Reviewerly/exHarmony huggingface dataset:
AIEOU: Automata-based improved equilibrium optimizer with U-shaped transfer function for feature selection

High dimensional data has become an essential challenge to address in Data Science and Machine Learning. Reducing the number of dimensions by excluding noisy, irrelevant, or correlated dimensions is often referred to as the feature selection (FS). The ultimate goal in FS is to identify an optimal set of dimensions (features) to realize better classification accuracy, decrease the computational time and optimize the memory requirements with the help of some methods. Recently, optimization algorithms have gained popularity in different fields because of their flexibility and effectiveness. Equilibrium optimizer (EO) is a physics-based meta-heuristic algorithm, which is inspired from a well-mixed dynamic mass balance on a control volume that has good exploration and exploitation capabilities. In this work, an improved version of EO is proposed with the inclusion of learning based automata to find proper values of its parameters and Adaptive β Hill Climbing (AβHC) to find a better equilibrium pool. The method is used as a feature selector, evaluated on 18 standard UCI datasets with the help of K-nearest neighbors (KNN) classifier, and compared with eight state-of-the-art methods including classical and hybrid meta-heuristic algorithms. Moreover, the proposed methods is applied on high dimensional Microarray datasets which generally contain a few samples but large number of features, and often lead to ‘curse of dimensionality’. The obtained results illustrate the supremacy of the proposed method over the other state-of-the-art methods mentioned in literature. The source code of this work is publicly available at https://github.com/ahmed-shameem/Feature_selection.
Secure Social Internet of Things Based on Post-Quantum Blockchain

With the advancement of the application of Internet of Things (IoTs), the IoT technology is combining with the social network, forming a new network with private object information as the media and social entertainment as the purpose. Social Internet of things (SIoTs) is a new application of IoT technology in social network. The current SIoT systems are centralized and the user's security and privacy is not properly protected. In order to address the challenges in SIoTs, we propose a privacy protection system for the users. First, we propose a post-quantum ring signature. Second, we propose a blockchain system based on the ring signature. Compared with the traditional SIoTs, our system is based on post-quantum techniques, which is secure against both traditional computers and quantum computers. The results of the blockchain system show that it is very suitable for SIoTs.
Consistent and diverse multi-View subspace clustering with structure constraint

Multi-view subspace clustering algorithms have recently been developed to process multi-view dataset clustering by accurately depicting the essential characteristics of multi-view data. Most existing methods focus on conduct self-representation property using a consistent representation and a set of specific representations with well-designed regularization to learn the common and specific knowledge among different views. However, specific representations only contain the unique information of each individual view, which limits their ability to fully excavate the diversity of multi-view data to enhance the complementarity among different views. Moreover, when conducting multi-view subspace clustering, the learned subspace self-representation and clustering are sequential and independent, which lacks consideration of the interaction between representation learning and the final clustering calculation. In this paper, a novel method termed consistent and diverse multi-view subspace clustering with structure constraint (CDMSC2) is proposed to overcome the above-described deficiencies. (1) An exclusivity constraint term is employed to enhance the diversity of specific representations among different views for modeling consistency and diversity in a unified framework. (2) A clustering structure constraint is imposed on the subspace self-representation by factorizing the learned subspace self-representation into the cluster centroids and the cluster assignments with the goal of obtaining a clustering-oriented subspace self-representation. In addition, we carefully designed an efficient optimization algorithm to solve the objective function through relaxation and alternating minimization. Extensive experiments on five benchmark datasets in terms of six evaluation metrics demonstrate that our method outperforms the state-of-the-art methods.
Two-Dimensional Parametric Polynomial Chaotic System

When used in engineering applications, most existing chaotic systems may have many disadvantages, including discontinuous chaotic parameter ranges, lack of robust chaos, and easy occurrence of chaos degradation. In this article, we propose a two-dimensional (2-D) parametric polynomial chaotic system (2D-PPCS) as a general system that can yield many 2-D chaotic maps with different exponent coefficient settings. The 2D-PPCS initializes two parametric polynomials and then applies modular chaotification to the polynomials. Setting different control parameters allows the 2D-PPCS to customize its Lyapunov exponents in order to obtain robust chaos and behaviors with desired complexity. Our theoretical analysis demonstrates the robust chaotic behavior of the 2D-PPCS. Two illustrative examples are provided and tested based on numeral experiments to verify the effectiveness of the 2D-PPCS. A chaos-based pseudorandom number generator is also developed to illustrate the applications of the 2D-PPCS. The experimental results demonstrate that these examples of the 2D-PPCS can achieve robust and desired chaos, have better performance, and generate higher randomness pseudorandom numbers than some representative 2-D chaotic maps.
A hybrid ensemble-filter wrapper feature selection approach for medical data classification

Medical data plays a decisive role in disease diagnosis. The classification accuracy of high-dimensional datasets is often diminished by several redundant and irrelevant features. In this context, feature selection becomes an indispensable process. Feature selection primarily intends to identify a feature subspace which retains the classification accuracy while reducing the high computational cost of learning model as well as eliminating noise. The suitability of an appropriate feature selection approach heavily depends upon the capability of that approach to match the problem framework and to discover the intrinsic patterns within the data. The prime objective of this paper is to develop an ensemble-filter based hybrid feature selection model for disease detection. In this paper, a four-step hybrid ensemble feature selection algorithm has been introduced. Firstly, the dataset is partitioned using the cross-validation procedure. Secondly, in the filter step, various filter methods based on weighted scores were ensembled to generate a ranking of features, and thirdly sequential forward selection algorithm is utilized as a wrapper technique to obtain an optimal subset of features. Finally, the resulting optimal subset is processed for subsequent classification tasks. Experiments have been performed on twenty benchmark medical datasets with different dimensionalities. The performance of the proposed hybrid approach is compared with fourteen state-of-the-art feature selection algorithms on four benchmark classifiers namely, Naïve Bayes, Support Vector Machine with Radial Basis Function, Random Forest and k-Nearest Neighbor. The empirical results clearly demonstrate the superiority of the proposed hybrid methodology over the competing methods with respect to accuracy, sensitivity, specificity, f1-score, area under curve evaluation measures and number of selected features. The statistical analysis of the obtained results shows the outperformance and the competitiveness of the proposed hybrid method with respect to various state-of-the-art algorithms. This study concludes that the proposed hybrid approach proves to be more effective and reliable feature selection technique in selecting highly discriminative features. The framework can be utilized as a promising tool by both clinicians and researchers in enhancing the classification performance of medical datasets.
On Feature Normalization and Data Augmentation

The moments (a.k.a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time. However, in the field of image generation, the moments play a much more central role. Studies have shown that the moments extracted from instance normalization and positional normalization can roughly capture style and shape information of an image. Instead of being discarded, these moments are instrumental to the generation process. In this paper we propose Moment Exchange, an implicit data augmentation method that encourages the model to utilize the moment information also for recognition models. Specifically, we replace the moments of the learned features of one training image by those of another, and also interpolate the target labels—forcing the model to extract training signal from the moments in addition to the normalized features. As our approach is fast, operates entirely in feature space, and mixes different signals than prior methods, one can effectively combine it with existing augmentation approaches. We demonstrate its efficacy across several recognition benchmark data sets where it improves the generalization capability of highly competitive baseline networks with remarkable consistency.
The Chatbot Usability Scale: the Design and Pilot of a Usability Scale for Interaction with AI-Based Conversational Agents

Abstract Standardised tools to assess a user’s satisfaction with the experience of using chatbots and conversational agents are currently unavailable. This work describes four studies, including a systematic literature review, with an overall sample of 141 participants in the survey (experts and novices), focus group sessions and testing of chatbots to (i) define attributes to assess the quality of interaction with chatbots and (ii) the designing and piloting a new scale to measure satisfaction after the experience with chatbots. Two instruments were developed: (i) A diagnostic tool in the form of a checklist (BOT-Check). This tool is a development of previous works which can be used reliably to check the quality of a chatbots experience in line with commonplace principles. (ii) A 15-item questionnaire (BOT Usability Scale, BUS-15) with estimated reliability between .76 and .87 distributed in five factors. BUS-15 strongly correlates with UMUX-LITE by enabling designers to consider a broader range of aspects usually not considered in satisfaction tools for non-conversational agents, e.g. conversational efficiency and accessibility, quality of the chatbot’s functionality and so on. Despite the convincing psychometric properties, BUS-15 requires further testing and validation. Designers can use it as a tool to assess products, thus building independent databases for future evaluation of its reliability, validity and sensitivity.
Do VR and AR versions of an immersive cultural experience engender different user experiences?

Although Virtual Reality (VR) and Augmented Reality (AR) user experiences have received large amounts of recent research interest, a direct comparison of different immersive technologies' user experiences has not often been conducted. This study compared user experiences of one VR and two AR versions of an immersive gallery experience ‘Virtual Veronese’, measuring multiple aspects of user experience, including enjoyment, presence, cognitive, emotional and behavioural engagement, using a between-subjects design, at the National Gallery in London, UK. Analysis of the self-reported survey data (N = 368) showed that enjoyment was high on all devices, with the Oculus Quest (VR) receiving higher mean scores than both AR devices, Magic Leap and Mira Prism. In relation to presence, the elements ‘spatial presence’, ‘involvement’, and ‘sense of being there’ received a higher mean score on the Oculus Quest than on both AR devices, and on ‘realism’ the Oculus Quest scored significantly higher than the Magic Leap. Cognitive engagement was similar between the three devices, with only ‘I knew what to do’ being rated higher for Quest than Mira Prism. Emotional engagement was similar between the devices. Behavioural engagement was high on all devices, with only ‘I would like to see more experiences like this’ being higher for Oculus Quest than Mira Prism. Negative effects including nausea were rarely reported. Differences in user experiences were likely partly driven by differences in immersion levels between the devices.
Chatbot for SMEs: Integrating customer and business owner perspectives

Chatbots have been widely adopted to create more positive customer experiences as customers now spend more time in digital environments. Despite the technological advancement and benefits of chatbots for customer service, research on chatbot applications for Small and medium-sized enterprises (SMEs) is limited. The absence of research explaining the struggles faced by SMEs contributes to the gap of SMEs' chatbot adoption. This research determines the features and elements that fit with SMEs’ characteristics and their customers with chatbots. A mixed-methods approach is used to understand SMEs' needs. Study 1 uses interviews with SME business owners and its customers; it aims to explore the features that should be provided by chatbots for SME by identifying combinations between chatbots' generic features and SMEs' customer characteristics. Study 2 tests features identified in Study 1 and surveys 315 SMEs customers to empirically test featured chatbots' influence to anthropomorphism, perceived enjoyment, perceived ease of use, perceived usefulness, and how they affect SMEs’ customer intentions to use chatbots and their shopping intentions. The findings suggest four combinations of chatbot features that match SME customer characteristics: responsive; simple steps to trigger customer actions; humanized conversations; and personalized recommendations. An experimental survey was designed by creating a chatbot prototype based on these features. The results show that the featured chatbot prototype affects higher anthropomorphism, perceived enjoyment, and perceived usefulness, compared to the standard chatbot. We also find that perceived enjoyment and usefulness positively affect customer's intention to shop and intention to use the chatbot. While anthropomorphism only affect customer's shopping intention to SMEs. This paper contributes to the emerging service literature on the use of chatbots service interactions, particularly for SMEs. This research provides robust explorations from the perspective of both SME owners and customers. For practice, the research provides guidelines on how to design a chatbot for SMEs that meet customers’ needs.
Pedestrian trajectory prediction with convolutional neural networks

Predicting the future trajectories of pedestrians is a challenging problem that has a range of application, from crowd surveillance to autonomous driving. In literature, methods to approach pedestrian trajectory prediction have evolved, transitioning from physics-based models to data-driven models based on recurrent neural networks. In this work, we propose a new approach to pedestrian trajectory prediction, with the introduction of a novel 2D convolutional model. This new model outperforms recurrent models, and it achieves state-of-the-art results on the ETH and TrajNet datasets. We also present an effective system to represent pedestrian positions and powerful data augmentation techniques, such as the addition of Gaussian noise and the use of random rotations, which can be applied to any model. As an additional exploratory analysis, we present experimental results on the inclusion of occupancy methods to model social information, which empirically show that these methods are ineffective in capturing social interaction.
Path Planning for Autonomous Underwater Vehicles Under the Influence of Ocean Currents Based on a Fusion Heuristic Algorithm

Recently, research on path planning for the autonomous underwater vehicles (AUVs) has developed rapidly. Heuristic algorithms have been widely used to plan a path for AUV, but most traditional heuristic algorithms are facing two problems, one is slow convergence speed, the other is premature convergence. To solve the above problems, this paper proposes a new heuristic algorithms fusion, which improves the genetic algorithm with the ant colony optimization algorithm and the simulated annealing algorithm. In addition, to accelerate convergence and expand the search space of the algorithm, some algorithms like trying to cross, path self-smoothing and probability of genetic operation adjust adaptively are proposed. The advantages of the proposed algorithm are reflected through simulated comparative experiments. Besides, this paper proposes an ocean current model and a kinematics model to solve the problem of AUV path planning under the influence of ocean currents.
Non-fungible token (NFT) markets on the Ethereum blockchain: Temporal development, cointegration and interrelations

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation

Sign language is designed to assist the deaf and hard of hearing community to convey messages and connect with society. Sign language recognition has been an important domain of research for a long time. Previously, sensor-based approaches have obtained higher accuracy than vision-based approaches. Due to the cost-effectiveness of vision-based approaches, researchers have been conducted here also despite the accuracy drop. The purpose of this research is to recognize American sign characters using hand images obtained from a web camera. In this work, the media-pipe hands algorithm was used for estimating hand joints from RGB images of hands obtained from a web camera and two types of features were generated from the estimated coordinates of the joints obtained for classification: one is the distances between the joint points and the other one is the angles between vectors and 3D axes. The classifiers utilized to classify the characters were support vector machine (SVM) and light gradient boosting machine (GBM). Three character datasets were used for recognition: the ASL Alphabet dataset, the Massey dataset, and the finger spelling A dataset. The results obtained were 99.39% for the Massey dataset, 87.60% for the ASL Alphabet dataset, and 98.45% for Finger Spelling A dataset. The proposed design for automatic American sign language recognition is cost-effective, computationally inexpensive, does not require any special sensors or devices, and has outperformed previous studies.
Deep Learning for Sign Language Recognition: Current Techniques, Benchmarks, and Open Issues

People with hearing impairments are found worldwide; therefore, the development of effective local level sign language recognition (SLR) tools is essential.We conducted a comprehensive review of automated sign language recognition based on machine/deep learning methods and techniques published between 2014 and 2021 and concluded that the current methods require conceptual classification to interpret all available data correctly.Thus, we turned our attention to elements that are common to almost all sign language recognition methodologies.This paper discusses their relative strengths and weaknesses, and we propose a general framework for researchers.This study also indicates that input modalities bear great significance in this field; it appears that recognition based on a combination of data sources, including vision-based and sensor-based channels, is superior to a unimodal analysis.In addition, recent advances have allowed researchers to move from simple recognition of sign language characters and words towards the capacity to translate continuous sign language communication with minimal delay.Many of the presented models are relatively effective for a range of tasks, but none currently possess the necessary generalization potential for commercial deployment.However, the pace of research is encouraging, and further progress is expected if specific difficulties are resolved.
Satellite Video Super-Resolution via Multiscale Deformable Convolution Alignment and Temporal Grouping Projection

As a new earth observation tool, satellite video has been widely used in remote-sensing field for dynamic analysis. Video super-resolution (VSR) technique has thus attracted increasing attention due to its improvement to spatial resolution of satellite video. However, the difficulty of remote-sensing image alignment and the low efficiency of spatial–temporal information fusion make poor generalization of the conventional VSR methods applied to satellite videos. In this article, a novel fusion strategy of temporal grouping projection and an accurate alignment module are proposed for satellite VSR. First, we propose a deformable convolution alignment module with a multiscale residual block to alleviate the alignment difficulties caused by scarce motion and various scales of moving objects in remote-sensing images. Second, a temporal grouping projection fusion strategy is proposed, which can reduce the complexity of projection and make the spatial features of reference frames play a continuous guiding role in spatial–temporal information fusion. Finally, a temporal attention module is designed to adaptively learn the different contributions of temporal information extracted from each group. Extensive experiments on Jilin-1 satellite video demonstrate that our method is superior to current state-of-the-art VSR methods.
An efficient Pareto-based feature selection algorithm for multi-label classification

Multi-label learning algorithms have significant challenges due to high-dimensional feature space and noises in multi-label datasets. Feature selection methods are effective techniques to deal with these problems. ParetoCluster is an effective multi-label feature selection algorithm based on Pareto dominance and cluster analysis concepts which considers each label an objective function. This algorithm loses its effectiveness to differentiate features when dealing with high labeled datasets and makes most features incomparable (e.g., when most features fall into the first layer). Thus, a cluster analysis criterion in ParetoCluster will play a decisive role in determining the most relevant features. Bearing this in mind, in this paper, we have modeled the multi-label feature selection problem into a bi-objective optimization problem regarding the relevancy and redundancy degree of the features. We then handle it using Pareto dominance in this bi-objective domain. To illustrate the optimality and efficiency of the proposed method, we have compared our approach against some similar techniques.