, results of this work are useful for impoverished countries as well as remote areas having similar environment conditions.
Quantum K-means clustering method for detecting heart disease using quantum circuit approach

The development of noisy intermediate- scale quantum computers is expected to signify the potential advantages of quantum computing over classical computing. This paper focuses on quantum paradigm usage to speed up unsupervised machine learning algorithms particularly the K-means clustering method. The main approach is to build a quantum circuit that performs the distance calculation required for the clustering process. This proposed technique is a collaboration of data mining techniques with quantum computation. Initially, extracted heart disease dataset is preprocessed and classical K-means clustering performance is evaluated. Later, the quantum concept is applied to the classical approach of the clustering algorithm. The comparative analysis is performed between quantum and classical processing to check performance metrics.
Label enhancement-based feature selection via fuzzy neighborhood discrimination index

As an essential preprocessing step, the feature selection for multi-label classification is an efficient tool to solve the high-dimensionality in training data under multiple semantics. However, in multi-label learning, the assumption of a uniform distribution is not practicable for some real applications where labels are related to the instance with different relative importance degrees. Motivated by this, the label enhancement algorithm is utilized to transform the logical values in multiple labels into label distributions with real values. To maintain the supervised information of label distributions, the discrimination index is combined with granular theory to describe the vagueness in feature and label spaces, and the fuzzy neighborhood discrimination index is presented to assess the discrimination information in features with continuous values. Furthermore, an innovative label enhancement-based feature selection approach is designed to handle the challenge caused by high dimensionality with multiple semantics. In this algorithm, the fuzzy conditional discrimination index is proposed by combining the fuzzy neighborhood discrimination index under the feature subset with the label similarity matrix of label distributions, and the significance of the feature is measured by the fuzzy conditional discrimination index, which is utilized to assess the discernibility information of labels under the feature. Furthermore, to validate the efficacy of our algorithm, a collection of experiments are conducted with five representative multi-label feature selection methods under twelve benchmark datasets. With six widespread evaluation indicators of multi-label classification, the experimental results indicate that our algorithm obtains superior performance against other algorithms.
Enhancing the Sense of Attention from an Assistance Mobile Robot by Improving Eye-Gaze Contact from Its Iconic Face Displayed on a Flat Screen

One direct way to express the sense of attention in a human interaction is through the gaze. This paper presents the enhancement of the sense of attention from the face of a human-sized mobile robot during an interaction. This mobile robot was designed as an assistance mobile robot and uses a flat screen at the top of the robot to display an iconic (simplified) face with big round eyes and a single line as a mouth. The implementation of eye-gaze contact from this iconic face is a problem because of the difficulty of simulating real 3D spherical eyes in a 2D image considering the perspective of the person interacting with the mobile robot. The perception of eye-gaze contact has been improved by manually calibrating the gaze of the robot relative to the location of the face of the person interacting with the robot. The sense of attention has been further enhanced by implementing cyclic face explorations with saccades in the gaze and by performing blinking and small movements of the mouth.
Automatic anomaly detection from X-ray images based on autoencoders

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The Effects of Chatbot Service Recovery With Emotion Words on Customer Satisfaction, Repurchase Intention, and Positive Word-Of-Mouth

This study sought to examine the effect of the quality of chatbot services on customer satisfaction, repurchase intention, and positive word-of-mouth by comparing two groups, namely chatbots with and without emotion words. An online survey was conducted for 2 weeks in May 2021. A total of 380 responses were collected and analyzed using structural equation modeling to test the hypothesis. The theoretical basis of the study was the SERVQUAL theory, which is widely used in measuring and managing service quality in various industries. The results showed that the assurance and reliability of chatbots positively impact customer satisfaction for both groups. However, empathy and interactivity positively affect customer satisfaction only for chatbots with emotion words. Responsiveness did not have an impact on customer satisfaction for both groups. Customer satisfaction positively impacts repurchase intention and positive word-of-mouth for both groups. The findings of this study can serve as a priori research to empirically prove the effectiveness of chatbots with emotion words.