Reviewerly/exHarmony huggingface dataset:
Biased parameter adaptation in differential evolution

The paper describes the problem of bias in parameter adaptation in Differential Evolution and other Evolutionary Algorithms. Based on the newly proposed Expected Fitness Improvement metric, the shift towards exploitation is demonstrated. The generalized Lehmer mean and Linear Bias Reduction are for the first time proposed to control the parameter adaptation bias for the fitness improvement based L-SHADE and distance based Db-L-SHADE algorithms. The experiments are performed on the benchmark functions of the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2017 competition on real-parameter optimization. The influence of the modified scaling factor and crossover rate adaptation is evaluated using Friedman ranking procedure and Mann–Whitney statistical test. The usage of Lehmer mean and Linear Bias Reduction is shown to deliver statistically better results for high-dimensional functions and improve the exploration properties of the search algorithm in the long-term perspective. To support the statements made, the search process is additionally analyzed using diversity measures and cluster analysis techniques.
A Broad Study on the Transferability of Visual Representations with Contrastive Learning

Tremendous progress has been made in visual representation learning, notably with the recent success of self-supervised contrastive learning methods. Supervised contrastive learning has also been shown to outperform its cross-entropy counterparts by leveraging labels for choosing where to contrast. However, there has been little work to explore the transfer capability of contrastive learning to a different domain. In this paper, we conduct a comprehensive study on the transferability of learned representations of different contrastive approaches for linear evaluation, full-network transfer, and few-shot recognition on 12 downstream datasets from different domains, and object detection tasks on MSCOCO and VOC0712. The results show that the contrastive approaches learn representations that are easily transferable to a different downstream task. We further observe that the joint objective of self-supervised contrastive loss with cross-entropy/supervised-contrastive loss leads to better transferability of these models over their supervised counterparts. Our analysis reveals that the representations learned from the contrastive approaches contain more low/mid-level semantics than cross-entropy models, which enables them to quickly adapt to a new task. Our codes and models will be publicly available to facilitate future research on transferability of visual representations. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Wearable Devices in Health Monitoring from the Environmental towards Multiple Domains: A Survey

The World Health Organization (WHO) recognizes the environmental, behavioral, physiological, and psychological domains that impact adversely human health, well-being, and quality of life (QoL) in general. The environmental domain has significant interaction with the others. With respect to proactive and personalized medicine and the Internet of medical things (IoMT), wearables are most important for continuous health monitoring. In this work, we analyze wearables in healthcare from a perspective of innovation by categorizing them according to the four domains. Furthermore, we consider the mode of wearability, costs, and prolonged monitoring. We identify features and investigate the wearable devices in the terms of sampling rate, resolution, data usage (propagation), and data transmission. We also investigate applications of wearable devices. Web of Science, Scopus, PubMed, IEEE Xplore, and ACM Library delivered wearables that we require to monitor at least one environmental parameter, e.g., a pollutant. According to the number of domains, from which the wearables record data, we identify groups: G1, environmental parameters only; G2, environmental and behavioral parameters; G3, environmental, behavioral, and physiological parameters; and G4 parameters from all domains. In total, we included 53 devices of which 35, 9, 9, and 0 belong to G1, G2, G3, and G4, respectively. Furthermore, 32, 11, 7, and 5 wearables are applied in general health and well-being monitoring, specific diagnostics, disease management, and non-medical. We further propose customized and quantified output for future wearables from both, the perspectives of users, as well as physicians. Our study shows a shift of wearable devices towards disease management and particular applications. It also indicates the significant role of wearables in proactive healthcare, having capability of creating big data and linking to external healthcare systems for real-time monitoring and care delivery at the point of perception.
Towards the adoption of virtual reality training systems for the self-tuition of industrial robot operators: A case study at KUKA

Interest is growing around Virtual Reality training systems (VRTSs), which started to be considered as a credible option to train companies’ workforce. Although the efficacy of VRTSs as a fancy alternative to traditional learning material used by trainers in their lectures has already been proved, their effectiveness as self-learning tools not requiring human instructors is still controversial, since experiments carried out within established training programmes are still rare. This paper reports the results of a user study aimed to investigate how an immersive VRTS designed to train industrial robot operators on light maintenance tasks actually compares to the training practice based on in-class and hands-on sessions that is adopted worldwide by an international company. After the training, study participants were evaluated by company's instructor while performing the taught task autonomously on a real robot. Obtained results showed that the effectiveness of the devised VRTS in making the trainees able to successfully complete the task was comparable to that of the traditional training. It was also found that there is still room for improvement, e.g., on virtually training interaction with physical tools and equipment, as well as on emulating and fortifying aspects of human trainer-trainee social dynamics.
Let's play: Me and my AI‐powered avatar as one team

Abstract Artificial intelligence (AI) tools have altered the gaming industry, thanks to their newly incepted functionalities, which have enhanced the consumer experience. Building on innovation diffusion theory, technology acceptance model, and flow theory, this study highlights an AI‐powered avatar concept. This study explores the roles of perceived easiness, usefulness, advantage, compatibility, enjoyment, customization, and interactivity in forming the gamers' intention to play with AI‐powered avatars. A survey data of 500 respondents from China having experience playing online video games is used to test the proposed hypotheses. The results offer significant support to the proposed relationships related to adopting an AI‐powered avatar and the consumers' psychological association with its adoption. Consequently, the results imply that AI‐powered avatars should allow gamers to customize, interact, and take assistance to move up levels with an enjoyable experience. Moreover, this study also suggests that digital technologies such as AI could be integrated into the gaming environment for a more pleasing and immersive experience.
Online Client Scheduling for Fast Federated Learning

Federated learning (FL) enables clients to collaboratively learn a shared task while keeping data privacy, which can be adopted at the edge of wireless networks to improve edge intelligence. In this letter, we aim to minimize the training latency of a wireless FL system for a given training loss by client scheduling. Instead of assuming that the prior information about wireless channel state and local computing power of the clients is available, we consider a more practical scenario without knowing the prior information. We first reformulate the client scheduling problem as a multi-armed bandit program and then propose an online scheduling scheme based on ϵ-greedy algorithm to achieve a tradeoff between exploration and exploitation. In addition, the proposed client scheduling scheme reduces the number of training rounds and the time interval per round simultaneously by jointly considering the significance of local updates and delay issue of each client. Simulation results show that in the case of non-independent and identically distributed data, the proposed scheme can save half the training time compared to the scheme which only considers the significance of local updates, and can improve more than 20% test accuracy compared to the scheme which only considers the time consumption per round of each client.
Where to go Next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments

Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing. Local trajectory optimization methods, such as model predictive control (MPC), can deal with those changes but require global guidance, which is not trivial to obtain in crowded scenarios. This letter proposes to learn, via deep Reinforcement Learning (RL), an interaction-aware policy that provides long-term guidance to the local planner. In particular, in simulations with cooperative and non-cooperative agents, we train a deep network to recommend a subgoal for the MPC planner. The recommended subgoal is expected to help the robot in making progress towards its goal and accounts for the expected interaction with other agents. Based on the recommended subgoal, the MPC planner then optimizes the inputs for the robot satisfying its kinodynamic and collision avoidance constraints. Our approach is shown to substantially improve the navigation performance in terms of number of collisions as compared to prior MPC frameworks, and in terms of both travel time and number of collisions compared to deep RL methods in cooperative, competitive and mixed multiagent scenarios.
What we think we know about cybersecurity: an investigation of the relationship between perceived knowledge, internet trust, and protection motivation in a cybercrime context

Individual internet users are commonly considered the weakest links in the cybersecurity chain. One reason for this is that they tend to be overoptimistic regarding their own online safety. To gain a better understanding of the cognitive processes involved in this assessment, the current study applies an extended version of the protection motivation theory. More specifically, this study includes perceived knowledge and internet trust to discover how these antecedents influence the threat and coping appraisal processes. Based on representative survey data collected from 967 respondents, we found that people who feel well-informed about online safety feel less vulnerable to cybercrime and are less inclined to take security measures. At the same time, feeling informed is associated with being more convinced of the severity of cybercrime. High levels of trust in the safety of the internet are linked to the feeling that one is less vulnerable to cybercrime and the perception that cybercrime is not a severe threat. Future interventions should remind internet users about their own perceived vulnerability and the risks that exist online while ensuring that internet users do not lose their trust in the internet and confidence in their own online knowledge.
Artificial Intelligence &amp; Tissue Biomarkers: Advantages, Risks and Perspectives for Pathology

Tissue Biomarkers are information written in the tissue and used in Pathology to recognize specific subsets of patients with diagnostic, prognostic or predictive purposes, thus representing the key elements of Personalized Medicine. The advent of Artificial Intelligence (AI) promises to further reinforce the role of Pathology in the scenario of Personalized Medicine: AI-based devices are expected to standardize the evaluation of tissue biomarkers and also to discover novel information, which would otherwise be ignored by human review, and use them to make specific predictions. In this review we will present how AI has been used to support Tissue Biomarkers evaluation in the specific field of Pathology, give an insight to the intriguing field of AI-based biomarkers and discuss possible advantages, risk and perspectives for Pathology.
Class-driven content-based medical image retrieval using hash codes of deep features

Medical imaging provides the convenience of physicians to analyze the disease by providing visual data of the body parts required for clinical research and treatment. Today, increasing medical images following technological developments are stored for a better understanding of diseases and future diagnoses. Effective medical image indexing and retrieval systems are required to use these images from storage repositories in real-time. In this quest, this paper provides an effective indexing and retrieval framework using deep features for MR and CT image indexing and searching. The proposed system aims to produce the most effective and least parameterized hash codes by using image features. For this reason, deep features are obtained from medical images using the convolutional neural network (CNN) architecture, which is the most effective automatic feature extraction method. The length of the acquired raw deep feature vectors for an image is relatively inefficient for retrieval speed. Feature reduction methods are used for the most effective reduction of the length of the deep feature vector. The most effective feature reduction algorithm is determined in this study. The main reason for producing a reduced class-driven hash code with feature selection algorithms is the drawbacks of medical image datasets. These drawbacks prevent the CNN output from being used directly as hash-code. The performance of the proposed method is tested on NEMA MRI and NEMA CT datasets. The proposed method is able to outperform the other state-of-the-art algorithms in terms of average precision performance.
From Resistance to Digital Technologies in the Context of the Reaction to Distance Learning in the School Context during COVID-19

In the relationship between teachers and distance learning in the context of COIVD-19, a series of unprecedented dynamics have emerged relating to a process of open-air experimentation that is going on in the world of school. The main constructs investigated in this paper concern the professional perceptions of teachers in terms of their skills and resistances towards digital technologies. To investigate this topic, a questionnaire on distance learning was administered to a sample of 658 teachers. From a methodological point of view, factor and reliability analyses and correlation and regression analyses were conducted. From the analysis of the results, it emerged that the questionnaire measures the resistance of teachers to distance learning and focuses on three main dimensions (two positive and one negative) that link teachers’ perceptions to the resistance to distance learning. In conclusion, the theme of the acceptance of technologies in the practice of teachers is still a subject full of meaning for professional perception and vision. A second issue concerns precisely the relationship between digital technologies and users, which must no longer focus only on the relationship with students but also on the perspective of the other training actors, including teachers.
Cyberbullying Detection: An Ensemble Based Machine Learning Approach

Research on cyberbullying detection is gaining increasing attention in recent years as both individual victims and societies are greatly affected by it. Moreover, ease of access to social media platforms such as Facebook, Instagram, Twitter, etc. has led to an exponential increase in the mistreatment of people in the form of hateful messages, bullying, sexism, racism, aggressive content, harassment, toxic comment etc. Thus there is an extensive need to identify, control and reduce the bullying contents spread over social media sites, which has motivated us to conduct this research to automate the detection process of offensive language or cyberbullying. Our main aim is to build single and double ensemble-based voting model to classify the contents into two groups: `offensive' or `non-offensive'. For this purpose, we have chosen four machine learning classifiers and three ensemble models with two different feature extraction techniques combined with various n-gram analysis on a dataset extracted from Twitter. In our work, Logistic Regression and Bagging ensemble model classifier have performed individually best in detecting cyberbullying which has been outperformed by our proposed SLE and DLE voting classifiers. Our proposed SLE and DLE models yield the best performance of 96% when TF-IDF (Unigram) feature extraction is applied with K-Fold cross-validation.
Dual VAEGAN: A generative model for generalized zero-shot learning

Generalized zero-shot learning (GZSL) aims to recognize samples from all classes based on training samples of seen classes by bridging the gap between the seen and unseen classes through the semantic descriptions (attributes). Recently, generative-based methods have been used to convert the GZSL task into a supervised learning problem by generating visual features for unseen classes. In this paper, we propose a dual framework based on variational auto-encoder (VAE) and generative adversarial network (GAN), known as dual VAEGAN, to produce more clear visual features than VAE and alleviate the model collapse problem of GAN. To avoid generating unconstraint visual features, the generated visual features are forced to map back into their respective common space using a cycle consistency loss. Meanwhile, the cycle-consistency loss promotes the diversity and preserves the semantic consistency of the generated visual features. The experimental results on the six standard datasets indicate that dual VAEGAN can produce promising results as compared with other methods reported in the literature.
Elk Audio OS

As the Internet of Musical Things (IoMusT) emerges, audio-specific operating systems (OSs) are required on embedded hardware to ease development and portability of IoMusT applications. Despite the increasing importance of IoMusT applications, in this article, we show that there is no OS able to fulfill the diverse requirements of IoMusT systems. To address such a gap, we propose the Elk Audio OS as a novel and open source OS in this space. It is a Linux-based OS optimized for ultra-low-latency and high-performance audio and sensor processing on embedded hardware, as well as for handling wireless connectivity to local and remote networks. Elk Audio OS uses the Xenomai real-time kernel extension, which makes it suitable for the most demanding of low-latency audio tasks. We provide the first comprehensive overview of Elk Audio OS, describing its architecture and the key components of interest to potential developers and users. We explain operational aspects like the configuration of the architecture and the control mechanisms of the internal sound engine, as well as the tools that enable an easier and faster development of connected musical devices. Finally, we discuss the implications of Elk Audio OS, including the development of an open source community around it.
Towards understanding the power of quantum kernels in the NISQ era

A key problem in the field of quantum computing is understanding whether quantum machine learning (QML) models implemented on noisy intermediate-scale quantum (NISQ) machines can achieve quantum advantages. Recently, Huang et al. [Nat Commun 12, 2631] partially answered this question by the lens of quantum kernel learning. Namely, they exhibited that quantum kernels can learn specific datasets with lower generalization error over the optimal classical kernel methods. However, most of their results are established on the ideal setting and ignore the caveats of near-term quantum machines. To this end, a crucial open question is: does the power of quantum kernels still hold under the NISQ setting? In this study, we fill this knowledge gap by exploiting the power of quantum kernels when the quantum system noise and sample error are considered. Concretely, we first prove that the advantage of quantum kernels is vanished for large size of datasets, few number of measurements, and large system noise. With the aim of preserving the superiority of quantum kernels in the NISQ era, we further devise an effective method via indefinite kernel learning. Numerical simulations accord with our theoretical results. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.
SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation

Abstract Detecting earthquake arrivals within seismic time series can be a challenging task. Visual, human detection has long been considered the gold standard but requires intensive manual labor that scales poorly to large data sets. In recent years, automatic detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, the accuracy of those methods relies on access to a sufficient amount of high‐quality labeled training data, often tens of thousands of records or more. We aim to resolve this dilemma by answering two questions: (1) provided with a limited amount of reliable labeled data, can we use them to generate additional, realistic synthetic waveform data? and (2) can we use those synthetic data to further enrich the training set through data augmentation, thereby enhancing detection algorithms? To address these questions, we use a generative adversarial network (GAN), a type of machine learning model which has shown supreme capability in generating high‐quality synthetic samples in multiple domains. Once trained, our GAN model is capable of producing realistic seismic waveforms of multiple labels (noise and event classes). Applied to real Earth seismic data sets in Oklahoma, we show that data augmentation from our GAN‐generated synthetic waveforms can be used to improve earthquake detection algorithms in instances when only small amounts of labeled training data are available.
Exploring Dense Context for Salient Object Detection

Contexts play an important role in salient object detection (SOD). High-level contexts describe the relations between different parts/objects and thus are helpful for discovering the specific locations of salient objects while low-level contexts could provide the fine detail information for delineating the boundary of the salient objects. However, the way of perceiving/leveraging rich contexts has not been fully investigated by existing SOD works. The common context extraction strategies (<i>e.g.</i>, leveraging convolutions with large kernels or atrous convolutions with large dilation rates) do not consider the effectiveness and efficiency simultaneously and may cause sub-optimal solutions. In this paper, we devote to exploring an effective and efficient way to learn rich contexts for accurate SOD. Specifically, we first build a dense context exploration (DCE) module to capture dense multi-scale contexts and further leverage the learned contexts to enhance the features discriminability. Then, we embed multiple DCE modules in an encoder-decoder architecture to harvest dense contexts of different levels. Furthermore, we propose an attentive skip-connection to transmit useful features from the encoder part to the decoder part for better dense context exploration. Finally, extensive experiments demonstrate that the proposed method achieves more superior detection results on the six benchmark datasets than 18 state-of-the-art SOD methods.