Reviewerly/exHarmony huggingface dataset:
Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions

In the developing world, parasites are responsible for causing several serious health problems, with relatively high infections in human beings. The traditional manual light microscopy process of parasite recognition remains the golden standard approach for the diagnosis of parasitic species, but this approach is time-consuming, highly tedious, and also difficult to maintain consistency but essential in parasitological classification for carrying out several experimental observations. Therefore, it is meaningful to apply deep learning to address these challenges. Convolution Neural Network and digital slide scanning show promising results that can revolutionize the clinical parasitology laboratory by automating the process of classification and detection of parasites. Image analysis using deep learning methods have the potential to achieve high efficiency and accuracy. For this review, we have conducted a thorough investigation in the field of image detection and classification of various parasites based on deep learning. Online databases and digital libraries such as ACM, IEEE, ScienceDirect, Springer, and Wiley Online Library were searched to identify sufficient related paper collections. After screening of 200 research papers, 70 of them met our filtering criteria, which became a part of this study. This paper presents a comprehensive review of existing parasite classification and detection methods and models in chronological order, from traditional machine learning based techniques to deep learning based techniques. In this review, we also demonstrate the summary of machine learning and deep learning methods along with dataset details, evaluation metrics, methods limitations, and future scope over the one decade. The majority of the technical publications from 2012 to the present have been examined and summarized. In addition, we have discussed the future directions and challenges of parasites classification and detection to help researchers in understanding the existing research gaps. Further, this review provides support to researchers who require an effective and comprehensive understanding of deep learning development techniques, research, and future trends in the field of parasites detection and classification.
Curb your enthusiasm: Examining the customer experience with Alexa and its marketing outcomes

Interactive Voice Assistants (IVAs) are intelligent conversational agents capable of communicating with users using natural language. Although IVAs are more frequent in our lives, customer experience research with these agents is still in its infancy. This article aims to identify the factors that form the customer experience (CX) with Alexa and assesses its impact on traditional marketing outcomes: satisfaction and recommendation. This research presents a conceptual model of CX with IVAs and an empirical validation of the model using Structural Equation Modelling based on a sample of 580 IVA users. The results confirm that CX is a multidimensional higher-order construct composed of six factors (usefulness, ease of use, trust, privacy concerns, communication skills, and enjoyment). We also highlight the positive impact of experience on satisfaction and recommendation. Finally, we test the enthusiasm moderating role, showing its negative influence on the investigated relationships. Theoretical and practical implications are discussed.
Federated learning for intelligent fault diagnosis based on similarity collaboration

Abstract In recent years, federated learning has received much attention because it involves the collaboration of each client to train a strong model without infringing data privacy. Machinery fault diagnosis also benefits from this technology. However, the different operating conditions and diagnostic tasks for each client lead to data heterogeneity among the devices, which brings a tough challenge to federated learning for machinery fault diagnosis. To solve this problem, we explore a federated learning method for machinery fault diagnosis based on similarity collaboration (FedSC). Considering the data heterogeneity of each client, the FedSC customizes a personalized model for each client, and then a similarity mechanism is used to weigh the aggregation of each personalization model. In addition, when a client’s model is updated, the distance constraint loss is employed to ensure that local model updates do not deviate from their personalized cloud model. Comprehensive experiments on two rotating machinery datasets demonstrate that our method achieves higher accuracy and faster convergence, providing promising application prospects in realistic industrial scenarios.
Solving multi-objective optimization problem of convolutional neural network using fast forward quantum optimization algorithm: Application in digital image classification

Convolutional neural network (CNN) has evolved as a new algorithm that has demonstrated its effectiveness in real-time issue solving over many other machine learning (ML) algorithms. However, many CNNs are created manually by considering randomly defined weights in the convolutional layer, pooling layer, and fully connected layer. During the training process, these weights may get stuck at the local minima. To avoid this, the weights must be initialized carefully in each of the layers. Furthermore, training cannot be accomplished until the classification error is minimized. The classification error largely depends on the optimal values of the connecting weights among the layers. Since optimization of weights requires many iterations, it increases the difficulty of selecting the minimum value of classification error. Therefore, weights and classification errors are considered to be two critical parameters that influence the performance of CNNs, and they must be carefully governed during architecture design. However, finding the optimal values for weights and classification error can be considered a multi-objective optimization problem (MOOP). To solve a MOOP, this study advocates the use of the recently proposed fast forward quantum optimization algorithm (FFQOA). This study also proposes a novel algorithm based on the hybridization of FFQOA with CNN, called the FFQOAconNetwork. In this algorithm, the FFQOA searches for the optimal weights associated with the layers by simultaneously achieving the minimal classification error. Application of the FFQOAconNetwork is demonstrated in the classification of images by adopting benchmark datasets. Empirical analyses indicate that the FFQOAconNetwork can solve the MOOP with effective performance as compared to other algorithms.
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming

Abstract Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity that is computationally intractable when the number of observations is large, especially when estimated with fully Bayesian methods such as Markov chain Monte Carlo. In this paper, we focus on a low-rank approximate Bayesian Gaussian processes, based on a basis function approximation via Laplace eigenfunctions for stationary covariance functions. The main contribution of this paper is a detailed analysis of the performance, and practical recommendations for how to select the number of basis functions and the boundary factor. Intuitive visualizations and recommendations, make it easier for users to improve approximation accuracy and computational performance. We also propose diagnostics for checking that the number of basis functions and the boundary factor are adequate given the data. The approach is simple and exhibits an attractive computational complexity due to its linear structure, and it is easy to implement in probabilistic programming frameworks. Several illustrative examples of the performance and applicability of the method in the probabilistic programming language Stan are presented together with the underlying Stan model code.
Correlated Pseudorandomness from Expand-Accumulate Codes

A pseudorandom correlation generator (PCG) is a recent tool for securely generating useful sources of correlated randomness, such as random oblivious transfers (OT) and vector oblivious linear evaluations (VOLE), with low communication cost. We introduce a simple new design for PCGs based on so-called expand-accumulate codes, which first apply a sparse random expander graph to replicate each message entry, and then accumulate the entries by computing the sum of each prefix. Our design offers the following advantages compared to state-of-the-art PCG constructions: To further improve the concrete computational cost, we propose a method for speeding up a full-domain evaluation of a puncturable pseudorandom function (PPRF). This is independently motivated by other cryptographic applications of PPRFs.
A Multi-Objective online streaming Multi-Label feature selection using mutual information

Multi-label classification methods aim at assigning more than one label to each instance. In many real-world classification problems such as image multi-label classification tasks such as cancer detection, and text classification, we faced with thousands of thousand features. The performance of machine learning methods will be reduced while faced with high dimensional problems. To tackle this issue, feature selection methods are introduced to choose a small set of prominent features which best describe the data. Traditional multi-label feature selection methods are required to access to whole feature space, while in online platforms such as Facebook and Twitter, we faced with streams of data added by the users of these platforms over the time. Traditional multi-label feature selection methods are failed while applied on data streams. To solve this issue, online methods are introduced to deal with data streams. Existing streaming multi-label feature selection methods consider the task as a single optimization process while there are several contradictory objectives that need to be optimize simultaneously. To solve this issue, this paper uses a multi-objective search strategy to choose streaming features by using the mutual information and Pareto optimal set theories. There are several objectives such as minimizing the redundancy of features, and maximizing the relevancy of features to a set of labels that are need to be optimized during the feature selection process. Here, we used the Pareto set theory to identify a set of no-dominant solutions which best describe the problem. The proposed method has compared with a set of state-of-the-art online feature selection methods and the obtained results demonstrate the effectiveness of the proposed strategy.
Digital Pandemic Stress in Higher Education in Venezuela

The COVID-19 pandemic had a great impact on the process of integrating digital technologies in higher education and caused digital stress among professors, mainly in countries with a lower level of digitalization. In this work, quantitative research was carried out on the stress of professors in Venezuela due to the digitalization of their teaching activities caused by the pandemic, and gender gaps were identified in this regard. This digital stress was compared with that of professors in other countries with a low level of digitalization. For this purpose, a questionnaire designed by the authors was used. The questionnaire was distributed to a sample of 129 Venezuelan professors and 132 professors from countries with low digitalization levels. As a result, it was found that Venezuelan professors have lower digital competence and lower digital stress than their colleagues in weakly digitized countries, and that digital stress decreases as digital competence increases. Moreover, among Venezuelan professors, there was a strong gender gap in digital stress, which was higher among females in all subject areas, except for Health Sciences. This gender gap is specific to Venezuela since it differs from that in countries with low digital levels. According to the results, we urgently recommend investing resources in the digital training of faculty members, especially in regards to the integration of female professors.
Occluded Person Re-Identification via Defending Against Attacks From Obstacles

Due to incomplete appearance features, the identity matching of occluded pedestrians under multiple cross-camera views is a long-term challenge. Although existing re-identification (re-ID) solutions of occluded pedestrians have made significant progress, most of them achieve accurate identity matching by extracting pedestrian appearance features from unoccluded areas. However, when a pedestrian is partially blocked by the body of another pedestrian, existing methods cannot accurately determine whether the unoccluded body parts belong to the target pedestrian, which brings great difficulties to pedestrian identity matching. To alleviate this problem, this paper introduces the idea of adversarial attack into occluded person re-ID and proposes an adversarial training framework that can defend against attacks from obstacles to resist the interference of obstacles on pedestrian identity matching. Unlike existing solutions, the proposed framework is not limited to extracting features of unoccluded human body areas to achieve occluded person re-ID, but explores how to make the re-ID model more resistant to obstacles. In the proposed framework, the occluded pedestrian images are regarded as adversarial examples and used to attack model training. If the trained model can defend against this kind of attack, its generalization is significantly improved, and the above-mentioned issues are also effectively solved. Specifically, a single-branch dual-stream collaborative network is designed. With the cooperation of the pre-trained verification guidance network, the model realizes the attack and defense of adversarial samples. This work broadens research horizons in robust model design of occluded person re-ID, and expands the scope of adversarial attacks. Compared with existing solutions, a lot of experimental results confirm that the proposed solution achieves better performance on two occluded re-ID datasets and two partial re-ID datasets.
A variant of the united multi-operator evolutionary algorithms using sequential quadratic programming and improved SHADE-cnEpSin

United multi-operator evolutionary algorithms (UMOEAs) combine multi-operator differential evolution (DE), the multi-operator genetic algorithm (MOGA), and the covariance matrix adaption evolution strategy (CMA-ES). UMOEAs-II, an improved version of UMOEAs, uses three differential evolution variants as multi-operator differential evolution (MODE), and CMA-ES. In this study, we further reform UMOEAs-II using an improved SHADE-cnEpSin that employs a novel adaptive strategy of scaling factor F, a crossover rate cri,j updating mechanism which can calculate crossover rate for the ith individual with a particular jth component, an improved rank-based selective pressure based mutation strategy, and nonlinear population size reduction along with sequential quadratic programming method. The effectiveness of the improved rank-based selective pressure based mutation strategy, nonlinear population size reduction, and sequential quadratic programming are evident from the individual validations. The novel framework, enhanced the exploration and exploitation abilities, is named UMOEAs-III and is evaluated using the CEC2017 benchmark functions. The experiments are tested on 10, 30, 50, and 100 dimensions. The experimental results demonstrate the outstanding performance of UMOEAs-III in both low and high-dimensional tests compared to the state-of-the-art DE-based variants and hybrid algorithms.
Multi-proxy Signature Scheme Using Five-qubit Entangled State Based on Controlled Quantum Teleportation

With the upgrading of communication technology and the rapid development of quantum computing, the classical digital signature schemes are faced with unprecedented challenges, so the research on quantum digital signature is imperative. In this paper, we propose a multi-proxy signature scheme based on controlled quantum teleportation of five-qubit entangled state. In this scheme, quantum fourier transform is used as an encryption method to encrypt message, which improves the quantum efficiency compared with the quantum one-time pad. The five-qubit maximally entangled state which is qubit threshold quantum error correction required is used as the quantum channel to ensure the stability of the scheme. Security analysis shows that our scheme is unforgeable and undeniable, and it can resist the intercept-resend attack.
Adaptive fusion network for RGB-D salient object detection

Existing state-of-the-art RGB-D saliency detection models mainly utilize the depth information as complementary cues to enhance the RGB information. However, depth maps can be easily influenced by environment and hence are full of noises. Thus, indiscriminately integrating multi-modality (i.e., RGB and depth) features may induce noise-degraded saliency maps. In this paper, we propose a novel Adaptive Fusion Network (AFNet) to solve this problem. Specifically, we design a triplet encoder network consisting of three subnetworks to process RGB, depth, and fused features, respectively. The three subnetworks are interlinked and form a grid net to facilitate mutual refinement of these multi-modality features. Moreover, we propose a Multi-modality Feature Interaction (MFI) module to exploit complementary cues between depth and RGB modalities and adaptively fuse the multi-modality features. Finally, we design the Cascaded Feature Interweaved Decoder (CFID) to exploit complementary information between multi-level features and refine them iteratively to achieve accurate saliency detection. Experimental results on six commonly used benchmark datasets verify that the proposed AFNet outperforms 20 state-of-the-art counterparts in terms of six widely adopted evaluation metrics. Source code will be publicly available athttps://github.com/clelouch/AFNet upon paper acceptance.
Real-world image dehazing with improved joint enhancement and exposure fusion

In this work, a single image dehazing method that improves the haze removal capacity of the Joint Contrast Enhancement and Exposure Fusion (CEEF) method with Smoothing-Sharpening Image Filter (SSIF) is presented. In this method, the hazy image is first sharpened with SSIF to obtain a sharper image. In this way, the difference between haze and objects is amplified. Then, the AHE procedure in CEEF is replaced by CLAHE to obtain an enhanced CEEF. The enhanced CEEF is applied to the filtering result to obtain the final dehazed image. Observations demonstrate that the proposed method obtains enhanced results while reducing the amount of haze. The visual and quantitative comparisons between the proposed method and state-of-the-art dehazing methods show that the proposed method has better dehazing performance and has a 50% improvement in terms of the FADE metric compared to the closest result.
An Image Encryption Algorithm Using Cascade Chaotic Map and S-Box

This paper proposed an image algorithm based on a cascaded chaotic system to improve the performance of the encryption algorithm. Firstly, this paper proposed an improved cascaded two-dimensional map 2D-Cosine-Logistic-Sine map (2D-CLSM). Cascade chaotic system offers good advantages in terms of key space, complexity and sensitivity to initial conditions. By using the control parameters and initial values associated with the plaintext, the system generates two chaotic sequences associated with the plaintext image. Then, an S-box construction method is proposed, and an encryption method is designed based on the S-box. Encryption is divided into bit-level encryption and pixel-level encryption, and a diffusion method was devised to improve security and efficiency in bit-level encryption. Performance analysis shows that the encryption algorithm has good security and is easily resistant to various attacks.
Reformulating the direct convolution for high-performance deep learning inference on ARM processors

We present two high-performance implementations of the convolution operator via the direct algorithm that outperform the so-called lowering approach based on the im2col transform plus the gemm kernel on an ARMv8-based processor. One of our methods presents the additional advantage of zero-memory overhead while the other employs an additional yet rather moderate workspace, substantially smaller than that required by the im2col+gemm solution. In contrast with a previous implementation of a similar zero-memory overhead direct convolution, this work exhibits the key advantage of preserving the conventional NHWC data layout for the input/output activations of the convolution layers.
Machine learning for predicting elections in Latin America based on social media engagement and polls

The popularity of contemporary social media (SM) has impacted democratic practices, and the success of presidential campaigns is frequently attributed to SM performance. Within this new scenario, many methodological proposals that use SM data have been put forward for predicting election results. However, the most common approach, based on the volume and sentiment analysis of mentions on Twitter, has been frequently criticized and challenged. Thus, recent surveys have indicated new directions, such as the use of data from more than one SM platform, the adoption of nonlinear machine learning (ML) models, and the validation of methodologies and experiments in different elections. In this context, the present paper proposes SoMEN, the Social Media framework for Election Nowcasting, a framework composed of a process and an ML model for nowcasting election results based on SM performance as features and with offline polls as labeled data. It also defines SoMEN-DC, an execution strategy for SoMEN that enables continuous prediction during the campaign (DC). The proposed metrics and framework were applied to some of the main recent presidential elections in Latin America: Argentina (2019), Brazil (2018), Colombia (2018), and Mexico (2018). More than 65,000 posts were collected from the SM profiles of candidates on Facebook, Twitter, and Instagram with data from 195 presidential polls. Results have demonstrated that it was possible to achieve a high level of accuracy in predicting the final vote share of the candidates and to make daily predictions, providing competitive or better results than the traditional polls. The strategies put forward in this study have attempted to address several of the current challenges in this research area and have indicated a new manner of how to face the problems. They may also be directly used for predicting future elections in similar scenarios.
Higher education personnel’s perceptions about telepresence robots

The interest towards using telepresence robots in a variety of educational contexts is growing, as they have a great potential to enhance the educational experience of remote learners and provide support for teachers. This paper describes a study, examining the perception of Georgian university personnel about the use of telepresence robots in education. This exploratory research aimed to obtain evidence-based information on how the personnel (16 persons) from eight Georgian universities perceived the telepresence robots' role in enhancing learning and teaching, and what challenges, benefits, opportunities, weaknesses and threats would characterise these robots. The results of the study revealed that the university personnel perceived telepresence robots to have a great potential to enhance educational activities. In addition, the participants indicated the major challenges, benefits, opportunities, weaknesses and threats, regarding integrating telepresence robotics into the teaching and learning in Georgia. Recommendations for future research are also presented.